R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(13
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+ ,2)
+ ,dim=c(5
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity')
+ ,1:156))
> y <- array(NA,dim=c(5,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity'),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 = '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
KnowingPeople Popularity FindingFriends Liked Celebrity t
1 14 13 13 13 3 1
2 8 12 12 13 5 2
3 12 15 10 16 6 3
4 7 12 9 12 6 4
5 10 10 10 11 5 5
6 7 12 12 12 3 6
7 16 15 13 18 8 7
8 11 9 12 11 4 8
9 14 12 12 14 4 9
10 6 11 6 9 4 10
11 16 11 5 14 6 11
12 11 11 12 12 6 12
13 16 15 11 11 5 13
14 12 7 14 12 4 14
15 7 11 14 13 6 15
16 13 11 12 11 4 16
17 11 10 12 12 6 17
18 15 14 11 16 6 18
19 7 10 11 9 4 19
20 9 6 7 11 4 20
21 7 11 9 13 2 21
22 14 15 11 15 7 22
23 15 11 11 10 5 23
24 7 12 12 11 4 24
25 15 14 12 13 6 25
26 17 15 11 16 6 26
27 15 9 11 15 7 27
28 14 13 8 14 5 28
29 14 13 9 14 6 29
30 8 16 12 14 4 30
31 8 13 10 8 4 31
32 14 12 10 13 7 32
33 14 14 12 15 7 33
34 8 11 8 13 4 34
35 11 9 12 11 4 35
36 16 16 11 15 6 36
37 10 12 12 15 6 37
38 8 10 7 9 5 38
39 14 13 11 13 6 39
40 16 16 11 16 7 40
41 13 14 12 13 6 41
42 5 15 9 11 3 42
43 8 5 15 12 3 43
44 10 8 11 12 4 44
45 8 11 11 12 6 45
46 13 16 11 14 7 46
47 15 17 11 14 5 47
48 6 9 15 8 4 48
49 12 9 11 13 5 49
50 16 13 12 16 6 50
51 5 10 12 13 6 51
52 15 6 9 11 6 52
53 12 12 12 14 5 53
54 8 8 12 13 4 54
55 13 14 13 13 5 55
56 14 12 11 13 5 56
57 12 11 9 12 4 57
58 16 16 9 16 6 58
59 10 8 11 15 2 59
60 15 15 11 15 8 60
61 8 7 12 12 3 61
62 16 16 12 14 6 62
63 19 14 9 12 6 63
64 14 16 11 15 6 64
65 6 9 9 12 5 65
66 13 14 12 13 5 66
67 15 11 12 12 6 67
68 7 13 12 12 5 68
69 13 15 12 13 6 69
70 4 5 14 5 2 70
71 14 15 11 13 5 71
72 13 13 12 13 5 72
73 11 11 11 14 5 73
74 14 11 6 17 6 74
75 12 12 10 13 6 75
76 15 12 12 13 6 76
77 14 12 13 12 5 77
78 13 12 8 13 5 78
79 8 14 12 14 4 79
80 6 6 12 11 2 80
81 7 7 12 12 4 81
82 13 14 6 12 6 82
83 13 14 11 16 6 83
84 11 10 10 12 5 84
85 5 13 12 12 3 85
86 12 12 13 12 6 86
87 8 9 11 10 4 87
88 11 12 7 15 5 88
89 14 16 11 15 8 89
90 9 10 11 12 4 90
91 10 14 11 16 6 91
92 13 10 11 15 6 92
93 16 16 12 16 7 93
94 16 15 10 13 6 94
95 11 12 11 12 5 95
96 8 10 12 11 4 96
97 4 8 7 13 6 97
98 7 8 13 10 3 98
99 14 11 8 15 5 99
100 11 13 12 13 6 100
101 17 16 11 16 7 101
102 15 16 12 15 7 102
103 17 14 14 18 6 103
104 5 11 10 13 3 104
105 4 4 10 10 2 105
106 10 14 13 16 8 106
107 11 9 10 13 3 107
108 15 14 11 15 8 108
109 10 8 10 14 3 109
110 9 8 7 15 4 110
111 12 11 10 14 5 111
112 15 12 8 13 7 112
113 7 11 12 13 6 113
114 13 14 12 15 6 114
115 12 15 12 16 7 115
116 14 16 11 14 6 116
117 14 16 12 14 6 117
118 8 11 12 16 6 118
119 15 14 12 14 6 119
120 12 14 11 12 4 120
121 12 12 12 13 4 121
122 16 14 11 12 5 122
123 9 8 11 12 4 123
124 15 13 13 14 6 124
125 15 16 12 14 6 125
126 6 12 12 14 5 126
127 14 16 12 16 8 127
128 15 12 12 13 6 128
129 10 11 8 14 5 129
130 6 4 8 4 4 130
131 14 16 12 16 8 131
132 12 15 11 13 6 132
133 8 10 12 16 4 133
134 11 13 13 15 6 134
135 13 15 12 14 6 135
136 9 12 12 13 4 136
137 15 14 11 14 6 137
138 13 7 12 12 3 138
139 15 19 12 15 6 139
140 14 12 10 14 5 140
141 16 12 11 13 4 141
142 14 13 12 14 6 142
143 14 15 12 16 4 143
144 10 8 10 6 4 144
145 10 12 12 13 4 145
146 4 10 13 13 6 146
