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)
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.
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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(13
+ ,14
+ ,16
+ ,18
+ ,19
+ ,11
+ ,15
+ ,12
+ ,14
+ ,16
+ ,13
+ ,18
+ ,19
+ ,14
+ ,15
+ ,14
+ ,14
+ ,15
+ ,15
+ ,15
+ ,16
+ ,17
+ ,16
+ ,19
+ ,16
+ ,10
+ ,16
+ ,16
+ ,17
+ ,18
+ ,15
+ ,14
+ ,15
+ ,14
+ ,20
+ ,17
+ ,18
+ ,14
+ ,16
+ ,16
+ ,16
+ ,18
+ ,16
+ ,11
+ ,19
+ ,14
+ ,16
+ ,12
+ ,17
+ ,17
+ ,17
+ ,9
+ ,16
+ ,16
+ ,15
+ ,14
+ ,16
+ ,15
+ ,14
+ ,11
+ ,15
+ ,16
+ ,12
+ ,13
+ ,14
+ ,17
+ ,16
+ ,15
+ ,14
+ ,14
+ ,7
+ ,16
+ ,10
+ ,9
+ ,14
+ ,15
+ ,16
+ ,17
+ ,16
+ ,13
+ ,16
+ ,15
+ ,14
+ ,16
+ ,20
+ ,16
+ ,14
+ ,12
+ ,14
+ ,12
+ ,11
+ ,11
+ ,14
+ ,15
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+ ,15
+ ,16
+ ,17
+ ,14
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+ ,9
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+ ,15
+ ,15
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+ ,12
+ ,12
+ ,8
+ ,16
+ ,13
+ ,16
+ ,11
+ ,14
+ ,14
+ ,16
+ ,15
+ ,17
+ ,10
+ ,18
+ ,11
+ ,18
+ ,12
+ ,12
+ ,15
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+ ,15
+ ,10
+ ,14
+ ,14
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+ ,18
+ ,15
+ ,18
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+ ,16
+ ,13
+ ,13
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+ ,16
+ ,13
+ ,16
+ ,15
+ ,20
+ ,16
+ ,16
+ ,15
+ ,15
+ ,16
+ ,15
+ ,15
+ ,16
+ ,14
+ ,14
+ ,15
+ ,16
+ ,14
+ ,16
+ ,13
+ ,15
+ ,7
+ ,12
+ ,17
+ ,17
+ ,13
+ ,16
+ ,15
+ ,15
+ ,14
+ ,13
+ ,13
+ ,16
+ ,16
+ ,16
+ ,12
+ ,16
+ ,14
+ ,16
+ ,17
+ ,14
+ ,15
+ ,16
+ ,17
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+ ,20
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+ ,15
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+ ,13
+ ,9
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+ ,16
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+ ,10
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+ ,10
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+ ,16
+ ,11
+ ,17
+ ,13
+ ,13
+ ,14
+ ,12
+ ,18
+ ,18
+ ,16
+ ,14
+ ,14
+ ,14
+ ,14
+ ,13
+ ,14
+ ,16
+ ,14
+ ,13
+ ,12
+ ,16
+ ,14
+ ,13
+ ,15
+ ,16
+ ,15
+ ,15
+ ,15
+ ,16
+ ,13
+ ,15
+ ,17
+ ,17
+ ,17
+ ,15
+ ,19
+ ,12
+ ,15
+ ,16
+ ,13
+ ,10
+ ,9
+ ,16
+ ,15
+ ,12
+ ,15
+ ,14
+ ,15
+ ,15
+ ,16
+ ,13
+ ,11
+ ,15
+ ,14
+ ,11
+ ,11
+ ,12
+ ,15
+ ,8
+ ,13
+ ,16
+ ,15
+ ,15
+ ,16
+ ,17
+ ,14
+ ,16
+ ,15
+ ,10
+ ,16
+ ,18
+ ,16
+ ,13
+ ,11
+ ,16
+ ,12
+ ,13
+ ,9
+ ,10
+ ,16
+ ,15
+ ,13
+ ,16
+ ,16
+ ,16
+ ,12
+ ,14
+ ,9
+ ,10
+ ,13
+ ,17
+ ,13
+ ,13
+ ,14
+ ,15
+ ,19
+ ,16
+ ,13
+ ,12
+ ,12
+ ,13
+ ,13)
+ ,dim=c(2
+ ,162)
+ ,dimnames=list(c('Learning'
+ ,'Hapiness')
+ ,1:162))
> y <- array(NA,dim=c(2,162),dimnames=list(c('Learning','Hapiness'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '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
Hapiness Learning t
1 14 13 1
2 18 16 2
3 11 19 3
4 12 15 4
5 16 14 5
6 18 13 6
7 14 19 7
8 14 15 8
9 15 14 9
10 15 15 10
11 17 16 11
12 19 16 12
13 10 16 13
14 16 16 14
15 18 17 15
16 14 15 16
17 14 15 17
18 17 20 18
19 14 18 19
20 16 16 20
21 18 16 21
22 11 16 22
23 14 19 23
24 12 16 24
25 17 17 25
26 9 17 26
27 16 16 27
28 14 15 28
29 15 16 29
30 11 14 30
31 16 15 31
32 13 12 32
33 17 14 33
34 15 16 34
35 14 14 35
36 16 7 36
37 9 10 37
38 15 14 38
39 17 16 39
40 13 16 40
41 15 16 41
42 16 14 42
43 16 20 43
44 12 14 44
45 12 14 45
46 11 11 46
47 15 14 47
48 15 15 48
49 17 16 49
50 13 14 50
51 16 16 51
52 14 14 52
53 11 12 53
54 12 16 54
55 12 9 55
56 15 14 56
57 16 16 57
58 15 16 58
59 12 15 59
60 12 16 60
