ASSESSMENT OF THE QUALITY FOR THE NADP/NTN DATA BASED ON THEIR PREDICTABILITY By SAULATI KOKU KOMUNGOMA B.Sc., University of Dar-es-Salaam, 1980 A THESIS SUBMITED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF STATISTICS We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA April 1992 © Saulati Koku Komungoma, 1992 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. (Signature) Department of .-Icrh cc The University of British Columbia Vancouver, Canada Date Qe1 4 1.4152 DE-6 (2/88) Abstract Three methods are used to predict the ion concentrations of a particular station using the concentrations of the other stations, for the data produced by the National Acidic Deposition (NADP) Network/ National Trends Network (NTN), during the period of 1983-86. We relate the degree of predictability to the quality of the data. Stations are ranked in the order in which they would be dropped if the network were, hypothetically, to be reduced in size. The agreement of the ranks given by different methods is assessed. Our study uses monthly volume weighted mean concentrations for each of the three selected ions, investigated one at a time. Since there a large number of stations (86 for hydrogen, 81 for each of the remaining ions) and only 48 months, analyses was carried out on clusters of stations. It was not possible to perform an ordinary regression analysis with a lot of missing data, so the analysis is done with missing values replaced by their estimates. ii Contents Abstract ii Contents iii List of Tables iv List of Figures vi Acknowledgements ix 1 Background 1 1.1 Introduction 1 1.2 Effects of Surface Water Chemistry to Biota 2 1.3 Network and Data Description 4 1.4 Review of the Entropy 5 2 Methods for Predicting One's Station Records From the Others 9 2.1 Introduction 9 2.2 Estimation of Missing Observations 10 2.3 Ordinary Regression Using Cross-Validatory Assessment 12 2.4 Regression Using a Bayesian Approach 14 2.5 Stone's Procedure 19 3 Assessment of the Network and the Agreement of the Methods 22 4 Discussion and Conclusion 35 References 38 Appendix 40 iii List of Tables Table A 1.1(a) Average Squared Prediction Errors and Ranks for Cluster 1 of Hydrogen 40 Table A 1.1(b) Average Squared Prediction Errors and Ranks for Cluster 2 of Hydrogen 41 Table A 1.1(c) Average Squared Prediction Errors and Ranks for Cluster 3 of Hydrogen 42 Table A 1.1(d) Average Squared Prediction Errors and Ranks for Cluster 4 of Hydrogen 42 Table A 1.1(e) Average Squared Prediction Errors and Ranks for Cluster 5 of Hydrogen 43 Table A 1.1(f) Average Squared Prediction Errors and Ranks for Cluster 6 of Hydrogen 44 oable A 1.2(a) Average Squared Prediction Errors and Ranks for Cluster 1 of Sulfate 45 Table A 1.2(b) Average Squared Prediction Errors and Ranks for Cluster 2 of Sulfate 47 Table A 1.2(c) Average Squared Prediction Errors and Ranks for Cluster 3 of Sulfate 49 Table A 1.3(a) Average Squared Prediction Errors and Ranks for Cluster 1 of Nitrate 50 Table A 1.3(b) Average Squared Prediction Errors and Ranks for Cluster 2 of Nitrate 52 Table A 1.3(c) Average Squared Prediction Errors and Ranks for Cluster 3 of Nitrate 54 iv Table A 2.1(a) Association Measure for Cluster 1 of Hydrogen 55 Table A 2.1(b) Association Measure for Cluster 2 of Hydrogen 55 Table A 2.1(c) Association Measure for Cluster 3 of Hydrogen 56 Table A 2.1(d) Association Measure for Cluster 4 of Hydrogen 57 Table A 2.1(e) Association Measure for Cluster 5 of Hydrogen 57 Table A 2.1(f) Association Measure for Cluster 6 of Hydrogen 58 Table A 2.2(a) Association Measure for Cluster 1 of Sulfate 59 Table A 2.2(b) Association Measure for Cluster 2 of Sulfate 59 Table A 2.2(c) Association Measure for Cluster 3 of Sulfate 60 Table A 2.3(a) Association Measure for Cluster 1 of Nitrates 61 Table A 2.3(b) Association Measure for Cluster 2 of Nitrates 61 Table A 2.3(c) Association Measure for Cluster 3 of Nitrates 62 Table A 3. Names and Identification, Codes for Sites Included in the Study 63 v List of Figures Figure A 1.1(a) Boxplots of Observed and Predicted Values for Cluster 1 of Hydrogen 65 Figure A 1.1(b) Boxplots of Observed and Predicted Values for Cluster 2 of Hydrogen 67 Figure A 1.1(c) Boxplots of Observed and Predicted Values for Cluster 3 of Hydrogen 69 Figure A 1.1(d) Boxplots of Observed and Predicted Values for Cluster 4 of Hydrogen 70 Figure A 1.1(e) Boxplots of Observed and Predicted Values for Cluster 5 of Hydrogen 71 Figure A 1.1(f) Boxplots of Observed and Predicted Values for Cluster 5 of Hydrogen 74 Figure A 1.2(a) Boxplots of Observed and Predicted Values for Cluster 1 of Sulfate 75 Figure A 1.2(b) Boxplots of Observed and Predicted Values for Cluster 2 of Sulfate 78 Figure A 1.2(c) Boxplots of Observed and Predicted Values for Cluster 3 of Sulfate 82 Figure A 1.3(a) Boxplots of Observed and Predicted Values for Cluster 1 of Nitrate 83 Figure A 1.3(b) Boxplots of Observed and Predicted Values for Cluster 2 of Nitrate 87 Figure A 1.3(c) Boxplots of Observed and Predicted Values for Cluster 3 of Nitrate 90 vi Figure A 2.1(a) Boxplots of Prediction Errors for Cluster 1 of Hydrogen 91 Figure A 2.1(b) Boxplots of Prediction Errors for Cluster 2 of Hydrogen 93 Figure A 2.1(c) Boxplots of Prediction Errors for Cluster 3 of Hydrogen 95 Figure A 2.1(d) Boxplots of Prediction Errors for Cluster 4 of Hydrogen 96 Figure A 2.1(e) Boxplots of Prediction Errors for Cluster 5 of Hydrogen 97 Figure A 2.1(f) Boxplots of Prediction Errors for Cluster 6 of Hydrogen 100 Figure A 2.2(a) Boxplots of Prediction Errors for Cluster 1 of Sulfate 101 Figure A 2.2(b) Boxplots of Prediction Errors for Cluster 2 of Sulfate 104 Figure A 2.2(c) Boxplots of Prediction Errors for Cluster 3 of Sulfate 108 Figure A 2.3(a) Boxplots of Prediction Errors for Cluster 1 of Nitrate 109 Figure A 2.3(b) Boxplots of Prediction Errors for Cluster 2 of Nitrate 113 Figure A 2.3(c) Boxplots of Prediction Errors for Cluster 3 of Nitrate 116 Figure A 3.1(a) Relative Measure of Agreement for Hydrogen 117 Figure A 3.1(b) P-Value for Tests of Agreement for Hydrogen 117 Figure A 3.2(a) Relative Measure of Agreement for Sulfate 118 Figure A 3.2(b) P-Value for Tests of Agreement for Sulfate 118 Figure A 3.3(a) Relative Measure of Agreement for Nitrate 119 Figure A 3.3(b) P-Value for Tests of Agreement for Nitrate 119 Figure A 4.1(a) Average Prediction Errors for Hydrogen (Cluster 1) in Ascending Order 120 Figure A 4.1(b) Average Prediction Errors for Hydrogen (Cluster 2) in Ascending Order 120 Figure A 4.1(c) Average Prediction Errors for Hydrogen (Cluster 3) in Ascending Order 121 Figure A 4.1(d) Average Prediction Errors for Hydrogen (Cluster 4) in vii Ascending Order 121 Figure A 4.1(e) Average Prediction Errors for Hydrogen (Cluster 5) in Ascending Order 122 Figure A 4.1(f) Average Prediction Errors for Hydrogen (Cluster 6) in Ascending Order 122 Figure A 4.2(a) Average Prediction Errors for Sulfate (Cluster 1) in Ascending Order 123 Figure A 4.2(b) Average Prediction Errors for Sulfate (Cluster 2) in Ascending Order 123 Figure A 4.2(c) Average Prediction Errors for Sulfate (Cluster 3) in Ascending Order 124 Figure A 4.3(a) Average Prediction Errors for Nitrate (Cluster 1) in Ascending Order 125 Figure A 4.3(b) Average Prediction Errors for Nitrate (Cluster 2) in Ascending Order 125 Figure A 4.3(c) Average Prediction Errors for Nitrate (Cluster 3) in Ascending Order 126 viii Ackowledgement I wish to thank members of the Department of Statiatics and Faculty of Graduate Studies at the University of British Columbia for their continued assistence throughout my studies. In particular I wish to thank my thesis supervisors, Professor J. Zidek and Profesor N. Heckman for their help and continued support during my research and thesis write-up. My special thanks goes to Professor M. Stone who suggested the idea of cross-validatory assessment in network design to my supervisors Professor J.V. Zidek. I would also like to thank my sponsors, the African Women 2000 Award who sup- ported me financially. It is my pleasure to acknowledge the kindness and assistence extended to me by my husband Mr Ayub Komungoma. I do not forget others who gave me moral support. ix CHAPTER 1 BACKGROUND 1.1 Introduction The National Deposition/National Trend Network (NADP/NTN) is one of the net- works which collect rainfall chemistry data in different locations in the U.S. Each location is called a station and is identified by a station code. Details of the network and the data are given in the next section. The goal of this study is to assess how well the rainfall chemistry of a particular station can be predicted from chemistries of other stations. This information might be used to reduce the size of the network, if the need arises, by dropping from the network a station whose rainfall chemistry is satisfactorily predicted from other stations' rainfall chemistries. The rainfall chemistries studied are the concentrations of 3 ions, namely hydrogen, sulfate and nitrate. In the following section, we discuss the nature of acidic deposition and its effect on biota. Various methods of predicting one station's chemistry from others are considered, namely, ordinary regression, regression using a Bayesian approach and Stone's cross validatory procedure. Descriptions of each method are included in the next chapter. Each month's rainfall chemistry at a particular station is predicted from the rest using each method, one at a time. This is, in turn, used to get a prediction error for each month, one at a time, for that particular station. The average squared prediction error at each station is used to assess each station's predictability. The smaller its average squared prediction error, the easier the prediction of a station. Stations are ranked by the above criterion. In this way we can rank the stations in the order in which they might hypothetically be dropped from the network, if necessary. Finally the rankings from the three different methods plus the rankings of Wu and Zidek (1992) from an entropy based approach are compared. A brief review of this entropy approach is given 1 in Section 1.3. For more details, see Wu and Zidek (1992). 1.2 Effect of Surface Water Chemistry to Biota The two main negatively charged ions that play a major role in the process of acidic deposition are sulfate and nitrate. These ions combine with hydrogen ions to form acidifying chemical compounds (sulfuric and/or nitric acid). Acidic deposition causes surface water to lose its acid neutralizing capacity (ANC), which results in increased acidity (lower pH) and increased inorganic aluminum, which is toxic to aquatic organisms. The extent to which acidic deposition causes surface water acidification is determined by the process occurring in the surrounding watershed. When water moves through the watershed, various processes change their chemical composition. The most prominent processes that take place are those that neutralize acids and release base cations (posi- tively charged ions such as calcium and magnesium). One of these processes is mineral weathering, in which minerals gradually dissolve with the passage of time. The other is a reaction in which the ions are exchanged in the soil, that is, the acidic hydrogen ion entering the soil is absorbed in the soil, replacing absorbed base cations, which in turn are released to the water. As most surface waters are well buffered, with pH values between 6.5 and 8.0, waters in which acid neutralizing and acid generating processes are nearly in balance are most likely to be affected by acidic deposition. Acidic deposition on surface water increases sulfate. The trend in many areas is that sulfate concentration increases as the rate of acidic deposition increases. However the nitrates remain low; although nitrate is a very important compound in acidic deposition, most watersheds retain it efficiently because of its importance in plant nutriency. On the other hand as acid inputs to a watershed increase, there is a nearly universal response 2 of increase in magnitude of acid neutralizing reactions that produce base cations. In watersheds almost all of the acid input is neutralized, with no change in pH or ANC of the surface waters. Even in the most sensitive waters, a substantial fraction of the acid input is neutralized by the processes that release base cations. As ANC and pH decrease, aluminum increases. When pH declines, aluminum, which is found in nearly all soil minerals, is leached from the soils, causing concentrations levels in lakes and streams to rise. Dissolved organic carbons tend to decrease with increase in acid input. This process reduces the decline in ANC and pH, thus partially making up for increased acidity. The harmful effects on aquatic life are not caused by ANC change alone, because aquatic organisms respond to many factors. Other factors affecting aquatic organisms are the change in pH and the release of calcium caused by acidification. The change in pH is the main variable that affects aquatic life. Aluminum concentrations are always low in non-acidic waters. When the pH value decreases below 5.5, the concentration of aluminum increases, very often to toxic levels. Both a decrease in pH and an increase in aluminum can cause acidification toxic to fish and other biota. In very dilute systems, low calcium levels could be stressful to fish, although in these waters the concentration of base cations increases in response to acidic deposition. Elevated base cations, especially calcium, may partially mitigate the toxic effects of low pH and high aluminum. Therefore, as surface water acidifies, the resulting combination of hydrogen ions, aluminum, and calcium determines the biological effects. Some types of organisms are sensitive to the chemical changes that accompany acid- ification and thus can not grow, survive, or reproduce in acidified water. As acidity increases, these acid-sensitive species perish, resulting in the decline of species richness. 3 Phenomenon of surface water chemistry and its effect on biota have been of much concern to researchers. Data are collected all over the world to enable researchers to come up with definite conclusions about the response of aquatic life to acidification. Detail on acidic deposition is found in the National Acid Precipitation Assessment Pro- gram, 1990 Integrated Assessment Report. 1.3 Network and Data Description The NADP/NTN is one of the networks in the U.S. which collect data on acidic deposition. This network collects weekly wet precipitation samples at more than 200 stations. These precipitation chemistry samples are analyzed by the Central Analytical Laboratory at the Illinois State Water Survey in Chicago, where ion concentrations are measured. Finally the data are transferred to the Acidic Deposition System (ADS) data base. This data base was established by the U.S. Environmental Protection agency at the Northwest Laboratory in the U.S. to provide an integrated centralized data base for the data collected by atmospheric deposition networks in North America. For more details about the NADP/NTN network and ADS refer to Olsen and Slavish ( 1986). Our study uses monthly volume weighted mean ion concentrations rather than weekly means since there is a large number of weeks with no precipitation. The analysis is done on one ion at a time using stations with less than five missing monthly volume weighted means. As a result only 86 stations are used for the hydrogen ion analysis, and 81 stations in the analysis of the remaining ions. The data included in this study were collected between 1983 and 1986 inclusive, so a total of 48 monthly volume weighted means are used in the analysis. Because of the small number of monthly volume weighted means relative to the number of stations, it is necessary to restrict our analysis to small clusters of stations and proceed from cluster to cluster. The clusters given by Wu and Zidek (1992) are used. Clustering was done for each ion separately using the k-means 4 algorithm of Hartigan and Wong (1979). Given k, the number of clusters, the method seeks to find k clusters so that the within-cluster sums of squares are minimized. The method proposed by Krzanowski and Lai (1988) was used to select k, the number of clusters for each ion. The numbers, k, selected by the method in this study for different ions, range from three to six. The cluster sizes range from 2 to 47 stations. A logarithmic transformation of the data was performed prior to the analysis. In certain clusters with more than 20 stations the number of complete records over all stations is less than the number of stations in the cluster. For example, the number of complete records in one of the sulfate clusters with 36 stations is 19. So for the analyses presented in this thesis, the missing values are replaced by their estimates. More about the estimation of missing values appears in Section 1 of Chapter 2. 