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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> x <- array(list(23
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+ ,3)
+ ,dim=c(8
+ ,142)
+ ,dimnames=list(c('AGE'
+ ,'PStress'
+ ,'BelInSprt'
+ ,'KunnenRekRel'
+ ,'ExtraCurAct'
+ ,'Depressie'
+ ,'Slaapgebrek'
+ ,'ToekZorgen')
+ ,1:142))
> y <- array(NA,dim=c(8,142),dimnames=list(c('AGE','PStress','BelInSprt','KunnenRekRel','ExtraCurAct','Depressie','Slaapgebrek','ToekZorgen'),1:142))
> 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
PStress AGE BelInSprt KunnenRekRel ExtraCurAct Depressie Slaapgebrek
1 10 23 53 7 6 12 2
2 6 21 86 4 6 11 4
3 13 21 66 6 5 14 7
4 12 21 67 5 4 12 3
5 8 24 76 4 4 21 7
6 6 22 78 3 6 12 2
7 10 21 53 5 7 22 7
8 10 22 80 6 5 11 2
9 9 21 74 5 4 10 1
10 9 20 76 6 6 13 2
11 7 22 79 7 1 10 6
12 5 21 54 6 4 8 1
13 14 21 67 7 6 15 1
14 6 23 87 6 6 10 1
15 10 22 58 4 5 14 2
16 10 23 75 6 3 14 2
17 7 22 88 4 7 11 2
18 10 24 64 5 2 10 1
19 8 23 57 3 5 13 7
20 6 21 66 3 5 7 1
21 10 23 54 4 3 12 2
22 12 23 56 5 5 14 4
23 7 21 86 3 5 11 2
24 15 20 80 7 6 9 1
25 8 32 76 7 4 11 1
26 10 22 69 4 4 15 5
27 13 21 67 4 4 13 2
28 8 21 80 5 2 9 1
29 11 21 54 6 3 15 3
30 7 22 71 5 6 10 1
31 9 21 84 4 6 11 2
32 10 21 74 6 5 13 5
33 8 21 71 5 3 8 2
34 15 22 63 5 3 20 6
35 9 21 71 6 4 12 4
36 7 21 76 2 4 10 1
37 11 21 69 6 5 10 3
38 9 21 74 7 3 9 6
39 8 23 75 5 5 14 7
40 8 21 54 5 4 8 4
41 12 23 69 5 3 11 5
42 13 23 68 6 3 13 3
43 9 21 75 4 4 11 2
44 11 21 75 6 6 11 2
45 8 20 72 5 5 10 2
46 10 21 67 5 3 14 2
47 13 21 63 3 4 18 1
48 12 22 62 4 2 14 2
49 12 21 63 4 3 11 1
50 9 21 76 2 5 12 2
51 8 22 74 3 5 13 2
52 9 20 67 6 5 9 5
53 12 22 73 5 4 10 5
54 12 22 70 6 5 15 2
55 16 21 53 2 3 20 1
56 11 23 77 3 6 12 1
57 13 22 77 6 3 12 2
58 10 24 52 3 2 14 3
59 9 23 54 6 3 13 7
60 14 21 80 6 4 11 4
61 13 22 66 4 3 17 4
62 12 22 73 7 4 12 1
63 9 21 63 6 4 13 2
64 9 21 69 3 7 14 2
65 10 21 67 7 2 13 2
66 8 21 54 2 2 15 5
67 9 20 81 4 5 13 1
68 9 22 69 6 3 10 6
69 11 22 84 4 6 11 2
70 7 22 70 1 6 13 2
71 11 23 69 4 4 17 4
72 9 21 77 7 6 13 6
73 11 23 54 4 6 9 2
74 9 22 79 4 4 11 2
75 8 21 30 4 2 10 2
76 9 21 71 6 6 9 1
77 8 20 73 2 3 12 1
78 9 24 72 3 5 12 2
79 10 24 77 4 3 13 2
80 9 21 75 4 4 13 3
81 17 20 70 4 6 22 3
82 7 21 73 6 2 13 5
83 11 21 54 2 7 15 2
84 9 21 77 4 2 13 5
85 10 21 82 3 3 15 3
86 11 22 80 7 6 10 1
87 8 22 80 4 4 11 2
88 12 21 69 5 4 16 2
