R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(2
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+ ,2)
+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('Yt'
+ ,'month'
+ ,'X1t'
+ ,'X2t'
+ ,'X3t'
+ ,'X4t'
+ ,'X5t'
+ ,'X6t'
+ ,'X7t')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('Yt','month','X1t','X2t','X3t','X4t','X5t','X6t','X7t'),1:156))
> 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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Yt month X1t X2t X3t X4t X5t X6t X7t t
1 2 9 2 1 4 3 3 3 3 1
2 3 9 2 3 4 3 3 4 3 2
3 3 9 4 2 3 4 4 4 3 3
4 3 9 3 3 2 3 3 3 3 4
5 3 9 3 2 3 3 2 2 2 5
6 3 9 1 2 4 3 3 2 2 6
7 2 9 4 4 5 4 4 5 4 7
8 3 9 2 2 4 2 2 3 2 8
9 3 9 2 2 4 4 3 2 3 9
10 4 9 2 2 2 2 2 2 2 10
11 3 9 4 2 2 3 2 4 4 11
12 3 9 3 3 4 3 2 3 3 12
13 2 9 3 2 4 4 4 3 3 13
14 3 9 2 2 5 3 4 2 3 14
15 9 3 3 5 3 3 4 3 3 15
16 9 2 2 4 3 2 2 2 3 16
17 9 3 3 3 3 3 3 3 3 17
18 9 3 3 4 4 4 4 3 2 18
19 9 2 2 4 2 2 2 2 4 19
20 9 2 2 2 3 2 2 3 3 20
21 9 1 1 4 3 3 3 2 2 21
22 9 4 3 4 4 4 4 3 3 22
23 9 3 2 4 3 3 2 3 3 23
24 9 2 2 4 3 3 2 2 2 24
25 9 3 3 4 3 4 3 3 2 25
26 9 3 3 4 4 4 4 3 4 26
27 9 4 3 4 4 2 4 4 2 27
28 9 3 2 3 4 3 3 3 3 28
29 9 3 3 3 4 3 3 3 2 29
30 9 2 2 4 4 4 4 2 4 30
31 9 2 2 3 2 4 2 2 3 31
32 9 4 3 4 3 3 3 4 2 32
33 9 4 3 4 4 3 4 4 3 33
34 9 2 2 4 3 2 3 3 3 34
35 9 2 2 4 3 2 2 3 1 35
36 9 3 3 4 4 4 4 4 3 36
37 9 3 3 4 3 3 4 3 3 37
38 9 3 2 3 2 2 2 2 3 38
39 9 3 3 4 3 3 3 3 2 39
40 9 4 3 4 4 4 4 4 3 40
41 9 3 3 4 3 4 4 3 9 41
42 1 2 3 2 2 3 3 5 9 42
43 2 1 5 2 1 4 2 4 9 43
44 2 2 4 3 2 3 2 3 9 44
45 3 3 4 3 2 3 3 2 9 45
46 4 3 4 4 4 3 4 2 9 46
47 3 2 4 4 4 3 4 3 9 47
48 2 2 5 2 2 2 2 4 9 48
49 2 3 4 3 3 4 3 2 9 49
50 3 3 4 4 3 4 3 3 9 50
51 3 3 4 3 2 4 3 4 10 51
52 4 2 3 3 1 2 2 3 10 52
53 3 2 4 4 3 3 4 4 10 53
54 2 2 4 3 2 3 3 3 10 54
55 2 3 5 3 4 3 4 3 10 55
56 2 3 4 3 3 3 3 4 10 56
57 2 2 3 3 4 2 3 2 10 57
58 3 3 3 4 4 4 4 4 10 58
59 1 1 4 3 4 4 1 2 10 59
60 5 3 4 4 4 4 4 4 10 60
61 2 1 4 3 1 3 2 2 10 61
62 3 3 4 4 4 4 3 3 10 62
63 4 2 3 3 4 3 3 2 10 63
64 4 2 3 4 4 4 3 3 10 64
65 2 3 3 3 1 3 3 3 10 65
66 3 2 4 3 4 3 4 3 10 66
67 3 3 4 3 3 3 2 3 10 67
68 3 2 4 3 3 3 2 2 10 68
69 3 3 4 3 4 4 4 4 10 69
70 1 1 5 2 1 1 1 2 10 70
71 3 2 3 3 4 4 4 3 10 71
72 3 2 4 3 3 4 4 4 10 72
73 3 2 3 4 3 3 3 2 10 73
74 4 2 2 4 2 5 2 3 10 74
75 3 3 4 3 3 3 3 3 10 75
76 4 2 4 3 3 3 3 3 10 76
77 3 2 5 3 3 3 3 3 10 77
78 3 2 2 3 4 4 3 2 10 78
79 2 2 4 4 3 4 4 4 10 79
80 1 1 4 2 1 3 2 4 10 80
81 2 2 4 3 3 3 2 10 3 81
82 3 3 3 3 3 2 10 3 3 82
83 4 4 4 4 3 4 10 2 3 83
84 3 3 3 2 2 3 10 2 1 84
85 4 3 4 4 2 3 10 3 3 85
86 5 3 3 3 3 3 10 2 2 86
87 2 3 2 2 2 3 10 3 2 87
88 2 4 3 4 3 2 10 4 4 88
89 4 4 4 4 3 4 10 2 2 89
90 4 3 3 3 3 3 10 3 3 90
91 3 4 4 4 3 4 10 3 3 91
92 4 4 3 4 4 2 10 4 3 92
93 4 4 4 4 4 2 10 3 3 93
94 4 3 4 3 4 3 10 2 3 94
95 4 3 3 3 3 3 10 2 2 95
96 4 2 2 4 2 3 10 3 3 96
97 2 3 1 5 3 4 10 2 1 97
98 4 3 2 3 2 2 10 3 2 98
99 4 4 2 4 3 3 10 3 3 99
100 4 3 3 4 3 2 10 4 3 100
101 4 4 4 4 4 2 10 4 3 101
102 4 3 4 4 3 3 10 3 3 102
103 5 3 5 5 3 3 10 1 2 103
104 4 3 2 4 2 5 10 1 1 104
105 4 3 1 3 1 2 10 4 4 105
106 4 4 3 4 3 4 10 2 1 106
107 3 3 3 4 3 3 10 4 4 107
108 4 4 4 4 4 4 10 2 1 108
109 4 3 2 4 2 4 10 2 2 109
110 4 3 2 4 3 10 3 2 4 110
111 3 2 4 3 3 10 4 3 3 111
112 3 3 3 4 3 10 3 3 4 112
113 3 3 4 3 3 10 3 3 4 113
114 3 4 4 4 2 10 4 3 4 114
115 4 4 4 4 2 10 2 4 5 115
116 3 4 4 3 2 10 3 3 4 116
117 3 4 4 3 2 10 3 3 4 117
118 4 2 4 3 1 10 3 3 4 118
119 3 3 4 3 2 10 2 2 4 119
120 3 3 3 3 3 10 2 2 4 120
121 3 3 4 3 3 10 2 3 4 121
122 3 4 3 3 4 10 2 2 4 122
123 3 3 3 3 3 10 4 2 4 123
