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 'contributors()' for more information and
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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(14
+ ,12
+ ,18
+ ,11
+ ,11
+ ,14
+ ,12
+ ,12
+ ,16
+ ,21
+ ,18
+ ,12
+ ,14
+ ,22
+ ,14
+ ,11
+ ,15
+ ,10
+ ,15
+ ,13
+ ,17
+ ,10
+ ,19
+ ,8
+ ,10
+ ,15
+ ,16
+ ,14
+ ,18
+ ,10
+ ,14
+ ,14
+ ,14
+ ,14
+ ,17
+ ,11
+ ,14
+ ,10
+ ,16
+ ,13
+ ,18
+ ,7
+ ,11
+ ,14
+ ,14
+ ,12
+ ,12
+ ,14
+ ,17
+ ,11
+ ,9
+ ,9
+ ,16
+ ,11
+ ,14
+ ,15
+ ,15
+ ,14
+ ,11
+ ,13
+ ,16
+ ,9
+ ,13
+ ,15
+ ,17
+ ,10
+ ,15
+ ,11
+ ,14
+ ,13
+ ,16
+ ,8
+ ,9
+ ,20
+ ,15
+ ,12
+ ,17
+ ,10
+ ,13
+ ,10
+ ,15
+ ,9
+ ,16
+ ,14
+ ,16
+ ,8
+ ,12
+ ,14
+ ,12
+ ,11
+ ,11
+ ,13
+ ,15
+ ,9
+ ,15
+ ,11
+ ,17
+ ,15
+ ,13
+ ,11
+ ,16
+ ,10
+ ,14
+ ,14
+ ,11
+ ,18
+ ,12
+ ,14
+ ,12
+ ,11
+ ,15
+ ,12
+ ,16
+ ,13
+ ,15
+ ,9
+ ,12
+ ,10
+ ,12
+ ,15
+ ,8
+ ,20
+ ,13
+ ,12
+ ,11
+ ,12
+ ,14
+ ,14
+ ,15
+ ,13
+ ,10
+ ,11
+ ,11
+ ,17
+ ,12
+ ,12
+ ,15
+ ,13
+ ,15
+ ,14
+ ,14
+ ,13
+ ,16
+ ,15
+ ,15
+ ,13
+ ,15
+ ,10
+ ,13
+ ,11
+ ,12
+ ,19
+ ,17
+ ,13
+ ,13
+ ,17
+ ,15
+ ,13
+ ,13
+ ,9
+ ,15
+ ,11
+ ,16
+ ,10
+ ,15
+ ,9
+ ,16
+ ,12
+ ,15
+ ,12
+ ,14
+ ,13
+ ,15
+ ,13
+ ,14
+ ,12
+ ,13
+ ,15
+ ,7
+ ,22
+ ,17
+ ,13
+ ,13
+ ,15
+ ,15
+ ,13
+ ,14
+ ,15
+ ,13
+ ,10
+ ,16
+ ,11
+ ,12
+ ,16
+ ,14
+ ,11
+ ,17
+ ,11
+ ,15
+ ,10
+ ,17
+ ,10
+ ,12
+ ,16
+ ,16
+ ,12
+ ,11
+ ,11
+ ,15
+ ,16
+ ,9
+ ,19
+ ,16
+ ,11
+ ,15
+ ,16
+ ,10
+ ,15
+ ,10
+ ,24
+ ,15
+ ,14
+ ,11
+ ,15
+ ,13
+ ,11
+ ,14
+ ,15
+ ,18
+ ,12
+ ,16
+ ,10
+ ,14
+ ,14
+ ,14
+ ,13
+ ,14
+ ,9
+ ,14
+ ,15
+ ,12
+ ,15
+ ,14
+ ,14
+ ,15
+ ,11
+ ,15
+ ,8
+ ,15
+ ,11
+ ,13
+ ,11
+ ,17
+ ,8
+ ,17
+ ,10
+ ,19
+ ,11
+ ,15
+ ,13
+ ,13
+ ,11
+ ,9
+ ,20
+ ,15
+ ,10
+ ,15
+ ,15
+ ,15
+ ,12
+ ,16
+ ,14
+ ,11
+ ,23
+ ,14
+ ,14
+ ,11
+ ,16
+ ,15
+ ,11
+ ,13
+ ,12
+ ,15
+ ,10
+ ,16
+ ,14
+ ,14
+ ,12
+ ,15
+ ,12
+ ,16
+ ,11
+ ,16
+ ,12
+ ,11
+ ,13
+ ,12
+ ,11
+ ,9
+ ,19
+ ,16
+ ,12
+ ,13
+ ,17
+ ,16
+ ,9
+ ,12
+ ,12
+ ,9
+ ,19
+ ,13
+ ,18
+ ,13
+ ,15
+ ,14
+ ,14
+ ,19
+ ,11
+ ,13
+ ,9
+ ,12
+ ,18
+ ,13
+ ,16)
+ ,dim=c(2
+ ,162)
+ ,dimnames=list(c('Happiness'
+ ,'Depression')
+ ,1:162))
> y <- array(NA,dim=c(2,162),dimnames=list(c('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])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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, 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
Happiness Depression t
1 14 12 1
2 18 11 2
3 11 14 3
4 12 12 4
5 16 21 5
6 18 12 6
7 14 22 7
8 14 11 8
9 15 10 9
10 15 13 10
11 17 10 11
12 19 8 12
13 10 15 13
14 16 14 14
15 18 10 15
16 14 14 16
17 14 14 17
18 17 11 18
19 14 10 19
20 16 13 20
21 18 7 21
22 11 14 22
23 14 12 23
24 12 14 24
25 17 11 25
26 9 9 26
27 16 11 27
28 14 15 28
29 15 14 29
30 11 13 30
31 16 9 31
32 13 15 32
33 17 10 33
34 15 11 34
35 14 13 35
36 16 8 36
37 9 20 37
38 15 12 38
39 17 10 39
40 13 10 40
41 15 9 41
42 16 14 42
43 16 8 43
44 12 14 44
45 12 11 45
46 11 13 46
47 15 9 47
48 15 11 48
49 17 15 49
50 13 11 50
51 16 10 51
52 14 14 52
53 11 18 53
54 12 14 54
55 12 11 55
56 15 12 56
57 16 13 57
58 15 9 58
59 12 10 59
60 12 15 60
61 8 20 61
62 13 12 62
63 11 12 63
64 14 14 64
65 15 13 65
66 10 11 66
67 11 17 67
68 12 12 68
69 15 13 69
70 15 14 70
71 14 13 71
72 16 15 72
73 15 13 73
74 15 10 74
75 13 11 75
76 12 19 76
77 17 13 77
78 13 17 78
79 15 13 79
80 13 9 80
81 15 11 81
82 16 10 82
83 15 9 83
84 16 12 84
85 15 12 85
86 14 13 86
87 15 13 87
88 14 12 88
89 13 15 89
90 7 22 90
91 17 13 91
92 13 15 92
93 15 13 93
94 14 15 94
95 13 10 95
96 16 11 96
97 12 16 97
98 14 11 98
99 17 11 99
100 15 10 100
101 17 10 101
102 12 16 102
103 16 12 103
104 11 11 104
105 15 16 105
106 9 19 106
107 16 11 107
108 15 16 108
