R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'q()' to quit R.
> x <- array(list(-1.2
+ ,23.6
+ ,-1.5
+ ,-0.1
+ ,0.8
+ ,-2.4
+ ,-1.2
+ ,-2.4
+ ,25.7
+ ,-4.4
+ ,-1.5
+ ,-0.1
+ ,0.8
+ ,-2.4
+ ,0.8
+ ,32.5
+ ,-4.2
+ ,-4.4
+ ,-1.5
+ ,-0.1
+ ,0.8
+ ,-0.1
+ ,33.5
+ ,3.5
+ ,-4.2
+ ,-4.4
+ ,-1.5
+ ,-0.1
+ ,-1.5
+ ,34.5
+ ,10
+ ,3.5
+ ,-4.2
+ ,-4.4
+ ,-1.5
+ ,-4.4
+ ,27.9
+ ,8.6
+ ,10
+ ,3.5
+ ,-4.2
+ ,-4.4
+ ,-4.2
+ ,45.3
+ ,9.5
+ ,8.6
+ ,10
+ ,3.5
+ ,-4.2
+ ,3.5
+ ,40.8
+ ,9.9
+ ,9.5
+ ,8.6
+ ,10
+ ,3.5
+ ,10
+ ,58.5
+ ,10.4
+ ,9.9
+ ,9.5
+ ,8.6
+ ,10
+ ,8.6
+ ,32.5
+ ,16
+ ,10.4
+ ,9.9
+ ,9.5
+ ,8.6
+ ,9.5
+ ,35.5
+ ,12.7
+ ,16
+ ,10.4
+ ,9.9
+ ,9.5
+ ,9.9
+ ,46.7
+ ,10.2
+ ,12.7
+ ,16
+ ,10.4
+ ,9.9
+ ,10.4
+ ,53.2
+ ,8.9
+ ,10.2
+ ,12.7
+ ,16
+ ,10.4
+ ,16
+ ,36.1
+ ,12.6
+ ,8.9
+ ,10.2
+ ,12.7
+ ,16
+ ,12.7
+ ,54
+ ,13.6
+ ,12.6
+ ,8.9
+ ,10.2
+ ,12.7
+ ,10.2
+ ,58.1
+ ,14.8
+ ,13.6
+ ,12.6
+ ,8.9
+ ,10.2
+ ,8.9
+ ,41.8
+ ,9.5
+ ,14.8
+ ,13.6
+ ,12.6
+ ,8.9
+ ,12.6
+ ,43.1
+ ,13.7
+ ,9.5
+ ,14.8
+ ,13.6
+ ,12.6
+ ,13.6
+ ,76
+ ,17
+ ,13.7
+ ,9.5
+ ,14.8
+ ,13.6
+ ,14.8
+ ,42.8
+ ,14.7
+ ,17
+ ,13.7
+ ,9.5
+ ,14.8
+ ,9.5
+ ,41
+ ,17.4
+ ,14.7
+ ,17
+ ,13.7
+ ,9.5
+ ,13.7
+ ,61.4
+ ,9
+ ,17.4
+ ,14.7
+ ,17
+ ,13.7
+ ,17
+ ,34.2
+ ,9.1
+ ,9
+ ,17.4
+ ,14.7
+ ,17
+ ,14.7
+ ,53.8
+ ,12.2
+ ,9.1
+ ,9
+ ,17.4
+ ,14.7
+ ,17.4
+ ,80.7
+ ,15.9
+ ,12.2
+ ,9.1
+ ,9
+ ,17.4
+ ,9
+ ,79.5
+ ,12.9
+ ,15.9
+ ,12.2
+ ,9.1
+ ,9
+ ,9.1
+ ,96.5
+ ,10.9
+ ,12.9
+ ,15.9
+ ,12.2
+ ,9.1
+ ,12.2
+ ,108.3
+ ,10.6
+ ,10.9
+ ,12.9
+ ,15.9
+ ,12.2
+ ,15.9
+ ,100.1
+ ,13.2
+ ,10.6
+ ,10.9
+ ,12.9
+ ,15.9
+ ,12.9
+ ,108.5
+ ,9.6
+ ,13.2
+ ,10.6
+ ,10.9
+ ,12.9
+ ,10.9
+ ,127.4
+ ,6.4
+ ,9.6
+ ,13.2
+ ,10.6
+ ,10.9
+ ,10.6
+ ,86.5
+ ,5.8
+ ,6.4
+ ,9.6
+ ,13.2
+ ,10.6
+ ,13.2
+ ,71.4
+ ,-1
+ ,5.8
+ ,6.4
+ ,9.6
+ ,13.2
+ ,9.6
+ ,88.2
+ ,-0.2
+ ,-1
+ ,5.8
+ ,6.4
+ ,9.6
+ ,6.4
+ ,135.6
+ ,2.7
+ ,-0.2
+ ,-1
+ ,5.8
+ ,6.4
+ ,5.8
+ ,70.5
+ ,3.6
+ ,2.7
+ ,-0.2
+ ,-1
+ ,5.8
+ ,-1
+ ,87.5
+ ,-0.9
+ ,3.6
+ ,2.7
+ ,-0.2
+ ,-1
+ ,-0.2
+ ,73.3
+ ,0.3
+ ,-0.9
+ ,3.6
+ ,2.7
+ ,-0.2
+ ,2.7
+ ,92.2
+ ,-1.1
+ ,0.3
+ ,-0.9
+ ,3.6
+ ,2.7
+ ,3.6
+ ,61.1
+ ,-2.5
+ ,-1.1
+ ,0.3
+ ,-0.9
+ ,3.6
+ ,-0.9
+ ,45.7
+ ,-3.4
+ ,-2.5
+ ,-1.1
+ ,0.3
+ ,-0.9
+ ,0.3
+ ,30.5
+ ,-3.5
+ ,-3.4
+ ,-2.5
+ ,-1.1
+ ,0.3
+ ,-1.1
+ ,34.8
+ ,-3.9
+ ,-3.5
+ ,-3.4
+ ,-2.5
+ ,-1.1
+ ,-2.5
+ ,29.2
+ ,-4.6
+ ,-3.9
+ ,-3.5
+ ,-3.4
+ ,-2.5
+ ,-3.4
+ ,56.7
+ ,-0.1
+ ,-4.6
+ ,-3.9
+ ,-3.5
+ ,-3.4
+ ,-3.5
+ ,67.1
+ ,4.3
+ ,-0.1
+ ,-4.6
+ ,-3.9
+ ,-3.5
