R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(9.2,0,9.1,0,9.1,0,9.1,0,9.1,0,9.2,0,9.3,0,9.3,0,9.3,0,9.3,0,9.3,0,9.4,0,9.4,0,9.4,0,9.5,0,9.5,0,9.4,0,9.4,0,9.3,0,9.4,0,9.4,0,9.2,0,9.1,0,9.1,0,9.1,0,9.1,0,9,0,8.9,0,8.8,0,8.7,0,8.5,0,8.3,0,8.1,0,7.8,0,7.6,0,7.5,0,7.4,0,7.3,0,7.1,0,6.9,0,6.8,0,6.8,0,6.8,0,6.9,0,6.7,0,6.6,0,6.5,0,6.4,0,6.3,0,6.3,0,6.3,0,6.5,0,6.6,0,6.5,0,6.4,0,6.5,0,6.7,0,7.1,0,7.1,0,7.2,1,7.2,1,7.3,1,7.3,1,7.3,1,7.4,1,7.4,1,7.6,1,7.6,1,7.6,1,7.7,1,7.8,1,7.9,1,8.1,1,8.1,1,8.1,1,8.2,1,8.2,1,8.2,1,8.2,1,8.2,1,8.2,1,8.3,1,8.3,1,8.4,1,8.4,1,8.4,1,8.3,1,8,1,8,1,8.2,1,8.6,1,8.7,1,8.7,1,8.5,1,8.4,1,8.4,1,8.4,1,8.5,1,8.5,1,8.5,1,8.5,1,8.5,1,8.4,1,8.4,1,8.4,1,8.5,1,8.5,1,8.6,1,8.6,1,8.6,1,8.5,1,8.4,1,8.4,1,8.3,1,8.2,1,8.1,1,8.2,1,8.1,1,8,1,7.9,1,7.8,1,7.7,1,7.7,1,7.9,1,7.8,1,7.6,1,7.4,1,7.3,1,7.1,1,7.1,1,7,1,7,1),dim=c(2,132),dimnames=list(c('Werkloosheid','SabenaFailliet'),1:132))
> y <- array(NA,dim=c(2,132),dimnames=list(c('Werkloosheid','SabenaFailliet'),1:132))
> 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
Werkloosheid SabenaFailliet M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.2 0 1 0 0 0 0 0 0 0 0 0 0 1
2 9.1 0 0 1 0 0 0 0 0 0 0 0 0 2
3 9.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 9.1 0 0 0 0 1 0 0 0 0 0 0 0 4
5 9.1 0 0 0 0 0 1 0 0 0 0 0 0 5
6 9.2 0 0 0 0 0 0 1 0 0 0 0 0 6
7 9.3 0 0 0 0 0 0 0 1 0 0 0 0 7
8 9.3 0 0 0 0 0 0 0 0 1 0 0 0 8
9 9.3 0 0 0 0 0 0 0 0 0 1 0 0 9
10 9.3 0 0 0 0 0 0 0 0 0 0 1 0 10
11 9.3 0 0 0 0 0 0 0 0 0 0 0 1 11
12 9.4 0 0 0 0 0 0 0 0 0 0 0 0 12
13 9.4 0 1 0 0 0 0 0 0 0 0 0 0 13
14 9.4 0 0 1 0 0 0 0 0 0 0 0 0 14
15 9.5 0 0 0 1 0 0 0 0 0 0 0 0 15
16 9.5 0 0 0 0 1 0 0 0 0 0 0 0 16
17 9.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 9.4 0 0 0 0 0 0 1 0 0 0 0 0 18
19 9.3 0 0 0 0 0 0 0 1 0 0 0 0 19
20 9.4 0 0 0 0 0 0 0 0 1 0 0 0 20
21 9.4 0 0 0 0 0 0 0 0 0 1 0 0 21
22 9.2 0 0 0 0 0 0 0 0 0 0 1 0 22
23 9.1 0 0 0 0 0 0 0 0 0 0 0 1 23
24 9.1 0 0 0 0 0 0 0 0 0 0 0 0 24
25 9.1 0 1 0 0 0 0 0 0 0 0 0 0 25
26 9.1 0 0 1 0 0 0 0 0 0 0 0 0 26
27 9.0 0 0 0 1 0 0 0 0 0 0 0 0 27
28 8.9 0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.8 0 0 0 0 0 1 0 0 0 0 0 0 29
30 8.7 0 0 0 0 0 0 1 0 0 0 0 0 30
31 8.5 0 0 0 0 0 0 0 1 0 0 0 0 31
32 8.3 0 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.8 0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.6 0 0 0 0 0 0 0 0 0 0 0 1 35
36 7.5 0 0 0 0 0 0 0 0 0 0 0 0 36
37 7.4 0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.3 0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.1 0 0 0 1 0 0 0 0 0 0 0 0 39
40 6.9 0 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 0 0 0 0 0 1 0 0 0 0 0 0 41
42 6.8 0 0 0 0 0 0 1 0 0 0 0 0 42
43 6.8 0 0 0 0 0 0 0 1 0 0 0 0 43
44 6.9 0 0 0 0 0 0 0 0 1 0 0 0 44
45 6.7 0 0 0 0 0 0 0 0 0 1 0 0 45
46 6.6 0 0 0 0 0 0 0 0 0 0 1 0 46
47 6.5 0 0 0 0 0 0 0 0 0 0 0 1 47
48 6.4 0 0 0 0 0 0 0 0 0 0 0 0 48
49 6.3 0 1 0 0 0 0 0 0 0 0 0 0 49
50 6.3 0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.3 0 0 0 1 0 0 0 0 0 0 0 0 51
52 6.5 0 0 0 0 1 0 0 0 0 0 0 0 52
53 6.6 0 0 0 0 0 1 0 0 0 0 0 0 53
54 6.5 0 0 0 0 0 0 1 0 0 0 0 0 54
55 6.4 0 0 0 0 0 0 0 1 0 0 0 0 55
56 6.5 0 0 0 0 0 0 0 0 1 0 0 0 56
57 6.7 0 0 0 0 0 0 0 0 0 1 0 0 57
58 7.1 0 0 0 0 0 0 0 0 0 0 1 0 58