109 10 15 109
110 10 24 110
111 15 14 111
112 11 15 112
113 13 11 113
114 14 15 114
115 18 12 115
116 16 10 116
117 14 14 117
118 14 13 118
119 14 9 119
120 14 15 120
121 12 15 121
122 14 14 122
123 15 11 123
124 15 8 124
125 15 11 125
126 13 11 126
127 17 8 127
128 17 10 128
129 19 11 129
130 15 13 130
131 13 11 131
132 9 20 132
133 15 10 133
134 15 15 134
135 15 12 135
136 16 14 136
137 11 23 137
138 14 14 138
139 11 16 139
140 15 11 140
141 13 12 141
142 15 10 142
143 16 14 143
144 14 12 144
145 15 12 145
146 16 11 146
147 16 12 147
148 11 13 148
149 12 11 149
150 9 19 150
151 16 12 151
152 13 17 152
153 16 9 153
154 12 12 154
155 9 19 155
156 13 18 156
157 13 15 157
158 14 14 158
159 19 11 159
160 13 9 160
161 12 18 161
162 13 16 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Depression t
19.31588 -0.39887 -0.00154
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.6860 -1.4334 0.2758 1.1930 5.0681
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.315879 0.679856 28.412 < 2e-16 ***
Depression -0.398871 0.049503 -8.057 1.74e-13 ***
t -0.001540 0.003341 -0.461 0.645
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.972 on 159 degrees of freedom
Multiple R-squared: 0.2971, Adjusted R-squared: 0.2883
F-statistic: 33.61 on 2 and 159 DF, p-value: 6.703e-13
> 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,] 0.9878179 0.024364171 0.012182086
[2,] 0.9787991 0.042401798 0.021200899
[3,] 0.9699729 0.060054265 0.030027133
[4,] 0.9451414 0.109717174 0.054858587
[5,] 0.9096927 0.180614618 0.090307309
[6,] 0.8842852 0.231429573 0.115714786
[7,] 0.8927725 0.214455049 0.107227524
[8,] 0.9858388 0.028322469 0.014161234
[9,] 0.9806428 0.038714445 0.019357222
[10,] 0.9786910 0.042617991 0.021308995
[11,] 0.9716880 0.056623908 0.028311954
[12,] 0.9612365 0.077527083 0.038763542
[13,] 0.9515806 0.096838844 0.048419422
[14,] 0.9479521 0.104095779 0.052047889
[15,] 0.9341347 0.131730625 0.065865313
[16,] 0.9190938 0.161812437 0.080906218
[17,] 0.9567940 0.086411919 0.043205959
[18,] 0.9426165 0.114767022 0.057383511
[19,] 0.9384110 0.123178093 0.061589046
[20,] 0.9401179 0.119764245 0.059882122
[21,] 0.9962389 0.007522218 0.003761109
[22,] 0.9956522 0.008695656 0.004347828
[23,] 0.9939228 0.012154398 0.006077199
[24,] 0.9926640 0.014672032 0.007336016
[25,] 0.9941598 0.011680326 0.005840163
[26,] 0.9923907 0.015218581 0.007609291
[27,] 0.9889604 0.022079267 0.011039634
[28,] 0.9901004 0.019799287 0.009899643
[29,] 0.9863208 0.027358403 0.013679202
[30,] 0.9809849 0.038030191 0.019015096
[31,] 0.9746133 0.050773464 0.025386732
[32,] 0.9747976 0.050404727 0.025202363
[33,] 0.9688931 0.062213842 0.031106921
[34,] 0.9710497 0.057900657 0.028950328
[35,] 0.9682008 0.063598313 0.031799156
[36,] 0.9579271 0.084145783 0.042072892
[37,] 0.9672562 0.065487641 0.032743820
[38,] 0.9576001 0.084799836 0.042399918
[39,] 0.9496838 0.100632373 0.050316187
[40,] 0.9523157 0.095368556 0.047684278
[41,] 0.9565551 0.086889858 0.043444929
[42,] 0.9446479 0.110704216 0.055352108
[43,] 0.9333783 0.133243353 0.066621676
[44,] 0.9738501 0.052299781 0.026149891
[45,] 0.9689571 0.062085874 0.031042937
[46,] 0.9644353 0.071129468 0.035564734
[47,] 0.9559995 0.088001053 0.044000527
[48,] 0.9451372 0.109725676 0.054862838
[49,] 0.9349835 0.130032962 0.065016481
[50,] 0.9377066 0.124586858 0.062293429
[51,] 0.9286817 0.142636638 0.071318319
[52,] 0.9387437 0.122512538 0.061256269
[53,] 0.9236908 0.152618338 0.076309169
[54,] 0.9351821 0.129635899 0.064817950
[55,] 0.9211099 0.157780103 0.078890051
[56,] 0.9339721 0.132055844 0.066027922
[57,] 0.9215461 0.156907738 0.078453869
[58,] 0.9377762 0.124447605 0.062223802
[59,] 0.9289205 0.142159091 0.071079546
[60,] 0.9256319 0.148736212 0.074368106
[61,] 0.9701151 0.059769729 0.029884864