147 8 8 12 14 5 147
148 15 10 15 15 6 148
149 16 15 11 14 6 149
150 12 16 12 15 8 150
151 12 13 11 13 7 151
152 15 16 12 16 7 152
153 9 9 11 12 4 153
154 12 14 10 15 6 154
155 14 14 11 12 6 155
156 11 12 11 14 2 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Popularity FindingFriends Liked Celebrity
0.725681 0.387097 -0.067559 0.273999 0.670539
t
-0.001877
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.0295 -1.4997 0.1820 1.7227 6.2700
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.725681 1.803242 0.402 0.68794
Popularity 0.387097 0.098004 3.950 0.00012 ***
FindingFriends -0.067559 0.122209 -0.553 0.58121
Liked 0.273999 0.126403 2.168 0.03176 *
Celebrity 0.670539 0.200407 3.346 0.00104 **
t -0.001877 0.004821 -0.389 0.69752
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.664 on 150 degrees of freedom
Multiple R-squared: 0.4269, Adjusted R-squared: 0.4078
F-statistic: 22.35 on 5 and 150 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.6729937 0.6540125 0.32700625
[2,] 0.6424957 0.7150087 0.35750434
[3,] 0.5084961 0.9830078 0.49150392
[4,] 0.4289003 0.8578006 0.57109972
[5,] 0.7753983 0.4492034 0.22460168
[6,] 0.7115979 0.5768041 0.28840206
[7,] 0.9090632 0.1818735 0.09093676
[8,] 0.8738436 0.2523129 0.12615643
[9,] 0.8240577 0.3518847 0.17594234
[10,] 0.8105644 0.3788712 0.18943558
[11,] 0.8079250 0.3841500 0.19207500
[12,] 0.7727507 0.4544986 0.22724930
[13,] 0.8926920 0.2146161 0.10730803
[14,] 0.8560851 0.2878298 0.14391489
[15,] 0.9039594 0.1920812 0.09604058
[16,] 0.9275998 0.1448005 0.07240025
[17,] 0.9115490 0.1769020 0.08845099
[18,] 0.8945058 0.2109883 0.10549417
[19,] 0.8712487 0.2575025 0.12875127
[20,] 0.8372299 0.3255402 0.16277011
[21,] 0.7975038 0.4049924 0.20249619
[22,] 0.8863549 0.2272902 0.11364509
[23,] 0.8619782 0.2760436 0.13802178
[24,] 0.8279024 0.3441952 0.17209761
[25,] 0.7923349 0.4153302 0.20766510
[26,] 0.8037969 0.3924062 0.19620309
[27,] 0.7706032 0.4587935 0.22939675
[28,] 0.7416520 0.5166959 0.25834797
[29,] 0.7721714 0.4556572 0.22782860
[30,] 0.7448096 0.5103808 0.25519041
[31,] 0.7086820 0.5826361 0.29131804
[32,] 0.6619487 0.6761027 0.33805133
[33,] 0.6102281 0.7795437 0.38977186
[34,] 0.7338435 0.5323130 0.26615651
[35,] 0.6993090 0.6013820 0.30069099
[36,] 0.6547606 0.6904789 0.34523945
[37,] 0.6927269 0.6145462 0.30727311
[38,] 0.6567761 0.6864478 0.34322389
[39,] 0.6436088 0.7127825 0.35639124
[40,] 0.6136709 0.7726583 0.38632914
[41,] 0.5778939 0.8442122 0.42210612
[42,] 0.5673143 0.8653715 0.43268573
[43,] 0.7823615 0.4352770 0.21763848
[44,] 0.8807979 0.2384043 0.11920213
[45,] 0.8544386 0.2911228 0.14556138
[46,] 0.8347145 0.3305710 0.16528549
[47,] 0.8109904 0.3780192 0.18900962
[48,] 0.8084706 0.3830587 0.19152935
[49,] 0.7874851 0.4250298 0.21251489
[50,] 0.7569173 0.4861653 0.24308265
[51,] 0.7286526 0.5426948 0.27134739
[52,] 0.6887176 0.6225649 0.31128244
[53,] 0.6488966 0.7022069 0.35110344
[54,] 0.6356458 0.7287083 0.36435417
[55,] 0.8070194 0.3859612 0.19298059
[56,] 0.7742912 0.4514176 0.22570879
[57,] 0.8314284 0.3371433 0.16857165
[58,] 0.8026027 0.3947945 0.19739725
[59,] 0.8324677 0.3350646 0.16753228
[60,] 0.8814614 0.2370771 0.11853856
[61,] 0.8563279 0.2873443 0.14367213
[62,] 0.8297318 0.3405364 0.17026822
[63,] 0.8055186 0.3889628 0.19448139
[64,] 0.7783432 0.4433137 0.22165683