59 7.1 0 0 0 0 0 0 0 0 0 0 0 1 59
60 7.2 1 0 0 0 0 0 0 0 0 0 0 0 60
61 7.2 1 1 0 0 0 0 0 0 0 0 0 0 61
62 7.3 1 0 1 0 0 0 0 0 0 0 0 0 62
63 7.3 1 0 0 1 0 0 0 0 0 0 0 0 63
64 7.3 1 0 0 0 1 0 0 0 0 0 0 0 64
65 7.4 1 0 0 0 0 1 0 0 0 0 0 0 65
66 7.4 1 0 0 0 0 0 1 0 0 0 0 0 66
67 7.6 1 0 0 0 0 0 0 1 0 0 0 0 67
68 7.6 1 0 0 0 0 0 0 0 1 0 0 0 68
69 7.6 1 0 0 0 0 0 0 0 0 1 0 0 69
70 7.7 1 0 0 0 0 0 0 0 0 0 1 0 70
71 7.8 1 0 0 0 0 0 0 0 0 0 0 1 71
72 7.9 1 0 0 0 0 0 0 0 0 0 0 0 72
73 8.1 1 1 0 0 0 0 0 0 0 0 0 0 73
74 8.1 1 0 1 0 0 0 0 0 0 0 0 0 74
75 8.1 1 0 0 1 0 0 0 0 0 0 0 0 75
76 8.2 1 0 0 0 1 0 0 0 0 0 0 0 76
77 8.2 1 0 0 0 0 1 0 0 0 0 0 0 77
78 8.2 1 0 0 0 0 0 1 0 0 0 0 0 78
79 8.2 1 0 0 0 0 0 0 1 0 0 0 0 79
80 8.2 1 0 0 0 0 0 0 0 1 0 0 0 80
81 8.2 1 0 0 0 0 0 0 0 0 1 0 0 81
82 8.3 1 0 0 0 0 0 0 0 0 0 1 0 82
83 8.3 1 0 0 0 0 0 0 0 0 0 0 1 83
84 8.4 1 0 0 0 0 0 0 0 0 0 0 0 84
85 8.4 1 1 0 0 0 0 0 0 0 0 0 0 85
86 8.4 1 0 1 0 0 0 0 0 0 0 0 0 86
87 8.3 1 0 0 1 0 0 0 0 0 0 0 0 87
88 8.0 1 0 0 0 1 0 0 0 0 0 0 0 88
89 8.0 1 0 0 0 0 1 0 0 0 0 0 0 89
90 8.2 1 0 0 0 0 0 1 0 0 0 0 0 90
91 8.6 1 0 0 0 0 0 0 1 0 0 0 0 91
92 8.7 1 0 0 0 0 0 0 0 1 0 0 0 92
93 8.7 1 0 0 0 0 0 0 0 0 1 0 0 93
94 8.5 1 0 0 0 0 0 0 0 0 0 1 0 94
95 8.4 1 0 0 0 0 0 0 0 0 0 0 1 95
96 8.4 1 0 0 0 0 0 0 0 0 0 0 0 96
97 8.4 1 1 0 0 0 0 0 0 0 0 0 0 97
98 8.5 1 0 1 0 0 0 0 0 0 0 0 0 98
99 8.5 1 0 0 1 0 0 0 0 0 0 0 0 99
100 8.5 1 0 0 0 1 0 0 0 0 0 0 0 100
101 8.5 1 0 0 0 0 1 0 0 0 0 0 0 101
102 8.5 1 0 0 0 0 0 1 0 0 0 0 0 102
103 8.4 1 0 0 0 0 0 0 1 0 0 0 0 103
104 8.4 1 0 0 0 0 0 0 0 1 0 0 0 104
105 8.4 1 0 0 0 0 0 0 0 0 1 0 0 105
106 8.5 1 0 0 0 0 0 0 0 0 0 1 0 106
107 8.5 1 0 0 0 0 0 0 0 0 0 0 1 107
108 8.6 1 0 0 0 0 0 0 0 0 0 0 0 108
109 8.6 1 1 0 0 0 0 0 0 0 0 0 0 109
110 8.6 1 0 1 0 0 0 0 0 0 0 0 0 110
111 8.5 1 0 0 1 0 0 0 0 0 0 0 0 111
112 8.4 1 0 0 0 1 0 0 0 0 0 0 0 112
113 8.4 1 0 0 0 0 1 0 0 0 0 0 0 113
114 8.3 1 0 0 0 0 0 1 0 0 0 0 0 114
115 8.2 1 0 0 0 0 0 0 1 0 0 0 0 115
116 8.1 1 0 0 0 0 0 0 0 1 0 0 0 116
117 8.2 1 0 0 0 0 0 0 0 0 1 0 0 117
118 8.1 1 0 0 0 0 0 0 0 0 0 1 0 118
119 8.0 1 0 0 0 0 0 0 0 0 0 0 1 119
120 7.9 1 0 0 0 0 0 0 0 0 0 0 0 120
121 7.8 1 1 0 0 0 0 0 0 0 0 0 0 121
122 7.7 1 0 1 0 0 0 0 0 0 0 0 0 122
123 7.7 1 0 0 1 0 0 0 0 0 0 0 0 123
124 7.9 1 0 0 0 1 0 0 0 0 0 0 0 124
125 7.8 1 0 0 0 0 1 0 0 0 0 0 0 125
126 7.6 1 0 0 0 0 0 1 0 0 0 0 0 126
127 7.4 1 0 0 0 0 0 0 1 0 0 0 0 127
128 7.3 1 0 0 0 0 0 0 0 1 0 0 0 128
129 7.1 1 0 0 0 0 0 0 0 0 1 0 0 129
130 7.1 1 0 0 0 0 0 0 0 0 0 1 0 130
131 7.0 1 0 0 0 0 0 0 0 0 0 0 1 131
132 7.0 1 0 0 0 0 0 0 0 0 0 0 0 132
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) SabenaFailliet M1 M2 M3
8.68377 1.37000 0.07502 0.08778 0.07328
M4 M5 M6 M7 M8
0.07695 0.08063 0.08431 0.09707 0.11893
M9 M10 M11 t
0.11352 0.11719 0.08451 -0.02186
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.5955 -0.5575 0.2206 0.5904 1.0890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.683772 0.283866 30.591 < 2e-16 ***
SabenaFailliet 1.369997 0.282594 4.848 3.84e-06 ***
M1 0.075017 0.347300 0.216 0.829
M2 0.087784 0.347123 0.253 0.801
M3 0.073278 0.346985 0.211 0.833
M4 0.076955 0.346887 0.222 0.825
M5 0.080631 0.346827 0.232 0.817
M6 0.084307 0.346808 0.243 0.808
M7 0.097074 0.346827 0.280 0.780