124 3 4 4 4 2 10 3 3 4 124
125 4 4 4 3 3 10 2 3 4 125
126 3 3 4 2 2 10 4 4 4 126
127 4 4 4 4 3 10 3 3 4 127
128 3 3 3 3 3 10 2 3 3 128
129 4 3 3 3 5 10 1 3 1 129
130 1 1 1 1 2 10 4 4 4 130
131 4 4 4 4 2 10 3 3 4 131
132 3 4 3 3 3 10 2 2 4 132
133 4 2 4 2 1 10 2 4 4 133
134 4 2 4 2 3 10 3 3 4 134
135 4 4 3 4 3 10 2 2 4 135
136 3 3 4 3 3 10 3 3 4 136
137 3 4 4 4 4 10 2 1 4 137
138 3 2 2 2 1 10 3 3 4 138
139 4 5 4 4 2 10 3 2 3 139
140 3 3 3 4 4 10 2 2 4 140
141 3 4 3 3 2 10 3 3 4 141
142 3 3 4 3 2 10 2 2 4 142
143 4 4 4 3 4 10 2 2 4 143
144 2 2 2 2 3 10 2 2 4 144
145 3 3 3 3 3 10 3 3 4 145
146 3 1 3 3 4 10 2 3 4 146
147 3 2 3 3 1 10 3 3 5 147
148 3 5 5 4 2 10 3 3 4 148
149 3 4 4 4 2 10 4 4 4 149
150 4 4 4 3 3 10 4 3 4 150
151 3 3 3 3 2 10 4 3 4 151
152 4 4 4 4 3 10 2 2 3 152
153 3 2 3 2 3 10 3 3 3 153
154 4 4 4 3 3 10 3 3 4 154
155 3 4 4 4 3 10 1 1 3 155
156 3 3 4 2 2 9 2 1 4 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month X1t X2t X3t X4t
9.352518 -0.640088 -0.029074 0.995492 0.114643 0.008803
X5t X6t X7t t
-0.243180 -0.104458 -0.542201 -0.033146
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.36174 -0.79352 0.08065 0.72533 4.81873
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.352518 0.819016 11.419 < 2e-16 ***
month -0.640088 0.068035 -9.408 < 2e-16 ***
X1t -0.029074 0.130130 -0.223 0.823518
X2t 0.995492 0.141128 7.054 6.47e-11 ***
X3t 0.114643 0.117472 0.976 0.330722
X4t 0.008803 0.086423 0.102 0.919011
X5t -0.243180 0.063292 -3.842 0.000181 ***
X6t -0.104458 0.102443 -1.020 0.309572
X7t -0.542201 0.047916 -11.316 < 2e-16 ***
t -0.033146 0.005988 -5.535 1.40e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.219 on 146 degrees of freedom
Multiple R-squared: 0.7468, Adjusted R-squared: 0.7312
F-statistic: 47.84 on 9 and 146 DF, p-value: < 2.2e-16
> 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,] 0.0616555127 1.233110e-01 9.383445e-01
[2,] 0.1002594976 2.005190e-01 8.997405e-01
[3,] 0.0425289853 8.505797e-02 9.574710e-01
[4,] 0.0271964549 5.439291e-02 9.728035e-01
[5,] 0.0210415892 4.208318e-02 9.789584e-01
[6,] 0.0088196537 1.763931e-02 9.911803e-01
[7,] 0.0045378290 9.075658e-03 9.954622e-01
[8,] 0.0021209900 4.241980e-03 9.978790e-01
[9,] 0.0148316772 2.966335e-02 9.851683e-01
[10,] 0.0410983106 8.219662e-02 9.589017e-01
[11,] 0.0281514497 5.630290e-02 9.718486e-01
[12,] 0.0246002460 4.920049e-02 9.753998e-01
[13,] 0.0141628252 2.832565e-02 9.858372e-01
[14,] 0.0096977290 1.939546e-02 9.903023e-01
[15,] 0.0053465673 1.069313e-02 9.946534e-01
[16,] 0.0031473743 6.294749e-03 9.968526e-01
[17,] 0.0018174047 3.634809e-03 9.981826e-01
[18,] 0.0012518662 2.503732e-03 9.987481e-01
[19,] 0.0008164270 1.632854e-03 9.991836e-01
[20,] 0.0004249305 8.498610e-04 9.995751e-01
[21,] 0.0002471954 4.943908e-04 9.997528e-01
[22,] 0.0006645935 1.329187e-03 9.993354e-01
[23,] 0.0016820401 3.364080e-03 9.983180e-01
[24,] 0.0013011025 2.602205e-03 9.986989e-01
[25,] 0.0013251481 2.650296e-03 9.986749e-01
[26,] 0.0014448754 2.889751e-03 9.985551e-01
[27,] 0.0026623519 5.324704e-03 9.973376e-01
[28,] 0.0138973979 2.779480e-02 9.861026e-01
[29,] 0.2114866285 4.229733e-01 7.885134e-01
[30,] 0.9995821799 8.356402e-04 4.178201e-04
[31,] 0.9999728399 5.432020e-05 2.716010e-05
[32,] 0.9999951771 9.645752e-06 4.822876e-06
[33,] 0.9999943277 1.134457e-05 5.672284e-06
[34,] 0.9999933965 1.320691e-05 6.603455e-06
[35,] 0.9999959292 8.141526e-06 4.070763e-06
[36,] 0.9999927035 1.459308e-05 7.296541e-06
[37,] 0.9999929908 1.401844e-05 7.009219e-06
[38,] 0.9999888787 2.224267e-05 1.112133e-05
