*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 21:47:07 +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/t12932271258kxide3olwww2at.htm/, Retrieved Fri, 24 Dec 2010 22:45:28 +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/t12932271258kxide3olwww2at.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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1221.53 2617.2 10168.52 6957.61 23448.78 1180.55 2506.13 9937.04 6688.49 23007.99 1183.26 2679.07 9202.45 6601.37 23096.32 1141.2 2589.73 9369.35 6229.02 22358.17 1049.33 2457.46 8824.06 5925.22 20536.49 1101.6 2517.3 9537.3 6147.97 21029.81 1030.71 2386.53 9382.64 5965.52 20128.99 1089.41 2453.37 9768.7 5964.33 19765.19 1186.69 2529.66 11057.4 6135.7 21108.59 1169.43 2475.14 11089.94 6153.55 21239.35 1104.49 2525.93 10126.03 5598.46 20608.7 1073.87 2480.93 10198.04 5608.79 20121.99 1115.1 2229.85 10546.44 5957.43 21872.5 1095.63 2169.14 9345.55 5625.95 21821.5 1036.19 2030.98 10034.74 5414.96 21752.87 1057.08 2071.37 10133.23 5675.16 20955.25 1020.62 1953.35 10492.53 5458.04 19724.19 987.48 1748.74 10356.83 5332.14 20573.33 919.32 1696.58 9958.44 4808.64 18378.73 919.14 1900.09 9522.5 4940.82 18171 872.81 1908.64 8828.26 4769.45 15520.99 797.87 1881.46 8109.53 4084.76 13576.02 735.09 2100.18 7568.42 3843.74 12811.57 825.88 2672.2 7994.05 4338.35 13278.21 903.25 3136 8859.56 4810.2 143 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 12 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Goodness of Fit Correlation NA R-squared NA RMSE 1926.8208 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 1221.53 1577.76777777778 -356.237777777777 2 1180.55 1577.76777777778 -397.217777777777 3 1183.26 1577.76777777778 -394.507777777777 4 1141.2 1577.76777777778 -436.567777777777 5 1049.33 1577.76777777778 -528.437777777777 6 1101.6 1577.76777777778 -476.167777777777 7 1030.71 1577.76777777778 -547.057777777777 8 1089.41 1577.76777777778 -488.357777777777 9 1186.69 1577.76777777778 -391.077777777777 10 1169.43 1577.76777777778 -408.337777777777 11 1104.49 1577.76777777778 -473.277777777777 12 1073.87 1577.76777777778 -503.897777777777 13 1115.1 1577.76777777778 -462.667777777777 14 1095.63 1577.76777777778 -482.137777777777 15 1036.19 1577.76777777778 -541.577777777777 16 1057.08 1577.76777777778 -520.687777777777 17 1020.62 1577.76777777778 -557.147777777777 18 987.48 1577.76777777778 -590.287777777777 19 919.32 1577.76777777778 -658.447777777777 20 919.14 1577.76777777778 -658.627777777777 21 872.81 1577.76777777778 -704.957777777777 22 797.87 1577.76777777778 -779.897777777777 23 735.09 1577.76777777778 -842.677777777777 24 825.88 1577.76777777778 -751.887777777777 25 903.25 1577.76777777778 -674.517777777777 26 896.24 1577.76777777778 -681.527777777777 27 968.75 1577.76777777778 -609.017777777777 28 1166.36 1577.76777777778 -411.407777777777 29 1282.83 1577.76777777778 -294.937777777777 30 1267.38 1577.76777777778 -310.387777777777 31 1280 1577.76777777778 -297.767777777777 32 1400.38 1577.76777777778 -177.387777777777 33 1385.59 1577.76777777778 -192.177777777777 34 1322.7 1577.76777777778 -255.067777777777 35 1330.63 1577.76777777778 -247.137777777777 36 1378.55 1577.76777777778 -199.217777777777 37 1468.36 1577.76777777778 -109.407777777777 38 1481.14 1577.76777777778 -96.6277777777773 39 1549.38 1577.76777777778 -28.3877777777773 40 1526.75 1577.76777777778 -51.0177777777774 41 1473.99 1577.76777777778 -103.777777777777 42 1455.27 1577.76777777778 -122.497777777777 43 1503.35 1577.76777777778 -74.4177777777775 44 1530.62 1577.76777777778 -47.1477777777775 45 1482.37 1577.76777777778 -95.3977777777775 46 1420.86 1577.76777777778 -156.907777777777 47 1406.82 1577.76777777778 -170.947777777777 48 1438.24 1577.76777777778 -139.527777777777 49 1418.3 1577.76777777778 -159.467777777777 50 1400.63 1577.76777777778 -177.137777777777 51 1377.94 1577.76777777778 -199.827777777777 52 1335.85 1577.76777777778 -241.917777777777 53 1303.82 1577.76777777778 -273.947777777777 54 1276.66 1577.76777777778 -301.107777777777 55 1270.2 1577.76777777778 -307.567777777777 56 1270.09 1577.76777777778 -307.677777777777 57 1310.61 1577.76777777778 -267.157777777777 58 1294.87 1577.76777777778 -282.897777777777 59 1280.66 1577.76777777778 -297.107777777777 60 1280.08 1577.76777777778 -297.687777777777 61 1248.29 1577.76777777778 -329.477777777777 62 1249.48 1577.76777777778 -328.287777777777 63 1207.01 1577.76777777778 -370.757777777777 64 1228.81 1577.76777777778 -348.957777777777 65 1220.33 1577.76777777778 -357.437777777777 66 1234.18 1577.76777777778 -343.587777777777 67 1191.33 1577.76777777778 -386.437777777777 68 1191.5 1577.76777777778 -386.267777777777 69 11008.9 1577.76777777778 9431.13222222222 70 4348.77 1577.76777777778 2771.00222222222 71 14195.35 1577.76777777778 12617.5822222222 72 1221.53 1577.76777777778 -356.237777777777

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932271258kxide3olwww2at/2u2t51293227214.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12932271258kxide3olwww2at/2u2t51293227214.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12932271258kxide3olwww2at/3u2t51293227214.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12932271258kxide3olwww2at/3u2t51293227214.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12932271258kxide3olwww2at/44tsq1293227214.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t12932271258kxide3olwww2at/44tsq1293227214.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 0 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 0 ; 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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