R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(41
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+ ,4)
+ ,dim=c(5
+ ,145)
+ ,dimnames=list(c('StudyForCareer'
+ ,'PersonalStandards'
+ ,'ParentalExpectations'
+ ,'Doubts'
+ ,'LeaderPreference')
+ ,1:145))
> y <- array(NA,dim=c(5,145),dimnames=list(c('StudyForCareer','PersonalStandards','ParentalExpectations','Doubts','LeaderPreference'),1:145))
> 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 42 18 10 8
5 40 23 18 15
6 43 25 14 9
7 40 23 11 11
8 45 30 17 6
9 45 32 21 10
10 44 25 7 11
11 42 26 18 16
12 32 25 13 11
13 32 25 13 11
14 41 35 18 7
15 38 20 12 10
16 38 21 9 9
17 24 23 11 15
18 46 17 11 6
19 42 27 16 12
20 46 25 12 10
21 43 18 14 14
22 38 22 13 9
23 39 23 17 14
24 40 25 13 14
25 37 19 13 9
26 41 20 12 8
27 46 26 12 10
28 26 16 12 9
29 37 22 9 9
30 39 25 17 9
31 44 29 18 11
32 38 22 12 10
33 38 32 12 8
34 38 23 9 14
35 33 18 13 10
36 43 26 11 14
37 41 14 13 15
38 49 20 6 8
39 45 25 11 10
40 31 21 18 13
41 30 21 18 13
42 38 23 15 10
43 39 24 11 11
44 40 21 14 10
45 36 17 12 16
46 49 29 8 6
47 41 25 11 11
48 42 25 17 14
49 41 25 16 9
50 43 21 13 11
51 46 23 15 8
52 41 25 16 8
53 39 25 7 11
54 42 24 16 16
55 35 21 13 12
56 36 22 15 14
57 48 14 12 8
58 41 20 12 10
59 47 21 24 14
60 41 22 15 10
61 31 19 8 5
62 36 28 18 12
63 46 25 17 9
64 44 21 15 8
65 43 27 11 16
66 40 19 12 13
67 40 20 14 8
68 46 17 11 14
69 39 22 10 8
70 44 26 11 7
71 38 17 12 11
72 39 15 6 6
73 41 27 15 9
74 39 25 14 14
75 40 19 16 12
76 44 18 16 8
77 42 15 11 8
78 46 29 15 12
79 44 24 12 13
80 37 24 13 11
81 39 22 14 12
82 40 22 12 13
83 42 25 17 14
84 37 21 11 9
85 33 21 13 8
86 35 18 9 8
87 42 10 12 9
88 36 18 10 14
89 44 23 9 14
90 45 24 11 14
91 47 32 9 14
92 40 24 16 9
93 49 17 14 14
94 48 30 24 8
95 29 25 9 10
96 45 23 11 11
97 29 19 14 13
98 41 21 12 9
99 34 24 8 13
100 38 23 5 16
101 37 19 10 12
102 48 27 15 4
103 39 26 10 10
104 34 26 18 14
105 35 16 12 10
106 41 27 13 9
107 43 14 11 8
108 41 18 12 9
109 39 21 7 15
110 36 22 17 8
111 32 31 9 11
112 46 23 10 12
113 42 24 12 9
114 42 19 10 13
115 45 22 7 7
116 39 24 13 10
117 45 28 9 11
118 48 24 9 8
119 28 15 12 14
120 35 21 11 9
121 38 21 14 16
122 42 13 8 11
123 36 20 11 12
124 37 22 11 8
125 38 19 12 7
126 43 26 20 13
127 35 19 8 20
128 36 20 11 11
129 33 14 15 10
130 39 17 12 16
131 32 29 12 12
132 45 21 12 8
133 35 19 11 10
134 38 17 9 11
135 36 19 8 14
136 42 17 12 10
137 41 19 13 12
138 47 21 17 11
139 35 20 16 11
140 43 20 11 14
141 40 29 9 16
142 46 23 11 9
143 44 23 11 11
144 35 19 13 9
145 29 22 15 14
LeaderPreference
1 3
2 4
3 4
4 4
5 3
6 4
7 4
8 5
9 4
10 4
11 4
12 5
13 5
14 4
15 4
16 4
17 3
18 5
19 4
20 4
21 5
22 4
23 4
24 3
25 2
26 4
27 4
28 3
29 3
30 4
31 5
32 2
33 0
34 4
35 3
36 4
37 2
38 4
39 5
40 3
41 3
42 4
43 4
44 4
45 2
46 5
47 4
48 3
49 5
50 4
51 3
52 5
53 4
54 4
55 5
56 3
57 4
58 4
59 3
60 3
61 5
62 4
63 4
64 4
65 2
66 5
67 3
68 3
69 4
70 4
71 2
72 4
73 5
74 3
75 4
76 4
77 4
78 5
79 4
80 4
81 2
82 3
83 3
84 3
85 2
86 4
87 2
88 2
89 4
90 4
91 4
92 4
93 4
94 5
95 4
96 5
97 2
98 4
99 2
100 2
101 3
102 5
103 4
104 4
105 2
106 3
107 4
108 3
109 2
110 4
111 4
112 4
113 4