M8 0.118932 0.346887 0.343 0.732
M9 0.113517 0.346985 0.327 0.744
M10 0.117193 0.347123 0.338 0.736
M11 0.084505 0.347300 0.243 0.808
t -0.021858 0.003698 -5.911 3.38e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8128 on 118 degrees of freedom
Multiple R-squared: 0.2337, Adjusted R-squared: 0.1493
F-statistic: 2.769 on 13 and 118 DF, p-value: 0.001807
> 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,] 9.293832e-04 1.858766e-03 9.990706e-01
[2,] 1.443965e-04 2.887931e-04 9.998556e-01
[3,] 1.763423e-04 3.526847e-04 9.998237e-01
[4,] 4.150124e-05 8.300248e-05 9.999585e-01
[5,] 9.527195e-06 1.905439e-05 9.999905e-01
[6,] 9.495985e-06 1.899197e-05 9.999905e-01
[7,] 1.297818e-05 2.595636e-05 9.999870e-01
[8,] 2.290220e-05 4.580439e-05 9.999771e-01
[9,] 2.487854e-05 4.975708e-05 9.999751e-01
[10,] 1.635429e-05 3.270858e-05 9.999836e-01
[11,] 1.984816e-05 3.969632e-05 9.999802e-01
[12,] 3.273911e-05 6.547822e-05 9.999673e-01
[13,] 5.496125e-05 1.099225e-04 9.999450e-01
[14,] 1.586921e-04 3.173843e-04 9.998413e-01
[15,] 7.685445e-04 1.537089e-03 9.992315e-01
[16,] 5.450679e-03 1.090136e-02 9.945493e-01
[17,] 3.059267e-02 6.118535e-02 9.694073e-01
[18,] 1.118046e-01 2.236091e-01 8.881954e-01
[19,] 2.674052e-01 5.348104e-01 7.325948e-01
[20,] 4.888315e-01 9.776631e-01 5.111685e-01
[21,] 6.238692e-01 7.522616e-01 3.761308e-01
[22,] 7.175750e-01 5.648500e-01 2.824250e-01
[23,] 8.035517e-01 3.928965e-01 1.964483e-01
[24,] 8.687635e-01 2.624731e-01 1.312365e-01
[25,] 9.043139e-01 1.913722e-01 9.568609e-02
[26,] 9.234176e-01 1.531647e-01 7.658237e-02
[27,] 9.297554e-01 1.404891e-01 7.024457e-02
[28,] 9.272509e-01 1.454983e-01 7.274913e-02
[29,] 9.262987e-01 1.474025e-01 7.370126e-02
[30,] 9.191854e-01 1.616291e-01 8.081457e-02
[31,] 9.099309e-01 1.801382e-01 9.006911e-02
[32,] 9.028473e-01 1.943054e-01 9.715269e-02
[33,] 8.884789e-01 2.230422e-01 1.115211e-01
[34,] 8.707951e-01 2.584097e-01 1.292049e-01
[35,] 8.490236e-01 3.019528e-01 1.509764e-01
[36,] 8.147219e-01 3.705561e-01 1.852781e-01
[37,] 7.765068e-01 4.469863e-01 2.234932e-01
[38,] 7.350481e-01 5.299038e-01 2.649519e-01
[39,] 6.988051e-01 6.023897e-01 3.011949e-01
[40,] 6.611573e-01 6.776854e-01 3.388427e-01
[41,] 6.295519e-01 7.408963e-01 3.704481e-01
[42,] 6.462093e-01 7.075814e-01 3.537907e-01
[43,] 6.677561e-01 6.644879e-01 3.322439e-01
[44,] 6.638042e-01 6.723917e-01 3.361958e-01
[45,] 6.990435e-01 6.019130e-01 3.009565e-01
[46,] 7.299538e-01 5.400925e-01 2.700462e-01
[47,] 7.630947e-01 4.738106e-01 2.369053e-01
[48,] 8.012612e-01 3.974777e-01 1.987388e-01
[49,] 8.321040e-01 3.357921e-01 1.678960e-01
[50,] 8.656755e-01 2.686491e-01 1.343245e-01
[51,] 8.871649e-01 2.256702e-01 1.128351e-01
[52,] 9.076033e-01 1.847933e-01 9.239667e-02
[53,] 9.288682e-01 1.422637e-01 7.113185e-02
[54,] 9.446497e-01 1.107006e-01 5.535031e-02
[55,] 9.549115e-01 9.017690e-02 4.508845e-02
[56,] 9.705633e-01 5.887335e-02 2.943667e-02
[57,] 9.855022e-01 2.899559e-02 1.449780e-02
[58,] 9.927767e-01 1.444662e-02 7.223310e-03
[59,] 9.962063e-01 7.587493e-03 3.793747e-03
[60,] 9.977357e-01 4.528579e-03 2.264290e-03
[61,] 9.985534e-01 2.893287e-03 1.446643e-03
[62,] 9.990225e-01 1.954986e-03 9.774930e-04
[63,] 9.993381e-01 1.323891e-03 6.619456e-04
