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(11
+ ,14
+ ,11
+ ,12
+ ,12
+ ,11
+ ,11
+ ,7
+ ,8
+ ,11
+ ,11
+ ,6
+ ,17
+ ,8
+ ,14
+ ,11
+ ,12
+ ,10
+ ,8
+ ,12
+ ,11
+ ,8
+ ,12
+ ,9
+ ,21
+ ,11
+ ,10
+ ,12
+ ,7
+ ,12
+ ,11
+ ,10
+ ,11
+ ,4
+ ,22
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,16
+ ,12
+ ,7
+ ,10
+ ,11
+ ,11
+ ,13
+ ,7
+ ,13
+ ,11
+ ,13
+ ,14
+ ,12
+ ,10
+ ,11
+ ,12
+ ,16
+ ,10
+ ,8
+ ,11
+ ,8
+ ,11
+ ,10
+ ,15
+ ,11
+ ,12
+ ,10
+ ,8
+ ,14
+ ,11
+ ,11
+ ,11
+ ,8
+ ,10
+ ,11
+ ,4
+ ,15
+ ,4
+ ,14
+ ,11
+ ,9
+ ,9
+ ,9
+ ,14
+ ,11
+ ,8
+ ,11
+ ,8
+ ,11
+ ,11
+ ,8
+ ,17
+ ,7
+ ,10
+ ,11
+ ,14
+ ,17
+ ,11
+ ,13
+ ,11
+ ,15
+ ,11
+ ,9
+ ,7
+ ,11
+ ,16
+ ,18
+ ,11
+ ,14
+ ,11
+ ,9
+ ,14
+ ,13
+ ,12
+ ,11
+ ,14
+ ,10
+ ,8
+ ,14
+ ,11
+ ,11
+ ,11
+ ,8
+ ,11
+ ,11
+ ,8
+ ,15
+ ,9
+ ,9
+ ,11
+ ,9
+ ,15
+ ,6
+ ,11
+ ,11
+ ,9
+ ,13
+ ,9
+ ,15
+ ,11
+ ,9
+ ,16
+ ,9
+ ,14
+ ,11
+ ,9
+ ,13
+ ,6
+ ,13
+ ,11
+ ,10
+ ,9
+ ,6
+ ,9
+ ,11
+ ,16
+ ,18
+ ,16
+ ,15
+ ,11
+ ,11
+ ,18
+ ,5
+ ,10
+ ,11
+ ,8
+ ,12
+ ,7
+ ,11
+ ,11
+ ,9
+ ,17
+ ,9
+ ,13
+ ,11
+ ,16
+ ,9
+ ,6
+ ,8
+ ,11
+ ,11
+ ,9
+ ,6
+ ,20
+ ,11
+ ,16
+ ,12
+ ,5
+ ,12
+ ,11
+ ,12
+ ,18
+ ,12
+ ,10
+ ,11
+ ,12
+ ,12
+ ,7
+ ,10
+ ,11
+ ,14
+ ,18
+ ,10
+ ,9
+ ,11
+ ,9
+ ,14
+ ,9
+ ,14
+ ,11
+ ,10
+ ,15
+ ,8
+ ,8
+ ,11
+ ,9
+ ,16
+ ,5
+ ,14
+ ,11
+ ,10
+ ,10
+ ,8
+ ,11
+ ,11
+ ,12
+ ,11
+ ,8
+ ,13
+ ,11
+ ,14
+ ,14
+ ,10
+ ,9
+ ,11
+ ,14
+ ,9
+ ,6
+ ,11
+ ,11
+ ,10
+ ,12
+ ,8
+ ,15
+ ,11
+ ,14
+ ,17
+ ,7
+ ,11
+ ,11
+ ,16
+ ,5
+ ,4
+ ,10
+ ,11
+ ,9
+ ,12
+ ,8
+ ,14
+ ,11
+ ,10
+ ,12
+ ,8
+ ,18
+ ,11
+ ,6
+ ,6
+ ,4
+ ,14
+ ,11
+ ,8
+ ,24
+ ,20
+ ,11
+ ,11
+ ,13
+ ,12
+ ,8
+ ,12
+ ,11
+ ,10
+ ,12
+ ,8
+ ,13
+ ,11
+ ,8
+ ,14
+ ,6
+ ,9
+ ,11
+ ,7
+ ,7
+ ,4
+ ,10
+ ,11
+ ,15
+ ,13
+ ,8
+ ,15
+ ,11
+ ,9
+ ,12
+ ,9
+ ,20
+ ,11
+ ,10
+ ,13
+ ,6
+ ,12
+ ,11
+ ,12
+ ,14
+ ,7
+ ,12
+ ,11
+ ,13
+ ,8
+ ,9
+ ,14
+ ,11
+ ,10
+ ,11
+ ,5
+ ,13
+ ,11
+ ,11
+ ,9
+ ,5
+ ,11
+ ,11
+ ,8
+ ,11
+ ,8
+ ,17
+ ,11
+ ,9
+ ,13
+ ,8
+ ,12
+ ,11
+ ,13
+ ,10
+ ,6
+ ,13
+ ,11
+ ,11
+ ,11
+ ,8
+ ,14
+ ,11
+ ,8
+ ,12
+ ,7
+ ,13
+ ,11
+ ,9
+ ,9
+ ,7
+ ,15
+ ,11
+ ,9
+ ,15
+ ,9
+ ,13
+ ,11
+ ,15
+ ,18
+ ,11
+ ,10
+ ,11
+ ,9
+ ,15
+ ,6
+ ,11
+ ,11
+ ,10
+ ,12
+ ,8
+ ,19
+ ,11
+ ,14
+ ,13
+ ,6
+ ,13
+ ,11
+ ,12
+ ,14
+ ,9
+ ,17
+ ,11
+ ,12
+ ,10
+ ,8
+ ,13
+ ,11
+ ,11
+ ,13
+ ,6
+ ,9
+ ,11
+ ,14
+ ,13
+ ,10
+ ,11
+ ,11
+ ,6
+ ,11
+ ,8
+ ,10
+ ,11
+ ,12
+ ,13
+ ,8
+ ,9
+ ,11
+ ,8
+ ,16
+ ,10
+ ,12
+ ,11
+ ,14
+ ,8
+ ,5
+ ,12
+ ,11
+ ,11
+ ,16
+ ,7
+ ,13
+ ,11
+ ,10
+ ,11
+ ,5
+ ,13
+ ,11
+ ,14
+ ,9
+ ,8
+ ,12
+ ,11
+ ,12
+ ,16
+ ,14
+ ,15
+ ,11
+ ,10
+ ,12
+ ,7
+ ,22
+ ,11
+ ,14
+ ,14
+ ,8
+ ,13
+ ,11
+ ,5
+ ,8
+ ,6
+ ,15
+ ,11
+ ,11
+ ,9
+ ,5
+ ,13
+ ,11
+ ,10
+ ,15
+ ,6
+ ,15
+ ,11
+ ,9
+ ,11
+ ,10
+ ,10
+ ,11
+ ,10
+ ,21
+ ,12
+ ,11
+ ,11
+ ,16
+ ,14
+ ,9
+ ,16
+ ,11
+ ,13
+ ,18
+ ,12
+ ,11
+ ,11
+ ,9
+ ,12
+ ,7
+ ,11
+ ,11
+ ,10
+ ,13
+ ,8
+ ,10
+ ,11
+ ,10
+ ,15
+ ,10
+ ,10
+ ,11
+ ,7
+ ,12
+ ,6
+ ,16
+ ,11
+ ,9
+ ,19
+ ,10
+ ,12
+ ,11
+ ,8
+ ,15
+ ,10
+ ,11
+ ,11
+ ,14
+ ,11
+ ,10
+ ,16
+ ,11
+ ,14
+ ,11
+ ,5
+ ,19
+ ,11
+ ,8
+ ,10
+ ,7
+ ,11
+ ,11
+ ,9
+ ,13
+ ,10
+ ,16
+ ,11
+ ,14
+ ,15
+ ,11
+ ,15
+ ,11
+ ,14
+ ,12
+ ,6
+ ,24
+ ,11
+ ,8
+ ,12
+ ,7
+ ,14
+ ,11
+ ,8
+ ,16
+ ,12
+ ,15
+ ,11
+ ,8
+ ,9
+ ,11
+ ,11
+ ,11
+ ,7
+ ,18
+ ,11
+ ,15
+ ,11
+ ,6
+ ,8
+ ,11
+ ,12
+ ,11
+ ,8
+ ,13
+ ,5
+ ,10
+ ,11
+ ,6
+ ,17
+ ,8
+ ,14
+ ,11
+ ,11
+ ,9
+ ,6
+ ,13
+ ,11
+ ,14
+ ,15
+ ,9
+ ,9
+ ,11
+ ,11
+ ,8
+ ,4
+ ,15
+ ,11
+ ,11
+ ,7
+ ,4
+ ,15
+ ,11
+ ,11
+ ,12
+ ,7
+ ,14
+ ,11
+ ,14
+ ,14
+ ,11
+ ,11
+ ,11
+ ,8
+ ,6
+ ,6
+ ,8
+ ,11
+ ,20
+ ,8
+ ,7
+ ,11
+ ,11
+ ,11
+ ,17
+ ,8
+ ,11
+ ,11
+ ,8
+ ,10
+ ,4
+ ,8
+ ,11
+ ,11
+ ,11
+ ,8
+ ,10
+ ,11
+ ,10
+ ,14
+ ,9
+ ,11
+ ,11
+ ,14
+ ,11
+ ,8
+ ,13
+ ,11
+ ,11
+ ,13
+ ,11
+ ,11
+ ,11
+ ,9
+ ,12
+ ,8
+ ,20
+ ,11
+ ,9
+ ,11
+ ,5
+ ,10
+ ,11
+ ,8
+ ,9
+ ,4
+ ,15
+ ,11
+ ,10
+ ,12
+ ,8
+ ,12
+ ,11
+ ,13
+ ,20
+ ,10
+ ,14
+ ,11
+ ,13
+ ,12
+ ,6
+ ,23
+ ,11
+ ,12
+ ,13
+ ,9
+ ,14
+ ,11
+ ,8
+ ,12
+ ,9
+ ,16
+ ,11
+ ,13
+ ,12
+ ,13
+ ,11
+ ,11
+ ,14
+ ,9
+ ,9
+ ,12
+ ,11
+ ,12
+ ,15
+ ,10
+ ,10
+ ,11
+ ,14
+ ,24
+ ,20
+ ,14
+ ,11
+ ,15
+ ,7
+ ,5
+ ,12
+ ,11
+ ,13
+ ,17
+ ,11
+ ,12
+ ,11
+ ,16
