R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-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(178421
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+ ,1.09
+ ,7105114
+ ,732417
+ ,2240876
+ ,8764326
+ ,87384
+ ,1.29
+ ,2.69
+ ,2.84
+ ,2.78
+ ,1.54
+ ,3.74
+ ,3.64
+ ,1.01
+ ,0.83
+ ,1.08
+ ,7647797
+ ,702229
+ ,2330906
+ ,9089938
+ ,84615
+ ,1.27
+ ,2.70
+ ,2.88
+ ,2.81
+ ,1.54
+ ,3.76
+ ,3.52
+ ,1.01
+ ,0.83
+ ,1.11
+ ,7440408
+ ,684271
+ ,2188360
+ ,8778446
+ ,80420
+ ,1.26
+ ,2.68
+ ,2.83
+ ,2.72
+ ,1.51
+ ,3.91
+ ,3.21
+ ,1.03
+ ,0.84
+ ,1.08
+ ,7255613
+ ,633638
+ ,2067367
+ ,8809264
+ ,80784
+ ,1.27
+ ,2.70
+ ,2.84
+ ,2.66
+ ,1.51
+ ,3.79
+ ,3.49
+ ,1.02
+ ,0.84
+ ,1.05
+ ,7231703
+ ,693374
+ ,2189597
+ ,9521789
+ ,79933
+ ,1.27
+ ,2.72
+ ,2.87
+ ,2.72
+ ,1.50
+ ,3.70
+ ,3.50
+ ,1.02
+ ,0.86
+ ,1.09
+ ,7278022
+ ,707616
+ ,2356724
+ ,9438993
+ ,82118
+ ,1.27
+ ,2.70
+ ,2.90
+ ,2.74
+ ,1.52
+ ,3.74
+ ,3.61
+ ,1.03
+ ,0.87
+ ,1.09
+ ,7382680
+ ,722553
+ ,2250295
+ ,9045288
+ ,91420
+ ,1.28
+ ,2.66
+ ,2.87
+ ,2.77
+ ,1.57
+ ,3.71
+ ,3.48
+ ,1.03
+ ,0.86
+ ,1.11
+ ,7622740
+ ,712532
+ ,2243913
+ ,9272049
+ ,112426
+ ,1.29
+ ,2.68
+ ,2.92
+ ,2.79
+ ,1.57
+ ,3.72
+ ,3.72
+ ,1.02
+ ,0.85
+ ,1.12
+ ,8295038
+ ,687023
+ ,2172504
+ ,9978418
+ ,114528
+ ,1.28
+ ,2.65
+ ,2.89
+ ,2.84
+ ,1.47
+ ,3.82
+ ,3.13
+ ,1.02
+ ,0.85
+ ,1.10
+ ,8136158
+ ,646716
+ ,2301051
+ ,9776284
+ ,131025
+ ,1.30
+ ,2.69
+ ,2.90
+ ,2.84
+ ,1.48
+ ,3.98
+ ,3.12
+ ,1.02
+ ,0.85
+ ,1.08
+ ,8240817
+ ,657284
+ ,2245784
+ ,9601480
+ ,116460
+ ,1.30
+ ,2.66
+ ,2.85
+ ,2.86
+ ,1.54
+ ,3.75
+ ,3.37
+ ,1.02
+ ,0.85
+ ,1.08
+ ,7993962
+ ,701042
+ ,2159896
+ ,11193789
+ ,111258
+ ,1.30
+ ,2.69
+ ,2.82
+ ,2.86
+ ,1.54
+ ,3.65
+ ,3.36
+ ,1.03
+ ,0.87
+ ,1.10
+ ,7997958
+ ,744939
+ ,2374240
+ ,9607554
+ ,155318
+ ,1.29
+ ,2.69
+ ,2.85
+ ,2.89
+ ,1.50
+ ,3.69
+ ,3.39
+ ,1.02
+ ,0.86
+ ,1.08
+ ,8914911
+ ,823561
+ ,2533022
+ ,9870457
+ ,155078
+ ,1.30
+ ,2.65
+ ,2.86
+ ,2.89
+ ,1.51
+ ,3.84
+ ,3.53
+ ,1.02
+ ,0.88
+ ,1.10
+ ,9082346
+ ,810516
+ ,2419167
+ ,10260040
+ ,134794
+ ,1.29
+ ,2.66
+ ,2.88
+ ,2.80
+ ,1.52
+ ,4.22
+ ,3.21
+ ,1.02
+ ,0.88
+ ,1.12
+ ,8690947
+ ,755964
+ ,2379061
+ ,9578120
+ ,139985
+ ,1.28
+ ,2.63
+ ,2.86
+ ,2.87
+ ,1.50
+ ,4.10
+ ,3.05
+ ,1.03
+ ,0.88
+ ,1.11
+ ,8678669
+ ,707347
+ ,2264684
+ ,9693065
+ ,198778
+ ,1.30
+ ,2.65
+ ,2.83
+ ,2.89
+ ,1.53
+ ,3.93
+ ,3.11
+ ,1.02
+ ,0.88
+ ,1.06
+ ,9768461
+ ,727181
+ ,2378165
+ ,12413462
+ ,172436
+ ,1.30
+ ,2.60
+ ,2.84
+ ,2.91
+ ,1.57
+ ,3.70
+ ,3.18
+ ,1.02
+ ,0.86
+ ,1.08
+ ,8751448
+ ,1110335
+ ,2536093
+ ,13143933
+ ,169585
+ ,1.31
+ ,2.57
+ ,2.86
+ ,2.90
+ ,1.56
+ ,3.81
+ ,2.87
+ ,1.02
+ ,0.89
+ ,1.11
+ ,8737854
+ ,939274
+ ,2559486
+ ,11118547
+ ,203702
+ ,1.32
+ ,2.65
+ ,2.85
+ ,2.90
+ ,1.52
+ ,3.83
+ ,2.89
+ ,1.02
+ ,0.89
+ ,1.10
+ ,9684075
+ ,842499
+ ,2340159
+ ,11289800
+ ,282392
+ ,1.33
+ ,2.69
+ ,2.86
+ ,2.90
+ ,1.49
+ ,4.18
+ ,2.81
+ ,1.02
+ ,0.88
+ ,1.08
+ ,11529582
+ ,785788
+ ,2235562
+ ,11573959
+ ,220658
+ ,1.32
+ ,2.71
+ ,2.89
+ ,2.76
+ ,1.49
+ ,4.10
+ ,2.89
+ ,1.00
+ ,0.89
+ ,1.07
+ ,9854882
+ ,812169
+ ,2300728
+ ,10511958
+ ,194472
+ ,1.30
+ ,2.72
+ ,2.87
+ ,2.71
+ ,1.49
+ ,4.26
+ ,2.82
+ ,1.04
+ ,0.91
+ ,1.08
+ ,9030507
+ ,730023
+ ,2090042
+ ,12515693
+ ,269246
+ ,1.31
+ ,2.73
+ ,2.84
+ ,2.74
+ ,1.49
+ ,4.32
+ ,2.64
+ ,1.04
+ ,0.90
+ ,1.07
+ ,10656814
+ ,823033
+ ,1976051
+ ,12966759
+ ,215340
+ ,1.30
+ ,2.72
+ ,2.79
+ ,2.79
+ ,1.51
+ ,4.19
+ ,2.55
+ ,1.03
+ ,0.88
+ ,1.08
+ ,9111428
+ ,976731
+ ,2104956
+ ,10668160
+ ,218319
+ ,1.30
+ ,2.73
+ ,2.86
+ ,2.85
+ ,1.52
+ ,3.86
+ ,2.54
+ ,1.02
+ ,0.87
+ ,1.08
+ ,9642906
+ ,738606
+ ,2489023
+ ,13948692
+ ,195724
+ ,1.30
+ ,2.72
+ ,2.86
+ ,2.87
+ ,1.54
+ ,3.84
+ ,2.46
+ ,1.04
+ ,0.89
+ ,1.07
+ ,9217060
+ ,685173
+ ,2598916
+ ,16087616
+ ,174614
+ ,1.29
+ ,2.70
+ ,2.87
+ ,2.89
+ ,1.53
+ ,3.91
+ ,2.59
+ ,1.05
+ ,0.88
+ ,1.09
+ ,8816389
+ ,642519
+ ,2302455
+ ,12159456
+ ,172085
+ ,1.29
+ ,2.72
+ ,2.85
+ ,2.90
+ ,1.53
+ ,4.01
+ ,2.68
+ ,1.03
+ ,0.85
+ ,1.08
+ ,9074790
+ ,677849
+ ,2427969
+ ,10633146
+ ,152347
+ ,1.30
+ ,2.70
+ ,2.88
+ ,2.90
+ ,1.55
+ ,3.66
+ ,3.33
+ ,0.99
+ ,0.86
+ ,1.16
+ ,8601172
+ ,826348
+ ,2132820
+ ,10770809
+ ,189615
+ ,1.30
+ ,2.72
+ ,2.88
+ ,2.88
+ ,1.58
+ ,3.63
+ ,3.41
+ ,1.03
+ ,0.87
+ ,1.13
+ ,9735782
+ ,757562
+ ,2560376
+ ,10548925
+ ,173804
+ ,1.29
+ ,2.70
+ ,2.87
+ ,2.91
+ ,1.58
+ ,3.57
+ ,3.30
+ ,1.08
+ ,0.88
+ ,1.14
+ ,9222117
+ ,825217
+ ,2454605
+ ,10123204
+ ,145683
+ ,1.27
+ ,2.65
+ ,2.86
+ ,2.90
+ ,1.54
+ ,3.66
+ ,3.51
+ ,1.09
+ ,0.91
+ ,1.10
+ ,8197462
+ ,831800
+ ,2169005
+ ,11471988
+ ,133550
+ ,1.26
+ ,2.66
+ ,2.85
+ ,2.91
+ ,1.53
+ ,3.74
+ ,3.50
+ ,1.08
+ ,0.89
+ ,1.10
+ ,8161117
+ ,890944
+ ,2072759
+ ,10599314
+ ,121156
+ ,1.25
+ ,2.69
+ ,2.81
+ ,2.91
+ ,1.53
+ ,3.85
+ ,3.46
+ ,1.05
+ ,0.86
+ ,1.11
+ ,8085780
+ ,818812
+ ,2201360
+ ,10501150
+ ,112040
+ ,1.26
+ ,2.70
+ ,2.81
+ ,2.91
+ ,1.52
+ ,3.98
+ ,3.36
+ ,1.06
+ ,0.87
+ ,1.12
+ ,7777563
+ ,813389
+ ,2215184
+ ,9476948
+ ,120767
+ ,1.27
+ ,2.71
+ ,2.83
+ ,2.90
+ ,1.52
+ ,3.84
+ ,3.52
+ ,1.04
+ ,0.87
+ ,1.11
+ ,8192525
+ ,791213
+ ,2140796
+ ,9854999
+ ,127019
+ ,1.26
+ ,2.69
+ ,2.93
+ ,2.91
+ ,1.52
+ ,3.81
+ ,3.48
+ ,1.06
+ ,0.86
+ ,1.10
+ ,8222640
+ ,753162
+ ,2064345
+ ,9020688
+ ,136295
+ ,1.25
+ ,2.72
+ ,2.88
+ ,2.89
+ ,1.49
+ ,3.90
+ ,3.17
+ ,1.06
+ ,0.85
+ ,1.09
+ ,8852425
+ ,744738
+ ,2246763
+ ,9639666
+ ,113425
+ ,1.25
+ ,2.71
+ ,2.86
+ ,2.88
+ ,1.50
+ ,3.91
+ ,3.08
+ ,1.07
+ ,0.86
+ ,1.08
+ ,8047626
+ ,740853
+ ,2196948
+ ,10016963
+ ,107815
+ ,1.25
+ ,2.71
+ ,2.86
+ ,2.90
+ ,1.50
+ ,3.93
+ ,3.32
+ ,1.08
+ ,0.88
+ ,1.11
+ ,8079925
+ ,828505
+ ,1987852
+ ,9221363
+ ,100298
+ ,1.26
+ ,2.74
+ ,2.90
+ ,2.90
+ ,1.50
+ ,3.80
+ ,3.51
+ ,1.08
+ ,0.86
+ ,1.10
+ ,8099820
+ ,764325
+ ,2013311
+ ,9163961
+ ,97048
+ ,1.26
+ ,2.82
+ ,2.96
+ ,2.90
+ ,1.51
+ ,3.75
+ ,3.57
+ ,1.05
+ ,0.85
+ ,1.10
+ ,7444464
+ ,779152
+ ,2024477
+ ,9600997
+ ,98750
+ ,1.26
+ ,2.76
+ ,3.02
+ ,2.90
+ ,1.50
+ ,3.86
+ ,3.67
+ ,1.04
+ ,0.84
+ ,1.09
+ ,8060967
+ ,780635
+ ,2175719
+ ,9629093
+ ,98235
+ ,1.27
+ ,2.77
+ ,3.15
+ ,2.90
+ ,1.82
+ ,4.03
+ ,0.85
+ ,1.04
+ ,0.85
+ ,1.08
+ ,7904184
+ ,772652
+ ,2459717
+ ,9266651
+ ,101254
+ ,1.28
+ ,2.77
+ ,3.21
+ ,2.91
+ ,1.45
+ ,4.34
+ ,2.97
+ ,1.04
+ ,0.84
+ ,1.05
+ ,8532755
+ ,796751
+ ,2436148
+ ,11454028
+ ,139589
+ ,1.29
+ ,2.81
+ ,3.30
+ ,2.91
+ ,1.36
+ ,5.00
+ ,2.88
+ ,1.04
+ ,0.84
+ ,1.04
+ ,10077590
+ ,774564
+ ,2533141
+ ,10051577
+ ,134921
+ ,1.30
+ ,2.77
+ ,3.14
+ ,2.90
+ ,1.38
+ ,4.86
+ ,2.99
+ ,1.06
+ ,0.85
+ ,1.08
+ ,9163186
+ ,781545
+ ,2438635
+ ,8887058
+ ,80355
+ ,1.26
+ ,2.76
+ ,2.99
+ ,2.91
+ ,1.53
+ ,3.79
+ ,3.48
+ ,1.08
+ ,0.85
+ ,1.09
+ ,7027349
+ ,846744
+ ,2294455
+ ,9590767
+ ,80396
+ ,1.25
+ ,2.73
+ ,2.97
+ ,2.83
+ ,1.60
+ ,3.80
+ ,3.57
+ ,1.08
+ ,0.87
+ ,1.09
+ ,7000371
+ ,852583
+ ,2233829
+ ,9269821
+ ,82183
+ ,1.26
+ ,2.72
+ ,2.98
+ ,2.76
+ ,1.58
+ ,3.90
+ ,3.54
+ ,1.08
+ ,0.87
+ ,1.10
+ ,7234027
+ ,837686
+ ,2231864
+ ,9242497
+ ,79709
+ ,1.25
+ ,2.73
+ ,2.95
+ ,2.84
+ ,1.55
+ ,3.89
+ ,3.67
+ ,1.07
+ ,0.84
+ ,1.09
+ ,7166769
+ ,872753
+ ,2248620
+ ,9621983
+ ,90781
+ ,1.24
+ ,2.71
+ ,2.92
+ ,2.88
+ ,1.57
+ ,3.77
+ ,3.50
+ ,1.06
+ ,0.85
+ ,1.09
+ ,7538708
+ ,863746
+ ,2348107
+ ,10101244)
+ ,dim=c(15
+ ,130)
+ ,dimnames=list(c('QBEFRU'
+ ,'PBEPIL'
+ ,'PBEFRU'
+ ,'PBEREG'
+ ,'PCHEXO'
+ ,'PAMMOORA'
+ ,'PAMMOAPP'
+ ,'PAMMOGRA'
+ ,'PSOCOLA'
+ ,'PSOLEM'
+ ,'PSTILL'
+ ,'BUDBEER'
+ ,'BUDCHIL'
+ ,'BUDAMB'
+ ,'BUDSISSS
')
+ ,1:130))
> y <- array(NA,dim=c(15,130),dimnames=list(c('QBEFRU','PBEPIL','PBEFRU','PBEREG','PCHEXO','PAMMOORA','PAMMOAPP','PAMMOGRA','PSOCOLA','PSOLEM','PSTILL','BUDBEER','BUDCHIL','BUDAMB','BUDSISSS
'),1:130))
> 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'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
QBEFRU PBEPIL PBEFRU PBEREG PCHEXO PAMMOORA PAMMOAPP PAMMOGRA PSOCOLA
1 178421 1.23 2.50 2.84 2.54 1.50 4.30 2.39 0.95
2 139871 1.22 2.59 2.85 2.58 1.48 4.30 2.59 0.97
3 118159 1.21 2.56 2.80 2.55 1.53 3.86 3.48 0.97
4 109763 1.22 2.59 2.83 2.56 1.53 3.67 3.36 0.95
5 97415 1.21 2.58 2.83 2.59 1.51 3.93 3.28 0.96
6 119190 1.22 2.62 2.80 2.57 1.52 4.09 3.41 0.96
7 97903 1.21 2.59 2.77 2.60 1.51 4.12 3.46 0.94
8 96953 1.20 2.58 2.75 2.57 1.47 4.05 3.38 0.96
9 87888 1.18 2.57 2.80 2.48 1.50 4.27 3.18 0.98
10 84637 1.19 2.57 2.85 2.51 1.52 4.14 3.47 0.97
11 90549 1.20 2.55 2.90 2.45 1.50 4.36 3.05 0.96
