R version 2.11.1 (2010-05-31)
Copyright (C) 2010 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(0,24,0,25,0,17,0,18,0,18,0,16,1,20,1,16,1,18,1,17,1,23,1,30,1,23,1,18,1,15,1,12,1,21,1,15,1,20,1,31,1,27,1,34,1,21,1,31,1,19,1,16,1,20,1,21,1,22,1,17,1,24,1,25,1,26,1,25,1,17,1,32,1,33,1,13,1,32,1,25,1,29,1,22,1,18,1,17,1,20,1,15,1,20,1,33,1,29,1,23,1,26,1,18,1,20,1,11,1,28,1,26,1,22,1,17,1,12,1,14,1,17,1,21,1,19,1,18,1,10,1,29,1,31,1,19,1,9,1,20,1,28,1,19,1,30,1,29,1,26,1,23,1,13,1,21,1,19,1,28,1,23,1,18,1,21,1,20,1,23,1,21,1,21,1,15,1,28,1,19,1,26,1,10,1,16,1,22,1,19,1,31,1,31,1,29,1,19,1,22,1,23,1,15,1,20,1,18,1,23,1,25,1,21,1,24,1,25,1,17,1,13,1,28,1,21,1,25,1,9,1,16,1,19,1,17,1,25,1,20,1,29,1,14,1,22,1,15,1,19,1,20,1,15,1,20,1,18,1,33,1,22,1,16,1,17,1,16,1,21,1,26,1,18,1,18,1,17,1,22,1,30,1,30,1,24,1,21,1,21,1,29,1,31,1,20,1,16,1,22,1,20,1,28,1,38,1,22,1,20,1,17,1,28,1,22,1,31),dim=c(2,159),dimnames=list(c('Month','Concernovermistakes'),1:159))
> y <- array(NA,dim=c(2,159),dimnames=list(c('Month','Concernovermistakes'),1:159))
> 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 = '2'
> #'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
Concernovermistakes Month M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 24 0 1 0 0 0 0 0 0 0 0 0 0 1
2 25 0 0 1 0 0 0 0 0 0 0 0 0 2
3 17 0 0 0 1 0 0 0 0 0 0 0 0 3
4 18 0 0 0 0 1 0 0 0 0 0 0 0 4
5 18 0 0 0 0 0 1 0 0 0 0 0 0 5
6 16 0 0 0 0 0 0 1 0 0 0 0 0 6
7 20 1 0 0 0 0 0 0 1 0 0 0 0 7
8 16 1 0 0 0 0 0 0 0 1 0 0 0 8
9 18 1 0 0 0 0 0 0 0 0 1 0 0 9
10 17 1 0 0 0 0 0 0 0 0 0 1 0 10
11 23 1 0 0 0 0 0 0 0 0 0 0 1 11
12 30 1 0 0 0 0 0 0 0 0 0 0 0 12
13 23 1 1 0 0 0 0 0 0 0 0 0 0 13
14 18 1 0 1 0 0 0 0 0 0 0 0 0 14
15 15 1 0 0 1 0 0 0 0 0 0 0 0 15
16 12 1 0 0 0 1 0 0 0 0 0 0 0 16
17 21 1 0 0 0 0 1 0 0 0 0 0 0 17
18 15 1 0 0 0 0 0 1 0 0 0 0 0 18
19 20 1 0 0 0 0 0 0 1 0 0 0 0 19
20 31 1 0 0 0 0 0 0 0 1 0 0 0 20
21 27 1 0 0 0 0 0 0 0 0 1 0 0 21
22 34 1 0 0 0 0 0 0 0 0 0 1 0 22
23 21 1 0 0 0 0 0 0 0 0 0 0 1 23
24 31 1 0 0 0 0 0 0 0 0 0 0 0 24
25 19 1 1 0 0 0 0 0 0 0 0 0 0 25
26 16 1 0 1 0 0 0 0 0 0 0 0 0 26
27 20 1 0 0 1 0 0 0 0 0 0 0 0 27
28 21 1 0 0 0 1 0 0 0 0 0 0 0 28
29 22 1 0 0 0 0 1 0 0 0 0 0 0 29
30 17 1 0 0 0 0 0 1 0 0 0 0 0 30
31 24 1 0 0 0 0 0 0 1 0 0 0 0 31
32 25 1 0 0 0 0 0 0 0 1 0 0 0 32
33 26 1 0 0 0 0 0 0 0 0 1 0 0 33
34 25 1 0 0 0 0 0 0 0 0 0 1 0 34
35 17 1 0 0 0 0 0 0 0 0 0 0 1 35
36 32 1 0 0 0 0 0 0 0 0 0 0 0 36
37 33 1 1 0 0 0 0 0 0 0 0 0 0 37
38 13 1 0 1 0 0 0 0 0 0 0 0 0 38
39 32 1 0 0 1 0 0 0 0 0 0 0 0 39
40 25 1 0 0 0 1 0 0 0 0 0 0 0 40
41 29 1 0 0 0 0 1 0 0 0 0 0 0 41
42 22 1 0 0 0 0 0 1 0 0 0 0 0 42
43 18 1 0 0 0 0 0 0 1 0 0 0 0 43
44 17 1 0 0 0 0 0 0 0 1 0 0 0 44
45 20 1 0 0 0 0 0 0 0 0 1 0 0 45
46 15 1 0 0 0 0 0 0 0 0 0 1 0 46
47 20 1 0 0 0 0 0 0 0 0 0 0 1 47
48 33 1 0 0 0 0 0 0 0 0 0 0 0 48
49 29 1 1 0 0 0 0 0 0 0 0 0 0 49
50 23 1 0 1 0 0 0 0 0 0 0 0 0 50
51 26 1 0 0 1 0 0 0 0 0 0 0 0 51
52 18 1 0 0 0 1 0 0 0 0 0 0 0 52
53 20 1 0 0 0 0 1 0 0 0 0 0 0 53
54 11 1 0 0 0 0 0 1 0 0 0 0 0 54
55 28 1 0 0 0 0 0 0 1 0 0 0 0 55
56 26 1 0 0 0 0 0 0 0 1 0 0 0 56
57 22 1 0 0 0 0 0 0 0 0 1 0 0 57
58 17 1 0 0 0 0 0 0 0 0 0 1 0 58
59 12 1 0 0 0 0 0 0 0 0 0 0 1 59
60 14 1 0 0 0 0 0 0 0 0 0 0 0 60
61 17 1 1 0 0 0 0 0 0 0 0 0 0 61
62 21 1 0 1 0 0 0 0 0 0 0 0 0 62
63 19 1 0 0 1 0 0 0 0 0 0 0 0 63
64 18 1 0 0 0 1 0 0 0 0 0 0 0 64
65 10 1 0 0 0 0 1 0 0 0 0 0 0 65
66 29 1 0 0 0 0 0 1 0 0 0 0 0 66
67 31 1 0 0 0 0 0 0 1 0 0 0 0 67
68 19 1 0 0 0 0 0 0 0 1 0 0 0 68
69 9 1 0 0 0 0 0 0 0 0 1 0 0 69
70 20 1 0 0 0 0 0 0 0 0 0 1 0 70
71 28 1 0 0 0 0 0 0 0 0 0 0 1 71
72 19 1 0 0 0 0 0 0 0 0 0 0 0 72
73 30 1 1 0 0 0 0 0 0 0 0 0 0 73
