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.
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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(46
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+ ,dim=c(5
+ ,146)
+ ,dimnames=list(c('Carrièremogelijkheden'
+ ,'Geen_Motivatie'
+ ,'Leermogelijkheden'
+ ,'Persoonlijke_redenen'
+ ,'Ouders')
+ ,1:146))
> y <- array(NA,dim=c(5,146),dimnames=list(c('Carrièremogelijkheden','Geen_Motivatie','Leermogelijkheden','Persoonlijke_redenen','Ouders'),1:146))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> #'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
Persoonlijke_redenen Carri\303\250remogelijkheden Geen_Motivatie
1 26 46 11
2 25 44 8
3 28 42 10
4 30 41 12
5 28 48 12
6 40 49 10
7 28 51 8
8 27 47 10
9 25 49 11
10 27 46 7
11 32 51 10
12 28 54 9
13 21 52 9
14 40 52 11
15 29 45 12
16 27 52 5
17 31 56 10
18 33 54 11
19 28 50 12
20 26 35 9
21 25 48 3
22 37 37 10
23 13 47 7
24 32 31 9
25 32 45 9
26 38 47 10
27 30 44 9
28 33 30 19
29 22 40 14
30 29 44 5
31 33 43 13
32 31 51 7
33 23 48 8
34 42 55 11
35 35 48 11
36 31 53 12
37 31 49 9
38 38 44 13
39 34 45 12
40 33 40 11
41 23 44 18
42 18 41 8
43 33 46 14
44 26 47 10
45 29 48 13
46 23 43 13
47 18 46 8
48 36 53 10
49 21 33 8
50 31 47 9
51 31 43 10
52 29 45 9
53 24 49 9
54 35 45 9
55 37 37 10
56 29 42 8
57 31 43 11
58 34 44 11
59 38 39 10
60 27 37 23
61 33 53 9
62 36 48 12
63 27 47 9
64 33 49 9
65 24 47 8
66 31 56 9
67 31 51 9
68 23 43 9
69 38 51 11
70 30 36 12
71 39 55 8
72 28 33 9
73 39 42 10
74 19 43 8
75 32 44 9
76 32 47 9
77 35 43 13
78 42 47 11
79 25 41 18
80 11 53 10
81 31 47 14
82 30 23 7
83 30 43 10
84 31 47 9
85 28 47 9
86 34 49 12
87 32 50 8
88 30 43 9
89 27 44 8
90 36 49 13
91 32 47 6
92 27 39 11
93 35 49 10
94 34 41 10
95 32 40 14
96 28 38 13
97 29 43 10
98 18 55 8
99 34 46 10
100 35 54 8
101 34 47 10
102 26 35 7
103 30 41 11
104 35 53 10
105 17 44 8
106 34 48 12
107 30 49 12
108 31 39 11
109 25 45 11
110 16 34 6
111 35 46 14
112 28 45 9
113 42 53 11
114 30 51 10
115 37 45 10
116 26 50 8
117 28 41 9
118 33 44 10
119 29 43 10
120 21 42 12
121 38 48 10
122 18 45 11
123 38 48 16
124 30 48 12
125 35 53 10
126 34 45 13
127 21 45 8
128 30 50 12
129 32 48 10
130 23 41 8
131 31 53 14
132 26 40 9
133 29 49 12
134 28 46 10
135 29 48 9
136 36 43 10
137 36 53 11
138 31 51 11
139 30 41 10
140 29 45 10
141 35 44 20
142 26 43 10
143 25 34 8
144 25 38 8
145 20 40 9
146 27 48 18
Leermogelijkheden Ouders
1 52 23
2 39 15
3 42 25
4 35 18
5 32 21
6 49 19
7 33 15
8 47 22
9 46 19
10 40 20
11 33 26
12 39 26
13 37 21
14 56 18
15 36 19
16 24 19
17 56 18
18 32 19
19 41 24
20 24 28
21 42 20
22 47 27
23 25 18
24 33 19
25 43 24
26 45 21
27 44 22
28 46 25
29 31 19
30 31 15
31 42 34
32 28 23
33 38 19
34 59 26
35 43 15
36 29 15
37 38 17
38 39 30
39 50 19
40 44 28
41 29 23
42 29 23
43 36 21
44 43 18
45 28 19
46 39 24
47 35 15
48 43 20
49 28 24
50 49 9
51 33 20
52 39 20
53 36 10
54 24 44
55 47 20
56 34 20
57 33 11
58 43 21
59 41 21
60 40 19
61 39 17
62 54 16
63 43 14
64 45 19
65 29 21
66 45 16
67 47 19
68 38 19
69 52 16
70 34 24
71 56 29
72 26 21
73 42 20
74 32 23
75 39 18
76 37 19
77 37 23
78 52 19
79 31 21
80 34 26
81 38 13
82 29 23
83 52 17
84 40 30
85 47 19
86 34 22
87 37 14
88 43 14