+ ,11
+ ,6
+ ,11
+ ,11
+ ,9
+ ,17
+ ,9
+ ,12
+ ,11
+ ,9
+ ,11
+ ,7
+ ,13
+ ,11
+ ,9
+ ,12
+ ,9
+ ,11
+ ,11
+ ,8
+ ,14
+ ,10
+ ,19
+ ,11
+ ,7
+ ,11
+ ,9
+ ,12
+ ,11
+ ,16
+ ,16
+ ,8
+ ,17
+ ,11
+ ,11
+ ,21
+ ,7
+ ,9
+ ,11
+ ,9
+ ,14
+ ,6
+ ,12
+ ,11
+ ,11
+ ,20
+ ,13
+ ,19
+ ,11
+ ,9
+ ,13
+ ,6
+ ,18
+ ,11
+ ,14
+ ,11
+ ,8
+ ,15
+ ,11
+ ,13
+ ,15
+ ,10
+ ,14
+ ,11
+ ,16
+ ,19
+ ,16
+ ,11
+ ,11
+ ,9
+ ,11
+ ,18
+ ,11
+ ,16)
+ ,dim=c(5
+ ,162)
+ ,dimnames=list(c('Month'
+ ,'Doubts'
+ ,'Expectations'
+ ,'Criticism'
+ ,'Depression')
+ ,1:162))
> y <- array(NA,dim=c(5,162),dimnames=list(c('Month','Doubts','Expectations','Criticism','Depression'),1:162))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '5'
> #'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
Depression Month Doubts Expectations Criticism t
1 12 11 14 11 12 1
2 11 11 11 7 8 2
3 14 11 6 17 8 3
4 12 11 12 10 8 4
5 21 11 8 12 9 5
6 12 11 10 12 7 6
7 22 11 10 11 4 7
8 11 11 11 11 11 8
9 10 11 16 12 7 9
10 13 11 11 13 7 10
11 10 11 13 14 12 11
12 8 11 12 16 10 12
13 15 11 8 11 10 13
14 14 11 12 10 8 14
15 10 11 11 11 8 15
16 14 11 4 15 4 16
17 14 11 9 9 9 17
18 11 11 8 11 8 18
19 10 11 8 17 7 19
20 13 11 14 17 11 20
21 7 11 15 11 9 21
22 14 11 16 18 11 22
23 12 11 9 14 13 23
24 14 11 14 10 8 24
25 11 11 11 11 8 25
26 9 11 8 15 9 26
27 11 11 9 15 6 27
28 15 11 9 13 9 28
29 14 11 9 16 9 29
30 13 11 9 13 6 30
31 9 11 10 9 6 31
32 15 11 16 18 16 32
33 10 11 11 18 5 33
34 11 11 8 12 7 34
35 13 11 9 17 9 35
36 8 11 16 9 6 36
37 20 11 11 9 6 37
38 12 11 16 12 5 38
39 10 11 12 18 12 39
40 10 11 12 12 7 40
41 9 11 14 18 10 41
42 14 11 9 14 9 42
43 8 11 10 15 8 43
44 14 11 9 16 5 44
45 11 11 10 10 8 45
46 13 11 12 11 8 46
47 9 11 14 14 10 47
48 11 11 14 9 6 48
49 15 11 10 12 8 49
50 11 11 14 17 7 50
51 10 11 16 5 4 51
52 14 11 9 12 8 52
53 18 11 10 12 8 53
54 14 11 6 6 4 54
55 11 11 8 24 20 55
56 12 11 13 12 8 56
57 13 11 10 12 8 57
58 9 11 8 14 6 58
59 10 11 7 7 4 59
60 15 11 15 13 8 60
61 20 11 9 12 9 61
62 12 11 10 13 6 62
63 12 11 12 14 7 63
64 14 11 13 8 9 64
65 13 11 10 11 5 65
66 11 11 11 9 5 66
67 17 11 8 11 8 67
68 12 11 9 13 8 68
69 13 11 13 10 6 69
70 14 11 11 11 8 70
71 13 11 8 12 7 71
72 15 11 9 9 7 72
73 13 11 9 15 9 73
74 10 11 15 18 11 74
75 11 11 9 15 6 75
76 19 11 10 12 8 76
77 13 11 14 13 6 77
78 17 11 12 14 9 78
79 13 11 12 10 8 79
80 9 11 11 13 6 80
81 11 11 14 13 10 81
82 10 11 6 11 8 82
83 9 11 12 13 8 83
84 12 11 8 16 10 84
85 12 11 14 8 5 85
86 13 11 11 16 7 86
87 13 11 10 11 5 87
88 12 11 14 9 8 88
89 15 11 12 16 14 89
90 22 11 10 12 7 90
91 13 11 14 14 8 91
92 15 11 5 8 6 92
93 13 11 11 9 5 93
94 15 11 10 15 6 94
95 10 11 9 11 10 95
96 11 11 10 21 12 96
97 16 11 16 14 9 97
98 11 11 13 18 12 98
99 11 11 9 12 7 99
100 10 11 10 13 8 100
101 10 11 10 15 10 101
102 16 11 7 12 6 102
103 12 11 9 19 10 103
104 11 11 8 15 10 104
105 16 11 14 11 10 105
106 19 11 14 11 5 106
107 11 11 8 10 7 107
108 16 11 9 13 10 108
109 15 11 14 15 11 109
110 24 11 14 12 6 110
111 14 11 8 12 7 111
112 15 11 8 16 12 112
113 11 11 8 9 11 113
114 15 11 7 18 11 114
115 12 11 6 8 11 115
116 10 11 8 13 5 116
117 14 11 6 17 8 117
118 13 11 11 9 6 118
119 9 11 14 15 9 119
120 15 11 11 8 4 120
121 15 11 11 7 4 121
122 14 11 11 12 7 122
123 11 11 14 14 11 123
124 8 11 8 6 6 124
125 11 11 20 8 7 125
126 11 11 11 17 8 126
127 8 11 8 10 4 127
128 10 11 11 11 8 128
129 11 11 10 14 9 129
130 13 11 14 11 8 130
131 11 11 11 13 11 131
132 20 11 9 12 8 132
133 10 11 9 11 5 133
134 15 11 8 9 4 134
135 12 11 10 12 8 135
136 14 11 13 20 10 136
137 23 11 13 12 6 137
138 14 11 12 13 9 138
139 16 11 8 12 9 139
140 11 11 13 12 13 140
141 12 11 14 9 9 141
142 10 11 12 15 10 142
143 14 11 14 24 20 143
144 12 11 15 7 5 144
145 12 11 13 17 11 145
146 11 11 16 11 6 146
147 12 11 9 17 9 147
148 13 11 9 11 7 148
149 11 11 9 12 9 149
150 19 11 8 14 10 150
151 12 11 7 11 9 151
152 17 11 16 16 8 152
153 9 11 11 21 7 153
154 12 11 9 14 6 154
155 19 11 11 20 13 155
156 18 11 9 13 6 156
157 15 11 14 11 8 157
158 14 11 13 15 10 158
159 11 11 16 19 16 159
160 11 11 9 11 18 160
161 12 16 11 14 11 161
162 8 12 11 11 7 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month Doubts Expectations Criticism
18.729464 -0.457083 -0.078015 -0.013447 -0.047892