[65,] 0.7466575 0.5066850 0.25334250
[66,] 0.7268404 0.5463193 0.27315965
[67,] 0.6889991 0.6220018 0.31100090
[68,] 0.7080060 0.5839879 0.29199396
[69,] 0.7259681 0.5480638 0.27403191
[70,] 0.6996154 0.6007693 0.30038463
[71,] 0.7414911 0.5170179 0.25850893
[72,] 0.7038349 0.5923302 0.29616511
[73,] 0.6771063 0.6457874 0.32289371
[74,] 0.6334693 0.7330614 0.36653069
[75,] 0.5959250 0.8081499 0.40407497
[76,] 0.5581106 0.8837788 0.44188939
[77,] 0.7074072 0.5851856 0.29259278
[78,] 0.6685502 0.6628997 0.33144984
[79,] 0.6276094 0.7447811 0.37239056
[80,] 0.5897286 0.8205428 0.41027140
[81,] 0.5577971 0.8844057 0.44220287
[82,] 0.5129863 0.9740273 0.48701367
[83,] 0.5474242 0.9051515 0.45257577
[84,] 0.5390245 0.9219510 0.46097549
[85,] 0.5039530 0.9920940 0.49604699
[86,] 0.5070701 0.9858598 0.49292991
[87,] 0.4585867 0.9171735 0.54141326
[88,] 0.4237666 0.8475332 0.57623341
[89,] 0.6235438 0.7529123 0.37645615
[90,] 0.5809058 0.8381883 0.41909416
[91,] 0.5818868 0.8362265 0.41811323
[92,] 0.5439452 0.9121095 0.45605476
[93,] 0.5347129 0.9305742 0.46528710
[94,] 0.4880755 0.9761510 0.51192451
[95,] 0.5715826 0.8568348 0.42841741
[96,] 0.7178604 0.5642792 0.28213960
[97,] 0.7050686 0.5898628 0.29493141
[98,] 0.7481537 0.5036926 0.25184629
[99,] 0.7244147 0.5511707 0.27558534
[100,] 0.7038109 0.5923782 0.29618910
[101,] 0.6669826 0.6660348 0.33301742
[102,] 0.6186660 0.7626680 0.38133400
[103,] 0.5769971 0.8460059 0.42300293
[104,] 0.6337927 0.7324146 0.36620731
[105,] 0.6921355 0.6157291 0.30786454
[106,] 0.6430669 0.7138662 0.35693310
[107,] 0.6084060 0.7831879 0.39159396
[108,] 0.5555082 0.8889836 0.44449180
[109,] 0.5025633 0.9948734 0.49743670
[110,] 0.5222558 0.9554883 0.47774415
[111,] 0.4983849 0.9967697 0.50161513
[112,] 0.4610545 0.9221089 0.53894553
[113,] 0.4110360 0.8220720 0.58896401
[114,] 0.4388687 0.8777375 0.56113125
[115,] 0.3808389 0.7616779 0.61916105
[116,] 0.3842359 0.7684719 0.61576407
[117,] 0.3397133 0.6794266 0.66028669
[118,] 0.5349922 0.9300156 0.46500778
[119,] 0.4750546 0.9501092 0.52494539
[120,] 0.5166956 0.9666088 0.48330442
[121,] 0.4553801 0.9107601 0.54461994
[122,] 0.3996884 0.7993769 0.60031157
[123,] 0.3403609 0.6807218 0.65963910
[124,] 0.2953250 0.5906500 0.70467501
[125,] 0.3137728 0.6275457 0.68622715
[126,] 0.2782415 0.5564831 0.72175847
[127,] 0.2282790 0.4565579 0.77172105
[128,] 0.2987177 0.5974354 0.70128228
[129,] 0.2397091 0.4794181 0.76029093
[130,] 0.2570509 0.5141018 0.74294912
[131,] 0.2547863 0.5095727 0.74521367
[132,] 0.2112340 0.4224681 0.78876596
[133,] 0.2952770 0.5905540 0.70472298
[134,] 0.2642379 0.5284759 0.73576206
[135,] 0.2007502 0.4015004 0.79924980
[136,] 0.2167331 0.4334661 0.78326695
[137,] 0.1414985 0.2829971 0.85850145
[138,] 0.6600139 0.6799721 0.33998607
[139,] 0.5452941 0.9094117 0.45470586
> postscript(file="/var/www/html/rcomp/tmp/1bf5l1290541781.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lo4o1290541781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lo4o1290541781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lo4o1290541781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wx3r1290541781.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 = 156
Frequency = 1
1 2 3 4 5 6
3.54860127 -3.47106145 -2.25813014 -5.06652439 -0.27835607 -2.84847571
7 8 9 10 11 12
