R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(91.6
+ ,0
+ ,104.6
+ ,111.6
+ ,98.3
+ ,0
+ ,91.6
+ ,104.6
+ ,97.7
+ ,0
+ ,98.3
+ ,91.6
+ ,106.3
+ ,0
+ ,97.7
+ ,98.3
+ ,102.3
+ ,0
+ ,106.3
+ ,97.7
+ ,106.6
+ ,0
+ ,102.3
+ ,106.3
+ ,108.1
+ ,0
+ ,106.6
+ ,102.3
+ ,93.8
+ ,0
+ ,108.1
+ ,106.6
+ ,88.2
+ ,0
+ ,93.8
+ ,108.1
+ ,108.9
+ ,0
+ ,88.2
+ ,93.8
+ ,114.2
+ ,0
+ ,108.9
+ ,88.2
+ ,102.5
+ ,0
+ ,114.2
+ ,108.9
+ ,94.2
+ ,0
+ ,102.5
+ ,114.2
+ ,97.4
+ ,0
+ ,94.2
+ ,102.5
+ ,98.5
+ ,0
+ ,97.4
+ ,94.2
+ ,106.5
+ ,0
+ ,98.5
+ ,97.4
+ ,102.9
+ ,0
+ ,106.5
+ ,98.5
+ ,97.1
+ ,0
+ ,102.9
+ ,106.5
+ ,103.7
+ ,0
+ ,97.1
+ ,102.9
+ ,93.4
+ ,0
+ ,103.7
+ ,97.1
+ ,85.8
+ ,0
+ ,93.4
+ ,103.7
+ ,108.6
+ ,0
+ ,85.8
+ ,93.4
+ ,110.2
+ ,0
+ ,108.6
+ ,85.8
+ ,101.2
+ ,0
+ ,110.2
+ ,108.6
+ ,101.2
+ ,0
+ ,101.2
+ ,110.2
+ ,96.9
+ ,0
+ ,101.2
+ ,101.2
+ ,99.4
+ ,0
+ ,96.9
+ ,101.2
+ ,118.7
+ ,0
+ ,99.4
+ ,96.9
+ ,108.0
+ ,0
+ ,118.7
+ ,99.4
+ ,101.2
+ ,0
+ ,108.0
+ ,118.7
+ ,119.9
+ ,0
+ ,101.2
+ ,108.0
+ ,94.8
+ ,0
+ ,119.9
+ ,101.2
+ ,95.3
+ ,0
+ ,94.8
+ ,119.9
+ ,118.0
+ ,0
+ ,95.3
+ ,94.8
+ ,115.9
+ ,0
+ ,118.0
+ ,95.3
+ ,111.4
+ ,0
+ ,115.9
+ ,118.0
+ ,108.2
+ ,0
+ ,111.4
+ ,115.9
+ ,108.8
+ ,0
+ ,108.2
+ ,111.4
+ ,109.5
+ ,0
+ ,108.8
+ ,108.2
+ ,124.8
+ ,0
+ ,109.5
+ ,108.8
+ ,115.3
+ ,0
+ ,124.8
+ ,109.5
+ ,109.5
+ ,0
+ ,115.3
+ ,124.8
+ ,124.2
+ ,0
+ ,109.5
+ ,115.3
+ ,92.9
+ ,0
+ ,124.2
+ ,109.5
+ ,98.4
+ ,0
+ ,92.9
+ ,124.2
+ ,120.9
+ ,0
+ ,98.4
+ ,92.9
+ ,111.7
+ ,0
+ ,120.9
+ ,98.4
+ ,116.1
+ ,0
+ ,111.7
+ ,120.9
+ ,109.4
+ ,0
+ ,116.1
+ ,111.7
+ ,111.7
+ ,0
+ ,109.4
+ ,116.1
+ ,114.3
+ ,0
+ ,111.7
+ ,109.4
+ ,133.7
+ ,0
+ ,114.3
+ ,111.7
+ ,114.3
+ ,0
+ ,133.7
+ ,114.3
+ ,126.5
+ ,0
+ ,114.3
+ ,133.7
+ ,131.0
+ ,0
+ ,126.5
+ ,114.3
+ ,104.0
+ ,0
+ ,131.0
+ ,126.5
+ ,108.9
+ ,0
+ ,104.0
+ ,131.0
+ ,128.5
+ ,0
+ ,108.9
+ ,104.0
+ ,132.4
+ ,0
+ ,128.5
+ ,108.9
+ ,128.0
+ ,0
+ ,132.4
+ ,128.5
+ ,116.4
+ ,0
+ ,128.0
+ ,132.4
+ ,120.9
+ ,0
+ ,116.4
+ ,128.0
+ ,118.6
+ ,0
+ ,120.9
+ ,116.4
+ ,133.1
+ ,0
+ ,118.6
+ ,120.9
