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 = 'Include Quarterly Dummies'
> par1 = '1'
> 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 Q1 Q2 Q3
1 0.81 0.97 8890176 484574 2254011 10064618 1 0 0
2 0.81 0.98 8194413 478106 2013875 11338363 0 1 0
3 0.81 1.00 7722000 506039 2308944 9435079 0 0 1
4 0.81 1.00 7769178 508171 2278649 8143581 0 0 0
5 0.81 0.98 7449343 468388 2109718 7775342 1 0 0
6 0.81 1.01 7929370 466709 2070365 7656876 0 1 0
7 0.80 1.00 7473017 499053 2041975 8203164 0 0 1
8 0.80 1.00 7472424 499697 2130112 8447687 0 0 0
9 0.79 1.01 7292436 456662 2012391 8482877 1 0 0
10 0.80 1.03 7215340 467478 1995215 8131924 0 1 0
11 0.80 1.00 7216230 453126 1959695 8184292 0 0 1
12 0.79 1.00 7378041 449584 2079820 8006102 0 0 0
13 0.81 0.99 7877412 423896 2201750 8052832 1 0 0
14 0.81 1.01 7158125 460454 1980527 7854934 0 1 0
15 0.79 1.02 7137912 454105 2023721 7609626 0 0 1
16 0.80 1.02 7290803 453042 2136317 7640934 0 0 0
17 0.77 1.01 7425266 433082 2205673 8422297 1 0 0
18 0.78 1.01 7450430 460163 2163485 7980377 0 1 0
19 0.76 0.96 9214042 421051 2844091 9541323 0 0 1
20 0.77 0.96 8158864 435182 2458147 8839590 0 0 0
21 0.81 1.02 6515759 495363 1972304 7677033 1 0 0
22 0.80 0.99 6308487 472805 2153601 8354688 0 1 0
23 0.80 1.02 6366367 452921 2066530 8150927 0 0 1
24 0.79 1.03 6770097 450870 2152437 7846633 0 0 0
25 0.79 1.04 6700697 472551 2189294 8461058 1 0 0
26 0.81 1.02 7140792 462772 2253024 8425126 0 1 0
27 0.81 1.04 6891715 507189 2151817 8351766 0 0 1
28 0.81 1.04 7057521 513235 2141496 7956264 0 0 0
29 0.80 1.04 6806593 602342 2240864 8502847 1 0 0
30 0.82 1.04 7068776 638260 2198530 8671279 0 1 0
31 0.81 1.04 6868085 618068 2213237 8230049 0 0 1
32 0.79 1.03 7245015 607338 2252202 8404517 0 0 0
33 0.81 1.01 7160726 1002379 2419597 8872254 1 0 0
34 0.80 0.98 7927365 755302 2334515 9651748 0 1 0
35 0.80 1.01 8275238 724580 2155819 9070647 0 0 1
36 0.82 1.04 7510220 706447 2532345 8649186 0 0 0
37 0.81 1.03 7751398 991278 2221561 9030492 1 0 0
38 0.82 1.04 8701633 852996 2302538 9069668 0 1 0
39 0.82 1.04 8164755 673183 2350319 9116009 0 0 1
40 0.82 1.04 8534307 686730 2287028 10336764 0 0 0
41 0.83 1.04 8333017 768403 2262802 8941018 1 0 0
42 0.84 1.05 8568251 720603 2641195 10163717 0 1 0
43 0.83 1.04 8613013 688646 2886395 10028886 0 0 1
44 0.84 1.03 9139357 717093 2430852 10190148 0 0 0
45 0.84 1.03 8385716 806356 2412703 11198930 1 0 0
46 0.83 1.00 8451237 649995 2365468 10355548 0 1 0
47 0.83 1.02 9033401 540044 2057798 9396952 0 0 1
48 0.84 1.03 8565930 591115 2390239 9238064 0 0 0
49 0.83 0.99 8562307 493197 2456918 9286880 1 0 0
50 0.85 1.01 9255216 574142 2048758 10943146 0 1 0
51 0.84 0.99 10502760 545220 2513095 11297607 0 0 1
52 0.84 0.99 10855161 484423 2887292 9982802 0 0 0
53 0.85 0.99 9473338 561620 2295291 11849225 1 0 0
54 0.84 1.03 8521439 554667 2160295 9895998 0 1 0
55 0.84 1.07 8169912 695658 2430452 10512292 0 0 1
56 0.84 1.07 8705590 694559 2381670 10001971 0 0 0
57 0.84 1.08 8600302 613095 2215665 9450060 1 0 0
58 0.83 1.07 7884570 602933 2350453 9047810 0 1 0
59 0.83 1.09 7509946 614260 2263940 9034858 0 0 1
60 0.84 1.06 7796000 580581 2223827 9626461 0 0 0
61 0.84 1.07 7651158 617713 2071658 8887882 1 0 0
62 0.84 1.07 7430052 605519 2118606 8699165 0 1 0
63 0.83 1.08 7581024 609843 1980701 8756626 0 0 1
64 0.82 1.08 8431470 592140 2141976 9120578 0 0 0
65 0.82 1.09 7903994 582844 2262595 9410935 1 0 0
