R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
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(2,14,2,18,2,11,1,12,2,16,2,18,2,14,2,14,2,15,2,15,1,17,2,19,1,10,2,16,2,18,1,14,1,14,2,17,1,14,2,16,1,18,2,11,2,14,2,12,1,17,2,9,1,16,2,14,2,15,1,11,2,16,1,13,2,17,2,15,1,14,1,16,1,9,1,15,2,17,1,13,1,15,2,16,1,16,1,12,2,12,2,11,2,15,2,15,2,17,1,13,2,16,1,14,1,11,2,12,1,12,2,15,2,16,2,15,1,12,2,12,1,8,1,13,2,11,2,14,2,15,1,10,2,11,1,12,2,15,1,15,1,14,2,16,2,15,1,15,1,13,2,12,2,17,2,13,1,15,1,13,1,15,1,16,2,15,1,16,2,15,2,14,1,15,2,14,2,13,2,7,2,17,2,13,2,15,2,14,2,13,2,16,2,12,2,14,1,17,1,15,2,17,1,12,2,16,1,11,2,15,1,9,2,16,1,15,1,10,2,10,2,15,2,11,2,13,1,14,2,18,1,16,2,14,2,14,2,14,2,14,2,12,2,14,2,15,2,15,2,15,2,13,1,17,2,17,2,19,2,15,1,13,1,9,2,15,1,15,1,15,2,16,1,11,1,14,2,11,2,15,1,13,2,15,1,16,2,14,1,15,2,16,2,16,1,11,1,12,1,9,2,16,2,13,1,16,2,12,2,9,2,13,2,13,2,14,2,19,2,13,2,12,2,13),dim=c(2,162),dimnames=list(c('x','y'),1:162))
> y <- array(NA,dim=c(2,162),dimnames=list(c('x','y'),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 = '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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 14 2 1 0 0 0 0 0 0 0 0 0 0 1
2 18 2 0 1 0 0 0 0 0 0 0 0 0 2
3 11 2 0 0 1 0 0 0 0 0 0 0 0 3
4 12 1 0 0 0 1 0 0 0 0 0 0 0 4
5 16 2 0 0 0 0 1 0 0 0 0 0 0 5
6 18 2 0 0 0 0 0 1 0 0 0 0 0 6
7 14 2 0 0 0 0 0 0 1 0 0 0 0 7
8 14 2 0 0 0 0 0 0 0 1 0 0 0 8
9 15 2 0 0 0 0 0 0 0 0 1 0 0 9
10 15 2 0 0 0 0 0 0 0 0 0 1 0 10
11 17 1 0 0 0 0 0 0 0 0 0 0 1 11
12 19 2 0 0 0 0 0 0 0 0 0 0 0 12
13 10 1 1 0 0 0 0 0 0 0 0 0 0 13
14 16 2 0 1 0 0 0 0 0 0 0 0 0 14
15 18 2 0 0 1 0 0 0 0 0 0 0 0 15
16 14 1 0 0 0 1 0 0 0 0 0 0 0 16
17 14 1 0 0 0 0 1 0 0 0 0 0 0 17
18 17 2 0 0 0 0 0 1 0 0 0 0 0 18
19 14 1 0 0 0 0 0 0 1 0 0 0 0 19
20 16 2 0 0 0 0 0 0 0 1 0 0 0 20
21 18 1 0 0 0 0 0 0 0 0 1 0 0 21
22 11 2 0 0 0 0 0 0 0 0 0 1 0 22
23 14 2 0 0 0 0 0 0 0 0 0 0 1 23
24 12 2 0 0 0 0 0 0 0 0 0 0 0 24
25 17 1 1 0 0 0 0 0 0 0 0 0 0 25
26 9 2 0 1 0 0 0 0 0 0 0 0 0 26
27 16 1 0 0 1 0 0 0 0 0 0 0 0 27
28 14 2 0 0 0 1 0 0 0 0 0 0 0 28
29 15 2 0 0 0 0 1 0 0 0 0 0 0 29
30 11 1 0 0 0 0 0 1 0 0 0 0 0 30
31 16 2 0 0 0 0 0 0 1 0 0 0 0 31
32 13 1 0 0 0 0 0 0 0 1 0 0 0 32
33 17 2 0 0 0 0 0 0 0 0 1 0 0 33
34 15 2 0 0 0 0 0 0 0 0 0 1 0 34
35 14 1 0 0 0 0 0 0 0 0 0 0 1 35
36 16 1 0 0 0 0 0 0 0 0 0 0 0 36
37 9 1 1 0 0 0 0 0 0 0 0 0 0 37
38 15 1 0 1 0 0 0 0 0 0 0 0 0 38
39 17 2 0 0 1 0 0 0 0 0 0 0 0 39
40 13 1 0 0 0 1 0 0 0 0 0 0 0 40
41 15 1 0 0 0 0 1 0 0 0 0 0 0 41
42 16 2 0 0 0 0 0 1 0 0 0 0 0 42
43 16 1 0 0 0 0 0 0 1 0 0 0 0 43
44 12 1 0 0 0 0 0 0 0 1 0 0 0 44
45 12 2 0 0 0 0 0 0 0 0 1 0 0 45
46 11 2 0 0 0 0 0 0 0 0 0 1 0 46
47 15 2 0 0 0 0 0 0 0 0 0 0 1 47
48 15 2 0 0 0 0 0 0 0 0 0 0 0 48
49 17 2 1 0 0 0 0 0 0 0 0 0 0 49
50 13 1 0 1 0 0 0 0 0 0 0 0 0 50
51 16 2 0 0 1 0 0 0 0 0 0 0 0 51
52 14 1 0 0 0 1 0 0 0 0 0 0 0 52
53 11 1 0 0 0 0 1 0 0 0 0 0 0 53
54 12 2 0 0 0 0 0 1 0 0 0 0 0 54
55 12 1 0 0 0 0 0 0 1 0 0 0 0 55
56 15 2 0 0 0 0 0 0 0 1 0 0 0 56
57 16 2 0 0 0 0 0 0 0 0 1 0 0 57
58 15 2 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 12 2 0 0 0 0 0 0 0 0 0 0 0 60
61 8 1 1 0 0 0 0 0 0 0 0 0 0 61
62 13 1 0 1 0 0 0 0 0 0 0 0 0 62
63 11 2 0 0 1 0 0 0 0 0 0 0 0 63
64 14 2 0 0 0 1 0 0 0 0 0 0 0 64
65 15 2 0 0 0 0 1 0 0 0 0 0 0 65
66 10 1 0 0 0 0 0 1 0 0 0 0 0 66
67 11 2 0 0 0 0 0 0 1 0 0 0 0 67
68 12 1 0 0 0 0 0 0 0 1 0 0 0 68
69 15 2 0 0 0 0 0 0 0 0 1 0 0 69
70 15 1 0 0 0 0 0 0 0 0 0 1 0 70
71 14 1 0 0 0 0 0 0 0 0 0 0 1 71
72 16 2 0 0 0 0 0 0 0 0 0 0 0 72
73 15 2 1 0 0 0 0 0 0 0 0 0 0 73
74 15 1 0 1 0 0 0 0 0 0 0 0 0 74
75 13 1 0 0 1 0 0 0 0 0 0 0 0 75
76 12 2 0 0 0 1 0 0 0 0 0 0 0 76
77 17 2 0 0 0 0 1 0 0 0 0 0 0 77
78 13 2 0 0 0 0 0 1 0 0 0 0 0 78
79 15 1 0 0 0 0 0 0 1 0 0 0 0 79
80 13 1 0 0 0 0 0 0 0 1 0 0 0 80
81 15 1 0 0 0 0 0 0 0 0 1 0 0 81
