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(2
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+ ,58)
+ ,dim=c(3
+ ,164)
+ ,dimnames=list(c('Gender'
+ ,'Nonverbal'
+ ,'Anxiety')
+ ,1:164))
> y <- array(NA,dim=c(3,164),dimnames=list(c('Gender','Nonverbal','Anxiety'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No 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
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
Nonverbal Gender Anxiety
1 73 2 69
2 58 1 53
3 68 1 43
4 69 2 60
5 62 1 49
6 68 1 62
7 65 1 45
8 65 1 50
9 81 1 75
10 73 1 82
11 64 2 60
12 68 1 59
13 51 1 21
14 68 1 40
15 61 1 62
16 77 2 54
17 69 1 47
18 73 1 59
19 61 2 37
20 62 2 43
21 63 1 48
22 69 1 59
23 47 2 79
24 66 2 62
25 58 1 16
26 63 2 38
27 69 1 58
28 59 2 60
29 59 1 72
30 63 2 67
31 65 2 55
32 65 1 47
33 71 2 59
34 60 1 49
35 66 2 47
36 67 1 57
37 81 2 39
38 62 1 49
39 63 1 26
40 73 2 53
41 55 2 75
42 59 1 65
43 64 1 49
44 63 2 48
45 74 2 45
46 67 2 31
47 64 1 67
48 73 1 61
49 54 1 49
50 76 1 69
51 74 2 54
52 63 2 80
53 73 2 57
54 67 2 34
55 68 2 69
56 66 1 44
57 62 2 70
58 71 2 51
59 68 1 66
60 63 1 18
61 75 1 74
62 77 1 59
63 62 2 48
64 74 1 55
65 67 2 44
66 56 2 56
67 60 2 65
68 58 2 77
69 65 1 46
70 49 2 70
71 61 1 39
72 66 2 55
73 64 2 44
74 65 2 45
75 46 1 45
76 81 2 25
77 65 2 49
78 72 1 65
79 65 2 45
80 74 2 71
81 69 1 48
82 59 1 41
83 58 2 40
84 71 1 64
85 79 2 56
86 68 2 52
87 66 1 41
88 62 2 45
89 69 1 49
90 60 1 42
91 63 2 54
92 62 1 40
93 61 1 40
94 65 2 51
95 64 1 48
96 67 2 80
97 56 2 38
98 56 2 57
99 48 1 28
100 74 1 51
101 69 1 46
102 62 1 58
103 73 1 67
104 64 1 72
105 57 1 26
106 57 1 54
107 60 2 53
108 61 2 69
109 72 1 64
110 57 1 47
111 51 1 43
112 63 1 66
113 54 1 54
114 72 1 62
115 62 1 52
116 68 1 64
117 62 1 55
118 63 2 57
119 77 1 74
120 57 1 32
121 57 1 38
122 61 1 66
123 66 2 37
124 65 1 26
125 63 1 64
126 59 1 28
127 66 2 66
128 68 1 65
129 72 1 48
130 68 1 44
131 68 2 64
132 67 1 39
133 59 1 50
134 56 1 52
135 62 1 66
136 55 2 48
137 72 2 70
138 68 2 66
139 67 1 61
140 54 1 31
141 69 2 61
142 61 1 54
143 55 1 34
144 75 2 62
145 55 1 47
146 49 1 52
147 54 2 37
148 51 1 46
149 66 1 50
150 73 1 61
151 63 2 70
152 61 2 38
153 74 1 63
154 81 2 34
155 58 1 46
156 62 1 40
157 64 1 30
158 62 1 35
159 85 1 51
160 74 1 56
161 51 1 68
162 66 1 39
163 61 2 44
164 72 1 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Anxiety
57.5800 0.6803 0.1172
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-21.20143 -4.18683 0.03828 4.73766 20.76120
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 57.57997 2.62245 21.957 < 2e-16 ***
Gender 0.68030 1.15872 0.587 0.55795
Anxiety 0.11723 0.04225 2.775 0.00618 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.196 on 161 degrees of freedom
Multiple R-squared: 0.0508, Adjusted R-squared: 0.03901
F-statistic: 4.309 on 2 and 161 DF, p-value: 0.01504
> 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]
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[2,] 0.172998790 0.345997581 0.8270012
[3,] 0.087369745 0.174739489 0.9126303
[4,] 0.166990481 0.333980961 0.8330095
[5,] 0.136134866 0.272269731 0.8638651
[6,] 0.106647401 0.213294803 0.8933526
[7,] 0.062476226 0.124952452 0.9375238
[8,] 0.040248176 0.080496352 0.9597518
[9,] 0.053327787 0.106655573 0.9466722
[10,] 0.077329885 0.154659771 0.9226701
[11,] 0.140720820 0.281441640 0.8592792
[12,] 0.123873630 0.247747259 0.8761264
[13,] 0.109973715 0.219947431 0.8900263
[14,] 0.078322531 0.156645062 0.9216775
[15,] 0.055490937 0.110981874 0.9445091
[16,] 0.037456357 0.074912714 0.9625436
