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Data X:
1 0.5 0.67 0.67 0 0.5 2011 1 0 149 0.89 0.5 0.83 0.33 0.5 1 2011 1 1 139 0.89 0.4 1 0.67 0 1 2011 1 0 148 0.89 0.5 0.83 0 0 0 2011 1 1 158 0.89 0.7 0.67 0 1 1 2011 1 1 128 0.78 0.3 0 0 0.5 0.5 2011 1 1 224 0.89 0.4 0.83 0.67 0.5 0 2011 1 0 159 1 0.4 0.5 0.67 1 1 2011 1 1 105 0.89 0.7 0.83 0 0.5 0 2011 1 1 159 0.78 0.6 0.33 0.67 0.5 0.5 2011 1 1 167 1 0.6 0.5 1 0 0.5 2011 1 1 165 0.78 0.2 0.67 0 0.5 0.5 2011 1 1 159 0.89 0.4 1 0 0.5 0.5 2011 1 1 119 0.89 0.4 0.5 0.67 0 1 2011 1 0 176 0.89 0.5 0.67 0.33 0 0 2011 1 0 54 0.89 0.3 0.17 0.67 0 0.5 2011 0 0 91 0.89 0.4 0.83 0.33 0.5 0.5 2011 1 1 163 0.67 0.7 0.67 0.33 0.5 1 2011 1 0 124 1 0.5 0.67 0.33 0 1 2011 0 1 137 0.78 0.2 0.67 0 0 1 2011 1 0 121 0.78 0.3 0.5 0.67 0 0.5 2011 1 1 153 0.89 0.6 1 0.33 0 1 2011 1 1 148 0.78 0.6 0.83 0.33 0 1 2011 1 0 221 0.89 0.2 0.83 0.33 0 1 2011 1 1 188 0.89 0.7 1 0.67 1 0 2011 1 1 149 0.33 0.2 0.67 0 0 0 2011 1 1 244 1 1 1 0.33 1 1 2011 0 1 148 0.89 0.4 0.83 0.67 0 0.5 2011 0 0 92 0.89 0.4 1 1 0 1 2011 1 1 150 0.67 0.2 0.83 0.67 0 0.5 2011 1 0 153 0.56 0.4 0.67 0.33 0 1 2011 1 0 94 0.89 0.4 0.67 0 0.5 1 2011 1 0 156 0.89 0.7 1 0.67 0.5 0.5 2011 1 1 132 1 0.2 0.67 0.67 0 0.5 2011 1 1 161 0.78 0.6 1 1 0 0.5 2011 1 1 105 0.78 0.3 1 1 0.5 0.5 2011 1 1 97 0.33 0.3 0.5 0.33 0 0 2011 1 0 151 0.78 0.2 0.67 0 0.5 0 2011 0 1 131 0.89 0.5 0.83 0.67 0.5 0.5 2011 1 1 166 0.89 0.7 1 0.67 0.5 1 2011 1 0 157 0.78 0.6 1 0.67 0.5 0.5 2011 1 1 111 0.89 0.4 1 0.67 0.5 1 2011 1 1 145 0.89 0.6 1 0.33 0.5 1 2011 1 1 162 1 0.4 1 1 0 1 2011 1 1 163 0.67 0.3 0.83 0.67 0 1 2011 0 1 59 1 0.5 0.83 0.67 0.5 0.5 2011 1 0 187 0.89 0.2 0.5 0 0 1 2011 1 1 109 0.89 0.3 0.83 0 0.5 1 2011 0 1 90 0.89 0.5 0.17 0 0 1 2011 1 0 105 0.78 0.7 0.83 1 0.5 1 2011 0 1 83 0.89 0.4 1 0.67 1 0.5 2011 0 1 116 0.78 0.3 1 0 0 0.5 2011 0 1 42 0.78 0.2 0.67 0.67 1 1 2011 1 1 148 1 0.5 1 0 0 0.5 2011 0 1 155 0.78 0.4 1 0 0.5 0 2011 1 1 125 1 0.6 1 0.67 1 1 2011 1 1 116 0.78 0.4 0.83 1 0 1 2011 0 0 128 0.67 0.4 0.33 0 0 0.5 2011 1 1 138 0.33 0.2 0.33 0.33 0 0 2011 0 0 49 1 0.9 1 0.67 0.5 1 2011 0 1 96 1 0.8 1 0.67 1 0.5 2011 1 1 164 0.78 0.8 0.83 0 0.5 1 2011 1 0 162 0.67 0.3 1 1 0.5 1 2011 1 0 99 1 0.2 0.83 0.67 0 0.5 2011 1 1 202 0.89 0.4 0.67 0 0.5 1 2011 1 0 186 0.89 0.2 0.83 1 0 1 2011 0 1 66 0.78 0.2 0.67 0.67 0.5 1 2011 1 0 183 1 0.1 0.83 0.67 0 1 2011 1 1 214 0.56 0.4 0.67 1 0.5 0 2011 1 1 188 0.67 0.5 1 0 0.5 0.5 2011 0 0 104 0.89 0.8 0.83 0.33 0.5 1 2011 1 0 177 0.89 0.4 0.67 0.67 0 0.5 2011 1 0 126 0.89 0.6 0.83 0.33 0.5 0.5 2011 