89 37 21
90 55 21
91 36 33
92 28 23
93 47 30
94 38 19
95 37 21
96 32 25
97 47 18
98 40 25
99 45 21
100 37 16
101 38 17
102 37 23
103 35 26
104 50 18
105 32 19
106 32 28
107 38 20
108 31 29
109 27 19
110 34 18
111 43 24
112 28 12
113 44 19
114 43 25
115 53 12
116 33 15
117 36 25
118 46 14
119 36 19
120 24 23
121 50 19
122 40 24
123 40 20
124 32 16
125 49 13
126 47 20
127 28 30
128 41 18
129 25 22
130 46 21
131 53 25
132 34 18
133 40 25
134 46 44
135 38 12
136 51 17
137 38 26
138 45 18
139 41 21
140 42 24
141 36 20
142 41 24
143 35 28
144 42 20
145 35 33
146 32 19
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Carri\303\250remogelijkheden`
7.80480 0.08035
Geen_Motivatie Leermogelijkheden
0.40176 0.34656
Ouders
0.02726
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.5729 -3.3735 0.4448 3.6020 10.4466
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.80480 4.46721 1.747 0.08279 .
`Carri\303\250remogelijkheden` 0.08035 0.07741 1.038 0.30103
Geen_Motivatie 0.40176 0.15155 2.651 0.00895 **
Leermogelijkheden 0.34656 0.05562 6.231 5.04e-09 ***
Ouders 0.02726 0.07777 0.351 0.72647
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.961 on 141 degrees of freedom
Multiple R-squared: 0.2919, Adjusted R-squared: 0.2718
F-statistic: 14.53 on 4 and 141 DF, p-value: 5.846e-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,] 0.7789044 0.44219113 0.22109557
[2,] 0.8154862 0.36902760 0.18451380
[3,] 0.7125464 0.57490719 0.28745360
[4,] 0.6382858 0.72342836 0.36171418
[5,] 0.5465187 0.90696268 0.45348134
[6,] 0.6618082 0.67638356 0.33819178
[7,] 0.7046093 0.59078150 0.29539075
[8,] 0.6168933 0.76621349 0.38310675
[9,] 0.5749518 0.85009638 0.42504819
[10,] 0.5325896 0.93482086 0.46741043
[11,] 0.4757451 0.95149014 0.52425493
[12,] 0.4119784 0.82395684 0.58802158
[13,] 0.3768355 0.75367097 0.62316452
[14,] 0.3073241 0.61464813 0.69267593
[15,] 0.4198960 0.83979193 0.58010404
[16,] 0.6755994 0.64880121 0.32440060
[17,] 0.6774153 0.64516947 0.32258473
[18,] 0.6244890 0.75102207 0.37551103
[19,] 0.6789821 0.64203584 0.32101792
[20,] 0.6180367 0.76392651 0.38196326
[21,] 0.5875942 0.82481169 0.41240584
[22,] 0.6067999 0.78640024 0.39320012
[23,] 0.5881911 0.82361774 0.41180887
[24,] 0.5295359 0.94092820 0.47046410
[25,] 0.5458810 0.90823798 0.45411899
[26,] 0.5499848 0.90003046 0.45001523
[27,] 0.5581302 0.88373954 0.44186977
[28,] 0.5562450 0.88750996 0.44375498
[29,] 0.5428400 0.91432002 0.45716001
[30,] 0.4966148 0.99322962 0.50338519
[31,] 0.5325420 0.93491607 0.46745804
[32,] 0.4769649 0.95392974 0.52303513
[33,] 0.4233468 0.84669356 0.57665322
[34,] 0.4492227 0.89844535 0.55077733
[35,] 0.5164795 0.96704095 0.48352048
[36,] 0.4849574 0.96991470 0.51504265
[37,] 0.4755459 0.95109181 0.52445410
[38,] 0.4328996 0.86579912 0.56710044
[39,] 0.4985303 0.99706060 0.50146970
[40,] 0.5986036 0.80279280 0.40139640
[41,] 0.5917640 0.81647198 0.40823599
[42,] 0.5565989 0.88680221 0.44340111
[43,] 0.5090302 0.98193954 0.49096977
[44,] 0.4921957 0.98439142 0.50780429
[45,] 0.4413272 0.88265442 0.55867279
[46,] 0.4133405 0.82668095 0.58665952
[47,] 0.5142986 0.97140281 0.48570141
[48,] 0.5380676 0.92386484 0.46193242
[49,] 0.4991795 0.99835894 0.50082053
[50,] 0.5003765 0.99924693 0.49962346
