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
+ ,0.651
+ ,3.063
+ ,5.951
+ ,15.234
+ ,0.736
+ ,3.547
+ ,6.789
+ ,14.718
+ ,0.878
+ ,3.240
+ ,6.302
+ ,16.961
+ ,0.916
+ ,3.708
+ ,6.961
+ ,13.945
+ ,0.724
+ ,3.337
+ ,6.162
+ ,15.876
+ ,0.841
+ ,4.104
+ ,7.534
+ ,16.226
+ ,1.028
+ ,4.846
+ ,7.462
+ ,18.316
+ ,0.994
+ ,4.590
+ ,8.894
+ ,16.748
+ ,0.855
+ ,3.917
+ ,7.734
+ ,17.904
+ ,0.889
+ ,4.376
+ ,8.968
+ ,17.209
+ ,1.117
+ ,4.312
+ ,8.383
+ ,18.950
+ ,1.132
+ ,4.941
+ ,9.790
+ ,17.225
+ ,0.899
+ ,4.659
+ ,9.656
+ ,18.710
+ ,0.944
+ ,5.227
+ ,10.440
+ ,17.236
+ ,1.167
+ ,4.933
+ ,9.820
+ ,18.687
+ ,1.089
+ ,5.381
+ ,10.947
+ ,17.580
+ ,0.970
+ ,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
+ ,0.882
+ ,4.985
+ ,9.217
+ ,17.475
+ ,0.911
+ ,5.332
+ ,9.371
+ ,17.725
+ ,1.076
+ ,5.377
+ ,9.526
+ ,19.562
+ ,1.147
+ ,5.948
+ ,10.837
+ ,16.368
+ ,0.946
+ ,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
+ ,0.870
+ ,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
+ ,0.891
+ ,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
+ ,0.771
+ ,5.019
+ ,7.894
+ ,17.795
+ ,0.938
+ ,5.935
+ ,7.949
+ ,16.761
+ ,0.995
+ ,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 0.651 3.063 5.951
2 15.234 0.736 3.547 6.789
3 14.718 0.878 3.240 6.302
4 16.961 0.916 3.708 6.961
5 13.945 0.724 3.337 6.162
6 15.876 0.841 4.104 7.534
7 16.226 1.028 4.846 7.462
8 18.316 0.994 4.590 8.894
9 16.748 0.855 3.917 7.734
10 17.904 0.889 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 0.899 4.659 9.656
14 18.710 0.944 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 0.970 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 0.882 4.985 9.217
26 17.475 0.911 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 0.946 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 0.870 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 0.891 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 0.771 5.019 7.894
42 17.795 0.938 5.935 7.949
43 16.761 0.995 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
7.4597 -0.5338 0.8270 0.6689
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.55191 -0.62352 0.07038 0.61145 2.26776
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.45966 0.77134 9.671 5.71e-16 ***
villa -0.53384 0.16383 -3.258 0.00154 **
app 0.82699 0.09477 8.726 6.51e-14 ***
grond 0.66888 0.06326 10.573 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9458 on 99 degrees of freedom
Multiple R-squared: 0.6569, Adjusted R-squared: 0.6465
F-statistic: 63.17 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,] 0.29481157 5.896231e-01 7.051884e-01
[2,] 0.23667210 4.733442e-01 7.633279e-01
[3,] 0.14616415 2.923283e-01 8.538359e-01
[4,] 0.09535338 1.907068e-01 9.046466e-01
[5,] 0.21762250 4.352450e-01 7.823775e-01
[6,] 0.20317985 4.063597e-01 7.968201e-01
[7,] 0.28337937 5.667587e-01 7.166206e-01
[8,] 0.20923783 4.184757e-01 7.907622e-01
[9,] 0.50609908 9.878018e-01 4.939009e-01
[10,] 0.46659464 9.331893e-01 5.334054e-01
[11,] 0.45809207 9.161841e-01 5.419079e-01
[12,] 0.41777824 8.355565e-01 5.822218e-01
[13,] 0.76377637 4.724473e-01 2.362236e-01
[14,] 0.73649312 5.270138e-01 2.635069e-01
[15,] 0.87544318 2.491136e-01 1.245568e-01