66 0.84 1.12 7462642 614646 2044949 8540660 0 1 0
67 0.82 1.11 7424743 607572 2055490 8577630 0 0 1
68 0.82 1.10 7480504 620835 2111968 8963865 0 0 0
69 0.81 1.09 7863944 581938 2153156 8831677 1 0 0
70 0.82 1.07 7703698 609333 2149987 8680975 0 1 0
71 0.81 1.04 8508132 619133 2805043 10889743 0 0 1
72 0.82 1.01 8933008 572585 2449477 9842291 0 0 0
73 0.84 1.08 8491850 599516 2168905 8005657 1 0 0
74 0.83 1.07 6940275 655034 2218929 8714475 0 1 0
75 0.84 1.10 6917191 668502 2144176 8555468 0 0 1
76 0.84 1.10 7096722 666124 2170967 8571300 0 0 0
77 0.83 1.09 7105114 732417 2240876 8764326 1 0 0
78 0.83 1.08 7647797 702229 2330906 9089938 0 1 0
79 0.83 1.11 7440408 684271 2188360 8778446 0 0 1
80 0.84 1.08 7255613 633638 2067367 8809264 0 0 0
81 0.84 1.05 7231703 693374 2189597 9521789 1 0 0
82 0.86 1.09 7278022 707616 2356724 9438993 0 1 0
83 0.87 1.09 7382680 722553 2250295 9045288 0 0 1
84 0.86 1.11 7622740 712532 2243913 9272049 0 0 0
85 0.85 1.12 8295038 687023 2172504 9978418 1 0 0
86 0.85 1.10 8136158 646716 2301051 9776284 0 1 0
87 0.85 1.08 8240817 657284 2245784 9601480 0 0 1
88 0.85 1.08 7993962 701042 2159896 11193789 0 0 0
89 0.87 1.10 7997958 744939 2374240 9607554 1 0 0
90 0.86 1.08 8914911 823561 2533022 9870457 0 1 0
91 0.88 1.10 9082346 810516 2419167 10260040 0 0 1
92 0.88 1.12 8690947 755964 2379061 9578120 0 0 0
93 0.88 1.11 8678669 707347 2264684 9693065 1 0 0
94 0.88 1.06 9768461 727181 2378165 12413462 0 1 0
95 0.86 1.08 8751448 1110335 2536093 13143933 0 0 1
96 0.89 1.11 8737854 939274 2559486 11118547 0 0 0
97 0.89 1.10 9684075 842499 2340159 11289800 1 0 0
98 0.88 1.08 11529582 785788 2235562 11573959 0 1 0
99 0.89 1.07 9854882 812169 2300728 10511958 0 0 1
100 0.91 1.08 9030507 730023 2090042 12515693 0 0 0
101 0.90 1.07 10656814 823033 1976051 12966759 1 0 0
102 0.88 1.08 9111428 976731 2104956 10668160 0 1 0
103 0.87 1.08 9642906 738606 2489023 13948692 0 0 1
104 0.89 1.07 9217060 685173 2598916 16087616 0 0 0
105 0.88 1.09 8816389 642519 2302455 12159456 1 0 0
106 0.85 1.08 9074790 677849 2427969 10633146 0 1 0
107 0.86 1.16 8601172 826348 2132820 10770809 0 0 1
108 0.87 1.13 9735782 757562 2560376 10548925 0 0 0
109 0.88 1.14 9222117 825217 2454605 10123204 1 0 0
110 0.91 1.10 8197462 831800 2169005 11471988 0 1 0
111 0.89 1.10 8161117 890944 2072759 10599314 0 0 1
112 0.86 1.11 8085780 818812 2201360 10501150 0 0 0
113 0.87 1.12 7777563 813389 2215184 9476948 1 0 0
114 0.87 1.11 8192525 791213 2140796 9854999 0 1 0
115 0.86 1.10 8222640 753162 2064345 9020688 0 0 1
116 0.85 1.09 8852425 744738 2246763 9639666 0 0 0
117 0.86 1.08 8047626 740853 2196948 10016963 1 0 0
118 0.88 1.11 8079925 828505 1987852 9221363 0 1 0
119 0.86 1.10 8099820 764325 2013311 9163961 0 0 1
120 0.85 1.10 7444464 779152 2024477 9600997 0 0 0
121 0.84 1.09 8060967 780635 2175719 9629093 1 0 0
122 0.85 1.08 7904184 772652 2459717 9266651 0 1 0
123 0.84 1.05 8532755 796751 2436148 11454028 0 0 1
124 0.84 1.04 10077590 774564 2533141 10051577 0 0 0
125 0.85 1.08 9163186 781545 2438635 8887058 1 0 0
126 0.85 1.09 7027349 846744 2294455 9590767 0 1 0
127 0.87 1.09 7000371 852583 2233829 9269821 0 0 1
128 0.87 1.10 7234027 837686 2231864 9242497 0 0 0
129 0.84 1.09 7166769 872753 2248620 9621983 1 0 0
130 0.85 1.09 7538708 863746 2348107 10101244 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PBEPIL PBEFRU PBEREG PCHEXO
-7.776e+04 4.331e+04 -1.337e+04 -7.180e+04 -2.505e+04