[51,] 0.4672685 0.93453693 0.53273153
[52,] 0.5540050 0.89199004 0.44599502
[53,] 0.5964251 0.80714972 0.40357486
[54,] 0.5745918 0.85081648 0.42540824
[55,] 0.5304139 0.93917220 0.46958610
[56,] 0.5033381 0.99332389 0.49666195
[57,] 0.4589544 0.91790889 0.54104556
[58,] 0.4157423 0.83148466 0.58425767
[59,] 0.3698887 0.73977733 0.63011134
[60,] 0.3278721 0.65574413 0.67212793
[61,] 0.3405743 0.68114851 0.65942574
[62,] 0.3192647 0.63852947 0.68073527
[63,] 0.2831302 0.56626047 0.71686977
[64,] 0.2649611 0.52992230 0.73503885
[65,] 0.2539132 0.50782638 0.74608681
[66,] 0.3413808 0.68276165 0.65861917
[67,] 0.3996191 0.79923829 0.60038085
[68,] 0.3705790 0.74115802 0.62942099
[69,] 0.3478642 0.69572846 0.65213577
[70,] 0.3494305 0.69886102 0.65056949
[71,] 0.4098314 0.81966278 0.59016861
[72,] 0.4143507 0.82870142 0.58564929
[73,] 0.8915247 0.21695068 0.10847534
[74,] 0.8721354 0.25572928 0.12786464
[75,] 0.9179838 0.16403236 0.08201618
[76,] 0.9083623 0.18327536 0.09163768
[77,] 0.8909870 0.21802610 0.10901305
[78,] 0.8827217 0.23455669 0.11727834
[79,] 0.8813156 0.23736883 0.11868441
[80,] 0.8684110 0.26317790 0.13158895
[81,] 0.8395591 0.32088177 0.16044088
[82,] 0.8083522 0.38329561 0.19164780
[83,] 0.7728100 0.45438003 0.22719002
[84,] 0.8074612 0.38507768 0.19253884
[85,] 0.7763057 0.44738869 0.22369434
[86,] 0.7600799 0.47984024 0.23992012
[87,] 0.7753810 0.44923797 0.22461899
[88,] 0.7409901 0.51801974 0.25900987
[89,] 0.6997337 0.60053268 0.30026634
[90,] 0.6681138 0.66377242 0.33188621
[91,] 0.8825349 0.23493029 0.11746515
[92,] 0.8637484 0.27250318 0.13625159
[93,] 0.8694940 0.26101197 0.13050599
[94,] 0.8675715 0.26485706 0.13242853
[95,] 0.8545543 0.29089143 0.14544571
[96,] 0.8397452 0.32050967 0.16025484
[97,] 0.8044853 0.39102947 0.19551474
[98,] 0.8836543 0.23269144 0.11634572
[99,] 0.8996220 0.20075600 0.10037800
[100,] 0.8736202 0.25275969 0.12637985
[101,] 0.9148537 0.17029260 0.08514630
[102,] 0.8900542 0.21989166 0.10994583
[103,] 0.9177346 0.16453078 0.08226539
[104,] 0.9055249 0.18895026 0.09447513
[105,] 0.8817023 0.23659544 0.11829772
[106,] 0.9459602 0.10807959 0.05403980
[107,] 0.9267939 0.14641210 0.07320605
[108,] 0.9107313 0.17853749 0.08926875
[109,] 0.8908763 0.21824740 0.10912370
[110,] 0.8689554 0.26208919 0.13104459
[111,] 0.8340930 0.33181403 0.16590701
[112,] 0.7932692 0.41346160 0.20673080
[113,] 0.7698716 0.46025683 0.23012842
[114,] 0.8009498 0.39810037 0.19905018
[115,] 0.9619297 0.07614069 0.03807034
[116,] 0.9700897 0.05982067 0.02991034
[117,] 0.9540650 0.09187000 0.04593500
[118,] 0.9321202 0.13575961 0.06787981
[119,] 0.9141585 0.17168297 0.08584148
[120,] 0.9134145 0.17317099 0.08658550
[121,] 0.8792556 0.24148871 0.12074435
[122,] 0.8878037 0.22439263 0.11219631
[123,] 0.9141247 0.17175069 0.08587534
[124,] 0.9393373 0.12132534 0.06066267
[125,] 0.9031339 0.19373225 0.09686613
[126,] 0.8524351 0.29512989 0.14756495
[127,] 0.7923928 0.41521440 0.20760720
[128,] 0.7000227 0.59995452 0.29997726
[129,] 0.5981027 0.80379466 0.40189733
[130,] 0.9127639 0.17447213 0.08723607
[131,] 0.8281808 0.34363835 0.17181918
