R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
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Type 'contributors()' for more information and
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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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+ ,2)
+ ,dim=c(5
+ ,143)
+ ,dimnames=list(c('StudyForCareer'
+ ,'PersonalStandards'
+ ,'ParentalExpectations'
+ ,'Doubts'
+ ,'LeaderPreference')
+ ,1:143))
> y <- array(NA,dim=c(5,143),dimnames=list(c('StudyForCareer','PersonalStandards','ParentalExpectations','Doubts','LeaderPreference'),1:143))
> 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
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
StudyForCareer PersonalStandards ParentalExpectations Doubts
1 41 25 15 9
2 38 25 15 9
3 37 19 14 9
4 36 18 10 14
5 42 18 10 8
6 44 23 9 14
7 40 23 18 15
8 43 25 14 9
9 40 23 11 11
10 45 24 11 14
11 47 32 9 14
12 45 30 17 6
13 45 32 21 10
14 40 24 16 9
15 49 17 14 14
16 48 30 24 8
17 44 25 7 11
18 29 25 9 10
19 42 26 18 16
20 44 23 11 11
21 35 19 13 9
22 32 25 13 11
23 32 25 13 11
24 41 35 18 7
25 29 19 14 13
26 38 20 12 10
27 41 21 12 9
28 38 21 9 9
29 24 23 11 15
30 34 24 8 13
31 38 23 5 16
32 37 19 10 12
33 46 17 11 6
34 48 27 15 4
35 42 27 16 12
36 46 25 12 10
37 43 18 14 14
38 38 22 13 9
39 39 26 10 10
40 34 26 18 14
41 39 23 17 14
42 35 16 12 10
43 41 27 13 9
44 40 25 13 14
45 43 14 11 8
46 37 19 13 9
47 41 20 12 8
48 46 26 12 10
49 26 16 12 9
50 41 18 12 9
51 37 22 9 9
52 39 25 17 9
53 44 29 18 11
54 39 21 7 15
55 36 22 17 8
56 38 22 12 10
57 38 32 12 8
58 38 23 9 14
59 32 31 9 11
60 33 18 13 10
61 46 23 10 12
62 42 24 12 9
63 42 19 10 13
64 43 26 11 14
65 41 14 13 15
66 49 20 6 8
67 45 22 7 7
68 39 24 13 10
69 45 25 11 10
70 31 21 18 13
71 30 21 18 13
72 45 28 9 11
73 48 24 9 8
74 28 15 12 14
75 35 21 11 9
76 38 23 15 10
77 39 24 11 11
78 40 21 14 10
79 38 21 14 16
80 42 13 8 11
81 36 17 12 16
82 49 29 8 6
83 41 25 11 11
84 18 16 10 12
85 36 20 11 12
86 42 25 17 14
87 41 25 16 9
88 43 21 13 11
89 46 23 15 8
90 37 22 11 8
91 38 19 12 7
92 43 26 20 13
93 41 25 16 8
94 35 19 8 20
95 42 24 16 16
96 36 20 11 11
97 35 21 13 12
98 33 14 15 10
99 36 22 15 14
100 48 14 12 8
101 41 20 12 10
102 47 21 24 14
103 41 22 15 10
104 31 19 8 5
105 36 28 18 12
106 46 25 17 9
107 39 17 12 16
108 44 21 15 8
109 43 27 11 16
110 32 29 12 12
111 40 19 12 13
112 40 20 14 8
113 46 17 11 14
114 45 21 12 8
115 39 22 10 8
116 44 26 11 7
117 35 19 11 10
118 38 17 9 11
119 38 17 12 11
120 36 19 8 14
121 42 17 12 10
122 39 15 6 6
123 41 27 15 9
124 41 19 13 12
125 47 21 17 11
126 39 25 14 14
127 40 19 16 12
128 44 18 16 8
129 42 15 11 8
130 35 20 16 11
131 46 29 15 12
132 43 20 11 14
133 40 29 9 16
134 44 24 12 13
135 37 24 13 11
136 46 23 11 9
137 39 22 14 12
138 40 22 12 13
139 37 21 11 9
140 29 22 15 14
141 33 21 13 8
142 35 18 9 8
143 42 10 12 9
LeaderPreference
1 3
2 4
3 4
4 2
5 4
6 4
7 3
8 4
9 4
10 4
11 4
12 5
13 4
14 4
15 4
16 5
17 4
18 4
19 4
20 5
21 3
22 5
23 5
24 4
25 2
26 4
27 4
28 4
29 3
30 2
31 2
32 3
33 5
34 5
35 4
36 4
37 5
38 4
39 4
40 4
41 4
42 2
43 3
44 3
45 4
46 2
47 4
48 4
49 3
50 3
51 3
52 4
53 5
54 2
55 4
56 2
57 0
58 4
59 4
60 3
61 4
62 4
63 2
64 4
65 2
66 4
67 3
68 4