[64,] 9.995282e-01 9.435280e-04 4.717640e-04
[65,] 9.996646e-01 6.707021e-04 3.353511e-04
[66,] 9.997201e-01 5.598890e-04 2.799445e-04
[67,] 9.997449e-01 5.102889e-04 2.551445e-04
[68,] 9.998093e-01 3.813020e-04 1.906510e-04
[69,] 9.998812e-01 2.376863e-04 1.188431e-04
[70,] 9.999229e-01 1.542545e-04 7.712724e-05
[71,] 9.999540e-01 9.195466e-05 4.597733e-05
[72,] 9.999920e-01 1.603404e-05 8.017020e-06
[73,] 9.999994e-01 1.290727e-06 6.453636e-07
[74,] 9.999999e-01 2.246265e-07 1.123133e-07
[75,] 9.999999e-01 2.463395e-07 1.231697e-07
[76,] 9.999998e-01 3.637705e-07 1.818852e-07
[77,] 9.999997e-01 5.861351e-07 2.930675e-07
[78,] 9.999996e-01 7.749701e-07 3.874851e-07
[79,] 9.999996e-01 8.976004e-07 4.488002e-07
[80,] 9.999996e-01 8.029252e-07 4.014626e-07
[81,] 9.999998e-01 4.048353e-07 2.024177e-07
[82,] 9.999998e-01 3.258470e-07 1.629235e-07
[83,] 9.999999e-01 2.806213e-07 1.403106e-07
[84,] 9.999999e-01 1.362910e-07 6.814548e-08
[85,] 1.000000e+00 5.406562e-08 2.703281e-08
[86,] 1.000000e+00 3.869823e-08 1.934912e-08
[87,] 1.000000e+00 2.505410e-08 1.252705e-08
[88,] 1.000000e+00 2.139849e-08 1.069924e-08
[89,] 1.000000e+00 1.415009e-08 7.075046e-09
[90,] 1.000000e+00 2.272881e-08 1.136440e-08
[91,] 1.000000e+00 6.156403e-08 3.078202e-08
[92,] 9.999998e-01 3.334935e-07 1.667467e-07
[93,] 9.999990e-01 2.034596e-06 1.017298e-06
[94,] 9.999943e-01 1.143854e-05 5.719269e-06
[95,] 9.999662e-01 6.769940e-05 3.384970e-05
[96,] 9.999501e-01 9.987295e-05 4.993647e-05
[97,] 9.999021e-01 1.958853e-04 9.794266e-05
[98,] 9.996981e-01 6.037257e-04 3.018629e-04
[99,] 9.982720e-01 3.455942e-03 1.727971e-03
> postscript(file="/var/www/html/rcomp/tmp/155pm1229950569.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2x7o71229950569.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3unkq1229950569.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43gqw1229950569.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cm7n1229950569.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 = 132
Frequency = 1
1 2 3 4 5 6
0.46306855 0.37215946 0.40852309 0.42670491 0.44488673 0.56306855
7 8 9 10 11 12
0.67215946 0.67215946 0.69943218 0.71761400 0.77215946 0.97852284
13 14 15 16 17 18
0.92536363 0.93445454 1.07081817 1.08899999 1.00718181 1.02536363
19 20 21 22 23 24
0.93445454 1.03445454 1.06172726 0.87990908 0.83445454 0.94081792
25 26 27 28 29 30
0.88765871 0.89674962 0.83311326 0.75129507 0.66947689 0.58765871
31 32 33 34 35 36
0.39674962 0.19674962 0.02402235 -0.25779584 -0.40325038 -0.39688699
37 38 39 40 41 42
-0.55004621 -0.64095530 -0.80459166 -0.98640984 -1.06822803 -1.05004621
43 44 45 46 47 48
-1.04095530 -0.94095530 -1.11368257 -1.19550075 -1.24095530 -1.23459191
49 50 51 52 53 54
-1.38775113 -1.37866022 -1.34229658 -1.12411476 -1.00593294 -1.08775113
55 56 57 58 59 60
-1.17866022 -1.07866022 -0.85138749 -0.43320567 -0.37866022 -1.54229408
61 62 63 64 65 66
-1.59545330 -1.48636239 -1.44999875 -1.43181693 -1.31363511 -1.29545330
67 68 69 70 71 72
-1.08636239 -1.08636239 -1.05908966 -0.94090784 -0.78636239 -0.57999900
73 74 75 76 77 78
-0.43315821 -0.42406731 -0.38770367 -0.26952185 -0.25134003 -0.23315821
79 80 81 82 83 84
-0.22406731 -0.22406731 -0.19679458 -0.07861276 -0.02406731 0.18229608
85 86 87 88 89 90
