R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-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(5
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+ ,13)
+ ,dim=c(3
+ ,156)
+ ,dimnames=list(c('WP'
+ ,'IEP'
+ ,'HS')
+ ,1:156))
> y <- array(NA,dim=c(3,156),dimnames=list(c('WP','IEP','HS'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
IEP WP HS t
1 13 5 14 1
2 12 3 18 2
3 15 0 11 3
4 12 7 12 4
5 10 4 16 5
6 12 1 18 6
7 15 6 14 7
8 9 3 14 8
9 12 12 15 9
10 11 0 15 10
11 11 5 17 11
12 11 6 19 12
13 15 6 10 13
14 7 6 16 14
15 11 2 18 15
16 11 1 14 16
17 10 5 14 17
18 14 7 17 18
19 10 3 14 19
20 6 3 16 20
21 11 3 18 21
22 15 7 11 22
23 11 8 14 23
24 12 6 12 24
25 14 3 17 25
26 15 5 9 26
27 9 5 16 27
28 13 10 14 28
29 13 2 15 29
30 16 6 11 30
31 13 4 16 31
32 12 6 13 32
33 14 8 17 33
34 11 4 15 34
35 9 5 14 35
36 16 10 16 36
37 12 6 9 37
38 10 7 15 38
39 13 4 17 39
40 16 10 13 40
41 14 4 15 41
42 15 3 16 42
43 5 3 16 43
44 8 3 12 44
45 11 3 12 45
46 16 7 11 46
47 17 15 15 47
48 9 0 15 48
49 9 0 17 49
50 13 4 13 50
51 10 5 16 51
52 6 5 14 52
53 12 2 11 53
54 8 3 12 54
55 14 0 12 55
56 12 9 15 56
57 11 2 16 57
58 16 7 15 58
59 8 7 12 59
60 15 0 12 60
61 7 0 8 61
62 16 10 13 62
63 14 2 11 63
64 16 1 14 64
65 9 8 15 65
66 14 6 10 66
67 11 11 11 67
68 13 3 12 68
69 15 8 15 69
70 5 6 15 70
71 15 9 14 71
72 13 9 16 72
73 11 8 15 73
74 11 8 15 74
75 12 7 13 75
76 12 6 12 76
77 12 5 17 77
78 12 4 13 78
79 14 6 15 79
80 6 3 13 80
81 7 2 15 81
82 14 12 16 82
83 14 8 15 83
84 10 5 16 84
85 13 9 15 85
86 12 6 14 86
87 9 5 15 87
88 12 2 14 88
89 16 4 13 89
90 10 7 7 90
91 14 5 17 91
92 10 6 13 92
93 16 7 15 93
94 15 8 14 94
95 12 6 13 95
96 10 0 16 96
97 8 1 12 97
98 8 5 14 98
99 11 5 17 99
100 13 5 15 100
101 16 7 17 101
102 16 7 12 102
103 14 1 16 103
104 11 3 11 104
105 4 4 15 105
106 14 8 9 106
107 9 6 16 107
108 14 6 15 108
109 8 2 10 109
110 8 2 10 110
111 11 3 15 111
112 12 3 11 112
113 11 0 13 113
114 14 2 14 114
115 15 8 18 115
116 16 8 16 116
117 16 0 14 117
118 11 5 14 118
119 14 9 14 119
120 14 6 14 120
121 12 6 12 121
122 14 3 14 122
123 8 9 15 123
124 13 7 15 124
125 16 8 15 125
126 12 0 13 126
127 16 7 17 127
128 12 0 17 128
129 11 5 19 129
130 4 0 15 130
131 16 14 13 131
132 15 5 9 132
133 10 2 15 133
134 13 8 15 134
135 15 4 15 135
136 12 2 16 136
137 14 6 11 137
138 7 3 14 138
139 19 5 11 139
140 12 9 15 140
141 12 3 13 141
142 13 3 15 142
143 15 0 16 143
144 8 10 14 144
145 12 4 15 145
146 10 2 16 146
147 8 3 16 147
148 10 10 11 148
149 15 7 12 149
150 16 0 9 150
151 13 6 16 151
152 16 8 13 152
153 9 0 16 153
154 14 4 12 154
155 14 10 9 155
156 12 5 13 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) WP HS t
11.966264 0.272511 -0.115233 0.004207
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.7696 -1.5668 0.1215 2.1096 6.3540
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.966264 1.528418 7.829 7.8e-13 ***
WP 0.272511 0.072667 3.750 0.000251 ***
HS -0.115233 0.097281 -1.185 0.238048
t 0.004207 0.005035 0.836 0.404728
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.816 on 152 degrees of freedom
Multiple R-squared: 0.09839, Adjusted R-squared: 0.0806
F-statistic: 5.529 on 3 and 152 DF, p-value: 0.001249