69 5
70 3
71 3
72 4
73 5
74 4
75 2
76 4
77 4
78 4
79 4
80 4
81 2
82 5
83 4
84 2
85 3
86 3
87 5
88 4
89 3
90 4
91 3
92 4
93 5
94 2
95 4
96 4
97 5
98 2
99 3
100 4
101 4
102 3
103 3
104 5
105 4
106 4
107 4
108 4
109 2
110 4
111 5
112 3
113 3
114 3
115 4
116 4
117 4
118 3
119 2
120 3
121 3
122 4
123 5
124 4
125 3
126 3
127 4
128 4
129 4
130 3
131 5
132 3
133 4
134 4
135 4
136 4
137 2
138 3
139 3
140 4
141 2
142 4
143 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PersonalStandards ParentalExpectations
32.79835 0.17434 0.03742
Doubts LeaderPreference
-0.22838 1.37571
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.9728 -2.2420 0.6441 3.2903 10.4085
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.79835 3.28819 9.975 < 2e-16 ***
PersonalStandards 0.17434 0.10506 1.659 0.09931 .
ParentalExpectations 0.03742 0.13147 0.285 0.77633
Doubts -0.22838 0.15669 -1.458 0.14723
LeaderPreference 1.37571 0.49129 2.800 0.00584 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.019 on 138 degrees of freedom
Multiple R-squared: 0.1337, Adjusted R-squared: 0.1086
F-statistic: 5.327 on 4 and 138 DF, p-value: 0.0005097
> 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,] 0.205696099 0.411392199 0.79430390
[2,] 0.121640829 0.243281659 0.87835917
[3,] 0.066793000 0.133586001 0.93320700
[4,] 0.031544759 0.063089518 0.96845524
[5,] 0.014010229 0.028020458 0.98598977
[6,] 0.006662163 0.013324326 0.99333784
[7,] 0.002741394 0.005482789 0.99725861
[8,] 0.028210757 0.056421513 0.97178924
[9,] 0.016497770 0.032995539 0.98350223
[10,] 0.009291356 0.018582712 0.99070864
[11,] 0.255029771 0.510059541 0.74497023
[12,] 0.278828171 0.557656341 0.72117183
[13,] 0.217753952 0.435507904 0.78224605
[14,] 0.170123790 0.340247580 0.82987621
[15,] 0.525614649 0.948770701 0.47438535
[16,] 0.729309337 0.541381326 0.27069066
[17,] 0.673896907 0.652206186 0.32610309
[18,] 0.761537961 0.476924078 0.23846204
[19,] 0.708695562 0.582608877 0.29130444
[20,] 0.660889420 0.678221160 0.33911058
[21,] 0.601799677 0.796400645 0.39820032
[22,] 0.895904989 0.208190022 0.10409501
[23,] 0.869155531 0.261688938 0.13084447
[24,] 0.850674640 0.298650721 0.14932536
[25,] 0.813825566 0.372348868 0.18617443
[26,] 0.814062556 0.371874889 0.18593744
[27,] 0.805657362 0.388685276 0.19434264
[28,] 0.764467627 0.471064746 0.23553237
[29,] 0.768894389 0.462211221 0.23110561
[30,] 0.728718562 0.542562877 0.27128144
[31,] 0.689363355 0.621273289 0.31063664
[32,] 0.642596516 0.714806967 0.35740348
[33,] 0.679662891 0.640674218 0.32033711
[34,] 0.630371267 0.739257466 0.36962873
[35,] 0.580634609 0.838730783 0.41936539
[36,] 0.536729761 0.926540477 0.46327024
[37,] 0.491473320 0.982946640 0.50852668
[38,] 0.466138508 0.932277015 0.53386149
[39,] 0.416530549 0.833061097 0.58346945
[40,] 0.365828803 0.731657605 0.63417120
[41,] 0.367590480 0.735180961 0.63240952
[42,] 0.579874754 0.840250492 0.42012525
[43,] 0.551994074 0.896011852 0.44800593
[44,] 0.505639385 0.988721229 0.49436061
[45,] 0.462437074 0.924874148 0.53756293
[46,] 0.413451319 0.826902637 0.58654868
[47,] 0.392043890 0.784087781 0.60795611
[48,] 0.382872937 0.765745874 0.61712706
[49,] 0.342576497 0.685152993 0.65742350
[50,] 0.311299065 0.622598131 0.68870093