1.4 Review of the Entropy Approach The purpose of an environmental monitoring network may be difficult to specify precisely. This presents a dilemma for the designer of such a network. Caselton and Zidek (1984) argue that the purposes of any network are in essence the reduction of uncertainty about some aspect of the world. They conclude that a rational design must minimize entropy, a measure of uncertainty. The theory of entropy and its potential role in assessing the quality of the data generated by an existing network are described by Caselton, Kan and Zidek (1990). If X is an uncertain, i.e. random, quantity or vector of such quantities, and f is the probability density function of X, then the uncertainty about X is expressible by the entropy of X's distribution, i.e. by H (X) = E[ — log f(X)1h(X)], where, according to Jaynes (1963), h is a "measure" representing complete ignorance. The inclusion of h in this definition of entropy makes this index of uncertainty satisfy the natural requirement of being invariant under one-to-one transformations of the scale of X. Although the 5 uncertainty about X is regarded as being of primary interest, often its distribution is determined by the conditional density of X, f (•I0), given an unspecified parameter, 0, which is of interest in its own right. In this case the total uncertainty of (X, 0) must be indexed. Conditional on the available data, the total uncertainty is then indexed by the total entropy, defined by H (X , 0) = —E [log[ f (X, Oldata) ]Idatai (1)h(X , 0) where the expectation is taken over both X and 0, and f (X, Oldata) = f (X10 , data)r(Oldata), (2) (01clat a) being the posterior distribution of 0. To assess the performance of stations in the network using the entropy approach, it is supposed that hypothetically, a specified number of stations, u, is to be dropped from the network and only g coordinates of X will be measured in future, with u g = p, where p is the number of stations. After rearranging subscripts as necessary, let X = (U, G) where U and G denote, respectively, the u and g dimensional vectors of values corresponding to the stations which are to be "ungauged" and "gauged". The process of measurement will eliminate the uncertainty about G assuming the measurement error to be negligible. The amount of uncertainty so eliminated would be MEAS defined by MEAS = — E[log[f (Giclata) I h(G)fidata]. The set of g stations would be chosen to maximize this entropy (MEAS). It can be shown that this same set of g stations can be found by minimizing PRED + MODEL, the residual uncertainty remaining after G is observed, where P RED = —E[log[f (UI0 ,G, data)! h(U)]Iclata] and MODEL = E[—log[f (0IG, data) I h(0)]Idataj. The g stations that maximize MEAS are considered to produce high quality data. On the other other hand, the u stations that maximize PRED + MODEL (the residual uncertainty after G is observed) are regarded as producing low quality data. 6 Wu and Zidek (1992) apply this theory to assess the quality of the same data set as in our study. The analysis was done one ion at a time, since there are ion-to-ion differences in data quality. The stations were clustered and the entropy analysis done within each cluster. This was necessary because the number of stations is greater than the number of observations (48 volume weighted ion concentration means). If the size of the network were, hypothetically, to be reduced, then only the selected g stations would be retained. In the implementation of the entropy theory, Caselton et al (1992) and Wu and Zidek (1992) found it was not computationally feasible to find the best subset of g gauged stations among all p stations in the cluster. Thus a stepwise suboptimization procedure was used. The first step consisted of finding p — 1 stations which maximize MEAS. The station left out would be the first one to be dropped from the network, hypothetically speaking. The next step was to find the p — 2 stations among the p — 1 selected stations in the first step which maximize MEAS, and this yielded a second station which might hypothetically be terminated. This process continued until just one station was hypothetically left in the network. This exercise left the stations in ranked order, starting with the station having the lowest quality data and finishing with the station having the highest quality data. Looking at the rank order within clusters, the station identified as having highest quality data is, in most cases, geographically isolated from the rest of the stations in the cluster. This seems reasonable since gauging such a station would be expected to substantially reduce the uncertainty. We may conclude that the entropy approach is promising as a way of assessing the quality of the data. But this approach has shortcomings. Entropy is a complex measure of uncertainty and therefore unintuitive. In addition we do not know how outliers might affect the ranking of the stations. Since 7 the entropy approach is not based on a predictive model it does not yield a method which could actually be used to predict the ion concentrations of the stations which would be dropped from the network. To address these shortcomings, a different approach is taken here to determine the relative quality of the environmental data. In this approach, a station is considered to yield high quality data if its observations are difficult to predict from observations of other stations. We do not mean to suggest that we are discrediting the entropy approach but rather we are aiming at developing a better understanding of it. 8 CHAPTER 2 METHODS FOR PREDICTING ONE STATION'S RECORDS FROM THE OTHERS 2.1 Introduction A multiplicity of plausible objectives can be foreseen for any data collection network, and at the same time some important future uses of the data may not be foreseen. This poses a dilemma as quality represents fitness for intended use (see Caselton, Kan and Zidek, 1990). As noted in the Introduction, to circumvent this difficulty, Wu and Zidek (1992) use an entropy based approach to assess the relative quality of the data produced by each station in a data gathering network, specifically that network which is the the subject of our study. The goal of the present study is similar to that of Wu and Zidek (1992) except that we use a concept other than entropy to define data quality. Like Wu and Zidek (1992), we look at the data quality for each station as the amount of additional information provided by the data in that station. However we define the amount of information as the extent to which the data from a particular station can be predicted from that of the other stations. A station whose data are hard to predict is considered as adding much information to the network. We would interpret such a station's data as being of high quality. By a similar argument, stations whose data are easily predicted are thought to add little information, which we interpret to mean the data are of low quality. We could argue that, if there is a need to reduce the size of the network, the stations with low quality data should be dropped out of the network first. The methods used in this study to predict one station's rainfall chemistry records from the others are ordinary regression, regression using the Bayesian approach and Stone's cross-validatory assessment method (See Stone, 1973). Cross validation is used 9 to assess the performance of all the methods. In all three methods, regression models are constructed to predict the ion concen- tration of station j from the ion concentrations of the remaining stations in a cluster. Months are considered as replicates. For instance, the ordinary regression method re- sults in a regression model for each month i = 1, , 48 and each station j = 1, , p, where p is the size of the cluster. For fixed i and j the regression model is constructed using the remaining 47 months as replicates. Thus, there are p parameters to be es- timated from 47 "replicates". The values of p range from 2 to 47 and, in some of the larger clusters, p is close to and sometimes even greater than the number of months with complete observations. For example, in one of the sulfate clusters with p = 36, there are only 19 months for which all the station values are available. It is not possible to perform an ordinary regression analysis when there are only 19 — 1 = 18 cases available to estimate 36 parameters. So our analysis is done with missing observations replaced by their estimates at the outset: for each cluster, we produce an X matrix of logarith- mically transformed ion concentrations with no missing entries. These completed data sets are used for all methods to achieve consistency. Our strategy for estimating missing observations is discussed in the next section. The last three sections of this chapter discuss in detail the three methods used to predict a station's rainfall chemistry from the others. 2.2 Estimation of Missing Observations. Various methods for estimating missing observations have been proposed by different authors. Afifi and Elashof (1961) suggest filling in the missing observations for each variable by that variable's mean. Another method uses regression instead. A variable with missing observations is regressed on the other variables in the study, using only complete cases. Regression is done separately for each variable with missing values. 10 The estimated regression model is used to impute the missing values. This method is discussed by Buck (1960) as well as Afifi and Elashof (1961). Stein and Shen (1991) regressed the logarithm of sulfate concentrations on the amount of precipitation and the month of the year when precipitation occurred. The model fitted by least squares was used to impute the missing sulfate values. A modified regression strategy was used in this study to impute the missing values. Each station with missing values was regressed on the other stations using only complete records. But as we saw above, in some of the clusters the number of complete records was less than the number of parameters to be estimated. So the regression method needed some modifications before it could be applied to our data. These modifications are found in a BMDP program, which estimates missing values. The program is called "Description and estimation of missing data" and is abbreviated as "PAM". This method uses stepwise regression to select the variables to be used. First, the variable most correlated with the variable with missing values is chosen to enter into the regression equation. If the chosen variable meets the "F-to-enter" criterion (explained below), then the next variable is chosen. The variable chosen next is the one with the highest partial correlation with the variable with missing values, with the partial correlation conditional on the variable already used in the equation. This variable must also meet the "F-to-enter" criterion. Additional variables are chosen in the same manner until all variables which meet the "F-to-enter" criterion have been used. If, during stepwise regression, no variables satisfy the criterion for admission into the regression, the mean of the variable with missing values is used to fill in that variable's missing values. The "F-to-enter" criterion is motivated by an approximate test of the coefficient of any predictor variable. That is, the square of the ratio of the predictor's regression 11 coefficient to its standard error is approximately distributed as an F statistic with one degree of freedom for the numerator. The square of this ratio is compared with the pre- set "F-to-enter" limit. If the square is greater than this limit, the variable has satisfied the "F-to-enter"criterion. For further details on this program, refer to BMDP Statistical Software, 1983 edition, page 217 and Frane (1978b). 2.3 Ordinary Regression Using Cross-Validatory Assessment Ordinary regression using cross-validatory assessment is one of the methods used in this study to assess how well each station's rainfall chemistry can be predicted from the chemistry records of other stations. In this study the sample is divided into two parts. The first part is used for estimation while the second part is for assessment. The size of the estimation subsample is taken to be n — 1 (with n = 48) and that of the prediction subsample to be 1. These are the same subsample sizes used by Stone (1973). We proceed as follows. One station is selected and its data values designated as the predictands. The remaining station records provide the predictors. Once a station is fixed, we set aside one month's record from among the n = 48 monthly records, and treat this as a "future" month for prediction. Now the data for the remaining 47 months are used as a "training set"; a linear model is fitted by ordinary least squares, using the 47 months as replicates. The estimated regression equation is then used to predict the ion concentration of the future month for the designated station, with the remaining stations' concentrations for that month as predictors. The selected month is now replaced by another and the process repeated until all 48 months have played the role of the future month. In this way we obtain 48 predictions and 48 prediction errors for the designated station. That station is now replaced by another from the cluster and the whole exercise repeated. A new station now provides the predictands and this continues until all stations in the cluster have played the designated role. At this point 12 we can assess the efficacy of ordinary regression and the relative difficulty of predicting the records of the various stations from the others. To state our procedures more precisely let p be the number of stations in a cluster and n = 48 the number of months in the study. Further let : x ii be the logarithmically transformed ion concentration for the i th month in the j th station, X = (xis ), i = 1,2, ... ,n , j = 1,2, ..., p, be the n x p observation matrix, = (x ii , , x ip),i = 1, , 48, and X. = (x ij , , xnj ) t , j = 1, ...,p, X-4 be the matrix X with column vector X.i deleted, • be the matrix X with row vector Xi. deleted, X-ij be the matrix X with both column and row vectors Xi and Xi . deleted, • be the column vector X. with the i th month deleted, Xi- ' be the row vector Xi. with the i th station deleted, • be the matrix X -ij with a column of ones added as the first column, be the row vector with one added as a first element. For fixed j and i = 1,2, ... ,48 the dependence of X..7 i on X -ij ,1 is modeled as 3 = 1 + ( 3 ) where is a 47 x 1 column vector, is a 47 x p matrix, /3 is the p