0.06298067 1.92002987 2.93861859 -3.70776667 3.51547709 -0.46173166
13 14 15 16 17 18
3.86873695 3.56660630 -4.59498062 3.16085408 -0.06524865 1.22468456
19 20 21 22 23 24
-1.96597804 0.76605053 -2.23935896 -0.55144274 4.70989599 -3.21122488
25 26 27 28 29 30
2.12738254 2.85260560 2.78052447 1.64641314 1.04531098 -4.57034683
31 32 33 34 35 36
-1.89830174 1.10905946 -0.07613698 -2.62359170 1.97071522 1.75828045
37 38 39 40 41 42
-2.62389563 -1.87108665 1.47320134 0.82125132 0.15741831 -5.87086444
43 44 45 46 47 48
1.13333786 1.03314851 -3.46734224 -1.61948660 1.33637126 -1.98020471
49 50 51 52 53 54
1.71089978 2.73941226 -6.27542199 5.62016326 0.35067820 -1.15451914
55 56 57 58 59 60
0.92179767 2.56274991 1.76114338 1.39046162 1.58038649 -0.15064653
61 62 63 64 65 66
0.19025641 2.14864723 6.27003881 -0.18915697 -4.12018380 0.87488792
67 68 69 70 71 72
3.64151627 -4.46026144 -0.17711595 -0.29500205 1.42961792 1.27324817
73 74 75 76 77 78
-0.29223959 0.87930423 -0.13968069 2.99731521 3.01128985 1.40137107
79 80 81 82 83 84
-3.70416863 -0.44244122 -1.44273762 0.10302827 -0.65329516 0.59594617
85 86 87 88 89 90
-5.08727098 0.35764623 -0.72522855 -1.19541458 -1.48330355 -0.65469236
91 92 93 94 95 96
-3.63827728 1.18598665 0.98830412 2.73469626 -0.09003859 -1.30187027
97 98 99 100 101 102
-6.75267217 -0.51182472 2.27989118 -1.34472798 1.93576266 0.27919857
103 104 105 106 107 108
3.03892888 -4.68652784 -1.48243600 -4.81607754 2.09329754 0.32655759
109 110 111 112 113 114
1.21014951 -0.93518932 0.71153601 2.12411960 -4.54613025 -0.25354220
115 116 117 118 119 120
-2.58329987 0.18245859 0.25189516 -4.35874208 2.02984331 0.85323735
121 122 123 124 125 126
1.42286826 4.18645308 0.18145008 2.49388566 1.26691305 -5.51228363
127 128 129 130 131 132
-1.61840862 3.09493144 -1.38979243 0.73229476 -1.61089968 -1.12640947
133 134 135 136 137 138
-2.60240925 -1.76134132 -0.32721776 -1.54897322 1.99607421 5.33480351
139 140 141 142 143 144
-0.14209551 2.37887899 5.39285362 1.46011656 1.48087891 2.79730869
145 146 147 148 149 150
-0.53207810 -7.02952532 -1.92447434 3.56134915 2.63150419 -3.30123288
151 152 153 154 155 156
-0.98708706 0.09906099 -0.14932970 -1.31357147 1.57786311 1.48809028
> postscript(file="/var/www/html/rcomp/tmp/6wx3r1290541781.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 3.54860127 NA
1 -3.47106145 3.54860127
2 -2.25813014 -3.47106145
3 -5.06652439 -2.25813014
4 -0.27835607 -5.06652439
5 -2.84847571 -0.27835607
6 0.06298067 -2.84847571
7 1.92002987 0.06298067
8 2.93861859 1.92002987
9 -3.70776667 2.93861859
10 3.51547709 -3.70776667
11 -0.46173166 3.51547709
12 3.86873695 -0.46173166
13 3.56660630 3.86873695
14 -4.59498062 3.56660630
15 3.16085408 -4.59498062
16 -0.06524865 3.16085408
17 1.22468456 -0.06524865
18 -1.96597804 1.22468456
19 0.76605053 -1.96597804
20 -2.23935896 0.76605053
21 -0.55144274 -2.23935896
22 4.70989599 -0.55144274
23 -3.21122488 4.70989599
24 2.12738254 -3.21122488
25 2.85260560 2.12738254
26 2.78052447 2.85260560
27 1.64641314 2.78052447
28 1.04531098 1.64641314
29 -4.57034683 1.04531098
30 -1.89830174 -4.57034683
31 1.10905946 -1.89830174
32 -0.07613698 1.10905946
33 -2.62359170 -0.07613698
34 1.97071522 -2.62359170
35 1.75828045 1.97071522
36 -2.62389563 1.75828045
37 -1.87108665 -2.62389563
38 1.47320134 -1.87108665
39 0.82125132 1.47320134
40 0.15741831 0.82125132