12 95680 1.19 2.51 2.79 2.47 1.48 4.29 3.37 0.95
13 99371 1.19 2.50 2.71 2.52 1.50 4.35 3.25 0.96
14 79984 1.20 2.59 2.79 2.50 1.51 4.20 3.30 0.96
15 86752 1.21 2.63 2.86 2.61 1.52 4.24 3.49 0.97
16 85733 1.20 2.63 2.95 2.60 1.51 4.35 3.55 0.96
17 84906 1.20 2.61 3.09 2.53 1.51 4.55 3.40 0.95
18 78356 1.20 2.64 3.15 2.53 1.49 4.58 3.11 0.95
19 108895 1.21 2.67 3.23 2.53 1.36 5.65 2.71 0.94
20 101768 1.21 2.63 3.13 2.53 1.37 5.66 2.71 0.94
21 73285 1.21 2.58 3.03 2.56 1.53 4.26 3.71 0.98
22 65724 1.20 2.56 2.88 2.68 1.52 4.12 3.74 0.93
23 67457 1.21 2.57 2.84 2.74 1.56 4.05 3.57 0.93
24 67203 1.21 2.55 2.85 2.75 1.57 4.20 3.32 0.96
25 69273 1.21 2.58 2.83 2.74 1.52 4.24 3.46 0.97
26 80807 1.20 2.50 2.82 2.75 1.53 4.26 3.51 0.97
27 75129 1.19 2.56 2.81 2.76 1.57 4.13 3.41 0.95
28 74991 1.20 2.62 2.75 2.78 1.56 4.06 3.56 0.95
29 68157 1.20 2.71 2.78 2.76 1.49 4.33 3.32 0.96
30 73858 1.20 2.74 2.80 2.75 1.57 4.08 3.47 0.98
31 71349 1.22 2.76 2.82 2.76 1.59 4.09 3.54 0.98
32 85634 1.22 2.66 2.86 2.73 1.59 4.03 3.19 0.97
33 91624 1.21 2.61 2.86 2.75 1.58 4.01 3.44 0.98
34 116014 1.25 2.68 2.84 2.78 1.53 4.13 3.54 0.98
35 120033 1.25 2.70 2.82 2.72 1.52 4.13 3.52 0.99
36 108651 1.27 2.70 2.83 2.69 1.50 4.25 3.10 0.99
37 105378 1.28 2.72 2.82 2.75 1.57 4.06 3.46 0.97
38 138939 1.27 2.77 2.85 2.79 1.51 4.30 3.24 0.98
39 132974 1.28 2.76 2.83 2.77 1.53 4.25 3.25 0.97
40 135277 1.29 2.72 2.82 2.77 1.54 4.24 3.60 0.97
41 152741 1.26 2.69 2.79 2.78 1.55 4.12 3.50 0.97
42 158417 1.27 2.70 2.76 2.78 1.53 4.21 2.99 0.98
43 157460 1.25 2.69 2.76 2.80 1.55 4.24 2.99 0.97
44 193997 1.27 2.66 2.79 2.79 1.58 4.04 3.07 0.97
45 154089 1.27 2.74 2.82 2.78 1.54 4.17 3.06 0.98
46 147570 1.27 2.76 2.81 2.76 1.51 4.31 2.98 0.98
47 162924 1.29 2.79 2.77 2.76 1.52 4.43 2.98 0.95
48 153629 1.26 2.78 2.78 2.77 1.52 4.49 2.53 0.97
49 155907 1.27 2.80 2.83 2.77 1.50 4.57 2.25 0.97
50 197675 1.27 2.78 2.83 2.70 1.52 4.45 2.43 0.97
51 250708 1.28 2.76 2.83 2.70 1.54 4.27 2.59 0.97
52 266652 1.28 2.73 2.79 2.68 1.58 4.16 2.21 0.98
53 209842 1.28 2.72 2.79 2.72 1.56 4.17 2.35 0.98
54 165826 1.27 2.73 2.77 2.74 1.57 3.88 2.40 0.98
55 137152 1.24 2.74 2.78 2.75 1.60 3.80 3.80 0.96
56 150581 1.25 2.72 2.79 2.75 1.57 3.92 3.53 0.98
57 145973 1.25 2.71 2.80 2.77 1.55 4.03 3.40 1.00
58 126532 1.24 2.66 2.77 2.77 1.55 3.93 3.65 1.01
59 115437 1.24 2.68 2.74 2.75 1.55 3.94 3.54 1.02
60 119526 1.23 2.67 2.77 2.76 1.55 4.02 3.55 1.01
61 110856 1.24 2.68 2.74 2.74 1.55 3.91 3.83 1.01
62 97243 1.23 2.67 2.81 2.73 1.56 3.93 3.82 1.02
63 103876 1.24 2.71 2.76 2.75 1.52 4.01 3.58 1.01
64 116370 1.24 2.69 2.87 2.73 1.51 4.07 3.69 1.01
65 109616 1.24 2.64 2.86 2.71 1.51 4.15 3.45 1.01
66 98365 1.25 2.66 2.84 2.70 1.53 4.08 3.38 1.02
67 90440 1.26 2.70 2.87 2.74 1.53 4.04 3.25 1.02
68 88899 1.26 2.69 2.93 2.73 1.53 3.99 3.63 1.02
69 92358 1.27 2.71 3.00 2.74 1.50 4.14 3.55 1.01
70 88394 1.26 2.74 3.03 2.73 1.48 4.18 3.46 1.01
71 98219 1.28 2.78 3.12 2.74 1.39 4.89 3.01 0.99
72 113546 1.29 2.79 3.20 2.75 1.36 5.10 3.09 1.00
73 107168 1.28 2.75 3.07 2.79 1.45 4.25 3.77 1.01
74 77540 1.27 2.69 2.93 2.80 1.51 3.70 3.84 0.99
75 74944 1.30 2.69 2.86 2.80 1.52 3.85 3.71 1.00
76 75641 1.30 2.69 2.84 2.78 1.52 3.87 3.72 1.02
77 75910 1.28 2.72 2.82 2.77 1.53 3.78 3.49 1.01
78 87384 1.29 2.69 2.84 2.78 1.54 3.74 3.64 1.01
79 84615 1.27 2.70 2.88 2.81 1.54 3.76 3.52 1.01
80 80420 1.26 2.68 2.83 2.72 1.51 3.91 3.21 1.03
81 80784 1.27 2.70 2.84 2.66 1.51 3.79 3.49 1.02
82 79933 1.27 2.72 2.87 2.72 1.50 3.70 3.50 1.02
83 82118 1.27 2.70 2.90 2.74 1.52 3.74 3.61 1.03
84 91420 1.28 2.66 2.87 2.77 1.57 3.71 3.48 1.03
85 112426 1.29 2.68 2.92 2.79 1.57 3.72 3.72 1.02
86 114528 1.28 2.65 2.89 2.84 1.47 3.82 3.13 1.02
87 131025 1.30 2.69 2.90 2.84 1.48 3.98 3.12 1.02
88 116460 1.30 2.66 2.85 2.86 1.54 3.75 3.37 1.02
89 111258 1.30 2.69 2.82 2.86 1.54 3.65 3.36 1.03
90 155318 1.29 2.69 2.85 2.89 1.50 3.69 3.39 1.02
91 155078 1.30 2.65 2.86 2.89 1.51 3.84 3.53 1.02
92 134794 1.29 2.66 2.88 2.80 1.52 4.22 3.21 1.02
93 139985 1.28 2.63 2.86 2.87 1.50 4.10 3.05 1.03
94 198778 1.30 2.65 2.83 2.89 1.53 3.93 3.11 1.02
95 172436 1.30 2.60 2.84 2.91 1.57 3.70 3.18 1.02
96 169585 1.31 2.57 2.86 2.90 1.56 3.81 2.87 1.02
