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*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 14:26:47 +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/t129294154722v3rrinzdmb6g0.htm/, Retrieved Tue, 21 Dec 2010 15:25:47 +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/t129294154722v3rrinzdmb6g0.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 «

1 1 14 41 38 13 12 1 1 18 39 32 16 11 1 1 11 30 35 19 15 1 0 12 31 33 15 6 1 1 16 34 37 14 13 1 1 18 35 29 13 10 1 1 14 39 31 19 12 1 1 14 34 36 15 14 1 1 15 36 35 14 12 1 1 15 37 38 15 9 1 0 17 38 31 16 10 1 1 19 36 34 16 12 1 0 10 38 35 16 12 1 1 16 39 38 16 11 1 1 18 33 37 17 15 1 0 14 32 33 15 12 1 0 14 36 32 15 10 1 1 17 38 38 20 12 1 0 14 39 38 18 11 1 1 16 32 32 16 12 1 0 18 32 33 16 11 1 1 11 31 31 16 12 1 1 14 39 38 19 13 1 1 12 37 39 16 11 1 0 17 39 32 17 12 1 1 9 41 32 17 13 1 0 16 36 35 16 10 1 1 14 33 37 15 14 1 1 15 33 33 16 12 1 0 11 34 33 14 10 1 1 16 31 31 15 12 1 0 13 27 32 12 8 1 1 17 37 31 14 10 1 1 15 34 37 16 12 1 0 14 34 30 14 12 1 0 16 32 33 10 7 1 0 9 29 31 10 9 1 0 15 36 33 14 12 1 1 17 29 31 16 10 1 0 13 35 33 16 10 1 0 15 37 32 16 10 1 1 16 34 33 14 12 1 0 16 38 32 20 15 1 0 12 35 33 14 10 1 1 15 38 28 14 10 1 1 11 37 35 11 12 1 1 15 38 39 14 13 1 1 15 33 34 15 11 1 1 17 36 38 16 11 1 0 13 38 32 14 12 1 1 16 32 38 16 14 1 0 14 32 3 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	8 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.6403
R-squared	0.41
RMSE	1.8068

