R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(15
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+ ,dim=c(8
+ ,137)
+ ,dimnames=list(c('Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Popularity'
+ ,'ConcernOverMistakes'
+ ,'ParentalExpectations'
+ ,'ParentalCriticism'
+ ,'Organization')
+ ,1:137))
> y <- array(NA,dim=c(8,137),dimnames=list(c('Happiness','Depression','Belonging','Popularity','ConcernOverMistakes','ParentalExpectations','ParentalCriticism','Organization'),1:137))
> 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 = '1'
> #'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
> 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
Happiness Depression Belonging Popularity ConcernOverMistakes
1 15 10 77 15 11
2 9 20 63 12 26
3 12 16 73 15 26
4 15 10 76 12 15
5 17 8 90 14 10
6 14 14 67 8 21
7 9 19 69 11 27
8 11 23 54 4 21
9 13 9 54 13 21
10 16 12 76 19 22
11 16 14 75 10 29
12 15 13 76 15 29
13 10 11 80 6 29
14 16 11 89 7 30
15 12 10 73 14 19
16 15 12 74 16 19
17 13 18 78 16 22
18 18 12 76 14 18
19 13 10 69 15 28
20 17 15 74 14 17
21 14 15 82 12 18
22 13 12 77 9 20
23 13 9 84 12 16
24 15 11 75 14 17
25 15 16 79 14 25
26 13 17 79 10 22
27 13 11 88 16 31
28 16 13 57 10 38
29 14 9 69 8 18
30 18 11 86 12 20
31 9 20 66 8 23
32 16 8 54 13 12
33 16 12 85 11 20
34 17 10 79 12 15
35 13 11 84 16 21
36 17 13 70 16 20
37 15 13 54 13 30
38 14 13 70 14 22
39 10 15 54 5 33
40 13 12 69 14 25
41 11 13 68 13 20
42 11 14 66 15 21
43 15 9 67 11 16
44 15 9 71 15 23
45 12 15 54 16 25
46 17 10 76 13 18
47 15 13 77 11 33
48 16 8 71 12 18
49 14 15 69 12 18
50 17 13 73 10 13
51 10 24 46 8 24
52 11 11 66 9 19
53 15 13 77 12 20
54 15 12 77 14 21
55 7 22 70 12 18
56 17 11 86 11 29
57 14 15 38 14 13
58 18 7 66 7 26
59 14 14 75 16 22
60 14 10 64 11 28
61 9 9 80 16 28
62 14 12 86 13 23
63 11 16 54 11 22
64 16 13 74 13 28
65 17 11 88 14 31
66 12 11 63 10 15
67 15 13 81 15 15
68 15 10 74 11 22
69 16 11 80 6 17
70 16 9 80 11 25
71 11 13 60 12 32
72 12 14 62 12 23
73 14 14 63 8 20
74 15 11 89 9 20
75 17 10 76 10 28
76 19 11 81 16 20
77 15 12 72 15 20
78 16 14 84 14 23
79 14 14 76 12 20
80 16 21 76 12 21
81 15 13 72 12 14
82 17 11 81 8 31
83 12 12 72 16 21
84 18 12 78 11 18
85 13 11 79 12 26
86 14 14 52 9 25
87 14 13 67 14 9
88 14 13 74 15 18
89 12 12 73 8 19
90 14 14 69 12 29
91 12 12 67 10 31
92 15 12 76 16 24
93 11 18 63 8 19
94 15 11 84 9 19
95 14 15 90 8 22
96 15 13 75 11 31
97 16 11 76 16 20
98 14 22 53 5 26
99 18 10 87 15 17
100 14 11 78 15 16
101 13 15 54 12 9
102 14 14 58 12 19
103 14 11 80 16 22
104 17 10 74 12 15
105 12 14 56 10 25
106 16 14 82 12 30
107 10 15 67 11 24
108 13 11 75 16 20
109 15 10 69 7 12
110 16 10 72 9 31
111 14 12 54 11 25
112 13 15 54 6 23
113 17 10 71 14 23
114 14 12 53 11 26
115 16 15 54 11 14
116 12 11 69 16 28
117 16 10 30 7 19
118 8 20 53 8 21
119 9 19 68 10 18
120 13 17 69 14 29
121 19 8 54 9 16
122 11 17 66 13 22
123 15 11 79 13 15
124 11 13 67 12 21
125 15 9 74 11 17
126 16 10 86 10 17
127 15 13 63 12 33
128 12 16 69 14 17
129 16 12 73 11 20
130 15 14 69 13 17
131 13 11 71 14 16
132 14 13 77 13 18
133 11 15 74 16 32
134 15 14 82 13 22
135 14 14 54 9 29
136 13 10 80 14 23
137 15 8 76 15 17
ParentalExpectations ParentalCriticism Organization
1 6 4 16
2 5 4 24
3 20 10 22
4 12 6 21
5 11 5 23
6 12 8 23
7 11 9 21
8 13 8 22
9 9 11 20
10 14 6 12
11 12 8 23
12 18 11 23
13 9 5 30
14 15 10 22
15 12 7 21
16 12 7 21
17 12 13 15
18 15 10 22
19 11 8 24
20 13 6 23
21 10 8 15
22 17 7 24
23 13 5 24
24 17 9 21
25 15 11 21
26 13 11 18
27 17 9 19
28 21 7 29
29 12 6 20
30 15 6 24
31 8 5 27
32 15 4 28
33 16 10 24
34 9 8 29
35 13 6 24
36 11 4 25
37 9 9 14
38 15 10 22
39 9 6 24
40 15 9 24
41 14 10 24
42 14 13 21
43 12 8 21
44 15 10 21
45 11 5 15
46 11 8 26
47 9 6 22
48 8 9 24
49 13 9 13
50 12 7 19
51 24 20 10
52 11 8 28
53 11 8 25
54 16 7 24
55 12 7 22
56 18 10 30
57 12 5 22
58 14 8 24
59 16 9 23
60 24 20 20
61 13 6 22
62 11 10 22
63 14 11 19
64 12 7 22
65 21 12 26
66 11 8 12
67 6 6 25
68 14 9 23
69 16 5 23
70 18 11 17
71 9 6 26
72 13 10 27
73 17 8 23
74 11 7 20
75 16 8 24
76 11 9 22
77 11 8 26
78 11 10 29
79 20 13 20
80 10 7 17
81 12 7 16
82 11 8 24
83 14 9 24
84 12 9 19
85 12 8 29
86 12 7 25
87 10 6 25
88 12 8 24
89 10 8 29
90 7 4 22
91 10 8 23
92 13 10 15
93 13 8 21
94 9 7 23
95 14 10 20
96 14 9 25
97 12 8 28
98 18 5 18
99 17 8 25
100 15 9 24
101 8 11 23
102 8 7 25
103 12 8 27
104 10 4 24
105 18 16 24
106 15 9 26
107 11 12 26
108 10 8 23
109 7 4 28
110 17 11 20
111 7 8 23
112 14 12 24
113 12 8 21
114 15 6 25
115 13 8 16
116 16 14 22
117 11 10 27
118 7 5 24
119 15 8 17
120 18 12 21
121 11 11 21
122 13 8 19
123 11 8 25
124 13 9 24
125 12 6 21
126 11 5 26
127 11 8 25
128 13 7 25
129 8 4 13
130 12 9 25
131 9 5 23
132 14 9 26
133 18 12 22
134 15 6 20
135 11 6 24
136 17 7 21
137 12 9 24
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Depression Belonging
18.47137 -0.37185 0.03458
Popularity ConcernOverMistakes ParentalExpectations
-0.05517 -0.04486 0.11165
ParentalCriticism Organization
-0.07922 -0.05481
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.52275 -1.32970 0.05296 1.28674 4.68317
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.47137 2.20371 8.382 7.71e-14 ***
Depression -0.37185 0.05838 -6.370 3.06e-09 ***
Belonging 0.03458 0.01718 2.014 0.0461 *
Popularity -0.05517 0.06232 -0.885 0.3776
ConcernOverMistakes -0.04486 0.03211 -1.397 0.1647
ParentalExpectations 0.11165 0.06177 1.808 0.0730 .
ParentalCriticism -0.07922 0.07844 -1.010 0.3144
Organization -0.05481 0.04683 -1.170 0.2440
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.966 on 129 degrees of freedom
Multiple R-squared: 0.3477, Adjusted R-squared: 0.3123
F-statistic: 9.822 on 7 and 129 DF, p-value: 8.891e-10
> 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,] 0.2679063 0.53581261 0.73209369
[2,] 0.2212448 0.44248955 0.77875522
[3,] 0.7308467 0.53830657 0.26915329
[4,] 0.6192638 0.76147240 0.38073620
[5,] 0.6576396 0.68472079 0.34236039
[6,] 0.6022411 0.79551771 0.39775886
[7,] 0.5203631 0.95927380 0.47963690
[8,] 0.6465989 0.70680227 0.35340114
[9,] 0.5662885 0.86742292 0.43371146
[10,] 0.6844091 0.63118171 0.31559086
[11,] 0.6130168 0.77396639 0.38698320
[12,] 0.6513692 0.69726162 0.34863081
[13,] 0.7095347 0.58093061 0.29046531
[14,] 0.6515423 0.69691536 0.34845768
[15,] 0.6045584 0.79088330 0.39544165
[16,] 0.5497544 0.90049116 0.45024558
[17,] 0.5460529 0.90789414 0.45394707
[18,] 0.6582523 0.68349548 0.34174774
[19,] 0.6182868 0.76342646 0.38171323
[20,] 0.6648345 0.67033096 0.33516548
[21,] 0.6348951 0.73020985 0.36510493
[22,] 0.5722799 0.85544014 0.42772007
[23,] 0.5144270 0.97114602 0.48557301
[24,] 0.5363790 0.92724205 0.46362102
[25,] 0.5398321 0.92033587 0.46016793
[26,] 0.6427393 0.71452139 0.35726069
[27,] 0.6894099 0.62118016 0.31059008
[28,] 0.6420959 0.71580813 0.35790406
[29,] 0.6223726 0.75525476 0.37762738
[30,] 0.5963234 0.80735313 0.40367656
[31,] 0.6787928 0.64241438 0.32120719
[32,] 0.7053800 0.58923997 0.29461999
[33,] 0.6578027 0.68439455 0.34219727
[34,] 0.6054953 0.78900943 0.39450471
[35,] 0.5623773 0.87524544 0.43762272
[36,] 0.5809809 0.83803819 0.41901909
[37,] 0.5830281 0.83394382 0.41697191
[38,] 0.5482857 0.90342861 0.45171430
[39,] 0.4944607 0.98892137 0.50553932
[40,] 0.5141293 0.97174144 0.48587072
[41,] 0.4686594 0.93731872 0.53134064
[42,] 0.5462879 0.90742415 0.45371207
[43,] 0.5090443 0.98191139 0.49095569
[44,] 0.4561960 0.91239196 0.54380402
[45,] 0.6057618 0.78847648 0.39423824
[46,] 0.5989118 0.80217645 0.40108823
[47,] 0.5853469 0.82930615 0.41465308
[48,] 0.5832033 0.83359333 0.41679666
[49,] 0.5334122 0.93317565 0.46658782
[50,] 0.5008217 0.99835661 0.49917831
[51,] 0.8900561 0.21988788 0.10994394
[52,] 0.8660918 0.26781631 0.13390816
[53,] 0.8502963 0.29940745 0.14970373
[54,] 0.8563563 0.28728731 0.14364365
[55,] 0.8504870 0.29902597 0.14951298
[56,] 0.9035135 0.19297298 0.09648649
[57,] 0.8878728 0.22425430 0.11212715
[58,] 0.8624153 0.27516943 0.13758472
[59,] 0.8327544 0.33449126 0.16724563
[60,] 0.8027562 0.39448755 0.19724378
[61,] 0.8000090 0.39998209 0.19999105
[62,] 0.7682785 0.46344309 0.23172155
[63,] 0.7268838 0.54623242 0.27311621
[64,] 0.6958879 0.60822427 0.30411213
[65,] 0.6786547 0.64269070 0.32134535
[66,] 0.8215232 0.35695355 0.17847677
[67,] 0.7976845 0.40463096 0.20231548
[68,] 0.8525414 0.29491725 0.14745862
[69,] 0.8221411 0.35571789 0.17785894
[70,] 0.9574407 0.08511858 0.04255929
[71,] 0.9441614 0.11167724 0.05583862
[72,] 0.9495654 0.10086920 0.05043460
[73,] 0.9524435 0.09511294 0.04755647
[74,] 0.9731692 0.05366152 0.02683076
[75,] 0.9679858 0.06402845 0.03201422
[76,] 0.9598188 0.08036248 0.04018124
[77,] 0.9463464 0.10730723 0.05365362
[78,] 0.9307283 0.13854349 0.06927175
[79,] 0.9340184 0.13196318 0.06598159
[80,] 0.9203448 0.15931032 0.07965516
