R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
R is a collaborative project with many contributors.
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(13.193
+ ,651
+ ,3.063
+ ,5.951
+ ,15.234
+ ,736
+ ,3.547
+ ,6.789
+ ,14.718
+ ,878
+ ,3.240
+ ,6.302
+ ,16.961
+ ,916
+ ,3.708
+ ,6.961
+ ,13.945
+ ,724
+ ,3.337
+ ,6.162
+ ,15.876
+ ,841
+ ,4.104
+ ,7.534
+ ,16.226
+ ,1.028
+ ,4.846
+ ,7.462
+ ,18.316
+ ,994
+ ,4.590
+ ,8.894
+ ,16.748
+ ,855
+ ,3.917
+ ,7.734
+ ,17.904
+ ,889
+ ,4.376
+ ,8.968
+ ,17.209
+ ,1.117
+ ,4.312
+ ,8.383
+ ,18.950
+ ,1.132
+ ,4.941
+ ,9.790
+ ,17.225
+ ,899
+ ,4.659
+ ,9.656
+ ,18.710
+ ,944
+ ,5.227
+ ,10.440
+ ,17.236
+ ,1.167
+ ,4.933
+ ,9.820
+ ,18.687
+ ,1.089
+ ,5.381
+ ,10.947
+ ,17.580
+ ,970
+ ,5.472
+ ,10.439
+ ,19.568
+ ,1.151
+ ,6.405
+ ,12.289
+ ,17.381
+ ,1.246
+ ,5.622
+ ,11.303
+ ,19.580
+ ,1.583
+ ,6.229
+ ,12.240
+ ,17.260
+ ,1.120
+ ,5.671
+ ,11.392
+ ,18.661
+ ,1.063
+ ,5.606
+ ,11.120
+ ,15.658
+ ,1.015
+ ,4.516
+ ,9.597
+ ,18.674
+ ,1.175
+ ,5.483
+ ,10.692
+ ,15.908
+ ,882
+ ,4.985
+ ,9.217
+ ,17.475
+ ,911
+ ,5.332
+ ,9.371
+ ,17.725
+ ,1.076
+ ,5.377
+ ,9.526
+ ,19.562
+ ,1.147
+ ,5.948
+ ,10.837
+ ,16.368
+ ,946
+ ,5.308
+ ,9.749
+ ,19.555
+ ,1.032
+ ,6.721
+ ,9.939
+ ,17.743
+ ,1.090
+ ,5.840
+ ,9.309
+ ,19.867
+ ,1.131
+ ,6.152
+ ,10.316
+ ,15.703
+ ,870
+ ,5.184
+ ,8.546
+ ,19.324
+ ,1.113
+ ,6.610
+ ,9.885
+ ,18.162
+ ,1.172
+ ,6.417
+ ,9.266
+ ,19.074
+ ,1.147
+ ,6.529
+ ,9.978
+ ,15.323
+ ,891
+ ,5.412
+ ,8.685
+ ,19.704
+ ,1.036
+ ,6.807
+ ,10.066
+ ,18.375
+ ,1.204
+ ,6.817
+ ,9.668
+ ,18.352
+ ,1.055
+ ,6.582
+ ,9.562
+ ,13.927
+ ,771
+ ,5.019
+ ,7.894
+ ,17.795
+ ,938
+ ,5.935
+ ,7.949
+ ,16.761
+ ,995
+ ,5.548
+ ,7.594
+ ,18.902
+ ,1.088
+ ,6.141
+ ,8.563
+ ,16.239
+ ,1.076
+ ,6.040
+ ,8.061
+ ,19.158
+ ,1.370
+ ,7.587
+ ,8.831
+ ,18.279
+ ,1.560
+ ,6.460
+ ,8.593
+ ,15.698
+ ,1.239
+ ,6.355
+ ,7.031
+ ,16.239
+ ,1.076
+ ,6.040
+ ,8.061
+ ,18.431
+ ,1.566
+ ,7.117
+ ,8.569
+ ,18.414
+ ,1.651
+ ,6.912
+ ,8.234
+ ,19.801
+ ,1.792
+ ,8.212
+ ,8.895
+ ,14.995
+ ,1.306
+ ,6.274
+ ,7.104
+ ,18.706
+ ,1.665
+ ,7.510
+ ,7.580
+ ,18.232
+ ,1.930
+ ,7.133
+ ,7.421
+ ,19.409
+ ,1.717
+ ,7.748
+ ,7.883
+ ,16.263
+ ,1.353
+ ,6.957
+ ,6.700
+ ,19.017
+ ,1.666
+ ,8.260
+ ,7.305
+ ,20.298
+ ,2.070
+ ,8.745
+ ,8.047
+ ,19.891
+ ,2.168
+ ,8.440
+ ,8.305
+ ,15.203
+ ,1.518
+ ,6.573
+ ,6.255
+ ,17.845
+ ,1.737
+ ,7.668
+ ,6.896
+ ,17.502
+ ,2.348
+ ,7.865
+ ,6.759
+ ,18.532
+ ,2.374
+ ,7.941
+ ,7.265
+ ,15.737
+ ,2.004
+ ,7.907
+ ,6.093
+ ,17.770
+ ,2.186
+ ,8.470
+ ,6.326
+ ,17.224
+ ,2.428
+ ,8.347
+ ,5.956
+ ,17.601
+ ,2.149
+ ,8.080
+ ,5.647
+ ,14.940
+ ,2.184
+ ,7.676
+ ,4.955
+ ,18.507
+ ,2.585
+ ,9.214
+ ,5.703
+ ,17.635
+ ,2.528
+ ,8.674
+ ,5.352
+ ,19.392
+ ,2.659
+ ,9.170
+ ,5.578
+ ,15.699
+ ,2.152
+ ,8.217
+ ,4.649
+ ,17.661
+ ,2.401
+ ,9.102
+ ,5.122
+ ,18.243
+ ,2.848
+ ,9.391
+ ,5.278
+ ,19.643
+ ,3.282
+ ,10.301
+ ,6.193
+ ,15.770