82 16 1 0 0 0 0 0 0 0 0 0 1 0 82
83 15 2 0 0 0 0 0 0 0 0 0 0 1 83
84 16 1 0 0 0 0 0 0 0 0 0 0 0 84
85 15 2 1 0 0 0 0 0 0 0 0 0 0 85
86 14 2 0 1 0 0 0 0 0 0 0 0 0 86
87 15 1 0 0 1 0 0 0 0 0 0 0 0 87
88 14 2 0 0 0 1 0 0 0 0 0 0 0 88
89 13 2 0 0 0 0 1 0 0 0 0 0 0 89
90 7 2 0 0 0 0 0 1 0 0 0 0 0 90
91 17 2 0 0 0 0 0 0 1 0 0 0 0 91
92 13 2 0 0 0 0 0 0 0 1 0 0 0 92
93 15 2 0 0 0 0 0 0 0 0 1 0 0 93
94 14 2 0 0 0 0 0 0 0 0 0 1 0 94
95 13 2 0 0 0 0 0 0 0 0 0 0 1 95
96 16 2 0 0 0 0 0 0 0 0 0 0 0 96
97 12 2 1 0 0 0 0 0 0 0 0 0 0 97
98 14 2 0 1 0 0 0 0 0 0 0 0 0 98
99 17 1 0 0 1 0 0 0 0 0 0 0 0 99
100 15 1 0 0 0 1 0 0 0 0 0 0 0 100
101 17 2 0 0 0 0 1 0 0 0 0 0 0 101
102 12 1 0 0 0 0 0 1 0 0 0 0 0 102
103 16 2 0 0 0 0 0 0 1 0 0 0 0 103
104 11 1 0 0 0 0 0 0 0 1 0 0 0 104
105 15 2 0 0 0 0 0 0 0 0 1 0 0 105
106 9 1 0 0 0 0 0 0 0 0 0 1 0 106
107 16 2 0 0 0 0 0 0 0 0 0 0 1 107
108 15 1 0 0 0 0 0 0 0 0 0 0 0 108
109 10 1 1 0 0 0 0 0 0 0 0 0 0 109
110 10 2 0 1 0 0 0 0 0 0 0 0 0 110
111 15 2 0 0 1 0 0 0 0 0 0 0 0 111
112 11 2 0 0 0 1 0 0 0 0 0 0 0 112
113 13 2 0 0 0 0 1 0 0 0 0 0 0 113
114 14 1 0 0 0 0 0 1 0 0 0 0 0 114
115 18 2 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 14 2 0 0 0 0 0 0 0 0 1 0 0 117
118 14 2 0 0 0 0 0 0 0 0 0 1 0 118
119 14 2 0 0 0 0 0 0 0 0 0 0 1 119
120 14 2 0 0 0 0 0 0 0 0 0 0 0 120
121 12 2 1 0 0 0 0 0 0 0 0 0 0 121
122 14 2 0 1 0 0 0 0 0 0 0 0 0 122
123 15 2 0 0 1 0 0 0 0 0 0 0 0 123
124 15 2 0 0 0 1 0 0 0 0 0 0 0 124
125 15 2 0 0 0 0 1 0 0 0 0 0 0 125
126 13 2 0 0 0 0 0 1 0 0 0 0 0 126
127 17 1 0 0 0 0 0 0 1 0 0 0 0 127
128 17 2 0 0 0 0 0 0 0 1 0 0 0 128
129 19 2 0 0 0 0 0 0 0 0 1 0 0 129
130 15 2 0 0 0 0 0 0 0 0 0 1 0 130
131 13 1 0 0 0 0 0 0 0 0 0 0 1 131
132 9 1 0 0 0 0 0 0 0 0 0 0 0 132
133 15 2 1 0 0 0 0 0 0 0 0 0 0 133
134 15 1 0 1 0 0 0 0 0 0 0 0 0 134
135 15 1 0 0 1 0 0 0 0 0 0 0 0 135
136 16 2 0 0 0 1 0 0 0 0 0 0 0 136
137 11 1 0 0 0 0 1 0 0 0 0 0 0 137
138 14 1 0 0 0 0 0 1 0 0 0 0 0 138
139 11 2 0 0 0 0 0 0 1 0 0 0 0 139
140 15 2 0 0 0 0 0 0 0 1 0 0 0 140
141 13 1 0 0 0 0 0 0 0 0 1 0 0 141
142 15 2 0 0 0 0 0 0 0 0 0 1 0 142
143 16 1 0 0 0 0 0 0 0 0 0 0 1 143
144 14 2 0 0 0 0 0 0 0 0 0 0 0 144
145 15 1 1 0 0 0 0 0 0 0 0 0 0 145
146 16 2 0 1 0 0 0 0 0 0 0 0 0 146
147 16 2 0 0 1 0 0 0 0 0 0 0 0 147
148 11 1 0 0 0 1 0 0 0 0 0 0 0 148
149 12 1 0 0 0 0 1 0 0 0 0 0 0 149
150 9 1 0 0 0 0 0 1 0 0 0 0 0 150
151 16 2 0 0 0 0 0 0 1 0 0 0 0 151
152 13 2 0 0 0 0 0 0 0 1 0 0 0 152
153 16 1 0 0 0 0 0 0 0 0 1 0 0 153
154 12 2 0 0 0 0 0 0 0 0 0 1 0 154
155 9 2 0 0 0 0 0 0 0 0 0 0 1 155
156 13 2 0 0 0 0 0 0 0 0 0 0 0 156
157 13 2 1 0 0 0 0 0 0 0 0 0 0 157
158 14 2 0 1 0 0 0 0 0 0 0 0 0 158
159 19 2 0 0 1 0 0 0 0 0 0 0 0 159
160 13 2 0 0 0 1 0 0 0 0 0 0 0 160
161 12 2 0 0 0 0 1 0 0 0 0 0 0 161
162 13 2 0 0 0 0 0 1 0 0 0 0 0 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
13.428303 0.832898 -1.310912 -0.365009 0.926100 -0.866155
M5 M6 M7 M8 M9 M10
-0.348823 -1.498221 0.498631 -0.431905 0.983814 -0.844090
M11 t
-0.261873 -0.005395
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1103 -1.2947 0.1908 1.4500 4.4365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.428303 0.923422 14.542 <2e-16 ***
x 0.832898 0.367901 2.264 0.0250 *
M1 -1.310912 0.865430 -1.515 0.1320
M2 -0.365009 0.864442 -0.422 0.6735
M3 0.926100 0.864388 1.071 0.2857
M4 -0.866155 0.865290 -1.001 0.3185
M5 -0.348823 0.864331 -0.404 0.6871
M6 -1.498221 0.865280 -1.731 0.0854 .
M7 0.498631 0.880616 0.566 0.5721
M8 -0.431905 0.881885 -0.490 0.6250
M9 0.983814 0.880066 1.118 0.2654
M10 -0.844090 0.880495 -0.959 0.3393
M11 -0.261873 0.881806 -0.297 0.7669
t -0.005395 0.003778 -1.428 0.1554
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.244 on 148 degrees of freedom
Multiple R-squared: 0.1532, Adjusted R-squared: 0.07886
F-statistic: 2.06 on 13 and 148 DF, p-value: 0.01975