[16,] 0.83970821 3.205836e-01 1.602918e-01
[17,] 0.90373116 1.925377e-01 9.626884e-02
[18,] 0.87434793 2.513041e-01 1.256521e-01
[19,] 0.89275648 2.144870e-01 1.072435e-01
[20,] 0.85977762 2.804448e-01 1.402224e-01
[21,] 0.82022990 3.595402e-01 1.797701e-01
[22,] 0.81341585 3.731683e-01 1.865842e-01
[23,] 0.85273933 2.945213e-01 1.472607e-01
[24,] 0.83483099 3.303380e-01 1.651690e-01
[25,] 0.80047390 3.990522e-01 1.995261e-01
[26,] 0.80953451 3.809310e-01 1.904655e-01
[27,] 0.84477952 3.104410e-01 1.552205e-01
[28,] 0.80828975 3.834205e-01 1.917103e-01
[29,] 0.78123237 4.375353e-01 2.187676e-01
[30,] 0.73640587 5.271883e-01 2.635941e-01
[31,] 0.86738929 2.652214e-01 1.326107e-01
[32,] 0.85445245 2.910951e-01 1.455475e-01
[33,] 0.86263374 2.747325e-01 1.373663e-01
[34,] 0.85242476 2.951505e-01 1.475752e-01
[35,] 0.97245071 5.509859e-02 2.754929e-02
[36,] 0.96714678 6.570645e-02 3.285322e-02
[37,] 0.95676195 8.647611e-02 4.323805e-02
[38,] 0.95975460 8.049081e-02 4.024540e-02
[39,] 0.97213328 5.573345e-02 2.786672e-02
[40,] 0.96517491 6.965018e-02 3.482509e-02
[41,] 0.95695459 8.609082e-02 4.304541e-02
[42,] 0.97031759 5.936483e-02 2.968241e-02
[43,] 0.97850098 4.299804e-02 2.149902e-02
[44,] 0.97326434 5.347133e-02 2.673566e-02
[45,] 0.96289671 7.420658e-02 3.710329e-02
[46,] 0.95558921 8.882159e-02 4.441079e-02
[47,] 0.99090301 1.819398e-02 9.096988e-03
[48,] 0.98707574 2.584851e-02 1.292426e-02
[49,] 0.98224071 3.551857e-02 1.775929e-02
[50,] 0.97756108 4.487785e-02 2.243892e-02
[51,] 0.97969750 4.060501e-02 2.030250e-02
[52,] 0.97219208 5.561584e-02 2.780792e-02
[53,] 0.96300469 7.399063e-02 3.699531e-02
[54,] 0.94916994 1.016601e-01 5.083006e-02
[55,] 0.97127399 5.745201e-02 2.872601e-02
[56,] 0.96095803 7.808395e-02 3.904197e-02
[57,] 0.95588589 8.822823e-02 4.411411e-02
[58,] 0.94038575 1.192285e-01 5.961425e-02
[59,] 0.98176366 3.647268e-02 1.823634e-02
[60,] 0.97592208 4.815585e-02 2.407792e-02
[61,] 0.96849638 6.300725e-02 3.150362e-02
[62,] 0.95891114 8.217772e-02 4.108886e-02
[63,] 0.97280373 5.439254e-02 2.719627e-02
[64,] 0.96665181 6.669637e-02 3.334819e-02
[65,] 0.95914325 8.171350e-02 4.085675e-02
[66,] 0.99381769 1.236462e-02 6.182311e-03
[67,] 0.99107593 1.784814e-02 8.924068e-03
[68,] 0.99175216 1.649568e-02 8.247838e-03
[69,] 0.99823293 3.534139e-03 1.767069e-03
[70,] 0.99993916 1.216813e-04 6.084063e-05
[71,] 0.99991273 1.745488e-04 8.727441e-05
[72,] 0.99996397 7.206317e-05 3.603159e-05
[73,] 0.99997821 4.357810e-05 2.178905e-05
[74,] 0.99999836 3.289769e-06 1.644884e-06
[75,] 0.99999778 4.444893e-06 2.222447e-06
[76,] 0.99999707 5.868816e-06 2.934408e-06
[77,] 0.99999921 1.575045e-06 7.875224e-07
[78,] 0.99999753 4.939251e-06 2.469626e-06
[79,] 0.99999604 7.914974e-06 3.957487e-06
[80,] 0.99999563 8.736299e-06 4.368149e-06
[81,] 0.99998451 3.098899e-05 1.549450e-05
[82,] 0.99994780 1.043915e-04 5.219575e-05
[83,] 0.99984014 3.197286e-04 1.598643e-04
[84,] 0.99957815 8.436996e-04 4.218498e-04
[85,] 0.99875914 2.481723e-03 1.240862e-03
[86,] 0.99648896 7.022078e-03 3.511039e-03
[87,] 0.99067920 1.864161e-02 9.320803e-03
[88,] 0.99235811 1.528378e-02 7.641890e-03
[89,] 0.97762845 4.474311e-02 2.237155e-02