[62,] 0.9643642 0.071271639 0.035635819
[63,] 0.9660122 0.067975506 0.033987753
[64,] 0.9648910 0.070217964 0.035108982
[65,] 0.9659482 0.068103686 0.034051843
[66,] 0.9587983 0.082403483 0.041201741
[67,] 0.9739867 0.052026576 0.026013288
[68,] 0.9710852 0.057829634 0.028914817
[69,] 0.9642572 0.071485630 0.035742815
[70,] 0.9619102 0.076179642 0.038089821
[71,] 0.9530574 0.093885215 0.046942607
[72,] 0.9710688 0.057862393 0.028931196
[73,] 0.9645724 0.070855267 0.035427633
[74,] 0.9596362 0.080727557 0.040363779
[75,] 0.9657553 0.068489409 0.034244704
[76,] 0.9578270 0.084346085 0.042173043
[77,] 0.9510706 0.097858712 0.048929356
[78,] 0.9414468 0.117106392 0.058553196
[79,] 0.9395572 0.120885587 0.060442794
[80,] 0.9276448 0.144710350 0.072355175
[81,] 0.9111984 0.177603127 0.088801563
[82,] 0.8988386 0.202322701 0.101161351
[83,] 0.8784706 0.243058882 0.121529441
[84,] 0.8539031 0.292193706 0.146096853
[85,] 0.8940549 0.211890207 0.105945104
[86,] 0.9231187 0.153762579 0.076881289
[87,] 0.9053375 0.189324948 0.094662474
[88,] 0.8916979 0.216604113 0.108302056
[89,] 0.8742071 0.251585733 0.125792866
[90,] 0.8811225 0.237754961 0.118877481
[91,] 0.8674089 0.265182254 0.132591127
[92,] 0.8445026 0.310994808 0.155497404
[93,] 0.8218328 0.356334479 0.178167240
[94,] 0.8304992 0.339001634 0.169500817
[95,] 0.8006831 0.398633732 0.199316866
[96,] 0.7958139 0.408372257 0.204186128
[97,] 0.7652394 0.469521172 0.234760586
[98,] 0.7543691 0.491261737 0.245630869
[99,] 0.8497727 0.300454527 0.150227263
[100,] 0.8589647 0.282070652 0.141035326
[101,] 0.8768217 0.246356527 0.123178263
[102,] 0.8595936 0.280812738 0.140406369
[103,] 0.8685536 0.262892762 0.131446381
[104,] 0.9105896 0.178820836 0.089410418
[105,] 0.8902699 0.219460153 0.109730077
[106,] 0.8782225 0.243555011 0.121777505
[107,] 0.8882140 0.223572088 0.111786044
[108,] 0.8938394 0.212321146 0.106160573
[109,] 0.8715610 0.256877991 0.128438995
[110,] 0.9220004 0.155999215 0.077999608
[111,] 0.9040850 0.191829957 0.095914978
[112,] 0.8808453 0.238309339 0.119154670
[113,] 0.8526128 0.294774403 0.147387201
[114,] 0.8507386 0.298522876 0.149261438
[115,] 0.8227055 0.354589089 0.177294545
[116,] 0.8029438 0.394112415 0.197056207
[117,] 0.7640635 0.471873075 0.235936538
[118,] 0.7209202 0.558159647 0.279079823
[119,] 0.7023395 0.595320986 0.297660493
[120,] 0.6548378 0.690324420 0.345162210
[121,] 0.6793121 0.641375789 0.320687895
[122,] 0.6323915 0.735216980 0.367608490
[123,] 0.6000945 0.799810924 0.399905462
[124,] 0.7540593 0.491881388 0.245940694
[125,] 0.7181609 0.563678273 0.281839136
[126,] 0.7179030 0.564194070 0.282097035
[127,] 0.7375389 0.524922248 0.262461124
[128,] 0.6914470 0.617106017 0.308553008
[129,] 0.6686251 0.662749876 0.331374938
[130,] 0.6111182 0.777763609 0.388881805
[131,] 0.6396288 0.720742499 0.360371249
[132,] 0.6259533 0.748093371 0.374046686
[133,] 0.5752790 0.849442043 0.424721022
[134,] 0.5323490 0.935302097 0.467651049
[135,] 0.4650834 0.930166766 0.534916617
[136,] 0.4223134 0.844626743 0.577686629
[137,] 0.3551894 0.710378711 0.644810644
[138,] 0.4165179 0.833035862 0.583482069
[139,] 0.3443697 0.688739400 0.655630300
[140,] 0.2958642 0.591728462 0.704135769
[141,] 0.2864953 0.572990551 0.713504725
[142,] 0.3638150 0.727630089 0.636184955
[143,] 0.3335146 0.667029180 0.666485410
[144,] 0.3389389 0.677877732 0.661061134
[145,] 0.3271067 0.654213497 0.672893252
[146,] 0.2993501 0.598700193 0.700649904
[147,] 0.2508746 0.501749284 0.749125358
[148,] 0.1978141 0.395628102 0.802185949
[149,] 0.1614444 0.322888803 0.838555598
[150,] 0.2044901 0.408980166 0.795509917
[151,] 0.1149921 0.229984263 0.885007868