+ ,2.572
+ ,9.081
+ ,5.036
+ ,17.344
+ ,2.985
+ ,9.771
+ ,5.472
+ ,17.229
+ ,3.477
+ ,9.778
+ ,5.649
+ ,17.322
+ ,3.336
+ ,10.256
+ ,5.678
+ ,16.152
+ ,3.668
+ ,7.022
+ ,6.382
+ ,17.919
+ ,4.210
+ ,8.307
+ ,7.225
+ ,16.918
+ ,4.161
+ ,7.942
+ ,6.161
+ ,18.114
+ ,4.572
+ ,9.643
+ ,7.145
+ ,16.308
+ ,3.886
+ ,8.561
+ ,6.745
+ ,17.759
+ ,4.165
+ ,9.162
+ ,6.840
+ ,16.021
+ ,4.048
+ ,8.579
+ ,5.898
+ ,17.952
+ ,4.595
+ ,10.054
+ ,6.408
+ ,15.954
+ ,3.886
+ ,9.367
+ ,5.540
+ ,17.762
+ ,4.345
+ ,10.714
+ ,5.859
+ ,16.610
+ ,4.424
+ ,9.726
+ ,5.429
+ ,17.751
+ ,4.513
+ ,10.460
+ ,5.950
+ ,15.458
+ ,3.773
+ ,9.611
+ ,4.924
+ ,18.106
+ ,4.368
+ ,11.436
+ ,5.688
+ ,15.990
+ ,4.218
+ ,9.620
+ ,4.710
+ ,15.349
+ ,4.040
+ ,9.378
+ ,4.555
+ ,13.185
+ ,3.225
+ ,7.856
+ ,3.792
+ ,15.409
+ ,3.861
+ ,9.079
+ ,4.265
+ ,16.007
+ ,4.323
+ ,9.279
+ ,4.345
+ ,16.633
+ ,4.602
+ ,10.345
+ ,5.062
+ ,14.800
+ ,3.909
+ ,9.281
+ ,4.312
+ ,15.974
+ ,4.212
+ ,10.047
+ ,4.582
+ ,15.693
+ ,4.328
+ ,9.352
+ ,4.229)
+ ,dim=c(4
+ ,103)
+ ,dimnames=list(c('huis'
+ ,'villa'
+ ,'app'
+ ,'grond')
+ ,1:103))
> y <- array(NA,dim=c(4,103),dimnames=list(c('huis','villa','app','grond'),1:103))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
huis villa app grond
1 13.193 651.000 3.063 5.951
2 15.234 736.000 3.547 6.789
3 14.718 878.000 3.240 6.302
4 16.961 916.000 3.708 6.961
5 13.945 724.000 3.337 6.162
6 15.876 841.000 4.104 7.534
7 16.226 1.028 4.846 7.462
8 18.316 994.000 4.590 8.894
9 16.748 855.000 3.917 7.734
10 17.904 889.000 4.376 8.968
11 17.209 1.117 4.312 8.383
12 18.950 1.132 4.941 9.790
13 17.225 899.000 4.659 9.656
14 18.710 944.000 5.227 10.440
15 17.236 1.167 4.933 9.820
16 18.687 1.089 5.381 10.947
17 17.580 970.000 5.472 10.439
18 19.568 1.151 6.405 12.289
19 17.381 1.246 5.622 11.303
20 19.580 1.583 6.229 12.240
21 17.260 1.120 5.671 11.392
22 18.661 1.063 5.606 11.120
23 15.658 1.015 4.516 9.597
24 18.674 1.175 5.483 10.692
25 15.908 882.000 4.985 9.217
26 17.475 911.000 5.332 9.371
27 17.725 1.076 5.377 9.526
28 19.562 1.147 5.948 10.837
29 16.368 946.000 5.308 9.749
30 19.555 1.032 6.721 9.939
31 17.743 1.090 5.840 9.309
32 19.867 1.131 6.152 10.316
33 15.703 870.000 5.184 8.546
34 19.324 1.113 6.610 9.885
35 18.162 1.172 6.417 9.266
36 19.074 1.147 6.529 9.978
37 15.323 891.000 5.412 8.685
38 19.704 1.036 6.807 10.066
39 18.375 1.204 6.817 9.668
40 18.352 1.055 6.582 9.562
41 13.927 771.000 5.019 7.894
42 17.795 938.000 5.935 7.949
43 16.761 995.000 5.548 7.594
44 18.902 1.088 6.141 8.563
45 16.239 1.076 6.040 8.061
46 19.158 1.370 7.587 8.831
47 18.279 1.560 6.460 8.593
48 15.698 1.239 6.355 7.031
49 16.239 1.076 6.040 8.061
50 18.431 1.566 7.117 8.569
51 18.414 1.651 6.912 8.234
52 19.801 1.792 8.212 8.895
53 14.995 1.306 6.274 7.104
54 18.706 1.665 7.510 7.580
55 18.232 1.930 7.133 7.421
56 19.409 1.717 7.748 7.883
57 16.263 1.353 6.957 6.700
58 19.017 1.666 8.260 7.305
59 20.298 2.070 8.745 8.047
60 19.891 2.168 8.440 8.305