97 203702 1.32 2.65 2.85 2.90 1.52 3.83 2.89 1.02
98 282392 1.33 2.69 2.86 2.90 1.49 4.18 2.81 1.02
99 220658 1.32 2.71 2.89 2.76 1.49 4.10 2.89 1.00
100 194472 1.30 2.72 2.87 2.71 1.49 4.26 2.82 1.04
101 269246 1.31 2.73 2.84 2.74 1.49 4.32 2.64 1.04
102 215340 1.30 2.72 2.79 2.79 1.51 4.19 2.55 1.03
103 218319 1.30 2.73 2.86 2.85 1.52 3.86 2.54 1.02
104 195724 1.30 2.72 2.86 2.87 1.54 3.84 2.46 1.04
105 174614 1.29 2.70 2.87 2.89 1.53 3.91 2.59 1.05
106 172085 1.29 2.72 2.85 2.90 1.53 4.01 2.68 1.03
107 152347 1.30 2.70 2.88 2.90 1.55 3.66 3.33 0.99
108 189615 1.30 2.72 2.88 2.88 1.58 3.63 3.41 1.03
109 173804 1.29 2.70 2.87 2.91 1.58 3.57 3.30 1.08
110 145683 1.27 2.65 2.86 2.90 1.54 3.66 3.51 1.09
111 133550 1.26 2.66 2.85 2.91 1.53 3.74 3.50 1.08
112 121156 1.25 2.69 2.81 2.91 1.53 3.85 3.46 1.05
113 112040 1.26 2.70 2.81 2.91 1.52 3.98 3.36 1.06
114 120767 1.27 2.71 2.83 2.90 1.52 3.84 3.52 1.04
115 127019 1.26 2.69 2.93 2.91 1.52 3.81 3.48 1.06
116 136295 1.25 2.72 2.88 2.89 1.49 3.90 3.17 1.06
117 113425 1.25 2.71 2.86 2.88 1.50 3.91 3.08 1.07
118 107815 1.25 2.71 2.86 2.90 1.50 3.93 3.32 1.08
119 100298 1.26 2.74 2.90 2.90 1.50 3.80 3.51 1.08
120 97048 1.26 2.82 2.96 2.90 1.51 3.75 3.57 1.05
121 98750 1.26 2.76 3.02 2.90 1.50 3.86 3.67 1.04
122 98235 1.27 2.77 3.15 2.90 1.82 4.03 0.85 1.04
123 101254 1.28 2.77 3.21 2.91 1.45 4.34 2.97 1.04
124 139589 1.29 2.81 3.30 2.91 1.36 5.00 2.88 1.04
125 134921 1.30 2.77 3.14 2.90 1.38 4.86 2.99 1.06
126 80355 1.26 2.76 2.99 2.91 1.53 3.79 3.48 1.08
127 80396 1.25 2.73 2.97 2.83 1.60 3.80 3.57 1.08
128 82183 1.26 2.72 2.98 2.76 1.58 3.90 3.54 1.08
129 79709 1.25 2.73 2.95 2.84 1.55 3.89 3.67 1.07
130 90781 1.24 2.71 2.92 2.88 1.57 3.77 3.50 1.06
PSOLEM PSTILL BUDBEER BUDCHIL BUDAMB BUDSISSS\r
1 0.81 0.97 8890176 484574 2254011 10064618
2 0.81 0.98 8194413 478106 2013875 11338363
3 0.81 1.00 7722000 506039 2308944 9435079
4 0.81 1.00 7769178 508171 2278649 8143581
5 0.81 0.98 7449343 468388 2109718 7775342
6 0.81 1.01 7929370 466709 2070365 7656876
7 0.80 1.00 7473017 499053 2041975 8203164
8 0.80 1.00 7472424 499697 2130112 8447687
9 0.79 1.01 7292436 456662 2012391 8482877
10 0.80 1.03 7215340 467478 1995215 8131924
11 0.80 1.00 7216230 453126 1959695 8184292
12 0.79 1.00 7378041 449584 2079820 8006102
13 0.81 0.99 7877412 423896 2201750 8052832
14 0.81 1.01 7158125 460454 1980527 7854934
15 0.79 1.02 7137912 454105 2023721 7609626
16 0.80 1.02 7290803 453042 2136317 7640934
17 0.77 1.01 7425266 433082 2205673 8422297
18 0.78 1.01 7450430 460163 2163485 7980377
19 0.76 0.96 9214042 421051 2844091 9541323
20 0.77 0.96 8158864 435182 2458147 8839590
21 0.81 1.02 6515759 495363 1972304 7677033
22 0.80 0.99 6308487 472805 2153601 8354688
23 0.80 1.02 6366367 452921 2066530 8150927
24 0.79 1.03 6770097 450870 2152437 7846633
25 0.79 1.04 6700697 472551 2189294 8461058
26 0.81 1.02 7140792 462772 2253024 8425126
27 0.81 1.04 6891715 507189 2151817 8351766
28 0.81 1.04 7057521 513235 2141496 7956264
29 0.80 1.04 6806593 602342 2240864 8502847
30 0.82 1.04 7068776 638260 2198530 8671279
31 0.81 1.04 6868085 618068 2213237 8230049
32 0.79 1.03 7245015 607338 2252202 8404517
33 0.81 1.01 7160726 1002379 2419597 8872254
34 0.80 0.98 7927365 755302 2334515 9651748
35 0.80 1.01 8275238 724580 2155819 9070647
36 0.82 1.04 7510220 706447 2532345 8649186
37 0.81 1.03 7751398 991278 2221561 9030492
38 0.82 1.04 8701633 852996 2302538 9069668
39 0.82 1.04 8164755 673183 2350319 9116009
40 0.82 1.04 8534307 686730 2287028 10336764
41 0.83 1.04 8333017 768403 2262802 8941018
42 0.84 1.05 8568251 720603 2641195 10163717
43 0.83 1.04 8613013 688646 2886395 10028886
44 0.84 1.03 9139357 717093 2430852 10190148
45 0.84 1.03 8385716 806356 2412703 11198930
46 0.83 1.00 8451237 649995 2365468 10355548
47 0.83 1.02 9033401 540044 2057798 9396952
48 0.84 1.03 8565930 591115 2390239 9238064
49 0.83 0.99 8562307 493197 2456918 9286880
50 0.85 1.01 9255216 574142 2048758 10943146
51 0.84 0.99 10502760 545220 2513095 11297607
52 0.84 0.99 10855161 484423 2887292 9982802
53 0.85 0.99 9473338 561620 2295291 11849225
54 0.84 1.03 8521439 554667 2160295 9895998
55 0.84 1.07 8169912 695658 2430452 10512292