114 2
115 3
116 4
117 4
118 5
119 4
120 2
121 4
122 4
123 3
124 4
125 3
126 4
127 2
128 4
129 2
130 4
131 4
132 3
133 4
134 3
135 3
136 3
137 4
138 3
139 3
140 3
141 4
142 4
143 5
144 3
145 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PersonalStandards ParentalExpectations
34.11213 0.14666 0.03216
Doubts LeaderPreference
-0.21615 1.20809
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.221 -2.307 0.535 3.222 10.138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.11213 3.12985 10.899 <2e-16 ***
PersonalStandards 0.14666 0.09936 1.476 0.1422
ParentalExpectations 0.03216 0.12218 0.263 0.7927
Doubts -0.21615 0.14791 -1.461 0.1462
LeaderPreference 1.20809 0.46291 2.610 0.0100 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.755 on 140 degrees of freedom
Multiple R-squared: 0.1148, Adjusted R-squared: 0.08954
F-statistic: 4.54 on 4 and 140 DF, p-value: 0.001770
> 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,] 0.23596916 0.47193832 0.7640308
[2,] 0.12958416 0.25916832 0.8704158
[3,] 0.06124303 0.12248607 0.9387570
[4,] 0.02709022 0.05418044 0.9729098
[5,] 0.23517826 0.47035652 0.7648217
[6,] 0.28509606 0.57019212 0.7149039
[7,] 0.31610811 0.63221623 0.6838919
[8,] 0.23299911 0.46599822 0.7670009
[9,] 0.17212486 0.34424971 0.8278751
[10,] 0.76859571 0.46280857 0.2314043
[11,] 0.75737599 0.48524803 0.2426240
[12,] 0.72007888 0.55984224 0.2799211
[13,] 0.77114348 0.45771304 0.2288565
[14,] 0.77322086 0.45355829 0.2267791
[15,] 0.73216488 0.53567023 0.2678351
[16,] 0.67011035 0.65977929 0.3298896
[17,] 0.63601105 0.72797790 0.3639889
[18,] 0.58169965 0.83660070 0.4183003
[19,] 0.51411005 0.97177990 0.4858899
[20,] 0.55743965 0.88512070 0.4425603
[21,] 0.81858165 0.36283671 0.1814184
[22,] 0.77647400 0.44705199 0.2235260
[23,] 0.73705614 0.52588772 0.2629439
[24,] 0.68597627 0.62804747 0.3140237
[25,] 0.63940896 0.72118208 0.3605910
[26,] 0.58381588 0.83236823 0.4161841
[27,] 0.52760541 0.94478919 0.4723946
[28,] 0.51252374 0.97495253 0.4874763
[29,] 0.48904145 0.97808290 0.5109585
[30,] 0.56871003 0.86257993 0.4312900
[31,] 0.70786920 0.58426160 0.2921308
[32,] 0.67507246 0.64985508 0.3249275
[33,] 0.70817884 0.58364232 0.2918212
[34,] 0.75756459 0.48487082 0.2424354
[35,] 0.72150041 0.55699918 0.2784996
[36,] 0.67896255 0.64207490 0.3210375
[37,] 0.63146256 0.73707487 0.3685374
[38,] 0.59196925 0.81606151 0.4080308
[39,] 0.58516711 0.82966578 0.4148329
[40,] 0.53316194 0.93367613 0.4668381
[41,] 0.52292763 0.95414473 0.4770724
[42,] 0.47339384 0.94678768 0.5266062
[43,] 0.44982103 0.89964205 0.5501790
[44,] 0.50246428 0.99507144 0.4975357
[45,] 0.45557449 0.91114899 0.5444255
[46,] 0.41788734 0.83577468 0.5821127
[47,] 0.39121474 0.78242948 0.6087853
[48,] 0.41442030 0.82884059 0.5855797
[49,] 0.37579495 0.75158989 0.6242051
[50,] 0.50062813 0.99874374 0.4993719
[51,] 0.45283029 0.90566057 0.5471697
[52,] 0.57996862 0.84006276 0.4200314
[53,] 0.53687292 0.92625416 0.4631271
[54,] 0.73172294 0.53655412 0.2682771
[55,] 0.73547627 0.52904745 0.2645237
[56,] 0.73328444 0.53343111 0.2667156
[57,] 0.70961201 0.58077597 0.2903880
[58,] 0.72959341 0.54081318 0.2704066