+ ,121.1
+ ,0
+ ,133.1
+ ,118.6
+ ,127.6
+ ,0
+ ,121.1
+ ,133.1
+ ,135.4
+ ,0
+ ,127.6
+ ,121.1
+ ,114.9
+ ,0
+ ,135.4
+ ,127.6
+ ,114.3
+ ,0
+ ,114.9
+ ,135.4
+ ,128.9
+ ,0
+ ,114.3
+ ,114.9
+ ,138.9
+ ,0
+ ,128.9
+ ,114.3
+ ,129.4
+ ,0
+ ,138.9
+ ,128.9
+ ,115.0
+ ,0
+ ,129.4
+ ,138.9
+ ,128.0
+ ,0
+ ,115.0
+ ,129.4
+ ,127.0
+ ,0
+ ,128.0
+ ,115.0
+ ,128.8
+ ,0
+ ,127.0
+ ,128.0
+ ,137.9
+ ,0
+ ,128.8
+ ,127.0
+ ,128.4
+ ,0
+ ,137.9
+ ,128.8
+ ,135.9
+ ,0
+ ,128.4
+ ,137.9
+ ,122.2
+ ,0
+ ,135.9
+ ,128.4
+ ,113.1
+ ,0
+ ,122.2
+ ,135.9
+ ,136.2
+ ,1
+ ,113.1
+ ,122.2
+ ,138.0
+ ,1
+ ,136.2
+ ,113.1
+ ,115.2
+ ,1
+ ,138.0
+ ,136.2
+ ,111.0
+ ,1
+ ,115.2
+ ,138.0
+ ,99.2
+ ,1
+ ,111.0
+ ,115.2
+ ,102.4
+ ,1
+ ,99.2
+ ,111.0
+ ,112.7
+ ,1
+ ,102.4
+ ,99.2
+ ,105.5
+ ,1
+ ,112.7
+ ,102.4
+ ,98.3
+ ,1
+ ,105.5
+ ,112.7
+ ,116.4
+ ,1
+ ,98.3
+ ,105.5
+ ,97.4
+ ,1
+ ,116.4
+ ,98.3
+ ,93.3
+ ,1
+ ,97.4
+ ,116.4
+ ,117.4
+ ,1
+ ,93.3
+ ,97.4)
+ ,dim=c(4
+ ,94)
+ ,dimnames=list(c('y'
+ ,'dummy'
+ ,'y1'
+ ,'y2')
+ ,1:94))
> y <- array(NA,dim=c(4,94),dimnames=list(c('y','dummy','y1','y2'),1:94))
> 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 dummy y1 y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 91.6 0 104.6 111.6 1 0 0 0 0 0 0 0 0 0 0 1
2 98.3 0 91.6 104.6 0 1 0 0 0 0 0 0 0 0 0 2
3 97.7 0 98.3 91.6 0 0 1 0 0 0 0 0 0 0 0 3
4 106.3 0 97.7 98.3 0 0 0 1 0 0 0 0 0 0 0 4
5 102.3 0 106.3 97.7 0 0 0 0 1 0 0 0 0 0 0 5
6 106.6 0 102.3 106.3 0 0 0 0 0 1 0 0 0 0 0 6
7 108.1 0 106.6 102.3 0 0 0 0 0 0 1 0 0 0 0 7
8 93.8 0 108.1 106.6 0 0 0 0 0 0 0 1 0 0 0 8
9 88.2 0 93.8 108.1 0 0 0 0 0 0 0 0 1 0 0 9
10 108.9 0 88.2 93.8 0 0 0 0 0 0 0 0 0 1 0 10
11 114.2 0 108.9 88.2 0 0 0 0 0 0 0 0 0 0 1 11
12 102.5 0 114.2 108.9 0 0 0 0 0 0 0 0 0 0 0 12
13 94.2 0 102.5 114.2 1 0 0 0 0 0 0 0 0 0 0 13
14 97.4 0 94.2 102.5 0 1 0 0 0 0 0 0 0 0 0 14
15 98.5 0 97.4 94.2 0 0 1 0 0 0 0 0 0 0 0 15
16 106.5 0 98.5 97.4 0 0 0 1 0 0 0 0 0 0 0 16
17 102.9 0 106.5 98.5 0 0 0 0 1 0 0 0 0 0 0 17
18 97.1 0 102.9 106.5 0 0 0 0 0 1 0 0 0 0 0 18