> postscript(file="/var/wessaorg/rcomp/tmp/1qs0k1355689100.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2kkqg1355689100.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hgnv1355689100.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/43sye1355689100.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/58d341355689100.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
-0.527891604 3.074778118 -2.727069946 -2.523270778 5.068104481 3.479809772
7 8 9 10 11 12
3.470055585 -0.915980232 -0.313310511 0.884841426 1.689770040 2.893569207
13 14 15 16 17 18
-3.312796641 2.289873080 2.695931140 0.292953630 0.294493905 2.099422519
19 20 21 22 23 24
-1.297907760 1.900244177 1.508561129 -2.697804719 -0.494005552 -1.694724169
25 26 27 28 29 30
2.110204444 -6.685996388 1.113284995 0.710307485 1.312977206 -3.084353073
31 32 33 34 35 36
0.321704987 -0.283531415 1.723656091 0.124066920 -0.076651697 -0.069464191
37 38 39 40 41 42
-2.281477271 0.529098574 1.732897742 -2.265561983 -0.662892262 2.333000782
43 44 45 46 47 48
-0.058682266 -1.663918668 -2.858990054 -3.059708671 -0.653650611 0.145630771
49 50 51 52 53 54
3.742653261 -1.851288679 0.751381043 0.348403533 -1.054573977 -1.648515917
55 56 57 58 59 60
-2.843587303 0.556823526 1.957234354 -0.636707586 -3.236296757 -1.240403713
61 62 63 64 65 66
-3.244510669 -1.433934824 -3.432394549 0.366886834 0.969556555 -4.826644277
67 68 69 70 71 72
-1.431880680 -2.424693173 0.975717655 1.376128484 -0.021201794 2.778079588
73 74 75 76 77 78
0.981878756 -0.213192631 -1.812781802 0.379722904 2.988039856 0.585062346
79 80 81 82 83 84
0.991120406 -2.602821534 0.196459849 0.799129570 -0.598200709 1.599951228
85 86 87 88 89 90
0.601491503 0.001902332 1.003442607 -0.393887672 -0.195735736 -3.402101584
91 92 93 94 95 96
3.009603707 -0.191114910 1.012684257 0.811965640 -2.180846854 1.219563975
97 98 99 100 101 102
-0.784542981 -0.777355475 2.224184800 -0.173145479 1.828394796 -0.776841606
103 104 105 106 107 108
1.629216454 -3.768113825 2.227779219 -2.574068844 1.236507001 2.232400045
109 110 111 112 113 114
-3.164930234 0.426445025 1.439279762 -2.160309409 -1.754251349 0.842771141
115 116 117 118 119 120
3.647699755 0.851498923 0.448521413 0.051191134 -1.542750806 0.852012792
121 122 123 124 125 126
-1.146446933 0.456222788 0.261151402 -0.933919984 0.264231952 -1.734227773
127 128 129 130 131 132
1.070700841 1.869982223 4.270393052 1.069674435 -1.726526397 -2.135151138
133 134 135 136 137 138
-0.122316401 1.873576643 0.678505257 2.477786639 1.069161898 0.480867189
139 140 141 142 143 144
-1.719851428 0.287336078 -1.312253093 -0.108453925 2.488568565 -0.307632268
145 146 147 148 149 150
0.693908007 1.296577729 1.696988558 -2.902600614 -2.698801446 -2.506296741
151 152 153 154 155 156
1.703149658 0.699042702 0.509618547 -2.292229517 -2.498595365 1.104074356
157 158 159 160 161 162
-0.090997030 0.511672691 4.316601305 -2.479599528 0.111775731 0.315574899
> postscript(file="/var/wessaorg/rcomp/tmp/6a7b01355689100.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 -0.527891604 NA
1 3.074778118 -0.527891604
2 -2.727069946 3.074778118
3 -2.523270778 -2.727069946
4 5.068104481 -2.523270778
5 3.479809772 5.068104481
6 3.470055585 3.479809772
7 -0.915980232 3.470055585
8 -0.313310511 -0.915980232
9 0.884841426 -0.313310511
10 1.689770040 0.884841426
11 2.893569207 1.689770040
12 -3.312796641 2.893569207
13 2.289873080 -3.312796641
14 2.695931140 2.289873080
15 0.292953630 2.695931140
16 0.294493905 0.292953630
17 2.099422519 0.294493905
18 -1.297907760 2.099422519
19 1.900244177 -1.297907760
20 1.508561129 1.900244177
21 -2.697804719 1.508561129
22 -0.494005552 -2.697804719
23 -1.694724169 -0.494005552
24 2.110204444 -1.694724169
25 -6.685996388 2.110204444
26 1.113284995 -6.685996388
27 0.710307485 1.113284995
28 1.312977206 0.710307485
29 -3.084353073 1.312977206