[81,] 0.9235960 0.15280796 0.07640398
[82,] 0.9061788 0.18764237 0.09382119
[83,] 0.8896554 0.22068914 0.11034457
[84,] 0.8600557 0.27988863 0.13994431
[85,] 0.8278861 0.34422783 0.17211392
[86,] 0.8047398 0.39052044 0.19526022
[87,] 0.8092308 0.38153838 0.19076919
[88,] 0.8441036 0.31179271 0.15589636
[89,] 0.8762884 0.24742316 0.12371158
[90,] 0.8464170 0.30716600 0.15358300
[91,] 0.8183922 0.36321570 0.18160785
[92,] 0.7976828 0.40463437 0.20231718
[93,] 0.7521205 0.49575905 0.24787952
[94,] 0.7325684 0.53486325 0.26743162
[95,] 0.6909852 0.61802951 0.30901475
[96,] 0.8105671 0.37886584 0.18943292
[97,] 0.8091646 0.38167080 0.19083540
[98,] 0.7770835 0.44583309 0.22291655
[99,] 0.7262649 0.54747017 0.27373509
[100,] 0.6658327 0.66833463 0.33416731
[101,] 0.6098227 0.78035464 0.39017732
[102,] 0.5438838 0.91223243 0.45611621
[103,] 0.5397371 0.92052585 0.46026292
[104,] 0.4618606 0.92372123 0.53813939
[105,] 0.5736008 0.85279842 0.42639921
[106,] 0.6613642 0.67727153 0.33863576
[107,] 0.6110357 0.77792853 0.38896426
[108,] 0.7055261 0.58894779 0.29447389
[109,] 0.8369650 0.32607009 0.16303505
[110,] 0.7875068 0.42498635 0.21249318
[111,] 0.8657883 0.26842348 0.13421174
[112,] 0.8325771 0.33484577 0.16742289
[113,] 0.7386163 0.52276739 0.26138370
[114,] 0.9025495 0.19490105 0.09745053
[115,] 0.8208562 0.35828756 0.17914378
[116,] 0.8190814 0.36183719 0.18091859
> postscript(file="/var/www/rcomp/tmp/198et1290350865.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/298et1290350865.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/398et1290350865.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/42zdv1290350865.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/52zdv1290350865.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 = 137
Frequency = 1
1 2 3 4 5 6
-0.57075628 -1.31049464 -1.28728685 -0.75965217 0.04055082 0.35556191
7 8 9 10 11 12
-2.33839356 0.76465104 -1.37003575 0.96770993 2.54814868 0.98533924
13 14 15 16 17 18
-4.48005384 0.59643823 -3.28687327 0.53259273 0.90642331 3.26573517
19 20 21 22 23 24
-1.33427925 3.36681601 0.07957252 -2.30634371 -3.38975323 -0.47373451
25 26 27 28 29 30
1.98784480 0.06327839 -2.29450739 2.44735758 -2.03033128 2.32015369
31 32 33 34 35 36
-1.86083588 0.06835323 0.87665376 2.06848878 -2.12181259 3.18087195
37 38 39 40 41 42
2.03382863 0.02454557 -2.21882877 -1.14773020 -2.82991335 -2.16044692
43 44 45 46 47 48
-0.67211100 -0.45221481 -0.76665185 1.97426808 1.46345953 0.65287036
49 50 51 52 53 54
0.16380829 2.22917674 -0.16677329 -3.37425130 1.03497863 0.12604370
55 56 57 58 59 60
-3.82131269 1.97955590 1.41000451 1.78784169 0.19776039 -1.10210691
61 62 63 64 65 66
-6.52275359 -0.46435055 -1.44563828 2.19750658 1.76988188 -3.27175988
67 68 69 70 71 72
1.23766223 -0.30762497 -0.18366555 -0.36941970 -1.71905642 -0.89505489
73 74 75 76 77 78
-0.10922798 -0.64253326 1.58949908 4.28842708 1.05638197 2.78737193
79 80 81 82 83 84
-0.44139978 4.68317252 0.25453940 2.37093493 -2.20893678 3.12234508
85 86 87 88 89 90
-1.40111557 1.13935162 -0.04912816 0.04806211 -2.13319938 1.05256050
91 92 93 94 95 96
-1.60584798 0.48489295 -1.32970131 -0.12673831 -0.25244910 1.28674033
97 98 99 100 101 102
1.59933923 2.69170164 1.93460232 -1.17943768 0.54361487 1.27479680
103 104 105 106 107 108
-0.50408050 1.53880853 -0.95552945 2.36997198 -2.37949509 -1.41682796
109 110 111 112 113 114