89 10 22 78 6 3 11 1
90 7 21 81 5 5 11 2
91 9 23 76 4 4 10 2
92 7 21 76 5 5 10 5
93 12 22 73 4 5 16 5
94 8 22 85 5 7 12 2
95 13 22 66 7 4 11 3
96 9 20 79 7 6 16 5
97 15 21 68 4 3 19 5
98 8 21 76 6 6 11 6
99 14 22 54 4 3 15 2
100 14 25 46 1 2 24 7
101 9 22 82 3 4 14 1
102 13 22 74 6 3 15 1
103 11 21 88 7 3 11 6
104 10 22 38 6 4 15 6
105 6 21 76 6 4 12 2
106 8 24 86 6 5 10 1
107 10 23 54 4 5 14 2
108 10 23 69 1 7 9 1
109 10 22 90 3 7 15 2
110 12 22 54 7 1 15 1
111 10 25 76 2 4 14 3
112 9 23 89 7 6 11 3
113 9 22 76 4 5 8 6
114 11 21 79 5 4 11 4
115 7 21 90 6 5 8 1
116 7 22 74 6 5 10 2
117 5 22 81 5 6 11 5
118 9 21 72 5 5 13 6
119 11 0 71 4 3 11 3
120 15 21 66 2 4 20 5
121 9 22 77 2 4 10 3
122 9 21 74 4 5 12 2
123 8 24 82 4 6 14 3
124 13 21 54 6 2 23 2
125 10 23 63 5 4 14 5
126 13 23 54 5 5 16 5
127 9 22 64 6 6 11 7
128 11 21 69 5 6 12 4
129 8 21 84 7 5 14 5
130 10 21 86 5 4 12 1
131 9 21 77 3 5 12 4
132 8 22 89 5 6 11 1
133 8 20 76 1 6 12 4
134 13 21 60 5 5 13 6
135 11 23 79 7 6 17 7
136 8 32 76 7 4 11 1
137 12 22 72 6 5 12 3
138 15 24 69 4 5 19 5
139 11 21 54 2 7 15 2
140 10 22 69 6 5 14 4
141 5 22 81 5 6 11 5
142 11 23 84 1 6 9 1
ToekZorgen
1 4
2 3
3 5
4 3
5 6
6 5
7 6
8 6
9 5
10 5
11 3
12 5
13 7
14 5
15 5
16 3
17 5
18 6
19 5
20 2
21 5
22 4
23 6
24 3
25 5
26 4
27 5
28 2
29 2
30 5
31 2
32 2
33 2
34 5
35 5
36 1
37 5
38 2
39 6
40 1
41 3
42 2
43 5
44 3
45 4
46 3
47 6
48 4
49 5
50 2
51 5
52 5
53 3
54 5
55 7
56 4
57 2
58 3
59 6
60 7
61 4
62 4
63 4
64 5
65 2
66 3
67 3
68 4
69 3
70 4
71 6
72 2
73 4
74 5
75 2
76 1
77 2
78 5
79 4
80 4
81 6
82 1
83 4
84 5
85 2
86 3
87 3
88 6
89 5
90 4
91 4
92 5
93 5
94 6
95 6
96 5
97 7
98 5
99 5
100 7
101 5
102 6
103 6
104 4
105 5
106 1
107 6
108 5
109 2
110 1
111 5
112 6
113 5
114 5
115 4
116 2
117 3
118 3
119 5
120 3
121 2
122 2
123 3
124 6
125 5
126 6
127 2
128 5
129 5
130 1
131 4
132 2
133 2
134 7
135 6
136 5
137 5
138 5
139 4
140 3
141 3
142 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) AGE BelInSprt KunnenRekRel ExtraCurAct
8.87667 -0.10427 -0.03216 0.20672 -0.13042
Depressie Slaapgebrek ToekZorgen
0.39429 -0.21057 0.19976
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.6117 -1.2516 -0.1580 1.4149 6.1795
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.87667 2.12472 4.178 5.27e-05 ***
AGE -0.10427 0.06666 -1.564 0.1202
BelInSprt -0.03216 0.01663 -1.934 0.0552 .
KunnenRekRel 0.20672 0.10821 1.910 0.0582 .
