Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
1 12.9 1 12.2 1 12.8 1 7.4 1 6.7 1 12.6 1 14.8 1 13.3 1 11.1 1 8.2 1 11.4 1 6.4 1 10.6 1 12 1 6.3 0 11.3 1 11.9 1 9.3 0 9.6 1 10 1 6.4 1 13.8 1 10.8 1 13.8 1 11.7 1 10.9 0 16.1 0 13.4 1 9.9 1 11.5 1 8.3 1 11.7 1 9 1 9.7 1 10.8 1 10.3 1 10.4 0 12.7 1 9.3 1 11.8 1 5.9 1 11.4 1 13 1 10.8 0 12.3 1 11.3 1 11.8 0 7.9 1 12.7 0 12.3 0 11.6 0 6.7 1 10.9 0 12.1 1 13.3 1 10.1 0 5.7 1 14.3 0 8 0 13.3 1 9.3 1 12.5 1 7.6 1 15.9 1 9.2 0 9.1 1 11.1 1 13 1 14.5 0 12.2 1 12.3 1 11.4 0 8.8 0 14.6 1 12.6 1 13 0 12.6 1 13.2 0 9.9 1 7.7 0 10.5 0 13.4 0 10.9 0 4.3 0 10.3 0 11.8 0 11.2 0 11.4 0 8.6 0 13.2 0 12.6 0 5.6 0 9.9 0 8.8 0 7.7 0 9 0 7.3 0 11.4 0 13.6 0 7.9 0 10.7 0 10.3 0 8.3 0 9.6 0 14.2 0 8.5 0 13.5 0 4.9 0 6.4 0 9.6 0 11.6 0 11.1 1 4.35 1 12.7 1 18.1 1 17.85 0 16.6 0 12.6 1 17.1 1 19.1 1 16.1 1 13.35 1 18.4 1 14.7 1 10.6 1 12.6 1 16.2 1 13.6 0 18.9 1 14.1 1 14.5 1 16.15 1 14.75 1 14.8 1 12.45 1 12.65 1 17.35 1 8.6 1 18.4 1 16.1 0 11.6 1 17.75 1 15.25 1 17.65 1 16.35 1 17.65 1 13.6 1 14.35 1 14.75 1 18.25 1 9.9 1 16 1 18.25 1 16.85 0 14.6 0 13.85 1 18.95 1 15.6 0 14.85 0 11.75 0 18.45 0 15.9 1 17.1 1 16.1 0 19.9 0 10.95 0 18.45 0 15.1 0 15 0 11.35 0 15.95 0 18.1 0 14.6 1 15.4 1 15.4 0 17.6 1 13.35 1 19.1 0 15.35 1 7.6 0 13.4 0 13.9 1 19.1 0 15.25 0 12.9 0 16.1 0 17.35 0 13.15 0 12.15 0 12.6 0 10.35 0 15.4 0 9.6 0 18.2 0 13.6 0 14.85 1 14.75 0 14.1 0 14.9 0 16.25 1 19.25 0 13.6 1 13.6 0 15.65 1 12.75 0 14.6 1 9.85 0 12.65 0 19.2 0 16.6 0 11.2 1 15.25 1 11.9 0 13.2 1 16.35 1 12.4 0 15.85 1 18.15 0 11.15 0 15.65 1 17.75 0 7.65 1 12.35 1 15.6 1 19.3 0 15.2 1 17.1 0 15.6 1 18.4 1 19.05 1 18.55 1 19.1 0 13.1 1 12.85 1 9.5 1 4.5 0 11.85 1 13.6 1 11.7 0 12.4 1 13.35 0 11.4 0 14.9 0 19.9 0 11.2 0 14.6 1 17.6 1 14.05 1 16.1 1 13.35 1 11.85 1 11.95 0 14.75 0 15.15 1 13.2 0 16.85 0 7.85 1 7.7 0 12.6 0 7.85 0 10.95 0 12.35 0 9.95 0 14.9 0 16.65 0 13.4 0 13.95 0 15.7 0 16.85 0 10.95 0 15.35 0 12.2 0 15.1 0 17.75 0 15.2 1 14.6 0 16.65 0 8.1
Names of X columns:
group TOT
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()
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation