R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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+ ,18
+ ,21
+ ,6
+ ,13
+ ,9
+ ,10
+ ,18
+ ,17
+ ,13
+ ,18
+ ,16
+ ,16
+ ,4
+ ,12
+ ,18
+ ,11
+ ,20
+ ,18
+ ,17
+ ,20
+ ,16
+ ,18
+ ,4
+ ,13
+ ,16)
+ ,dim=c(10
+ ,162)
+ ,dimnames=list(c('Month'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A'
+ ,'Happiness'
+ ,'Depression
')
+ ,1:162))
> y <- array(NA,dim=c(10,162),dimnames=list(c('Month','I1','I2','I3','E1','E2','E3','A','Happiness','Depression
'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
I1 Month I2 I3 E1 E2 E3 A Happiness Depression\r t
1 26 9 21 21 23 17 23 4 14 12 1
2 20 9 16 15 24 17 20 4 18 11 2
3 19 9 19 18 22 18 20 6 11 14 3
4 19 9 18 11 20 21 21 8 12 12 4
5 20 9 16 8 24 20 24 8 16 21 5
6 25 9 23 19 27 28 22 4 18 12 6
7 25 9 17 4 28 19 23 4 14 22 7
8 22 9 12 20 27 22 20 8 14 11 8
9 26 9 19 16 24 16 25 5 15 10 9
10 22 9 16 14 23 18 23 4 15 13 10
11 17 9 19 10 24 25 27 4 17 10 11
12 22 9 20 13 27 17 27 4 19 8 12
13 19 9 13 14 27 14 22 4 10 15 13
14 24 9 20 8 28 11 24 4 16 14 14
15 26 9 27 23 27 27 25 4 18 10 15
16 21 9 17 11 23 20 22 8 14 14 16
17 13 9 8 9 24 22 28 4 14 14 17
18 26 9 25 24 28 22 28 4 17 11 18
19 20 9 26 5 27 21 27 4 14 10 19
20 22 9 13 15 25 23 25 8 16 13 20
21 14 9 19 5 19 17 16 4 18 7 21
22 21 9 15 19 24 24 28 7 11 14 22
23 7 9 5 6 20 14 21 4 14 12 23
24 23 9 16 13 28 17 24 4 12 14 24
25 17 9 14 11 26 23 27 5 17 11 25
26 25 9 24 17 23 24 14 4 9 9 26
27 25 9 24 17 23 24 14 4 16 11 27
28 19 9 9 5 20 8 27 4 14 15 28
29 20 9 19 9 11 22 20 4 15 14 29
30 23 9 19 15 24 23 21 4 11 13 30
31 22 9 25 17 25 25 22 4 16 9 31
32 22 9 19 17 23 21 21 4 13 15 32
33 21 9 18 20 18 24 12 15 17 10 33
34 15 9 15 12 20 15 20 10 15 11 34
35 20 9 12 7 20 22 24 4 14 13 35
36 22 9 21 16 24 21 19 8 16 8 36
37 18 9 12 7 23 25 28 4 9 20 37
38 20 9 15 14 25 16 23 4 15 12 38
39 28 9 28 24 28 28 27 4 17 10 39
40 22 9 25 15 26 23 22 4 13 10 40
41 18 9 19 15 26 21 27 7 15 9 41
42 23 9 20 10 23 21 26 4 16 14 42
43 20 9 24 14 22 26 22 6 16 8 43
44 25 9 26 18 24 22 21 5 12 14 44
45 26 9 25 12 21 21 19 4 12 11 45
46 15 9 12 9 20 18 24 16 11 13 46
47 17 9 12 9 22 12 19 5 15 9 47
48 23 9 15 8 20 25 26 12 15 11 48
49 21 9 17 18 25 17 22 6 17 15 49
50 13 9 14 10 20 24 28 9 13 11 50
51 18 9 16 17 22 15 21 9 16 10 51
52 19 9 11 14 23 13 23 4 14 14 52
53 22 9 20 16 25 26 28 5 11 18 53
54 16 9 11 10 23 16 10 4 12 14 54
55 24 9 22 19 23 24 24 4 12 11 55
56 18 9 20 10 22 21 21 5 15 12 56
57 20 9 19 14 24 20 21 4 16 13 57
58 24 9 17 10 25 14 24 4 15 9 58
59 14 9 21 4 21 25 24 4 12 10 59
60 22 9 23 19 12 25 25 5 12 15 60
61 24 9 18 9 17 20 25 4 8 20 61
62 18 9 17 12 20 22 23 6 13 12 62
63 21 9 27 16 23 20 21 4 11 12 63
64 23 9 25 11 23 26 16 4 14 14 64
65 17 9 19 18 20 18 17 18 15 13 65
66 22 10 22 11 28 22 25 4 10 11 66
67 24 10 24 24 24 24 24 6 11 17 67
68 21 10 20 17 24 17 23 4 12 12 68
69 22 10 19 18 24 24 25 4 15 13 69
70 16 10 11 9 24 20 23 5 15 14 70
71 21 10 22 19 28 19 28 4 14 13 71
72 23 10 22 18 25 20 26 4 16 15 72
73 22 10 16 12 21 15 22 5 15 13 73
74 24 10 20 23 25 23 19 10 15 10 74
75 24 10 24 22 25 26 26 5 13 11 75
76 16 10 16 14 18 22 18 8 12 19 76
77 16 10 16 14 17 20 18 8 17 13 77
78 21 10 22 16 26 24 25 5 13 17 78
79 26 10 24 23 28 26 27 4 15 13 79
80 15 10 16 7 21 21 12 4 13 9 80
81 25 10 27 10 27 25 15 4 15 11 81