x 1 vector of regression coefficients and U is a 47 x 1 column vector of random errors. Denote the estimated regression coefficients (which depend on i and j) and predicted value of x ij by andand XESTJ , respectively. Then ;3: is multiplied by )0' 1 to get XESTi.i . The prediction error made in predicting x is is given by x i; — XESTi; and denoted by 13 Both XESTij and PEii are on a logarithmically transformed scale. Each station yields a vector of 48 elements for both predicted values and prediction errors, one element for each month. The average of the squared prediction errors for each station is used to assess how well each station's rainfall chemistry can be predicted from that of the other stations. For fixed station, j, let .1a be the set of indices for which xij was not imputed, that is, the set of i's for which xij was not missing in the original data set. Then, the average of the squared prediction errors for station j is calculated using prediction errors only for months i in I. Since the x i; values for the months with i .13 were imputed using regression, the prediction errors from these months are omitted from the average to avoid misleadingly small estimates of the prediction error. Now if we denote the average squared prediction error of station j by APE; , then APE.; = (E PEij)/(48 — kj), (4) JET, where ki is the number of months for which station j had missing data. Stations are ranked using APE. Within a cluster the station with the minimum value of APE is ranked first and has the lowest quality data according to the criterion described above. The station with the highest quality data is the one with maximum APE and is ranked last. 2.4 Regression Using a Bayesian Approach In the previous section, a least squares approach is used to estimate the unknown regression coefficients in predicting one station's ion concentrations from the others. But if prior information about the regression coefficients is available, then this information should be exploited to find improved regression coefficient estimates. In this section, we assume that such prior information exists. Specifically, when station j and month i are fixed, the model X cor = X cor r + (I , (5) 14 is assumed, where X,,,„ denotes the matrix of data values centred about station averages. Prior knowledge on the regression coefficients, (flu, , Pp') is expressed in terms of a density which is exchangeable, where p' = p — 1. That is, prior knowledge of the regression coefficients would be unaltered by any permutation of the suffixes. This implies that our opinion of p; is the same as that of pg . This idea of exchangeability derives from the work of de Finetti (1964). Lindley and Smith (1971) apply this idea and give explicit expressions for the Bayesian estimates. They use the mixture approach to construct an exchangeable prior distribution of the parameters of interest. This mixture approach is supported by Hewitt and Savage (1955). To give a simple example from Lindley and Smith (1971), suppose E(y110) = 0, and E(0 1 ) = II, with var(y2 10) = cr 2 and var(Oi ) = T 2 . If it is assumed that the O's are exchangeable, then, P, the prior distribution on 0, is of the form n PO f) = P(19t1/1)d0(11), (6) ,=i where P(•111) and OH are arbitrary measures. In the language of Lindley and Smith (1971), this example is a two stage-model. If in turn, depends on unspecified "hyper- parameters", then we can have a three stage-model. The choice of the number of stages to be used depends on the individual. Lindley and Smith (1971) consider the linear regression model of the form, Y=Ted-U, (7) where the prior opinion on Q is exchangeable. We modify their approach by first "cen- tering" the data about their mean to eliminate the need for an intercept coefficient in Equation 7. Because of the exchangeability assumption, we can not include the intercept in the regression, since its inclusion would make the exchangeability assumption unre- alistic. To be precise, after setting aside the values for the "future month", we subtract 15 each station's mean (based on 47 months) from each station's concentrations. Then we work with these corrected values. This forces the regression line to pass through the origin. We apply Lindley and Smith's method (1971) with the regression model of Equation 5, where = X t — 1(E koi x k3 )/47 and 1 is a 47 x 1 vector all of whosej elements are 1. Each station's values in X;oirjr are obtained from the corresponding sta- tion's values in X -ij in the same way as X.,0171 r.i . The resulting Bayesian estimate of 13, ,Q*, is then used to calculate the prediction of station j during month i, by, ii; = (E x0/47 + (8) koi where Xc7rri. is obtained from by subtracting from each element of X27-3 the appro- priate station average. Suppose , a- 2 , cr,3 N (. X ;,,?r , 0-2 (9) where In , is the n' x n' identity matrix and n' = 47. Assume O t = (01 , ... , 9) is exchangeable and that ,3:71, 4 N(, 4). (10) Assuming vague prior knowledge for Lindley and Smith (1971) give an explicit ex- pression for the Bayesian estimate as O.* = + k((X co t4) t X jr)- 1 ( 1.p , (ip') where k = Q21 , ,3 is the usual least squares estimate, p' is the number of parameters, and Jp, is a matrix of dimension p' x p', all of whose elements are 1. In practice a2 and cr,i will be unknown, and they can be estimated from the data. To estimate a 2 and 4, it is now assumed that a 2 and ai23 , which are independent of 0 and are independently distributed as v /0.2 xv2 (12) 16 voAo/a 20 x2, , ( 13) (see Lindley and Smith, 1971), where v, A, vo and Ao are prior parameters. The joint distribution of X -i j) e.14 a-2 and ap2 is given bycorr. (0.2)-1/2nexpf _(xc—oirr. — dy(xc-oirr.; — xcT,i7.3,.,#)1(20- 2 )} x (4) -112Pexp{—(g — — 1)1(24)} x (0- 2 ) - 2 (P+ 2) exp{—vA/(2a2 )} x (4) - 1 ("+2) ex p{ — v I (2a-P} (14) Integrating the joint distribution with respect to e, one calculates the posterior distribution of 0, u 2 and • 20 , which is proportional to (a2 ) _1/2(.+,+2) exp[_ { , A pc_07.2 ,4 — i3)t(x_i — x.c_02,,,,q)}1(20.2)] X COTT COT T.3 101 x(o.i2,3) ,./*+,,A-n exp[_ { ,„A, E (ok — #.) 2 1/(2a 20 )], (15) k=1 where 0. (E7;:_ i 13k) (p'). Using the results in Equation 11 and the modal equations for a 2 and ar23 from the posterior distribution, we get the Bayes estimates of 13, •2 and 20" 0.* = + k*((x;o1r37-) tXc—otrjr ) -1 — (4 1 )/P1)}:0) S 2 = vA (X;oirr.i — X;oirjritT) t (X;;irr. i — X ,3*)} I (n' v + 2), 711 2 = { VO APL^ (Qk 13.' ) 2 } API VO 1)7 (16) k=1 where k* = s 2/s4. Lindley and Smith (1971, p17) suggest that the parameters v and vo may well be small positive numbers and that, in any case, the solution is very insensitive to changes in these numbers, so that they may be set to zero. In the example in Lindley and Smith (1971), the values of v and vo were set to zero as well as was the starting value of k*. The resulting equations were solved iteratively, starting with initial values for s 2 and 4 or by setting the starting value of k* to zero. The initial value of 0* was 17 found via (16) and then used to find the next values of s 2 and 3 2 , and so on. However, if v and vv are set to zero and if during iteration, the values of the estimates of the A's become close to each other, then .5 20 becomes very close to zero. Hence k* = ,2 /,2 becomes very big. This can result in an overflow in the computer calculations. From (16), it can be seen that /3* 0 as k* oo. Thus if a small estimate of /3 is used in our prediction model, Equation (8), the prediction will be close to the the station's average. Because of the problem of overflow, we suggest two alternatives of estimating 8. The first alternative is to set voilt o in the last Equation of (16) to a small number such that vo is negligible and can be ignored in the denominator of the same expression. This modification keeps the estimate, Sp, bounded away from zero in the iterations. The second alternative is to not iterate, but rather to pick a value of k* and use it in the first Equation of (16) to calculate the estimate, /3*. Because we do not have grounds to prefer one of the alternatives over the other, both are used. In the first alternative, which we will call Bayesian regression alternative (1), the initial value of k* is chosen to be zero, as in Lindley and Smith (1971). In subsequent iterations, vf3 A0 is set equal to 0.1, with vo negligible, so that 7/1 S 20 = {O.1 E(QZ — 0 .*)}1(p + 1). k=1 In the second, non-iterative alternative which we will call Bayesian regression alternative (2), we take S = x—ik corr..? — Xcorr.j X;o174 13. n + 2 )— s 20 = ()k — 13.) 2 /(" — 1). k=1 However for a cluster of size two the second alternative can not work, because after fixing a station, there is only one station as a predictor, and thus p' = 1, and s 20 is undefined. 18 These two approaches are applied to our data to give the Bayesian estimates of the regression coefficients for each data set constructed by fixing station j and month i, and using the remaining 47 months to construct a model for the prediction of the ion concentration of station j. For fixed station j and month i, we get the Bayesian estimate /3*. This 13* is used to calculate the predicted value of x ii , denoted by 4. We get the prediction error made by predicting x ii , namely xis — 4. For each station j, we obtain the vector of 48 predicted values and prediction errors, one for each month. The average of the squared prediction errors for station j, calculated using only the months which were not imputed, is used to give the assessment of the station's predictability. For each cluster, stations are ranked using the average of the squared prediction error. The station with the minimum average squared prediction error is ranked first, and is considered as having the lowest data quality. Similarly, the station with the maximum average squared prediction error is ranked last and considered as having the highest data quality. The results are included in the Appendix and the discussion is in Chapter 3. 2.5 Stone's Procedure Both the ordinary regression method and regression based on a Bayesian procedure, for predicting rainfall chemistry of a particular station from the chemistries of the other stations, were discussed in the previous two sections. These methods were assessed by cross-validation. If it is accepted in advance that cross-validation will be used in this way, then the ordinary least squares and the Bayesian predictors can be modified to do well in the intended assessment. The resulting predictor differs markedly from that produced by the previous two methods. It is due to Stone (1973) and we include it in our study as a competitor to the first two methods. Stone's procedure begins by choosing a statistical predictor, which is a function of the data, (y i , t i) i = 1, , n, and a parameter denoted by a. Here tf = (t il , . , t i„). 19 The parameter, a, is estimated from the data by cross-validation, using a loss function. This loss function is also used to calculate C+, an assessment of the prediction efficiency. The score is constructed by calculating yi , a statistical predictor of y i based on the data set (yi, ti), 1 = 1, , n, 1 i. The assessment score, C+, is the average of the losses incurred from estimating yi by We apply Stone's method to each station in a cluster. That is, for each fixed station j, we take n = 48, is = p —1, yi = x i; and t! = XiTi , the vector of ion concentrations of the remaining stations in month i. Stone's procedure yields a linear model for predicting station j's ion concentrations from the other stations', and a score, C;F, which assesses the predictability of station j. These scores are then used to rank the stations within a cluster. In Section 3 of his paper, Stone (1973) gives a number of statistical predictors which can be used in different problems. In the case of the present study, the statistical predictor of Stone's Example 3.3 is appropriate. Letting ,5 = {1, , n} the statistical predictor based on (yi , i E s, is given by: a, s) = ay + (1 — a)(y. E bk(tik — t•c)), (17) where :7; Ei yi , and the f.k = n 1 tik, and the bk 's are the estimated regression coefficients in the least squares multiple regression of y on t. The parameter, a, in the statistical predictor, is estimated via cross-validation, using squared error loss. That is, a is chosen to minimize : n C(a) n E(yi — a, s -1 )) 2 (18) where s' = {1, , n}/{i}. The value of a so obtained is denoted by a+ (s), and the resulting model is Equation 17 with a = a+ (s). 20 Stone gives the explicit form of a+ (s) corresponding to the selected statistical pre- dictor and loss function as ri2 nri(yi — y) r i n(yi — c'+(s) (1 — Aii) 2 (n — 1)(1 — A ii) jj/ — (n — 1) if (19)t_i where ri is the it' residual in the least squares multiple regression using the data (y i , t i ), i E ,s, and A = T(VT) -1V, where T is the design matrix corresponding to this regression. The cross-validatory assessment employs C+, which is calculated as follows. For each i = 1, , n, the statistical predictor of Equation 17 is constructed as described above, but using the reduced data set (yk, tk), k E . Thus for each i, we have a cross-validatory choice of a, a+(s -i ), and an estimate of y i, (t i , cx+ s-i). The assessment of predictability is given by n C+ = n E(Yi Mi,a+ (s -1),s -i )) 2 . (20) This statistical predictor and assessment procedure are applied in the present study to each station. The station whose predictability score, C -P, is lowest is the most easily predicted, so it is considered to have low quality data according to this assessment procedure. Similarly a station with a larger value of C;E . is hard to predict, and thus is considered to be producing data of high quality. Stations are ranked from low to high according to this definition of their data quality. Discussion of the results is given in Chapter 3. The rankings are given in the Appendix. 