R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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
+ ,53
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+ ,69)
+ ,dim=c(3
+ ,162)
+ ,dimnames=list(c('Happiness'
+ ,'Depression'
+ ,'Belonging')
+ ,1:162))
> y <- array(NA,dim=c(3,162),dimnames=list(c('Happiness','Depression','Belonging'),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 = '3'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Belonging Happiness Depression t
1 53 14 12 1
2 86 18 11 2
3 66 11 14 3
4 67 12 12 4
5 76 16 21 5
6 78 18 12 6
7 53 14 22 7
8 80 14 11 8
9 74 15 10 9
10 76 15 13 10
11 79 17 10 11
12 54 19 8 12
13 67 10 15 13
14 54 16 14 14
15 87 18 10 15
16 58 14 14 16
17 75 14 14 17
18 88 17 11 18
19 64 14 10 19
20 57 16 13 20
21 66 18 7 21
22 68 11 14 22
23 54 14 12 23
24 56 12 14 24
25 86 17 11 25
26 80 9 9 26
27 76 16 11 27
28 69 14 15 28
29 78 15 14 29
30 67 11 13 30
31 80 16 9 31
32 54 13 15 32
33 71 17 10 33
34 84 15 11 34
35 74 14 13 35
36 71 16 8 36
37 63 9 20 37
38 71 15 12 38
39 76 17 10 39
40 69 13 10 40
41 74 15 9 41
42 75 16 14 42
43 54 16 8 43
44 52 12 14 44
45 69 12 11 45
46 68 11 13 46
47 65 15 9 47
48 75 15 11 48
49 74 17 15 49
50 75 13 11 50
51 72 16 10 51
52 67 14 14 52
53 63 11 18 53
54 62 12 14 54
55 63 12 11 55
56 76 15 12 56
57 74 16 13 57
58 67 15 9 58
59 73 12 10 59
60 70 12 15 60
61 53 8 20 61
62 77 13 12 62
63 77 11 12 63
64 52 14 14 64
65 54 15 13 65
66 80 10 11 66
67 66 11 17 67
68 73 12 12 68
69 63 15 13 69
70 69 15 14 70
71 67 14 13 71
72 54 16 15 72
73 81 15 13 73
74 69 15 10 74
75 84 13 11 75
76 80 12 19 76
77 70 17 13 77
78 69 13 17 78
79 77 15 13 79
80 54 13 9 80
81 79 15 11 81
82 30 16 10 82
83 71 15 9 83
84 73 16 12 84
85 72 15 12 85
86 77 14 13 86
87 75 15 13 87
88 69 14 12 88
89 54 13 15 89
90 70 7 22 90
91 73 17 13 91
92 54 13 15 92
93 77 15 13 93
94 82 14 15 94
95 80 13 10 95
96 80 16 11 96
97 69 12 16 97
98 78 14 11 98
99 81 17 11 99
100 76 15 10 100
101 76 17 10 101
102 73 12 16 102
103 85 16 12 103
104 66 11 11 104
105 79 15 16 105
106 68 9 19 106
107 76 16 11 107
108 71 15 16 108
109 54 10 15 109
110 46 10 24 110
111 82 15 14 111
112 74 11 15 112
113 88 13 11 113
114 38 14 15 114
115 76 18 12 115
116 86 16 10 116
117 54 14 14 117
118 70 14 13 118
119 69 14 9 119
120 90 14 15 120
121 54 12 15 121
122 76 14 14 122
123 89 15 11 123
124 76 15 8 124
125 73 15 11 125
126 79 13 11 126
127 90 17 8 127
128 74 17 10 128
129 81 19 11 129
130 72 15 13 130
131 71 13 11 131
132 66 9 20 132
133 77 15 10 133
134 65 15 15 134
135 74 15 12 135
136 82 16 14 136
137 54 11 23 137
138 63 14 14 138
139 54 11 16 139
140 64 15 11 140
141 69 13 12 141
142 54 15 10 142
143 84 16 14 143
144 86 14 12 144
145 77 15 12 145
146 89 16 11 146
147 76 16 12 147
148 60 11 13 148
149 75 12 11 149
150 73 9 19 150
151 85 16 12 151
152 79 13 17 152
153 71 16 9 153
154 72 12 12 154
155 69 9 19 155
156 78 13 18 156
157 54 13 15 157
158 69 14 14 158
159 81 19 11 159
160 84 13 9 160
161 84 12 18 161
162 69 13 16 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Happiness Depression t
62.55114 0.92560 -0.63535 0.04141
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-44.403 -4.789 1.538 6.197 19.052
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 62.55114 8.56179 7.306 1.28e-11 ***
