R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(10708
+ ,12.3
+ ,23
+ ,154.25
+ ,67.75
+ ,36.2
+ ,93.1
+ ,85.2
+ ,94.5
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+ ,17.1
+ ,10853
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+ ,72.25
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+ ,58.7
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+ ,28.9
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+ ,16.6
+ ,10751
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+ ,72.25
+ ,37.4
+ ,101.8
+ ,86.4
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+ ,60.1
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+ ,22.8
+ ,32.4
+ ,29.4
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+ ,10340
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+ ,24
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+ ,71.25
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+ ,42.2
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+ ,17.7
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+ ,210.25
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+ ,39
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+ ,35.7
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+ ,181
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+ ,36.4
+ ,105.1
+ ,90.7
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+ ,58.4
+ ,38.3
+ ,22.9
+ ,31.9
+ ,27.8
+ ,17.7
+ ,10704
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+ ,25
+ ,176
+ ,72.5
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+ ,99.6
+ ,88.5
+ ,97.1
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+ ,30.5
+ ,29
+ ,18.8
+ ,10900
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+ ,191
+ ,74
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+ ,100.9
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+ ,38.3
+ ,23.8
+ ,35.9
+ ,31.1
+ ,18.2
+ ,10722
+ ,11.7
+ ,23
+ ,198.25
+ ,73.5
+ ,42.1
+ ,99.6
+ ,88.6
+ ,104.1
+ ,63.1
+ ,41.7
+ ,25
+ ,35.6
+ ,30
+ ,19.2
+ ,10830
+ ,7.1
+ ,26
+ ,186.25
+ ,74.5
+ ,38.5
+ ,101.5
+ ,83.6
+ ,98.2
+ ,59.7
+ ,39.7
+ ,25.2
+ ,32.8
+ ,29.4
+ ,18.5
+ ,10812
+ ,7.8
+ ,27
+ ,216
+ ,76
+ ,39.4
+ ,103.6
+ ,90.9
+ ,107.7
+ ,66.2
+ ,39.2
+ ,25.9
+ ,37.2
+ ,30.2
+ ,19
+ ,10513
+ ,20.8
+ ,32
+ ,180.5
+ ,69.5
+ ,38.4
+ ,102
+ ,91.6
+ ,103.9
+ ,63.4
+ ,38.3
+ ,21.5
+ ,32.5
+ ,28.6
+ ,17.7
+ ,10505
+ ,21.2
+ ,30
+ ,205.25
+ ,71.25
+ ,39.4
+ ,104.1
+ ,101.8
+ ,108.6
+ ,66
+ ,41.5
+ ,23.7
+ ,36.9
+ ,31.6
+ ,18.8
+ ,10484
+ ,22.1
+ ,35
+ ,187.75
+ ,69.5
+ ,40.5
+ ,101.3
+ ,96.4
+ ,100.1
+ ,69
+ ,39
+ ,23.1
+ ,36.1
+ ,30.5
+ ,18.2
+ ,10512
+ ,20.9
+ ,35
+ ,162.75
+ ,66
+ ,36.4
+ ,99.1
+ ,92.8
+ ,99.2
+ ,63.1
+ ,38.7
+ ,21.7
+ ,31.1
+ ,26.4
+ ,16.9
+ ,10333
+ ,29
+ ,34
+ ,195.75
+ ,71
+ ,38.9
+ ,101.9
+ ,96.4
+ ,105.2
+ ,64.8
+ ,40.8
+ ,23.1
+ ,36.2
+ ,30.8
+ ,17.3
+ ,10468
+ ,22.9
+ ,32
+ ,209.25
+ ,71
+ ,42.1
+ ,107.6
+ ,97.5
+ ,107
+ ,66.9
+ ,40
+ ,24.4
+ ,38.2
+ ,31.6
+ ,19.3
+ ,10622
+ ,16
+ ,28
+ ,183.75
+ ,67.75
+ ,38
+ ,106.8
+ ,89.6
+ ,102.4
+ ,64.2
+ ,38.7
+ ,22.9
+ ,37.2
+ ,30.5
+ ,18.5
+ ,10610
+ ,16.5
+ ,33
+ ,211.75
+ ,73.5
+ ,40
+ ,106.2
+ ,100.5
+ ,109
+ ,65.8
+ ,40.6
+ ,24
+ ,37.1
+ ,30.1
+ ,18.2
+ ,10551
+ ,19.1
+ ,28
+ ,179
+ ,68
+ ,39.1
+ ,103.3
+ ,95.9
+ ,104.9
+ ,63.5
+ ,38
+ ,22.1
+ ,32.5
+ ,30.3
+ ,18.4
+ ,10640
+ ,15.2
+ ,28
+ ,200.5
+ ,69.75
+ ,41.3
+ ,111.4
+ ,98.8
+ ,104.8
+ ,63.4
+ ,40.6
+ ,24.6
+ ,33
+ ,32.8
+ ,19.9
+ ,10631
+ ,15.6
+ ,31
+ ,140.25
+ ,68.25
+ ,33.9
+ ,86
+ ,76.4
+ ,94.6
+ ,57.4
+ ,35.3
+ ,22.2
+ ,27.9
+ ,25.9
+ ,16.7
+ ,10584
+ ,17.7
+ ,32
+ ,148.75
+ ,70
+ ,35.5
+ ,86.7
+ ,80
+ ,93.4
+ ,54.9
+ ,36.2
+ ,22.1
+ ,29.8
+ ,26.7
+ ,17.1
+ ,10668
+ ,14
+ ,28
+ ,151.25
+ ,67.75
+ ,34.5
+ ,90.2
+ ,76.3
+ ,95.8
+ ,58.4
+ ,35.5
+ ,22.9
+ ,31.1
+ ,28
+ ,17.6
+ ,10911
+ ,3.7
+ ,27
+ ,159.25
+ ,71.5
+ ,35.7
+ ,89.6
+ ,79.7
+ ,96.5
+ ,55
+ ,36.7
+ ,22.5
+ ,29.9
+ ,28.2
+ ,17.7
+ ,10811
+ ,7.9
+ ,34
+ ,131.5
+ ,67.5
+ ,36.2
+ ,88.6
+ ,74.6
+ ,85.3
+ ,51.7
+ ,34.7
+ ,21.4
+ ,28.7
+ ,27
+ ,16.5
+ ,10468
+ ,22.9
+ ,31
+ ,148
+ ,67.5
+ ,38.8
+ ,97.4
+ ,88.7
+ ,94.7
+ ,57.5
+ ,36
+ ,21
+ ,29.2
+ ,26.6
+ ,17
+ ,10910
+ ,3.7
+ ,27
+ ,133.25
+ ,64.75
+ ,36.4
+ ,93.5
+ ,73.9
+ ,88.5
+ ,50.1
+ ,34.5
+ ,21.3
+ ,30.5
+ ,27.9
+ ,17.2
+ ,10790
+ ,8.8
+ ,29
+ ,160.75
+ ,69
+ ,36.7
+ ,97.4
+ ,83.5
+ ,98.7
+ ,58.9
+ ,35.3
+ ,22.6
+ ,30.1
+ ,26.7
+ ,17.6
+ ,10716
+ ,11.9
+ ,32
+ ,182
+ ,73.75
+ ,38.7
+ ,100.5
+ ,88.7
+ ,99.8
+ ,57.5
+ ,38.7
+ ,33.9
+ ,32.5
+ ,27.7
+ ,18.4
+ ,10862
+ ,5.7
+ ,29
+ ,160.25
+ ,71.25
+ ,37.3
+ ,93.5
+ ,84.5
+ ,100.6
+ ,58.5
+ ,38.8
+ ,21.5
+ ,30.1
+ ,26.4
+ ,17.9
+ ,10719
+ ,11.8
+ ,27
+ ,168
+ ,71.25
+ ,38.1
+ ,93
+ ,79.1
+ ,94.5
+ ,57.3
+ ,36.2
+ ,24.5
+ ,29
+ ,30
+ ,18.8
+ ,10502
+ ,21.3
+ ,41
+ ,218.5
+ ,71
+ ,39.8
+ ,111.7
+ ,100.5
+ ,108.3
+ ,67.1
+ ,44.2
+ ,25.2
+ ,37.5
+ ,31.5
+ ,18.7
+ ,10263
+ ,32.3
+ ,41
+ ,247.25
+ ,73.5
+ ,42.1
+ ,117
+ ,115.6
+ ,116.1
+ ,71.2
+ ,43.3
+ ,26.3
+ ,37.3
+ ,31.7
+ ,19.7
+ ,10101
+ ,40.1
+ ,49
+ ,191.75
+ ,65
+ ,38.4
+ ,118.5
+ ,113.1
+ ,113.8
+ ,61.9
+ ,38.3
+ ,21.9
+ ,32
+ ,29.8
+ ,17
+ ,10438
+ ,24.2
+ ,40
+ ,202.25
+ ,70
+ ,38.5
+ ,106.5
+ ,100.9
+ ,106.2
+ ,63.5
+ ,39.9
+ ,22.6
+ ,35.1
+ ,30.6
+ ,19
+ ,10346
+ ,28.4
+ ,50
+ ,196.75
+ ,68.25
+ ,42.1
+ ,105.6
+ ,98.8
+ ,104.8
+ ,66
+ ,41.5
+ ,24.7
+ ,33.2
+ ,30.5
+ ,19.4
+ ,10202
+ ,35.2
+ ,46
+ ,363.15
+ ,72.25
+ ,51.2
+ ,136.2
+ ,148.1
+ ,147.7
+ ,87.3
+ ,49.1
+ ,29.6
+ ,45
+ ,29
+ ,21.4
+ ,10258
+ ,32.6
+ ,50
+ ,203
+ ,67
+ ,40.2
+ ,114.8
+ ,108.1
+ ,102.5
+ ,61.3
+ ,41.1
+ ,24.7
+ ,34.1
+ ,31
+ ,18.3
+ ,10217
+ ,34.5
+ ,45
+ ,262.75
+ ,68.75
+ ,43.2
+ ,128.3
+ ,126.2
+ ,125.6
+ ,72.5
+ ,39.6
+ ,26.6
+ ,36.4
+ ,32.7
+ ,21.4
+ ,10250
+ ,32.9
+ ,44
+ ,205
+ ,29.5
+ ,36.6
+ ,106
+ ,104.3
+ ,115.5
+ ,70.6
+ ,42.5
+ ,23.7
+ ,33.6
+ ,28.7
+ ,17.4
+ ,10279
+ ,31.6
+ ,48
+ ,217
+ ,70
+ ,37.3
+ ,113.3
+ ,111.2
+ ,114.1
+ ,67.7
+ ,40.9
+ ,25
+ ,36.7
+ ,29.8
+ ,18.4
+ ,10269
+ ,32
+ ,41
+ ,212
+ ,71.5
+ ,41.5
+ ,106.6
+ ,104.3
+ ,106
+ ,65
+ ,40.2
+ ,23
+ ,35.8
+ ,31.5
+ ,18.8
+ ,10814
+ ,7.7
+ ,39
+ ,125.25
+ ,68
+ ,31.5
+ ,85.1
+ ,76
+ ,88.2
+ ,50
+ ,34.7
+ ,21
+ ,26.1
+ ,23.1
+ ,16.1
+ ,10670
+ ,13.9
+ ,43
+ ,164.25
+ ,73.25
+ ,35.7
+ ,96.6
+ ,81.5
+ ,97.2
+ ,58.4
+ ,38.2
+ ,23.4
+ ,29.7
+ ,27.4
+ ,18.3
+ ,10742
+ ,10.8
+ ,40
+ ,133.5
+ ,67.5
+ ,33.6
+ ,88.2
+ ,73.7
+ ,88.5
+ ,53.3
+ ,34.5
+ ,22.5
+ ,27.9
+ ,26.2
+ ,17.3
+ ,10665
+ ,5.6
+ ,39
+ ,148.5
+ ,71.25
+ ,34.6
+ ,89.8
+ ,79.5
+ ,92.7
+ ,52.7
+ ,37.5
+ ,21.9
+ ,28.8
+ ,26.8
+ ,17.9
+ ,10678
+ ,13.6
+ ,45
+ ,135.75
+ ,68.5
+ ,32.8
+ ,92.3
+ ,83.4
+ ,90.4
+ ,52
+ ,35.8
+ ,20.6
+ ,28.8
+ ,25.5
+ ,16.3
+ ,10903
+ ,4
+ ,47
+ ,127.5
+ ,66.75
+ ,34
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+ ,70.4
+ ,87.2
+ ,50.6
+ ,34.4
+ ,21.9
+ ,26.8
+ ,25.8
+ ,16.8
+ ,10756
+ ,10.2
+ ,47
+ ,158.25
+ ,72.25
+ ,34.9
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+ ,22.4
+ ,26
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+ ,17.3
+ ,10840
+ ,6.6
+ ,40
+ ,139.25
+ ,69
+ ,34.3
+ ,89.2
+ ,77.9
+ ,91
+ ,51.4
+ ,34.9
+ ,21
+ ,26.7
+ ,26.1
+ ,17.2
+ ,10807
+ ,8
+ ,51
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+ ,67.75
+ ,36.5
+ ,89.7
+ ,82
+ ,89.1
+ ,49.3
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+ ,26
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+ ,49
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+ ,35.1
+ ,93.3
+ ,79.6
+ ,91.6
+ ,52.6
+ ,37.6
+ ,22.6
+ ,38.5
+ ,27.4
+ ,18.5
+ ,10906
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+ ,22.5
+ ,27.7
+ ,27.5
+ ,18.5
+ ,10473
+ ,22.6
+ ,54
+ ,198
+ ,72
+ ,39.9
+ ,107.6
+ ,100
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+ ,38
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+ ,10396
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+ ,32.7
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+ ,57
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+ ,70
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+ ,31.1
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+ ,10499
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+ ,55
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+ ,25.7
+ ,18.8
+ ,10498
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+ ,81
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+ ,21.5
+ ,31.4
+ ,26.8
+ ,18.3
+ ,10560
+ ,18.8
+ ,66
+ ,171.25
+ ,69.25
+ ,37.4
+ ,102.7
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+ ,100.2
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+ ,39.3
+ ,22.7
+ ,30.3
+ ,28.7
+ ,19
+ ,10283
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+ ,67
+ ,163.75
+ ,67.75
+ ,38.4
+ ,97.7
+ ,95.8
+ ,97.1
+ ,54.8
+ ,38.2
+ ,23.7
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+ ,107.6
+ ,110
+ ,63.3
+ ,44
+ ,22.6
+ ,37.5
+ ,32.6
+ ,18.8
+ ,10736
+ ,11
+ ,70
+ ,134.25
+ ,67
+ ,34.9
+ ,89.2
+ ,83.6
+ ,88.8
+ ,49.6
+ ,34.8
+ ,21.5
+ ,25.6
+ ,25.7
+ ,18.5
+ ,10236
+ ,33.6
+ ,72
+ ,201
+ ,69.75
+ ,40.9
+ ,108.5
+ ,105
+ ,104.5
+ ,59.6
+ ,40.8
+ ,23.2
+ ,35.2
+ ,28.6
+ ,20.1
+ ,10328
+ ,29.3
+ ,72
+ ,186.75
+ ,66
+ ,38.9
+ ,111.1