> 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,] 0.30785753 0.61571507 0.6921425
[2,] 0.43067967 0.86135934 0.5693203
[3,] 0.30724043 0.61448086 0.6927596
[4,] 0.19608896 0.39217791 0.8039110
[5,] 0.12121665 0.24243330 0.8787834
[6,] 0.07560303 0.15120606 0.9243970
[7,] 0.06360935 0.12721870 0.9363907
[8,] 0.13651418 0.27302836 0.8634858
[9,] 0.10393600 0.20787200 0.8960640
[10,] 0.06635939 0.13271877 0.9336406
[11,] 0.04381900 0.08763801 0.9561810
[12,] 0.08852950 0.17705900 0.9114705
[13,] 0.06355059 0.12710118 0.9364494
[14,] 0.11045673 0.22091346 0.8895433
[15,] 0.09705995 0.19411991 0.9029400
[16,] 0.11249364 0.22498729 0.8875064
[17,] 0.08146667 0.16293333 0.9185333
[18,] 0.05698771 0.11397542 0.9430123
[19,] 0.09668211 0.19336422 0.9033179
[20,] 0.08219872 0.16439743 0.9178013
[21,] 0.06983628 0.13967256 0.9301637
[22,] 0.05309335 0.10618671 0.9469066
[23,] 0.04675489 0.09350978 0.9532451
[24,] 0.05154310 0.10308620 0.9484569
[25,] 0.04273538 0.08547076 0.9572646
[26,] 0.03093737 0.06187474 0.9690626
[27,] 0.03081783 0.06163567 0.9691822
[28,] 0.02263314 0.04526628 0.9773669
[29,] 0.02780104 0.05560207 0.9721990
[30,] 0.03995142 0.07990284 0.9600486
[31,] 0.03426795 0.06853590 0.9657321
[32,] 0.03067703 0.06135407 0.9693230
[33,] 0.02654487 0.05308973 0.9734551
[34,] 0.02686898 0.05373796 0.9731310
[35,] 0.02415373 0.04830746 0.9758463
[36,] 0.02968394 0.05936788 0.9703161
[37,] 0.11816539 0.23633077 0.8818346
[38,] 0.15065829 0.30131659 0.8493417
[39,] 0.12324816 0.24649632 0.8767518
[40,] 0.12997241 0.25994481 0.8700276
[41,] 0.12415943 0.24831887 0.8758406
[42,] 0.10312295 0.20624591 0.8968770
[43,] 0.08263813 0.16527626 0.9173619
[44,] 0.06832782 0.13665565 0.9316722
[45,] 0.05676006 0.11352012 0.9432399
[46,] 0.12360000 0.24719999 0.8764000
[47,] 0.10143023 0.20286046 0.8985698
[48,] 0.11035911 0.22071823 0.8896409
[49,] 0.13063451 0.26126902 0.8693655
[50,] 0.10671971 0.21343943 0.8932803
[51,] 0.08763594 0.17527187 0.9123641
[52,] 0.11123871 0.22247742 0.8887613
[53,] 0.15663372 0.31326744 0.8433663
[54,] 0.20715820 0.41431639 0.7928418
[55,] 0.25197707 0.50395414 0.7480229
[56,] 0.25155244 0.50310488 0.7484476
[57,] 0.25192708 0.50385417 0.7480729
[58,] 0.36944594 0.73889187 0.6305541
[59,] 0.38983704 0.77967408 0.6101630
[60,] 0.36012201 0.72024402 0.6398780
[61,] 0.35655015 0.71310030 0.6434498
[62,] 0.33055540 0.66111080 0.6694446
[63,] 0.32760583 0.65521165 0.6723942
[64,] 0.53516120 0.92967760 0.4648388
[65,] 0.52174620 0.95650760 0.4782538
[66,] 0.47732249 0.95464499 0.5226775
[67,] 0.43946453 0.87892905 0.5605355
[68,] 0.40216542 0.80433084 0.5978346
[69,] 0.35785169 0.71570337 0.6421483
[70,] 0.31534470 0.63068940 0.6846553
[71,] 0.27921187 0.55842373 0.7207881
[72,] 0.24322590 0.48645180 0.7567741
[73,] 0.23114333 0.46228666 0.7688567
[74,] 0.31679777 0.63359554 0.6832022
[75,] 0.34084385 0.68168771 0.6591561
[76,] 0.30258807 0.60517615 0.6974119
[77,] 0.27736646 0.55473292 0.7226335
[78,] 0.24862131 0.49724262 0.7513787
[79,] 0.21408720 0.42817441 0.7859128
[80,] 0.18193744 0.36387489 0.8180626
[81,] 0.17640874 0.35281748 0.8235913
[82,] 0.15331675 0.30663350 0.8466832
[83,] 0.19817777 0.39635553 0.8018222
[84,] 0.20337563 0.40675127 0.7966244
[85,] 0.19859379 0.39718758 0.8014062