61 15.203 1.518 6.573 6.255
62 17.845 1.737 7.668 6.896
63 17.502 2.348 7.865 6.759
64 18.532 2.374 7.941 7.265
65 15.737 2.004 7.907 6.093
66 17.770 2.186 8.470 6.326
67 17.224 2.428 8.347 5.956
68 17.601 2.149 8.080 5.647
69 14.940 2.184 7.676 4.955
70 18.507 2.585 9.214 5.703
71 17.635 2.528 8.674 5.352
72 19.392 2.659 9.170 5.578
73 15.699 2.152 8.217 4.649
74 17.661 2.401 9.102 5.122
75 18.243 2.848 9.391 5.278
76 19.643 3.282 10.301 6.193
77 15.770 2.572 9.081 5.036
78 17.344 2.985 9.771 5.472
79 17.229 3.477 9.778 5.649
80 17.322 3.336 10.256 5.678
81 16.152 3.668 7.022 6.382
82 17.919 4.210 8.307 7.225
83 16.918 4.161 7.942 6.161
84 18.114 4.572 9.643 7.145
85 16.308 3.886 8.561 6.745
86 17.759 4.165 9.162 6.840
87 16.021 4.048 8.579 5.898
88 17.952 4.595 10.054 6.408
89 15.954 3.886 9.367 5.540
90 17.762 4.345 10.714 5.859
91 16.610 4.424 9.726 5.429
92 17.751 4.513 10.460 5.950
93 15.458 3.773 9.611 4.924
94 18.106 4.368 11.436 5.688
95 15.990 4.218 9.620 4.710
96 15.349 4.040 9.378 4.555
97 13.185 3.225 7.856 3.792
98 15.409 3.861 9.079 4.265
99 16.007 4.323 9.279 4.345
100 16.633 4.602 10.345 5.062
101 14.800 3.909 9.281 4.312
102 15.974 4.212 10.047 4.582
103 15.693 4.328 9.352 4.229
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) villa app grond
7.537e+00 -7.668e-05 5.749e-01 7.517e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.3698 -0.7360 -0.1012 0.7011 2.3909
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.537e+00 9.859e-01 7.645 1.37e-11 ***
villa -7.668e-05 3.707e-04 -0.207 0.837
app 5.749e-01 8.005e-02 7.181 1.30e-10 ***
grond 7.517e-01 6.302e-02 11.927 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.995 on 99 degrees of freedom
Multiple R-squared: 0.6202, Adjusted R-squared: 0.6087
F-statistic: 53.89 on 3 and 99 DF, p-value: < 2.2e-16
> 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,] 0.4552028 9.104055e-01 5.447972e-01
[2,] 0.3029212 6.058423e-01 6.970788e-01
[3,] 0.2314292 4.628584e-01 7.685708e-01
[4,] 0.1498386 2.996773e-01 8.501614e-01
[5,] 0.1608802 3.217603e-01 8.391198e-01
[6,] 0.1045788 2.091577e-01 8.954212e-01
[7,] 0.3237643 6.475286e-01 6.762357e-01
[8,] 0.3107251 6.214501e-01 6.892749e-01
[9,] 0.3334154 6.668309e-01 6.665846e-01
[10,] 0.2700512 5.401023e-01 7.299488e-01
[11,] 0.3947418 7.894837e-01 6.052582e-01
[12,] 0.3688018 7.376036e-01 6.311982e-01
[13,] 0.5469925 9.060149e-01 4.530075e-01
[14,] 0.4963082 9.926164e-01 5.036918e-01
[15,] 0.6865601 6.268797e-01 3.134399e-01
[16,] 0.6380879 7.238243e-01 3.619121e-01
[17,] 0.7043780 5.912441e-01 2.956220e-01
[18,] 0.6669478 6.661044e-01 3.330522e-01
[19,] 0.7760858 4.478284e-01 2.239142e-01
[20,] 0.7294145 5.411710e-01 2.705855e-01
[21,] 0.6756895 6.486211e-01 3.243105e-01
[22,] 0.6685324 6.629352e-01 3.314676e-01
[23,] 0.7442561 5.114878e-01 2.557439e-01
[24,] 0.7078357 5.843285e-01 2.921643e-01
[25,] 0.6600735 6.798530e-01 3.399265e-01
[26,] 0.6634549 6.730903e-01 3.365451e-01