[51,] 0.271965931 0.543931862 0.72803407
[52,] 0.397711514 0.795423028 0.60228849
[53,] 0.395841210 0.791682420 0.60415879
[54,] 0.418432613 0.836865226 0.58156739
[55,] 0.373279229 0.746558459 0.62672077
[56,] 0.391408543 0.782817087 0.60859146
[57,] 0.358589442 0.717178885 0.64141056
[58,] 0.377184234 0.754368469 0.62281577
[59,] 0.476321002 0.952642004 0.52367900
[60,] 0.483164946 0.966329891 0.51683505
[61,] 0.440045116 0.880090232 0.55995488
[62,] 0.405740365 0.811480730 0.59425963
[63,] 0.455607420 0.911214839 0.54439258
[64,] 0.539727499 0.920545002 0.46027250
[65,] 0.521890081 0.956219837 0.47810992
[66,] 0.541138864 0.917722272 0.45886114
[67,] 0.679312006 0.641375988 0.32068799
[68,] 0.645487116 0.709025768 0.35451288
[69,] 0.612842504 0.774314992 0.38715750
[70,] 0.568015199 0.863969602 0.43198480
[71,] 0.519647641 0.960704717 0.48035236
[72,] 0.472466226 0.944932451 0.52753377
[73,] 0.457677151 0.915354303 0.54232285
[74,] 0.413450602 0.826901203 0.58654940
[75,] 0.458661791 0.917323582 0.54133821
[76,] 0.414041659 0.828083317 0.58595834
[77,] 0.930635692 0.138728616 0.06936431
[78,] 0.917335345 0.165329310 0.08266465
[79,] 0.905401764 0.189196471 0.09459824
[80,] 0.883129043 0.233741913 0.11687096
[81,] 0.868569734 0.262860531 0.13143027
[82,] 0.884077804 0.231844393 0.11592220
[83,] 0.867762923 0.264474155 0.13223708
[84,] 0.840751662 0.318496677 0.15924834
[85,] 0.813904181 0.372191638 0.18609582
[86,] 0.779055215 0.441889569 0.22094478
[87,] 0.743405520 0.513188959 0.25659448
[88,] 0.707398880 0.585202240 0.29260112
[89,] 0.683341483 0.633317034 0.31665852
[90,] 0.700470876 0.599058247 0.29952912
[91,] 0.749065866 0.501868269 0.25093413
[92,] 0.738912135 0.522175730 0.26108786
[93,] 0.815781268 0.368437464 0.18421873
[94,] 0.779092882 0.441814237 0.22090712
[95,] 0.798635349 0.402729301 0.20136465
[96,] 0.759389836 0.481220328 0.24061016
[97,] 0.858074406 0.283851187 0.14192559
[98,] 0.866354437 0.267291125 0.13364556
[99,] 0.861324322 0.277351355 0.13867568
[100,] 0.826664525 0.346670949 0.17333547
[101,] 0.804120913 0.391758174 0.19587909
[102,] 0.809099424 0.381801152 0.19090058
[103,] 0.877813362 0.244373276 0.12218664
[104,] 0.843361092 0.313277817 0.15663891
[105,] 0.802783162 0.394433675 0.19721684
[106,] 0.870271318 0.259457364 0.12972868
[107,] 0.874397180 0.251205639 0.12560282
[108,] 0.839566744 0.320866513 0.16043326
[109,] 0.814078572 0.371842855 0.18592143
[110,] 0.817689590 0.364620820 0.18231041
[111,] 0.767501088 0.464997824 0.23249891
[112,] 0.709916843 0.580166315 0.29008316
[113,] 0.658858474 0.682283053 0.34114153
[114,] 0.610528434 0.778943132 0.38947157
[115,] 0.538384177 0.923231646 0.46161582
[116,] 0.464775958 0.929551916 0.53522404
[117,] 0.387960876 0.775921752 0.61203912
[118,] 0.508857154 0.982285691 0.49114285
[119,] 0.426067427 0.852134853 0.57393257
[120,] 0.342122922 0.684245844 0.65787708
[121,] 0.332515011 0.665030023 0.66748499
[122,] 0.263159257 0.526318513 0.73684074
[123,] 0.197952883 0.395905767 0.80204712
[124,] 0.327804316 0.655608632 0.67219568
[125,] 0.249597575 0.499195149 0.75040243
[126,] 0.223368800 0.446737600 0.77663120
[127,] 0.194484118 0.388968236 0.80551588
[128,] 0.119511060 0.239022120 0.88048894