PAMMOORA PAMMOAPP PAMMOGRA PSOCOLA PSOLEM
7.971e+04 1.884e+03 -4.758e+03 -1.077e+05 2.609e+05
PSTILL BUDBEER BUDCHIL BUDAMB `BUDSISSS\\r`
-9.274e+04 3.905e-02 8.936e-03 -1.401e-02 4.192e-03
Q1 Q2 Q3
2.224e+03 1.809e+03 2.022e+03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-28259.4 -6284.4 -139.7 6652.7 27154.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.776e+04 1.108e+05 -0.702 0.484241
PBEPIL 4.331e+04 5.977e+04 0.725 0.470231
PBEFRU -1.337e+04 1.888e+04 -0.708 0.480328
PBEREG -7.180e+04 1.249e+04 -5.747 7.94e-08 ***
PCHEXO -2.505e+04 1.529e+04 -1.638 0.104233
PAMMOORA 7.971e+04 2.946e+04 2.706 0.007880 **
PAMMOAPP 1.884e+03 6.128e+03 0.307 0.759047
PAMMOGRA -4.758e+03 3.569e+03 -1.333 0.185179
PSOCOLA -1.077e+05 5.758e+04 -1.870 0.064061 .
PSOLEM 2.609e+05 8.071e+04 3.233 0.001611 **
PSTILL -9.274e+04 4.718e+04 -1.966 0.051810 .
BUDBEER 3.905e-02 1.786e-03 21.870 < 2e-16 ***
BUDCHIL 8.936e-03 1.113e-02 0.803 0.423819
BUDAMB -1.401e-02 6.249e-03 -2.242 0.026951 *
`BUDSISSS\\r` 4.192e-03 1.135e-03 3.692 0.000345 ***
Q1 2.224e+03 2.565e+03 0.867 0.387643
Q2 1.809e+03 2.608e+03 0.693 0.489453
Q3 2.022e+03 2.579e+03 0.784 0.434535
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10120 on 112 degrees of freedom
Multiple R-squared: 0.9584, Adjusted R-squared: 0.9521
F-statistic: 151.9 on 17 and 112 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,] 0.20943082 0.41886163 0.7905692
[2,] 0.26177452 0.52354904 0.7382255
[3,] 0.15773963 0.31547927 0.8422604
[4,] 0.53182908 0.93634184 0.4681709
[5,] 0.42748888 0.85497775 0.5725111
[6,] 0.31957695 0.63915389 0.6804231
[7,] 0.24181789 0.48363577 0.7581821
[8,] 0.18190510 0.36381020 0.8180949
[9,] 0.13003041 0.26006082 0.8699696
[10,] 0.09424821 0.18849642 0.9057518
[11,] 0.06153789 0.12307579 0.9384621
[12,] 0.04813326 0.09626652 0.9518667
[13,] 0.03796085 0.07592171 0.9620391
[14,] 0.03227765 0.06455531 0.9677223
[15,] 0.04020103 0.08040205 0.9597990
[16,] 0.02898692 0.05797384 0.9710131
[17,] 0.10889751 0.21779502 0.8911025
[18,] 0.10508463 0.21016926 0.8949154
[19,] 0.08633553 0.17267106 0.9136645
[20,] 0.09068341 0.18136682 0.9093166
[21,] 0.22585553 0.45171106 0.7741445
[22,] 0.22495591 0.44991183 0.7750441
[23,] 0.24551596 0.49103193 0.7544840
[24,] 0.33006033 0.66012066 0.6699397
[25,] 0.28358931 0.56717862 0.7164107
[26,] 0.23546760 0.47093520 0.7645324
[27,] 0.25024957 0.50049914 0.7497504
[28,] 0.20393258 0.40786515 0.7960674
[29,] 0.16624676 0.33249351 0.8337532
[30,] 0.14476047 0.28952094 0.8552395
[31,] 0.20370381 0.40740762 0.7962962
[32,] 0.28359388 0.56718776 0.7164061
[33,] 0.27912272 0.55824545 0.7208773
[34,] 0.24619436 0.49238871 0.7538056
[35,] 0.20821967 0.41643933 0.7917803
[36,] 0.18897026 0.37794052 0.8110297
[37,] 0.16073016 0.32146032 0.8392698
[38,] 0.17236490 0.34472981 0.8276351
[39,] 0.18145072 0.36290143 0.8185493
[40,] 0.18279896 0.36559792 0.8172010
[41,] 0.15630394 0.31260788 0.8436961
[42,] 0.15189238 0.30378476 0.8481076
[43,] 0.12082712 0.24165425 0.8791729
[44,] 0.11322582 0.22645164 0.8867742
[45,] 0.11501164 0.23002328 0.8849884
[46,] 0.09809279 0.19618558 0.9019072
[47,] 0.08473978 0.16947955 0.9152602
[48,] 0.08980081 0.17960162 0.9101992
[49,] 0.09758515 0.19517030 0.9024148
[50,] 0.10097192 0.20194385 0.8990281
[51,] 0.13761043 0.27522087 0.8623896
[52,] 0.12863853 0.25727705 0.8713615
[53,] 0.12550072 0.25100143 0.8744993
[54,] 0.36500005 0.73000011 0.6349999