0.12913687 0.13822778 0.07459141 -0.20722677 -0.18904495 0.02913687
91 92 93 94 95 96
0.43822778 0.53822778 0.56550050 0.38368232 0.33822778 0.44459116
97 98 99 100 101 102
0.39143195 0.50052286 0.53688649 0.55506831 0.57325013 0.59143195
103 104 105 106 107 108
0.50052286 0.50052286 0.52779559 0.64597740 0.70052286 0.90688624
109 110 111 112 113 114
0.85372703 0.86281794 0.79918158 0.71736339 0.73554521 0.65372703
115 116 117 118 119 120
0.56281794 0.46281794 0.59009067 0.50827249 0.46281794 0.46918133
121 122 123 124 125 126
0.31602211 0.22511302 0.26147666 0.47965848 0.39784030 0.21602211
127 128 129 130 131 132
0.02511302 -0.07488698 -0.24761425 -0.22943243 -0.27488698 -0.16852359
> postscript(file="/var/www/html/rcomp/tmp/6odtf1229950569.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 = 132
Frequency = 1
lag(myerror, k = 1) myerror
0 0.46306855 NA
1 0.37215946 0.46306855
2 0.40852309 0.37215946
3 0.42670491 0.40852309
4 0.44488673 0.42670491
5 0.56306855 0.44488673
6 0.67215946 0.56306855
7 0.67215946 0.67215946
8 0.69943218 0.67215946
9 0.71761400 0.69943218
10 0.77215946 0.71761400
11 0.97852284 0.77215946
12 0.92536363 0.97852284
13 0.93445454 0.92536363
14 1.07081817 0.93445454
15 1.08899999 1.07081817
16 1.00718181 1.08899999
17 1.02536363 1.00718181
18 0.93445454 1.02536363
19 1.03445454 0.93445454
20 1.06172726 1.03445454
21 0.87990908 1.06172726
22 0.83445454 0.87990908
23 0.94081792 0.83445454
24 0.88765871 0.94081792
25 0.89674962 0.88765871
26 0.83311326 0.89674962
27 0.75129507 0.83311326
28 0.66947689 0.75129507
29 0.58765871 0.66947689
30 0.39674962 0.58765871
31 0.19674962 0.39674962
32 0.02402235 0.19674962
33 -0.25779584 0.02402235
34 -0.40325038 -0.25779584
35 -0.39688699 -0.40325038
36 -0.55004621 -0.39688699
37 -0.64095530 -0.55004621
38 -0.80459166 -0.64095530
39 -0.98640984 -0.80459166
40 -1.06822803 -0.98640984
41 -1.05004621 -1.06822803
42 -1.04095530 -1.05004621
43 -0.94095530 -1.04095530
44 -1.11368257 -0.94095530
45 -1.19550075 -1.11368257
46 -1.24095530 -1.19550075
47 -1.23459191 -1.24095530
48 -1.38775113 -1.23459191
49 -1.37866022 -1.38775113
50 -1.34229658 -1.37866022
51 -1.12411476 -1.34229658
52 -1.00593294 -1.12411476
53 -1.08775113 -1.00593294
54 -1.17866022 -1.08775113
55 -1.07866022 -1.17866022
56 -0.85138749 -1.07866022
57 -0.43320567 -0.85138749
58 -0.37866022 -0.43320567
59 -1.54229408 -0.37866022
60 -1.59545330 -1.54229408
61 -1.48636239 -1.59545330
62 -1.44999875 -1.48636239
63 -1.43181693 -1.44999875
64 -1.31363511 -1.43181693
65 -1.29545330 -1.31363511
66 -1.08636239 -1.29545330
67 -1.08636239 -1.08636239
68 -1.05908966 -1.08636239
69 -0.94090784 -1.05908966
70 -0.78636239 -0.94090784
71 -0.57999900 -0.78636239
72 -0.43315821 -0.57999900
73 -0.42406731 -0.43315821
74 -0.38770367 -0.42406731
75 -0.26952185 -0.38770367
76 -0.25134003 -0.26952185
77 -0.23315821 -0.25134003
78 -0.22406731 -0.23315821
79 -0.22406731 -0.22406731
80 -0.19679458 -0.22406731
81 -0.07861276 -0.19679458
82 -0.02406731 -0.07861276
83 0.18229608 -0.02406731
84 0.12913687 0.18229608
85 0.13822778 0.12913687
86 0.07459141 0.13822778
87 -0.20722677 0.07459141
88 -0.18904495 -0.20722677
89 0.02913687 -0.18904495
90 0.43822778 0.02913687
91 0.53822778 0.43822778
92 0.56550050 0.53822778