56 0.84 1.07 8705590 694559 2381670 10001971
57 0.84 1.08 8600302 613095 2215665 9450060
58 0.83 1.07 7884570 602933 2350453 9047810
59 0.83 1.09 7509946 614260 2263940 9034858
60 0.84 1.06 7796000 580581 2223827 9626461
61 0.84 1.07 7651158 617713 2071658 8887882
62 0.84 1.07 7430052 605519 2118606 8699165
63 0.83 1.08 7581024 609843 1980701 8756626
64 0.82 1.08 8431470 592140 2141976 9120578
65 0.82 1.09 7903994 582844 2262595 9410935
66 0.84 1.12 7462642 614646 2044949 8540660
67 0.82 1.11 7424743 607572 2055490 8577630
68 0.82 1.10 7480504 620835 2111968 8963865
69 0.81 1.09 7863944 581938 2153156 8831677
70 0.82 1.07 7703698 609333 2149987 8680975
71 0.81 1.04 8508132 619133 2805043 10889743
72 0.82 1.01 8933008 572585 2449477 9842291
73 0.84 1.08 8491850 599516 2168905 8005657
74 0.83 1.07 6940275 655034 2218929 8714475
75 0.84 1.10 6917191 668502 2144176 8555468
76 0.84 1.10 7096722 666124 2170967 8571300
77 0.83 1.09 7105114 732417 2240876 8764326
78 0.83 1.08 7647797 702229 2330906 9089938
79 0.83 1.11 7440408 684271 2188360 8778446
80 0.84 1.08 7255613 633638 2067367 8809264
81 0.84 1.05 7231703 693374 2189597 9521789
82 0.86 1.09 7278022 707616 2356724 9438993
83 0.87 1.09 7382680 722553 2250295 9045288
84 0.86 1.11 7622740 712532 2243913 9272049
85 0.85 1.12 8295038 687023 2172504 9978418
86 0.85 1.10 8136158 646716 2301051 9776284
87 0.85 1.08 8240817 657284 2245784 9601480
88 0.85 1.08 7993962 701042 2159896 11193789
89 0.87 1.10 7997958 744939 2374240 9607554
90 0.86 1.08 8914911 823561 2533022 9870457
91 0.88 1.10 9082346 810516 2419167 10260040
92 0.88 1.12 8690947 755964 2379061 9578120
93 0.88 1.11 8678669 707347 2264684 9693065
94 0.88 1.06 9768461 727181 2378165 12413462
95 0.86 1.08 8751448 1110335 2536093 13143933
96 0.89 1.11 8737854 939274 2559486 11118547
97 0.89 1.10 9684075 842499 2340159 11289800
98 0.88 1.08 11529582 785788 2235562 11573959
99 0.89 1.07 9854882 812169 2300728 10511958
100 0.91 1.08 9030507 730023 2090042 12515693
101 0.90 1.07 10656814 823033 1976051 12966759
102 0.88 1.08 9111428 976731 2104956 10668160
103 0.87 1.08 9642906 738606 2489023 13948692
104 0.89 1.07 9217060 685173 2598916 16087616
105 0.88 1.09 8816389 642519 2302455 12159456
106 0.85 1.08 9074790 677849 2427969 10633146
107 0.86 1.16 8601172 826348 2132820 10770809
108 0.87 1.13 9735782 757562 2560376 10548925
109 0.88 1.14 9222117 825217 2454605 10123204
110 0.91 1.10 8197462 831800 2169005 11471988
111 0.89 1.10 8161117 890944 2072759 10599314
112 0.86 1.11 8085780 818812 2201360 10501150
113 0.87 1.12 7777563 813389 2215184 9476948
114 0.87 1.11 8192525 791213 2140796 9854999
115 0.86 1.10 8222640 753162 2064345 9020688
116 0.85 1.09 8852425 744738 2246763 9639666
117 0.86 1.08 8047626 740853 2196948 10016963
118 0.88 1.11 8079925 828505 1987852 9221363
119 0.86 1.10 8099820 764325 2013311 9163961
120 0.85 1.10 7444464 779152 2024477 9600997
121 0.84 1.09 8060967 780635 2175719 9629093
122 0.85 1.08 7904184 772652 2459717 9266651
123 0.84 1.05 8532755 796751 2436148 11454028
124 0.84 1.04 10077590 774564 2533141 10051577
125 0.85 1.08 9163186 781545 2438635 8887058
126 0.85 1.09 7027349 846744 2294455 9590767
127 0.87 1.09 7000371 852583 2233829 9269821
128 0.87 1.10 7234027 837686 2231864 9242497
129 0.84 1.09 7166769 872753 2248620 9621983
130 0.85 1.09 7538708 863746 2348107 10101244
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PBEPIL PBEFRU PBEREG PCHEXO
-6.429e+04 4.161e+04 -1.300e+04 -7.161e+04 -2.435e+04
PAMMOORA PAMMOAPP PAMMOGRA PSOCOLA PSOLEM
7.780e+04 1.408e+03 -5.022e+03 -1.099e+05 2.584e+05
PSTILL BUDBEER BUDCHIL BUDAMB `BUDSISSS\\r`
-9.471e+04 3.905e-02 1.023e-02 -1.485e-02 4.172e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-27376 -6744 -164 6401 27039
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.429e+04 1.087e+05 -0.592 0.555343
PBEPIL 4.161e+04 5.898e+04 0.705 0.481927
PBEFRU -1.300e+04 1.860e+04 -0.699 0.485755
PBEREG -7.161e+04 1.233e+04 -5.809 5.7e-08 ***
PCHEXO -2.435e+04 1.510e+04 -1.613 0.109504
PAMMOORA 7.780e+04 2.910e+04 2.674 0.008592 **
PAMMOAPP 1.408e+03 6.014e+03 0.234 0.815370
PAMMOGRA -5.022e+03 3.500e+03 -1.435 0.154009
PSOCOLA -1.099e+05 5.624e+04 -1.953 0.053190 .