30 0.321704987 -3.084353073
31 -0.283531415 0.321704987
32 1.723656091 -0.283531415
33 0.124066920 1.723656091
34 -0.076651697 0.124066920
35 -0.069464191 -0.076651697
36 -2.281477271 -0.069464191
37 0.529098574 -2.281477271
38 1.732897742 0.529098574
39 -2.265561983 1.732897742
40 -0.662892262 -2.265561983
41 2.333000782 -0.662892262
42 -0.058682266 2.333000782
43 -1.663918668 -0.058682266
44 -2.858990054 -1.663918668
45 -3.059708671 -2.858990054
46 -0.653650611 -3.059708671
47 0.145630771 -0.653650611
48 3.742653261 0.145630771
49 -1.851288679 3.742653261
50 0.751381043 -1.851288679
51 0.348403533 0.751381043
52 -1.054573977 0.348403533
53 -1.648515917 -1.054573977
54 -2.843587303 -1.648515917
55 0.556823526 -2.843587303
56 1.957234354 0.556823526
57 -0.636707586 1.957234354
58 -3.236296757 -0.636707586
59 -1.240403713 -3.236296757
60 -3.244510669 -1.240403713
61 -1.433934824 -3.244510669
62 -3.432394549 -1.433934824
63 0.366886834 -3.432394549
64 0.969556555 0.366886834
65 -4.826644277 0.969556555
66 -1.431880680 -4.826644277
67 -2.424693173 -1.431880680
68 0.975717655 -2.424693173
69 1.376128484 0.975717655
70 -0.021201794 1.376128484
71 2.778079588 -0.021201794
72 0.981878756 2.778079588
73 -0.213192631 0.981878756
74 -1.812781802 -0.213192631
75 0.379722904 -1.812781802
76 2.988039856 0.379722904
77 0.585062346 2.988039856
78 0.991120406 0.585062346
79 -2.602821534 0.991120406
80 0.196459849 -2.602821534
81 0.799129570 0.196459849
82 -0.598200709 0.799129570
83 1.599951228 -0.598200709
84 0.601491503 1.599951228
85 0.001902332 0.601491503
86 1.003442607 0.001902332
87 -0.393887672 1.003442607
88 -0.195735736 -0.393887672
89 -3.402101584 -0.195735736
90 3.009603707 -3.402101584
91 -0.191114910 3.009603707
92 1.012684257 -0.191114910
93 0.811965640 1.012684257
94 -2.180846854 0.811965640
95 1.219563975 -2.180846854
96 -0.784542981 1.219563975
97 -0.777355475 -0.784542981
98 2.224184800 -0.777355475
99 -0.173145479 2.224184800
100 1.828394796 -0.173145479
101 -0.776841606 1.828394796
102 1.629216454 -0.776841606
103 -3.768113825 1.629216454
104 2.227779219 -3.768113825
105 -2.574068844 2.227779219
106 1.236507001 -2.574068844
107 2.232400045 1.236507001
108 -3.164930234 2.232400045
109 0.426445025 -3.164930234
110 1.439279762 0.426445025
111 -2.160309409 1.439279762
112 -1.754251349 -2.160309409
113 0.842771141 -1.754251349
114 3.647699755 0.842771141
115 0.851498923 3.647699755
116 0.448521413 0.851498923
117 0.051191134 0.448521413
118 -1.542750806 0.051191134
119 0.852012792 -1.542750806
120 -1.146446933 0.852012792
121 0.456222788 -1.146446933
122 0.261151402 0.456222788
123 -0.933919984 0.261151402
124 0.264231952 -0.933919984
125 -1.734227773 0.264231952
126 1.070700841 -1.734227773
127 1.869982223 1.070700841
128 4.270393052 1.869982223
129 1.069674435 4.270393052
130 -1.726526397 1.069674435
131 -2.135151138 -1.726526397
132 -0.122316401 -2.135151138
133 1.873576643 -0.122316401
134 0.678505257 1.873576643
135 2.477786639 0.678505257
136 1.069161898 2.477786639
137 0.480867189 1.069161898
138 -1.719851428 0.480867189
139 0.287336078 -1.719851428
140 -1.312253093 0.287336078
141 -0.108453925 -1.312253093
142 2.488568565 -0.108453925
143 -0.307632268 2.488568565
144 0.693908007 -0.307632268
145 1.296577729 0.693908007
146 1.696988558 1.296577729
147 -2.902600614 1.696988558
148 -2.698801446 -2.902600614
149 -2.506296741 -2.698801446
150 1.703149658 -2.506296741
151 0.699042702 1.703149658
152 0.509618547 0.699042702
153 -2.292229517 0.509618547
154 -2.498595365 -2.292229517
155 1.104074356 -2.498595365
156 -0.090997030 1.104074356
157 0.511672691 -0.090997030
158 4.316601305 0.511672691
159 -2.479599528 4.316601305
160 0.111775731 -2.479599528
161 0.315574899 0.111775731
162 NA 0.315574899