[55,] 0.34710929 0.69421858 0.6528907
[56,] 0.32453804 0.64907609 0.6754620
[57,] 0.30257512 0.60515023 0.6974249
[58,] 0.38584258 0.77168516 0.6141574
[59,] 0.35063031 0.70126061 0.6493697
[60,] 0.32464162 0.64928324 0.6753584
[61,] 0.32321144 0.64642287 0.6767886
[62,] 0.32477565 0.64955129 0.6752244
[63,] 0.33403278 0.66806556 0.6659672
[64,] 0.38072340 0.76144679 0.6192766
[65,] 0.44381542 0.88763084 0.5561846
[66,] 0.38341579 0.76683158 0.6165842
[67,] 0.35942708 0.71885417 0.6405729
[68,] 0.35144037 0.70288074 0.6485596
[69,] 0.63545305 0.72909389 0.3645469
[70,] 0.59264975 0.81470049 0.4073502
[71,] 0.59596919 0.80806161 0.4040308
[72,] 0.59521835 0.80956331 0.4047817
[73,] 0.52444940 0.95110121 0.4755506
[74,] 0.50305652 0.99388696 0.4969435
[75,] 0.53296035 0.93407929 0.4670396
[76,] 0.46607053 0.93214106 0.5339295
[77,] 0.54650399 0.90699201 0.4534960
[78,] 0.50615546 0.98768909 0.4938445
[79,] 0.55219651 0.89560699 0.4478035
[80,] 0.49741513 0.99483025 0.5025849
[81,] 0.51304679 0.97390641 0.4869532
[82,] 0.49376452 0.98752904 0.5062355
[83,] 0.72842130 0.54315739 0.2715787
[84,] 0.63271054 0.73457893 0.3672895
[85,] 0.63541243 0.72917514 0.3645876
[86,] 0.74316376 0.51367248 0.2568362
[87,] 0.73255649 0.53488702 0.2674435
[88,] 0.59478310 0.81043379 0.4052169
[89,] 0.45885347 0.91770694 0.5411465
> postscript(file="/var/wessaorg/rcomp/tmp/1lgfc1356109628.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/275fm1356109628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/302sl1356109628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4c0l01356109628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5d58z1356109628.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
4168.423559 -6455.357230 553.962320 -2686.081991 -4238.009023
6 7 8 9 10
-1085.410975 -8604.621150 -4100.073909 -5771.497642 -2354.036496
11 12 13 14 15
-570.668836 6.341605 -28259.376605 -14144.087527 10026.968224
16 17 18 19 20
10348.938262 12684.083249 8844.533630 -14707.102782 7666.266648
21 22 23 24 25
25640.367178 14538.156547 10857.531369 4554.635780 10152.863258
26 27 28 29 30
-2727.888721 -3349.643543 -8276.649047 1240.714065 -11171.331990
31 32 33 34 35
-1403.450010 2538.217036 2744.296377 -2060.910089 -9745.236112
36 37 38 39 40
14009.683648 -12765.125319 -6756.730863 5080.584809 -11950.403882
41 42 43 44 45
11271.039625 4718.214431 6272.405543 12430.320003 1396.437119
46 47 48 49 50
-2372.965529 -15014.137402 1188.660881 5423.485889 1266.533865
51 52 53 54 55
10201.178420 18214.878120 -3158.915127 2712.390265 -4127.708930
56 57 58 59 60
-5079.473531 -2148.870457 12190.072788 14254.884304 2586.584028
61 62 63 64 65
-2505.963558 -241.037370 -561.390728 -7420.786885 2503.874785
66 67 68 69 70
4213.711580 4616.226964 6872.196416 2054.336268 4471.442219
71 72 73 74 75
-9754.621212 -5415.412749 -1680.706519 12879.657610 3696.893059
76 77 78 79 80
-38.436684 -4279.963969 -13501.418925 -2118.470316 -6728.490212
81 82 83 84 85
-12714.585352 -8967.491412 -11348.132259 -12803.788601 -16503.455503
86 87 88 89 90
-2521.700793 6924.137555 -10896.447435 -13117.409257 2238.738811
91 92 93 94 95
-11892.128304 -13516.763910 -10161.960652 -13256.088289 834.501136
96 97 98 99 100
5304.408007 -445.117789 7361.188993 10134.903593 5243.717716
101 102 103 104 105
8970.909830 27154.745304 10892.341700 -5559.540273 7657.423472
106 107 108 109 110
7360.267120 3980.613325 3222.674053 8438.399819 3824.303594