[17,] 0.024547000 0.049093999 0.9754530
[18,] 0.616261801 0.767476399 0.3837382
[19,] 0.550615560 0.898768880 0.4493844
[20,] 0.486306876 0.972613752 0.5136931
[21,] 0.425548727 0.851097454 0.5744513
[22,] 0.369581342 0.739162683 0.6304187
[23,] 0.356718587 0.713437174 0.6432814
[24,] 0.413122366 0.826244733 0.5868776
[25,] 0.363823835 0.727647671 0.6361762
[26,] 0.309875724 0.619751447 0.6901243
[27,] 0.259163076 0.518326152 0.7408369
[28,] 0.247964078 0.495928157 0.7520359
[29,] 0.224007082 0.448014165 0.7759929
[30,] 0.188544316 0.377088632 0.8114557
[31,] 0.153109785 0.306219570 0.8468902
[32,] 0.396833247 0.793666494 0.6031668
[33,] 0.352246208 0.704492416 0.6477538
[34,] 0.303642744 0.607285487 0.6963573
[35,] 0.307147092 0.614294184 0.6928529
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[38,] 0.361397539 0.722795079 0.6386025
[39,] 0.316705786 0.633411571 0.6832942
[40,] 0.348143233 0.696286465 0.6518568
[41,] 0.311450973 0.622901945 0.6885490
[42,] 0.270490447 0.540980894 0.7295096
[43,] 0.275412969 0.550825939 0.7245870
[44,] 0.326742551 0.653485102 0.6732574
[45,] 0.366426771 0.732853543 0.6335732
[46,] 0.380981136 0.761962271 0.6190189
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[48,] 0.354946486 0.709892972 0.6450535
[49,] 0.319405081 0.638810163 0.6805949
[50,] 0.277795223 0.555590446 0.7222048
[51,] 0.241677253 0.483354506 0.7583227
[52,] 0.224507628 0.449015256 0.7754924
[53,] 0.210247248 0.420494495 0.7897528
[54,] 0.179855844 0.359711687 0.8201442
[55,] 0.153389693 0.306779386 0.8466103
[56,] 0.162565941 0.325131883 0.8374341
[57,] 0.211911337 0.423822674 0.7880887
[58,] 0.186370486 0.372740971 0.8136295
[59,] 0.202171568 0.404343136 0.7978284
[60,] 0.174728746 0.349457492 0.8252713
[61,] 0.203729872 0.407459744 0.7962701
[62,] 0.198878101 0.397756201 0.8011219
[63,] 0.226878328 0.453756656 0.7731217
[64,] 0.195164123 0.390328247 0.8048359
[65,] 0.402580997 0.805161994 0.5974190
[66,] 0.368644382 0.737288765 0.6313556
[67,] 0.326818579 0.653637158 0.6731814
[68,] 0.286900971 0.573801942 0.7130990
[69,] 0.249619145 0.499238289 0.7503809
[70,] 0.474738048 0.949476097 0.5252620
[71,] 0.734575167 0.530849667 0.2654248
[72,] 0.696735290 0.606529419 0.3032647
[73,] 0.683060818 0.633878364 0.3169392
[74,] 0.643470593 0.713058814 0.3565294
[75,] 0.639738265 0.720523470 0.3602617
[76,] 0.618066841 0.763866317 0.3819332
[77,] 0.592893688 0.814212625 0.4071063
[78,] 0.576138592 0.847722815 0.4238614
[79,] 0.552507004 0.894985992 0.4474930
[80,] 0.667042957 0.665914085 0.3329570
[81,] 0.635032733 0.729934534 0.3649673
[82,] 0.602095087 0.795809826 0.3979049
[83,] 0.562467244 0.875065512 0.4375328
[84,] 0.540536091 0.918927817 0.4594639
[85,] 0.506789090 0.986421820 0.4932109
[86,] 0.465110438 0.930220875 0.5348896
[87,] 0.423967927 0.847935853 0.5760321
[88,] 0.385424786 0.770849573 0.6145752
[89,] 0.343758237 0.687516475 0.6562418
[90,] 0.304070917 0.608141834 0.6959291
[91,] 0.267788805 0.535577611 0.7322112
[92,] 0.264567989 0.529135979 0.7354320
[93,] 0.292490098 0.584980195 0.7075099
[94,] 0.386751622 0.773503244 0.6132484
[95,] 0.427310386 0.854620771 0.5726896
[96,] 0.412012319 0.824024638 0.5879877
[97,] 0.375036168 0.750072336 0.6249638
[98,] 0.370145561 0.740291122 0.6298544
[99,] 0.333396067 0.666792135 0.6666039
[100,] 0.303960503 0.607921005 0.6960395
[101,] 0.304692879 0.609385758 0.6953071
[102,] 0.283789111 0.567578222 0.7162109
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[104,] 0.267127334 0.534254668 0.7328727
[105,] 0.257287666 0.514575331 0.7427123