> postscript(file="/var/www/html/rcomp/tmp/1bytx1292754260.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lpa01292754260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lpa01292754260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lpa01292754260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wgs31292754260.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 = 146
Frequency = 1
1 2 3 4 5 6
-8.56854642 -3.47915857 -2.43427363 1.45933517 -0.14520754 6.74089544
7 8 9 10 11 12
1.03777284 -5.48705985 -7.62117055 -1.72097036 3.93438768 -1.98428785
13 14 15 16 17 18
-7.99415522 3.69940293 -0.23589004 4.17272356 -5.22024044 4.82897083
19 20 21 22 23 24
-3.50676418 2.68629825 -2.96776952 5.18013644 -10.54834279 6.13397424
25 26 27 28 29 30
1.40713040 6.23332857 -0.80456121 -1.47214115 -5.90483510 4.49862253
31 32 33 34 35 36
1.03475594 5.95426061 -5.56303932 4.20057349 3.60791468 3.65629892
37 38 39 40 41 42
2.00937541 7.10314092 -0.08778203 1.54975909 -6.24918065 -6.99055898
43 44 45 46 47 48
2.82572360 -4.99176103 1.89381217 -7.65294301 -9.25360397 4.47161624
49 50 51 52 53 54
-3.02845526 -1.42403727 3.74075462 -0.09757078 -4.10667023 10.44662116
55 56 57 58 59 60
5.37096338 2.27805529 3.58434641 2.76574924 8.26238442 -7.39867232
61 62 63 64 65 66
3.34141119 0.36669571 -3.48095994 1.52890743 -1.41813775 -0.95176048
67 68 69 70 71 72
-1.32492025 -5.56304583 3.22052989 2.04408356 3.36375305 4.34469801
73 74 75 76 77 78
8.70203132 -7.19095037 3.03730133 3.46211739 5.06744541 7.46014742
79 80 81 82 83 84
-4.64673573 -17.57287628 0.27033397 6.85750039 -3.76217297 1.12255535
85 86 87 88 89 90
-4.00351975 5.05405392 3.75912910 -0.15955944 -0.94959709 -0.59828021
91 92 93 94 95 96
4.63229863 1.31143359 2.13415197 5.19589729 1.96126063 0.14749262
97 98 99 100 101 102
-3.05661539 -11.98218357 2.31367869 6.38320662 4.76831853 -0.87921082
103 104 105 106 107 108
1.64300437 1.10019222 -9.16225654 5.66396553 -0.27767895 4.10817651
109 110 111 112 113 114
-0.71505948 -9.22110747 2.31799463 2.93271800 9.75055638 -1.50398847
115 116 117 118 119 120
2.86686802 -0.88187704 0.12721591 1.31864216 0.72832447 -3.94511906
121 122 123 124 125 126
4.47468185 -12.35669271 5.50251522 1.99109742 1.58306088 0.52289099
127 128 129 130 131 132
-4.15622270 -1.34319824 7.05699173 -7.82762014 -5.73735437 -0.90847961
133 134 135 136 137 138
-2.10711133 -4.65988780 0.22603048 2.58439074 5.63911173 -1.40804609
139 140 141 142 143 144
0.10168417 -1.64806301 2.60314206 -4.14079905 -1.64379534 -4.17305392
145 146
-7.66395818 -3.50122833
> postscript(file="/var/www/html/rcomp/tmp/6wgs31292754260.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 = 146
Frequency = 1
lag(myerror, k = 1) myerror
0 -8.56854642 NA
1 -3.47915857 -8.56854642
2 -2.43427363 -3.47915857
3 1.45933517 -2.43427363
4 -0.14520754 1.45933517
5 6.74089544 -0.14520754
6 1.03777284 6.74089544
7 -5.48705985 1.03777284
8 -7.62117055 -5.48705985
9 -1.72097036 -7.62117055
10 3.93438768 -1.72097036
11 -1.98428785 3.93438768
12 -7.99415522 -1.98428785
13 3.69940293 -7.99415522
14 -0.23589004 3.69940293
15 4.17272356 -0.23589004
16 -5.22024044 4.17272356
17 4.82897083 -5.22024044
18 -3.50676418 4.82897083
19 2.68629825 -3.50676418
20 -2.96776952 2.68629825
21 5.18013644 -2.96776952
22 -10.54834279 5.18013644
23 6.13397424 -10.54834279
24 1.40713040 6.13397424
