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(41
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+ ,8
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+ ,10
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+ ,9
+ ,2)
+ ,dim=c(5
+ ,146)
+ ,dimnames=list(c('Career'
+ ,'PersonalStandards'
+ ,'ParentalExpectations'
+ ,'Doubts'
+ ,'LeadershipPreference')
+ ,1:146))
> y <- array(NA,dim=c(5,146),dimnames=list(c('Career','PersonalStandards','ParentalExpectations','Doubts','LeadershipPreference'),1:146))
> 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 = '5'
> #'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
LeadershipPreference Career PersonalStandards ParentalExpectations Doubts
1 3 41 25 15 9
2 4 38 25 15 9
3 4 37 19 14 9
4 2 36 18 10 14
5 4 42 18 10 8
6 4 44 23 9 14
7 3 40 23 18 15
8 4 43 25 14 9
9 4 40 23 11 11
10 4 45 24 11 14
11 4 47 32 9 14
12 5 45 30 17 6
13 4 45 32 21 10
14 4 40 24 16 9
15 4 49 17 14 14
16 5 48 30 24 8
17 4 44 25 7 11
18 4 29 25 9 10
19 4 42 26 18 16
20 5 45 23 11 11
21 5 32 25 13 11
22 5 32 25 13 11
23 4 41 35 18 7
24 2 29 19 14 13
25 4 38 20 12 10
26 4 41 21 12 9
27 4 38 21 9 9
28 3 24 23 11 15
29 2 34 24 8 13
30 2 38 23 5 16
31 3 37 19 10 12
32 5 46 17 11 6
33 5 48 27 15 4
34 4 42 27 16 12
35 4 46 25 12 10
36 5 43 18 14 14
37 4 38 22 13 9
38 4 39 26 10 10
39 4 34 26 18 14
40 4 39 23 17 14
41 2 35 16 12 10
42 3 41 27 13 9
43 3 40 25 13 14
44 4 43 14 11 8
45 2 37 19 13 9
46 4 41 20 12 8
47 4 46 26 12 10
48 3 26 16 12 9
49 3 41 18 12 9
50 3 37 22 9 9
51 4 39 25 17 9
52 5 44 29 18 11
53 2 39 21 7 15
54 4 36 22 17 8
55 2 38 22 12 10
56 0 38 32 12 8
57 4 38 23 9 14
58 4 32 31 9 11
59 3 33 18 13 10
60 4 46 23 10 12
61 4 42 24 12 9
62 2 42 19 10 13
63 4 43 26 11 14
64 2 41 14 13 15
65 4 49 20 6 8
66 3 45 22 7 7
67 4 39 24 13 10
68 5 45 25 11 10
69 3 31 21 18 13
70 3 30 21 18 13
71 4 45 28 9 11
72 5 48 24 9 8
73 4 28 15 12 14
74 2 35 21 11 9
75 4 38 23 15 10
76 4 39 24 11 11
77 4 40 21 14 10
78 4 38 21 14 16
79 4 42 13 8 11
80 2 36 17 12 16
81 5 49 29 8 6
82 4 41 25 11 11
83 2 18 16 10 12
84 3 36 20 11 12
85 3 42 25 17 14
86 5 41 25 16 9
87 4 43 21 13 11
88 3 46 23 15 8
89 4 37 22 11 8
90 3 38 19 12 7
91 4 43 26 20 13
92 5 41 25 16 8
93 2 35 19 8 20
94 4 39 25 7 11
95 4 42 24 16 16
96 4 36 20 11 11
97 5 35 21 13 12
98 2 33 14 15 10
99 3 36 22 15 14
100 4 48 14 12 8
101 4 41 20 12 10
102 3 47 21 24 14
103 3 41 22 15 10
104 5 31 19 8 5
105 4 36 28 18 12
106 4 46 25 17 9
107 4 39 17 12 16
108 4 44 21 15 8
109 2 43 27 11 16
110 4 32 29 12 12
111 5 40 19 12 13
112 3 40 20 14 8
113 3 46 17 11 14
114 3 45 21 12 8
115 4 39 22 10 8
116 4 44 26 11 7
117 4 35 19 11 10
118 3 38 17 9 11
119 2 38 17 12 11
120 3 36 19 8 14
121 3 42 17 12 10
122 4 39 15 6 6
123 5 41 27 15 9
124 4 41 19 13 12
125 3 47 21 17 11
126 3 39 25 14 14
127 4 40 19 16 12
128 4 44 18 16 8
129 4 42 15 11 8
130 3 35 20 16 11
131 5 46 29 15 12
132 3 43 20 11 14
133 4 40 29 9 16
134 4 44 24 12 13
135 4 37 24 13 11
136 4 46 23 11 9
137 5 44 23 11 11
138 3 35 19 13 9
139 2 39 22 14 12
140 3 40 22 12 13
141 3 42 25 17 14
142 3 37 21 11 9
143 4 29 22 15 14
144 2 33 21 13 8
145 4 35 18 9 8
146 2 42 10 12 9
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
65 65
66 66
67 67
68 68
69 69
70 70
71 71
72 72
73 73
74 74
75 75