ExtraCurAct -0.13042 0.12298 -1.061 0.2908
Depressie 0.39429 0.06198 6.362 2.91e-09 ***
Slaapgebrek -0.21057 0.09250 -2.277 0.0244 *
ToekZorgen 0.19976 0.11280 1.771 0.0789 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.92 on 134 degrees of freedom
Multiple R-squared: 0.3851, Adjusted R-squared: 0.3529
F-statistic: 11.99 on 7 and 134 DF, p-value: 8.176e-12
> 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,] 0.9003253 0.19934950 0.09967475
[2,] 0.9884258 0.02314849 0.01157424
[3,] 0.9845834 0.03083318 0.01541659
[4,] 0.9829163 0.03416745 0.01708373
[5,] 0.9780435 0.04391297 0.02195649
[6,] 0.9619956 0.07600881 0.03800440
[7,] 0.9432316 0.11353682 0.05676841
[8,] 0.9453906 0.10921872 0.05460936
[9,] 0.9304647 0.13907069 0.06953535
[10,] 0.9009331 0.19813386 0.09906693
[11,] 0.8687819 0.26243613 0.13121806
[12,] 0.8828290 0.23434207 0.11717103
[13,] 0.8517328 0.29653431 0.14826715
[14,] 0.9833310 0.03333798 0.01666899
[15,] 0.9767675 0.04646506 0.02323253
[16,] 0.9688325 0.06233494 0.03116747
[17,] 0.9831638 0.03367233 0.01683617
[18,] 0.9773044 0.04539114 0.02269557
[19,] 0.9707294 0.05854116 0.02927058
[20,] 0.9699156 0.06016886 0.03008443
[21,] 0.9606443 0.07871148 0.03935574
[22,] 0.9461440 0.10771204 0.05385602
[23,] 0.9279494 0.14410121 0.07205061
[24,] 0.9442580 0.11148399 0.05574200
[25,] 0.9303189 0.13936223 0.06968112
[26,] 0.9106252 0.17874963 0.08937482
[27,] 0.9035669 0.19286619 0.09643310
[28,] 0.8793415 0.24131694 0.12065847
[29,] 0.8603198 0.27936038 0.13968019
[30,] 0.8269131 0.34617371 0.17308685
[31,] 0.8966371 0.20672580 0.10336290
[32,] 0.9160444 0.16791126 0.08395563
[33,] 0.8954743 0.20905130 0.10452565
[34,] 0.8843586 0.23128283 0.11564141
[35,] 0.8666792 0.26664170 0.13332085
[36,] 0.8462514 0.30749710 0.15374855
[37,] 0.8274430 0.34511410 0.17255705
[38,] 0.8040641 0.39187186 0.19593593
[39,] 0.8087283 0.38254334 0.19127167
[40,] 0.7799078 0.44018436 0.22009218
[41,] 0.7662438 0.46751250 0.23375625
[42,] 0.7254270 0.54914597 0.27457299
[43,] 0.8464058 0.30718834 0.15359417
[44,] 0.8156911 0.36861775 0.18430887
[45,] 0.8315019 0.33699616 0.16849808
[46,] 0.8511122 0.29777557 0.14888779
[47,] 0.8950619 0.20987612 0.10493806
[48,] 0.8709519 0.25809626 0.12904813
[49,] 0.8538189 0.29236215 0.14618107
[50,] 0.9426503 0.11469937 0.05734968
[51,] 0.9358374 0.12832521 0.06416261
[52,] 0.9298470 0.14030600 0.07015300
[53,] 0.9352433 0.12951333 0.06475667
[54,] 0.9286807 0.14263866 0.07131933
[55,] 0.9292318 0.14153630 0.07076815
[56,] 0.9379493 0.12410131 0.06205065
[57,] 0.9258577 0.14828461 0.07414230
[58,] 0.9082328 0.18353435 0.09176717
[59,] 0.9271788 0.14564235 0.07282117
[60,] 0.9406152 0.11876962 0.05938481
[61,] 0.9278932 0.14421369 0.07210684
[62,] 0.9123153 0.17536939 0.08768470
[63,] 0.9277964 0.14440711 0.07220355
[64,] 0.9094026 0.18119486 0.09059743
[65,] 0.9120566 0.17588686 0.08794343
[66,] 0.8982420 0.20351607 0.10175804
[67,] 0.8888215 0.22235704 0.11117852
[68,] 0.8707316 0.25853684 0.12926842
[69,] 0.8421082 0.31578361 0.15789180
[70,] 0.8180301 0.36393985 0.18196992
[71,] 0.8705520 0.25889594 0.12944797
[72,] 0.8913957 0.21720867 0.10860434
[73,] 0.8694974 0.26100527 0.13050264
[74,] 0.8545941 0.29081170 0.14540585
[75,] 0.8229488 0.35410236 0.17705118
[76,] 0.8595827 0.28083458 0.14041729
[77,] 0.8381179 0.32376429 0.16188215
[78,] 0.8039039 0.39219230 0.19609615
[79,] 0.7659278 0.46814445 0.23407222
[80,] 0.7786814 0.44263715 0.22131857
[81,] 0.7405095 0.51898101 0.25949050
[82,] 0.7399333 0.52013332 0.26006666