25 6.23332857 1.40713040
26 -0.80456121 6.23332857
27 -1.47214115 -0.80456121
28 -5.90483510 -1.47214115
29 4.49862253 -5.90483510
30 1.03475594 4.49862253
31 5.95426061 1.03475594
32 -5.56303932 5.95426061
33 4.20057349 -5.56303932
34 3.60791468 4.20057349
35 3.65629892 3.60791468
36 2.00937541 3.65629892
37 7.10314092 2.00937541
38 -0.08778203 7.10314092
39 1.54975909 -0.08778203
40 -6.24918065 1.54975909
41 -6.99055898 -6.24918065
42 2.82572360 -6.99055898
43 -4.99176103 2.82572360
44 1.89381217 -4.99176103
45 -7.65294301 1.89381217
46 -9.25360397 -7.65294301
47 4.47161624 -9.25360397
48 -3.02845526 4.47161624
49 -1.42403727 -3.02845526
50 3.74075462 -1.42403727
51 -0.09757078 3.74075462
52 -4.10667023 -0.09757078
53 10.44662116 -4.10667023
54 5.37096338 10.44662116
55 2.27805529 5.37096338
56 3.58434641 2.27805529
57 2.76574924 3.58434641
58 8.26238442 2.76574924
59 -7.39867232 8.26238442
60 3.34141119 -7.39867232
61 0.36669571 3.34141119
62 -3.48095994 0.36669571
63 1.52890743 -3.48095994
64 -1.41813775 1.52890743
65 -0.95176048 -1.41813775
66 -1.32492025 -0.95176048
67 -5.56304583 -1.32492025
68 3.22052989 -5.56304583
69 2.04408356 3.22052989
70 3.36375305 2.04408356
71 4.34469801 3.36375305
72 8.70203132 4.34469801
73 -7.19095037 8.70203132
74 3.03730133 -7.19095037
75 3.46211739 3.03730133
76 5.06744541 3.46211739
77 7.46014742 5.06744541
78 -4.64673573 7.46014742
79 -17.57287628 -4.64673573
80 0.27033397 -17.57287628
81 6.85750039 0.27033397
82 -3.76217297 6.85750039
83 1.12255535 -3.76217297
84 -4.00351975 1.12255535
85 5.05405392 -4.00351975
86 3.75912910 5.05405392
87 -0.15955944 3.75912910
88 -0.94959709 -0.15955944
89 -0.59828021 -0.94959709
90 4.63229863 -0.59828021
91 1.31143359 4.63229863
92 2.13415197 1.31143359
93 5.19589729 2.13415197
94 1.96126063 5.19589729
95 0.14749262 1.96126063
96 -3.05661539 0.14749262
97 -11.98218357 -3.05661539
98 2.31367869 -11.98218357
99 6.38320662 2.31367869
100 4.76831853 6.38320662
101 -0.87921082 4.76831853
102 1.64300437 -0.87921082
103 1.10019222 1.64300437
104 -9.16225654 1.10019222
105 5.66396553 -9.16225654
106 -0.27767895 5.66396553
107 4.10817651 -0.27767895
108 -0.71505948 4.10817651
109 -9.22110747 -0.71505948
110 2.31799463 -9.22110747
111 2.93271800 2.31799463
112 9.75055638 2.93271800
113 -1.50398847 9.75055638
114 2.86686802 -1.50398847
115 -0.88187704 2.86686802
116 0.12721591 -0.88187704
117 1.31864216 0.12721591
118 0.72832447 1.31864216
119 -3.94511906 0.72832447
120 4.47468185 -3.94511906
121 -12.35669271 4.47468185
122 5.50251522 -12.35669271
123 1.99109742 5.50251522
124 1.58306088 1.99109742
125 0.52289099 1.58306088
126 -4.15622270 0.52289099
127 -1.34319824 -4.15622270
128 7.05699173 -1.34319824
129 -7.82762014 7.05699173
130 -5.73735437 -7.82762014
131 -0.90847961 -5.73735437
132 -2.10711133 -0.90847961
133 -4.65988780 -2.10711133
134 0.22603048 -4.65988780
135 2.58439074 0.22603048
136 5.63911173 2.58439074
137 -1.40804609 5.63911173
138 0.10168417 -1.40804609
139 -1.64806301 0.10168417
140 2.60314206 -1.64806301
141 -4.14079905 2.60314206
142 -1.64379534 -4.14079905
143 -4.17305392 -1.64379534
144 -7.66395818 -4.17305392
145 -3.50122833 -7.66395818
146 NA -3.50122833
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.47915857 -8.56854642
