R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(5
+ ,2
+ ,1
+ ,3
+ ,73
+ ,62
+ ,66
+ ,12
+ ,1
+ ,1
+ ,1
+ ,58
+ ,54
+ ,54
+ ,11
+ ,1
+ ,1
+ ,3
+ ,68
+ ,41
+ ,82
+ ,6
+ ,1
+ ,1
+ ,3
+ ,62
+ ,49
+ ,61
+ ,12
+ ,1
+ ,2
+ ,3
+ ,65
+ ,49
+ ,65
+ ,11
+ ,1
+ ,1
+ ,3
+ ,81
+ ,72
+ ,77
+ ,12
+ ,1
+ ,1
+ ,1
+ ,73
+ ,78
+ ,66
+ ,7
+ ,2
+ ,4
+ ,3
+ ,64
+ ,58
+ ,66
+ ,8
+ ,1
+ ,1
+ ,3
+ ,68
+ ,58
+ ,66
+ ,13
+ ,1
+ ,1
+ ,1
+ ,51
+ ,23
+ ,48
+ ,12
+ ,1
+ ,1
+ ,1
+ ,68
+ ,39
+ ,57
+ ,13
+ ,1
+ ,1
+ ,3
+ ,61
+ ,63
+ ,80
+ ,12
+ ,1
+ ,1
+ ,1
+ ,69
+ ,46
+ ,60
+ ,12
+ ,1
+ ,3
+ ,3
+ ,73
+ ,58
+ ,70
+ ,11
+ ,2
+ ,1
+ ,3
+ ,61
+ ,39
+ ,85
+ ,12
+ ,2
+ ,1
+ ,1
+ ,62
+ ,44
+ ,59
+ ,12
+ ,1
+ ,1
+ ,1
+ ,63
+ ,49
+ ,72
+ ,12
+ ,1
+ ,6
+ ,1
+ ,69
+ ,57
+ ,70
+ ,11
+ ,2
+ ,1
+ ,3
+ ,47
+ ,76
+ ,74
+ ,13
+ ,2
+ ,1
+ ,1
+ ,66
+ ,63
+ ,70
+ ,9
+ ,1
+ ,1
+ ,3
+ ,58
+ ,18
+ ,51
+ ,11
+ ,2
+ ,1
+ ,3
+ ,63
+ ,40
+ ,70
+ ,11
+ ,1
+ ,1
+ ,1
+ ,69
+ ,59
+ ,71
+ ,11
+ ,2
+ ,1
+ ,3
+ ,59
+ ,62
+ ,72
+ ,9
+ ,1
+ ,1
+ ,1
+ ,59
+ ,70
+ ,50
+ ,11
+ ,2
+ ,1
+ ,4
+ ,63
+ ,65
+ ,69
+ ,12
+ ,2
+ ,1
+ ,3
+ ,65
+ ,56
+ ,73
+ ,12
+ ,1
+ ,1
+ ,3
+ ,65
+ ,45
+ ,66
+ ,10
+ ,2
+ ,1
+ ,3
+ ,71
+ ,57
+ ,73
+ ,12
+ ,1
+ ,4
+ ,3
+ ,60
+ ,50
+ ,58
+ ,12
+ ,2
+ ,1
+ ,1
+ ,81
+ ,40
+ ,78
+ ,12
+ ,1
+ ,1
+ ,3
+ ,67
+ ,58
+ ,83
+ ,9
+ ,2
+ ,1
+ ,3
+ ,66
+ ,49
+ ,76
+ ,9
+ ,1
+ ,1
+ ,3
+ ,62
+ ,49
+ ,77
+ ,12
+ ,1
+ ,1
+ ,3
+ ,63
+ ,27
+ ,79
+ ,14
+ ,2
+ ,1
+ ,1
+ ,73
+ ,51
+ ,71
+ ,12
+ ,2
+ ,1
+ ,3
+ ,55
+ ,75
+ ,79
+ ,11
+ ,1
+ ,1
+ ,1
+ ,59
+ ,65
+ ,60
+ ,9
+ ,1
+ ,1
+ ,2
+ ,64
+ ,47
+ ,73
+ ,11
+ ,2
+ ,1
+ ,3
+ ,63
+ ,49
+ ,70
+ ,7
+ ,1
+ ,1
+ ,1
+ ,64
+ ,65
+ ,42
+ ,15
+ ,1
+ ,1
+ ,1
+ ,73
+ ,61
+ ,74
+ ,11
+ ,1
+ ,1
+ ,3
+ ,54
+ ,46
+ ,68
+ ,12
+ ,1
+ ,1
+ ,3
+ ,76
+ ,69
+ ,83
+ ,12
+ ,2
+ ,2
+ ,1
+ ,74
+ ,55
+ ,62
+ ,9
+ ,2
+ ,1
+ ,3
+ ,63
+ ,78
+ ,79
+ ,12
+ ,2
+ ,1
+ ,3
+ ,73
+ ,58
+ ,61
+ ,11
+ ,2
+ ,1
+ ,3
+ ,67
+ ,34
+ ,86
+ ,11
+ ,2
+ ,2
+ ,3
+ ,68
+ ,67
+ ,64
+ ,8
+ ,1
+ ,4
+ ,3
+ ,66
+ ,45
+ ,75
+ ,7
+ ,2
+ ,1
+ ,1
+ ,62
+ ,68
+ ,59
+ ,12
+ ,2
+ ,4
+ ,3
+ ,71
+ ,49
+ ,82
+ ,8
+ ,1
+ ,1
+ ,2
+ ,63
+ ,19
+ ,61
+ ,10
+ ,1
+ ,1
+ ,1
+ ,75
+ ,72
+ ,69
+ ,12
+ ,1
+ ,2
+ ,2
+ ,77
+ ,59
+ ,60
+ ,15
+ ,2
+ ,3
+ ,3
+ ,62
+ ,46
+ ,59
+ ,12
+ ,1
+ ,1
+ ,3
+ ,74
+ ,56
+ ,81
+ ,12
+ ,2
+ ,2
+ ,1
+ ,67
+ ,45
+ ,65
+ ,12
+ ,2
+ ,1
+ ,3
+ ,56
+ ,53
+ ,60
+ ,12
+ ,2
+ ,1
+ ,1
+ ,60
+ ,67
+ ,60
+ ,8
+ ,2
+ ,1
+ ,3
+ ,58
+ ,73
+ ,45
+ ,10
+ ,1
+ ,1
+ ,3
+ ,65
+ ,46
+ ,75
+ ,14
+ ,2
+ ,1
+ ,3
+ ,49
+ ,70
+ ,84
+ ,10
+ ,1
+ ,1
+ ,3
+ ,61
+ ,38
+ ,77
+ ,12
+ ,2
+ ,1
+ ,3
+ ,66
+ ,54
+ ,64
+ ,14
+ ,2
+ ,1
+ ,3
+ ,64
+ ,46
+ ,54
+ ,6
+ ,2
+ ,1
+ ,1
+ ,65
+ ,46
+ ,72
+ ,11
+ ,1
+ ,1
+ ,3
+ ,46
+ ,45
+ ,56
+ ,10
+ ,2
+ ,1
+ ,3
+ ,65
+ ,47
+ ,67
+ ,14
+ ,2
+ ,1
+ ,3
+ ,81
+ ,25
+ ,81
+ ,12
+ ,1
+ ,1
+ ,1
+ ,72
+ ,63
+ ,73
+ ,13
+ ,2
+ ,1
+ ,1
+ ,65
+ ,46
+ ,67
+ ,11
+ ,2
+ ,1
+ ,3
+ ,74
+ ,69
+ ,72
+ ,11
+ ,1
+ ,1
+ ,3
+ ,59
+ ,43
+ ,69
+ ,12
+ ,1
+ ,1
+ ,1
+ ,69
+ ,49
+ ,71
+ ,13
+ ,2
+ ,2
+ ,3
+ ,58
+ ,39
+ ,77
+ ,12
+ ,1
+ ,1
+ ,1
+ ,71
+ ,65
+ ,63
+ ,8
+ ,2
+ ,1
+ ,3
+ ,79
+ ,54
+ ,49
+ ,12
+ ,2
+ ,1
+ ,3
+ ,68
+ ,50
+ ,74
+ ,11
+ ,1
+ ,1
+ ,3
+ ,66
+ ,42
+ ,76
+ ,10
+ ,2
+ ,1
+ ,3
+ ,62
+ ,45
+ ,65
+ ,12
+ ,1
+ ,1
+ ,3
+ ,69
+ ,50
+ ,65
+ ,11
+ ,2
+ ,2
+ ,7
+ ,63
+ ,55
+ ,69
+ ,12
+ ,1
+ ,1
+ ,1
+ ,62
+ ,38
+ ,71
+ ,12
+ ,1
+ ,1
+ ,3
+ ,61
+ ,40
+ ,68
+ ,10
+ ,2
+ ,1
+ ,1
+ ,65
+ ,51
+ ,49
+ ,12
+ ,1
+ ,1
+ ,3
+ ,64
+ ,49
+ ,86
+ ,12
+ ,2
+ ,1
+ ,1
+ ,56
+ ,39
+ ,63
+ ,11
+ ,2
+ ,1
+ ,3
+ ,56
+ ,57
+ ,77
+ ,10
+ ,1
+ ,1
+ ,3
+ ,48
+ ,30
+ ,52
+ ,12
+ ,1
+ ,1
+ ,1
+ ,74
+ ,51
+ ,73
+ ,11
+ ,1
+ ,1
+ ,1
+ ,69
+ ,48
+ ,63
+ ,12
+ ,1
+ ,4
+ ,3
+ ,62
+ ,56
+ ,54
+ ,12
+ ,1
+ ,1
+ ,2
+ ,73
+ ,66
+ ,56
+ ,10
+ ,1
+ ,1
+ ,1
+ ,64
+ ,72
+ ,54
+ ,11
+ ,1
+ ,1
+ ,1
+ ,57
+ ,28
+ ,61
+ ,10
+ ,1
+ ,1
+ ,2
+ ,57
+ ,52
+ ,70
+ ,11
+ ,2
+ ,1
+ ,2
+ ,60
+ ,53
+ ,68
+ ,11
+ ,2
+ ,1
+ ,1
+ ,61
+ ,70
+ ,63
+ ,12
+ ,1
+ ,1
+ ,2
+ ,72
+ ,63
+ ,76
+ ,11
+ ,1
+ ,1
+ ,3
+ ,57
+ ,46