Happiness 0.92560 0.40514 2.285 0.0237 *
Depression -0.63535 0.30012 -2.117 0.0358 *
t 0.04141 0.01708 2.424 0.0165 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.07 on 158 degrees of freedom
Multiple R-squared: 0.1338, Adjusted R-squared: 0.1173
F-statistic: 8.133 on 3 and 158 DF, p-value: 4.53e-05
> 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,] 0.80244338 0.3951132 0.19755662
[2,] 0.70330075 0.5933985 0.29669925
[3,] 0.61348992 0.7730202 0.38651008
[4,] 0.48822902 0.9764580 0.51177098
[5,] 0.39703539 0.7940708 0.60296461
[6,] 0.88082259 0.2383548 0.11917741
[7,] 0.83348179 0.3330364 0.16651821
[8,] 0.85822737 0.2835453 0.14177263
[9,] 0.89716556 0.2056689 0.10283444
[10,] 0.87630374 0.2473925 0.12369626
[11,] 0.86310818 0.2737836 0.13689182
[12,] 0.89328711 0.2134258 0.10671289
[13,] 0.87129710 0.2574058 0.12870290
[14,] 0.88360021 0.2327996 0.11639979
[15,] 0.86613657 0.2677269 0.13386343
[16,] 0.83837237 0.3232553 0.16162763
[17,] 0.84624924 0.3075015 0.15375076
[18,] 0.81519123 0.3696175 0.18480877
[19,] 0.87301377 0.2539725 0.12698623
[20,] 0.90638948 0.1872210 0.09361052
[21,] 0.88525593 0.2294881 0.11474407
[22,] 0.85462334 0.2907533 0.14537666
[23,] 0.84566289 0.3086742 0.15433711
[24,] 0.80749890 0.3850022 0.19250110
[25,] 0.77931547 0.4413691 0.22068453
[26,] 0.79696479 0.4060704 0.20303521
[27,] 0.75578345 0.4884331 0.24421655
[28,] 0.77765933 0.4446813 0.22234067
[29,] 0.74280021 0.5143996 0.25719979
[30,] 0.70400951 0.5919810 0.29599049
[31,] 0.65905694 0.6818861 0.34094306
[32,] 0.60786614 0.7842677 0.39213386
[33,] 0.55675361 0.8864928 0.44324639
[34,] 0.50541608 0.9891678 0.49458392
[35,] 0.45294337 0.9058867 0.54705663
[36,] 0.41048364 0.8209673 0.58951636
[37,] 0.56449101 0.8710180 0.43550899
[38,] 0.60528383 0.7894323 0.39471617
[39,] 0.55604140 0.8879172 0.44395860
[40,] 0.50840621 0.9831876 0.49159379
[41,] 0.47664230 0.9532846 0.52335770
[42,] 0.43830847 0.8766169 0.56169153
[43,] 0.39783159 0.7956632 0.60216841
[44,] 0.36826009 0.7365202 0.63173991
[45,] 0.32195392 0.6439078 0.67804608
[46,] 0.27882689 0.5576538 0.72117311
[47,] 0.23857058 0.4771412 0.76142942
[48,] 0.20741360 0.4148272 0.79258640
[49,] 0.18086092 0.3617218 0.81913908
[50,] 0.16126499 0.3225300 0.83873501
[51,] 0.13638879 0.2727776 0.86361121
[52,] 0.11713540 0.2342708 0.88286460
[53,] 0.09936827 0.1987365 0.90063173
[54,] 0.08313791 0.1662758 0.91686209
[55,] 0.07181610 0.1436322 0.92818390
[56,] 0.06762215 0.1352443 0.93237785
[57,] 0.06828341 0.1365668 0.93171659
[58,] 0.09913522 0.1982704 0.90086478
[59,] 0.12956748 0.2591350 0.87043252
[60,] 0.15085326 0.3017065 0.84914674
[61,] 0.12682813 0.2536563 0.87317187
[62,] 0.11026350 0.2205270 0.88973650
[63,] 0.09757088 0.1951418 0.90242912
[64,] 0.07910220 0.1582044 0.92089780
[65,] 0.06364306 0.1272861 0.93635694
[66,] 0.08224683 0.1644937 0.91775317
[67,] 0.08994202 0.1798840 0.91005798
[68,] 0.07362580 0.1472516 0.92637420
[69,] 0.09422195 0.1884439 0.90577805
[70,] 0.13233184 0.2646637 0.86766816
[71,] 0.11020110 0.2204022 0.88979890
[72,] 0.09178540 0.1835708 0.90821460
[73,] 0.08178288 0.1635658 0.91821712
[74,] 0.11741907 0.2348381 0.88258093
[75,] 0.10778579 0.2155716 0.89221421
[76,] 0.76021890 0.4795622 0.23978110
[77,] 0.72786124 0.5442775 0.27213876