21 CHAPTER 3 ASSESSMENT OF THE NETWORK AND THE AGREEMENT OF THE METHODS The methods described in Chapter 2 were used to assess the quality of the data described in Section 2 of Chapter 1, obtained over the period, 1983-86; and the results are presented in this section. The data used in this study were collected by the National Deposition/National Trend Network (NADP/NTN) which we discussed in Section 2 of Chapter 1. The NADP/NTN network stations used in this study are tabulated in the Appendix. For reasons given in Section 1 of Chapter 2, we use the data with missing values replaced by their estimates as described in Section 2 of Chapter 2. After filling in the missing values by their estimates, we have a total of 48 volume weighted monthly average concentrations for each station and ion, and these average concentrations are used in all the methods. In Section 2 of Chapter 1, we discussed the nature and the effect of acid deposition. Our study involves the concentrations of three ions, namely: hydrogen, sulfate and nitrate. The data were logarithmically transformed, to achieve a more nearly Gaussian data distribution and to be consistent with the earlier work done on the same data set. The clusters from the study by Wu and Zidek (1992) are used. Our results for sulfate ion concentrations will be discussed in detail to illustrate our findings. This ion is selected for detailed consideration, so that one can compare our results with those from the entropy based analysis of Wu and Zidek (1992) where the same ion was selected for detailed discussion. Alongside this focused discussion we shall be commenting generally on all the ions and their clusters. Data for sulfate ion concentrations yield 3 station clusters with 37, 36 and 8 stations. Tables A1.2(a)-A1.2(c) in the Appendix give the ranks of the stations for each cluster 22 determined by the methods used in our study and those given by entropy analysis. Also included in the tables are the average squared prediction errors, an index of quality used to rank stations. The corresponding measure used to rank stations in entropy analysis is not included in the tables, because it is not comparable to average prediction error. To focus our discussion, consider the third sulfate cluster which contains 8 stations, with identification codes 037a, 059a, 061a, 074a, 078a, 271a, 281a, and 354a. The cor- responding station names can be found in the Appendix. Table 3.1 below (identical to Table A1.2(a) in the Appendix, but reproduced here for convenience) contains the ranks and average prediction errors for the 8 stations in the cluster. Since this cluster contains 8 stations, a rank of 8 corresponds to the best station, a rank of 7 to the second best station, and so on. Four of the stations in the cluster (half the cluster size) are given the same ranks by all the methods. The station with identification code 037a is ranked first by the three methods used here, as well as by the entropy analysis. For this station, the average squared prediction errors produced by ordinary regression, regres- sion using the Bayesian approach and Stone's cross-validatory assessment method are respectively, 0.1484, 0.2242 and 0.1483. This means that this station (Glacier National Park, Montana) has the lowest quality (most easily predicted) data according to the four methods. If there were a need to reduce the size of the network, this station might be considered first for possible termination. Two other stations in the cluster compete for "best". Identification codes for these stations are 281a and 354a, with respective names Bull Run, Oregon, and St. Mary Ranger Station, Montana. Bull Run, Oregon, is ranked best by the ordinary regression method and Stone's procedure, while both the Bayesian alternative approaches and entropy rank it second best. St. Mary Ranger Station, Montana, is ranked best by both the Bayesian alternatives approaches and entropy, while ordinary regression and Stone's procedure rank it second best. 23 Table 3.1: Average Squared Prediction Errors and Ranks in One of Sulfate Cluster station methods code Regression Bayesian 1 Bayesian 2 Stone Entropy APE rank APE rank APE rank APE rank rank 037a 0.1484 1 0.1343 1 0.1334 1 0.1483 1 1 059a 0.3091 5 0.2683 5 0.2711 5 0.2836 5 4 061a 0.2537 4 0.2354 4 0.2403 4 0.2279 4 5 074a 0.4518 6 0.3693 6 0.3812 6 0.4096 6 6 078a 0.1796 2 0.1635 2 0.1659 2 0.1610 2 2 271a 0.2498 3 0.1979 3 0.1983 3 0.2276 3 3 281a 0.5258 8 0.4044 7 0.4604 7 0.5398 8 7 354a 0.5053 7 0.4773 8 0.4767 8 0.4472 7 8 In general, the four different methods do not give the same best station. However, as can be seen from the table above, the difference is not very important, since a station ranked best by one method is either ranked best or second best or third best by the other methods. Similarly, the ranks for the intermediate stations do not have significant differences. There is no cluster where a station is identified as the best by one method, and the poorest by the other methods. If it were literally necessary to select the best single station it would be necessary to pay careful attention to its selection. One might well face a decision problem, and other nonstatistical issues might be invoked to resolve the conflict. For example, the geographical positions of the stations might be taken into consideration. It is not unusual for different methods, adopted for a single purpose, to give different results, or for different judges to give different ranks to various contestants. But it is important that there be a reasonably strong association between the ranks given by the 24 different methods. Below we use association measures to give a more precise assessment of the agreement of the ranks from the five methods. We use association measures to assess the degree of agreement between pairs of ranking methods and among all five ranking methods. This is done within each cluster. The ten pairwise associations we consider are for ordinary regression with the Bayesian regression alternative (1) approach, ordinary regression with the Bayesian regression alternative (2) approach, ordinary regression with the Stone's procedure, ordinary re- gression with the entropy approach, the Bayesian regression alternative (1) approach with the Bayesian regression alternative (2) approach, the Bayesian regression alter- native (1) approach with the Stone's procedure, the Bayesian regression alternative (1) approach with the entropy approach, the Bayesian regression alternative (2) with Stone's procedure, Bayesian regression alternative (2) with entropy and Stone's procedure with entropy approach. We test the null hypothesis that the rankings are random against the alternative hypothesis that a direct association exists between the ranking methods. For pairwise associations, Spearman's coefficient of rank correlation is used. The test statistic is R = 1 — 6 D?/(p3 — p), where D i is the the difference between the two ranks given the i t ' station, and p is the number of stations in the cluster. The value of R lies between -1 and 1, where a value of -1 means perfect disagreement or inverse association and a value of 1 means perfect agreement or direct association, which is of interest in our study. A value of zero indicates there is no association, that is neither agreement nor disagreement. For p < 30 exact p-values for the calculated values of R are given in Table I of Gibbons (1976) while for larger values of p , p> 30, an approximate normal distribution is used to calculate the p-values. (R-/p —1 is approximately standard normal under the null hypothesis) For the five different ranking methods, Kendall's coefficient of concordance is used to 25 test the same hypothesis of no association. Three different test statistics are available. The first of these, denoted by S, measures the departure from lack of agreement and is given by, S = 7E[C; — k(p + 1)/21 2 , (21) J.1 where Ci is the sum of rankings of the jth station and k = 5 is the number of ranking methods. This quantity is expected to be small if there is no agreement and big if a positive association exists. But it is difficult to have an intuition about the size of S, so a second test statistic denoted by W is used. W is a relative measure of association, defined by W S/S*, a ratio of the observed measure of departure from lack of agreement, S, to S*, where S* is given by S* = lE[jk k(p + 1)121 2 . (22) S* is the value of S under perfect agreement. The value of W lies between 0 and 1, where a value of 0 means no association and a value of 1 means perfect agreement. Accordingly, large values of W call for rejection of the null hypothesis in favor of the alternative. Since the test statistic, S, is a monotonically increasing function of W, and is large when W is large and zero when W is zero, the appropriate p-values are the right tail probabilities. Table K of Gibbons (1976) gives exact right-tail probabilities for S for p = 3, k < 8, and p = 4, k < 5. For combinations of p and k that are not covered by that table, an equivalent test statistic to S and W, denoted by Q, is used. Either of the following two expressions can be used to calculate Q: Q k(p —1)W, Q = 12S I kp(p + 1). (23) The distribution of this test statistic, Q, can be approximated by the chi-square distri- 26 bution with p — 1 degrees of freedom for large k. As with the test statistic S, Q is also a monotonically increasing function of W, so the appropriate p-values are also the right tail probabilities. Details of the association test procedures for both pairwise and any number, k, of ranking methods is found in Gibbons (1976). The association measures for all the clusters and all the ions are given in Tables A2.1- A2.3 in the Appendix. The entries in the table are R for pairwise associations or W for all five ranking methods, Q or S for all four ranking methods, depending on which statistic is used to calculate the p-value and, the p-values. Table 3.2 below (identical to Table A2.2(c) in the Appendix but given a different label to be consistent within this chapter) contains the association measures for the third sulfate cluster with 8 stations. Table 3.2: Association measures for the third sulfate cluster methods being compared R or W Q p-value regression versus Bayesian 1 0.976 - 0.000 regression versus Bayesian 2 0.976 - 0.000 regression versus Stone 1.000 - 0.000 regression versus entropy 0.952 - 0.001 Bayesian 1 versus Bayesian 2 1.000 - 0.000 Bayesian 1 versus Stone 0.976 - 0.0000 Bayesian 1 versus entropy 0.976 - 0.000 Bayesian 2 versus Stone 0.976 - 0.000 Bayesian 2 versus entropy 0.976 - 0.000 Stone versus entropy 0.952 0.001 all methods 0.981 34.3333 0.000 The values of R and W for this cluster are greater than 0.9. Also the p-values are less than or equal to 0.001. These results give evidence for the rejection of the null 27 hypothesis of random ranking in favor of the alternative hypothesis. That is, in this cluster the ranks of the stations given by the all five methods have a strong agreement. Looking at the association measure, W, for all rankings, in all the clusters, we see that, for most of the clusters, W is greater than 0.8 and the p-value is less than 0.001 . This indicates good agreement of the ranks from all the methods. Results for nitrate show a different pattern from that discussed above. The association measures for nitrate clusters are relatively low. For example, for one of nitrate clusters with 41 stations W is 0.5. But the p-value for this cluster is small (0.00001) so this gives grounds to reject the random ranking hypothesis in favor of the alternative. Generally small clusters of size less than five have large association measures but relatively large p-values. But this cannot be used to support the null hypothesis of random ranking, since these clusters have inadequate sample sizes to yield a reliable conclusion. This study was motivated by the results of the entropy analysis. The entropy ap- proach seems to work well in ranking stations on the basis of data quality, but it is not intuitive. So we seek an intuitive method which gives ranks close to those from entropy analysis; it could be used in combination with the entropy approach to get a better understanding of the data. The scatterplots of the p-values against cluster sizes and relative measures, R, against cluster sizes are used to find out which of the meth- ods used here is in close agreement with the entropy analysis and to see if agreement depends on cluster size. The plots are done ion-by-ion. Scatterplots for all the ions are included in the Appendix. Figures 3.1(a) and Figure 3.1(b) below are scatterplots for respectively, the relative measure of agreement, R, against cluster sizes and the p-values against cluster sizes for sulfate ion. From Figure 3.1(a) we see that the line corresponding to the Bayesian alternative (1) and entropy is above the other lines and the next highest line is the one correspond- 28 ing to Stone's procedure and entropy. This means that the agreement, as measured by R, between the Bayesian alternative (1) and entropy is higher than for the others. In addition, the corresponding line in Figure 3.1(b) is below the other lines. This indicates that the p-values for this comparison are smaller than for the others. These observa- tions suggest that the ranks produced by the Bayesian alternative (1) approach agree with those produced by entropy more often than the others. The same phenomenon is observed with the hydrogen ion rankings except for one cluster in which the ordinary regression and entropy comparison has a bigger value of R and a lower p-value. But this one cluster among the six clusters of hydrogen cannot change our conclusion. That is, even for hydrogen, the Bayesian alternative (1) approach and Stone's procedure agree strongly with entropy. Nitrate gives exceptional results. For this ion Stone's procedure seems to rank last in agreement with entropy, but the Bayesian alternative (1) once again shows strong agreement with entropy. The scatterplots indicate that there is no consistent relationship between agreement and cluster size. The need for diagnostic checking is one of the reasons which motivated this study of various approaches to assessing data quality. We need to know more about the predicted values and the prediction errors. We acquire this knowledge by comparing the boxplots of the predicted values to those of the observed values, and by studying the boxplots of the prediction errors. For each cluster, we look at two sets of boxplots. One set of boxplots shows the observed and predicted values from all four methods used in this study. For easy reading, we frame the five boxes for each station separately with the station identification code above the frame. The first boxplot in each frame is for the observed values, while the second, third, fourth and fifth represent the predicted values from respectively, ordinary 29 regression, regression using the Bayesian alternative (1) approach, regression using the Bayesian alternative (2) approach and Stone's procedure. The second set of boxplots shows the prediction errors from the four methods used. The same format as above is repeated. The four consecutive boxes in each small frame represent the prediction errors for each given station, where the first boxplot in a group is for the prediction errors from the ordinary regression method, the second and third boxes are, respectively, for alternative 1 and alternative 2 of the Bayesian approach while the last boxplot represents the prediction errors from Stone's procedure. Boxplots for all the clusters are included in the Appendix. Both sets for the third sulfate cluster are included in this chapter for easy reference, and are labelled 3.2(a) and 3.2(b) for consistency within the chapter. Figure 3.2(a) is for the boxplots of the observed and predicted values, while figure 3.2(b) is for the boxplots of the prediction errors. From Figure 3.2(a) we see that the dispersion of the predicted values is less than that of the observed values. Among the predicted values from different methods, those from Stone's procedure have the smallest dispersion, followed by those from the Bayesian alternative (1) approach. A careful examination of the plot indicates that the predictions of Stone's procedure are pulled toward the center of the data. In other words, Stone's procedure shrinks the prediction towards the center of the data. This phenomenon is strong in other clusters. Also, in other clusters, the Bayesian alternative (1) approach reveals the same shrinkage behavior. On the other hand the predicted values from the ordinary regression method have the widest dispersion and sometimes a dispersion wider than the observed values. This might be caused by outliers. From Figure 3.2(b), the boxplots for prediction errors, we see that the prediction errors from the ordinary regression method have the widest dispersion, while the other 30 three methods produce prediction errors with almost the same dispersion. But in other clusters there are some features which are not present in the cluster we have highlighted in our discussion. In particular, the dispersions of the prediction errors from the Bayesian alternative (1) are sometimes smaller than those from Stone's procedure. In general, however, the Bayesian alternative (1) approach and Stone's procedure compete for the distinction of having prediction errors with smallest dispersion. 