+ ,111.5
+ ,101.7
+ ,60.3
+ ,37.3
+ ,21.5
+ ,31.3
+ ,27.2
+ ,18
+ ,10399
+ ,26
+ ,72
+ ,190.75
+ ,70.5
+ ,38.9
+ ,108.3
+ ,101.3
+ ,97.8
+ ,56
+ ,41.6
+ ,22.7
+ ,30.5
+ ,29.4
+ ,19.8
+ ,10271
+ ,31.9
+ ,74
+ ,207.5
+ ,70
+ ,40.8
+ ,112.4
+ ,108.5
+ ,107.1
+ ,59.3
+ ,42.2
+ ,24.6
+ ,33.7
+ ,30
+ ,20.9)
+ ,dim=c(15
+ ,252)
+ ,dimnames=list(c('dichtheid'
+ ,'lichaamsvet'
+ ,'leeftijd'
+ ,'gewicht'
+ ,'grootte'
+ ,'nekomtrek'
+ ,'borstomtrek'
+ ,'buikomtrek'
+ ,'heupomtrek'
+ ,'dijomtrek'
+ ,'knieomtrek'
+ ,'enkelomtrek'
+ ,'bicepsomtrek'
+ ,'voorarmomtrek'
+ ,'polsomtrek
')
+ ,1:252))
> y <- array(NA,dim=c(15,252),dimnames=list(c('dichtheid','lichaamsvet','leeftijd','gewicht','grootte','nekomtrek','borstomtrek','buikomtrek','heupomtrek','dijomtrek','knieomtrek','enkelomtrek','bicepsomtrek','voorarmomtrek','polsomtrek
'),1:252))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
gewicht dichtheid lichaamsvet leeftijd grootte nekomtrek borstomtrek
1 154.25 10708 12.3 23 67.75 36.2 93.1
2 173.25 10853 6.1 22 72.25 38.5 93.6
3 154.00 10414 25.3 22 66.25 34.0 95.8
4 184.75 10751 10.4 26 72.25 37.4 101.8
5 184.25 10340 28.7 24 71.25 34.4 97.3
6 210.25 10502 20.9 24 74.75 39.0 104.5
7 181.00 10549 19.2 26 69.75 36.4 105.1
8 176.00 10704 12.4 25 72.50 37.8 99.6
9 191.00 10900 4.1 25 74.00 38.1 100.9
10 198.25 10722 11.7 23 73.50 42.1 99.6
11 186.25 10830 7.1 26 74.50 38.5 101.5
12 216.00 10812 7.8 27 76.00 39.4 103.6
13 180.50 10513 20.8 32 69.50 38.4 102.0
14 205.25 10505 21.2 30 71.25 39.4 104.1
15 187.75 10484 22.1 35 69.50 40.5 101.3
16 162.75 10512 20.9 35 66.00 36.4 99.1
17 195.75 10333 29.0 34 71.00 38.9 101.9
18 209.25 10468 22.9 32 71.00 42.1 107.6
19 183.75 10622 16.0 28 67.75 38.0 106.8
20 211.75 10610 16.5 33 73.50 40.0 106.2
21 179.00 10551 19.1 28 68.00 39.1 103.3
22 200.50 10640 15.2 28 69.75 41.3 111.4
23 140.25 10631 15.6 31 68.25 33.9 86.0
24 148.75 10584 17.7 32 70.00 35.5 86.7
25 151.25 10668 14.0 28 67.75 34.5 90.2
26 159.25 10911 3.7 27 71.50 35.7 89.6
27 131.50 10811 7.9 34 67.50 36.2 88.6
28 148.00 10468 22.9 31 67.50 38.8 97.4
29 133.25 10910 3.7 27 64.75 36.4 93.5
30 160.75 10790 8.8 29 69.00 36.7 97.4
31 182.00 10716 11.9 32 73.75 38.7 100.5
32 160.25 10862 5.7 29 71.25 37.3 93.5
33 168.00 10719 11.8 27 71.25 38.1 93.0
34 218.50 10502 21.3 41 71.00 39.8 111.7
35 247.25 10263 32.3 41 73.50 42.1 117.0
36 191.75 10101 40.1 49 65.00 38.4 118.5
37 202.25 10438 24.2 40 70.00 38.5 106.5
38 196.75 10346 28.4 50 68.25 42.1 105.6
39 363.15 10202 35.2 46 72.25 51.2 136.2
40 203.00 10258 32.6 50 67.00 40.2 114.8
41 262.75 10217 34.5 45 68.75 43.2 128.3
42 205.00 10250 32.9 44 29.50 36.6 106.0
43 217.00 10279 31.6 48 70.00 37.3 113.3
44 212.00 10269 32.0 41 71.50 41.5 106.6
45 125.25 10814 7.7 39 68.00 31.5 85.1
46 164.25 10670 13.9 43 73.25 35.7 96.6
47 133.50 10742 10.8 40 67.50 33.6 88.2
48 148.50 10665 5.6 39 71.25 34.6 89.8
49 135.75 10678 13.6 45 68.50 32.8 92.3
50 127.50 10903 4.0 47 66.75 34.0 83.4
51 158.25 10756 10.2 47 72.25 34.9 90.2
52 139.25 10840 6.6 40 69.00 34.3 89.2
53 137.25 10807 8.0 51 67.75 36.5 89.7
54 152.75 10848 6.3 49 73.50 35.1 93.3
55 136.25 10906 3.9 42 67.50 37.8 87.6
56 198.00 10473 22.6 54 72.00 39.9 107.6
57 181.50 10524 20.4 58 68.00 39.1 100.0
58 201.25 10356 28.0 62 69.50 40.5 111.5
59 202.50 10280 31.5 54 70.75 40.5 115.4
60 179.75 10430 24.6 61 65.75 38.4 104.8
61 216.00 10396 26.1 62 73.25 41.4 112.3
62 178.75 10317 29.8 56 68.50 35.6 102.9
63 193.25 10298 30.7 54 70.25 38.0 107.6
64 178.00 10403 25.8 61 67.00 37.4 105.3
65 205.50 10264 32.3 57 70.00 40.1 105.3
66 183.50 10313 30.0 55 67.50 40.9 103.0
67 151.50 10499 21.5 54 70.75 35.6 90.0
68 154.75 10673 13.8 55 71.50 36.9 95.4
69 155.25 10847 6.3 54 69.25 37.5 89.3
70 156.75 10693 12.9 55 71.50 36.3 94.4
71 167.50 10439 24.3 62 71.50 35.5 97.6
72 146.75 10788 8.8 55 68.75 38.7 88.5
73 160.75 10796 8.5 56 73.75 36.4 93.6
74 125.00 10680 13.5 55 64.00 33.2 87.7
75 143.00 10720 11.8 61 65.75 36.5 93.4
76 148.25 10666 18.5 61 67.50 36.0 91.6
77 162.50 10790 8.8 57 69.50 38.7 91.6
78 177.75 10483 22.2 69 68.50 38.7 102.0
79 161.25 10498 21.5 81 70.25 37.8 96.4
80 171.25 10560 18.8 66 69.25 37.4 102.7
81 163.75 10283 31.4 67 67.75 38.4 97.7
82 150.25 10382 26.8 64 67.25 38.1 97.1
83 190.25 10568 18.4 64 72.75 39.3 103.1
84 170.75 10377 27.0 70 70.00 38.7 101.8
85 168.00 10378 27.0 72 69.25 38.5 101.4
86 167.00 10386 26.6 67 67.50 36.5 98.9
87 157.75 10648 14.9 72 67.25 37.7 97.5
88 160.00 10462 23.1 64 65.75 36.5 104.3
89 176.75 10800 8.3 46 72.50 38.0 97.3
90 176.00 10666 14.1 48 73.00 36.7 96.7
91 177.00 10520 20.5 46 70.00 37.2 99.7
92 179.75 10573 18.2 44 69.50 39.2 101.9
93 165.25 10795 8.5 47 70.50 37.5 97.2
94 192.50 10424 24.9 46 71.75 38.0 106.6
95 184.25 10785 9.0 47 74.50 37.3 99.6
96 224.50 10991 17.4 53 77.75 41.1 113.2
97 188.75 10770 9.6 38 73.25 37.5 99.1
98 162.50 10730 11.3 50 66.50 38.7 99.4
99 156.50 10582 17.8 46 68.25 35.9 95.1
100 197.00 10484 22.2 47 72.00 40.0 107.5
101 198.50 10506 21.2 49 73.50 40.1 106.5
102 173.75 10524 20.4 48 72.00 37.0 99.1
103 172.75 10530 20.1 41 71.25 36.3 96.7
104 196.75 10480 22.3 49 73.75 40.7 103.5
105 177.00 10412 25.4 43 69.25 39.6 104.0
106 165.50 10578 18.0 43 68.50 31.1 93.1
107 200.25 10547 19.3 43 73.50 38.6 105.2
108 203.25 10569 18.3 52 74.25 42.0 110.0
109 194.00 10593 17.3 43 75.50 38.5 110.1
110 168.50 10500 21.4 40 69.25 34.2 97.8
111 170.75 10538 19.7 43 68.50 37.2 96.3
112 183.25 10355 28.0 43 70.00 37.1 108.0
113 178.25 10486 22.1 47 70.00 40.2 99.7
114 163.00 10503 21.3 42 70.25 35.3 93.5
115 175.25 10384 26.7 48 71.75 38.0 100.7
116 158.00 10607 16.7 40 69.25 36.3 97.0
117 177.25 10529 20.1 48 72.75 36.8 96.0
118 179.00 10671 13.9 51 72.00 41.0 99.2
119 191.00 10404 25.8 40 74.00 38.3 95.4
120 187.50 10575 18.1 44 72.25 38.0 101.8
121 206.50 10358 27.9 52 74.50 40.8 104.3
122 185.25 10414 25.3 44 71.50 39.5 99.2
123 160.25 10652 14.7 40 68.75 36.9 99.3
124 151.50 10623 16.0 47 66.75 36.9 94.0
125 161.00 10674 13.8 50 66.50 37.7 98.9
126 167.00 10587 17.5 46 67.00 36.6 101.0
127 177.50 10373 27.2 42 68.75 38.9 98.7
128 152.25 10590 17.4 43 67.75 37.5 95.9
129 192.25 10515 20.8 40 73.25 39.8 103.9
130 165.25 10648 14.9 42 69.75 38.3 96.2
131 171.75 10575 18.1 49 71.50 35.5 97.8
132 171.25 10472 22.7 40 70.50 36.3 94.6
133 197.00 10452 23.6 47 73.25 37.8 103.6
134 157.00 10398 26.1 50 66.75 37.8 100.4
135 168.25 10435 24.4 41 69.50 36.5 98.4
136 186.00 10374 27.1 44 69.75 37.8 104.6
137 166.75 10491 21.8 39 70.75 37.0 92.9
138 187.75 10325 29.4 43 74.00 37.7 97.8
139 168.25 10481 22.4 40 71.25 34.3 98.3
140 212.75 10522 20.4 49 75.00 40.8 104.7
141 176.75 10422 24.9 40 71.00 37.4 98.6
142 173.25 10571 18.3 40 69.50 36.5 99.5
143 167.00 10459 23.3 52 67.75 37.5 102.7
144 159.75 10775 9.4 23 72.25 35.5 92.1
145 188.15 10754 10.3 23 77.50 38.0 96.6
146 156.00 10664 14.2 24 70.75 35.7 92.7
147 208.50 10550 19.2 24 72.75 39.2 102.0
148 206.50 10322 29.6 25 69.75 40.9 110.9
149 143.75 10873 5.3 25 72.50 35.2 92.3
150 223.00 10416 25.2 26 70.25 40.6 114.1
151 152.25 10776 9.4 26 69.00 35.4 92.9
152 241.75 10542 19.6 26 74.50 41.8 108.3
153 146.00 10758 10.1 27 72.25 34.1 88.5
154 156.75 10610 16.5 27 67.25 37.9 94.0
155 200.25 10510 21.0 27 73.50 38.2 101.1
156 171.50 10594 17.3 28 75.25 35.6 92.1
157 205.75 10287 31.2 28 69.00 38.5 105.6
158 182.50 10761 10.0 28 72.25 37.0 98.5
159 136.50 10704 12.5 30 68.75 35.9 88.7
160 177.25 10477 22.5 31 71.50 36.2 101.1
161 151.25 10775 9.4 31 72.25 35.0 94.0
162 196.00 10653 14.6 33 73.00 38.5 103.8
163 184.25 10690 13.0 33 68.75 40.7 98.9
164 140.00 10644 15.1 34 70.50 36.0 89.2
165 218.75 10370 27.3 34 72.00 39.5 111.4
166 217.00 10549 19.2 35 73.75 40.5 107.5
167 166.25 10492 21.8 35 68.00 38.5 99.1
168 224.75 10525 20.3 35 72.25 43.9 108.2
169 228.25 10180 34.3 35 69.50 40.4 114.9
170 172.75 10610 16.5 35 69.50 37.6 99.1
171 152.25 10926 3.0 35 67.75 37.0 92.2
172 125.75 10983 0.7 35 65.50 34.0 90.8
173 177.25 10521 20.5 35 71.00 38.4 100.5
174 176.25 10603 16.9 36 71.50 38.7 98.2
175 226.75 10414 25.3 36 71.75 41.5 115.3
176 145.25 10763 9.9 37 69.25 36.0 96.8
177 151.00 10689 13.1 37 67.00 35.3 92.6
178 241.25 10316 29.9 37 71.50 42.1 119.2
179 187.25 10477 22.5 38 69.25 38.0 102.7
180 234.75 10603 16.9 39 74.50 42.8 109.5
181 219.25 10387 26.6 39 74.25 40.0 108.5
182 118.50 11089 0.0 40 68.00 33.8 79.3
183 145.75 10725 11.5 40 67.25 35.5 95.5
184 159.25 10713 12.1 40 69.75 35.3 92.3
185 170.50 10587 17.5 40 74.25 37.7 98.9
186 167.50 10794 8.6 40 71.50 39.4 89.5
187 232.75 10453 23.6 41 74.25 41.9 117.5
188 210.50 10524 20.4 41 72.00 38.5 107.4
189 202.25 10520 20.5 41 72.50 40.8 109.2
190 185.00 10434 24.4 41 68.25 38.0 103.4
191 153.00 10728 11.4 41 69.25 36.4 91.4
192 244.25 10140 38.1 42 76.00 41.8 115.2
193 193.50 10624 15.9 42 70.50 40.7 104.9
194 224.75 10429 24.7 42 74.75 38.5 106.7
195 162.75 10470 22.8 42 72.75 35.4 92.2
196 180.00 10411 25.5 42 68.25 38.5 101.6
197 156.25 10488 22.0 42 69.00 35.5 97.8
198 168.00 10583 17.7 42 71.50 36.5 92.0
199 167.25 10841 6.6 42 72.75 37.6 94.0