[27,] 0.7213277 5.573446e-01 2.786723e-01
[28,] 0.6723434 6.553131e-01 3.276566e-01
[29,] 0.6277861 7.444278e-01 3.722139e-01
[30,] 0.5753037 8.493926e-01 4.246963e-01
[31,] 0.7354974 5.290053e-01 2.645026e-01
[32,] 0.7007656 5.984688e-01 2.992344e-01
[33,] 0.6942857 6.114287e-01 3.057143e-01
[34,] 0.6724541 6.550917e-01 3.275459e-01
[35,] 0.9154441 1.691118e-01 8.455590e-02
[36,] 0.9022212 1.955577e-01 9.777885e-02
[37,] 0.9761594 4.768110e-02 2.384055e-02
[38,] 0.9785295 4.294103e-02 2.147052e-02
[39,] 0.9840883 3.182349e-02 1.591174e-02
[40,] 0.9795569 4.088617e-02 2.044309e-02
[41,] 0.9715720 5.685600e-02 2.842800e-02
[42,] 0.9777819 4.443613e-02 2.221807e-02
[43,] 0.9844101 3.117975e-02 1.558987e-02
[44,] 0.9802141 3.957176e-02 1.978588e-02
[45,] 0.9730505 5.389900e-02 2.694950e-02
[46,] 0.9691998 6.160045e-02 3.080022e-02
[47,] 0.9935013 1.299737e-02 6.498684e-03
[48,] 0.9912460 1.750808e-02 8.754040e-03
[49,] 0.9883328 2.333449e-02 1.166725e-02
[50,] 0.9859130 2.817394e-02 1.408697e-02
[51,] 0.9875994 2.480111e-02 1.240055e-02
[52,] 0.9826124 3.477515e-02 1.738758e-02
[53,] 0.9784071 4.318585e-02 2.159292e-02
[54,] 0.9704634 5.907315e-02 2.953657e-02
[55,] 0.9823374 3.532515e-02 1.766258e-02
[56,] 0.9752365 4.952707e-02 2.476353e-02
[57,] 0.9666773 6.664541e-02 3.332271e-02
[58,] 0.9538310 9.233796e-02 4.616898e-02
[59,] 0.9853517 2.929658e-02 1.464829e-02
[60,] 0.9802485 3.950303e-02 1.975151e-02
[61,] 0.9728380 5.432409e-02 2.716205e-02
[62,] 0.9645476 7.090487e-02 3.545243e-02
[63,] 0.9752350 4.953004e-02 2.476502e-02
[64,] 0.9698456 6.030871e-02 3.015436e-02
[65,] 0.9630300 7.393996e-02 3.696998e-02
[66,] 0.9947754 1.044911e-02 5.224555e-03
[67,] 0.9924138 1.517245e-02 7.586223e-03
[68,] 0.9929736 1.405282e-02 7.026410e-03
[69,] 0.9985284 2.943221e-03 1.471610e-03
[70,] 0.9999513 9.745197e-05 4.872599e-05
[71,] 0.9999270 1.459988e-04 7.299938e-05
[72,] 0.9999684 6.312000e-05 3.156000e-05
[73,] 0.9999790 4.203948e-05 2.101974e-05
[74,] 0.9999984 3.255631e-06 1.627815e-06
[75,] 0.9999978 4.449199e-06 2.224600e-06
[76,] 0.9999971 5.841979e-06 2.920990e-06
[77,] 0.9999992 1.556740e-06 7.783698e-07
[78,] 0.9999976 4.708310e-06 2.354155e-06
[79,] 0.9999965 7.012179e-06 3.506090e-06
[80,] 0.9999962 7.584877e-06 3.792439e-06
[81,] 0.9999873 2.546785e-05 1.273393e-05
[82,] 0.9999575 8.499547e-05 4.249774e-05
[83,] 0.9998728 2.544847e-04 1.272424e-04
[84,] 0.9996619 6.762488e-04 3.381244e-04
[85,] 0.9990086 1.982895e-03 9.914477e-04
[86,] 0.9971438 5.712495e-03 2.856248e-03
[87,] 0.9922553 1.548942e-02 7.744710e-03
[88,] 0.9934983 1.300334e-02 6.501670e-03
[89,] 0.9805204 3.895928e-02 1.947964e-02
[90,] 0.9364766 1.270468e-01 6.352341e-02
> postscript(file="/var/www/rcomp/tmp/1l8ws1292682070.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2dzvv1292682070.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3dzvv1292682070.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4dzvv1292682070.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5o9vy1292682070.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 = 103