74 29 1 0 1 0 0 0 0 0 0 0 0 0 74
75 26 1 0 0 1 0 0 0 0 0 0 0 0 75
76 23 1 0 0 0 1 0 0 0 0 0 0 0 76
77 13 1 0 0 0 0 1 0 0 0 0 0 0 77
78 21 1 0 0 0 0 0 1 0 0 0 0 0 78
79 19 1 0 0 0 0 0 0 1 0 0 0 0 79
80 28 1 0 0 0 0 0 0 0 1 0 0 0 80
81 23 1 0 0 0 0 0 0 0 0 1 0 0 81
82 18 1 0 0 0 0 0 0 0 0 0 1 0 82
83 21 1 0 0 0 0 0 0 0 0 0 0 1 83
84 20 1 0 0 0 0 0 0 0 0 0 0 0 84
85 23 1 1 0 0 0 0 0 0 0 0 0 0 85
86 21 1 0 1 0 0 0 0 0 0 0 0 0 86
87 21 1 0 0 1 0 0 0 0 0 0 0 0 87
88 15 1 0 0 0 1 0 0 0 0 0 0 0 88
89 28 1 0 0 0 0 1 0 0 0 0 0 0 89
90 19 1 0 0 0 0 0 1 0 0 0 0 0 90
91 26 1 0 0 0 0 0 0 1 0 0 0 0 91
92 10 1 0 0 0 0 0 0 0 1 0 0 0 92
93 16 1 0 0 0 0 0 0 0 0 1 0 0 93
94 22 1 0 0 0 0 0 0 0 0 0 1 0 94
95 19 1 0 0 0 0 0 0 0 0 0 0 1 95
96 31 1 0 0 0 0 0 0 0 0 0 0 0 96
97 31 1 1 0 0 0 0 0 0 0 0 0 0 97
98 29 1 0 1 0 0 0 0 0 0 0 0 0 98
99 19 1 0 0 1 0 0 0 0 0 0 0 0 99
100 22 1 0 0 0 1 0 0 0 0 0 0 0 100
101 23 1 0 0 0 0 1 0 0 0 0 0 0 101
102 15 1 0 0 0 0 0 1 0 0 0 0 0 102
103 20 1 0 0 0 0 0 0 1 0 0 0 0 103
104 18 1 0 0 0 0 0 0 0 1 0 0 0 104
105 23 1 0 0 0 0 0 0 0 0 1 0 0 105
106 25 1 0 0 0 0 0 0 0 0 0 1 0 106
107 21 1 0 0 0 0 0 0 0 0 0 0 1 107
108 24 1 0 0 0 0 0 0 0 0 0 0 0 108
109 25 1 1 0 0 0 0 0 0 0 0 0 0 109
110 17 1 0 1 0 0 0 0 0 0 0 0 0 110
111 13 1 0 0 1 0 0 0 0 0 0 0 0 111
112 28 1 0 0 0 1 0 0 0 0 0 0 0 112
113 21 1 0 0 0 0 1 0 0 0 0 0 0 113
114 25 1 0 0 0 0 0 1 0 0 0 0 0 114
115 9 1 0 0 0 0 0 0 1 0 0 0 0 115
116 16 1 0 0 0 0 0 0 0 1 0 0 0 116
117 19 1 0 0 0 0 0 0 0 0 1 0 0 117
118 17 1 0 0 0 0 0 0 0 0 0 1 0 118
119 25 1 0 0 0 0 0 0 0 0 0 0 1 119
120 20 1 0 0 0 0 0 0 0 0 0 0 0 120
121 29 1 1 0 0 0 0 0 0 0 0 0 0 121
122 14 1 0 1 0 0 0 0 0 0 0 0 0 122
123 22 1 0 0 1 0 0 0 0 0 0 0 0 123
124 15 1 0 0 0 1 0 0 0 0 0 0 0 124
125 19 1 0 0 0 0 1 0 0 0 0 0 0 125
126 20 1 0 0 0 0 0 1 0 0 0 0 0 126
127 15 1 0 0 0 0 0 0 1 0 0 0 0 127
128 20 1 0 0 0 0 0 0 0 1 0 0 0 128
129 18 1 0 0 0 0 0 0 0 0 1 0 0 129
130 33 1 0 0 0 0 0 0 0 0 0 1 0 130
131 22 1 0 0 0 0 0 0 0 0 0 0 1 131
132 16 1 0 0 0 0 0 0 0 0 0 0 0 132
133 17 1 1 0 0 0 0 0 0 0 0 0 0 133
134 16 1 0 1 0 0 0 0 0 0 0 0 0 134
135 21 1 0 0 1 0 0 0 0 0 0 0 0 135
136 26 1 0 0 0 1 0 0 0 0 0 0 0 136
137 18 1 0 0 0 0 1 0 0 0 0 0 0 137
138 18 1 0 0 0 0 0 1 0 0 0 0 0 138
139 17 1 0 0 0 0 0 0 1 0 0 0 0 139
140 22 1 0 0 0 0 0 0 0 1 0 0 0 140
141 30 1 0 0 0 0 0 0 0 0 1 0 0 141
142 30 1 0 0 0 0 0 0 0 0 0 1 0 142
143 24 1 0 0 0 0 0 0 0 0 0 0 1 143
144 21 1 0 0 0 0 0 0 0 0 0 0 0 144
145 21 1 1 0 0 0 0 0 0 0 0 0 0 145
146 29 1 0 1 0 0 0 0 0 0 0 0 0 146
147 31 1 0 0 1 0 0 0 0 0 0 0 0 147
148 20 1 0 0 0 1 0 0 0 0 0 0 0 148
149 16 1 0 0 0 0 1 0 0 0 0 0 0 149
150 22 1 0 0 0 0 0 1 0 0 0 0 0 150
151 20 1 0 0 0 0 0 0 1 0 0 0 0 151
152 28 1 0 0 0 0 0 0 0 1 0 0 0 152
153 38 1 0 0 0 0 0 0 0 0 1 0 0 153
154 22 1 0 0 0 0 0 0 0 0 0 1 0 154
155 20 1 0 0 0 0 0 0 0 0 0 0 1 155
156 17 1 0 0 0 0 0 0 0 0 0 0 0 156
157 28 1 1 0 0 0 0 0 0 0 0 0 0 157
158 22 1 0 1 0 0 0 0 0 0 0 0 0 158
159 31 1 0 0 1 0 0 0 0 0 0 0 0 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month M1 M2 M3 M4
21.995338 1.401515 1.353959 -2.649559 -1.224505 -3.479437
M5 M6 M7 M8 M9 M10
-3.713724 -4.332626 -3.136260 -2.447469 -1.450987 -0.992965
M11 t
-2.688790 0.003517
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.1886 -3.9043 -0.3808 3.8485 15.5160
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.995338 2.899528 7.586 3.66e-12 ***
Month 1.401515 2.571152 0.545 0.5865
M1 1.353959 2.209902 0.613 0.5410
M2 -2.649559 2.209966 -1.199 0.2325
M3 -1.224505 2.210080 -0.554 0.5804
M4 -3.479437 2.251334 -1.546 0.1244
M5 -3.713724 2.251272 -1.650 0.1012
M6 -4.332626 2.251259 -1.925 0.0562 .