+ ,-3.9
+ ,41.8
+ ,10.2
+ ,4.3
+ ,-0.1
+ ,-4.6
+ ,-3.9
+ ,-4.6
+ ,46.8
+ ,8.7
+ ,10.2
+ ,4.3
+ ,-0.1
+ ,-4.6
+ ,-0.1
+ ,50.1
+ ,13.3
+ ,8.7
+ ,10.2
+ ,4.3
+ ,-0.1
+ ,4.3
+ ,81.9
+ ,15
+ ,13.3
+ ,8.7
+ ,10.2
+ ,4.3
+ ,10.2
+ ,115.8
+ ,20.7
+ ,15
+ ,13.3
+ ,8.7
+ ,10.2
+ ,8.7
+ ,102.5
+ ,20.7
+ ,20.7
+ ,15
+ ,13.3
+ ,8.7
+ ,13.3
+ ,106.6
+ ,26.4
+ ,20.7
+ ,20.7
+ ,15
+ ,13.3
+ ,15
+ ,101.4
+ ,31.2
+ ,26.4
+ ,20.7
+ ,20.7
+ ,15
+ ,20.7
+ ,136.1
+ ,31.4
+ ,31.2
+ ,26.4
+ ,20.7
+ ,20.7
+ ,20.7
+ ,143.4
+ ,26.6
+ ,31.4
+ ,31.2
+ ,26.4
+ ,20.7
+ ,26.4
+ ,127.5
+ ,26.6
+ ,26.6
+ ,31.4
+ ,31.2
+ ,26.4
+ ,31.2
+ ,113.8
+ ,19.2
+ ,26.6
+ ,26.6
+ ,31.4
+ ,31.2
+ ,31.4
+ ,75.3
+ ,6.5
+ ,19.2
+ ,26.6
+ ,26.6
+ ,31.4
+ ,26.6
+ ,98.5
+ ,3.1
+ ,6.5
+ ,19.2
+ ,26.6
+ ,26.6
+ ,26.6
+ ,113.7
+ ,-0.2
+ ,3.1
+ ,6.5
+ ,19.2
+ ,26.6
+ ,19.2
+ ,103.7
+ ,-4
+ ,-0.2
+ ,3.1
+ ,6.5
+ ,19.2
+ ,6.5
+ ,73.9
+ ,-12.6
+ ,-4
+ ,-0.2
+ ,3.1
+ ,6.5
+ ,3.1
+ ,52.5
+ ,-13
+ ,-12.6
+ ,-4
+ ,-0.2
+ ,3.1
+ ,-0.2
+ ,63.9
+ ,-17.6
+ ,-13
+ ,-12.6
+ ,-4
+ ,-0.2
+ ,-4
+ ,44.9
+ ,-21.7
+ ,-17.6
+ ,-13
+ ,-12.6
+ ,-4
+ ,-12.6
+ ,31.3
+ ,-23.2
+ ,-21.7
+ ,-17.6
+ ,-13
+ ,-12.6
+ ,-13
+ ,24.9
+ ,-16.8
+ ,-23.2
+ ,-21.7
+ ,-17.6
+ ,-13)
+ ,dim=c(7
+ ,68)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4'
+ ,'Y5')
+ ,1:68))
> y <- array(NA,dim=c(7,68),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4','Y5'),1:68))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 Y5 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 -1.2 23.6 -1.5 -0.1 0.8 -2.4 -1.2 1 0 0 0 0 0 0 0 0 0 0
2 -2.4 25.7 -4.4 -1.5 -0.1 0.8 -2.4 0 1 0 0 0 0 0 0 0 0 0
3 0.8 32.5 -4.2 -4.4 -1.5 -0.1 0.8 0 0 1 0 0 0 0 0 0 0 0
4 -0.1 33.5 3.5 -4.2 -4.4 -1.5 -0.1 0 0 0 1 0 0 0 0 0 0 0
5 -1.5 34.5 10.0 3.5 -4.2 -4.4 -1.5 0 0 0 0 1 0 0 0 0 0 0
6 -4.4 27.9 8.6 10.0 3.5 -4.2 -4.4 0 0 0 0 0 1 0 0 0 0 0
7 -4.2 45.3 9.5 8.6 10.0 3.5 -4.2 0 0 0 0 0 0 1 0 0 0 0
8 3.5 40.8 9.9 9.5 8.6 10.0 3.5 0 0 0 0 0 0 0 1 0 0 0
9 10.0 58.5 10.4 9.9 9.5 8.6 10.0 0 0 0 0 0 0 0 0 1 0 0
10 8.6 32.5 16.0 10.4 9.9 9.5 8.6 0 0 0 0 0 0 0 0 0 1 0
11 9.5 35.5 12.7 16.0 10.4 9.9 9.5 0 0 0 0 0 0 0 0 0 0 1
12 9.9 46.7 10.2 12.7 16.0 10.4 9.9 0 0 0 0 0 0 0 0 0 0 0
13 10.4 53.2 8.9 10.2 12.7 16.0 10.4 1 0 0 0 0 0 0 0 0 0 0
14 16.0 36.1 12.6 8.9 10.2 12.7 16.0 0 1 0 0 0 0 0 0 0 0 0
15 12.7 54.0 13.6 12.6 8.9 10.2 12.7 0 0 1 0 0 0 0 0 0 0 0
16 10.2 58.1 14.8 13.6 12.6 8.9 10.2 0 0 0 1 0 0 0 0 0 0 0
17 8.9 41.8 9.5 14.8 13.6 12.6 8.9 0 0 0 0 1 0 0 0 0 0 0