19 103.7 0 97.1 102.9 0 0 0 0 0 0 1 0 0 0 0 19
20 93.4 0 103.7 97.1 0 0 0 0 0 0 0 1 0 0 0 20
21 85.8 0 93.4 103.7 0 0 0 0 0 0 0 0 1 0 0 21
22 108.6 0 85.8 93.4 0 0 0 0 0 0 0 0 0 1 0 22
23 110.2 0 108.6 85.8 0 0 0 0 0 0 0 0 0 0 1 23
24 101.2 0 110.2 108.6 0 0 0 0 0 0 0 0 0 0 0 24
25 101.2 0 101.2 110.2 1 0 0 0 0 0 0 0 0 0 0 25
26 96.9 0 101.2 101.2 0 1 0 0 0 0 0 0 0 0 0 26
27 99.4 0 96.9 101.2 0 0 1 0 0 0 0 0 0 0 0 27
28 118.7 0 99.4 96.9 0 0 0 1 0 0 0 0 0 0 0 28
29 108.0 0 118.7 99.4 0 0 0 0 1 0 0 0 0 0 0 29
30 101.2 0 108.0 118.7 0 0 0 0 0 1 0 0 0 0 0 30
31 119.9 0 101.2 108.0 0 0 0 0 0 0 1 0 0 0 0 31
32 94.8 0 119.9 101.2 0 0 0 0 0 0 0 1 0 0 0 32
33 95.3 0 94.8 119.9 0 0 0 0 0 0 0 0 1 0 0 33
34 118.0 0 95.3 94.8 0 0 0 0 0 0 0 0 0 1 0 34
35 115.9 0 118.0 95.3 0 0 0 0 0 0 0 0 0 0 1 35
36 111.4 0 115.9 118.0 0 0 0 0 0 0 0 0 0 0 0 36
37 108.2 0 111.4 115.9 1 0 0 0 0 0 0 0 0 0 0 37
38 108.8 0 108.2 111.4 0 1 0 0 0 0 0 0 0 0 0 38
39 109.5 0 108.8 108.2 0 0 1 0 0 0 0 0 0 0 0 39
40 124.8 0 109.5 108.8 0 0 0 1 0 0 0 0 0 0 0 40
41 115.3 0 124.8 109.5 0 0 0 0 1 0 0 0 0 0 0 41
42 109.5 0 115.3 124.8 0 0 0 0 0 1 0 0 0 0 0 42
43 124.2 0 109.5 115.3 0 0 0 0 0 0 1 0 0 0 0 43
44 92.9 0 124.2 109.5 0 0 0 0 0 0 0 1 0 0 0 44
45 98.4 0 92.9 124.2 0 0 0 0 0 0 0 0 1 0 0 45
46 120.9 0 98.4 92.9 0 0 0 0 0 0 0 0 0 1 0 46
47 111.7 0 120.9 98.4 0 0 0 0 0 0 0 0 0 0 1 47
48 116.1 0 111.7 120.9 0 0 0 0 0 0 0 0 0 0 0 48
49 109.4 0 116.1 111.7 1 0 0 0 0 0 0 0 0 0 0 49
50 111.7 0 109.4 116.1 0 1 0 0 0 0 0 0 0 0 0 50
51 114.3 0 111.7 109.4 0 0 1 0 0 0 0 0 0 0 0 51
52 133.7 0 114.3 111.7 0 0 0 1 0 0 0 0 0 0 0 52
53 114.3 0 133.7 114.3 0 0 0 0 1 0 0 0 0 0 0 53
54 126.5 0 114.3 133.7 0 0 0 0 0 1 0 0 0 0 0 54
55 131.0 0 126.5 114.3 0 0 0 0 0 0 1 0 0 0 0 55
56 104.0 0 131.0 126.5 0 0 0 0 0 0 0 1 0 0 0 56
57 108.9 0 104.0 131.0 0 0 0 0 0 0 0 0 1 0 0 57
58 128.5 0 108.9 104.0 0 0 0 0 0 0 0 0 0 1 0 58
59 132.4 0 128.5 108.9 0 0 0 0 0 0 0 0 0 0 1 59
60 128.0 0 132.4 128.5 0 0 0 0 0 0 0 0 0 0 0 60
61 116.4 0 128.0 132.4 1 0 0 0 0 0 0 0 0 0 0 61