PSOLEM 2.584e+05 7.968e+04 3.243 0.001550 **
PSTILL -9.471e+04 4.640e+04 -2.041 0.043529 *
BUDBEER 3.905e-02 1.769e-03 22.083 < 2e-16 ***
BUDCHIL 1.023e-02 1.095e-02 0.935 0.351789
BUDAMB -1.485e-02 6.119e-03 -2.427 0.016783 *
`BUDSISSS\\r` 4.172e-03 1.118e-03 3.733 0.000296 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10030 on 115 degrees of freedom
Multiple R-squared: 0.9581, Adjusted R-squared: 0.953
F-statistic: 187.7 on 14 and 115 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,] 0.32426129 0.64852258 0.6757387
[2,] 0.23052565 0.46105131 0.7694743
[3,] 0.15066172 0.30132343 0.8493383
[4,] 0.20155086 0.40310173 0.7984491
[5,] 0.16069138 0.32138276 0.8393086
[6,] 0.16405216 0.32810432 0.8359478
[7,] 0.27511951 0.55023902 0.7248805
[8,] 0.20853633 0.41707267 0.7914637
[9,] 0.15715200 0.31430400 0.8428480
[10,] 0.11998784 0.23997568 0.8800122
[11,] 0.08022154 0.16044308 0.9197785
[12,] 0.05656614 0.11313228 0.9434339
[13,] 0.04692924 0.09385848 0.9530708
[14,] 0.03103263 0.06206526 0.9689674
[15,] 0.02875244 0.05750488 0.9712476
[16,] 0.02434616 0.04869233 0.9756538
[17,] 0.02554830 0.05109659 0.9744517
[18,] 0.02930127 0.05860253 0.9706987
[19,] 0.02193350 0.04386699 0.9780665
[20,] 0.06308906 0.12617813 0.9369109
[21,] 0.05812337 0.11624674 0.9418766
[22,] 0.04998339 0.09996677 0.9500166
[23,] 0.05171511 0.10343023 0.9482849
[24,] 0.19084733 0.38169466 0.8091527
[25,] 0.17675029 0.35350057 0.8232497
[26,] 0.20812929 0.41625859 0.7918707
[27,] 0.27243222 0.54486443 0.7275678
[28,] 0.22347616 0.44695232 0.7765238
[29,] 0.18129518 0.36259037 0.8187048
[30,] 0.18346216 0.36692433 0.8165378
[31,] 0.15014830 0.30029659 0.8498517
[32,] 0.12291007 0.24582015 0.8770899
[33,] 0.10279308 0.20558617 0.8972069
[34,] 0.16908002 0.33816004 0.8309200
[35,] 0.21777218 0.43554436 0.7822278
[36,] 0.20003217 0.40006433 0.7999678
[37,] 0.18176043 0.36352086 0.8182396
[38,] 0.14909552 0.29819104 0.8509045
[39,] 0.13577558 0.27155117 0.8642244
[40,] 0.11012296 0.22024591 0.8898770
[41,] 0.12411752 0.24823504 0.8758825
[42,] 0.14271800 0.28543601 0.8572820
[43,] 0.13161315 0.26322629 0.8683869
[44,] 0.10763229 0.21526457 0.8923677
[45,] 0.11409240 0.22818481 0.8859076
[46,] 0.08954299 0.17908597 0.9104570
[47,] 0.08644471 0.17288942 0.9135553
[48,] 0.08436034 0.16872068 0.9156397
[49,] 0.08001191 0.16002381 0.9199881
[50,] 0.06937416 0.13874831 0.9306258
[51,] 0.07238435 0.14476871 0.9276156
[52,] 0.07943625 0.15887249 0.9205638
[53,] 0.09423056 0.18846113 0.9057694
[54,] 0.12843290 0.25686580 0.8715671
[55,] 0.12146872 0.24293744 0.8785313
[56,] 0.11644581 0.23289161 0.8835542
[57,] 0.33132701 0.66265403 0.6686730
[58,] 0.33515731 0.67031463 0.6648427
[59,] 0.32060792 0.64121584 0.6793921
[60,] 0.30011920 0.60023840 0.6998808
[61,] 0.37981212 0.75962425 0.6201879
[62,] 0.36101470 0.72202940 0.6389853
[63,] 0.34216292 0.68432585 0.6578371
[64,] 0.34772071 0.69544143 0.6522793
[65,] 0.34325674 0.68651347 0.6567433
[66,] 0.34855176 0.69710352 0.6514482
[67,] 0.42598137 0.85196274 0.5740186
[68,] 0.47556788 0.95113576 0.5244321
[69,] 0.41562519 0.83125037 0.5843748
[70,] 0.40651443 0.81302886 0.5934856
[71,] 0.43339524 0.86679048 0.5666048
[72,] 0.73472072 0.53055855 0.2652793
[73,] 0.69655891 0.60688218 0.3034411
[74,] 0.70612147 0.58775707 0.2938785
[75,] 0.71962390 0.56075221 0.2803761
[76,] 0.65889123 0.68221754 0.3411088
[77,] 0.63844075 0.72311851 0.3615593
[78,] 0.67767794 0.64464412 0.3223221
[79,] 0.61586422 0.76827157 0.3841358
[80,] 0.67439213 0.65121574 0.3256079
[81,] 0.65112473 0.69775054 0.3488753
[82,] 0.66560647 0.66878706 0.3343935
[83,] 0.61639368 0.76721264 0.3836063
[84,] 0.62649911 0.74700179 0.3735009
[85,] 0.64883219 0.70233562 0.3511678
[86,] 0.79103502 0.41792996 0.2089650
[87,] 0.72342880 0.55314240 0.2765712
[88,] 0.68407816 0.63184369 0.3159218
[89,] 0.65878107 0.68243787 0.3412189
[90,] 0.57194808 0.85610385 0.4280519
[91,] 0.45951591 0.91903182 0.5404841
[92,] 0.34841866 0.69683732 0.6515813
[93,] 0.28094991 0.56189983 0.7190501
[94,] 0.46048333 0.92096666 0.5395167
[95,] 0.47212094 0.94424188 0.5278791
> postscript(file="/var/wessaorg/rcomp/tmp/12slc1356016373.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2jczn1356016373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3b7uw1356016373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ulgs1356016373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/524251356016373.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 = 130
Frequency = 1
1 2 3 4 5 6
4772.1076 -6437.8156 1144.9401 -4343.5898 -3756.3747 -841.7481
7 8 9 10 11 12
-8303.3884 -5818.5078 -5137.7212 -2089.3988 -170.9662 -1542.9729
13 14 15 16 17 18
-27376.3205 -13931.1758 10484.2808 8905.8693 13532.6269 9100.3318
19 20 21 22 23 24
-13607.7346 6469.9664 26383.3680 14725.0791 11242.9801 3069.3880
25 26 27 28 29 30
10908.9599 -2262.0895 -2874.4391 -9868.3420 1755.7888 -10992.2764
31 32 33 34 35 36
-928.7975 877.6565 3002.2640 -2025.5374 -9522.7531 12613.5506
37 38 39 40 41 42
-12542.5239 -6762.0429 5597.6065 -13362.6214 11848.8768 5240.9993
43 44 45 46 47 48
7195.5490 10948.0706 2004.0128 -2121.6488 -14603.0151 -269.7497
49 50 51 52 53 54
6195.2550 1380.8801 10918.0955 17255.5686 -2451.0441 2779.3868
55 56 57 58 59 60
-3429.9531 -6459.2176 -1318.1391 12726.5555 14967.0798 1256.6223
61 62 63 64 65 66
-1728.0721 200.7682 -156.9875 -8859.8408 3437.4606 4628.5811
67 68 69 70 71 72
5108.4919 5435.6563 2839.9489 4716.3868 -8799.2879 -6699.5866
73 74 75 76 77 78
-896.4958 13040.2105 4214.7077 -1443.4798 -3669.5636 -13148.6990
79 80 81 82 83 84
-1683.8914 -8346.4548 -12083.7290 -8616.7673 -10779.8638 -14168.4368
85 86 87 88 89 90
-15630.5736 -2280.9383 7385.3287 -12478.1231 -12326.3347 2470.9523
91 92 93 94 95 96
-11270.4101 -14699.4746 -9338.1119 -12835.6567 1095.3375 3862.7497
97 98 99 100 101 102
189.8094 7595.0230 10596.0397 3867.8015 9544.7451 27038.9431
103 104 105 106 107 108
11439.6086 -6759.4472 8469.9100 7709.0239 4262.4260 2049.9702
109 110 111 112 113 114
9372.1782 4154.0611 -195.4376 -3600.9146 3123.2294 -8052.3049
115 116 117 118 119 120