76 76
77 77
78 78
79 79
80 80
81 81
82 82
83 83
84 84
85 85
86 86
87 87
88 88
89 89
90 90
91 91
92 92
93 93
94 94
95 95
96 96
97 97
98 98
99 99
100 100
101 101
102 102
103 103
104 104
105 105
106 106
107 107
108 108
109 109
110 110
111 111
112 112
113 113
114 114
115 115
116 116
117 117
118 118
119 119
120 120
121 121
122 122
123 123
124 124
125 125
126 126
127 127
128 128
129 129
130 130
131 131
132 132
133 133
134 134
135 135
136 136
137 137
138 138
139 139
140 140
141 141
142 142
143 143
144 144
145 145
146 146
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Career PersonalStandards
1.6359677 0.0405311 0.0478901
ParentalExpectations Doubts t
0.0137974 -0.0745267 -0.0008554
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.2301 -0.5038 0.1624 0.4590 1.7377
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6359677 0.7348550 2.226 0.02760 *
Career 0.0405311 0.0139293 2.910 0.00421 **
PersonalStandards 0.0478901 0.0178853 2.678 0.00830 **
ParentalExpectations 0.0137974 0.0217582 0.634 0.52704
Doubts -0.0745267 0.0257897 -2.890 0.00447 **
t -0.0008554 0.0017188 -0.498 0.61948
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8475 on 140 degrees of freedom
Multiple R-squared: 0.2079, Adjusted R-squared: 0.1797
F-statistic: 7.351 on 5 and 140 DF, p-value: 3.799e-06
> 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,] 0.511551365 0.976897271 0.48844864
[2,] 0.343316904 0.686633809 0.65668310
[3,] 0.223754599 0.447509198 0.77624540
[4,] 0.138319636 0.276639272 0.86168036
[5,] 0.092668193 0.185336386 0.90733181
[6,] 0.052724097 0.105448194 0.94727590
[7,] 0.028067984 0.056135967 0.97193202
[8,] 0.014988840 0.029977680 0.98501116
[9,] 0.009488436 0.018976872 0.99051156
[10,] 0.006895512 0.013791025 0.99310449
[11,] 0.004163587 0.008327174 0.99583641
[12,] 0.002673830 0.005347661 0.99732617
[13,] 0.004083818 0.008167636 0.99591618
[14,] 0.003373184 0.006746369 0.99662682
[15,] 0.015661377 0.031322754 0.98433862
[16,] 0.123332226 0.246664451 0.87666777
[17,] 0.097601114 0.195202228 0.90239889
[18,] 0.083654538 0.167309076 0.91634546
[19,] 0.065945858 0.131891715 0.93405414
[20,] 0.046995400 0.093990799 0.95300460
[21,] 0.151228234 0.302456468 0.84877177
[22,] 0.202900253 0.405800507 0.79709975
[23,] 0.172547601 0.345095201 0.82745240
[24,] 0.149722230 0.299444460 0.85027777
[25,] 0.122600954 0.245201909 0.87739905
[26,] 0.093383130 0.186766259 0.90661687
[27,] 0.072390019 0.144780039 0.92760998
[28,] 0.126644467 0.253288933 0.87335553
[29,] 0.100758122 0.201516243 0.89924188
[30,] 0.077319953 0.154639907 0.92268005
[31,] 0.063501215 0.127002431 0.93649878
[32,] 0.049950738 0.099901475 0.95004926
[33,] 0.118497439 0.236994878 0.88150256
[34,] 0.150130856 0.300261712 0.84986914
[35,] 0.132300949 0.264601898 0.86769905
[36,] 0.109131117 0.218262234 0.89086888
[37,] 0.195103915 0.390207830 0.80489608
[38,] 0.162296353 0.324592706 0.83770365
[39,] 0.131289170 0.262578341 0.86871083
[40,] 0.105199652 0.210399305 0.89480035
[41,] 0.094821685 0.189643371 0.90517831
[42,] 0.079865343 0.159730685 0.92013466
[43,] 0.062549089 0.125098179 0.93745091
[44,] 0.066029879 0.132059758 0.93397012
[45,] 0.072755815 0.145511631 0.92724418
[46,] 0.059030203 0.118060406 0.94096980
[47,] 0.096902359 0.193804718 0.90309764