93 0.38368232 0.56550050
94 0.33822778 0.38368232
95 0.44459116 0.33822778
96 0.39143195 0.44459116
97 0.50052286 0.39143195
98 0.53688649 0.50052286
99 0.55506831 0.53688649
100 0.57325013 0.55506831
101 0.59143195 0.57325013
102 0.50052286 0.59143195
103 0.50052286 0.50052286
104 0.52779559 0.50052286
105 0.64597740 0.52779559
106 0.70052286 0.64597740
107 0.90688624 0.70052286
108 0.85372703 0.90688624
109 0.86281794 0.85372703
110 0.79918158 0.86281794
111 0.71736339 0.79918158
112 0.73554521 0.71736339
113 0.65372703 0.73554521
114 0.56281794 0.65372703
115 0.46281794 0.56281794
116 0.59009067 0.46281794
117 0.50827249 0.59009067
118 0.46281794 0.50827249
119 0.46918133 0.46281794
120 0.31602211 0.46918133
121 0.22511302 0.31602211
122 0.26147666 0.22511302
123 0.47965848 0.26147666
124 0.39784030 0.47965848
125 0.21602211 0.39784030
126 0.02511302 0.21602211
127 -0.07488698 0.02511302
128 -0.24761425 -0.07488698
129 -0.22943243 -0.24761425
130 -0.27488698 -0.22943243
131 -0.16852359 -0.27488698
132 NA -0.16852359
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.37215946 0.46306855
[2,] 0.40852309 0.37215946
[3,] 0.42670491 0.40852309
[4,] 0.44488673 0.42670491
[5,] 0.56306855 0.44488673
[6,] 0.67215946 0.56306855
[7,] 0.67215946 0.67215946
[8,] 0.69943218 0.67215946
[9,] 0.71761400 0.69943218
[10,] 0.77215946 0.71761400
[11,] 0.97852284 0.77215946
[12,] 0.92536363 0.97852284
[13,] 0.93445454 0.92536363
[14,] 1.07081817 0.93445454
[15,] 1.08899999 1.07081817
[16,] 1.00718181 1.08899999
[17,] 1.02536363 1.00718181
[18,] 0.93445454 1.02536363
[19,] 1.03445454 0.93445454
[20,] 1.06172726 1.03445454
[21,] 0.87990908 1.06172726
[22,] 0.83445454 0.87990908
[23,] 0.94081792 0.83445454
[24,] 0.88765871 0.94081792
[25,] 0.89674962 0.88765871
[26,] 0.83311326 0.89674962
[27,] 0.75129507 0.83311326
[28,] 0.66947689 0.75129507
[29,] 0.58765871 0.66947689
[30,] 0.39674962 0.58765871
[31,] 0.19674962 0.39674962
[32,] 0.02402235 0.19674962
[33,] -0.25779584 0.02402235
[34,] -0.40325038 -0.25779584
[35,] -0.39688699 -0.40325038
[36,] -0.55004621 -0.39688699
[37,] -0.64095530 -0.55004621
[38,] -0.80459166 -0.64095530
[39,] -0.98640984 -0.80459166
[40,] -1.06822803 -0.98640984
[41,] -1.05004621 -1.06822803
[42,] -1.04095530 -1.05004621
[43,] -0.94095530 -1.04095530
[44,] -1.11368257 -0.94095530
[45,] -1.19550075 -1.11368257
[46,] -1.24095530 -1.19550075
[47,] -1.23459191 -1.24095530
[48,] -1.38775113 -1.23459191
[49,] -1.37866022 -1.38775113
[50,] -1.34229658 -1.37866022
[51,] -1.12411476 -1.34229658
[52,] -1.00593294 -1.12411476
[53,] -1.08775113 -1.00593294
[54,] -1.17866022 -1.08775113
[55,] -1.07866022 -1.17866022
[56,] -0.85138749 -1.07866022
[57,] -0.43320567 -0.85138749
[58,] -0.37866022 -0.43320567
[59,] -1.54229408 -0.37866022
[60,] -1.59545330 -1.54229408
[61,] -1.48636239 -1.59545330
[62,] -1.44999875 -1.48636239
[63,] -1.43181693 -1.44999875
[64,] -1.31363511 -1.43181693
[65,] -1.29545330 -1.31363511
[66,] -1.08636239 -1.29545330
[67,] -1.08636239 -1.08636239
[68,] -1.05908966 -1.08636239
[69,] -0.94090784 -1.05908966
[70,] -0.78636239 -0.94090784
[71,] -0.57999900 -0.78636239
[72,] -0.43315821 -0.57999900
[73,] -0.42406731 -0.43315821
[74,] -0.38770367 -0.42406731
[75,] -0.26952185 -0.38770367