31 10 15 20 25 30 35 o - o d 1rgent 2baylent — - — 3bay2ent — — 4stent Figure 3.1(a): Relative Measure of Figure 3.1(b): P-value for Tests of Agreement for Sulfate 0 3 -o 0. Q ci CO 0 o o 0 0 cm 0 Agreement for Sulfate 10 15 20 25 30 35 ci CO o cluster size cluster size Legend: rgent = regression with entropy, bay1ent= Bayesian alternative (1) with entropy bayl ent= Bayesian alternative (2) with entropy, stent = Stone's procedure with entropy CHAPTER 4 DISCUSSION AND CONCLUSION In the analysis of the monthly ion concentrations of stations in the NADP/NTN, we attempted to determine which station's ion concentrations were most accurately pre- dicted from the other stations' concentrations. However, our analyses did not answer the question completely, due to three things. First, the predictability of stations were ranked within cluster, rather than across the entire network. Secondly, our analyses were conducted ion by ion. Thirdly, our different methods of prediction (ordinary re- gression, Bayes regression, and Stone's cross-validatory regression) resulted in different rankings. It may not be possible to completely resolve these three issues with this data set. However, we have gotten a clear picture of the relationship between the entropy approach and our prediction methods. For each ion the analysis was done in clusters, each of size less than 48. This analysis by cluster makes the comparison of stations in different clusters impossible. But we argue that, since ion concentrations in different clusters are statistically different, we would not expect stations in different clusters to do well in predicting each other. However we cannot determine a single worst station from the network, since each cluster gives its own worst station. We suggest a tentative solution to this problem: to take the worst station from a larger cluster to be the overall network worst station. Since a station to be dropped out should be redundant, it seems logical to think of a redundant station coming from a larger cluster. Alternatively one might use geographical knowledge to make the decision. If one could accurately estimate missing values in the original data set, then one could use the weekly average concentrations with missing values replaced by their estimates. This would give us more than the 48 months as replicates, allowing analysis of the entire network, and hence give only one worst station for each ion. We 33 think this idea is feasible since Sten, Shen and Styr (1992) have used multiple regression to impute missing daily sulfate concentrations. The same problem would arise if we would want the overall network best station. We do not consider this problem to be of as much concern as the first one, since the need to retain only one station in a network is not realistic. But if there are reasons to identify the overall network best station, then other issues like geographical positions might be invoked in selecting the best station. We did not carry out a formal analysis to compare each station's ranking in different ions, so we cannot say that, for instance, all of a particular station's ion concentrations are difficult to predict. So we cannot give a strong statement, but we have traced worst and best stations for all sulfate clusters and have given their ranks for each ion using the different prediction methods. Table 4.1 contains the ranks of the selected stations for the different ions. Since the clustering depends upon the ion under study, when looking at the rank of a station, we should note the cluster size to judge whether a station is towards the worst or the best position. From this table we see that the worst station as given by one ion is not necessarily the worst for another ion, and similarly for the best station. But some of the stations are put in the same category in either two of the ions or in all three ions. By category, we mean a ranking towards either the worst or best position. For example the station with identification code 025a is towards the worst position category in sulfate and nitrate while the station with identification code 163a is in the same category (towards the worst) in all three ions. Wu and Zidek (1992) found ion-to-ion differences in station clustering and station ranking. They pointed out that those differences might be informative and so resisted the use of a multivariate analysis at this stage. However, a multivariate analysis might give some indication of the simultaneous predictability of all of a station's ion concentrations. 34 Table 4.1: The Ranks of the Selected Stations in Different Ions. ion method station identification code 025a 249a 068a 281a 163a 037a regression 1 36 37 8 1 1 Stone 2 36 36 7 1 1 sulfate Bayesian 1 1 36 29 7 5 1 Bayesian 2 1 36 37 7 1 1 cluster size 36 36 37 8 37 8 regression 27 33 18 20 6 1 Stone 32 26 19 5 2 4 hydrogen Bayesian 1 31 24 19 6 2 4 Bayesian 2 32 24 19 6 2 4 cluster size 33 33 20 20 18 20 regression 10 7 20 30 11 27 Stone 2 28 20 31 4 19 nitrate Bayesian 1 8 14 20 30 6 16 Bayesian 2 7 4 19 31 5 26 cluster size 41 41 31 31 31 31 For the analysis of each ion within a cluster the different prediction methods' rankings did not always agree. In Chapter 3 we discussed the agreement of the station rankings from different methods in each cluster for each ion. It was found that the degree of agreement is reasonably high. We are interested in knowing which of the three ranking methods used in our study agrees strongly with those from the entropy based analysis of Wu and Zidek (1992). We found in Chapter 3 that the ranks from regression using a Bayesian alternative (1) approach agree with those from the entropy based approach 35 more often than the others. This implies that if, by using the entropy approach, a station found to be producing data of low quality is terminated, then in the future, regression using the Bayesian alternative (1) approach can be used to predict ion concentrations of the deleted station. The close agreement of the ranking based on entropy analysis and the ranking from the methods used in our study reflects the relationship between the degree of the sta- tion's predictability and the amount of uncertainty reduced by the inclusion of a station into the network. That is, a station which is easily predicted will generally not notice- ably reduce uncertainty when added into the network. Such a station is considered as producing data of low quality. On the other hand a station which is hard to predict will generally greatly reduce uncertainty when added into the network. Such a station is considered as producing data of high quality. This exercise of ranking stations, starting with the worst station to the best station does, not mean that there is a worst station in an absolute sense. When we look at the trend of the average squared prediction errors (APE), we see that there is not much difference among the APE's for the first few worst stations. But the difference becomes sharper as we move towards the best station. Figure 4.1, a scatterplot of the average prediction errors (of cluster 1 of sulfate ) against the ordered rankings, supports this fact. Another fact revealed by this figure is the close agreement of the average prediction errors from Stone's procedure and the regression using a Bayesian alternative (1) approach. These two methods have low average prediction errors when compared with the other methods. Scatterplots for other clusters for all three ions are included in the Appendix. This ranking exercise, as pointed out by Wu and Zidek (1992), can only be used to suggest the station which could be closed if there were budgetary constraints. 36 .3 1 reg 2bay1 3bay2 4stone 1 /1 / '1 1-1-1 ,4 Average Prediction Errors for Cluster 1 of Sulfate in Ascending Order 1 0 0 0 10 20 30 ordered ranks Legend: reg=regression,bayl = Bayesian alternative (1), bay2= Bayesian alternative (2) Stone = Stone's procedure References [1] Afifi, A.A and Elashoff R.M. (1961). Missing Observations in Multivariate Statis- tics, Journal of the American Statistical Association, 61, 595-604. [2] Buck, S.F. (1960). A Method of Estimation of Missing Values in Multivariate Data Suitable for Use with an Electronic Computer Journal of the Royal Statiatical So- ciety, B, 22, 302-307. [3] Caselton, W.F., Kan. L. and Zidek, J.V. (1990). Quality Data Network Designs Based on Entropy. Unpublished Manuscript. [4] Caselton, W.F., and Zidek, J.V. (1984). Optimal Monitoring Network Designs, Statistics and Probability Letters, 2, 223-227. [5] De Finetti, B. (1964). Foresight: its Logical Laws, its Subjective Sources. In Studies in Subjective Probability (H.E. Kyburgs Jr, and H.E. Smoker, Eds) pp 93-158. New York: Wiley. [6] Frane J.W. (1976). Missing Data and BMDP: Some Pragmatic Approaches, Tech- nical Report, No 45 Department of Biomathematics UCLA. [7] Gibbons, J.D. (1976). Nonparametric Methods for Quantitative Analysis, Holt Rinehart and Winston. [8] Hartigan, J.A. and Wong, M.A. (1979). A K-Means Clustering Algorithm, Applied Statistics, 28, 101-108. [9] Hewitt, E. and Savage, L.G. (1955). Symmetric Measures on Cartesian Products. Trans. Amer. Math. Soc., 80, 470-501. 38 [10] Krzanowski, W.J. and Lai Y.T. (1988). A Criterion for Determining the Number of Groups in a Data set Using Sum-of-Squares Clustering. Biometrics. 44, 23-34. [11] Lindley, D.V. and Smith, F.M. (1972). Bayes Esimation for Linear Model (with discussion). Journal of the Royal Statistical Society, B, 34, 1-41. [12] National Acid Precipitation Assessment Program, 1990. Integrated Assessment Re- port. [13] Olsen, A.R. and Slavich A.1. (1986). Acid Precipitation in North America: 1984 Annual Data Summary fron Acid Deposition System Data Base. U.S Environmental Protection Agency , Research Triangle Park, NC. EPL/600/4-86/033. [14] Sten, M.L., Shen, X and Styer, P.E. (1991). Applications of a Simple Regression Model to Acid Rain Data. Technical Report No 276. Department of Statistics. The University of Chicago, Chicago, IL. [15] Stone, M. (1973). Cross-Validatory Choice and Assessment of Statistical Prediction Journal of the Royal Statistical Society, B, 36, 111-132. [16] Styer, P.E., and Stein, M.L. (1991). Acid Deposition Models for Detecting the Effects of Changes in Emissions, Technical Report No 331. Department of Statistics, University of Chicago, Chicago, IL. [17] Wu, S. and Zidek, J.V. (1992) An Entropy Based Analysis of Data from Selected NAP/NTN Network Sites for 1983-86. Unpublished Manuscript. 39 APPENDIX Table A1.1(a) Average Squared Prediction Errors and Ranks for the Stations in Cluster 1 of Hydrogen station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 015a 1.9848 16 1.0338 15 1.1952 15 1.0514 15 15 017a 0.6188 8 0.3383 6 0.3962 7 0.3787 7 5 024a 2.23042 17 1.2851 17 1.4310 17 1.2975 17 17 030a 0.37024 3 0.2929 4 0.3007 4 0.3143 4 7 036a 0.5956 7 0.4432 8 0.4952 9 0.4828 9 8 049a 0.3846 4 0.2732 3 0.2858 3 0.2965 3 3 051a 1.1721 13 0.6348 13 8179 13 0.7802 13 13 052a 0.5551 6 0.4543 9 0.4610 8 0.4527 8 10 053a 0.7862 11 0.5383 10 0.5945 11 0.6120 10 11 076a 0.9970 12 0.5514 11 0.7170 12 0.6206 11 9 077a 1.7739 15 1.1356 16 1.2522 16 1.1314 16 16 163a 0.3159 1 0.2261 2 2425 2 0.2370 2 2 252a 0.4828 5 0.3616 7 0.3893 6 0.3747 6 6 253a 0.3353 2 0.2160 1 0.2363 1 0.1979 1 1 258a 0.6336 9 0.3220 5 0.3720 5 0.3331 5 4 268a 1.3925 14 0.7729 14 0.9564 14 0.9725 14 14 283a 0.7388 10 0.5531 12 0.5669 10 0.6377 12 12 339a 2.5667 18 1.9549 18 1.8939 18 1.6876 18 18 40 Table A1.1(b) Average Squared Prediction Errors and Ranks for the Stations in Cluster 2 of Hydrogen station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 010a 1.0896 15 0.6209 13 o.7394 13 0.8512 14 14 011a 0.9865 12 0.5561 12 0.6864 12 0.7569 13 10 012a 1.7235 17 1.0377 16 1.3725 17 1.2563 17 16 016a 1.0326 14 0.7130 15 0.7983 15 0.7188 12 13 037a 0.3457 5 0.2518 4 0.2723 4 0.2668 4 4 038a 2.3707 19 1.4686 18 1.8701 18 1.4813 18 18 059a 0.6713 8 0.3935 7 0.4654 7 0.3520 7 6 061a 0.1679 1 0.1211 1 0.1266 1 0.1220 2 1 062a 3.4879 20 1.6470 20 2.2785 20 1.9624 20 20 068a 2.2189 18 1.6361 19 1.9070 19 1.6308 19 19 074a 0.3380 4 0.2929 5 0.2939 5 0.2885 6 8 078a 1.0004 13 0.5332 11 0.6751 11 0.6500 11 11 172a 0.1815 2 0.1264 2 0.1282 2 0.1001 1 2 173a 0.7678 10 0.4809 9 0.5937 10 0.5093 9 9 255a 1.3043 16 1.0602 17 1.1346 16 1.0255 16 17 271a 0.1905 3 0.1457 3 0.1520 3 0.1532 3 3 279a 0.7196 9 0.4438 8 0.5664 9 0.6342 10 7 280a 0.8923 11 0.6490 14 0.7683 14 0.9509 15 15 281a 0.3760 6 0.3082 6 0.3118 6 0.2765 5 5 354a 0.5384 7 0.5012 10 0.4830 8 0.4112 8 12 41 Table A1.1(c) Average Squared Prediction Errors and Ranks for the Stations in Cluster 3 of Hydrogen station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 007a 035a 070a 1.1711 2.0774 1.2163 1 3 2 1.1696 1.5934 1.1539 2 3 1 1.1837 1.5712 1.1760 2 3 1 1.1278 1.8186 1.1095 2 3 1 1 3 2 Table A1.1(d) Average Squared Prediction Errors and Ranks for the Stations in Cluster 4 of Hydrogen station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 004a 0.5734 1 0.4583 1 0.4688 1 0.5149 1 1 029a 3.5054 10 2.2386 10 2.6179 10 2.3558 9 9 034a 0.8194 6 0.7349 8 0.7374 7 0.7552 7 8 071a 1.7075 9 1.4480 9 1.5129 9 2.4643 10 10 166a 0.7058 4 0.6485 5 0.6552 5 0.5889 2 2 254a 0.8472 7 0.7255 7 0.7419 8 0.7550 6 4 273a 0.7375 5 0.6436 4 0.6527 4 0.6293 3 6 275a 0.7041 3 0.6008 2 0.6105 2 0.6944 5 3 282a 0.6755 2 0.6275 3 0.6359 3 0.6629 4 7 349a 0.8792 8 0.6991 6 0.7233 6 0.8074 8 5 42 Table A1.1(e) Average Squared Prediction Errors and Ranks for the Stations in Cluster 5 of Hydrogen station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 020a 0.4309 15 0.1250 10 0.1601 7 0.1851 12 12 021a 1.2228 30 0.6873 33 0.7766 33 0.5594 31 31 022a 0.5742 19 0.2189 16 0.3526 14 0.2292 16 22 023a 0.3755 13 0.1311 12 0.1838 10 0.1721 9 9 025a 0.9739 27 0.5283 31 0.7410 32 0.6173 32 26 028a 1.1945 29 0.4046 28 0.6830 27 0.5428 29 32 031a 0.7658 24 0.1811 14 0.3620 15 0.1566 7 20 032a 0.5223 18 0.1411 13 0.1724 8 0.1700 8 13 033a 0.6182 21 0.1816 15 0.3987 18 0.1735 10 16 039a 1.7682 32 0.2804 23 0.4872 20 0.4494 28 30 040a 0.3632 11 0.1001 7 0.1820 9 0.2355 17 10 041a 1.0391 28 0.2714 22 0.4540 19 0.28484 21 23 046a 0.2554 5 0.0919 5 0.1358 5 0.1256 4 2 047a 0.3441 10 0.1139 8 0.1947 11 0.17474 11 6 055a 0.2656 6 0.0948 6 0.2010 12 0.0984 3 7 056a 0.1266 2 0.0892 4 0.1034 3 0.0972 2 3 058a 0.4510 16 0.0849 3 0.1181 4 0.1857 13 4 063a 0.3636 12 0.1302 11 0.2541 13 0.2176 15 14 064a 0.1625 3 0.0806 2 0.0923 2 0.1259 5 8 43 Table A1.1(e) Continued station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 065b 0.1201 1 0.0645 1 0.0905 1 0.0961 1 1 073a 1.7031 31 0.2712 21 0.7115 29 0.5487 30 27 075a 0.3038 8 0.1238 9 0.1473 6 0.1546 6 11 161a 0.6085 20 0.5617 32 0.6372 25 0.7598 33 33 164a 0.7928 25 0.2387 18 0.3891 16 0.4198 27 24 168a 0.3324 9 0.4531 29 0.6983 28 0.3153 22 19 171a 0.6740 23 0.5213 30 0.6705 26 0.2420 18 17 249a 2.2977 33 0.2988 24 0.5887 24 0.4195 26 22 251a 0.4256 14 0.3009 25 0.5016 22 0.3832 25 28 257a 0.9505 26 0.2470 20 0.4876 21 0.3260 24 18 272a 0.6684 22 0.2424 19 0.5166 23 0.2503 20 21 277a 0.2901 7 0.3835 27 0.7221 30 0.1886 14 5 285a 0.2542 4 0.2363 17 0.3905 17 0.2461 19 15 350a 0.4782 17 0.3247 26 0.7305 31 0.3239 23 25 Table A1.1(f) Average Squared Prediction Errors and Ranks for the Stations in Cluster 6 of Hydrogen station regression Bayes'anl Stone entropy code APE rank APE rank APE rank rank 160a 1.5016 1 1.5016 1 1.4745 1 1 278a 1.9126 2 1.9125 2 1.8781 2 2 44 Table A1.2(a): Average Squared prediction Errors and Ranks for the Stations in Cluster 1 of Sulfate station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 004a 0.4139 6 0.1514 3 0.2577 7 0.1512 8 5 010a 0.5927 12 0.3185 25 0.3822 14 0.2303 15 20 01la 0.8626 21 0.2037 16 0.5076 21 0.4108 30 16 012a 0.9378 24 0.4955 34 0.6559 27 0.4509 33 33 029a 0.6013 13 0.2853 22 0.4086 16 0.2763 20 22 030a 0.4442 7 0.3079 24 0.3234 11 0.2680 18 24 034a 0.4524 8 0.1286 2 0.2102 3 0.1356 4 7 035a 0.7034 16 0. 