200 170.75 10462 23.6 43 67.50 37.4 103.7
201 178.25 10709 12.2 43 70.25 37.8 102.7
202 150.00 10484 22.1 43 69.25 35.2 91.1
203 200.50 10340 28.7 43 71.50 37.9 107.2
204 184.00 10854 6.0 44 74.00 37.9 100.8
205 223.00 10209 34.8 44 69.75 40.9 121.6
206 208.75 10610 16.6 44 73.00 41.9 105.6
207 166.00 10250 32.9 44 65.50 39.1 100.6
208 195.00 10254 32.8 47 72.50 40.2 102.7
209 160.50 10771 9.6 47 70.25 36.0 99.8
210 159.75 10742 10.8 47 70.75 34.5 92.9
211 140.50 10829 7.1 49 68.00 35.8 91.2
212 216.25 10373 27.2 49 74.50 40.2 115.6
213 168.25 10543 19.5 49 71.75 38.3 98.3
214 194.75 10561 18.7 50 70.75 39.0 103.7
215 172.75 10543 19.5 50 73.00 37.4 98.7
216 219.00 1 47.5 51 64.00 41.2 119.8
217 149.25 10678 13.6 51 69.75 34.8 92.8
218 154.50 10819 7.5 51 70.00 36.9 93.3
219 199.25 10433 24.5 52 71.75 39.4 106.8
220 154.50 10646 15.0 53 69.25 37.6 93.9
221 153.25 10706 12.4 54 70.50 38.5 99.0
222 230.00 10399 26.0 54 72.25 42.5 119.9
223 161.75 10726 11.5 54 67.50 37.4 94.2
224 142.25 10874 5.2 55 67.25 35.2 92.7
225 179.75 10740 10.9 55 68.75 41.1 106.9
226 126.50 10703 12.5 55 66.75 33.4 88.8
227 169.50 10650 14.8 55 68.25 37.2 101.7
228 198.50 10418 25.2 55 74.25 38.3 105.3
229 174.50 10647 14.9 56 69.50 38.1 104.0
230 167.75 10601 17.0 56 68.50 37.4 98.6
231 147.75 10745 10.6 57 65.75 35.2 99.6
232 182.25 10620 16.1 57 71.75 39.4 103.4
233 175.50 10636 15.4 58 71.50 38.0 100.2
234 161.75 10384 26.7 58 67.25 35.1 94.9
235 157.75 10403 25.8 60 67.50 40.4 97.2
236 168.75 10563 18.6 62 67.50 38.3 104.7
237 191.50 10424 24.8 62 72.25 40.6 104.0
238 219.15 10372 27.3 63 69.50 40.2 117.6
239 155.25 10705 12.4 64 69.50 37.9 95.8
240 189.75 10316 29.9 65 65.75 40.8 106.4
241 127.50 10599 17.0 65 65.75 34.7 93.0
242 224.50 10207 35.0 65 68.25 38.8 119.6
243 234.25 10304 30.4 66 72.00 41.4 119.7
244 227.75 10256 32.6 67 72.75 41.3 115.8
245 199.50 10334 29.0 67 68.50 40.7 118.3
246 155.50 10641 15.2 68 69.25 36.3 97.4
247 215.50 10308 30.2 69 70.50 40.8 113.7
248 134.25 10736 11.0 70 67.00 34.9 89.2
249 201.00 10236 33.6 72 69.75 40.9 108.5
250 186.75 10328 29.3 72 66.00 38.9 111.1
251 190.75 10399 26.0 72 70.50 38.9 108.3
252 207.50 10271 31.9 74 70.00 40.8 112.4
buikomtrek heupomtrek dijomtrek knieomtrek enkelomtrek bicepsomtrek
1 85.2 94.5 59.0 37.3 21.9 32.0
2 83.0 98.7 58.7 37.3 23.4 30.5
3 87.9 99.2 59.6 38.9 24.0 28.8
4 86.4 101.2 60.1 37.3 22.8 32.4
5 100.0 101.9 63.2 42.2 24.0 32.2
6 94.4 107.8 66.0 42.0 25.6 35.7
7 90.7 100.3 58.4 38.3 22.9 31.9
8 88.5 97.1 60.0 39.4 23.2 30.5
9 82.5 99.9 62.9 38.3 23.8 35.9
10 88.6 104.1 63.1 41.7 25.0 35.6
11 83.6 98.2 59.7 39.7 25.2 32.8
12 90.9 107.7 66.2 39.2 25.9 37.2
13 91.6 103.9 63.4 38.3 21.5 32.5
14 101.8 108.6 66.0 41.5 23.7 36.9
15 96.4 100.1 69.0 39.0 23.1 36.1
16 92.8 99.2 63.1 38.7 21.7 31.1
17 96.4 105.2 64.8 40.8 23.1 36.2
18 97.5 107.0 66.9 40.0 24.4 38.2
19 89.6 102.4 64.2 38.7 22.9 37.2
20 100.5 109.0 65.8 40.6 24.0 37.1
21 95.9 104.9 63.5 38.0 22.1 32.5
22 98.8 104.8 63.4 40.6 24.6 33.0
23 76.4 94.6 57.4 35.3 22.2 27.9
24 80.0 93.4 54.9 36.2 22.1 29.8
25 76.3 95.8 58.4 35.5 22.9 31.1
26 79.7 96.5 55.0 36.7 22.5 29.9
27 74.6 85.3 51.7 34.7 21.4 28.7
28 88.7 94.7 57.5 36.0 21.0 29.2
29 73.9 88.5 50.1 34.5 21.3 30.5
30 83.5 98.7 58.9 35.3 22.6 30.1
31 88.7 99.8 57.5 38.7 33.9 32.5
32 84.5 100.6 58.5 38.8 21.5 30.1
33 79.1 94.5 57.3 36.2 24.5 29.0
34 100.5 108.3 67.1 44.2 25.2 37.5
35 115.6 116.1 71.2 43.3 26.3 37.3
36 113.1 113.8 61.9 38.3 21.9 32.0
37 100.9 106.2 63.5 39.9 22.6 35.1
38 98.8 104.8 66.0 41.5 24.7 33.2
39 148.1 147.7 87.3 49.1 29.6 45.0
40 108.1 102.5 61.3 41.1 24.7 34.1
41 126.2 125.6 72.5 39.6 26.6 36.4
42 104.3 115.5 70.6 42.5 23.7 33.6
43 111.2 114.1 67.7 40.9 25.0 36.7
44 104.3 106.0 65.0 40.2 23.0 35.8
45 76.0 88.2 50.0 34.7 21.0 26.1
46 81.5 97.2 58.4 38.2 23.4 29.7
47 73.7 88.5 53.3 34.5 22.5 27.9
48 79.5 92.7 52.7 37.5 21.9 28.8
49 83.4 90.4 52.0 35.8 20.6 28.8
50 70.4 87.2 50.6 34.4 21.9 26.8
51 86.7 98.3 52.6 37.2 22.4 26.0
52 77.9 91.0 51.4 34.9 21.0 26.7
53 82.0 89.1 49.3 33.7 21.4 29.6
54 79.6 91.6 52.6 37.6 22.6 38.5
55 77.6 88.6 51.9 34.9 22.5 27.7
56 100.0 99.6 57.2 38.0 22.0 35.9
57 99.8 102.5 62.1 39.6 22.5 33.1
58 104.2 105.8 61.8 39.8 22.7 37.7
59 105.3 97.0 59.1 38.0 22.5 31.6
60 98.3 99.6 60.6 37.7 22.9 34.5
61 104.8 103.1 61.6 40.9 23.1 36.2
62 94.7 100.8 60.9 38.0 22.1 32.5
63 102.4 99.4 61.0 39.4 23.6 32.7
64 99.7 99.7 60.8 40.1 22.7 33.6
65 105.5 108.3 65.0 41.2 24.7 35.3
66 100.3 104.2 64.8 40.2 22.7 34.8
67 83.9 93.9 55.0 36.1 21.7 29.6
68 86.6 91.8 54.3 35.4 21.5 32.8
69 78.4 96.1 56.0 37.4 22.4 32.6
70 84.6 94.3 51.2 37.4 21.6 27.3
71 91.5 98.5 56.6 38.6 22.4 31.5
72 82.8 95.5 58.9 37.6 21.6 30.3
73 82.9 96.3 52.9 37.5 23.1 29.7
74 76.0 88.6 50.9 35.4 19.1 29.3
75 83.3 93.0 55.5 35.2 20.9 29.4
76 81.8 94.8 54.5 37.0 21.4 29.3
77 78.8 94.3 56.7 39.7 24.2 30.2
78 95.0 98.3 55.0 38.3 21.8 30.8
79 95.4 99.3 53.5 37.5 21.5 31.4
80 98.6 100.2 56.5 39.3 22.7 30.3
81 95.8 97.1 54.8 38.2 23.7 29.4
82 89.0 96.9 54.8 38.0 22.0 29.9
83 97.8 99.6 58.9 39.0 23.0 34.3
84 94.9 95.0 56.0 36.5 24.1 31.2
85 99.8 96.2 56.3 36.6 22.0 29.7
86 89.7 96.2 54.7 37.8 33.7 32.4
87 88.1 96.9 57.2 37.7 21.8 32.6
88 90.9 93.8 57.8 39.5 23.3 29.2
89 86.0 99.3 61.0 38.4 23.8 30.2
90 86.5 98.3 60.4 39.9 24.4 28.8
91 95.6 102.2 58.3 38.2 22.5 29.1
92 93.2 100.6 58.9 39.7 23.1 31.4
93 83.1 95.4 56.9 38.3 22.1 30.1
94 97.5 100.6 58.9 40.5 24.5 33.3
95 88.8 101.4 57.4 39.6 24.6 30.3
96 99.2 107.5 61.7 42.3 23.2 32.9
97 91.6 102.4 60.6 39.4 22.9 31.6
98 86.7 96.2 62.1 39.3 23.3 30.6
99 88.2 92.8 54.7 37.3 21.9 31.6
100 94.0 103.7 62.7 39.0 22.3 35.3
101 95.0 101.7 59.0 39.4 22.3 32.2
102 92.0 98.3 59.3 38.4 22.4 27.9
103 89.2 98.3 60.0 38.4 23.2 31.0
104 95.5 101.6 59.1 39.8 25.4 31.0
105 98.6 99.5 59.5 36.1 22.0 30.1
106 87.3 96.6 54.7 39.0 24.8 31.0
107 102.8 103.6 61.2 39.3 23.5 30.5
108 101.6 100.7 55.8 38.7 23.4 35.1
109 88.7 102.1 57.5 40.0 24.8 35.1
110 92.3 100.6 57.5 36.8 22.8 32.1
111 90.6 99.3 61.9 38.0 22.3 33.3
112 105.0 103.0 63.7 40.0 23.6 33.5
113 95.0 98.6 62.3 38.1 23.9 35.3
114 89.6 99.8 61.5 37.8 21.9 30.7
115 92.4 97.5 59.3 38.1 21.8 31.8
116 86.6 92.6 55.9 36.3 22.1 29.8
117 90.0 99.7 58.8 38.4 22.8 29.9
118 90.0 96.4 56.8 38.8 23.3 33.4
119 92.4 104.3 64.6 41.1 24.8 33.6
120 87.5 101.0 58.5 39.2 24.5 32.1
121 99.2 104.1 58.5 39.3 24.6 33.9
122 98.1 101.4 57.1 40.5 23.2 33.0
123 83.3 97.5 60.5 38.7 22.6 34.4
124 86.1 95.2 58.1 36.5 22.1 30.6
125 84.1 94.0 58.5 36.6 23.5 34.4
126 89.9 100.0 60.7 36.0 21.9 35.6
127 92.1 98.5 60.7 36.8 22.2 33.8
128 78.0 93.2 53.5 35.8 20.8 33.9
129 93.5 99.5 61.7 39.0 21.8 33.3
130 87.0 97.8 57.4 36.9 22.2 31.6
131 90.1 95.8 57.0 38.7 23.2 27.5
132 90.3 99.1 60.3 38.5 23.0 31.2
133 99.8 103.2 61.2 38.1 22.6 33.5
134 89.4 92.3 56.1 35.6 20.5 33.6
135 87.2 98.4 56.0 36.9 23.0 34.0
136 101.1 102.1 58.9 37.9 22.7 30.9
137 86.1 95.6 58.8 36.1 22.4 32.7
138 98.6 100.6 63.6 39.2 23.8 34.3
139 88.5 98.3 58.1 38.4 22.5 31.7
140 106.6 107.7 66.5 42.5 24.5 35.5
141 93.1 101.6 59.1 39.6 21.6 30.8
142 93.0 99.3 60.4 38.2 22.0 32.0
143 91.0 98.9 57.1 36.7 22.3 31.6
144 77.1 93.9 56.1 36.1 22.7 30.5
145 85.3 102.5 59.1 37.6 23.2 31.8
146 81.9 95.3 56.4 36.5 22.0 33.5
147 99.1 110.1 71.2 43.5 25.2 36.1
148 100.5 106.2 68.4 40.8 24.6 33.3
149 76.5 92.1 51.9 35.7 22.0 25.8
150 106.8 113.9 67.6 42.7 24.7 36.0
151 77.6 93.5 56.9 35.9 20.4 31.6
152 102.9 114.4 72.9 43.5 25.1 38.5
153 72.8 91.1 53.6 36.8 23.8 27.8
154 88.2 95.2 56.8 37.4 22.8 30.6
155 100.1 105.0 62.1 40.0 24.9 33.7
156 83.5 98.3 57.3 37.8 21.7 32.2
157 105.0 106.4 68.6 40.0 25.2 35.2
158 90.8 102.5 60.8 38.5 25.0 31.6
159 76.6 89.8 50.1 34.8 21.8 27.0
160 92.4 99.3 59.4 39.0 24.6 30.1
161 81.2 91.5 52.5 36.6 21.0 27.0
162 95.6 105.1 61.4 40.6 25.0 31.3
163 92.1 103.5 64.0 37.3 23.5 33.5
164 83.4 89.6 52.4 35.6 20.4 28.3
165 106.0 108.8 63.8 42.0 23.4 34.0
166 95.1 104.5 64.8 41.3 25.6 36.4
167 90.4 95.6 55.5 34.2 21.9 30.2
168 100.4 106.8 63.3 41.7 24.6 37.2
169 115.9 111.9 74.4 40.6 24.0 36.1
170 90.8 98.1 60.1 39.1 23.4 32.5
171 81.9 92.8 54.7 36.2 22.1 30.4
172 75.0 89.2 50.0 34.8 22.0 24.8
173 90.3 98.7 57.8 37.3 22.4 31.0
174 90.3 99.9 59.2 37.7 21.5 32.4
175 108.8 114.4 69.2 42.4 24.0 35.4
176 79.4 89.2 50.3 34.8 22.2 31.0
177 83.2 96.4 60.0 38.1 22.0 31.5
178 110.3 113.9 69.8 42.6 24.8 34.4
179 92.7 101.9 64.7 39.5 24.7 34.8
180 104.5 109.9 69.5 43.1 25.8 39.1
181 104.6 109.8 68.1 42.8 24.1 35.6
182 69.4 85.0 47.2 33.5 20.2 27.7
183 83.6 91.6 54.1 36.2 21.8 31.4
184 86.8 96.1 58.0 39.4 22.7 30.0
185 90.4 95.5 55.4 38.9 22.4 30.5
186 83.7 98.1 57.3 39.7 22.6 32.9
187 109.3 108.8 67.7 41.3 24.7 37.2
188 98.9 104.1 63.5 39.8 23.5 36.4
189 98.0 101.8 62.8 41.3 24.8 36.6
190 101.2 103.1 61.5 40.4 22.9 33.4
191 80.6 92.3 54.3 36.3 21.8 29.6
192 113.7 112.4 68.5 45.0 25.5 37.1
193 94.1 102.7 60.6 38.6 24.7 34.0
194 105.7 111.8 65.3 43.3 26.0 33.7
195 85.6 96.5 60.2 38.9 22.4 31.7
196 96.6 100.6 61.1 38.4 24.1 32.9
197 86.0 96.2 57.7 38.6 24.0 31.2
198 89.7 101.0 62.3 38.0 22.3 30.8
199 78.0 99.0 57.5 40.0 22.5 30.6