> postscript(file="/var/www/html/freestat/rcomp/tmp/1z2zx1290454078.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2styi1290454078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3styi1290454078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4styi1290454078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5k2x31290454078.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 = 143
Frequency = 1
1 2 3 4 5 6
1.21016493 -3.16554050 -3.08209089 0.13525654 2.01356166 4.54958149
7 8 9 10 11 12
1.81685249 1.87188340 -0.21040832 5.30039607 5.98054293 0.82707617
13 14 15 16 17 18
2.61793344 -1.02862678 10.40848770 4.02187021 3.59061205 -11.71261642
19 20 21 22 23 24
2.14651487 2.41388625 -3.66896156 -10.00963679 -10.00963679 -2.47794972
25 26 27 28 29 30
-7.41715735 -1.95320004 0.64408168 -2.24364662 -13.92118021 -3.06430203
31 32 33 34 35 36
1.90744930 -0.87154785 4.31800861 3.96817549 1.13350238 5.17511187
37 38 39 40 41 42
2.85844465 -2.56767984 -1.92437794 -6.31024647 -0.74980971 -1.50443870
43 44 45 46 47 48
0.93633750 1.42691608 3.67348823 -0.29325613 0.59003862 5.00077426
49 50 51 52 53 54
-12.10852480 2.54279996 -2.04227881 -2.24038830 1.10589324 2.95289606
55 56 57 58 59 60
-4.94575611 0.44953559 1.00080894 -1.45041851 -9.53026146 -5.26624327
61 62 63 64 65 66
6.05539625 1.12106882 5.73253826 2.95172084 5.94871598 8.81458203
67 68 69 70 71 72
5.57580765 -1.68797441 2.83683034 -7.29123362 -8.29123362 3.99275139
73 74 75 76 77 78
5.62925442 -10.16798927 -2.56708356 -2.58848459 -1.38474594 -0.20238546
79 80 81 82 83 84
-0.83210144 3.64523956 0.69150770 5.33822889 0.44091644 -17.97282956
85 86 87 88 89 90
-2.08330936 3.27722048 -1.57866983 3.06341911 6.33045950 -3.72121271
91 92 93 94 95 96
-1.08829900 2.38652506 -1.80705050 0.40605075 2.57003791 -3.68739547
97 98 99 100 101 102
-6.08390565 -3.26803517 -2.12491886 8.63606433 1.04679996 8.71260365
103 104 105 106 107 108
1.96155846 -11.14677560 -5.11568304 4.75961170 0.94009684 3.30342930
109 110 111 112 113 114
5.98555542 -9.06547726 -0.46942584 0.89089625 8.89646483 5.79140644
115 116 117 118 119 120
-1.68378881 2.35305615 -4.74143852 0.28617062 1.54960435 -1.33993871
121 122 123 124 125 126
3.94551825 -0.77049122 -1.88992116 1.64047502 8.28942894 0.38949218
127 128 129 130 131 132
0.52820332 3.78901826 2.49915061 -3.49880954 3.44654561 5.37345197
133 134 135 136 137 138
-0.03968288 4.03459150 -3.45959374 5.33283034 1.83144913 1.75897217
139 140 141 142 143
-1.94278899 -10.50062429 -4.87031203 -4.94901444 6.31320634
> postscript(file="/var/www/html/freestat/rcomp/tmp/6k2x31290454078.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 = 143
Frequency = 1
lag(myerror, k = 1) myerror
0 1.21016493 NA
1 -3.16554050 1.21016493
2 -3.08209089 -3.16554050
3 0.13525654 -3.08209089
4 2.01356166 0.13525654
5 4.54958149 2.01356166
6 1.81685249 4.54958149
7 1.87188340 1.81685249
8 -0.21040832 1.87188340
9 5.30039607 -0.21040832
10 5.98054293 5.30039607
11 0.82707617 5.98054293
12 2.61793344 0.82707617
13 -1.02862678 2.61793344
14 10.40848770 -1.02862678
15 4.02187021 10.40848770
16 3.59061205 4.02187021
17 -11.71261642 3.59061205
18 2.14651487 -11.71261642
19 2.41388625 2.14651487
20 -3.66896156 2.41388625
21 -10.00963679 -3.66896156