[59,] 0.69105702 0.61788596 0.3089430
[60,] 0.64757256 0.70485488 0.3524274
[61,] 0.73541790 0.52916419 0.2645821
[62,] 0.70011827 0.59976346 0.2998817
[63,] 0.66727640 0.66544719 0.3327236
[64,] 0.62433013 0.75133974 0.3756699
[65,] 0.58183502 0.83632997 0.4181650
[66,] 0.54021159 0.91957683 0.4597884
[67,] 0.49211484 0.98422968 0.5078852
[68,] 0.44399509 0.88799018 0.5560049
[69,] 0.42131238 0.84262476 0.5786876
[70,] 0.38177144 0.76354289 0.6182286
[71,] 0.36315573 0.72631146 0.6368443
[72,] 0.34761090 0.69522181 0.6523891
[73,] 0.32617277 0.65234553 0.6738272
[74,] 0.28983932 0.57967863 0.7101607
[75,] 0.25354766 0.50709531 0.7464523
[76,] 0.23749541 0.47499083 0.7625046
[77,] 0.20831197 0.41662395 0.7916880
[78,] 0.21009353 0.42018707 0.7899065
[79,] 0.22506354 0.45012709 0.7749365
[80,] 0.23567531 0.47135062 0.7643247
[81,] 0.19996417 0.39992834 0.8000358
[82,] 0.19125455 0.38250911 0.8087454
[83,] 0.19636871 0.39273741 0.8036313
[84,] 0.22592322 0.45184644 0.7740768
[85,] 0.19055335 0.38110671 0.8094466
[86,] 0.33746163 0.67492326 0.6625384
[87,] 0.37245524 0.74491048 0.6275448
[88,] 0.65271030 0.69457939 0.3472897
[89,] 0.62734282 0.74531436 0.3726572
[90,] 0.68190502 0.63618997 0.3180950
[91,] 0.63350776 0.73298449 0.3664922
[92,] 0.61356185 0.77287630 0.3864382
[93,] 0.56345621 0.87308759 0.4365438
[94,] 0.51449811 0.97100378 0.4855019
[95,] 0.50770776 0.98458448 0.4922922
[96,] 0.46243283 0.92486567 0.5375672
[97,] 0.45252877 0.90505755 0.5474712
[98,] 0.41388388 0.82776776 0.5861161
[99,] 0.36289171 0.72578342 0.6371083
[100,] 0.32845069 0.65690137 0.6715493
[101,] 0.28641826 0.57283651 0.7135817
[102,] 0.24555355 0.49110711 0.7544464
[103,] 0.23229541 0.46459081 0.7677046
[104,] 0.40712127 0.81424255 0.5928787
[105,] 0.42886638 0.85773276 0.5711336
[106,] 0.37162571 0.74325141 0.6283743
[107,] 0.37706247 0.75412495 0.6229375
[108,] 0.35497044 0.70994088 0.6450296
[109,] 0.30503975 0.61007950 0.6949603
[110,] 0.27659815 0.55319630 0.7234019
[111,] 0.29497138 0.58994276 0.7050286
[112,] 0.49834249 0.99668498 0.5016575
[113,] 0.44556692 0.89113384 0.5544331
[114,] 0.37850039 0.75700079 0.6214996
[115,] 0.34135393 0.68270786 0.6586461
[116,] 0.28818642 0.57637284 0.7118136
[117,] 0.25403540 0.50807080 0.7459646
[118,] 0.20542199 0.41084397 0.7945780
[119,] 0.19485594 0.38971188 0.8051441
[120,] 0.14579468 0.29158936 0.8542053
[121,] 0.12581734 0.25163468 0.8741827
[122,] 0.12453115 0.24906229 0.8754689
[123,] 0.09541888 0.19083776 0.9045811
[124,] 0.22324884 0.44649768 0.7767512
[125,] 0.16338819 0.32677639 0.8366118
[126,] 0.16189225 0.32378451 0.8381077
[127,] 0.10969730 0.21939460 0.8903027
[128,] 0.07733732 0.15467464 0.9226627
[129,] 0.04204189 0.08408378 0.9579581
[130,] 0.02382211 0.04764422 0.9761779
> postscript(file="/var/www/html/rcomp/tmp/179eh1292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/279eh1292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/379eh1292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hiv21292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hiv21292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 145
Frequency = 1
1 2 3 4 5 6