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.074778118 -0.527891604
[2,] -2.727069946 3.074778118
[3,] -2.523270778 -2.727069946
[4,] 5.068104481 -2.523270778
[5,] 3.479809772 5.068104481
[6,] 3.470055585 3.479809772
[7,] -0.915980232 3.470055585
[8,] -0.313310511 -0.915980232
[9,] 0.884841426 -0.313310511
[10,] 1.689770040 0.884841426
[11,] 2.893569207 1.689770040
[12,] -3.312796641 2.893569207
[13,] 2.289873080 -3.312796641
[14,] 2.695931140 2.289873080
[15,] 0.292953630 2.695931140
[16,] 0.294493905 0.292953630
[17,] 2.099422519 0.294493905
[18,] -1.297907760 2.099422519
[19,] 1.900244177 -1.297907760
[20,] 1.508561129 1.900244177
[21,] -2.697804719 1.508561129
[22,] -0.494005552 -2.697804719
[23,] -1.694724169 -0.494005552
[24,] 2.110204444 -1.694724169
[25,] -6.685996388 2.110204444
[26,] 1.113284995 -6.685996388
[27,] 0.710307485 1.113284995
[28,] 1.312977206 0.710307485
[29,] -3.084353073 1.312977206
[30,] 0.321704987 -3.084353073
[31,] -0.283531415 0.321704987
[32,] 1.723656091 -0.283531415
[33,] 0.124066920 1.723656091
[34,] -0.076651697 0.124066920
[35,] -0.069464191 -0.076651697
[36,] -2.281477271 -0.069464191
[37,] 0.529098574 -2.281477271
[38,] 1.732897742 0.529098574
[39,] -2.265561983 1.732897742
[40,] -0.662892262 -2.265561983
[41,] 2.333000782 -0.662892262
[42,] -0.058682266 2.333000782
[43,] -1.663918668 -0.058682266
[44,] -2.858990054 -1.663918668
[45,] -3.059708671 -2.858990054
[46,] -0.653650611 -3.059708671
[47,] 0.145630771 -0.653650611
[48,] 3.742653261 0.145630771
[49,] -1.851288679 3.742653261
[50,] 0.751381043 -1.851288679
[51,] 0.348403533 0.751381043
[52,] -1.054573977 0.348403533
[53,] -1.648515917 -1.054573977
[54,] -2.843587303 -1.648515917
[55,] 0.556823526 -2.843587303
[56,] 1.957234354 0.556823526
[57,] -0.636707586 1.957234354
[58,] -3.236296757 -0.636707586
[59,] -1.240403713 -3.236296757
[60,] -3.244510669 -1.240403713
[61,] -1.433934824 -3.244510669
[62,] -3.432394549 -1.433934824
[63,] 0.366886834 -3.432394549
[64,] 0.969556555 0.366886834
[65,] -4.826644277 0.969556555
[66,] -1.431880680 -4.826644277
[67,] -2.424693173 -1.431880680
[68,] 0.975717655 -2.424693173
[69,] 1.376128484 0.975717655
[70,] -0.021201794 1.376128484
[71,] 2.778079588 -0.021201794
[72,] 0.981878756 2.778079588
[73,] -0.213192631 0.981878756
[74,] -1.812781802 -0.213192631
[75,] 0.379722904 -1.812781802
[76,] 2.988039856 0.379722904
[77,] 0.585062346 2.988039856
[78,] 0.991120406 0.585062346
[79,] -2.602821534 0.991120406
[80,] 0.196459849 -2.602821534
[81,] 0.799129570 0.196459849
[82,] -0.598200709 0.799129570
[83,] 1.599951228 -0.598200709
[84,] 0.601491503 1.599951228
[85,] 0.001902332 0.601491503
[86,] 1.003442607 0.001902332
[87,] -0.393887672 1.003442607
[88,] -0.195735736 -0.393887672
[89,] -3.402101584 -0.195735736
[90,] 3.009603707 -3.402101584
[91,] -0.191114910 3.009603707
[92,] 1.012684257 -0.191114910
[93,] 0.811965640 1.012684257
[94,] -2.180846854 0.811965640
[95,] 1.219563975 -2.180846854
[96,] -0.784542981 1.219563975
[97,] -0.777355475 -0.784542981
[98,] 2.224184800 -0.777355475
[99,] -0.173145479 2.224184800
[100,] 1.828394796 -0.173145479
[101,] -0.776841606 1.828394796
[102,] 1.629216454 -0.776841606
[103,] -3.768113825 1.629216454
[104,] 2.227779219 -3.768113825
[105,] -2.574068844 2.227779219
[106,] 1.236507001 -2.574068844
[107,] 2.232400045 1.236507001
[108,] -3.164930234 2.232400045
[109,] 0.426445025 -3.164930234
[110,] 1.439279762 0.426445025
[111,] -2.160309409 1.439279762
[112,] -1.754251349 -2.160309409
[113,] 0.842771141 -1.754251349
[114,] 3.647699755 0.842771141
[115,] 0.851498923 3.647699755
[116,] 0.448521413 0.851498923
[117,] 0.051191134 0.448521413
[118,] -1.542750806 0.051191134
[119,] 0.852012792 -1.542750806
[120,] -1.146446933 0.852012792