> 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,] 0.7757051 0.44858986 0.224294932
[2,] 0.7461282 0.50774368 0.253871839
[3,] 0.6877758 0.62444843 0.312224216
[4,] 0.5734685 0.85306310 0.426531548
[5,] 0.6638957 0.67220850 0.336104252
[6,] 0.7796051 0.44078989 0.220394944
[7,] 0.7856082 0.42878368 0.214391839
[8,] 0.9033672 0.19326563 0.096632815
[9,] 0.9592302 0.08153969 0.040769847
[10,] 0.9934333 0.01313339 0.006566694
[11,] 0.9900975 0.01980493 0.009902467
[12,] 0.9896046 0.02079071 0.010395353
[13,] 0.9842655 0.03146898 0.015734491
[14,] 0.9928276 0.01434486 0.007172428
[15,] 0.9914077 0.01718460 0.008592302
[16,] 0.9868653 0.02626942 0.013134708
[17,] 0.9814858 0.03702834 0.018514169
[18,] 0.9781964 0.04360719 0.021803596
[19,] 0.9689258 0.06214839 0.031074196
[20,] 0.9622945 0.07541107 0.037705536
[21,] 0.9702177 0.05956468 0.029782340
[22,] 0.9646505 0.07069895 0.035349477
[23,] 0.9609978 0.07800434 0.039002172
[24,] 0.9468413 0.10631738 0.053158690
[25,] 0.9336080 0.13278405 0.066392027
[26,] 0.9298216 0.14035677 0.070178387
[27,] 0.9209148 0.15817043 0.079085214
[28,] 0.9054805 0.18903903 0.094519516
[29,] 0.9316277 0.13674459 0.068372294
[30,] 0.9277307 0.14453866 0.072269328
[31,] 0.9089196 0.18216080 0.091080398
[32,] 0.8852304 0.22953918 0.114769591
[33,] 0.9335477 0.13290469 0.066452346
[34,] 0.9145769 0.17084625 0.085423124
[35,] 0.8939243 0.21215139 0.106075694
[36,] 0.8754452 0.24910961 0.124554806
[37,] 0.8800356 0.23992871 0.119964353
[38,] 0.8761180 0.24776406 0.123882030
[39,] 0.8666866 0.26662670 0.133313351
[40,] 0.8465212 0.30695752 0.153478758
[41,] 0.8178214 0.36435722 0.182178608
[42,] 0.8083120 0.38337606 0.191688030
[43,] 0.7872759 0.42544815 0.212724075
[44,] 0.7917147 0.41657061 0.208285306
[45,] 0.8580149 0.28397023 0.141985114
[46,] 0.8292505 0.34149892 0.170749462
[47,] 0.8827592 0.23448155 0.117240773
[48,] 0.8600318 0.27993642 0.139968210
[49,] 0.8381744 0.32365116 0.161825579
[50,] 0.8344594 0.33108122 0.165540608
[51,] 0.8800070 0.23998601 0.119993005
[52,] 0.8624184 0.27516316 0.137581578
[53,] 0.8365330 0.32693403 0.163467017
[54,] 0.8492550 0.30148990 0.150744952
[55,] 0.8225640 0.35487206 0.177436029
[56,] 0.8096613 0.38067732 0.190338661
[57,] 0.8096882 0.38062351 0.190311753
[58,] 0.8001743 0.39965143 0.199825716
[59,] 0.7905127 0.41897455 0.209487274
[60,] 0.7710688 0.45786236 0.228931180
[61,] 0.7977712 0.40445754 0.202228769
[62,] 0.7633843 0.47323137 0.236615685
[63,] 0.7463597 0.50728067 0.253640337
[64,] 0.7113758 0.57724831 0.288624154
[65,] 0.6725089 0.65498225 0.327491124
[66,] 0.7119579 0.57608426 0.288042131
[67,] 0.6763370 0.64732608 0.323663041
[68,] 0.6813350 0.63732996 0.318664981
[69,] 0.6714322 0.65713567 0.328567835
[70,] 0.6257533 0.74849345 0.374246727
[71,] 0.5890443 0.82191150 0.410955748
[72,] 0.5433148 0.91337032 0.456685161
[73,] 0.5032525 0.99349495 0.496747473
[74,] 0.7524535 0.49509305 0.247546523
[75,] 0.7457908 0.50841835 0.254209177
[76,] 0.7210668 0.55786639 0.278933197
[77,] 0.6826099 0.63478012 0.317390059
[78,] 0.6384255 0.72314898 0.361574490
[79,] 0.6025884 0.79482314 0.397411571
[80,] 0.5851294 0.82974114 0.414870568
[81,] 0.5507194 0.89856126 0.449280632
[82,] 0.5010757 0.99784860 0.498924300
[83,] 0.4994043 0.99880850 0.500595750
[84,] 0.4978438 0.99568763 0.502156186
[85,] 0.5505493 0.89890136 0.449450682
[86,] 0.4991888 0.99837765 0.500811176
[87,] 0.4572772 0.91455445 0.542722774
[88,] 0.4807411 0.96148221 0.519258897
[89,] 0.4344430 0.86888608 0.565556959
[90,] 0.5371969 0.92560617 0.462803087
[91,] 0.5285554 0.94288929 0.471444643
[92,] 0.5210482 0.95790352 0.478951758
[93,] 0.5526880 0.89462400 0.447312000
[94,] 0.7060960 0.58780805 0.293904023
[95,] 0.6830741 0.63385189 0.316925944
[96,] 0.7309084 0.53818310 0.269091550
[97,] 0.6899914 0.62001717 0.310008585
[98,] 0.6585297 0.68294056 0.341470279
