| | *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, 10 Dec 2010 13:21:51 +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/10/t1291987230c3tx596vhimzrye.htm/, Retrieved Fri, 10 Dec 2010 14:20:33 +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/10/t1291987230c3tx596vhimzrye.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: | » Textbox « » Textfile « » CSV « | 356 182 89
386 213 97
444 227 154
387 209 81
327 219 110
448 221 116
225 114 73
182 97 73
460 205 174
411 215 103
342 224 130
361 189 91
377 182 136
331 201 106
428 198 136
340 173 122
352 238 131
461 258 135
221 122 75
198 101 68
422 259 143
329 243 115
320 188 93
375 173 128
364 224 152
351 215 125
380 196 107
319 159 116
322 187 220
386 208 137
221 131 34
187 93 51
344 210 153
342 228 145
365 176 116
313 195 145
356 188 98
337 188 118
389 190 139
326 188 140
343 176 113
357 225 149
220 93 79
218 79 47
391 235 166
425 247 180
332 195 122
298 197 134
360 211 114
336 156 125
325 209 181
393 180 142
301 185 143
426 303 187
265 129 137
210 85 62
429 249 239
440 231 157
357 212 139
431 240 187
442 234 99
442 217 146
544 287 175
420 221 148
396 208 130
482 241 183
261 156 115
211 96 80
448 320 223
468 242 131
464 227 201
425 200 157
| | 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!
Goodness of Fit | Correlation | 0.8406 | R-squared | 0.7067 | RMSE | 42.7464 |
Actuals, Predictions, and Residuals | # | Actuals | Forecasts | Residuals | 1 | 356 | 364.634146341463 | -8.6341463414634 | 2 | 386 | 364.634146341463 | 21.3658536585366 | 3 | 444 | 430 | 14 | 4 | 387 | 364.634146341463 | 22.3658536585366 | 5 | 327 | 364.634146341463 | -37.6341463414634 | 6 | 448 | 364.634146341463 | 83.3658536585366 | 7 | 225 | 227.307692307692 | -2.30769230769232 | 8 | 182 | 227.307692307692 | -45.3076923076923 | 9 | 460 | 364.634146341463 | 95.3658536585366 | 10 | 411 | 364.634146341463 | 46.3658536585366 | 11 | 342 | 364.634146341463 | -22.6341463414634 | 12 | 361 | 364.634146341463 | -3.63414634146341 | 13 | 377 | 364.634146341463 | 12.3658536585366 | 14 | 331 | 364.634146341463 | -33.6341463414634 | 15 | 428 | 364.634146341463 | 63.3658536585366 | 16 | 340 | 364.634146341463 | -24.6341463414634 | 17 | 352 | 430 | -78 | 18 | 461 | 430 | 31 | 19 | 221 | 227.307692307692 | -6.30769230769232 | 20 | 198 | 227.307692307692 | -29.3076923076923 | 21 | 422 | 430 | -8 | 22 | 329 | 430 | -101 | 23 | 320 | 364.634146341463 | -44.6341463414634 | 24 | 375 | 364.634146341463 | 10.3658536585366 | 25 | 364 | 364.634146341463 | -0.634146341463406 | 26 | 351 | 364.634146341463 | -13.6341463414634 | 27 | 380 | 364.634146341463 | 15.3658536585366 | 28 | 319 | 364.634146341463 | -45.6341463414634 | 29 | 322 | 364.634146341463 | -42.6341463414634 | 30 | 386 | 364.634146341463 | 21.3658536585366 | 31 | 221 | 227.307692307692 | -6.30769230769232 | 32 | 187 | 227.307692307692 | -40.3076923076923 | 33 | 344 | 364.634146341463 | -20.6341463414634 | 34 | 342 | 430 | -88 | 35 | 365 | 364.634146341463 | 0.365853658536594 | 36 | 313 | 364.634146341463 | -51.6341463414634 | 37 | 356 | 364.634146341463 | -8.6341463414634 | 38 | 337 | 364.634146341463 | -27.6341463414634 | 39 | 389 | 364.634146341463 | 24.3658536585366 | 40 | 326 | 364.634146341463 | -38.6341463414634 | 41 | 343 | 364.634146341463 | -21.6341463414634 | 42 | 357 | 364.634146341463 | -7.6341463414634 | 43 | 220 | 227.307692307692 | -7.30769230769232 | 44 | 218 | 227.307692307692 | -9.30769230769232 | 45 | 391 | 430 | -39 | 46 | 425 | 430 | -5 | 47 | 332 | 364.634146341463 | -32.6341463414634 | 48 | 298 | 364.634146341463 | -66.6341463414634 | 49 | 360 | 364.634146341463 | -4.63414634146341 | 50 | 336 | 227.307692307692 | 108.692307692308 | 51 | 325 | 364.634146341463 | -39.6341463414634 | 52 | 393 | 364.634146341463 | 28.3658536585366 | 53 | 301 | 364.634146341463 | -63.6341463414634 | 54 | 426 | 430 | -4 | 55 | 265 | 227.307692307692 | 37.6923076923077 | 56 | 210 | 227.307692307692 | -17.3076923076923 | 57 | 429 | 430 | -1 | 58 | 440 | 430 | 10 | 59 | 357 | 364.634146341463 | -7.6341463414634 | 60 | 431 | 430 | 1 | 61 | 442 | 430 | 12 | 62 | 442 | 364.634146341463 | 77.3658536585366 | 63 | 544 | 430 | 114 | 64 | 420 | 364.634146341463 | 55.3658536585366 | 65 | 396 | 364.634146341463 | 31.3658536585366 | 66 | 482 | 430 | 52 | 67 | 261 | 227.307692307692 | 33.6923076923077 | 68 | 211 | 227.307692307692 | -16.3076923076923 | 69 | 448 | 430 | 18 | 70 | 468 | 430 | 38 | 71 | 464 | 430 | 34 | 72 | 425 | 364.634146341463 | 60.3658536585366 |
| | Charts produced by software: | | http://www.freestatistics.org/blog/date/2010/Dec/10/t1291987230c3tx596vhimzrye/2w0z81291987304.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1291987230c3tx596vhimzrye/2w0z81291987304.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1291987230c3tx596vhimzrye/3w0z81291987304.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1291987230c3tx596vhimzrye/3w0z81291987304.ps (open in new window) |
| http://www.freestatistics.org/blog/date/2010/Dec/10/t1291987230c3tx596vhimzrye/4hjyw1291987304.png (open in new window) | http://www.freestatistics.org/blog/date/2010/Dec/10/t1291987230c3tx596vhimzrye/4hjyw1291987304.ps (open in new window) |
| | Parameters (Session): | par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ; | | Parameters (R input): | par1 = 1 ; par2 = none ; par3 = 3 ; 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')
}
| |
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