[2,] -2.43427363 -3.47915857
[3,] 1.45933517 -2.43427363
[4,] -0.14520754 1.45933517
[5,] 6.74089544 -0.14520754
[6,] 1.03777284 6.74089544
[7,] -5.48705985 1.03777284
[8,] -7.62117055 -5.48705985
[9,] -1.72097036 -7.62117055
[10,] 3.93438768 -1.72097036
[11,] -1.98428785 3.93438768
[12,] -7.99415522 -1.98428785
[13,] 3.69940293 -7.99415522
[14,] -0.23589004 3.69940293
[15,] 4.17272356 -0.23589004
[16,] -5.22024044 4.17272356
[17,] 4.82897083 -5.22024044
[18,] -3.50676418 4.82897083
[19,] 2.68629825 -3.50676418
[20,] -2.96776952 2.68629825
[21,] 5.18013644 -2.96776952
[22,] -10.54834279 5.18013644
[23,] 6.13397424 -10.54834279
[24,] 1.40713040 6.13397424
[25,] 6.23332857 1.40713040
[26,] -0.80456121 6.23332857
[27,] -1.47214115 -0.80456121
[28,] -5.90483510 -1.47214115
[29,] 4.49862253 -5.90483510
[30,] 1.03475594 4.49862253
[31,] 5.95426061 1.03475594
[32,] -5.56303932 5.95426061
[33,] 4.20057349 -5.56303932
[34,] 3.60791468 4.20057349
[35,] 3.65629892 3.60791468
[36,] 2.00937541 3.65629892
[37,] 7.10314092 2.00937541
[38,] -0.08778203 7.10314092
[39,] 1.54975909 -0.08778203
[40,] -6.24918065 1.54975909
[41,] -6.99055898 -6.24918065
[42,] 2.82572360 -6.99055898
[43,] -4.99176103 2.82572360
[44,] 1.89381217 -4.99176103
[45,] -7.65294301 1.89381217
[46,] -9.25360397 -7.65294301
[47,] 4.47161624 -9.25360397
[48,] -3.02845526 4.47161624
[49,] -1.42403727 -3.02845526
[50,] 3.74075462 -1.42403727
[51,] -0.09757078 3.74075462
[52,] -4.10667023 -0.09757078
[53,] 10.44662116 -4.10667023
[54,] 5.37096338 10.44662116
[55,] 2.27805529 5.37096338
[56,] 3.58434641 2.27805529
[57,] 2.76574924 3.58434641
[58,] 8.26238442 2.76574924
[59,] -7.39867232 8.26238442
[60,] 3.34141119 -7.39867232
[61,] 0.36669571 3.34141119
[62,] -3.48095994 0.36669571
[63,] 1.52890743 -3.48095994
[64,] -1.41813775 1.52890743
[65,] -0.95176048 -1.41813775
[66,] -1.32492025 -0.95176048
[67,] -5.56304583 -1.32492025
[68,] 3.22052989 -5.56304583
[69,] 2.04408356 3.22052989
[70,] 3.36375305 2.04408356
[71,] 4.34469801 3.36375305
[72,] 8.70203132 4.34469801
[73,] -7.19095037 8.70203132
[74,] 3.03730133 -7.19095037
[75,] 3.46211739 3.03730133
[76,] 5.06744541 3.46211739
[77,] 7.46014742 5.06744541
[78,] -4.64673573 7.46014742
[79,] -17.57287628 -4.64673573
[80,] 0.27033397 -17.57287628
[81,] 6.85750039 0.27033397
[82,] -3.76217297 6.85750039
[83,] 1.12255535 -3.76217297
[84,] -4.00351975 1.12255535
[85,] 5.05405392 -4.00351975
[86,] 3.75912910 5.05405392
[87,] -0.15955944 3.75912910
[88,] -0.94959709 -0.15955944
[89,] -0.59828021 -0.94959709
[90,] 4.63229863 -0.59828021
[91,] 1.31143359 4.63229863
[92,] 2.13415197 1.31143359
[93,] 5.19589729 2.13415197
[94,] 1.96126063 5.19589729
[95,] 0.14749262 1.96126063
[96,] -3.05661539 0.14749262
[97,] -11.98218357 -3.05661539
[98,] 2.31367869 -11.98218357
[99,] 6.38320662 2.31367869
[100,] 4.76831853 6.38320662
[101,] -0.87921082 4.76831853
[102,] 1.64300437 -0.87921082
[103,] 1.10019222 1.64300437
[104,] -9.16225654 1.10019222
[105,] 5.66396553 -9.16225654
[106,] -0.27767895 5.66396553
[107,] 4.10817651 -0.27767895
[108,] -0.71505948 4.10817651
[109,] -9.22110747 -0.71505948
[110,] 2.31799463 -9.22110747
[111,] 2.93271800 2.31799463