[39,] 0.9999841427 3.171466e-05 1.585733e-05
[40,] 0.9999934436 1.311279e-05 6.556394e-06
[41,] 0.9999913839 1.723224e-05 8.616119e-06
[42,] 0.9999895866 2.082682e-05 1.041341e-05
[43,] 0.9999888646 2.227072e-05 1.113536e-05
[44,] 0.9999845888 3.082244e-05 1.541122e-05
[45,] 0.9999810365 3.792709e-05 1.896354e-05
[46,] 0.9999696026 6.079485e-05 3.039743e-05
[47,] 0.9999885623 2.287541e-05 1.143771e-05
[48,] 0.9999980319 3.936187e-06 1.968094e-06
[49,] 0.9999981929 3.614174e-06 1.807087e-06
[50,] 0.9999971113 5.777316e-06 2.888658e-06
[51,] 0.9999980070 3.985992e-06 1.992996e-06
[52,] 0.9999977760 4.448079e-06 2.224039e-06
[53,] 0.9999974485 5.103083e-06 2.551542e-06
[54,] 0.9999954538 9.092430e-06 4.546215e-06
[55,] 0.9999952110 9.577903e-06 4.788952e-06
[56,] 0.9999925558 1.488833e-05 7.444164e-06
[57,] 0.9999873503 2.529948e-05 1.264974e-05
[58,] 0.9999945348 1.093048e-05 5.465239e-06
[59,] 0.9999917186 1.656274e-05 8.281369e-06
[60,] 0.9999867036 2.659280e-05 1.329640e-05
[61,] 0.9999822191 3.556174e-05 1.778087e-05
[62,] 0.9999905090 1.898195e-05 9.490975e-06
[63,] 0.9999859427 2.811453e-05 1.405726e-05
[64,] 0.9999938374 1.232529e-05 6.162644e-06
[65,] 0.9999902500 1.949991e-05 9.749954e-06
[66,] 0.9999937679 1.246412e-05 6.232061e-06
[67,] 0.9999964083 7.183458e-06 3.591729e-06
[68,] 0.9999980288 3.942380e-06 1.971190e-06
[69,] 0.9999998226 3.547342e-07 1.773671e-07
[70,] 0.9999999941 1.187040e-08 5.935201e-09
[71,] 0.9999999923 1.538738e-08 7.693688e-09
[72,] 0.9999999923 1.536929e-08 7.684647e-09
[73,] 0.9999999850 2.992288e-08 1.496144e-08
[74,] 0.9999999932 1.361762e-08 6.808811e-09
[75,] 0.9999999978 4.426806e-09 2.213403e-09
[76,] 0.9999999999 2.220999e-10 1.110500e-10
[77,] 0.9999999998 4.990562e-10 2.495281e-10
[78,] 0.9999999995 9.767713e-10 4.883856e-10
[79,] 0.9999999996 7.449598e-10 3.724799e-10
[80,] 0.9999999992 1.688610e-09 8.443050e-10
[81,] 0.9999999983 3.340287e-09 1.670143e-09
[82,] 0.9999999963 7.382897e-09 3.691448e-09
[83,] 0.9999999918 1.630322e-08 8.151608e-09
[84,] 0.9999999870 2.594843e-08 1.297422e-08
[85,] 0.9999999999 1.017521e-10 5.087603e-11
[86,] 0.9999999999 2.296366e-10 1.148183e-10
[87,] 0.9999999998 4.746061e-10 2.373031e-10
[88,] 0.9999999994 1.156936e-09 5.784680e-10
[89,] 0.9999999988 2.340708e-09 1.170354e-09
[90,] 0.9999999972 5.536585e-09 2.768292e-09
[91,] 0.9999999946 1.079254e-08 5.396272e-09
[92,] 0.9999999902 1.964213e-08 9.821067e-09
[93,] 0.9999999927 1.455331e-08 7.276654e-09
[94,] 0.9999999826 3.474980e-08 1.737490e-08
[95,] 0.9999999803 3.936162e-08 1.968081e-08
[96,] 0.9999999633 7.336385e-08 3.668193e-08
[97,] 0.9999999193 1.614464e-07 8.072321e-08
[98,] 0.9999999863 2.736140e-08 1.368070e-08
[99,] 0.9999999675 6.500828e-08 3.250414e-08
[100,] 0.9999999196 1.608777e-07 8.043887e-08
[101,] 0.9999998443 3.113818e-07 1.556909e-07
[102,] 0.9999997021 5.957641e-07 2.978821e-07
[103,] 0.9999995667 8.665800e-07 4.332900e-07
[104,] 0.9999992635 1.473054e-06 7.365272e-07
[105,] 0.9999988175 2.364991e-06 1.182495e-06
[106,] 0.9999986579 2.684207e-06 1.342104e-06
[107,] 0.9999971998 5.600499e-06 2.800249e-06
[108,] 0.9999934127 1.317461e-05 6.587305e-06
[109,] 0.9999905259 1.894821e-05 9.474106e-06
[110,] 0.9999796996 4.060076e-05 2.030038e-05
[111,] 0.9999584438 8.311235e-05 4.155618e-05
[112,] 0.9999399711 1.200579e-04 6.002893e-05
[113,] 0.9998889158 2.221684e-04 1.110842e-04
[114,] 0.9998502726 2.994547e-04 1.497274e-04
[115,] 0.9997047388 5.905223e-04 2.952612e-04
[116,] 0.9994727051 1.054590e-03 5.272949e-04
[117,] 0.9989579451 2.084110e-03 1.042055e-03