1.06001219 -3.14807958 -3.23596592 1.82320197 1.55372660 1.88408501
7 8 9 10 11 12
-0.29380760 1.19776268 2.84847428 3.54153438 2.12180893 -9.85954490
13 14 15 16 17 18
-9.85954490 -2.14345240 -2.10214629 -2.36845934 -14.22112129 4.29730653
19 20 21 22 23 24
1.17488538 5.16456282 2.78334367 -2.64377587 -0.83834922 1.20508454
25 26 27 28 29 30
-0.78761779 0.46555644 5.01790464 -12.52357044 -2.30702575 -2.21240875
31 32 33 34 35 36
1.39299945 0.02072089 0.53802536 -1.58103252 -5.63290275 2.91466377
37 38 39 40 41 42
5.24256491 8.65854396 2.98863564 -7.58525431 -8.58525431 -2.63861458
43 44 45 46 47 48
-1.44046578 -0.31313364 0.05090360 5.63390214 0.41287604 3.07642619
49 50 51 52 53 54
-1.38833593 2.93517958 6.13717991 -1.60448457 -1.45846562 2.47945446
55 56 57 58 59 60
-6.05676355 -2.41927010 8.34550552 0.89785371 8.43790680 1.71613536
61 62 63 64 65 66
-11.11566471 -5.03610197 4.78759125 3.22240450 5.61648639 -0.51513397
67 68 69 70 71 72
0.60931904 8.44267915 -1.76343074 2.40162332 0.97016042 -1.04046241
73 74 75 76 77 78
-1.64948770 0.17291995 0.34815082 3.63021445 2.23101193 3.70564184
79 80 81 82 83 84
3.95966690 -3.50479496 1.38868899 1.46107503 3.07642619 -2.22469675
85 86 87 88 89 90
-5.29708279 -5.14463344 5.56447041 -0.46372268 4.41896748 5.20798012
91 92 93 94 95 96
6.09904386 -1.03358599 10.13809362 4.40490784 -11.73894342 3.49810063
97 98 99 100 101 102
-7.95518784 0.53504690 -3.49549122 1.39610663 -1.25076990 4.26976912
103 104 105 106 107 108
-1.91776619 -6.31048834 -2.09933003 0.83102500 3.37767010 2.18311320
109 110 111 112 113 114
2.40894518 -4.98858285 -9.40274386 5.95450562 1.09507236 5.17347051
115 116 117 118 119 120
5.32500615 -1.72094359 4.03723067 5.76732572 -10.50426085 -3.01660498
121 122 123 124 125 126
-1.01624183 3.26926795 -2.42959266 -3.79559533 -1.39584225 2.40903385
127 128 129 130 131 132
-0.24915987 -3.85383307 -3.90250744 0.63472006 -8.98977263 5.52699003
133 134 135 136 137 138
-4.92332352 -0.14143759 -1.75414345 3.54592002 1.44464458 8.01461300
139 140 141 142 143 144
-3.80656423 5.00270461 -0.02868433 5.27389513 2.49810063 -3.99570956
145
-10.62736186
> postscript(file="/var/www/html/rcomp/tmp/6hiv21292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 1.06001219 NA
1 -3.14807958 1.06001219
2 -3.23596592 -3.14807958
3 1.82320197 -3.23596592
4 1.55372660 1.82320197
5 1.88408501 1.55372660
6 -0.29380760 1.88408501
7 1.19776268 -0.29380760
8 2.84847428 1.19776268
9 3.54153438 2.84847428
10 2.12180893 3.54153438
11 -9.85954490 2.12180893
12 -9.85954490 -9.85954490
13 -2.14345240 -9.85954490
14 -2.10214629 -2.14345240
15 -2.36845934 -2.10214629
16 -14.22112129 -2.36845934
17 4.29730653 -14.22112129
18 1.17488538 4.29730653
19 5.16456282 1.17488538
20 2.78334367 5.16456282
21 -2.64377587 2.78334367
22 -0.83834922 -2.64377587
23 1.20508454 -0.83834922
24 -0.78761779 1.20508454
25 0.46555644 -0.78761779
26 5.01790464 0.46555644
27 -12.52357044 5.01790464
28 -2.30702575 -12.52357044
29 -2.21240875 -2.30702575
30 1.39299945 -2.21240875
31 0.02072089 1.39299945
32 0.53802536 0.02072089
33 -1.58103252 0.53802536
34 -5.63290275 -1.58103252
35 2.91466377 -5.63290275