[112,] 9.75055638 2.93271800
[113,] -1.50398847 9.75055638
[114,] 2.86686802 -1.50398847
[115,] -0.88187704 2.86686802
[116,] 0.12721591 -0.88187704
[117,] 1.31864216 0.12721591
[118,] 0.72832447 1.31864216
[119,] -3.94511906 0.72832447
[120,] 4.47468185 -3.94511906
[121,] -12.35669271 4.47468185
[122,] 5.50251522 -12.35669271
[123,] 1.99109742 5.50251522
[124,] 1.58306088 1.99109742
[125,] 0.52289099 1.58306088
[126,] -4.15622270 0.52289099
[127,] -1.34319824 -4.15622270
[128,] 7.05699173 -1.34319824
[129,] -7.82762014 7.05699173
[130,] -5.73735437 -7.82762014
[131,] -0.90847961 -5.73735437
[132,] -2.10711133 -0.90847961
[133,] -4.65988780 -2.10711133
[134,] 0.22603048 -4.65988780
[135,] 2.58439074 0.22603048
[136,] 5.63911173 2.58439074
[137,] -1.40804609 5.63911173
[138,] 0.10168417 -1.40804609
[139,] -1.64806301 0.10168417
[140,] 2.60314206 -1.64806301
[141,] -4.14079905 2.60314206
[142,] -1.64379534 -4.14079905
[143,] -4.17305392 -1.64379534
[144,] -7.66395818 -4.17305392
[145,] -3.50122833 -7.66395818
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.47915857 -8.56854642
2 -2.43427363 -3.47915857
3 1.45933517 -2.43427363
4 -0.14520754 1.45933517
5 6.74089544 -0.14520754
6 1.03777284 6.74089544
7 -5.48705985 1.03777284
8 -7.62117055 -5.48705985
9 -1.72097036 -7.62117055
10 3.93438768 -1.72097036
11 -1.98428785 3.93438768
12 -7.99415522 -1.98428785
13 3.69940293 -7.99415522
14 -0.23589004 3.69940293
15 4.17272356 -0.23589004
16 -5.22024044 4.17272356
17 4.82897083 -5.22024044
18 -3.50676418 4.82897083
19 2.68629825 -3.50676418
20 -2.96776952 2.68629825
21 5.18013644 -2.96776952
22 -10.54834279 5.18013644
23 6.13397424 -10.54834279
24 1.40713040 6.13397424
25 6.23332857 1.40713040
26 -0.80456121 6.23332857
27 -1.47214115 -0.80456121
28 -5.90483510 -1.47214115
29 4.49862253 -5.90483510
30 1.03475594 4.49862253
31 5.95426061 1.03475594
32 -5.56303932 5.95426061
33 4.20057349 -5.56303932
34 3.60791468 4.20057349
35 3.65629892 3.60791468
36 2.00937541 3.65629892
37 7.10314092 2.00937541
38 -0.08778203 7.10314092
39 1.54975909 -0.08778203
40 -6.24918065 1.54975909
41 -6.99055898 -6.24918065
42 2.82572360 -6.99055898
43 -4.99176103 2.82572360
44 1.89381217 -4.99176103
45 -7.65294301 1.89381217
46 -9.25360397 -7.65294301
47 4.47161624 -9.25360397
48 -3.02845526 4.47161624
49 -1.42403727 -3.02845526
50 3.74075462 -1.42403727
51 -0.09757078 3.74075462
52 -4.10667023 -0.09757078
53 10.44662116 -4.10667023
54 5.37096338 10.44662116
55 2.27805529 5.37096338
56 3.58434641 2.27805529
57 2.76574924 3.58434641
58 8.26238442 2.76574924
59 -7.39867232 8.26238442
60 3.34141119 -7.39867232
61 0.36669571 3.34141119
62 -3.48095994 0.36669571
63 1.52890743 -3.48095994
64 -1.41813775 1.52890743
65 -0.95176048 -1.41813775
66 -1.32492025 -0.95176048
67 -5.56304583 -1.32492025
68 3.22052989 -5.56304583
69 2.04408356 3.22052989
70 3.36375305 2.04408356
71 4.34469801 3.36375305
72 8.70203132 4.34469801
73 -7.19095037 8.70203132
74 3.03730133 -7.19095037
75 3.46211739 3.03730133
76 5.06744541 3.46211739
77 7.46014742 5.06744541
78 -4.64673573 7.46014742
79 -17.57287628 -4.64673573
80 0.27033397 -17.57287628
81 6.85750039 0.27033397
82 -3.76217297 6.85750039
83 1.12255535 -3.76217297
84 -4.00351975 1.12255535