[86,] 0.18493067 0.36986135 0.8150693
[87,] 0.20939234 0.41878468 0.7906077
[88,] 0.19982523 0.39965046 0.8001748
[89,] 0.16822515 0.33645030 0.8317748
[90,] 0.14008143 0.28016286 0.8599186
[91,] 0.14105130 0.28210260 0.8589487
[92,] 0.16422699 0.32845398 0.8357730
[93,] 0.13796514 0.27593028 0.8620349
[94,] 0.11750068 0.23500136 0.8824993
[95,] 0.13901133 0.27802266 0.8609887
[96,] 0.14897024 0.29794049 0.8510298
[97,] 0.16305289 0.32610578 0.8369471
[98,] 0.13589081 0.27178163 0.8641092
[99,] 0.34872406 0.69744813 0.6512759
[100,] 0.30550015 0.61100030 0.6944999
[101,] 0.31398612 0.62797223 0.6860139
[102,] 0.28602274 0.57204548 0.7139773
[103,] 0.33571509 0.67143018 0.6642849
[104,] 0.43146648 0.86293296 0.5685335
[105,] 0.39481484 0.78962969 0.6051852
[106,] 0.36981085 0.73962170 0.6301892
[107,] 0.34902348 0.69804695 0.6509765
[108,] 0.32250643 0.64501285 0.6774936
[109,] 0.31656185 0.63312371 0.6834381
[110,] 0.33197105 0.66394211 0.6680289
[111,] 0.39290529 0.78581057 0.6070947
[112,] 0.35895283 0.71790567 0.6410472
[113,] 0.31077671 0.62155342 0.6892233
[114,] 0.27166482 0.54332965 0.7283352
[115,] 0.24127398 0.48254797 0.7587260
[116,] 0.21367875 0.42735750 0.7863213
[117,] 0.31960980 0.63921960 0.6803902
[118,] 0.26983356 0.53966712 0.7301664
[119,] 0.26796880 0.53593761 0.7320312
[120,] 0.22435243 0.44870485 0.7756476
[121,] 0.28164188 0.56328376 0.7183581
[122,] 0.25072804 0.50145609 0.7492720
[123,] 0.21915103 0.43830207 0.7808490
[124,] 0.56184071 0.87631858 0.4381593
[125,] 0.56248400 0.87503199 0.4375160
[126,] 0.50915380 0.98169241 0.4908462
[127,] 0.49213965 0.98427931 0.5078603
[128,] 0.43348824 0.86697648 0.5665118
[129,] 0.44787485 0.89574969 0.5521252
[130,] 0.38209700 0.76419401 0.6179030
[131,] 0.31337891 0.62675781 0.6866211
[132,] 0.57034524 0.85930952 0.4296548
[133,] 0.70068964 0.59862072 0.2993104
[134,] 0.64815434 0.70369132 0.3518457
[135,] 0.56942155 0.86115691 0.4305785
[136,] 0.50001773 0.99996453 0.4999823
[137,] 0.65026780 0.69946440 0.3497322
[138,] 0.65822503 0.68354994 0.3417750
[139,] 0.57749640 0.84500720 0.4225036
[140,] 0.45894845 0.91789691 0.5410515
[141,] 0.44330128 0.88660255 0.5566987
[142,] 0.88268623 0.23462754 0.1173138
[143,] 0.82833285 0.34333430 0.1716671
> postscript(file="/var/www/rcomp/tmp/1h43n1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2se2q1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3se2q1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4se2q1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5se2q1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 156
Frequency = 1
1 2 3 4 5 6
1.28024261 1.28198985 4.28868118 -0.50786653 -1.23360873 1.81018243
7 8 9 10 11 12
2.98248926 -2.20418620 -1.54575502 0.72016452 -0.41612877 -0.46237983
13 14 15 16 17 18
2.49631321 -4.81649403 0.49980769 0.30717785 -1.78707150 2.00940020
19 20 21 22 23 24
-1.25046465 -5.02420516 0.20205434 2.30117178 -1.62984596 -0.31949861
25 26 27 28 29 30
3.06999250 2.59889774 -2.59867619 -0.19590273 2.09520790 3.54002528
31 32 33 34 35 36
1.65700583 -0.23792235 1.67378266 -0.47084888 -2.86279988 3.00090684
37 38 39 40 41 42
-0.71989127 -2.30520907 1.73858209 2.63837837 2.49970120 3.88323794
43 44 45 46 47 48