61 8 12 61
62 13 16 62
63 11 16 63
64 14 14 64
65 15 16 65
66 10 17 66
67 11 18 67
68 12 18 68
69 15 12 69
70 15 16 70
71 14 10 71
72 16 14 72
73 15 18 73
74 15 18 74
75 13 16 75
76 12 17 76
77 17 16 77
78 13 16 78
79 15 13 79
80 13 16 80
81 15 16 81
82 16 20 82
83 15 16 83
84 16 15 84
85 15 15 85
86 14 16 86
87 15 14 87
88 14 16 88
89 13 16 89
90 7 15 90
91 17 12 91
92 13 17 92
93 15 16 93
94 14 15 94
95 13 13 95
96 16 16 96
97 12 16 97
98 14 16 98
99 17 16 99
100 15 14 100
101 17 16 101
102 12 16 102
103 16 20 103
104 11 15 104
105 15 16 105
106 9 13 106
107 16 17 107
108 15 16 108
109 10 16 109
110 10 12 110
111 15 16 111
112 11 16 112
113 13 17 113
114 14 13 114
115 18 12 115
116 16 18 116
117 14 14 117
118 14 14 118
119 14 13 119
120 14 16 120
121 12 13 121
122 14 16 122
123 15 13 123
124 15 16 124
125 15 15 125
126 13 16 126
127 17 15 127
128 17 17 128
129 19 15 129
130 15 12 130
131 13 16 131
132 9 10 132
133 15 16 133
134 15 12 134
135 15 14 135
136 16 15 136
137 11 13 137
138 14 15 138
139 11 11 139
140 15 12 140
141 13 8 141
142 15 16 142
143 16 15 143
144 14 17 144
145 15 16 145
146 16 10 146
147 16 18 147
148 11 13 148
149 12 16 149
150 9 13 150
151 16 10 151
152 13 15 152
153 16 16 153
154 12 16 154
155 9 14 155
156 13 10 156
157 13 17 157
158 14 13 158
159 19 15 159
160 13 16 160
161 12 12 161
162 13 13 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Learning t
11.68324 0.17586 -0.00338
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.0170 -1.2901 0.3996 1.5655 5.2162
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.683235 1.340991 8.712 3.68e-15 ***
Learning 0.175864 0.082150 2.141 0.0338 *
t -0.003380 0.003951 -0.855 0.3936
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.307 on 159 degrees of freedom
Multiple R-squared: 0.03787, Adjusted R-squared: 0.02577
F-statistic: 3.13 on 2 and 159 DF, p-value: 0.04645
> 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,] 0.88680056 0.2263989 0.11319944
[2,] 0.80141754 0.3971649 0.19858246
[3,] 0.74448874 0.5110225 0.25551126
[4,] 0.64525417 0.7094917 0.35474583
[5,] 0.53184531 0.9363094 0.46815469
[6,] 0.50196512 0.9960698 0.49803488
[7,] 0.57209485 0.8558103 0.42790515
[8,] 0.89023430 0.2195314 0.10976570
[9,] 0.85093458 0.2981308 0.14906542
[10,] 0.86144525 0.2771095 0.13855475
[11,] 0.85126128 0.2974774 0.14873872
[12,] 0.82739737 0.3452053 0.17260263
[13,] 0.81868060 0.3626388 0.18131940
[14,] 0.78319497 0.4336101 0.21680503
[15,] 0.73151472 0.5369706 0.26848528
[16,] 0.72757157 0.5448569 0.27242843
[17,] 0.85652180 0.2869564 0.14347820
[18,] 0.82051278 0.3589744 0.17948722
[19,] 0.83756153 0.3248769 0.16243847
[20,] 0.83306460 0.3338708 0.16693540
[21,] 0.94360937 0.1127813 0.05639063
[22,] 0.93385539 0.1322892 0.06614461
[23,] 0.91311848 0.1737630 0.08688152
[24,] 0.88938532 0.2212294 0.11061468
[25,] 0.90657614 0.1868477 0.09342386
[26,] 0.89851741 0.2029652 0.10148259
[27,] 0.87547974 0.2490405 0.12452026
[28,] 0.89017820 0.2196436 0.10982180
[29,] 0.86522867 0.2695427 0.13477133
[30,] 0.83359766 0.3328047 0.16640234
[31,] 0.82977276 0.3404545 0.17022724
[32,] 0.91571178 0.1685764 0.08428822
[33,] 0.89961540 0.2007692 0.10038460
[34,] 0.91111277 0.1777745 0.08888723
[35,] 0.89316542 0.2136692 0.10683458
[36,] 0.87160465 0.2567907 0.12839535
[37,] 0.86572106 0.2685579 0.13427894
[38,] 0.84606285 0.3078743 0.15393715
[39,] 0.83684394 0.3263121 0.16315606
[40,] 0.82393731 0.3521254 0.17606269
[41,] 0.82142003 0.3571599 0.17857997
[42,] 0.79909547 0.4018091 0.20090453
[43,] 0.77223304 0.4555339 0.22776696
[44,] 0.79370862 0.4125828 0.20629138
[45,] 0.76134368 0.4773126 0.23865632
[46,] 0.74940664 0.5011867 0.25059336