-0.14453165 0.71402412 0.96469315 0.30478273 2.04097314 0.10209796
115 116 117 118 119 120
2.53315009 -2.09980105 2.49220979 -2.55375220 -3.50783592 0.62925702
121 122 123 124 125 126
3.64460371 -1.50446405 0.05296401 -2.77321022 -1.02778143 0.18037220
127 128 129 130 131 132
2.10238512 -0.89956755 1.10662053 1.57165503 -1.69431206 -0.20049794
133 134 135 136 137
-1.98761360 0.49969924 1.22725711 -2.90776846 -0.84600080
> postscript(file="/var/www/rcomp/tmp/62zdv1290350865.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 = 137
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.57075628 NA
1 -1.31049464 -0.57075628
2 -1.28728685 -1.31049464
3 -0.75965217 -1.28728685
4 0.04055082 -0.75965217
5 0.35556191 0.04055082
6 -2.33839356 0.35556191
7 0.76465104 -2.33839356
8 -1.37003575 0.76465104
9 0.96770993 -1.37003575
10 2.54814868 0.96770993
11 0.98533924 2.54814868
12 -4.48005384 0.98533924
13 0.59643823 -4.48005384
14 -3.28687327 0.59643823
15 0.53259273 -3.28687327
16 0.90642331 0.53259273
17 3.26573517 0.90642331
18 -1.33427925 3.26573517
19 3.36681601 -1.33427925
20 0.07957252 3.36681601
21 -2.30634371 0.07957252
22 -3.38975323 -2.30634371
23 -0.47373451 -3.38975323
24 1.98784480 -0.47373451
25 0.06327839 1.98784480
26 -2.29450739 0.06327839
27 2.44735758 -2.29450739
28 -2.03033128 2.44735758
29 2.32015369 -2.03033128
30 -1.86083588 2.32015369
31 0.06835323 -1.86083588
32 0.87665376 0.06835323
33 2.06848878 0.87665376
34 -2.12181259 2.06848878
35 3.18087195 -2.12181259
36 2.03382863 3.18087195
37 0.02454557 2.03382863
38 -2.21882877 0.02454557
39 -1.14773020 -2.21882877
40 -2.82991335 -1.14773020
41 -2.16044692 -2.82991335
42 -0.67211100 -2.16044692
43 -0.45221481 -0.67211100
44 -0.76665185 -0.45221481
45 1.97426808 -0.76665185
46 1.46345953 1.97426808
47 0.65287036 1.46345953
48 0.16380829 0.65287036
49 2.22917674 0.16380829
50 -0.16677329 2.22917674
51 -3.37425130 -0.16677329
52 1.03497863 -3.37425130
53 0.12604370 1.03497863
54 -3.82131269 0.12604370
55 1.97955590 -3.82131269
56 1.41000451 1.97955590
57 1.78784169 1.41000451
58 0.19776039 1.78784169
59 -1.10210691 0.19776039
60 -6.52275359 -1.10210691
61 -0.46435055 -6.52275359
62 -1.44563828 -0.46435055
63 2.19750658 -1.44563828
64 1.76988188 2.19750658
65 -3.27175988 1.76988188
66 1.23766223 -3.27175988
67 -0.30762497 1.23766223
68 -0.18366555 -0.30762497
69 -0.36941970 -0.18366555
70 -1.71905642 -0.36941970
71 -0.89505489 -1.71905642
72 -0.10922798 -0.89505489
73 -0.64253326 -0.10922798
74 1.58949908 -0.64253326
75 4.28842708 1.58949908
76 1.05638197 4.28842708
77 2.78737193 1.05638197
78 -0.44139978 2.78737193
79 4.68317252 -0.44139978
80 0.25453940 4.68317252
81 2.37093493 0.25453940
82 -2.20893678 2.37093493
83 3.12234508 -2.20893678
84 -1.40111557 3.12234508
85 1.13935162 -1.40111557
86 -0.04912816 1.13935162
87 0.04806211 -0.04912816
88 -2.13319938 0.04806211
89 1.05256050 -2.13319938
90 -1.60584798 1.05256050
91 0.48489295 -1.60584798
92 -1.32970131 0.48489295
93 -0.12673831 -1.32970131
94 -0.25244910 -0.12673831
95 1.28674033 -0.25244910
96 1.59933923 1.28674033
97 2.69170164 1.59933923
98 1.93460232 2.69170164
99 -1.17943768 1.93460232
100 0.54361487 -1.17943768
101 1.27479680 0.54361487
102 -0.50408050 1.27479680
103 1.53880853 -0.50408050
104 -0.95552945 1.53880853
105 2.36997198 -0.95552945
106 -2.37949509 2.36997198
107 -1.41682796 -2.37949509