-6.12096919 -3.58610958 -0.59031672 3.20020061 2.47684229 -1.43970650
49 50 51 52 53 54
-1.21344700 1.23137038 -1.69964736 -5.93432112 0.53330347 -3.62818090
55 56 57 58 59 60
3.18514363 -0.92595856 0.09264151 3.61064829 -4.73925879 4.16410797
61 62 63 64 65 66
-4.30103242 2.54582146 2.49123215 5.10523552 -3.69131219 1.27333522
67 68 69 70 71 72
-2.97819138 1.31291925 2.29185928 -7.16732674 1.89570115 0.12196064
73 74 75 76 77 78
-1.72496925 -1.72917638 -0.69133958 -0.53826948 0.30620052 0.11357069
79 80 81 82 83 84
1.79480907 -5.62233302 -4.12356297 0.26235766 1.23295943 -1.83848272
85 86 87 88 89 90
-0.04796539 -0.34987417 -2.96633743 0.73175379 4.06729224 -3.44584645
91 92 93 94 95 96
2.24730067 -2.49035027 3.46339867 2.07144767 -0.50297167 -0.52641552
97 98 99 100 101 102
-3.26406647 -4.12784919 -0.78635638 0.97896986 3.66020824 3.07983454
103 104 105 106 107 108
3.17162400 -0.95377082 -7.76955525 0.44479551 -3.20775731 1.67280224
109 110 111 112 113 114
-3.81752924 -3.82173637 -0.52228748 0.01257213 0.05636329 2.62236836
115 116 117 118 119 120
2.44403115 3.20935739 5.15476808 -1.21199183 0.69375881 1.50708335
121 122 123 124 125 126
-0.72759041 2.31620075 -5.20783640 0.33297758 3.05625989 1.00167057
127 128 129 130 131 132
3.55082281 1.45418957 -0.68210371 -6.78469132 1.16548713 2.15294175
133 134 135 136 137 138
-1.34233383 0.01839570 3.10423079 0.76027809 1.08986216 -4.75111336
139 140 141 142 143 144
6.35395845 -1.27935765 0.12103193 1.34729142 4.27584927 -5.68393005
145 146 147 148 149 150
0.06215947 -1.28179323 -3.55851092 -4.04645852 1.88209933 4.43976615
151 152 153 154 155 156
0.60712888 2.71220070 -1.76622205 1.67859534 -0.30637507 -0.48709616
> postscript(file="/var/www/rcomp/tmp/6kn2b1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.28024261 NA
1 1.28198985 1.28024261
2 4.28868118 1.28198985
3 -0.50786653 4.28868118
4 -1.23360873 -0.50786653
5 1.81018243 -1.23360873
6 2.98248926 1.81018243
7 -2.20418620 2.98248926
8 -1.54575502 -2.20418620
9 0.72016452 -1.54575502
10 -0.41612877 0.72016452
11 -0.46237983 -0.41612877
12 2.49631321 -0.46237983
13 -4.81649403 2.49631321
14 0.49980769 -4.81649403
15 0.30717785 0.49980769
16 -1.78707150 0.30717785
17 2.00940020 -1.78707150
18 -1.25046465 2.00940020
19 -5.02420516 -1.25046465
20 0.20205434 -5.02420516
21 2.30117178 0.20205434
22 -1.62984596 2.30117178
23 -0.31949861 -1.62984596
24 3.06999250 -0.31949861
25 2.59889774 3.06999250
26 -2.59867619 2.59889774
27 -0.19590273 -2.59867619
28 2.09520790 -0.19590273
29 3.54002528 2.09520790
30 1.65700583 3.54002528
31 -0.23792235 1.65700583
32 1.67378266 -0.23792235
33 -0.47084888 1.67378266
34 -2.86279988 -0.47084888
35 3.00090684 -2.86279988
36 -0.71989127 3.00090684
37 -2.30520907 -0.71989127
38 1.73858209 -2.30520907
39 2.63837837 1.73858209
40 2.49970120 2.63837837
41 3.88323794 2.49970120
42 -6.12096919 3.88323794
43 -3.58610958 -6.12096919
44 -0.59031672 -3.58610958
45 3.20020061 -0.59031672
46 2.47684229 3.20020061
47 -1.43970650 2.47684229
48 -1.21344700 -1.43970650
49 1.23137038 -1.21344700
50 -1.69964736 1.23137038
51 -5.93432112 -1.69964736
52 0.53330347 -5.93432112
53 -3.62818090 0.53330347
54 3.18514363 -3.62818090
55 -0.92595856 3.18514363