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	12	10.203125	1.796875
2	11	11.225	-0.225000000000000
3	15	12.9642857142857	2.03571428571429
4	6	11.225	-5.225
5	13	10.203125	2.796875
6	10	10.203125	-0.203125
7	12	12.9642857142857	-0.964285714285714
8	14	11.225	2.775
9	12	10.203125	1.796875
10	9	11.225	-2.225
11	10	11.225	-1.225
12	12	11.225	0.775
13	12	11.225	0.775
14	11	11.225	-0.225000000000000
15	15	12.9642857142857	2.03571428571429
16	12	11.225	0.775
17	10	11.225	-1.225
18	12	12.9642857142857	-0.964285714285714
19	11	12.9642857142857	-1.96428571428571
20	12	11.225	0.775
21	11	11.225	-0.225000000000000
22	12	11.225	0.775
23	13	12.9642857142857	0.0357142857142865
24	11	11.225	-0.225000000000000
25	12	12.9642857142857	-0.964285714285714
26	13	12.9642857142857	0.0357142857142865
27	10	11.225	-1.225
28	14	11.225	2.775
29	12	11.225	0.775
30	10	10.203125	-0.203125
31	12	11.225	0.775
32	8	8.6969696969697	-0.696969696969697
33	10	10.203125	-0.203125
34	12	11.225	0.775
35	12	10.203125	1.796875
36	7	8.6969696969697	-1.69696969696970
37	9	8.6969696969697	0.303030303030303
38	12	10.203125	1.796875
39	10	11.225	-1.225
40	10	11.225	-1.225
41	10	11.225	-1.225
42	12	10.203125	1.796875
43	15	12.9642857142857	2.03571428571429
44	10	10.203125	-0.203125
45	10	10.203125	-0.203125
46	12	8.6969696969697	3.3030303030303
47	13	10.203125	2.796875
48	11	11.225	-0.225000000000000
49	11	11.225	-0.225000000000000
50	12	10.203125	1.796875
51	14	11.225	2.775
52	10	10.203125	-0.203125
53	12	8.6969696969697	3.3030303030303
54	13	11.225	1.775
55	5	6	-1
56	6	10.203125	-4.203125
57	12	11.225	0.775
58	12	11.225	0.775
59	11	11.225	-0.225000000000000
60	10	11.225	-1.225
61	7	8.6969696969697	-1.69696969696970
62	12	11.225	0.775
63	14	11.225	2.775
64	11	10.203125	0.796875
65	12	11.225	0.775
66	13	12.9642857142857	0.0357142857142865
67	14	12.9642857142857	1.03571428571429
68	11	12.9642857142857	-1.96428571428571
69	12	8.6969696969697	3.3030303030303
70	12	11.225	0.775
71	8	8.6969696969697	-0.696969696969697
72	11	10.203125	0.796875
73	14	12.9642857142857	1.03571428571429
74	14	12.9642857142857	1.03571428571429
75	12	11.225	0.775
76	9	12.9642857142857	-3.96428571428571
77	13	11.225	1.775
78	11	11.225	-0.225000000000000
79	12	10.203125	1.796875
80	12	11.225	0.775
81	12	11.225	0.775
82	12	11.225	0.775
83	12	11.225	0.775
84	11	11.225	-0.225000000000000
85	10	11.225	-1.225
86	9	10.203125	-1.203125
87	12	11.225	0.775
88	12	11.225	0.775
89	12	11.225	0.775
90	9	8.6969696969697	0.303030303030303
91	15	12.9642857142857	2.03571428571429
92	12	11.225	0.775
93	12	11.225	0.775
94	12	10.203125	1.796875
95	10	11.225	-1.225
96	13	11.225	1.775
97	9	11.225	-2.225
98	12	11.225	0.775
99	10	10.203125	-0.203125
100	14	11.225	2.775
101	11	11.225	-0.225000000000000
102	15	12.9642857142857	2.03571428571429
103	11	11.225	-0.225000000000000
104	11	11.225	-0.225000000000000
105	12	10.203125	1.796875
106	12	12.9642857142857	-0.964285714285714
107	12	11.225	0.775
108	11	11.225	-0.225000000000000
109	7	8.6969696969697	-1.69696969696970
110	12	11.225	0.775
111	14	11.225	2.775
112	11	12.9642857142857	-1.96428571428571
113	10	8.6969696969697	1.30303030303030
114	13	12.9642857142857	0.0357142857142865
115	13	10.203125	2.796875
116	8	10.203125	-2.203125
117	11	10.203125	0.796875
118	12	11.225	0.775
119	11	10.203125	0.796875
120	13	11.225	1.775
121	12	10.203125	1.796875
122	14	11.225	2.775
123	13	11.225	1.775
124	15	11.225	3.775
125	10	11.225	-1.225
126	11	12.9642857142857	-1.96428571428571
127	9	11.225	-2.225
128	11	8.6969696969697	2.30303030303030
129	10	11.225	-1.225
130	11	8.6969696969697	2.30303030303030
131	8	11.225	-3.225
132	11	8.6969696969697	2.30303030303030
133	12	10.203125	1.796875
134	12	11.225	0.775
135	9	10.203125	-1.203125
136	11	11.225	-0.225000000000000
137	10	8.6969696969697	1.30303030303030
138	8	8.6969696969697	-0.696969696969697
139	9	8.6969696969697	0.303030303030303
140	8	11.225	-3.225
141	9	11.225	-2.225
142	15	12.9642857142857	2.03571428571429
143	11	11.225	-0.225000000000000
144	8	8.6969696969697	-0.696969696969697
145	13	12.9642857142857	0.0357142857142865
146	12	10.203125	1.796875
147	12	11.225	0.775
148	9	10.203125	-1.203125
149	7	8.6969696969697	-1.69696969696970
150	13	11.225	1.775
151	9	11.225	-2.225
152	6	11.225	-5.225
153	8	10.203125	-2.203125
154	8	8.6969696969697	-0.696969696969697
155	6	10.203125	-4.203125
156	9	11.225	-2.225
157	11	11.225	-0.225000000000000
158	8	8.6969696969697	-0.696969696969697
159	10	10.203125	-0.203125
160	8	8.6969696969697	-0.696969696969697
161	14	12.9642857142857	1.03571428571429
162	10	11.225	-1.225