3696 30 0.4618 18 0.3128 23 25 036a 0.5708 10 0.1943 13 0.3089 9 0.1424 6 9 038a 0.6547 15 0.1985 15 0.3573 13 0.2309 16 17 039a 0.9386 25 0.4242 32 0.6640 28 0.4877 35 36 052a 0.7357 17 0.2816 21 0.4071 15 0.3000 22 21 068a 3.1738 37 0.3626 29 1.5558 37 0.5189 36 31 070a 1.7942 35 0.4356 33 0.9571 35 0.4019 29 35 071a 0.8449 20 0.3428 28 0.6376 25 0.3743 28 32 076a 0.9870 26 0.1917 12 0.5618 24 0.2728 19 14 077a 1.1916 31 0.3854 31 0.6642 29 0.3134 31 27 160a 1.2444 32 0.6412 35 0.7963 33 0.7200 37 37 163a 0.1773 1 0.1577 5 0.1482 1 0.1185 1 10 45 Table A1.2(a) Continued station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 164a 0.3223 5 0.1818 11 0.2514 6 0.2022 10 13 166a 0.6382 14 0.2902 23 0.4827 20 0.2271 14 8 172a 0.7576 18 0.2284 17 0.3448 12 0.2814 21 29 173a 0.3154 3 0.1681 7 0.2182 4 0.1789 9 2 252a 0.3208 4 0. 1746 9 0.2260 5 0.1285 2 6 253a 0.8422 19 0.1529 4 0.4661 19 0.1419 5 11 254a 1.3770 34 0.2710 20 0.6783 30 0.3467 26 23 255a 1.1435 30 0.6489 37 0.7348 31 0.4369 32 30 257a 0.5785 11 0.1604 6 0.3167 10 0.2579 17 4 258a 0.1.0605 27 0.1800 8 0.5520 22 0.2261 12 18 268a 1.1218 29 0.6447 36 0.9270 34 0.4865 34 34 273a 0.2449 2 0.1973 14 0.1844 2 0.1504 7 3 275a 0.8560 22 0.2527 18 0.6461 26 0.2268 13 12 278 1.3028 33 0.3208 26 0.7546 32 0.3410 24 28 279a 2.3086 36 0.2603 19 1.1182 36 0.3448 25 26 280a 0.9371 23 0.3369 27 0.5617 23 0.3590 27 19 282a 1.1098 28 0.1778 10 0.4526 17 0.2030 11 15 349a 0.4750 9 0.1192 1 0.2893 8 0.1321 3 1 46 Table A1.2(b): Average Squared Prediction Errors and Ranks for the Stations in Cluster 2 of Sulfate station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 017a 1.3291 34 0.1449 23 0.5271 34 0.2528 28 29 020a 0.1888 4 0.0608 2 0.1078 4 0.0843 1 3 021a 0.3333 14 0.1646 26 0.1762 12 0.1181 7 21 022a 0.5234 22 0.1127 17 0.2933 23 0.1222 9 19 023a 0.4187 17 0.1028 12 0.2672 19 0.1168 6 14 024a 0.6716 28 0.1069 14 2899 21 0.1224 10 17 025a 0.1418 1 0.0576 1 0.0901 1 0.0849 2 4 028a 0.3180 12 0.2334 32 0.2201 16 0.2571 29 34 031a 1.3176 33 0.1814 29 0.4555 32 0.2687 31 28 032a 0.6416 27 0.1403 22 0.2983 25 0.1907 22 23 033a 0.4842 19 0.0987 10 0.2341 18 0.1457 13 9 040a 0.4650 18 0.0824 8 0.2211 17 0.2070 23 32 041a 0.3411 16 0.1044 13 0.1637 8 0.1551 15 12 046a 0.1753 2 0.0724 4 0.1288 6 0.1203 8 5 047a 0.2546 7 0.1173 18 0.1725 11 0.1658 20 13 049a 1.1425 32 0.2818 35 0.3762 28 0.3556 33 33 051a 0.6200 26 0.1074 15 0.3843 30 0.2287 26 15 053a 1.3871 35 0.2607 34 0.6844 35 0.2801 32 31 47 Table A1.2(b) Continued station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 055a 0.1759 3 0.0710 3 0.1091 5 0.1058 3 2 056a 0.3197 13 0.0774 6 0.2069 15 0.1463 14 6 058a 0.5563 24 0.1076 16 0.3542 27 0.1588 17 11 063a 0.2831 10 0.1369 21 0.1696 10 0.2126 24 16 064a 0.2331 6 0.0800 7 0.0904 2 0.1114 5 20 065b 0.2114 5 0.0877 9 0.1069 3 0.1113 4 1 073a 0.4927 20 0.1788 27 0.3248 26 0.3566 34 26 075a 0.3340 15 0.0737 5 0.1616 7 0.1614 19 7 161a 0.3083 11 0.1519 25 0.1645 9 0.1581 16 27 168a 0.9466 31 0.2448 33 0.4810 33 0.3853 35 35 171a 0.5840 25 0.1354 20 0.3917 31 0.1766 21 22 249a 1.9059 36 0.4026 36 0.8033 36 0.5058 36 36 251a 0.9152 30 0.1861 30 0.2904 22 0.2146 25 30 272a 0.2783 9 0.1025 11 0.1894 13 0.1289 11 8 277a 0.2635 8 0.1335 19 0.1986 14 0.1449 12 10 283a 0.5486 23 0.2327 31 0.3832 29 0.1608 18 25 285a 0.5149 21 0.1798 28 0.2947 24 0.2379 27 24 350a 0.8198 29 0.1493 24 0.2854 20 0.2680 30 18 48 Table A1.2(c): Average Squared Prediction Errors and Ranks for the Stations in Cluster 3 of Sulfate station code Regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 037a 0.1484 1 0.1343 1 0.1334 1 0.1483 1 1 059a 0.3091 5 0.2683 5 0.2711 5 0.2836 5 4 061a 0.2537 4 0.2354 4 0.2403 4 0.2279 4 5 074a 0.4518 6 0.3693 6 0.3812 6 0.4096 6 6 078a 0.1796 2 0.1635 2 0.1659 2 0.1610 2 2 271a 0.2498 3 0.1979 3 0.1983 3 0.2276 3 3 281a 0.5258 8 0.4044 7 0.4604 7 0.5398 8 7 354a 0.5053 7 0.4773 8 0.4767 8 0.4472 7 8 49 Table A1.3(a): Average Squared Prediction Errors and Ranks for the Stations in Cluster 1 of Nitrate station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 004a 3.044 40 0.1882 9 0.1.2349 38 0.182 15 29 011a 1.999 33 0.3615 26 0.6625 28 0.217 24 32 012a 0.752 14 0.6384 39 0.6911 29 0.326 36 39 020a 2.109 34 0.2576 18 0.7614 34 0.157 8 3 021a 0.999 20 0.2444 15 0.7079 30 0.261 30 17 022a 1.815 30 0.4309 32 0.5707 24 0.210 20 11 023a 0.660 11 0.2570 17 0.3090 12 0.161 9 7 024a 0.782 15 0.4638 34 0.4864 17 0.298 33 37 025a 0.613 10 0.1856 8 0.2672 7 0.095 2 2 031a 1.738 29 0.3347 24 0.4893 18 0.192 17 30 032a 0.461 8 0.2451 16 0.2904 9 0.235 27 20 033a 0.555 9 0.1725 7 0.3006 10 0. 155 6 12 038a 1.471 26 0.6027 37 0.5710 25 0.217 23 21 040a 2.632 37 0.1124 3 0.7176 31 0.162 10 33 041a 0.701 13 0.3007 21 0.3036 11 0.211 21 18 046a 1.522 27 0.1496 4 0.3389 13 0.132 3 5 047a 0.850 17 0.2017 11 0.5010 21 0.171 11 6 051a 1.823 31 0.5791 36 0.7359 33 0.296 32 22 50 Table A1.3(a) Continued station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 053a 1.173 21 0.4834 35 0.8777 35 0.417 39 40 055a 0.393 4 0.1682 5 0.2056 5 0.151 5 4 056a 0.952 19 0.1925 10 0.4903 19 0.178 13 10 058a 0.401 5 0.2761 20 0.2590 6 0.172 12 8 063a 0.246 3 0.2021 12 0.1865 3 0.156 7 14 064a 1.462 25 0.1712 6 0.5427 23 0.186 16 13 065a 0.227 2 0.1086 2 0.1122 2 0.134 4 1 076a 0.860 18 0.3605 25 0.4248 16 0.210 19 15 077a 2.716 38 0.7553 40 1.8189 40 0.343 37 38 161a 0.435 6 0.2675 19 0.2726 8 0.234 26 25 166a 0.844 16 0.3775 29 0.5725 26 0.219 25 23 168a 2.435 36 0.4186 31 0.8832 36 0.314 35 31 171a 2.760 39 0.3222 22 0.6364 27 0.201 18 27 249a 0.436 7 0.2332 14 0.2053 4 0.241 28 35 251a 1.265 23 0.3682 27 0.5335 22 0.472 40 36 252a 1.705 28 0.6175 38 0.7186 32 0.284 31 28 253a 2.374 35 0.3954 30 0.9132 37 0.211 22 24 258a 1.263 22 0.2126 13 0.3455 14 0.181 14 16 51 Table A1.3(a) Continued station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 272a 1.437 24 0.3252 23 0.4922 20 0.255 29 19 273a 1.920 32 0.8116 41 1.4461 39 0.298 34 34 277a 3.651 41 0.3754 28 1.8964 41 0.512 41 41 283a 0.106 1 0.0525 1 0.0539 1 0.094 1 9 285a 0.684 12 0.4368 33 0.3922 15 0.370 38 26 Table A1.3(b) Average Squared Prediction Error and Ranks for the Stations in Cluster 2 of Nitrate station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 010a 0.777 4 0.2596 8 0.4208 8 0.299 5 8 017a 0.341 1 0.2267 2 0.2502 1 0.235 1 3 028a 1.511 16 0.8503 26 1.0421 18 0.477 14 10 029a 0.791 5 0.2202 1 0.3050 4 0.465 13 2 030a 2.351 24 0.3922 13 0.9411 16 1.133 27 21 036a 1.201 12 0.3111 9 0.5561 10 0.368 7 6 037a 3.059 27 0.4473 16 1.5074 26 0.659 19 20 049a 1.400 14 0.9118 27 1.2118 25 0.745 21 27 052a 0.569 3 0.2420 5 0.2613 2 0.251 2 5 068a 1.910 20 0.5580 20 1.044 19 0.704 20 26 070a 2.112 22 0.4465 15 0.9874 17 0.394 9 24 071a 0.866 8 0.3748 11 0.4883 9 0.390 8 14 52 Table A1.3 (b) Continued station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 073a 1.626 17 0.5946 21 0.8578 14 0.502 15 15 078a 1.421 15 0.4677 17 0.5850 12 0.644 18 13 160a 1.670 18 1.0074 29 1.1883 23 1.023 25 28 163a 1.164 11 0.2510 6 0.3503 5 0.280 4 11 164a 0.393 2 0.2272 3 0.2668 3 0.310 6 7 173a 1.104 9 0.2545 7 0.5617 11 0.403 10 1 254a 1.728 19 0.4967 18 0.8702 15 0.587 17 12 255a 2.831 26 0.3751 12 1.1103 21 0.412 11 16 257a 0.850 7 0.3524 10 0.4157 7 0.453 12 17 268a 2.190 23 1.9298 31 1.9351 29 1.628 30 29 271a 4.333 31 0.3952 14 1.6762 27 0.516 16 18 275a 1.338 13 0.6996 24 1.1334 22 1.077 26 22 278a 2.420 25 0.9904 28 1.2061 24 1.249 29 31 279a 3.426 28 0.7868 25 1.7865 28 1.209 28 19 280a 3.694 29 0.5129 19 2.1104 30 0.872 22 23 281a 4.231 30 1.0187 30 2.4678 31 1.850 31 30 282a 0.811 6 0.2357 4 0.0.4108 6 0.278 3 4 349a 1.921 21 0.6867 23 1.0605 20 0.887 23 9 354a 1.155 10 0.6291 22 0.7954 13 0.997 24 25 53 Table A1.3(c) Average Squared Prediction Errors and Ranks for the Stations in Cluster 3 of Nitrate station code regression Bayesian 1 Bayesian 2 Stone entropy APE rank APE rank APE rank APE rank rank 059a 0.982 4 0.9444 4 0.9478 4 1.005 4 4 061a 0.911 3 0.8603 3 0.8442 3 0.858 3 3 074a 0.653 2 0.6563 2 0.6572 2 0.737 2 2 172a 0.400 1 0.3996 1 0.4045 1 0.371 1 1 54 Table A2.1(a): Association Measures for Cluster 1 of Hydrogen methods being compared R or W Q p-value regression versus Bayesian 1 0.952 - 0.00004 regression versus Bayesian 2 0.967 - 0.00003 regression versus Stone 0.961 - 0.00004 regression versus entropy 0.911 - 0.00008 Bayesian 1 versus Bayesian 2 0.989 - 0.00002 Bayesian 1 versus Stone 0.996 - 0.00002 Bayesian 1 versus entropy 0.981 - 0.00003 Bayesian 2 versus Stone 0.994 - 0.00002 Bayesian 2 versus entropy 0.967 - 0.00003 Stone versus entropy 0.975 - 0.00003 all methods 0.975 82.923 0.000 Table A2.1(b): Association Measures for Cluster 2 of Hydrogen methods being compared R or W Q p-value regression versus Bayesian 1 0.973 - 0.00001 regression versus Bayesian 2 0.982 - 0.0.000009 regression versus Stone 0.970 - 0.00001 regression versus entropy 0.938 - 0.00002 Bayesian 1 versus Bayesian 2 0.994 - 0.000007 55 Table A2.1(b): Continued methods being compared R or W Q p-value Bayesian 1 versus Stone 0.980 - 0.000009 Bayesian 1 versus entropy 0.980 - 0.000009 Bayesian 2 versus Stone 0.986 - 0.000008 Bayesian 2 versus entropy 0.967 - 0.00001 Stone versus entropy 0.967 - 0.00001 all methods 0.979 93.011 0.0000 Table A2.1(c): Association Measures for Cluster 3 of Hydrogen methods being compared R or W S p-value regression versus Bayesian 1 0.50 - 0.500 regression versus Bayesian 2 0.50 - 0.500 regression versus Stone 0.50 - 0.500 regression versus entropy 1.00 - 0.167 Bayesian 1 versus Bayesian 2 1.00 0.167 Bayesian 1 versus Stone 1.000 0.167 Bayesian 1 versus entropy 0.50 - 0.500 Bayesian 2 versus Stone 1.00 - 0.167 Bayesian 2 versus entropy 0.50 - 0.500 Stone versus entropy 0.50 - 0.500 all methods 0.76 38 0.024 56 Table A2.1(d): Association Measures for Cluster 4 of Hydrogen methods being compared R or W Q p-value regression versus Bayesian 1 0.927 - 0.000 regression versus Bayesian 2 0.939 - 0.000 regression versus Stone 0.879 - 0.001 regression versus entropy 0.673 - 0.019 Bayesian 1 versus Bayesian 2 0.988 - 0.000 Bayesian 1 versus Stone 0.830 - 0.002 Bayesian 1 versus entropy 0.745 - 0.009 Bayesian 2 versus Stone 0.818 - 0.003 Bayesian 2 versus entropy 0.697 - 0.015 Stone versus entropy 0.782 - 0.005 all methods 0.862 38.804 0.000012 Table A2.1(e): Association Measures for Cluster 5 of Hydrogen methods being compared R or W Q p-value regression versus Bayesian 1 0.637 - 0.00016 regression versus Bayesian 2 0.618 - 0.00024 regression versus Stone 0.739 - 0.000015 regression versus entropy 0.810 - 0.000002 Bayesian 1 versus Bayesian 2 0.945 - 0.0000 57 Table A2.1(e): Continued Bayesian 1 versus Stone 0.839 - 0.0000011 Bayesian 1 versus entropy 0.811 - 0.0000022 Bayesian 2 versus Stone 0.813 - 0.0000021 Bayesian 2 versus entropy 0.753 - 0.00001 Stone versus entropy 0.881 - 0.00000029 all methods 0.828 132.42 0.0000 Table A2.1(f): Association Measures for Cluster 6 of Hydrogen methods being compared R or W S p-value regression versus Bayesian 1 1.0 - 0.5 regression versus Bayesian 2 1.0 - 0.5 regression versus Stone 1.0 - 0.5 regression versus entropy 1.0 - 0.5 Bayesian 1 versus Bayesian 2 1.0 - 0.5 Bayesian 1 versus Stone 1.0 - 0.5 Bayesian 1 versus entropy 1.0 - 0.5 Bayesian 2 versus Stone 1.0 - 0.5 Bayesian 2 versus entropy 1.0 0.5 Stone versus entropy 1.0 0.5 all methods 1.0 8 - 58 Table A2.2(a): Association Measures for Cluster 1 of Sulfate methods being compared R or W Q p-value regression versus Bayesian 1 0.705 - 0.000015 regression versus Bayesian 2 0.919 - 0.000 regression versus Stone 0.752 - 0.000004 regression versus entropy 0.713 - 0.000012 Bayesian 1 versus Bayesian 2 0.726 - 0.0000086 Bayesian 1 versus Stone 0.793 - 0.0000014 Bayesian 1 versus entropy 0.847 - 0.0000002 Bayesian 2 versus Stone 0.755 - 0.0000039 Bayesian 2 versus entropy 0.697 0.000018 Stone versus entropy 0.780 - 0.0000019 all methods 0.815 142.61 0.0000 Table A2.2(b): Association Measures for Cluster 2 of Sulfate methods being compared R or W S p-value regression versus Bayesian 1 0.973 - 0.000011 regression versus Bayesian 2 0.982 - 0.0000093 regression versus Stone 0.970 - 0.000012 regression versus entropy 0.938 - 0.000022 Bayesian 1 versus Bayesian 2 0.994 - 0.0000074 59 Table A2.2(b) : Continued Bayesian 1 versus Stone 0.980 - 0.0000096 Bayesian 1 versus entropy 0.980 - 0.0000096 Bayesian 2 versus Stone 0.986 - 0.0000086 Bayesian 2 versus entropy 0.967 - 0.000012 Stone versus entropy 0.967 - 0.000012 all methods 0.979 93.011 0.0000 Table A2.2(c): Association Measures for Cluster 3 of Sulfate methods being compared R or W Q p-value regression versus Bayesian 1 0.976 - 0.000 regression versus Bayesian 2 0.976 - 0.000 regression versus Stone 1.000 - 0.000 regression versus entropy 0.952 - 0.001 Bayesian 1 versus Bayesian 2 1.000 - 0.000 Bayesian 1 versus Stone 0.976 - 0.0000 Bayesian 1 versus entropy 0.976 - 0.000 Bayesian 2 versus Stone 0.976 - 0.000 Bayesian 2 versus entropy 0.976 0.000 Stone versus entropy 0.952 0.001 all methods 0.981 34.3333 0.000 60 Table A2.3(a): Association Measures for Cluster 1 of Nitrate methods being compared R or W Q p-value regression versus Bayesian 1 0.394 - 0.0064 regression versus Bayesian 2 0.877 0.0000 regression versus Stone 0.082 - 0.3 regression versus entropy 0.455 - 0.002 Bayesian 1 versus Bayesian 2 0.587 - 0.00010 Bayesian 1 versus Stone 0.033 - 0.4 Bayesian 1 versus entropy 0.638 - 0.000027 Bayesian 2 versus Stone 0.108 - 0.2 Bayesian 2 versus entropy 0.572 - 0.00015 Stone versus entropy 0.241 - 0.06 all methods 0.514 102.73 0.0000 Table A2.3(b): Association Measures for Cluster 2 of Nitrate methods being compared R or W Q p-value regression versus Bayesian 1 0.65 - 0.00018 regression versus Bayesian 2 0.897 - 0.00000047 regression versus Stone 0.0411 - 0.4 regression versus entropy 0.664 - 0.00014 Bayesian 1 versus Bayesian 2 0.829 - 0.0000029 61 Table A2.3(b): Continued Bayesian 1 versus Stone 0.002 - 0.49 Bayesian 1 versus entropy 0.799 - 0.0000059 Bayesian 2 versus Stone 0.083 - 0.32 Bayesian 2 versus entropy 0.772 - 0.000012 Stone versus entropy 0.102 - 0.28 all methods 0.567 85.059 0.0000 Table A2.3(c): Association Measures for Cluster 3 of Nitrate methods being compared R or W S p-value regression versus Bayesian 1 1.0 0.042 regression versus Bayesian 2 1.0 - 0.042 regression versus Stone 1.0 - 0.042 regression versus entropy 1.0 - 0.042 Bayesian 1 versus Bayesian 2 1.0 - 0.042 Bayesian 1 versus Stone 1.0 - 0.042 Bayesian 1 versus entropy 1.0 - 0.042 Bayesian 2 versus Stone 1.0 - 0.042 Bayesian 2 versus entropy 1.0 - 0.042 Stone versus entropy 1.0 - 0.042 all methods 1.0 125 0.0018 62 Table A3: Names and Identification Codes for the Sites Included in the Study site ID site name site ID site name 004a Fayetteville, Arkansas 070a K-Bar, Texas 007a Hopland (Ukiah), California 071a Victoria, Texas 010a Rocky Mt. Net park, colorado 073a Horton's Station, Virginia 011a Manitou, Colorado 074a Olympic Nat.park, Washington 012a Pawnee, Colorado 075a Parsons, West Virginia 015a Bradford Forest, Florida 076a Trout Lake, Wisconsin 016a Everglades Nat.Pa, Florida 077a Spooner, Wisconsin 017a Georgia Station, Georgia 078a Yellowstone, Wyoming 020a Bondville, Illinois 160a Alamosa, Colorado 021a Argonne, Illinois 161a Salem, Illinois 022a Southern Ill U, Illinois 163a Caribou (a), Maine 023a Dixon Springs Illinois 164a Bridgton, Maine 024a NIARC, Illinois 166a Fernberg, Minnesota 025a Idiaana Dunes, Indiana 168a Huntington, New York 028a Elkmont, Tennessee 171a WalkerBranch, Tennessee 029a Mesa Verde, Colorado 172a American Samoa, American Samoa 030a Greenville Station, Maine 173a Sand Spring, Colorado 031a Douglas Lake, Michigan 249a Bennington, Vermont 032a Kellogg, Micigan 251a