22 -10.00963679 -10.00963679
23 -2.47794972 -10.00963679
24 -7.41715735 -2.47794972
25 -1.95320004 -7.41715735
26 0.64408168 -1.95320004
27 -2.24364662 0.64408168
28 -13.92118021 -2.24364662
29 -3.06430203 -13.92118021
30 1.90744930 -3.06430203
31 -0.87154785 1.90744930
32 4.31800861 -0.87154785
33 3.96817549 4.31800861
34 1.13350238 3.96817549
35 5.17511187 1.13350238
36 2.85844465 5.17511187
37 -2.56767984 2.85844465
38 -1.92437794 -2.56767984
39 -6.31024647 -1.92437794
40 -0.74980971 -6.31024647
41 -1.50443870 -0.74980971
42 0.93633750 -1.50443870
43 1.42691608 0.93633750
44 3.67348823 1.42691608
45 -0.29325613 3.67348823
46 0.59003862 -0.29325613
47 5.00077426 0.59003862
48 -12.10852480 5.00077426
49 2.54279996 -12.10852480
50 -2.04227881 2.54279996
51 -2.24038830 -2.04227881
52 1.10589324 -2.24038830
53 2.95289606 1.10589324
54 -4.94575611 2.95289606
55 0.44953559 -4.94575611
56 1.00080894 0.44953559
57 -1.45041851 1.00080894
58 -9.53026146 -1.45041851
59 -5.26624327 -9.53026146
60 6.05539625 -5.26624327
61 1.12106882 6.05539625
62 5.73253826 1.12106882
63 2.95172084 5.73253826
64 5.94871598 2.95172084
65 8.81458203 5.94871598
66 5.57580765 8.81458203
67 -1.68797441 5.57580765
68 2.83683034 -1.68797441
69 -7.29123362 2.83683034
70 -8.29123362 -7.29123362
71 3.99275139 -8.29123362
72 5.62925442 3.99275139
73 -10.16798927 5.62925442
74 -2.56708356 -10.16798927
75 -2.58848459 -2.56708356
76 -1.38474594 -2.58848459
77 -0.20238546 -1.38474594
78 -0.83210144 -0.20238546
79 3.64523956 -0.83210144
80 0.69150770 3.64523956
81 5.33822889 0.69150770
82 0.44091644 5.33822889
83 -17.97282956 0.44091644
84 -2.08330936 -17.97282956
85 3.27722048 -2.08330936
86 -1.57866983 3.27722048
87 3.06341911 -1.57866983
88 6.33045950 3.06341911
89 -3.72121271 6.33045950
90 -1.08829900 -3.72121271
91 2.38652506 -1.08829900
92 -1.80705050 2.38652506
93 0.40605075 -1.80705050
94 2.57003791 0.40605075
95 -3.68739547 2.57003791
96 -6.08390565 -3.68739547
97 -3.26803517 -6.08390565
98 -2.12491886 -3.26803517
99 8.63606433 -2.12491886
100 1.04679996 8.63606433
101 8.71260365 1.04679996
102 1.96155846 8.71260365
103 -11.14677560 1.96155846
104 -5.11568304 -11.14677560
105 4.75961170 -5.11568304
106 0.94009684 4.75961170
107 3.30342930 0.94009684
108 5.98555542 3.30342930
109 -9.06547726 5.98555542
110 -0.46942584 -9.06547726
111 0.89089625 -0.46942584
112 8.89646483 0.89089625
113 5.79140644 8.89646483
114 -1.68378881 5.79140644
115 2.35305615 -1.68378881
116 -4.74143852 2.35305615
117 0.28617062 -4.74143852
118 1.54960435 0.28617062
119 -1.33993871 1.54960435
120 3.94551825 -1.33993871
121 -0.77049122 3.94551825
122 -1.88992116 -0.77049122
123 1.64047502 -1.88992116
124 8.28942894 1.64047502
125 0.38949218 8.28942894
126 0.52820332 0.38949218
127 3.78901826 0.52820332
128 2.49915061 3.78901826
129 -3.49880954 2.49915061
130 3.44654561 -3.49880954
131 5.37345197 3.44654561
132 -0.03968288 5.37345197
133 4.03459150 -0.03968288
134 -3.45959374 4.03459150
135 5.33283034 -3.45959374
136 1.83144913 5.33283034
137 1.75897217 1.83144913
138 -1.94278899 1.75897217
139 -10.50062429 -1.94278899
140 -4.87031203 -10.50062429
141 -4.94901444 -4.87031203
142 6.31320634 -4.94901444
143 NA 6.31320634