163	8	8.6969696969697	-0.696969696969697
164	11	10.203125	0.796875
165	12	8.6969696969697	3.3030303030303
166	12	10.203125	1.796875
167	12	11.225	0.775
168	5	8.6969696969697	-3.6969696969697
169	12	11.225	0.775
170	10	8.6969696969697	1.30303030303030
171	7	6	1
172	12	8.6969696969697	3.3030303030303
173	11	11.225	-0.225000000000000
174	8	8.6969696969697	-0.696969696969697
175	9	8.6969696969697	0.303030303030303
176	10	10.203125	-0.203125
177	9	8.6969696969697	0.303030303030303
178	12	11.225	0.775
179	6	8.6969696969697	-2.69696969696970
180	15	12.9642857142857	2.03571428571429
181	12	11.225	0.775
182	12	6	6
183	12	11.225	0.775
184	11	12.9642857142857	-1.96428571428571
185	7	8.6969696969697	-1.69696969696970
186	7	8.6969696969697	-1.69696969696970
187	5	8.6969696969697	-3.6969696969697
188	12	10.203125	1.796875
189	12	11.225	0.775
190	3	8.6969696969697	-5.6969696969697
191	11	11.225	-0.225000000000000
192	10	10.203125	-0.203125
193	12	11.225	0.775
194	9	11.225	-2.225
195	12	11.225	0.775
196	9	10.203125	-1.203125
197	12	11.225	0.775
198	10	10.203125	-0.203125
199	9	8.6969696969697	0.303030303030303
200	12	8.6969696969697	3.3030303030303
201	8	11.225	-3.225
202	11	11.225	-0.225000000000000
203	11	11.225	-0.225000000000000
204	12	11.225	0.775
205	10	8.6969696969697	1.30303030303030
206	10	11.225	-1.225
207	12	8.6969696969697	3.3030303030303
208	12	8.6969696969697	3.3030303030303
209	11	11.225	-0.225000000000000
210	8	11.225	-3.225
211	12	11.225	0.775
212	10	10.203125	-0.203125
213	11	12.9642857142857	-1.96428571428571
214	10	10.203125	-0.203125
215	8	10.203125	-2.203125
216	12	11.225	0.775
217	12	10.203125	1.796875
218	10	10.203125	-0.203125
219	12	11.225	0.775
220	9	8.6969696969697	0.303030303030303
221	6	6	0
222	10	10.203125	-0.203125
223	9	10.203125	-1.203125
224	9	8.6969696969697	0.303030303030303
225	9	8.6969696969697	0.303030303030303
226	6	10.203125	-4.203125
227	10	8.6969696969697	1.30303030303030
228	6	11.225	-5.225
229	14	12.9642857142857	1.03571428571429
230	10	10.203125	-0.203125
231	10	8.6969696969697	1.30303030303030
232	6	6	0
233	12	10.203125	1.796875
234	12	11.225	0.775
235	7	8.6969696969697	-1.69696969696970
236	8	8.6969696969697	-0.696969696969697
237	11	8.6969696969697	2.30303030303030
238	3	8.6969696969697	-5.6969696969697
239	6	10.203125	-4.203125
240	8	8.6969696969697	-0.696969696969697
241	9	10.203125	-1.203125
242	9	8.6969696969697	0.303030303030303
243	8	8.6969696969697	-0.696969696969697
244	9	8.6969696969697	0.303030303030303
245	7	8.6969696969697	-1.69696969696970
246	7	8.6969696969697	-1.69696969696970
247	6	8.6969696969697	-2.69696969696970
248	9	11.225	-2.225
249	10	8.6969696969697	1.30303030303030
250	11	10.203125	0.796875
251	12	11.225	0.775
252	8	10.203125	-2.203125
253	11	10.203125	0.796875
254	3	6	-3
255	11	11.225	-0.225000000000000
256	12	8.6969696969697	3.3030303030303
257	7	8.6969696969697	-1.69696969696970
258	9	10.203125	-1.203125
259	12	11.225	0.775
260	8	10.203125	-2.203125
261	11	10.203125	0.796875
262	8	8.6969696969697	-0.696969696969697
263	10	11.225	-1.225
264	8	8.6969696969697	-0.696969696969697
265	7	10.203125	-3.203125
266	8	10.203125	-2.203125
267	10	11.225	-1.225
268	8	10.203125	-2.203125
269	12	11.225	0.775
270	14	11.225	2.775
271	7	8.6969696969697	-1.69696969696970
272	6	6	0
273	11	11.225	-0.225000000000000
274	4	6	-2
275	9	11.225	-2.225
276	5	6	-1
277	9	8.6969696969697	0.303030303030303
278	11	11.225	-0.225000000000000
279	12	10.203125	1.796875
280	9	8.6969696969697	0.303030303030303
281	12	11.225	0.775
282	10	11.225	-1.225
283	9	8.6969696969697	0.303030303030303
284	6	6	0
285	10	11.225	-1.225
286	9	8.6969696969697	0.303030303030303
287	13	11.225	1.775
288	12	10.203125	1.796875

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294154722v3rrinzdmb6g0/2khsi1292941596.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294154722v3rrinzdmb6g0/2khsi1292941596.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294154722v3rrinzdmb6g0/3khsi1292941596.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294154722v3rrinzdmb6g0/3khsi1292941596.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294154722v3rrinzdmb6g0/4u8r31292941596.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t129294154722v3rrinzdmb6g0/4u8r31292941596.ps (open in new window)

Parameters (Session):

par1 = 7 ; par2 = none ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 7 ; par2 = none ; 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')

}

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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