Home » date » 2010 » Dec » 21 »

Paper DMA Recursive Catigorization (none)

*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, 21 Dec 2010 13:23:07 +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/21/t1292937668lc2qp7aiwrtkxz8.htm/, Retrieved Tue, 21 Dec 2010 14:21:08 +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/21/t1292937668lc2qp7aiwrtkxz8.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:

Paper DMA

Dataseries X:

» Textbox « » Textfile « » CSV «

3030.29 25.64 2803.47 27.97 2767.63 27.62 2882.6 23.31 2863.36 29.07 2897.06 29.58 3012.61 28.63 3142.95 29.92 3032.93 32.68 3045.78 31.54 3110.52 32.43 3013.24 26.54 2987.1 25.85 2995.55 27.6 2833.18 25.71 2848.96 25.38 2794.83 28.57 2845.26 27.64 2915.03 25.36 2892.63 25.9 2604.42 26.29 2641.65 21.74 2659.81 19.2 2638.53 19.32 2720.25 19.82 2745.88 20.36 2735.7 24.31 2811.7 25.97 2799.43 25.61 2555.28 24.67 2304.98 25.59 2214.95 26.09 2065.81 28.37 1940.49 27.34 2042 24.46 1995.37 27.46 1946.81 30.23 1765.9 32.33 1635.25 29.87 1833.42 24.87 1910.43 25.48 1959.67 27.28 1969.6 28.24 2061.41 29.58 2093.48 26.95 2120.88 29.08 2174.56 28.76 2196.72 29.59 2350.44 30.7 2440.25 30.52 2408.64 32.67 2472.81 33.19 2407.6 37.13 2454.62 35.54 2448.05 37.75 2497.84 41.84 2645.64 42.94 2756.76 49.14 2849.27 44.61 2921.44 40.22 2981.85 44.23 3080.58 45.85 3106.22 53.38 3119.31 53.26 3061.26 51.8 3097.31 55.3 3161.69 57.81 3257.16 63.96 3277.01 63.77 3295. 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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.7633
R-squared	0.5826
RMSE	498.0036

