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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 computationMon, 20 Dec 2010 12:33:09 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/20/t12928483186chgjyaq0s6z9yb.htm/, Retrieved Fri, 29 Mar 2024 15:21:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112876, Retrieved Fri, 29 Mar 2024 15:21:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact177
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Sleep in Mammals ...] [2010-03-18 19:12:28] [b98453cac15ba1066b407e146608df68]
- RMP   [Recursive Partitioning (Regression Trees)] [Review of Sleep A...] [2010-05-01 12:04:20] [b98453cac15ba1066b407e146608df68]
- RMP     [Recursive Partitioning (Regression Trees)] [] [2010-12-17 13:55:13] [94f495cfd7e7946e5228cbd267a6841d]
- RMPD        [Recursive Partitioning (Regression Trees)] [bonustaak, regres...] [2010-12-20 12:33:09] [39ab8462d2190635c809d7a35eacc961] [Current]
-    D          [Recursive Partitioning (Regression Trees)] [bonustaak, regres...] [2010-12-20 12:40:26] [94f495cfd7e7946e5228cbd267a6841d]
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Dataseries X:
6.3	0	3
2.1	3.406028945	4
9.1	1.02325246	4
15.8	-1.698970004	1
5.2	2.204119983	4
10.9	0.51851394	1
8.3	1.717337583	1
11	-0.366531544	4
3.2	2.667452953	5
6.3	-1.096910013	1
6.6	-0.102372909	2
9.5	-0.698970004	2
3.3	1.441852176	5
11	-0.920818754	2
4.7	1.929418926	1
10.4	-1	3
7.4	0.017033339	4
2.1	2.716837723	5
17.9	-2	1
6.1	1.792391689	1
11.9	-1.698970004	3
13.8	0.230448921	1
14.3	0.544068044	1
15.2	-0.318758763	2
10	1	4
6.5	0.209515015	4
7.5	2.283301229	5
10.6	0.397940009	3
7.4	-0.552841969	1
8.4	0.627365857	2
5.7	0.832508913	2
4.9	-0.124938737	3
3.2	0.556302501	5
11	1.744292983	2
4.9	-0.045757491	3
13.2	0.301029996	2
9.7	-1	4
12.8	0.622214023	1
11.9	0.544068044	2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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'.

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
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'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=112876&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
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'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=112876&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112876&T=0

As an alternative you can also use a 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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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
Correlation0.6393
R-squared0.4086
RMSE3.0122

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6393 \tabularnewline
R-squared & 0.4086 \tabularnewline
RMSE & 3.0122 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112876&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6393[/C][/ROW]
[ROW][C]R-squared[/C][C]0.4086[/C][/ROW]
[ROW][C]RMSE[/C][C]3.0122[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112876&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112876&T=1

As an alternative you can also use a QR Code:  

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

Goodness of Fit
Correlation0.6393
R-squared0.4086
RMSE3.0122







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
16.37.8-1.50000000000000
22.14.41428571428571-2.31428571428571
39.17.81.30000000000000
415.811.11111111111114.68888888888889
55.24.414285714285710.785714285714286
610.911.1111111111111-0.211111111111110
78.311.1111111111111-2.81111111111111
8117.83.2
93.24.41428571428571-1.21428571428571
106.311.1111111111111-4.81111111111111
116.611.1111111111111-4.51111111111111
129.511.1111111111111-1.61111111111111
133.37.8-4.5
141111.1111111111111-0.111111111111111
154.74.414285714285710.285714285714286
1610.47.82.6
177.47.8-0.400000000000001
182.14.41428571428571-2.31428571428571
1917.911.11111111111116.78888888888889
206.14.414285714285711.68571428571429
2111.97.84.1
2213.811.11111111111112.68888888888889
2314.311.11111111111113.18888888888889
2415.211.11111111111114.08888888888889
25107.82.2
266.57.8-1.30000000000000
277.54.414285714285713.08571428571429
2810.67.82.8
297.411.1111111111111-3.71111111111111
308.411.1111111111111-2.71111111111111
315.711.1111111111111-5.41111111111111
324.97.8-2.9
333.27.8-4.6
341111.1111111111111-0.111111111111111
354.97.8-2.9
3613.211.11111111111112.08888888888889
379.77.81.90000000000000
3812.811.11111111111111.68888888888889
3911.911.11111111111110.78888888888889