[106,] 0.321917992 0.643835984 0.6780820
[107,] 0.287076997 0.574153995 0.7129230
[108,] 0.331026714 0.662053429 0.6689733
[109,] 0.320059602 0.640119204 0.6799404
[110,] 0.280868680 0.561737360 0.7191313
[111,] 0.243998441 0.487996881 0.7560016
[112,] 0.210963220 0.421926441 0.7890368
[113,] 0.182776318 0.365552637 0.8172237
[114,] 0.211024944 0.422049888 0.7889751
[115,] 0.188346953 0.376693906 0.8116530
[116,] 0.171611676 0.343223352 0.8283883
[117,] 0.152682838 0.305365676 0.8473172
[118,] 0.126971475 0.253942951 0.8730285
[119,] 0.110535214 0.221070427 0.8894648
[120,] 0.090622279 0.181244557 0.9093777
[121,] 0.072026679 0.144053358 0.9279733
[122,] 0.056227463 0.112454927 0.9437725
[123,] 0.043509508 0.087019016 0.9564905
[124,] 0.047021305 0.094042611 0.9529787
[125,] 0.040866325 0.081732650 0.9591337
[126,] 0.030341865 0.060683730 0.9696581
[127,] 0.025982259 0.051964519 0.9740177
[128,] 0.020629513 0.041259025 0.9793705
[129,] 0.020426373 0.040852746 0.9795736
[130,] 0.015930931 0.031861861 0.9840691
[131,] 0.020282264 0.040564528 0.9797177
[132,] 0.014937719 0.029875439 0.9850623
[133,] 0.010267303 0.020534607 0.9897327
[134,] 0.006932873 0.013865745 0.9930671
[135,] 0.006132565 0.012265130 0.9938674
[136,] 0.004006839 0.008013678 0.9959932
[137,] 0.002697727 0.005395454 0.9973023
[138,] 0.002340791 0.004681582 0.9976592
[139,] 0.002458066 0.004916133 0.9975419
[140,] 0.002716703 0.005433406 0.9972833
[141,] 0.010609451 0.021218902 0.9893905
[142,] 0.014033420 0.028066839 0.9859666
[143,] 0.039548173 0.079096347 0.9604518
[144,] 0.025974052 0.051948104 0.9740259
[145,] 0.021327892 0.042655784 0.9786721
[146,] 0.014306546 0.028613092 0.9856935
[147,] 0.012725958 0.025451916 0.9872740
[148,] 0.011583729 0.023167457 0.9884163
[149,] 0.031952827 0.063905654 0.9680472
[150,] 0.029757759 0.059515518 0.9702422
[151,] 0.019271244 0.038542487 0.9807288
[152,] 0.011686379 0.023372759 0.9883136
[153,] 0.018568108 0.037136217 0.9814319
> postscript(file="/var/www/html/rcomp/tmp/192r01292347764.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ktql1292347764.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3ktql1292347764.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ktql1292347764.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vkqo1292347764.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 = 164
Frequency = 1
1 2 3 4 5
5.970830878 -6.473252082 4.699009999 3.025866752 -2.004347250
6 7 8 9 10
2.471712045 1.464557583 0.878426542 13.947771339 5.127187882
11 12 13 14 15
-1.974133248 2.823390669 -9.722013422 5.050688624 -4.528287955
16 17 18 19 20
11.729224001 5.230105167 7.823390669 -2.277930461 -1.981287710
21 22 23 24 25
-0.887121041 3.823390669 -21.201431203 -0.208585665 -2.135882381
26 27 28 29 30
-0.395156669 3.940616877 -6.974133248 -7.700550037 -3.794716705
31 32 33 34 35
-0.388002208 1.230105167 5.143092960 -4.004347250 1.549807458
36 37 38 39 40
2.057843085 17.487617123 -2.004347250 1.691855538 7.846450209
41 42 43 44 45
-12.732526370 -6.879966580 -0.004347250 -1.567418751 9.784259874
46 47 48 49 50
4.425426788 -2.114418996 7.588938253 -10.004347250 9.651128588
51 52 53 54 55
8.729224001 -5.318657411 7.377545376 4.073748163 0.970830878
56 57 58 59 60
2.581783791 -5.146395330 6.080902625 2.002807212 2.629665203
61 62 63 64 65
8.064997547 11.823390669 -2.567418751 9.292295502 2.901486082
66 67 68 69 70
-9.505228416 -6.560264289 -9.966978787 1.347331375 -18.146395330
71 72 73 74 75
-1.832085168 0.611997792 -0.098513918 0.784259874 -17.535442417
76 77 78 79 80