[83,] 0.7152607 0.56947865 0.28473932
[84,] 0.6903062 0.61938756 0.30969378
[85,] 0.7514412 0.49711753 0.24855876
[86,] 0.7460556 0.50788889 0.25394444
[87,] 0.7461890 0.50762198 0.25381099
[88,] 0.7054898 0.58902043 0.29451021
[89,] 0.7177145 0.56457093 0.28228547
[90,] 0.7380392 0.52392167 0.26196084
[91,] 0.7502023 0.49959538 0.24979769
[92,] 0.7268533 0.54629347 0.27314673
[93,] 0.7219267 0.55614662 0.27807331
[94,] 0.7068064 0.58638721 0.29319360
[95,] 0.8628872 0.27422560 0.13711280
[96,] 0.8357452 0.32850955 0.16425477
[97,] 0.8464130 0.30717396 0.15358698
[98,] 0.8295497 0.34090064 0.17045032
[99,] 0.8023259 0.39534814 0.19767407
[100,] 0.7774815 0.44503708 0.22251854
[101,] 0.7788167 0.44236657 0.22118329
[102,] 0.7303077 0.53938453 0.26969227
[103,] 0.6843957 0.63120863 0.31560432
[104,] 0.6578669 0.68426617 0.34213308
[105,] 0.5952340 0.80953197 0.40476598
[106,] 0.5352753 0.92944938 0.46472469
[107,] 0.6449011 0.71019773 0.35509886
[108,] 0.5742791 0.85144189 0.42572094
[109,] 0.4994339 0.99886784 0.50056608
[110,] 0.5515034 0.89699330 0.44849665
[111,] 0.4746417 0.94928335 0.52535832
[112,] 0.3904791 0.78095818 0.60952091
[113,] 0.3432646 0.68652922 0.65673539
[114,] 0.4287359 0.85747187 0.57126407
[115,] 0.4195381 0.83907615 0.58046193
[116,] 0.3351103 0.67022069 0.66488965
[117,] 0.5447857 0.91042865 0.45521432
[118,] 0.4754644 0.95092887 0.52453557
[119,] 0.6935332 0.61293356 0.30646678
[120,] 0.5581004 0.88379921 0.44189961
[121,] 0.8198406 0.36031888 0.18015944
> postscript(file="/var/www/html/rcomp/tmp/1fj6c1291471767.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qtnx1291471767.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qtnx1291471767.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qtnx1291471767.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5jk501291471767.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 = 142
Frequency = 1
1 2 3 4 5 6
-0.54799499 -2.05996078 2.80234128 2.25661598 -4.24002834 -3.22117952
7 8 9 10 11 12
-3.50223518 0.28708639 -0.55035169 -1.50849145 -1.63762126 -4.61165725
13 14 15 16 17 18
1.70096820 -2.86964223 -0.99006285 -0.61386896 -1.58161194 -0.01971629
19 20 21 22 23 24
-1.26408682 -1.48161444 -0.48668096 1.46406669 -2.00406855 6.17952308
25 26 27 28 29 30
-1.14680082 -0.32956203 2.45896334 -0.62467673 -0.48169305 -2.28172110
31 32 33 34 35 36
0.65433893 0.63202694 -0.17882224 2.17971256 -1.01041681 -1.06684137
37 38 39 40 41 42
1.63369405 0.95220032 -1.69273045 0.02580427 3.21448541 2.96564130
43 44 45 46 47 48
-0.49519112 1.75171436 -1.17819276 -0.87295220 0.15527486 0.94707399
49 50 51 52 53 54
1.77792254 0.28580965 -1.87451994 0.28053882 3.76355597 0.58810145
55 56 57 58 59 60
1.92166021 1.84011870 3.33451466 -0.54891869 -1.44131951 4.27378281
61 62 63 64 65 66
1.44439523 1.51949308 -1.88335624 -1.27303546 -0.82276665 -2.56383903
67 68 69 70 71 72
-0.87573070 0.30859709 2.55885175 -2.25953412 -0.62395679 -0.13723232
73 74 75 76 77 78
2.28719911 -0.26228853 -2.20958356 0.60060528 -1.38634052 -0.33600656
79 80 81 82 83 84
0.16269414 -0.87344367 3.17420647 -2.59162714 0.25678072 -0.84857867
85 86 87 88 89 90
0.03890192 1.99377276 -0.83061628 -0.06606936 -0.04887871 -2.17878928
91 92 93 94 95 96
0.33955384 -1.51333577 1.33544128 -1.47885550 2.71030270 -2.16989749
97 98 99 100 101 102
2.22716105 -0.77335961 2.22617909 -0.12380063 -1.35253262 1.04557139
103 104 105 106 107 108
1.81480669 -1.52933914 -4.27076685 -0.12892006 -1.21418277 2.10982781
109 110 111 112 113 114