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 6.3 & 7.8 & -1.50000000000000 \tabularnewline
2 & 2.1 & 4.41428571428571 & -2.31428571428571 \tabularnewline
3 & 9.1 & 7.8 & 1.30000000000000 \tabularnewline
4 & 15.8 & 11.1111111111111 & 4.68888888888889 \tabularnewline
5 & 5.2 & 4.41428571428571 & 0.785714285714286 \tabularnewline
6 & 10.9 & 11.1111111111111 & -0.211111111111110 \tabularnewline
7 & 8.3 & 11.1111111111111 & -2.81111111111111 \tabularnewline
8 & 11 & 7.8 & 3.2 \tabularnewline
9 & 3.2 & 4.41428571428571 & -1.21428571428571 \tabularnewline
10 & 6.3 & 11.1111111111111 & -4.81111111111111 \tabularnewline
11 & 6.6 & 11.1111111111111 & -4.51111111111111 \tabularnewline
12 & 9.5 & 11.1111111111111 & -1.61111111111111 \tabularnewline
13 & 3.3 & 7.8 & -4.5 \tabularnewline
14 & 11 & 11.1111111111111 & -0.111111111111111 \tabularnewline
15 & 4.7 & 4.41428571428571 & 0.285714285714286 \tabularnewline
16 & 10.4 & 7.8 & 2.6 \tabularnewline
17 & 7.4 & 7.8 & -0.400000000000001 \tabularnewline
18 & 2.1 & 4.41428571428571 & -2.31428571428571 \tabularnewline
19 & 17.9 & 11.1111111111111 & 6.78888888888889 \tabularnewline
20 & 6.1 & 4.41428571428571 & 1.68571428571429 \tabularnewline
21 & 11.9 & 7.8 & 4.1 \tabularnewline
22 & 13.8 & 11.1111111111111 & 2.68888888888889 \tabularnewline
23 & 14.3 & 11.1111111111111 & 3.18888888888889 \tabularnewline
24 & 15.2 & 11.1111111111111 & 4.08888888888889 \tabularnewline
25 & 10 & 7.8 & 2.2 \tabularnewline
26 & 6.5 & 7.8 & -1.30000000000000 \tabularnewline
27 & 7.5 & 4.41428571428571 & 3.08571428571429 \tabularnewline
28 & 10.6 & 7.8 & 2.8 \tabularnewline
29 & 7.4 & 11.1111111111111 & -3.71111111111111 \tabularnewline
30 & 8.4 & 11.1111111111111 & -2.71111111111111 \tabularnewline
31 & 5.7 & 11.1111111111111 & -5.41111111111111 \tabularnewline
32 & 4.9 & 7.8 & -2.9 \tabularnewline
33 & 3.2 & 7.8 & -4.6 \tabularnewline
34 & 11 & 11.1111111111111 & -0.111111111111111 \tabularnewline
35 & 4.9 & 7.8 & -2.9 \tabularnewline
36 & 13.2 & 11.1111111111111 & 2.08888888888889 \tabularnewline
37 & 9.7 & 7.8 & 1.90000000000000 \tabularnewline
38 & 12.8 & 11.1111111111111 & 1.68888888888889 \tabularnewline
39 & 11.9 & 11.1111111111111 & 0.78888888888889 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112876&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]6.3[/C][C]7.8[/C][C]-1.50000000000000[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]4.41428571428571[/C][C]-2.31428571428571[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]7.8[/C][C]1.30000000000000[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]11.1111111111111[/C][C]4.68888888888889[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]4.41428571428571[/C][C]0.785714285714286[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.1111111111111[/C][C]-0.211111111111110[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]11.1111111111111[/C][C]-2.81111111111111[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]7.8[/C][C]3.2[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]4.41428571428571[/C][C]-1.21428571428571[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.1111111111111[/C][C]-4.81111111111111[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]11.1111111111111[/C][C]-4.51111111111111[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]11.1111111111111[/C][C]-1.61111111111111[/C][/ROW]
[ROW][C]13[/C][C]3.3[/C][C]7.8[/C][C]-4.5[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.1111111111111[/C][C]-0.111111111111111[/C][/ROW]
[ROW][C]15[/C][C]4.7[/C][C]4.41428571428571[/C][C]0.285714285714286[/C][/ROW]
[ROW][C]16[/C][C]10.4[/C][C]7.8[/C][C]2.6[/C][/ROW]
[ROW][C]17[/C][C]7.4[/C][C]7.8[/C][C]-0.400000000000001[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]4.41428571428571[/C][C]-2.31428571428571[/C][/ROW]
[ROW][C]19[/C][C]17.9[/C][C]11.1111111111111[/C][C]6.78888888888889[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]4.41428571428571[/C][C]1.68571428571429[/C][/ROW]
[ROW][C]21[/C][C]11.9[/C][C]7.8[/C][C]4.1[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.1111111111111[/C][C]2.68888888888889[/C][/ROW]
[ROW][C]23[/C][C]14.3[/C][C]11.1111111111111[/C][C]3.18888888888889[/C][/ROW]
[ROW][C]24[/C][C]15.2[/C][C]11.1111111111111[/C][C]4.08888888888889[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]7.8[/C][C]2.2[/C][/ROW]
[ROW][C]26[/C][C]6.5[/C][C]7.8[/C][C]-1.30000000000000[/C][/ROW]
[ROW][C]27[/C][C]7.5[/C][C]4.41428571428571[/C][C]3.08571428571429[/C][/ROW]
[ROW][C]28[/C][C]10.6[/C][C]7.8[/C][C]2.8[/C][/ROW]
[ROW][C]29[/C][C]7.4[/C][C]11.1111111111111[/C][C]-3.71111111111111[/C][/ROW]
[ROW][C]30[/C][C]8.4[/C][C]11.1111111111111[/C][C]-2.71111111111111[/C][/ROW]
[ROW][C]31[/C][C]5.7[/C][C]11.1111111111111[/C][C]-5.41111111111111[/C][/ROW]
[ROW][C]32[/C][C]4.9[/C][C]7.8[/C][C]-2.9[/C][/ROW]
[ROW][C]33[/C][C]3.2[/C][C]7.8[/C][C]-4.6[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.1111111111111[/C][C]-0.111111111111111[/C][/ROW]
[ROW][C]35[/C][C]4.9[/C][C]7.8[/C][C]-2.9[/C][/ROW]
[ROW][C]36[/C][C]13.2[/C][C]11.1111111111111[/C][C]2.08888888888889[/C][/ROW]
[ROW][C]37[/C][C]9.7[/C][C]7.8[/C][C]1.90000000000000[/C][/ROW]
[ROW][C]38[/C][C]12.8[/C][C]11.1111111111111[/C][C]1.68888888888889[/C][/ROW]
[ROW][C]39[/C][C]11.9[/C][C]11.1111111111111[/C][C]0.78888888888889[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112876&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112876&T=2

As an alternative you can also use a QR Code:  

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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
16.37.8-1.50000000000000
22.14.41428571428571-2.31428571428571
39.17.81.30000000000000
415.811.11111111111114.68888888888889
55.24.414285714285710.785714285714286
610.911.1111111111111-0.211111111111110
78.311.1111111111111-2.81111111111111
8117.83.2
93.24.41428571428571-1.21428571428571
106.311.1111111111111-4.81111111111111
116.611.1111111111111-4.51111111111111
129.511.1111111111111-1.61111111111111
133.37.8-4.5
141111.1111111111111-0.111111111111111
154.74.414285714285710.285714285714286
1610.47.82.6
177.47.8-0.400000000000001
182.14.41428571428571-2.31428571428571
1917.911.11111111111116.78888888888889
206.14.414285714285711.68571428571429
2111.97.84.1
2213.811.11111111111112.68888888888889
2314.311.11111111111113.18888888888889
2415.211.11111111111114.08888888888889
25107.82.2
266.57.8-1.30000000000000
277.54.414285714285713.08571428571429
2810.67.82.8
297.411.1111111111111-3.71111111111111
308.411.1111111111111-2.71111111111111
315.711.1111111111111-5.41111111111111
324.97.8-2.9
333.27.8-4.6
341111.1111111111111-0.111111111111111
354.97.8-2.9
3613.211.11111111111112.08888888888889
379.77.81.90000000000000
3812.811.11111111111111.68888888888889
3911.911.11111111111110.78888888888889



Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 1 ; 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')
}