19.128784037 0.315355041 6.120033420 0.784259874 6.736378462
81 82 83 84 85
5.112878959 -4.066537584 -5.629609085 5.237259628 13.494771584
86 87 88 89 90
2.963676417 2.933462416 -2.215740126 4.995652750 -3.183763793
91 92 93 94 95
-2.270775999 -0.949311376 -1.949311376 0.080902625 0.112878959
96 97 98 99 100
-1.318657411 -7.395156669 -9.622454624 -13.542596879 9.761200334
101 102 103 104 105
5.347331375 -3.059383123 6.885581004 -2.700550037 -4.308144462
106 107 108 109 110
-7.590478290 -5.153549791 -6.029169122 6.237259628 -6.769894833
111 112 113 114 115
-12.300990001 -2.997192788 -10.590478290 6.471712045 -2.356025874
116 117 118 119 120
2.237259628 -2.707704498 -2.622454624 10.064997547 -5.011501711
121 122 123 124 125
-5.714858960 -4.997192788 2.722069539 3.691855538 -2.762740372
126 127 128 129 130
-2.542596879 -0.677490497 2.120033420 8.112878959 4.581783791
131 132 133 134 135
1.556961919 4.167914832 -5.121573458 -8.356025874 -3.997192788
136 137 138 139 140
-9.567418751 4.853604670 1.322509503 1.588938253 -7.894275503
141 142 143 144 145
2.908640544 -3.590478290 -7.245954127 8.791414335 -8.769894833
146 147 148 149 150
-15.356025874 -9.277930461 -12.652668625 1.878426542 7.588938253
151 152 153 154 155
-4.146395330 -2.395156669 8.354485836 18.073748163 -5.652668625
156 157 158 159 160
-0.949311376 2.222950705 -0.363180336 20.761200334 9.175069293
161 162 163 164
-15.231645204 3.167914832 -3.098513918 6.940616877
> postscript(file="/var/www/html/rcomp/tmp/6vkqo1292347764.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 5.970830878 NA
1 -6.473252082 5.970830878
2 4.699009999 -6.473252082
3 3.025866752 4.699009999
4 -2.004347250 3.025866752
5 2.471712045 -2.004347250
6 1.464557583 2.471712045
7 0.878426542 1.464557583
8 13.947771339 0.878426542
9 5.127187882 13.947771339
10 -1.974133248 5.127187882
11 2.823390669 -1.974133248
12 -9.722013422 2.823390669
13 5.050688624 -9.722013422
14 -4.528287955 5.050688624
15 11.729224001 -4.528287955
16 5.230105167 11.729224001
17 7.823390669 5.230105167
18 -2.277930461 7.823390669
19 -1.981287710 -2.277930461
20 -0.887121041 -1.981287710
21 3.823390669 -0.887121041
22 -21.201431203 3.823390669
23 -0.208585665 -21.201431203
24 -2.135882381 -0.208585665
25 -0.395156669 -2.135882381
26 3.940616877 -0.395156669
27 -6.974133248 3.940616877
28 -7.700550037 -6.974133248
29 -3.794716705 -7.700550037
30 -0.388002208 -3.794716705
31 1.230105167 -0.388002208
32 5.143092960 1.230105167
33 -4.004347250 5.143092960
34 1.549807458 -4.004347250
35 2.057843085 1.549807458
36 17.487617123 2.057843085
37 -2.004347250 17.487617123
38 1.691855538 -2.004347250
39 7.846450209 1.691855538
40 -12.732526370 7.846450209
41 -6.879966580 -12.732526370
42 -0.004347250 -6.879966580
43 -1.567418751 -0.004347250
44 9.784259874 -1.567418751
45 4.425426788 9.784259874
46 -2.114418996 4.425426788
47 7.588938253 -2.114418996
48 -10.004347250 7.588938253
49 9.651128588 -10.004347250
50 8.729224001 9.651128588
51 -5.318657411 8.729224001
52 7.377545376 -5.318657411
53 4.073748163 7.377545376
54 0.970830878 4.073748163
55 2.581783791 0.970830878
56 -5.146395330 2.581783791
57 6.080902625 -5.146395330
58 2.002807212 6.080902625
59 2.629665203 2.002807212
60 8.064997547 2.629665203
61 11.823390669 8.064997547
62 -2.567418751 11.823390669
63 9.292295502 -2.567418751
64 2.901486082 9.292295502
65 -9.505228416 2.901486082
66 -6.560264289 -9.505228416
67 -9.966978787 -6.560264289
68 1.347331375 -9.966978787
69 -18.146395330 1.347331375
70 -1.832085168 -18.146395330
71 0.611997792 -1.832085168