36 5.24256491 2.91466377
37 8.65854396 5.24256491
38 2.98863564 8.65854396
39 -7.58525431 2.98863564
40 -8.58525431 -7.58525431
41 -2.63861458 -8.58525431
42 -1.44046578 -2.63861458
43 -0.31313364 -1.44046578
44 0.05090360 -0.31313364
45 5.63390214 0.05090360
46 0.41287604 5.63390214
47 3.07642619 0.41287604
48 -1.38833593 3.07642619
49 2.93517958 -1.38833593
50 6.13717991 2.93517958
51 -1.60448457 6.13717991
52 -1.45846562 -1.60448457
53 2.47945446 -1.45846562
54 -6.05676355 2.47945446
55 -2.41927010 -6.05676355
56 8.34550552 -2.41927010
57 0.89785371 8.34550552
58 8.43790680 0.89785371
59 1.71613536 8.43790680
60 -11.11566471 1.71613536
61 -5.03610197 -11.11566471
62 4.78759125 -5.03610197
63 3.22240450 4.78759125
64 5.61648639 3.22240450
65 -0.51513397 5.61648639
66 0.60931904 -0.51513397
67 8.44267915 0.60931904
68 -1.76343074 8.44267915
69 2.40162332 -1.76343074
70 0.97016042 2.40162332
71 -1.04046241 0.97016042
72 -1.64948770 -1.04046241
73 0.17291995 -1.64948770
74 0.34815082 0.17291995
75 3.63021445 0.34815082
76 2.23101193 3.63021445
77 3.70564184 2.23101193
78 3.95966690 3.70564184
79 -3.50479496 3.95966690
80 1.38868899 -3.50479496
81 1.46107503 1.38868899
82 3.07642619 1.46107503
83 -2.22469675 3.07642619
84 -5.29708279 -2.22469675
85 -5.14463344 -5.29708279
86 5.56447041 -5.14463344
87 -0.46372268 5.56447041
88 4.41896748 -0.46372268
89 5.20798012 4.41896748
90 6.09904386 5.20798012
91 -1.03358599 6.09904386
92 10.13809362 -1.03358599
93 4.40490784 10.13809362
94 -11.73894342 4.40490784
95 3.49810063 -11.73894342
96 -7.95518784 3.49810063
97 0.53504690 -7.95518784
98 -3.49549122 0.53504690
99 1.39610663 -3.49549122
100 -1.25076990 1.39610663
101 4.26976912 -1.25076990
102 -1.91776619 4.26976912
103 -6.31048834 -1.91776619
104 -2.09933003 -6.31048834
105 0.83102500 -2.09933003
106 3.37767010 0.83102500
107 2.18311320 3.37767010
108 2.40894518 2.18311320
109 -4.98858285 2.40894518
110 -9.40274386 -4.98858285
111 5.95450562 -9.40274386
112 1.09507236 5.95450562
113 5.17347051 1.09507236
114 5.32500615 5.17347051
115 -1.72094359 5.32500615
116 4.03723067 -1.72094359
117 5.76732572 4.03723067
118 -10.50426085 5.76732572
119 -3.01660498 -10.50426085
120 -1.01624183 -3.01660498
121 3.26926795 -1.01624183
122 -2.42959266 3.26926795
123 -3.79559533 -2.42959266
124 -1.39584225 -3.79559533
125 2.40903385 -1.39584225
126 -0.24915987 2.40903385
127 -3.85383307 -0.24915987
128 -3.90250744 -3.85383307
129 0.63472006 -3.90250744
130 -8.98977263 0.63472006
131 5.52699003 -8.98977263
132 -4.92332352 5.52699003
133 -0.14143759 -4.92332352
134 -1.75414345 -0.14143759
135 3.54592002 -1.75414345
136 1.44464458 3.54592002
137 8.01461300 1.44464458
138 -3.80656423 8.01461300
139 5.00270461 -3.80656423
140 -0.02868433 5.00270461
141 5.27389513 -0.02868433
142 2.49810063 5.27389513
143 -3.99570956 2.49810063
144 -10.62736186 -3.99570956
145 NA -10.62736186
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.14807958 1.06001219
[2,] -3.23596592 -3.14807958
[3,] 1.82320197 -3.23596592
[4,] 1.55372660 1.82320197
[5,] 1.88408501 1.55372660
[6,] -0.29380760 1.88408501
[7,] 1.19776268 -0.29380760