[121,] 0.456222788 -1.146446933
[122,] 0.261151402 0.456222788
[123,] -0.933919984 0.261151402
[124,] 0.264231952 -0.933919984
[125,] -1.734227773 0.264231952
[126,] 1.070700841 -1.734227773
[127,] 1.869982223 1.070700841
[128,] 4.270393052 1.869982223
[129,] 1.069674435 4.270393052
[130,] -1.726526397 1.069674435
[131,] -2.135151138 -1.726526397
[132,] -0.122316401 -2.135151138
[133,] 1.873576643 -0.122316401
[134,] 0.678505257 1.873576643
[135,] 2.477786639 0.678505257
[136,] 1.069161898 2.477786639
[137,] 0.480867189 1.069161898
[138,] -1.719851428 0.480867189
[139,] 0.287336078 -1.719851428
[140,] -1.312253093 0.287336078
[141,] -0.108453925 -1.312253093
[142,] 2.488568565 -0.108453925
[143,] -0.307632268 2.488568565
[144,] 0.693908007 -0.307632268
[145,] 1.296577729 0.693908007
[146,] 1.696988558 1.296577729
[147,] -2.902600614 1.696988558
[148,] -2.698801446 -2.902600614
[149,] -2.506296741 -2.698801446
[150,] 1.703149658 -2.506296741
[151,] 0.699042702 1.703149658
[152,] 0.509618547 0.699042702
[153,] -2.292229517 0.509618547
[154,] -2.498595365 -2.292229517
[155,] 1.104074356 -2.498595365
[156,] -0.090997030 1.104074356
[157,] 0.511672691 -0.090997030
[158,] 4.316601305 0.511672691
[159,] -2.479599528 4.316601305
[160,] 0.111775731 -2.479599528
[161,] 0.315574899 0.111775731
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.074778118 -0.527891604
2 -2.727069946 3.074778118
3 -2.523270778 -2.727069946
4 5.068104481 -2.523270778
5 3.479809772 5.068104481
6 3.470055585 3.479809772
7 -0.915980232 3.470055585
8 -0.313310511 -0.915980232
9 0.884841426 -0.313310511
10 1.689770040 0.884841426
11 2.893569207 1.689770040
12 -3.312796641 2.893569207
13 2.289873080 -3.312796641
14 2.695931140 2.289873080
15 0.292953630 2.695931140
16 0.294493905 0.292953630
17 2.099422519 0.294493905
18 -1.297907760 2.099422519
19 1.900244177 -1.297907760
20 1.508561129 1.900244177
21 -2.697804719 1.508561129
22 -0.494005552 -2.697804719
23 -1.694724169 -0.494005552
24 2.110204444 -1.694724169
25 -6.685996388 2.110204444
26 1.113284995 -6.685996388
27 0.710307485 1.113284995
28 1.312977206 0.710307485
29 -3.084353073 1.312977206
30 0.321704987 -3.084353073
31 -0.283531415 0.321704987
32 1.723656091 -0.283531415
33 0.124066920 1.723656091
34 -0.076651697 0.124066920
35 -0.069464191 -0.076651697
36 -2.281477271 -0.069464191
37 0.529098574 -2.281477271
38 1.732897742 0.529098574
39 -2.265561983 1.732897742
40 -0.662892262 -2.265561983
41 2.333000782 -0.662892262
42 -0.058682266 2.333000782
43 -1.663918668 -0.058682266
44 -2.858990054 -1.663918668
45 -3.059708671 -2.858990054
46 -0.653650611 -3.059708671
47 0.145630771 -0.653650611
48 3.742653261 0.145630771
49 -1.851288679 3.742653261
50 0.751381043 -1.851288679
51 0.348403533 0.751381043
52 -1.054573977 0.348403533
53 -1.648515917 -1.054573977
54 -2.843587303 -1.648515917
55 0.556823526 -2.843587303
56 1.957234354 0.556823526
57 -0.636707586 1.957234354
58 -3.236296757 -0.636707586
59 -1.240403713 -3.236296757
60 -3.244510669 -1.240403713
61 -1.433934824 -3.244510669
62 -3.432394549 -1.433934824
63 0.366886834 -3.432394549
64 0.969556555 0.366886834
65 -4.826644277 0.969556555
66 -1.431880680 -4.826644277
67 -2.424693173 -1.431880680
68 0.975717655 -2.424693173
69 1.376128484 0.975717655
70 -0.021201794 1.376128484
71 2.778079588 -0.021201794
72 0.981878756 2.778079588
73 -0.213192631 0.981878756
74 -1.812781802 -0.213192631
75 0.379722904 -1.812781802
76 2.988039856 0.379722904
77 0.585062346 2.988039856
78 0.991120406 0.585062346
79 -2.602821534 0.991120406
80 0.196459849 -2.602821534
81 0.799129570 0.196459849
82 -0.598200709 0.799129570
83 1.599951228 -0.598200709
84 0.601491503 1.599951228
85 0.001902332 0.601491503
86 1.003442607 0.001902332