+ ,69
+ ,11
+ ,1
+ ,2
+ ,3
+ ,51
+ ,45
+ ,71
+ ,7
+ ,1
+ ,1
+ ,2
+ ,63
+ ,68
+ ,39
+ ,12
+ ,1
+ ,1
+ ,3
+ ,54
+ ,54
+ ,54
+ ,8
+ ,1
+ ,1
+ ,1
+ ,72
+ ,60
+ ,64
+ ,10
+ ,1
+ ,1
+ ,3
+ ,62
+ ,50
+ ,70
+ ,12
+ ,1
+ ,1
+ ,2
+ ,68
+ ,66
+ ,76
+ ,11
+ ,1
+ ,1
+ ,3
+ ,62
+ ,56
+ ,71
+ ,13
+ ,2
+ ,1
+ ,2
+ ,63
+ ,54
+ ,73
+ ,9
+ ,1
+ ,1
+ ,3
+ ,77
+ ,72
+ ,81
+ ,11
+ ,1
+ ,1
+ ,1
+ ,57
+ ,34
+ ,50
+ ,13
+ ,1
+ ,1
+ ,1
+ ,57
+ ,39
+ ,42
+ ,8
+ ,1
+ ,1
+ ,3
+ ,61
+ ,66
+ ,66
+ ,12
+ ,1
+ ,1
+ ,3
+ ,65
+ ,27
+ ,77
+ ,11
+ ,1
+ ,1
+ ,3
+ ,63
+ ,63
+ ,62
+ ,11
+ ,2
+ ,1
+ ,1
+ ,66
+ ,65
+ ,66
+ ,12
+ ,1
+ ,1
+ ,3
+ ,68
+ ,63
+ ,69
+ ,13
+ ,1
+ ,1
+ ,3
+ ,72
+ ,49
+ ,72
+ ,11
+ ,1
+ ,1
+ ,1
+ ,68
+ ,42
+ ,67
+ ,10
+ ,1
+ ,1
+ ,1
+ ,59
+ ,51
+ ,59
+ ,10
+ ,1
+ ,4
+ ,3
+ ,56
+ ,50
+ ,66
+ ,10
+ ,1
+ ,1
+ ,1
+ ,62
+ ,64
+ ,68
+ ,12
+ ,2
+ ,1
+ ,3
+ ,72
+ ,68
+ ,72
+ ,12
+ ,2
+ ,1
+ ,3
+ ,68
+ ,66
+ ,73
+ ,13
+ ,1
+ ,1
+ ,3
+ ,67
+ ,59
+ ,69
+ ,11
+ ,1
+ ,2
+ ,1
+ ,54
+ ,32
+ ,57
+ ,11
+ ,2
+ ,1
+ ,1
+ ,69
+ ,62
+ ,55
+ ,12
+ ,1
+ ,2
+ ,3
+ ,61
+ ,52
+ ,72
+ ,9
+ ,1
+ ,1
+ ,3
+ ,55
+ ,34
+ ,68
+ ,11
+ ,2
+ ,1
+ ,3
+ ,75
+ ,63
+ ,83
+ ,12
+ ,1
+ ,1
+ ,3
+ ,55
+ ,48
+ ,74
+ ,12
+ ,1
+ ,1
+ ,3
+ ,49
+ ,53
+ ,72
+ ,13
+ ,2
+ ,1
+ ,3
+ ,54
+ ,39
+ ,66
+ ,6
+ ,1
+ ,1
+ ,3
+ ,66
+ ,51
+ ,61
+ ,11
+ ,1
+ ,1
+ ,3
+ ,73
+ ,60
+ ,86
+ ,10
+ ,2
+ ,1
+ ,2
+ ,63
+ ,70
+ ,81
+ ,12
+ ,2
+ ,4
+ ,3
+ ,61
+ ,40
+ ,79
+ ,11
+ ,1
+ ,1
+ ,3
+ ,74
+ ,61
+ ,73
+ ,12
+ ,2
+ ,5
+ ,3
+ ,81
+ ,35
+ ,59
+ ,12
+ ,1
+ ,1
+ ,1
+ ,62
+ ,39
+ ,64
+ ,7
+ ,1
+ ,1
+ ,2
+ ,64
+ ,31
+ ,75
+ ,12
+ ,1
+ ,1
+ ,3
+ ,62
+ ,36
+ ,68
+ ,12
+ ,1
+ ,1
+ ,1
+ ,85
+ ,51
+ ,84
+ ,9
+ ,1
+ ,1
+ ,1
+ ,74
+ ,55
+ ,68
+ ,12
+ ,1
+ ,1
+ ,3
+ ,51
+ ,67
+ ,68
+ ,12
+ ,1
+ ,1
+ ,3
+ ,66
+ ,40
+ ,69)
+ ,dim=c(7
+ ,146)
+ ,dimnames=list(c('FF'
+ ,'Geslacht'
+ ,'Opvoeding'
+ ,'Huwelijksstatus'
+ ,'TotNV'
+ ,'TotAngst'
+ ,'TotGroep')
+ ,1:146))
> y <- array(NA,dim=c(7,146),dimnames=list(c('FF','Geslacht','Opvoeding','Huwelijksstatus','TotNV','TotAngst','TotGroep'),1:146))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
> 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
FF Geslacht Opvoeding Huwelijksstatus TotNV TotAngst TotGroep
1 5 2 1 3 73 62 66
2 12 1 1 1 58 54 54
3 11 1 1 3 68 41 82
4 6 1 1 3 62 49 61
5 12 1 2 3 65 49 65
6 11 1 1 3 81 72 77
7 12 1 1 1 73 78 66
8 7 2 4 3 64 58 66
9 8 1 1 3 68 58 66
10 13 1 1 1 51 23 48
11 12 1 1 1 68 39 57
12 13 1 1 3 61 63 80
13 12 1 1 1 69 46 60
14 12 1 3 3 73 58 70
15 11 2 1 3 61 39 85
16 12 2 1 1 62 44 59
17 12 1 1 1 63 49 72
18 12 1 6 1 69 57 70
19 11 2 1 3 47 76 74
20 13 2 1 1 66 63 70
21 9 1 1 3 58 18 51
22 11 2 1 3 63 40 70
23 11 1 1 1 69 59 71
24 11 2 1 3 59 62 72
25 9 1 1 1 59 70 50
26 11 2 1 4 63 65 69
27 12 2 1 3 65 56 73
28 12 1 1 3 65 45 66
29 10 2 1 3 71 57 73
30 12 1 4 3 60 50 58
31 12 2 1 1 81 40 78
32 12 1 1 3 67 58 83
33 9 2 1 3 66 49 76
34 9 1 1 3 62 49 77
35 12 1 1 3 63 27 79
36 14 2 1 1 73 51 71
37 12 2 1 3 55 75 79
38 11 1 1 1 59 65 60
39 9 1 1 2 64 47 73
40 11 2 1 3 63 49 70
41 7 1 1 1 64 65 42
42 15 1 1 1 73 61 74
43 11 1 1 3 54 46 68
44 12 1 1 3 76 69 83
45 12 2 2 1 74 55 62
46 9 2 1 3 63 78 79
47 12 2 1 3 73 58 61
48 11 2 1 3 67 34 86
49 11 2 2 3 68 67 64
50 8 1 4 3 66 45 75
51 7 2 1 1 62 68 59
52 12 2 4 3 71 49 82
53 8 1 1 2 63 19 61
54 10 1 1 1 75 72 69
55 12 1 2 2 77 59 60
56 15 2 3 3 62 46 59
57 12 1 1 3 74 56 81
58 12 2 2 1 67 45 65
59 12 2 1 3 56 53 60
60 12 2 1 1 60 67 60
61 8 2 1 3 58 73 45
62 10 1 1 3 65 46 75
63 14 2 1 3 49 70 84
64 10 1 1 3 61 38 77
65 12 2 1 3 66 54 64
66 14 2 1 3 64 46 54
67 6 2 1 1 65 46 72
68 11 1 1 3 46 45 56
69 10 2 1 3 65 47 67
70 14 2 1 3 81 25 81
71 12 1 1 1 72 63 73
72 13 2 1 1 65 46 67
73 11 2 1 3 74 69 72
74 11 1 1 3 59 43 69
75 12 1 1 1 69 49 71
76 13 2 2 3 58 39 77
77 12 1 1 1 71 65 63
78 8 2 1 3 79 54 49
79 12 2 1 3 68 50 74
80 11 1 1 3 66 42 76
81 10 2 1 3 62 45 65
82 12 1 1 3 69 50 65
83 11 2 2 7 63 55 69
84 12 1 1 1 62 38 71
85 12 1 1 3 61 40 68
86 10 2 1 1 65 51 49