18 12.6 43.1 13.7 9.5 14.8 13.6 12.6 0 0 0 0 0 1 0 0 0 0 0
19 13.6 76.0 17.0 13.7 9.5 14.8 13.6 0 0 0 0 0 0 1 0 0 0 0
20 14.8 42.8 14.7 17.0 13.7 9.5 14.8 0 0 0 0 0 0 0 1 0 0 0
21 9.5 41.0 17.4 14.7 17.0 13.7 9.5 0 0 0 0 0 0 0 0 1 0 0
22 13.7 61.4 9.0 17.4 14.7 17.0 13.7 0 0 0 0 0 0 0 0 0 1 0
23 17.0 34.2 9.1 9.0 17.4 14.7 17.0 0 0 0 0 0 0 0 0 0 0 1
24 14.7 53.8 12.2 9.1 9.0 17.4 14.7 0 0 0 0 0 0 0 0 0 0 0
25 17.4 80.7 15.9 12.2 9.1 9.0 17.4 1 0 0 0 0 0 0 0 0 0 0
26 9.0 79.5 12.9 15.9 12.2 9.1 9.0 0 1 0 0 0 0 0 0 0 0 0
27 9.1 96.5 10.9 12.9 15.9 12.2 9.1 0 0 1 0 0 0 0 0 0 0 0
28 12.2 108.3 10.6 10.9 12.9 15.9 12.2 0 0 0 1 0 0 0 0 0 0 0
29 15.9 100.1 13.2 10.6 10.9 12.9 15.9 0 0 0 0 1 0 0 0 0 0 0
30 12.9 108.5 9.6 13.2 10.6 10.9 12.9 0 0 0 0 0 1 0 0 0 0 0
31 10.9 127.4 6.4 9.6 13.2 10.6 10.9 0 0 0 0 0 0 1 0 0 0 0
32 10.6 86.5 5.8 6.4 9.6 13.2 10.6 0 0 0 0 0 0 0 1 0 0 0
33 13.2 71.4 -1.0 5.8 6.4 9.6 13.2 0 0 0 0 0 0 0 0 1 0 0
34 9.6 88.2 -0.2 -1.0 5.8 6.4 9.6 0 0 0 0 0 0 0 0 0 1 0
35 6.4 135.6 2.7 -0.2 -1.0 5.8 6.4 0 0 0 0 0 0 0 0 0 0 1
36 5.8 70.5 3.6 2.7 -0.2 -1.0 5.8 0 0 0 0 0 0 0 0 0 0 0
37 -1.0 87.5 -0.9 3.6 2.7 -0.2 -1.0 1 0 0 0 0 0 0 0 0 0 0
38 -0.2 73.3 0.3 -0.9 3.6 2.7 -0.2 0 1 0 0 0 0 0 0 0 0 0
39 2.7 92.2 -1.1 0.3 -0.9 3.6 2.7 0 0 1 0 0 0 0 0 0 0 0
40 3.6 61.1 -2.5 -1.1 0.3 -0.9 3.6 0 0 0 1 0 0 0 0 0 0 0
41 -0.9 45.7 -3.4 -2.5 -1.1 0.3 -0.9 0 0 0 0 1 0 0 0 0 0 0
42 0.3 30.5 -3.5 -3.4 -2.5 -1.1 0.3 0 0 0 0 0 1 0 0 0 0 0
43 -1.1 34.8 -3.9 -3.5 -3.4 -2.5 -1.1 0 0 0 0 0 0 1 0 0 0 0
44 -2.5 29.2 -4.6 -3.9 -3.5 -3.4 -2.5 0 0 0 0 0 0 0 1 0 0 0
45 -3.4 56.7 -0.1 -4.6 -3.9 -3.5 -3.4 0 0 0 0 0 0 0 0 1 0 0
46 -3.5 67.1 4.3 -0.1 -4.6 -3.9 -3.5 0 0 0 0 0 0 0 0 0 1 0
47 -3.9 41.8 10.2 4.3 -0.1 -4.6 -3.9 0 0 0 0 0 0 0 0 0 0 1
48 -4.6 46.8 8.7 10.2 4.3 -0.1 -4.6 0 0 0 0 0 0 0 0 0 0 0
49 -0.1 50.1 13.3 8.7 10.2 4.3 -0.1 1 0 0 0 0 0 0 0 0 0 0
50 4.3 81.9 15.0 13.3 8.7 10.2 4.3 0 1 0 0 0 0 0 0 0 0 0
51 10.2 115.8 20.7 15.0 13.3 8.7 10.2 0 0 1 0 0 0 0 0 0 0 0
52 8.7 102.5 20.7 20.7 15.0 13.3 8.7 0 0 0 1 0 0 0 0 0 0 0
53 13.3 106.6 26.4 20.7 20.7 15.0 13.3 0 0 0 0 1 0 0 0 0 0 0
54 15.0 101.4 31.2 26.4 20.7 20.7 15.0 0 0 0 0 0 1 0 0 0 0 0
55 20.7 136.1 31.4 31.2 26.4 20.7 20.7 0 0 0 0 0 0 1 0 0 0 0
56 20.7 143.4 26.6 31.4 31.2 26.4 20.7 0 0 0 0 0 0 0 1 0 0 0
57 26.4 127.5 26.6 26.6 31.4 31.2 26.4 0 0 0 0 0 0 0 0 1 0 0
58 31.2 113.8 19.2 26.6 26.6 31.4 31.2 0 0 0 0 0 0 0 0 0 1 0
59 31.4 75.3 6.5 19.2 26.6 26.6 31.4 0 0 0 0 0 0 0 0 0 0 1
60 26.6 98.5 3.1 6.5 19.2 26.6 26.6 0 0 0 0 0 0 0 0 0 0 0
