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Data X:
3 13 10 12 15 7 15 13 4 16 11 11 12 14 13 11 15 2 12 20 9 9 16 8 21 4 15 2 18 3 14 6 18 12 6 10 16 3 9 9 14 8 20 7 15 12 20 8 11 1 14 7 11 0 10 9 9 6 16 7 11 13 15 8 13 10 11 9 10 12 9 11 18 8 9 2 11 12 7 5 6 13 15 13 10 11 10 9 12 7 14 14 13 13 11 16 9 10 14 3 11 13 17 4 9 4 17 5 11 10 11 4 4 14 11 6 18 3 14 11 27 10 8 8 18 8 6 8 20 4 9 8 20 6 9 13 11 11 10 6 16 6 16 2 13 13 11 7 14 5 16 13 17 4 19 4 14 9 11 15 7 8 5 14 12 11 8 4 22 6 14 13 4 7 16 6 11 3 6 14 13 9 15 11 19 2 4 24 12 10 15 19 18 11 7 11 19 6 17 4 7 13 5 5 11 10 12 7 8 11 13 11 12 1 6 10 9 10 9 4 10 9 7 7 10 10 12 17 10 21 9 9 18 13 12 3 15 7 12 5 8 16 11 11 11 13 20 9 15 8 13 15 16 5 3 8 6 12 4 10 11 7 14 16 10 5 13 9 10 16 8 15 15 8 10 8 17 10 16 10 19 8 8 9 14 5 5 8 10 17 14 10 11 7 14 6 13 6 14 14 11 4 23 6 8 6 11 8 21 7 16 8 12 7 12 15 11 17 13 7 8 10 17 6 5 4 11 10 18 5 7 11 11 8 12 10 8 18 15 9 21 2 17 5 8 7 11 14 11 8 9 7 10 21 6 6 11 11 10 12 6 9 5 11 6 7 4 8 11 10 14 8 12 9 27 4 18 4 12 8 17 9 13 9 13 9 9 13 8 6 12 8 12 15 13 11 12 10 10 8 9 13 6 17 8 16 13 8 24 5 14 10 18 3 11 14 12 6 16 6 12 13 14 9 10 21 27 7 9 10 11 9 9 14 7 16 12 6 21 4 10 7 12 9 7 11 9 12 12 12 12 7 14 7 14 8 16 4 6 11 7 12 24 3 14 4 13 8 13 9 12 7 10 7 21 10 9 10 14 3 9 10 11 7 10 12 14 7 6 13 16 5 13 9 5 18 11 11 7 17 15 6 5 7 16 12 9 10 13 17 22 6 12 5 10 8 13 7 14 8 6 10 13 11 17 5 18 12 13 10 20 6 17 5 17 11 7 18 21 3 12 14 9 11 6 7 7 13 15 6 10 6 18 9 6 10 12 12 19 9 13 4 15 8 12 9 13 5 11 8 5 9 28 5 19 8 16 2 6 9 5 7 16 6 12 9 7 10 8 20 13 12 7 10 8 12 10 9 19 5 17 8 10 7 12 5 11 15 8 7 15 11 11 13 23 9 20 9 4 13 7 14 16 16 10 10 14 10 8 13 9 16 16 8 11 12 7 8 9 7 22 4 23 14 13 11 6 7 14 6 17 7 14 10 13 14 10 9 24 12 16 8 7 5 8 10 6 10 11 13 16 12 8 6 9 12 6 7 16 11 15 4 12 14 12 9 15 11 9 8 18 9 12 12 9 9 13 5 17 6 14 5 23 7 12 21 10 15 11 13 15 4 7 10 11 7 15 7 19 12 11 8 13 16 9 14 17 10 7 7 16 10 6 7 17 5 10 17 3 10 13 8 16 14 6 11 16 5 22 6 8 6 20 8 16 10 6 8 12 14
Names of X columns:
HS AS
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
TRUE
FALSE
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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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