10994.0428 -5087.1544 -4282.1865 -11399.6936 -10223.5161 14530.1026
121 122 123 124 125 126
-552.1771 -22939.0170 -11327.9178 -14584.2033 9801.8758 19383.3657
127 128 129 130
7326.8844 902.9982 8414.8419 -2410.5228
> postscript(file="/var/wessaorg/rcomp/tmp/6pqae1356016373.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 = 130
Frequency = 1
lag(myerror, k = 1) myerror
0 4772.1076 NA
1 -6437.8156 4772.1076
2 1144.9401 -6437.8156
3 -4343.5898 1144.9401
4 -3756.3747 -4343.5898
5 -841.7481 -3756.3747
6 -8303.3884 -841.7481
7 -5818.5078 -8303.3884
8 -5137.7212 -5818.5078
9 -2089.3988 -5137.7212
10 -170.9662 -2089.3988
11 -1542.9729 -170.9662
12 -27376.3205 -1542.9729
13 -13931.1758 -27376.3205
14 10484.2808 -13931.1758
15 8905.8693 10484.2808
16 13532.6269 8905.8693
17 9100.3318 13532.6269
18 -13607.7346 9100.3318
19 6469.9664 -13607.7346
20 26383.3680 6469.9664
21 14725.0791 26383.3680
22 11242.9801 14725.0791
23 3069.3880 11242.9801
24 10908.9599 3069.3880
25 -2262.0895 10908.9599
26 -2874.4391 -2262.0895
27 -9868.3420 -2874.4391
28 1755.7888 -9868.3420
29 -10992.2764 1755.7888
30 -928.7975 -10992.2764
31 877.6565 -928.7975
32 3002.2640 877.6565
33 -2025.5374 3002.2640
34 -9522.7531 -2025.5374
35 12613.5506 -9522.7531
36 -12542.5239 12613.5506
37 -6762.0429 -12542.5239
38 5597.6065 -6762.0429
39 -13362.6214 5597.6065
40 11848.8768 -13362.6214
41 5240.9993 11848.8768
42 7195.5490 5240.9993
43 10948.0706 7195.5490
44 2004.0128 10948.0706
45 -2121.6488 2004.0128
46 -14603.0151 -2121.6488
47 -269.7497 -14603.0151
48 6195.2550 -269.7497
49 1380.8801 6195.2550
50 10918.0955 1380.8801
51 17255.5686 10918.0955
52 -2451.0441 17255.5686
53 2779.3868 -2451.0441
54 -3429.9531 2779.3868
55 -6459.2176 -3429.9531
56 -1318.1391 -6459.2176
57 12726.5555 -1318.1391
58 14967.0798 12726.5555
59 1256.6223 14967.0798
60 -1728.0721 1256.6223
61 200.7682 -1728.0721
62 -156.9875 200.7682
63 -8859.8408 -156.9875
64 3437.4606 -8859.8408
65 4628.5811 3437.4606
66 5108.4919 4628.5811
67 5435.6563 5108.4919
68 2839.9489 5435.6563
69 4716.3868 2839.9489
70 -8799.2879 4716.3868
71 -6699.5866 -8799.2879
72 -896.4958 -6699.5866
73 13040.2105 -896.4958
74 4214.7077 13040.2105
75 -1443.4798 4214.7077
76 -3669.5636 -1443.4798
77 -13148.6990 -3669.5636
78 -1683.8914 -13148.6990
79 -8346.4548 -1683.8914
80 -12083.7290 -8346.4548
81 -8616.7673 -12083.7290
82 -10779.8638 -8616.7673
83 -14168.4368 -10779.8638
84 -15630.5736 -14168.4368
85 -2280.9383 -15630.5736
86 7385.3287 -2280.9383
87 -12478.1231 7385.3287
88 -12326.3347 -12478.1231
89 2470.9523 -12326.3347
90 -11270.4101 2470.9523
91 -14699.4746 -11270.4101
92 -9338.1119 -14699.4746
93 -12835.6567 -9338.1119
94 1095.3375 -12835.6567
95 3862.7497 1095.3375
96 189.8094 3862.7497
97 7595.0230 189.8094
98 10596.0397 7595.0230
99 3867.8015 10596.0397
100 9544.7451 3867.8015
101 27038.9431 9544.7451
102 11439.6086 27038.9431
103 -6759.4472 11439.6086
104 8469.9100 -6759.4472
105 7709.0239 8469.9100
106 4262.4260 7709.0239
107 2049.9702 4262.4260
108 9372.1782 2049.9702
109 4154.0611 9372.1782
110 -195.4376 4154.0611
111 -3600.9146 -195.4376
112 3123.2294 -3600.9146
113 -8052.3049 3123.2294
114 10994.0428 -8052.3049
115 -5087.1544 10994.0428
116 -4282.1865 -5087.1544
117 -11399.6936 -4282.1865
118 -10223.5161 -11399.6936
119 14530.1026 -10223.5161
120 -552.1771 14530.1026
121 -22939.0170 -552.1771
122 -11327.9178 -22939.0170
123 -14584.2033 -11327.9178
124 9801.8758 -14584.2033
125 19383.3657 9801.8758
126 7326.8844 19383.3657
127 902.9982 7326.8844
128 8414.8419 902.9982
129 -2410.5228 8414.8419
130 NA -2410.5228
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6437.8156 4772.1076
[2,] 1144.9401 -6437.8156
[3,] -4343.5898 1144.9401
[4,] -3756.3747 -4343.5898
[5,] -841.7481 -3756.3747
[6,] -8303.3884 -841.7481
[7,] -5818.5078 -8303.3884
[8,] -5137.7212 -5818.5078
[9,] -2089.3988 -5137.7212
[10,] -170.9662 -2089.3988
[11,] -1542.9729 -170.9662
[12,] -27376.3205 -1542.9729
[13,] -13931.1758 -27376.3205
[14,] 10484.2808 -13931.1758
[15,] 8905.8693 10484.2808
[16,] 13532.6269 8905.8693
[17,] 9100.3318 13532.6269
[18,] -13607.7346 9100.3318
[19,] 6469.9664 -13607.7346
[20,] 26383.3680 6469.9664
[21,] 14725.0791 26383.3680
[22,] 11242.9801 14725.0791
[23,] 3069.3880 11242.9801
[24,] 10908.9599 3069.3880
[25,] -2262.0895 10908.9599
[26,] -2874.4391 -2262.0895
[27,] -9868.3420 -2874.4391
[28,] 1755.7888 -9868.3420
[29,] -10992.2764 1755.7888
[30,] -928.7975 -10992.2764
[31,] 877.6565 -928.7975
[32,] 3002.2640 877.6565
[33,] -2025.5374 3002.2640
[34,] -9522.7531 -2025.5374
[35,] 12613.5506 -9522.7531
[36,] -12542.5239 12613.5506
[37,] -6762.0429 -12542.5239
[38,] 5597.6065 -6762.0429
[39,] -13362.6214 5597.6065
[40,] 11848.8768 -13362.6214
[41,] 5240.9993 11848.8768
[42,] 7195.5490 5240.9993
[43,] 10948.0706 7195.5490
[44,] 2004.0128 10948.0706
[45,] -2121.6488 2004.0128
[46,] -14603.0151 -2121.6488
[47,] -269.7497 -14603.0151
[48,] 6195.2550 -269.7497
[49,] 1380.8801 6195.2550
[50,] 10918.0955 1380.8801
[51,] 17255.5686 10918.0955
[52,] -2451.0441 17255.5686
[53,] 2779.3868 -2451.0441
[54,] -3429.9531 2779.3868
[55,] -6459.2176 -3429.9531
[56,] -1318.1391 -6459.2176
[57,] 12726.5555 -1318.1391
[58,] 14967.0798 12726.5555
[59,] 1256.6223 14967.0798
[60,] -1728.0721 1256.6223
[61,] 200.7682 -1728.0721
[62,] -156.9875 200.7682
[63,] -8859.8408 -156.9875
[64,] 3437.4606 -8859.8408
[65,] 4628.5811 3437.4606
[66,] 5108.4919 4628.5811
[67,] 5435.6563 5108.4919
[68,] 2839.9489 5435.6563
[69,] 4716.3868 2839.9489
[70,] -8799.2879 4716.3868
[71,] -6699.5866 -8799.2879
[72,] -896.4958 -6699.5866
[73,] 13040.2105 -896.4958
[74,] 4214.7077 13040.2105
[75,] -1443.4798 4214.7077
[76,] -3669.5636 -1443.4798
[77,] -13148.6990 -3669.5636
[78,] -1683.8914 -13148.6990
[79,] -8346.4548 -1683.8914
[80,] -12083.7290 -8346.4548
[81,] -8616.7673 -12083.7290
[82,] -10779.8638 -8616.7673
[83,] -14168.4368 -10779.8638
[84,] -15630.5736 -14168.4368
[85,] -2280.9383 -15630.5736
[86,] 7385.3287 -2280.9383
[87,] -12478.1231 7385.3287
[88,] -12326.3347 -12478.1231
[89,] 2470.9523 -12326.3347