108 -0.14453165 -1.41682796
109 0.71402412 -0.14453165
110 0.96469315 0.71402412
111 0.30478273 0.96469315
112 2.04097314 0.30478273
113 0.10209796 2.04097314
114 2.53315009 0.10209796
115 -2.09980105 2.53315009
116 2.49220979 -2.09980105
117 -2.55375220 2.49220979
118 -3.50783592 -2.55375220
119 0.62925702 -3.50783592
120 3.64460371 0.62925702
121 -1.50446405 3.64460371
122 0.05296401 -1.50446405
123 -2.77321022 0.05296401
124 -1.02778143 -2.77321022
125 0.18037220 -1.02778143
126 2.10238512 0.18037220
127 -0.89956755 2.10238512
128 1.10662053 -0.89956755
129 1.57165503 1.10662053
130 -1.69431206 1.57165503
131 -0.20049794 -1.69431206
132 -1.98761360 -0.20049794
133 0.49969924 -1.98761360
134 1.22725711 0.49969924
135 -2.90776846 1.22725711
136 -0.84600080 -2.90776846
137 NA -0.84600080
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.31049464 -0.57075628
[2,] -1.28728685 -1.31049464
[3,] -0.75965217 -1.28728685
[4,] 0.04055082 -0.75965217
[5,] 0.35556191 0.04055082
[6,] -2.33839356 0.35556191
[7,] 0.76465104 -2.33839356
[8,] -1.37003575 0.76465104
[9,] 0.96770993 -1.37003575
[10,] 2.54814868 0.96770993
[11,] 0.98533924 2.54814868
[12,] -4.48005384 0.98533924
[13,] 0.59643823 -4.48005384
[14,] -3.28687327 0.59643823
[15,] 0.53259273 -3.28687327
[16,] 0.90642331 0.53259273
[17,] 3.26573517 0.90642331
[18,] -1.33427925 3.26573517
[19,] 3.36681601 -1.33427925
[20,] 0.07957252 3.36681601
[21,] -2.30634371 0.07957252
[22,] -3.38975323 -2.30634371
[23,] -0.47373451 -3.38975323
[24,] 1.98784480 -0.47373451
[25,] 0.06327839 1.98784480
[26,] -2.29450739 0.06327839
[27,] 2.44735758 -2.29450739
[28,] -2.03033128 2.44735758
[29,] 2.32015369 -2.03033128
[30,] -1.86083588 2.32015369
[31,] 0.06835323 -1.86083588
[32,] 0.87665376 0.06835323
[33,] 2.06848878 0.87665376
[34,] -2.12181259 2.06848878
[35,] 3.18087195 -2.12181259
[36,] 2.03382863 3.18087195
[37,] 0.02454557 2.03382863
[38,] -2.21882877 0.02454557
[39,] -1.14773020 -2.21882877
[40,] -2.82991335 -1.14773020
[41,] -2.16044692 -2.82991335
[42,] -0.67211100 -2.16044692
[43,] -0.45221481 -0.67211100
[44,] -0.76665185 -0.45221481
[45,] 1.97426808 -0.76665185
[46,] 1.46345953 1.97426808
[47,] 0.65287036 1.46345953
[48,] 0.16380829 0.65287036
[49,] 2.22917674 0.16380829
[50,] -0.16677329 2.22917674
[51,] -3.37425130 -0.16677329
[52,] 1.03497863 -3.37425130
[53,] 0.12604370 1.03497863
[54,] -3.82131269 0.12604370
[55,] 1.97955590 -3.82131269
[56,] 1.41000451 1.97955590
[57,] 1.78784169 1.41000451
[58,] 0.19776039 1.78784169
[59,] -1.10210691 0.19776039
[60,] -6.52275359 -1.10210691
[61,] -0.46435055 -6.52275359
[62,] -1.44563828 -0.46435055
[63,] 2.19750658 -1.44563828
[64,] 1.76988188 2.19750658
[65,] -3.27175988 1.76988188
[66,] 1.23766223 -3.27175988
[67,] -0.30762497 1.23766223
[68,] -0.18366555 -0.30762497
[69,] -0.36941970 -0.18366555
[70,] -1.71905642 -0.36941970
[71,] -0.89505489 -1.71905642
[72,] -0.10922798 -0.89505489
[73,] -0.64253326 -0.10922798
[74,] 1.58949908 -0.64253326
[75,] 4.28842708 1.58949908
[76,] 1.05638197 4.28842708
[77,] 2.78737193 1.05638197
[78,] -0.44139978 2.78737193
[79,] 4.68317252 -0.44139978
[80,] 0.25453940 4.68317252
[81,] 2.37093493 0.25453940
[82,] -2.20893678 2.37093493
[83,] 3.12234508 -2.20893678
[84,] -1.40111557 3.12234508
[85,] 1.13935162 -1.40111557
[86,] -0.04912816 1.13935162
[87,] 0.04806211 -0.04912816