[78,] 0.69244309 0.6151138 0.30755691
[79,] 0.65391630 0.6921674 0.34608370
[80,] 0.63271139 0.7345772 0.36728861
[81,] 0.59712105 0.8057579 0.40287895
[82,] 0.55498964 0.8900207 0.44501036
[83,] 0.60054798 0.7989040 0.39945202
[84,] 0.62219653 0.7556069 0.37780347
[85,] 0.58299567 0.8340087 0.41700433
[86,] 0.63138102 0.7372380 0.36861898
[87,] 0.60183887 0.7963223 0.39816113
[88,] 0.62518093 0.7496381 0.37481907
[89,] 0.61199492 0.7760102 0.38800508
[90,] 0.58344487 0.8331103 0.41655513
[91,] 0.53957599 0.9208480 0.46042401
[92,] 0.50921647 0.9815671 0.49078353
[93,] 0.47806646 0.9561329 0.52193354
[94,] 0.43382668 0.8676534 0.56617332
[95,] 0.38962151 0.7792430 0.61037849
[96,] 0.36186320 0.7237264 0.63813680
[97,] 0.37127314 0.7425463 0.62872686
[98,] 0.33059627 0.6611925 0.66940373
[99,] 0.32210891 0.6442178 0.67789109
[100,] 0.30842497 0.6168499 0.69157503
[101,] 0.26883432 0.5376686 0.73116568
[102,] 0.23228253 0.4645651 0.76771747
[103,] 0.23523314 0.4704663 0.76476686
[104,] 0.25664035 0.5132807 0.74335965
[105,] 0.26001249 0.5200250 0.73998751
[106,] 0.24750005 0.4950001 0.75249995
[107,] 0.33947037 0.6789407 0.66052963
[108,] 0.73970383 0.5205923 0.26029617
[109,] 0.69846861 0.6030628 0.30153139
[110,] 0.70573456 0.5885309 0.29426544
[111,] 0.78355011 0.4328998 0.21644989
[112,] 0.74432919 0.5113416 0.25567081
[113,] 0.70916931 0.5816614 0.29083069
[114,] 0.82107093 0.3578581 0.17892907
[115,] 0.85430533 0.2913893 0.14569467
[116,] 0.82749353 0.3450129 0.17250647
[117,] 0.87415058 0.2516988 0.12584942
[118,] 0.84338102 0.3132380 0.15661898
[119,] 0.80665098 0.3866980 0.19334902
[120,] 0.80226820 0.3954636 0.19773180
[121,] 0.84637089 0.3072582 0.15362911
[122,] 0.80914050 0.3817190 0.19085950
[123,] 0.77187962 0.4562408 0.22812038
[124,] 0.72531704 0.5493659 0.27468296
[125,] 0.68065932 0.6386814 0.31934068
[126,] 0.65265988 0.6946802 0.34734012
[127,] 0.62399450 0.7520110 0.37600550
[128,] 0.57696969 0.8460606 0.42303031
[129,] 0.52333038 0.9533392 0.47666962
[130,] 0.53475155 0.9304969 0.46524845
[131,] 0.52192578 0.9561484 0.47807422
[132,] 0.48508936 0.9701787 0.51491064
[133,] 0.53489283 0.9302143 0.46510717
[134,] 0.53471557 0.9305689 0.46528443
[135,] 0.46911423 0.9382285 0.53088577
[136,] 0.78531575 0.4293685 0.21468425
[137,] 0.73082949 0.5383410 0.26917051
[138,] 0.72382924 0.5523415 0.27617076
[139,] 0.64970163 0.7005967 0.35029837
[140,] 0.68451641 0.6309672 0.31548359
[141,] 0.59929974 0.8014005 0.40070026
[142,] 0.63029446 0.7394111 0.36970554
[143,] 0.53419020 0.9316196 0.46580980
[144,] 0.43940358 0.8788072 0.56059642
[145,] 0.42825387 0.8565077 0.57174613
[146,] 0.42141145 0.8428229 0.57858855
[147,] 0.30451996 0.6090399 0.69548004
[148,] 0.20188709 0.4037742 0.79811291
[149,] 0.12129994 0.2425999 0.87870006
> postscript(file="/var/www/html/rcomp/tmp/1v4ax1290561126.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2v4ax1290561126.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/35vr01290561126.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45vr01290561126.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55vr01290561126.ps",horizontal=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
-14.9268223 13.6940121 2.0378709 0.8001681 11.7744732 6.1637375
7 8 9 10 11 12
-8.8217840 11.1479928 3.5456370 7.4102728 6.6116196 -21.5516865