56 0.09264151 -0.92595856
57 3.61064829 0.09264151
58 -4.73925879 3.61064829
59 4.16410797 -4.73925879
60 -4.30103242 4.16410797
61 2.54582146 -4.30103242
62 2.49123215 2.54582146
63 5.10523552 2.49123215
64 -3.69131219 5.10523552
65 1.27333522 -3.69131219
66 -2.97819138 1.27333522
67 1.31291925 -2.97819138
68 2.29185928 1.31291925
69 -7.16732674 2.29185928
70 1.89570115 -7.16732674
71 0.12196064 1.89570115
72 -1.72496925 0.12196064
73 -1.72917638 -1.72496925
74 -0.69133958 -1.72917638
75 -0.53826948 -0.69133958
76 0.30620052 -0.53826948
77 0.11357069 0.30620052
78 1.79480907 0.11357069
79 -5.62233302 1.79480907
80 -4.12356297 -5.62233302
81 0.26235766 -4.12356297
82 1.23295943 0.26235766
83 -1.83848272 1.23295943
84 -0.04796539 -1.83848272
85 -0.34987417 -0.04796539
86 -2.96633743 -0.34987417
87 0.73175379 -2.96633743
88 4.06729224 0.73175379
89 -3.44584645 4.06729224
90 2.24730067 -3.44584645
91 -2.49035027 2.24730067
92 3.46339867 -2.49035027
93 2.07144767 3.46339867
94 -0.50297167 2.07144767
95 -0.52641552 -0.50297167
96 -3.26406647 -0.52641552
97 -4.12784919 -3.26406647
98 -0.78635638 -4.12784919
99 0.97896986 -0.78635638
100 3.66020824 0.97896986
101 3.07983454 3.66020824
102 3.17162400 3.07983454
103 -0.95377082 3.17162400
104 -7.76955525 -0.95377082
105 0.44479551 -7.76955525
106 -3.20775731 0.44479551
107 1.67280224 -3.20775731
108 -3.81752924 1.67280224
109 -3.82173637 -3.81752924
110 -0.52228748 -3.82173637
111 0.01257213 -0.52228748
112 0.05636329 0.01257213
113 2.62236836 0.05636329
114 2.44403115 2.62236836
115 3.20935739 2.44403115
116 5.15476808 3.20935739
117 -1.21199183 5.15476808
118 0.69375881 -1.21199183
119 1.50708335 0.69375881
120 -0.72759041 1.50708335
121 2.31620075 -0.72759041
122 -5.20783640 2.31620075
123 0.33297758 -5.20783640
124 3.05625989 0.33297758
125 1.00167057 3.05625989
126 3.55082281 1.00167057
127 1.45418957 3.55082281
128 -0.68210371 1.45418957
129 -6.78469132 -0.68210371
130 1.16548713 -6.78469132
131 2.15294175 1.16548713
132 -1.34233383 2.15294175
133 0.01839570 -1.34233383
134 3.10423079 0.01839570
135 0.76027809 3.10423079
136 1.08986216 0.76027809
137 -4.75111336 1.08986216
138 6.35395845 -4.75111336
139 -1.27935765 6.35395845
140 0.12103193 -1.27935765
141 1.34729142 0.12103193
142 4.27584927 1.34729142
143 -5.68393005 4.27584927
144 0.06215947 -5.68393005
145 -1.28179323 0.06215947
146 -3.55851092 -1.28179323
147 -4.04645852 -3.55851092
148 1.88209933 -4.04645852
149 4.43976615 1.88209933
150 0.60712888 4.43976615
151 2.71220070 0.60712888
152 -1.76622205 2.71220070
153 1.67859534 -1.76622205
154 -0.30637507 1.67859534
155 -0.48709616 -0.30637507
156 NA -0.48709616
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.28198985 1.28024261
[2,] 4.28868118 1.28198985
[3,] -0.50786653 4.28868118
[4,] -1.23360873 -0.50786653
[5,] 1.81018243 -1.23360873
[6,] 2.98248926 1.81018243
[7,] -2.20418620 2.98248926
[8,] -1.54575502 -2.20418620
[9,] 0.72016452 -1.54575502
[10,] -0.41612877 0.72016452
[11,] -0.46237983 -0.41612877
[12,] 2.49631321 -0.46237983
[13,] -4.81649403 2.49631321
[14,] 0.49980769 -4.81649403
[15,] 0.30717785 0.49980769
[16,] -1.78707150 0.30717785
[17,] 2.00940020 -1.78707150
[18,] -1.25046465 2.00940020
[19,] -5.02420516 -1.25046465