72 -0.098513918 0.611997792
73 0.784259874 -0.098513918
74 -17.535442417 0.784259874
75 19.128784037 -17.535442417
76 0.315355041 19.128784037
77 6.120033420 0.315355041
78 0.784259874 6.120033420
79 6.736378462 0.784259874
80 5.112878959 6.736378462
81 -4.066537584 5.112878959
82 -5.629609085 -4.066537584
83 5.237259628 -5.629609085
84 13.494771584 5.237259628
85 2.963676417 13.494771584
86 2.933462416 2.963676417
87 -2.215740126 2.933462416
88 4.995652750 -2.215740126
89 -3.183763793 4.995652750
90 -2.270775999 -3.183763793
91 -0.949311376 -2.270775999
92 -1.949311376 -0.949311376
93 0.080902625 -1.949311376
94 0.112878959 0.080902625
95 -1.318657411 0.112878959
96 -7.395156669 -1.318657411
97 -9.622454624 -7.395156669
98 -13.542596879 -9.622454624
99 9.761200334 -13.542596879
100 5.347331375 9.761200334
101 -3.059383123 5.347331375
102 6.885581004 -3.059383123
103 -2.700550037 6.885581004
104 -4.308144462 -2.700550037
105 -7.590478290 -4.308144462
106 -5.153549791 -7.590478290
107 -6.029169122 -5.153549791
108 6.237259628 -6.029169122
109 -6.769894833 6.237259628
110 -12.300990001 -6.769894833
111 -2.997192788 -12.300990001
112 -10.590478290 -2.997192788
113 6.471712045 -10.590478290
114 -2.356025874 6.471712045
115 2.237259628 -2.356025874
116 -2.707704498 2.237259628
117 -2.622454624 -2.707704498
118 10.064997547 -2.622454624
119 -5.011501711 10.064997547
120 -5.714858960 -5.011501711
121 -4.997192788 -5.714858960
122 2.722069539 -4.997192788
123 3.691855538 2.722069539
124 -2.762740372 3.691855538
125 -2.542596879 -2.762740372
126 -0.677490497 -2.542596879
127 2.120033420 -0.677490497
128 8.112878959 2.120033420
129 4.581783791 8.112878959
130 1.556961919 4.581783791
131 4.167914832 1.556961919
132 -5.121573458 4.167914832
133 -8.356025874 -5.121573458
134 -3.997192788 -8.356025874
135 -9.567418751 -3.997192788
136 4.853604670 -9.567418751
137 1.322509503 4.853604670
138 1.588938253 1.322509503
139 -7.894275503 1.588938253
140 2.908640544 -7.894275503
141 -3.590478290 2.908640544
142 -7.245954127 -3.590478290
143 8.791414335 -7.245954127
144 -8.769894833 8.791414335
145 -15.356025874 -8.769894833
146 -9.277930461 -15.356025874
147 -12.652668625 -9.277930461
148 1.878426542 -12.652668625
149 7.588938253 1.878426542
150 -4.146395330 7.588938253
151 -2.395156669 -4.146395330
152 8.354485836 -2.395156669
153 18.073748163 8.354485836
154 -5.652668625 18.073748163
155 -0.949311376 -5.652668625
156 2.222950705 -0.949311376
157 -0.363180336 2.222950705
158 20.761200334 -0.363180336
159 9.175069293 20.761200334
160 -15.231645204 9.175069293
161 3.167914832 -15.231645204
162 -3.098513918 3.167914832
163 6.940616877 -3.098513918
164 NA 6.940616877
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.473252082 5.970830878
[2,] 4.699009999 -6.473252082
[3,] 3.025866752 4.699009999
[4,] -2.004347250 3.025866752
[5,] 2.471712045 -2.004347250
[6,] 1.464557583 2.471712045
[7,] 0.878426542 1.464557583
[8,] 13.947771339 0.878426542
[9,] 5.127187882 13.947771339
[10,] -1.974133248 5.127187882
[11,] 2.823390669 -1.974133248
[12,] -9.722013422 2.823390669
[13,] 5.050688624 -9.722013422
[14,] -4.528287955 5.050688624
[15,] 11.729224001 -4.528287955
[16,] 5.230105167 11.729224001
[17,] 7.823390669 5.230105167
[18,] -2.277930461 7.823390669
[19,] -1.981287710 -2.277930461
[20,] -0.887121041 -1.981287710
[21,] 3.823390669 -0.887121041
[22,] -21.201431203 3.823390669
[23,] -0.208585665 -21.201431203
[24,] -2.135882381 -0.208585665
[25,] -0.395156669 -2.135882381
[26,] 3.940616877 -0.395156669