111 112 113 114 115
-482.668040 -1966.596478 2438.053566 -8223.895659 10702.473150
116 117 118 119 120
-3387.580235 -4819.442397 -11406.176684 -10463.549834 16433.749245
121 122 123 124 125
-1001.760293 -23155.161926 -11665.499789 -13066.266632 8988.680745
126 127 128 129 130
19250.995169 6779.423775 2271.521008 7798.770660 -2623.261447
> postscript(file="/var/wessaorg/rcomp/tmp/6jjue1356109628.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 4168.423559 NA
1 -6455.357230 4168.423559
2 553.962320 -6455.357230
3 -2686.081991 553.962320
4 -4238.009023 -2686.081991
5 -1085.410975 -4238.009023
6 -8604.621150 -1085.410975
7 -4100.073909 -8604.621150
8 -5771.497642 -4100.073909
9 -2354.036496 -5771.497642
10 -570.668836 -2354.036496
11 6.341605 -570.668836
12 -28259.376605 6.341605
13 -14144.087527 -28259.376605
14 10026.968224 -14144.087527
15 10348.938262 10026.968224
16 12684.083249 10348.938262
17 8844.533630 12684.083249
18 -14707.102782 8844.533630
19 7666.266648 -14707.102782
20 25640.367178 7666.266648
21 14538.156547 25640.367178
22 10857.531369 14538.156547
23 4554.635780 10857.531369
24 10152.863258 4554.635780
25 -2727.888721 10152.863258
26 -3349.643543 -2727.888721
27 -8276.649047 -3349.643543
28 1240.714065 -8276.649047
29 -11171.331990 1240.714065
30 -1403.450010 -11171.331990
31 2538.217036 -1403.450010
32 2744.296377 2538.217036
33 -2060.910089 2744.296377
34 -9745.236112 -2060.910089
35 14009.683648 -9745.236112
36 -12765.125319 14009.683648
37 -6756.730863 -12765.125319
38 5080.584809 -6756.730863
39 -11950.403882 5080.584809
40 11271.039625 -11950.403882
41 4718.214431 11271.039625
42 6272.405543 4718.214431
43 12430.320003 6272.405543
44 1396.437119 12430.320003
45 -2372.965529 1396.437119
46 -15014.137402 -2372.965529
47 1188.660881 -15014.137402
48 5423.485889 1188.660881
49 1266.533865 5423.485889
50 10201.178420 1266.533865
51 18214.878120 10201.178420
52 -3158.915127 18214.878120
53 2712.390265 -3158.915127
54 -4127.708930 2712.390265
55 -5079.473531 -4127.708930
56 -2148.870457 -5079.473531
57 12190.072788 -2148.870457
58 14254.884304 12190.072788
59 2586.584028 14254.884304
60 -2505.963558 2586.584028
61 -241.037370 -2505.963558
62 -561.390728 -241.037370
63 -7420.786885 -561.390728
64 2503.874785 -7420.786885
65 4213.711580 2503.874785
66 4616.226964 4213.711580
67 6872.196416 4616.226964
68 2054.336268 6872.196416
69 4471.442219 2054.336268
70 -9754.621212 4471.442219
71 -5415.412749 -9754.621212
72 -1680.706519 -5415.412749
73 12879.657610 -1680.706519
74 3696.893059 12879.657610
75 -38.436684 3696.893059
76 -4279.963969 -38.436684
77 -13501.418925 -4279.963969
78 -2118.470316 -13501.418925
79 -6728.490212 -2118.470316
80 -12714.585352 -6728.490212
81 -8967.491412 -12714.585352
82 -11348.132259 -8967.491412
83 -12803.788601 -11348.132259
84 -16503.455503 -12803.788601
85 -2521.700793 -16503.455503
86 6924.137555 -2521.700793
87 -10896.447435 6924.137555
88 -13117.409257 -10896.447435
89 2238.738811 -13117.409257
90 -11892.128304 2238.738811
91 -13516.763910 -11892.128304
92 -10161.960652 -13516.763910
93 -13256.088289 -10161.960652
94 834.501136 -13256.088289
95 5304.408007 834.501136
96 -445.117789 5304.408007
97 7361.188993 -445.117789
98 10134.903593 7361.188993
99 5243.717716 10134.903593
100 8970.909830 5243.717716
101 27154.745304 8970.909830
102 10892.341700 27154.745304
103 -5559.540273 10892.341700
