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p_Stress_RP1

*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: Sat, 11 Dec 2010 13:00:40 +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/11/t12920748315macddk4ldwivjn.htm/, Retrieved Sat, 11 Dec 2010 14:40:34 +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/11/t12920748315macddk4ldwivjn.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 «

23 10 53 7 12 2 4 0 0 21 6 86 4 11 4 3 0 0 21 13 66 6 14 7 5 0 0 21 12 67 5 12 3 3 0 1 24 8 76 4 21 7 6 0 0 22 6 78 3 12 2 5 0 0 21 10 53 5 22 7 6 0 0 22 10 80 6 11 2 6 0 0 21 9 74 5 10 1 5 0 0 20 9 76 6 13 2 5 0 0 22 7 79 7 10 6 3 0 1 21 5 54 6 8 1 5 0 0 21 14 67 7 15 1 7 0 1 23 6 87 6 10 1 5 0 0 22 10 58 4 14 2 5 0 1 23 10 75 6 14 2 3 0 1 22 7 88 4 11 2 5 0 0 24 10 64 5 10 1 6 0 1 23 8 57 3 13 7 5 0 0 21 6 66 3 7 1 2 0 1 23 10 54 4 12 2 5 0 0 23 12 56 5 14 4 4 0 0 21 7 86 3 11 2 6 0 1 20 15 80 7 9 1 3 0 0 32 8 76 7 11 1 5 0 1 22 10 69 4 15 5 4 0 0 21 13 67 4 13 2 5 0 1 21 8 80 5 9 1 2 0 0 21 11 54 6 15 3 2 0 1 22 7 71 5 10 1 5 0 0 21 9 84 4 11 2 2 0 0 21 10 74 6 13 5 2 0 1 21 8 71 5 8 2 2 0 1 22 15 63 5 20 6 5 0 1 21 9 71 6 12 4 5 0 1 21 7 76 2 10 1 1 0 0 21 11 69 6 10 3 5 0 1 21 9 74 7 9 6 2 0 1 23 8 75 5 14 7 6 0 0 21 8 54 5 8 4 1 0 1 23 12 69 5 11 5 3 0 0 23 13 68 6 13 3 2 0 0 21 9 75 4 11 2 5 0 0 21 11 75 6 11 2 3 0 1 20 8 72 5 10 2 4 0 0 21 10 67 5 14 2 3 0 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	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.5063
R-squared	0.2563
RMSE	2.0507