[27,] -6.974133248 3.940616877
[28,] -7.700550037 -6.974133248
[29,] -3.794716705 -7.700550037
[30,] -0.388002208 -3.794716705
[31,] 1.230105167 -0.388002208
[32,] 5.143092960 1.230105167
[33,] -4.004347250 5.143092960
[34,] 1.549807458 -4.004347250
[35,] 2.057843085 1.549807458
[36,] 17.487617123 2.057843085
[37,] -2.004347250 17.487617123
[38,] 1.691855538 -2.004347250
[39,] 7.846450209 1.691855538
[40,] -12.732526370 7.846450209
[41,] -6.879966580 -12.732526370
[42,] -0.004347250 -6.879966580
[43,] -1.567418751 -0.004347250
[44,] 9.784259874 -1.567418751
[45,] 4.425426788 9.784259874
[46,] -2.114418996 4.425426788
[47,] 7.588938253 -2.114418996
[48,] -10.004347250 7.588938253
[49,] 9.651128588 -10.004347250
[50,] 8.729224001 9.651128588
[51,] -5.318657411 8.729224001
[52,] 7.377545376 -5.318657411
[53,] 4.073748163 7.377545376
[54,] 0.970830878 4.073748163
[55,] 2.581783791 0.970830878
[56,] -5.146395330 2.581783791
[57,] 6.080902625 -5.146395330
[58,] 2.002807212 6.080902625
[59,] 2.629665203 2.002807212
[60,] 8.064997547 2.629665203
[61,] 11.823390669 8.064997547
[62,] -2.567418751 11.823390669
[63,] 9.292295502 -2.567418751
[64,] 2.901486082 9.292295502
[65,] -9.505228416 2.901486082
[66,] -6.560264289 -9.505228416
[67,] -9.966978787 -6.560264289
[68,] 1.347331375 -9.966978787
[69,] -18.146395330 1.347331375
[70,] -1.832085168 -18.146395330
[71,] 0.611997792 -1.832085168
[72,] -0.098513918 0.611997792
[73,] 0.784259874 -0.098513918
[74,] -17.535442417 0.784259874
[75,] 19.128784037 -17.535442417
[76,] 0.315355041 19.128784037
[77,] 6.120033420 0.315355041
[78,] 0.784259874 6.120033420
[79,] 6.736378462 0.784259874
[80,] 5.112878959 6.736378462
[81,] -4.066537584 5.112878959
[82,] -5.629609085 -4.066537584
[83,] 5.237259628 -5.629609085
[84,] 13.494771584 5.237259628
[85,] 2.963676417 13.494771584
[86,] 2.933462416 2.963676417
[87,] -2.215740126 2.933462416
[88,] 4.995652750 -2.215740126
[89,] -3.183763793 4.995652750
[90,] -2.270775999 -3.183763793
[91,] -0.949311376 -2.270775999
[92,] -1.949311376 -0.949311376
[93,] 0.080902625 -1.949311376
[94,] 0.112878959 0.080902625
[95,] -1.318657411 0.112878959
[96,] -7.395156669 -1.318657411
[97,] -9.622454624 -7.395156669
[98,] -13.542596879 -9.622454624
[99,] 9.761200334 -13.542596879
[100,] 5.347331375 9.761200334
[101,] -3.059383123 5.347331375
[102,] 6.885581004 -3.059383123
[103,] -2.700550037 6.885581004
[104,] -4.308144462 -2.700550037
[105,] -7.590478290 -4.308144462
[106,] -5.153549791 -7.590478290
[107,] -6.029169122 -5.153549791
[108,] 6.237259628 -6.029169122
[109,] -6.769894833 6.237259628
[110,] -12.300990001 -6.769894833
[111,] -2.997192788 -12.300990001
[112,] -10.590478290 -2.997192788
[113,] 6.471712045 -10.590478290
[114,] -2.356025874 6.471712045
[115,] 2.237259628 -2.356025874
[116,] -2.707704498 2.237259628
[117,] -2.622454624 -2.707704498
[118,] 10.064997547 -2.622454624
[119,] -5.011501711 10.064997547
[120,] -5.714858960 -5.011501711
[121,] -4.997192788 -5.714858960
[122,] 2.722069539 -4.997192788
[123,] 3.691855538 2.722069539
[124,] -2.762740372 3.691855538
[125,] -2.542596879 -2.762740372
[126,] -0.677490497 -2.542596879
[127,] 2.120033420 -0.677490497
[128,] 8.112878959 2.120033420
[129,] 4.581783791 8.112878959
[130,] 1.556961919 4.581783791
[131,] 4.167914832 1.556961919
[132,] -5.121573458 4.167914832
[133,] -8.356025874 -5.121573458
[134,] -3.997192788 -8.356025874
[135,] -9.567418751 -3.997192788
[136,] 4.853604670 -9.567418751
[137,] 1.322509503 4.853604670
[138,] 1.588938253 1.322509503
[139,] -7.894275503 1.588938253