Frequency = 1
1 2 3 4 5 6
-0.52802719 0.61136092 0.64879367 2.13032268 -0.08654570 0.38122077
7 8 9 10 11 12
0.29437422 1.53130789 1.21146466 1.17865765 0.89208845 1.21390971
13 14 15 16 17 18
-0.17940620 0.39321467 -0.51803833 -0.17170735 -0.87488485 -0.88810104
19 20 21 22 23 24
-1.88383075 -0.73805838 -2.09990691 -0.45709339 -1.68870671 -0.05166546
25 26 27 28 29 30
-1.35514180 -0.10115565 -0.06330320 0.46002435 -1.47580150 0.68362447
31 32 33 34 35 36
-0.14836032 1.03936195 -1.17110006 0.55703151 -0.02873662 0.28369497
37 38 39 40 41 42
-1.78504214 0.68772485 -0.34785126 -0.15609094 -2.36975568 0.94312129
43 44 45 46 47 48
0.40280772 1.39833830 -0.82926788 0.62164391 0.56943909 -0.77713409
49 50 51 52 53 54
-0.82926788 0.36178533 0.71444691 0.85727222 -1.48843495 1.15425587
55 56 57 58 59 60
1.01651829 1.49268690 -0.30940242 1.24080428 1.68529000 1.25970722
61 62 63 64 65 66
-0.81414942 0.71656475 0.36333800 0.96931055 -0.92522915 0.60899296
67 68 69 70 71 72
0.41183471 1.17456764 -0.73403232 1.38659804 1.08885896 2.39085551
73 74 75 76 77 78
-0.05603582 1.04168327 1.34031956 1.52944820 -0.77258865 0.07705531
79 80 81 82 83 84
-0.17497452 -0.37857448 -0.21856474 0.17611276 0.18470283 -0.33676217
85 86 87 88 89 90
-1.22013506 -0.18602206 -0.88081632 -0.18106299 -1.13173813 -0.33784070
91 92 93 94 95 96
-0.59864361 -0.27120998 -1.30499555 -0.28036646 -0.61728057 -1.00266710
97 98 99 100 101 102
-1.71825237 -0.55281194 -0.12988448 -0.65562027 -1.31326127 -0.78254120
103
-0.39865780
> postscript(file="/var/www/rcomp/tmp/6o9vy1292682070.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 = 103
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.52802719 NA
1 0.61136092 -0.52802719
2 0.64879367 0.61136092
3 2.13032268 0.64879367
4 -0.08654570 2.13032268
5 0.38122077 -0.08654570
6 0.29437422 0.38122077
7 1.53130789 0.29437422
8 1.21146466 1.53130789
9 1.17865765 1.21146466
10 0.89208845 1.17865765
11 1.21390971 0.89208845
12 -0.17940620 1.21390971
13 0.39321467 -0.17940620
14 -0.51803833 0.39321467
15 -0.17170735 -0.51803833
16 -0.87488485 -0.17170735
17 -0.88810104 -0.87488485
18 -1.88383075 -0.88810104
19 -0.73805838 -1.88383075
20 -2.09990691 -0.73805838
21 -0.45709339 -2.09990691
22 -1.68870671 -0.45709339
23 -0.05166546 -1.68870671
24 -1.35514180 -0.05166546
25 -0.10115565 -1.35514180
26 -0.06330320 -0.10115565
27 0.46002435 -0.06330320
28 -1.47580150 0.46002435
29 0.68362447 -1.47580150
30 -0.14836032 0.68362447
31 1.03936195 -0.14836032
32 -1.17110006 1.03936195
33 0.55703151 -1.17110006
34 -0.02873662 0.55703151
35 0.28369497 -0.02873662
36 -1.78504214 0.28369497
37 0.68772485 -1.78504214
38 -0.34785126 0.68772485
39 -0.15609094 -0.34785126
40 -2.36975568 -0.15609094
41 0.94312129 -2.36975568
42 0.40280772 0.94312129
43 1.39833830 0.40280772
44 -0.82926788 1.39833830