13 14 15 16 17 18
4.1847672 -15.0456050 13.5203947 -9.2772092 7.6813854 15.9571289
19 20 21 22 23 24
-5.9428137 -12.9293845 -9.6340789 3.2511682 -14.8377412 -9.7572459
25 26 27 28 29 30
13.6672910 13.7600178 4.5100835 1.8612730 9.2589172 1.2845779
31 32 33 34 35 36
7.0737677 -12.3787453 -2.2992993 13.1458490 5.3007410 -2.7686064
37 38 39 40 41 42
3.2933762 0.6155744 2.4522683 -0.8867239 1.5853170 4.7950437
43 44 45 46 47 48
-20.0584442 -14.5853540 0.4671994 1.6220914 -7.6631154 3.5661733
49 50 51 52 53 54
3.2149496 5.3345691 -1.1189933 -1.7678038 -0.4910110 -4.9994080
55 56 57 58 59 60
-5.9468547 4.8702772 2.5386155 -6.1185749 3.2521767 3.3875066
61 62 63 64 65 66
-6.7747502 7.4730513 9.2828525 -17.2646686 -16.8670244 12.4488925
67 68 69 70 71 72
1.2939662 4.1502222 -8.0326460 -1.4387044 -3.1898535 -16.8117714
73 74 75 76 77 78
9.8017324 -4.1457143 13.2994340 15.2664085 -3.2150958 1.9873002
79 80 81 82 83 84
5.5532999 -18.1782872 6.1997950 -44.4025608 -3.1537100 -0.2146775
85 86 87 88 89 90
-0.3304796 6.1890654 3.2220567 -2.5290925 -14.7388534 11.2207905
91 92 93 94 95 96
-0.7947715 -14.8630696 4.9736243 12.1285163 7.8359788 5.6531106
97 98 99 100 101 102
1.4908537 5.4215064 5.6032911 1.7777452 -0.1148668 5.2838267
103 104 105 106 107 108
10.9986198 -4.0501162 8.3828006 4.8010562 1.1976512 0.2585844
109 110 111 112 113 114
-12.7901516 -15.1134333 9.8636741 6.1600289 15.7260286 -32.6995918
115 116 117 118 119 120
-0.3494516 10.1896554 -17.4591551 -2.1359076 -5.7187013 19.0519758
121 122 123 124 125 126
-15.1382230 4.3338179 14.4607680 -0.4866787 -1.6220428 6.1877583
127 128 129 130 131 132
11.5378985 -3.2328127 2.5099224 -1.5583757 -2.0192687 2.3598628
133 134 135 136 137 138
1.4113668 -7.4533032 -0.4007498 7.9029356 -9.7923296 -9.3286686
139 140 141 142 143 144
-14.3225700 -11.2431239 -3.7979757 -21.9612818 9.6130978 12.1522048
145 146 147 148 149 150
2.1851961 12.5828404 0.1767820 -10.6012598 2.1610373 7.9792184
151 152 153 154 155 156
9.0111604 8.9233003 -6.9776916 -0.4106426 3.7721914 8.3930257
157 158 159 160 161 162
-17.5544209 -4.1567767 1.2677602 8.5092804 15.1116020 -2.1261008
> postscript(file="/var/www/html/rcomp/tmp/63qfr1290561126.ps",horizontal=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 -14.9268223 NA
1 13.6940121 -14.9268223
2 2.0378709 13.6940121
3 0.8001681 2.0378709
4 11.7744732 0.8001681
5 6.1637375 11.7744732
6 -8.8217840 6.1637375
7 11.1479928 -8.8217840
8 3.5456370 11.1479928
9 7.4102728 3.5456370
10 6.6116196 7.4102728
11 -21.5516865 6.6116196
12 4.1847672 -21.5516865
13 -15.0456050 4.1847672
14 13.5203947 -15.0456050
15 -9.2772092 13.5203947
16 7.6813854 -9.2772092
17 15.9571289 7.6813854
18 -5.9428137 15.9571289
19 -12.9293845 -5.9428137
20 -9.6340789 -12.9293845
21 3.2511682 -9.6340789
22 -14.8377412 3.2511682
23 -9.7572459 -14.8377412
24 13.6672910 -9.7572459
25 13.7600178 13.6672910
26 4.5100835 13.7600178
27 1.8612730 4.5100835
28 9.2589172 1.8612730
29 1.2845779 9.2589172
30 7.0737677 1.2845779
31 -12.3787453 7.0737677
32 -2.2992993 -12.3787453
33 13.1458490 -2.2992993
34 5.3007410 13.1458490
35 -2.7686064 5.3007410
36 3.2933762 -2.7686064
37 0.6155744 3.2933762
38 2.4522683 0.6155744
39 -0.8867239 2.4522683
40 1.5853170 -0.8867239
41 4.7950437 1.5853170
42 -20.0584442 4.7950437