[118,] 0.9997179003 5.641994e-04 2.820997e-04
[119,] 0.9994434576 1.113085e-03 5.565424e-04
[120,] 0.9989873056 2.025389e-03 1.012694e-03
[121,] 0.9982976122 3.404776e-03 1.702388e-03
[122,] 0.9979644192 4.071162e-03 2.035581e-03
[123,] 0.9981433384 3.713323e-03 1.856662e-03
[124,] 0.9962409280 7.518144e-03 3.759072e-03
[125,] 0.9942276346 1.154473e-02 5.772365e-03
[126,] 0.9922813436 1.543731e-02 7.718656e-03
[127,] 0.9897734016 2.045320e-02 1.022660e-02
[128,] 0.9751215963 4.975681e-02 2.487840e-02
[129,] 0.9624745567 7.505089e-02 3.752544e-02
[130,] 0.9115326935 1.769346e-01 8.846731e-02
[131,] 0.8496636117 3.006728e-01 1.503364e-01
> postscript(file="/var/www/html/rcomp/tmp/10bmq1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20bmq1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3blmt1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4blmt1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5blmt1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 156
Frequency = 1
1 2 3 4 5 6
-0.31139230 -1.16477246 0.27103288 -0.94457929 -0.92042283 -0.81688857
7 8 9 10 11 12
-2.16999512 -0.85144256 -0.15497951 0.33967645 0.71548454 -1.15187959
13 14 15 16 17 18
-0.64568560 0.14808817 -0.28294974 -0.50548853 1.53114518 0.14633256
19 20 21 22 23 24
0.25079180 1.72253494 -1.31674607 1.46120338 0.46227326 -0.79132718
25 26 27 28 29 30
0.24981483 1.49589898 1.20679365 1.75202995 1.27204892 0.85486198
31 32 33 34 35 36
1.08422457 1.23518200 1.93906536 0.43876968 -0.85566599 1.38961187
37 38 39 40 41 42
1.44174525 1.97393680 0.72265598 2.16228190 4.81872971 -1.70804787
43 44 45 46 47 48
-1.49863887 -2.06026918 -0.24831392 -0.19676578 -1.69925014 -0.78986057
49 50 51 52 53 54
-1.23917693 -1.09706538 0.69287308 0.84146770 -0.73907536 -0.94343266
55 56 57 58 59 60
-0.22723105 -0.24723893 -1.19801040 -0.08577931 -3.24669741 2.00958606
61 62 63 64 65 66
-1.58449565 -0.27176007 0.99206053 0.12536938 0.14682515 0.46820883
67 68 69 70 71 72
0.76972539 0.05832574 1.30338820 -1.48719365 0.59606000 0.87738012
73 74 75 76 77 78
-0.55733241 0.40505432 1.27806991 1.67112790 0.73334767 0.45136774
79 80 81 82 83 84
-0.88609235 -0.75032219 -3.47052580 -0.60333004 -0.01857741 -0.62456633
85 86 87 88 89 90
-0.36447075 0.87379125 -0.90754524 -1.11320208 -0.36190457 0.65303210
91 92 93 94 95 96
-0.64895493 0.36253683 0.32029895 0.59558831 0.17210168 -0.69810503
97 98 99 100 101 102
-4.36174232 0.47036740 0.56686428 0.10225677 0.68992142 0.08436180
103 104 105 106 107 108
-0.60002622 -1.10377485 1.97681441 -0.36970425 -0.13232591 -0.38898161
109 110 111 112 113 114
-0.28258570 -1.03475523 -1.78262067 -1.83493222 -0.77722076 -0.74165684
115 116 117 118 119 120
0.45178783 0.07694639 0.11009199 -0.02229490 -0.81134174 -0.92191301
121 122 123 124 125 126
-0.75523561 -0.33017692 -0.33611683 -0.65338048 1.01743441 1.11144385
127 128 129 130 131 132
0.33141361 -1.09449133 -1.61821244 -1.12787972 0.57863875 0.11592184
133 134 135 136 137 138
1.33165882 1.27424102 0.21986695 -0.01487186 -0.90386805 0.57796057
139 140 141 142 143 144
0.83723275 -1.36913536 0.87651233 -0.04899285 1.39495492 -0.80008858
145 146 147 148 149 150
0.25436442 -1.35048761 0.45205425 0.81127578 0.52289695 2.33243389
151 152 153 154 155 156
0.81106045 0.27021559 0.33273255 2.22183662 -0.97798493 1.31488237
> postscript(file="/var/www/html/rcomp/tmp/63clv1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.31139230 NA
1 -1.16477246 -0.31139230
2 0.27103288 -1.16477246
3 -0.94457929 0.27103288
4 -0.92042283 -0.94457929
5 -0.81688857 -0.92042283