[47,] 0.71067838 0.5786432 0.28932162
[48,] 0.71117772 0.5776446 0.28882228
[49,] 0.69834775 0.6033045 0.30165225
[50,] 0.65857891 0.6828422 0.34142109
[51,] 0.63208678 0.7358264 0.36791322
[52,] 0.62293073 0.7541385 0.37706927
[53,] 0.58672789 0.8265442 0.41327211
[54,] 0.56697582 0.8660484 0.43302418
[55,] 0.55019893 0.8996021 0.44980107
[56,] 0.71142399 0.5771520 0.28857601
[57,] 0.67439614 0.6512077 0.32560386
[58,] 0.68793009 0.6241398 0.31206991
[59,] 0.65121216 0.6975757 0.34878784
[60,] 0.62312981 0.7537404 0.37687019
[61,] 0.69624142 0.6075172 0.30375858
[62,] 0.72168645 0.5566271 0.27831355
[63,] 0.71314820 0.5737036 0.28685180
[64,] 0.70882896 0.5823421 0.29117104
[65,] 0.68785477 0.6242905 0.31214523
[66,] 0.66117249 0.6776550 0.33882751
[67,] 0.67695601 0.6460880 0.32304399
[68,] 0.64612828 0.7077434 0.35387172
[69,] 0.61288682 0.7742264 0.38711318
[70,] 0.57513534 0.8497293 0.42486466
[71,] 0.56428489 0.8714302 0.43571511
[72,] 0.60890028 0.7821994 0.39109972
[73,] 0.57199048 0.8560190 0.42800952
[74,] 0.55283817 0.8943237 0.44716183
[75,] 0.51520719 0.9695856 0.48479281
[76,] 0.48326412 0.9665282 0.51673588
[77,] 0.45764702 0.9152940 0.54235298
[78,] 0.42424171 0.8484834 0.57575829
[79,] 0.42378376 0.8475675 0.57621624
[80,] 0.39357284 0.7871457 0.60642716
[81,] 0.35083420 0.7016684 0.64916580
[82,] 0.32630098 0.6526020 0.67369902
[83,] 0.28648667 0.5729733 0.71351333
[84,] 0.25441274 0.5088255 0.74558726
[85,] 0.57578684 0.8484263 0.42421316
[86,] 0.65097854 0.6980429 0.34902146
[87,] 0.62093449 0.7581310 0.37906551
[88,] 0.58599462 0.8280108 0.41400538
[89,] 0.54161864 0.9167627 0.45838136
[90,] 0.49706769 0.9941354 0.50293231
[91,] 0.48576599 0.9715320 0.51423401
[92,] 0.47569490 0.9513898 0.52430510
[93,] 0.43104046 0.8620809 0.56895954
[94,] 0.45994584 0.9198917 0.54005416
[95,] 0.43080566 0.8616113 0.56919434
[96,] 0.46303114 0.9260623 0.53696886
[97,] 0.44939288 0.8987858 0.55060712
[98,] 0.41527551 0.8305510 0.58472449
[99,] 0.43589814 0.8717963 0.56410186
[100,] 0.39736262 0.7947252 0.60263738
[101,] 0.52612629 0.9477474 0.47387371
[102,] 0.50274547 0.9945091 0.49725453
[103,] 0.46181964 0.9236393 0.53818036
[104,] 0.57524564 0.8495087 0.42475436
[105,] 0.64700387 0.7059923 0.35299613
[106,] 0.60599261 0.7880148 0.39400739
[107,] 0.67908882 0.6418224 0.32091118
[108,] 0.67549976 0.6490005 0.32450024
[109,] 0.63581096 0.7283781 0.36418904
[110,] 0.74002351 0.5199530 0.25997649
[111,] 0.70683678 0.5863264 0.29316322
[112,] 0.66403525 0.6719295 0.33596475
[113,] 0.61885012 0.7622998 0.38114988
[114,] 0.57051946 0.8589611 0.42948054
[115,] 0.52848032 0.9430394 0.47151968
[116,] 0.52667461 0.9466508 0.47332539
[117,] 0.48927593 0.9785519 0.51072407
[118,] 0.44434316 0.8886863 0.55565684
[119,] 0.39644793 0.7928959 0.60355207
[120,] 0.34932881 0.6986576 0.65067119
[121,] 0.34394097 0.6878819 0.65605903
[122,] 0.33952058 0.6790412 0.66047942
[123,] 0.32257576 0.6451515 0.67742424
[124,] 0.48016456 0.9603291 0.51983544
[125,] 0.44786447 0.8957289 0.55213553
[126,] 0.40723023 0.8144605 0.59276977
[127,] 0.54816300 0.9036740 0.45183700
[128,] 0.48988838 0.9797768 0.51011162
[129,] 0.44445107 0.8889021 0.55554893
[130,] 0.39402957 0.7880591 0.60597043
[131,] 0.37728833 0.7545767 0.62271167
[132,] 0.39483619 0.7896724 0.60516381
[133,] 0.33346408 0.6669282 0.66653592
[134,] 0.35622265 0.7124453 0.64377735
[135,] 0.30521481 0.6104296 0.69478519
[136,] 0.25225245 0.5045049 0.74774755
[137,] 0.20258403 0.4051681 0.79741597
[138,] 0.18456553 0.3691311 0.81543447
[139,] 0.13962597 0.2792519 0.86037403
[140,] 0.11113032 0.2222606 0.88886968
[141,] 0.14346472 0.2869294 0.85653528