0.71153734 -0.06636434 0.39519074 -0.18495391 1.79680657 1.84786063
115 116 117 118 119 120
-1.12378793 -1.71254454 -3.11263330 -0.21475405 -0.73333799 3.11144431
121 122 123 124 125 126
1.29097059 -0.19195059 -1.26922453 -2.77602958 -0.43043022 1.42222666
127 128 129 130 131 132
0.75485362 1.39282405 -2.24667429 0.84599460 0.13287278 -0.49789271
133 134 135 136 137 138
-0.06017967 2.60031959 -0.02999404 -1.14680082 2.04585839 3.23247816
139 140 141 142
0.25678072 -0.22911167 -3.11263330 3.86129700
> postscript(file="/var/www/html/rcomp/tmp/6jk501291471767.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 = 142
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.54799499 NA
1 -2.05996078 -0.54799499
2 2.80234128 -2.05996078
3 2.25661598 2.80234128
4 -4.24002834 2.25661598
5 -3.22117952 -4.24002834
6 -3.50223518 -3.22117952
7 0.28708639 -3.50223518
8 -0.55035169 0.28708639
9 -1.50849145 -0.55035169
10 -1.63762126 -1.50849145
11 -4.61165725 -1.63762126
12 1.70096820 -4.61165725
13 -2.86964223 1.70096820
14 -0.99006285 -2.86964223
15 -0.61386896 -0.99006285
16 -1.58161194 -0.61386896
17 -0.01971629 -1.58161194
18 -1.26408682 -0.01971629
19 -1.48161444 -1.26408682
20 -0.48668096 -1.48161444
21 1.46406669 -0.48668096
22 -2.00406855 1.46406669
23 6.17952308 -2.00406855
24 -1.14680082 6.17952308
25 -0.32956203 -1.14680082
26 2.45896334 -0.32956203
27 -0.62467673 2.45896334
28 -0.48169305 -0.62467673
29 -2.28172110 -0.48169305
30 0.65433893 -2.28172110
31 0.63202694 0.65433893
32 -0.17882224 0.63202694
33 2.17971256 -0.17882224
34 -1.01041681 2.17971256
35 -1.06684137 -1.01041681
36 1.63369405 -1.06684137
37 0.95220032 1.63369405
38 -1.69273045 0.95220032
39 0.02580427 -1.69273045
40 3.21448541 0.02580427
41 2.96564130 3.21448541
42 -0.49519112 2.96564130
43 1.75171436 -0.49519112
44 -1.17819276 1.75171436
45 -0.87295220 -1.17819276
46 0.15527486 -0.87295220
47 0.94707399 0.15527486
48 1.77792254 0.94707399
49 0.28580965 1.77792254
50 -1.87451994 0.28580965
51 0.28053882 -1.87451994
52 3.76355597 0.28053882
53 0.58810145 3.76355597
54 1.92166021 0.58810145
55 1.84011870 1.92166021
56 3.33451466 1.84011870
57 -0.54891869 3.33451466
58 -1.44131951 -0.54891869
59 4.27378281 -1.44131951
60 1.44439523 4.27378281
61 1.51949308 1.44439523
62 -1.88335624 1.51949308
63 -1.27303546 -1.88335624
64 -0.82276665 -1.27303546
65 -2.56383903 -0.82276665
66 -0.87573070 -2.56383903
67 0.30859709 -0.87573070
68 2.55885175 0.30859709
69 -2.25953412 2.55885175
70 -0.62395679 -2.25953412
71 -0.13723232 -0.62395679
72 2.28719911 -0.13723232
73 -0.26228853 2.28719911
74 -2.20958356 -0.26228853
75 0.60060528 -2.20958356
76 -1.38634052 0.60060528
77 -0.33600656 -1.38634052
78 0.16269414 -0.33600656
79 -0.87344367 0.16269414
80 3.17420647 -0.87344367
81 -2.59162714 3.17420647
82 0.25678072 -2.59162714
83 -0.84857867 0.25678072
84 0.03890192 -0.84857867
85 1.99377276 0.03890192
86 -0.83061628 1.99377276
87 -0.06606936 -0.83061628
88 -0.04887871 -0.06606936
89 -2.17878928 -0.04887871
90 0.33955384 -2.17878928
91 -1.51333577 0.33955384
92 1.33544128 -1.51333577
93 -1.47885550 1.33544128
94 2.71030270 -1.47885550
95 -2.16989749 2.71030270
96 2.22716105 -2.16989749
97 -0.77335961 2.22716105
98 2.22617909 -0.77335961
99 -0.12380063 2.22617909
100 -1.35253262 -0.12380063
101 1.04557139 -1.35253262
102 1.81480669 1.04557139
103 -1.52933914 1.81480669
104 -4.27076685 -1.52933914
105 -0.12892006 -4.27076685
106 -1.21418277 -0.12892006
107 2.10982781 -1.21418277
108 0.71153734 2.10982781
