Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
13 12 16 11 19 14 15 12 14 21 13 12 19 22 15 11 14 10 15 13 16 10 16 8 16 15 16 14 17 10 15 14 15 14 20 11 18 10 16 13 16 9.5 16 14 19 12 16 14 17 11 17 9 16 11 15 15 16 14 14 13 15 9 12 15 14 10 16 11 14 13 10 8 10 20 14 12 16 10 16 10 16 9 14 14 20 8 14 14 14 11 11 13 14 9 15 11 16 15 14 11 16 10 14 14 12 18 16 14 9 11 14 14.5 16 13 16 9 15 10 16 15 12 20 16 12 16 12 14 14 16 13 17 11 18 17 18 12 12 13 16 14 10 13 14 15 18 13 18 10 16 11 17 19 16 13 16 17 13 13 16 9 16 11 16 9 15 12 15 12 16 13 14 13 16 12 16 15 15 22 12 13 17 15 16 13 15 15 13 12.5 16 11 16 16 16 11 16 11 14 10 16 10 16 16 20 12 15 11 16 16 13 19 17 11 16 16 16 15 12 24 16 14 16 15 17 11 13 15 12 12 18 10 14 14 14 13 13 9 16 15 13 15 16 14 13 11 16 8 15 11 16 11 15 8 17 10 15 11 12 13 16 11 10 20 16 10 12 15 14 12 15 14 13 23 15 14 11 16 12 11 11 12 16 10 15 14 17 12 16 12 10 11 18 12 13 13 16 11 13 19 10 12 15 17 16 9 16 12 14 19 10 18 17 15 13 14 15 11 16 9 12 18 13 16 13 24 12 14 17 20 15 18 10 23 14 12 11 14 13 16 16 18 12 20 16 12 12 12 9 17 12 13 15 9 12 16 12 18 14 10 12 14 16 11 11 9 19 11 15 10 8 11 16 19 17 14 12 12 11 14 11 21 14 13 16 10 12 15 16 16 13 14 15 12 16 19 16 15 14 19 16 13 16 17 14 12 11 11 12 14 15 11 15 13 16 12 16 15 11 14 15 12 12 17 12 11 15 18 15 13 16 17 14 13 17 11 14 12 13 22 15 14 13 12 14 12 15 17 12 9 13 21 8 10 14 11 14 12 11 23 12 13 13 12 10 16 16 9 18 17 13 9 11 14 4 17 13 13 16 11 10 12 12 10 12 19 10 16 13 16 15 14 12 20 14 15 10 23 12 20 12 16 11 14 10 17 12 11 16 13 12 17 14 15 16 21 14 18 13 15 4 8 15 12 11 12 11 22 14 12
Names of X columns:
learning depression
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
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()
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