t
0.007117
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.6553 -2.0699 -0.4099 1.4382 11.0565
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.729464 7.033314 2.663 0.00856 **
Month -0.457083 0.631823 -0.723 0.47050
Doubts -0.078015 0.090654 -0.861 0.39079
Expectations -0.013447 0.088020 -0.153 0.87877
Criticism -0.047892 0.108905 -0.440 0.66072
t 0.007117 0.005404 1.317 0.18974
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.162 on 156 degrees of freedom
Multiple R-squared: 0.01985, Adjusted R-squared: -0.01157
F-statistic: 0.6317 on 5 and 156 DF, p-value: 0.6758
> 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,] 0.97961805 0.04076390 0.02038195
[2,] 0.95973601 0.08052798 0.04026399
[3,] 0.92513428 0.14973144 0.07486572
[4,] 0.90478677 0.19042645 0.09521323
[5,] 0.85281192 0.29437615 0.14718808
[6,] 0.78907386 0.42185227 0.21092614
[7,] 0.78487010 0.43025979 0.21512990
[8,] 0.79595862 0.40808275 0.20404138
[9,] 0.73191424 0.53617152 0.26808576
[10,] 0.69040612 0.61918776 0.30959388
[11,] 0.62391462 0.75217076 0.37608538
[12,] 0.74981740 0.50036521 0.25018260
[13,] 0.73025027 0.53949946 0.26974973
[14,] 0.82057307 0.35885386 0.17942693
[15,] 0.77501230 0.44997540 0.22498770
[16,] 0.76353810 0.47292380 0.23646190
[17,] 0.71217119 0.57565762 0.28782881
[18,] 0.70170848 0.59658304 0.29829152
[19,] 0.64839285 0.70321430 0.35160715
[20,] 0.65503278 0.68993444 0.34496722
[21,] 0.62339881 0.75320238 0.37660119
[22,] 0.56250677 0.87498646 0.43749323
[23,] 0.55892284 0.88215431 0.44107716
[24,] 0.64116566 0.71766868 0.35883434
[25,] 0.60059587 0.79880826 0.39940413
[26,] 0.54723962 0.90552076 0.45276038
[27,] 0.49433932 0.98867864 0.50566068
[28,] 0.46519610 0.93039220 0.53480390
[29,] 0.77211151 0.45577699 0.22788849
[30,] 0.73343410 0.53313181 0.26656590
[31,] 0.69765960 0.60468081 0.30234040
[32,] 0.66457441 0.67085119 0.33542559
[33,] 0.63683902 0.72632197 0.36316098
[34,] 0.60108268 0.79783464 0.39891732
[35,] 0.62664647 0.74670707 0.37335353
[36,] 0.59559697 0.80880606 0.40440303
[37,] 0.55035855 0.89928290 0.44964145
[38,] 0.50922805 0.98154391 0.49077195
[39,] 0.48566955 0.97133909 0.51433045
[40,] 0.44046456 0.88092911 0.55953544
[41,] 0.43320481 0.86640962 0.56679519
[42,] 0.39230270 0.78460540 0.60769730
[43,] 0.36279577 0.72559155 0.63720423
[44,] 0.32764888 0.65529777 0.67235112
[45,] 0.42512384 0.85024768 0.57487616
[46,] 0.37911404 0.75822807 0.62088596
[47,] 0.33829631 0.67659262 0.66170369
[48,] 0.29701164 0.59402328 0.70298836
[49,] 0.25645104 0.51290208 0.74354896
[50,] 0.27729766 0.55459531 0.72270234
[51,] 0.28207431 0.56414862 0.71792569
[52,] 0.29846296 0.59692593 0.70153704
[53,] 0.48912782 0.97825563 0.51087218
[54,] 0.44439371 0.88878741 0.55560629
[55,] 0.40066297 0.80132594 0.59933703
[56,] 0.36352227 0.72704454 0.63647773
[57,] 0.32018634 0.64037268 0.67981366
[58,] 0.29354635 0.58709270 0.70645365
[59,] 0.30934738 0.61869476 0.69065262
[60,] 0.27298745 0.54597491 0.72701255
[61,] 0.23706576 0.47413152 0.76293424
[62,] 0.20632351 0.41264703 0.79367649
[63,] 0.17473141 0.34946281 0.82526859
[64,] 0.15416439 0.30832879 0.84583561
[65,] 0.12792250 0.25584499 0.87207750
[66,] 0.11583335 0.23166670 0.88416665
[67,] 0.10275414 0.20550829 0.89724586
[68,] 0.16750080 0.33500161 0.83249920
[69,] 0.14318296 0.28636593 0.85681704
[70,] 0.16503036 0.33006072 0.83496964
[71,] 0.13813371 0.27626742 0.86186629
[72,] 0.15434351 0.30868703 0.84565649
[73,] 0.13537257 0.27074515 0.86462743
[74,] 0.14747349 0.29494699 0.85252651
[75,] 0.16200493 0.32400985 0.83799507
[76,] 0.13854304 0.27708608 0.86145696
[77,] 0.11823337 0.23646674 0.88176663
[78,] 0.09961350 0.19922701 0.90038650
[79,] 0.08161615 0.16323231 0.91838385
[80,] 0.06763421 0.13526842 0.93236579
[81,] 0.06000985 0.12001969 0.93999015
[82,] 0.21266559 0.42533118 0.78733441
[83,] 0.18250013 0.36500025 0.81749987
[84,] 0.16331049 0.32662097 0.83668951
[85,] 0.13640797 0.27281593 0.86359203
[86,] 0.11962643 0.23925287 0.88037357
[87,] 0.12204109 0.24408218 0.87795891
[88,] 0.10867956 0.21735912 0.89132044
[89,] 0.10928060 0.21856120 0.89071940
[90,] 0.09706496 0.19412993 0.90293504
[91,] 0.08882426 0.17764852 0.91117574
[92,] 0.09138830 0.18277661 0.90861170