M7 -3.136260 2.244117 -1.398 0.1644
M8 -2.447469 2.243896 -1.091 0.2772
M9 -1.450987 2.243725 -0.647 0.5189
M10 -0.992965 2.243603 -0.443 0.6587
M11 -2.688790 2.243529 -1.198 0.2327
t 0.003517 0.010483 0.336 0.7377
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.72 on 145 degrees of freedom
Multiple R-squared: 0.08323, Adjusted R-squared: 0.001042
F-statistic: 1.013 on 13 and 145 DF, p-value: 0.4423
> 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,] 0.18601825 0.37203649 0.8139818
[2,] 0.08719651 0.17439301 0.9128035
[3,] 0.03555638 0.07111276 0.9644436
[4,] 0.14682814 0.29365627 0.8531719
[5,] 0.08521086 0.17042173 0.9147891
[6,] 0.11100531 0.22201061 0.8889947
[7,] 0.19888196 0.39776392 0.8011180
[8,] 0.18491054 0.36982108 0.8150895
[9,] 0.24764662 0.49529324 0.7523534
[10,] 0.28044952 0.56089903 0.7195505
[11,] 0.21782132 0.43564264 0.7821787
[12,] 0.17494416 0.34988832 0.8250558
[13,] 0.12593877 0.25187755 0.8740612
[14,] 0.08942307 0.17884614 0.9105769
[15,] 0.06431445 0.12862889 0.9356856
[16,] 0.05168322 0.10336643 0.9483168
[17,] 0.03572991 0.07145982 0.9642701
[18,] 0.03119843 0.06239685 0.9688016
[19,] 0.04491927 0.08983854 0.9550807
[20,] 0.03628672 0.07257344 0.9637133
[21,] 0.05160556 0.10321112 0.9483944
[22,] 0.08894690 0.17789379 0.9110531
[23,] 0.17674347 0.35348695 0.8232565
[24,] 0.15658979 0.31317958 0.8434102
[25,] 0.15871656 0.31743311 0.8412834
[26,] 0.12666050 0.25332100 0.8733395
[27,] 0.15523601 0.31047202 0.8447640
[28,] 0.23085718 0.46171436 0.7691428
[29,] 0.23317953 0.46635907 0.7668205
[30,] 0.35683720 0.71367440 0.6431628
[31,] 0.30923222 0.61846445 0.6907678
[32,] 0.32130091 0.64260183 0.6786991
[33,] 0.28629265 0.57258530 0.7137074
[34,] 0.24742088 0.49484175 0.7525791
[35,] 0.21664688 0.43329376 0.7833531
[36,] 0.18813571 0.37627142 0.8118643
[37,] 0.17120738 0.34241476 0.8287926
[38,] 0.20761874 0.41523749 0.7923813
[39,] 0.21997439 0.43994879 0.7800256
[40,] 0.20012014 0.40024029 0.7998799
[41,] 0.17157487 0.34314974 0.8284251
[42,] 0.18310777 0.36621553 0.8168922
[43,] 0.23853608 0.47707217 0.7614639
[44,] 0.47223917 0.94447833 0.5277608
[45,] 0.51010435 0.97979131 0.4898957
[46,] 0.46237893 0.92475785 0.5376211
[47,] 0.42265021 0.84530041 0.5773498
[48,] 0.37632429 0.75264857 0.6236757
[49,] 0.47026994 0.94053988 0.5297301
[50,] 0.59981924 0.80036151 0.4001808
[51,] 0.70719379 0.58561243 0.2928062
[52,] 0.67247246 0.65505508 0.3275275
[53,] 0.81644207 0.36711586 0.1835579
[54,] 0.78633164 0.42733673 0.2136684
[55,] 0.81787052 0.36425895 0.1821295
[56,] 0.81480409 0.37039183 0.1851959
[57,] 0.81376631 0.37246737 0.1862337
[58,] 0.85241216 0.29517567 0.1475878
[59,] 0.83877887 0.32244226 0.1612211
[60,] 0.81780880 0.36438239 0.1821912
[61,] 0.82439163 0.35121673 0.1756084
[62,] 0.79575217 0.40849565 0.2042478
[63,] 0.77089206 0.45821588 0.2291079
[64,] 0.80483513 0.39032975 0.1951649
[65,] 0.77169454 0.45661091 0.2283055
[66,] 0.75292671 0.49414658 0.2470733
[67,] 0.71259264 0.57481472 0.2874074
[68,] 0.68966250 0.62067500 0.3103375
[69,] 0.64658743 0.70682515 0.3534126
[70,] 0.60076184 0.79847633 0.3992382
[71,] 0.55244861 0.89510278 0.4475514
[72,] 0.53609127 0.92781745 0.4639087
[73,] 0.60251954 0.79496091 0.3974805
[74,] 0.55355638 0.89288723 0.4464436
[75,] 0.61092884 0.77814233 0.3890712
[76,] 0.70105638 0.59788725 0.2989436
[77,] 0.71005940 0.57988120 0.2899406
[78,] 0.66933240 0.66133521 0.3306676
[79,] 0.62550460 0.74899079 0.3744954
[80,] 0.72477294 0.55045412 0.2752271
[81,] 0.76057922 0.47884156 0.2394208
[82,] 0.84483234 0.31033532 0.1551677
[83,] 0.81630097 0.36739805 0.1836990
[84,] 0.78582019 0.42835962 0.2141798
[85,] 0.78696517 0.42606966 0.2130348
[86,] 0.76141526 0.47716948 0.2385847
[87,] 0.76993592 0.46012815 0.2300641
[88,] 0.73112477 0.53775046 0.2688752
[89,] 0.68800816 0.62398368 0.3119918
[90,] 0.65140953 0.69718094 0.3485905
[91,] 0.60181931 0.79636137 0.3981807
[92,] 0.63495837 0.73008326 0.3650416
[93,] 0.61147256 0.77705489 0.3885274
[94,] 0.56723380 0.86553240 0.4327662
[95,] 0.63613227 0.72773546 0.3638677
[96,] 0.73072825 0.53854350 0.2692718
[97,] 0.72971659 0.54056682 0.2702834
[98,] 0.77602181 0.44795639 0.2239782