45 0.62164391 -0.82926788
46 0.56943909 0.62164391
47 -0.77713409 0.56943909
48 -0.82926788 -0.77713409
49 0.36178533 -0.82926788
50 0.71444691 0.36178533
51 0.85727222 0.71444691
52 -1.48843495 0.85727222
53 1.15425587 -1.48843495
54 1.01651829 1.15425587
55 1.49268690 1.01651829
56 -0.30940242 1.49268690
57 1.24080428 -0.30940242
58 1.68529000 1.24080428
59 1.25970722 1.68529000
60 -0.81414942 1.25970722
61 0.71656475 -0.81414942
62 0.36333800 0.71656475
63 0.96931055 0.36333800
64 -0.92522915 0.96931055
65 0.60899296 -0.92522915
66 0.41183471 0.60899296
67 1.17456764 0.41183471
68 -0.73403232 1.17456764
69 1.38659804 -0.73403232
70 1.08885896 1.38659804
71 2.39085551 1.08885896
72 -0.05603582 2.39085551
73 1.04168327 -0.05603582
74 1.34031956 1.04168327
75 1.52944820 1.34031956
76 -0.77258865 1.52944820
77 0.07705531 -0.77258865
78 -0.17497452 0.07705531
79 -0.37857448 -0.17497452
80 -0.21856474 -0.37857448
81 0.17611276 -0.21856474
82 0.18470283 0.17611276
83 -0.33676217 0.18470283
84 -1.22013506 -0.33676217
85 -0.18602206 -1.22013506
86 -0.88081632 -0.18602206
87 -0.18106299 -0.88081632
88 -1.13173813 -0.18106299
89 -0.33784070 -1.13173813
90 -0.59864361 -0.33784070
91 -0.27120998 -0.59864361
92 -1.30499555 -0.27120998
93 -0.28036646 -1.30499555
94 -0.61728057 -0.28036646
95 -1.00266710 -0.61728057
96 -1.71825237 -1.00266710
97 -0.55281194 -1.71825237
98 -0.12988448 -0.55281194
99 -0.65562027 -0.12988448
100 -1.31326127 -0.65562027
101 -0.78254120 -1.31326127
102 -0.39865780 -0.78254120
103 NA -0.39865780
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.61136092 -0.52802719
[2,] 0.64879367 0.61136092
[3,] 2.13032268 0.64879367
[4,] -0.08654570 2.13032268
[5,] 0.38122077 -0.08654570
[6,] 0.29437422 0.38122077
[7,] 1.53130789 0.29437422
[8,] 1.21146466 1.53130789
[9,] 1.17865765 1.21146466
[10,] 0.89208845 1.17865765
[11,] 1.21390971 0.89208845
[12,] -0.17940620 1.21390971
[13,] 0.39321467 -0.17940620
[14,] -0.51803833 0.39321467
[15,] -0.17170735 -0.51803833
[16,] -0.87488485 -0.17170735
[17,] -0.88810104 -0.87488485
[18,] -1.88383075 -0.88810104
[19,] -0.73805838 -1.88383075
[20,] -2.09990691 -0.73805838
[21,] -0.45709339 -2.09990691
[22,] -1.68870671 -0.45709339
[23,] -0.05166546 -1.68870671
[24,] -1.35514180 -0.05166546
[25,] -0.10115565 -1.35514180
[26,] -0.06330320 -0.10115565
[27,] 0.46002435 -0.06330320
[28,] -1.47580150 0.46002435
[29,] 0.68362447 -1.47580150
[30,] -0.14836032 0.68362447
[31,] 1.03936195 -0.14836032
[32,] -1.17110006 1.03936195
[33,] 0.55703151 -1.17110006
[34,] -0.02873662 0.55703151
[35,] 0.28369497 -0.02873662
[36,] -1.78504214 0.28369497
[37,] 0.68772485 -1.78504214
[38,] -0.34785126 0.68772485
[39,] -0.15609094 -0.34785126
[40,] -2.36975568 -0.15609094
[41,] 0.94312129 -2.36975568
[42,] 0.40280772 0.94312129
[43,] 1.39833830 0.40280772
[44,] -0.82926788 1.39833830
[45,] 0.62164391 -0.82926788
[46,] 0.56943909 0.62164391
[47,] -0.77713409 0.56943909
[48,] -0.82926788 -0.77713409