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	10	9.27927927927928	0.72072072072072
2	6	9.27927927927928	-3.27927927927928
3	13	9.27927927927928	3.72072072072072
4	12	9.27927927927928	2.72072072072072
5	8	12.1935483870968	-4.19354838709677
6	6	9.27927927927928	-3.27927927927928
7	10	12.1935483870968	-2.19354838709677
8	10	9.27927927927928	0.72072072072072
9	9	9.27927927927928	-0.279279279279280
10	9	9.27927927927928	-0.279279279279280
11	7	9.27927927927928	-2.27927927927928
12	5	9.27927927927928	-4.27927927927928
13	14	12.1935483870968	1.80645161290323
14	6	9.27927927927928	-3.27927927927928
15	10	9.27927927927928	0.72072072072072
16	10	9.27927927927928	0.72072072072072
17	7	9.27927927927928	-2.27927927927928
18	10	9.27927927927928	0.72072072072072
19	8	9.27927927927928	-1.27927927927928
20	6	9.27927927927928	-3.27927927927928
21	10	9.27927927927928	0.72072072072072
22	12	9.27927927927928	2.72072072072072
23	7	9.27927927927928	-2.27927927927928
24	15	9.27927927927928	5.72072072072072
25	8	9.27927927927928	-1.27927927927928
26	10	12.1935483870968	-2.19354838709677
27	13	9.27927927927928	3.72072072072072
28	8	9.27927927927928	-1.27927927927928
29	11	12.1935483870968	-1.19354838709677
30	7	9.27927927927928	-2.27927927927928
31	9	9.27927927927928	-0.279279279279280
32	10	9.27927927927928	0.72072072072072
33	8	9.27927927927928	-1.27927927927928
34	15	12.1935483870968	2.80645161290323
35	9	9.27927927927928	-0.279279279279280
36	7	9.27927927927928	-2.27927927927928
37	11	9.27927927927928	1.72072072072072
38	9	9.27927927927928	-0.279279279279280
39	8	9.27927927927928	-1.27927927927928
40	8	9.27927927927928	-1.27927927927928
41	12	9.27927927927928	2.72072072072072
42	13	9.27927927927928	3.72072072072072
43	9	9.27927927927928	-0.279279279279280
44	11	9.27927927927928	1.72072072072072
45	8	9.27927927927928	-1.27927927927928
46	10	9.27927927927928	0.72072072072072
47	13	12.1935483870968	0.806451612903226
48	12	9.27927927927928	2.72072072072072
49	12	9.27927927927928	2.72072072072072
50	9	9.27927927927928	-0.279279279279280
51	8	9.27927927927928	-1.27927927927928
52	9	9.27927927927928	-0.279279279279280
53	12	9.27927927927928	2.72072072072072
54	12	12.1935483870968	-0.193548387096774
55	16	12.1935483870968	3.80645161290323
56	11	9.27927927927928	1.72072072072072
57	13	9.27927927927928	3.72072072072072
58	10	9.27927927927928	0.72072072072072
59	9	9.27927927927928	-0.279279279279280
60	14	9.27927927927928	4.72072072072072
61	13	12.1935483870968	0.806451612903226
62	12	9.27927927927928	2.72072072072072
63	9	9.27927927927928	-0.279279279279280
64	9	9.27927927927928	-0.279279279279280
65	10	9.27927927927928	0.72072072072072
66	8	12.1935483870968	-4.19354838709677
67	9	9.27927927927928	-0.279279279279280
68	9	9.27927927927928	-0.279279279279280
69	11	9.27927927927928	1.72072072072072
70	7	9.27927927927928	-2.27927927927928
71	11	12.1935483870968	-1.19354838709677
72	9	9.27927927927928	-0.279279279279280
73	11	9.27927927927928	1.72072072072072
74	9	9.27927927927928	-0.279279279279280
75	8	9.27927927927928	-1.27927927927928
76	9	9.27927927927928	-0.279279279279280
77	8	9.27927927927928	-1.27927927927928
78	9	9.27927927927928	-0.279279279279280
79	10	9.27927927927928	0.72072072072072
80	9	9.27927927927928	-0.279279279279280
81	17	12.1935483870968	4.80645161290323
82	7	9.27927927927928	-2.27927927927928
83	11	12.1935483870968	-1.19354838709677
84	9	9.27927927927928	-0.279279279279280
85	10	12.1935483870968	-2.19354838709677
86	11	9.27927927927928	1.72072072072072
87	8	9.27927927927928	-1.27927927927928
88	12	12.1935483870968	-0.193548387096774
89	10	9.27927927927928	0.72072072072072
90	7	9.27927927927928	-2.27927927927928
91	9	9.27927927927928	-0.279279279279280
92	7	9.27927927927928	-2.27927927927928
93	12	12.1935483870968	-0.193548387096774
94	8	9.27927927927928	-1.27927927927928
95	13	9.27927927927928	3.72072072072072
96	9	12.1935483870968	-3.19354838709677
97	15	12.1935483870968	2.80645161290323
98	8	9.27927927927928	-1.27927927927928
99	14	12.1935483870968	1.80645161290323
100	14	12.1935483870968	1.80645161290323
101	9	9.27927927927928	-0.279279279279280
102	13	12.1935483870968	0.806451612903226
103	11	9.27927927927928	1.72072072072072
104	10	12.1935483870968	-2.19354838709677
105	6	9.27927927927928	-3.27927927927928
106	8	9.27927927927928	-1.27927927927928
107	10	9.27927927927928	0.72072072072072
108	10	9.27927927927928	0.72072072072072
109	10	12.1935483870968	-2.19354838709677
110	12	12.1935483870968	-0.193548387096774
111	10	9.27927927927928	0.72072072072072
112	9	9.27927927927928	-0.279279279279280
113	9	9.27927927927928	-0.279279279279280
114	11	9.27927927927928	1.72072072072072
115	7	9.27927927927928	-2.27927927927928
116	7	9.27927927927928	-2.27927927927928
117	5	9.27927927927928	-4.27927927927928
118	9	9.27927927927928	-0.279279279279280
119	11	9.27927927927928	1.72072072072072
120	15	12.1935483870968	2.80645161290323
121	9	9.27927927927928	-0.279279279279280
122	9	9.27927927927928	-0.279279279279280
123	8	9.27927927927928	-1.27927927927928
124	13	12.1935483870968	0.806451612903226
125	10	9.27927927927928	0.72072072072072
126	13	12.1935483870968	0.806451612903226
127	9	9.27927927927928	-0.279279279279280
128	11	9.27927927927928	1.72072072072072
129	8	9.27927927927928	-1.27927927927928
130	10	9.27927927927928	0.72072072072072
131	9	9.27927927927928	-0.279279279279280
132	8	9.27927927927928	-1.27927927927928
133	8	9.27927927927928	-1.27927927927928
134	13	9.27927927927928	3.72072072072072
135	11	12.1935483870968	-1.19354838709677
136	8	9.27927927927928	-1.27927927927928
137	12	9.27927927927928	2.72072072072072
138	15	12.1935483870968	2.80645161290323
139	11	12.1935483870968	-1.19354838709677
140	10	9.27927927927928	0.72072072072072
141	5	9.27927927927928	-4.27927927927928
142	11	9.27927927927928	1.72072072072072

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920748315macddk4ldwivjn/2jabi1292072433.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920748315macddk4ldwivjn/2jabi1292072433.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920748315macddk4ldwivjn/3jabi1292072433.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920748315macddk4ldwivjn/3jabi1292072433.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920748315macddk4ldwivjn/4cjs31292072433.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920748315macddk4ldwivjn/4cjs31292072433.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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We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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