Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
41 38 13 12 14 12 53 32 39 32 16 11 18 11 86 51 30 35 19 15 11 14 66 42 31 33 15 6 12 12 67 41 34 37 14 13 16 21 76 46 35 29 13 10 18 12 78 47 39 31 19 12 14 22 53 37 34 36 15 14 14 11 80 49 36 35 14 12 15 10 74 45 37 38 15 6 15 13 76 47 38 31 16 10 17 10 79 49 36 34 16 12 19 8 54 33 38 35 16 12 10 15 67 42 39 38 16 11 16 14 54 33 33 37 17 15 18 10 87 53 32 33 15 12 14 14 58 36 36 32 15 10 14 14 75 45 38 38 20 12 17 11 88 54 39 38 18 11 14 10 64 41 32 32 16 12 16 13 57 36 32 33 16 11 18 7 66 41 31 31 16 12 11 14 68 44 39 38 19 13 14 12 54 33 37 39 16 11 12 14 56 37 39 32 17 9 17 11 86 52 41 32 17 13 9 9 80 47 36 35 16 10 16 11 76 43 33 37 15 14 14 15 69 44 33 33 16 12 15 14 78 45 34 33 14 10 11 13 67 44 31 28 15 12 16 9 80 49 27 32 12 8 13 15 54 33 37 31 14 10 17 10 71 43 34 37 16 12 15 11 84 54 34 30 14 12 14 13 74 42 32 33 7 7 16 8 71 44 29 31 10 6 9 20 63 37 36 33 14 12 15 12 71 43 29 31 16 10 17 10 76 46 35 33 16 10 13 10 69 42 37 32 16 10 15 9 74 45 34 33 14 12 16 14 75 44 38 32 20 15 16 8 54 33 35 33 14 10 12 14 52 31 38 28 14 10 12 11 69 42 37 35 11 12 11 13 68 40 38 39 14 13 15 9 65 43 33 34 15 11 15 11 75 46 36 38 16 11 17 15 74 42 38 32 14 12 13 11 75 45 32 38 16 14 16 10 72 44 32 30 14 10 14 14 67 40 32 33 12 12 11 18 63 37 34 38 16 13 12 14 62 46 32 32 9 5 12 11 63 36 37 32 14 6 15 12 76 47 39 34 16 12 16 13 74 45 29 34 16 12 15 9 67 42 37 36 15 11 12 10 73 43 35 34 16 10 12 15 70 43 30 28 12 7 8 20 53 32 38 34 16 12 13 12 77 45 34 35 16 14 11 12 77 45 31 35 14 11 14 14 52 31 34 31 16 12 15 13 54 33 35 37 17 13 10 11 80 49 36 35 18 14 11 17 66 42 30 27 18 11 12 12 73 41 39 40 12 12 15 13 63 38 35 37 16 12 15 14 69 42 38 36 10 8 14 13 67 44 31 38 14 11 16 15 54 33 34 39 18 14 15 13 81 48 38 41 18 14 15 10 69 40 34 27 16 12 13 11 84 50 39 30 17 9 12 19 80 49 37 37 16 13 17 13 70 43 34 31 16 11 13 17 69 44 28 31 13 12 15 13 77 47 37 27 16 12 13 9 54 33 33 36 16 12 15 11 79 46 37 38 20 12 16 10 30 0 35 37 16 12 15 9 71 45 37 33 15 12 16 12 73 43 32 34 15 11 15 12 72 44 33 31 16 10 14 13 77 47 38 39 14 9 15 13 75 45 33 34 16 12 14 12 69 42 29 32 16 12 13 15 54 33 33 33 15 12 7 22 70 43 31 36 12 9 17 13 73 46 36 32 17 15 13 15 54 33 35 41 16 12 15 13 77 46 32 28 15 12 14 15 82 48 29 30 13 12 13 10 80 47 39 36 16 10 16 11 80 47 37 35 16 13 12 16 69 43 35 31 16 9 14 11 78 46 37 34 16 12 17 11 81 48 32 36 14 10 15 10 76 46 38 36 16 14 17 10 76 45 37 35 16 11 12 16 73 45 36 37 20 15 16 12 85 52 32 28 15 11 11 11 66 42 33 39 16 11 15 16 79 47 40 32 13 12 9 19 68 41 38 35 17 12 16 11 76 47 41 39 16 12 15 16 71 43 36 35 16 11 10 15 54 33 43 42 12 7 10 24 46 30 30 34 16 12 15 14 82 49 31 33 16 14 11 15 74 44 32 41 17 11 13 11 88 55 32 33 13 11 14 15 38 11 37 34 12 10 18 12 76 47 37 32 18 13 16 10 86 53 33 40 14 13 14 14 54 33 34 40 14 8 14 13 70 44 33 35 13 11 14 9 69 42 38 36 16 12 14 15 90 55 33 37 13 11 12 15 54 33 31 27 16 13 14 14 76 46 38 39 13 12 15 11 89 54 37 38 16 14 15 8 76 47 33 31 15 13 15 11 73 45 31 33 16 15 13 11 79 47 39 32 15 10 17 8 90 55 44 39 17 11 17 10 74 44 33 36 15 9 19 11 81 53 35 33 12 11 15 13 72 44 32 33 16 10 13 11 71 42 28 32 10 11 9 20 66 40 40 37 16 8 15 10 77 46 27 30 12 11 15 15 65 40 37 38 14 12 15 12 74 46 32 29 15 12 16 14 82 53 28 22 13 9 11 23 54 33 34 35 15 11 14 14 63 42 30 35 11 10 11 16 54 35 35 34 12 8 15 11 64 40 31 35 8 9 13 12 69 41 32 34 16 8 15 10 54 33 30 34 15 9 16 14 84 51 30 35 17 15 14 12 86 53 31 23 16 11 15 12 77 46 40 31 10 8 16 11 89 55 32 27 18 13 16 12 76 47 36 36 13 12 11 13 60 38 32 31 16 12 12 11 75 46 35 32 13 9 9 19 73 46 38 39 10 7 16 12 85 53 42 37 15 13 13 17 79 47 34 38 16 9 16 9 71 41 35 39 16 6 12 12 72 44 35 34 14 8 9 19 69 43 33 31 10 8 13 18 78 51 36 32 17 15 13 15 54 33 32 37 13 6 14 14 69 43 33 36 15 9 19 11 81 53 34 32 16 11 13 9 84 51 32 35 12 8 12 18 84 50 34 36 13 8 13 16 69 46
Names of X columns:
connected seperated learning software happiness depression belonging belonging_all
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()
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