[20,] 0.20205434 -5.02420516
[21,] 2.30117178 0.20205434
[22,] -1.62984596 2.30117178
[23,] -0.31949861 -1.62984596
[24,] 3.06999250 -0.31949861
[25,] 2.59889774 3.06999250
[26,] -2.59867619 2.59889774
[27,] -0.19590273 -2.59867619
[28,] 2.09520790 -0.19590273
[29,] 3.54002528 2.09520790
[30,] 1.65700583 3.54002528
[31,] -0.23792235 1.65700583
[32,] 1.67378266 -0.23792235
[33,] -0.47084888 1.67378266
[34,] -2.86279988 -0.47084888
[35,] 3.00090684 -2.86279988
[36,] -0.71989127 3.00090684
[37,] -2.30520907 -0.71989127
[38,] 1.73858209 -2.30520907
[39,] 2.63837837 1.73858209
[40,] 2.49970120 2.63837837
[41,] 3.88323794 2.49970120
[42,] -6.12096919 3.88323794
[43,] -3.58610958 -6.12096919
[44,] -0.59031672 -3.58610958
[45,] 3.20020061 -0.59031672
[46,] 2.47684229 3.20020061
[47,] -1.43970650 2.47684229
[48,] -1.21344700 -1.43970650
[49,] 1.23137038 -1.21344700
[50,] -1.69964736 1.23137038
[51,] -5.93432112 -1.69964736
[52,] 0.53330347 -5.93432112
[53,] -3.62818090 0.53330347
[54,] 3.18514363 -3.62818090
[55,] -0.92595856 3.18514363
[56,] 0.09264151 -0.92595856
[57,] 3.61064829 0.09264151
[58,] -4.73925879 3.61064829
[59,] 4.16410797 -4.73925879
[60,] -4.30103242 4.16410797
[61,] 2.54582146 -4.30103242
[62,] 2.49123215 2.54582146
[63,] 5.10523552 2.49123215
[64,] -3.69131219 5.10523552
[65,] 1.27333522 -3.69131219
[66,] -2.97819138 1.27333522
[67,] 1.31291925 -2.97819138
[68,] 2.29185928 1.31291925
[69,] -7.16732674 2.29185928
[70,] 1.89570115 -7.16732674
[71,] 0.12196064 1.89570115
[72,] -1.72496925 0.12196064
[73,] -1.72917638 -1.72496925
[74,] -0.69133958 -1.72917638
[75,] -0.53826948 -0.69133958
[76,] 0.30620052 -0.53826948
[77,] 0.11357069 0.30620052
[78,] 1.79480907 0.11357069
[79,] -5.62233302 1.79480907
[80,] -4.12356297 -5.62233302
[81,] 0.26235766 -4.12356297
[82,] 1.23295943 0.26235766
[83,] -1.83848272 1.23295943
[84,] -0.04796539 -1.83848272
[85,] -0.34987417 -0.04796539
[86,] -2.96633743 -0.34987417
[87,] 0.73175379 -2.96633743
[88,] 4.06729224 0.73175379
[89,] -3.44584645 4.06729224
[90,] 2.24730067 -3.44584645
[91,] -2.49035027 2.24730067
[92,] 3.46339867 -2.49035027
[93,] 2.07144767 3.46339867
[94,] -0.50297167 2.07144767
[95,] -0.52641552 -0.50297167
[96,] -3.26406647 -0.52641552
[97,] -4.12784919 -3.26406647
[98,] -0.78635638 -4.12784919
[99,] 0.97896986 -0.78635638
[100,] 3.66020824 0.97896986
[101,] 3.07983454 3.66020824
[102,] 3.17162400 3.07983454
[103,] -0.95377082 3.17162400
[104,] -7.76955525 -0.95377082
[105,] 0.44479551 -7.76955525
[106,] -3.20775731 0.44479551
[107,] 1.67280224 -3.20775731
[108,] -3.81752924 1.67280224
[109,] -3.82173637 -3.81752924
[110,] -0.52228748 -3.82173637
[111,] 0.01257213 -0.52228748
[112,] 0.05636329 0.01257213
[113,] 2.62236836 0.05636329
[114,] 2.44403115 2.62236836
[115,] 3.20935739 2.44403115
[116,] 5.15476808 3.20935739
[117,] -1.21199183 5.15476808
[118,] 0.69375881 -1.21199183
[119,] 1.50708335 0.69375881
[120,] -0.72759041 1.50708335
[121,] 2.31620075 -0.72759041
[122,] -5.20783640 2.31620075
[123,] 0.33297758 -5.20783640
[124,] 3.05625989 0.33297758
[125,] 1.00167057 3.05625989
[126,] 3.55082281 1.00167057
[127,] 1.45418957 3.55082281
[128,] -0.68210371 1.45418957
[129,] -6.78469132 -0.68210371