109 -0.06636434 0.71153734
110 0.39519074 -0.06636434
111 -0.18495391 0.39519074
112 1.79680657 -0.18495391
113 1.84786063 1.79680657
114 -1.12378793 1.84786063
115 -1.71254454 -1.12378793
116 -3.11263330 -1.71254454
117 -0.21475405 -3.11263330
118 -0.73333799 -0.21475405
119 3.11144431 -0.73333799
120 1.29097059 3.11144431
121 -0.19195059 1.29097059
122 -1.26922453 -0.19195059
123 -2.77602958 -1.26922453
124 -0.43043022 -2.77602958
125 1.42222666 -0.43043022
126 0.75485362 1.42222666
127 1.39282405 0.75485362
128 -2.24667429 1.39282405
129 0.84599460 -2.24667429
130 0.13287278 0.84599460
131 -0.49789271 0.13287278
132 -0.06017967 -0.49789271
133 2.60031959 -0.06017967
134 -0.02999404 2.60031959
135 -1.14680082 -0.02999404
136 2.04585839 -1.14680082
137 3.23247816 2.04585839
138 0.25678072 3.23247816
139 -0.22911167 0.25678072
140 -3.11263330 -0.22911167
141 3.86129700 -3.11263330
142 NA 3.86129700
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.05996078 -0.54799499
[2,] 2.80234128 -2.05996078
[3,] 2.25661598 2.80234128
[4,] -4.24002834 2.25661598
[5,] -3.22117952 -4.24002834
[6,] -3.50223518 -3.22117952
[7,] 0.28708639 -3.50223518
[8,] -0.55035169 0.28708639
[9,] -1.50849145 -0.55035169
[10,] -1.63762126 -1.50849145
[11,] -4.61165725 -1.63762126
[12,] 1.70096820 -4.61165725
[13,] -2.86964223 1.70096820
[14,] -0.99006285 -2.86964223
[15,] -0.61386896 -0.99006285
[16,] -1.58161194 -0.61386896
[17,] -0.01971629 -1.58161194
[18,] -1.26408682 -0.01971629
[19,] -1.48161444 -1.26408682
[20,] -0.48668096 -1.48161444
[21,] 1.46406669 -0.48668096
[22,] -2.00406855 1.46406669
[23,] 6.17952308 -2.00406855
[24,] -1.14680082 6.17952308
[25,] -0.32956203 -1.14680082
[26,] 2.45896334 -0.32956203
[27,] -0.62467673 2.45896334
[28,] -0.48169305 -0.62467673
[29,] -2.28172110 -0.48169305
[30,] 0.65433893 -2.28172110
[31,] 0.63202694 0.65433893
[32,] -0.17882224 0.63202694
[33,] 2.17971256 -0.17882224
[34,] -1.01041681 2.17971256
[35,] -1.06684137 -1.01041681
[36,] 1.63369405 -1.06684137
[37,] 0.95220032 1.63369405
[38,] -1.69273045 0.95220032
[39,] 0.02580427 -1.69273045
[40,] 3.21448541 0.02580427
[41,] 2.96564130 3.21448541
[42,] -0.49519112 2.96564130
[43,] 1.75171436 -0.49519112
[44,] -1.17819276 1.75171436
[45,] -0.87295220 -1.17819276
[46,] 0.15527486 -0.87295220
[47,] 0.94707399 0.15527486
[48,] 1.77792254 0.94707399
[49,] 0.28580965 1.77792254
[50,] -1.87451994 0.28580965
[51,] 0.28053882 -1.87451994
[52,] 3.76355597 0.28053882
[53,] 0.58810145 3.76355597
[54,] 1.92166021 0.58810145
[55,] 1.84011870 1.92166021
[56,] 3.33451466 1.84011870
[57,] -0.54891869 3.33451466
[58,] -1.44131951 -0.54891869
[59,] 4.27378281 -1.44131951
[60,] 1.44439523 4.27378281
[61,] 1.51949308 1.44439523
[62,] -1.88335624 1.51949308
[63,] -1.27303546 -1.88335624
[64,] -0.82276665 -1.27303546
[65,] -2.56383903 -0.82276665
[66,] -0.87573070 -2.56383903
[67,] 0.30859709 -0.87573070
[68,] 2.55885175 0.30859709
[69,] -2.25953412 2.55885175
[70,] -0.62395679 -2.25953412
[71,] -0.13723232 -0.62395679
[72,] 2.28719911 -0.13723232
[73,] -0.26228853 2.28719911
[74,] -2.20958356 -0.26228853
[75,] 0.60060528 -2.20958356
[76,] -1.38634052 0.60060528
[77,] -0.33600656 -1.38634052
[78,] 0.16269414 -0.33600656
[79,] -0.87344367 0.16269414
[80,] 3.17420647 -0.87344367
[81,] -2.59162714 3.17420647
[82,] 0.25678072 -2.59162714
[83,] -0.84857867 0.25678072
[84,] 0.03890192 -0.84857867
[85,] 1.99377276 0.03890192
[86,] -0.83061628 1.99377276
[87,] -0.06606936 -0.83061628
[88,] -0.04887871 -0.06606936