[142,] 0.17022965 0.3404593 0.82977035
[143,] 0.13353575 0.2670715 0.86646425
[144,] 0.09582826 0.1916565 0.90417174
[145,] 0.17384970 0.3476994 0.82615030
[146,] 0.20786341 0.4157268 0.79213659
[147,] 0.14225554 0.2845111 0.85774446
[148,] 0.18116865 0.3623373 0.81883135
[149,] 0.11539735 0.2307947 0.88460265
[150,] 0.23265024 0.4653005 0.76734976
[151,] 0.13761617 0.2752323 0.86238383
> postscript(file="/var/wessaorg/rcomp/tmp/10my21356015859.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/wessaorg/rcomp/tmp/2m1sf1356015859.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3m6w21356015859.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4s5491356015859.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5pqdm1356015859.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.03391477 3.50970329 -4.01450820 -2.30767285 1.87157100 4.05081485
7 8 9 10 11 12
-1.00098814 -0.29415279 0.88509106 0.71260724 2.54012342 4.54350344
13 14 15 16 17 18
-4.45311655 1.55026347 3.37777965 -0.26711267 -0.26373265 1.86032820
19 20 21 22 23 24
-0.78456412 1.57054356 3.57392357 -3.42269641 -0.94690789 -2.41593638
25 26 27 28 29 30
2.41157980 -5.58504018 1.59420367 -0.22655249 0.60096370 -3.04392862
31 32 33 34 35 36
1.78358756 -0.68544093 2.96621142 0.61786377 -0.02702855 3.20739830
37 38 39 40 41 42
-4.31681318 0.98311150 2.63476385 -1.36185614 0.64152388 1.99663156
43 44 45 46 47 48
0.94482858 -1.99660841 -1.99322839 -2.46225688 1.01353164 0.84104782
49 50 51 52 53 54
2.66856400 -0.97632832 1.67532403 0.03043171 -2.61446061 -2.31453592
55 56 57 58 59 60
-1.08010908 1.04395177 1.69560412 0.69898414 -2.12177201 -2.29425583
61 62 63 64 65 66
-5.58742048 -1.28749580 -3.28411579 0.07099190 0.72264424 -4.44983957
67 68 69 70 71 72
-3.62232339 -2.61894338 1.43961964 0.73954432 0.79810733 2.09803202
73 74 75 76 77 78
0.39795670 0.40133672 -1.24355560 -2.41603942 2.76320443 -1.23341556
79 80 81 82 83 84
1.29755596 -1.22665553 0.77672449 1.07664917 0.78348452 1.96272837
85 86 87 88 89 90
0.96610838 -0.20637544 1.14873225 -0.19961540 -1.19623539 -7.01699154
91 92 93 94 95 96
3.51397997 -1.36195918 0.81728467 -0.00347148 -0.64836380 1.82742472
97 98 99 100 101 102
-2.16919527 -0.16581525 2.83756476 1.19267244 2.84432479 -2.15229519
103 104 105 106 107 108
1.14762949 -2.96967133 0.85784485 -4.61118363 1.68874105 0.86798490
109 110 111 112 113 114
-4.12863508 -3.42179974 0.87812495 -3.11849504 -1.29097886 0.41585649
115 116 117 118 119 120
4.59510034 1.54329736 0.25013270 0.25351272 0.43275657 -0.09145492
121 122 123 124 125 126
-1.56048340 -0.08469489 1.44627663 0.92206514 1.10130899 -1.07117483
127 128 129 130 131 132
3.10806902 2.75972137 5.11482905 1.64580057 -1.05427475 -3.99571174
133 134 135 136 137 138
0.95248528 1.65932063 1.31097298 2.13848916 -2.50640316 0.14524919
139 140 141 142 143 144
-2.14791546 1.67960072 0.38643607 0.98290542 2.16214927 -0.18619838
145 146 147 148 149 150
0.99304546 3.05160848 1.64807783 -2.46922299 -1.99343448 -4.46246296
151 152 153 154 155 156
3.06850855 -0.80743060 2.02008559 -1.97653440 -4.62142672 0.08540863
157 158 159 160 161 162
-1.14225819 0.56457716 5.21622951 -0.95625431 -1.24941896 -0.42190278
> postscript(file="/var/wessaorg/rcomp/tmp/6hxhh1356015859.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 0.03391477 NA
1 3.50970329 0.03391477
2 -4.01450820 3.50970329
3 -2.30767285 -4.01450820
4 1.87157100 -2.30767285
5 4.05081485 1.87157100
6 -1.00098814 4.05081485
7 -0.29415279 -1.00098814
8 0.88509106 -0.29415279
9 0.71260724 0.88509106
10 2.54012342 0.71260724
11 4.54350344 2.54012342
12 -4.45311655 4.54350344
13 1.55026347 -4.45311655
14 3.37777965 1.55026347
