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W10-gender RP

*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 19:14:56 +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/t1292094865v9rcnd1537swct0.htm/, Retrieved Sat, 11 Dec 2010 20:14:25 +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/t1292094865v9rcnd1537swct0.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 «

0 24 14 11 12 24 26 0 25 11 7 8 25 23 0 17 6 17 8 30 25 1 18 12 10 8 19 23 1 18 8 12 9 22 19 1 16 10 12 7 22 29 1 20 10 11 4 25 25 1 16 11 11 11 23 21 1 18 16 12 7 17 22 1 17 11 13 7 21 25 0 23 13 14 12 19 24 0 30 12 16 10 19 18 1 23 8 11 10 15 22 1 18 12 10 8 16 15 1 15 11 11 8 23 22 1 12 4 15 4 27 28 0 21 9 9 9 22 20 1 15 8 11 8 14 12 1 20 8 17 7 22 24 0 31 14 17 11 23 20 0 27 15 11 9 23 21 1 34 16 18 11 21 20 1 21 9 14 13 19 21 1 31 14 10 8 18 23 1 19 11 11 8 20 28 0 16 8 15 9 23 24 1 20 9 15 6 25 24 1 21 9 13 9 19 24 1 22 9 16 9 24 23 1 17 9 13 6 22 23 1 24 10 9 6 25 29 0 25 16 18 16 26 24 0 26 11 18 5 29 18 1 25 8 12 7 32 25 1 17 9 17 9 25 21 1 32 16 9 6 29 26 1 33 11 9 6 28 22 1 13 16 12 5 17 22 1 32 12 18 12 28 22 1 25 12 12 7 29 23 1 29 14 18 10 26 30 1 22 9 14 9 25 23 1 18 10 15 8 14 17 1 17 9 16 5 25 23 0 20 10 10 8 26 23 1 15 12 11 8 20 25 1 20 14 14 10 18 24 1 33 14 9 6 32 24 0 29 10 12 8 25 23 1 23 14 17 7 25 21 0 26 16 5 4 23 24 1 18 9 12 8 21 2 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.938
R-squared	0.8798
RMSE	3.7431