200 89.7 94.2 58.5 39.0 24.1 33.8
201 89.2 99.2 60.2 39.2 23.8 31.7
202 85.7 96.9 55.5 35.7 22.0 29.4
203 103.1 105.5 68.8 38.3 23.7 32.1
204 89.1 102.6 60.6 39.0 24.0 32.9
205 113.9 107.1 63.5 40.3 21.8 34.8
206 96.3 102.0 63.3 39.8 24.1 37.3
207 93.9 100.1 58.9 37.6 21.4 33.1
208 101.3 101.7 60.7 39.4 23.3 36.7
209 83.9 91.8 53.0 36.2 22.5 31.4
210 84.4 94.0 56.0 38.2 22.6 29.0
211 79.4 89.0 51.1 35.0 21.7 30.9
212 104.0 109.0 63.7 40.3 23.2 36.8
213 89.7 99.1 56.3 38.8 23.0 29.5
214 97.6 104.2 60.0 40.9 25.5 32.7
215 87.6 96.1 57.1 38.1 21.8 28.6
216 122.1 112.8 62.5 36.9 23.6 34.7
217 81.1 96.3 53.8 36.5 21.5 31.3
218 81.5 94.4 54.7 39.0 22.6 27.5
219 100.0 105.0 63.9 39.2 22.9 35.7
220 88.7 94.5 53.7 36.2 22.0 28.5
221 91.8 96.2 57.7 38.1 23.9 31.4
222 110.4 105.5 64.2 42.7 27.0 38.4
223 87.6 95.6 59.7 40.2 23.4 27.9
224 82.8 91.9 54.4 35.2 22.5 29.4
225 95.3 98.2 57.4 37.1 21.8 34.1
226 78.2 87.5 50.8 33.0 19.7 25.3
227 91.1 97.1 56.6 38.5 22.6 33.4
228 96.7 106.6 64.0 42.6 23.4 33.2
229 89.4 98.4 58.4 37.4 22.5 34.6
230 93.0 97.0 55.4 38.8 23.2 32.4
231 86.4 90.1 53.0 35.0 21.3 31.7
232 96.7 100.7 59.3 38.6 22.8 31.8
233 88.1 97.8 57.1 38.9 23.6 30.9
234 94.9 100.2 56.8 35.9 21.0 27.8
235 93.3 94.0 54.3 35.7 21.0 31.3
236 95.6 93.7 54.4 37.1 22.7 30.3
237 98.2 101.1 59.3 40.3 23.0 32.6
238 113.8 111.8 63.4 41.1 22.3 35.1
239 82.8 94.5 61.2 39.1 22.3 29.8
240 100.5 100.5 59.2 38.1 24.0 35.9
241 79.7 87.6 50.7 33.4 20.1 28.5
242 118.0 114.3 61.3 42.1 23.4 34.9
243 109.0 109.1 63.7 42.4 24.6 35.6
244 113.4 109.8 65.6 46.0 25.4 35.3
245 106.1 101.6 58.2 38.8 24.1 32.1
246 84.3 94.4 54.3 37.5 22.6 29.2
247 107.6 110.0 63.3 44.0 22.6 37.5
248 83.6 88.8 49.6 34.8 21.5 25.6
249 105.0 104.5 59.6 40.8 23.2 35.2
250 111.5 101.7 60.3 37.3 21.5 31.3
251 101.3 97.8 56.0 41.6 22.7 30.5
252 108.5 107.1 59.3 42.2 24.6 33.7
voorarmomtrek polsomtrek\r
1 27.4 17.1
2 28.9 18.2
3 25.2 16.6
4 29.4 18.2
5 27.7 17.7
6 30.6 18.8
7 27.8 17.7
8 29.0 18.8
9 31.1 18.2
10 30.0 19.2
11 29.4 18.5
12 30.2 19.0
13 28.6 17.7
14 31.6 18.8
15 30.5 18.2
16 26.4 16.9
17 30.8 17.3
18 31.6 19.3
19 30.5 18.5
20 30.1 18.2
21 30.3 18.4
22 32.8 19.9
23 25.9 16.7
24 26.7 17.1
25 28.0 17.6
26 28.2 17.7
27 27.0 16.5
28 26.6 17.0
29 27.9 17.2
30 26.7 17.6
31 27.7 18.4
32 26.4 17.9
33 30.0 18.8
34 31.5 18.7
35 31.7 19.7
36 29.8 17.0
37 30.6 19.0
38 30.5 19.4
39 29.0 21.4
40 31.0 18.3
41 32.7 21.4
42 28.7 17.4
43 29.8 18.4
44 31.5 18.8
45 23.1 16.1
46 27.4 18.3
47 26.2 17.3
48 26.8 17.9
49 25.5 16.3
50 25.8 16.8
51 25.8 17.3
52 26.1 17.2
53 26.0 16.9
54 27.4 18.5
55 27.5 18.5
56 30.2 18.9
57 28.3 18.5
58 30.9 19.2
59 28.8 18.2
60 29.6 18.5
61 31.8 20.2
62 29.8 18.3
63 29.9 19.1
64 29.0 18.8
65 31.1 18.4
66 30.1 18.7
67 27.4 17.4
68 27.4 18.7
69 28.1 18.1
70 27.1 17.3
71 27.3 18.6
72 27.3 18.3
73 27.3 18.2
74 25.7 16.9
75 27.0 16.8
76 27.0 18.3
77 29.2 18.1
78 25.7 18.8
79 26.8 18.3
80 28.7 19.0
81 27.2 19.0
82 25.2 17.7
83 29.6 19.0
84 27.3 19.2
85 26.3 18.0
86 27.7 18.2
87 28.0 18.8
88 28.4 18.1
89 29.3 18.8
90 29.6 18.7
91 27.7 17.7
92 28.4 18.8
93 28.2 18.4
94 29.6 19.1
95 27.9 17.8
96 30.8 20.4
97 30.1 18.5
98 27.8 18.2
99 27.5 18.2
100 30.9 18.3
101 31.0 18.6
102 26.2 17.0
103 29.2 18.4
104 30.3 19.7
105 27.2 17.7
106 29.4 18.8
107 28.5 18.1
108 29.6 19.1
109 30.7 19.2
110 26.0 17.3
111 28.2 18.1
112 27.8 17.4
113 31.1 19.8
114 27.6 17.4
115 27.3 17.5
116 26.3 17.3
117 28.0 18.1
118 29.8 19.5
119 29.5 18.5
120 28.6 18.0
121 31.2 19.5
122 29.6 18.4
123 28.0 17.6
124 27.5 17.6
125 29.2 18.0
126 30.2 17.6
127 30.3 17.2
128 28.2 17.4
129 29.6 18.1
130 27.8 17.7
131 26.5 17.6
132 28.4 17.1
133 28.6 17.9
134 29.3 17.3
135 29.8 18.1
136 28.8 17.6
137 28.3 17.1
138 28.4 17.7
139 27.4 17.6
140 29.8 18.7
141 27.9 16.6
142 28.5 17.8
143 27.5 17.9
144 27.2 18.2
145 29.7 18.3
146 28.3 17.3
147 30.3 18.7
148 29.7 18.4
149 25.2 16.9
150 30.4 18.4
151 29.0 17.8
152 33.8 19.6
153 26.3 17.4
154 28.3 17.9
155 29.2 19.4
156 27.7 17.7
157 30.7 19.1
158 28.0 18.6
159 34.9 16.9
160 28.2 18.2
161 26.3 16.5
162 29.2 19.1
163 30.6 19.7
164 26.2 16.5
165 31.2 18.5
166 33.7 19.4
167 28.7 17.7
168 33.1 19.8
169 31.8 18.8
170 29.8 17.4
171 27.4 17.7
172 25.9 16.9
173 28.7 17.7
174 28.4 17.8
175 21.0 20.1
176 26.9 16.9
177 26.6 16.7
178 29.5 18.4
179 30.3 18.1
180 32.5 19.9
181 29.0 19.0
182 24.6 16.5
183 28.3 17.2
184 26.4 17.4
185 28.9 17.7
186 29.3 18.2
187 31.8 20.0
188 30.4 19.1
189 32.4 18.8
190 29.2 18.5
191 27.3 17.9
192 31.2 19.9
193 30.1 18.7
194 29.9 18.5
195 27.1 17.1
196 29.8 18.8
197 27.3 17.4
198 27.8 16.9
199 30.0 18.5
200 28.8 18.8
201 28.4 18.6
202 26.6 17.4
203 28.9 18.7
204 29.2 18.4
205 30.7 17.4
206 23.1 19.4
207 29.5 17.3
208 31.6 18.4
209 27.5 17.7
210 26.2 17.6
211 28.8 17.4
212 31.0 18.9
213 27.9 18.6
214 30.0 19.0
215 26.7 18.0
216 29.1 18.4
217 26.3 17.8
218 25.9 18.6
219 30.4 19.2
220 25.7 17.1
221 29.9 18.9
222 32.0 19.6
223 27.0 17.8
224 26.8 17.0
225 31.1 19.2
226 22.0 15.8
227 29.3 18.8
228 30.0 18.4
229 30.1 18.8
230 29.7 19.0
231 27.3 16.9
232 29.1 19.0
233 29.6 18.0
234 26.1 17.6
235 28.7 18.3
236 26.3 18.3
237 28.5 19.0
238 29.6 18.5
239 28.9 18.3
240 30.5 19.1
241 24.8 16.5
242 30.1 19.4
243 30.7 19.5
244 29.8 19.5
245 29.3 18.5
246 27.3 18.5
247 32.6 18.8
248 25.7 18.5
249 28.6 20.1
250 27.2 18.0
251 29.4 19.8
252 30.0 20.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dichtheid lichaamsvet leeftijd grootte
-2.913e+02 1.410e-04 -1.231e-01 -1.169e-01 7.941e-01
nekomtrek borstomtrek buikomtrek heupomtrek dijomtrek
1.196e+00 7.444e-01 4.475e-01 1.435e+00 1.754e-01
knieomtrek enkelomtrek bicepsomtrek voorarmomtrek `polsomtrek\r`
1.128e+00 7.761e-01 5.818e-01 9.707e-02 1.202e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-20.3911 -2.9491 -0.0355 3.1226 23.5501
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.913e+02 1.057e+01 -27.565 < 2e-16 ***
dichtheid 1.410e-04 5.617e-04 0.251 0.802031
lichaamsvet -1.231e-01 8.046e-02 -1.529 0.127516
leeftijd -1.169e-01 3.879e-02 -3.013 0.002864 **
grootte 7.941e-01 1.040e-01 7.635 5.52e-13 ***
nekomtrek 1.196e+00 2.723e-01 4.394 1.68e-05 ***
borstomtrek 7.444e-01 1.094e-01 6.803 8.30e-11 ***
buikomtrek 4.475e-01 1.252e-01 3.573 0.000427 ***
heupomtrek 1.435e+00 1.511e-01 9.492 < 2e-16 ***
dijomtrek 1.754e-01 1.761e-01 0.996 0.320242
knieomtrek 1.128e+00 2.873e-01 3.927 0.000113 ***
enkelomtrek 7.761e-01 2.632e-01 2.949 0.003508 **
bicepsomtrek 5.818e-01 2.035e-01 2.859 0.004624 **
voorarmomtrek 9.707e-02 2.428e-01 0.400 0.689654
`polsomtrek\r` 1.202e+00 6.531e-01 1.841 0.066860 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.194 on 237 degrees of freedom
Multiple R-squared: 0.9705, Adjusted R-squared: 0.9688
F-statistic: 557 on 14 and 237 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,] 3.377190e-02 6.754379e-02 9.662281e-01
[2,] 9.852962e-03 1.970592e-02 9.901470e-01
[3,] 1.284566e-02 2.569132e-02 9.871543e-01
[4,] 6.315124e-03 1.263025e-02 9.936849e-01
[5,] 1.095910e-02 2.191820e-02 9.890409e-01
[6,] 4.151401e-03 8.302802e-03 9.958486e-01
[7,] 1.595040e-03 3.190079e-03 9.984050e-01
[8,] 8.299818e-04 1.659964e-03 9.991700e-01
[9,] 2.890491e-04 5.780982e-04 9.997110e-01
[10,] 9.668108e-05 1.933622e-04 9.999033e-01
[11,] 1.282057e-03 2.564114e-03 9.987179e-01
[12,] 5.599341e-04 1.119868e-03 9.994401e-01
[13,] 3.144285e-04 6.288569e-04 9.996856e-01
[14,] 8.681713e-04 1.736343e-03 9.991318e-01
[15,] 5.878138e-03 1.175628e-02 9.941219e-01
[16,] 7.877998e-03 1.575600e-02 9.921220e-01
[17,] 1.083138e-02 2.166276e-02 9.891686e-01
[18,] 1.382514e-02 2.765028e-02 9.861749e-01
[19,] 5.853194e-02 1.170639e-01 9.414681e-01
[20,] 4.136622e-02 8.273244e-02 9.586338e-01
[21,] 2.935046e-02 5.870091e-02 9.706495e-01
[22,] 9.423255e-01 1.153491e-01 5.767454e-02
[23,] 9.574372e-01 8.512569e-02 4.256284e-02
[24,] 9.497418e-01 1.005164e-01 5.025820e-02
[25,] 9.999016e-01 1.967183e-04 9.835914e-05
[26,] 9.998963e-01 2.073719e-04 1.036860e-04
[27,] 9.999266e-01 1.467316e-04 7.336579e-05
[28,] 9.999276e-01 1.447295e-04 7.236476e-05
[29,] 9.998950e-01 2.099876e-04 1.049938e-04
[30,] 9.998588e-01 2.823904e-04 1.411952e-04
[31,] 9.998688e-01 2.623844e-04 1.311922e-04
[32,] 9.997998e-01 4.003684e-04 2.001842e-04
[33,] 9.998193e-01 3.613664e-04 1.806832e-04
[34,] 9.997213e-01 5.573225e-04 2.786613e-04
[35,] 9.996253e-01 7.493167e-04 3.746583e-04
[36,] 9.994808e-01 1.038339e-03 5.191693e-04
[37,] 9.998877e-01 2.246973e-04 1.123486e-04
[38,] 9.998847e-01 2.305390e-04 1.152695e-04
[39,] 9.999724e-01 5.519495e-05 2.759747e-05
[40,] 9.999856e-01 2.887907e-05 1.443954e-05
[41,] 9.999831e-01 3.380264e-05 1.690132e-05
[42,] 9.999998e-01 3.511422e-07 1.755711e-07
[43,] 9.999997e-01 5.115281e-07 2.557641e-07
[44,] 9.999997e-01 5.142685e-07 2.571343e-07
[45,] 9.999997e-01 6.868460e-07 3.434230e-07
[46,] 9.999996e-01 8.778839e-07 4.389419e-07
[47,] 9.999997e-01 5.755826e-07 2.877913e-07
[48,] 9.999995e-01 9.379073e-07 4.689536e-07
[49,] 9.999999e-01 1.955553e-07 9.777763e-08
[50,] 9.999999e-01 2.416440e-07 1.208220e-07
[51,] 9.999998e-01 3.497146e-07 1.748573e-07
[52,] 9.999998e-01 4.494386e-07 2.247193e-07