62 120.9 0 116.4 128.0 0 1 0 0 0 0 0 0 0 0 0 62
63 118.6 0 120.9 116.4 0 0 1 0 0 0 0 0 0 0 0 63
64 133.1 0 118.6 120.9 0 0 0 1 0 0 0 0 0 0 0 64
65 121.1 0 133.1 118.6 0 0 0 0 1 0 0 0 0 0 0 65
66 127.6 0 121.1 133.1 0 0 0 0 0 1 0 0 0 0 0 66
67 135.4 0 127.6 121.1 0 0 0 0 0 0 1 0 0 0 0 67
68 114.9 0 135.4 127.6 0 0 0 0 0 0 0 1 0 0 0 68
69 114.3 0 114.9 135.4 0 0 0 0 0 0 0 0 1 0 0 69
70 128.9 0 114.3 114.9 0 0 0 0 0 0 0 0 0 1 0 70
71 138.9 0 128.9 114.3 0 0 0 0 0 0 0 0 0 0 1 71
72 129.4 0 138.9 128.9 0 0 0 0 0 0 0 0 0 0 0 72
73 115.0 0 129.4 138.9 1 0 0 0 0 0 0 0 0 0 0 73
74 128.0 0 115.0 129.4 0 1 0 0 0 0 0 0 0 0 0 74
75 127.0 0 128.0 115.0 0 0 1 0 0 0 0 0 0 0 0 75
76 128.8 0 127.0 128.0 0 0 0 1 0 0 0 0 0 0 0 76
77 137.9 0 128.8 127.0 0 0 0 0 1 0 0 0 0 0 0 77
78 128.4 0 137.9 128.8 0 0 0 0 0 1 0 0 0 0 0 78
79 135.9 0 128.4 137.9 0 0 0 0 0 0 1 0 0 0 0 79
80 122.2 0 135.9 128.4 0 0 0 0 0 0 0 1 0 0 0 80
81 113.1 0 122.2 135.9 0 0 0 0 0 0 0 0 1 0 0 81
82 136.2 1 113.1 122.2 0 0 0 0 0 0 0 0 0 1 0 82
83 138.0 1 136.2 113.1 0 0 0 0 0 0 0 0 0 0 1 83
84 115.2 1 138.0 136.2 0 0 0 0 0 0 0 0 0 0 0 84
85 111.0 1 115.2 138.0 1 0 0 0 0 0 0 0 0 0 0 85
86 99.2 1 111.0 115.2 0 1 0 0 0 0 0 0 0 0 0 86
87 102.4 1 99.2 111.0 0 0 1 0 0 0 0 0 0 0 0 87
88 112.7 1 102.4 99.2 0 0 0 1 0 0 0 0 0 0 0 88
89 105.5 1 112.7 102.4 0 0 0 0 1 0 0 0 0 0 0 89
90 98.3 1 105.5 112.7 0 0 0 0 0 1 0 0 0 0 0 90
91 116.4 1 98.3 105.5 0 0 0 0 0 0 1 0 0 0 0 91
92 97.4 1 116.4 98.3 0 0 0 0 0 0 0 1 0 0 0 92
93 93.3 1 97.4 116.4 0 0 0 0 0 0 0 0 1 0 0 93
94 117.4 1 93.3 97.4 0 0 0 0 0 0 0 0 0 1 0 94
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy y1 y2 M1 M2
36.8473 -11.7370 0.1817 0.3982 -6.5794 -0.3858
M3 M4 M5 M6 M7 M8
2.9364 14.0524 4.1872 -1.0484 11.8171 -9.7204
M9 M10 M11 t
-12.2102 18.7224 16.9993 0.1864
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.059 -2.865 0.370 2.986 10.634
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36.84729 10.45983 3.523 0.000717 ***
dummy -11.73702 2.62168 -4.477 2.55e-05 ***
y1 0.18170 0.10082 1.802 0.075368 .