87 12 1 1 3 64 49 86
88 12 2 1 1 56 39 63
89 11 2 1 3 56 57 77
90 10 1 1 3 48 30 52
91 12 1 1 1 74 51 73
92 11 1 1 1 69 48 63
93 12 1 4 3 62 56 54
94 12 1 1 2 73 66 56
95 10 1 1 1 64 72 54
96 11 1 1 1 57 28 61
97 10 1 1 2 57 52 70
98 11 2 1 2 60 53 68
99 11 2 1 1 61 70 63
100 12 1 1 2 72 63 76
101 11 1 1 3 57 46 69
102 11 1 2 3 51 45 71
103 7 1 1 2 63 68 39
104 12 1 1 3 54 54 54
105 8 1 1 1 72 60 64
106 10 1 1 3 62 50 70
107 12 1 1 2 68 66 76
108 11 1 1 3 62 56 71
109 13 2 1 2 63 54 73
110 9 1 1 3 77 72 81
111 11 1 1 1 57 34 50
112 13 1 1 1 57 39 42
113 8 1 1 3 61 66 66
114 12 1 1 3 65 27 77
115 11 1 1 3 63 63 62
116 11 2 1 1 66 65 66
117 12 1 1 3 68 63 69
118 13 1 1 3 72 49 72
119 11 1 1 1 68 42 67
120 10 1 1 1 59 51 59
121 10 1 4 3 56 50 66
122 10 1 1 1 62 64 68
123 12 2 1 3 72 68 72
124 12 2 1 3 68 66 73
125 13 1 1 3 67 59 69
126 11 1 2 1 54 32 57
127 11 2 1 1 69 62 55
128 12 1 2 3 61 52 72
129 9 1 1 3 55 34 68
130 11 2 1 3 75 63 83
131 12 1 1 3 55 48 74
132 12 1 1 3 49 53 72
133 13 2 1 3 54 39 66
134 6 1 1 3 66 51 61
135 11 1 1 3 73 60 86
136 10 2 1 2 63 70 81
137 12 2 4 3 61 40 79
138 11 1 1 3 74 61 73
139 12 2 5 3 81 35 59
140 12 1 1 1 62 39 64
141 7 1 1 2 64 31 75
142 12 1 1 3 62 36 68
143 12 1 1 1 85 51 84
144 9 1 1 1 74 55 68
145 12 1 1 3 51 67 68
146 12 1 1 3 66 40 69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Geslacht Opvoeding Huwelijksstatus
9.18224 0.29899 0.16316 -0.25708
TotNV TotAngst TotGroep
-0.01003 -0.01481 0.04739
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.7655 -0.8382 0.3178 1.0997 4.0084
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.18224 1.55786 5.894 2.72e-08 ***
Geslacht 0.29899 0.30714 0.973 0.33201
Opvoeding 0.16316 0.17309 0.943 0.34750
Huwelijksstatus -0.25708 0.16104 -1.596 0.11268
TotNV -0.01003 0.02141 -0.468 0.64035
TotAngst -0.01481 0.01190 -1.245 0.21513
TotGroep 0.04739 0.01672 2.835 0.00527 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.764 on 139 degrees of freedom
Multiple R-squared: 0.0756, Adjusted R-squared: 0.0357
F-statistic: 1.895 on 6 and 139 DF, p-value: 0.08588
> 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,] 0.50976378 0.980472431 0.4902362153
[2,] 0.73808186 0.523836286 0.2619181428
[3,] 0.75903366 0.481932690 0.2409663450
[4,] 0.66397687 0.672046251 0.3360231256
[5,] 0.57216714 0.855665712 0.4278328562
[6,] 0.51547594 0.969048119 0.4845240596
[7,] 0.57843996 0.843120086 0.4215600428
[8,] 0.76182995 0.476340095 0.2381700474
[9,] 0.80738329 0.385233418 0.1926167089
[10,] 0.76751931 0.464961383 0.2324806916
[11,] 0.74313256 0.513734890 0.2568674448
[12,] 0.68808906 0.623821882 0.3119109412
[13,] 0.69569269 0.608614625 0.3043073124
[14,] 0.71423970 0.571520600 0.2857603001
[15,] 0.68073311 0.638533784 0.3192668920
[16,] 0.66470359 0.670592828 0.3352964141
[17,] 0.76273944 0.474521111 0.2372605554
[18,] 0.75931471 0.481370576 0.2406852881
[19,] 0.75351855 0.492962903 0.2464814517
[20,] 0.70536471 0.589270582 0.2946352911
[21,] 0.72948251 0.541034989 0.2705174945
[22,] 0.67436202 0.651275963 0.3256379817
[23,] 0.61744466 0.765110672 0.3825553362
[24,] 0.62217364 0.755652726 0.3778263628
[25,] 0.68320202 0.633595966 0.3167979829
[26,] 0.62805471 0.743890590 0.3719452948
[27,] 0.67004945 0.659901109 0.3299505546
[28,] 0.62600497 0.747990067 0.3739950334
[29,] 0.58207529 0.835849424 0.4179247121
[30,] 0.65896594 0.682068119 0.3410340596
[31,] 0.61359976 0.772800477 0.3864002384
[32,] 0.66128243 0.677435143 0.3387175715
[33,] 0.76254330 0.474913396 0.2374566978
[34,] 0.71805597 0.563888064 0.2819440319
[35,] 0.68493414 0.630131724 0.3150658620
[36,] 0.65072616 0.698547678 0.3492738390
[37,] 0.66139218 0.677215639 0.3386078193
[38,] 0.72948295 0.541034099 0.2705170495
[39,] 0.70352190 0.592956209 0.2964781047
[40,] 0.68012653 0.639746947 0.3198734736
[41,] 0.78796165 0.424076698 0.2120383491
[42,] 0.90671077 0.186578454 0.0932892272
[43,] 0.88861431 0.222771390 0.1113856950
[44,] 0.92714378 0.145712430 0.0728562150