[88,] -2.13319938 0.04806211
[89,] 1.05256050 -2.13319938
[90,] -1.60584798 1.05256050
[91,] 0.48489295 -1.60584798
[92,] -1.32970131 0.48489295
[93,] -0.12673831 -1.32970131
[94,] -0.25244910 -0.12673831
[95,] 1.28674033 -0.25244910
[96,] 1.59933923 1.28674033
[97,] 2.69170164 1.59933923
[98,] 1.93460232 2.69170164
[99,] -1.17943768 1.93460232
[100,] 0.54361487 -1.17943768
[101,] 1.27479680 0.54361487
[102,] -0.50408050 1.27479680
[103,] 1.53880853 -0.50408050
[104,] -0.95552945 1.53880853
[105,] 2.36997198 -0.95552945
[106,] -2.37949509 2.36997198
[107,] -1.41682796 -2.37949509
[108,] -0.14453165 -1.41682796
[109,] 0.71402412 -0.14453165
[110,] 0.96469315 0.71402412
[111,] 0.30478273 0.96469315
[112,] 2.04097314 0.30478273
[113,] 0.10209796 2.04097314
[114,] 2.53315009 0.10209796
[115,] -2.09980105 2.53315009
[116,] 2.49220979 -2.09980105
[117,] -2.55375220 2.49220979
[118,] -3.50783592 -2.55375220
[119,] 0.62925702 -3.50783592
[120,] 3.64460371 0.62925702
[121,] -1.50446405 3.64460371
[122,] 0.05296401 -1.50446405
[123,] -2.77321022 0.05296401
[124,] -1.02778143 -2.77321022
[125,] 0.18037220 -1.02778143
[126,] 2.10238512 0.18037220
[127,] -0.89956755 2.10238512
[128,] 1.10662053 -0.89956755
[129,] 1.57165503 1.10662053
[130,] -1.69431206 1.57165503
[131,] -0.20049794 -1.69431206
[132,] -1.98761360 -0.20049794
[133,] 0.49969924 -1.98761360
[134,] 1.22725711 0.49969924
[135,] -2.90776846 1.22725711
[136,] -0.84600080 -2.90776846
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.31049464 -0.57075628
2 -1.28728685 -1.31049464
3 -0.75965217 -1.28728685
4 0.04055082 -0.75965217
5 0.35556191 0.04055082
6 -2.33839356 0.35556191
7 0.76465104 -2.33839356
8 -1.37003575 0.76465104
9 0.96770993 -1.37003575
10 2.54814868 0.96770993
11 0.98533924 2.54814868
12 -4.48005384 0.98533924
13 0.59643823 -4.48005384
14 -3.28687327 0.59643823
15 0.53259273 -3.28687327
16 0.90642331 0.53259273
17 3.26573517 0.90642331
18 -1.33427925 3.26573517
19 3.36681601 -1.33427925
20 0.07957252 3.36681601
21 -2.30634371 0.07957252
22 -3.38975323 -2.30634371
23 -0.47373451 -3.38975323
24 1.98784480 -0.47373451
25 0.06327839 1.98784480
26 -2.29450739 0.06327839
27 2.44735758 -2.29450739
28 -2.03033128 2.44735758
29 2.32015369 -2.03033128
30 -1.86083588 2.32015369
31 0.06835323 -1.86083588
32 0.87665376 0.06835323
33 2.06848878 0.87665376
34 -2.12181259 2.06848878
35 3.18087195 -2.12181259
36 2.03382863 3.18087195
37 0.02454557 2.03382863
38 -2.21882877 0.02454557
39 -1.14773020 -2.21882877
40 -2.82991335 -1.14773020
41 -2.16044692 -2.82991335
42 -0.67211100 -2.16044692
43 -0.45221481 -0.67211100
44 -0.76665185 -0.45221481
45 1.97426808 -0.76665185
46 1.46345953 1.97426808
47 0.65287036 1.46345953
48 0.16380829 0.65287036
49 2.22917674 0.16380829
50 -0.16677329 2.22917674
51 -3.37425130 -0.16677329
52 1.03497863 -3.37425130
53 0.12604370 1.03497863
54 -3.82131269 0.12604370
55 1.97955590 -3.82131269
56 1.41000451 1.97955590
57 1.78784169 1.41000451
58 0.19776039 1.78784169
59 -1.10210691 0.19776039
60 -6.52275359 -1.10210691
61 -0.46435055 -6.52275359
62 -1.44563828 -0.46435055
63 2.19750658 -1.44563828
64 1.76988188 2.19750658
65 -3.27175988 1.76988188
66 1.23766223 -3.27175988
67 -0.30762497 1.23766223
68 -0.18366555 -0.30762497
69 -0.36941970 -0.18366555
70 -1.71905642 -0.36941970
71 -0.89505489 -1.71905642
72 -0.10922798 -0.89505489
73 -0.64253326 -0.10922798