[89,] -2.17878928 -0.04887871
[90,] 0.33955384 -2.17878928
[91,] -1.51333577 0.33955384
[92,] 1.33544128 -1.51333577
[93,] -1.47885550 1.33544128
[94,] 2.71030270 -1.47885550
[95,] -2.16989749 2.71030270
[96,] 2.22716105 -2.16989749
[97,] -0.77335961 2.22716105
[98,] 2.22617909 -0.77335961
[99,] -0.12380063 2.22617909
[100,] -1.35253262 -0.12380063
[101,] 1.04557139 -1.35253262
[102,] 1.81480669 1.04557139
[103,] -1.52933914 1.81480669
[104,] -4.27076685 -1.52933914
[105,] -0.12892006 -4.27076685
[106,] -1.21418277 -0.12892006
[107,] 2.10982781 -1.21418277
[108,] 0.71153734 2.10982781
[109,] -0.06636434 0.71153734
[110,] 0.39519074 -0.06636434
[111,] -0.18495391 0.39519074
[112,] 1.79680657 -0.18495391
[113,] 1.84786063 1.79680657
[114,] -1.12378793 1.84786063
[115,] -1.71254454 -1.12378793
[116,] -3.11263330 -1.71254454
[117,] -0.21475405 -3.11263330
[118,] -0.73333799 -0.21475405
[119,] 3.11144431 -0.73333799
[120,] 1.29097059 3.11144431
[121,] -0.19195059 1.29097059
[122,] -1.26922453 -0.19195059
[123,] -2.77602958 -1.26922453
[124,] -0.43043022 -2.77602958
[125,] 1.42222666 -0.43043022
[126,] 0.75485362 1.42222666
[127,] 1.39282405 0.75485362
[128,] -2.24667429 1.39282405
[129,] 0.84599460 -2.24667429
[130,] 0.13287278 0.84599460
[131,] -0.49789271 0.13287278
[132,] -0.06017967 -0.49789271
[133,] 2.60031959 -0.06017967
[134,] -0.02999404 2.60031959
[135,] -1.14680082 -0.02999404
[136,] 2.04585839 -1.14680082
[137,] 3.23247816 2.04585839
[138,] 0.25678072 3.23247816
[139,] -0.22911167 0.25678072
[140,] -3.11263330 -0.22911167
[141,] 3.86129700 -3.11263330
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.05996078 -0.54799499
2 2.80234128 -2.05996078
3 2.25661598 2.80234128
4 -4.24002834 2.25661598
5 -3.22117952 -4.24002834
6 -3.50223518 -3.22117952
7 0.28708639 -3.50223518
8 -0.55035169 0.28708639
9 -1.50849145 -0.55035169
10 -1.63762126 -1.50849145
11 -4.61165725 -1.63762126
12 1.70096820 -4.61165725
13 -2.86964223 1.70096820
14 -0.99006285 -2.86964223
15 -0.61386896 -0.99006285
16 -1.58161194 -0.61386896
17 -0.01971629 -1.58161194
18 -1.26408682 -0.01971629
19 -1.48161444 -1.26408682
20 -0.48668096 -1.48161444
21 1.46406669 -0.48668096
22 -2.00406855 1.46406669
23 6.17952308 -2.00406855
24 -1.14680082 6.17952308
25 -0.32956203 -1.14680082
26 2.45896334 -0.32956203
27 -0.62467673 2.45896334
28 -0.48169305 -0.62467673
29 -2.28172110 -0.48169305
30 0.65433893 -2.28172110
31 0.63202694 0.65433893
32 -0.17882224 0.63202694
33 2.17971256 -0.17882224
34 -1.01041681 2.17971256
35 -1.06684137 -1.01041681
36 1.63369405 -1.06684137
37 0.95220032 1.63369405
38 -1.69273045 0.95220032
39 0.02580427 -1.69273045
40 3.21448541 0.02580427
41 2.96564130 3.21448541
42 -0.49519112 2.96564130
43 1.75171436 -0.49519112
44 -1.17819276 1.75171436
45 -0.87295220 -1.17819276
46 0.15527486 -0.87295220
47 0.94707399 0.15527486
48 1.77792254 0.94707399
49 0.28580965 1.77792254
50 -1.87451994 0.28580965
51 0.28053882 -1.87451994
52 3.76355597 0.28053882
53 0.58810145 3.76355597
54 1.92166021 0.58810145
55 1.84011870 1.92166021
56 3.33451466 1.84011870
57 -0.54891869 3.33451466
58 -1.44131951 -0.54891869
59 4.27378281 -1.44131951
60 1.44439523 4.27378281
61 1.51949308 1.44439523
62 -1.88335624 1.51949308
63 -1.27303546 -1.88335624
64 -0.82276665 -1.27303546
65 -2.56383903 -0.82276665
66 -0.87573070 -2.56383903
67 0.30859709 -0.87573070
68 2.55885175 0.30859709
69 -2.25953412 2.55885175
70 -0.62395679 -2.25953412
71 -0.13723232 -0.62395679
72 2.28719911 -0.13723232