[99,] 0.79734792 0.40530417 0.2026521
[100,] 0.76864934 0.46270133 0.2313507
[101,] 0.75749744 0.48500512 0.2425026
[102,] 0.78246821 0.43506359 0.2175318
[103,] 0.78030470 0.43939060 0.2196953
[104,] 0.77178651 0.45642697 0.2282135
[105,] 0.86125171 0.27749658 0.1387483
[106,] 0.84258184 0.31483631 0.1574182
[107,] 0.79998547 0.40002906 0.2000145
[108,] 0.77920508 0.44158984 0.2207949
[109,] 0.75480371 0.49039258 0.2451963
[110,] 0.71030117 0.57939766 0.2896988
[111,] 0.65214320 0.69571359 0.3478568
[112,] 0.58395917 0.83208165 0.4160408
[113,] 0.77017018 0.45965965 0.2298298
[114,] 0.86116099 0.27767801 0.1388390
[115,] 0.82461829 0.35076342 0.1753817
[116,] 0.77519262 0.44961476 0.2248074
[117,] 0.74293325 0.51413349 0.2570667
[118,] 0.75738802 0.48522395 0.2426120
[119,] 0.83647067 0.32705867 0.1635293
[120,] 0.82272279 0.35455443 0.1772772
[121,] 0.75112756 0.49774488 0.2488724
[122,] 0.67629722 0.64740556 0.3237028
[123,] 0.57910368 0.84179265 0.4208963
[124,] 0.54507911 0.90984178 0.4549209
[125,] 0.66097681 0.67804639 0.3390232
[126,] 0.60505931 0.78988138 0.3949407
> postscript(file="/var/www/rcomp/tmp/10hcs1290853552.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/20hcs1290853552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3tqbd1290853552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4tqbd1290853552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5tqbd1290853552.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 = 159
Frequency = 1
1 2 3 4 5
6.471861e-01 5.647186e+00 -3.781385e+00 -5.299700e-01 -2.992008e-01
6 7 8 9 10
-1.683816e+00 -2.852148e-01 -4.977522e+00 -3.977522e+00 -5.439061e+00
11 12 13 14 15
2.253247e+00 6.560939e+00 -1.796537e+00 -2.796537e+00 -7.225108e+00
16 17 18 19 20
-7.973693e+00 1.257076e+00 -4.127539e+00 -3.274226e-01 9.980270e+00
21 22 23 24 25
4.980270e+00 1.151873e+01 2.110390e-01 7.518731e+00 -5.838745e+00
26 27 28 29 30
-4.838745e+00 -2.267316e+00 9.840992e-01 2.214868e+00 -2.169747e+00
31 32 33 34 35
3.630370e+00 3.938062e+00 3.938062e+00 2.476523e+00 -3.831169e+00
36 37 38 39 40
8.476523e+00 8.119048e+00 -7.880952e+00 9.690476e+00 4.941891e+00
41 42 43 44 45
9.172661e+00 2.788045e+00 -2.411838e+00 -4.104146e+00 -2.104146e+00
46 47 48 49 50
-7.565684e+00 -8.733766e-01 9.434316e+00 4.076840e+00 2.076840e+00
51 52 53 54 55
3.648268e+00 -2.100316e+00 1.304529e-01 -8.254163e+00 7.545954e+00
56 57 58 59 60
4.853646e+00 -1.463536e-01 -5.607892e+00 -8.915584e+00 -9.607892e+00
61 62 63 64 65
-7.965368e+00 3.463203e-02 -3.393939e+00 -2.142524e+00 -9.911755e+00
66 67 68 69 70
9.703630e+00 1.050375e+01 -2.188561e+00 -1.318856e+01 -2.650100e+00
71 72 73 74 75
7.042208e+00 -4.650100e+00 4.992424e+00 7.992424e+00 3.563853e+00
76 77 78 79 80
2.815268e+00 -6.953963e+00 1.661422e+00 -1.538462e+00 6.769231e+00
81 82 83 84 85
7.692308e-01 -4.692308e+00 -5.431175e-17 -3.692308e+00 -2.049784e+00
86 87 88 89 90
-4.978355e-02 -1.478355e+00 -5.226940e+00 8.003830e+00 -3.807859e-01
91 92 93 94 95
5.419331e+00 -1.127298e+01 -6.272977e+00 -7.345155e-01 -2.042208e+00
96 97 98 99 100
7.265485e+00 5.908009e+00 7.908009e+00 -3.520563e+00 1.730852e+00
101 102 103 104 105
2.961622e+00 -4.422994e+00 -6.228771e-01 -3.315185e+00 6.848152e-01
106 107 108 109 110
2.223277e+00 -8.441558e-02 2.232767e-01 -1.341991e-01 -4.134199e+00
111 112 113 114 115
-9.562771e+00 7.688645e+00 9.194139e-01 5.534799e+00 -1.166508e+01
116 117 118 119 120
-5.357393e+00 -3.357393e+00 -5.818931e+00 3.873377e+00 -3.818931e+00
121 122 123 124 125
3.823593e+00 -7.176407e+00 -6.049784e-01 -5.353563e+00 -1.122794e+00
126 127 128 129 130
4.925907e-01 -5.707293e+00 -1.399600e+00 -4.399600e+00 1.013886e+01
131 132 133 134 135
8.311688e-01 -7.861139e+00 -8.218615e+00 -5.218615e+00 -1.647186e+00
136 137 138 139 140
5.604229e+00 -2.165002e+00 -1.549617e+00 -3.749500e+00 5.581918e-01
141 142 143 144 145
7.558192e+00 7.096653e+00 2.788961e+00 -2.903347e+00 -4.260823e+00
146 147 148 149 150
7.739177e+00 8.310606e+00 -4.379787e-01 -4.207209e+00 2.408175e+00
151 152 153 154 155
-7.917083e-01 6.515984e+00 1.551598e+01 -9.455544e-01 -1.253247e+00
156 157 158 159
-6.945554e+00 2.696970e+00 6.969697e-01 8.268398e+00