[90,] -11270.4101 2470.9523
[91,] -14699.4746 -11270.4101
[92,] -9338.1119 -14699.4746
[93,] -12835.6567 -9338.1119
[94,] 1095.3375 -12835.6567
[95,] 3862.7497 1095.3375
[96,] 189.8094 3862.7497
[97,] 7595.0230 189.8094
[98,] 10596.0397 7595.0230
[99,] 3867.8015 10596.0397
[100,] 9544.7451 3867.8015
[101,] 27038.9431 9544.7451
[102,] 11439.6086 27038.9431
[103,] -6759.4472 11439.6086
[104,] 8469.9100 -6759.4472
[105,] 7709.0239 8469.9100
[106,] 4262.4260 7709.0239
[107,] 2049.9702 4262.4260
[108,] 9372.1782 2049.9702
[109,] 4154.0611 9372.1782
[110,] -195.4376 4154.0611
[111,] -3600.9146 -195.4376
[112,] 3123.2294 -3600.9146
[113,] -8052.3049 3123.2294
[114,] 10994.0428 -8052.3049
[115,] -5087.1544 10994.0428
[116,] -4282.1865 -5087.1544
[117,] -11399.6936 -4282.1865
[118,] -10223.5161 -11399.6936
[119,] 14530.1026 -10223.5161
[120,] -552.1771 14530.1026
[121,] -22939.0170 -552.1771
[122,] -11327.9178 -22939.0170
[123,] -14584.2033 -11327.9178
[124,] 9801.8758 -14584.2033
[125,] 19383.3657 9801.8758
[126,] 7326.8844 19383.3657
[127,] 902.9982 7326.8844
[128,] 8414.8419 902.9982
[129,] -2410.5228 8414.8419
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6437.8156 4772.1076
2 1144.9401 -6437.8156
3 -4343.5898 1144.9401
4 -3756.3747 -4343.5898
5 -841.7481 -3756.3747
6 -8303.3884 -841.7481
7 -5818.5078 -8303.3884
8 -5137.7212 -5818.5078
9 -2089.3988 -5137.7212
10 -170.9662 -2089.3988
11 -1542.9729 -170.9662
12 -27376.3205 -1542.9729
13 -13931.1758 -27376.3205
14 10484.2808 -13931.1758
15 8905.8693 10484.2808
16 13532.6269 8905.8693
17 9100.3318 13532.6269
18 -13607.7346 9100.3318
19 6469.9664 -13607.7346
20 26383.3680 6469.9664
21 14725.0791 26383.3680
22 11242.9801 14725.0791
23 3069.3880 11242.9801
24 10908.9599 3069.3880
25 -2262.0895 10908.9599
26 -2874.4391 -2262.0895
27 -9868.3420 -2874.4391
28 1755.7888 -9868.3420
29 -10992.2764 1755.7888
30 -928.7975 -10992.2764
31 877.6565 -928.7975
32 3002.2640 877.6565
33 -2025.5374 3002.2640
34 -9522.7531 -2025.5374
35 12613.5506 -9522.7531
36 -12542.5239 12613.5506
37 -6762.0429 -12542.5239
38 5597.6065 -6762.0429
39 -13362.6214 5597.6065
40 11848.8768 -13362.6214
41 5240.9993 11848.8768
42 7195.5490 5240.9993
43 10948.0706 7195.5490
44 2004.0128 10948.0706
45 -2121.6488 2004.0128
46 -14603.0151 -2121.6488
47 -269.7497 -14603.0151
48 6195.2550 -269.7497
49 1380.8801 6195.2550
50 10918.0955 1380.8801
51 17255.5686 10918.0955
52 -2451.0441 17255.5686
53 2779.3868 -2451.0441
54 -3429.9531 2779.3868
55 -6459.2176 -3429.9531
56 -1318.1391 -6459.2176
57 12726.5555 -1318.1391
58 14967.0798 12726.5555
59 1256.6223 14967.0798
60 -1728.0721 1256.6223
61 200.7682 -1728.0721
62 -156.9875 200.7682
63 -8859.8408 -156.9875
64 3437.4606 -8859.8408
65 4628.5811 3437.4606
66 5108.4919 4628.5811
67 5435.6563 5108.4919
68 2839.9489 5435.6563
69 4716.3868 2839.9489
70 -8799.2879 4716.3868
71 -6699.5866 -8799.2879
72 -896.4958 -6699.5866
73 13040.2105 -896.4958
74 4214.7077 13040.2105
75 -1443.4798 4214.7077
76 -3669.5636 -1443.4798
77 -13148.6990 -3669.5636
78 -1683.8914 -13148.6990
79 -8346.4548 -1683.8914
80 -12083.7290 -8346.4548
81 -8616.7673 -12083.7290
82 -10779.8638 -8616.7673
83 -14168.4368 -10779.8638
84 -15630.5736 -14168.4368
85 -2280.9383 -15630.5736
86 7385.3287 -2280.9383
87 -12478.1231 7385.3287
88 -12326.3347 -12478.1231
89 2470.9523 -12326.3347
90 -11270.4101 2470.9523
91 -14699.4746 -11270.4101
92 -9338.1119 -14699.4746
93 -12835.6567 -9338.1119
94 1095.3375 -12835.6567
95 3862.7497 1095.3375
96 189.8094 3862.7497
97 7595.0230 189.8094
98 10596.0397 7595.0230
99 3867.8015 10596.0397
100 9544.7451 3867.8015
101 27038.9431 9544.7451
102 11439.6086 27038.9431
103 -6759.4472 11439.6086
104 8469.9100 -6759.4472
105 7709.0239 8469.9100
106 4262.4260 7709.0239
107 2049.9702 4262.4260
108 9372.1782 2049.9702
109 4154.0611 9372.1782
110 -195.4376 4154.0611
111 -3600.9146 -195.4376
112 3123.2294 -3600.9146
113 -8052.3049 3123.2294
114 10994.0428 -8052.3049
115 -5087.1544 10994.0428
116 -4282.1865 -5087.1544
117 -11399.6936 -4282.1865
118 -10223.5161 -11399.6936
119 14530.1026 -10223.5161
120 -552.1771 14530.1026
121 -22939.0170 -552.1771
122 -11327.9178 -22939.0170
123 -14584.2033 -11327.9178
124 9801.8758 -14584.2033
125 19383.3657 9801.8758
126 7326.8844 19383.3657
127 902.9982 7326.8844
128 8414.8419 902.9982
129 -2410.5228 8414.8419
> 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/wessaorg/rcomp/tmp/7fxjs1356016373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8bube1356016373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/90aod1356016373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10g4sg1356016373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11r60e1356016373.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/wessaorg/rcomp/tmp/12odak1356016373.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/wessaorg/rcomp/tmp/13irms1356016373.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/wessaorg/rcomp/tmp/14415g1356016373.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/wessaorg/rcomp/tmp/15muzs1356016373.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/wessaorg/rcomp/tmp/1665031356016373.tab")
+ }
>
> try(system("convert tmp/12slc1356016373.ps tmp/12slc1356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jczn1356016373.ps tmp/2jczn1356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b7uw1356016373.ps tmp/3b7uw1356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ulgs1356016373.ps tmp/4ulgs1356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/524251356016373.ps tmp/524251356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pqae1356016373.ps tmp/6pqae1356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fxjs1356016373.ps tmp/7fxjs1356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bube1356016373.ps tmp/8bube1356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/90aod1356016373.ps tmp/90aod1356016373.png",intern=TRUE))
character(0)
> try(system("convert tmp/10g4sg1356016373.ps tmp/10g4sg1356016373.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.377 1.336 11.028