NACL, Massachusetts 033a Wellston, Michigan 252a Ashland, Missouri 034a Marcell, Minnesota 253a University Forest, Missouri 035a Lamberton, Minnesota 254a Forest Seed Ctr, Texas 63 Table A3: Continued 036a Meridian, Mississippi 255a Newcastie, Wyoming 037a Glacier Nat. Park, Montana 257a Acadia > 11/81, Maine 038a Mead, Nebraska 258 Chassell, Michigan 039a Hubbard Brook, New Hampshire 268a Warren ZWSW, Arkansas 040a Aurora, New York 271a Headquarters, Idaho 041a Chautauqua, New York 272a Purdue U Ag Farm, Indiana 046a Bennett Bridge, New York 273a Konza Prairie, Kansas 047a Jasper, New York 275 Iberia, Louisiana 049a Lewiston, North Carolina 277a East, Massachusetts 051a Piedmont Station, North Carolina 278a Give Out Morgan, Montana 052a Clinton Station, North Carolina 279a Bandelier, New Mexico 053a Finley (a), North Calorina 280 Cuba, New Mexico 055a Delaware, Ohio 281a Bull Run, Oregon 056a Caldwell, Ohio 282a Longview, Texas 058a Wooster, Ohio 283a Lake Bubay, Wisconsin 059a Alsea, Oregon 285a Washington Xing, New Jersey 061a H.J. Andrews, Oregon 339a Bellville, Georgia 062a Teddy Roosevelt NP, North Dakota 349a Southeast, Louisiana 063a Kane, Pennsylvania 350 Wye, Maryland 064a Leading Ridge, Pennsylvania 354a St. Mary Ranger St, Montana 065b Penn State, Pennsylvania 068a Grand Canyon, Arizona 64 Nitobs reg bayt bay2 atobs reg bayt bay2 N stobs reg bayt bay2 O IV 024a method 051a Eti method 030a method 052a obi rag bayt bay2 st method 1 63a obs reg bay I bay2 method Stobi rag bays bay2 O obs reg bayt bay2 obs reg bay I bay2 at method method 015a obi rag bayt bay2 at method 036a obit rag bay 1 bay2 method 053a 017a method 049a obs rag bay 1 bay2 at method 076a N obs rag bayt bay2 at mehod 077a i 1-"" 43*I LTj "rj 1 Figure Al .1(a): Boxplots of Observed and Predicted Values for Cluster 1 of Hydrogen Legend: obs=observed, reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure 0r) 0in O stobs reg bay 1 bay2 0 obs rag bay 1 bay2 at obs reg bay 1 bay2 at method methodmethod 283a I 04 I I yEls i 0 04 obs reg beyl bay2 st method 339a obs reg beyl bay2 st obs reg bay 1 bay2 Figure A1.1(a): Continued 252a 253a 258a 268a method method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure obs rag bay I bay2 at rd I I ÷ ble1 1+1' I • obe rag bay 1 bay2 method 061a obs rag bays bay2 method 078a method atobs reg bayl bay2 Itobe rag bayl bay2 obs rep bey I bay2 a method 037a obs rag bald bay2 at method 062a obs reg bays bay2 st method obe reg bayl bay2 st method 038a obs rag bays bay2 method 068a obe reg bayl bay2 at method method 059a method 074a obs reg bay 1 bay2 method O 0 N O N 0 O O Figure A1.1(b): Boxplots of Observed and Predicted Values for Cluster 2 of Hydrogen 010a 011a 012a 016a Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure 173a . obs rag bayt bay2 It Figure A1.1 (b): Continued 255a -LI + 0 i43 the reg bay 1 bay2 st 271a 1-1-pp&i. 1 I polio i I Lij l'T obs rag bay 1 bay2 at Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure It St method 280a method 281a method method method 279a F-Li 1 1 'FA EiI I 1 I obs rag bayt bay2 method method 354a 43 IIE il riI I obs rag bayl bay2 method N 0 g st I 1I obs rag bayt bay2 the rag bayl bay2 st _L. 1 ± I qa 1 I I 172a obs rag bayt bay2 st Figure A1.1(c): Boxplots of Observed and Predicted Values for Cluster 3 of Hydrogen 007a 070a 035a . T I I I I ,., T 1.{1=1 Erf] 1=3 -i-- -+- -+- --I- C ,,, 1 1 1 °be rag butyl method bay2 st obs rag bay 1 method bay2 et obs rag bay' method bay2 et Legend: obs=observed, reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure Figure A1.1(d): Boxplots of Observed and Predicted Values for Cluster 4 of Hydrogen 004a [-Li [19 Ggim 1 • • rep bey 1 bay2 it method 166a obs rep bap bay2 at method 282a rag bayl bay2 at method atobs rep bay 1 bay2 O O the rep bayl bay2 st obs rep bayl bay2 st obs rep bay 1 bay2 at method 349a method method 029a the rep bay I bay2 it method 254a 034a method 273a 071a the rep bay I bay2 method 275a 0 0 + EP 13 E13 E1.3 obs reg bayl bay2 st method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure ,„ O obs reg bays bay2 method 032a at 0 0 0 4 0 01 obs rag bays bay2 method at method 033a obs reg bay 1 bay2 at method method 039a obs mg bays bay2 at method method 040a obs rag bay 1 bay2 at method atobs reg bayl bay2 atobs reg bays bay2obs reg bay 1 bay2 et m *Mod 025a obs rig bayl bay2 at ota reg bay 1 bay2 at method 028a metiod 031a obs rag bays bay2 at obs rag bays bay2 at mabod 041a Figure A1.1(e): Boxplots of Observed and Predicted Values for Cluster 5 of Hydrogen 020a 021a 022a 023a Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure eta nYI obs reg bey I bay2 st obs reg bay 1 bay2 at stobs reg bays bay2 method 058a the rag beyl bay2 method 073a the rug bayl bay2 st method method 063a the reg bayt bay2 method 075a method O O O obs rag bay bay2 obs rag bays bay2 at method 064a method 065b obs rag bays bay2 st abs rag bayl bay2 st metiod 161a method 164a * -'1 E1=4 + the rag bay 1 bay2 st the rep bayt bay2 st method method Figure A1.1(e): Continued 046a 047a 055a 056a Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure r. obs rep bay 1 bey2 at obs rep bay 1 bay2 method st 171a 249a O 168a stobs mg bay 1 bey2 obs repag bay I bay2 st obs rep bay 1 bey2 st method 350a method obs rep bey 1 bay2 et method 257a method 272a T 251a obs rag bays bay2 st method 285a obs rep bay 1 bay2 method m ehod 277a I Figure A1.1 (e): Continued obs rep bay I bay2 St method Legend: obs=observed, reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure r t 1 EE!=i mmosale∎.L. T 1 1 J. • T T I 1 I=Ei=1 i Figure A1.1(f): Boxplots of Observed and Predicted Values for Cluster 6 of Hydrogen C \ i 0 Cij 160a 278a obs reg bayl st obs reg bayl st method method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure r? obi reg bay 1 bay2 st method 023a method 024a stobi rag bay 1 bsy2 itobi reg bay 1 bay2 itobs nog bay 1 bay2 Figure A1.2(a): Boxplots of Observed and Predicted Values for Cluster 1 of Sulfate 017a 020a 021a 022a i I YT l i' EP •1 1- method 031a obs rag bay 1 bsy2 method meted 032a 17r-11 E0E1m* method O o Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure obs rag beyt bay2 st obs rag bays bay2 at method 025a method 028a O T stobs rag bay 1 bsy2 cbs reg bays bsy2 st method 033a meted obs rag bays bay2 v method 040a cbs reg hart bay2 method O obs rig beyt bay2 +11 4'4' 43 EIP* 44 E15 I Figure Al .2(a): Continued 041a 046a 047a 049a O + 42-.°I I I i l l 1 - 1,4;11 1 1 T I obs rag bay I bay2 to obs reg bayl bay2 at obs rag bay 1 bay2 st obi reg bay 1 bay2 it atobs rag bay 1 bay2 atobs reg bays bay2 I I T Itobs rag bays bay2 obsat method 051a obs rag bays bay2 method 058a obs rig bays bay2 method method 053a ft, method 063a [4g* ,=L1 obs rag bays bay2 st method method 055a 1*3 method 064a obs reg bay 1 bay2 mehod method 056a method 065b rag bay I bay2 method O Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure 075a a obs leg bayt bay2 st I I 145 rtobs rag bayt bay2 Figure A1.2(a): Continued 161a El=1 EJ* 3 obs rag bayl bay2 at 168a Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure TT I I method 272a Ei t11+1 41 + obs reg bay I bay2 method 350a obs rag bayt bay2 st method obs reg bays bay2 at method 277a obs rag bayt bay2 st method method 283a obs rag bayt bay2 st method method 285a method O meted m 'Cod 251a st method 249a oba leg bayt bay2 obs rag bayl bay2 171a obs rag bay 1 bay2 st O 9 N 0 O 073a obs rag bay 1 bay2 N 0 N O Figure Al .2(b): Boxplots of Observed and Predicted Values for Cluster 2 of Sulfate 004a 010a 011a O E=4] 6E1=3 • • I t*i I obs reg bayl bay2 St obs cog bay I bay2 obs tog bayl bay2 method method method 01 2a 029a 030a Ill It II Ea91 1: O obs teg bar! bay2 at obs rag bay l bay2 a abs nag bay I bay2 method method method 034a 035a 036a Cg i*a I -4— 43 ÷ obs rag bays bay2 st obs rag bayl bay2 obs cog bayl bay2 st method method method O Legend obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure Eli3 I 1 rtriO Figure A1.2(b): Continued 038a 039a 052a 0 O obs rep bay' bay2 St abs rep bay I bay2 at obt rep bay 1 bay2 at method method method 068a 070a 071a O 0 abs rag bayl bay2 St abs rep bay2 obs rag bar! bay2 IR method method method 076a 077a 160a obs nag bay 1 method bay2 St abs reg bay l method bay2 abs rag bay 1 method bay2 It Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure ON 0 E1E atoba it abs rugbay2baylreg bayl bay2 method 253a EI=A I 1 I --I- 1 method 254a N 0 0 Figure A1.2(b): Continued 163a 164a 166a rag bayl method 172a bay2 obi reg bays method 173a bay2 at obi rag bap method 252a bay2 obi reg bay I bay2 It obe reg bay 1 bay2 at obi rag bayl bey2 at method melhod method Legend: obs=observed, reg=ordinary regression, bayl=regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure Figure A1.2(b): Continued 257a 258a 268a 273a E Elf]+*t EHi14]Ert N 0 Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure abs rag bays bay2 It the reg bayt bay2 st obs reg bayt bay2 . obs reg bays bay2 st obs reg bayt bay2 st method 275a method 278a method 279a N `1, '=*' + ±43 ri-' obs rag buy I bay2 It obe reg bayt bay2 st obs reg bayt bay2 at method 282a method 349a method itobs reg bayt bay2 m N obs re; bayt bay2 st method El i i i43+43 method 0 method 280a method ,„ . * 1 63EIDEI, I 0oba rag bay' bay2 method 078a 14=i*Epit oba rag bayl bay2 at method 271a O the rag bay' bay2 at abs rag bayt bay2 method method 281a 354a Figure A1.2(c): Boxplots of Observed and Predicted Values for Cluster 3 of Sulfate 037a 059a 061a 074a O O 0 r.TEt3E .m] Eia oba rag bay' bay2 at oba rag bay' bay2 at oba rag bayl bay2 It abs rag bayl bay2 method method method method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure NO N 0 004a the rig bay 1 bay2 011a obs reg bay I bay2 012a abs rig bays bay2 st 020a obs rag bayl bay2 O the rag bays bay2 st Figure A1.3(a): Boxplots of Observed and Predicted Values for Cluster 1 of Nitrate method method method method 021a 022a 023a 024a to, N 0 N obs rig toy I bay2 it [+] the reg bay I bay2 it 441 EH3 -9- obs rig bays bay2 st method method matted method 025a 031a 032a 033a C 0 Ei]N O Plq EP El2 N 0 the rig bayl bay2 it obs reg bay 1 bay2 st obs reg Dart bay2 st the rag bayl bay2 method method method method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure 0obs reg bayl bay2 method 051a st obe reg bayl bay2 method 058a at N N Itthe reg bays bay2 038a obs reg bowl bay2 at method 047a obs rag bay I bay2 method 056a 041a 046a obs rag bayl bay2 method 055a method 064a 9 040a N O obs rag bayl bay2 et method 053a abs rag bay 1 bay2 method 063a at 0 obsatobs rag beyl bay2 I e+s ril I 1 obs reg bayl bay2 st the reg bayl bay2 rag bay 1 bay2 N O Figure Al .3(a): Continued method method method method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure 076a 077a <NI O oba rag bayl bay2 at obs reg bays bay2 at method 168a method 171a obe rag bays bay2 at the reg bays bay2 at method 252a method 253a N O °bet reg bayl bay2 at the rag bayl bay2 it method method atobs rag bay/ bay2 N O O N O itthe reg bayl bay2 065b obs reg bays bay2 at method 166a method 251a obs reg beyt bay2 method 161a the rag bay 1 bay2 method 249a obs rag bayl bay2 method 258a method Figure A1.3(a): Continued • • O Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure Figure A1.3(a): Continued <NI O O 272a obe reg bey I bay2 at method 285a obs reg bay I bay2 273a oba reg bay 1 bay2 it method 277a 1-1,-1 4g Ejg obs reg bay 1 bay2 it method 283a E43 El3 4E, obs rag bays bay2 method method Legend: obs=observed, reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure + [1 obs rag bey* bay2 st method 030a obs rag bay* bay2 at method 036a O obs rig bayl bay2 St ate rag bays bay2 st method method 037a 049a 0 •n••••n• 1=]0 O atobe rig bayl bay2 stobs reg bay I bay2 14g obs rag bawl bay2 obs rig bays bay2 O O O Figure Al .3(b): Boxplots of Observed and Predicted Values for Cluster 2 of Nitrate 010a 017a 028a 029a obe rag beyl bay2 st obs rag beyl bay2 st obs rag bay 1 bay2 at obs rig bayl bay2 st method method method method 052a 068a 070a 071a method method method method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure Oobit rag bays bay2 st Figure A1.3(b): Continued 078a 160a obs reg beyt bay2 st 163a 4;3 obs reg bay 1 bay2 073a obs nag beyt bay2 st O obs rag beyl bay2 method st O obs rag bay 1 bay2 st Stobs rag beyt bay2 atobs rag beyt bay2 obs rag bayl bay2 at O 257a obe rag bay 1 bay2 st T T • r—:— 0 I 0 • 4 t i reg bays method 268a bay2 st m eihod 271a ita --1-- mahod 275a obs reg bayl bay2 oba 49 method method method method 164a 173a 254a 255a method method method method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure 278a obs reg bey1 bay2 method 282a a N Figure A1.3(b): Continued 279a 280a N obs reg bay 1 bay2 st obs reg bey 1 bay2 at method method 349a 354a 281a abs nag bay 1 bay2 so method O I+ [43 E4-3 N 0 obs reg bay I bay2 obs reg bay 1 bay2 St obs mg bay I bay2 st method method method Legend: obs=observed, reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure 1Figure A1.3(c): Boxplots of Observed and Predicted Values for Cluster 3 of Nitrate 059a 061a 074a 172a a O , • Ei3 E1E13--1- • oba reg bayl bay2 ohs rag bays bay2 at obs reg bayl bay2 at abs rig bayl bay2 method method method method Legend: obs=observed, reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2 = Bayesian alternative (2) approach, st= Stone's procedure rag bayl bay2 at 0 0 reg bays bay2 method at 077a •••S N • El=30 0 N 1 1E 1 1 i 1* at atrag rag baysbayl bay2 bay2 method 053a method 076a method 163a Figure A2.1(a): Boxplots of Prediction Errors for Cluster 1 of Hydrogen a 015a E4 g 113 ELI 017a 1 024a 030a rag bay t bay2 at rag bays bay2 at reg bays bay2 it rag bay 1 bay2 st method method method method 036a 049a 051a 052a rag bay.' bay2 t reg bay 1 bay2 at rag bay 1 bay2 It rag bayl bay2 at method method mehod method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure rep bayl bay2 mehod method ..,T I N 0 I ''':' 1 at method 339a i 1 iElEit+39E stbay2baylrag method 283a Figure A2.1 (a): Continued 253a 258a 0., . T 252a I T T I I I II I 11 ' 1 268a 1 T 1 ......... +1 1 'TI T T [ 1 1 I T 1- I I .s. I CM O reg bayl bay2 st rag bayt bay2 at reg bayl bay2 st rag bays bay2 at method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure rag bay I bay2 rag bays bay2 St I I rag bays bay2 st rag bays bay2 at O O O O rep bay 1 bay2 at N O 011a 0.1 0 O 012a T T I 1] Fg I at rag bayl bay2 at method rag bay I bay2 st mettod at method 074a 010a method 037a method 062a O rag 038a 1 bayl bay2 method 068a 059a 016a method 061a method 078a I I Figure A2.1(b): Boxplots of Prediction Errors for Cluster 2 of Hydrogen 0 i • i I 1 , 1 I Eta TN O rag bay I bay2 at rep bay bay2 at reg bay 1 bay2 at rag bays bay2 at method method m °Cod method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure OT Figure A2.1 (b): Continued 172a 173a 255a 271a orO O O T I T II [49 ElE3 . . reg bey1 bay2 et rag bay I bay2 at reg bay 1 bay2 st rog bey t bay2 it method method mehod method 279a 280a 281a 354a rag beyl bay2 it reg bay 1 bay2 it reg beyl bay2 at rig bays bay2 at method method meted method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure T1 . Figure A2.1 (c): Boxplots of Prediction Errors for Cluster 3 of Hydrogen 007a 070a 035a T reg bayl baY2 at nog bay I bay2 reg bey I bey2 at method method method Legend: reg=ordinary regression, bay1 =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure Figure A2.1(d): Boxplots of Prediction Errors for Cluster 4 of Hydrogen 004a 029a 034a 071a at at atat reg rag nagreg beyl baylbayl baylbay2 bay2 bay2bay2 method 282a method 349a method method at atrag rag baytbayt bay2 bay2 method method method 166a Eig+11=1=1 method 254a ÷+Ii+IE+9 method 273a method 275a filE19 +EI0 ifJp[4=1Elf=iO Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure at atragbay2 bay2bayl baylrag atbay2baylrag N 0 reg ITT T 1 11 1 11 1 111 bayl bay2 at Itat atbaylreg rag baysbay 1rag regbay2 method 025a bay2 method 032a bay2 method 031a bays bay2 method 028a Figure A2.1(e): Boxplots of Prediction Errors for Cluster 5 of Hydrogen 020a 021a 022a 023a I (I I I + I 1 C C Elm T 1[41C C 1 1 rag bay 1 bay2 at rag bay I bay2 at reg bay 1 bay2 at rag bayl bay2 It mthhod method method method 033a 039a 040a 041a 1 1 1 E43 41 Etie tElE3 C 17 rag bays bay2 at bays bay2 at reg bay I bay2 at rag bayl bay2 at method method method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, . bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure 0 0I TI j I I EIE1 1 I T 1 1 T I i 7 19' 0 N O O 1 I E*31 +1 43 I • • ei=3[1E3 Figure A2.1 (e): Continued 046a 047a 055a 056a reg bayt bay2 et bay 1 bay2 at reg bayl bay2 et nag bayt bay2 matted method mehod method 058a 063a 064a 065b reg bayl bay2 et reg beyl bay2 at reg bays bay2 st reg bayt bay2 et method method method moe,od 073a 075a 161a 164a rag beyt bay2 reg beyt bay2 et reg bayl bay2 et reg bayt bay2 method method method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure N N C N C T 1 I T IT 1 I 1 T I I I+ ( I II i T 1 Figure A2.1 (e): Continued 168a 171a 249a 251a I Ti 1 ÷ 1 II 1E? N N ,, N i IEi 1 T1 I [=IC .. rag bap bay2 st rag bap bay2 at ray bayl bay2 at rag bayt bay2 st method method mathod method 257a 272a 277a 285a 09 bayt bey2 st nag bayt bay2 St reg bayt bay2 St reg bayt bay2 at method method method method 350a I T I I 1II nag bayt bey2 st method Legend: reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure I1 I IJ_ 1.1. 