R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> x <- c(93.2
+ ,96
+ ,95.2
+ ,77.1
+ ,70.9
+ ,64.8
+ ,70.1
+ ,77.3
+ ,79.5
+ ,100.6
+ ,100.7
+ ,107.1
+ ,95.9
+ ,82.8
+ ,83.3
+ ,80
+ ,80.4
+ ,67.5
+ ,75.7
+ ,71.1
+ ,89.3
+ ,101.1
+ ,105.2
+ ,114.1
+ ,96.3
+ ,84.4
+ ,91.2
+ ,81.9
+ ,80.5
+ ,70.4
+ ,74.8
+ ,75.9
+ ,86.3
+ ,98.7
+ ,100.9
+ ,113.8
+ ,89.8
+ ,84.4
+ ,87.2
+ ,85.6
+ ,72
+ ,69.2
+ ,77.5
+ ,78.1
+ ,94.3
+ ,97.7
+ ,100.2
+ ,116.4
+ ,97.1
+ ,93
+ ,96
+ ,80.5
+ ,76.1
+ ,69.9
+ ,73.6
+ ,92.6
+ ,94.2
+ ,93.5
+ ,108.5
+ ,109.4
+ ,105.1
+ ,92.5
+ ,97.1
+ ,81.4
+ ,79.1
+ ,72.1
+ ,78.7
+ ,87.1
+ ,91.4
+ ,109.9
+ ,116.3
+ ,113
+ ,100
+ ,84.8
+ ,94.3
+ ,87.1
+ ,90.3
+ ,72.4
+ ,84.9
+ ,92.7
+ ,92.2
+ ,114.9
+ ,112.5
+ ,118.3
+ ,106
+ ,91.2
+ ,96.6
+ ,96.3
+ ,88.2
+ ,70.2
+ ,86.5
+ ,88.2
+ ,102.8
+ ,119.1
+ ,119.2
+ ,125.1
+ ,106.1
+ ,102.1
+ ,105.2
+ ,101
+ ,84.3
+ ,87.5
+ ,92.7
+ ,94.4
+ ,113
+ ,113.9
+ ,122.9
+ ,132.7
+ ,106.9
+ ,96.6
+ ,127.3
+ ,98.2
+ ,100.2
+ ,89.4
+ ,95.3
+ ,104.2
+ ,106.4
+ ,116.2
+ ,135.9
+ ,134
+ ,104.6
+ ,107.1
+ ,123.5
+ ,98.8
+ ,98.6
+ ,90.6
+ ,89.1
+ ,105.2
+ ,114
+ ,122.1
+ ,138
+ ,142.2
+ ,116.4
+ ,112.6
+ ,123.8
+ ,103.6
+ ,113.9
+ ,98.6
+ ,95
+ ,116
+ ,113.9
+ ,127.5
+ ,131.4
+ ,145.9
+ ,131.5
+ ,131
+ ,130.5
+ ,118.9
+ ,114.3
+ ,85.7
+ ,104.6
+ ,105.1
+ ,117.3
+ ,142.5
+ ,140
+ ,159.8
+ ,131.2
+ ,125.4
+ ,126.5
+ ,119.4
+ ,113.5
+ ,98.7
+ ,114.5
+ ,113.8
+ ,133.1
+ ,143.4
+ ,137.3
+ ,165.2
+ ,126.9
+ ,124
+ ,135.7
+ ,130
+ ,109.4
+ ,117.8
+ ,120.3
+ ,121
+ ,132.3
+ ,142.9
+ ,147.4
+ ,175.9
+ ,132.6
+ ,123.7
+ ,153.3
+ ,134
+ ,119.6
+ ,116.2
+ ,118.6
+ ,130.7
+ ,129.3
+ ,144.4
+ ,163.2
+ ,179.4
+ ,128.1
+ ,138.4
+ ,152.7
+ ,120
+ ,140.5
+ ,116.2
+ ,121.4
+ ,127.8
+ ,143.6
+ ,157.6
+ ,166.2
+ ,182.3
+ ,153.1
+ ,147.6
+ ,157.7
+ ,137.2
+ ,151.5
+ ,98.7
+ ,145.8
+ ,151.7
+ ,129.4
+ ,174.1
+ ,197
+ ,193.9
+ ,164.1
+ ,142.8
+ ,157.9
+ ,159.2
+ ,162.2
+ ,123.1
+ ,130
+ ,150.1
+ ,169.4
+ ,179.7
+ ,182.1
+ ,194.3
+ ,161.4
+ ,169.4
+ ,168.8
+ ,158.1
+ ,158.5
+ ,135.3
+ ,149.3
+ ,143.4
+ ,142.2
+ ,188.4