15 -0.26711267 3.37777965
16 -0.26373265 -0.26711267
17 1.86032820 -0.26373265
18 -0.78456412 1.86032820
19 1.57054356 -0.78456412
20 3.57392357 1.57054356
21 -3.42269641 3.57392357
22 -0.94690789 -3.42269641
23 -2.41593638 -0.94690789
24 2.41157980 -2.41593638
25 -5.58504018 2.41157980
26 1.59420367 -5.58504018
27 -0.22655249 1.59420367
28 0.60096370 -0.22655249
29 -3.04392862 0.60096370
30 1.78358756 -3.04392862
31 -0.68544093 1.78358756
32 2.96621142 -0.68544093
33 0.61786377 2.96621142
34 -0.02702855 0.61786377
35 3.20739830 -0.02702855
36 -4.31681318 3.20739830
37 0.98311150 -4.31681318
38 2.63476385 0.98311150
39 -1.36185614 2.63476385
40 0.64152388 -1.36185614
41 1.99663156 0.64152388
42 0.94482858 1.99663156
43 -1.99660841 0.94482858
44 -1.99322839 -1.99660841
45 -2.46225688 -1.99322839
46 1.01353164 -2.46225688
47 0.84104782 1.01353164
48 2.66856400 0.84104782
49 -0.97632832 2.66856400
50 1.67532403 -0.97632832
51 0.03043171 1.67532403
52 -2.61446061 0.03043171
53 -2.31453592 -2.61446061
54 -1.08010908 -2.31453592
55 1.04395177 -1.08010908
56 1.69560412 1.04395177
57 0.69898414 1.69560412
58 -2.12177201 0.69898414
59 -2.29425583 -2.12177201
60 -5.58742048 -2.29425583
61 -1.28749580 -5.58742048
62 -3.28411579 -1.28749580
63 0.07099190 -3.28411579
64 0.72264424 0.07099190
65 -4.44983957 0.72264424
66 -3.62232339 -4.44983957
67 -2.61894338 -3.62232339
68 1.43961964 -2.61894338
69 0.73954432 1.43961964
70 0.79810733 0.73954432
71 2.09803202 0.79810733
72 0.39795670 2.09803202
73 0.40133672 0.39795670
74 -1.24355560 0.40133672
75 -2.41603942 -1.24355560
76 2.76320443 -2.41603942
77 -1.23341556 2.76320443
78 1.29755596 -1.23341556
79 -1.22665553 1.29755596
80 0.77672449 -1.22665553
81 1.07664917 0.77672449
82 0.78348452 1.07664917
83 1.96272837 0.78348452
84 0.96610838 1.96272837
85 -0.20637544 0.96610838
86 1.14873225 -0.20637544
87 -0.19961540 1.14873225
88 -1.19623539 -0.19961540
89 -7.01699154 -1.19623539
90 3.51397997 -7.01699154
91 -1.36195918 3.51397997
92 0.81728467 -1.36195918
93 -0.00347148 0.81728467
94 -0.64836380 -0.00347148
95 1.82742472 -0.64836380
96 -2.16919527 1.82742472
97 -0.16581525 -2.16919527
98 2.83756476 -0.16581525
99 1.19267244 2.83756476
100 2.84432479 1.19267244
101 -2.15229519 2.84432479
102 1.14762949 -2.15229519
103 -2.96967133 1.14762949
104 0.85784485 -2.96967133
105 -4.61118363 0.85784485
106 1.68874105 -4.61118363
107 0.86798490 1.68874105
108 -4.12863508 0.86798490
109 -3.42179974 -4.12863508
110 0.87812495 -3.42179974
111 -3.11849504 0.87812495
112 -1.29097886 -3.11849504
113 0.41585649 -1.29097886
114 4.59510034 0.41585649
115 1.54329736 4.59510034
116 0.25013270 1.54329736
117 0.25351272 0.25013270
118 0.43275657 0.25351272
119 -0.09145492 0.43275657
120 -1.56048340 -0.09145492
121 -0.08469489 -1.56048340
122 1.44627663 -0.08469489
123 0.92206514 1.44627663
124 1.10130899 0.92206514
125 -1.07117483 1.10130899
126 3.10806902 -1.07117483
127 2.75972137 3.10806902
128 5.11482905 2.75972137
129 1.64580057 5.11482905
130 -1.05427475 1.64580057
131 -3.99571174 -1.05427475
132 0.95248528 -3.99571174
133 1.65932063 0.95248528
134 1.31097298 1.65932063
135 2.13848916 1.31097298
136 -2.50640316 2.13848916
137 0.14524919 -2.50640316
138 -2.14791546 0.14524919
139 1.67960072 -2.14791546
140 0.38643607 1.67960072
141 0.98290542 0.38643607
142 2.16214927 0.98290542
143 -0.18619838 2.16214927
144 0.99304546 -0.18619838
145 3.05160848 0.99304546
146 1.64807783 3.05160848
147 -2.46922299 1.64807783
148 -1.99343448 -2.46922299
149 -4.46246296 -1.99343448
150 3.06850855 -4.46246296
151 -0.80743060 3.06850855
152 2.02008559 -0.80743060
153 -1.97653440 2.02008559
154 -4.62142672 -1.97653440