[130,] 1.16548713 -6.78469132
[131,] 2.15294175 1.16548713
[132,] -1.34233383 2.15294175
[133,] 0.01839570 -1.34233383
[134,] 3.10423079 0.01839570
[135,] 0.76027809 3.10423079
[136,] 1.08986216 0.76027809
[137,] -4.75111336 1.08986216
[138,] 6.35395845 -4.75111336
[139,] -1.27935765 6.35395845
[140,] 0.12103193 -1.27935765
[141,] 1.34729142 0.12103193
[142,] 4.27584927 1.34729142
[143,] -5.68393005 4.27584927
[144,] 0.06215947 -5.68393005
[145,] -1.28179323 0.06215947
[146,] -3.55851092 -1.28179323
[147,] -4.04645852 -3.55851092
[148,] 1.88209933 -4.04645852
[149,] 4.43976615 1.88209933
[150,] 0.60712888 4.43976615
[151,] 2.71220070 0.60712888
[152,] -1.76622205 2.71220070
[153,] 1.67859534 -1.76622205
[154,] -0.30637507 1.67859534
[155,] -0.48709616 -0.30637507
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.28198985 1.28024261
2 4.28868118 1.28198985
3 -0.50786653 4.28868118
4 -1.23360873 -0.50786653
5 1.81018243 -1.23360873
6 2.98248926 1.81018243
7 -2.20418620 2.98248926
8 -1.54575502 -2.20418620
9 0.72016452 -1.54575502
10 -0.41612877 0.72016452
11 -0.46237983 -0.41612877
12 2.49631321 -0.46237983
13 -4.81649403 2.49631321
14 0.49980769 -4.81649403
15 0.30717785 0.49980769
16 -1.78707150 0.30717785
17 2.00940020 -1.78707150
18 -1.25046465 2.00940020
19 -5.02420516 -1.25046465
20 0.20205434 -5.02420516
21 2.30117178 0.20205434
22 -1.62984596 2.30117178
23 -0.31949861 -1.62984596
24 3.06999250 -0.31949861
25 2.59889774 3.06999250
26 -2.59867619 2.59889774
27 -0.19590273 -2.59867619
28 2.09520790 -0.19590273
29 3.54002528 2.09520790
30 1.65700583 3.54002528
31 -0.23792235 1.65700583
32 1.67378266 -0.23792235
33 -0.47084888 1.67378266
34 -2.86279988 -0.47084888
35 3.00090684 -2.86279988
36 -0.71989127 3.00090684
37 -2.30520907 -0.71989127
38 1.73858209 -2.30520907
39 2.63837837 1.73858209
40 2.49970120 2.63837837
41 3.88323794 2.49970120
42 -6.12096919 3.88323794
43 -3.58610958 -6.12096919
44 -0.59031672 -3.58610958
45 3.20020061 -0.59031672
46 2.47684229 3.20020061
47 -1.43970650 2.47684229
48 -1.21344700 -1.43970650
49 1.23137038 -1.21344700
50 -1.69964736 1.23137038
51 -5.93432112 -1.69964736
52 0.53330347 -5.93432112
53 -3.62818090 0.53330347
54 3.18514363 -3.62818090
55 -0.92595856 3.18514363
56 0.09264151 -0.92595856
57 3.61064829 0.09264151
58 -4.73925879 3.61064829
59 4.16410797 -4.73925879
60 -4.30103242 4.16410797
61 2.54582146 -4.30103242
62 2.49123215 2.54582146
63 5.10523552 2.49123215
64 -3.69131219 5.10523552
65 1.27333522 -3.69131219
66 -2.97819138 1.27333522
67 1.31291925 -2.97819138
68 2.29185928 1.31291925
69 -7.16732674 2.29185928
70 1.89570115 -7.16732674
71 0.12196064 1.89570115
72 -1.72496925 0.12196064
73 -1.72917638 -1.72496925
74 -0.69133958 -1.72917638
75 -0.53826948 -0.69133958
76 0.30620052 -0.53826948
77 0.11357069 0.30620052
78 1.79480907 0.11357069
79 -5.62233302 1.79480907
80 -4.12356297 -5.62233302
81 0.26235766 -4.12356297
82 1.23295943 0.26235766
83 -1.83848272 1.23295943
84 -0.04796539 -1.83848272
85 -0.34987417 -0.04796539
86 -2.96633743 -0.34987417
87 0.73175379 -2.96633743
88 4.06729224 0.73175379
89 -3.44584645 4.06729224
90 2.24730067 -3.44584645
91 -2.49035027 2.24730067
92 3.46339867 -2.49035027
93 2.07144767 3.46339867