> postscript(file="/var/www/rcomp/tmp/6miag1290853552.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 6.471861e-01 NA
1 5.647186e+00 6.471861e-01
2 -3.781385e+00 5.647186e+00
3 -5.299700e-01 -3.781385e+00
4 -2.992008e-01 -5.299700e-01
5 -1.683816e+00 -2.992008e-01
6 -2.852148e-01 -1.683816e+00
7 -4.977522e+00 -2.852148e-01
8 -3.977522e+00 -4.977522e+00
9 -5.439061e+00 -3.977522e+00
10 2.253247e+00 -5.439061e+00
11 6.560939e+00 2.253247e+00
12 -1.796537e+00 6.560939e+00
13 -2.796537e+00 -1.796537e+00
14 -7.225108e+00 -2.796537e+00
15 -7.973693e+00 -7.225108e+00
16 1.257076e+00 -7.973693e+00
17 -4.127539e+00 1.257076e+00
18 -3.274226e-01 -4.127539e+00
19 9.980270e+00 -3.274226e-01
20 4.980270e+00 9.980270e+00
21 1.151873e+01 4.980270e+00
22 2.110390e-01 1.151873e+01
23 7.518731e+00 2.110390e-01
24 -5.838745e+00 7.518731e+00
25 -4.838745e+00 -5.838745e+00
26 -2.267316e+00 -4.838745e+00
27 9.840992e-01 -2.267316e+00
28 2.214868e+00 9.840992e-01
29 -2.169747e+00 2.214868e+00
30 3.630370e+00 -2.169747e+00
31 3.938062e+00 3.630370e+00
32 3.938062e+00 3.938062e+00
33 2.476523e+00 3.938062e+00
34 -3.831169e+00 2.476523e+00
35 8.476523e+00 -3.831169e+00
36 8.119048e+00 8.476523e+00
37 -7.880952e+00 8.119048e+00
38 9.690476e+00 -7.880952e+00
39 4.941891e+00 9.690476e+00
40 9.172661e+00 4.941891e+00
41 2.788045e+00 9.172661e+00
42 -2.411838e+00 2.788045e+00
43 -4.104146e+00 -2.411838e+00
44 -2.104146e+00 -4.104146e+00
45 -7.565684e+00 -2.104146e+00
46 -8.733766e-01 -7.565684e+00
47 9.434316e+00 -8.733766e-01
48 4.076840e+00 9.434316e+00
49 2.076840e+00 4.076840e+00
50 3.648268e+00 2.076840e+00
51 -2.100316e+00 3.648268e+00
52 1.304529e-01 -2.100316e+00
53 -8.254163e+00 1.304529e-01
54 7.545954e+00 -8.254163e+00
55 4.853646e+00 7.545954e+00
56 -1.463536e-01 4.853646e+00
57 -5.607892e+00 -1.463536e-01
58 -8.915584e+00 -5.607892e+00
59 -9.607892e+00 -8.915584e+00
60 -7.965368e+00 -9.607892e+00
61 3.463203e-02 -7.965368e+00
62 -3.393939e+00 3.463203e-02
63 -2.142524e+00 -3.393939e+00
64 -9.911755e+00 -2.142524e+00
65 9.703630e+00 -9.911755e+00
66 1.050375e+01 9.703630e+00
67 -2.188561e+00 1.050375e+01
68 -1.318856e+01 -2.188561e+00
69 -2.650100e+00 -1.318856e+01
70 7.042208e+00 -2.650100e+00
71 -4.650100e+00 7.042208e+00
72 4.992424e+00 -4.650100e+00
73 7.992424e+00 4.992424e+00
74 3.563853e+00 7.992424e+00
75 2.815268e+00 3.563853e+00
76 -6.953963e+00 2.815268e+00
77 1.661422e+00 -6.953963e+00
78 -1.538462e+00 1.661422e+00
79 6.769231e+00 -1.538462e+00
80 7.692308e-01 6.769231e+00
81 -4.692308e+00 7.692308e-01
82 -5.431175e-17 -4.692308e+00
83 -3.692308e+00 -5.431175e-17
84 -2.049784e+00 -3.692308e+00
85 -4.978355e-02 -2.049784e+00
86 -1.478355e+00 -4.978355e-02
87 -5.226940e+00 -1.478355e+00
88 8.003830e+00 -5.226940e+00
89 -3.807859e-01 8.003830e+00
90 5.419331e+00 -3.807859e-01
91 -1.127298e+01 5.419331e+00
92 -6.272977e+00 -1.127298e+01
93 -7.345155e-01 -6.272977e+00
94 -2.042208e+00 -7.345155e-01
95 7.265485e+00 -2.042208e+00
96 5.908009e+00 7.265485e+00
97 7.908009e+00 5.908009e+00
98 -3.520563e+00 7.908009e+00
99 1.730852e+00 -3.520563e+00
100 2.961622e+00 1.730852e+00
101 -4.422994e+00 2.961622e+00
102 -6.228771e-01 -4.422994e+00
103 -3.315185e+00 -6.228771e-01
104 6.848152e-01 -3.315185e+00
105 2.223277e+00 6.848152e-01
106 -8.441558e-02 2.223277e+00
107 2.232767e-01 -8.441558e-02
108 -1.341991e-01 2.232767e-01
109 -4.134199e+00 -1.341991e-01
110 -9.562771e+00 -4.134199e+00
111 7.688645e+00 -9.562771e+00
112 9.194139e-01 7.688645e+00
113 5.534799e+00 9.194139e-01
114 -1.166508e+01 5.534799e+00
115 -5.357393e+00 -1.166508e+01
116 -3.357393e+00 -5.357393e+00
117 -5.818931e+00 -3.357393e+00
118 3.873377e+00 -5.818931e+00
119 -3.818931e+00 3.873377e+00
120 3.823593e+00 -3.818931e+00
121 -7.176407e+00 3.823593e+00
122 -6.049784e-01 -7.176407e+00
123 -5.353563e+00 -6.049784e-01
124 -1.122794e+00 -5.353563e+00
125 4.925907e-01 -1.122794e+00
126 -5.707293e+00 4.925907e-01
127 -1.399600e+00 -5.707293e+00
128 -4.399600e+00 -1.399600e+00
129 1.013886e+01 -4.399600e+00
130 8.311688e-01 1.013886e+01
131 -7.861139e+00 8.311688e-01
132 -8.218615e+00 -7.861139e+00
133 -5.218615e+00 -8.218615e+00
134 -1.647186e+00 -5.218615e+00
