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*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: Tue, 14 Dec 2010 17:41:08 +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/14/t1292348342ob2xuk3n7mcpgmz.htm/, Retrieved Tue, 14 Dec 2010 18:39:05 +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/14/t1292348342ob2xuk3n7mcpgmz.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 «

2 73 69 1 58 53 1 68 43 2 69 60 1 62 49 1 68 62 1 65 45 1 65 50 1 81 75 1 73 82 2 64 60 1 68 59 1 51 21 1 68 40 1 61 62 2 77 54 1 69 47 1 73 59 2 61 37 2 62 43 1 63 48 1 69 59 2 47 79 2 66 62 1 58 16 2 63 38 1 69 58 2 59 60 1 59 72 2 63 67 2 65 55 1 65 47 2 71 59 1 60 49 2 66 47 1 67 57 2 81 39 1 62 49 1 63 26 2 73 53 2 55 75 1 59 65 1 64 49 2 63 48 2 74 45 2 67 31 1 64 67 1 73 61 1 54 49 1 76 69 2 74 54 2 63 80 2 73 57 2 67 34 2 68 69 1 66 44 2 62 70 2 71 51 1 68 66 1 63 18 1 75 74 1 77 59 2 62 48 1 74 55 2 67 44 2 56 56 2 60 65 2 58 77 1 65 46 2 49 70 1 61 39 2 66 55 2 64 44 2 65 45 1 46 45 2 81 25 2 65 49 1 72 65 2 65 45 2 74 71 1 69 48 1 59 41 2 58 40 1 71 64 2 79 56 2 68 52 1 66 41 2 62 45 1 69 49 1 60 42 2 63 54 1 62 40 1 61 40 2 65 51 1 64 48 2 67 80 2 56 38 2 56 57 1 48 28 1 74 51 1 69 46 1 62 58 1 73 67 1 64 72 1 57 26 1 57 54 2 60 53 2 61 69 1 72 64 1 57 47 1 51 43 1 63 66 1 54 54 1 72 62 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	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.314
R-squared	0.0986
RMSE	6.9479