[76,] -0.25134003 -0.26952185
[77,] -0.23315821 -0.25134003
[78,] -0.22406731 -0.23315821
[79,] -0.22406731 -0.22406731
[80,] -0.19679458 -0.22406731
[81,] -0.07861276 -0.19679458
[82,] -0.02406731 -0.07861276
[83,] 0.18229608 -0.02406731
[84,] 0.12913687 0.18229608
[85,] 0.13822778 0.12913687
[86,] 0.07459141 0.13822778
[87,] -0.20722677 0.07459141
[88,] -0.18904495 -0.20722677
[89,] 0.02913687 -0.18904495
[90,] 0.43822778 0.02913687
[91,] 0.53822778 0.43822778
[92,] 0.56550050 0.53822778
[93,] 0.38368232 0.56550050
[94,] 0.33822778 0.38368232
[95,] 0.44459116 0.33822778
[96,] 0.39143195 0.44459116
[97,] 0.50052286 0.39143195
[98,] 0.53688649 0.50052286
[99,] 0.55506831 0.53688649
[100,] 0.57325013 0.55506831
[101,] 0.59143195 0.57325013
[102,] 0.50052286 0.59143195
[103,] 0.50052286 0.50052286
[104,] 0.52779559 0.50052286
[105,] 0.64597740 0.52779559
[106,] 0.70052286 0.64597740
[107,] 0.90688624 0.70052286
[108,] 0.85372703 0.90688624
[109,] 0.86281794 0.85372703
[110,] 0.79918158 0.86281794
[111,] 0.71736339 0.79918158
[112,] 0.73554521 0.71736339
[113,] 0.65372703 0.73554521
[114,] 0.56281794 0.65372703
[115,] 0.46281794 0.56281794
[116,] 0.59009067 0.46281794
[117,] 0.50827249 0.59009067
[118,] 0.46281794 0.50827249
[119,] 0.46918133 0.46281794
[120,] 0.31602211 0.46918133
[121,] 0.22511302 0.31602211
[122,] 0.26147666 0.22511302
[123,] 0.47965848 0.26147666
[124,] 0.39784030 0.47965848
[125,] 0.21602211 0.39784030
[126,] 0.02511302 0.21602211
[127,] -0.07488698 0.02511302
[128,] -0.24761425 -0.07488698
[129,] -0.22943243 -0.24761425
[130,] -0.27488698 -0.22943243
[131,] -0.16852359 -0.27488698
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.37215946 0.46306855
2 0.40852309 0.37215946
3 0.42670491 0.40852309
4 0.44488673 0.42670491
5 0.56306855 0.44488673
6 0.67215946 0.56306855
7 0.67215946 0.67215946
8 0.69943218 0.67215946
9 0.71761400 0.69943218
10 0.77215946 0.71761400
11 0.97852284 0.77215946
12 0.92536363 0.97852284
13 0.93445454 0.92536363
14 1.07081817 0.93445454
15 1.08899999 1.07081817
16 1.00718181 1.08899999
17 1.02536363 1.00718181
18 0.93445454 1.02536363
19 1.03445454 0.93445454
20 1.06172726 1.03445454
21 0.87990908 1.06172726
22 0.83445454 0.87990908
23 0.94081792 0.83445454
24 0.88765871 0.94081792
25 0.89674962 0.88765871
26 0.83311326 0.89674962
27 0.75129507 0.83311326
28 0.66947689 0.75129507
29 0.58765871 0.66947689
30 0.39674962 0.58765871
31 0.19674962 0.39674962
32 0.02402235 0.19674962
33 -0.25779584 0.02402235
34 -0.40325038 -0.25779584
35 -0.39688699 -0.40325038
36 -0.55004621 -0.39688699
37 -0.64095530 -0.55004621
38 -0.80459166 -0.64095530
39 -0.98640984 -0.80459166
40 -1.06822803 -0.98640984
41 -1.05004621 -1.06822803
42 -1.04095530 -1.05004621
43 -0.94095530 -1.04095530
44 -1.11368257 -0.94095530
45 -1.19550075 -1.11368257
46 -1.24095530 -1.19550075
47 -1.23459191 -1.24095530
48 -1.38775113 -1.23459191
49 -1.37866022 -1.38775113
50 -1.34229658 -1.37866022
51 -1.12411476 -1.34229658
52 -1.00593294 -1.12411476
53 -1.08775113 -1.00593294
54 -1.17866022 -1.08775113
55 -1.07866022 -1.17866022
56 -0.85138749 -1.07866022
57 -0.43320567 -0.85138749
58 -0.37866022 -0.43320567
59 -1.54229408 -0.37866022
60 -1.59545330 -1.54229408
61 -1.48636239 -1.59545330
62 -1.44999875 -1.48636239