[53,] 9.999997e-01 5.814467e-07 2.907234e-07
[54,] 9.999996e-01 8.997974e-07 4.498987e-07
[55,] 9.999999e-01 1.889568e-07 9.447841e-08
[56,] 9.999998e-01 3.070251e-07 1.535125e-07
[57,] 9.999998e-01 3.738921e-07 1.869461e-07
[58,] 9.999997e-01 5.864014e-07 2.932007e-07
[59,] 9.999996e-01 8.988989e-07 4.494494e-07
[60,] 9.999996e-01 8.456035e-07 4.228017e-07
[61,] 9.999996e-01 7.391085e-07 3.695542e-07
[62,] 9.999997e-01 5.509733e-07 2.754867e-07
[63,] 9.999999e-01 2.320046e-07 1.160023e-07
[64,] 9.999998e-01 3.505581e-07 1.752791e-07
[65,] 9.999999e-01 1.457488e-07 7.287439e-08
[66,] 9.999999e-01 2.180791e-07 1.090395e-07
[67,] 9.999998e-01 3.537184e-07 1.768592e-07
[68,] 9.999997e-01 5.628852e-07 2.814426e-07
[69,] 9.999996e-01 7.802942e-07 3.901471e-07
[70,] 9.999997e-01 6.361704e-07 3.180852e-07
[71,] 9.999995e-01 9.327486e-07 4.663743e-07
[72,] 9.999992e-01 1.522478e-06 7.612389e-07
[73,] 9.999989e-01 2.195657e-06 1.097828e-06
[74,] 9.999983e-01 3.475666e-06 1.737833e-06
[75,] 9.999975e-01 4.915510e-06 2.457755e-06
[76,] 9.999963e-01 7.358821e-06 3.679411e-06
[77,] 9.999945e-01 1.096481e-05 5.482404e-06
[78,] 9.999921e-01 1.583018e-05 7.915088e-06
[79,] 9.999890e-01 2.198359e-05 1.099180e-05
[80,] 9.999851e-01 2.971982e-05 1.485991e-05
[81,] 9.999873e-01 2.539265e-05 1.269632e-05
[82,] 9.999814e-01 3.723520e-05 1.861760e-05
[83,] 9.999753e-01 4.944768e-05 2.472384e-05
[84,] 9.999821e-01 3.584906e-05 1.792453e-05
[85,] 9.999800e-01 4.008764e-05 2.004382e-05
[86,] 9.999716e-01 5.684075e-05 2.842038e-05
[87,] 9.999615e-01 7.694112e-05 3.847056e-05
[88,] 9.999485e-01 1.030498e-04 5.152491e-05
[89,] 9.999790e-01 4.200202e-05 2.100101e-05
[90,] 9.999736e-01 5.282034e-05 2.641017e-05
[91,] 9.999653e-01 6.934120e-05 3.467060e-05
[92,] 9.999722e-01 5.553842e-05 2.776921e-05
[93,] 9.999589e-01 8.213440e-05 4.106720e-05
[94,] 9.999407e-01 1.186347e-04 5.931736e-05
[95,] 9.999911e-01 1.773999e-05 8.869997e-06
[96,] 9.999926e-01 1.475837e-05 7.379183e-06
[97,] 9.999894e-01 2.110857e-05 1.055428e-05
[98,] 9.999879e-01 2.420396e-05 1.210198e-05
[99,] 9.999849e-01 3.029337e-05 1.514669e-05
[100,] 9.999846e-01 3.084805e-05 1.542403e-05
[101,] 9.999769e-01 4.625820e-05 2.312910e-05
[102,] 9.999660e-01 6.797365e-05 3.398682e-05
[103,] 9.999690e-01 6.190434e-05 3.095217e-05
[104,] 9.999712e-01 5.751002e-05 2.875501e-05
[105,] 9.999589e-01 8.213026e-05 4.106513e-05
[106,] 9.999786e-01 4.277964e-05 2.138982e-05
[107,] 9.999709e-01 5.814910e-05 2.907455e-05
[108,] 9.999572e-01 8.554380e-05 4.277190e-05
[109,] 9.999480e-01 1.040970e-04 5.204848e-05
[110,] 9.999568e-01 8.642168e-05 4.321084e-05
[111,] 9.999401e-01 1.198960e-04 5.994799e-05
[112,] 9.999374e-01 1.252342e-04 6.261710e-05
[113,] 9.999099e-01 1.802159e-04 9.010797e-05
[114,] 9.999464e-01 1.072957e-04 5.364787e-05
[115,] 9.999306e-01 1.387036e-04 6.935182e-05
[116,] 9.999366e-01 1.268902e-04 6.344510e-05
[117,] 9.999116e-01 1.768926e-04 8.844631e-05
[118,] 9.998708e-01 2.584344e-04 1.292172e-04
[119,] 9.998165e-01 3.670528e-04 1.835264e-04
[120,] 9.998663e-01 2.673444e-04 1.336722e-04
[121,] 9.998193e-01 3.614952e-04 1.807476e-04
[122,] 9.997423e-01 5.153296e-04 2.576648e-04
[123,] 9.996962e-01 6.076842e-04 3.038421e-04
[124,] 9.995657e-01 8.685121e-04 4.342560e-04
[125,] 9.993868e-01 1.226460e-03 6.132300e-04
[126,] 9.992104e-01 1.579140e-03 7.895702e-04
[127,] 9.991668e-01 1.666358e-03 8.331788e-04
[128,] 9.988944e-01 2.211110e-03 1.105555e-03
[129,] 9.986470e-01 2.705967e-03 1.352984e-03
[130,] 9.988058e-01 2.388472e-03 1.194236e-03
[131,] 9.984643e-01 3.071382e-03 1.535691e-03
[132,] 9.982137e-01 3.572615e-03 1.786307e-03
[133,] 9.982323e-01 3.535356e-03 1.767678e-03
[134,] 9.977176e-01 4.564817e-03 2.282409e-03
[135,] 9.985171e-01 2.965787e-03 1.482894e-03
[136,] 9.981694e-01 3.661211e-03 1.830605e-03
[137,] 9.979651e-01 4.069758e-03 2.034879e-03
[138,] 9.972163e-01 5.567397e-03 2.783698e-03
[139,] 9.967463e-01 6.507492e-03 3.253746e-03
[140,] 9.957206e-01 8.558718e-03 4.279359e-03
[141,] 9.946562e-01 1.068761e-02 5.343806e-03
[142,] 9.930995e-01 1.380109e-02 6.900543e-03
[143,] 9.913873e-01 1.722543e-02 8.612717e-03
[144,] 9.894343e-01 2.113149e-02 1.056575e-02
[145,] 9.876027e-01 2.479455e-02 1.239728e-02
[146,] 9.853078e-01 2.938439e-02 1.469219e-02
[147,] 9.825434e-01 3.491313e-02 1.745656e-02
[148,] 9.796932e-01 4.061358e-02 2.030679e-02
[149,] 9.871864e-01 2.562729e-02 1.281364e-02
[150,] 9.857430e-01 2.851404e-02 1.425702e-02
[151,] 9.923192e-01 1.536164e-02 7.680819e-03
[152,] 9.901032e-01 1.979368e-02 9.896842e-03
[153,] 9.873430e-01 2.531410e-02 1.265705e-02
[154,] 9.846801e-01 3.063970e-02 1.531985e-02
[155,] 9.813234e-01 3.735323e-02 1.867661e-02
[156,] 9.774086e-01 4.518280e-02 2.259140e-02
[157,] 9.708785e-01 5.824291e-02 2.912145e-02
[158,] 9.871668e-01 2.566646e-02 1.283323e-02
[159,] 9.840017e-01 3.199666e-02 1.599833e-02
[160,] 9.827330e-01 3.453409e-02 1.726704e-02
[161,] 9.843431e-01 3.131375e-02 1.565687e-02
[162,] 9.800826e-01 3.983479e-02 1.991739e-02
[163,] 9.833598e-01 3.328032e-02 1.664016e-02
[164,] 9.786365e-01 4.272705e-02 2.136352e-02
[165,] 9.808869e-01 3.822618e-02 1.911309e-02
[166,] 9.766917e-01 4.661663e-02 2.330832e-02
[167,] 9.701831e-01 5.963387e-02 2.981693e-02
[168,] 9.673237e-01 6.535262e-02 3.267631e-02
[169,] 9.615505e-01 7.689900e-02 3.844950e-02
[170,] 9.517553e-01 9.648936e-02 4.824468e-02
[171,] 9.797768e-01 4.044632e-02 2.022316e-02
[172,] 9.735088e-01 5.298236e-02 2.649118e-02
[173,] 9.714594e-01 5.708117e-02 2.854058e-02
[174,] 9.762046e-01 4.759089e-02 2.379545e-02
[175,] 9.720233e-01 5.595345e-02 2.797672e-02
[176,] 9.668854e-01 6.622926e-02 3.311463e-02
[177,] 9.745504e-01 5.089923e-02 2.544961e-02
[178,] 9.667590e-01 6.648204e-02 3.324102e-02
[179,] 9.576284e-01 8.474314e-02 4.237157e-02
[180,] 9.774129e-01 4.517419e-02 2.258709e-02
[181,] 9.698313e-01 6.033734e-02 3.016867e-02
[182,] 9.674566e-01 6.508673e-02 3.254337e-02
[183,] 9.625561e-01 7.488782e-02 3.744391e-02
[184,] 9.504821e-01 9.903573e-02 4.951786e-02
[185,] 9.391311e-01 1.217378e-01 6.086890e-02
[186,] 9.225891e-01 1.548218e-01 7.741090e-02
[187,] 9.026620e-01 1.946759e-01 9.733797e-02
[188,] 8.885301e-01 2.229397e-01 1.114699e-01
[189,] 8.759449e-01 2.481101e-01 1.240551e-01
[190,] 8.924781e-01 2.150438e-01 1.075219e-01
[191,] 8.643810e-01 2.712380e-01 1.356190e-01
[192,] 8.368344e-01 3.263311e-01 1.631656e-01
[193,] 8.427771e-01 3.144458e-01 1.572229e-01
[194,] 8.365418e-01 3.269164e-01 1.634582e-01
[195,] 8.042812e-01 3.914376e-01 1.957188e-01
[196,] 8.198952e-01 3.602096e-01 1.801048e-01
[197,] 7.864200e-01 4.271600e-01 2.135800e-01
[198,] 7.658221e-01 4.683557e-01 2.341779e-01
[199,] 7.325958e-01 5.348084e-01 2.674042e-01
[200,] 7.114845e-01 5.770310e-01 2.885155e-01
[201,] 6.533249e-01 6.933501e-01 3.466751e-01
[202,] 6.102303e-01 7.795394e-01 3.897697e-01
[203,] 5.398020e-01 9.203959e-01 4.601980e-01
[204,] 9.718560e-01 5.628806e-02 2.814403e-02
[205,] 9.553136e-01 8.937286e-02 4.468643e-02
[206,] 9.440519e-01 1.118962e-01 5.594808e-02
[207,] 9.179770e-01 1.640461e-01 8.202303e-02
[208,] 8.800946e-01 2.398108e-01 1.199054e-01
[209,] 8.732256e-01 2.535488e-01 1.267744e-01
[210,] 8.146306e-01 3.707387e-01 1.853694e-01
[211,] 8.010366e-01 3.979268e-01 1.989634e-01
[212,] 7.640504e-01 4.718991e-01 2.359496e-01
[213,] 7.183259e-01 5.633482e-01 2.816741e-01
[214,] 6.301604e-01 7.396791e-01 3.698396e-01
[215,] 9.091847e-01 1.816305e-01 9.081526e-02
[216,] 9.416303e-01 1.167393e-01 5.836967e-02
[217,] 9.533109e-01 9.337815e-02 4.668907e-02
> postscript(file="/var/wessaorg/rcomp/tmp/171ze1321893165.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2fp1l1321893165.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mgq31321893165.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4tk351321893165.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/501ek1321893165.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 = 252
Frequency = 1
1 2 3 4 5
-3.157456166 1.497030500 -8.884364922 3.177964113 -0.723514916
6 7 8 9 10
-0.454692725 0.770952715 -1.772731838 5.971078805 -3.951540373
11 12 13 14 15
1.844323417 6.715420750 -4.496540984 -5.333959892 -1.313534618
16 17 18 19 20
-6.910737362 0.100548544 -2.669873649 -6.537375905 -1.568143243
21 22 23 24 25
-11.802966638 -9.194682504 0.945371325 3.949323930 2.176304818
26 27 28 29 30
2.376506962 3.594248951 -10.845268497 -3.377839371 -4.151932617
31 32 33 34 35
-9.353108479 -11.174451821 7.718849714 -0.333541265 1.511521385
36 37 38 39 40
-20.391086123 1.343137665 -4.996637107 16.106977394 -0.636644933
41 42 43 44 45
-4.475830516 23.550104745 -5.466997389 4.938165516 3.929226898
46 47 48 49 50
-1.389159322 4.555111395 0.706063662 -1.229826505 7.877844350
51 52 53 54 55
1.653443958 3.409724162 1.846435674 -4.333970086 -1.458669661
56 57 58 59 60
7.724284674 -3.623604853 -4.676148341 11.931274290 0.545004023
61 62 63 64 65
6.855683334 3.700983333 5.034266717 -4.358290063 -1.293713513
66 67 68 69 70
-10.125454828 4.209373490 -0.119455696 -0.056276224 3.659913099
71 72 73 74 75
