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Data X:
7.00 .00 109.00 13.10 68.00 32.00 258.00 183.00 137.00 95.00 7.00 1.00 112.00 12.90 65.00 19.00 449.00 245.00 134.00 85.00 8.00 .00 124.00 14.10 64.00 22.00 441.00 268.00 147.00 100.00 8.00 1.00 125.00 16.20 67.00 41.00 234.00 146.00 124.00 85.00 8.00 .00 127.00 21.50 93.00 52.00 202.00 131.00 104.00 95.00 9.00 .00 130.00 17.50 68.00 44.00 308.00 155.00 118.00 80.00 11.00 1.00 139.00 30.70 89.00 28.00 305.00 179.00 119.00 65.00 12.00 1.00 150.00 28.40 69.00 18.00 369.00 198.00 103.00 110.00 12.00 .00 146.00 25.10 67.00 24.00 312.00 194.00 128.00 70.00 13.00 1.00 155.00 31.50 68.00 23.00 413.00 225.00 136.00 95.00 13.00 .00 156.00 39.90 89.00 39.00 206.00 142.00 95.00 110.00 14.00 1.00 153.00 42.10 90.00 26.00 253.00 191.00 121.00 90.00 14.00 .00 160.00 45.60 93.00 45.00 174.00 139.00 108.00 100.00 15.00 1.00 158.00 51.20 93.00 45.00 158.00 124.00 90.00 80.00 16.00 1.00 160.00 35.90 66.00 31.00 302.00 133.00 101.00 134.00 17.00 1.00 153.00 34.80 70.00 29.00 204.00 118.00 120.00 134.00 17.00 .00 174.00 44.70 70.00 49.00 187.00 104.00 103.00 165.00 17.00 1.00 176.00 60.10 92.00 29.00 188.00 129.00 130.00 120.00 17.00 .00 171.00 42.60 69.00 38.00 172.00 130.00 103.00 130.00 19.00 1.00 156.00 37.20 72.00 21.00 216.00 119.00 81.00 85.00 19.00 .00 174.00 54.60 86.00 37.00 184.00 118.00 101.00 85.00 20.00 .00 178.00 64.00 86.00 34.00 225.00 148.00 135.00 160.00 23.00 .00 180.00 73.80 97.00 57.00 171.00 108.00 98.00 165.00 23.00 .00 175.00 51.10 71.00 33.00 224.00 131.00 113.00 95.00 23.00 .00 179.00 71.50 95.00 52.00 225.00 127.00 101.00 195.00
Names of X columns:
Age Sex Height Weight BMP Fev1 Rv Frc Tlc Pemax
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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