[93,] 0.09447779 0.18895557 0.90552221
[94,] 0.08511859 0.17023717 0.91488141
[95,] 0.07340355 0.14680710 0.92659645
[96,] 0.06835269 0.13670538 0.93164731
[97,] 0.06389610 0.12779220 0.93610390
[98,] 0.09799520 0.19599040 0.90200480
[99,] 0.09033555 0.18067111 0.90966445
[100,] 0.08304521 0.16609042 0.91695479
[101,] 0.07111103 0.14222206 0.92888897
[102,] 0.43108646 0.86217293 0.56891354
[103,] 0.38862343 0.77724686 0.61137657
[104,] 0.36362786 0.72725572 0.63637214
[105,] 0.33482930 0.66965861 0.66517070
[106,] 0.30955751 0.61911502 0.69044249
[107,] 0.27485227 0.54970454 0.72514773
[108,] 0.27115662 0.54231325 0.72884338
[109,] 0.23271998 0.46543995 0.76728002
[110,] 0.19795477 0.39590954 0.80204523
[111,] 0.20473750 0.40947500 0.79526250
[112,] 0.18697884 0.37395768 0.81302116
[113,] 0.17641520 0.35283040 0.82358480
[114,] 0.15208686 0.30417372 0.84791314
[115,] 0.12634789 0.25269578 0.87365211
[116,] 0.15099455 0.30198909 0.84900545
[117,] 0.12377864 0.24755728 0.87622136
[118,] 0.10764123 0.21528246 0.89235877
[119,] 0.15744789 0.31489578 0.84255211
[120,] 0.15724861 0.31449722 0.84275139
[121,] 0.14911711 0.29823423 0.85088289
[122,] 0.11836359 0.23672718 0.88163641
[123,] 0.11089727 0.22179454 0.88910273
[124,] 0.18460564 0.36921127 0.81539436
[125,] 0.20825996 0.41651992 0.79174004
[126,] 0.16741858 0.33483717 0.83258142
[127,] 0.14577949 0.29155899 0.85422051
[128,] 0.11612895 0.23225790 0.88387105
[129,] 0.48899626 0.97799253 0.51100374
[130,] 0.42696519 0.85393038 0.57303481
[131,] 0.41987257 0.83974515 0.58012743
[132,] 0.35661188 0.71322377 0.64338812
[133,] 0.29031495 0.58062989 0.70968505
[134,] 0.27420044 0.54840088 0.72579956
[135,] 0.21426163 0.42852325 0.78573837
[136,] 0.16039186 0.32078372 0.83960814
[137,] 0.12395644 0.24791287 0.87604356
[138,] 0.10902173 0.21804347 0.89097827
[139,] 0.09351072 0.18702143 0.90648928
[140,] 0.06770458 0.13540916 0.93229542
[141,] 0.10314532 0.20629063 0.89685468
[142,] 0.09407980 0.18815960 0.90592020
[143,] 0.10539307 0.21078614 0.89460693
[144,] 0.06170720 0.12341440 0.93829280
[145,] 0.20799461 0.41598922 0.79200539
> postscript(file="/var/www/html/rcomp/tmp/18urb1290547640.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28urb1290547640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30mqe1290547640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40mqe1290547640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50mqe1290547640.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 = 162
Frequency = 1
1 2 3 4 5 6
0.106168215 -1.380350974 1.356932271 -0.276228612 8.479382145 -0.467489813
7 8 9 10 11 12
9.368269371 -1.225588892 -2.020753418 0.595503184 -2.002677058 -4.156698320
13 14 15 16 17 18
2.456888559 1.652598606 -2.419085972 0.889915258 1.431647297 -1.674481921
19 20 21 22 23 24
-2.648806815 1.003732313 -5.101838778 2.158974570 -0.352251172 1.737455233
25 26 27 28 29 30
-1.490258755 -3.629738484 -1.702517190 2.407146855 1.440371791 0.249236165
31 32 33 34 35 36
-3.733656027 3.327262010 -2.596741280 -1.822803013 0.411115526 -4.301154189
37 38 39 40 41 42
7.301655008 -0.322938576 -2.226185934 -2.553447863 -3.180175170 1.320952364
43 44 45 46 47 48
-4.642594849 1.142044440 -1.724066429 0.438293107 -3.276668458 -1.542590938
49 50 51 52 53 54
2.274359268 -1.401354212 -2.557487071 1.174992728 5.245890155 0.654461451
55 56 57 58 59 60
-1.188300422 -0.541417566 0.217421042 -4.014614925 -3.289662830 2.599590136
61 62 63 64 65 66
7.158829268 -0.900502033 -0.690250453 1.395746634 0.003359278 -1.952638105
67 68 69 70 71 72
3.976771445 -0.925436319 0.243378919 1.189463725 -0.086142307 1.944412905
73 74 75 76 77 78
0.113764144 -2.289138603 -2.044146546 6.082192755 0.304797612 4.298774462
79 80 81 82 83 84
0.189975521 -3.950598337 -1.532103323 -3.286017138 -3.798151378 -0.981201172
85 86 87 88 89 90
-0.867269682 0.094932253 -0.153220843 -0.731497979 2.486839433 8.934658816
91 92 93 94 95 96
0.314387211 1.428669072 -0.144804617 1.898639873 -3.048713552 -1.747557989
97 98 99 100 101 102
3.475604995 -1.568090645 -2.207411393 -3.075174518 -2.959612898 2.567315318
103 104 105 106 107 108
-0.998072540 -2.136994142 3.270187190 6.023609690 -2.369259133 2.885656641
109 110 111 112 113 114