y2 0.39820 0.09382 4.244 5.99e-05 ***
M1 -6.57945 2.63894 -2.493 0.014777 *
M2 -0.38581 2.85053 -0.135 0.892686
M3 2.93644 2.86148 1.026 0.307972
M4 14.05240 2.82477 4.975 3.81e-06 ***
M5 4.18719 2.72053 1.539 0.127825
M6 -1.04841 2.66237 -0.394 0.694811
M7 11.81711 2.70270 4.372 3.76e-05 ***
M8 -9.72043 2.64382 -3.677 0.000432 ***
M9 -12.21018 3.36392 -3.630 0.000505 ***
M10 18.72244 3.26035 5.742 1.71e-07 ***
M11 16.99933 3.16941 5.364 8.09e-07 ***
t 0.18642 0.04651 4.008 0.000139 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.799 on 78 degrees of freedom
Multiple R-squared: 0.8894, Adjusted R-squared: 0.8682
F-statistic: 41.84 on 15 and 78 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,] 0.350948088 0.70189618 0.6490519
[2,] 0.261698533 0.52339707 0.7383015
[3,] 0.149225385 0.29845077 0.8507746
[4,] 0.082073379 0.16414676 0.9179266
[5,] 0.047265718 0.09453144 0.9527343
[6,] 0.023105150 0.04621030 0.9768949
[7,] 0.115319968 0.23063994 0.8846800
[8,] 0.071336656 0.14267331 0.9286633
[9,] 0.043110028 0.08622006 0.9568900
[10,] 0.181867309 0.36373462 0.8181327
[11,] 0.129328148 0.25865630 0.8706719
[12,] 0.106164615 0.21232923 0.8938354
[13,] 0.251529029 0.50305806 0.7484710
[14,] 0.192607751 0.38521550 0.8073922
[15,] 0.152293820 0.30458764 0.8477062
[16,] 0.141030077 0.28206015 0.8589699
[17,] 0.106006533 0.21201307 0.8939935
[18,] 0.086754603 0.17350921 0.9132454
[19,] 0.095567114 0.19113423 0.9044329
[20,] 0.072306482 0.14461296 0.9276935
[21,] 0.049828544 0.09965709 0.9501715
[22,] 0.043182933 0.08636587 0.9568171
[23,] 0.028666621 0.05733324 0.9713334
[24,] 0.022719867 0.04543973 0.9772801
[25,] 0.017690639 0.03538128 0.9823094
[26,] 0.040596120 0.08119224 0.9594039
[27,] 0.027210706 0.05442141 0.9727893
[28,] 0.019805215 0.03961043 0.9801948
[29,] 0.102976110 0.20595222 0.8970239
[30,] 0.082203240 0.16440648 0.9177968
[31,] 0.071228821 0.14245764 0.9287712
[32,] 0.050291643 0.10058329 0.9497084
[33,] 0.036006281 0.07201256 0.9639937
[34,] 0.070577293 0.14115459 0.9294227
[35,] 0.078091653 0.15618331 0.9219083
[36,] 0.105993067 0.21198613 0.8940069
[37,] 0.096771296 0.19354259 0.9032287
[38,] 0.156235173 0.31247035 0.8437648
[39,] 0.123630163 0.24726033 0.8763698
[40,] 0.091889392 0.18377878 0.9081106
[41,] 0.097680345 0.19536069 0.9023197
[42,] 0.089404447 0.17880889 0.9105956
[43,] 0.063783125 0.12756625 0.9362169
[44,] 0.042881002 0.08576200 0.9571190
[45,] 0.028812171 0.05762434 0.9711878
[46,] 0.021732638 0.04346528 0.9782674
[47,] 0.023148962 0.04629792 0.9768510
[48,] 0.021146396 0.04229279 0.9788536
[49,] 0.013890422 0.02778084 0.9861096
[50,] 0.010335031 0.02067006 0.9896650
[51,] 0.006206862 0.01241372 0.9937931
[52,] 0.020822844 0.04164569 0.9791772
[53,] 0.045969838 0.09193968 0.9540302
[54,] 0.026447725 0.05289545 0.9735523
[55,] 0.480976423 0.96195285 0.5190236
[56,] 0.376352545 0.75270509 0.6236475
[57,] 0.316415104 0.63283021 0.6835849
> postscript(file="/var/www/html/rcomp/tmp/1kdnf1262014207.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2tnof1262014207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b5zc1262014207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4t1zr1262014207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5022r1262014207.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 = 94
Frequency = 1
1 2 3 4 5 6
-2.2994377 3.1700402 3.0205704 -2.2407233 2.1143451 8.7658208