[45,] 0.91871626 0.162567473 0.0812837367
[46,] 0.91687519 0.166249623 0.0831248117
[47,] 0.97964284 0.040714312 0.0203571562
[48,] 0.97443094 0.051138121 0.0255690603
[49,] 0.96629414 0.067411726 0.0337058631
[50,] 0.96470961 0.070580778 0.0352903889
[51,] 0.95753454 0.084930928 0.0424654642
[52,] 0.95528835 0.089423293 0.0447116466
[53,] 0.94701520 0.105969603 0.0529848013
[54,] 0.95525259 0.089494823 0.0447474117
[55,] 0.95004864 0.099902717 0.0499513583
[56,] 0.94542952 0.109140967 0.0545704833
[57,] 0.97795391 0.044092185 0.0220460923
[58,] 0.99941013 0.001179741 0.0005898707
[59,] 0.99914116 0.001717672 0.0008588361
[60,] 0.99891740 0.002165196 0.0010825981
[61,] 0.99908113 0.001837737 0.0009188683
[62,] 0.99874309 0.002513813 0.0012569066
[63,] 0.99856961 0.002860772 0.0014303859
[64,] 0.99787024 0.004259512 0.0021297562
[65,] 0.99687984 0.006240320 0.0031201601
[66,] 0.99577962 0.008440767 0.0042203836
[67,] 0.99475079 0.010498429 0.0052492143
[68,] 0.99409569 0.011808614 0.0059043072
[69,] 0.99513051 0.009738974 0.0048694868
[70,] 0.99333421 0.013331573 0.0066657864
[71,] 0.99063263 0.018734744 0.0093673718
[72,] 0.98912218 0.021755647 0.0108778236
[73,] 0.98773539 0.024529226 0.0122646128
[74,] 0.98528853 0.029422942 0.0147114710
[75,] 0.98103621 0.037927581 0.0189637904
[76,] 0.97657602 0.046847964 0.0234239818
[77,] 0.97068640 0.058627210 0.0293136048
[78,] 0.96229078 0.075418442 0.0377092211
[79,] 0.95113061 0.097738781 0.0488693907
[80,] 0.93873328 0.122533450 0.0612667249
[81,] 0.92629680 0.147406393 0.0737031964
[82,] 0.91589292 0.168214168 0.0841070841
[83,] 0.89524193 0.209516150 0.1047580748
[84,] 0.88958089 0.220838224 0.1104191122
[85,] 0.89696124 0.206077518 0.1030387588
[86,] 0.87278940 0.254421195 0.1272105977
[87,] 0.84308690 0.313826199 0.1569130995
[88,] 0.81893086 0.362138287 0.1810691434
[89,] 0.78738009 0.425239819 0.2126199096
[90,] 0.74572010 0.508559791 0.2542798956
[91,] 0.73123334 0.537533320 0.2687666601
[92,] 0.68428013 0.631439743 0.3157198713
[93,] 0.63458846 0.730823076 0.3654115379
[94,] 0.70549954 0.589000915 0.2945004575
[95,] 0.68433602 0.631327955 0.3156639773
[96,] 0.73146024 0.537079516 0.2685397580
[97,] 0.69679070 0.606418600 0.3032093000
[98,] 0.67991064 0.640178728 0.3200893638
[99,] 0.62598935 0.748021310 0.3740106550
[100,] 0.60374830 0.792503406 0.3962517030
[101,] 0.59369717 0.812605670 0.4063028349
[102,] 0.53437104 0.931257914 0.4656289571
[103,] 0.60443720 0.791125605 0.3955628023
[104,] 0.69254464 0.614910710 0.3074553551
[105,] 0.64629480 0.707410408 0.3537052041
[106,] 0.58732843 0.825343147 0.4126715734
[107,] 0.52276026 0.954479475 0.4772397375
[108,] 0.47853548 0.957070953 0.5214645234
[109,] 0.50469944 0.990601128 0.4953005642
[110,] 0.44796707 0.895934148 0.5520329262
[111,] 0.38384432 0.767688634 0.6161556829
[112,] 0.36487965 0.729759305 0.6351203476
[113,] 0.31406850 0.628137005 0.6859314975
[114,] 0.25963363 0.519267269 0.7403663654
[115,] 0.20910768 0.418215366 0.7908923170
[116,] 0.23572308 0.471446154 0.7642769229
[117,] 0.17899569 0.357991379 0.8210043103
[118,] 0.13545475 0.270909490 0.8645452548
[119,] 0.10028341 0.200566819 0.8997165903
[120,] 0.09769764 0.195395285 0.9023023576
[121,] 0.06438893 0.128777854 0.9356110730
[122,] 0.04254062 0.085081237 0.9574593817
[123,] 0.02722053 0.054441062 0.9727794688
[124,] 0.03741511 0.074830213 0.9625848933
[125,] 0.22263544 0.445270874 0.7773645630
[126,] 0.13785348 0.275706964 0.8621465182
[127,] 0.08344326 0.166886512 0.9165567438
> postscript(file="/var/www/rcomp/tmp/17ry01292185129.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2z0fk1292185129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3z0fk1292185129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4z0fk1292185129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5sawn1292185129.ps",horizontal=F,onefile=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 = 146
Frequency = 1
1 2 3 4 5 6