[8,] 2.84847428 1.19776268
[9,] 3.54153438 2.84847428
[10,] 2.12180893 3.54153438
[11,] -9.85954490 2.12180893
[12,] -9.85954490 -9.85954490
[13,] -2.14345240 -9.85954490
[14,] -2.10214629 -2.14345240
[15,] -2.36845934 -2.10214629
[16,] -14.22112129 -2.36845934
[17,] 4.29730653 -14.22112129
[18,] 1.17488538 4.29730653
[19,] 5.16456282 1.17488538
[20,] 2.78334367 5.16456282
[21,] -2.64377587 2.78334367
[22,] -0.83834922 -2.64377587
[23,] 1.20508454 -0.83834922
[24,] -0.78761779 1.20508454
[25,] 0.46555644 -0.78761779
[26,] 5.01790464 0.46555644
[27,] -12.52357044 5.01790464
[28,] -2.30702575 -12.52357044
[29,] -2.21240875 -2.30702575
[30,] 1.39299945 -2.21240875
[31,] 0.02072089 1.39299945
[32,] 0.53802536 0.02072089
[33,] -1.58103252 0.53802536
[34,] -5.63290275 -1.58103252
[35,] 2.91466377 -5.63290275
[36,] 5.24256491 2.91466377
[37,] 8.65854396 5.24256491
[38,] 2.98863564 8.65854396
[39,] -7.58525431 2.98863564
[40,] -8.58525431 -7.58525431
[41,] -2.63861458 -8.58525431
[42,] -1.44046578 -2.63861458
[43,] -0.31313364 -1.44046578
[44,] 0.05090360 -0.31313364
[45,] 5.63390214 0.05090360
[46,] 0.41287604 5.63390214
[47,] 3.07642619 0.41287604
[48,] -1.38833593 3.07642619
[49,] 2.93517958 -1.38833593
[50,] 6.13717991 2.93517958
[51,] -1.60448457 6.13717991
[52,] -1.45846562 -1.60448457
[53,] 2.47945446 -1.45846562
[54,] -6.05676355 2.47945446
[55,] -2.41927010 -6.05676355
[56,] 8.34550552 -2.41927010
[57,] 0.89785371 8.34550552
[58,] 8.43790680 0.89785371
[59,] 1.71613536 8.43790680
[60,] -11.11566471 1.71613536
[61,] -5.03610197 -11.11566471
[62,] 4.78759125 -5.03610197
[63,] 3.22240450 4.78759125
[64,] 5.61648639 3.22240450
[65,] -0.51513397 5.61648639
[66,] 0.60931904 -0.51513397
[67,] 8.44267915 0.60931904
[68,] -1.76343074 8.44267915
[69,] 2.40162332 -1.76343074
[70,] 0.97016042 2.40162332
[71,] -1.04046241 0.97016042
[72,] -1.64948770 -1.04046241
[73,] 0.17291995 -1.64948770
[74,] 0.34815082 0.17291995
[75,] 3.63021445 0.34815082
[76,] 2.23101193 3.63021445
[77,] 3.70564184 2.23101193
[78,] 3.95966690 3.70564184
[79,] -3.50479496 3.95966690
[80,] 1.38868899 -3.50479496
[81,] 1.46107503 1.38868899
[82,] 3.07642619 1.46107503
[83,] -2.22469675 3.07642619
[84,] -5.29708279 -2.22469675
[85,] -5.14463344 -5.29708279
[86,] 5.56447041 -5.14463344
[87,] -0.46372268 5.56447041
[88,] 4.41896748 -0.46372268
[89,] 5.20798012 4.41896748
[90,] 6.09904386 5.20798012
[91,] -1.03358599 6.09904386
[92,] 10.13809362 -1.03358599
[93,] 4.40490784 10.13809362
[94,] -11.73894342 4.40490784
[95,] 3.49810063 -11.73894342
[96,] -7.95518784 3.49810063
[97,] 0.53504690 -7.95518784
[98,] -3.49549122 0.53504690
[99,] 1.39610663 -3.49549122
[100,] -1.25076990 1.39610663
[101,] 4.26976912 -1.25076990
[102,] -1.91776619 4.26976912
[103,] -6.31048834 -1.91776619
[104,] -2.09933003 -6.31048834
[105,] 0.83102500 -2.09933003
[106,] 3.37767010 0.83102500
[107,] 2.18311320 3.37767010
[108,] 2.40894518 2.18311320
[109,] -4.98858285 2.40894518
[110,] -9.40274386 -4.98858285
[111,] 5.95450562 -9.40274386
[112,] 1.09507236 5.95450562
[113,] 5.17347051 1.09507236
[114,] 5.32500615 5.17347051
[115,] -1.72094359 5.32500615