104 7657.423472 -5559.540273
105 7360.267120 7657.423472
106 3980.613325 7360.267120
107 3222.674053 3980.613325
108 8438.399819 3222.674053
109 3824.303594 8438.399819
110 -482.668040 3824.303594
111 -1966.596478 -482.668040
112 2438.053566 -1966.596478
113 -8223.895659 2438.053566
114 10702.473150 -8223.895659
115 -3387.580235 10702.473150
116 -4819.442397 -3387.580235
117 -11406.176684 -4819.442397
118 -10463.549834 -11406.176684
119 16433.749245 -10463.549834
120 -1001.760293 16433.749245
121 -23155.161926 -1001.760293
122 -11665.499789 -23155.161926
123 -13066.266632 -11665.499789
124 8988.680745 -13066.266632
125 19250.995169 8988.680745
126 6779.423775 19250.995169
127 2271.521008 6779.423775
128 7798.770660 2271.521008
129 -2623.261447 7798.770660
130 NA -2623.261447
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6455.357230 4168.423559
[2,] 553.962320 -6455.357230
[3,] -2686.081991 553.962320
[4,] -4238.009023 -2686.081991
[5,] -1085.410975 -4238.009023
[6,] -8604.621150 -1085.410975
[7,] -4100.073909 -8604.621150
[8,] -5771.497642 -4100.073909
[9,] -2354.036496 -5771.497642
[10,] -570.668836 -2354.036496
[11,] 6.341605 -570.668836
[12,] -28259.376605 6.341605
[13,] -14144.087527 -28259.376605
[14,] 10026.968224 -14144.087527
[15,] 10348.938262 10026.968224
[16,] 12684.083249 10348.938262
[17,] 8844.533630 12684.083249
[18,] -14707.102782 8844.533630
[19,] 7666.266648 -14707.102782
[20,] 25640.367178 7666.266648
[21,] 14538.156547 25640.367178
[22,] 10857.531369 14538.156547
[23,] 4554.635780 10857.531369
[24,] 10152.863258 4554.635780
[25,] -2727.888721 10152.863258
[26,] -3349.643543 -2727.888721
[27,] -8276.649047 -3349.643543
[28,] 1240.714065 -8276.649047
[29,] -11171.331990 1240.714065
[30,] -1403.450010 -11171.331990
[31,] 2538.217036 -1403.450010
[32,] 2744.296377 2538.217036
[33,] -2060.910089 2744.296377
[34,] -9745.236112 -2060.910089
[35,] 14009.683648 -9745.236112
[36,] -12765.125319 14009.683648
[37,] -6756.730863 -12765.125319
[38,] 5080.584809 -6756.730863
[39,] -11950.403882 5080.584809
[40,] 11271.039625 -11950.403882
[41,] 4718.214431 11271.039625
[42,] 6272.405543 4718.214431
[43,] 12430.320003 6272.405543
[44,] 1396.437119 12430.320003
[45,] -2372.965529 1396.437119
[46,] -15014.137402 -2372.965529
[47,] 1188.660881 -15014.137402
[48,] 5423.485889 1188.660881
[49,] 1266.533865 5423.485889
[50,] 10201.178420 1266.533865
[51,] 18214.878120 10201.178420
[52,] -3158.915127 18214.878120
[53,] 2712.390265 -3158.915127
[54,] -4127.708930 2712.390265
[55,] -5079.473531 -4127.708930
[56,] -2148.870457 -5079.473531
[57,] 12190.072788 -2148.870457
[58,] 14254.884304 12190.072788
[59,] 2586.584028 14254.884304
[60,] -2505.963558 2586.584028
[61,] -241.037370 -2505.963558
[62,] -561.390728 -241.037370
[63,] -7420.786885 -561.390728
[64,] 2503.874785 -7420.786885
[65,] 4213.711580 2503.874785
[66,] 4616.226964 4213.711580
[67,] 6872.196416 4616.226964
[68,] 2054.336268 6872.196416
[69,] 4471.442219 2054.336268
[70,] -9754.621212 4471.442219
[71,] -5415.412749 -9754.621212
[72,] -1680.706519 -5415.412749
[73,] 12879.657610 -1680.706519
[74,] 3696.893059 12879.657610
[75,] -38.436684 3696.893059
[76,] -4279.963969 -38.436684
[77,] -13501.418925 -4279.963969
[78,] -2118.470316 -13501.418925
[79,] -6728.490212 -2118.470316
[80,] -12714.585352 -6728.490212
[81,] -8967.491412 -12714.585352
[82,] -11348.132259 -8967.491412
[83,] -12803.788601 -11348.132259