87 -0.393887672 1.003442607
88 -0.195735736 -0.393887672
89 -3.402101584 -0.195735736
90 3.009603707 -3.402101584
91 -0.191114910 3.009603707
92 1.012684257 -0.191114910
93 0.811965640 1.012684257
94 -2.180846854 0.811965640
95 1.219563975 -2.180846854
96 -0.784542981 1.219563975
97 -0.777355475 -0.784542981
98 2.224184800 -0.777355475
99 -0.173145479 2.224184800
100 1.828394796 -0.173145479
101 -0.776841606 1.828394796
102 1.629216454 -0.776841606
103 -3.768113825 1.629216454
104 2.227779219 -3.768113825
105 -2.574068844 2.227779219
106 1.236507001 -2.574068844
107 2.232400045 1.236507001
108 -3.164930234 2.232400045
109 0.426445025 -3.164930234
110 1.439279762 0.426445025
111 -2.160309409 1.439279762
112 -1.754251349 -2.160309409
113 0.842771141 -1.754251349
114 3.647699755 0.842771141
115 0.851498923 3.647699755
116 0.448521413 0.851498923
117 0.051191134 0.448521413
118 -1.542750806 0.051191134
119 0.852012792 -1.542750806
120 -1.146446933 0.852012792
121 0.456222788 -1.146446933
122 0.261151402 0.456222788
123 -0.933919984 0.261151402
124 0.264231952 -0.933919984
125 -1.734227773 0.264231952
126 1.070700841 -1.734227773
127 1.869982223 1.070700841
128 4.270393052 1.869982223
129 1.069674435 4.270393052
130 -1.726526397 1.069674435
131 -2.135151138 -1.726526397
132 -0.122316401 -2.135151138
133 1.873576643 -0.122316401
134 0.678505257 1.873576643
135 2.477786639 0.678505257
136 1.069161898 2.477786639
137 0.480867189 1.069161898
138 -1.719851428 0.480867189
139 0.287336078 -1.719851428
140 -1.312253093 0.287336078
141 -0.108453925 -1.312253093
142 2.488568565 -0.108453925
143 -0.307632268 2.488568565
144 0.693908007 -0.307632268
145 1.296577729 0.693908007
146 1.696988558 1.296577729
147 -2.902600614 1.696988558
148 -2.698801446 -2.902600614
149 -2.506296741 -2.698801446
150 1.703149658 -2.506296741
151 0.699042702 1.703149658
152 0.509618547 0.699042702
153 -2.292229517 0.509618547
154 -2.498595365 -2.292229517
155 1.104074356 -2.498595365
156 -0.090997030 1.104074356
157 0.511672691 -0.090997030
158 4.316601305 0.511672691
159 -2.479599528 4.316601305
160 0.111775731 -2.479599528
161 0.315574899 0.111775731
> 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/7izl91355689100.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/89n2o1355689100.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9fh7x1355689100.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10cxnb1355689100.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113dnq1355689100.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/12l6p71355689100.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/130eoe1355689100.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/14skt51355689100.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/158z0b1355689100.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/16pcop1355689100.tab")
+ }
>
> try(system("convert tmp/1qs0k1355689100.ps tmp/1qs0k1355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kkqg1355689100.ps tmp/2kkqg1355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hgnv1355689100.ps tmp/3hgnv1355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/43sye1355689100.ps tmp/43sye1355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/58d341355689100.ps tmp/58d341355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/6a7b01355689100.ps tmp/6a7b01355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/7izl91355689100.ps tmp/7izl91355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/89n2o1355689100.ps tmp/89n2o1355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fh7x1355689100.ps tmp/9fh7x1355689100.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cxnb1355689100.ps tmp/10cxnb1355689100.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.715 1.139 10.877