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.16554050 1.21016493
[2,] -3.08209089 -3.16554050
[3,] 0.13525654 -3.08209089
[4,] 2.01356166 0.13525654
[5,] 4.54958149 2.01356166
[6,] 1.81685249 4.54958149
[7,] 1.87188340 1.81685249
[8,] -0.21040832 1.87188340
[9,] 5.30039607 -0.21040832
[10,] 5.98054293 5.30039607
[11,] 0.82707617 5.98054293
[12,] 2.61793344 0.82707617
[13,] -1.02862678 2.61793344
[14,] 10.40848770 -1.02862678
[15,] 4.02187021 10.40848770
[16,] 3.59061205 4.02187021
[17,] -11.71261642 3.59061205
[18,] 2.14651487 -11.71261642
[19,] 2.41388625 2.14651487
[20,] -3.66896156 2.41388625
[21,] -10.00963679 -3.66896156
[22,] -10.00963679 -10.00963679
[23,] -2.47794972 -10.00963679
[24,] -7.41715735 -2.47794972
[25,] -1.95320004 -7.41715735
[26,] 0.64408168 -1.95320004
[27,] -2.24364662 0.64408168
[28,] -13.92118021 -2.24364662
[29,] -3.06430203 -13.92118021
[30,] 1.90744930 -3.06430203
[31,] -0.87154785 1.90744930
[32,] 4.31800861 -0.87154785
[33,] 3.96817549 4.31800861
[34,] 1.13350238 3.96817549
[35,] 5.17511187 1.13350238
[36,] 2.85844465 5.17511187
[37,] -2.56767984 2.85844465
[38,] -1.92437794 -2.56767984
[39,] -6.31024647 -1.92437794
[40,] -0.74980971 -6.31024647
[41,] -1.50443870 -0.74980971
[42,] 0.93633750 -1.50443870
[43,] 1.42691608 0.93633750
[44,] 3.67348823 1.42691608
[45,] -0.29325613 3.67348823
[46,] 0.59003862 -0.29325613
[47,] 5.00077426 0.59003862
[48,] -12.10852480 5.00077426
[49,] 2.54279996 -12.10852480
[50,] -2.04227881 2.54279996
[51,] -2.24038830 -2.04227881
[52,] 1.10589324 -2.24038830
[53,] 2.95289606 1.10589324
[54,] -4.94575611 2.95289606
[55,] 0.44953559 -4.94575611
[56,] 1.00080894 0.44953559
[57,] -1.45041851 1.00080894
[58,] -9.53026146 -1.45041851
[59,] -5.26624327 -9.53026146
[60,] 6.05539625 -5.26624327
[61,] 1.12106882 6.05539625
[62,] 5.73253826 1.12106882
[63,] 2.95172084 5.73253826
[64,] 5.94871598 2.95172084
[65,] 8.81458203 5.94871598
[66,] 5.57580765 8.81458203
[67,] -1.68797441 5.57580765
[68,] 2.83683034 -1.68797441
[69,] -7.29123362 2.83683034
[70,] -8.29123362 -7.29123362
[71,] 3.99275139 -8.29123362
[72,] 5.62925442 3.99275139
[73,] -10.16798927 5.62925442
[74,] -2.56708356 -10.16798927
[75,] -2.58848459 -2.56708356
[76,] -1.38474594 -2.58848459
[77,] -0.20238546 -1.38474594
[78,] -0.83210144 -0.20238546
[79,] 3.64523956 -0.83210144
[80,] 0.69150770 3.64523956
[81,] 5.33822889 0.69150770
[82,] 0.44091644 5.33822889
[83,] -17.97282956 0.44091644
[84,] -2.08330936 -17.97282956
[85,] 3.27722048 -2.08330936
[86,] -1.57866983 3.27722048
[87,] 3.06341911 -1.57866983
[88,] 6.33045950 3.06341911
[89,] -3.72121271 6.33045950
[90,] -1.08829900 -3.72121271
[91,] 2.38652506 -1.08829900
[92,] -1.80705050 2.38652506
[93,] 0.40605075 -1.80705050
[94,] 2.57003791 0.40605075
[95,] -3.68739547 2.57003791
[96,] -6.08390565 -3.68739547
[97,] -3.26803517 -6.08390565
[98,] -2.12491886 -3.26803517
[99,] 8.63606433 -2.12491886
[100,] 1.04679996 8.63606433
[101,] 8.71260365 1.04679996
[102,] 1.96155846 8.71260365
[103,] -11.14677560 1.96155846
[104,] -5.11568304 -11.14677560
[105,] 4.75961170 -5.11568304
[106,] 0.94009684 4.75961170
[107,] 3.30342930 0.94009684
[108,] 5.98555542 3.30342930
[109,] -9.06547726 5.98555542
[110,] -0.46942584 -9.06547726
