Apple Inc - Recursive Partitioning Trend

*The author of this computation has been verified*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Fri, 24 Dec 2010 13:24:35 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197592548ust8s96hr0vp.htm/, Retrieved Fri, 24 Dec 2010 14:33:18 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197592548ust8s96hr0vp.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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10.81 -0,2643 0 0 24563400 24.45 2772.73 0,0373 115.7 1 9.12 -0,2643 0 0 14163200 23.62 2151.83 0,0353 109.2 2 11.03 -0,2643 0 0 18184800 21.90 1840.26 0,0292 116.9 3 12.74 -0,1918 0 0 20810300 27.12 2116.24 0,0327 109.9 4 9.98 -0,1918 0 0 12843000 27.70 2110.49 0,0362 116.1 5 11.62 -0,1918 0 0 13866700 29.23 2160.54 0,0325 118.9 6 9.40 -0,2246 0 0 15119200 26.50 2027.13 0,0272 116.3 7 9.27 -0,2246 0 0 8301600 22.84 1805.43 0,0272 114.0 8 7.76 -0,2246 0 0 14039600 20.49 1498.80 0,0265 97.0 9 8.78 0,3654 0 0 12139700 23.28 1690.20 0,0213 85.3 10 10.65 0,3654 0 0 9649000 25.71 1930.58 0,019 84.9 11 10.95 0,3654 0 0 8513600 26.52 1950.40 0,0155 94.6 12 12.36 0,0447 0 0 15278600 25.51 1934.03 0,0114 97.8 13 10.85 0,0447 0 0 15590900 23.36 1731.49 0,0114 95.0 14 11.84 0,0447 0 0 9691100 24.15 1845.35 0,0148 110.7 15 12.14 -0,0312 0 0 10882700 20.92 1688.23 0,0164 108.5 16 11.65 -0,0312 0 0 10294800 20.38 1615.73 0,0118 110.3 17 8.86 -0,0312 0 0 16031900 21.90 1463.21 0,0107 106.3 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 13 seconds R Server 'George Udny Yule' @ 72.249.76.132 Goodness of Fit Correlation 0.9756 R-squared 0.9518 RMSE 16.6129 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 10.81 10.83 -0.0199999999999978 2 9.12 10.83 -1.71 3 11.03 10.83 0.200000000000001 4 12.74 10.83 1.91000000000000 5 9.98 10.83 -0.849999999999998 6 11.62 10.83 0.790000000000001 7 9.4 10.83 -1.43000000000000 8 9.27 10.83 -1.56 9 7.76 7.66692307692308 0.0930769230769215 10 8.78 10.83 -2.05 11 10.65 10.83 -0.179999999999998 12 10.95 10.83 0.120000000000001 13 12.36 10.83 1.53 14 10.85 10.83 0.0200000000000014 15 11.84 10.83 1.01000000000000 16 12.14 10.83 1.31000000000000 17 11.65 10.83 0.820000000000002 18 8.86 7.66692307692308 1.19307692307692 19 7.63 7.66692307692308 -0.0369230769230784 20 7.38 7.66692307692308 -0.286923076923078 21 7.25 7.66692307692308 -0.416923076923078 22 8.03 7.66692307692308 0.363076923076921 23 7.75 7.66692307692308 0.0830769230769217 24 7.16 7.66692307692308 -0.506923076923078 25 7.18 7.66692307692308 -0.486923076923079 26 7.51 7.66692307692308 -0.156923076923078 27 7.07 7.66692307692308 -0.596923076923078 28 7.11 7.66692307692308 -0.556923076923078 29 8.98 7.66692307692308 1.31307692307692 30 9.53 10.83 -1.3 31 10.54 10.83 -0.289999999999999 32 11.31 10.83 0.480000000000002 33 10.36 10.83 -0.469999999999999 34 11.44 10.83 0.610000000000001 35 10.45 10.83 -0.379999999999999 36 10.69 10.83 -0.139999999999999 37 11.28 10.83 0.450000000000001 38 11.96 10.83 1.13000000000000 39 13.52 15.6442857142857 -2.12428571428571 40 12.89 15.6442857142857 -2.75428571428571 41 14.03 15.6442857142857 -1.61428571428571 42 16.27 15.6442857142857 0.625714285714286 43 16.17 15.6442857142857 0.525714285714288 44 17.25 15.6442857142857 1.60571428571429 45 19.38 15.6442857142857 3.73571428571429 46 26.2 37.219 -11.019 47 33.53 37.219 -3.689 48 32.2 37.219 -5.019 49 38.45 37.219 1.23100000000000 50 44.86 37.219 7.641 51 41.67 37.219 4.451 52 36.06 37.219 -1.159 53 39.76 37.219 2.54100000000000 54 