85 5.05405392 -4.00351975
86 3.75912910 5.05405392
87 -0.15955944 3.75912910
88 -0.94959709 -0.15955944
89 -0.59828021 -0.94959709
90 4.63229863 -0.59828021
91 1.31143359 4.63229863
92 2.13415197 1.31143359
93 5.19589729 2.13415197
94 1.96126063 5.19589729
95 0.14749262 1.96126063
96 -3.05661539 0.14749262
97 -11.98218357 -3.05661539
98 2.31367869 -11.98218357
99 6.38320662 2.31367869
100 4.76831853 6.38320662
101 -0.87921082 4.76831853
102 1.64300437 -0.87921082
103 1.10019222 1.64300437
104 -9.16225654 1.10019222
105 5.66396553 -9.16225654
106 -0.27767895 5.66396553
107 4.10817651 -0.27767895
108 -0.71505948 4.10817651
109 -9.22110747 -0.71505948
110 2.31799463 -9.22110747
111 2.93271800 2.31799463
112 9.75055638 2.93271800
113 -1.50398847 9.75055638
114 2.86686802 -1.50398847
115 -0.88187704 2.86686802
116 0.12721591 -0.88187704
117 1.31864216 0.12721591
118 0.72832447 1.31864216
119 -3.94511906 0.72832447
120 4.47468185 -3.94511906
121 -12.35669271 4.47468185
122 5.50251522 -12.35669271
123 1.99109742 5.50251522
124 1.58306088 1.99109742
125 0.52289099 1.58306088
126 -4.15622270 0.52289099
127 -1.34319824 -4.15622270
128 7.05699173 -1.34319824
129 -7.82762014 7.05699173
130 -5.73735437 -7.82762014
131 -0.90847961 -5.73735437
132 -2.10711133 -0.90847961
133 -4.65988780 -2.10711133
134 0.22603048 -4.65988780
135 2.58439074 0.22603048
136 5.63911173 2.58439074
137 -1.40804609 5.63911173
138 0.10168417 -1.40804609
139 -1.64806301 0.10168417
140 2.60314206 -1.64806301
141 -4.14079905 2.60314206
142 -1.64379534 -4.14079905
143 -4.17305392 -1.64379534
144 -7.66395818 -4.17305392
145 -3.50122833 -7.66395818
> 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/7ppr61292754260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hg891292754260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hg891292754260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hg891292754260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11er6i1292754260.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/12z9n61292754260.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/13dj2w1292754260.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/14y1jk1292754260.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/152kh81292754260.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/16nkgw1292754260.tab")
+ }
>
> try(system("convert tmp/1bytx1292754260.ps tmp/1bytx1292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lpa01292754260.ps tmp/2lpa01292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lpa01292754260.ps tmp/3lpa01292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lpa01292754260.ps tmp/4lpa01292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wgs31292754260.ps tmp/5wgs31292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wgs31292754260.ps tmp/6wgs31292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ppr61292754260.ps tmp/7ppr61292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hg891292754260.ps tmp/8hg891292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hg891292754260.ps tmp/9hg891292754260.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hg891292754260.ps tmp/10hg891292754260.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.852 1.803 9.530