[48,] 0.897705797 0.204588405 0.10229420
[49,] 0.909744850 0.180510300 0.09025515
[50,] 0.921168303 0.157663394 0.07883170
[51,] 0.902325152 0.195349695 0.09767485
[52,] 0.884775911 0.230448178 0.11522409
[53,] 0.863035132 0.273929737 0.13696487
[54,] 0.895106782 0.209786436 0.10489322
[55,] 0.881831576 0.236336848 0.11816842
[56,] 0.885574667 0.228850666 0.11442533
[57,] 0.864312691 0.271374618 0.13568731
[58,] 0.881966612 0.236066775 0.11803339
[59,] 0.865554260 0.268891479 0.13444574
[60,] 0.882123056 0.235753889 0.11787694
[61,] 0.856976976 0.286046048 0.14302302
[62,] 0.828617123 0.342765755 0.17138288
[63,] 0.808013606 0.383972787 0.19198639
[64,] 0.805817252 0.388365495 0.19418275
[65,] 0.868452769 0.263094463 0.13154723
[66,] 0.924210212 0.151579576 0.07578979
[67,] 0.908323706 0.183352588 0.09167629
[68,] 0.892318324 0.215363353 0.10768168
[69,] 0.871343869 0.257312263 0.12865613
[70,] 0.872059898 0.255880204 0.12794010
[71,] 0.871218893 0.257562215 0.12878111
[72,] 0.869408735 0.261182531 0.13059127
[73,] 0.851987437 0.296025125 0.14801256
[74,] 0.824825840 0.350348320 0.17517416
[75,] 0.811212669 0.377574662 0.18878733
[76,] 0.786759776 0.426480448 0.21324022
[77,] 0.778678132 0.442643737 0.22132187
[78,] 0.784875932 0.430248137 0.21512407
[79,] 0.749662130 0.500675741 0.25033787
[80,] 0.793260966 0.413478068 0.20673903
[81,] 0.761828035 0.476343929 0.23817196
[82,] 0.770441337 0.459117326 0.22955866
[83,] 0.730112388 0.539775225 0.26988761
[84,] 0.723693823 0.552612353 0.27630618
[85,] 0.725905480 0.548189041 0.27409452
[86,] 0.702327463 0.595345073 0.29767254
[87,] 0.668813574 0.662372853 0.33118643
[88,] 0.634997026 0.730005949 0.36500297
[89,] 0.743349985 0.513300030 0.25665002
[90,] 0.770683546 0.458632908 0.22931645
[91,] 0.739483937 0.521032126 0.26051606
[92,] 0.702855925 0.594288149 0.29714407
[93,] 0.659083810 0.681832380 0.34091619
[94,] 0.632887486 0.734225027 0.36711251
[95,] 0.627901063 0.744197875 0.37209894
[96,] 0.671608015 0.656783971 0.32839199
[97,] 0.621418938 0.757162125 0.37858106
[98,] 0.568008866 0.863982268 0.43199113
[99,] 0.599506091 0.800987817 0.40049391
[100,] 0.545451761 0.909096478 0.45454824
[101,] 0.748571789 0.502856422 0.25142821
[102,] 0.710077392 0.579845216 0.28992261
[103,] 0.850955144 0.298089712 0.14904486
[104,] 0.836413144 0.327173712 0.16358686
[105,] 0.797201137 0.405597725 0.20279886
[106,] 0.822952700 0.354094600 0.17704730
[107,] 0.779799007 0.440401986 0.22020099
[108,] 0.793077777 0.413844447 0.20692222
[109,] 0.763709358 0.472581285 0.23629064
[110,] 0.714843808 0.570312385 0.28515619
[111,] 0.791926687 0.416146626 0.20807331
[112,] 0.759444352 0.481111297 0.24055565
[113,] 0.748935148 0.502129704 0.25106485
[114,] 0.708100208 0.583799584 0.29189979
[115,] 0.659974657 0.680050686 0.34002534
[116,] 0.598175834 0.803648333 0.40182417
[117,] 0.588490004 0.823019993 0.41151000
[118,] 0.632563337 0.734873325 0.36743666
[119,] 0.576255297 0.847489407 0.42374470
[120,] 0.507078687 0.985842626 0.49292131
[121,] 0.457739507 0.915479014 0.54226049
[122,] 0.369108115 0.738216229 0.63089189
[123,] 0.365306539 0.730613077 0.63469346
[124,] 0.312983211 0.625966421 0.68701679
[125,] 0.444011227 0.888022454 0.55598877
[126,] 0.357175023 0.714350047 0.64282498