0 0 76 0.89 0.5 0.83 0.67 0.5 1 2011 0 1 99 0.78 0.3 0.67 0 0 0 2011 1 0 139 1 0.4 0.33 0 0.5 0 2011 1 0 162 1 0.6 0.83 0.67 0.5 0.5 2011 0 1 108 0.89 0.4 1 0.33 0 0.5 2011 1 0 159 0.44 0.3 0.83 0 0 0 2011 0 0 74 0.78 0.8 0.83 0 1 1 2011 1 1 110 0.89 0.6 0.5 0.33 1 1 2011 0 0 96 0.67 0.3 0.5 0 0 0 2011 0 0 116 0.78 0.5 0.83 0.67 0.5 1 2011 0 0 87 0.78 0.4 1 0.33 0 1 2011 0 1 97 0.33 0.3 0.33 0.67 0 0 2011 0 0 127 0.89 0.7 1 0.33 0 0.5 2011 0 1 106 0.89 0.2 0.67 0.33 0.5 0.5 2011 0 1 80 0.89 0.4 0.83 1 0 1 2011 0 0 74 0.89 0.6 1 0.67 0.5 0.5 2011 0 0 91 0.56 0.6 0.83 0 0 1 2011 0 0 133 0.67 0.6 0.83 0.67 0.5 0.5 2011 0 1 74 0.67 0.4 1 0.33 0.5 1 2011 0 1 114 0.78 0.6 0.83 0 0 1 2011 0 1 140 0.78 0.5 1 0.33 0.5 1 2011 0 0 95 0.78 0.5 0.83 0 0 1 2011 0 1 98 0.89 0.6 0.67 0 0 1 2011 0 0 121 1 0.8 0.83 0.33 0.5 1 2011 0 1 126 0.89 0.5 0.83 0.67 1 0.5 2011 0 1 98 0.89 0.6 0.83 0.67 0.5 1 2011 0 1 95 0.78 0.4 0.83 0.67 0.5 1 2011 0 1 110 1 0.3 0.67 0.67 0.5 1 2011 0 1 70 0.78 0.3 0.83 1 0 0.5 2011 0 0 102 0.67 0.2 0 0 0 0 2011 0 1 86 0.78 0.4 0.83 0 0 0.5 2011 0 1 130 0.89 0.5 1 0 0 0.5 2011 0 1 96 0.67 0.3 0.17 0 0.5 0 2011 0 0 102 0.22 0.4 0.17 0 0.5 0 2011 0 0 100 0.44 0.5 0.5 1 0 0 2011 0 0 94 0.89 0.3 0.5 0.67 0 1 2011 0 0 52 0.67 0.5 1 0 0 0.5 2011 0 0 98 0.89 0.4 0.67 0.67 0 0.5 2011 0 0 118 0.67 0.4 0.83 0.67 0 1 2011 0 1 99 0.78 0.6 1 0 1 1 2012 1 1 48 0.78 0.3 1 0.67 1 1 2012 1 1 50 0.78 0.4 1 0.33 1 0.5 2012 1 1 150 1 0.3 1 1 1 1 2012 1 1 154 0.78 1 1 1 1 1 2012 0 0 109 0.67 0.4 1 0 0 0.5 2012 0 1 68 0.89 0.8 0.83 1 0.5 1 2012 1 1 194 0.89 0.3 1 0.67 1 1 2012 1 0 158 1 0.5 0.83 0.67 0 1 2012 1 1 159 0.78 0.4 1 0 0 0.5 2012 1 0 67 0.67 0.3 0.83 0.67 0 1 2012 1 0 147 0.89 0.5 0.83 1 0 1 2012 1 1 39 0.67 0.3 1 0.67 0 1 2012 1 1 100 0.67 0.3 0.67 0 0 1 2012 1 1 111 1 0.4 0.83 0 0 1 2012 1 1 138 0.67 0.3 1 0 0 0.5 2012 1 1 101 1 0.6 1 0.33 0.5 0.5 2012 0 1 131 0.89 0.6 0.83 0.67 1 1 2012 1 1 101 0.89 0.4 1 1 1 1 2012 1 1 114 1 0.4 1 0 0 0 2012 1 0 165 0.67 0.4 1 0.67 0 0.5 2012 1 1 114 0.44 0.3 0.67 0.67 0.5 1 2012 1 1 111 0.89 0.2 1 0.33 1 0 2012 1 1 75 0.56 0.5 0.83 0.67 0 1 2012 1 1 82 0.78 0.4 1 0.67 1 1 2012 1 1 121 1 0.4 1 0.67 0 0 2012 1 1 32 1 0.4 0.83 0.67 0 1 2012 1 0 150 0.89 0.3 0.67 0.67 0.5 0.5 2012 1 1 117 0.67 0.4 0.83 0.67 1 0.5 2012 0 1 71 0.89 0.2 1 0.33 0.5 1 2012 1 1 165 0.33 0 0 0 0 0 2012 1 1 154 0.89 0.4 1 0.67 0.5 1 2012 1 1 126 0.78 0.6 1 0 1 1 2012 1 0 149 1 0.4 0.67 0.67 0 0.5 2012 1 0 145 0.44 0.4 1 0 0 0.5 2012 1 1 120 0.67 0.4 0.83 0 0.5 0 2012 1 0 109 0.33 0.2 0.17 0 0.5 0 2012 1 0 132 0.89 