73 -0.26228853 2.28719911
74 -2.20958356 -0.26228853
75 0.60060528 -2.20958356
76 -1.38634052 0.60060528
77 -0.33600656 -1.38634052
78 0.16269414 -0.33600656
79 -0.87344367 0.16269414
80 3.17420647 -0.87344367
81 -2.59162714 3.17420647
82 0.25678072 -2.59162714
83 -0.84857867 0.25678072
84 0.03890192 -0.84857867
85 1.99377276 0.03890192
86 -0.83061628 1.99377276
87 -0.06606936 -0.83061628
88 -0.04887871 -0.06606936
89 -2.17878928 -0.04887871
90 0.33955384 -2.17878928
91 -1.51333577 0.33955384
92 1.33544128 -1.51333577
93 -1.47885550 1.33544128
94 2.71030270 -1.47885550
95 -2.16989749 2.71030270
96 2.22716105 -2.16989749
97 -0.77335961 2.22716105
98 2.22617909 -0.77335961
99 -0.12380063 2.22617909
100 -1.35253262 -0.12380063
101 1.04557139 -1.35253262
102 1.81480669 1.04557139
103 -1.52933914 1.81480669
104 -4.27076685 -1.52933914
105 -0.12892006 -4.27076685
106 -1.21418277 -0.12892006
107 2.10982781 -1.21418277
108 0.71153734 2.10982781
109 -0.06636434 0.71153734
110 0.39519074 -0.06636434
111 -0.18495391 0.39519074
112 1.79680657 -0.18495391
113 1.84786063 1.79680657
114 -1.12378793 1.84786063
115 -1.71254454 -1.12378793
116 -3.11263330 -1.71254454
117 -0.21475405 -3.11263330
118 -0.73333799 -0.21475405
119 3.11144431 -0.73333799
120 1.29097059 3.11144431
121 -0.19195059 1.29097059
122 -1.26922453 -0.19195059
123 -2.77602958 -1.26922453
124 -0.43043022 -2.77602958
125 1.42222666 -0.43043022
126 0.75485362 1.42222666
127 1.39282405 0.75485362
128 -2.24667429 1.39282405
129 0.84599460 -2.24667429
130 0.13287278 0.84599460
131 -0.49789271 0.13287278
132 -0.06017967 -0.49789271
133 2.60031959 -0.06017967
134 -0.02999404 2.60031959
135 -1.14680082 -0.02999404
136 2.04585839 -1.14680082
137 3.23247816 2.04585839
138 0.25678072 3.23247816
139 -0.22911167 0.25678072
140 -3.11263330 -0.22911167
141 3.86129700 -3.11263330
> 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/74l7y1291471768.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84l7y1291471768.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fv6j1291471768.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fv6j1291471768.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t4ls1291471768.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/123elv1291471768.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/13afip1291471768.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/14loh91291471768.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/156ofx1291471768.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/16kyvo1291471768.tab")
+ }
>
> try(system("convert tmp/1fj6c1291471767.ps tmp/1fj6c1291471767.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qtnx1291471767.ps tmp/2qtnx1291471767.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qtnx1291471767.ps tmp/3qtnx1291471767.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qtnx1291471767.ps tmp/4qtnx1291471767.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jk501291471767.ps tmp/5jk501291471767.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jk501291471767.ps tmp/6jk501291471767.png",intern=TRUE))
character(0)
> try(system("convert tmp/74l7y1291471768.ps tmp/74l7y1291471768.png",intern=TRUE))
character(0)
> try(system("convert tmp/84l7y1291471768.ps tmp/84l7y1291471768.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fv6j1291471768.ps tmp/9fv6j1291471768.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fv6j1291471768.ps tmp/10fv6j1291471768.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.971 1.809 11.336