82 18 10 11 12 22 13 21 5 16 10 82
83 23 10 21 12 21 20 23 4 15 9 83
84 20 10 20 12 25 22 22 4 16 12 84
85 17 10 20 17 22 23 21 8 15 12 85
86 25 10 27 21 23 28 24 4 14 13 86
87 24 10 20 16 26 22 27 5 15 13 87
88 17 10 12 11 19 20 22 14 14 12 88
89 19 10 8 14 25 6 28 8 13 15 89
90 20 10 21 13 21 21 26 8 7 22 90
91 15 10 18 9 13 20 10 4 17 13 91
92 27 10 24 19 24 18 19 4 13 15 92
93 22 10 16 13 25 23 22 6 15 13 93
94 23 10 18 19 26 20 21 4 14 15 94
95 16 10 20 13 25 24 24 7 13 10 95
96 19 10 20 13 25 22 25 7 16 11 96
97 25 10 19 13 22 21 21 4 12 16 97
98 19 10 17 14 21 18 20 6 14 11 98
99 19 10 16 12 23 21 21 4 17 11 99
100 26 10 26 22 25 23 24 7 15 10 100
101 21 10 15 11 24 23 23 4 17 10 101
102 20 10 22 5 21 15 18 4 12 16 102
103 24 10 17 18 21 21 24 8 16 12 103
104 22 10 23 19 25 24 24 4 11 11 104
105 20 10 21 14 22 23 19 4 15 16 105
106 18 10 19 15 20 21 20 10 9 19 106
107 18 10 14 12 20 21 18 8 16 11 107
108 24 10 17 19 23 20 20 6 15 16 108
109 24 10 12 15 28 11 27 4 10 15 109
110 22 10 24 17 23 22 23 4 10 24 110
111 23 10 18 8 28 27 26 4 15 14 111
112 22 10 20 10 24 25 23 5 11 15 112
113 20 10 16 12 18 18 17 4 13 11 113
114 18 10 20 12 20 20 21 6 14 15 114
115 25 10 22 20 28 24 25 4 18 12 115
116 18 10 12 12 21 10 23 5 16 10 116
117 16 10 16 12 21 27 27 7 14 14 117
118 20 10 17 14 25 21 24 8 14 13 118
119 19 10 22 6 19 21 20 5 14 9 119
120 15 10 12 10 18 18 27 8 14 15 120
121 19 10 14 18 21 15 21 10 12 15 121
122 19 10 23 18 22 24 24 8 14 14 122
123 16 10 15 7 24 22 21 5 15 11 123
124 17 10 17 18 15 14 15 12 15 8 124
125 28 10 28 9 28 28 25 4 15 11 125
126 23 10 20 17 26 18 25 5 13 11 126
127 25 10 23 22 23 26 22 4 17 8 127
128 20 10 13 11 26 17 24 6 17 10 128
129 17 10 18 15 20 19 21 4 19 11 129
130 23 10 23 17 22 22 22 4 15 13 130
131 16 10 19 15 20 18 23 7 13 11 131
132 23 10 23 22 23 24 22 7 9 20 132
133 11 10 12 9 22 15 20 10 15 10 133
134 18 10 16 13 24 18 23 4 15 15 134
135 24 10 23 20 23 26 25 5 15 12 135
136 23 10 13 14 22 11 23 8 16 14 136
137 21 10 22 14 26 26 22 11 11 23 137
138 16 10 18 12 23 21 25 7 14 14 138
139 24 10 23 20 27 23 26 4 11 16 139
140 23 10 20 20 23 23 22 8 15 11 140
141 18 10 10 8 21 15 24 6 13 12 141
142 20 10 17 17 26 22 24 7 15 10 142
143 9 10 18 9 23 26 25 5 16 14 143
144 24 10 15 18 21 16 20 4 14 12 144
145 25 10 23 22 27 20 26 8 15 12 145
146 20 10 17 10 19 18 21 4 16 11 146
147 21 10 17 13 23 22 26 8 16 12 147
148 25 10 22 15 25 16 21 6 11 13 148
149 22 10 20 18 23 19 22 4 12 11 149
150 21 10 20 18 22 20 16 9 9 19 150
151 21 10 19 12 22 19 26 5 16 12 151
152 22 10 18 12 25 23 28 6 13 17 152
153 27 10 22 20 25 24 18 4 16 9 153
154 24 9 20 12 28 25 25 4 12 12 154
155 24 10 22 16 28 21 23 4 9 19 155
156 21 10 18 16 20 21 21 5 13 18 156
157 18 10 16 18 25 23 20 6 13 15 157
158 16 10 16 16 19 27 25 16 14 14 158
159 22 10 16 13 25 23 22 6 19 11 159
160 20 10 16 17 22 18 21 6 13 9 160
161 18 10 17 13 18 16 16 4 12 18 161
162 20 11 18 17 20 16 18 4 13 16 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month I2 I3
12.242614 -0.812022 0.366415 0.263119
E1 E2 E3 A
0.267207 -0.123305 0.022526 -0.215226
Happiness `Depression\\r` t
0.059722 0.127970 0.003644
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.781 -1.461 0.074 1.688 7.721
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.242614 6.737058 1.817 0.071168 .