6 -2.16999512 -0.81688857
7 -0.85144256 -2.16999512
8 -0.15497951 -0.85144256
9 0.33967645 -0.15497951
10 0.71548454 0.33967645
11 -1.15187959 0.71548454
12 -0.64568560 -1.15187959
13 0.14808817 -0.64568560
14 -0.28294974 0.14808817
15 -0.50548853 -0.28294974
16 1.53114518 -0.50548853
17 0.14633256 1.53114518
18 0.25079180 0.14633256
19 1.72253494 0.25079180
20 -1.31674607 1.72253494
21 1.46120338 -1.31674607
22 0.46227326 1.46120338
23 -0.79132718 0.46227326
24 0.24981483 -0.79132718
25 1.49589898 0.24981483
26 1.20679365 1.49589898
27 1.75202995 1.20679365
28 1.27204892 1.75202995
29 0.85486198 1.27204892
30 1.08422457 0.85486198
31 1.23518200 1.08422457
32 1.93906536 1.23518200
33 0.43876968 1.93906536
34 -0.85566599 0.43876968
35 1.38961187 -0.85566599
36 1.44174525 1.38961187
37 1.97393680 1.44174525
38 0.72265598 1.97393680
39 2.16228190 0.72265598
40 4.81872971 2.16228190
41 -1.70804787 4.81872971
42 -1.49863887 -1.70804787
43 -2.06026918 -1.49863887
44 -0.24831392 -2.06026918
45 -0.19676578 -0.24831392
46 -1.69925014 -0.19676578
47 -0.78986057 -1.69925014
48 -1.23917693 -0.78986057
49 -1.09706538 -1.23917693
50 0.69287308 -1.09706538
51 0.84146770 0.69287308
52 -0.73907536 0.84146770
53 -0.94343266 -0.73907536
54 -0.22723105 -0.94343266
55 -0.24723893 -0.22723105
56 -1.19801040 -0.24723893
57 -0.08577931 -1.19801040
58 -3.24669741 -0.08577931
59 2.00958606 -3.24669741
60 -1.58449565 2.00958606
61 -0.27176007 -1.58449565
62 0.99206053 -0.27176007
63 0.12536938 0.99206053
64 0.14682515 0.12536938
65 0.46820883 0.14682515
66 0.76972539 0.46820883
67 0.05832574 0.76972539
68 1.30338820 0.05832574
69 -1.48719365 1.30338820
70 0.59606000 -1.48719365
71 0.87738012 0.59606000
72 -0.55733241 0.87738012
73 0.40505432 -0.55733241
74 1.27806991 0.40505432
75 1.67112790 1.27806991
76 0.73334767 1.67112790
77 0.45136774 0.73334767
78 -0.88609235 0.45136774
79 -0.75032219 -0.88609235
80 -3.47052580 -0.75032219
81 -0.60333004 -3.47052580
82 -0.01857741 -0.60333004
83 -0.62456633 -0.01857741
84 -0.36447075 -0.62456633
85 0.87379125 -0.36447075
86 -0.90754524 0.87379125
87 -1.11320208 -0.90754524
88 -0.36190457 -1.11320208
89 0.65303210 -0.36190457
90 -0.64895493 0.65303210
91 0.36253683 -0.64895493
92 0.32029895 0.36253683
93 0.59558831 0.32029895
94 0.17210168 0.59558831
95 -0.69810503 0.17210168
96 -4.36174232 -0.69810503
97 0.47036740 -4.36174232
98 0.56686428 0.47036740
99 0.10225677 0.56686428
100 0.68992142 0.10225677
101 0.08436180 0.68992142
102 -0.60002622 0.08436180
103 -1.10377485 -0.60002622
104 1.97681441 -1.10377485
105 -0.36970425 1.97681441
106 -0.13232591 -0.36970425
107 -0.38898161 -0.13232591
108 -0.28258570 -0.38898161
109 -1.03475523 -0.28258570
110 -1.78262067 -1.03475523
111 -1.83493222 -1.78262067
112 -0.77722076 -1.83493222
113 -0.74165684 -0.77722076
114 0.45178783 -0.74165684
115 0.07694639 0.45178783
116 0.11009199 0.07694639
117 -0.02229490 0.11009199
118 -0.81134174 -0.02229490
119 -0.92191301 -0.81134174
120 -0.75523561 -0.92191301
121 -0.33017692 -0.75523561
122 -0.33611683 -0.33017692
123 -0.65338048 -0.33611683
124 1.01743441 -0.65338048
125 1.11144385 1.01743441
126 0.33141361 1.11144385
127 -1.09449133 0.33141361
128 -1.61821244 -1.09449133
129 -1.12787972 -1.61821244
130 0.57863875 -1.12787972
131 0.11592184 0.57863875
132 1.33165882 0.11592184
133 1.27424102 1.33165882
134 0.21986695 1.27424102
135 -0.01487186 0.21986695
136 -0.90386805 -0.01487186
137 0.57796057 -0.90386805
138 0.83723275 0.57796057
139 -1.36913536 0.83723275
140 0.87651233 -1.36913536
141 -0.04899285 0.87651233