43 -14.5853540 -20.0584442
44 0.4671994 -14.5853540
45 1.6220914 0.4671994
46 -7.6631154 1.6220914
47 3.5661733 -7.6631154
48 3.2149496 3.5661733
49 5.3345691 3.2149496
50 -1.1189933 5.3345691
51 -1.7678038 -1.1189933
52 -0.4910110 -1.7678038
53 -4.9994080 -0.4910110
54 -5.9468547 -4.9994080
55 4.8702772 -5.9468547
56 2.5386155 4.8702772
57 -6.1185749 2.5386155
58 3.2521767 -6.1185749
59 3.3875066 3.2521767
60 -6.7747502 3.3875066
61 7.4730513 -6.7747502
62 9.2828525 7.4730513
63 -17.2646686 9.2828525
64 -16.8670244 -17.2646686
65 12.4488925 -16.8670244
66 1.2939662 12.4488925
67 4.1502222 1.2939662
68 -8.0326460 4.1502222
69 -1.4387044 -8.0326460
70 -3.1898535 -1.4387044
71 -16.8117714 -3.1898535
72 9.8017324 -16.8117714
73 -4.1457143 9.8017324
74 13.2994340 -4.1457143
75 15.2664085 13.2994340
76 -3.2150958 15.2664085
77 1.9873002 -3.2150958
78 5.5532999 1.9873002
79 -18.1782872 5.5532999
80 6.1997950 -18.1782872
81 -44.4025608 6.1997950
82 -3.1537100 -44.4025608
83 -0.2146775 -3.1537100
84 -0.3304796 -0.2146775
85 6.1890654 -0.3304796
86 3.2220567 6.1890654
87 -2.5290925 3.2220567
88 -14.7388534 -2.5290925
89 11.2207905 -14.7388534
90 -0.7947715 11.2207905
91 -14.8630696 -0.7947715
92 4.9736243 -14.8630696
93 12.1285163 4.9736243
94 7.8359788 12.1285163
95 5.6531106 7.8359788
96 1.4908537 5.6531106
97 5.4215064 1.4908537
98 5.6032911 5.4215064
99 1.7777452 5.6032911
100 -0.1148668 1.7777452
101 5.2838267 -0.1148668
102 10.9986198 5.2838267
103 -4.0501162 10.9986198
104 8.3828006 -4.0501162
105 4.8010562 8.3828006
106 1.1976512 4.8010562
107 0.2585844 1.1976512
108 -12.7901516 0.2585844
109 -15.1134333 -12.7901516
110 9.8636741 -15.1134333
111 6.1600289 9.8636741
112 15.7260286 6.1600289
113 -32.6995918 15.7260286
114 -0.3494516 -32.6995918
115 10.1896554 -0.3494516
116 -17.4591551 10.1896554
117 -2.1359076 -17.4591551
118 -5.7187013 -2.1359076
119 19.0519758 -5.7187013
120 -15.1382230 19.0519758
121 4.3338179 -15.1382230
122 14.4607680 4.3338179
123 -0.4866787 14.4607680
124 -1.6220428 -0.4866787
125 6.1877583 -1.6220428
126 11.5378985 6.1877583
127 -3.2328127 11.5378985
128 2.5099224 -3.2328127
129 -1.5583757 2.5099224
130 -2.0192687 -1.5583757
131 2.3598628 -2.0192687
132 1.4113668 2.3598628
133 -7.4533032 1.4113668
134 -0.4007498 -7.4533032
135 7.9029356 -0.4007498
136 -9.7923296 7.9029356
137 -9.3286686 -9.7923296
138 -14.3225700 -9.3286686
139 -11.2431239 -14.3225700
140 -3.7979757 -11.2431239
141 -21.9612818 -3.7979757
142 9.6130978 -21.9612818
143 12.1522048 9.6130978
144 2.1851961 12.1522048
145 12.5828404 2.1851961
146 0.1767820 12.5828404
147 -10.6012598 0.1767820
148 2.1610373 -10.6012598
149 7.9792184 2.1610373
150 9.0111604 7.9792184
151 8.9233003 9.0111604
152 -6.9776916 8.9233003
153 -0.4106426 -6.9776916
154 3.7721914 -0.4106426
155 8.3930257 3.7721914
156 -17.5544209 8.3930257
157 -4.1567767 -17.5544209
158 1.2677602 -4.1567767
159 8.5092804 1.2677602
160 15.1116020 8.5092804
161 -2.1261008 15.1116020
162 NA -2.1261008
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 13.6940121 -14.9268223
[2,] 2.0378709 13.6940121
[3,] 0.8001681 2.0378709
[4,] 11.7744732 0.8001681
[5,] 6.1637375 11.7744732
[6,] -8.8217840 6.1637375
[7,] 11.1479928 -8.8217840
[8,] 3.5456370 11.1479928
[9,] 7.4102728 3.5456370
[10,] 6.6116196 7.4102728