Month -0.812022 0.718578 -1.130 0.260251
I2 0.366415 0.063483 5.772 4.31e-08 ***
I3 0.263119 0.050992 5.160 7.66e-07 ***
E1 0.267207 0.075251 3.551 0.000512 ***
E2 -0.123305 0.059075 -2.087 0.038544 *
E3 0.022526 0.061702 0.365 0.715557
A -0.215226 0.084393 -2.550 0.011759 *
Happiness 0.059722 0.102460 0.583 0.560843
`Depression\\r` 0.127970 0.076284 1.678 0.095505 .
t 0.003644 0.007684 0.474 0.636045
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.498 on 151 degrees of freedom
Multiple R-squared: 0.5625, Adjusted R-squared: 0.5335
F-statistic: 19.41 on 10 and 151 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,] 0.95013761 0.09972479 0.04986239
[2,] 0.90114705 0.19770591 0.09885295
[3,] 0.86202524 0.27594953 0.13797476
[4,] 0.79992422 0.40015157 0.20007578
[5,] 0.72768476 0.54463049 0.27231524
[6,] 0.65953465 0.68093070 0.34046535
[7,] 0.64743623 0.70512754 0.35256377
[8,] 0.58337771 0.83324458 0.41662229
[9,] 0.50486356 0.99027288 0.49513644
[10,] 0.58501340 0.82997320 0.41498660
[11,] 0.55865916 0.88268169 0.44134084
[12,] 0.48632384 0.97264769 0.51367616
[13,] 0.56036508 0.87926984 0.43963492
[14,] 0.49871524 0.99743048 0.50128476
[15,] 0.64692358 0.70615284 0.35307642
[16,] 0.65684049 0.68631902 0.34315951
[17,] 0.59746304 0.80507392 0.40253696
[18,] 0.59461780 0.81076440 0.40538220
[19,] 0.57658314 0.84683371 0.42341686
[20,] 0.55409831 0.89180338 0.44590169
[21,] 0.62369113 0.75261774 0.37630887
[22,] 0.75360070 0.49279860 0.24639930
[23,] 0.70537159 0.58925683 0.29462841
[24,] 0.65920572 0.68158856 0.34079428
[25,] 0.60747403 0.78505195 0.39252597
[26,] 0.55060939 0.89878122 0.44939061
[27,] 0.52670981 0.94658039 0.47329019
[28,] 0.53381482 0.93237035 0.46618518
[29,] 0.49921788 0.99843576 0.50078212
[30,] 0.45108866 0.90217732 0.54891134
[31,] 0.41836717 0.83673435 0.58163283
[32,] 0.47431195 0.94862390 0.52568805
[33,] 0.42055078 0.84110156 0.57944922
[34,] 0.37464204 0.74928409 0.62535796
[35,] 0.77792927 0.44414146 0.22207073
[36,] 0.77392323 0.45215354 0.22607677
[37,] 0.80087769 0.39824463 0.19912231
[38,] 0.78463780 0.43072439 0.21536220
[39,] 0.74823643 0.50352714 0.25176357
[40,] 0.72723513 0.54552974 0.27276487
[41,] 0.70024117 0.59951765 0.29975883
[42,] 0.66208543 0.67582913 0.33791457
[43,] 0.65022992 0.69954015 0.34977008
[44,] 0.62810561 0.74378878 0.37189439
[45,] 0.73086872 0.53826255 0.26913128
[46,] 0.78598342 0.42803316 0.21401658
[47,] 0.75512383 0.48975234 0.24487617
[48,] 0.83763542 0.32472917 0.16236458
[49,] 0.80647934 0.38704132 0.19352066
[50,] 0.85143228 0.29713544 0.14856772
[51,] 0.82251142 0.35497717 0.17748858
[52,] 0.84156048 0.31687904 0.15843952
[53,] 0.81047440 0.37905120 0.18952560
[54,] 0.79444988 0.41110024 0.20555012
[55,] 0.76870953 0.46258093 0.23129047
[56,] 0.73489875 0.53020249 0.26510125
[57,] 0.69692297 0.60615406 0.30307703
[58,] 0.74809709 0.50380581 0.25190291
[59,] 0.71555991 0.56888018 0.28444009
[60,] 0.74096779 0.51806441 0.25903221
[61,] 0.72933489 0.54133023 0.27066511
[62,] 0.69004225 0.61991551 0.30995775
[63,] 0.69466043 0.61067913 0.30533957
[64,] 0.66093448 0.67813103 0.33906552
[65,] 0.64799025 0.70401949 0.35200975
[66,] 0.61255490 0.77489021 0.38744510
[67,] 0.58845725 0.82308550 0.41154275
[68,] 0.57567176 0.84865647 0.42432824
[69,] 0.55208140 0.89583720 0.44791860
[70,] 0.57302290 0.85395421 0.42697710
[71,] 0.53929625 0.92140750 0.46070375
[72,] 0.59078521 0.81842959 0.40921479
[73,] 0.54393733 0.91212533 0.45606267
[74,] 0.52118119 0.95763763 0.47881881
[75,] 0.54081828 0.91836344 0.45918172
[76,] 0.50131061 0.99737878 0.49868939
[77,] 0.47416530 0.94833059 0.52583470
[78,] 0.44089130 0.88178260 0.55910870
[79,] 0.42259078 0.84518155 0.57740922
[80,] 0.42513525 0.85027051 0.57486475
[81,] 0.38444088 0.76888176 0.61555912
[82,] 0.47239933 0.94479866 0.52760067
[83,] 0.45490805 0.90981609 0.54509195
[84,] 0.55914304 0.88171393 0.44085696
[85,] 0.51415711 0.97168578 0.48584289
[86,] 0.47230702 0.94461404 0.52769298
