FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationThu, 06 Dec 2012 08:03:15 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/06/t1354799011zjkxnjw4zs2oc00.htm/, Retrieved Thu, 18 Apr 2024 18:28:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197060, Retrieved Thu, 18 Apr 2024 18:28:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 20:21:33] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [] [2012-12-06 12:48:07] [8ab8078357d7493428921287469fd527]
-           [Recursive Partitioning (Regression Trees)] [] [2012-12-06 13:03:15] [eace0511beeaae09dbb51bfebd62c02b] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	7	53	145	56
39	32	5	86	101	56
30	35	5	66	98	54
31	33	5	67	132	89
34	37	8	76	60	40
35	29	6	78	38	25
39	31	5	53	144	92
34	36	6	80	5	18
36	35	5	74	28	63
37	38	4	76	84	44
38	31	6	79	79	33
36	34	5	54	127	84
38	35	5	67	78	88
39	38	6	54	60	55
33	37	7	87	131	60
32	33	6	58	84	66
36	32	7	75	133	154
38	38	6	88	150	53
39	38	8	64	91	119
32	32	7	57	132	41
32	33	5	66	136	61
31	31	5	68	124	58
39	38	7	54	118	75
37	39	7	56	70	33
39	32	5	86	107	40
41	32	4	80	119	92
36	35	10	76	89	100
33	37	6	69	112	112
33	33	5	78	108	73
34	33	5	67	52	40
31	28	5	80	112	45
27	32	5	54	116	60
37	31	6	71	123	62
34	37	5	84	125	75
34	30	5	74	27	31
32	33	5	71	162	77
29	31	5	63	32	34
36	33	5	71	64	46
29	31	5	76	92	99
35	33	5	69	0	17
37	32	5	74	83	66
34	33	7	75	41	30
38	32	5	54	47	76
35	33	6	52	120	146
38	28	7	69	105	67
37	35	7	68	79	56
38	39	5	65	65	107
33	34	5	75	70	58
36	38	4	74	55	34
38	32	5	75	39	61
32	38	4	72	67	119
32	30	5	67	21	42
32	33	5	63	127	66
34	38	7	62	152	89
32	32	5	63	113	44
37	32	5	76	99	66
39	34	6	74	7	24
29	34	4	67	141	259
37	36	6	73	21	17
35	34	6	70	35	64
30	28	5	53	109	41
38	34	7	77	133	68
34	35	6	77	123	168
31	35	8	52	26	43
34	31	7	54	230	132
35	37	5	80	166	105
36	35	6	66	68	71
30	27	6	73	147	112
39	40	5	63	179	94
35	37	5	69	61	82
38	36	5	67	101	70
31	38	5	54	108	57
34	39	4	81	90	53
38	41	6	69	114	103
34	27	6	84	103	121
39	30	6	80	142	62
37	37	6	70	79	52
34	31	7	69	88	52
28	31	5	77	25	32
37	27	7	54	83	62
33	36	6	79	113	45
37	38	5	30	118	46
35	37	5	71	110	63
37	33	4	73	129	75
32	34	8	72	51	88
33	31	8	77	93	46
38	39	5	75	76	53
33	34	5	69	49	37
29	32	6	54	118	90
33	33	4	70	38	63
31	36	5	73	141	78
36	32	5	54	58	25
35	41	5	77	27	45
32	28	5	82	91	46
29	30	6	80	48	41
39	36	6	80	63	144
37	35	5	69	56	82
35	31	6	78	144	91
37	34	5	81	73	71
32	36	7	76	168	63
38	36	5	76	64	53
37	35	6	73	97	62
36	37	6	85	117	63
32	28	6	66	100	32
33	39	4	79	149	39
40	32	5	68	187	62
38	35	5	76	127	117
41	39	7	71	37	34
36	35	6	54	245	92
43	42	9	46	87	93
30	34	6	82	177	54
31	33	6	74	49	144
32	41	5	88	49	14
32	33	6	38	73	61
37	34	5	76	177	109
37	32	8	86	94	38
33	40	7	54	117	73
34	40	5	70	60	75
33	35	7	69	55	50
38	36	6	90	39	61
33	37	6	54	64	55
31	27	9	76	26	77
38	39	7	89	64	75
37	38	6	76	58	72
33	31	5	73	95	50
31	33	5	79	25	32
39	32	6	90	26	53
44	39	6	74	76	42
33	36	7	81	129	71
35	33	5	72	11	10
32	33	5	71	2	35
28	32	5	66	101	65
40	37	6	77	28	25
27	30	4	65	36	66
37	38	5	74	89	41
32	29	7	82	193	86
28	22	5	54	4	16
34	35	7	63	84	42
30	35	7	54	23	19
35	34	6	64	39	19
31	35	5	69	14	45
32	34	8	54	78	65
30	34	5	84	14	35
30	35	5	86	101	95
31	23	5	77	82	49
40	31	6	89	24	37
32	27	4	76	36	64
36	36	5	60	75	38
32	31	5	75	16	34
35	32	7	73	55	32
38	39	6	85	131	65
42	37	7	79	131	52
34	38	10	71	39	62
35	39	6	72	144	65
35	34	8	69	139	83
33	31	4	78	211	95
36	32	5	54	78	29
32	37	6	69	50	18
33	36	7	81	39	33
34	32	7	84	90	247
32	35	6	84	166	139
34	36	6	69	12	29

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197060&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197060&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197060&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C17310
C24138

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 73 & 10 \tabularnewline
C2 & 41 & 38 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197060&T=1

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][/ROW]
[ROW][C]C1[/C][C]73[/C][C]10[/C][/ROW]
[ROW][C]C2[/C][C]41[/C][C]38[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197060&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197060&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C17310
C24138


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = none ; par3 = 5 ; par4 = no ;
Parameters (R input):
par1 = 6 ; par2 = quantiles ; par3 = 2 ; 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')
}