[49,] 0.36178533 -0.82926788
[50,] 0.71444691 0.36178533
[51,] 0.85727222 0.71444691
[52,] -1.48843495 0.85727222
[53,] 1.15425587 -1.48843495
[54,] 1.01651829 1.15425587
[55,] 1.49268690 1.01651829
[56,] -0.30940242 1.49268690
[57,] 1.24080428 -0.30940242
[58,] 1.68529000 1.24080428
[59,] 1.25970722 1.68529000
[60,] -0.81414942 1.25970722
[61,] 0.71656475 -0.81414942
[62,] 0.36333800 0.71656475
[63,] 0.96931055 0.36333800
[64,] -0.92522915 0.96931055
[65,] 0.60899296 -0.92522915
[66,] 0.41183471 0.60899296
[67,] 1.17456764 0.41183471
[68,] -0.73403232 1.17456764
[69,] 1.38659804 -0.73403232
[70,] 1.08885896 1.38659804
[71,] 2.39085551 1.08885896
[72,] -0.05603582 2.39085551
[73,] 1.04168327 -0.05603582
[74,] 1.34031956 1.04168327
[75,] 1.52944820 1.34031956
[76,] -0.77258865 1.52944820
[77,] 0.07705531 -0.77258865
[78,] -0.17497452 0.07705531
[79,] -0.37857448 -0.17497452
[80,] -0.21856474 -0.37857448
[81,] 0.17611276 -0.21856474
[82,] 0.18470283 0.17611276
[83,] -0.33676217 0.18470283
[84,] -1.22013506 -0.33676217
[85,] -0.18602206 -1.22013506
[86,] -0.88081632 -0.18602206
[87,] -0.18106299 -0.88081632
[88,] -1.13173813 -0.18106299
[89,] -0.33784070 -1.13173813
[90,] -0.59864361 -0.33784070
[91,] -0.27120998 -0.59864361
[92,] -1.30499555 -0.27120998
[93,] -0.28036646 -1.30499555
[94,] -0.61728057 -0.28036646
[95,] -1.00266710 -0.61728057
[96,] -1.71825237 -1.00266710
[97,] -0.55281194 -1.71825237
[98,] -0.12988448 -0.55281194
[99,] -0.65562027 -0.12988448
[100,] -1.31326127 -0.65562027
[101,] -0.78254120 -1.31326127
[102,] -0.39865780 -0.78254120
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.61136092 -0.52802719
2 0.64879367 0.61136092
3 2.13032268 0.64879367
4 -0.08654570 2.13032268
5 0.38122077 -0.08654570
6 0.29437422 0.38122077
7 1.53130789 0.29437422
8 1.21146466 1.53130789
9 1.17865765 1.21146466
10 0.89208845 1.17865765
11 1.21390971 0.89208845
12 -0.17940620 1.21390971
13 0.39321467 -0.17940620
14 -0.51803833 0.39321467
15 -0.17170735 -0.51803833
16 -0.87488485 -0.17170735
17 -0.88810104 -0.87488485
18 -1.88383075 -0.88810104
19 -0.73805838 -1.88383075
20 -2.09990691 -0.73805838
21 -0.45709339 -2.09990691
22 -1.68870671 -0.45709339
23 -0.05166546 -1.68870671
24 -1.35514180 -0.05166546
25 -0.10115565 -1.35514180
26 -0.06330320 -0.10115565
27 0.46002435 -0.06330320
28 -1.47580150 0.46002435
29 0.68362447 -1.47580150
30 -0.14836032 0.68362447
31 1.03936195 -0.14836032
32 -1.17110006 1.03936195
33 0.55703151 -1.17110006
34 -0.02873662 0.55703151
35 0.28369497 -0.02873662
36 -1.78504214 0.28369497
37 0.68772485 -1.78504214
38 -0.34785126 0.68772485
39 -0.15609094 -0.34785126
40 -2.36975568 -0.15609094
41 0.94312129 -2.36975568
42 0.40280772 0.94312129
43 1.39833830 0.40280772
44 -0.82926788 1.39833830
45 0.62164391 -0.82926788
46 0.56943909 0.62164391
47 -0.77713409 0.56943909
48 -0.82926788 -0.77713409
49 0.36178533 -0.82926788
50 0.71444691 0.36178533
51 0.85727222 0.71444691