[87,] 0.43069867 0.86139734 0.56930133
[88,] 0.42578568 0.85157137 0.57421432
[89,] 0.37820191 0.75640383 0.62179809
[90,] 0.47123463 0.94246925 0.52876537
[91,] 0.45443517 0.90887034 0.54556483
[92,] 0.42109768 0.84219537 0.57890232
[93,] 0.38718678 0.77437357 0.61281322
[94,] 0.35348013 0.70696026 0.64651987
[95,] 0.35975457 0.71950914 0.64024543
[96,] 0.34747343 0.69494686 0.65252657
[97,] 0.33222135 0.66444270 0.66777865
[98,] 0.36737837 0.73475675 0.63262163
[99,] 0.38051587 0.76103174 0.61948413
[100,] 0.40431764 0.80863528 0.59568236
[101,] 0.36603746 0.73207492 0.63396254
[102,] 0.31624876 0.63249752 0.68375124
[103,] 0.26977736 0.53955473 0.73022264
[104,] 0.24508243 0.49016487 0.75491757
[105,] 0.20915745 0.41831490 0.79084255
[106,] 0.18769555 0.37539109 0.81230445
[107,] 0.17847663 0.35695326 0.82152337
[108,] 0.15364926 0.30729851 0.84635074
[109,] 0.14660204 0.29320409 0.85339796
[110,] 0.12219695 0.24439390 0.87780305
[111,] 0.09535778 0.19071556 0.90464222
[112,] 0.28668984 0.57337967 0.71331016
[113,] 0.23760607 0.47521214 0.76239393
[114,] 0.23276075 0.46552150 0.76723925
[115,] 0.22756306 0.45512611 0.77243694
[116,] 0.24594470 0.49188939 0.75405530
[117,] 0.20959030 0.41918059 0.79040970
[118,] 0.22450795 0.44901590 0.77549205
[119,] 0.17941763 0.35883526 0.82058237
[120,] 0.27647955 0.55295911 0.72352045
[121,] 0.26617720 0.53235440 0.73382280
[122,] 0.25918704 0.51837408 0.74081296
[123,] 0.23386237 0.46772474 0.76613763
[124,] 0.23689281 0.47378563 0.76310719
[125,] 0.22400011 0.44800021 0.77599989
[126,] 0.17338821 0.34677643 0.82661179
[127,] 0.14394279 0.28788557 0.85605721
[128,] 0.12269134 0.24538268 0.87730866
[129,] 0.08350830 0.16701659 0.91649170
[130,] 0.88390279 0.23219441 0.11609721
[131,] 0.97600469 0.04799063 0.02399531
[132,] 0.95542354 0.08915292 0.04457646
[133,] 0.90809621 0.18380758 0.09190379
[134,] 0.82613239 0.34773523 0.17386761
[135,] 0.70182461 0.59635079 0.29817539
> postscript(file="/var/fisher/rcomp/tmp/1jwfg1353336441.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/fisher/rcomp/tmp/2r6a61353336441.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/30eoi1353336441.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4c5fs1353336441.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5fgeu1353336441.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5 6
1.76319266 -1.14020247 -2.91013777 0.80294171 0.67115768 0.60955075
7 8 9 10 11 12
4.31089726 1.90269497 3.75835001 1.33992541 -2.94019730 -0.75117929
13 14 15 16 17 18
-2.06861207 1.02901744 -0.87632552 1.80279753 -3.39352775 -1.43710530
19 20 21 22 23 24
-2.33433268 2.97785543 -3.73946387 0.46360552 -7.03077353 1.12136311
25 26 27 28 29 30
-2.11601934 2.37372593 1.69608991 2.48956656 3.12635888 1.53794573
31 32 33 34 35 36
-2.02028054 -0.35036725 2.90018004 -2.80828924 3.88836729 0.52072062
37 38 39 40 41 42
0.76208749 -1.04873540 0.27745917 -1.98945646 -3.49964720 1.92478875
43 44 45 46 47 48
-1.42488386 0.03689794 3.87043863 0.59070162 -0.66904357 7.72152064
49 50 51 52 53 54
-1.80119430 -3.14018175 -2.25616115 -0.66580509 -0.65499435 -1.83841161
55 56 57 58 59 60
0.81428167 -2.21549556 -1.96583764 3.31281172 -4.10139820 1.17304758
61 62 63 64 65 66
5.06392584 -0.71701518 -3.75147531 0.71066638 -2.06225965 0.20545143
67 68 69 70 71 72
-1.01070112 -1.39778016 0.21281961 -0.85233876 -3.85004371 -0.99597292
73 74 75 76 77 78
2.85088698 1.93250043 -0.14611033 -1.87439816 -1.38823390 -2.10460504
79 80 81 82 83 84
0.16143941 -1.47782821 2.14561766 0.48306852 2.87302720 -1.00752358
85 86 87 88 89 90
-3.45868741 0.27287226 1.69620610 2.80067523 0.39312076 -0.68480544
91 92 93 94 95 96
-1.46834230 2.29265138 2.64773378 0.09127787 -4.02838348 -1.60829915
97 98 99 100 101 102
4.47625380 -0.18700258 -0.09463369 1.14408326 2.58993412 0.05870019
103 104 105 106 107 108
4.20514887 -1.39334218 -1.43634754 -1.43922820 1.39887234 2.47358872
109 110 111 112 113 114
2.74716101 -2.54893918 3.20798674 2.16121304 2.14946710 -1.83889778