41 -5.87086444 0.15741831
42 1.13333786 -5.87086444
43 1.03314851 1.13333786
44 -3.46734224 1.03314851
45 -1.61948660 -3.46734224
46 1.33637126 -1.61948660
47 -1.98020471 1.33637126
48 1.71089978 -1.98020471
49 2.73941226 1.71089978
50 -6.27542199 2.73941226
51 5.62016326 -6.27542199
52 0.35067820 5.62016326
53 -1.15451914 0.35067820
54 0.92179767 -1.15451914
55 2.56274991 0.92179767
56 1.76114338 2.56274991
57 1.39046162 1.76114338
58 1.58038649 1.39046162
59 -0.15064653 1.58038649
60 0.19025641 -0.15064653
61 2.14864723 0.19025641
62 6.27003881 2.14864723
63 -0.18915697 6.27003881
64 -4.12018380 -0.18915697
65 0.87488792 -4.12018380
66 3.64151627 0.87488792
67 -4.46026144 3.64151627
68 -0.17711595 -4.46026144
69 -0.29500205 -0.17711595
70 1.42961792 -0.29500205
71 1.27324817 1.42961792
72 -0.29223959 1.27324817
73 0.87930423 -0.29223959
74 -0.13968069 0.87930423
75 2.99731521 -0.13968069
76 3.01128985 2.99731521
77 1.40137107 3.01128985
78 -3.70416863 1.40137107
79 -0.44244122 -3.70416863
80 -1.44273762 -0.44244122
81 0.10302827 -1.44273762
82 -0.65329516 0.10302827
83 0.59594617 -0.65329516
84 -5.08727098 0.59594617
85 0.35764623 -5.08727098
86 -0.72522855 0.35764623
87 -1.19541458 -0.72522855
88 -1.48330355 -1.19541458
89 -0.65469236 -1.48330355
90 -3.63827728 -0.65469236
91 1.18598665 -3.63827728
92 0.98830412 1.18598665
93 2.73469626 0.98830412
94 -0.09003859 2.73469626
95 -1.30187027 -0.09003859
96 -6.75267217 -1.30187027
97 -0.51182472 -6.75267217
98 2.27989118 -0.51182472
99 -1.34472798 2.27989118
100 1.93576266 -1.34472798
101 0.27919857 1.93576266
102 3.03892888 0.27919857
103 -4.68652784 3.03892888
104 -1.48243600 -4.68652784
105 -4.81607754 -1.48243600
106 2.09329754 -4.81607754
107 0.32655759 2.09329754
108 1.21014951 0.32655759
109 -0.93518932 1.21014951
110 0.71153601 -0.93518932
111 2.12411960 0.71153601
112 -4.54613025 2.12411960
113 -0.25354220 -4.54613025
114 -2.58329987 -0.25354220
115 0.18245859 -2.58329987
116 0.25189516 0.18245859
117 -4.35874208 0.25189516
118 2.02984331 -4.35874208
119 0.85323735 2.02984331
120 1.42286826 0.85323735
121 4.18645308 1.42286826
122 0.18145008 4.18645308
123 2.49388566 0.18145008
124 1.26691305 2.49388566
125 -5.51228363 1.26691305
126 -1.61840862 -5.51228363
127 3.09493144 -1.61840862
128 -1.38979243 3.09493144
129 0.73229476 -1.38979243
130 -1.61089968 0.73229476
131 -1.12640947 -1.61089968
132 -2.60240925 -1.12640947
133 -1.76134132 -2.60240925
134 -0.32721776 -1.76134132
135 -1.54897322 -0.32721776
136 1.99607421 -1.54897322
137 5.33480351 1.99607421
138 -0.14209551 5.33480351
139 2.37887899 -0.14209551
140 5.39285362 2.37887899
141 1.46011656 5.39285362
142 1.48087891 1.46011656
143 2.79730869 1.48087891
144 -0.53207810 2.79730869
145 -7.02952532 -0.53207810
146 -1.92447434 -7.02952532
147 3.56134915 -1.92447434
148 2.63150419 3.56134915
149 -3.30123288 2.63150419
150 -0.98708706 -3.30123288
151 0.09906099 -0.98708706
152 -0.14932970 0.09906099
153 -1.31357147 -0.14932970
154 1.57786311 -1.31357147
155 1.48809028 1.57786311
156 NA 1.48809028
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.47106145 3.54860127
[2,] -2.25813014 -3.47106145
[3,] -5.06652439 -2.25813014
[4,] -0.27835607 -5.06652439
[5,] -2.84847571 -0.27835607
[6,] 0.06298067 -2.84847571
[7,] 1.92002987 0.06298067
[8,] 2.93861859 1.92002987
[9,] -3.70776667 2.93861859
[10,] 3.51547709 -3.70776667
[11,] -0.46173166 3.51547709
[12,] 3.86873695 -0.46173166
[13,] 3.56660630 3.86873695
[14,] -4.59498062 3.56660630
[15,] 3.16085408 -4.59498062
[16,] -0.06524865 3.16085408
[17,] 1.22468456 -0.06524865
[18,] -1.96597804 1.22468456
[19,] 0.76605053 -1.96597804
[20,] -2.23935896 0.76605053
[21,] -0.55144274 -2.23935896
[22,] 4.70989599 -0.55144274
[23,] -3.21122488 4.70989599
[24,] 2.12738254 -3.21122488
[25,] 2.85260560 2.12738254
[26,] 2.78052447 2.85260560
[27,] 1.64641314 2.78052447
[28,] 1.04531098 1.64641314
[29,] -4.57034683 1.04531098
[30,] -1.89830174 -4.57034683
[31,] 1.10905946 -1.89830174
[32,] -0.07613698 1.10905946
[33,] -2.62359170 -0.07613698
[34,] 1.97071522 -2.62359170
[35,] 1.75828045 1.97071522
[36,] -2.62389563 1.75828045
[37,] -1.87108665 -2.62389563
[38,] 1.47320134 -1.87108665
[39,] 0.82125132 1.47320134
[40,] 0.15741831 0.82125132
[41,] -5.87086444 0.15741831
[42,] 1.13333786 -5.87086444
[43,] 1.03314851 1.13333786
[44,] -3.46734224 1.03314851
[45,] -1.61948660 -3.46734224
[46,] 1.33637126 -1.61948660
[47,] -1.98020471 1.33637126
[48,] 1.71089978 -1.98020471
[49,] 2.73941226 1.71089978
[50,] -6.27542199 2.73941226
[51,] 5.62016326 -6.27542199
[52,] 0.35067820 5.62016326
[53,] -1.15451914 0.35067820
[54,] 0.92179767 -1.15451914
[55,] 2.56274991 0.92179767
[56,] 1.76114338 2.56274991
[57,] 1.39046162 1.76114338
[58,] 1.58038649 1.39046162
[59,] -0.15064653 1.58038649
[60,] 0.19025641 -0.15064653
[61,] 2.14864723 0.19025641
[62,] 6.27003881 2.14864723
[63,] -0.18915697 6.27003881
[64,] -4.12018380 -0.18915697
[65,] 0.87488792 -4.12018380
[66,] 3.64151627 0.87488792
[67,] -4.46026144 3.64151627
[68,] -0.17711595 -4.46026144
[69,] -0.29500205 -0.17711595
[70,] 1.42961792 -0.29500205
[71,] 1.27324817 1.42961792
[72,] -0.29223959 1.27324817
[73,] 0.87930423 -0.29223959
[74,] -0.13968069 0.87930423
[75,] 2.99731521 -0.13968069
[76,] 3.01128985 2.99731521
[77,] 1.40137107 3.01128985
[78,] -3.70416863 1.40137107
[79,] -0.44244122 -3.70416863
[80,] -1.44273762 -0.44244122
[81,] 0.10302827 -1.44273762
[82,] -0.65329516 0.10302827
[83,] 0.59594617 -0.65329516
[84,] -5.08727098 0.59594617
[85,] 0.35764623 -5.08727098
[86,] -0.72522855 0.35764623
[87,] -1.19541458 -0.72522855
[88,] -1.48330355 -1.19541458
[89,] -0.65469236 -1.48330355
[90,] -3.63827728 -0.65469236
[91,] 1.18598665 -3.63827728
[92,] 0.98830412 1.18598665
[93,] 2.73469626 0.98830412
[94,] -0.09003859 2.73469626
[95,] -1.30187027 -0.09003859
[96,] -6.75267217 -1.30187027
[97,] -0.51182472 -6.75267217
[98,] 2.27989118 -0.51182472
[99,] -1.34472798 2.27989118
[100,] 1.93576266 -1.34472798
[101,] 0.27919857 1.93576266
[102,] 3.03892888 0.27919857
[103,] -4.68652784 3.03892888
[104,] -1.48243600 -4.68652784
[105,] -4.81607754 -1.48243600
[106,] 2.09329754 -4.81607754
[107,] 0.32655759 2.09329754
[108,] 1.21014951 0.32655759
[109,] -0.93518932 1.21014951
[110,] 0.71153601 -0.93518932
[111,] 2.12411960 0.71153601
[112,] -4.54613025 2.12411960
[113,] -0.25354220 -4.54613025
[114,] -2.58329987 -0.25354220
[115,] 0.18245859 -2.58329987
[116,] 0.25189516 0.18245859
[117,] -4.35874208 0.25189516
[118,] 2.02984331 -4.35874208
[119,] 0.85323735 2.02984331
[120,] 1.42286826 0.85323735
[121,] 4.18645308 1.42286826
[122,] 0.18145008 4.18645308
[123,] 2.49388566 0.18145008