61 26.6 113.7 -0.2 3.1 6.5 19.2 26.6 1 0 0 0 0 0 0 0 0 0 0
62 19.2 103.7 -4.0 -0.2 3.1 6.5 19.2 0 1 0 0 0 0 0 0 0 0 0
63 6.5 73.9 -12.6 -4.0 -0.2 3.1 6.5 0 0 1 0 0 0 0 0 0 0 0
64 3.1 52.5 -13.0 -12.6 -4.0 -0.2 3.1 0 0 0 1 0 0 0 0 0 0 0
65 -0.2 63.9 -17.6 -13.0 -12.6 -4.0 -0.2 0 0 0 0 1 0 0 0 0 0 0
66 -4.0 44.9 -21.7 -17.6 -13.0 -12.6 -4.0 0 0 0 0 0 1 0 0 0 0 0
67 -12.6 31.3 -23.2 -21.7 -17.6 -13.0 -12.6 0 0 0 0 0 0 1 0 0 0 0
68 -13.0 24.9 -16.8 -23.2 -21.7 -17.6 -13.0 0 0 0 0 0 0 0 1 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
-6.741e-16 -5.520e-18 1.645e-16 -1.029e-16 5.917e-17 -6.484e-16
Y5 M1 M2 M3 M4 M5
1.000e+00 5.089e-16 6.136e-16 -1.923e-16 4.237e-16 -3.734e-16
M6 M7 M8 M9 M10 M11
5.181e-16 -2.913e-15 1.628e-16 3.053e-16 -9.533e-17 -1.796e-16
t
1.450e-17
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.434e-14 -5.361e-16 -3.819e-17 6.888e-16 3.656e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.741e-16 1.298e-15 -5.190e-01 0.6058
X -5.520e-18 1.504e-17 -3.670e-01 0.7151
Y1 1.645e-16 8.246e-17 1.995e+00 0.0517 .
Y2 -1.029e-16 1.288e-16 -7.990e-01 0.4282
Y3 5.917e-17 1.311e-16 4.510e-01 0.6538
Y4 -6.484e-16 1.297e-16 -4.997e+00 7.8e-06 ***
Y5 1.000e+00 8.954e-17 1.117e+16 < 2e-16 ***
M1 5.089e-16 1.509e-15 3.370e-01 0.7374
M2 6.136e-16 1.507e-15 4.070e-01 0.6856
M3 -1.923e-16 1.541e-15 -1.250e-01 0.9012
M4 4.237e-16 1.518e-15 2.790e-01 0.7813
M5 -3.734e-16 1.516e-15 -2.460e-01 0.8064
M6 5.181e-16 1.517e-15 3.420e-01 0.7341
M7 -2.913e-15 1.537e-15 -1.895e+00 0.0640 .
M8 1.628e-16 1.501e-15 1.080e-01 0.9141
M9 3.053e-16 1.563e-15 1.950e-01 0.8460
M10 -9.533e-17 1.565e-15 -6.100e-02 0.9517
M11 -1.796e-16 1.568e-15 -1.150e-01 0.9093
t 1.449e-17 2.034e-17 7.130e-01 0.4794
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.457e-15 on 49 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 5.757e+31 on 18 and 49 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 8.052648e-01 3.894705e-01 1.947352e-01
[2,] 7.105883e-01 5.788233e-01 2.894117e-01
[3,] 2.221743e-01 4.443486e-01 7.778257e-01
[4,] 0.000000e+00 0.000000e+00 1.000000e+00
[5,] 8.193679e-01 3.612641e-01 1.806321e-01
[6,] 7.368246e-01 5.263508e-01 2.631754e-01
[7,] 5.498402e-01 9.003196e-01 4.501598e-01
[8,] 9.772822e-01 4.543563e-02 2.271782e-02
[9,] 1.000000e+00 4.464129e-15 2.232064e-15
[10,] 9.342488e-01 1.315023e-01 6.575117e-02
[11,] 6.000268e-03 1.200054e-02 9.939997e-01
[12,] 7.807576e-01 4.384848e-01 2.192424e-01
[13,] 9.996713e-01 6.573606e-04 3.286803e-04
[14,] 8.310393e-01 3.379213e-01 1.689607e-01