7 8 9 10 11 12
-1.9746315 3.0916743 1.7960494 -1.9111902 3.3941790 -0.6986642
13 14 15 16 17 18
-2.5901758 0.3968404 0.7117894 -4.0646987 0.1224513 -3.1598340
19 20 21 22 23 24
-7.1243721 5.0370705 -1.0161835 -3.8528179 -1.8326240 -3.3893880
25 26 27 28 29 30
4.0018435 -3.0944100 -3.3217521 5.9338756 0.4103086 -7.0815484
31 32 33 34 35 36
4.0628352 -0.3761214 -0.4583986 1.0265361 -3.8605189 -0.2051641
37 38 39 40 41 42
4.6417462 1.2350409 -0.4084033 3.2231092 0.3431116 -4.7739887
43 44 45 46 47 48
1.7108526 -8.5994943 -0.9624170 1.8828450 -12.0588690 1.8662129
49 50 51 52 53 54
4.4231911 -0.1915349 1.1498264 7.8591646 -6.4223926 6.6267412
55 56 57 58 59 60
3.5831183 -7.7414622 2.5759336 0.9179575 0.8421004 4.7416605
61 62 63 64 65 66
-1.2187987 0.7609761 -1.2462284 0.5774118 -3.4626242 4.4930914
67 68 69 70 71 72
2.8384930 -0.3159661 2.0063045 -6.2406077 2.8821466 2.5643216
73 74 75 76 77 78
-7.6984748 5.3208862 4.1841710 -10.3131010 8.5368236 1.7157573
79 80 81 82 83 84
-5.7336206 4.3376294 -2.9562164 7.8706028 10.6335858 -4.8789787
85 86 87 88 89 90
0.7401061 -7.5978388 -4.0899734 -0.9750381 -1.6420234 -6.5860398
91 92 93 94
2.6373251 4.5666699 -0.9850720 0.3066744
> postscript(file="/var/www/html/rcomp/tmp/6eeeo1262014208.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 = 94
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.2994377 NA
1 3.1700402 -2.2994377
2 3.0205704 3.1700402
3 -2.2407233 3.0205704
4 2.1143451 -2.2407233
5 8.7658208 2.1143451
6 -1.9746315 8.7658208
7 3.0916743 -1.9746315
8 1.7960494 3.0916743
9 -1.9111902 1.7960494
10 3.3941790 -1.9111902
11 -0.6986642 3.3941790
12 -2.5901758 -0.6986642
13 0.3968404 -2.5901758
14 0.7117894 0.3968404
15 -4.0646987 0.7117894
16 0.1224513 -4.0646987
17 -3.1598340 0.1224513
18 -7.1243721 -3.1598340
19 5.0370705 -7.1243721
20 -1.0161835 5.0370705
21 -3.8528179 -1.0161835
22 -1.8326240 -3.8528179
23 -3.3893880 -1.8326240
24 4.0018435 -3.3893880
25 -3.0944100 4.0018435
26 -3.3217521 -3.0944100
27 5.9338756 -3.3217521
28 0.4103086 5.9338756
29 -7.0815484 0.4103086
30 4.0628352 -7.0815484
31 -0.3761214 4.0628352
32 -0.4583986 -0.3761214
33 1.0265361 -0.4583986
34 -3.8605189 1.0265361
35 -0.2051641 -3.8605189
36 4.6417462 -0.2051641
37 1.2350409 4.6417462
38 -0.4084033 1.2350409
39 3.2231092 -0.4084033
40 0.3431116 3.2231092
41 -4.7739887 0.3431116
42 1.7108526 -4.7739887
43 -8.5994943 1.7108526
44 -0.9624170 -8.5994943
45 1.8828450 -0.9624170
46 -12.0588690 1.8828450
47 1.8662129 -12.0588690
48 4.4231911 1.8662129
49 -0.1915349 4.4231911
50 1.1498264 -0.1915349
51 7.8591646 1.1498264
52 -6.4223926 7.8591646
53 6.6267412 -6.4223926
54 3.5831183 6.6267412
55 -7.7414622 3.5831183
56 2.5759336 -7.7414622
57 0.9179575 2.5759336
58 0.8421004 0.9179575
59 4.7416605 0.8421004
60 -1.2187987 4.7416605
61 0.7609761 -1.2187987
62 -1.2462284 0.7609761
63 0.5774118 -1.2462284
64 -3.4626242 0.5774118
65 4.4930914 -3.4626242
66 2.8384930 4.4930914
67 -0.3159661 2.8384930
68 2.0063045 -0.3159661
69 -6.2406077 2.0063045
70 2.8821466 -6.2406077
71 2.5643216 2.8821466
72 -7.6984748 2.5643216
73 5.3208862 -7.6984748
74 4.1841710 5.3208862
75 -10.3131010 4.1841710
76 8.5368236 -10.3131010
77 1.7157573 8.5368236
78 -5.7336206 1.7157573
79 4.3376294 -5.7336206
80 -2.9562164 4.3376294
81 7.8706028 -2.9562164
82 10.6335858 7.8706028
83 -4.8789787 10.6335858
84 0.7401061 -4.8789787