115 116 117 118 119 120
0.29970424 -0.15499246 -1.58020173 0.12561772 0.95440237 -0.82010237
121 122 123 124 125 126
-0.14801363 -3.09636056 -1.30930165 -0.49601944 4.75944454 0.21820041
127 128 129 130 131 132
1.58515208 1.35806675 -3.29056525 0.14339783 -3.82861832 -1.09186541
133 134 135 136 137 138
-4.87482797 -2.55989259 0.83745127 3.86937572 -0.83617444 -3.61874044
139 140 141 142 143 144
-1.12661124 1.38979030 2.27167441 -0.78612719 -9.78096996 3.52071098
145 146 147 148 149 150
0.08926859 1.65257188 1.90426348 2.12087994 -0.29172177 -0.53816085
151 152 153 154 155 156
0.67159199 1.43545149 3.62390503 1.66500079 -0.47672095 0.27231314
157 158 159 160 161 162
-2.99250033 -0.26557098 2.42430573 0.19007667 -1.71511498 -0.70887752
> postscript(file="/var/fisher/rcomp/tmp/6xv8t1353336441.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 1.76319266 NA
1 -1.14020247 1.76319266
2 -2.91013777 -1.14020247
3 0.80294171 -2.91013777
4 0.67115768 0.80294171
5 0.60955075 0.67115768
6 4.31089726 0.60955075
7 1.90269497 4.31089726
8 3.75835001 1.90269497
9 1.33992541 3.75835001
10 -2.94019730 1.33992541
11 -0.75117929 -2.94019730
12 -2.06861207 -0.75117929
13 1.02901744 -2.06861207
14 -0.87632552 1.02901744
15 1.80279753 -0.87632552
16 -3.39352775 1.80279753
17 -1.43710530 -3.39352775
18 -2.33433268 -1.43710530
19 2.97785543 -2.33433268
20 -3.73946387 2.97785543
21 0.46360552 -3.73946387
22 -7.03077353 0.46360552
23 1.12136311 -7.03077353
24 -2.11601934 1.12136311
25 2.37372593 -2.11601934
26 1.69608991 2.37372593
27 2.48956656 1.69608991
28 3.12635888 2.48956656
29 1.53794573 3.12635888
30 -2.02028054 1.53794573
31 -0.35036725 -2.02028054
32 2.90018004 -0.35036725
33 -2.80828924 2.90018004
34 3.88836729 -2.80828924
35 0.52072062 3.88836729
36 0.76208749 0.52072062
37 -1.04873540 0.76208749
38 0.27745917 -1.04873540
39 -1.98945646 0.27745917
40 -3.49964720 -1.98945646
41 1.92478875 -3.49964720
42 -1.42488386 1.92478875
43 0.03689794 -1.42488386
44 3.87043863 0.03689794
45 0.59070162 3.87043863
46 -0.66904357 0.59070162
47 7.72152064 -0.66904357
48 -1.80119430 7.72152064
49 -3.14018175 -1.80119430
50 -2.25616115 -3.14018175
51 -0.66580509 -2.25616115
52 -0.65499435 -0.66580509
53 -1.83841161 -0.65499435
54 0.81428167 -1.83841161
55 -2.21549556 0.81428167
56 -1.96583764 -2.21549556
57 3.31281172 -1.96583764
58 -4.10139820 3.31281172
59 1.17304758 -4.10139820
60 5.06392584 1.17304758
61 -0.71701518 5.06392584
62 -3.75147531 -0.71701518
63 0.71066638 -3.75147531
64 -2.06225965 0.71066638
65 0.20545143 -2.06225965
66 -1.01070112 0.20545143
67 -1.39778016 -1.01070112
68 0.21281961 -1.39778016
69 -0.85233876 0.21281961
70 -3.85004371 -0.85233876
71 -0.99597292 -3.85004371
72 2.85088698 -0.99597292
73 1.93250043 2.85088698
74 -0.14611033 1.93250043
75 -1.87439816 -0.14611033
76 -1.38823390 -1.87439816
77 -2.10460504 -1.38823390
78 0.16143941 -2.10460504
79 -1.47782821 0.16143941
80 2.14561766 -1.47782821
81 0.48306852 2.14561766
82 2.87302720 0.48306852
83 -1.00752358 2.87302720
84 -3.45868741 -1.00752358
85 0.27287226 -3.45868741
86 1.69620610 0.27287226
87 2.80067523 1.69620610
88 0.39312076 2.80067523
89 -0.68480544 0.39312076
90 -1.46834230 -0.68480544
91 2.29265138 -1.46834230
92 2.64773378 2.29265138
93 0.09127787 2.64773378
94 -4.02838348 0.09127787
95 -1.60829915 -4.02838348
96 4.47625380 -1.60829915
97 -0.18700258 4.47625380
98 -0.09463369 -0.18700258
99 1.14408326 -0.09463369
100 2.58993412 1.14408326
101 0.05870019 2.58993412
102 4.20514887 0.05870019
103 -1.39334218 4.20514887
104 -1.43634754 -1.39334218
105 -1.43922820 -1.43634754
106 1.39887234 -1.43922820
107 2.47358872 1.39887234
108 2.74716101 2.47358872
109 -2.54893918 2.74716101
110 3.20798674 -2.54893918
111 2.16121304 3.20798674
112 2.14946710 2.16121304
113 -1.83889778 2.14946710
114 0.29970424 -1.83889778
115 -0.15499246 0.29970424
116 -1.58020173 -0.15499246
117 0.12561772 -1.58020173
118 0.95440237 0.12561772
119 -0.82010237 0.95440237