[140,] 2.908640544 -7.894275503
[141,] -3.590478290 2.908640544
[142,] -7.245954127 -3.590478290
[143,] 8.791414335 -7.245954127
[144,] -8.769894833 8.791414335
[145,] -15.356025874 -8.769894833
[146,] -9.277930461 -15.356025874
[147,] -12.652668625 -9.277930461
[148,] 1.878426542 -12.652668625
[149,] 7.588938253 1.878426542
[150,] -4.146395330 7.588938253
[151,] -2.395156669 -4.146395330
[152,] 8.354485836 -2.395156669
[153,] 18.073748163 8.354485836
[154,] -5.652668625 18.073748163
[155,] -0.949311376 -5.652668625
[156,] 2.222950705 -0.949311376
[157,] -0.363180336 2.222950705
[158,] 20.761200334 -0.363180336
[159,] 9.175069293 20.761200334
[160,] -15.231645204 9.175069293
[161,] 3.167914832 -15.231645204
[162,] -3.098513918 3.167914832
[163,] 6.940616877 -3.098513918
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.473252082 5.970830878
2 4.699009999 -6.473252082
3 3.025866752 4.699009999
4 -2.004347250 3.025866752
5 2.471712045 -2.004347250
6 1.464557583 2.471712045
7 0.878426542 1.464557583
8 13.947771339 0.878426542
9 5.127187882 13.947771339
10 -1.974133248 5.127187882
11 2.823390669 -1.974133248
12 -9.722013422 2.823390669
13 5.050688624 -9.722013422
14 -4.528287955 5.050688624
15 11.729224001 -4.528287955
16 5.230105167 11.729224001
17 7.823390669 5.230105167
18 -2.277930461 7.823390669
19 -1.981287710 -2.277930461
20 -0.887121041 -1.981287710
21 3.823390669 -0.887121041
22 -21.201431203 3.823390669
23 -0.208585665 -21.201431203
24 -2.135882381 -0.208585665
25 -0.395156669 -2.135882381
26 3.940616877 -0.395156669
27 -6.974133248 3.940616877
28 -7.700550037 -6.974133248
29 -3.794716705 -7.700550037
30 -0.388002208 -3.794716705
31 1.230105167 -0.388002208
32 5.143092960 1.230105167
33 -4.004347250 5.143092960
34 1.549807458 -4.004347250
35 2.057843085 1.549807458
36 17.487617123 2.057843085
37 -2.004347250 17.487617123
38 1.691855538 -2.004347250
39 7.846450209 1.691855538
40 -12.732526370 7.846450209
41 -6.879966580 -12.732526370
42 -0.004347250 -6.879966580
43 -1.567418751 -0.004347250
44 9.784259874 -1.567418751
45 4.425426788 9.784259874
46 -2.114418996 4.425426788
47 7.588938253 -2.114418996
48 -10.004347250 7.588938253
49 9.651128588 -10.004347250
50 8.729224001 9.651128588
51 -5.318657411 8.729224001
52 7.377545376 -5.318657411
53 4.073748163 7.377545376
54 0.970830878 4.073748163
55 2.581783791 0.970830878
56 -5.146395330 2.581783791
57 6.080902625 -5.146395330
58 2.002807212 6.080902625
59 2.629665203 2.002807212
60 8.064997547 2.629665203
61 11.823390669 8.064997547
62 -2.567418751 11.823390669
63 9.292295502 -2.567418751
64 2.901486082 9.292295502
65 -9.505228416 2.901486082
66 -6.560264289 -9.505228416
67 -9.966978787 -6.560264289
68 1.347331375 -9.966978787
69 -18.146395330 1.347331375
70 -1.832085168 -18.146395330
71 0.611997792 -1.832085168
72 -0.098513918 0.611997792
73 0.784259874 -0.098513918
74 -17.535442417 0.784259874
75 19.128784037 -17.535442417
76 0.315355041 19.128784037
77 6.120033420 0.315355041
78 0.784259874 6.120033420
79 6.736378462 0.784259874
80 5.112878959 6.736378462
81 -4.066537584 5.112878959
82 -5.629609085 -4.066537584
83 5.237259628 -5.629609085
84 13.494771584 5.237259628
85 2.963676417 13.494771584
86 2.933462416 2.963676417
87 -2.215740126 2.933462416
88 4.995652750 -2.215740126
89 -3.183763793 4.995652750
90 -2.270775999 -3.183763793
91 -0.949311376 -2.270775999
92 -1.949311376 -0.949311376
93 0.080902625 -1.949311376
94 0.112878959 0.080902625
95 -1.318657411 0.112878959
96 -7.395156669 -1.318657411
97 -9.622454624 -7.395156669