[127,] 0.253500828 0.507001656 0.74649917
[128,] 0.171939618 0.343879237 0.82806038
[129,] 0.382671608 0.765343216 0.61732839
> postscript(file="/var/www/html/rcomp/tmp/15yyo1290458249.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2y7fr1290458249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3y7fr1290458249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4y7fr1290458249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59zxc1290458249.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 = 146
Frequency = 1
1 2 3 4 5 6
-1.03036237 0.09208643 0.43461120 -1.04828932 0.26221935 0.40351919
7 8 9 10 11 12
-0.48315045 -0.09163925 0.31703526 0.29092493 -0.14480831 0.32629742
13 14 15 16 17 18
-0.52571020 0.05538213 0.42691649 0.26059758 0.12116336 0.62786429
19 20 21 22 23 24
0.37690871 1.12378937 1.52817443 1.52902986 -0.68088974 -0.92506907
25 26 27 28 29 30
0.46713080 0.22397604 0.38781693 0.27989313 -1.28011410 -1.12852094
31 32 33 34 35 36
-0.26266729 1.00823092 0.24487990 0.07133808 -0.08801461 1.64017715
37 38 39 40 41 42
0.29329166 0.17797414 0.56921299 0.52488058 -1.20602836 -1.06347528
43 44 45 46 47 48
-0.55367509 0.43281323 -1.51566335 0.21444813 -0.12563958 0.09021311
49 50 51 52 53 54
-0.61267862 -0.59986718 0.06587669 0.80777190 -1.15571826 0.25918012
55 56 57 58 59 60
-1.60298656 -4.23008589 0.69033291 0.32767395 -0.21914600 0.20579950
61 62 63 64 65 66
0.06971457 -1.36427790 0.32154472 -0.97492394 -0.01076353 -1.03188791
67 68 69 70 71 72
0.25716984 0.99454309 -0.11860667 -0.07722011 -0.04443965 0.80280296
73 74 75 76 77 78
1.45106012 -1.47797917 0.32483983 0.36699010 0.35506608 0.88414378
79 80 81 82 83 84
0.81614666 -0.81392782 0.39526403 0.24317029 -0.30442308 -0.23848584
85 86 87 88 89 90
-0.65399870 1.02855188 0.33035104 -1.13734191 0.33137323 -0.65295606
91 92 93 94 95 96
0.14679387 0.95915779 -0.50476026 0.38968716 0.56529645 0.69725266
97 98 99 100 101 102
1.73768103 -1.02181836 -0.22757076 0.26426437 0.41055016 -0.74713295
103 104 105 106 107 108
-0.72491134 1.54887395 0.29977555 -0.17079249 1.08757543 0.05660924
109 110 111 112 113 114
-1.53794227 0.50107123 1.73110574 -0.71615703 -0.32626584 -0.93739722
115 116 117 118 119 120
0.28634954 -0.19533525 0.72911131 -0.19372496 -1.23426161 0.03064523
121 122 123 124 125 126
-0.46920192 0.53370466 0.97821989 0.61337119 -0.85445653 -0.45594058
127 128 129 130 131 132
0.61507652 0.20359092 0.49816583 -0.30211837 0.91020743 -0.33208970
133 134 135 136 137 138
0.53599588 0.34920544 0.47092805 0.03343487 1.26440589 -0.35504604
139 140 141 142 143 144
-1.45020289 -0.38775719 -0.60609457 -0.50087213 1.09378607 -1.43915815
145 146
0.67949489 -1.18711183
> postscript(file="/var/www/html/rcomp/tmp/69zxc1290458249.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 = 146
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.03036237 NA
1 0.09208643 -1.03036237
2 0.43461120 0.09208643
3 -1.04828932 0.43461120
4 0.26221935 -1.04828932
5 0.40351919 0.26221935
6 -0.48315045 0.40351919
7 -0.09163925 -0.48315045
8 0.31703526 -0.09163925
9 0.29092493 0.31703526
10 -0.14480831 0.29092493
11 0.32629742 -0.14480831
12 -0.52571020 0.32629742
13 0.05538213 -0.52571020
14 0.42691649 0.05538213
15 0.26059758 0.42691649
16 0.12116336 0.26059758
17 0.62786429 0.12116336