52 -1.48843495 0.85727222
53 1.15425587 -1.48843495
54 1.01651829 1.15425587
55 1.49268690 1.01651829
56 -0.30940242 1.49268690
57 1.24080428 -0.30940242
58 1.68529000 1.24080428
59 1.25970722 1.68529000
60 -0.81414942 1.25970722
61 0.71656475 -0.81414942
62 0.36333800 0.71656475
63 0.96931055 0.36333800
64 -0.92522915 0.96931055
65 0.60899296 -0.92522915
66 0.41183471 0.60899296
67 1.17456764 0.41183471
68 -0.73403232 1.17456764
69 1.38659804 -0.73403232
70 1.08885896 1.38659804
71 2.39085551 1.08885896
72 -0.05603582 2.39085551
73 1.04168327 -0.05603582
74 1.34031956 1.04168327
75 1.52944820 1.34031956
76 -0.77258865 1.52944820
77 0.07705531 -0.77258865
78 -0.17497452 0.07705531
79 -0.37857448 -0.17497452
80 -0.21856474 -0.37857448
81 0.17611276 -0.21856474
82 0.18470283 0.17611276
83 -0.33676217 0.18470283
84 -1.22013506 -0.33676217
85 -0.18602206 -1.22013506
86 -0.88081632 -0.18602206
87 -0.18106299 -0.88081632
88 -1.13173813 -0.18106299
89 -0.33784070 -1.13173813
90 -0.59864361 -0.33784070
91 -0.27120998 -0.59864361
92 -1.30499555 -0.27120998
93 -0.28036646 -1.30499555
94 -0.61728057 -0.28036646
95 -1.00266710 -0.61728057
96 -1.71825237 -1.00266710
97 -0.55281194 -1.71825237
98 -0.12988448 -0.55281194
99 -0.65562027 -0.12988448
100 -1.31326127 -0.65562027
101 -0.78254120 -1.31326127
102 -0.39865780 -0.78254120
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7hic11292682070.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8hic11292682070.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9a9t31292682070.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10a9t31292682070.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11vaa91292682070.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12ztqx1292682070.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13nt591292682070.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14glmu1292682070.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/151ll01292682070.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16xvj91292682070.tab")
+ }
>
> try(system("convert tmp/1l8ws1292682070.ps tmp/1l8ws1292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dzvv1292682070.ps tmp/2dzvv1292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dzvv1292682070.ps tmp/3dzvv1292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dzvv1292682070.ps tmp/4dzvv1292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o9vy1292682070.ps tmp/5o9vy1292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/6o9vy1292682070.ps tmp/6o9vy1292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hic11292682070.ps tmp/7hic11292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hic11292682070.ps tmp/8hic11292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/9a9t31292682070.ps tmp/9a9t31292682070.png",intern=TRUE))
character(0)
> try(system("convert tmp/10a9t31292682070.ps tmp/10a9t31292682070.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.700 1.620 5.331