[116,] 4.03723067 -1.72094359
[117,] 5.76732572 4.03723067
[118,] -10.50426085 5.76732572
[119,] -3.01660498 -10.50426085
[120,] -1.01624183 -3.01660498
[121,] 3.26926795 -1.01624183
[122,] -2.42959266 3.26926795
[123,] -3.79559533 -2.42959266
[124,] -1.39584225 -3.79559533
[125,] 2.40903385 -1.39584225
[126,] -0.24915987 2.40903385
[127,] -3.85383307 -0.24915987
[128,] -3.90250744 -3.85383307
[129,] 0.63472006 -3.90250744
[130,] -8.98977263 0.63472006
[131,] 5.52699003 -8.98977263
[132,] -4.92332352 5.52699003
[133,] -0.14143759 -4.92332352
[134,] -1.75414345 -0.14143759
[135,] 3.54592002 -1.75414345
[136,] 1.44464458 3.54592002
[137,] 8.01461300 1.44464458
[138,] -3.80656423 8.01461300
[139,] 5.00270461 -3.80656423
[140,] -0.02868433 5.00270461
[141,] 5.27389513 -0.02868433
[142,] 2.49810063 5.27389513
[143,] -3.99570956 2.49810063
[144,] -10.62736186 -3.99570956
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.14807958 1.06001219
2 -3.23596592 -3.14807958
3 1.82320197 -3.23596592
4 1.55372660 1.82320197
5 1.88408501 1.55372660
6 -0.29380760 1.88408501
7 1.19776268 -0.29380760
8 2.84847428 1.19776268
9 3.54153438 2.84847428
10 2.12180893 3.54153438
11 -9.85954490 2.12180893
12 -9.85954490 -9.85954490
13 -2.14345240 -9.85954490
14 -2.10214629 -2.14345240
15 -2.36845934 -2.10214629
16 -14.22112129 -2.36845934
17 4.29730653 -14.22112129
18 1.17488538 4.29730653
19 5.16456282 1.17488538
20 2.78334367 5.16456282
21 -2.64377587 2.78334367
22 -0.83834922 -2.64377587
23 1.20508454 -0.83834922
24 -0.78761779 1.20508454
25 0.46555644 -0.78761779
26 5.01790464 0.46555644
27 -12.52357044 5.01790464
28 -2.30702575 -12.52357044
29 -2.21240875 -2.30702575
30 1.39299945 -2.21240875
31 0.02072089 1.39299945
32 0.53802536 0.02072089
33 -1.58103252 0.53802536
34 -5.63290275 -1.58103252
35 2.91466377 -5.63290275
36 5.24256491 2.91466377
37 8.65854396 5.24256491
38 2.98863564 8.65854396
39 -7.58525431 2.98863564
40 -8.58525431 -7.58525431
41 -2.63861458 -8.58525431
42 -1.44046578 -2.63861458
43 -0.31313364 -1.44046578
44 0.05090360 -0.31313364
45 5.63390214 0.05090360
46 0.41287604 5.63390214
47 3.07642619 0.41287604
48 -1.38833593 3.07642619
49 2.93517958 -1.38833593
50 6.13717991 2.93517958
51 -1.60448457 6.13717991
52 -1.45846562 -1.60448457
53 2.47945446 -1.45846562
54 -6.05676355 2.47945446
55 -2.41927010 -6.05676355
56 8.34550552 -2.41927010
57 0.89785371 8.34550552
58 8.43790680 0.89785371
59 1.71613536 8.43790680
60 -11.11566471 1.71613536
61 -5.03610197 -11.11566471
62 4.78759125 -5.03610197
63 3.22240450 4.78759125
64 5.61648639 3.22240450
65 -0.51513397 5.61648639
66 0.60931904 -0.51513397
67 8.44267915 0.60931904
68 -1.76343074 8.44267915
69 2.40162332 -1.76343074
70 0.97016042 2.40162332
71 -1.04046241 0.97016042
72 -1.64948770 -1.04046241
73 0.17291995 -1.64948770
74 0.34815082 0.17291995
75 3.63021445 0.34815082
76 2.23101193 3.63021445
77 3.70564184 2.23101193
78 3.95966690 3.70564184
79 -3.50479496 3.95966690
80 1.38868899 -3.50479496
81 1.46107503 1.38868899
82 3.07642619 1.46107503
83 -2.22469675 3.07642619
84 -5.29708279 -2.22469675
85 -5.14463344 -5.29708279
86 5.56447041 -5.14463344