120 -0.14801363 -0.82010237
121 -3.09636056 -0.14801363
122 -1.30930165 -3.09636056
123 -0.49601944 -1.30930165
124 4.75944454 -0.49601944
125 0.21820041 4.75944454
126 1.58515208 0.21820041
127 1.35806675 1.58515208
128 -3.29056525 1.35806675
129 0.14339783 -3.29056525
130 -3.82861832 0.14339783
131 -1.09186541 -3.82861832
132 -4.87482797 -1.09186541
133 -2.55989259 -4.87482797
134 0.83745127 -2.55989259
135 3.86937572 0.83745127
136 -0.83617444 3.86937572
137 -3.61874044 -0.83617444
138 -1.12661124 -3.61874044
139 1.38979030 -1.12661124
140 2.27167441 1.38979030
141 -0.78612719 2.27167441
142 -9.78096996 -0.78612719
143 3.52071098 -9.78096996
144 0.08926859 3.52071098
145 1.65257188 0.08926859
146 1.90426348 1.65257188
147 2.12087994 1.90426348
148 -0.29172177 2.12087994
149 -0.53816085 -0.29172177
150 0.67159199 -0.53816085
151 1.43545149 0.67159199
152 3.62390503 1.43545149
153 1.66500079 3.62390503
154 -0.47672095 1.66500079
155 0.27231314 -0.47672095
156 -2.99250033 0.27231314
157 -0.26557098 -2.99250033
158 2.42430573 -0.26557098
159 0.19007667 2.42430573
160 -1.71511498 0.19007667
161 -0.70887752 -1.71511498
162 NA -0.70887752
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.14020247 1.76319266
[2,] -2.91013777 -1.14020247
[3,] 0.80294171 -2.91013777
[4,] 0.67115768 0.80294171
[5,] 0.60955075 0.67115768
[6,] 4.31089726 0.60955075
[7,] 1.90269497 4.31089726
[8,] 3.75835001 1.90269497
[9,] 1.33992541 3.75835001
[10,] -2.94019730 1.33992541
[11,] -0.75117929 -2.94019730
[12,] -2.06861207 -0.75117929
[13,] 1.02901744 -2.06861207
[14,] -0.87632552 1.02901744
[15,] 1.80279753 -0.87632552
[16,] -3.39352775 1.80279753
[17,] -1.43710530 -3.39352775
[18,] -2.33433268 -1.43710530
[19,] 2.97785543 -2.33433268
[20,] -3.73946387 2.97785543
[21,] 0.46360552 -3.73946387
[22,] -7.03077353 0.46360552
[23,] 1.12136311 -7.03077353
[24,] -2.11601934 1.12136311
[25,] 2.37372593 -2.11601934
[26,] 1.69608991 2.37372593
[27,] 2.48956656 1.69608991
[28,] 3.12635888 2.48956656
[29,] 1.53794573 3.12635888
[30,] -2.02028054 1.53794573
[31,] -0.35036725 -2.02028054
[32,] 2.90018004 -0.35036725
[33,] -2.80828924 2.90018004
[34,] 3.88836729 -2.80828924
[35,] 0.52072062 3.88836729
[36,] 0.76208749 0.52072062
[37,] -1.04873540 0.76208749
[38,] 0.27745917 -1.04873540
[39,] -1.98945646 0.27745917
[40,] -3.49964720 -1.98945646
[41,] 1.92478875 -3.49964720
[42,] -1.42488386 1.92478875
[43,] 0.03689794 -1.42488386
[44,] 3.87043863 0.03689794
[45,] 0.59070162 3.87043863
[46,] -0.66904357 0.59070162
[47,] 7.72152064 -0.66904357
[48,] -1.80119430 7.72152064
[49,] -3.14018175 -1.80119430
[50,] -2.25616115 -3.14018175
[51,] -0.66580509 -2.25616115
[52,] -0.65499435 -0.66580509
[53,] -1.83841161 -0.65499435
[54,] 0.81428167 -1.83841161
[55,] -2.21549556 0.81428167
[56,] -1.96583764 -2.21549556
[57,] 3.31281172 -1.96583764
[58,] -4.10139820 3.31281172
[59,] 1.17304758 -4.10139820
[60,] 5.06392584 1.17304758
[61,] -0.71701518 5.06392584
[62,] -3.75147531 -0.71701518
[63,] 0.71066638 -3.75147531
[64,] -2.06225965 0.71066638
[65,] 0.20545143 -2.06225965
[66,] -1.01070112 0.20545143
[67,] -1.39778016 -1.01070112
[68,] 0.21281961 -1.39778016
[69,] -0.85233876 0.21281961
[70,] -3.85004371 -0.85233876
[71,] -0.99597292 -3.85004371
[72,] 2.85088698 -0.99597292
[73,] 1.93250043 2.85088698
[74,] -0.14611033 1.93250043
[75,] -1.87439816 -0.14611033
[76,] -1.38823390 -1.87439816
[77,] -2.10460504 -1.38823390
[78,] 0.16143941 -2.10460504
[79,] -1.47782821 0.16143941
[80,] 2.14561766 -1.47782821
[81,] 0.48306852 2.14561766
[82,] 2.87302720 0.48306852
[83,] -1.00752358 2.87302720
[84,] -3.45868741 -1.00752358
[85,] 0.27287226 -3.45868741
[86,] 1.69620610 0.27287226
[87,] 2.80067523 1.69620610
[88,] 0.39312076 2.80067523
[89,] -0.68480544 0.39312076
[90,] -1.46834230 -0.68480544
[91,] 2.29265138 -1.46834230
[92,] 2.64773378 2.29265138
[93,] 0.09127787 2.64773378
[94,] -4.02838348 0.09127787
[95,] -1.60829915 -4.02838348
[96,] 4.47625380 -1.60829915