-5.64970067 1.43494423 -0.47023852 -4.41662036 1.26072348 0.35631574
7 8 9 10 11 12
1.37216204 -4.28868335 -2.46010045 2.18987918 1.17081757 1.88027388
13 14 15 16 17 18
1.14236285 1.07413479 -1.01122464 0.79095097 0.55792261 0.01557349
19 20 21 22 23 24
-0.08214420 1.59119799 -1.44203113 -0.26545370 -0.18638194 -0.07443551
25 26 27 28 29 30
-1.12842793 0.40937170 0.84944590 1.31723514 -1.07557986 1.23083530
31 32 33 34 35 36
0.02172346 0.72418235 -2.38640739 -2.17491732 0.41441021 2.43622097
37 38 39 40 41 42
0.74628724 0.32356555 -2.25199537 -0.13212606 -2.77321698 3.74117245
43 44 45 46 47 48
0.12696876 0.97737845 0.76888447 -2.12905680 1.52801040 -1.07252905
49 50 51 52 53 54
0.30586203 -3.58876608 -3.85350864 -0.11012122 -3.10809643 -0.83885038
55 56 57 58 59 60
1.50907596 4.00840998 0.85952784 0.40837523 1.33087954 1.06423026
61 62 63 64 65 66
-1.64188005 -1.09449272 2.37508846 -1.34790001 1.25638625 3.59175504
67 68 69 70 71 72
-5.76545692 0.60066388 -0.99952039 2.17148458 0.80816770 1.47151089
73 74 75 76 77 78
0.17966393 0.08526603 0.66547623 1.17468186 1.30170499 -1.90236354
79 80 81 82 83 84
0.74324727 -0.19111633 -0.96444166 1.47880632 0.86929974 0.43233348
85 86 87 88 89 90
1.10827040 -0.60133411 0.41859399 0.46714575 -0.41555426 -0.41192126
91 92 93 94 95 96
0.65045086 0.02981053 1.52934799 1.92540473 -0.23824042 -0.29200612
97 98 99 100 101 102
-1.10593051 -0.26523951 -0.02348120 0.92306429 0.10965523 -0.22326809
103 104 105 106 107 108
-2.33954315 1.90899206 -2.80973280 -0.82834822 0.92740013 0.21314332
109 110 111 112 113 114
1.54268690 -1.87336521 0.31820814 2.77142753 -2.41177373 0.52925069
115 116 117 118 119 120
0.75341132 -0.18959940 1.47178978 2.16231725 -0.25867548 -0.83643945
121 122 123 124 125 126
-1.18841990 -1.04031708 1.14479639 1.02766775 2.40250637 -0.23641714
127 128 129 130 131 132
0.31736724 0.93330440 -2.04077476 -0.42052366 0.88226244 0.99096041
133 134 135 136 137 138
1.81906626 -4.34688528 -0.32820991 -1.59943466 -0.20153495 0.31274724
139 140 141 142 143 144
0.70963693 0.77890259 -4.58380941 1.05904035 0.23941515 -2.05332460
145 146
1.40798656 1.11101022
> postscript(file="/var/www/rcomp/tmp/6sawn1292185129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 146
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.64970067 NA
1 1.43494423 -5.64970067
2 -0.47023852 1.43494423
3 -4.41662036 -0.47023852
4 1.26072348 -4.41662036
5 0.35631574 1.26072348
6 1.37216204 0.35631574
7 -4.28868335 1.37216204
8 -2.46010045 -4.28868335
9 2.18987918 -2.46010045
10 1.17081757 2.18987918
11 1.88027388 1.17081757
12 1.14236285 1.88027388
13 1.07413479 1.14236285
14 -1.01122464 1.07413479
15 0.79095097 -1.01122464
16 0.55792261 0.79095097
17 0.01557349 0.55792261
18 -0.08214420 0.01557349
19 1.59119799 -0.08214420
20 -1.44203113 1.59119799
21 -0.26545370 -1.44203113
22 -0.18638194 -0.26545370
23 -0.07443551 -0.18638194
24 -1.12842793 -0.07443551
25 0.40937170 -1.12842793
26 0.84944590 0.40937170
27 1.31723514 0.84944590
28 -1.07557986 1.31723514
29 1.23083530 -1.07557986
30 0.02172346 1.23083530
31 0.72418235 0.02172346
32 -2.38640739 0.72418235
33 -2.17491732 -2.38640739
34 0.41441021 -2.17491732
35 2.43622097 0.41441021
36 0.74628724 2.43622097
37 0.32356555 0.74628724
38 -2.25199537 0.32356555
39 -0.13212606 -2.25199537
40 -2.77321698 -0.13212606
41 3.74117245 -2.77321698
42 0.12696876 3.74117245
43 0.97737845 0.12696876
44 0.76888447 0.97737845
45 -2.12905680 0.76888447
46 1.52801040 -2.12905680
47 -1.07252905 1.52801040
48 0.30586203 -1.07252905
49 -3.58876608 0.30586203
50 -3.85350864 -3.58876608
51 -0.11012122 -3.85350864
52 -3.10809643 -0.11012122
53 -0.83885038 -3.10809643
54 1.50907596 -0.83885038
55 4.00840998 1.50907596
56 0.85952784 4.00840998
57 0.40837523 0.85952784
58 1.33087954 0.40837523
59 1.06423026 1.33087954
60 -1.64188005 1.06423026
61 -1.09449272 -1.64188005
62 2.37508846 -1.09449272
63 -1.34790001 2.37508846
64 1.25638625 -1.34790001
65 3.59175504 1.25638625