155 0.08540863 -4.62142672
156 -1.14225819 0.08540863
157 0.56457716 -1.14225819
158 5.21622951 0.56457716
159 -0.95625431 5.21622951
160 -1.24941896 -0.95625431
161 -0.42190278 -1.24941896
162 NA -0.42190278
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.50970329 0.03391477
[2,] -4.01450820 3.50970329
[3,] -2.30767285 -4.01450820
[4,] 1.87157100 -2.30767285
[5,] 4.05081485 1.87157100
[6,] -1.00098814 4.05081485
[7,] -0.29415279 -1.00098814
[8,] 0.88509106 -0.29415279
[9,] 0.71260724 0.88509106
[10,] 2.54012342 0.71260724
[11,] 4.54350344 2.54012342
[12,] -4.45311655 4.54350344
[13,] 1.55026347 -4.45311655
[14,] 3.37777965 1.55026347
[15,] -0.26711267 3.37777965
[16,] -0.26373265 -0.26711267
[17,] 1.86032820 -0.26373265
[18,] -0.78456412 1.86032820
[19,] 1.57054356 -0.78456412
[20,] 3.57392357 1.57054356
[21,] -3.42269641 3.57392357
[22,] -0.94690789 -3.42269641
[23,] -2.41593638 -0.94690789
[24,] 2.41157980 -2.41593638
[25,] -5.58504018 2.41157980
[26,] 1.59420367 -5.58504018
[27,] -0.22655249 1.59420367
[28,] 0.60096370 -0.22655249
[29,] -3.04392862 0.60096370
[30,] 1.78358756 -3.04392862
[31,] -0.68544093 1.78358756
[32,] 2.96621142 -0.68544093
[33,] 0.61786377 2.96621142
[34,] -0.02702855 0.61786377
[35,] 3.20739830 -0.02702855
[36,] -4.31681318 3.20739830
[37,] 0.98311150 -4.31681318
[38,] 2.63476385 0.98311150
[39,] -1.36185614 2.63476385
[40,] 0.64152388 -1.36185614
[41,] 1.99663156 0.64152388
[42,] 0.94482858 1.99663156
[43,] -1.99660841 0.94482858
[44,] -1.99322839 -1.99660841
[45,] -2.46225688 -1.99322839
[46,] 1.01353164 -2.46225688
[47,] 0.84104782 1.01353164
[48,] 2.66856400 0.84104782
[49,] -0.97632832 2.66856400
[50,] 1.67532403 -0.97632832
[51,] 0.03043171 1.67532403
[52,] -2.61446061 0.03043171
[53,] -2.31453592 -2.61446061
[54,] -1.08010908 -2.31453592
[55,] 1.04395177 -1.08010908
[56,] 1.69560412 1.04395177
[57,] 0.69898414 1.69560412
[58,] -2.12177201 0.69898414
[59,] -2.29425583 -2.12177201
[60,] -5.58742048 -2.29425583
[61,] -1.28749580 -5.58742048
[62,] -3.28411579 -1.28749580
[63,] 0.07099190 -3.28411579
[64,] 0.72264424 0.07099190
[65,] -4.44983957 0.72264424
[66,] -3.62232339 -4.44983957
[67,] -2.61894338 -3.62232339
[68,] 1.43961964 -2.61894338
[69,] 0.73954432 1.43961964
[70,] 0.79810733 0.73954432
[71,] 2.09803202 0.79810733
[72,] 0.39795670 2.09803202
[73,] 0.40133672 0.39795670
[74,] -1.24355560 0.40133672
[75,] -2.41603942 -1.24355560
[76,] 2.76320443 -2.41603942
[77,] -1.23341556 2.76320443
[78,] 1.29755596 -1.23341556
[79,] -1.22665553 1.29755596
[80,] 0.77672449 -1.22665553
[81,] 1.07664917 0.77672449
[82,] 0.78348452 1.07664917
[83,] 1.96272837 0.78348452
[84,] 0.96610838 1.96272837
[85,] -0.20637544 0.96610838
[86,] 1.14873225 -0.20637544
[87,] -0.19961540 1.14873225
[88,] -1.19623539 -0.19961540
[89,] -7.01699154 -1.19623539
[90,] 3.51397997 -7.01699154
[91,] -1.36195918 3.51397997
[92,] 0.81728467 -1.36195918
[93,] -0.00347148 0.81728467
[94,] -0.64836380 -0.00347148
[95,] 1.82742472 -0.64836380
[96,] -2.16919527 1.82742472
[97,] -0.16581525 -2.16919527
[98,] 2.83756476 -0.16581525
[99,] 1.19267244 2.83756476
[100,] 2.84432479 1.19267244
[101,] -2.15229519 2.84432479
[102,] 1.14762949 -2.15229519
[103,] -2.96967133 1.14762949
[104,] 0.85784485 -2.96967133
[105,] -4.61118363 0.85784485
[106,] 1.68874105 -4.61118363
[107,] 0.86798490 1.68874105
[108,] -4.12863508 0.86798490
[109,] -3.42179974 -4.12863508
[110,] 0.87812495 -3.42179974
[111,] -3.11849504 0.87812495
[112,] -1.29097886 -3.11849504
[113,] 0.41585649 -1.29097886
[114,] 4.59510034 0.41585649
[115,] 1.54329736 4.59510034
[116,] 0.25013270 1.54329736
[117,] 0.25351272 0.25013270