[97,] -0.18700258 4.47625380
[98,] -0.09463369 -0.18700258
[99,] 1.14408326 -0.09463369
[100,] 2.58993412 1.14408326
[101,] 0.05870019 2.58993412
[102,] 4.20514887 0.05870019
[103,] -1.39334218 4.20514887
[104,] -1.43634754 -1.39334218
[105,] -1.43922820 -1.43634754
[106,] 1.39887234 -1.43922820
[107,] 2.47358872 1.39887234
[108,] 2.74716101 2.47358872
[109,] -2.54893918 2.74716101
[110,] 3.20798674 -2.54893918
[111,] 2.16121304 3.20798674
[112,] 2.14946710 2.16121304
[113,] -1.83889778 2.14946710
[114,] 0.29970424 -1.83889778
[115,] -0.15499246 0.29970424
[116,] -1.58020173 -0.15499246
[117,] 0.12561772 -1.58020173
[118,] 0.95440237 0.12561772
[119,] -0.82010237 0.95440237
[120,] -0.14801363 -0.82010237
[121,] -3.09636056 -0.14801363
[122,] -1.30930165 -3.09636056
[123,] -0.49601944 -1.30930165
[124,] 4.75944454 -0.49601944
[125,] 0.21820041 4.75944454
[126,] 1.58515208 0.21820041
[127,] 1.35806675 1.58515208
[128,] -3.29056525 1.35806675
[129,] 0.14339783 -3.29056525
[130,] -3.82861832 0.14339783
[131,] -1.09186541 -3.82861832
[132,] -4.87482797 -1.09186541
[133,] -2.55989259 -4.87482797
[134,] 0.83745127 -2.55989259
[135,] 3.86937572 0.83745127
[136,] -0.83617444 3.86937572
[137,] -3.61874044 -0.83617444
[138,] -1.12661124 -3.61874044
[139,] 1.38979030 -1.12661124
[140,] 2.27167441 1.38979030
[141,] -0.78612719 2.27167441
[142,] -9.78096996 -0.78612719
[143,] 3.52071098 -9.78096996
[144,] 0.08926859 3.52071098
[145,] 1.65257188 0.08926859
[146,] 1.90426348 1.65257188
[147,] 2.12087994 1.90426348
[148,] -0.29172177 2.12087994
[149,] -0.53816085 -0.29172177
[150,] 0.67159199 -0.53816085
[151,] 1.43545149 0.67159199
[152,] 3.62390503 1.43545149
[153,] 1.66500079 3.62390503
[154,] -0.47672095 1.66500079
[155,] 0.27231314 -0.47672095
[156,] -2.99250033 0.27231314
[157,] -0.26557098 -2.99250033
[158,] 2.42430573 -0.26557098
[159,] 0.19007667 2.42430573
[160,] -1.71511498 0.19007667
[161,] -0.70887752 -1.71511498
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.14020247 1.76319266
2 -2.91013777 -1.14020247
3 0.80294171 -2.91013777
4 0.67115768 0.80294171
5 0.60955075 0.67115768
6 4.31089726 0.60955075
7 1.90269497 4.31089726
8 3.75835001 1.90269497
9 1.33992541 3.75835001
10 -2.94019730 1.33992541
11 -0.75117929 -2.94019730
12 -2.06861207 -0.75117929
13 1.02901744 -2.06861207
14 -0.87632552 1.02901744
15 1.80279753 -0.87632552
16 -3.39352775 1.80279753
17 -1.43710530 -3.39352775
18 -2.33433268 -1.43710530
19 2.97785543 -2.33433268
20 -3.73946387 2.97785543
21 0.46360552 -3.73946387
22 -7.03077353 0.46360552
23 1.12136311 -7.03077353
24 -2.11601934 1.12136311
25 2.37372593 -2.11601934
26 1.69608991 2.37372593
27 2.48956656 1.69608991
28 3.12635888 2.48956656
29 1.53794573 3.12635888
30 -2.02028054 1.53794573
31 -0.35036725 -2.02028054
32 2.90018004 -0.35036725
33 -2.80828924 2.90018004
34 3.88836729 -2.80828924
35 0.52072062 3.88836729
36 0.76208749 0.52072062
37 -1.04873540 0.76208749
38 0.27745917 -1.04873540
39 -1.98945646 0.27745917
40 -3.49964720 -1.98945646
41 1.92478875 -3.49964720
42 -1.42488386 1.92478875
43 0.03689794 -1.42488386
44 3.87043863 0.03689794
45 0.59070162 3.87043863
46 -0.66904357 0.59070162
47 7.72152064 -0.66904357
48 -1.80119430 7.72152064
49 -3.14018175 -1.80119430
50 -2.25616115 -3.14018175
51 -0.66580509 -2.25616115
52 -0.65499435 -0.66580509
53 -1.83841161 -0.65499435
54 0.81428167 -1.83841161
55 -2.21549556 0.81428167
56 -1.96583764 -2.21549556
57 3.31281172 -1.96583764
58 -4.10139820 3.31281172
59 1.17304758 -4.10139820
60 5.06392584 1.17304758
61 -0.71701518 5.06392584
62 -3.75147531 -0.71701518
63 0.71066638 -3.75147531
64 -2.06225965 0.71066638
65 0.20545143 -2.06225965
66 -1.01070112 0.20545143
67 -1.39778016 -1.01070112
68 0.21281961 -1.39778016
69 -0.85233876 0.21281961
70 -3.85004371 -0.85233876
71 -0.99597292 -3.85004371
72 2.85088698 -0.99597292
73 1.93250043 2.85088698
74 -0.14611033 1.93250043
75 -1.87439816 -0.14611033
76 -1.38823390 -1.87439816