2.343399740 11.056480026 0.629166563 1.915299126 -2.233842030 1.802052630
115 116 117 118 119 120
-1.417553401 -3.488756512 0.545562552 -0.274844529 -3.823557131 1.601689421
121 122 123 124 125 126
1.581124738 0.784920617 -1.769689560 -5.591934527 -1.588088494 -2.128419428
127 128 129 130 131 132
-5.655280831 -3.223338413 -2.220236137 -0.003528855 -2.074119305 6.605610469
133 134 135 136 137 138
-3.558630347 1.281450816 -1.337726661 1.092563502 9.786298809 0.858290363
139 140 141 142 143 144
2.525666861 -1.899808716 -1.060821681 -3.095391896 1.653467322 -1.222621798
145 146 147 148 149 150
-0.963942172 -2.057159986 -1.386019636 -0.569605431 -2.467491216 5.522163655
151 152 153 154 155 156
-1.651202587 4.063157457 -4.314688365 -1.619858931 5.944981939 4.352459108
157 158 159 160 161 162
1.804304633 0.868746357 -1.563184921 -2.128200282 1.011226898 -5.056134488
> postscript(file="/var/www/html/rcomp/tmp/6tdpz1290547640.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 0.106168215 NA
1 -1.380350974 0.106168215
2 1.356932271 -1.380350974
3 -0.276228612 1.356932271
4 8.479382145 -0.276228612
5 -0.467489813 8.479382145
6 9.368269371 -0.467489813
7 -1.225588892 9.368269371
8 -2.020753418 -1.225588892
9 0.595503184 -2.020753418
10 -2.002677058 0.595503184
11 -4.156698320 -2.002677058
12 2.456888559 -4.156698320
13 1.652598606 2.456888559
14 -2.419085972 1.652598606
15 0.889915258 -2.419085972
16 1.431647297 0.889915258
17 -1.674481921 1.431647297
18 -2.648806815 -1.674481921
19 1.003732313 -2.648806815
20 -5.101838778 1.003732313
21 2.158974570 -5.101838778
22 -0.352251172 2.158974570
23 1.737455233 -0.352251172
24 -1.490258755 1.737455233
25 -3.629738484 -1.490258755
26 -1.702517190 -3.629738484
27 2.407146855 -1.702517190
28 1.440371791 2.407146855
29 0.249236165 1.440371791
30 -3.733656027 0.249236165
31 3.327262010 -3.733656027
32 -2.596741280 3.327262010
33 -1.822803013 -2.596741280
34 0.411115526 -1.822803013
35 -4.301154189 0.411115526
36 7.301655008 -4.301154189
37 -0.322938576 7.301655008
38 -2.226185934 -0.322938576
39 -2.553447863 -2.226185934
40 -3.180175170 -2.553447863
41 1.320952364 -3.180175170
42 -4.642594849 1.320952364
43 1.142044440 -4.642594849
44 -1.724066429 1.142044440
45 0.438293107 -1.724066429
46 -3.276668458 0.438293107
47 -1.542590938 -3.276668458
48 2.274359268 -1.542590938
49 -1.401354212 2.274359268
50 -2.557487071 -1.401354212
51 1.174992728 -2.557487071
52 5.245890155 1.174992728
53 0.654461451 5.245890155
54 -1.188300422 0.654461451
55 -0.541417566 -1.188300422
56 0.217421042 -0.541417566
57 -4.014614925 0.217421042
58 -3.289662830 -4.014614925
59 2.599590136 -3.289662830
60 7.158829268 2.599590136
61 -0.900502033 7.158829268
62 -0.690250453 -0.900502033
63 1.395746634 -0.690250453
64 0.003359278 1.395746634
65 -1.952638105 0.003359278
66 3.976771445 -1.952638105
67 -0.925436319 3.976771445
68 0.243378919 -0.925436319
69 1.189463725 0.243378919
70 -0.086142307 1.189463725
71 1.944412905 -0.086142307
72 0.113764144 1.944412905
73 -2.289138603 0.113764144
74 -2.044146546 -2.289138603
75 6.082192755 -2.044146546
76 0.304797612 6.082192755
77 4.298774462 0.304797612
78 0.189975521 4.298774462
79 -3.950598337 0.189975521
80 -1.532103323 -3.950598337
81 -3.286017138 -1.532103323
82 -3.798151378 -3.286017138
83 -0.981201172 -3.798151378
84 -0.867269682 -0.981201172
85 0.094932253 -0.867269682
86 -0.153220843 0.094932253
87 -0.731497979 -0.153220843
88 2.486839433 -0.731497979
89 8.934658816 2.486839433
90 0.314387211 8.934658816
91 1.428669072 0.314387211
92 -0.144804617 1.428669072
93 1.898639873 -0.144804617
94 -3.048713552 1.898639873
95 -1.747557989 -3.048713552
96 3.475604995 -1.747557989
97 -1.568090645 3.475604995
98 -2.207411393 -1.568090645
99 -3.075174518 -2.207411393
100 -2.959612898 -3.075174518
101 2.567315318 -2.959612898
102 -0.998072540 2.567315318
103 -2.136994142 -0.998072540
104 3.270187190 -2.136994142
105 6.023609690 3.270187190
106 -2.369259133 6.023609690
107 2.885656641 -2.369259133
108 2.343399740 2.885656641
109 11.056480026 2.343399740
110 0.629166563 11.056480026
111 1.915299126 0.629166563
112 -2.233842030 1.915299126
113 1.802052630 -2.233842030
114 -1.417553401 1.802052630
115 -3.488756512 -1.417553401