85 -7.5978388 0.7401061
86 -4.0899734 -7.5978388
87 -0.9750381 -4.0899734
88 -1.6420234 -0.9750381
89 -6.5860398 -1.6420234
90 2.6373251 -6.5860398
91 4.5666699 2.6373251
92 -0.9850720 4.5666699
93 0.3066744 -0.9850720
94 NA 0.3066744
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.1700402 -2.2994377
[2,] 3.0205704 3.1700402
[3,] -2.2407233 3.0205704
[4,] 2.1143451 -2.2407233
[5,] 8.7658208 2.1143451
[6,] -1.9746315 8.7658208
[7,] 3.0916743 -1.9746315
[8,] 1.7960494 3.0916743
[9,] -1.9111902 1.7960494
[10,] 3.3941790 -1.9111902
[11,] -0.6986642 3.3941790
[12,] -2.5901758 -0.6986642
[13,] 0.3968404 -2.5901758
[14,] 0.7117894 0.3968404
[15,] -4.0646987 0.7117894
[16,] 0.1224513 -4.0646987
[17,] -3.1598340 0.1224513
[18,] -7.1243721 -3.1598340
[19,] 5.0370705 -7.1243721
[20,] -1.0161835 5.0370705
[21,] -3.8528179 -1.0161835
[22,] -1.8326240 -3.8528179
[23,] -3.3893880 -1.8326240
[24,] 4.0018435 -3.3893880
[25,] -3.0944100 4.0018435
[26,] -3.3217521 -3.0944100
[27,] 5.9338756 -3.3217521
[28,] 0.4103086 5.9338756
[29,] -7.0815484 0.4103086
[30,] 4.0628352 -7.0815484
[31,] -0.3761214 4.0628352
[32,] -0.4583986 -0.3761214
[33,] 1.0265361 -0.4583986
[34,] -3.8605189 1.0265361
[35,] -0.2051641 -3.8605189
[36,] 4.6417462 -0.2051641
[37,] 1.2350409 4.6417462
[38,] -0.4084033 1.2350409
[39,] 3.2231092 -0.4084033
[40,] 0.3431116 3.2231092
[41,] -4.7739887 0.3431116
[42,] 1.7108526 -4.7739887
[43,] -8.5994943 1.7108526
[44,] -0.9624170 -8.5994943
[45,] 1.8828450 -0.9624170
[46,] -12.0588690 1.8828450
[47,] 1.8662129 -12.0588690
[48,] 4.4231911 1.8662129
[49,] -0.1915349 4.4231911
[50,] 1.1498264 -0.1915349
[51,] 7.8591646 1.1498264
[52,] -6.4223926 7.8591646
[53,] 6.6267412 -6.4223926
[54,] 3.5831183 6.6267412
[55,] -7.7414622 3.5831183
[56,] 2.5759336 -7.7414622
[57,] 0.9179575 2.5759336
[58,] 0.8421004 0.9179575
[59,] 4.7416605 0.8421004
[60,] -1.2187987 4.7416605
[61,] 0.7609761 -1.2187987
[62,] -1.2462284 0.7609761
[63,] 0.5774118 -1.2462284
[64,] -3.4626242 0.5774118
[65,] 4.4930914 -3.4626242
[66,] 2.8384930 4.4930914
[67,] -0.3159661 2.8384930
[68,] 2.0063045 -0.3159661
[69,] -6.2406077 2.0063045
[70,] 2.8821466 -6.2406077
[71,] 2.5643216 2.8821466
[72,] -7.6984748 2.5643216
[73,] 5.3208862 -7.6984748
[74,] 4.1841710 5.3208862
[75,] -10.3131010 4.1841710
[76,] 8.5368236 -10.3131010
[77,] 1.7157573 8.5368236
[78,] -5.7336206 1.7157573
[79,] 4.3376294 -5.7336206
[80,] -2.9562164 4.3376294
[81,] 7.8706028 -2.9562164
[82,] 10.6335858 7.8706028
[83,] -4.8789787 10.6335858
[84,] 0.7401061 -4.8789787
[85,] -7.5978388 0.7401061
[86,] -4.0899734 -7.5978388
[87,] -0.9750381 -4.0899734
[88,] -1.6420234 -0.9750381
[89,] -6.5860398 -1.6420234
[90,] 2.6373251 -6.5860398
[91,] 4.5666699 2.6373251
[92,] -0.9850720 4.5666699
[93,] 0.3066744 -0.9850720
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.1700402 -2.2994377
2 3.0205704 3.1700402
3 -2.2407233 3.0205704
4 2.1143451 -2.2407233
5 8.7658208 2.1143451
6 -1.9746315 8.7658208
7 3.0916743 -1.9746315
8 1.7960494 3.0916743
9 -1.9111902 1.7960494
10 3.3941790 -1.9111902
11 -0.6986642 3.3941790
12 -2.5901758 -0.6986642
13 0.3968404 -2.5901758
14 0.7117894 0.3968404
15 -4.0646987 0.7117894
16 0.1224513 -4.0646987
17 -3.1598340 0.1224513
18 -7.1243721 -3.1598340
19 5.0370705 -7.1243721
20 -1.0161835 5.0370705
21 -3.8528179 -1.0161835
22 -1.8326240 -3.8528179
23 -3.3893880 -1.8326240
