Full_Name: Melinda Teng
Version: 1.6.2
OS: windows M E
Submission from: (NULL) (136.152.196.249)
Greatly appreciate for any advice on the following. Many grateful thanks. Please
do not hesitate to contact me for further details.
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Error message :
"Error in model.frame.default(formula = Y ~ X4, data = TS,
drop.unused.levels TRUE) : Object "TS" not found"
Actions taken :
debug(), traceback(), and looking into model.frame.default.
Situation :
stepAIC() is used to select variables into a regression model by forward
selection procedure. This stepAIC() is programmed within a function where the
data.frame of the required data are defined and computed.
Problem : When I cut and paste the body of the function Q and run in the
workspace, it will give me the required output and the data.frame from gfit (T)
is passed into stepAIC() for the selection procedure. However when I execute the
same codes, but now through the function (Q(), see below), the above error
message appears.
Code snippets :
rm(list=ls(all=TRUE))
library(base); library(boot); library(MASS); library(methods); library(rpart);
library(nls)
B <- data.frame
Y X1 X2 X3 X4
1 0 -1.14 -0.47 -0.05 -0.98
2 0 -1.14 -0.47 -0.05 -0.98
3 0 -1.14 -0.47 -0.05 -0.98
4 0 -1.14 -0.47 -0.05 -0.98
5 0 -1.14 -0.47 -0.05 -0.98
6 0 -1.14 -0.47 -0.05 -0.98
7 0 -1.14 -0.47 -0.05 -0.98
8 0 -1.14 -0.47 -0.05 -0.98
9 0 -1.14 -0.47 -0.05 -0.98
10 0 -1.14 -0.47 -0.05 -0.98
11 0 -1.14 -0.47 -0.05 -0.98
12 0 -1.14 -0.47 -0.05 -0.98
13 0 -1.14 -0.47 -0.05 -0.98
14 0 -1.14 -0.47 -0.05 -0.98
15 0 -1.14 -0.47 -0.05 -0.98
16 0 -1.14 -0.47 -0.05 -0.98
17 0 -1.14 -0.47 -0.05 -0.98
18 0 -1.14 -0.47 -0.05 -0.98
19 0 -1.14 -0.47 -0.05 -0.98
20 0 -1.14 -0.47 -0.05 -0.98
21 0 -1.14 -0.47 -0.05 -0.98
22 1 -1.14 -0.47 -0.05 -0.98
23 0 -1.14 -0.47 -0.05 -0.98
24 1 -1.14 -0.47 -0.05 -0.98
25 0 -1.14 -0.47 -0.05 -0.98
26 0 -1.14 -0.47 -0.05 -0.98
27 0 -1.14 -0.47 -0.05 -0.98
28 0 -1.14 -0.47 -0.05 -0.98
29 1 -1.14 -0.47 -0.05 -0.98
30 1 -1.14 -0.47 -0.05 -0.98
31 0 -1.14 -0.47 -0.05 -0.98
32 0 -1.14 -0.47 -0.05 -0.98
33 0 -1.14 -0.47 -0.05 -0.98
34 0 -1.14 -0.47 -0.05 -0.98
35 0 -1.14 -0.47 -0.05 -0.98
36 0 -1.14 -0.47 -0.05 -0.98
37 0 -1.14 -0.47 -0.05 -0.98
38 0 -1.14 -0.47 -0.05 -0.98
39 0 -1.14 -0.47 -0.05 -0.98
40 1 -1.14 -0.47 -0.05 -0.98
41 0 -1.14 -0.47 -0.05 -0.98
42 0 -1.14 -0.47 -0.05 -0.98
43 0 -1.14 -0.47 -0.05 -0.98
44 1 -1.14 -0.47 -0.05 -0.98
45 0 -1.14 -0.47 -0.05 -0.98
46 0 -1.14 -0.47 -0.05 -0.98
47 0 -1.14 -0.47 -0.05 -0.98
48 0 -1.14 -0.47 -0.05 -0.98
49 0 -1.14 -0.47 -0.05 -0.98
50 1 -1.14 -0.47 -0.05 -0.98
Z <- data.frame
Y X1 X2 X3 X4
1 1 2.30 0.48 1.27 0.29
2 1 2.30 0.48 1.27 0.29
3 1 2.30 0.48 1.27 0.29
4 1 2.30 0.48 1.27 0.29
5 1 2.30 0.48 1.27 0.29
6 0 -1.67 1.13 -1.63 0.22