[90,] 0.92900789 1.419842e-01 7.099211e-02
> postscript(file="/var/www/rcomp/tmp/1tzg81292684573.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2tzg81292684573.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lqfb1292684573.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4lqfb1292684573.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5lqfb1292684573.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
-0.432729966 0.692858371 0.832297139 2.267756709 -0.009489645 0.431959242
7 8 9 10 11 12
0.316319097 1.642038125 1.332304136 1.301463554 1.172404241 1.460115070
13 14 15 16 17 18
-0.066428459 0.448458369 -0.248650909 0.036385294 -0.869606031 -0.793997953
19 20 21 22 23 24
-1.623228692 -0.373051008 -1.911546366 -0.305284814 -1.413778394 0.155507832
25 26 27 28 29 30
-1.368463862 -0.175956859 0.021235988 0.547020475 -1.497262228 0.440019768
31 32 33 34 35 36
-0.191040267 1.023260447 -1.295621100 0.380177080 -0.176677899 0.153108106
37 38 39 40 41 42
-1.945939462 0.435085625 -0.546282960 -0.383580916 -2.551906214 0.610931853
43 44 45 46 47 48
0.164860690 1.216953896 -1.033146712 0.248405830 0.562051418 -1.058683298
49 50 51 52 53 54
-1.033146712 0.189973379 0.611959513 0.557009283 -1.707757736 0.854341084
55 56 57 58 59 60
0.939938641 1.185605174 -0.709274423 0.729573113 1.328843702 1.053821500
61 62 63 64 65 66
-1.065971862 0.358628991 0.270527519 0.913101242 -1.267372719 0.241340324
67 68 69 70 71 72
0.173737577 0.829286816 -1.016056491 0.992775801 0.771700709 2.037278371
73 74 75 76 77 78
-0.516864462 0.529792571 1.007074504 1.274171723 -1.195029189 -0.262809340
79 80 81 82 83 84
-0.239338953 -0.636311250 0.574526457 1.004316168 0.990701646 0.341216171
85 86 87 88 89 90
-0.668641891 0.370734216 -0.317501032 0.344567292 -0.883194141 -0.157492424
91 92 93 94 95 96
-0.162630079 0.070381329 -1.229272942 -0.283923839 -0.324014001 -0.756229240
97 98 99 100 101 102
-1.586271905 -0.350540530 0.275186544 -0.311034230 -1.132406036 -0.610725993
103
-0.018924284
> postscript(file="/var/www/rcomp/tmp/6ezee1292684573.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 -0.432729966 NA
1 0.692858371 -0.432729966
2 0.832297139 0.692858371
3 2.267756709 0.832297139
4 -0.009489645 2.267756709
5 0.431959242 -0.009489645
6 0.316319097 0.431959242
7 1.642038125 0.316319097
8 1.332304136 1.642038125
9 1.301463554 1.332304136
10 1.172404241 1.301463554
11 1.460115070 1.172404241
12 -0.066428459 1.460115070
13 0.448458369 -0.066428459
14 -0.248650909 0.448458369
15 0.036385294 -0.248650909
16 -0.869606031 0.036385294
17 -0.793997953 -0.869606031
18 -1.623228692 -0.793997953
19 -0.373051008 -1.623228692
20 -1.911546366 -0.373051008
21 -0.305284814 -1.911546366
22 -1.413778394 -0.305284814
23 0.155507832 -1.413778394
24 -1.368463862 0.155507832
25 -0.175956859 -1.368463862
26 0.021235988 -0.175956859
27 0.547020475 0.021235988
28 -1.497262228 0.547020475
29 0.440019768 -1.497262228
30 -0.191040267 0.440019768
31 1.023260447 -0.191040267
32 -1.295621100 1.023260447
33 0.380177080 -1.295621100
34 -0.176677899 0.380177080
35 0.153108106 -0.176677899
36 -1.945939462 0.153108106
37 0.435085625 -1.945939462