[84,] -16503.455503 -12803.788601
[85,] -2521.700793 -16503.455503
[86,] 6924.137555 -2521.700793
[87,] -10896.447435 6924.137555
[88,] -13117.409257 -10896.447435
[89,] 2238.738811 -13117.409257
[90,] -11892.128304 2238.738811
[91,] -13516.763910 -11892.128304
[92,] -10161.960652 -13516.763910
[93,] -13256.088289 -10161.960652
[94,] 834.501136 -13256.088289
[95,] 5304.408007 834.501136
[96,] -445.117789 5304.408007
[97,] 7361.188993 -445.117789
[98,] 10134.903593 7361.188993
[99,] 5243.717716 10134.903593
[100,] 8970.909830 5243.717716
[101,] 27154.745304 8970.909830
[102,] 10892.341700 27154.745304
[103,] -5559.540273 10892.341700
[104,] 7657.423472 -5559.540273
[105,] 7360.267120 7657.423472
[106,] 3980.613325 7360.267120
[107,] 3222.674053 3980.613325
[108,] 8438.399819 3222.674053
[109,] 3824.303594 8438.399819
[110,] -482.668040 3824.303594
[111,] -1966.596478 -482.668040
[112,] 2438.053566 -1966.596478
[113,] -8223.895659 2438.053566
[114,] 10702.473150 -8223.895659
[115,] -3387.580235 10702.473150
[116,] -4819.442397 -3387.580235
[117,] -11406.176684 -4819.442397
[118,] -10463.549834 -11406.176684
[119,] 16433.749245 -10463.549834
[120,] -1001.760293 16433.749245
[121,] -23155.161926 -1001.760293
[122,] -11665.499789 -23155.161926
[123,] -13066.266632 -11665.499789
[124,] 8988.680745 -13066.266632
[125,] 19250.995169 8988.680745
[126,] 6779.423775 19250.995169
[127,] 2271.521008 6779.423775
[128,] 7798.770660 2271.521008
[129,] -2623.261447 7798.770660
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6455.357230 4168.423559
2 553.962320 -6455.357230
3 -2686.081991 553.962320
4 -4238.009023 -2686.081991
5 -1085.410975 -4238.009023
6 -8604.621150 -1085.410975
7 -4100.073909 -8604.621150
8 -5771.497642 -4100.073909
9 -2354.036496 -5771.497642
10 -570.668836 -2354.036496
11 6.341605 -570.668836
12 -28259.376605 6.341605
13 -14144.087527 -28259.376605
14 10026.968224 -14144.087527
15 10348.938262 10026.968224
16 12684.083249 10348.938262
17 8844.533630 12684.083249
18 -14707.102782 8844.533630
19 7666.266648 -14707.102782
20 25640.367178 7666.266648
21 14538.156547 25640.367178
22 10857.531369 14538.156547
23 4554.635780 10857.531369
24 10152.863258 4554.635780
25 -2727.888721 10152.863258
26 -3349.643543 -2727.888721
27 -8276.649047 -3349.643543
28 1240.714065 -8276.649047
29 -11171.331990 1240.714065
30 -1403.450010 -11171.331990
31 2538.217036 -1403.450010
32 2744.296377 2538.217036
33 -2060.910089 2744.296377
34 -9745.236112 -2060.910089
35 14009.683648 -9745.236112
36 -12765.125319 14009.683648
37 -6756.730863 -12765.125319
38 5080.584809 -6756.730863
39 -11950.403882 5080.584809
40 11271.039625 -11950.403882
41 4718.214431 11271.039625
42 6272.405543 4718.214431
43 12430.320003 6272.405543
44 1396.437119 12430.320003
45 -2372.965529 1396.437119
46 -15014.137402 -2372.965529
47 1188.660881 -15014.137402
48 5423.485889 1188.660881
49 1266.533865 5423.485889
50 10201.178420 1266.533865
51 18214.878120 10201.178420
52 -3158.915127 18214.878120
53 2712.390265 -3158.915127
54 -4127.708930 2712.390265
55 -5079.473531 -4127.708930
56 -2148.870457 -5079.473531
57 12190.072788 -2148.870457
58 14254.884304 12190.072788
59 2586.584028 14254.884304
60 -2505.963558 2586.584028
61 -241.037370 -2505.963558
62 -561.390728 -241.037370
63 -7420.786885 -561.390728
64 2503.874785 -7420.786885
65 4213.711580 2503.874785
66 4616.226964 4213.711580
67 6872.196416 4616.226964