66 -5.76545692 3.59175504
67 0.60066388 -5.76545692
68 -0.99952039 0.60066388
69 2.17148458 -0.99952039
70 0.80816770 2.17148458
71 1.47151089 0.80816770
72 0.17966393 1.47151089
73 0.08526603 0.17966393
74 0.66547623 0.08526603
75 1.17468186 0.66547623
76 1.30170499 1.17468186
77 -1.90236354 1.30170499
78 0.74324727 -1.90236354
79 -0.19111633 0.74324727
80 -0.96444166 -0.19111633
81 1.47880632 -0.96444166
82 0.86929974 1.47880632
83 0.43233348 0.86929974
84 1.10827040 0.43233348
85 -0.60133411 1.10827040
86 0.41859399 -0.60133411
87 0.46714575 0.41859399
88 -0.41555426 0.46714575
89 -0.41192126 -0.41555426
90 0.65045086 -0.41192126
91 0.02981053 0.65045086
92 1.52934799 0.02981053
93 1.92540473 1.52934799
94 -0.23824042 1.92540473
95 -0.29200612 -0.23824042
96 -1.10593051 -0.29200612
97 -0.26523951 -1.10593051
98 -0.02348120 -0.26523951
99 0.92306429 -0.02348120
100 0.10965523 0.92306429
101 -0.22326809 0.10965523
102 -2.33954315 -0.22326809
103 1.90899206 -2.33954315
104 -2.80973280 1.90899206
105 -0.82834822 -2.80973280
106 0.92740013 -0.82834822
107 0.21314332 0.92740013
108 1.54268690 0.21314332
109 -1.87336521 1.54268690
110 0.31820814 -1.87336521
111 2.77142753 0.31820814
112 -2.41177373 2.77142753
113 0.52925069 -2.41177373
114 0.75341132 0.52925069
115 -0.18959940 0.75341132
116 1.47178978 -0.18959940
117 2.16231725 1.47178978
118 -0.25867548 2.16231725
119 -0.83643945 -0.25867548
120 -1.18841990 -0.83643945
121 -1.04031708 -1.18841990
122 1.14479639 -1.04031708
123 1.02766775 1.14479639
124 2.40250637 1.02766775
125 -0.23641714 2.40250637
126 0.31736724 -0.23641714
127 0.93330440 0.31736724
128 -2.04077476 0.93330440
129 -0.42052366 -2.04077476
130 0.88226244 -0.42052366
131 0.99096041 0.88226244
132 1.81906626 0.99096041
133 -4.34688528 1.81906626
134 -0.32820991 -4.34688528
135 -1.59943466 -0.32820991
136 -0.20153495 -1.59943466
137 0.31274724 -0.20153495
138 0.70963693 0.31274724
139 0.77890259 0.70963693
140 -4.58380941 0.77890259
141 1.05904035 -4.58380941
142 0.23941515 1.05904035
143 -2.05332460 0.23941515
144 1.40798656 -2.05332460
145 1.11101022 1.40798656
146 NA 1.11101022
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.43494423 -5.64970067
[2,] -0.47023852 1.43494423
[3,] -4.41662036 -0.47023852
[4,] 1.26072348 -4.41662036
[5,] 0.35631574 1.26072348
[6,] 1.37216204 0.35631574
[7,] -4.28868335 1.37216204
[8,] -2.46010045 -4.28868335
[9,] 2.18987918 -2.46010045
[10,] 1.17081757 2.18987918
[11,] 1.88027388 1.17081757
[12,] 1.14236285 1.88027388
[13,] 1.07413479 1.14236285
[14,] -1.01122464 1.07413479
[15,] 0.79095097 -1.01122464
[16,] 0.55792261 0.79095097
[17,] 0.01557349 0.55792261
[18,] -0.08214420 0.01557349
[19,] 1.59119799 -0.08214420
[20,] -1.44203113 1.59119799
[21,] -0.26545370 -1.44203113
[22,] -0.18638194 -0.26545370
[23,] -0.07443551 -0.18638194
[24,] -1.12842793 -0.07443551
[25,] 0.40937170 -1.12842793
[26,] 0.84944590 0.40937170
[27,] 1.31723514 0.84944590
[28,] -1.07557986 1.31723514
[29,] 1.23083530 -1.07557986
[30,] 0.02172346 1.23083530
[31,] 0.72418235 0.02172346
[32,] -2.38640739 0.72418235
[33,] -2.17491732 -2.38640739
[34,] 0.41441021 -2.17491732
[35,] 2.43622097 0.41441021
[36,] 0.74628724 2.43622097
[37,] 0.32356555 0.74628724
[38,] -2.25199537 0.32356555
[39,] -0.13212606 -2.25199537
[40,] -2.77321698 -0.13212606
[41,] 3.74117245 -2.77321698
[42,] 0.12696876 3.74117245
[43,] 0.97737845 0.12696876
[44,] 0.76888447 0.97737845
[45,] -2.12905680 0.76888447
[46,] 1.52801040 -2.12905680
[47,] -1.07252905 1.52801040
[48,] 0.30586203 -1.07252905
[49,] -3.58876608 0.30586203
[50,] -3.85350864 -3.58876608
[51,] -0.11012122 -3.85350864
[52,] -3.10809643 -0.11012122
[53,] -0.83885038 -3.10809643
[54,] 1.50907596 -0.83885038
[55,] 4.00840998 1.50907596
[56,] 0.85952784 4.00840998
[57,] 0.40837523 0.85952784
[58,] 1.33087954 0.40837523
[59,] 1.06423026 1.33087954
[60,] -1.64188005 1.06423026
[61,] -1.09449272 -1.64188005
[62,] 2.37508846 -1.09449272