[99,] 0.6742072 0.65158558 0.325792791
[100,] 0.6568408 0.68631843 0.343159216
[101,] 0.6714115 0.65717703 0.328588517
[102,] 0.6222195 0.75556109 0.377780543
[103,] 0.5641680 0.87166404 0.435832022
[104,] 0.5112411 0.97751786 0.488758929
[105,] 0.5427045 0.91459108 0.457295540
[106,] 0.5301097 0.93978054 0.469890268
[107,] 0.5653384 0.86932320 0.434661598
[108,] 0.5111178 0.97776442 0.488882212
[109,] 0.4529751 0.90595025 0.547024876
[110,] 0.4093661 0.81873215 0.590633926
[111,] 0.4707701 0.94154022 0.529229890
[112,] 0.4516703 0.90334053 0.548329737
[113,] 0.4415774 0.88315477 0.558422615
[114,] 0.3810729 0.76214587 0.618927067
[115,] 0.3162765 0.63255298 0.683723512
[116,] 0.4188269 0.83765387 0.581173064
[117,] 0.3543766 0.70875322 0.645623391
[118,] 0.2906115 0.58122301 0.709388497
[119,] 0.2779461 0.55589224 0.722053879
[120,] 0.3100598 0.62011954 0.689940232
[121,] 0.2593783 0.51875658 0.740621708
[122,] 0.2478723 0.49574460 0.752127702
[123,] 0.3399469 0.67989375 0.660053126
[124,] 0.2757577 0.55151531 0.724242344
[125,] 0.2658224 0.53164489 0.734177556
[126,] 0.2238067 0.44761341 0.776193294
[127,] 0.6510302 0.69793968 0.348969840
[128,] 0.5274063 0.94518739 0.472593696
[129,] 0.5721191 0.85576181 0.427880905
> postscript(file="/var/www/html/freestat/rcomp/tmp/13ub81291035088.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/290351291035088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/390351291035088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/490351291035088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/529kq1291035088.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
0.22220856 3.28170127 -5.00401302 -1.37346502 1.28170127 4.43649428
7 8 9 10 11 12
-1.55496245 -0.61903151 -1.02935492 0.80394491 3.06002018 3.97064508
13 14 15 16 17 18
-2.88014971 1.34644515 2.06073086 0.69127886 0.17934299 3.50123816
19 20 21 22 23 24
-0.65732073 1.44571237 2.86828680 -3.13131121 -0.70813379 -2.96461104
25 26 27 28 29 30
4.18459417 -5.58881097 0.95837258 -0.07687511 0.41118903 -1.60112012
31 32 33 34 35 36
0.57452531 -0.65664591 1.10013284 0.93343267 0.18950794 1.93303068
37 38 39 40 41 42
-3.75066195 1.30883075 1.19021862 -0.17923338 1.30883075 2.63072592
43 44 45 46 47 48
1.47216703 -1.59190203 -3.83512328 -3.00182345 0.42135397 0.16487672
49 50 51 52 53 54
3.48118408 -0.62642537 0.25496250 0.88551050 -2.62642537 -1.30453020
55 56 57 58 59 60
-2.46308909 0.63994401 0.22962060 1.06292043 -1.68100430 -2.77037940
61 62 63 64 65 66
-4.62117419 -0.56168149 -4.68029362 0.11735653 0.60542066 -2.40688848
67 68 69 70 71 72
-4.23124305 -1.46241427 -0.70563552 1.96056215 0.38373957 1.29436448
73 74 75 76 77 78
1.61067184 1.50306239 -1.78265190 -1.81789959 2.67016454 -0.17504244
79 80 81 82 83 84
0.66639867 -0.39767039 0.19200620 3.02530603 0.61558561 2.19200620
85 86 87 88 89 90
1.67541572 -0.26509158 0.28209198 0.24684429 -1.26509158 -6.11029856
91 92 93 94 95 96
1.89824471 -1.16582436 -0.57614776 0.25715207 -1.31967051 1.42385224
97 98 99 100 101 102
-1.25984040 -0.20034770 2.34683586 2.14448602 2.79965230 -0.21265684
103 104 105 106 107 108
0.96298859 -2.26818263 -0.51140388 -3.84520621 1.74507337 1.32149396
109 110 111 112 113 114
-2.36219868 -4.13560382 -0.42131810 -2.62366795 -1.13560382 1.85208704
115 116 117 118 119 120
3.02773247 2.79656125 -1.44666001 0.38663983 -0.19018275 -0.44666001
121 122 123 124 125 126
-1.13035264 -0.07085994 -0.35657422 1.44107593 0.92914006 0.08393307
127 128 129 130 131 132
2.92537419 3.02840728 3.61808387 1.45138371 -0.29254103 -4.54901828
133 134 135 136 137 138
1.93439124 1.82678179 0.54106750 2.50581981 -2.17321821 1.98157480
139 140 141 142 143 144
-3.84277977 1.09315116 -1.48427440 1.51612759 2.77220285 -0.31717225
145 146 147 148 149 150
2.83203296 2.05862782 0.77291354 -1.59653847 -1.10847433 -2.95368132