[11,] -21.5516865 6.6116196
[12,] 4.1847672 -21.5516865
[13,] -15.0456050 4.1847672
[14,] 13.5203947 -15.0456050
[15,] -9.2772092 13.5203947
[16,] 7.6813854 -9.2772092
[17,] 15.9571289 7.6813854
[18,] -5.9428137 15.9571289
[19,] -12.9293845 -5.9428137
[20,] -9.6340789 -12.9293845
[21,] 3.2511682 -9.6340789
[22,] -14.8377412 3.2511682
[23,] -9.7572459 -14.8377412
[24,] 13.6672910 -9.7572459
[25,] 13.7600178 13.6672910
[26,] 4.5100835 13.7600178
[27,] 1.8612730 4.5100835
[28,] 9.2589172 1.8612730
[29,] 1.2845779 9.2589172
[30,] 7.0737677 1.2845779
[31,] -12.3787453 7.0737677
[32,] -2.2992993 -12.3787453
[33,] 13.1458490 -2.2992993
[34,] 5.3007410 13.1458490
[35,] -2.7686064 5.3007410
[36,] 3.2933762 -2.7686064
[37,] 0.6155744 3.2933762
[38,] 2.4522683 0.6155744
[39,] -0.8867239 2.4522683
[40,] 1.5853170 -0.8867239
[41,] 4.7950437 1.5853170
[42,] -20.0584442 4.7950437
[43,] -14.5853540 -20.0584442
[44,] 0.4671994 -14.5853540
[45,] 1.6220914 0.4671994
[46,] -7.6631154 1.6220914
[47,] 3.5661733 -7.6631154
[48,] 3.2149496 3.5661733
[49,] 5.3345691 3.2149496
[50,] -1.1189933 5.3345691
[51,] -1.7678038 -1.1189933
[52,] -0.4910110 -1.7678038
[53,] -4.9994080 -0.4910110
[54,] -5.9468547 -4.9994080
[55,] 4.8702772 -5.9468547
[56,] 2.5386155 4.8702772
[57,] -6.1185749 2.5386155
[58,] 3.2521767 -6.1185749
[59,] 3.3875066 3.2521767
[60,] -6.7747502 3.3875066
[61,] 7.4730513 -6.7747502
[62,] 9.2828525 7.4730513
[63,] -17.2646686 9.2828525
[64,] -16.8670244 -17.2646686
[65,] 12.4488925 -16.8670244
[66,] 1.2939662 12.4488925
[67,] 4.1502222 1.2939662
[68,] -8.0326460 4.1502222
[69,] -1.4387044 -8.0326460
[70,] -3.1898535 -1.4387044
[71,] -16.8117714 -3.1898535
[72,] 9.8017324 -16.8117714
[73,] -4.1457143 9.8017324
[74,] 13.2994340 -4.1457143
[75,] 15.2664085 13.2994340
[76,] -3.2150958 15.2664085
[77,] 1.9873002 -3.2150958
[78,] 5.5532999 1.9873002
[79,] -18.1782872 5.5532999
[80,] 6.1997950 -18.1782872
[81,] -44.4025608 6.1997950
[82,] -3.1537100 -44.4025608
[83,] -0.2146775 -3.1537100
[84,] -0.3304796 -0.2146775
[85,] 6.1890654 -0.3304796
[86,] 3.2220567 6.1890654
[87,] -2.5290925 3.2220567
[88,] -14.7388534 -2.5290925
[89,] 11.2207905 -14.7388534
[90,] -0.7947715 11.2207905
[91,] -14.8630696 -0.7947715
[92,] 4.9736243 -14.8630696
[93,] 12.1285163 4.9736243
[94,] 7.8359788 12.1285163
[95,] 5.6531106 7.8359788
[96,] 1.4908537 5.6531106
[97,] 5.4215064 1.4908537
[98,] 5.6032911 5.4215064
[99,] 1.7777452 5.6032911
[100,] -0.1148668 1.7777452
[101,] 5.2838267 -0.1148668
[102,] 10.9986198 5.2838267
[103,] -4.0501162 10.9986198
[104,] 8.3828006 -4.0501162
[105,] 4.8010562 8.3828006
[106,] 1.1976512 4.8010562
[107,] 0.2585844 1.1976512
[108,] -12.7901516 0.2585844
[109,] -15.1134333 -12.7901516
[110,] 9.8636741 -15.1134333
[111,] 6.1600289 9.8636741
[112,] 15.7260286 6.1600289
[113,] -32.6995918 15.7260286
[114,] -0.3494516 -32.6995918
[115,] 10.1896554 -0.3494516
[116,] -17.4591551 10.1896554
[117,] -2.1359076 -17.4591551
[118,] -5.7187013 -2.1359076
[119,] 19.0519758 -5.7187013
[120,] -15.1382230 19.0519758
[121,] 4.3338179 -15.1382230
[122,] 14.4607680 4.3338179
[123,] -0.4866787 14.4607680
[124,] -1.6220428 -0.4866787
[125,] 6.1877583 -1.6220428
[126,] 11.5378985 6.1877583
[127,] -3.2328127 11.5378985
[128,] 2.5099224 -3.2328127