0.4 0.83 1 1 1 2012 1 1 172 0.89 0.3 0.83 0 0 0.5 2012 1 0 169 1 0.6 0.83 0.67 1 0 2012 1 1 114 0.89 0.6 0.83 1 0 1 2012 1 1 156 0.89 0.4 0.83 0 0 1 2012 1 0 172 1 0.5 1 0.67 1 0.5 2012 0 1 68 0.89 0.4 0.83 0 0.5 1 2012 0 1 89 1 0.6 1 1 1 1 2012 1 1 167 0.78 0.6 0.83 0.67 0.5 1 2012 1 0 113 0.78 0.9 1 0.67 0.5 1 2012 0 0 115 0.67 0.4 0.83 0.67 0.5 0 2012 0 0 78 0.89 0.8 1 1 0.5 1 2012 0 0 118 0.67 0.5 0.83 1 0 1 2012 0 1 87 0.78 0.4 0.83 1 0 0 2012 1 0 173 0.89 0.4 1 0.67 1 0.5 2012 1 1 2 0.89 0.7 1 1 1 0.5 2012 0 0 162 0.78 0.4 1 0.33 1 1 2012 0 1 49 1 0.8 1 0.67 0.5 1 2012 0 0 122 1 0.4 1 1 1 0.5 2012 0 1 96 1 0.3 1 0.67 0 0.5 2012 0 0 100 0.67 0.5 1 0.67 0.5 1 2012 0 0 82 0.89 0.8 1 0.67 1 1 2012 0 1 100 1 0.4 0.83 0.33 0 0.5 2012 0 0 115 1 1 1 1 0.5 0 2012 0 1 141 0.89 0.5 1 0.67 1 1 2012 1 1 165 0.89 0.5 1 0.67 1 1 2012 1 1 165 0.89 0.3 1 0.33 0 1 2012 0 1 110 0.89 0.3 0.83 0.33 0.5 1 2012 1 1 118 0.89 0.3 0.5 0 0 1 2012 1 0 158 1 0.4 0.67 0.33 0.5 0.5 2012 0 1 146 0.67 0.5 1 0.33 0 1 2012 1 0 49 1 0.5 0.67 0.67 0.5 1 2012 0 0 90 0.89 0.4 1 0 0 0 2012 0 0 121 0.89 0.7 1 1 0.5 0 2012 1 1 155 0.89 0.5 0.5 0.33 0 0.5 2012 0 0 104 0.89 0.4 0.67 0.33 1 0 2012 0 1 147 1 0.7 0.67 1 0 1 2012 0 0 110 1 0.7 0.67 1 0 1 2012 0 0 108 1 0.7 0.67 1 0 1 2012 0 0 113 0.89 0.7 0.67 1 0 1 2012 0 0 115 0.89 0.7 0.67 0 0 0 2012 0 1 61 0.89 0.7 1 0.67 0.5 1 2012 0 1 60 0.33 0.1 0.67 0.33 0.5 0 2012 0 1 109 0.67 0.2 0.67 0.67 0.5 1 2012 0 1 68 0.56 0.3 0.33 0.33 0 1 2012 0 0 111 0.44 0.6 0.83 0.33 0 0.5 2012 0 0 77 1 0.8 1 1 1 1 2012 0 1 73 0.89 0.8 1 0.33 0.5 0.5 2012 1 0 151 0.33 0 0.17 0 0 0 2012 0 0 89 0.67 0.3 0.67 0.33 0 1 2012 0 0 78 0.67 0.6 0.83 0.33 0.5 1 2012 0 0 110 1 0.5 0.83 0.67 0 1 2012 1 1 220 0.78 0.7 1 0.33 0 0.5 2012 0 1 65 0.67 0.3 0.83 0 0.5 1 2012 1 0 141 1 0.3 1 0.67 0 0 2012 0 0 117 0.78 0.4 1 0.67 0 0.5 2012 1 1 122 0.89 0.4 0.83 1 0 1 2012 0 0 63 0.89 0.1 0.83 0 0 1 2012 1 1 44 0.89 0.5 1 0.67 0 1 2012 0 1 52 0 0 0 0 0 0 2012 0 0 131 0.67 0.4 1 0.33 0.5 0 2012 0 1 101 1 0.6 0.83 0.67 1 0.5 2012 0 1 42 1 0.4 1 0.33 0.5 1 2012 1 1 152 0.67 0.1 0.33 0 0.5 1 2012 1 0 107 0.89 0.3 0.83 0 0 1 2012 0 0 77 0.89 0.7 0.83 0.67 0 1 2012 1 0 154 0.56 0.3 0.17 0 0 1 2012 1 1 103 0.67 0.5 0.83 0.33 0.5 0 2012 0 1 96 1 0.3 0.83 0.67 1 1 2012 1 1 175 1 0.6 0.67 0.67 0.5 1 2012 0 1 57 1 0.9 1 1 0 1 2012 0 0 112 0.67 0.4 0.83 0 0.5 1 2012 1 0 143 0.44 0.3 1 0 0.5 0.5 2012 0 0 49 0.89 0.9 1 0.67 1 1 2012 1 1 110 0.44 0.5 1 0 0.5 0 2012 1 1 131 0.56 0.3 1 1 0.5 0.5 2012 1 0 167 0.89 0.6 0.83 0.67 0 0.5 2012 0 0 56 0.67 0.2 1 0.33 0 0.5 