-0.819279943 -8.612629725 -0.271522862 2.366054903 -0.349639875
76 77 78 79 80
0.381017905 4.091836767 5.390158213 -6.009440647 -8.118336448
81 82 83 84 85
-3.062566749 -9.949273936 3.736064113 2.050520589 0.629701823
86 87 88 89 90
-2.943238547 -5.525062690 -2.935069266 0.981891400 2.866913829
91 92 93 94 95
-0.701852571 -3.732272260 1.574720101 0.750973763 1.895128509
96 97 98 99 100
5.560558335 3.029444450 -6.109636065 1.699132866 1.290365401
101 102 103 104 105
6.384093617 3.934731370 2.029566717 2.578427028 -1.366680239
106 107 108 109 110
7.632488431 4.368055135 2.470417246 -4.151773036 -0.030901226
111 112 113 114 115
-0.507089384 -11.310140411 -5.427121009 -2.455152826 2.836429749
116 117 118 119 120
3.683264497 5.351319711 0.208571048 0.476764775 5.391220470
121 122 123 124 125
6.588430655 -1.844737850 -9.009486825 -3.646510934 0.009911368
126 127 128 129 130
-4.618733675 5.758841232 1.238270164 5.814929925 -1.584829559
131 132 133 134 135
8.283650136 2.773969708 6.928799464 1.369539246 -0.185561179
136 137 138 139 140
1.456491798 7.747099314 3.252731434 0.823403539 -2.397663147
141 142 143 144 145
-0.040066489 -0.589241944 -3.277959464 5.281154924 3.552210410
146 147 148 149 150
-2.127559378 -6.990506080 -2.819417893 -1.918193537 -6.762876536
151 152 153 154 155
2.123368445 6.445363435 3.798324081 -4.973066956 -0.084187951
156 157 158 159 160
5.146392116 0.452399110 -2.851352746 0.247639536 -1.354518272
161 162 163 164 165
4.062738655 -3.416822468 -4.822773343 0.255787213 3.228636143
166 167 168 169 170
8.122826875 4.433582411 6.642328278 0.773415292 -2.198440052
171 172 173 174 175
0.747756712 -4.525866468 2.807878844 -0.199008003 -6.786279491
176 177 178 179 180
0.652461061 -4.762168539 5.882911792 -0.347735546 4.194505878
181 182 183 184 185
-0.328138959 6.328248955 -3.127719935 -1.037779710 -0.855281426
186 187 188 189 190
-1.991146350 2.277118967 9.436806376 -2.115104898 -5.876225944
191 192 193 194 195
5.106719573 4.332149697 -0.600079935 4.730439235 1.297345458
196 197 198 199 200
-2.966882941 -6.916998655 -1.140116946 -2.575957291 -0.371176873
201 202 203 204 205
-1.965645311 -2.434004725 1.829828063 -3.933194814 5.133279635
206 207 208 209 210
7.605633760 -6.563644775 1.287084082 4.723490969 6.062256299
211 212 213 214 215
1.933532973 -2.872108225 -5.852148806 -1.756239239 6.736976016
216 217 218 219 220
0.279407528 -3.360735816 -1.961899264 -0.865587840 0.206346488
221 222 223 224 225
-19.526210666 1.687641355 0.180757183 -1.217725507 -4.373508130
226 227 228 229 230
7.360040455 -2.736488530 -3.334054075 -2.265548644 -3.220020521
231 232 233 234 235
2.360554984 -3.967640194 2.679193305 2.738798999 -2.248488819
236 237 238 239 240
2.305416866 0.960091213 -2.789400881 -4.747573689 2.659823010
241 242 243 244 245
1.675185292 -3.150123371 9.184919628 -2.220276526 -0.119724384
246 247 248 249 250
-0.236384539 -4.159600774 3.104146503 -0.522927930 -0.696266942
251 252
4.616928656 -5.120157995
> postscript(file="/var/wessaorg/rcomp/tmp/6oc8u1321893165.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 = 252
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.157456166 NA
1 1.497030500 -3.157456166
2 -8.884364922 1.497030500
3 3.177964113 -8.884364922
4 -0.723514916 3.177964113
5 -0.454692725 -0.723514916
6 0.770952715 -0.454692725
7 -1.772731838 0.770952715
8 5.971078805 -1.772731838
9 -3.951540373 5.971078805
10 1.844323417 -3.951540373
11 6.715420750 1.844323417
12 -4.496540984 6.715420750
13 -5.333959892 -4.496540984
14 -1.313534618 -5.333959892
15 -6.910737362 -1.313534618
16 0.100548544 -6.910737362
17 -2.669873649 0.100548544
18 -6.537375905 -2.669873649
19 -1.568143243 -6.537375905
20 -11.802966638 -1.568143243
21 -9.194682504 -11.802966638
22 0.945371325 -9.194682504
23 3.949323930 0.945371325
24 2.176304818 3.949323930
25 2.376506962 2.176304818
26 3.594248951 2.376506962
27 -10.845268497 3.594248951
28 -3.377839371 -10.845268497
29 -4.151932617 -3.377839371
30 -9.353108479 -4.151932617
31 -11.174451821 -9.353108479
32 7.718849714 -11.174451821
33 -0.333541265 7.718849714
34 1.511521385 -0.333541265
35 -20.391086123 1.511521385
36 1.343137665 -20.391086123
37 -4.996637107 1.343137665
38 16.106977394 -4.996637107
39 -0.636644933 16.106977394
40 -4.475830516 -0.636644933
41 23.550104745 -4.475830516
42 -5.466997389 23.550104745
43 4.938165516 -5.466997389
44 3.929226898 4.938165516
45 -1.389159322 3.929226898
46 4.555111395 -1.389159322
47 0.706063662 4.555111395
48 -1.229826505 0.706063662
49 7.877844350 -1.229826505
50 1.653443958 7.877844350
51 3.409724162 1.653443958
52 1.846435674 3.409724162
53 -4.333970086 1.846435674
54 -1.458669661 -4.333970086
55 7.724284674 -1.458669661
56 -3.623604853 7.724284674
57 -4.676148341 -3.623604853
58 11.931274290 -4.676148341
59 0.545004023 11.931274290
60 6.855683334 0.545004023
61 3.700983333 6.855683334
62 5.034266717 3.700983333
63 -4.358290063 5.034266717
64 -1.293713513 -4.358290063
65 -10.125454828 -1.293713513
66 4.209373490 -10.125454828
67 -0.119455696 4.209373490
68 -0.056276224 -0.119455696
69 3.659913099 -0.056276224
70 -0.819279943 3.659913099
71 -8.612629725 -0.819279943
72 -0.271522862 -8.612629725
73 2.366054903 -0.271522862
74 -0.349639875 2.366054903
75 0.381017905 -0.349639875
76 4.091836767 0.381017905
77 5.390158213 4.091836767
78 -6.009440647 5.390158213
79 -8.118336448 -6.009440647
80 -3.062566749 -8.118336448
81 -9.949273936 -3.062566749
82 3.736064113 -9.949273936
83 2.050520589 3.736064113
84 0.629701823 2.050520589
85 -2.943238547 0.629701823
86 -5.525062690 -2.943238547
87 -2.935069266 -5.525062690
88 0.981891400 -2.935069266
89 2.866913829 0.981891400
90 -0.701852571 2.866913829
91 -3.732272260 -0.701852571
92 1.574720101 -3.732272260
93 0.750973763 1.574720101
94 1.895128509 0.750973763
95 5.560558335 1.895128509
96 3.029444450 5.560558335
97 -6.109636065 3.029444450
98 1.699132866 -6.109636065
99 1.290365401 1.699132866
100 6.384093617 1.290365401
101 3.934731370 6.384093617
102 2.029566717 3.934731370
103 2.578427028 2.029566717
104 -1.366680239 2.578427028
105 7.632488431 -1.366680239
106 4.368055135 7.632488431
107 2.470417246 4.368055135
108 -4.151773036 2.470417246
109 -0.030901226 -4.151773036
110 -0.507089384 -0.030901226
111 -11.310140411 -0.507089384
112 -5.427121009 -11.310140411
113 -2.455152826 -5.427121009
114 2.836429749 -2.455152826
115 3.683264497 2.836429749
116 5.351319711 3.683264497
117 0.208571048 5.351319711
118 0.476764775 0.208571048
119 5.391220470 0.476764775
120 6.588430655 5.391220470
121 -1.844737850 6.588430655
122 -9.009486825 -1.844737850
123 -3.646510934 -9.009486825
124 0.009911368 -3.646510934
125 -4.618733675 0.009911368
126 5.758841232 -4.618733675
127 1.238270164 5.758841232
128 5.814929925 1.238270164
129 -1.584829559 5.814929925
130 8.283650136 -1.584829559
131 2.773969708 8.283650136
132 6.928799464 2.773969708
133 1.369539246 6.928799464
134 -0.185561179 1.369539246
135 1.456491798 -0.185561179
136 7.747099314 1.456491798
137 3.252731434 7.747099314
138 0.823403539 3.252731434
139 -2.397663147 0.823403539
140 -0.040066489 -2.397663147
141 -0.589241944 -0.040066489
142 -3.277959464 -0.589241944
143 5.281154924 -3.277959464
144 3.552210410 5.281154924
145 -2.127559378 3.552210410
146 -6.990506080 -2.127559378
147 -2.819417893 -6.990506080
148 -1.918193537 -2.819417893
149 -6.762876536 -1.918193537
150 2.123368445 -6.762876536
151 6.445363435 2.123368445
152 3.798324081 6.445363435
153 -4.973066956 3.798324081
154 -0.084187951 -4.973066956
155 5.146392116 -0.084187951
156 0.452399110 5.146392116
157 -2.851352746 0.452399110
158 0.247639536 -2.851352746
159 -1.354518272 0.247639536
160 4.062738655 -1.354518272
161 -3.416822468 4.062738655
162 -4.822773343 -3.416822468
163 0.255787213 -4.822773343
164 3.228636143 0.255787213
165 8.122826875 3.228636143
166 4.433582411 8.122826875
167 6.642328278 4.433582411
168 0.773415292 6.642328278
169 -2.198440052 0.773415292
170 0.747756712 -2.198440052
171 -4.525866468 0.747756712
172 2.807878844 -4.525866468
173 -0.199008003 2.807878844
174 -6.786279491 -0.199008003
175 0.652461061 -6.786279491
176 -4.762168539 0.652461061
177 5.882911792 -4.762168539
178 -0.347735546 5.882911792
179 4.194505878 -0.347735546
180 -0.328138959 4.194505878
181 6.328248955 -0.328138959
182 -3.127719935 6.328248955
183 -1.037779710 -3.127719935
184 -0.855281426 -1.037779710
185 -1.991146350 -0.855281426
186 2.277118967 -1.991146350
187 9.436806376 2.277118967
188 -2.115104898 9.436806376
189 -5.876225944 -2.115104898
190 5.106719573 -5.876225944
191 4.332149697 5.106719573
192 -0.600079935 4.332149697
193 4.730439235 -0.600079935
194 1.297345458 4.730439235
195 -2.966882941 1.297345458
196 -6.916998655 -2.966882941
197 -1.140116946 -6.916998655
198 -2.575957291 -1.140116946
199 -0.371176873 -2.575957291
200 -1.965645311 -0.371176873
201 -2.434004725 -1.965645311
202 1.829828063 -2.434004725
203 -3.933194814 1.829828063
204 5.133279635 -3.933194814
205 7.605633760 5.133279635
206 -6.563644775 7.605633760
207 1.287084082 -6.563644775
208 4.723490969 1.287084082
