Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
BNP % CAC % 9.94879114850037 6.01067015836525 -0.399198276766438 -1.21977613260762 -6.66332988204191 2.31112066881356 11.7910200714085 4.75142720236295 3.68117637644047 3.01548292053592 -2.74776015356222 0.112773821391325 0.777780952380947 -2.63946570143053 4.5203951954514 -0.665672192109903 -2.83302889040041 0.00566825627927877 5.45905216536669 4.21578998409902 -1.11765 3.59015255807922 -5.4134445393648 -2.71976227526205 -2.12263843152951 -1.03067194514993 8.0000048192771 3.60704981930878 -3.15335699152059 -3.73426059324766 -7.58716468150568 -3.01952707483359 6.33206270008518 7.13034924460085 -15.3016583307283 -1.2889513759093 -2.74958115426428 -3.23541238587502 8.02656341505572 4.86152611435862 -11.206746882136 -2.16707844113809 3.87734725771212 1.7447734108114 -13.1593987110798 -7.61393425574986 -0.734646962045911 0.709739377805638 -12.7913423791305 -9.20083196396636 3.8758786004245 7.80817874915677 10.7182072742786 5.24492702067135 -4.97797995691718 2.41800786930533 9.99073273042959 3.21994985633516 -14.4362547010273 -7.12241510921467 4.42118740991072 7.97662683241735 -0.448161339780641 -1.49028996000521 -3.05651211673916 -0.317765233466577 9.7397064709836 3.8802357679552 4.67705547095521 0.935356609249225 8.8936170212766 2.79399419701954 3.22391949980461 1.80182033345972 -8.93431917719784 -3.2338780215173 -7.19184977776242 -6.97384449918244 -40.2015681880597 -21.3455650963417 7.3969959776406 3.59629943430699 16.4428950808333 8.23857993884503 6.22941617937754 3.41404944522168 -3.62277971179083 -2.88958564384573 8.23460265512352 3.69726167316263 -14.7972972972973 -2.49717010119355 -4.20301030927835 -4.85031525590461 43.0297965884173 20.0979366365621 -0.208357448778802 1.29148570316575 -6.15937594246606 -3.07461047304441 23.5599475016082 6.05465751362791 4.12165066026411 5.35064396773492 2.80553222894827 3.38570755716761 5.00934579439252 2.94184820140171 -5.32218049127803 1.22712131449202 -2.29366050561497 1.6508907711291 4.36790640046359 1.17888755099045 -0.018440264806775 -4.68110523899644 7.7632288401254 6.99536964296674 -4.89390323227076 -1.38975388176277
Names of X columns:
IQ Add MumAge Grade
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
par3 <- 'TRUE' par2 <- '2' par1 <- '1' 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