24 4.0018435 -3.3893880
25 -3.0944100 4.0018435
26 -3.3217521 -3.0944100
27 5.9338756 -3.3217521
28 0.4103086 5.9338756
29 -7.0815484 0.4103086
30 4.0628352 -7.0815484
31 -0.3761214 4.0628352
32 -0.4583986 -0.3761214
33 1.0265361 -0.4583986
34 -3.8605189 1.0265361
35 -0.2051641 -3.8605189
36 4.6417462 -0.2051641
37 1.2350409 4.6417462
38 -0.4084033 1.2350409
39 3.2231092 -0.4084033
40 0.3431116 3.2231092
41 -4.7739887 0.3431116
42 1.7108526 -4.7739887
43 -8.5994943 1.7108526
44 -0.9624170 -8.5994943
45 1.8828450 -0.9624170
46 -12.0588690 1.8828450
47 1.8662129 -12.0588690
48 4.4231911 1.8662129
49 -0.1915349 4.4231911
50 1.1498264 -0.1915349
51 7.8591646 1.1498264
52 -6.4223926 7.8591646
53 6.6267412 -6.4223926
54 3.5831183 6.6267412
55 -7.7414622 3.5831183
56 2.5759336 -7.7414622
57 0.9179575 2.5759336
58 0.8421004 0.9179575
59 4.7416605 0.8421004
60 -1.2187987 4.7416605
61 0.7609761 -1.2187987
62 -1.2462284 0.7609761
63 0.5774118 -1.2462284
64 -3.4626242 0.5774118
65 4.4930914 -3.4626242
66 2.8384930 4.4930914
67 -0.3159661 2.8384930
68 2.0063045 -0.3159661
69 -6.2406077 2.0063045
70 2.8821466 -6.2406077
71 2.5643216 2.8821466
72 -7.6984748 2.5643216
73 5.3208862 -7.6984748
74 4.1841710 5.3208862
75 -10.3131010 4.1841710
76 8.5368236 -10.3131010
77 1.7157573 8.5368236
78 -5.7336206 1.7157573
79 4.3376294 -5.7336206
80 -2.9562164 4.3376294
81 7.8706028 -2.9562164
82 10.6335858 7.8706028
83 -4.8789787 10.6335858
84 0.7401061 -4.8789787
85 -7.5978388 0.7401061
86 -4.0899734 -7.5978388
87 -0.9750381 -4.0899734
88 -1.6420234 -0.9750381
89 -6.5860398 -1.6420234
90 2.6373251 -6.5860398
91 4.5666699 2.6373251
92 -0.9850720 4.5666699
93 0.3066744 -0.9850720
> 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/7c2a41262014208.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8484u1262014208.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9lzck1262014208.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qh5h1262014208.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11oag61262014208.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/12ea2a1262014208.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/13636t1262014208.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/14raah1262014208.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/1521rg1262014208.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/16y5sy1262014208.tab")
+ }
>
> try(system("convert tmp/1kdnf1262014207.ps tmp/1kdnf1262014207.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tnof1262014207.ps tmp/2tnof1262014207.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b5zc1262014207.ps tmp/3b5zc1262014207.png",intern=TRUE))
character(0)
> try(system("convert tmp/4t1zr1262014207.ps tmp/4t1zr1262014207.png",intern=TRUE))
character(0)
> try(system("convert tmp/5022r1262014207.ps tmp/5022r1262014207.png",intern=TRUE))
character(0)
> try(system("convert tmp/6eeeo1262014208.ps tmp/6eeeo1262014208.png",intern=TRUE))
character(0)
> try(system("convert tmp/7c2a41262014208.ps tmp/7c2a41262014208.png",intern=TRUE))
character(0)
> try(system("convert tmp/8484u1262014208.ps tmp/8484u1262014208.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lzck1262014208.ps tmp/9lzck1262014208.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qh5h1262014208.ps tmp/10qh5h1262014208.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.014 1.651 6.803