18 0.37690871 0.62786429
19 1.12378937 0.37690871
20 1.52817443 1.12378937
21 1.52902986 1.52817443
22 -0.68088974 1.52902986
23 -0.92506907 -0.68088974
24 0.46713080 -0.92506907
25 0.22397604 0.46713080
26 0.38781693 0.22397604
27 0.27989313 0.38781693
28 -1.28011410 0.27989313
29 -1.12852094 -1.28011410
30 -0.26266729 -1.12852094
31 1.00823092 -0.26266729
32 0.24487990 1.00823092
33 0.07133808 0.24487990
34 -0.08801461 0.07133808
35 1.64017715 -0.08801461
36 0.29329166 1.64017715
37 0.17797414 0.29329166
38 0.56921299 0.17797414
39 0.52488058 0.56921299
40 -1.20602836 0.52488058
41 -1.06347528 -1.20602836
42 -0.55367509 -1.06347528
43 0.43281323 -0.55367509
44 -1.51566335 0.43281323
45 0.21444813 -1.51566335
46 -0.12563958 0.21444813
47 0.09021311 -0.12563958
48 -0.61267862 0.09021311
49 -0.59986718 -0.61267862
50 0.06587669 -0.59986718
51 0.80777190 0.06587669
52 -1.15571826 0.80777190
53 0.25918012 -1.15571826
54 -1.60298656 0.25918012
55 -4.23008589 -1.60298656
56 0.69033291 -4.23008589
57 0.32767395 0.69033291
58 -0.21914600 0.32767395
59 0.20579950 -0.21914600
60 0.06971457 0.20579950
61 -1.36427790 0.06971457
62 0.32154472 -1.36427790
63 -0.97492394 0.32154472
64 -0.01076353 -0.97492394
65 -1.03188791 -0.01076353
66 0.25716984 -1.03188791
67 0.99454309 0.25716984
68 -0.11860667 0.99454309
69 -0.07722011 -0.11860667
70 -0.04443965 -0.07722011
71 0.80280296 -0.04443965
72 1.45106012 0.80280296
73 -1.47797917 1.45106012
74 0.32483983 -1.47797917
75 0.36699010 0.32483983
76 0.35506608 0.36699010
77 0.88414378 0.35506608
78 0.81614666 0.88414378
79 -0.81392782 0.81614666
80 0.39526403 -0.81392782
81 0.24317029 0.39526403
82 -0.30442308 0.24317029
83 -0.23848584 -0.30442308
84 -0.65399870 -0.23848584
85 1.02855188 -0.65399870
86 0.33035104 1.02855188
87 -1.13734191 0.33035104
88 0.33137323 -1.13734191
89 -0.65295606 0.33137323
90 0.14679387 -0.65295606
91 0.95915779 0.14679387
92 -0.50476026 0.95915779
93 0.38968716 -0.50476026
94 0.56529645 0.38968716
95 0.69725266 0.56529645
96 1.73768103 0.69725266
97 -1.02181836 1.73768103
98 -0.22757076 -1.02181836
99 0.26426437 -0.22757076
100 0.41055016 0.26426437
101 -0.74713295 0.41055016
102 -0.72491134 -0.74713295
103 1.54887395 -0.72491134
104 0.29977555 1.54887395
105 -0.17079249 0.29977555
106 1.08757543 -0.17079249
107 0.05660924 1.08757543
108 -1.53794227 0.05660924
109 0.50107123 -1.53794227
110 1.73110574 0.50107123
111 -0.71615703 1.73110574
112 -0.32626584 -0.71615703
113 -0.93739722 -0.32626584
114 0.28634954 -0.93739722
115 -0.19533525 0.28634954
116 0.72911131 -0.19533525
117 -0.19372496 0.72911131
118 -1.23426161 -0.19372496
119 0.03064523 -1.23426161
120 -0.46920192 0.03064523
121 0.53370466 -0.46920192
122 0.97821989 0.53370466
123 0.61337119 0.97821989
124 -0.85445653 0.61337119
125 -0.45594058 -0.85445653
126 0.61507652 -0.45594058
127 0.20359092 0.61507652
128 0.49816583 0.20359092
129 -0.30211837 0.49816583
130 0.91020743 -0.30211837
131 -0.33208970 0.91020743
132 0.53599588 -0.33208970
133 0.34920544 0.53599588
134 0.47092805 0.34920544
135 0.03343487 0.47092805
136 1.26440589 0.03343487
137 -0.35504604 1.26440589
138 -1.45020289 -0.35504604
139 -0.38775719 -1.45020289
140 -0.60609457 -0.38775719
141 -0.50087213 -0.60609457
142 1.09378607 -0.50087213