38 -0.546282960 0.435085625
39 -0.383580916 -0.546282960
40 -2.551906214 -0.383580916
41 0.610931853 -2.551906214
42 0.164860690 0.610931853
43 1.216953896 0.164860690
44 -1.033146712 1.216953896
45 0.248405830 -1.033146712
46 0.562051418 0.248405830
47 -1.058683298 0.562051418
48 -1.033146712 -1.058683298
49 0.189973379 -1.033146712
50 0.611959513 0.189973379
51 0.557009283 0.611959513
52 -1.707757736 0.557009283
53 0.854341084 -1.707757736
54 0.939938641 0.854341084
55 1.185605174 0.939938641
56 -0.709274423 1.185605174
57 0.729573113 -0.709274423
58 1.328843702 0.729573113
59 1.053821500 1.328843702
60 -1.065971862 1.053821500
61 0.358628991 -1.065971862
62 0.270527519 0.358628991
63 0.913101242 0.270527519
64 -1.267372719 0.913101242
65 0.241340324 -1.267372719
66 0.173737577 0.241340324
67 0.829286816 0.173737577
68 -1.016056491 0.829286816
69 0.992775801 -1.016056491
70 0.771700709 0.992775801
71 2.037278371 0.771700709
72 -0.516864462 2.037278371
73 0.529792571 -0.516864462
74 1.007074504 0.529792571
75 1.274171723 1.007074504
76 -1.195029189 1.274171723
77 -0.262809340 -1.195029189
78 -0.239338953 -0.262809340
79 -0.636311250 -0.239338953
80 0.574526457 -0.636311250
81 1.004316168 0.574526457
82 0.990701646 1.004316168
83 0.341216171 0.990701646
84 -0.668641891 0.341216171
85 0.370734216 -0.668641891
86 -0.317501032 0.370734216
87 0.344567292 -0.317501032
88 -0.883194141 0.344567292
89 -0.157492424 -0.883194141
90 -0.162630079 -0.157492424
91 0.070381329 -0.162630079
92 -1.229272942 0.070381329
93 -0.283923839 -1.229272942
94 -0.324014001 -0.283923839
95 -0.756229240 -0.324014001
96 -1.586271905 -0.756229240
97 -0.350540530 -1.586271905
98 0.275186544 -0.350540530
99 -0.311034230 0.275186544
100 -1.132406036 -0.311034230
101 -0.610725993 -1.132406036
102 -0.018924284 -0.610725993
103 NA -0.018924284
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.692858371 -0.432729966
[2,] 0.832297139 0.692858371
[3,] 2.267756709 0.832297139
[4,] -0.009489645 2.267756709
[5,] 0.431959242 -0.009489645
[6,] 0.316319097 0.431959242
[7,] 1.642038125 0.316319097
[8,] 1.332304136 1.642038125
[9,] 1.301463554 1.332304136
[10,] 1.172404241 1.301463554
[11,] 1.460115070 1.172404241
[12,] -0.066428459 1.460115070
[13,] 0.448458369 -0.066428459
[14,] -0.248650909 0.448458369
[15,] 0.036385294 -0.248650909
[16,] -0.869606031 0.036385294
[17,] -0.793997953 -0.869606031
[18,] -1.623228692 -0.793997953
[19,] -0.373051008 -1.623228692
[20,] -1.911546366 -0.373051008
[21,] -0.305284814 -1.911546366
[22,] -1.413778394 -0.305284814
[23,] 0.155507832 -1.413778394
[24,] -1.368463862 0.155507832
[25,] -0.175956859 -1.368463862
[26,] 0.021235988 -0.175956859
[27,] 0.547020475 0.021235988
[28,] -1.497262228 0.547020475
[29,] 0.440019768 -1.497262228
[30,] -0.191040267 0.440019768
[31,] 1.023260447 -0.191040267
[32,] -1.295621100 1.023260447
[33,] 0.380177080 -1.295621100
[34,] -0.176677899 0.380177080
[35,] 0.153108106 -0.176677899
[36,] -1.945939462 0.153108106
[37,] 0.435085625 -1.945939462
[38,] -0.546282960 0.435085625
[39,] -0.383580916 -0.546282960
[40,] -2.551906214 -0.383580916
[41,] 0.610931853 -2.551906214
[42,] 0.164860690 0.610931853