[129,] -1.5583757 2.5099224
[130,] -2.0192687 -1.5583757
[131,] 2.3598628 -2.0192687
[132,] 1.4113668 2.3598628
[133,] -7.4533032 1.4113668
[134,] -0.4007498 -7.4533032
[135,] 7.9029356 -0.4007498
[136,] -9.7923296 7.9029356
[137,] -9.3286686 -9.7923296
[138,] -14.3225700 -9.3286686
[139,] -11.2431239 -14.3225700
[140,] -3.7979757 -11.2431239
[141,] -21.9612818 -3.7979757
[142,] 9.6130978 -21.9612818
[143,] 12.1522048 9.6130978
[144,] 2.1851961 12.1522048
[145,] 12.5828404 2.1851961
[146,] 0.1767820 12.5828404
[147,] -10.6012598 0.1767820
[148,] 2.1610373 -10.6012598
[149,] 7.9792184 2.1610373
[150,] 9.0111604 7.9792184
[151,] 8.9233003 9.0111604
[152,] -6.9776916 8.9233003
[153,] -0.4106426 -6.9776916
[154,] 3.7721914 -0.4106426
[155,] 8.3930257 3.7721914
[156,] -17.5544209 8.3930257
[157,] -4.1567767 -17.5544209
[158,] 1.2677602 -4.1567767
[159,] 8.5092804 1.2677602
[160,] 15.1116020 8.5092804
[161,] -2.1261008 15.1116020
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 13.6940121 -14.9268223
2 2.0378709 13.6940121
3 0.8001681 2.0378709
4 11.7744732 0.8001681
5 6.1637375 11.7744732
6 -8.8217840 6.1637375
7 11.1479928 -8.8217840
8 3.5456370 11.1479928
9 7.4102728 3.5456370
10 6.6116196 7.4102728
11 -21.5516865 6.6116196
12 4.1847672 -21.5516865
13 -15.0456050 4.1847672
14 13.5203947 -15.0456050
15 -9.2772092 13.5203947
16 7.6813854 -9.2772092
17 15.9571289 7.6813854
18 -5.9428137 15.9571289
19 -12.9293845 -5.9428137
20 -9.6340789 -12.9293845
21 3.2511682 -9.6340789
22 -14.8377412 3.2511682
23 -9.7572459 -14.8377412
24 13.6672910 -9.7572459
25 13.7600178 13.6672910
26 4.5100835 13.7600178
27 1.8612730 4.5100835
28 9.2589172 1.8612730
29 1.2845779 9.2589172
30 7.0737677 1.2845779
31 -12.3787453 7.0737677
32 -2.2992993 -12.3787453
33 13.1458490 -2.2992993
34 5.3007410 13.1458490
35 -2.7686064 5.3007410
36 3.2933762 -2.7686064
37 0.6155744 3.2933762
38 2.4522683 0.6155744
39 -0.8867239 2.4522683
40 1.5853170 -0.8867239
41 4.7950437 1.5853170
42 -20.0584442 4.7950437
43 -14.5853540 -20.0584442
44 0.4671994 -14.5853540
45 1.6220914 0.4671994
46 -7.6631154 1.6220914
47 3.5661733 -7.6631154
48 3.2149496 3.5661733
49 5.3345691 3.2149496
50 -1.1189933 5.3345691
51 -1.7678038 -1.1189933
52 -0.4910110 -1.7678038
53 -4.9994080 -0.4910110
54 -5.9468547 -4.9994080
55 4.8702772 -5.9468547
56 2.5386155 4.8702772
57 -6.1185749 2.5386155
58 3.2521767 -6.1185749
59 3.3875066 3.2521767
60 -6.7747502 3.3875066
61 7.4730513 -6.7747502
62 9.2828525 7.4730513
63 -17.2646686 9.2828525
64 -16.8670244 -17.2646686
65 12.4488925 -16.8670244
66 1.2939662 12.4488925
67 4.1502222 1.2939662
68 -8.0326460 4.1502222
69 -1.4387044 -8.0326460
70 -3.1898535 -1.4387044
71 -16.8117714 -3.1898535
72 9.8017324 -16.8117714
73 -4.1457143 9.8017324
74 13.2994340 -4.1457143
75 15.2664085 13.2994340
76 -3.2150958 15.2664085
77 1.9873002 -3.2150958
78 5.5532999 1.9873002
79 -18.1782872 5.5532999
80 6.1997950 -18.1782872
81 -44.4025608 6.1997950
82 -3.1537100 -44.4025608
83 -0.2146775 -3.1537100
84 -0.3304796 -0.2146775
85 6.1890654 -0.3304796
86 3.2220567 6.1890654
87 -2.5290925 3.2220567
88 -14.7388534 -2.5290925
89 11.2207905 -14.7388534
90 -0.7947715 11.2207905
91 -14.8630696 -0.7947715
92 4.9736243 -14.8630696
93 12.1285163 4.9736243
94 7.8359788 12.1285163
95 5.6531106 7.8359788