[111,] 0.89089625 -0.46942584
[112,] 8.89646483 0.89089625
[113,] 5.79140644 8.89646483
[114,] -1.68378881 5.79140644
[115,] 2.35305615 -1.68378881
[116,] -4.74143852 2.35305615
[117,] 0.28617062 -4.74143852
[118,] 1.54960435 0.28617062
[119,] -1.33993871 1.54960435
[120,] 3.94551825 -1.33993871
[121,] -0.77049122 3.94551825
[122,] -1.88992116 -0.77049122
[123,] 1.64047502 -1.88992116
[124,] 8.28942894 1.64047502
[125,] 0.38949218 8.28942894
[126,] 0.52820332 0.38949218
[127,] 3.78901826 0.52820332
[128,] 2.49915061 3.78901826
[129,] -3.49880954 2.49915061
[130,] 3.44654561 -3.49880954
[131,] 5.37345197 3.44654561
[132,] -0.03968288 5.37345197
[133,] 4.03459150 -0.03968288
[134,] -3.45959374 4.03459150
[135,] 5.33283034 -3.45959374
[136,] 1.83144913 5.33283034
[137,] 1.75897217 1.83144913
[138,] -1.94278899 1.75897217
[139,] -10.50062429 -1.94278899
[140,] -4.87031203 -10.50062429
[141,] -4.94901444 -4.87031203
[142,] 6.31320634 -4.94901444
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.16554050 1.21016493
2 -3.08209089 -3.16554050
3 0.13525654 -3.08209089
4 2.01356166 0.13525654
5 4.54958149 2.01356166
6 1.81685249 4.54958149
7 1.87188340 1.81685249
8 -0.21040832 1.87188340
9 5.30039607 -0.21040832
10 5.98054293 5.30039607
11 0.82707617 5.98054293
12 2.61793344 0.82707617
13 -1.02862678 2.61793344
14 10.40848770 -1.02862678
15 4.02187021 10.40848770
16 3.59061205 4.02187021
17 -11.71261642 3.59061205
18 2.14651487 -11.71261642
19 2.41388625 2.14651487
20 -3.66896156 2.41388625
21 -10.00963679 -3.66896156
22 -10.00963679 -10.00963679
23 -2.47794972 -10.00963679
24 -7.41715735 -2.47794972
25 -1.95320004 -7.41715735
26 0.64408168 -1.95320004
27 -2.24364662 0.64408168
28 -13.92118021 -2.24364662
29 -3.06430203 -13.92118021
30 1.90744930 -3.06430203
31 -0.87154785 1.90744930
32 4.31800861 -0.87154785
33 3.96817549 4.31800861
34 1.13350238 3.96817549
35 5.17511187 1.13350238
36 2.85844465 5.17511187
37 -2.56767984 2.85844465
38 -1.92437794 -2.56767984
39 -6.31024647 -1.92437794
40 -0.74980971 -6.31024647
41 -1.50443870 -0.74980971
42 0.93633750 -1.50443870
43 1.42691608 0.93633750
44 3.67348823 1.42691608
45 -0.29325613 3.67348823
46 0.59003862 -0.29325613
47 5.00077426 0.59003862
48 -12.10852480 5.00077426
49 2.54279996 -12.10852480
50 -2.04227881 2.54279996
51 -2.24038830 -2.04227881
52 1.10589324 -2.24038830
53 2.95289606 1.10589324
54 -4.94575611 2.95289606
55 0.44953559 -4.94575611
56 1.00080894 0.44953559
57 -1.45041851 1.00080894
58 -9.53026146 -1.45041851
59 -5.26624327 -9.53026146
60 6.05539625 -5.26624327
61 1.12106882 6.05539625
62 5.73253826 1.12106882
63 2.95172084 5.73253826
64 5.94871598 2.95172084
65 8.81458203 5.94871598
66 5.57580765 8.81458203
67 -1.68797441 5.57580765
68 2.83683034 -1.68797441
69 -7.29123362 2.83683034
70 -8.29123362 -7.29123362
71 3.99275139 -8.29123362
72 5.62925442 3.99275139
73 -10.16798927 5.62925442
74 -2.56708356 -10.16798927
75 -2.58848459 -2.56708356
76 -1.38474594 -2.58848459
77 -0.20238546 -1.38474594
78 -0.83210144 -0.20238546
79 3.64523956 -0.83210144
80 0.69150770 3.64523956
81 5.33822889 0.69150770
82 0.44091644 5.33822889
83 -17.97282956 0.44091644
84 -2.08330936 -17.97282956
85 3.27722048 -2.08330936
86 -1.57866983 3.27722048