143 -1.43915815 1.09378607
144 0.67949489 -1.43915815
145 -1.18711183 0.67949489
146 NA -1.18711183
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.09208643 -1.03036237
[2,] 0.43461120 0.09208643
[3,] -1.04828932 0.43461120
[4,] 0.26221935 -1.04828932
[5,] 0.40351919 0.26221935
[6,] -0.48315045 0.40351919
[7,] -0.09163925 -0.48315045
[8,] 0.31703526 -0.09163925
[9,] 0.29092493 0.31703526
[10,] -0.14480831 0.29092493
[11,] 0.32629742 -0.14480831
[12,] -0.52571020 0.32629742
[13,] 0.05538213 -0.52571020
[14,] 0.42691649 0.05538213
[15,] 0.26059758 0.42691649
[16,] 0.12116336 0.26059758
[17,] 0.62786429 0.12116336
[18,] 0.37690871 0.62786429
[19,] 1.12378937 0.37690871
[20,] 1.52817443 1.12378937
[21,] 1.52902986 1.52817443
[22,] -0.68088974 1.52902986
[23,] -0.92506907 -0.68088974
[24,] 0.46713080 -0.92506907
[25,] 0.22397604 0.46713080
[26,] 0.38781693 0.22397604
[27,] 0.27989313 0.38781693
[28,] -1.28011410 0.27989313
[29,] -1.12852094 -1.28011410
[30,] -0.26266729 -1.12852094
[31,] 1.00823092 -0.26266729
[32,] 0.24487990 1.00823092
[33,] 0.07133808 0.24487990
[34,] -0.08801461 0.07133808
[35,] 1.64017715 -0.08801461
[36,] 0.29329166 1.64017715
[37,] 0.17797414 0.29329166
[38,] 0.56921299 0.17797414
[39,] 0.52488058 0.56921299
[40,] -1.20602836 0.52488058
[41,] -1.06347528 -1.20602836
[42,] -0.55367509 -1.06347528
[43,] 0.43281323 -0.55367509
[44,] -1.51566335 0.43281323
[45,] 0.21444813 -1.51566335
[46,] -0.12563958 0.21444813
[47,] 0.09021311 -0.12563958
[48,] -0.61267862 0.09021311
[49,] -0.59986718 -0.61267862
[50,] 0.06587669 -0.59986718
[51,] 0.80777190 0.06587669
[52,] -1.15571826 0.80777190
[53,] 0.25918012 -1.15571826
[54,] -1.60298656 0.25918012
[55,] -4.23008589 -1.60298656
[56,] 0.69033291 -4.23008589
[57,] 0.32767395 0.69033291
[58,] -0.21914600 0.32767395
[59,] 0.20579950 -0.21914600
[60,] 0.06971457 0.20579950
[61,] -1.36427790 0.06971457
[62,] 0.32154472 -1.36427790
[63,] -0.97492394 0.32154472
[64,] -0.01076353 -0.97492394
[65,] -1.03188791 -0.01076353
[66,] 0.25716984 -1.03188791
[67,] 0.99454309 0.25716984
[68,] -0.11860667 0.99454309
[69,] -0.07722011 -0.11860667
[70,] -0.04443965 -0.07722011
[71,] 0.80280296 -0.04443965
[72,] 1.45106012 0.80280296
[73,] -1.47797917 1.45106012
[74,] 0.32483983 -1.47797917
[75,] 0.36699010 0.32483983
[76,] 0.35506608 0.36699010
[77,] 0.88414378 0.35506608
[78,] 0.81614666 0.88414378
[79,] -0.81392782 0.81614666
[80,] 0.39526403 -0.81392782
[81,] 0.24317029 0.39526403
[82,] -0.30442308 0.24317029
[83,] -0.23848584 -0.30442308
[84,] -0.65399870 -0.23848584
[85,] 1.02855188 -0.65399870
[86,] 0.33035104 1.02855188
[87,] -1.13734191 0.33035104
[88,] 0.33137323 -1.13734191
[89,] -0.65295606 0.33137323
[90,] 0.14679387 -0.65295606
[91,] 0.95915779 0.14679387
[92,] -0.50476026 0.95915779
[93,] 0.38968716 -0.50476026
[94,] 0.56529645 0.38968716
[95,] 0.69725266 0.56529645
[96,] 1.73768103 0.69725266
[97,] -1.02181836 1.73768103
[98,] -0.22757076 -1.02181836
[99,] 0.26426437 -0.22757076
[100,] 0.41055016 0.26426437
[101,] -0.74713295 0.41055016
[102,] -0.72491134 -0.74713295
[103,] 1.54887395 -0.72491134
[104,] 0.29977555 1.54887395
[105,] -0.17079249 0.29977555
[106,] 1.08757543 -0.17079249
[107,] 0.05660924 1.08757543
[108,] -1.53794227 0.05660924
[109,] 0.50107123 -1.53794227