96 1.4908537 5.6531106
97 5.4215064 1.4908537
98 5.6032911 5.4215064
99 1.7777452 5.6032911
100 -0.1148668 1.7777452
101 5.2838267 -0.1148668
102 10.9986198 5.2838267
103 -4.0501162 10.9986198
104 8.3828006 -4.0501162
105 4.8010562 8.3828006
106 1.1976512 4.8010562
107 0.2585844 1.1976512
108 -12.7901516 0.2585844
109 -15.1134333 -12.7901516
110 9.8636741 -15.1134333
111 6.1600289 9.8636741
112 15.7260286 6.1600289
113 -32.6995918 15.7260286
114 -0.3494516 -32.6995918
115 10.1896554 -0.3494516
116 -17.4591551 10.1896554
117 -2.1359076 -17.4591551
118 -5.7187013 -2.1359076
119 19.0519758 -5.7187013
120 -15.1382230 19.0519758
121 4.3338179 -15.1382230
122 14.4607680 4.3338179
123 -0.4866787 14.4607680
124 -1.6220428 -0.4866787
125 6.1877583 -1.6220428
126 11.5378985 6.1877583
127 -3.2328127 11.5378985
128 2.5099224 -3.2328127
129 -1.5583757 2.5099224
130 -2.0192687 -1.5583757
131 2.3598628 -2.0192687
132 1.4113668 2.3598628
133 -7.4533032 1.4113668
134 -0.4007498 -7.4533032
135 7.9029356 -0.4007498
136 -9.7923296 7.9029356
137 -9.3286686 -9.7923296
138 -14.3225700 -9.3286686
139 -11.2431239 -14.3225700
140 -3.7979757 -11.2431239
141 -21.9612818 -3.7979757
142 9.6130978 -21.9612818
143 12.1522048 9.6130978
144 2.1851961 12.1522048
145 12.5828404 2.1851961
146 0.1767820 12.5828404
147 -10.6012598 0.1767820
148 2.1610373 -10.6012598
149 7.9792184 2.1610373
150 9.0111604 7.9792184
151 8.9233003 9.0111604
152 -6.9776916 8.9233003
153 -0.4106426 -6.9776916
154 3.7721914 -0.4106426
155 8.3930257 3.7721914
156 -17.5544209 8.3930257
157 -4.1567767 -17.5544209
158 1.2677602 -4.1567767
159 8.5092804 1.2677602
160 15.1116020 8.5092804
161 -2.1261008 15.1116020
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7rwqo1290561126.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rwqo1290561126.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rwqo1290561126.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101n791290561126.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11no5f1290561126.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/128om21290561126.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13mg1t1290561126.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14qh0z1290561126.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15thzn1290561126.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16x0xb1290561126.tab")
+ }
>
> try(system("convert tmp/1v4ax1290561126.ps tmp/1v4ax1290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/2v4ax1290561126.ps tmp/2v4ax1290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/35vr01290561126.ps tmp/35vr01290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/45vr01290561126.ps tmp/45vr01290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/55vr01290561126.ps tmp/55vr01290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/63qfr1290561126.ps tmp/63qfr1290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rwqo1290561126.ps tmp/7rwqo1290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rwqo1290561126.ps tmp/8rwqo1290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rwqo1290561126.ps tmp/9rwqo1290561126.png",intern=TRUE))
character(0)
> try(system("convert tmp/101n791290561126.ps tmp/101n791290561126.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.911 1.718 9.375