[63,] -1.34790001 2.37508846
[64,] 1.25638625 -1.34790001
[65,] 3.59175504 1.25638625
[66,] -5.76545692 3.59175504
[67,] 0.60066388 -5.76545692
[68,] -0.99952039 0.60066388
[69,] 2.17148458 -0.99952039
[70,] 0.80816770 2.17148458
[71,] 1.47151089 0.80816770
[72,] 0.17966393 1.47151089
[73,] 0.08526603 0.17966393
[74,] 0.66547623 0.08526603
[75,] 1.17468186 0.66547623
[76,] 1.30170499 1.17468186
[77,] -1.90236354 1.30170499
[78,] 0.74324727 -1.90236354
[79,] -0.19111633 0.74324727
[80,] -0.96444166 -0.19111633
[81,] 1.47880632 -0.96444166
[82,] 0.86929974 1.47880632
[83,] 0.43233348 0.86929974
[84,] 1.10827040 0.43233348
[85,] -0.60133411 1.10827040
[86,] 0.41859399 -0.60133411
[87,] 0.46714575 0.41859399
[88,] -0.41555426 0.46714575
[89,] -0.41192126 -0.41555426
[90,] 0.65045086 -0.41192126
[91,] 0.02981053 0.65045086
[92,] 1.52934799 0.02981053
[93,] 1.92540473 1.52934799
[94,] -0.23824042 1.92540473
[95,] -0.29200612 -0.23824042
[96,] -1.10593051 -0.29200612
[97,] -0.26523951 -1.10593051
[98,] -0.02348120 -0.26523951
[99,] 0.92306429 -0.02348120
[100,] 0.10965523 0.92306429
[101,] -0.22326809 0.10965523
[102,] -2.33954315 -0.22326809
[103,] 1.90899206 -2.33954315
[104,] -2.80973280 1.90899206
[105,] -0.82834822 -2.80973280
[106,] 0.92740013 -0.82834822
[107,] 0.21314332 0.92740013
[108,] 1.54268690 0.21314332
[109,] -1.87336521 1.54268690
[110,] 0.31820814 -1.87336521
[111,] 2.77142753 0.31820814
[112,] -2.41177373 2.77142753
[113,] 0.52925069 -2.41177373
[114,] 0.75341132 0.52925069
[115,] -0.18959940 0.75341132
[116,] 1.47178978 -0.18959940
[117,] 2.16231725 1.47178978
[118,] -0.25867548 2.16231725
[119,] -0.83643945 -0.25867548
[120,] -1.18841990 -0.83643945
[121,] -1.04031708 -1.18841990
[122,] 1.14479639 -1.04031708
[123,] 1.02766775 1.14479639
[124,] 2.40250637 1.02766775
[125,] -0.23641714 2.40250637
[126,] 0.31736724 -0.23641714
[127,] 0.93330440 0.31736724
[128,] -2.04077476 0.93330440
[129,] -0.42052366 -2.04077476
[130,] 0.88226244 -0.42052366
[131,] 0.99096041 0.88226244
[132,] 1.81906626 0.99096041
[133,] -4.34688528 1.81906626
[134,] -0.32820991 -4.34688528
[135,] -1.59943466 -0.32820991
[136,] -0.20153495 -1.59943466
[137,] 0.31274724 -0.20153495
[138,] 0.70963693 0.31274724
[139,] 0.77890259 0.70963693
[140,] -4.58380941 0.77890259
[141,] 1.05904035 -4.58380941
[142,] 0.23941515 1.05904035
[143,] -2.05332460 0.23941515
[144,] 1.40798656 -2.05332460
[145,] 1.11101022 1.40798656
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.43494423 -5.64970067
2 -0.47023852 1.43494423
3 -4.41662036 -0.47023852
4 1.26072348 -4.41662036
5 0.35631574 1.26072348
6 1.37216204 0.35631574
7 -4.28868335 1.37216204
8 -2.46010045 -4.28868335
9 2.18987918 -2.46010045
10 1.17081757 2.18987918
11 1.88027388 1.17081757
12 1.14236285 1.88027388
13 1.07413479 1.14236285
14 -1.01122464 1.07413479
15 0.79095097 -1.01122464
16 0.55792261 0.79095097
17 0.01557349 0.55792261
18 -0.08214420 0.01557349
19 1.59119799 -0.08214420
20 -1.44203113 1.59119799
21 -0.26545370 -1.44203113
22 -0.18638194 -0.26545370
23 -0.07443551 -0.18638194
24 -1.12842793 -0.07443551
25 0.40937170 -1.12842793
26 0.84944590 0.40937170
27 1.31723514 0.84944590
28 -1.07557986 1.31723514
29 1.23083530 -1.07557986
30 0.02172346 1.23083530
31 0.72418235 0.02172346
32 -2.38640739 0.72418235
33 -2.17491732 -2.38640739
34 0.41441021 -2.17491732
35 2.43622097 0.41441021
36 0.74628724 2.43622097
37 0.32356555 0.74628724
38 -2.25199537 0.32356555
39 -0.13212606 -2.25199537
40 -2.77321698 -0.13212606
41 3.74117245 -2.77321698
42 0.12696876 3.74117245
43 0.97737845 0.12696876
44 0.76888447 0.97737845
45 -2.12905680 0.76888447
46 1.52801040 -2.12905680
47 -1.07252905 1.52801040
48 0.30586203 -1.07252905
49 -3.58876608 0.30586203
50 -3.85350864 -3.58876608
51 -0.11012122 -3.85350864
52 -3.10809643 -0.11012122
53 -0.83885038 -3.10809643
54 1.50907596 -0.83885038
55 4.00840998 1.50907596
56 0.85952784 4.00840998
57 0.40837523 0.85952784
58 1.33087954 0.40837523