[110,] 1.73110574 0.50107123
[111,] -0.71615703 1.73110574
[112,] -0.32626584 -0.71615703
[113,] -0.93739722 -0.32626584
[114,] 0.28634954 -0.93739722
[115,] -0.19533525 0.28634954
[116,] 0.72911131 -0.19533525
[117,] -0.19372496 0.72911131
[118,] -1.23426161 -0.19372496
[119,] 0.03064523 -1.23426161
[120,] -0.46920192 0.03064523
[121,] 0.53370466 -0.46920192
[122,] 0.97821989 0.53370466
[123,] 0.61337119 0.97821989
[124,] -0.85445653 0.61337119
[125,] -0.45594058 -0.85445653
[126,] 0.61507652 -0.45594058
[127,] 0.20359092 0.61507652
[128,] 0.49816583 0.20359092
[129,] -0.30211837 0.49816583
[130,] 0.91020743 -0.30211837
[131,] -0.33208970 0.91020743
[132,] 0.53599588 -0.33208970
[133,] 0.34920544 0.53599588
[134,] 0.47092805 0.34920544
[135,] 0.03343487 0.47092805
[136,] 1.26440589 0.03343487
[137,] -0.35504604 1.26440589
[138,] -1.45020289 -0.35504604
[139,] -0.38775719 -1.45020289
[140,] -0.60609457 -0.38775719
[141,] -0.50087213 -0.60609457
[142,] 1.09378607 -0.50087213
[143,] -1.43915815 1.09378607
[144,] 0.67949489 -1.43915815
[145,] -1.18711183 0.67949489
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.09208643 -1.03036237
2 0.43461120 0.09208643
3 -1.04828932 0.43461120
4 0.26221935 -1.04828932
5 0.40351919 0.26221935
6 -0.48315045 0.40351919
7 -0.09163925 -0.48315045
8 0.31703526 -0.09163925
9 0.29092493 0.31703526
10 -0.14480831 0.29092493
11 0.32629742 -0.14480831
12 -0.52571020 0.32629742
13 0.05538213 -0.52571020
14 0.42691649 0.05538213
15 0.26059758 0.42691649
16 0.12116336 0.26059758
17 0.62786429 0.12116336
18 0.37690871 0.62786429
19 1.12378937 0.37690871
20 1.52817443 1.12378937
21 1.52902986 1.52817443
22 -0.68088974 1.52902986
23 -0.92506907 -0.68088974
24 0.46713080 -0.92506907
25 0.22397604 0.46713080
26 0.38781693 0.22397604
27 0.27989313 0.38781693
28 -1.28011410 0.27989313
29 -1.12852094 -1.28011410
30 -0.26266729 -1.12852094
31 1.00823092 -0.26266729
32 0.24487990 1.00823092
33 0.07133808 0.24487990
34 -0.08801461 0.07133808
35 1.64017715 -0.08801461
36 0.29329166 1.64017715
37 0.17797414 0.29329166
38 0.56921299 0.17797414
39 0.52488058 0.56921299
40 -1.20602836 0.52488058
41 -1.06347528 -1.20602836
42 -0.55367509 -1.06347528
43 0.43281323 -0.55367509
44 -1.51566335 0.43281323
45 0.21444813 -1.51566335
46 -0.12563958 0.21444813
47 0.09021311 -0.12563958
48 -0.61267862 0.09021311
49 -0.59986718 -0.61267862
50 0.06587669 -0.59986718
51 0.80777190 0.06587669
52 -1.15571826 0.80777190
53 0.25918012 -1.15571826
54 -1.60298656 0.25918012
55 -4.23008589 -1.60298656
56 0.69033291 -4.23008589
57 0.32767395 0.69033291
58 -0.21914600 0.32767395
59 0.20579950 -0.21914600
60 0.06971457 0.20579950
61 -1.36427790 0.06971457
62 0.32154472 -1.36427790
63 -0.97492394 0.32154472
64 -0.01076353 -0.97492394
65 -1.03188791 -0.01076353
66 0.25716984 -1.03188791
67 0.99454309 0.25716984
68 -0.11860667 0.99454309
69 -0.07722011 -0.11860667
70 -0.04443965 -0.07722011
71 0.80280296 -0.04443965
72 1.45106012 0.80280296
73 -1.47797917 1.45106012
74 0.32483983 -1.47797917
75 0.36699010 0.32483983
76 0.35506608 0.36699010
77 0.88414378 0.35506608
78 0.81614666 0.88414378
79 -0.81392782 0.81614666
80 0.39526403 -0.81392782
81 0.24317029 0.39526403
82 -0.30442308 0.24317029
83 -0.23848584 -0.30442308