[15,] 9.999206e-01 1.588330e-04 7.941649e-05
[16,] 6.023450e-04 1.204690e-03 9.993977e-01
[17,] 9.994211e-01 1.157789e-03 5.788943e-04
[18,] 4.637446e-07 9.274892e-07 9.999995e-01
[19,] 9.745101e-01 5.097971e-02 2.548986e-02
[20,] 1.163637e-02 2.327274e-02 9.883636e-01
[21,] 9.666501e-01 6.669981e-02 3.334991e-02
[22,] 7.093006e-01 5.813987e-01 2.906994e-01
[23,] 8.520997e-02 1.704199e-01 9.147900e-01
[24,] 6.678182e-09 1.335636e-08 1.000000e+00
[25,] 3.005457e-01 6.010913e-01 6.994543e-01
> postscript(file="/var/www/html/rcomp/tmp/1xr811261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gyk71261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3xzhm1261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pugu1261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5i3m01261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 68
Frequency = 1
1 2 3 4 5
1.830497e-15 1.950635e-15 -4.887810e-16 6.498318e-16 -1.591761e-15
6 7 8 9 10
3.085354e-15 -1.433707e-14 7.582611e-16 4.615507e-17 6.843872e-16
11 12 13 14 15
-9.296114e-17 1.136440e-15 8.558004e-16 -5.701972e-16 -9.740048e-17
16 17 18 19 20
1.644229e-17 1.116605e-15 4.805509e-16 2.556671e-15 -9.832104e-16
21 22 23 24 25
7.737711e-16 2.663709e-16 9.779978e-16 -1.855197e-16 -9.815675e-16
26 27 28 29 30
-6.532705e-16 1.160680e-15 3.416723e-16 -1.029776e-16 -9.873376e-16
31 32 33 34 35
3.656404e-15 4.855206e-16 -7.290991e-16 3.572934e-16 -2.844050e-16
36 37 38 39 40
-8.797703e-16 6.782148e-17 9.821130e-16 -2.249979e-16 -5.130897e-16
41 42 43 44 45
6.847937e-16 -5.309716e-16 2.857891e-15 -8.885368e-17 1.247543e-17
46 47 48 49 50
-1.842456e-16 2.148776e-16 -1.412266e-16 4.429224e-16 -3.420742e-16
51 52 53 54 55
9.508470e-17 -1.175538e-16 7.006458e-16 -1.052947e-15 2.457639e-15
56 57 58 59 60
3.797489e-16 -1.033025e-16 -1.123806e-15 -8.155092e-16 7.007711e-17
61 62 63 64 65
-2.215473e-15 -1.367206e-15 -4.445849e-16 -3.773029e-16 -8.073063e-16
66 67 68
-9.946488e-16 2.808463e-15 -5.514664e-16
> postscript(file="/var/www/html/rcomp/tmp/62qho1261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 1.830497e-15 NA
1 1.950635e-15 1.830497e-15
2 -4.887810e-16 1.950635e-15
3 6.498318e-16 -4.887810e-16
4 -1.591761e-15 6.498318e-16
5 3.085354e-15 -1.591761e-15
6 -1.433707e-14 3.085354e-15
7 7.582611e-16 -1.433707e-14
8 4.615507e-17 7.582611e-16
9 6.843872e-16 4.615507e-17
10 -9.296114e-17 6.843872e-16
11 1.136440e-15 -9.296114e-17
12 8.558004e-16 1.136440e-15
13 -5.701972e-16 8.558004e-16
14 -9.740048e-17 -5.701972e-16