68 2054.336268 6872.196416
69 4471.442219 2054.336268
70 -9754.621212 4471.442219
71 -5415.412749 -9754.621212
72 -1680.706519 -5415.412749
73 12879.657610 -1680.706519
74 3696.893059 12879.657610
75 -38.436684 3696.893059
76 -4279.963969 -38.436684
77 -13501.418925 -4279.963969
78 -2118.470316 -13501.418925
79 -6728.490212 -2118.470316
80 -12714.585352 -6728.490212
81 -8967.491412 -12714.585352
82 -11348.132259 -8967.491412
83 -12803.788601 -11348.132259
84 -16503.455503 -12803.788601
85 -2521.700793 -16503.455503
86 6924.137555 -2521.700793
87 -10896.447435 6924.137555
88 -13117.409257 -10896.447435
89 2238.738811 -13117.409257
90 -11892.128304 2238.738811
91 -13516.763910 -11892.128304
92 -10161.960652 -13516.763910
93 -13256.088289 -10161.960652
94 834.501136 -13256.088289
95 5304.408007 834.501136
96 -445.117789 5304.408007
97 7361.188993 -445.117789
98 10134.903593 7361.188993
99 5243.717716 10134.903593
100 8970.909830 5243.717716
101 27154.745304 8970.909830
102 10892.341700 27154.745304
103 -5559.540273 10892.341700
104 7657.423472 -5559.540273
105 7360.267120 7657.423472
106 3980.613325 7360.267120
107 3222.674053 3980.613325
108 8438.399819 3222.674053
109 3824.303594 8438.399819
110 -482.668040 3824.303594
111 -1966.596478 -482.668040
112 2438.053566 -1966.596478
113 -8223.895659 2438.053566
114 10702.473150 -8223.895659
115 -3387.580235 10702.473150
116 -4819.442397 -3387.580235
117 -11406.176684 -4819.442397
118 -10463.549834 -11406.176684
119 16433.749245 -10463.549834
120 -1001.760293 16433.749245
121 -23155.161926 -1001.760293
122 -11665.499789 -23155.161926
123 -13066.266632 -11665.499789
124 8988.680745 -13066.266632
125 19250.995169 8988.680745
126 6779.423775 19250.995169
127 2271.521008 6779.423775
128 7798.770660 2271.521008
129 -2623.261447 7798.770660
> 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/7zfz21356109628.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/8qg311356109628.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/98cmp1356109628.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/10xhgd1356109628.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/119qt71356109628.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12iznq1356109628.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13lx3j1356109628.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14lei01356109628.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/15zj0v1356109628.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/16xs0p1356109628.tab")
+ }
>
> try(system("convert tmp/1lgfc1356109628.ps tmp/1lgfc1356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/275fm1356109628.ps tmp/275fm1356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/302sl1356109628.ps tmp/302sl1356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c0l01356109628.ps tmp/4c0l01356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/5d58z1356109628.ps tmp/5d58z1356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jjue1356109628.ps tmp/6jjue1356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zfz21356109628.ps tmp/7zfz21356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qg311356109628.ps tmp/8qg311356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/98cmp1356109628.ps tmp/98cmp1356109628.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xhgd1356109628.ps tmp/10xhgd1356109628.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.840 1.144 10.059