84 -0.65399870 -0.23848584
85 1.02855188 -0.65399870
86 0.33035104 1.02855188
87 -1.13734191 0.33035104
88 0.33137323 -1.13734191
89 -0.65295606 0.33137323
90 0.14679387 -0.65295606
91 0.95915779 0.14679387
92 -0.50476026 0.95915779
93 0.38968716 -0.50476026
94 0.56529645 0.38968716
95 0.69725266 0.56529645
96 1.73768103 0.69725266
97 -1.02181836 1.73768103
98 -0.22757076 -1.02181836
99 0.26426437 -0.22757076
100 0.41055016 0.26426437
101 -0.74713295 0.41055016
102 -0.72491134 -0.74713295
103 1.54887395 -0.72491134
104 0.29977555 1.54887395
105 -0.17079249 0.29977555
106 1.08757543 -0.17079249
107 0.05660924 1.08757543
108 -1.53794227 0.05660924
109 0.50107123 -1.53794227
110 1.73110574 0.50107123
111 -0.71615703 1.73110574
112 -0.32626584 -0.71615703
113 -0.93739722 -0.32626584
114 0.28634954 -0.93739722
115 -0.19533525 0.28634954
116 0.72911131 -0.19533525
117 -0.19372496 0.72911131
118 -1.23426161 -0.19372496
119 0.03064523 -1.23426161
120 -0.46920192 0.03064523
121 0.53370466 -0.46920192
122 0.97821989 0.53370466
123 0.61337119 0.97821989
124 -0.85445653 0.61337119
125 -0.45594058 -0.85445653
126 0.61507652 -0.45594058
127 0.20359092 0.61507652
128 0.49816583 0.20359092
129 -0.30211837 0.49816583
130 0.91020743 -0.30211837
131 -0.33208970 0.91020743
132 0.53599588 -0.33208970
133 0.34920544 0.53599588
134 0.47092805 0.34920544
135 0.03343487 0.47092805
136 1.26440589 0.03343487
137 -0.35504604 1.26440589
138 -1.45020289 -0.35504604
139 -0.38775719 -1.45020289
140 -0.60609457 -0.38775719
141 -0.50087213 -0.60609457
142 1.09378607 -0.50087213
143 -1.43915815 1.09378607
144 0.67949489 -1.43915815
145 -1.18711183 0.67949489
> 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/7j8ex1290458249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8uhv01290458249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uhv01290458249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10uhv01290458249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gic61290458249.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/121isc1290458249.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/13qj7o1290458249.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/14jaor1290458249.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/154b5e1290458249.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/1603l51290458249.tab")
+ }
>
> try(system("convert tmp/15yyo1290458249.ps tmp/15yyo1290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/2y7fr1290458249.ps tmp/2y7fr1290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y7fr1290458249.ps tmp/3y7fr1290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y7fr1290458249.ps tmp/4y7fr1290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/59zxc1290458249.ps tmp/59zxc1290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/69zxc1290458249.ps tmp/69zxc1290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/7j8ex1290458249.ps tmp/7j8ex1290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uhv01290458249.ps tmp/8uhv01290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/9uhv01290458249.ps tmp/9uhv01290458249.png",intern=TRUE))
character(0)
> try(system("convert tmp/10uhv01290458249.ps tmp/10uhv01290458249.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.796 1.707 9.303