+ ,166.2
+ ,199.2
+ ,182.7
+ ,145.2
+ ,182.1
+ ,158.7
+ ,141.6
+ ,132.6
+ ,139.6
+ ,147
+ ,166.6
+ ,157
+ ,180.4
+ ,210.2
+ ,159.8
+ ,157.8
+ ,168.2
+ ,158.4
+ ,152
+ ,142.2
+ ,137.2
+ ,152.6
+ ,166.8
+ ,165.6
+ ,198.6
+ ,201.5
+ ,170.7
+ ,164.4
+ ,179.7
+ ,157
+ ,168
+ ,139.3
+ ,138.6
+ ,153.4
+ ,138.9
+ ,172.1
+ ,198.4
+ ,217.8
+ ,173.7
+ ,153.8
+ ,175.6
+ ,147.1
+ ,160.3
+ ,135.2
+ ,148.8
+ ,151
+ ,148.2
+ ,182.2
+ ,189.2
+ ,183.1
+ ,170
+ ,158.4
+ ,176.1
+ ,156.2
+ ,153.2
+ ,117.9
+ ,149.8
+ ,156.6
+ ,166.7
+ ,156.8
+ ,158.6
+ ,210.8
+ ,203.6
+ ,175.2
+ ,168.7
+ ,155.9
+ ,147.3
+ ,137
+ ,141.1
+ ,167.4
+ ,160.2
+ ,191.9
+ ,174.4
+ ,208.2
+ ,159.4
+ ,161.1
+ ,172.1
+ ,158.4
+ ,114.6
+ ,159.6
+ ,159.7
+ ,159.4
+ ,160.7
+ ,165.5
+ ,205
+ ,205.2
+ ,141.6
+ ,148.1
+ ,184.9
+ ,132.5
+ ,137.3
+ ,135.5
+ ,121.7
+ ,166.1
+ ,146.8
+ ,162.8
+ ,186.8
+ ,185.5
+ ,151.5
+ ,158.1
+ ,143
+ ,151.2
+ ,147.6
+ ,130.7
+ ,137.5
+ ,146.1
+ ,133.6
+ ,167.9
+ ,181.9
+ ,202
+ ,166.5
+ ,151.3
+ ,146.2
+ ,148.3
+ ,144.7
+ ,123.6
+ ,151.6
+ ,133.9
+ ,137.4
+ ,181.6
+ ,182
+ ,190
+ ,161.2
+ ,155.5
+ ,141.9
+ ,164.6
+ ,136.2
+ ,126.8
+ ,152.5
+ ,126.6
+ ,150.1
+ ,186.3
+ ,147.5
+ ,200.4
+ ,177.2
+ ,127.4
+ ,177.1
+ ,154.4
+ ,135.2
+ ,126.4
+ ,147.3
+ ,140.6
+ ,152.3
+ ,151.2
+ ,172.2
+ ,215.3
+ ,154.1
+ ,159.3
+ ,160.4
+ ,151.9
+ ,148.4
+ ,139.6
+ ,148.2
+ ,153.5
+ ,145.1
+ ,183.7
+ ,210.5
+ ,203.3
+ ,153.3
+ ,144.3
+ ,169.6
+ ,143.7
+ ,160.1
+ ,135.6
+ ,141.8
+ ,159.9
+ ,145.7
+ ,183.5
+ ,198.2
+ ,186.8
+ ,172
+ ,150.6
+ ,163.3
+ ,153.7
+ ,152.9
+ ,135.5
+ ,148.5
+ ,148.4
+ ,133.6
+ ,194.1
+ ,208.6
+ ,197.3
+ ,164.4
+ ,148.1
+ ,152
+ ,144.1
+ ,155
+ ,124.5
+ ,153
+ ,146
+ ,138
+ ,190
+ ,192
+ ,192
+ ,147
+ ,133
+ ,163
+ ,150
+ ,129
+ ,131
+ ,145
+ ,137
+ ,138
+ ,168
+ ,176
+ ,188
+ ,139
+ ,143
+ ,150
+ ,154
+ ,137
+ ,129
+ ,128
+ ,140
+ ,143
+ ,151
+ ,177
+ ,184
+ ,151
+ ,134
+ ,164
+ ,126
+ ,131
+ ,125
+ ,127
+ ,143
+ ,143
+ ,160
+ ,190
+ ,182
+ ,138
+ ,136
+ ,152
+ ,127
+ ,151
+ ,130
+ ,119
+ ,153)
> 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!)