[43,] 1.216953896 0.164860690
[44,] -1.033146712 1.216953896
[45,] 0.248405830 -1.033146712
[46,] 0.562051418 0.248405830
[47,] -1.058683298 0.562051418
[48,] -1.033146712 -1.058683298
[49,] 0.189973379 -1.033146712
[50,] 0.611959513 0.189973379
[51,] 0.557009283 0.611959513
[52,] -1.707757736 0.557009283
[53,] 0.854341084 -1.707757736
[54,] 0.939938641 0.854341084
[55,] 1.185605174 0.939938641
[56,] -0.709274423 1.185605174
[57,] 0.729573113 -0.709274423
[58,] 1.328843702 0.729573113
[59,] 1.053821500 1.328843702
[60,] -1.065971862 1.053821500
[61,] 0.358628991 -1.065971862
[62,] 0.270527519 0.358628991
[63,] 0.913101242 0.270527519
[64,] -1.267372719 0.913101242
[65,] 0.241340324 -1.267372719
[66,] 0.173737577 0.241340324
[67,] 0.829286816 0.173737577
[68,] -1.016056491 0.829286816
[69,] 0.992775801 -1.016056491
[70,] 0.771700709 0.992775801
[71,] 2.037278371 0.771700709
[72,] -0.516864462 2.037278371
[73,] 0.529792571 -0.516864462
[74,] 1.007074504 0.529792571
[75,] 1.274171723 1.007074504
[76,] -1.195029189 1.274171723
[77,] -0.262809340 -1.195029189
[78,] -0.239338953 -0.262809340
[79,] -0.636311250 -0.239338953
[80,] 0.574526457 -0.636311250
[81,] 1.004316168 0.574526457
[82,] 0.990701646 1.004316168
[83,] 0.341216171 0.990701646
[84,] -0.668641891 0.341216171
[85,] 0.370734216 -0.668641891
[86,] -0.317501032 0.370734216
[87,] 0.344567292 -0.317501032
[88,] -0.883194141 0.344567292
[89,] -0.157492424 -0.883194141
[90,] -0.162630079 -0.157492424
[91,] 0.070381329 -0.162630079
[92,] -1.229272942 0.070381329
[93,] -0.283923839 -1.229272942
[94,] -0.324014001 -0.283923839
[95,] -0.756229240 -0.324014001
[96,] -1.586271905 -0.756229240
[97,] -0.350540530 -1.586271905
[98,] 0.275186544 -0.350540530
[99,] -0.311034230 0.275186544
[100,] -1.132406036 -0.311034230
[101,] -0.610725993 -1.132406036
[102,] -0.018924284 -0.610725993
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.692858371 -0.432729966
2 0.832297139 0.692858371
3 2.267756709 0.832297139
4 -0.009489645 2.267756709
5 0.431959242 -0.009489645
6 0.316319097 0.431959242
7 1.642038125 0.316319097
8 1.332304136 1.642038125
9 1.301463554 1.332304136
10 1.172404241 1.301463554
11 1.460115070 1.172404241
12 -0.066428459 1.460115070
13 0.448458369 -0.066428459
14 -0.248650909 0.448458369
15 0.036385294 -0.248650909
16 -0.869606031 0.036385294
17 -0.793997953 -0.869606031
18 -1.623228692 -0.793997953
19 -0.373051008 -1.623228692
20 -1.911546366 -0.373051008
21 -0.305284814 -1.911546366
22 -1.413778394 -0.305284814
23 0.155507832 -1.413778394
24 -1.368463862 0.155507832
25 -0.175956859 -1.368463862
26 0.021235988 -0.175956859
27 0.547020475 0.021235988
28 -1.497262228 0.547020475
29 0.440019768 -1.497262228
30 -0.191040267 0.440019768
31 1.023260447 -0.191040267
32 -1.295621100 1.023260447
33 0.380177080 -1.295621100
34 -0.176677899 0.380177080
35 0.153108106 -0.176677899
36 -1.945939462 0.153108106
37 0.435085625 -1.945939462
38 -0.546282960 0.435085625
39 -0.383580916 -0.546282960
40 -2.551906214 -0.383580916
41 0.610931853 -2.551906214
42 0.164860690 0.610931853
43 1.216953896 0.164860690
44 -1.033146712 1.216953896
45 0.248405830 -1.033146712
46 0.562051418 0.248405830