94 -0.50297167 2.07144767
95 -0.52641552 -0.50297167
96 -3.26406647 -0.52641552
97 -4.12784919 -3.26406647
98 -0.78635638 -4.12784919
99 0.97896986 -0.78635638
100 3.66020824 0.97896986
101 3.07983454 3.66020824
102 3.17162400 3.07983454
103 -0.95377082 3.17162400
104 -7.76955525 -0.95377082
105 0.44479551 -7.76955525
106 -3.20775731 0.44479551
107 1.67280224 -3.20775731
108 -3.81752924 1.67280224
109 -3.82173637 -3.81752924
110 -0.52228748 -3.82173637
111 0.01257213 -0.52228748
112 0.05636329 0.01257213
113 2.62236836 0.05636329
114 2.44403115 2.62236836
115 3.20935739 2.44403115
116 5.15476808 3.20935739
117 -1.21199183 5.15476808
118 0.69375881 -1.21199183
119 1.50708335 0.69375881
120 -0.72759041 1.50708335
121 2.31620075 -0.72759041
122 -5.20783640 2.31620075
123 0.33297758 -5.20783640
124 3.05625989 0.33297758
125 1.00167057 3.05625989
126 3.55082281 1.00167057
127 1.45418957 3.55082281
128 -0.68210371 1.45418957
129 -6.78469132 -0.68210371
130 1.16548713 -6.78469132
131 2.15294175 1.16548713
132 -1.34233383 2.15294175
133 0.01839570 -1.34233383
134 3.10423079 0.01839570
135 0.76027809 3.10423079
136 1.08986216 0.76027809
137 -4.75111336 1.08986216
138 6.35395845 -4.75111336
139 -1.27935765 6.35395845
140 0.12103193 -1.27935765
141 1.34729142 0.12103193
142 4.27584927 1.34729142
143 -5.68393005 4.27584927
144 0.06215947 -5.68393005
145 -1.28179323 0.06215947
146 -3.55851092 -1.28179323
147 -4.04645852 -3.55851092
148 1.88209933 -4.04645852
149 4.43976615 1.88209933
150 0.60712888 4.43976615
151 2.71220070 0.60712888
152 -1.76622205 2.71220070
153 1.67859534 -1.76622205
154 -0.30637507 1.67859534
155 -0.48709616 -0.30637507
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7de1w1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8de1w1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9o5iz1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10o5iz1292940178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11r6z51292940178.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12nyin1292940179.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/131qgw1292940179.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14n9wk1292940179.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/158rv81292940179.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16tste1292940179.tab")
+ }
>
> try(system("convert tmp/1h43n1292940178.ps tmp/1h43n1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/2se2q1292940178.ps tmp/2se2q1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/3se2q1292940178.ps tmp/3se2q1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/4se2q1292940178.ps tmp/4se2q1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/5se2q1292940178.ps tmp/5se2q1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kn2b1292940178.ps tmp/6kn2b1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/7de1w1292940178.ps tmp/7de1w1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/8de1w1292940178.ps tmp/8de1w1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/9o5iz1292940178.ps tmp/9o5iz1292940178.png",intern=TRUE))
character(0)
> try(system("convert tmp/10o5iz1292940178.ps tmp/10o5iz1292940178.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.350 1.940 6.328