209 6.062256299 4.723490969
210 1.933532973 6.062256299
211 -2.872108225 1.933532973
212 -5.852148806 -2.872108225
213 -1.756239239 -5.852148806
214 6.736976016 -1.756239239
215 0.279407528 6.736976016
216 -3.360735816 0.279407528
217 -1.961899264 -3.360735816
218 -0.865587840 -1.961899264
219 0.206346488 -0.865587840
220 -19.526210666 0.206346488
221 1.687641355 -19.526210666
222 0.180757183 1.687641355
223 -1.217725507 0.180757183
224 -4.373508130 -1.217725507
225 7.360040455 -4.373508130
226 -2.736488530 7.360040455
227 -3.334054075 -2.736488530
228 -2.265548644 -3.334054075
229 -3.220020521 -2.265548644
230 2.360554984 -3.220020521
231 -3.967640194 2.360554984
232 2.679193305 -3.967640194
233 2.738798999 2.679193305
234 -2.248488819 2.738798999
235 2.305416866 -2.248488819
236 0.960091213 2.305416866
237 -2.789400881 0.960091213
238 -4.747573689 -2.789400881
239 2.659823010 -4.747573689
240 1.675185292 2.659823010
241 -3.150123371 1.675185292
242 9.184919628 -3.150123371
243 -2.220276526 9.184919628
244 -0.119724384 -2.220276526
245 -0.236384539 -0.119724384
246 -4.159600774 -0.236384539
247 3.104146503 -4.159600774
248 -0.522927930 3.104146503
249 -0.696266942 -0.522927930
250 4.616928656 -0.696266942
251 -5.120157995 4.616928656
252 NA -5.120157995
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.497030500 -3.157456166
[2,] -8.884364922 1.497030500
[3,] 3.177964113 -8.884364922
[4,] -0.723514916 3.177964113
[5,] -0.454692725 -0.723514916
[6,] 0.770952715 -0.454692725
[7,] -1.772731838 0.770952715
[8,] 5.971078805 -1.772731838
[9,] -3.951540373 5.971078805
[10,] 1.844323417 -3.951540373
[11,] 6.715420750 1.844323417
[12,] -4.496540984 6.715420750
[13,] -5.333959892 -4.496540984
[14,] -1.313534618 -5.333959892
[15,] -6.910737362 -1.313534618
[16,] 0.100548544 -6.910737362
[17,] -2.669873649 0.100548544
[18,] -6.537375905 -2.669873649
[19,] -1.568143243 -6.537375905
[20,] -11.802966638 -1.568143243
[21,] -9.194682504 -11.802966638
[22,] 0.945371325 -9.194682504
[23,] 3.949323930 0.945371325
[24,] 2.176304818 3.949323930
[25,] 2.376506962 2.176304818
[26,] 3.594248951 2.376506962
[27,] -10.845268497 3.594248951
[28,] -3.377839371 -10.845268497
[29,] -4.151932617 -3.377839371
[30,] -9.353108479 -4.151932617
[31,] -11.174451821 -9.353108479
[32,] 7.718849714 -11.174451821
[33,] -0.333541265 7.718849714
[34,] 1.511521385 -0.333541265
[35,] -20.391086123 1.511521385
[36,] 1.343137665 -20.391086123
[37,] -4.996637107 1.343137665
[38,] 16.106977394 -4.996637107
[39,] -0.636644933 16.106977394
[40,] -4.475830516 -0.636644933
[41,] 23.550104745 -4.475830516
[42,] -5.466997389 23.550104745
[43,] 4.938165516 -5.466997389
[44,] 3.929226898 4.938165516
[45,] -1.389159322 3.929226898
[46,] 4.555111395 -1.389159322
[47,] 0.706063662 4.555111395
[48,] -1.229826505 0.706063662
[49,] 7.877844350 -1.229826505
[50,] 1.653443958 7.877844350
[51,] 3.409724162 1.653443958
[52,] 1.846435674 3.409724162
[53,] -4.333970086 1.846435674
[54,] -1.458669661 -4.333970086
[55,] 7.724284674 -1.458669661
[56,] -3.623604853 7.724284674
[57,] -4.676148341 -3.623604853
[58,] 11.931274290 -4.676148341
[59,] 0.545004023 11.931274290
[60,] 6.855683334 0.545004023
[61,] 3.700983333 6.855683334
[62,] 5.034266717 3.700983333
[63,] -4.358290063 5.034266717
[64,] -1.293713513 -4.358290063
[65,] -10.125454828 -1.293713513
[66,] 4.209373490 -10.125454828
[67,] -0.119455696 4.209373490
[68,] -0.056276224 -0.119455696
[69,] 3.659913099 -0.056276224
[70,] -0.819279943 3.659913099
[71,] -8.612629725 -0.819279943
[72,] -0.271522862 -8.612629725
[73,] 2.366054903 -0.271522862
[74,] -0.349639875 2.366054903
[75,] 0.381017905 -0.349639875
[76,] 4.091836767 0.381017905
[77,] 5.390158213 4.091836767
[78,] -6.009440647 5.390158213
[79,] -8.118336448 -6.009440647
[80,] -3.062566749 -8.118336448
[81,] -9.949273936 -3.062566749
[82,] 3.736064113 -9.949273936
[83,] 2.050520589 3.736064113
[84,] 0.629701823 2.050520589
[85,] -2.943238547 0.629701823
[86,] -5.525062690 -2.943238547
[87,] -2.935069266 -5.525062690
[88,] 0.981891400 -2.935069266
[89,] 2.866913829 0.981891400
[90,] -0.701852571 2.866913829
[91,] -3.732272260 -0.701852571
[92,] 1.574720101 -3.732272260
[93,] 0.750973763 1.574720101
[94,] 1.895128509 0.750973763
[95,] 5.560558335 1.895128509
[96,] 3.029444450 5.560558335
[97,] -6.109636065 3.029444450
[98,] 1.699132866 -6.109636065
[99,] 1.290365401 1.699132866
[100,] 6.384093617 1.290365401
[101,] 3.934731370 6.384093617
[102,] 2.029566717 3.934731370
[103,] 2.578427028 2.029566717
[104,] -1.366680239 2.578427028
[105,] 7.632488431 -1.366680239
[106,] 4.368055135 7.632488431
[107,] 2.470417246 4.368055135
[108,] -4.151773036 2.470417246
[109,] -0.030901226 -4.151773036
[110,] -0.507089384 -0.030901226
[111,] -11.310140411 -0.507089384
[112,] -5.427121009 -11.310140411
[113,] -2.455152826 -5.427121009
[114,] 2.836429749 -2.455152826
[115,] 3.683264497 2.836429749
[116,] 5.351319711 3.683264497
[117,] 0.208571048 5.351319711
[118,] 0.476764775 0.208571048
[119,] 5.391220470 0.476764775
[120,] 6.588430655 5.391220470
[121,] -1.844737850 6.588430655
[122,] -9.009486825 -1.844737850
[123,] -3.646510934 -9.009486825
[124,] 0.009911368 -3.646510934
[125,] -4.618733675 0.009911368
[126,] 5.758841232 -4.618733675
[127,] 1.238270164 5.758841232
[128,] 5.814929925 1.238270164
[129,] -1.584829559 5.814929925
[130,] 8.283650136 -1.584829559
[131,] 2.773969708 8.283650136
[132,] 6.928799464 2.773969708
[133,] 1.369539246 6.928799464
[134,] -0.185561179 1.369539246
[135,] 1.456491798 -0.185561179
[136,] 7.747099314 1.456491798
[137,] 3.252731434 7.747099314
[138,] 0.823403539 3.252731434
[139,] -2.397663147 0.823403539
[140,] -0.040066489 -2.397663147
[141,] -0.589241944 -0.040066489
[142,] -3.277959464 -0.589241944
[143,] 5.281154924 -3.277959464
[144,] 3.552210410 5.281154924
[145,] -2.127559378 3.552210410
[146,] -6.990506080 -2.127559378
[147,] -2.819417893 -6.990506080
[148,] -1.918193537 -2.819417893
[149,] -6.762876536 -1.918193537
[150,] 2.123368445 -6.762876536
[151,] 6.445363435 2.123368445
[152,] 3.798324081 6.445363435
[153,] -4.973066956 3.798324081
[154,] -0.084187951 -4.973066956
[155,] 5.146392116 -0.084187951
[156,] 0.452399110 5.146392116
[157,] -2.851352746 0.452399110
[158,] 0.247639536 -2.851352746
[159,] -1.354518272 0.247639536
[160,] 4.062738655 -1.354518272
[161,] -3.416822468 4.062738655
[162,] -4.822773343 -3.416822468
[163,] 0.255787213 -4.822773343
[164,] 3.228636143 0.255787213
[165,] 8.122826875 3.228636143
[166,] 4.433582411 8.122826875
[167,] 6.642328278 4.433582411
[168,] 0.773415292 6.642328278
[169,] -2.198440052 0.773415292
[170,] 0.747756712 -2.198440052
[171,] -4.525866468 0.747756712
[172,] 2.807878844 -4.525866468
[173,] -0.199008003 2.807878844
[174,] -6.786279491 -0.199008003
[175,] 0.652461061 -6.786279491
[176,] -4.762168539 0.652461061
[177,] 5.882911792 -4.762168539
[178,] -0.347735546 5.882911792
[179,] 4.194505878 -0.347735546
[180,] -0.328138959 4.194505878
[181,] 6.328248955 -0.328138959
[182,] -3.127719935 6.328248955
[183,] -1.037779710 -3.127719935
[184,] -0.855281426 -1.037779710
[185,] -1.991146350 -0.855281426
[186,] 2.277118967 -1.991146350
[187,] 9.436806376 2.277118967
[188,] -2.115104898 9.436806376
[189,] -5.876225944 -2.115104898
[190,] 5.106719573 -5.876225944
[191,] 4.332149697 5.106719573
[192,] -0.600079935 4.332149697
[193,] 4.730439235 -0.600079935
[194,] 1.297345458 4.730439235
[195,] -2.966882941 1.297345458
[196,] -6.916998655 -2.966882941
[197,] -1.140116946 -6.916998655
[198,] -2.575957291 -1.140116946
[199,] -0.371176873 -2.575957291
[200,] -1.965645311 -0.371176873
[201,] -2.434004725 -1.965645311
[202,] 1.829828063 -2.434004725
[203,] -3.933194814 1.829828063
[204,] 5.133279635 -3.933194814
[205,] 7.605633760 5.133279635
[206,] -6.563644775 7.605633760
[207,] 1.287084082 -6.563644775
[208,] 4.723490969 1.287084082
[209,] 6.062256299 4.723490969
[210,] 1.933532973 6.062256299
[211,] -2.872108225 1.933532973
[212,] -5.852148806 -2.872108225
[213,] -1.756239239 -5.852148806
[214,] 6.736976016 -1.756239239
[215,] 0.279407528 6.736976016
[216,] -3.360735816 0.279407528
[217,] -1.961899264 -3.360735816
[218,] -0.865587840 -1.961899264
[219,] 0.206346488 -0.865587840
[220,] -19.526210666 0.206346488
[221,] 1.687641355 -19.526210666
[222,] 0.180757183 1.687641355
[223,] -1.217725507 0.180757183
[224,] -4.373508130 -1.217725507
[225,] 7.360040455 -4.373508130
[226,] -2.736488530 7.360040455
[227,] -3.334054075 -2.736488530
[228,] -2.265548644 -3.334054075
[229,] -3.220020521 -2.265548644
[230,] 2.360554984 -3.220020521
[231,] -3.967640194 2.360554984
[232,] 2.679193305 -3.967640194
[233,] 2.738798999 2.679193305
[234,] -2.248488819 2.738798999
[235,] 2.305416866 -2.248488819
[236,] 0.960091213 2.305416866
[237,] -2.789400881 0.960091213
[238,] -4.747573689 -2.789400881
[239,] 2.659823010 -4.747573689
[240,] 1.675185292 2.659823010
[241,] -3.150123371 1.675185292
[242,] 9.184919628 -3.150123371
[243,] -2.220276526 9.184919628
[244,] -0.119724384 -2.220276526
[245,] -0.236384539 -0.119724384