[118,] 0.43275657 0.25351272
[119,] -0.09145492 0.43275657
[120,] -1.56048340 -0.09145492
[121,] -0.08469489 -1.56048340
[122,] 1.44627663 -0.08469489
[123,] 0.92206514 1.44627663
[124,] 1.10130899 0.92206514
[125,] -1.07117483 1.10130899
[126,] 3.10806902 -1.07117483
[127,] 2.75972137 3.10806902
[128,] 5.11482905 2.75972137
[129,] 1.64580057 5.11482905
[130,] -1.05427475 1.64580057
[131,] -3.99571174 -1.05427475
[132,] 0.95248528 -3.99571174
[133,] 1.65932063 0.95248528
[134,] 1.31097298 1.65932063
[135,] 2.13848916 1.31097298
[136,] -2.50640316 2.13848916
[137,] 0.14524919 -2.50640316
[138,] -2.14791546 0.14524919
[139,] 1.67960072 -2.14791546
[140,] 0.38643607 1.67960072
[141,] 0.98290542 0.38643607
[142,] 2.16214927 0.98290542
[143,] -0.18619838 2.16214927
[144,] 0.99304546 -0.18619838
[145,] 3.05160848 0.99304546
[146,] 1.64807783 3.05160848
[147,] -2.46922299 1.64807783
[148,] -1.99343448 -2.46922299
[149,] -4.46246296 -1.99343448
[150,] 3.06850855 -4.46246296
[151,] -0.80743060 3.06850855
[152,] 2.02008559 -0.80743060
[153,] -1.97653440 2.02008559
[154,] -4.62142672 -1.97653440
[155,] 0.08540863 -4.62142672
[156,] -1.14225819 0.08540863
[157,] 0.56457716 -1.14225819
[158,] 5.21622951 0.56457716
[159,] -0.95625431 5.21622951
[160,] -1.24941896 -0.95625431
[161,] -0.42190278 -1.24941896
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.50970329 0.03391477
2 -4.01450820 3.50970329
3 -2.30767285 -4.01450820
4 1.87157100 -2.30767285
5 4.05081485 1.87157100
6 -1.00098814 4.05081485
7 -0.29415279 -1.00098814
8 0.88509106 -0.29415279
9 0.71260724 0.88509106
10 2.54012342 0.71260724
11 4.54350344 2.54012342
12 -4.45311655 4.54350344
13 1.55026347 -4.45311655
14 3.37777965 1.55026347
15 -0.26711267 3.37777965
16 -0.26373265 -0.26711267
17 1.86032820 -0.26373265
18 -0.78456412 1.86032820
19 1.57054356 -0.78456412
20 3.57392357 1.57054356
21 -3.42269641 3.57392357
22 -0.94690789 -3.42269641
23 -2.41593638 -0.94690789
24 2.41157980 -2.41593638
25 -5.58504018 2.41157980
26 1.59420367 -5.58504018
27 -0.22655249 1.59420367
28 0.60096370 -0.22655249
29 -3.04392862 0.60096370
30 1.78358756 -3.04392862
31 -0.68544093 1.78358756
32 2.96621142 -0.68544093
33 0.61786377 2.96621142
34 -0.02702855 0.61786377
35 3.20739830 -0.02702855
36 -4.31681318 3.20739830
37 0.98311150 -4.31681318
38 2.63476385 0.98311150
39 -1.36185614 2.63476385
40 0.64152388 -1.36185614
41 1.99663156 0.64152388
42 0.94482858 1.99663156
43 -1.99660841 0.94482858
44 -1.99322839 -1.99660841
45 -2.46225688 -1.99322839
46 1.01353164 -2.46225688
47 0.84104782 1.01353164
48 2.66856400 0.84104782
49 -0.97632832 2.66856400
50 1.67532403 -0.97632832
51 0.03043171 1.67532403
52 -2.61446061 0.03043171
53 -2.31453592 -2.61446061
54 -1.08010908 -2.31453592
55 1.04395177 -1.08010908
56 1.69560412 1.04395177
57 0.69898414 1.69560412
58 -2.12177201 0.69898414
59 -2.29425583 -2.12177201
60 -5.58742048 -2.29425583
61 -1.28749580 -5.58742048
62 -3.28411579 -1.28749580
63 0.07099190 -3.28411579
64 0.72264424 0.07099190
65 -4.44983957 0.72264424
66 -3.62232339 -4.44983957
67 -2.61894338 -3.62232339
68 1.43961964 -2.61894338
69 0.73954432 1.43961964
70 0.79810733 0.73954432
71 2.09803202 0.79810733
72 0.39795670 2.09803202
73 0.40133672 0.39795670
74 -1.24355560 0.40133672
75 -2.41603942 -1.24355560
76 2.76320443 -2.41603942
77 -1.23341556 2.76320443
78 1.29755596 -1.23341556
79 -1.22665553 1.29755596
80 0.77672449 -1.22665553
81 1.07664917 0.77672449
82 0.78348452 1.07664917
83 1.96272837 0.78348452
84 0.96610838 1.96272837
85 -0.20637544 0.96610838
86 1.14873225 -0.20637544
87 -0.19961540 1.14873225
88 -1.19623539 -0.19961540
89 -7.01699154 -1.19623539