7 1 -1.67 1.13 -1.63 0.22
8 1 -1.67 1.13 -1.63 0.22
9 1 -1.67 1.13 -1.63 0.22
10 1 -1.67 1.13 -1.63 0.22
11 0 0.61 1.11 -0.37 -1.43
12 1 0.61 1.11 -0.37 -1.43
13 0 0.61 1.11 -0.37 -1.43
14 0 0.61 1.11 -0.37 -1.43
15 0 0.61 1.11 -0.37 -1.43
16 1 -0.49 0.68 -1.15 0.32
17 0 -0.49 0.68 -1.15 0.32
18 1 -0.49 0.68 -1.15 0.32
19 1 -0.49 0.68 -1.15 0.32
20 1 -0.49 0.68 -1.15 0.32
21 1 -1.05 0.19 -0.21 -0.54
22 0 -1.05 0.19 -0.21 -0.54
23 0 -1.05 0.19 -0.21 -0.54
24 1 -1.05 0.19 -0.21 -0.54
25 1 -1.05 0.19 -0.21 -0.54
26 1 -0.48 -1.45 0.15 0.90
27 1 -0.48 -1.45 0.15 0.90
28 1 -0.48 -1.45 0.15 0.90
29 1 -0.48 -1.45 0.15 0.90
30 1 -0.48 -1.45 0.15 0.90
31 1 1.33 1.17 0.70 0.50
32 1 1.33 1.17 0.70 0.50
33 1 1.33 1.17 0.70 0.50
34 1 1.33 1.17 0.70 0.50
35 1 1.33 1.17 0.70 0.50
36 0 -0.62 -0.15 0.44 0.94
37 1 -0.62 -0.15 0.44 0.94
38 1 -0.62 -0.15 0.44 0.94
39 1 -0.62 -0.15 0.44 0.94
40 1 -0.62 -0.15 0.44 0.94
41 1 0.90 0.68 0.50 -0.37
42 0 0.90 0.68 0.50 -0.37
43 1 0.90 0.68 0.50 -0.37
44 0 0.90 0.68 0.50 -0.37
45 0 0.90 0.68 0.50 -0.37
46 1 1.26 -0.22 -1.83 -0.37
47 1 1.26 -0.22 -1.83 -0.37
48 0 1.26 -0.22 -1.83 -0.37
49 0 1.26 -0.22 -1.83 -0.37
50 1 1.26 -0.22 -1.83 -0.37
S <- matrix
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 0 0 0 1 0 0 0 0 1 1 0 0 0 0
0 0 0 0 0 0 0
[2,] 0 0 0 0 0 1 0 0 0 0 0 0 0 0
1 0 0 0 0 0 1
[3,] 0 0 0 0 1 0 0 1 0 0 0 0 0 0
0 0 0 1 0 0 0
[4,] 0 1 0 0 0 0 0 0 0 0 1 0 0 0
0 1 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 0 1 1 0
0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0
[7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0
[8,] 1 0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 1 0
[10,] 0 0 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 0
[,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33]
[,34] [,35] [,36] [,37] [,38] [,39] [,40]
[1,] 0 0 0 0 0 0 0 0 0 0 1 0
0 0 0 1 0 0 0
[2,] 0 0 0 0 0 1 0 0 0 0 0 0
1 0 0 0 0 0 0
[3,] 0 0 1 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 1 0 0
[6,] 1 0 0 0 0 0 1 0 0 0 0 0
0 1 0 0 0 0 0
[7,] 0 1 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 1 0
[10,] 0 0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 0 0 1
[,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
[1,] 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0
[4,] 0 1 0 0 0 0 0 0 0 0
[5,] 0 0 0 1 0 0 0 0 0 0
[6,] 0 0 0 0 0 1 0 0 0 0
[7,] 1 0 0 0 0 0 0 1 0 0
[8,] 0 0 1 0 0 0 0 0 1 0
[9,] 0 0 0 0 1 0 0 0 0 0
[10,] 0 0 0 0 0 0 1 0 0 1
v <- 10
Q <- function(v, Z, B, S) {
for (m in 1:v) {
T <- Z[S[m,]==0,]
options(contrasts=c("contr.treatment", "contr.poly"))
gfit <- glm(Y~1, family=binomial(link=logit), data = T)
ffit <- stepAIC(gfit, as.formula(paste(paste(colnames(Z)[1]),
paste("~"),
paste(colnames(Z)[2:NCOL(Z)], collapse= "+"))), direction =
c("forward"), trace
= FALSE, k=0)
}
}