15 1.644229e-17 -9.740048e-17
16 1.116605e-15 1.644229e-17
17 4.805509e-16 1.116605e-15
18 2.556671e-15 4.805509e-16
19 -9.832104e-16 2.556671e-15
20 7.737711e-16 -9.832104e-16
21 2.663709e-16 7.737711e-16
22 9.779978e-16 2.663709e-16
23 -1.855197e-16 9.779978e-16
24 -9.815675e-16 -1.855197e-16
25 -6.532705e-16 -9.815675e-16
26 1.160680e-15 -6.532705e-16
27 3.416723e-16 1.160680e-15
28 -1.029776e-16 3.416723e-16
29 -9.873376e-16 -1.029776e-16
30 3.656404e-15 -9.873376e-16
31 4.855206e-16 3.656404e-15
32 -7.290991e-16 4.855206e-16
33 3.572934e-16 -7.290991e-16
34 -2.844050e-16 3.572934e-16
35 -8.797703e-16 -2.844050e-16
36 6.782148e-17 -8.797703e-16
37 9.821130e-16 6.782148e-17
38 -2.249979e-16 9.821130e-16
39 -5.130897e-16 -2.249979e-16
40 6.847937e-16 -5.130897e-16
41 -5.309716e-16 6.847937e-16
42 2.857891e-15 -5.309716e-16
43 -8.885368e-17 2.857891e-15
44 1.247543e-17 -8.885368e-17
45 -1.842456e-16 1.247543e-17
46 2.148776e-16 -1.842456e-16
47 -1.412266e-16 2.148776e-16
48 4.429224e-16 -1.412266e-16
49 -3.420742e-16 4.429224e-16
50 9.508470e-17 -3.420742e-16
51 -1.175538e-16 9.508470e-17
52 7.006458e-16 -1.175538e-16
53 -1.052947e-15 7.006458e-16
54 2.457639e-15 -1.052947e-15
55 3.797489e-16 2.457639e-15
56 -1.033025e-16 3.797489e-16
57 -1.123806e-15 -1.033025e-16
58 -8.155092e-16 -1.123806e-15
59 7.007711e-17 -8.155092e-16
60 -2.215473e-15 7.007711e-17
61 -1.367206e-15 -2.215473e-15
62 -4.445849e-16 -1.367206e-15
63 -3.773029e-16 -4.445849e-16
64 -8.073063e-16 -3.773029e-16
65 -9.946488e-16 -8.073063e-16
66 2.808463e-15 -9.946488e-16
67 -5.514664e-16 2.808463e-15
68 NA -5.514664e-16
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.950635e-15 1.830497e-15
[2,] -4.887810e-16 1.950635e-15
[3,] 6.498318e-16 -4.887810e-16
[4,] -1.591761e-15 6.498318e-16
[5,] 3.085354e-15 -1.591761e-15
[6,] -1.433707e-14 3.085354e-15
[7,] 7.582611e-16 -1.433707e-14
[8,] 4.615507e-17 7.582611e-16
[9,] 6.843872e-16 4.615507e-17
[10,] -9.296114e-17 6.843872e-16
[11,] 1.136440e-15 -9.296114e-17
[12,] 8.558004e-16 1.136440e-15
[13,] -5.701972e-16 8.558004e-16
[14,] -9.740048e-17 -5.701972e-16
[15,] 1.644229e-17 -9.740048e-17
[16,] 1.116605e-15 1.644229e-17
[17,] 4.805509e-16 1.116605e-15
[18,] 2.556671e-15 4.805509e-16
[19,] -9.832104e-16 2.556671e-15
[20,] 7.737711e-16 -9.832104e-16
[21,] 2.663709e-16 7.737711e-16
[22,] 9.779978e-16 2.663709e-16
[23,] -1.855197e-16 9.779978e-16
[24,] -9.815675e-16 -1.855197e-16
[25,] -6.532705e-16 -9.815675e-16
[26,] 1.160680e-15 -6.532705e-16