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 476
> (np <- floor(n / par1))
[1] 119
> arr <- array(NA,dim=c(par1,np))
> j <- 0
> k <- 1
> for (i in 1:(np*par1))
+ {
+ j = j + 1
+ arr[j,k] <- x[i]
+ if (j == par1) {
+ j = 0
+ k=k+1
+ }
+ }
> arr
     [,1] [,2]  [,3] [,4] [,5]  [,6] [,7] [,8]  [,9] [,10] [,11] [,12] [,13]
[1,] 93.2 70.9  79.5 95.9 80.4  89.3 96.3 80.5  86.3  89.8  72.0  94.3  97.1
[2,] 96.0 64.8 100.6 82.8 67.5 101.1 84.4 70.4  98.7  84.4  69.2  97.7  93.0
[3,] 95.2 70.1 100.7 83.3 75.7 105.2 91.2 74.8 100.9  87.2  77.5 100.2  96.0
[4,] 77.1 77.3 107.1 80.0 71.1 114.1 81.9 75.9 113.8  85.6  78.1 116.4  80.5
     [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
[1,]  76.1  94.2 105.1  79.1  91.4 100.0  90.3  92.2 106.0  88.2 102.8 106.1
[2,]  69.9  93.5  92.5  72.1 109.9  84.8  72.4 114.9  91.2  70.2 119.1 102.1
[3,]  73.6 108.5  97.1  78.7 116.3  94.3  84.9 112.5  96.6  86.5 119.2 105.2
[4,]  92.6 109.4  81.4  87.1 113.0  87.1  92.7 118.3  96.3  88.2 125.1 101.0
     [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
[1,]  84.3 113.0 106.9 100.2 106.4 104.6  98.6 114.0 116.4 113.9 113.9 131.5
[2,]  87.5 113.9  96.6  89.4 116.2 107.1  90.6 122.1 112.6  98.6 127.5 131.0
[3,]  92.7 122.9 127.3  95.3 135.9 123.5  89.1 138.0 123.8  95.0 131.4 130.5
[4,]  94.4 132.7  98.2 104.2 134.0  98.8 105.2 142.2 103.6 116.0 145.9 118.9
     [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
[1,] 114.3 117.3 131.2 113.5 133.1 126.9 109.4 132.3 132.6 119.6 129.3 128.1
[2,]  85.7 142.5 125.4  98.7 143.4 124.0 117.8 142.9 123.7 116.2 144.4 138.4
[3,] 104.6 140.0 126.5 114.5 137.3 135.7 120.3 147.4 153.3 118.6 163.2 152.7
[4,] 105.1 159.8 119.4 113.8 165.2 130.0 121.0 175.9 134.0 130.7 179.4 120.0
     [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61]
[1,] 140.5 143.6 153.1 151.5 129.4 164.1 162.2 169.4 161.4 158.5 142.2 182.7
[2,] 116.2 157.6 147.6  98.7 174.1 142.8 123.1 179.7 169.4 135.3 188.4 145.2
[3,] 121.4 166.2 157.7 145.8 197.0 157.9 130.0 182.1 168.8 149.3 166.2 182.1
[4,] 127.8 182.3 137.2 151.7 193.9 159.2 150.1 194.3 158.1 143.4 199.2 158.7
     [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73]
[1,] 141.6 166.6 159.8 152.0 166.8 170.7 168.0 138.9 173.7 160.3 148.2 170.0
[2,] 132.6 157.0 157.8 142.2 165.6 164.4 139.3 172.1 153.8 135.2 182.2 158.4
[3,] 139.6 180.4 168.2 137.2 198.6 179.7 138.6 198.4 175.6 148.8 189.2 176.1
[4,] 147.0 210.2 158.4 152.6 201.5 157.0 153.4 217.8 147.1 151.0 183.1 156.2
     [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85]
[1,] 153.2 166.7 203.6 147.3 160.2 159.4 114.6 160.7 141.6 137.3 146.8 151.5
[2,] 117.9 156.8 175.2 137.0 191.9 161.1 159.6 165.5 148.1 135.5 162.8 158.1
[3,] 149.8 158.6 168.7 141.1 174.4 172.1 159.7 205.0 184.9 121.7 186.8 143.0
[4,] 156.6 210.8 155.9 167.4 208.2 158.4 159.4 205.2 132.5 166.1 185.5 151.2
     [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97]
[1,] 147.6 133.6 166.5 144.7 137.4 161.2 136.2 150.1 177.2 135.2 152.3 154.1
[2,] 130.7 167.9 151.3 123.6 181.6 155.5 126.8 186.3 127.4 126.4 151.2 159.3
[3,] 137.5 181.9 146.2 151.6 182.0 141.9 152.5 147.5 177.1 147.3 172.2 160.4
[4,] 146.1 202.0 148.3 133.9 190.0 164.6 126.6 200.4 154.4 140.6 215.3 151.9
     [,98] [,99] [,100] [,101] [,102] [,103] [,104] [,105] [,106] [,107] [,108]
[1,] 148.4 145.1  153.3  160.1  145.7  172.0  152.9  133.6  164.4  155.0    138
[2,] 139.6 183.7  144.3  135.6  183.5  150.6  135.5  194.1  148.1  124.5    190