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	0	0.716981132075472	-0.716981132075472
2	0	0.716981132075472	-0.716981132075472
3	0	0.716981132075472	-0.716981132075472
4	1	0.716981132075472	0.283018867924528
5	1	0.716981132075472	0.283018867924528
6	1	0.716981132075472	0.283018867924528
7	1	0.716981132075472	0.283018867924528
8	1	0.716981132075472	0.283018867924528
9	1	0.716981132075472	0.283018867924528
10	1	0.716981132075472	0.283018867924528
11	0	0.716981132075472	-0.716981132075472
12	0	0.716981132075472	-0.716981132075472
13	1	0.716981132075472	0.283018867924528
14	1	0.716981132075472	0.283018867924528
15	1	0.716981132075472	0.283018867924528
16	1	0.716981132075472	0.283018867924528
17	0	0.716981132075472	-0.716981132075472
18	1	0.716981132075472	0.283018867924528
19	1	0.716981132075472	0.283018867924528
20	0	0.716981132075472	-0.716981132075472
21	0	0.716981132075472	-0.716981132075472
22	1	0.716981132075472	0.283018867924528
23	1	0.716981132075472	0.283018867924528
24	1	0.716981132075472	0.283018867924528
25	1	0.716981132075472	0.283018867924528
26	0	0.716981132075472	-0.716981132075472
27	1	0.716981132075472	0.283018867924528
28	1	0.716981132075472	0.283018867924528
29	1	0.716981132075472	0.283018867924528
30	1	0.716981132075472	0.283018867924528
31	1	0.716981132075472	0.283018867924528
32	0	0.716981132075472	-0.716981132075472
33	0	0.716981132075472	-0.716981132075472
34	1	0.716981132075472	0.283018867924528
35	1	0.716981132075472	0.283018867924528
36	1	0.716981132075472	0.283018867924528
37	1	0.716981132075472	0.283018867924528
38	1	0.716981132075472	0.283018867924528
39	1	0.716981132075472	0.283018867924528
40	1	0.716981132075472	0.283018867924528
41	1	0.716981132075472	0.283018867924528
42	1	0.716981132075472	0.283018867924528
43	1	0.716981132075472	0.283018867924528
44	1	0.716981132075472	0.283018867924528
45	0	0.716981132075472	-0.716981132075472
46	1	0.716981132075472	0.283018867924528
47	1	0.716981132075472	0.283018867924528
48	1	0.716981132075472	0.283018867924528
49	0	0.716981132075472	-0.716981132075472
50	1	0.716981132075472	0.283018867924528
51	0	0.716981132075472	-0.716981132075472
52	1	0.716981132075472	0.283018867924528
53	0	0.716981132075472	-0.716981132075472
54	11	13.5714285714286	-2.57142857142857
55	28	25.4482758620690	2.55172413793104
56	26	19.6101694915254	6.38983050847458
57	22	25	-3
58	17	19.6101694915254	-2.61016949152542
59	12	13.5714285714286	-1.57142857142857
60	14	19.6101694915254	-5.61016949152542
61	17	19.6101694915254	-2.61016949152542
62	21	19.6101694915254	1.38983050847458
63	19	19.6101694915254	-0.610169491525422
64	18	19.6101694915254	-1.61016949152542
65	10	19.6101694915254	-9.61016949152542
66	29	25	4
67	31	19.6101694915254	11.3898305084746
68	19	25	-6
69	9	19.6101694915254	-10.6101694915254
70	20	25	-5
71	28	19.6101694915254	8.38983050847458
72	19	19.6101694915254	-0.610169491525422
73	30	25.4482758620690	4.55172413793104
74	29	25.4482758620690	3.55172413793104
75	26	25.4482758620690	0.551724137931036
76	23	19.6101694915254	3.38983050847458
77	13	19.6101694915254	-6.61016949152542
78	21	19.6101694915254	1.38983050847458
79	19	19.6101694915254	-0.610169491525422
80	28	25	3
81	23	25	-2
82	18	13.5714285714286	4.42857142857143
83	21	19.6101694915254	1.38983050847458
84	20	25.4482758620690	-5.44827586206896
85	23	19.6101694915254	3.38983050847458
86	21	25.4482758620690	-4.44827586206896
87	21	25	-4
88	15	19.6101694915254	-4.61016949152542
89	28	25.4482758620690	2.55172413793104
90	19	19.6101694915254	-0.610169491525422
91	26	19.6101694915254	6.38983050847458
92	10	13.5714285714286	-3.57142857142857
93	16	19.6101694915254	-3.61016949152542
94	22	25.4482758620690	-3.44827586206896
95	19	19.6101694915254	-0.610169491525422
96	31	25.4482758620690	5.55172413793104
97	31	19.6101694915254	11.3898305084746
98	29	25.4482758620690	3.55172413793104
99	19	19.6101694915254	-0.610169491525422
100	22	19.6101694915254	2.38983050847458
101	23	25.4482758620690	-2.44827586206896
102	15	13.5714285714286	1.42857142857143
103	20	25.4482758620690	-5.44827586206896
104	18	25.4482758620690	-7.44827586206896
105	23	19.6101694915254	3.38983050847458
106	25	19.6101694915254	5.38983050847458
107	21	19.6101694915254	1.38983050847458
108	24	19.6101694915254	4.38983050847458
109	25	25.4482758620690	-0.448275862068964
110	17	19.6101694915254	-2.61016949152542
111	13	19.6101694915254	-6.61016949152542
112	28	25.4482758620690	2.55172413793104
113	21	19.6101694915254	1.38983050847458
114	25	25.4482758620690	-0.448275862068964
115	9	13.5714285714286	-4.57142857142857
116	16	19.6101694915254	-3.61016949152542
117	19	25.4482758620690	-6.44827586206896
118	17	19.6101694915254	-2.61016949152542
119	25	25.4482758620690	-0.448275862068964
120	20	19.6101694915254	0.389830508474578
121	29	25	4
122	14	19.6101694915254	-5.61016949152542
123	22	25	-3
124	15	19.6101694915254	-4.61016949152542
125	19	19.6101694915254	-0.610169491525422
126	20	25.4482758620690	-5.44827586206896
127	15	19.6101694915254	-4.61016949152542
128	20	19.6101694915254	0.389830508474578
129	18	19.6101694915254	-1.61016949152542
130	33	25	8
131	22	19.6101694915254	2.38983050847458
132	16	19.6101694915254	-3.61016949152542
133	17	19.6101694915254	-2.61016949152542
134	16	19.6101694915254	-3.61016949152542
135	21	19.6101694915254	1.38983050847458
136	26	25.4482758620690	0.551724137931036
137	18	19.6101694915254	-1.61016949152542
138	18	19.6101694915254	-1.61016949152542
139	17	19.6101694915254	-2.61016949152542
140	22	19.6101694915254	2.38983050847458
141	30	19.6101694915254	10.3898305084746
142	30	25.4482758620690	4.55172413793104
143	24	25.4482758620690	-1.44827586206896
144	21	19.6101694915254	1.38983050847458
145	21	25.4482758620690	-4.44827586206896
146	29	25	4
147	31	25.4482758620690	5.55172413793104
148	20	19.6101694915254	0.389830508474578
149	16	19.6101694915254	-3.61016949152542
150	22	19.6101694915254	2.38983050847458
151	20	13.5714285714286	6.42857142857143
152	28	25.4482758620690	2.55172413793104
153	38	25.4482758620690	12.5517241379310
154	22	19.6101694915254	2.38983050847458
155	20	25.4482758620690	-5.44827586206896
156	17	19.6101694915254	-2.61016949152542
157	28	19.6101694915254	8.38983050847458
158	22	25.4482758620690	-3.44827586206896
159	31	25.4482758620690	5.55172413793104

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292094865v9rcnd1537swct0/2bf6q1292094888.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292094865v9rcnd1537swct0/2bf6q1292094888.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292094865v9rcnd1537swct0/3bf6q1292094888.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292094865v9rcnd1537swct0/3bf6q1292094888.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292094865v9rcnd1537swct0/44o5t1292094888.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292094865v9rcnd1537swct0/44o5t1292094888.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')

}

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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