151 152 153 154 155 156
1.22196411 -0.84210496 1.58046948 -1.41912853 -4.99595111 -1.25242837
157 158 159 160 161 162
0.06387900 0.12337170 3.83765742 -0.36469243 -1.87662830 0.27816471
> postscript(file="/var/www/html/freestat/rcomp/tmp/629kq1291035088.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 0.22220856 NA
1 3.28170127 0.22220856
2 -5.00401302 3.28170127
3 -1.37346502 -5.00401302
4 1.28170127 -1.37346502
5 4.43649428 1.28170127
6 -1.55496245 4.43649428
7 -0.61903151 -1.55496245
8 -1.02935492 -0.61903151
9 0.80394491 -1.02935492
10 3.06002018 0.80394491
11 3.97064508 3.06002018
12 -2.88014971 3.97064508
13 1.34644515 -2.88014971
14 2.06073086 1.34644515
15 0.69127886 2.06073086
16 0.17934299 0.69127886
17 3.50123816 0.17934299
18 -0.65732073 3.50123816
19 1.44571237 -0.65732073
20 2.86828680 1.44571237
21 -3.13131121 2.86828680
22 -0.70813379 -3.13131121
23 -2.96461104 -0.70813379
24 4.18459417 -2.96461104
25 -5.58881097 4.18459417
26 0.95837258 -5.58881097
27 -0.07687511 0.95837258
28 0.41118903 -0.07687511
29 -1.60112012 0.41118903
30 0.57452531 -1.60112012
31 -0.65664591 0.57452531
32 1.10013284 -0.65664591
33 0.93343267 1.10013284
34 0.18950794 0.93343267
35 1.93303068 0.18950794
36 -3.75066195 1.93303068
37 1.30883075 -3.75066195
38 1.19021862 1.30883075
39 -0.17923338 1.19021862
40 1.30883075 -0.17923338
41 2.63072592 1.30883075
42 1.47216703 2.63072592
43 -1.59190203 1.47216703
44 -3.83512328 -1.59190203
45 -3.00182345 -3.83512328
46 0.42135397 -3.00182345
47 0.16487672 0.42135397
48 3.48118408 0.16487672
49 -0.62642537 3.48118408
50 0.25496250 -0.62642537
51 0.88551050 0.25496250
52 -2.62642537 0.88551050
53 -1.30453020 -2.62642537
54 -2.46308909 -1.30453020
55 0.63994401 -2.46308909
56 0.22962060 0.63994401
57 1.06292043 0.22962060
58 -1.68100430 1.06292043
59 -2.77037940 -1.68100430
60 -4.62117419 -2.77037940
61 -0.56168149 -4.62117419
62 -4.68029362 -0.56168149
63 0.11735653 -4.68029362
64 0.60542066 0.11735653
65 -2.40688848 0.60542066
66 -4.23124305 -2.40688848
67 -1.46241427 -4.23124305
68 -0.70563552 -1.46241427
69 1.96056215 -0.70563552
70 0.38373957 1.96056215
71 1.29436448 0.38373957
72 1.61067184 1.29436448
73 1.50306239 1.61067184
74 -1.78265190 1.50306239
75 -1.81789959 -1.78265190
76 2.67016454 -1.81789959
77 -0.17504244 2.67016454
78 0.66639867 -0.17504244
79 -0.39767039 0.66639867
80 0.19200620 -0.39767039
81 3.02530603 0.19200620
82 0.61558561 3.02530603
83 2.19200620 0.61558561
84 1.67541572 2.19200620
85 -0.26509158 1.67541572
86 0.28209198 -0.26509158
87 0.24684429 0.28209198
88 -1.26509158 0.24684429
89 -6.11029856 -1.26509158
90 1.89824471 -6.11029856
91 -1.16582436 1.89824471
92 -0.57614776 -1.16582436
93 0.25715207 -0.57614776
94 -1.31967051 0.25715207
95 1.42385224 -1.31967051
96 -1.25984040 1.42385224
97 -0.20034770 -1.25984040
98 2.34683586 -0.20034770
99 2.14448602 2.34683586
100 2.79965230 2.14448602
101 -0.21265684 2.79965230
102 0.96298859 -0.21265684
103 -2.26818263 0.96298859
104 -0.51140388 -2.26818263
105 -3.84520621 -0.51140388
106 1.74507337 -3.84520621
107 1.32149396 1.74507337
108 -2.36219868 1.32149396
109 -4.13560382 -2.36219868
110 -0.42131810 -4.13560382
111 -2.62366795 -0.42131810
112 -1.13560382 -2.62366795
113 1.85208704 -1.13560382
114 3.02773247 1.85208704
115 2.79656125 3.02773247
116 -1.44666001 2.79656125
117 0.38663983 -1.44666001
118 -0.19018275 0.38663983
119 -0.44666001 -0.19018275
120 -1.13035264 -0.44666001
121 -0.07085994 -1.13035264
122 -0.35657422 -0.07085994
123 1.44107593 -0.35657422
124 0.92914006 1.44107593
125 0.08393307 0.92914006
126 2.92537419 0.08393307
127 3.02840728 2.92537419
128 3.61808387 3.02840728
129 1.45138371 3.61808387
130 -0.29254103 1.45138371
131 -4.54901828 -0.29254103
132 1.93439124 -4.54901828
133 1.82678179 1.93439124
134 0.54106750 1.82678179