[27,] 3.416723e-16 1.160680e-15
[28,] -1.029776e-16 3.416723e-16
[29,] -9.873376e-16 -1.029776e-16
[30,] 3.656404e-15 -9.873376e-16
[31,] 4.855206e-16 3.656404e-15
[32,] -7.290991e-16 4.855206e-16
[33,] 3.572934e-16 -7.290991e-16
[34,] -2.844050e-16 3.572934e-16
[35,] -8.797703e-16 -2.844050e-16
[36,] 6.782148e-17 -8.797703e-16
[37,] 9.821130e-16 6.782148e-17
[38,] -2.249979e-16 9.821130e-16
[39,] -5.130897e-16 -2.249979e-16
[40,] 6.847937e-16 -5.130897e-16
[41,] -5.309716e-16 6.847937e-16
[42,] 2.857891e-15 -5.309716e-16
[43,] -8.885368e-17 2.857891e-15
[44,] 1.247543e-17 -8.885368e-17
[45,] -1.842456e-16 1.247543e-17
[46,] 2.148776e-16 -1.842456e-16
[47,] -1.412266e-16 2.148776e-16
[48,] 4.429224e-16 -1.412266e-16
[49,] -3.420742e-16 4.429224e-16
[50,] 9.508470e-17 -3.420742e-16
[51,] -1.175538e-16 9.508470e-17
[52,] 7.006458e-16 -1.175538e-16
[53,] -1.052947e-15 7.006458e-16
[54,] 2.457639e-15 -1.052947e-15
[55,] 3.797489e-16 2.457639e-15
[56,] -1.033025e-16 3.797489e-16
[57,] -1.123806e-15 -1.033025e-16
[58,] -8.155092e-16 -1.123806e-15
[59,] 7.007711e-17 -8.155092e-16
[60,] -2.215473e-15 7.007711e-17
[61,] -1.367206e-15 -2.215473e-15
[62,] -4.445849e-16 -1.367206e-15
[63,] -3.773029e-16 -4.445849e-16
[64,] -8.073063e-16 -3.773029e-16
[65,] -9.946488e-16 -8.073063e-16
[66,] 2.808463e-15 -9.946488e-16
[67,] -5.514664e-16 2.808463e-15
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.950635e-15 1.830497e-15
2 -4.887810e-16 1.950635e-15
3 6.498318e-16 -4.887810e-16
4 -1.591761e-15 6.498318e-16
5 3.085354e-15 -1.591761e-15
6 -1.433707e-14 3.085354e-15
7 7.582611e-16 -1.433707e-14
8 4.615507e-17 7.582611e-16
9 6.843872e-16 4.615507e-17
10 -9.296114e-17 6.843872e-16
11 1.136440e-15 -9.296114e-17
12 8.558004e-16 1.136440e-15
13 -5.701972e-16 8.558004e-16
14 -9.740048e-17 -5.701972e-16
15 1.644229e-17 -9.740048e-17
16 1.116605e-15 1.644229e-17
17 4.805509e-16 1.116605e-15
18 2.556671e-15 4.805509e-16
19 -9.832104e-16 2.556671e-15
20 7.737711e-16 -9.832104e-16
21 2.663709e-16 7.737711e-16
22 9.779978e-16 2.663709e-16
23 -1.855197e-16 9.779978e-16
24 -9.815675e-16 -1.855197e-16
25 -6.532705e-16 -9.815675e-16
26 1.160680e-15 -6.532705e-16
27 3.416723e-16 1.160680e-15
28 -1.029776e-16 3.416723e-16
29 -9.873376e-16 -1.029776e-16
30 3.656404e-15 -9.873376e-16
31 4.855206e-16 3.656404e-15
32 -7.290991e-16 4.855206e-16
33 3.572934e-16 -7.290991e-16
34 -2.844050e-16 3.572934e-16
35 -8.797703e-16 -2.844050e-16
36 6.782148e-17 -8.797703e-16
37 9.821130e-16 6.782148e-17