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	3030.29	2561.64447761194	468.64552238806
2	2803.47	2561.64447761194	241.825522388060
3	2767.63	2561.64447761194	205.98552238806
4	2882.6	2561.64447761194	320.95552238806
5	2863.36	2561.64447761194	301.71552238806
6	2897.06	2561.64447761194	335.41552238806
7	3012.61	2561.64447761194	450.96552238806
8	3142.95	2561.64447761194	581.30552238806
9	3032.93	2561.64447761194	471.28552238806
10	3045.78	2561.64447761194	484.13552238806
11	3110.52	2561.64447761194	548.87552238806
12	3013.24	2561.64447761194	451.59552238806
13	2987.1	2561.64447761194	425.45552238806
14	2995.55	2561.64447761194	433.90552238806
15	2833.18	2561.64447761194	271.535522388060
16	2848.96	2561.64447761194	287.31552238806
17	2794.83	2561.64447761194	233.18552238806
18	2845.26	2561.64447761194	283.61552238806
19	2915.03	2561.64447761194	353.38552238806
20	2892.63	2561.64447761194	330.98552238806
21	2604.42	2561.64447761194	42.7755223880599
22	2641.65	2561.64447761194	80.00552238806
23	2659.81	2561.64447761194	98.1655223880598
24	2638.53	2561.64447761194	76.88552238806
25	2720.25	2561.64447761194	158.60552238806
26	2745.88	2561.64447761194	184.23552238806
27	2735.7	2561.64447761194	174.055522388060
28	2811.7	2561.64447761194	250.055522388060
29	2799.43	2561.64447761194	237.785522388060
30	2555.28	2561.64447761194	-6.36447761193995
31	2304.98	2561.64447761194	-256.66447761194
32	2214.95	2561.64447761194	-346.69447761194
33	2065.81	2561.64447761194	-495.83447761194
34	1940.49	2561.64447761194	-621.15447761194
35	2042	2561.64447761194	-519.64447761194
36	1995.37	2561.64447761194	-566.27447761194
37	1946.81	2561.64447761194	-614.83447761194
38	1765.9	2561.64447761194	-795.74447761194
39	1635.25	2561.64447761194	-926.39447761194
40	1833.42	2561.64447761194	-728.22447761194
41	1910.43	2561.64447761194	-651.21447761194
42	1959.67	2561.64447761194	-601.97447761194
43	1969.6	2561.64447761194	-592.04447761194
44	2061.41	2561.64447761194	-500.23447761194
45	2093.48	2561.64447761194	-468.16447761194
46	2120.88	2561.64447761194	-440.76447761194
47	2174.56	2561.64447761194	-387.08447761194
48	2196.72	2561.64447761194	-364.92447761194
49	2350.44	2561.64447761194	-211.20447761194
50	2440.25	2561.64447761194	-121.394477611940
51	2408.64	2561.64447761194	-153.004477611940
52	2472.81	2561.64447761194	-88.8344776119402
53	2407.6	2561.64447761194	-154.044477611940
54	2454.62	2561.64447761194	-107.024477611940
55	2448.05	2561.64447761194	-113.59447761194
56	2497.84	2561.64447761194	-63.80447761194
57	2645.64	2561.64447761194	83.9955223880597
58	2756.76	2561.64447761194	195.11552238806
59	2849.27	2561.64447761194	287.62552238806
60	2921.44	2561.64447761194	359.79552238806
61	2981.85	2561.64447761194	420.20552238806
62	3080.58	2561.64447761194	518.93552238806
63	3106.22	2561.64447761194	544.57552238806
64	3119.31	2561.64447761194	557.66552238806
65	3061.26	2561.64447761194	499.61552238806
66	3097.31	3770.56642857143	-673.256428571429
67	3161.69	3770.56642857143	-608.876428571429
68	3257.16	3770.56642857143	-513.406428571429
69	3277.01	3770.56642857143	-493.556428571429
70	3295.32	3770.56642857143	-475.246428571429
71	3363.99	3770.56642857143	-406.576428571429
72	3494.17	3770.56642857143	-276.396428571429
73	3667.03	3770.56642857143	-103.536428571429
74	3813.06	3770.56642857143	42.4935714285712
75	3917.96	3770.56642857143	147.393571428571
76	3895.51	3770.56642857143	124.943571428571
77	3801.06	3770.56642857143	30.4935714285712
78	3570.12	3770.56642857143	-200.446428571429
79	3701.61	3770.56642857143	-68.9564285714287
80	3862.27	3770.56642857143	91.7035714285712
81	3970.1	3770.56642857143	199.533571428571
82	4138.52	3770.56642857143	367.953571428572
83	4199.75	3770.56642857143	429.183571428571
84	4290.89	3770.56642857143	520.323571428572
85	4443.91	3770.56642857143	673.343571428571
86	4502.64	3770.56642857143	732.073571428572
87	4356.98	3770.56642857143	586.413571428571
88	4591.27	3770.56642857143	820.703571428572
89	4696.96	3770.56642857143	926.393571428571
90	4621.4	3770.56642857143	850.83357142857
91	4562.84	3770.56642857143	792.273571428571
92	4202.52	3770.56642857143	431.953571428572
93	4296.49	3770.56642857143	525.923571428571
94	4435.23	3770.56642857143	664.663571428571
95	4105.18	3770.56642857143	334.613571428572
96	4116.68	3770.56642857143	346.113571428572
97	3844.49	3770.56642857143	73.923571428571
98	3720.98	3770.56642857143	-49.5864285714288
99	3674.4	3770.56642857143	-96.1664285714287
100	3857.62	3770.56642857143	87.0535714285711
101	3801.06	3770.56642857143	30.4935714285712
102	3504.37	3770.56642857143	-266.196428571429
103	3032.6	3770.56642857143	-737.966428571429
104	3047.03	3770.56642857143	-723.536428571429
105	2962.34	3770.56642857143	-808.226428571429
106	2197.82	3770.56642857143	-1572.74642857143
107	2014.45	3770.56642857143	-1756.11642857143
108	1862.83	2561.64447761194	-698.81447761194
109	1905.41	2561.64447761194	-656.23447761194

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937668lc2qp7aiwrtkxz8/2jt1d1292937779.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937668lc2qp7aiwrtkxz8/2jt1d1292937779.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937668lc2qp7aiwrtkxz8/3jt1d1292937779.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937668lc2qp7aiwrtkxz8/3jt1d1292937779.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937668lc2qp7aiwrtkxz8/4uk1g1292937779.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937668lc2qp7aiwrtkxz8/4uk1g1292937779.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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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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