135 2.50581981 0.54106750
136 -2.17321821 2.50581981
137 1.98157480 -2.17321821
138 -3.84277977 1.98157480
139 1.09315116 -3.84277977
140 -1.48427440 1.09315116
141 1.51612759 -1.48427440
142 2.77220285 1.51612759
143 -0.31717225 2.77220285
144 2.83203296 -0.31717225
145 2.05862782 2.83203296
146 0.77291354 2.05862782
147 -1.59653847 0.77291354
148 -1.10847433 -1.59653847
149 -2.95368132 -1.10847433
150 1.22196411 -2.95368132
151 -0.84210496 1.22196411
152 1.58046948 -0.84210496
153 -1.41912853 1.58046948
154 -4.99595111 -1.41912853
155 -1.25242837 -4.99595111
156 0.06387900 -1.25242837
157 0.12337170 0.06387900
158 3.83765742 0.12337170
159 -0.36469243 3.83765742
160 -1.87662830 -0.36469243
161 0.27816471 -1.87662830
162 NA 0.27816471
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.28170127 0.22220856
[2,] -5.00401302 3.28170127
[3,] -1.37346502 -5.00401302
[4,] 1.28170127 -1.37346502
[5,] 4.43649428 1.28170127
[6,] -1.55496245 4.43649428
[7,] -0.61903151 -1.55496245
[8,] -1.02935492 -0.61903151
[9,] 0.80394491 -1.02935492
[10,] 3.06002018 0.80394491
[11,] 3.97064508 3.06002018
[12,] -2.88014971 3.97064508
[13,] 1.34644515 -2.88014971
[14,] 2.06073086 1.34644515
[15,] 0.69127886 2.06073086
[16,] 0.17934299 0.69127886
[17,] 3.50123816 0.17934299
[18,] -0.65732073 3.50123816
[19,] 1.44571237 -0.65732073
[20,] 2.86828680 1.44571237
[21,] -3.13131121 2.86828680
[22,] -0.70813379 -3.13131121
[23,] -2.96461104 -0.70813379
[24,] 4.18459417 -2.96461104
[25,] -5.58881097 4.18459417
[26,] 0.95837258 -5.58881097
[27,] -0.07687511 0.95837258
[28,] 0.41118903 -0.07687511
[29,] -1.60112012 0.41118903
[30,] 0.57452531 -1.60112012
[31,] -0.65664591 0.57452531
[32,] 1.10013284 -0.65664591
[33,] 0.93343267 1.10013284
[34,] 0.18950794 0.93343267
[35,] 1.93303068 0.18950794
[36,] -3.75066195 1.93303068
[37,] 1.30883075 -3.75066195
[38,] 1.19021862 1.30883075
[39,] -0.17923338 1.19021862
[40,] 1.30883075 -0.17923338
[41,] 2.63072592 1.30883075
[42,] 1.47216703 2.63072592
[43,] -1.59190203 1.47216703
[44,] -3.83512328 -1.59190203
[45,] -3.00182345 -3.83512328
[46,] 0.42135397 -3.00182345
[47,] 0.16487672 0.42135397
[48,] 3.48118408 0.16487672
[49,] -0.62642537 3.48118408
[50,] 0.25496250 -0.62642537
[51,] 0.88551050 0.25496250
[52,] -2.62642537 0.88551050
[53,] -1.30453020 -2.62642537
[54,] -2.46308909 -1.30453020
[55,] 0.63994401 -2.46308909
[56,] 0.22962060 0.63994401
[57,] 1.06292043 0.22962060
[58,] -1.68100430 1.06292043
[59,] -2.77037940 -1.68100430
[60,] -4.62117419 -2.77037940
[61,] -0.56168149 -4.62117419
[62,] -4.68029362 -0.56168149
[63,] 0.11735653 -4.68029362
[64,] 0.60542066 0.11735653
[65,] -2.40688848 0.60542066
[66,] -4.23124305 -2.40688848
[67,] -1.46241427 -4.23124305
[68,] -0.70563552 -1.46241427
[69,] 1.96056215 -0.70563552
[70,] 0.38373957 1.96056215
[71,] 1.29436448 0.38373957
[72,] 1.61067184 1.29436448
[73,] 1.50306239 1.61067184
[74,] -1.78265190 1.50306239
[75,] -1.81789959 -1.78265190
[76,] 2.67016454 -1.81789959
[77,] -0.17504244 2.67016454
[78,] 0.66639867 -0.17504244
[79,] -0.39767039 0.66639867
[80,] 0.19200620 -0.39767039
[81,] 3.02530603 0.19200620
[82,] 0.61558561 3.02530603
[83,] 2.19200620 0.61558561
[84,] 1.67541572 2.19200620
[85,] -0.26509158 1.67541572
[86,] 0.28209198 -0.26509158
[87,] 0.24684429 0.28209198
[88,] -1.26509158 0.24684429
[89,] -6.11029856 -1.26509158
[90,] 1.89824471 -6.11029856
[91,] -1.16582436 1.89824471
[92,] -0.57614776 -1.16582436
[93,] 0.25715207 -0.57614776
[94,] -1.31967051 0.25715207
[95,] 1.42385224 -1.31967051
[96,] -1.25984040 1.42385224
[97,] -0.20034770 -1.25984040
[98,] 2.34683586 -0.20034770
[99,] 2.14448602 2.34683586
[100,] 2.79965230 2.14448602
[101,] -0.21265684 2.79965230
[102,] 0.96298859 -0.21265684
[103,] -2.26818263 0.96298859
[104,] -0.51140388 -2.26818263
[105,] -3.84520621 -0.51140388
[106,] 1.74507337 -3.84520621