74 1.58949908 -0.64253326
75 4.28842708 1.58949908
76 1.05638197 4.28842708
77 2.78737193 1.05638197
78 -0.44139978 2.78737193
79 4.68317252 -0.44139978
80 0.25453940 4.68317252
81 2.37093493 0.25453940
82 -2.20893678 2.37093493
83 3.12234508 -2.20893678
84 -1.40111557 3.12234508
85 1.13935162 -1.40111557
86 -0.04912816 1.13935162
87 0.04806211 -0.04912816
88 -2.13319938 0.04806211
89 1.05256050 -2.13319938
90 -1.60584798 1.05256050
91 0.48489295 -1.60584798
92 -1.32970131 0.48489295
93 -0.12673831 -1.32970131
94 -0.25244910 -0.12673831
95 1.28674033 -0.25244910
96 1.59933923 1.28674033
97 2.69170164 1.59933923
98 1.93460232 2.69170164
99 -1.17943768 1.93460232
100 0.54361487 -1.17943768
101 1.27479680 0.54361487
102 -0.50408050 1.27479680
103 1.53880853 -0.50408050
104 -0.95552945 1.53880853
105 2.36997198 -0.95552945
106 -2.37949509 2.36997198
107 -1.41682796 -2.37949509
108 -0.14453165 -1.41682796
109 0.71402412 -0.14453165
110 0.96469315 0.71402412
111 0.30478273 0.96469315
112 2.04097314 0.30478273
113 0.10209796 2.04097314
114 2.53315009 0.10209796
115 -2.09980105 2.53315009
116 2.49220979 -2.09980105
117 -2.55375220 2.49220979
118 -3.50783592 -2.55375220
119 0.62925702 -3.50783592
120 3.64460371 0.62925702
121 -1.50446405 3.64460371
122 0.05296401 -1.50446405
123 -2.77321022 0.05296401
124 -1.02778143 -2.77321022
125 0.18037220 -1.02778143
126 2.10238512 0.18037220
127 -0.89956755 2.10238512
128 1.10662053 -0.89956755
129 1.57165503 1.10662053
130 -1.69431206 1.57165503
131 -0.20049794 -1.69431206
132 -1.98761360 -0.20049794
133 0.49969924 -1.98761360
134 1.22725711 0.49969924
135 -2.90776846 1.22725711
136 -0.84600080 -2.90776846
> 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/rcomp/tmp/7urcg1290350865.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/85it11290350865.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/95it11290350865.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/105it11290350865.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/118ia71290350865.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/rcomp/tmp/12u18v1290350865.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/rcomp/tmp/13buq21290350866.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/rcomp/tmp/14eupp1290350866.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/rcomp/tmp/15iv5d1290350866.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/rcomp/tmp/163dm11290350866.tab")
+ }
>
> try(system("convert tmp/198et1290350865.ps tmp/198et1290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/298et1290350865.ps tmp/298et1290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/398et1290350865.ps tmp/398et1290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/42zdv1290350865.ps tmp/42zdv1290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/52zdv1290350865.ps tmp/52zdv1290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/62zdv1290350865.ps tmp/62zdv1290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/7urcg1290350865.ps tmp/7urcg1290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/85it11290350865.ps tmp/85it11290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/95it11290350865.ps tmp/95it11290350865.png",intern=TRUE))
character(0)
> try(system("convert tmp/105it11290350865.ps tmp/105it11290350865.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.160 2.200 7.352