36.81 37.219 -0.408999999999999 55 42.65 37.219 5.431 56 46.89 58.7057142857143 -11.8157142857143 57 53.61 58.7057142857143 -5.09571428571428 58 57.59 58.7057142857143 -1.11571428571428 59 67.82 78.0484615384615 -10.2284615384615 60 71.89 78.0484615384615 -6.15846153846154 61 75.51 78.0484615384615 -2.53846153846153 62 68.49 78.0484615384615 -9.55846153846154 63 62.72 78.0484615384615 -15.3284615384615 64 70.39 78.0484615384615 -7.65846153846154 65 59.77 58.7057142857143 1.06428571428572 66 57.27 58.7057142857143 -1.43571428571428 67 67.96 58.7057142857143 9.25428571428571 68 67.85 58.7057142857143 9.14428571428572 69 76.98 78.0484615384615 -1.06846153846153 70 81.08 78.0484615384615 3.03153846153846 71 91.66 78.0484615384615 13.6115384615385 72 84.84 78.0484615384615 6.79153846153847 73 85.73 78.0484615384615 7.68153846153847 74 84.61 78.0484615384615 6.56153846153846 75 92.91 78.0484615384615 14.8615384615385 76 99.8 151.770476190476 -51.9704761904762 77 121.19 151.770476190476 -30.5804761904762 78 122.04 151.770476190476 -29.7304761904762 79 131.76 151.770476190476 -20.0104761904762 80 138.48 151.770476190476 -13.2904761904762 81 153.47 151.770476190476 1.69952380952384 82 189.95 151.770476190476 38.1795238095238 83 182.22 151.770476190476 30.4495238095238 84 198.08 151.770476190476 46.3095238095239 85 135.36 151.770476190476 -16.4104761904761 86 125.02 151.770476190476 -26.7504761904762 87 143.5 151.770476190476 -8.27047619047616 88 173.95 151.770476190476 22.1795238095238 89 188.75 151.770476190476 36.9795238095238 90 167.44 151.770476190476 15.6695238095238 91 158.95 151.770476190476 7.17952380952383 92 169.53 151.770476190476 17.7595238095238 93 113.66 151.770476190476 -38.1104761904762 94 107.59 103.97625 3.61374999999998 95 92.67 103.97625 -11.3062500000000 96 85.35 103.97625 -18.6262500000000 97 90.13 103.97625 -13.8462500000000 98 89.31 103.97625 -14.6662500000000 99 105.12 103.97625 1.14374999999998 100 125.83 103.97625 21.8537500000000 101 135.81 103.97625 31.83375 102 142.43 151.770476190476 -9.34047619047615 103 163.39 151.770476190476 11.6195238095238 104 168.21 151.770476190476 16.4395238095238 105 185.35 228.443846153846 -43.0938461538462 106 188.5 228.443846153846 -39.9438461538462 107 199.91 228.443846153846 -28.5338461538462 108 210.73 228.443846153846 -17.7138461538462 109 192.06 228.443846153846 -36.3838461538462 110 204.62 228.443846153846 -23.8238461538461 111 235 228.443846153846 6.55615384615385 112 261.09 228.443846153846 32.6461538461538 113 256.88 228.443846153846 28.4361538461538 114 251.53 228.443846153846 23.0861538461538 115 257.25 228.443846153846 28.8061538461538 116 243.1 228.443846153846 14.6561538461538 117 283.75 228.443846153846 55.3061538461538

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197592548ust8s96hr0vp/2wupo1293197061.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197592548ust8s96hr0vp/2wupo1293197061.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197592548ust8s96hr0vp/3wupo1293197061.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197592548ust8s96hr0vp/3wupo1293197061.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197592548ust8s96hr0vp/473791293197061.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197592548ust8s96hr0vp/473791293197061.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par4 = no ;

R code (references can be found in the software module):

library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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