[107,] 1.32149396 1.74507337
[108,] -2.36219868 1.32149396
[109,] -4.13560382 -2.36219868
[110,] -0.42131810 -4.13560382
[111,] -2.62366795 -0.42131810
[112,] -1.13560382 -2.62366795
[113,] 1.85208704 -1.13560382
[114,] 3.02773247 1.85208704
[115,] 2.79656125 3.02773247
[116,] -1.44666001 2.79656125
[117,] 0.38663983 -1.44666001
[118,] -0.19018275 0.38663983
[119,] -0.44666001 -0.19018275
[120,] -1.13035264 -0.44666001
[121,] -0.07085994 -1.13035264
[122,] -0.35657422 -0.07085994
[123,] 1.44107593 -0.35657422
[124,] 0.92914006 1.44107593
[125,] 0.08393307 0.92914006
[126,] 2.92537419 0.08393307
[127,] 3.02840728 2.92537419
[128,] 3.61808387 3.02840728
[129,] 1.45138371 3.61808387
[130,] -0.29254103 1.45138371
[131,] -4.54901828 -0.29254103
[132,] 1.93439124 -4.54901828
[133,] 1.82678179 1.93439124
[134,] 0.54106750 1.82678179
[135,] 2.50581981 0.54106750
[136,] -2.17321821 2.50581981
[137,] 1.98157480 -2.17321821
[138,] -3.84277977 1.98157480
[139,] 1.09315116 -3.84277977
[140,] -1.48427440 1.09315116
[141,] 1.51612759 -1.48427440
[142,] 2.77220285 1.51612759
[143,] -0.31717225 2.77220285
[144,] 2.83203296 -0.31717225
[145,] 2.05862782 2.83203296
[146,] 0.77291354 2.05862782
[147,] -1.59653847 0.77291354
[148,] -1.10847433 -1.59653847
[149,] -2.95368132 -1.10847433
[150,] 1.22196411 -2.95368132
[151,] -0.84210496 1.22196411
[152,] 1.58046948 -0.84210496
[153,] -1.41912853 1.58046948
[154,] -4.99595111 -1.41912853
[155,] -1.25242837 -4.99595111
[156,] 0.06387900 -1.25242837
[157,] 0.12337170 0.06387900
[158,] 3.83765742 0.12337170
[159,] -0.36469243 3.83765742
[160,] -1.87662830 -0.36469243
[161,] 0.27816471 -1.87662830
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.28170127 0.22220856
2 -5.00401302 3.28170127
3 -1.37346502 -5.00401302
4 1.28170127 -1.37346502
5 4.43649428 1.28170127
6 -1.55496245 4.43649428
7 -0.61903151 -1.55496245
8 -1.02935492 -0.61903151
9 0.80394491 -1.02935492
10 3.06002018 0.80394491
11 3.97064508 3.06002018
12 -2.88014971 3.97064508
13 1.34644515 -2.88014971
14 2.06073086 1.34644515
15 0.69127886 2.06073086
16 0.17934299 0.69127886
17 3.50123816 0.17934299
18 -0.65732073 3.50123816
19 1.44571237 -0.65732073
20 2.86828680 1.44571237
21 -3.13131121 2.86828680
22 -0.70813379 -3.13131121
23 -2.96461104 -0.70813379
24 4.18459417 -2.96461104
25 -5.58881097 4.18459417
26 0.95837258 -5.58881097
27 -0.07687511 0.95837258
28 0.41118903 -0.07687511
29 -1.60112012 0.41118903
30 0.57452531 -1.60112012
31 -0.65664591 0.57452531
32 1.10013284 -0.65664591
33 0.93343267 1.10013284
34 0.18950794 0.93343267
35 1.93303068 0.18950794
36 -3.75066195 1.93303068
37 1.30883075 -3.75066195
38 1.19021862 1.30883075
39 -0.17923338 1.19021862
40 1.30883075 -0.17923338
41 2.63072592 1.30883075
42 1.47216703 2.63072592
43 -1.59190203 1.47216703
44 -3.83512328 -1.59190203
45 -3.00182345 -3.83512328
46 0.42135397 -3.00182345
47 0.16487672 0.42135397
48 3.48118408 0.16487672
49 -0.62642537 3.48118408
50 0.25496250 -0.62642537
51 0.88551050 0.25496250
52 -2.62642537 0.88551050
53 -1.30453020 -2.62642537
54 -2.46308909 -1.30453020
55 0.63994401 -2.46308909
56 0.22962060 0.63994401
57 1.06292043 0.22962060
58 -1.68100430 1.06292043
59 -2.77037940 -1.68100430
60 -4.62117419 -2.77037940
61 -0.56168149 -4.62117419
62 -4.68029362 -0.56168149
63 0.11735653 -4.68029362
64 0.60542066 0.11735653
65 -2.40688848 0.60542066
66 -4.23124305 -2.40688848
67 -1.46241427 -4.23124305
68 -0.70563552 -1.46241427
69 1.96056215 -0.70563552
70 0.38373957 1.96056215
71 1.29436448 0.38373957
72 1.61067184 1.29436448
73 1.50306239 1.61067184
74 -1.78265190 1.50306239
75 -1.81789959 -1.78265190
76 2.67016454 -1.81789959
77 -0.17504244 2.67016454
78 0.66639867 -0.17504244
79 -0.39767039 0.66639867
80 0.19200620 -0.39767039
81 3.02530603 0.19200620
82 0.61558561 3.02530603
83 2.19200620 0.61558561