47 -1.058683298 0.562051418
48 -1.033146712 -1.058683298
49 0.189973379 -1.033146712
50 0.611959513 0.189973379
51 0.557009283 0.611959513
52 -1.707757736 0.557009283
53 0.854341084 -1.707757736
54 0.939938641 0.854341084
55 1.185605174 0.939938641
56 -0.709274423 1.185605174
57 0.729573113 -0.709274423
58 1.328843702 0.729573113
59 1.053821500 1.328843702
60 -1.065971862 1.053821500
61 0.358628991 -1.065971862
62 0.270527519 0.358628991
63 0.913101242 0.270527519
64 -1.267372719 0.913101242
65 0.241340324 -1.267372719
66 0.173737577 0.241340324
67 0.829286816 0.173737577
68 -1.016056491 0.829286816
69 0.992775801 -1.016056491
70 0.771700709 0.992775801
71 2.037278371 0.771700709
72 -0.516864462 2.037278371
73 0.529792571 -0.516864462
74 1.007074504 0.529792571
75 1.274171723 1.007074504
76 -1.195029189 1.274171723
77 -0.262809340 -1.195029189
78 -0.239338953 -0.262809340
79 -0.636311250 -0.239338953
80 0.574526457 -0.636311250
81 1.004316168 0.574526457
82 0.990701646 1.004316168
83 0.341216171 0.990701646
84 -0.668641891 0.341216171
85 0.370734216 -0.668641891
86 -0.317501032 0.370734216
87 0.344567292 -0.317501032
88 -0.883194141 0.344567292
89 -0.157492424 -0.883194141
90 -0.162630079 -0.157492424
91 0.070381329 -0.162630079
92 -1.229272942 0.070381329
93 -0.283923839 -1.229272942
94 -0.324014001 -0.283923839
95 -0.756229240 -0.324014001
96 -1.586271905 -0.756229240
97 -0.350540530 -1.586271905
98 0.275186544 -0.350540530
99 -0.311034230 0.275186544
100 -1.132406036 -0.311034230
101 -0.610725993 -1.132406036
102 -0.018924284 -0.610725993
> 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/779eh1292684573.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/879eh1292684573.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/979eh1292684573.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10hiv21292684573.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lib81292684573.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/12o1se1292684573.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/132t841292684573.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/146bos1292684573.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/15rc4y1292684573.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/16cclm1292684573.tab")
+ }
>
> try(system("convert tmp/1tzg81292684573.ps tmp/1tzg81292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tzg81292684573.ps tmp/2tzg81292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lqfb1292684573.ps tmp/3lqfb1292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lqfb1292684573.ps tmp/4lqfb1292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lqfb1292684573.ps tmp/5lqfb1292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ezee1292684573.ps tmp/6ezee1292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/779eh1292684573.ps tmp/779eh1292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/879eh1292684573.ps tmp/879eh1292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/979eh1292684573.ps tmp/979eh1292684573.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hiv21292684573.ps tmp/10hiv21292684573.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.640 1.720 5.319