[246,] -4.159600774 -0.236384539
[247,] 3.104146503 -4.159600774
[248,] -0.522927930 3.104146503
[249,] -0.696266942 -0.522927930
[250,] 4.616928656 -0.696266942
[251,] -5.120157995 4.616928656
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.497030500 -3.157456166
2 -8.884364922 1.497030500
3 3.177964113 -8.884364922
4 -0.723514916 3.177964113
5 -0.454692725 -0.723514916
6 0.770952715 -0.454692725
7 -1.772731838 0.770952715
8 5.971078805 -1.772731838
9 -3.951540373 5.971078805
10 1.844323417 -3.951540373
11 6.715420750 1.844323417
12 -4.496540984 6.715420750
13 -5.333959892 -4.496540984
14 -1.313534618 -5.333959892
15 -6.910737362 -1.313534618
16 0.100548544 -6.910737362
17 -2.669873649 0.100548544
18 -6.537375905 -2.669873649
19 -1.568143243 -6.537375905
20 -11.802966638 -1.568143243
21 -9.194682504 -11.802966638
22 0.945371325 -9.194682504
23 3.949323930 0.945371325
24 2.176304818 3.949323930
25 2.376506962 2.176304818
26 3.594248951 2.376506962
27 -10.845268497 3.594248951
28 -3.377839371 -10.845268497
29 -4.151932617 -3.377839371
30 -9.353108479 -4.151932617
31 -11.174451821 -9.353108479
32 7.718849714 -11.174451821
33 -0.333541265 7.718849714
34 1.511521385 -0.333541265
35 -20.391086123 1.511521385
36 1.343137665 -20.391086123
37 -4.996637107 1.343137665
38 16.106977394 -4.996637107
39 -0.636644933 16.106977394
40 -4.475830516 -0.636644933
41 23.550104745 -4.475830516
42 -5.466997389 23.550104745
43 4.938165516 -5.466997389
44 3.929226898 4.938165516
45 -1.389159322 3.929226898
46 4.555111395 -1.389159322
47 0.706063662 4.555111395
48 -1.229826505 0.706063662
49 7.877844350 -1.229826505
50 1.653443958 7.877844350
51 3.409724162 1.653443958
52 1.846435674 3.409724162
53 -4.333970086 1.846435674
54 -1.458669661 -4.333970086
55 7.724284674 -1.458669661
56 -3.623604853 7.724284674
57 -4.676148341 -3.623604853
58 11.931274290 -4.676148341
59 0.545004023 11.931274290
60 6.855683334 0.545004023
61 3.700983333 6.855683334
62 5.034266717 3.700983333
63 -4.358290063 5.034266717
64 -1.293713513 -4.358290063
65 -10.125454828 -1.293713513
66 4.209373490 -10.125454828
67 -0.119455696 4.209373490
68 -0.056276224 -0.119455696
69 3.659913099 -0.056276224
70 -0.819279943 3.659913099
71 -8.612629725 -0.819279943
72 -0.271522862 -8.612629725
73 2.366054903 -0.271522862
74 -0.349639875 2.366054903
75 0.381017905 -0.349639875
76 4.091836767 0.381017905
77 5.390158213 4.091836767
78 -6.009440647 5.390158213
79 -8.118336448 -6.009440647
80 -3.062566749 -8.118336448
81 -9.949273936 -3.062566749
82 3.736064113 -9.949273936
83 2.050520589 3.736064113
84 0.629701823 2.050520589
85 -2.943238547 0.629701823
86 -5.525062690 -2.943238547
87 -2.935069266 -5.525062690
88 0.981891400 -2.935069266
89 2.866913829 0.981891400
90 -0.701852571 2.866913829
91 -3.732272260 -0.701852571
92 1.574720101 -3.732272260
93 0.750973763 1.574720101
94 1.895128509 0.750973763
95 5.560558335 1.895128509
96 3.029444450 5.560558335
97 -6.109636065 3.029444450
98 1.699132866 -6.109636065
99 1.290365401 1.699132866
100 6.384093617 1.290365401
101 3.934731370 6.384093617
102 2.029566717 3.934731370
103 2.578427028 2.029566717
104 -1.366680239 2.578427028
105 7.632488431 -1.366680239
106 4.368055135 7.632488431
107 2.470417246 4.368055135
108 -4.151773036 2.470417246
109 -0.030901226 -4.151773036
110 -0.507089384 -0.030901226
111 -11.310140411 -0.507089384
112 -5.427121009 -11.310140411
113 -2.455152826 -5.427121009
114 2.836429749 -2.455152826
115 3.683264497 2.836429749
116 5.351319711 3.683264497
117 0.208571048 5.351319711
118 0.476764775 0.208571048
119 5.391220470 0.476764775
120 6.588430655 5.391220470
121 -1.844737850 6.588430655
122 -9.009486825 -1.844737850
123 -3.646510934 -9.009486825
124 0.009911368 -3.646510934
125 -4.618733675 0.009911368
126 5.758841232 -4.618733675
127 1.238270164 5.758841232
128 5.814929925 1.238270164
129 -1.584829559 5.814929925
130 8.283650136 -1.584829559
131 2.773969708 8.283650136
132 6.928799464 2.773969708
133 1.369539246 6.928799464
134 -0.185561179 1.369539246
135 1.456491798 -0.185561179
136 7.747099314 1.456491798
137 3.252731434 7.747099314
138 0.823403539 3.252731434
139 -2.397663147 0.823403539
140 -0.040066489 -2.397663147
141 -0.589241944 -0.040066489
142 -3.277959464 -0.589241944
143 5.281154924 -3.277959464
144 3.552210410 5.281154924
145 -2.127559378 3.552210410
146 -6.990506080 -2.127559378
147 -2.819417893 -6.990506080
148 -1.918193537 -2.819417893
149 -6.762876536 -1.918193537
150 2.123368445 -6.762876536
151 6.445363435 2.123368445
152 3.798324081 6.445363435
153 -4.973066956 3.798324081
154 -0.084187951 -4.973066956
155 5.146392116 -0.084187951
156 0.452399110 5.146392116
157 -2.851352746 0.452399110
158 0.247639536 -2.851352746
159 -1.354518272 0.247639536
160 4.062738655 -1.354518272
161 -3.416822468 4.062738655
162 -4.822773343 -3.416822468
163 0.255787213 -4.822773343
164 3.228636143 0.255787213
165 8.122826875 3.228636143
166 4.433582411 8.122826875
167 6.642328278 4.433582411
168 0.773415292 6.642328278
169 -2.198440052 0.773415292
170 0.747756712 -2.198440052
171 -4.525866468 0.747756712
172 2.807878844 -4.525866468
173 -0.199008003 2.807878844
174 -6.786279491 -0.199008003
175 0.652461061 -6.786279491
176 -4.762168539 0.652461061
177 5.882911792 -4.762168539
178 -0.347735546 5.882911792
179 4.194505878 -0.347735546
180 -0.328138959 4.194505878
181 6.328248955 -0.328138959
182 -3.127719935 6.328248955
183 -1.037779710 -3.127719935
184 -0.855281426 -1.037779710
185 -1.991146350 -0.855281426
186 2.277118967 -1.991146350
187 9.436806376 2.277118967
188 -2.115104898 9.436806376
189 -5.876225944 -2.115104898
190 5.106719573 -5.876225944
191 4.332149697 5.106719573
192 -0.600079935 4.332149697
193 4.730439235 -0.600079935
194 1.297345458 4.730439235
195 -2.966882941 1.297345458
196 -6.916998655 -2.966882941
197 -1.140116946 -6.916998655
198 -2.575957291 -1.140116946
199 -0.371176873 -2.575957291
200 -1.965645311 -0.371176873
201 -2.434004725 -1.965645311
202 1.829828063 -2.434004725
203 -3.933194814 1.829828063
204 5.133279635 -3.933194814
205 7.605633760 5.133279635
206 -6.563644775 7.605633760
207 1.287084082 -6.563644775
208 4.723490969 1.287084082
209 6.062256299 4.723490969
210 1.933532973 6.062256299
211 -2.872108225 1.933532973
212 -5.852148806 -2.872108225
213 -1.756239239 -5.852148806
214 6.736976016 -1.756239239
215 0.279407528 6.736976016
216 -3.360735816 0.279407528
217 -1.961899264 -3.360735816
218 -0.865587840 -1.961899264
219 0.206346488 -0.865587840
220 -19.526210666 0.206346488
221 1.687641355 -19.526210666
222 0.180757183 1.687641355
223 -1.217725507 0.180757183
224 -4.373508130 -1.217725507
225 7.360040455 -4.373508130
226 -2.736488530 7.360040455
227 -3.334054075 -2.736488530
228 -2.265548644 -3.334054075
229 -3.220020521 -2.265548644
230 2.360554984 -3.220020521
231 -3.967640194 2.360554984
232 2.679193305 -3.967640194
233 2.738798999 2.679193305
234 -2.248488819 2.738798999
235 2.305416866 -2.248488819
236 0.960091213 2.305416866
237 -2.789400881 0.960091213
238 -4.747573689 -2.789400881
239 2.659823010 -4.747573689
240 1.675185292 2.659823010
241 -3.150123371 1.675185292
242 9.184919628 -3.150123371
243 -2.220276526 9.184919628
244 -0.119724384 -2.220276526
245 -0.236384539 -0.119724384
246 -4.159600774 -0.236384539
247 3.104146503 -4.159600774
248 -0.522927930 3.104146503
249 -0.696266942 -0.522927930
250 4.616928656 -0.696266942
251 -5.120157995 4.616928656
> 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/7exc91321893165.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/82m8a1321893165.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9n7o31321893165.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wppj1321893165.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116fm01321893165.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/12zp5z1321893165.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/13rkkf1321893165.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/14xsac1321893165.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/15qnlt1321893165.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/169t3x1321893165.tab")
+ }
>
> try(system("convert tmp/171ze1321893165.ps tmp/171ze1321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fp1l1321893165.ps tmp/2fp1l1321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mgq31321893165.ps tmp/3mgq31321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tk351321893165.ps tmp/4tk351321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/501ek1321893165.ps tmp/501ek1321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oc8u1321893165.ps tmp/6oc8u1321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/7exc91321893165.ps tmp/7exc91321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/82m8a1321893165.ps tmp/82m8a1321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/9n7o31321893165.ps tmp/9n7o31321893165.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wppj1321893165.ps tmp/10wppj1321893165.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.458 0.581 11.098