77 -2.10460504 -1.38823390
78 0.16143941 -2.10460504
79 -1.47782821 0.16143941
80 2.14561766 -1.47782821
81 0.48306852 2.14561766
82 2.87302720 0.48306852
83 -1.00752358 2.87302720
84 -3.45868741 -1.00752358
85 0.27287226 -3.45868741
86 1.69620610 0.27287226
87 2.80067523 1.69620610
88 0.39312076 2.80067523
89 -0.68480544 0.39312076
90 -1.46834230 -0.68480544
91 2.29265138 -1.46834230
92 2.64773378 2.29265138
93 0.09127787 2.64773378
94 -4.02838348 0.09127787
95 -1.60829915 -4.02838348
96 4.47625380 -1.60829915
97 -0.18700258 4.47625380
98 -0.09463369 -0.18700258
99 1.14408326 -0.09463369
100 2.58993412 1.14408326
101 0.05870019 2.58993412
102 4.20514887 0.05870019
103 -1.39334218 4.20514887
104 -1.43634754 -1.39334218
105 -1.43922820 -1.43634754
106 1.39887234 -1.43922820
107 2.47358872 1.39887234
108 2.74716101 2.47358872
109 -2.54893918 2.74716101
110 3.20798674 -2.54893918
111 2.16121304 3.20798674
112 2.14946710 2.16121304
113 -1.83889778 2.14946710
114 0.29970424 -1.83889778
115 -0.15499246 0.29970424
116 -1.58020173 -0.15499246
117 0.12561772 -1.58020173
118 0.95440237 0.12561772
119 -0.82010237 0.95440237
120 -0.14801363 -0.82010237
121 -3.09636056 -0.14801363
122 -1.30930165 -3.09636056
123 -0.49601944 -1.30930165
124 4.75944454 -0.49601944
125 0.21820041 4.75944454
126 1.58515208 0.21820041
127 1.35806675 1.58515208
128 -3.29056525 1.35806675
129 0.14339783 -3.29056525
130 -3.82861832 0.14339783
131 -1.09186541 -3.82861832
132 -4.87482797 -1.09186541
133 -2.55989259 -4.87482797
134 0.83745127 -2.55989259
135 3.86937572 0.83745127
136 -0.83617444 3.86937572
137 -3.61874044 -0.83617444
138 -1.12661124 -3.61874044
139 1.38979030 -1.12661124
140 2.27167441 1.38979030
141 -0.78612719 2.27167441
142 -9.78096996 -0.78612719
143 3.52071098 -9.78096996
144 0.08926859 3.52071098
145 1.65257188 0.08926859
146 1.90426348 1.65257188
147 2.12087994 1.90426348
148 -0.29172177 2.12087994
149 -0.53816085 -0.29172177
150 0.67159199 -0.53816085
151 1.43545149 0.67159199
152 3.62390503 1.43545149
153 1.66500079 3.62390503
154 -0.47672095 1.66500079
155 0.27231314 -0.47672095
156 -2.99250033 0.27231314
157 -0.26557098 -2.99250033
158 2.42430573 -0.26557098
159 0.19007667 2.42430573
160 -1.71511498 0.19007667
161 -0.70887752 -1.71511498
> 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/fisher/rcomp/tmp/7ddet1353336441.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/fisher/rcomp/tmp/8fog71353336441.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/fisher/rcomp/tmp/9nhxe1353336441.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/fisher/rcomp/tmp/10zw0d1353336441.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1151301353336441.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12oplp1353336441.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13596z1353336441.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/141iwb1353336441.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/fisher/rcomp/tmp/155xpv1353336441.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/fisher/rcomp/tmp/160gov1353336441.tab")
+ }
>
> try(system("convert tmp/1jwfg1353336441.ps tmp/1jwfg1353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r6a61353336441.ps tmp/2r6a61353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/30eoi1353336441.ps tmp/30eoi1353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c5fs1353336441.ps tmp/4c5fs1353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fgeu1353336441.ps tmp/5fgeu1353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xv8t1353336441.ps tmp/6xv8t1353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ddet1353336441.ps tmp/7ddet1353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fog71353336441.ps tmp/8fog71353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nhxe1353336441.ps tmp/9nhxe1353336441.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zw0d1353336441.ps tmp/10zw0d1353336441.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.656 1.356 10.013