278a reg bayl st Figure 2.1(f): Boxplots of Prediction Errors for Cluster 6 of Hydrogen 160a T 0? T T T I I -1- I I reg bayl st method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure Iat atatnag reg ragreghartbayt bayl bar!bay2bay2 bay2 bay2 l i I I I I rag bayt bay2 at rag bayt bay2 at rag WTI bay2 it rag bayt bay2 at mehod 033a method method 040a032a I I T 1 atat at atrigrag ragregbayl aytbayl aytbay2bay2 bay2 bay2 method method mehod method Figure A2.2(a): Boxplots of Prediction Errors for Cluster 1 of Sulfate 017a 020a 021a 022a 4iBimi4E00 method 023a I I ÷ method 024a mehod 025a 4p4aE#1* method 028a ITIE13+÷0 0 method 031a I T Eti I 1 =1 I • 4-36+2E*40 Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure T rag bay 1 bay2 method 051a I ., bay 1 bay2 T I I I st st at stNgrag regMg bay 1bity1 bay I bapbay2 bay2 bay2bay2 method method method m (shod Figure A2.2(a): Continued 041a bay t bay2 method 058a I I falm 1 I 1 Ela1 1 . 046a 047a 1 1 45 Et' ElEi m "hod 055a i • • 1 1 (# *1 + 049a 43 EEi + i] I I 43j-"E+1i t 1 1 ElEi 43 ÷1 . ,1, . C . nag Legend: reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure I 13 etIt ragbay2baysnag method 053a st sttog bay2bay 1 method 056a st St st sttagbay2bay 1rag tag bay2bay2bays bay 1 method 063a method 064a method 065b : ,,, . C tV . I NO 49 ÷ etet at atrag rag ragreg baylbayt bayt bayl bay2bay2bay2 bay2 method 171a method 249a method 251a method 272a at atatat ragrag rag regbaylbayl bayt bayt bay2bay2 bay2 bay2 [4A at at atatrag rag ragragbayl bayt bayl bayl bay2bay2 bay2 bay2 method method method method Figure A2.2(a): Continued 073a 075a 161a 168a method 277a method 283a method 285a method 350a ,„ O O O Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure T 43* Figure A2.2(b): Boxplots of Prediction Errors for Cluster 2 of Sulfate 004a 010a 011a N 0 T 14:1 I' E+, lag bayl bay2 st rag bay 1 bay2 a rep bay/ bay2 method 012a method 029a method 030a T t 0 rag bar! bay2 st rep bays bay2 rep bay bay2 C N 0 rf method 034a m othod 035a TIT li I ► T 1 1 method 036a N 0 rep beyl bay2 St rag beyl bay2 mg bay 1 bay2 method method method Legend: reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure I 0 0 T 1 baylrag bay2 bay2rep bayl 4:4 reg bayl bay2 at Figure A2.2(b): Continued 038a 039a 052a reg bayl bay2 at rag bay I bay2 rep beyl bay2 at method method method 068a 070a 071a N to 0 method 076a 0 . T __ }3 I . method 077a method 160a rT 11 T 11 I EE:Ei 1 1 • reg bayl bay2 at reg bayl bay2 at rag bayl bay2 at method method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure T I 1 1 1 1=PI 1 0 0 T II T I T T Figure A2.2(b): Continued 163a 164a 166a rag bayl bay2 a t reg bays bay2 bayt bay2 It method 172a method 173a method 252a 1 1 rag bay' bay2 It rag bay' bay2 to reg bays bay2 at method method method 253a 254a 255a 0 0 rag bays bsY2 at reg bayl bay2 at rag bayl bay2 method method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure Figure A2.2(b): Continued 257a 258a 268a 273a N O N 0 O rag bayt bay2 at rag bayl bay2 at reg bayl bay2 at rag bayl bay2 at method method method method 275a 278a 279a 280a 0 E] Ela 41 rag bayl bay2 at reg bayl bay2 t reg bayl bay2 at rag Dart bay2 at method method method method 282a 349a O 13 r N O rag bay t bay2 at reg bayt bay2 at method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure 0 • O reg bay', bay2 Figure A2.2(c): Boxplots of Prediction Errors for Cluster 3 of Sulfate 037a 059a 061a 074a N 0 0 ilil I- 11 IL 1 l y N O [43EiJ* E1E' reg beyl bay2 at rag bays bay2 at tog bayt bay2 $1 reg bayl bay2 It 9 method 078a method method 271a method method 281a method method 354a FT, 1E+9 +E13. matod rig bayt bay2 at rag bayt bay2 at tog bay2 O at Legend: reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure Figure A2.3(a): Boxplots of Prediction Errors for Cluster 1 of Nitrate 004a 011a 012a 020a reg tart bay2 et reg beyl bay2 et Sag bayt bay2 at rag bayt bay2 at O method 021a I I ÷ I I 14aI I method 022a method 023a Ef3 method 024a t 1 El] [iA q= nig bayt bay2 St rag bayt bay2 at rag bayt bay2 at rag bayl bay2 al method method method method 025a 031a 032a 033a N N O 4E1 1E1E3 nag bayt bay2 St reg bayt bay2 at reg bayt bay2 It reg bayl bay2 It method method method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure Figure A2.3(a): Continued 038a 040a 041a 046a O • 0 Eep O El=a 14=g rag Day 1 bay2 St rep bayt bay2 at reg bay/ bay2 at reg bayl bay2 at method method method method 047a 051a 053a 055a O O rag bayl bay2 st rag bayt bay2 st reg twirl bay2 at reg bayl bay2 at method method method method 056a 058a 063a 064a O reg bayi bay2 st rag bayt bay2 at rag bayl bay2 st rag bayt bay2 St method method method method Legend: reg=ordinary regression, bay1=regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure o . E 1 E N 0 0 atrag bay I bay2 Ejl+ E=D+ atbey2bey 1tO9 method Ei;3 14a EO E 4 4 s reg bayl bay2 method at rag bay biy2 methodmethod N 0 • N O Figure A2.3(a): Continued 065b 076a 077a 161a I Ti I If [ = Eif O E = + N O rag bayl bay2 reg bay 1 bay2 It reg bayl bay2 at reg bays bay2 at method method method method 166a 168a 171a 249a reg bayl bey2 at rag bey 1 bay2 at nag bayl bey2 at rag bayl bay2 or method method method method 251a 252a 253a 258a Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure +G;PEI=J 4 Figure A2.3(a): Continued 272a 273a 277a 283a . , i 1 IEElat1 6+3 1 : Fi] E4=J Ejq÷ ElE t* *{Ig rag bayt bay2 or reg bayl bay2 st reg bey1 bay2 at rag bayl bay2 at method method method method 285a [4=]Eig*[+] rag bays bay2 at method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure Figure A2.3(b): Boxplots of Prediction Errors for Cluster 2 of Nitrate 010a 017a 028a 029a O 0O r+a rag beyl bay2 It rag beyl bay2 st bayl bay2 st reg beyl bay2 st method method method method 030a 036a 037a 049a Ei="1=3 11E3 ÷ EiE O N O 0 O rig beyt bay2 st rag bays bay2 st reg bayl bay2 rag bayl bay2 st method 052a method 068a mettod 070a method 071a O O N O rag bays bay2 It rag bayl bay2 st rag bayl bay2 st bays bay2 It method method method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure $1 at atst regrag ragreg beyl bayl1340 bayl bay2bay2 bay2 bay2 at at atregrag reg ragbaylbay l bayl baytbay2 bay2bay2 bay2 atst at regreg rag ragbeylbayl baytbay 1 bay2bay2bay2 bay2 method matted methodmethod Figure A2.3(b): Continued 073a 078a 160a 163a method 255a method 254a ; • I i 1 11, 1 1I 1 1 i El=;EEitE49 142÷E=Ine== G#I ET3 14S3 O method 257a m ertmd 268a t*3 14ataa÷ method 271a method 275a O CS Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure [4]E144] O N O 1 T method 164a method 173a N O Figure A2.3(b): Continued 278a 279a 280a 281a 0 0 O O tog bays bay2 it rag bayt bay2 at rig bay t bay2 at rag bayt bay2 Mr at method method method method 282a 349a 354a N Mr co • nag bayt bay2 st rag bayt bay2 it rag bay 1 bay2 method method method Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure NO a a O 2 T 1 T st stragbay2bayl bayl regbay2 bay2bayt tog methodmethod method Figure A2.3(c): Boxplots of Prediction Errors for Cluster 3 of Nitrate 059a 061a 074a 172a tog bay1 bay2 m oOtod Legend: reg=ordinary regression, bayl =regression using a Bayesian alternative (1) approach, bay2= regression using a Bayesian alternative (2) approach, st= Stone's procedure SI 5 10 15 20 25 30 1rgent 2bay1ent --- 3bay2ent -- 4stent L 0 0 co. 0 a) a) a_ 1rgent 2bay1 ent --- 3bay2ent -- 4stent N .0 T". co 0O 5 10 15 20 25 30 Figure A3.1 (a): Relative Measure of Figure A3.1 (b): P-value for Tests of Agreement for Hydrogen Agreement for Hydrogen cluster size cluster size Legend: rgent = regression with entropy, bayl ent= Bayesian alternative (1) with entropy bayl ent= Bayesian alternative (2) with entropy, stent = Stone's procedure with entropy Figure A3.2(a): Relative Measure of Figure A3.2(b): P-value for Tests of Agreement for Sulfate Agreement for Sulfate 0 00) 0 1rgent 2bay1ent --- 3bay2ent -- 4stent u") r-- _ 0 0N _ ci 10 15 20 25 30 35 cluster size 10 15 20 25 30 35 cluster size Legend: rgent = regression with entropy, bayl ent= Bayesian alternative (1) with entropy bay1ent= Bayesian alternative (2) with entropy, stent = Stone's procedure with entropy 0. T.... CO 0 10 20 30 40 10 20 30 40 0 cv _ / / 4 d / / / / / / / / / 1rgent -------- 2bay1ent --- 3bay2ent -- 4stent 0. o d 0 ci Figure A3.3(a): Relative Measure of Figure A3.3(b): P-value for Tests of Agreement for Nitrate Agreement for Nitrate cluster size cluster size Legend: rgent = regression with entropy, bayl ent= Bayesian alternative (1) with entropy bayl ent= Bayesian alternative (2) with entropy, stent = Stone's procedure with entropy 00. a) rn > 0 1 reg 2bay1 3bay2 4stone 1 3/ 1 1 0 /1 3/. / / 1 -1-1 4-4z4,2 ,1 - /2-2 ,1 food 1 1 reg 2bay1 3bay2 4stone Average Prediction Errors for Hydrogen in Ascending Order Figure A4.1 (a): Cluster 1 Figure A4.1 (b): Cluster 2 co (.1 o 5 10 15 5 10 15 20 ordered ranks ordered ranks Legend: reg=regression,bayl = Bayesian (alternative)), bay2= Bayesian (alternative2) Stone = Stone's procedure CO T-- (0. , 0 Cn1 N T Average Prediction Errors for Hydrogen in Ascending Order Figure A4.1(c): Cluster 3 Figure A4.1 (d): Cluster 4 1.0 1.5 2.0 2.5 3.0 2 4 6 8 10 ordered ranks ordered ranks Legend: reg=regression, bay1= Bayesian alternative (1), bay2= Bayesian alternative (2) Stone = Stone's procedure 1 1 1.0 1.2 1.4 1.6 1.8 2.0 T in. N. T. CO T 0. ,-I i 11 11 11 .3 3 33333332 )11113 3333 ,4 4402 11 11 111313 iiiMiliffliftg ggag4W22 1 reg 2bay1 --- 3bay2 -- 4stone 1 / 1 1 / 1 a 0 cv 0 5 10 15 20 25 30 Average Prediction Errors for Hydrogen in Ascending Order Figure A4.1(e): Cluster 5 Figure A4.1(f): Cluster 6 ordered ranks ordered ranks Legend: reg=regression, bay1= Bayesian alternative (1), bay2= Bayesian alternative (2) Stone = Stone's procedure 1 reg 2bay1 3bay2 4stone 1 1 1 1 1/ ' i4H°°°°WP122? 3333333333333 444?3 .333 /4 111 1 1 reg 2bay1 3bay2 4stone 1 3 1 1 1 1 11 11 111 1 111 333333333 ,33 33333333333:31 311 24333 111 1 ;111 11 4 44AAAA4A4d444AA44 4f14 ' 4 111 1 1 0 0 0 0o - O a) rn E. B. tr?0 r) 0.1 0 Average Prediction Errors for Sulfate in Ascending Order Figure A4.2(a): Cluster 1 Figure A4.2(b): Cluster 2 0 10 20 30 0 10 20 30 ordered ranks ordered ranks Legend: reg=regression, bay1= Bayesian alternative (1), bay2= Bayesian alternative (2) Stone = Stone's procedure 2 4 6 8 (N0 Average Prediction Errors for Sulfate in Ascending Order Figure A4.2(c): Cluster 3 ordered ranks Legend: reg=regression, bay1= Bayesian alternative (1), bay2= Bayesian alternative (2) Stone = Stone's procedure 1 reg 2bay1 3bay2 4stone 1 ,1 1 1 1 1 1 11 1 ,3/ 11 3 1 1 .33 4 /3 / 3 3 3 3:- 3334 43 34 4 24 ‘2 42 2 A22222222 1 - 0 5 10 15 20 25 30 0 10 20 30 40 - 1 reg 2bay1 3bay2 4stone 1 1/1111 33 /1111 i ;1 11 3 /3 1 1 I 1111 333 3 22 33333333333333333 2222 23,0222V2222222222222 44 44"4" 14"4444444444444444 0 - T". Average Prediction Errors for Nitrate in Ascending Order Figure A4.3(a): Cluster 1 Figure A4.3(b): Cluster 2 ordered ranks ordered ranks Legend: reg=regression, bay1= Bayesian alternative (1), bay2= Bayesian alternative (2) Stone = Stone's procedure 0. T cr? 0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Average Prediction Errors for Nitrate in Ascending Order Figure A4.3(c): Cluster 3 ordered ranks Legend: reg=regression, bay1= Bayesian alternative (1), bay2= Bayesian alternative (2) Stone = Stone's procedure BIOGRAPHICAL INFORMATION NAME: Iko tau tac.,,Ot\A ) MAILING ADDRESS: 1--4P 'cm ' t`"1 STLC-5 thi-k.1/4‘) et-a-5 0 s ft-L.h itt4 P - f=t7X 5o4 G ft-L. ft-A-I,A rti-V1.ft+al pt PLACE AND DATE OF BIRTH: ri • mot-iv—CV z ke E4t-r-V2A-1,0 EDUCATION (Colleges and Universities attended, dates, and degrees): V NI 12-511Y er -F A-vs-6-s S Prisik4 Cyl, c=z-h c-t tac^tv c-QS tI`f voe% its vt co Lult, •st teki 2_ MSc_ POSITIONS HELD: S PUBLICATIONS (if necessary, use a second sheet): AWARDS: Complete one biographical form for each copy of a thesis presented to the Special Collections Division, University Library. DE-5
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Assessment of the quality for the NADP/NTN date based on their predictability Komungoma, Saulati Koku 1992
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Title | Assessment of the quality for the NADP/NTN date based on their predictability |
Creator |
Komungoma, Saulati Koku |
Date | 1992 |
Date Created | 2009-01-05 |
Date Issued | 2009-01-05 |
Description | Three methods are used to predict the ion concentrations of a particular station using the concentrations of the other stations, for the data produced by the National Acidic Deposition (NADP) Network/ National Trends Network (NTN), during the period of1983-86. We relate the degree of predictability to the quality of the data. Stations are ranked in the order in which they would be dropped if the network were, hypothetically, to be reduced in size. The agreement of the ranks given by different methods is assessed. Our study uses monthly volume weighted mean concentrations for each of the three selected ions, investigated one at a time. Since there a large number of stations (86 for hydrogen, 81 for each of the remaining ions) and only 48 months, analyses was carried out on clusters of stations. It was not possible to perform an ordinary regression analysis with a lot of missing data, so the analysis is done with missing values replaced by their estimates. |
Extent | 5106475 bytes |
Genre |
Thesis/Dissertation |
Type |
Text |
File Format | application/pdf |
Language | Eng |
Collection |
Retrospective Theses and Dissertations, 1919-2007 |
Series | UBC Retrospective Theses Digitization Project |
Date Available | 2009-01-05 |
Rights | For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. |
DOI | 10.14288/1.0086707 |
Degree |
Master of Science - MSc |
Program |
Statistics |
Affiliation |
Science, Faculty of |
Degree Grantor | University of British Columbia |
Graduation Date | 1992-05 |
Campus |
UBCV |
Scholarly Level | Graduate |
URI | http://hdl.handle.net/2429/3344 |
Aggregated Source Repository | DSpace |
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