116 0.545562552 -3.488756512
117 -0.274844529 0.545562552
118 -3.823557131 -0.274844529
119 1.601689421 -3.823557131
120 1.581124738 1.601689421
121 0.784920617 1.581124738
122 -1.769689560 0.784920617
123 -5.591934527 -1.769689560
124 -1.588088494 -5.591934527
125 -2.128419428 -1.588088494
126 -5.655280831 -2.128419428
127 -3.223338413 -5.655280831
128 -2.220236137 -3.223338413
129 -0.003528855 -2.220236137
130 -2.074119305 -0.003528855
131 6.605610469 -2.074119305
132 -3.558630347 6.605610469
133 1.281450816 -3.558630347
134 -1.337726661 1.281450816
135 1.092563502 -1.337726661
136 9.786298809 1.092563502
137 0.858290363 9.786298809
138 2.525666861 0.858290363
139 -1.899808716 2.525666861
140 -1.060821681 -1.899808716
141 -3.095391896 -1.060821681
142 1.653467322 -3.095391896
143 -1.222621798 1.653467322
144 -0.963942172 -1.222621798
145 -2.057159986 -0.963942172
146 -1.386019636 -2.057159986
147 -0.569605431 -1.386019636
148 -2.467491216 -0.569605431
149 5.522163655 -2.467491216
150 -1.651202587 5.522163655
151 4.063157457 -1.651202587
152 -4.314688365 4.063157457
153 -1.619858931 -4.314688365
154 5.944981939 -1.619858931
155 4.352459108 5.944981939
156 1.804304633 4.352459108
157 0.868746357 1.804304633
158 -1.563184921 0.868746357
159 -2.128200282 -1.563184921
160 1.011226898 -2.128200282
161 -5.056134488 1.011226898
162 NA -5.056134488
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.380350974 0.106168215
[2,] 1.356932271 -1.380350974
[3,] -0.276228612 1.356932271
[4,] 8.479382145 -0.276228612
[5,] -0.467489813 8.479382145
[6,] 9.368269371 -0.467489813
[7,] -1.225588892 9.368269371
[8,] -2.020753418 -1.225588892
[9,] 0.595503184 -2.020753418
[10,] -2.002677058 0.595503184
[11,] -4.156698320 -2.002677058
[12,] 2.456888559 -4.156698320
[13,] 1.652598606 2.456888559
[14,] -2.419085972 1.652598606
[15,] 0.889915258 -2.419085972
[16,] 1.431647297 0.889915258
[17,] -1.674481921 1.431647297
[18,] -2.648806815 -1.674481921
[19,] 1.003732313 -2.648806815
[20,] -5.101838778 1.003732313
[21,] 2.158974570 -5.101838778
[22,] -0.352251172 2.158974570
[23,] 1.737455233 -0.352251172
[24,] -1.490258755 1.737455233
[25,] -3.629738484 -1.490258755
[26,] -1.702517190 -3.629738484
[27,] 2.407146855 -1.702517190
[28,] 1.440371791 2.407146855
[29,] 0.249236165 1.440371791
[30,] -3.733656027 0.249236165
[31,] 3.327262010 -3.733656027
[32,] -2.596741280 3.327262010
[33,] -1.822803013 -2.596741280
[34,] 0.411115526 -1.822803013
[35,] -4.301154189 0.411115526
[36,] 7.301655008 -4.301154189
[37,] -0.322938576 7.301655008
[38,] -2.226185934 -0.322938576
[39,] -2.553447863 -2.226185934
[40,] -3.180175170 -2.553447863
[41,] 1.320952364 -3.180175170
[42,] -4.642594849 1.320952364
[43,] 1.142044440 -4.642594849
[44,] -1.724066429 1.142044440
[45,] 0.438293107 -1.724066429
[46,] -3.276668458 0.438293107
[47,] -1.542590938 -3.276668458
[48,] 2.274359268 -1.542590938
[49,] -1.401354212 2.274359268
[50,] -2.557487071 -1.401354212
[51,] 1.174992728 -2.557487071
[52,] 5.245890155 1.174992728
[53,] 0.654461451 5.245890155
[54,] -1.188300422 0.654461451
[55,] -0.541417566 -1.188300422
[56,] 0.217421042 -0.541417566
[57,] -4.014614925 0.217421042
[58,] -3.289662830 -4.014614925
[59,] 2.599590136 -3.289662830
[60,] 7.158829268 2.599590136
[61,] -0.900502033 7.158829268
[62,] -0.690250453 -0.900502033
[63,] 1.395746634 -0.690250453
[64,] 0.003359278 1.395746634
[65,] -1.952638105 0.003359278
[66,] 3.976771445 -1.952638105
[67,] -0.925436319 3.976771445
[68,] 0.243378919 -0.925436319
[69,] 1.189463725 0.243378919
[70,] -0.086142307 1.189463725
[71,] 1.944412905 -0.086142307
[72,] 0.113764144 1.944412905
[73,] -2.289138603 0.113764144
[74,] -2.044146546 -2.289138603
[75,] 6.082192755 -2.044146546
[76,] 0.304797612 6.082192755
[77,] 4.298774462 0.304797612
[78,] 0.189975521 4.298774462
[79,] -3.950598337 0.189975521
[80,] -1.532103323 -3.950598337
[81,] -3.286017138 -1.532103323
[82,] -3.798151378 -3.286017138
[83,] -0.981201172 -3.798151378
[84,] -0.867269682 -0.981201172
[85,] 0.094932253 -0.867269682
[86,] -0.153220843 0.094932253
[87,] -0.731497979 -0.153220843
[88,] 2.486839433 -0.731497979
[89,] 8.934658816 2.486839433
[90,] 0.314387211 8.934658816
[91,] 1.428669072 0.314387211