87 3.06341911 -1.57866983
88 6.33045950 3.06341911
89 -3.72121271 6.33045950
90 -1.08829900 -3.72121271
91 2.38652506 -1.08829900
92 -1.80705050 2.38652506
93 0.40605075 -1.80705050
94 2.57003791 0.40605075
95 -3.68739547 2.57003791
96 -6.08390565 -3.68739547
97 -3.26803517 -6.08390565
98 -2.12491886 -3.26803517
99 8.63606433 -2.12491886
100 1.04679996 8.63606433
101 8.71260365 1.04679996
102 1.96155846 8.71260365
103 -11.14677560 1.96155846
104 -5.11568304 -11.14677560
105 4.75961170 -5.11568304
106 0.94009684 4.75961170
107 3.30342930 0.94009684
108 5.98555542 3.30342930
109 -9.06547726 5.98555542
110 -0.46942584 -9.06547726
111 0.89089625 -0.46942584
112 8.89646483 0.89089625
113 5.79140644 8.89646483
114 -1.68378881 5.79140644
115 2.35305615 -1.68378881
116 -4.74143852 2.35305615
117 0.28617062 -4.74143852
118 1.54960435 0.28617062
119 -1.33993871 1.54960435
120 3.94551825 -1.33993871
121 -0.77049122 3.94551825
122 -1.88992116 -0.77049122
123 1.64047502 -1.88992116
124 8.28942894 1.64047502
125 0.38949218 8.28942894
126 0.52820332 0.38949218
127 3.78901826 0.52820332
128 2.49915061 3.78901826
129 -3.49880954 2.49915061
130 3.44654561 -3.49880954
131 5.37345197 3.44654561
132 -0.03968288 5.37345197
133 4.03459150 -0.03968288
134 -3.45959374 4.03459150
135 5.33283034 -3.45959374
136 1.83144913 5.33283034
137 1.75897217 1.83144913
138 -1.94278899 1.75897217
139 -10.50062429 -1.94278899
140 -4.87031203 -10.50062429
141 -4.94901444 -4.87031203
142 6.31320634 -4.94901444
> 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/freestat/rcomp/tmp/7vue61290454078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8vue61290454078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9nlwr1290454078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10nlwr1290454078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/119luf1290454078.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/freestat/rcomp/tmp/12c4t31290454078.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/freestat/rcomp/tmp/13158x1290454078.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/freestat/rcomp/tmp/145ook1290454078.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/freestat/rcomp/tmp/15qo581290454078.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/freestat/rcomp/tmp/16t7le1290454078.tab")
+ }
>
> try(system("convert tmp/1z2zx1290454078.ps tmp/1z2zx1290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/2styi1290454078.ps tmp/2styi1290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/3styi1290454078.ps tmp/3styi1290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/4styi1290454078.ps tmp/4styi1290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/5k2x31290454078.ps tmp/5k2x31290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/6k2x31290454078.ps tmp/6k2x31290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vue61290454078.ps tmp/7vue61290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vue61290454078.ps tmp/8vue61290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nlwr1290454078.ps tmp/9nlwr1290454078.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nlwr1290454078.ps tmp/10nlwr1290454078.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.376 2.741 10.966