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Data X:
0.79 0.18 2.21 0.87 2.12 1.14 0.93 0.2 5.38 NA 3.14 1.08 2.23 0.89 11.88 NA 9.31 4.85 6.06 4.14 2.31 1.25 6.84 4.46 7.49 6.19 0.72 0.26 4.48 3.28 5.09 2.57 7.44 4.43 1.41 0.51 5.77 NA 4.84 0.63 2.96 0.67 3.12 1.74 3.83 2.36 3.11 0.91 2.86 NA 4.06 3.24 3.32 2.08 1.21 0.12 0.8 0.04 2.52 NA 1.21 NA 1.17 0.19 8.17 5 5.65 3.56 1.24 0.08 1.46 0.01 4.36 2.04 3.38 2.32 1.87 0.67 1.03 0.25 1.29 0.47 0.82 0.07 2.84 1.37 1.27 0.26 3.92 2.21 1.95 1.23 4.21 2.94 5.19 3.42 5.51 2.6 2.19 NA 2.57 1.47 1.53 0.86 2.17 1.08 2.15 1.02 2.07 0.84 3.97 3.17 0.42 0.03 6.86 NA 1.02 0.07 2.9 1.06 5.87 NA 5.14 2.71 2.34 1.58 4.73 2.39 2.02 0.43 1.03 0.21 1.58 0.83 5.3 3.28 1.97 0.43 4.38 2.58 2.98 NA 3.23 2.61 1.89 0.7 1.41 0.16 1.53 0.09 3.07 1.25 0.61 0.15 1.68 0.6 2.92 1.9 1.16 0.61 1.58 0.64 2.79 1.72 1.88 1.36 5.57 3.22 6.22 4.59 4.61 2.77 1.89 1.09 5.02 3.69 2.1 1.09 5.55 4.59 1.03 0.2 1.17 0.68 5.69 4.17 8.13 6.89 1.91 0.95 1.22 0.09 6.29 1.66 3.84 2.52 1.66 0.51 1.21 0.14 3.69 2.33 5.83 2.15 15.82 12.65 3.26 2.06 0.99 0.07 0.81 0.07 3.71 2.1 1.53 0.1 2.08 1.73 2.54 0.55 3.46 1.99 2.89 1.74 1.78 1.03 6.08 2.09 3.78 2.13 7.78 NA 1.68 0.67 0.87 0.17 1.43 0.09 2.48 1.02 2.94 NA 0.98 0.16 5.28 3.23 3.58 1.78 5.6 2.84 1.39 0.45 1.56 0.1 1.16 0.21 4.98 NA 7.52 5.8 0.79 0.38 2.79 1.44 1.91 0.35 4.16 0.97 2.28 0.67 1.1 0.34 4.44 2.64 3.88 2.15 10.8 9.57 3.65 3.27 2.71 1.46 5.69 3.87 0.87 0.07 4.94 3.34 2.45 1.56 3.11 NA 2.77 0.96 1.49 0.37 5.61 4.21 1.21 0.3 2.7 1.66 1.24 0.07 7.97 5.91 4.06 2.82 5.81 4.27 1.29 0 1.24 0.07 3.31 2.34 3.67 2.22 1.32 0.52 4.25 3.01 2.01 0.67 7.25 3.88 5.79 4.26 1.51 0.81 0.91 0.13 1.32 0.17 2.66 1.54 0.48 0.06 1.13 0.31 2.7 0.88 7.92 6.89 2.34 1.11 3.33 1.92 5.47 4.13 1.24 0.08 2.84 1.92 4.94 3.14 7.93 6.37 8.22 5.9 2.91 0.98 2.32 1.41 3.57 2.13 1.65 0.79 2.07 NA 1.03 0.42 0.99 0.24 1.37 0.53
Names of X columns:
Total_Ecological_Footprint Carbon_Footprint
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()
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