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	73	66.4772727272727	6.52272727272727
2	58	66.4772727272727	-8.47727272727273
3	68	61.313725490196	6.68627450980392
4	69	66.4772727272727	2.52272727272727
5	62	61.313725490196	0.686274509803923
6	68	66.4772727272727	1.52272727272727
7	65	61.313725490196	3.68627450980392
8	65	61.313725490196	3.68627450980392
9	81	66.4772727272727	14.5227272727273
10	73	66.4772727272727	6.52272727272727
11	64	66.4772727272727	-2.47727272727273
12	68	66.4772727272727	1.52272727272727
13	51	61.313725490196	-10.3137254901961
14	68	61.313725490196	6.68627450980392
15	61	66.4772727272727	-5.47727272727273
16	77	66.4772727272727	10.5227272727273
17	69	61.313725490196	7.68627450980392
18	73	66.4772727272727	6.52272727272727
19	61	65.08	-4.08
20	62	65.08	-3.08
21	63	61.313725490196	1.68627450980392
22	69	66.4772727272727	2.52272727272727
23	47	66.4772727272727	-19.4772727272727
24	66	66.4772727272727	-0.477272727272734
25	58	61.313725490196	-3.31372549019608
26	63	65.08	-2.08
27	69	66.4772727272727	2.52272727272727
28	59	66.4772727272727	-7.47727272727273
29	59	66.4772727272727	-7.47727272727273
30	63	66.4772727272727	-3.47727272727273
31	65	66.4772727272727	-1.47727272727273
32	65	61.313725490196	3.68627450980392
33	71	66.4772727272727	4.52272727272727
34	60	61.313725490196	-1.31372549019608
35	66	65.08	0.920000000000002
36	67	66.4772727272727	0.522727272727266
37	81	65.08	15.92
38	62	61.313725490196	0.686274509803923
39	63	61.313725490196	1.68627450980392
40	73	66.4772727272727	6.52272727272727
41	55	66.4772727272727	-11.4772727272727
42	59	66.4772727272727	-7.47727272727273
43	64	61.313725490196	2.68627450980392
44	63	65.08	-2.08
45	74	65.08	8.92
46	67	65.08	1.92
47	64	66.4772727272727	-2.47727272727273
48	73	66.4772727272727	6.52272727272727
49	54	61.313725490196	-7.31372549019608
50	76	66.4772727272727	9.52272727272727
51	74	66.4772727272727	7.52272727272727
52	63	66.4772727272727	-3.47727272727273
53	73	66.4772727272727	6.52272727272727
54	67	65.08	1.92
55	68	66.4772727272727	1.52272727272727
56	66	61.313725490196	4.68627450980392
57	62	66.4772727272727	-4.47727272727273
58	71	66.4772727272727	4.52272727272727
59	68	66.4772727272727	1.52272727272727
60	63	61.313725490196	1.68627450980392
61	75	66.4772727272727	8.52272727272727
62	77	66.4772727272727	10.5227272727273
63	62	65.08	-3.08
64	74	66.4772727272727	7.52272727272727
65	67	65.08	1.92
66	56	66.4772727272727	-10.4772727272727
67	60	66.4772727272727	-6.47727272727273
68	58	66.4772727272727	-8.47727272727273
69	65	61.313725490196	3.68627450980392
70	49	66.4772727272727	-17.4772727272727
71	61	61.313725490196	-0.313725490196077
72	66	66.4772727272727	-0.477272727272734
73	64	65.08	-1.08000000000000
74	65	65.08	-0.0799999999999983
75	46	61.313725490196	-15.3137254901961
76	81	65.08	15.92
77	65	65.08	-0.0799999999999983
78	72	66.4772727272727	5.52272727272727
79	65	65.08	-0.0799999999999983
80	74	66.4772727272727	7.52272727272727
81	69	61.313725490196	7.68627450980392
82	59	61.313725490196	-2.31372549019608
83	58	65.08	-7.08
84	71	66.4772727272727	4.52272727272727
85	79	66.4772727272727	12.5227272727273
86	68	66.4772727272727	1.52272727272727
87	66	61.313725490196	4.68627450980392
88	62	65.08	-3.08
89	69	61.313725490196	7.68627450980392
90	60	61.313725490196	-1.31372549019608
91	63	66.4772727272727	-3.47727272727273
92	62	61.313725490196	0.686274509803923
93	61	61.313725490196	-0.313725490196077
94	65	66.4772727272727	-1.47727272727273
95	64	61.313725490196	2.68627450980392
96	67	66.4772727272727	0.522727272727266
97	56	65.08	-9.08
98	56	66.4772727272727	-10.4772727272727
99	48	61.313725490196	-13.3137254901961
100	74	66.4772727272727	7.52272727272727
101	69	61.313725490196	7.68627450980392
102	62	66.4772727272727	-4.47727272727273
103	73	66.4772727272727	6.52272727272727
104	64	66.4772727272727	-2.47727272727273
105	57	61.313725490196	-4.31372549019608
106	57	66.4772727272727	-9.47727272727273
107	60	66.4772727272727	-6.47727272727273
108	61	66.4772727272727	-5.47727272727273
109	72	66.4772727272727	5.52272727272727
110	57	61.313725490196	-4.31372549019608
111	51	61.313725490196	-10.3137254901961
112	63	66.4772727272727	-3.47727272727273
113	54	66.4772727272727	-12.4772727272727
114	72	66.4772727272727	5.52272727272727
115	62	66.4772727272727	-4.47727272727273
116	68	66.4772727272727	1.52272727272727
117	62	66.4772727272727	-4.47727272727273
118	63	66.4772727272727	-3.47727272727273
119	77	66.4772727272727	10.5227272727273
120	57	61.313725490196	-4.31372549019608
121	57	61.313725490196	-4.31372549019608
122	61	66.4772727272727	-5.47727272727273
123	66	65.08	0.920000000000002
124	65	61.313725490196	3.68627450980392
125	63	66.4772727272727	-3.47727272727273
126	59	61.313725490196	-2.31372549019608
127	66	66.4772727272727	-0.477272727272734
128	68	66.4772727272727	1.52272727272727
129	72	61.313725490196	10.6862745098039
130	68	61.313725490196	6.68627450980392
131	68	66.4772727272727	1.52272727272727
132	67	61.313725490196	5.68627450980392
133	59	61.313725490196	-2.31372549019608
134	56	66.4772727272727	-10.4772727272727
135	62	66.4772727272727	-4.47727272727273
136	55	65.08	-10.08
137	72	66.4772727272727	5.52272727272727
138	68	66.4772727272727	1.52272727272727
139	67	66.4772727272727	0.522727272727266
140	54	61.313725490196	-7.31372549019608
141	69	66.4772727272727	2.52272727272727
142	61	66.4772727272727	-5.47727272727273
143	55	61.313725490196	-6.31372549019608
144	75	66.4772727272727	8.52272727272727
145	55	61.313725490196	-6.31372549019608
146	49	66.4772727272727	-17.4772727272727
147	54	65.08	-11.08
148	51	61.313725490196	-10.3137254901961
149	66	61.313725490196	4.68627450980392
150	73	66.4772727272727	6.52272727272727
151	63	66.4772727272727	-3.47727272727273
152	61	65.08	-4.08
153	74	66.4772727272727	7.52272727272727
154	81	65.08	15.92
155	58	61.313725490196	-3.31372549019608
156	62	61.313725490196	0.686274509803923
157	64	61.313725490196	2.68627450980392
158	62	61.313725490196	0.686274509803923
159	85	66.4772727272727	18.5227272727273
160	74	66.4772727272727	7.52272727272727
161	51	66.4772727272727	-15.4772727272727
162	66	61.313725490196	4.68627450980392
163	61	65.08	-4.08
164	72	66.4772727272727	5.52272727272727

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292348342ob2xuk3n7mcpgmz/2o1ac1292348461.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292348342ob2xuk3n7mcpgmz/2o1ac1292348461.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292348342ob2xuk3n7mcpgmz/3o1ac1292348461.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292348342ob2xuk3n7mcpgmz/3o1ac1292348461.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292348342ob2xuk3n7mcpgmz/4haaf1292348461.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292348342ob2xuk3n7mcpgmz/4haaf1292348461.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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')

}

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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