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Data X:
1 221 2 219 3 214 4 210 5 207 6 206 7 217 8 231 9 234 10 233 11 228 12 226 13 227 14 225 15 219 16 215 17 210 18 206 19 215 20 228 21 229 22 222 23 215 24 212 25 211 26 208 27 205 28 201 29 198 30 198 31 210 32 224 33 226 34 222 35 216 36 215 37 215 38 214 39 211 40 207 41 203 42 200 43 209 44 223 45 225 46 216 47 206 48 203 49 203 50 201 51 197 52 192 53 187 54 184 55 194 56 203 57 197 58 191 59 182 60 175 61 163 62 155 63 151 64 156 65 154 66 153 67 167 68 177 69 171 70 169 71 160 72 151 73 139 74 130 75 126 76 130 77 127 78 122 79 129 80 135 81 142 82 156 83 157 84 165 85 170 86 169 87 162 88 148 89 143 90 146 91 175 92 181 93 178 94 166 95 161 96 164 97 173 98 174 99 167 100 156 101 148 102 150 103 174 104 181 105 183 106 178 107 176 108 184 109 193 110 192 111 182 112 163 113 157 114 167 115 205 116 219 117 214 118 198 119 183 120 184 121 192 122 196 123 194 124 185 125 181 126 184 127 206 128 210 129 208 130 197 131 189 132 190 133 191 134 190 135 187 136 184 137 183 138 184 139 203 140 208 141 205 142 195 143 189 144 188 145 190 146 190 147 190 148 193 149 185 150 173 151 176 152 170 153 163 154 170 155 171 156 173 157 171 158 162 159 152 160 142 161 136 162 146 163 179 164 191 165 181 166 170 167 161 168 168 169 180 170 182 171 176 172 164 173 154 174 160 175 189 176 196 177 186 178 171 179 169 180 181 181 198 182 202 183 196 184 183 185 173 186 175 187 198 188 203 189 197 190 191 191 182 192 172 193 158 194 147 195 143 196 146 197 147 198 152 199 177 200 184 201 174 202 162 203 157 204 155 205 159 206 158 207 156 208 157 209 156 210 158 211 173 212 179 213 172 214 169 215 168 216 172 217 180 218 182 219 182 220 181 221 178 222 178 223 196 224 199 225 192 226 187 227 184 228 184 229 188 230 183 231 176 232 168 233 163 234 166 235 189 236 195 237 192 238 189 239 187 240 187 241 190 242 187 243 179 244 168 245 160 246 161 247 177 248 182 249 176
Names of X columns:
Tijd Werklozen
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
TRUE
TRUE
FALSE
Chart options
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R Code
par3 <- 'TRUE' par2 <- '1' par1 <- '2' library(boot) cat1 <- as.numeric(par1) cat2<- as.numeric(par2) intercept<-as.logical(par3) x <- t(x) rsq <- function(formula, data, indices) { d <- data[indices,] # allows boot to select sample fit <- lm(formula, data=d) return(summary(fit)$r.square) } xdf<-data.frame(t(y)) (V1<-dimnames(y)[[1]][cat1]) (V2<-dimnames(y)[[1]][cat2]) xdf <- data.frame(xdf[[cat1]], xdf[[cat2]]) names(xdf)<-c('Y', 'X') if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) ) (results <- boot(data=xdf, statistic=rsq, R=1000, formula=Y~X)) sumlmxdf<-summary(lmxdf) (aov.xdf<-aov(lmxdf) ) (anova.xdf<-anova(lmxdf) ) load(file='createtable') a<-table.start() nc <- ncol(sumlmxdf$'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$'Mean Sq'[2], digits=3)) a<-table.element(a, ' ') a<-table.element(a, ' ') a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') bitmap(file='regressionplot.png') plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution') if(intercept == TRUE) abline(coef(lmxdf), col='red') if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red') dev.off() library(car) bitmap(file='residualsQQplot.png') qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit') dev.off() bitmap(file='residualsplot.png') plot(xdf$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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Big Analytics Cloud Computing Center
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