84 1.67541572 2.19200620
85 -0.26509158 1.67541572
86 0.28209198 -0.26509158
87 0.24684429 0.28209198
88 -1.26509158 0.24684429
89 -6.11029856 -1.26509158
90 1.89824471 -6.11029856
91 -1.16582436 1.89824471
92 -0.57614776 -1.16582436
93 0.25715207 -0.57614776
94 -1.31967051 0.25715207
95 1.42385224 -1.31967051
96 -1.25984040 1.42385224
97 -0.20034770 -1.25984040
98 2.34683586 -0.20034770
99 2.14448602 2.34683586
100 2.79965230 2.14448602
101 -0.21265684 2.79965230
102 0.96298859 -0.21265684
103 -2.26818263 0.96298859
104 -0.51140388 -2.26818263
105 -3.84520621 -0.51140388
106 1.74507337 -3.84520621
107 1.32149396 1.74507337
108 -2.36219868 1.32149396
109 -4.13560382 -2.36219868
110 -0.42131810 -4.13560382
111 -2.62366795 -0.42131810
112 -1.13560382 -2.62366795
113 1.85208704 -1.13560382
114 3.02773247 1.85208704
115 2.79656125 3.02773247
116 -1.44666001 2.79656125
117 0.38663983 -1.44666001
118 -0.19018275 0.38663983
119 -0.44666001 -0.19018275
120 -1.13035264 -0.44666001
121 -0.07085994 -1.13035264
122 -0.35657422 -0.07085994
123 1.44107593 -0.35657422
124 0.92914006 1.44107593
125 0.08393307 0.92914006
126 2.92537419 0.08393307
127 3.02840728 2.92537419
128 3.61808387 3.02840728
129 1.45138371 3.61808387
130 -0.29254103 1.45138371
131 -4.54901828 -0.29254103
132 1.93439124 -4.54901828
133 1.82678179 1.93439124
134 0.54106750 1.82678179
135 2.50581981 0.54106750
136 -2.17321821 2.50581981
137 1.98157480 -2.17321821
138 -3.84277977 1.98157480
139 1.09315116 -3.84277977
140 -1.48427440 1.09315116
141 1.51612759 -1.48427440
142 2.77220285 1.51612759
143 -0.31717225 2.77220285
144 2.83203296 -0.31717225
145 2.05862782 2.83203296
146 0.77291354 2.05862782
147 -1.59653847 0.77291354
148 -1.10847433 -1.59653847
149 -2.95368132 -1.10847433
150 1.22196411 -2.95368132
151 -0.84210496 1.22196411
152 1.58046948 -0.84210496
153 -1.41912853 1.58046948
154 -4.99595111 -1.41912853
155 -1.25242837 -4.99595111
156 0.06387900 -1.25242837
157 0.12337170 0.06387900
158 3.83765742 0.12337170
159 -0.36469243 3.83765742
160 -1.87662830 -0.36469243
161 0.27816471 -1.87662830
> 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/freestat/rcomp/tmp/7c01b1291035088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8nrje1291035088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9nrje1291035088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10nrje1291035088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/111jgm1291035088.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/freestat/rcomp/tmp/12nkxs1291035088.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/freestat/rcomp/tmp/131cdj1291035088.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/freestat/rcomp/tmp/14mut71291035088.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/freestat/rcomp/tmp/158dav1291035088.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/freestat/rcomp/tmp/16bd811291035088.tab")
+ }
>
> try(system("convert tmp/13ub81291035088.ps tmp/13ub81291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/290351291035088.ps tmp/290351291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/390351291035088.ps tmp/390351291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/490351291035088.ps tmp/490351291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/529kq1291035088.ps tmp/529kq1291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/629kq1291035088.ps tmp/629kq1291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/7c01b1291035088.ps tmp/7c01b1291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nrje1291035088.ps tmp/8nrje1291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nrje1291035088.ps tmp/9nrje1291035088.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nrje1291035088.ps tmp/10nrje1291035088.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.685 2.722 6.011