38 -2.249979e-16 9.821130e-16
39 -5.130897e-16 -2.249979e-16
40 6.847937e-16 -5.130897e-16
41 -5.309716e-16 6.847937e-16
42 2.857891e-15 -5.309716e-16
43 -8.885368e-17 2.857891e-15
44 1.247543e-17 -8.885368e-17
45 -1.842456e-16 1.247543e-17
46 2.148776e-16 -1.842456e-16
47 -1.412266e-16 2.148776e-16
48 4.429224e-16 -1.412266e-16
49 -3.420742e-16 4.429224e-16
50 9.508470e-17 -3.420742e-16
51 -1.175538e-16 9.508470e-17
52 7.006458e-16 -1.175538e-16
53 -1.052947e-15 7.006458e-16
54 2.457639e-15 -1.052947e-15
55 3.797489e-16 2.457639e-15
56 -1.033025e-16 3.797489e-16
57 -1.123806e-15 -1.033025e-16
58 -8.155092e-16 -1.123806e-15
59 7.007711e-17 -8.155092e-16
60 -2.215473e-15 7.007711e-17
61 -1.367206e-15 -2.215473e-15
62 -4.445849e-16 -1.367206e-15
63 -3.773029e-16 -4.445849e-16
64 -8.073063e-16 -3.773029e-16
65 -9.946488e-16 -8.073063e-16
66 2.808463e-15 -9.946488e-16
67 -5.514664e-16 2.808463e-15
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7b19t1261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nw7f1261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tklp1261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10u8jp1261315544.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11b83t1261315544.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12jtt31261315544.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/131hms1261315544.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/147vj61261315544.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/152cs71261315544.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16vgmc1261315544.tab")
+ }
>
> try(system("convert tmp/1xr811261315544.ps tmp/1xr811261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gyk71261315544.ps tmp/2gyk71261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xzhm1261315544.ps tmp/3xzhm1261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pugu1261315544.ps tmp/4pugu1261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/5i3m01261315544.ps tmp/5i3m01261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/62qho1261315544.ps tmp/62qho1261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/7b19t1261315544.ps tmp/7b19t1261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nw7f1261315544.ps tmp/8nw7f1261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tklp1261315544.ps tmp/9tklp1261315544.png",intern=TRUE))
character(0)
> try(system("convert tmp/10u8jp1261315544.ps tmp/10u8jp1261315544.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.512 1.538 3.188