2012 1 0 137 0.89 0.4 0.83 1 0.5 1 2012 0 1 86 1 0.5 0.83 0.67 0.5 0.5 2012 1 1 121 0.78 0.4 0.83 0.67 0 0.5 2012 1 0 149 0.44 0 0 0 0 0 2012 1 0 168 0.89 0.2 1 0.33 0.5 1 2012 1 0 140 0.89 0.5 1 0.67 0.5 1 2012 0 1 88 0.89 0.3 1 0.67 0 0.5 2012 1 1 168 0.44 0 0 0 0 0 2012 1 1 94 1 0.5 0.83 1 0 1 2012 1 1 51 0.89 0.6 0.83 0.33 0 1 2012 0 0 48 0.67 0.3 0.83 0 0.5 0.5 2012 1 1 145 0.33 0 0 0 0 0 2012 1 1 66 0.78 0.3 0.67 0 0.5 0 2012 0 1 85 0.89 0.5 1 0.67 0.5 1 2012 1 0 109 0.78 0.4 0.67 0 0 1 2012 0 0 63 0.78 0.5 0.83 0.67 0 0.5 2012 0 1 102 0.89 0.7 1 1 1 0.5 2012 0 0 162 0.78 0.8 1 0.67 0.5 1 2012 0 1 86 0.78 0.6 1 0.33 0.5 1 2012 0 1 114 0.67 0.4 0.83 0.33 0 0.5 2012 1 0 164 0.89 0.5 0.83 0.33 0.5 0 2012 1 1 119 0.89 0.5 1 0 0.5 1 2012 1 0 126 0.78 0.3 1 0.33 0 1 2012 1 1 132 1 0.6 1 0 0.5 1 2012 1 1 142 1 0.3 0.67 0.67 0 0.5 2012 1 0 83 0.78 0.6 0.83 1 0.5 0.5 2012 0 1 94 0.78 0.3 0.33 0.33 0 1 2012 0 0 81 0.89 0.7 1 0.67 1 1 2012 1 1 166 0.89 0.7 1 1 0 1 2012 0 0 110 0.67 0.6 0.67 1 0.5 1 2012 0 1 64 1 0.5 1 0.33 0.5 0 2012 1 0 93 0.67 0.5 0.83 0.33 0 0.5 2012 0 0 104 0.56 0.4 0.67 0 0 1 2012 0 1 105 0.78 0.4 1 0.33 1 1 2012 0 1 49 1 0.7 1 1 0 1 2012 0 0 88 0.67 0.2 0.17 0 0.5 0 2012 0 1 95 0.78 0.5 0.83 0.67 0 0.5 2012 0 1 102 0.56 0.4 0.83 0.67 0.5 0 2012 0 0 99 1 0.2 1 0.67 1 1 2012 0 1 63 0.89 0.5 0.67 0.67 0 0 2012 0 0 76 0.44 0.4 0.5 0 0 1 2012 0 0 109 1 0.7 0.67 1 1 1 2012 0 1 117 0.89 0.6 0.83 0.67 1 0 2012 0 1 57 0.78 0.4 0.83 0 0 0 2012 0 0 120 0.89 0.5 1 0.67 1 1 2012 0 1 73 0.11 0 0.17 0 0 0 2012 0 0 91 0.89 0.7 1 0.67 0.5 1 2012 0 0 108 0.89 0.4 0.67 0.67 0 1 2012 0 1 105 1 0.5 0.67 1 0 1 2012 1 0 117 0.89 0.6 0.83 0.67 0 0.5 2012 0 0 119 1 0.8 0.5 0.67 0.5 0.5 2012 0 1 31
Names of X columns:
Calculation Algebraic_Reasoning Graphical_Interpretation Proportionality_and_Ratio Probability_and_Sampling Estimation year group gender LFM
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
TRUE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
library(boot) cat1 <- as.numeric(par1) cat2<- as.numeric(par2) intercept<-as.logical(par3) x <- na.omit(t(x)) rsq <- function(formula, data, indices) { d <- data[indices,] # allows boot to select sample fit <- lm(formula, data=d) return(summary(fit)$r.square) } xdf<-data.frame(na.omit(t(y))) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) xdf <- data.frame(xdf[[cat1]], xdf[[cat2]]) names(xdf)<-c('Y', 'X') if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) ) (results <- boot(data=xdf, statistic=rsq, R=1000, formula=Y~X)) sumlmxdf<-summary(lmxdf) (aov.xdf<-aov(lmxdf) ) (anova.xdf<-anova(lmxdf) ) load(file='createtable') a<-table.start() nc <- ncol(sumlmxdf$'coefficients') nr <- nrow(sumlmxdf$'coefficients') a<-table.row.start(a) a<-table.element(a,'Linear Regression Model', nc+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, lmxdf$call['formula'],nc+1) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'coefficients:',1,TRUE) a<-table.element(a, ' ',nc,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) for(i in 1 : nc){ a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE) }#end header a<-table.row.end(a) for(i in 1: nr){ a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE) for(j in 1 : nc){ a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE) } a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a, '- - - ',1,TRUE) a<-table.element(a, ' ',nc,FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Std. Err. ',1,TRUE) a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, '95% CI Multiple R-sq. ',1,TRUE) a<-table.element(a, paste('[',round(boot.ci(results,type='bca')$bca[1,4], digits=3),', ', round(boot.ci(results,type='bca')$bca[1,5], digits=3), ']',sep='') ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'ANOVA Statistics', 5+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, ' ',1,TRUE) a<-table.element(a, 'Df',1,TRUE) a<-table.element(a, 'Sum Sq',1,TRUE) a<-table.element(a, 'Mean Sq',1,TRUE) a<-table.element(a, 'F value',1,TRUE) a<-table.element(a, 'Pr(>F)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, V2,1,TRUE) a<-table.element(a, anova.xdf$Df[1]) a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3)) a<-table.element(a, round(anova.xdf$'F value'[1], digits=3)) a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residuals',1,TRUE) a<-table.element(a, anova.xdf$Df[2]) a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3)) a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3)) a<-table.element(a, ' ') a<-table.element(a, ' ') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') bitmap(file='regressionplot.png') plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution') if(intercept == TRUE) abline(coef(lmxdf), col='red') if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red') dev.off() library(car) bitmap(file='residualsQQplot.png') qqPlot(resid(lmxdf), main='QQplot of Residuals of Fit') dev.off() bitmap(file='residualsplot.png') plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit') dev.off() bitmap(file='cooksDistanceLmplot.png') plot(lmxdf, which=4) dev.off()
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