142 1.39495492 -0.04899285
143 -0.80008858 1.39495492
144 0.25436442 -0.80008858
145 -1.35048761 0.25436442
146 0.45205425 -1.35048761
147 0.81127578 0.45205425
148 0.52289695 0.81127578
149 2.33243389 0.52289695
150 0.81106045 2.33243389
151 0.27021559 0.81106045
152 0.33273255 0.27021559
153 2.22183662 0.33273255
154 -0.97798493 2.22183662
155 1.31488237 -0.97798493
156 NA 1.31488237
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.16477246 -0.31139230
[2,] 0.27103288 -1.16477246
[3,] -0.94457929 0.27103288
[4,] -0.92042283 -0.94457929
[5,] -0.81688857 -0.92042283
[6,] -2.16999512 -0.81688857
[7,] -0.85144256 -2.16999512
[8,] -0.15497951 -0.85144256
[9,] 0.33967645 -0.15497951
[10,] 0.71548454 0.33967645
[11,] -1.15187959 0.71548454
[12,] -0.64568560 -1.15187959
[13,] 0.14808817 -0.64568560
[14,] -0.28294974 0.14808817
[15,] -0.50548853 -0.28294974
[16,] 1.53114518 -0.50548853
[17,] 0.14633256 1.53114518
[18,] 0.25079180 0.14633256
[19,] 1.72253494 0.25079180
[20,] -1.31674607 1.72253494
[21,] 1.46120338 -1.31674607
[22,] 0.46227326 1.46120338
[23,] -0.79132718 0.46227326
[24,] 0.24981483 -0.79132718
[25,] 1.49589898 0.24981483
[26,] 1.20679365 1.49589898
[27,] 1.75202995 1.20679365
[28,] 1.27204892 1.75202995
[29,] 0.85486198 1.27204892
[30,] 1.08422457 0.85486198
[31,] 1.23518200 1.08422457
[32,] 1.93906536 1.23518200
[33,] 0.43876968 1.93906536
[34,] -0.85566599 0.43876968
[35,] 1.38961187 -0.85566599
[36,] 1.44174525 1.38961187
[37,] 1.97393680 1.44174525
[38,] 0.72265598 1.97393680
[39,] 2.16228190 0.72265598
[40,] 4.81872971 2.16228190
[41,] -1.70804787 4.81872971
[42,] -1.49863887 -1.70804787
[43,] -2.06026918 -1.49863887
[44,] -0.24831392 -2.06026918
[45,] -0.19676578 -0.24831392
[46,] -1.69925014 -0.19676578
[47,] -0.78986057 -1.69925014
[48,] -1.23917693 -0.78986057
[49,] -1.09706538 -1.23917693
[50,] 0.69287308 -1.09706538
[51,] 0.84146770 0.69287308
[52,] -0.73907536 0.84146770
[53,] -0.94343266 -0.73907536
[54,] -0.22723105 -0.94343266
[55,] -0.24723893 -0.22723105
[56,] -1.19801040 -0.24723893
[57,] -0.08577931 -1.19801040
[58,] -3.24669741 -0.08577931
[59,] 2.00958606 -3.24669741
[60,] -1.58449565 2.00958606
[61,] -0.27176007 -1.58449565
[62,] 0.99206053 -0.27176007
[63,] 0.12536938 0.99206053
[64,] 0.14682515 0.12536938
[65,] 0.46820883 0.14682515
[66,] 0.76972539 0.46820883
[67,] 0.05832574 0.76972539
[68,] 1.30338820 0.05832574
[69,] -1.48719365 1.30338820
[70,] 0.59606000 -1.48719365
[71,] 0.87738012 0.59606000
[72,] -0.55733241 0.87738012
[73,] 0.40505432 -0.55733241
[74,] 1.27806991 0.40505432
[75,] 1.67112790 1.27806991
[76,] 0.73334767 1.67112790
[77,] 0.45136774 0.73334767
[78,] -0.88609235 0.45136774
[79,] -0.75032219 -0.88609235
[80,] -3.47052580 -0.75032219
[81,] -0.60333004 -3.47052580
[82,] -0.01857741 -0.60333004
[83,] -0.62456633 -0.01857741
[84,] -0.36447075 -0.62456633
[85,] 0.87379125 -0.36447075
[86,] -0.90754524 0.87379125
[87,] -1.11320208 -0.90754524
[88,] -0.36190457 -1.11320208
[89,] 0.65303210 -0.36190457
[90,] -0.64895493 0.65303210
[91,] 0.36253683 -0.64895493
[92,] 0.32029895 0.36253683
[93,] 0.59558831 0.32029895
[94,] 0.17210168 0.59558831
[95,] -0.69810503 0.17210168
[96,] -4.36174232 -0.69810503
[97,] 0.47036740 -4.36174232
[98,] 0.56686428 0.47036740
[99,] 0.10225677 0.56686428
[100,] 0.68992142 0.10225677
[101,] 0.08436180 0.68992142
[102,] -0.60002622 0.08436180
[103,] -1.10377485 -0.60002622
[104,] 1.97681441 -1.10377485
[105,] -0.36970425 1.97681441
[106,] -0.13232591 -0.36970425
[107,] -0.38898161 -0.13232591
[108,] -0.28258570 -0.38898161