59 1.06423026 1.33087954
60 -1.64188005 1.06423026
61 -1.09449272 -1.64188005
62 2.37508846 -1.09449272
63 -1.34790001 2.37508846
64 1.25638625 -1.34790001
65 3.59175504 1.25638625
66 -5.76545692 3.59175504
67 0.60066388 -5.76545692
68 -0.99952039 0.60066388
69 2.17148458 -0.99952039
70 0.80816770 2.17148458
71 1.47151089 0.80816770
72 0.17966393 1.47151089
73 0.08526603 0.17966393
74 0.66547623 0.08526603
75 1.17468186 0.66547623
76 1.30170499 1.17468186
77 -1.90236354 1.30170499
78 0.74324727 -1.90236354
79 -0.19111633 0.74324727
80 -0.96444166 -0.19111633
81 1.47880632 -0.96444166
82 0.86929974 1.47880632
83 0.43233348 0.86929974
84 1.10827040 0.43233348
85 -0.60133411 1.10827040
86 0.41859399 -0.60133411
87 0.46714575 0.41859399
88 -0.41555426 0.46714575
89 -0.41192126 -0.41555426
90 0.65045086 -0.41192126
91 0.02981053 0.65045086
92 1.52934799 0.02981053
93 1.92540473 1.52934799
94 -0.23824042 1.92540473
95 -0.29200612 -0.23824042
96 -1.10593051 -0.29200612
97 -0.26523951 -1.10593051
98 -0.02348120 -0.26523951
99 0.92306429 -0.02348120
100 0.10965523 0.92306429
101 -0.22326809 0.10965523
102 -2.33954315 -0.22326809
103 1.90899206 -2.33954315
104 -2.80973280 1.90899206
105 -0.82834822 -2.80973280
106 0.92740013 -0.82834822
107 0.21314332 0.92740013
108 1.54268690 0.21314332
109 -1.87336521 1.54268690
110 0.31820814 -1.87336521
111 2.77142753 0.31820814
112 -2.41177373 2.77142753
113 0.52925069 -2.41177373
114 0.75341132 0.52925069
115 -0.18959940 0.75341132
116 1.47178978 -0.18959940
117 2.16231725 1.47178978
118 -0.25867548 2.16231725
119 -0.83643945 -0.25867548
120 -1.18841990 -0.83643945
121 -1.04031708 -1.18841990
122 1.14479639 -1.04031708
123 1.02766775 1.14479639
124 2.40250637 1.02766775
125 -0.23641714 2.40250637
126 0.31736724 -0.23641714
127 0.93330440 0.31736724
128 -2.04077476 0.93330440
129 -0.42052366 -2.04077476
130 0.88226244 -0.42052366
131 0.99096041 0.88226244
132 1.81906626 0.99096041
133 -4.34688528 1.81906626
134 -0.32820991 -4.34688528
135 -1.59943466 -0.32820991
136 -0.20153495 -1.59943466
137 0.31274724 -0.20153495
138 0.70963693 0.31274724
139 0.77890259 0.70963693
140 -4.58380941 0.77890259
141 1.05904035 -4.58380941
142 0.23941515 1.05904035
143 -2.05332460 0.23941515
144 1.40798656 -2.05332460
145 1.11101022 1.40798656
> 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/rcomp/tmp/731vq1292185129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/831vq1292185129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9vsub1292185129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10vsub1292185129.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11ztbz1292185129.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/rcomp/tmp/122t9n1292185129.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/rcomp/tmp/139c6g1292185129.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/rcomp/tmp/14k3o11292185129.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/rcomp/tmp/155mmp1292185129.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/rcomp/tmp/161wkg1292185129.tab")
+ }
>
> try(system("convert tmp/17ry01292185129.ps tmp/17ry01292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z0fk1292185129.ps tmp/2z0fk1292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z0fk1292185129.ps tmp/3z0fk1292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z0fk1292185129.ps tmp/4z0fk1292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/5sawn1292185129.ps tmp/5sawn1292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sawn1292185129.ps tmp/6sawn1292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/731vq1292185129.ps tmp/731vq1292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/831vq1292185129.ps tmp/831vq1292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vsub1292185129.ps tmp/9vsub1292185129.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vsub1292185129.ps tmp/10vsub1292185129.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.320 1.850 6.173