87 -0.46372268 5.56447041
88 4.41896748 -0.46372268
89 5.20798012 4.41896748
90 6.09904386 5.20798012
91 -1.03358599 6.09904386
92 10.13809362 -1.03358599
93 4.40490784 10.13809362
94 -11.73894342 4.40490784
95 3.49810063 -11.73894342
96 -7.95518784 3.49810063
97 0.53504690 -7.95518784
98 -3.49549122 0.53504690
99 1.39610663 -3.49549122
100 -1.25076990 1.39610663
101 4.26976912 -1.25076990
102 -1.91776619 4.26976912
103 -6.31048834 -1.91776619
104 -2.09933003 -6.31048834
105 0.83102500 -2.09933003
106 3.37767010 0.83102500
107 2.18311320 3.37767010
108 2.40894518 2.18311320
109 -4.98858285 2.40894518
110 -9.40274386 -4.98858285
111 5.95450562 -9.40274386
112 1.09507236 5.95450562
113 5.17347051 1.09507236
114 5.32500615 5.17347051
115 -1.72094359 5.32500615
116 4.03723067 -1.72094359
117 5.76732572 4.03723067
118 -10.50426085 5.76732572
119 -3.01660498 -10.50426085
120 -1.01624183 -3.01660498
121 3.26926795 -1.01624183
122 -2.42959266 3.26926795
123 -3.79559533 -2.42959266
124 -1.39584225 -3.79559533
125 2.40903385 -1.39584225
126 -0.24915987 2.40903385
127 -3.85383307 -0.24915987
128 -3.90250744 -3.85383307
129 0.63472006 -3.90250744
130 -8.98977263 0.63472006
131 5.52699003 -8.98977263
132 -4.92332352 5.52699003
133 -0.14143759 -4.92332352
134 -1.75414345 -0.14143759
135 3.54592002 -1.75414345
136 1.44464458 3.54592002
137 8.01461300 1.44464458
138 -3.80656423 8.01461300
139 5.00270461 -3.80656423
140 -0.02868433 5.00270461
141 5.27389513 -0.02868433
142 2.49810063 5.27389513
143 -3.99570956 2.49810063
144 -10.62736186 -3.99570956
> 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/7a9c51292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lib81292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9lib81292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lib81292684824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11zs9g1292684824.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/12ak8j1292684824.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/13g35d1292684824.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/14234j1292684824.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/15cclm1292684824.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/161elq1292684825.tab")
+ }
>
> try(system("convert tmp/179eh1292684824.ps tmp/179eh1292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/279eh1292684824.ps tmp/279eh1292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/379eh1292684824.ps tmp/379eh1292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hiv21292684824.ps tmp/4hiv21292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hiv21292684824.ps tmp/5hiv21292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hiv21292684824.ps tmp/6hiv21292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a9c51292684824.ps tmp/7a9c51292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lib81292684824.ps tmp/8lib81292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lib81292684824.ps tmp/9lib81292684824.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lib81292684824.ps tmp/10lib81292684824.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.811 1.808 10.263