[109,] -1.03475523 -0.28258570
[110,] -1.78262067 -1.03475523
[111,] -1.83493222 -1.78262067
[112,] -0.77722076 -1.83493222
[113,] -0.74165684 -0.77722076
[114,] 0.45178783 -0.74165684
[115,] 0.07694639 0.45178783
[116,] 0.11009199 0.07694639
[117,] -0.02229490 0.11009199
[118,] -0.81134174 -0.02229490
[119,] -0.92191301 -0.81134174
[120,] -0.75523561 -0.92191301
[121,] -0.33017692 -0.75523561
[122,] -0.33611683 -0.33017692
[123,] -0.65338048 -0.33611683
[124,] 1.01743441 -0.65338048
[125,] 1.11144385 1.01743441
[126,] 0.33141361 1.11144385
[127,] -1.09449133 0.33141361
[128,] -1.61821244 -1.09449133
[129,] -1.12787972 -1.61821244
[130,] 0.57863875 -1.12787972
[131,] 0.11592184 0.57863875
[132,] 1.33165882 0.11592184
[133,] 1.27424102 1.33165882
[134,] 0.21986695 1.27424102
[135,] -0.01487186 0.21986695
[136,] -0.90386805 -0.01487186
[137,] 0.57796057 -0.90386805
[138,] 0.83723275 0.57796057
[139,] -1.36913536 0.83723275
[140,] 0.87651233 -1.36913536
[141,] -0.04899285 0.87651233
[142,] 1.39495492 -0.04899285
[143,] -0.80008858 1.39495492
[144,] 0.25436442 -0.80008858
[145,] -1.35048761 0.25436442
[146,] 0.45205425 -1.35048761
[147,] 0.81127578 0.45205425
[148,] 0.52289695 0.81127578
[149,] 2.33243389 0.52289695
[150,] 0.81106045 2.33243389
[151,] 0.27021559 0.81106045
[152,] 0.33273255 0.27021559
[153,] 2.22183662 0.33273255
[154,] -0.97798493 2.22183662
[155,] 1.31488237 -0.97798493
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.16477246 -0.31139230
2 0.27103288 -1.16477246
3 -0.94457929 0.27103288
4 -0.92042283 -0.94457929
5 -0.81688857 -0.92042283
6 -2.16999512 -0.81688857
7 -0.85144256 -2.16999512
8 -0.15497951 -0.85144256
9 0.33967645 -0.15497951
10 0.71548454 0.33967645
11 -1.15187959 0.71548454
12 -0.64568560 -1.15187959
13 0.14808817 -0.64568560
14 -0.28294974 0.14808817
15 -0.50548853 -0.28294974
16 1.53114518 -0.50548853
17 0.14633256 1.53114518
18 0.25079180 0.14633256
19 1.72253494 0.25079180
20 -1.31674607 1.72253494
21 1.46120338 -1.31674607
22 0.46227326 1.46120338
23 -0.79132718 0.46227326
24 0.24981483 -0.79132718
25 1.49589898 0.24981483
26 1.20679365 1.49589898
27 1.75202995 1.20679365
28 1.27204892 1.75202995
29 0.85486198 1.27204892
30 1.08422457 0.85486198
31 1.23518200 1.08422457
32 1.93906536 1.23518200
33 0.43876968 1.93906536
34 -0.85566599 0.43876968
35 1.38961187 -0.85566599
36 1.44174525 1.38961187
37 1.97393680 1.44174525
38 0.72265598 1.97393680
39 2.16228190 0.72265598
40 4.81872971 2.16228190
41 -1.70804787 4.81872971
42 -1.49863887 -1.70804787
43 -2.06026918 -1.49863887
44 -0.24831392 -2.06026918
45 -0.19676578 -0.24831392
46 -1.69925014 -0.19676578
47 -0.78986057 -1.69925014
48 -1.23917693 -0.78986057
49 -1.09706538 -1.23917693
50 0.69287308 -1.09706538
51 0.84146770 0.69287308
52 -0.73907536 0.84146770
53 -0.94343266 -0.73907536
54 -0.22723105 -0.94343266
55 -0.24723893 -0.22723105
56 -1.19801040 -0.24723893
57 -0.08577931 -1.19801040
58 -3.24669741 -0.08577931
59 2.00958606 -3.24669741
60 -1.58449565 2.00958606
61 -0.27176007 -1.58449565
62 0.99206053 -0.27176007
63 0.12536938 0.99206053
64 0.14682515 0.12536938
65 0.46820883 0.14682515
66 0.76972539 0.46820883
67 0.05832574 0.76972539
68 1.30338820 0.05832574
69 -1.48719365 1.30338820
70 0.59606000 -1.48719365
71 0.87738012 0.59606000
72 -0.55733241 0.87738012
73 0.40505432 -0.55733241
74 1.27806991 0.40505432
75 1.67112790 1.27806991
76 0.73334767 1.67112790
77 0.45136774 0.73334767
78 -0.88609235 0.45136774
79 -0.75032219 -0.88609235
80 -3.47052580 -0.75032219
81 -0.60333004 -3.47052580
