Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
426 7.1 3.2 24776 3 396 7.2 2.9 19814 3 458 7.2 2.7 12738 3 315 7.1 3.1 31566 3 337 6.9 2.7 30111 3 386 6.8 2.6 30019 3 352 6.8 1.8 31934 3 384 6.8 2.3 25826 3 439 6.9 2.2 26835 3.18 397 7.1 1.8 20205 3.25 453 7.2 1.4 17789 3.25 364 7.2 0.3 20520 3.23 367 7.1 0.8 22518 2.92 474 7.1 -0.5 15572 2.25 373 7.2 -2.2 11509 1.62 404 7.5 -2.9 25447 1 385 7.7 -5.1 24090 0.66 365 7.8 -7.2 27786 0.31 366 7.7 -7.9 26195 0.25 421 7.7 -10.9 20516 0.25 354 7.8 -12.7 22759 0.25 367 8 -14 19028 0.25 413 8.1 -15.6 16971 0.25 362 8.1 -16 20036 0.25 385 8 -17.2 22485 0.25 343 8.1 -17.6 18730 0.25 369 8.2 -15.5 14538 0.25 363 8.4 -13.7 27561 0.25 318 8.5 -11.4 25985 0.25 393 8.5 -9.2 34670 0.25 325 8.5 -6.3 32066 0.25 403 8.5 -3.1 27186 0.25 392 8.5 0 29586 0.25 409 8.4 3 21359 0.25 485 8.3 5.4 21553 0.25 423 8.2 7.6 19573 0.25 428 8.1 9.7 24256 0.25 431 7.9 12 22380 0.25 416 7.6 11.6 16167 0.25 330 7.3 10 27297 0.25 314 7.1 10.8 28287 0.25 345 7 11.3 33474 0.39 365 7.1 10.1 28229 0.5 417 7.1 9.4 28785 0.5 356 7.1 9.6 25597 0.65 477 7.3 7.9 18130 0.75 423 7.3 7.3 20198 0.75 386 7.3 6.2 22849 0.75 390 7.2 4.9 23118 0.57 407 7.2 3.6 21925 0.36 398 7.1 2.9 20801 0.25 327 7.1 3.1 18785 0.25 304 7.1 1.7 20659 0.25 378 7.2 0.6 29367 0.25 311 7.3 -0.4 23992 0.25 376 7.4 -1.1 20645 0.25 340 7.4 -2.9 22356 0.08 383 7.5 -2.8 17902 0 467 7.4 -3 15879 0 439 7.4 -3.2 16963 0
Names of X columns:
bouwvergunningen werkloosheidsgraad uitvoer personenwagens rentetarieven
Response Variable (column number)
Explanatory Variable (column number)
Include Intercept Term ?
FALSE
TRUE
FALSE
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
par3 <- 'FALSE' par2 <- '4' par1 <- '2' 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
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation