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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 computationSun, 11 Dec 2011 06:24:23 -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/2011/Dec/11/t1323604240a3toqtllmml6ei3.htm/, Retrieved Sun, 28 Apr 2024 22:58:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153679, Retrieved Sun, 28 Apr 2024 22:58:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

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 19:35:21] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [recursive partiti...] [2010-12-09 21:27:00] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
-   P     [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-09 21:41:03] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- R PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-11 11:24:23] [ce4468323d272130d499477f5e05a6d2] [Current]
- R P           [Recursive Partitioning (Regression Trees)] [Cross Validation] [2011-12-13 11:01:21] [d1ce18d003fa52f731d1c3ce8b58d5f9]
-   PD            [Recursive Partitioning (Regression Trees)] [] [2011-12-20 21:27:44] [f1de53e71fac758e9834be8effee591f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	13	12	14	12	53	32
2	5	39	32	16	11	18	11	86	51
2	5	30	35	19	15	11	14	66	42
1	5	31	33	15	6	12	12	67	41
2	8	34	37	14	13	16	21	76	46
2	6	35	29	13	10	18	12	78	47
2	5	39	31	19	12	14	22	53	37
2	6	34	36	15	14	14	11	80	49
2	5	36	35	14	12	15	10	74	45
2	4	37	38	15	6	15	13	76	47
1	6	38	31	16	10	17	10	79	49
2	5	36	34	16	12	19	8	54	33
1	5	38	35	16	12	10	15	67	42
2	6	39	38	16	11	16	14	54	33
2	7	33	37	17	15	18	10	87	53
1	6	32	33	15	12	14	14	58	36
1	7	36	32	15	10	14	14	75	45
2	6	38	38	20	12	17	11	88	54
1	8	39	38	18	11	14	10	64	41
2	7	32	32	16	12	16	13	57	36
1	5	32	33	16	11	18	7	66	41
2	5	31	31	16	12	11	14	68	44
2	7	39	38	19	13	14	12	54	33
2	7	37	39	16	11	12	14	56	37
1	5	39	32	17	9	17	11	86	52
2	4	41	32	17	13	9	9	80	47
1	10	36	35	16	10	16	11	76	43
2	6	33	37	15	14	14	15	69	44
2	5	33	33	16	12	15	14	78	45
1	5	34	33	14	10	11	13	67	44
2	5	31	28	15	12	16	9	80	49
1	5	27	32	12	8	13	15	54	33
2	6	37	31	14	10	17	10	71	43
2	5	34	37	16	12	15	11	84	54
1	5	34	30	14	12	14	13	74	42
1	5	32	33	7	7	16	8	71	44
1	5	29	31	10	6	9	20	63	37
1	5	36	33	14	12	15	12	71	43
2	5	29	31	16	10	17	10	76	46
1	5	35	33	16	10	13	10	69	42
1	5	37	32	16	10	15	9	74	45
2	7	34	33	14	12	16	14	75	44
1	5	38	32	20	15	16	8	54	33
1	6	35	33	14	10	12	14	52	31
2	7	38	28	14	10	12	11	69	42
2	7	37	35	11	12	11	13	68	40
2	5	38	39	14	13	15	9	65	43
2	5	33	34	15	11	15	11	75	46
2	4	36	38	16	11	17	15	74	42
1	5	38	32	14	12	13	11	75	45
2	4	32	38	16	14	16	10	72	44
1	5	32	30	14	10	14	14	67	40
1	5	32	33	12	12	11	18	63	37
2	7	34	38	16	13	12	14	62	46
1	5	32	32	9	5	12	11	63	36
2	5	37	32	14	6	15	12	76	47
2	6	39	34	16	12	16	13	74	45
2	4	29	34	16	12	15	9	67	42
1	6	37	36	15	11	12	10	73	43
2	6	35	34	16	10	12	15	70	43
1	5	30	28	12	7	8	20	53	32
1	7	38	34	16	12	13	12	77	45
2	6	34	35	16	14	11	12	77	45
2	8	31	35	14	11	14	14	52	31
2	7	34	31	16	12	15	13	54	33
1	5	35	37	17	13	10	11	80	49
2	6	36	35	18	14	11	17	66	42
1	6	30	27	18	11	12	12	73	41
2	5	39	40	12	12	15	13	63	38
1	5	35	37	16	12	15	14	69	42
1	5	38	36	10	8	14	13	67	44
2	5	31	38	14	11	16	15	54	33
2	4	34	39	18	14	15	13	81	48
1	6	38	41	18	14	15	10	69	40
1	6	34	27	16	12	13	11	84	50
2	6	39	30	17	9	12	19	80	49
2	6	37	37	16	13	17	13	70	43
2	7	34	31	16	11	13	17	69	44
1	5	28	31	13	12	15	13	77	47
1	7	37	27	16	12	13	9	54	33
1	6	33	36	16	12	15	11	79	46
1	5	37	38	20	12	16	10	30	0
2	5	35	37	16	12	15	9	71	45
1	4	37	33	15	12	16	12	73	43
2	8	32	34	15	11	15	12	72	44
2	8	33	31	16	10	14	13	77	47
1	5	38	39	14	9	15	13	75	45
2	5	33	34	16	12	14	12	69	42
2	6	29	32	16	12	13	15	54	33
2	4	33	33	15	12	7	22	70	43
2	5	31	36	12	9	17	13	73	46
2	5	36	32	17	15	13	15	54	33
2	5	35	41	16	12	15	13	77	46
2	5	32	28	15	12	14	15	82	48
2	6	29	30	13	12	13	10	80	47
2	6	39	36	16	10	16	11	80	47
2	5	37	35	16	13	12	16	69	43
2	6	35	31	16	9	14	11	78	46
1	5	37	34	16	12	17	11	81	48
1	7	32	36	14	10	15	10	76	46
2	5	38	36	16	14	17	10	76	45
1	6	37	35	16	11	12	16	73	45
2	6	36	37	20	15	16	12	85	52
1	6	32	28	15	11	11	11	66	42
2	4	33	39	16	11	15	16	79	47
1	5	40	32	13	12	9	19	68	41
2	5	38	35	17	12	16	11	76	47
1	7	41	39	16	12	15	16	71	43
1	6	36	35	16	11	10	15	54	33
2	9	43	42	12	7	10	24	46	30
2	6	30	34	16	12	15	14	82	49
2	6	31	33	16	14	11	15	74	44
2	5	32	41	17	11	13	11	88	55
1	6	32	33	13	11	14	15	38	11
2	5	37	34	12	10	18	12	76	47
1	8	37	32	18	13	16	10	86	53
2	7	33	40	14	13	14	14	54	33
2	5	34	40	14	8	14	13	70	44
2	7	33	35	13	11	14	9	69	42
2	6	38	36	16	12	14	15	90	55
2	6	33	37	13	11	12	15	54	33
2	9	31	27	16	13	14	14	76	46
2	7	38	39	13	12	15	11	89	54
2	6	37	38	16	14	15	8	76	47
2	5	33	31	15	13	15	11	73	45
2	5	31	33	16	15	13	11	79	47
1	6	39	32	15	10	17	8	90	55
2	6	44	39	17	11	17	10	74	44
2	7	33	36	15	9	19	11	81	53
2	5	35	33	12	11	15	13	72	44
1	5	32	33	16	10	13	11	71	42
1	5	28	32	10	11	9	20	66	40
2	6	40	37	16	8	15	10	77	46
1	4	27	30	12	11	15	15	65	40
1	5	37	38	14	12	15	12	74	46
2	7	32	29	15	12	16	14	82	53
1	5	28	22	13	9	11	23	54	33
1	7	34	35	15	11	14	14	63	42
2	7	30	35	11	10	11	16	54	35
2	6	35	34	12	8	15	11	64	40
1	5	31	35	8	9	13	12	69	41
2	8	32	34	16	8	15	10	54	33
1	5	30	34	15	9	16	14	84	51
2	5	30	35	17	15	14	12	86	53
1	5	31	23	16	11	15	12	77	46
2	6	40	31	10	8	16	11	89	55
2	4	32	27	18	13	16	12	76	47
1	5	36	36	13	12	11	13	60	38
1	5	32	31	16	12	12	11	75	46
1	7	35	32	13	9	9	19	73	46
2	6	38	39	10	7	16	12	85	53
2	7	42	37	15	13	13	17	79	47
1	10	34	38	16	9	16	9	71	41
2	6	35	39	16	6	12	12	72	44
2	8	35	34	14	8	9	19	69	43
2	4	33	31	10	8	13	18	78	51
2	5	36	32	17	15	13	15	54	33
2	6	32	37	13	6	14	14	69	43
2	7	33	36	15	9	19	11	81	53
2	7	34	32	16	11	13	9	84	51
2	6	32	35	12	8	12	18	84	50
2	6	34	36	13	8	13	16	69	46

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=153679&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=153679&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153679&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






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2C3CVC1C2C3CV
C115734430.3115125400.1818
C260650120.900386820.8718
C34162540.245512720.0667
Overall---0.5954---0.4713

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & C3 & CV & C1 & C2 & C3 & CV \tabularnewline
C1 & 157 & 344 & 3 & 0.3115 & 12 & 54 & 0 & 0.1818 \tabularnewline
C2 & 60 & 650 & 12 & 0.9003 & 8 & 68 & 2 & 0.8718 \tabularnewline
C3 & 4 & 162 & 54 & 0.2455 & 1 & 27 & 2 & 0.0667 \tabularnewline
Overall & - & - & - & 0.5954 & - & - & - & 0.4713 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153679&T=1

[TABLE]
[ROW][C]10-Fold Cross Validation[/C][/ROW]
[ROW][C][/C][C]Prediction (training)[/C][C]Prediction (testing)[/C][/ROW]
[ROW][C]Actual[/C][C]C1[/C][C]C2[/C][C]C3[/C][C]CV[/C][C]C1[/C][C]C2[/C][C]C3[/C][C]CV[/C][/ROW]
[ROW][C]C1[/C][C]157[/C][C]344[/C][C]3[/C][C]0.3115[/C][C]12[/C][C]54[/C][C]0[/C][C]0.1818[/C][/ROW]
[ROW][C]C2[/C][C]60[/C][C]650[/C][C]12[/C][C]0.9003[/C][C]8[/C][C]68[/C][C]2[/C][C]0.8718[/C][/ROW]
[ROW][C]C3[/C][C]4[/C][C]162[/C][C]54[/C][C]0.2455[/C][C]1[/C][C]27[/C][C]2[/C][C]0.0667[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]-[/C][C]0.5954[/C][C]-[/C][C]-[/C][C]-[/C][C]0.4713[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153679&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153679&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:

10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2C3CVC1C2C3CV
C115734430.3115125400.1818
C260650120.900386820.8718
C34162540.245512720.0667
Overall---0.5954---0.4713






Confusion Matrix (predicted in columns / actuals in rows)
C1C2C3
C117400
C25741
C30187

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 & C3 \tabularnewline
C1 & 17 & 40 & 0 \tabularnewline
C2 & 5 & 74 & 1 \tabularnewline
C3 & 0 & 18 & 7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153679&T=2

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][C]C3[/C][/ROW]
[ROW][C]C1[/C][C]17[/C][C]40[/C][C]0[/C][/ROW]
[ROW][C]C2[/C][C]5[/C][C]74[/C][C]1[/C][/ROW]
[ROW][C]C3[/C][C]0[/C][C]18[/C][C]7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153679&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153679&T=2

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)
C1C2C3
C117400
C25741
C30187


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = quantiles ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 5 ; par2 = quantiles ; par3 = 3 ; par4 = yes ;
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')
}