135 5.604229e+00 -1.647186e+00
136 -2.165002e+00 5.604229e+00
137 -1.549617e+00 -2.165002e+00
138 -3.749500e+00 -1.549617e+00
139 5.581918e-01 -3.749500e+00
140 7.558192e+00 5.581918e-01
141 7.096653e+00 7.558192e+00
142 2.788961e+00 7.096653e+00
143 -2.903347e+00 2.788961e+00
144 -4.260823e+00 -2.903347e+00
145 7.739177e+00 -4.260823e+00
146 8.310606e+00 7.739177e+00
147 -4.379787e-01 8.310606e+00
148 -4.207209e+00 -4.379787e-01
149 2.408175e+00 -4.207209e+00
150 -7.917083e-01 2.408175e+00
151 6.515984e+00 -7.917083e-01
152 1.551598e+01 6.515984e+00
153 -9.455544e-01 1.551598e+01
154 -1.253247e+00 -9.455544e-01
155 -6.945554e+00 -1.253247e+00
156 2.696970e+00 -6.945554e+00
157 6.969697e-01 2.696970e+00
158 8.268398e+00 6.969697e-01
159 NA 8.268398e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.647186e+00 6.471861e-01
[2,] -3.781385e+00 5.647186e+00
[3,] -5.299700e-01 -3.781385e+00
[4,] -2.992008e-01 -5.299700e-01
[5,] -1.683816e+00 -2.992008e-01
[6,] -2.852148e-01 -1.683816e+00
[7,] -4.977522e+00 -2.852148e-01
[8,] -3.977522e+00 -4.977522e+00
[9,] -5.439061e+00 -3.977522e+00
[10,] 2.253247e+00 -5.439061e+00
[11,] 6.560939e+00 2.253247e+00
[12,] -1.796537e+00 6.560939e+00
[13,] -2.796537e+00 -1.796537e+00
[14,] -7.225108e+00 -2.796537e+00
[15,] -7.973693e+00 -7.225108e+00
[16,] 1.257076e+00 -7.973693e+00
[17,] -4.127539e+00 1.257076e+00
[18,] -3.274226e-01 -4.127539e+00
[19,] 9.980270e+00 -3.274226e-01
[20,] 4.980270e+00 9.980270e+00
[21,] 1.151873e+01 4.980270e+00
[22,] 2.110390e-01 1.151873e+01
[23,] 7.518731e+00 2.110390e-01
[24,] -5.838745e+00 7.518731e+00
[25,] -4.838745e+00 -5.838745e+00
[26,] -2.267316e+00 -4.838745e+00
[27,] 9.840992e-01 -2.267316e+00
[28,] 2.214868e+00 9.840992e-01
[29,] -2.169747e+00 2.214868e+00
[30,] 3.630370e+00 -2.169747e+00
[31,] 3.938062e+00 3.630370e+00
[32,] 3.938062e+00 3.938062e+00
[33,] 2.476523e+00 3.938062e+00
[34,] -3.831169e+00 2.476523e+00
[35,] 8.476523e+00 -3.831169e+00
[36,] 8.119048e+00 8.476523e+00
[37,] -7.880952e+00 8.119048e+00
[38,] 9.690476e+00 -7.880952e+00
[39,] 4.941891e+00 9.690476e+00
[40,] 9.172661e+00 4.941891e+00
[41,] 2.788045e+00 9.172661e+00
[42,] -2.411838e+00 2.788045e+00
[43,] -4.104146e+00 -2.411838e+00
[44,] -2.104146e+00 -4.104146e+00
[45,] -7.565684e+00 -2.104146e+00
[46,] -8.733766e-01 -7.565684e+00
[47,] 9.434316e+00 -8.733766e-01
[48,] 4.076840e+00 9.434316e+00
[49,] 2.076840e+00 4.076840e+00
[50,] 3.648268e+00 2.076840e+00
[51,] -2.100316e+00 3.648268e+00
[52,] 1.304529e-01 -2.100316e+00
[53,] -8.254163e+00 1.304529e-01
[54,] 7.545954e+00 -8.254163e+00
[55,] 4.853646e+00 7.545954e+00
[56,] -1.463536e-01 4.853646e+00
[57,] -5.607892e+00 -1.463536e-01
[58,] -8.915584e+00 -5.607892e+00
[59,] -9.607892e+00 -8.915584e+00
[60,] -7.965368e+00 -9.607892e+00
[61,] 3.463203e-02 -7.965368e+00
[62,] -3.393939e+00 3.463203e-02
[63,] -2.142524e+00 -3.393939e+00
[64,] -9.911755e+00 -2.142524e+00
[65,] 9.703630e+00 -9.911755e+00
[66,] 1.050375e+01 9.703630e+00
[67,] -2.188561e+00 1.050375e+01
[68,] -1.318856e+01 -2.188561e+00
[69,] -2.650100e+00 -1.318856e+01
[70,] 7.042208e+00 -2.650100e+00
[71,] -4.650100e+00 7.042208e+00
[72,] 4.992424e+00 -4.650100e+00
[73,] 7.992424e+00 4.992424e+00
[74,] 3.563853e+00 7.992424e+00
[75,] 2.815268e+00 3.563853e+00
[76,] -6.953963e+00 2.815268e+00
[77,] 1.661422e+00 -6.953963e+00
[78,] -1.538462e+00 1.661422e+00
[79,] 6.769231e+00 -1.538462e+00
[80,] 7.692308e-01 6.769231e+00
[81,] -4.692308e+00 7.692308e-01
[82,] -5.431175e-17 -4.692308e+00
[83,] -3.692308e+00 -5.431175e-17
[84,] -2.049784e+00 -3.692308e+00
[85,] -4.978355e-02 -2.049784e+00
[86,] -1.478355e+00 -4.978355e-02
[87,] -5.226940e+00 -1.478355e+00
[88,] 8.003830e+00 -5.226940e+00
[89,] -3.807859e-01 8.003830e+00
[90,] 5.419331e+00 -3.807859e-01
[91,] -1.127298e+01 5.419331e+00
[92,] -6.272977e+00 -1.127298e+01
[93,] -7.345155e-01 -6.272977e+00
[94,] -2.042208e+00 -7.345155e-01
[95,] 7.265485e+00 -2.042208e+00
[96,] 5.908009e+00 7.265485e+00
[97,] 7.908009e+00 5.908009e+00
[98,] -3.520563e+00 7.908009e+00
[99,] 1.730852e+00 -3.520563e+00
[100,] 2.961622e+00 1.730852e+00
[101,] -4.422994e+00 2.961622e+00
[102,] -6.228771e-01 -4.422994e+00
[103,] -3.315185e+00 -6.228771e-01