[124,] 1.26691305 2.49388566
[125,] -5.51228363 1.26691305
[126,] -1.61840862 -5.51228363
[127,] 3.09493144 -1.61840862
[128,] -1.38979243 3.09493144
[129,] 0.73229476 -1.38979243
[130,] -1.61089968 0.73229476
[131,] -1.12640947 -1.61089968
[132,] -2.60240925 -1.12640947
[133,] -1.76134132 -2.60240925
[134,] -0.32721776 -1.76134132
[135,] -1.54897322 -0.32721776
[136,] 1.99607421 -1.54897322
[137,] 5.33480351 1.99607421
[138,] -0.14209551 5.33480351
[139,] 2.37887899 -0.14209551
[140,] 5.39285362 2.37887899
[141,] 1.46011656 5.39285362
[142,] 1.48087891 1.46011656
[143,] 2.79730869 1.48087891
[144,] -0.53207810 2.79730869
[145,] -7.02952532 -0.53207810
[146,] -1.92447434 -7.02952532
[147,] 3.56134915 -1.92447434
[148,] 2.63150419 3.56134915
[149,] -3.30123288 2.63150419
[150,] -0.98708706 -3.30123288
[151,] 0.09906099 -0.98708706
[152,] -0.14932970 0.09906099
[153,] -1.31357147 -0.14932970
[154,] 1.57786311 -1.31357147
[155,] 1.48809028 1.57786311
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.47106145 3.54860127
2 -2.25813014 -3.47106145
3 -5.06652439 -2.25813014
4 -0.27835607 -5.06652439
5 -2.84847571 -0.27835607
6 0.06298067 -2.84847571
7 1.92002987 0.06298067
8 2.93861859 1.92002987
9 -3.70776667 2.93861859
10 3.51547709 -3.70776667
11 -0.46173166 3.51547709
12 3.86873695 -0.46173166
13 3.56660630 3.86873695
14 -4.59498062 3.56660630
15 3.16085408 -4.59498062
16 -0.06524865 3.16085408
17 1.22468456 -0.06524865
18 -1.96597804 1.22468456
19 0.76605053 -1.96597804
20 -2.23935896 0.76605053
21 -0.55144274 -2.23935896
22 4.70989599 -0.55144274
23 -3.21122488 4.70989599
24 2.12738254 -3.21122488
25 2.85260560 2.12738254
26 2.78052447 2.85260560
27 1.64641314 2.78052447
28 1.04531098 1.64641314
29 -4.57034683 1.04531098
30 -1.89830174 -4.57034683
31 1.10905946 -1.89830174
32 -0.07613698 1.10905946
33 -2.62359170 -0.07613698
34 1.97071522 -2.62359170
35 1.75828045 1.97071522
36 -2.62389563 1.75828045
37 -1.87108665 -2.62389563
38 1.47320134 -1.87108665
39 0.82125132 1.47320134
40 0.15741831 0.82125132
41 -5.87086444 0.15741831
42 1.13333786 -5.87086444
43 1.03314851 1.13333786
44 -3.46734224 1.03314851
45 -1.61948660 -3.46734224
46 1.33637126 -1.61948660
47 -1.98020471 1.33637126
48 1.71089978 -1.98020471
49 2.73941226 1.71089978
50 -6.27542199 2.73941226
51 5.62016326 -6.27542199
52 0.35067820 5.62016326
53 -1.15451914 0.35067820
54 0.92179767 -1.15451914
55 2.56274991 0.92179767
56 1.76114338 2.56274991
57 1.39046162 1.76114338
58 1.58038649 1.39046162
59 -0.15064653 1.58038649
60 0.19025641 -0.15064653
61 2.14864723 0.19025641
62 6.27003881 2.14864723
63 -0.18915697 6.27003881
64 -4.12018380 -0.18915697
65 0.87488792 -4.12018380
66 3.64151627 0.87488792
67 -4.46026144 3.64151627
68 -0.17711595 -4.46026144
69 -0.29500205 -0.17711595
70 1.42961792 -0.29500205
71 1.27324817 1.42961792
72 -0.29223959 1.27324817
73 0.87930423 -0.29223959
74 -0.13968069 0.87930423
75 2.99731521 -0.13968069
76 3.01128985 2.99731521
77 1.40137107 3.01128985
78 -3.70416863 1.40137107
79 -0.44244122 -3.70416863
80 -1.44273762 -0.44244122
81 0.10302827 -1.44273762
82 -0.65329516 0.10302827
83 0.59594617 -0.65329516
84 -5.08727098 0.59594617
85 0.35764623 -5.08727098
86 -0.72522855 0.35764623
87 -1.19541458 -0.72522855
88 -1.48330355 -1.19541458
89 -0.65469236 -1.48330355
90 -3.63827728 -0.65469236