82 -0.01857741 -0.60333004
83 -0.62456633 -0.01857741
84 -0.36447075 -0.62456633
85 0.87379125 -0.36447075
86 -0.90754524 0.87379125
87 -1.11320208 -0.90754524
88 -0.36190457 -1.11320208
89 0.65303210 -0.36190457
90 -0.64895493 0.65303210
91 0.36253683 -0.64895493
92 0.32029895 0.36253683
93 0.59558831 0.32029895
94 0.17210168 0.59558831
95 -0.69810503 0.17210168
96 -4.36174232 -0.69810503
97 0.47036740 -4.36174232
98 0.56686428 0.47036740
99 0.10225677 0.56686428
100 0.68992142 0.10225677
101 0.08436180 0.68992142
102 -0.60002622 0.08436180
103 -1.10377485 -0.60002622
104 1.97681441 -1.10377485
105 -0.36970425 1.97681441
106 -0.13232591 -0.36970425
107 -0.38898161 -0.13232591
108 -0.28258570 -0.38898161
109 -1.03475523 -0.28258570
110 -1.78262067 -1.03475523
111 -1.83493222 -1.78262067
112 -0.77722076 -1.83493222
113 -0.74165684 -0.77722076
114 0.45178783 -0.74165684
115 0.07694639 0.45178783
116 0.11009199 0.07694639
117 -0.02229490 0.11009199
118 -0.81134174 -0.02229490
119 -0.92191301 -0.81134174
120 -0.75523561 -0.92191301
121 -0.33017692 -0.75523561
122 -0.33611683 -0.33017692
123 -0.65338048 -0.33611683
124 1.01743441 -0.65338048
125 1.11144385 1.01743441
126 0.33141361 1.11144385
127 -1.09449133 0.33141361
128 -1.61821244 -1.09449133
129 -1.12787972 -1.61821244
130 0.57863875 -1.12787972
131 0.11592184 0.57863875
132 1.33165882 0.11592184
133 1.27424102 1.33165882
134 0.21986695 1.27424102
135 -0.01487186 0.21986695
136 -0.90386805 -0.01487186
137 0.57796057 -0.90386805
138 0.83723275 0.57796057
139 -1.36913536 0.83723275
140 0.87651233 -1.36913536
141 -0.04899285 0.87651233
142 1.39495492 -0.04899285
143 -0.80008858 1.39495492
144 0.25436442 -0.80008858
145 -1.35048761 0.25436442
146 0.45205425 -1.35048761
147 0.81127578 0.45205425
148 0.52289695 0.81127578
149 2.33243389 0.52289695
150 0.81106045 2.33243389
151 0.27021559 0.81106045
152 0.33273255 0.27021559
153 2.22183662 0.33273255
154 -0.97798493 2.22183662
155 1.31488237 -0.97798493
> 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/7elky1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8elky1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9elky1291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10puj11291333662.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sd071291333662.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/12dvyv1291333662.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/13snwm1291333662.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/14d6vs1291333662.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/15g6bg1291333662.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/162pa31291333662.tab")
+ }
>
> try(system("convert tmp/10bmq1291333662.ps tmp/10bmq1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/20bmq1291333662.ps tmp/20bmq1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/3blmt1291333662.ps tmp/3blmt1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/4blmt1291333662.ps tmp/4blmt1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/5blmt1291333662.ps tmp/5blmt1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/63clv1291333662.ps tmp/63clv1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/7elky1291333662.ps tmp/7elky1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/8elky1291333662.ps tmp/8elky1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/9elky1291333662.ps tmp/9elky1291333662.png",intern=TRUE))
character(0)
> try(system("convert tmp/10puj11291333662.ps tmp/10puj11291333662.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.208 1.753 9.887