[104,] 6.848152e-01 -3.315185e+00
[105,] 2.223277e+00 6.848152e-01
[106,] -8.441558e-02 2.223277e+00
[107,] 2.232767e-01 -8.441558e-02
[108,] -1.341991e-01 2.232767e-01
[109,] -4.134199e+00 -1.341991e-01
[110,] -9.562771e+00 -4.134199e+00
[111,] 7.688645e+00 -9.562771e+00
[112,] 9.194139e-01 7.688645e+00
[113,] 5.534799e+00 9.194139e-01
[114,] -1.166508e+01 5.534799e+00
[115,] -5.357393e+00 -1.166508e+01
[116,] -3.357393e+00 -5.357393e+00
[117,] -5.818931e+00 -3.357393e+00
[118,] 3.873377e+00 -5.818931e+00
[119,] -3.818931e+00 3.873377e+00
[120,] 3.823593e+00 -3.818931e+00
[121,] -7.176407e+00 3.823593e+00
[122,] -6.049784e-01 -7.176407e+00
[123,] -5.353563e+00 -6.049784e-01
[124,] -1.122794e+00 -5.353563e+00
[125,] 4.925907e-01 -1.122794e+00
[126,] -5.707293e+00 4.925907e-01
[127,] -1.399600e+00 -5.707293e+00
[128,] -4.399600e+00 -1.399600e+00
[129,] 1.013886e+01 -4.399600e+00
[130,] 8.311688e-01 1.013886e+01
[131,] -7.861139e+00 8.311688e-01
[132,] -8.218615e+00 -7.861139e+00
[133,] -5.218615e+00 -8.218615e+00
[134,] -1.647186e+00 -5.218615e+00
[135,] 5.604229e+00 -1.647186e+00
[136,] -2.165002e+00 5.604229e+00
[137,] -1.549617e+00 -2.165002e+00
[138,] -3.749500e+00 -1.549617e+00
[139,] 5.581918e-01 -3.749500e+00
[140,] 7.558192e+00 5.581918e-01
[141,] 7.096653e+00 7.558192e+00
[142,] 2.788961e+00 7.096653e+00
[143,] -2.903347e+00 2.788961e+00
[144,] -4.260823e+00 -2.903347e+00
[145,] 7.739177e+00 -4.260823e+00
[146,] 8.310606e+00 7.739177e+00
[147,] -4.379787e-01 8.310606e+00
[148,] -4.207209e+00 -4.379787e-01
[149,] 2.408175e+00 -4.207209e+00
[150,] -7.917083e-01 2.408175e+00
[151,] 6.515984e+00 -7.917083e-01
[152,] 1.551598e+01 6.515984e+00
[153,] -9.455544e-01 1.551598e+01
[154,] -1.253247e+00 -9.455544e-01
[155,] -6.945554e+00 -1.253247e+00
[156,] 2.696970e+00 -6.945554e+00
[157,] 6.969697e-01 2.696970e+00
[158,] 8.268398e+00 6.969697e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.647186e+00 6.471861e-01
2 -3.781385e+00 5.647186e+00
3 -5.299700e-01 -3.781385e+00
4 -2.992008e-01 -5.299700e-01
5 -1.683816e+00 -2.992008e-01
6 -2.852148e-01 -1.683816e+00
7 -4.977522e+00 -2.852148e-01
8 -3.977522e+00 -4.977522e+00
9 -5.439061e+00 -3.977522e+00
10 2.253247e+00 -5.439061e+00
11 6.560939e+00 2.253247e+00
12 -1.796537e+00 6.560939e+00
13 -2.796537e+00 -1.796537e+00
14 -7.225108e+00 -2.796537e+00
15 -7.973693e+00 -7.225108e+00
16 1.257076e+00 -7.973693e+00
17 -4.127539e+00 1.257076e+00
18 -3.274226e-01 -4.127539e+00
19 9.980270e+00 -3.274226e-01
20 4.980270e+00 9.980270e+00
21 1.151873e+01 4.980270e+00
22 2.110390e-01 1.151873e+01
23 7.518731e+00 2.110390e-01
24 -5.838745e+00 7.518731e+00
25 -4.838745e+00 -5.838745e+00
26 -2.267316e+00 -4.838745e+00
27 9.840992e-01 -2.267316e+00
28 2.214868e+00 9.840992e-01
29 -2.169747e+00 2.214868e+00
30 3.630370e+00 -2.169747e+00
31 3.938062e+00 3.630370e+00
32 3.938062e+00 3.938062e+00
33 2.476523e+00 3.938062e+00
34 -3.831169e+00 2.476523e+00
35 8.476523e+00 -3.831169e+00
36 8.119048e+00 8.476523e+00
37 -7.880952e+00 8.119048e+00
38 9.690476e+00 -7.880952e+00
39 4.941891e+00 9.690476e+00
40 9.172661e+00 4.941891e+00
41 2.788045e+00 9.172661e+00
42 -2.411838e+00 2.788045e+00
43 -4.104146e+00 -2.411838e+00
44 -2.104146e+00 -4.104146e+00
45 -7.565684e+00 -2.104146e+00
46 -8.733766e-01 -7.565684e+00
47 9.434316e+00 -8.733766e-01
48 4.076840e+00 9.434316e+00
49 2.076840e+00 4.076840e+00
50 3.648268e+00 2.076840e+00
51 -2.100316e+00 3.648268e+00
52 1.304529e-01 -2.100316e+00
53 -8.254163e+00 1.304529e-01
54 7.545954e+00 -8.254163e+00
55 4.853646e+00 7.545954e+00
56 -1.463536e-01 4.853646e+00
57 -5.607892e+00 -1.463536e-01
58 -8.915584e+00 -5.607892e+00
59 -9.607892e+00 -8.915584e+00
60 -7.965368e+00 -9.607892e+00
61 3.463203e-02 -7.965368e+00
62 -3.393939e+00 3.463203e-02
63 -2.142524e+00 -3.393939e+00
64 -9.911755e+00 -2.142524e+00
65 9.703630e+00 -9.911755e+00
66 1.050375e+01 9.703630e+00
67 -2.188561e+00 1.050375e+01
68 -1.318856e+01 -2.188561e+00
69 -2.650100e+00 -1.318856e+01
70 7.042208e+00 -2.650100e+00
71 -4.650100e+00 7.042208e+00
72 4.992424e+00 -4.650100e+00
73 7.992424e+00 4.992424e+00
74 3.563853e+00 7.992424e+00
75 2.815268e+00 3.563853e+00