91 1.18598665 -3.63827728
92 0.98830412 1.18598665
93 2.73469626 0.98830412
94 -0.09003859 2.73469626
95 -1.30187027 -0.09003859
96 -6.75267217 -1.30187027
97 -0.51182472 -6.75267217
98 2.27989118 -0.51182472
99 -1.34472798 2.27989118
100 1.93576266 -1.34472798
101 0.27919857 1.93576266
102 3.03892888 0.27919857
103 -4.68652784 3.03892888
104 -1.48243600 -4.68652784
105 -4.81607754 -1.48243600
106 2.09329754 -4.81607754
107 0.32655759 2.09329754
108 1.21014951 0.32655759
109 -0.93518932 1.21014951
110 0.71153601 -0.93518932
111 2.12411960 0.71153601
112 -4.54613025 2.12411960
113 -0.25354220 -4.54613025
114 -2.58329987 -0.25354220
115 0.18245859 -2.58329987
116 0.25189516 0.18245859
117 -4.35874208 0.25189516
118 2.02984331 -4.35874208
119 0.85323735 2.02984331
120 1.42286826 0.85323735
121 4.18645308 1.42286826
122 0.18145008 4.18645308
123 2.49388566 0.18145008
124 1.26691305 2.49388566
125 -5.51228363 1.26691305
126 -1.61840862 -5.51228363
127 3.09493144 -1.61840862
128 -1.38979243 3.09493144
129 0.73229476 -1.38979243
130 -1.61089968 0.73229476
131 -1.12640947 -1.61089968
132 -2.60240925 -1.12640947
133 -1.76134132 -2.60240925
134 -0.32721776 -1.76134132
135 -1.54897322 -0.32721776
136 1.99607421 -1.54897322
137 5.33480351 1.99607421
138 -0.14209551 5.33480351
139 2.37887899 -0.14209551
140 5.39285362 2.37887899
141 1.46011656 5.39285362
142 1.48087891 1.46011656
143 2.79730869 1.48087891
144 -0.53207810 2.79730869
145 -7.02952532 -0.53207810
146 -1.92447434 -7.02952532
147 3.56134915 -1.92447434
148 2.63150419 3.56134915
149 -3.30123288 2.63150419
150 -0.98708706 -3.30123288
151 0.09906099 -0.98708706
152 -0.14932970 0.09906099
153 -1.31357147 -0.14932970
154 1.57786311 -1.31357147
155 1.48809028 1.57786311
> 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/77okc1290541781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87okc1290541781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zgjf1290541781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zgjf1290541781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lgil1290541781.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/12ohy91290541781.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/13diw31290541781.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/14gicq1290541781.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/15k1aw1290541781.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/16n1r21290541781.tab")
+ }
>
> try(system("convert tmp/1bf5l1290541781.ps tmp/1bf5l1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lo4o1290541781.ps tmp/2lo4o1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lo4o1290541781.ps tmp/3lo4o1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lo4o1290541781.ps tmp/4lo4o1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wx3r1290541781.ps tmp/5wx3r1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wx3r1290541781.ps tmp/6wx3r1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/77okc1290541781.ps tmp/77okc1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/87okc1290541781.ps tmp/87okc1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zgjf1290541781.ps tmp/9zgjf1290541781.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zgjf1290541781.ps tmp/10zgjf1290541781.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.885 1.688 8.600