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Data X:
210907 56 120982 56 176508 54 179321 89 123185 40 52746 25 385534 92 33170 18 101645 63 149061 44 165446 33 237213 84 173326 88 133131 55 258873 60 180083 66 324799 154 230964 53 236785 119 135473 41 202925 61 215147 58 344297 75 153935 33 132943 40 174724 92 174415 100 225548 112 223632 73 124817 40 221698 45 210767 60 170266 62 260561 75 84853 31 294424 77 101011 34 215641 46 325107 99 7176 17 167542 66 106408 30 96560 76 265769 146 269651 67 149112 56 175824 107 152871 58 111665 34 116408 61 362301 119 78800 42 183167 66 277965 89 150629 44 168809 66 24188 24 329267 259 65029 17 101097 64 218946 41 244052 68 341570 168 103597 43 233328 132 256462 105 206161 71 311473 112 235800 94 177939 82 207176 70 196553 57 174184 53 143246 103 187559 121 187681 62 119016 52 182192 52 73566 32 194979 62 167488 45 143756 46 275541 63 243199 75 182999 88 135649 46 152299 53 120221 37 346485 90 145790 63 193339 78 80953 25 122774 45 130585 46 112611 41 286468 144 241066 82 148446 91 204713 71 182079 63 140344 53 220516 62 243060 63 162765 32 182613 39 232138 62 265318 117 85574 34 310839 92 225060 93 232317 54 144966 144 43287 14 155754 61 164709 109 201940 38 235454 73 220801 75 99466 50 92661 61 133328 55 61361 77 125930 75 100750 72 224549 50 82316 32 102010 53 101523 42 243511 71 22938 10 41566 35 152474 65 61857 25 99923 66 132487 41 317394 86 21054 16 209641 42 22648 19 31414 19 46698 45 131698 65 91735 35 244749 95 184510 49 79863 37 128423 64 97839 38 38214 34 151101 32 272458 65 172494 52 108043 62 328107 65 250579 83 351067 95 158015 29 98866 18 85439 33 229242 247 351619 139 84207 29 120445 118 324598 110 131069 67 204271 42 165543 65 141722 94 116048 64 250047 81 299775 95 195838 67 173260 63 254488 83 104389 45 136084 30 199476 70 92499 32 224330 83 135781 31 74408 67 81240 66 14688 10 181633 70 271856 103 7199 5 46660 20 17547 5 133368 36 95227 34 152601 48 98146 40 79619 43 59194 31 139942 42 118612 46 72880 33 65475 18 99643 55 71965 35 77272 59 49289 19 135131 66 108446 60 89746 36 44296 25 77648 47 181528 54 134019 53 124064 40 92630 40 121848 39 52915 14 81872 45 58981 36 53515 28 60812 44 56375 30 65490 22 80949 17 76302 31 104011 55 98104 54 67989 21 30989 14 135458 81 73504 35 63123 43 61254 46 74914 30 31774 23 81437 38 87186 54 50090 20 65745 53 56653 45 158399 39 46455 20 73624 24 38395 31 91899 35 139526 151 52164 52 51567 30 70551 31 84856 29 102538 57 86678 40 85709 44 34662 25 150580 77 99611 35 19349 11 99373 63 86230 44 30837 19 31706 13 89806 42 62088 38 40151 29 27634 20 76990 27 37460 20 54157 19 49862 37 84337 26 64175 42 59382 49 119308 30 76702 49 103425 67 70344 28 43410 19 104838 49 62215 27 69304 30 53117 22 19764 12 86680 31 84105 20 77945 20 89113 39 91005 29 40248 16 64187 27 50857 21 56613 19 62792 35 72535 14
Names of X columns:
A B
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
cat1 <- as.numeric(par1) cat2<- as.numeric(par2) intercept<-as.logical(par3) x <- t(x) 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) ) 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$'r.squared', digits=3) ,nc, FALSE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-sq. ',1,TRUE) a<-table.element(a, round(sumlmxdf$'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.lm(lmxdf, which=4) dev.off()
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