90 3.51397997 -7.01699154
91 -1.36195918 3.51397997
92 0.81728467 -1.36195918
93 -0.00347148 0.81728467
94 -0.64836380 -0.00347148
95 1.82742472 -0.64836380
96 -2.16919527 1.82742472
97 -0.16581525 -2.16919527
98 2.83756476 -0.16581525
99 1.19267244 2.83756476
100 2.84432479 1.19267244
101 -2.15229519 2.84432479
102 1.14762949 -2.15229519
103 -2.96967133 1.14762949
104 0.85784485 -2.96967133
105 -4.61118363 0.85784485
106 1.68874105 -4.61118363
107 0.86798490 1.68874105
108 -4.12863508 0.86798490
109 -3.42179974 -4.12863508
110 0.87812495 -3.42179974
111 -3.11849504 0.87812495
112 -1.29097886 -3.11849504
113 0.41585649 -1.29097886
114 4.59510034 0.41585649
115 1.54329736 4.59510034
116 0.25013270 1.54329736
117 0.25351272 0.25013270
118 0.43275657 0.25351272
119 -0.09145492 0.43275657
120 -1.56048340 -0.09145492
121 -0.08469489 -1.56048340
122 1.44627663 -0.08469489
123 0.92206514 1.44627663
124 1.10130899 0.92206514
125 -1.07117483 1.10130899
126 3.10806902 -1.07117483
127 2.75972137 3.10806902
128 5.11482905 2.75972137
129 1.64580057 5.11482905
130 -1.05427475 1.64580057
131 -3.99571174 -1.05427475
132 0.95248528 -3.99571174
133 1.65932063 0.95248528
134 1.31097298 1.65932063
135 2.13848916 1.31097298
136 -2.50640316 2.13848916
137 0.14524919 -2.50640316
138 -2.14791546 0.14524919
139 1.67960072 -2.14791546
140 0.38643607 1.67960072
141 0.98290542 0.38643607
142 2.16214927 0.98290542
143 -0.18619838 2.16214927
144 0.99304546 -0.18619838
145 3.05160848 0.99304546
146 1.64807783 3.05160848
147 -2.46922299 1.64807783
148 -1.99343448 -2.46922299
149 -4.46246296 -1.99343448
150 3.06850855 -4.46246296
151 -0.80743060 3.06850855
152 2.02008559 -0.80743060
153 -1.97653440 2.02008559
154 -4.62142672 -1.97653440
155 0.08540863 -4.62142672
156 -1.14225819 0.08540863
157 0.56457716 -1.14225819
158 5.21622951 0.56457716
159 -0.95625431 5.21622951
160 -1.24941896 -0.95625431
161 -0.42190278 -1.24941896
> 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/wessaorg/rcomp/tmp/7vzro1356015859.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/wessaorg/rcomp/tmp/8bxdh1356015859.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/wessaorg/rcomp/tmp/98azm1356015859.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/wessaorg/rcomp/tmp/10w8331356015859.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11b9af1356015859.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12kf1f1356015859.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/138jy01356015859.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/149wzm1356015859.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/wessaorg/rcomp/tmp/15xi021356015859.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/wessaorg/rcomp/tmp/16fzly1356015859.tab")
+ }
>
> try(system("convert tmp/10my21356015859.ps tmp/10my21356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m1sf1356015859.ps tmp/2m1sf1356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m6w21356015859.ps tmp/3m6w21356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/4s5491356015859.ps tmp/4s5491356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pqdm1356015859.ps tmp/5pqdm1356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hxhh1356015859.ps tmp/6hxhh1356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vzro1356015859.ps tmp/7vzro1356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bxdh1356015859.ps tmp/8bxdh1356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/98azm1356015859.ps tmp/98azm1356015859.png",intern=TRUE))
character(0)
> try(system("convert tmp/10w8331356015859.ps tmp/10w8331356015859.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.274 1.159 9.422