98 -13.542596879 -9.622454624
99 9.761200334 -13.542596879
100 5.347331375 9.761200334
101 -3.059383123 5.347331375
102 6.885581004 -3.059383123
103 -2.700550037 6.885581004
104 -4.308144462 -2.700550037
105 -7.590478290 -4.308144462
106 -5.153549791 -7.590478290
107 -6.029169122 -5.153549791
108 6.237259628 -6.029169122
109 -6.769894833 6.237259628
110 -12.300990001 -6.769894833
111 -2.997192788 -12.300990001
112 -10.590478290 -2.997192788
113 6.471712045 -10.590478290
114 -2.356025874 6.471712045
115 2.237259628 -2.356025874
116 -2.707704498 2.237259628
117 -2.622454624 -2.707704498
118 10.064997547 -2.622454624
119 -5.011501711 10.064997547
120 -5.714858960 -5.011501711
121 -4.997192788 -5.714858960
122 2.722069539 -4.997192788
123 3.691855538 2.722069539
124 -2.762740372 3.691855538
125 -2.542596879 -2.762740372
126 -0.677490497 -2.542596879
127 2.120033420 -0.677490497
128 8.112878959 2.120033420
129 4.581783791 8.112878959
130 1.556961919 4.581783791
131 4.167914832 1.556961919
132 -5.121573458 4.167914832
133 -8.356025874 -5.121573458
134 -3.997192788 -8.356025874
135 -9.567418751 -3.997192788
136 4.853604670 -9.567418751
137 1.322509503 4.853604670
138 1.588938253 1.322509503
139 -7.894275503 1.588938253
140 2.908640544 -7.894275503
141 -3.590478290 2.908640544
142 -7.245954127 -3.590478290
143 8.791414335 -7.245954127
144 -8.769894833 8.791414335
145 -15.356025874 -8.769894833
146 -9.277930461 -15.356025874
147 -12.652668625 -9.277930461
148 1.878426542 -12.652668625
149 7.588938253 1.878426542
150 -4.146395330 7.588938253
151 -2.395156669 -4.146395330
152 8.354485836 -2.395156669
153 18.073748163 8.354485836
154 -5.652668625 18.073748163
155 -0.949311376 -5.652668625
156 2.222950705 -0.949311376
157 -0.363180336 2.222950705
158 20.761200334 -0.363180336
159 9.175069293 20.761200334
160 -15.231645204 9.175069293
161 3.167914832 -15.231645204
162 -3.098513918 3.167914832
163 6.940616877 -3.098513918
> 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/76upr1292347764.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86upr1292347764.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9y3oc1292347764.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10y3oc1292347764.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1123ni1292347764.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/12nm361292347764.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/13uniz1292347764.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/144whk1292347764.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/15qxyq1292347764.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/1646wh1292347764.tab")
+ }
>
> try(system("convert tmp/192r01292347764.ps tmp/192r01292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ktql1292347764.ps tmp/2ktql1292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ktql1292347764.ps tmp/3ktql1292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ktql1292347764.ps tmp/4ktql1292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vkqo1292347764.ps tmp/5vkqo1292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vkqo1292347764.ps tmp/6vkqo1292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/76upr1292347764.ps tmp/76upr1292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/86upr1292347764.ps tmp/86upr1292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/9y3oc1292347764.ps tmp/9y3oc1292347764.png",intern=TRUE))
character(0)
> try(system("convert tmp/10y3oc1292347764.ps tmp/10y3oc1292347764.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.211 1.835 11.191