[92,] -0.144804617 1.428669072
[93,] 1.898639873 -0.144804617
[94,] -3.048713552 1.898639873
[95,] -1.747557989 -3.048713552
[96,] 3.475604995 -1.747557989
[97,] -1.568090645 3.475604995
[98,] -2.207411393 -1.568090645
[99,] -3.075174518 -2.207411393
[100,] -2.959612898 -3.075174518
[101,] 2.567315318 -2.959612898
[102,] -0.998072540 2.567315318
[103,] -2.136994142 -0.998072540
[104,] 3.270187190 -2.136994142
[105,] 6.023609690 3.270187190
[106,] -2.369259133 6.023609690
[107,] 2.885656641 -2.369259133
[108,] 2.343399740 2.885656641
[109,] 11.056480026 2.343399740
[110,] 0.629166563 11.056480026
[111,] 1.915299126 0.629166563
[112,] -2.233842030 1.915299126
[113,] 1.802052630 -2.233842030
[114,] -1.417553401 1.802052630
[115,] -3.488756512 -1.417553401
[116,] 0.545562552 -3.488756512
[117,] -0.274844529 0.545562552
[118,] -3.823557131 -0.274844529
[119,] 1.601689421 -3.823557131
[120,] 1.581124738 1.601689421
[121,] 0.784920617 1.581124738
[122,] -1.769689560 0.784920617
[123,] -5.591934527 -1.769689560
[124,] -1.588088494 -5.591934527
[125,] -2.128419428 -1.588088494
[126,] -5.655280831 -2.128419428
[127,] -3.223338413 -5.655280831
[128,] -2.220236137 -3.223338413
[129,] -0.003528855 -2.220236137
[130,] -2.074119305 -0.003528855
[131,] 6.605610469 -2.074119305
[132,] -3.558630347 6.605610469
[133,] 1.281450816 -3.558630347
[134,] -1.337726661 1.281450816
[135,] 1.092563502 -1.337726661
[136,] 9.786298809 1.092563502
[137,] 0.858290363 9.786298809
[138,] 2.525666861 0.858290363
[139,] -1.899808716 2.525666861
[140,] -1.060821681 -1.899808716
[141,] -3.095391896 -1.060821681
[142,] 1.653467322 -3.095391896
[143,] -1.222621798 1.653467322
[144,] -0.963942172 -1.222621798
[145,] -2.057159986 -0.963942172
[146,] -1.386019636 -2.057159986
[147,] -0.569605431 -1.386019636
[148,] -2.467491216 -0.569605431
[149,] 5.522163655 -2.467491216
[150,] -1.651202587 5.522163655
[151,] 4.063157457 -1.651202587
[152,] -4.314688365 4.063157457
[153,] -1.619858931 -4.314688365
[154,] 5.944981939 -1.619858931
[155,] 4.352459108 5.944981939
[156,] 1.804304633 4.352459108
[157,] 0.868746357 1.804304633
[158,] -1.563184921 0.868746357
[159,] -2.128200282 -1.563184921
[160,] 1.011226898 -2.128200282
[161,] -5.056134488 1.011226898
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.380350974 0.106168215
2 1.356932271 -1.380350974
3 -0.276228612 1.356932271
4 8.479382145 -0.276228612
5 -0.467489813 8.479382145
6 9.368269371 -0.467489813
7 -1.225588892 9.368269371
8 -2.020753418 -1.225588892
9 0.595503184 -2.020753418
10 -2.002677058 0.595503184
11 -4.156698320 -2.002677058
12 2.456888559 -4.156698320
13 1.652598606 2.456888559
14 -2.419085972 1.652598606
15 0.889915258 -2.419085972
16 1.431647297 0.889915258
17 -1.674481921 1.431647297
18 -2.648806815 -1.674481921
19 1.003732313 -2.648806815
20 -5.101838778 1.003732313
21 2.158974570 -5.101838778
22 -0.352251172 2.158974570
23 1.737455233 -0.352251172
24 -1.490258755 1.737455233
25 -3.629738484 -1.490258755
26 -1.702517190 -3.629738484
27 2.407146855 -1.702517190
28 1.440371791 2.407146855
29 0.249236165 1.440371791
30 -3.733656027 0.249236165
31 3.327262010 -3.733656027
32 -2.596741280 3.327262010
33 -1.822803013 -2.596741280
34 0.411115526 -1.822803013
35 -4.301154189 0.411115526
36 7.301655008 -4.301154189
37 -0.322938576 7.301655008
38 -2.226185934 -0.322938576
39 -2.553447863 -2.226185934
40 -3.180175170 -2.553447863
41 1.320952364 -3.180175170
42 -4.642594849 1.320952364
43 1.142044440 -4.642594849
44 -1.724066429 1.142044440
45 0.438293107 -1.724066429
46 -3.276668458 0.438293107
47 -1.542590938 -3.276668458
48 2.274359268 -1.542590938
49 -1.401354212 2.274359268
50 -2.557487071 -1.401354212
51 1.174992728 -2.557487071
52 5.245890155 1.174992728
53 0.654461451 5.245890155
54 -1.188300422 0.654461451
55 -0.541417566 -1.188300422
56 0.217421042 -0.541417566
57 -4.014614925 0.217421042
58 -3.289662830 -4.014614925
59 2.599590136 -3.289662830
60 7.158829268 2.599590136
61 -0.900502033 7.158829268
62 -0.690250453 -0.900502033
63 1.395746634 -0.690250453
64 0.003359278 1.395746634
65 -1.952638105 0.003359278
66 3.976771445 -1.952638105
67 -0.925436319 3.976771445
68 0.243378919 -0.925436319
69 1.189463725 0.243378919