63 -1.43181693 -1.44999875
64 -1.31363511 -1.43181693
65 -1.29545330 -1.31363511
66 -1.08636239 -1.29545330
67 -1.08636239 -1.08636239
68 -1.05908966 -1.08636239
69 -0.94090784 -1.05908966
70 -0.78636239 -0.94090784
71 -0.57999900 -0.78636239
72 -0.43315821 -0.57999900
73 -0.42406731 -0.43315821
74 -0.38770367 -0.42406731
75 -0.26952185 -0.38770367
76 -0.25134003 -0.26952185
77 -0.23315821 -0.25134003
78 -0.22406731 -0.23315821
79 -0.22406731 -0.22406731
80 -0.19679458 -0.22406731
81 -0.07861276 -0.19679458
82 -0.02406731 -0.07861276
83 0.18229608 -0.02406731
84 0.12913687 0.18229608
85 0.13822778 0.12913687
86 0.07459141 0.13822778
87 -0.20722677 0.07459141
88 -0.18904495 -0.20722677
89 0.02913687 -0.18904495
90 0.43822778 0.02913687
91 0.53822778 0.43822778
92 0.56550050 0.53822778
93 0.38368232 0.56550050
94 0.33822778 0.38368232
95 0.44459116 0.33822778
96 0.39143195 0.44459116
97 0.50052286 0.39143195
98 0.53688649 0.50052286
99 0.55506831 0.53688649
100 0.57325013 0.55506831
101 0.59143195 0.57325013
102 0.50052286 0.59143195
103 0.50052286 0.50052286
104 0.52779559 0.50052286
105 0.64597740 0.52779559
106 0.70052286 0.64597740
107 0.90688624 0.70052286
108 0.85372703 0.90688624
109 0.86281794 0.85372703
110 0.79918158 0.86281794
111 0.71736339 0.79918158
112 0.73554521 0.71736339
113 0.65372703 0.73554521
114 0.56281794 0.65372703
115 0.46281794 0.56281794
116 0.59009067 0.46281794
117 0.50827249 0.59009067
118 0.46281794 0.50827249
119 0.46918133 0.46281794
120 0.31602211 0.46918133
121 0.22511302 0.31602211
122 0.26147666 0.22511302
123 0.47965848 0.26147666
124 0.39784030 0.47965848
125 0.21602211 0.39784030
126 0.02511302 0.21602211
127 -0.07488698 0.02511302
128 -0.24761425 -0.07488698
129 -0.22943243 -0.24761425
130 -0.27488698 -0.22943243
131 -0.16852359 -0.27488698
> 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/7tah11229950569.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gvh91229950569.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gs5o1229950569.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10r5hy1229950569.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tmwb1229950569.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/127k4c1229950569.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/13c0u71229950569.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/14eshp1229950570.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/1543cl1229950570.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/16lha21229950570.tab")
+ }
>
> system("convert tmp/155pm1229950569.ps tmp/155pm1229950569.png")
> system("convert tmp/2x7o71229950569.ps tmp/2x7o71229950569.png")
> system("convert tmp/3unkq1229950569.ps tmp/3unkq1229950569.png")
> system("convert tmp/43gqw1229950569.ps tmp/43gqw1229950569.png")
> system("convert tmp/5cm7n1229950569.ps tmp/5cm7n1229950569.png")
> system("convert tmp/6odtf1229950569.ps tmp/6odtf1229950569.png")
> system("convert tmp/7tah11229950569.ps tmp/7tah11229950569.png")
> system("convert tmp/8gvh91229950569.ps tmp/8gvh91229950569.png")
> system("convert tmp/9gs5o1229950569.ps tmp/9gs5o1229950569.png")
> system("convert tmp/10r5hy1229950569.ps tmp/10r5hy1229950569.png")
>
>
> proc.time()
user system elapsed
3.518 1.669 5.324