76 -6.953963e+00 2.815268e+00
77 1.661422e+00 -6.953963e+00
78 -1.538462e+00 1.661422e+00
79 6.769231e+00 -1.538462e+00
80 7.692308e-01 6.769231e+00
81 -4.692308e+00 7.692308e-01
82 -5.431175e-17 -4.692308e+00
83 -3.692308e+00 -5.431175e-17
84 -2.049784e+00 -3.692308e+00
85 -4.978355e-02 -2.049784e+00
86 -1.478355e+00 -4.978355e-02
87 -5.226940e+00 -1.478355e+00
88 8.003830e+00 -5.226940e+00
89 -3.807859e-01 8.003830e+00
90 5.419331e+00 -3.807859e-01
91 -1.127298e+01 5.419331e+00
92 -6.272977e+00 -1.127298e+01
93 -7.345155e-01 -6.272977e+00
94 -2.042208e+00 -7.345155e-01
95 7.265485e+00 -2.042208e+00
96 5.908009e+00 7.265485e+00
97 7.908009e+00 5.908009e+00
98 -3.520563e+00 7.908009e+00
99 1.730852e+00 -3.520563e+00
100 2.961622e+00 1.730852e+00
101 -4.422994e+00 2.961622e+00
102 -6.228771e-01 -4.422994e+00
103 -3.315185e+00 -6.228771e-01
104 6.848152e-01 -3.315185e+00
105 2.223277e+00 6.848152e-01
106 -8.441558e-02 2.223277e+00
107 2.232767e-01 -8.441558e-02
108 -1.341991e-01 2.232767e-01
109 -4.134199e+00 -1.341991e-01
110 -9.562771e+00 -4.134199e+00
111 7.688645e+00 -9.562771e+00
112 9.194139e-01 7.688645e+00
113 5.534799e+00 9.194139e-01
114 -1.166508e+01 5.534799e+00
115 -5.357393e+00 -1.166508e+01
116 -3.357393e+00 -5.357393e+00
117 -5.818931e+00 -3.357393e+00
118 3.873377e+00 -5.818931e+00
119 -3.818931e+00 3.873377e+00
120 3.823593e+00 -3.818931e+00
121 -7.176407e+00 3.823593e+00
122 -6.049784e-01 -7.176407e+00
123 -5.353563e+00 -6.049784e-01
124 -1.122794e+00 -5.353563e+00
125 4.925907e-01 -1.122794e+00
126 -5.707293e+00 4.925907e-01
127 -1.399600e+00 -5.707293e+00
128 -4.399600e+00 -1.399600e+00
129 1.013886e+01 -4.399600e+00
130 8.311688e-01 1.013886e+01
131 -7.861139e+00 8.311688e-01
132 -8.218615e+00 -7.861139e+00
133 -5.218615e+00 -8.218615e+00
134 -1.647186e+00 -5.218615e+00
135 5.604229e+00 -1.647186e+00
136 -2.165002e+00 5.604229e+00
137 -1.549617e+00 -2.165002e+00
138 -3.749500e+00 -1.549617e+00
139 5.581918e-01 -3.749500e+00
140 7.558192e+00 5.581918e-01
141 7.096653e+00 7.558192e+00
142 2.788961e+00 7.096653e+00
143 -2.903347e+00 2.788961e+00
144 -4.260823e+00 -2.903347e+00
145 7.739177e+00 -4.260823e+00
146 8.310606e+00 7.739177e+00
147 -4.379787e-01 8.310606e+00
148 -4.207209e+00 -4.379787e-01
149 2.408175e+00 -4.207209e+00
150 -7.917083e-01 2.408175e+00
151 6.515984e+00 -7.917083e-01
152 1.551598e+01 6.515984e+00
153 -9.455544e-01 1.551598e+01
154 -1.253247e+00 -9.455544e-01
155 -6.945554e+00 -1.253247e+00
156 2.696970e+00 -6.945554e+00
157 6.969697e-01 2.696970e+00
158 8.268398e+00 6.969697e-01
> 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/7wra11290853552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8wra11290853552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9wra11290853552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1070rm1290853552.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11s1pa1290853552.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/12w16g1290853552.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/13stm71290853552.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/14323s1290853552.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/15631x1290853552.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/162cz61290853552.tab")
+ }
>
> try(system("convert tmp/10hcs1290853552.ps tmp/10hcs1290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/20hcs1290853552.ps tmp/20hcs1290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tqbd1290853552.ps tmp/3tqbd1290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tqbd1290853552.ps tmp/4tqbd1290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tqbd1290853552.ps tmp/5tqbd1290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/6miag1290853552.ps tmp/6miag1290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wra11290853552.ps tmp/7wra11290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wra11290853552.ps tmp/8wra11290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wra11290853552.ps tmp/9wra11290853552.png",intern=TRUE))
character(0)
> try(system("convert tmp/1070rm1290853552.ps tmp/1070rm1290853552.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.580 1.100 6.666