70 -0.086142307 1.189463725
71 1.944412905 -0.086142307
72 0.113764144 1.944412905
73 -2.289138603 0.113764144
74 -2.044146546 -2.289138603
75 6.082192755 -2.044146546
76 0.304797612 6.082192755
77 4.298774462 0.304797612
78 0.189975521 4.298774462
79 -3.950598337 0.189975521
80 -1.532103323 -3.950598337
81 -3.286017138 -1.532103323
82 -3.798151378 -3.286017138
83 -0.981201172 -3.798151378
84 -0.867269682 -0.981201172
85 0.094932253 -0.867269682
86 -0.153220843 0.094932253
87 -0.731497979 -0.153220843
88 2.486839433 -0.731497979
89 8.934658816 2.486839433
90 0.314387211 8.934658816
91 1.428669072 0.314387211
92 -0.144804617 1.428669072
93 1.898639873 -0.144804617
94 -3.048713552 1.898639873
95 -1.747557989 -3.048713552
96 3.475604995 -1.747557989
97 -1.568090645 3.475604995
98 -2.207411393 -1.568090645
99 -3.075174518 -2.207411393
100 -2.959612898 -3.075174518
101 2.567315318 -2.959612898
102 -0.998072540 2.567315318
103 -2.136994142 -0.998072540
104 3.270187190 -2.136994142
105 6.023609690 3.270187190
106 -2.369259133 6.023609690
107 2.885656641 -2.369259133
108 2.343399740 2.885656641
109 11.056480026 2.343399740
110 0.629166563 11.056480026
111 1.915299126 0.629166563
112 -2.233842030 1.915299126
113 1.802052630 -2.233842030
114 -1.417553401 1.802052630
115 -3.488756512 -1.417553401
116 0.545562552 -3.488756512
117 -0.274844529 0.545562552
118 -3.823557131 -0.274844529
119 1.601689421 -3.823557131
120 1.581124738 1.601689421
121 0.784920617 1.581124738
122 -1.769689560 0.784920617
123 -5.591934527 -1.769689560
124 -1.588088494 -5.591934527
125 -2.128419428 -1.588088494
126 -5.655280831 -2.128419428
127 -3.223338413 -5.655280831
128 -2.220236137 -3.223338413
129 -0.003528855 -2.220236137
130 -2.074119305 -0.003528855
131 6.605610469 -2.074119305
132 -3.558630347 6.605610469
133 1.281450816 -3.558630347
134 -1.337726661 1.281450816
135 1.092563502 -1.337726661
136 9.786298809 1.092563502
137 0.858290363 9.786298809
138 2.525666861 0.858290363
139 -1.899808716 2.525666861
140 -1.060821681 -1.899808716
141 -3.095391896 -1.060821681
142 1.653467322 -3.095391896
143 -1.222621798 1.653467322
144 -0.963942172 -1.222621798
145 -2.057159986 -0.963942172
146 -1.386019636 -2.057159986
147 -0.569605431 -1.386019636
148 -2.467491216 -0.569605431
149 5.522163655 -2.467491216
150 -1.651202587 5.522163655
151 4.063157457 -1.651202587
152 -4.314688365 4.063157457
153 -1.619858931 -4.314688365
154 5.944981939 -1.619858931
155 4.352459108 5.944981939
156 1.804304633 4.352459108
157 0.868746357 1.804304633
158 -1.563184921 0.868746357
159 -2.128200282 -1.563184921
160 1.011226898 -2.128200282
161 -5.056134488 1.011226898
> 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/7m4pk1290547640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8m4pk1290547640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m4pk1290547640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wv6n1290547640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ienb1290547640.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/123wlg1290547640.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/13hoj71290547640.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/14lphd1290547640.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/1567gj1290547640.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/16r8wp1290547640.tab")
+ }
>
> try(system("convert tmp/18urb1290547640.ps tmp/18urb1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/28urb1290547640.ps tmp/28urb1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/30mqe1290547640.ps tmp/30mqe1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/40mqe1290547640.ps tmp/40mqe1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/50mqe1290547640.ps tmp/50mqe1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tdpz1290547640.ps tmp/6tdpz1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m4pk1290547640.ps tmp/7m4pk1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/8m4pk1290547640.ps tmp/8m4pk1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m4pk1290547640.ps tmp/9m4pk1290547640.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wv6n1290547640.ps tmp/10wv6n1290547640.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.113 1.737 9.351