[3,] 148.2 210.5  169.6  141.8  198.2  163.3  148.5  208.6  152.0  153.0    192
[4,] 153.5 203.3  143.7  159.9  186.8  153.7  148.4  197.3  144.1  146.0    192
     [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116] [,117] [,118]
[1,]    147    129    138    139    137    143    151    131    143    138
[2,]    133    131    168    143    129    151    134    125    160    136
[3,]    163    145    176    150    128    177    164    127    190    152
[4,]    150    137    188    154    140    184    126    143    182    127
     [,119]
[1,]    151
[2,]    130
[3,]    119
[4,]    153
> arr.mean <- array(NA,dim=np)
> arr.sd <- array(NA,dim=np)
> arr.range <- array(NA,dim=np)
> for (j in 1:np)
+ {
+ arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
+ arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
+ arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
+ }
> arr.mean
  [1]  90.375  70.775  96.975  85.500  73.675 102.425  88.450  75.400  99.925
 [10]  86.750  74.200 102.150  91.650  78.050 101.400  94.025  79.250 107.650
 [19]  91.550  85.075 109.475  97.525  83.275 116.550 103.600  89.725 120.625
 [28] 107.250  97.275 123.125 108.500  95.875 129.075 114.100 105.875 129.675
 [37] 127.975 102.425 139.900 125.625 110.125 144.750 129.150 117.125 149.625
 [46] 135.900 121.275 154.075 134.800 126.475 162.425 148.900 136.925 173.600
 [55] 156.000 141.350 181.375 164.425 146.625 174.000 167.175 140.200 178.550
 [64] 161.050 146.000 183.125 167.950 149.825 181.800 162.550 148.825 175.675
 [73] 165.175 144.375 173.225 175.850 148.200 183.675 162.750 148.325 184.100
 [82] 151.775 140.150 170.475 150.950 140.475 171.350 153.075 138.450 172.750
 [91] 155.800 135.525 171.075 159.025 137.375 172.750 156.425 147.425 185.650
[100] 152.725 149.350 178.550 159.900 146.325 183.400 152.150 144.625 178.000
[109] 148.250 135.500 167.500 146.500 133.500 163.750 143.750 131.500 168.750
[118] 138.250 138.250
> arr.sd
  [1]  8.927999  5.123394 12.040314  7.083784  5.600223 10.296075  6.544463
  [8]  4.148092 11.263621  2.334524  4.318179  9.802891  7.632605 10.028792
 [15]  8.730407  9.896590  6.139218 11.144057  6.936137  9.057732 11.759925
 [22]  6.169481  8.753428  9.586275  2.437212  4.657879  9.207741 14.112052
 [29]  6.388205 14.249532 10.587099  7.485709 13.264332  8.403967 10.616143
 [36] 13.163175  6.063758 12.008712 17.450310  4.852748  7.628182 14.274102
 [43]  5.006995  5.330025 18.625319 12.464617  6.443278 21.848169 14.110280
 [50] 10.484711 16.195961  8.825342 25.629719 31.163761  9.196014 18.010830
 [57] 10.227210  5.569186  9.777994 25.261829 18.425594  5.953150 23.182968
 [64]  4.839766  7.559541 19.585262  9.628603 13.903087 34.186060 14.258214
 [71] 10.360944 18.578729  9.470788 17.866985 25.417366 20.162754 13.482087
 [78] 20.866460  6.332193 22.483679 24.327899 22.991502 18.650737 19.249827
 [85]  6.183041  7.891081 28.796354  9.191436 12.291054 23.882141  9.998333
 [92] 12.170833 26.378574 23.654228  8.833412 29.963367  4.080339  5.764475
 [99] 29.309555 12.076527 12.555610 22.787497  9.710819  7.515484 33.777606
[106]  8.780471 13.960510 26.683328 12.311918  7.187953 21.315096  6.757712
[113]  5.916080 19.822126 17.056279  8.062258 21.344398 10.340052 16.520190
> arr.range
  [1] 18.9 12.5 27.6 15.9 12.9 24.8 14.4 10.1 27.5  5.4  8.9 22.1 16.6 22.7 15.9
 [16] 23.7 15.0 24.9 15.2 20.3 26.1 14.8 18.0 22.3  5.1 10.1 19.7 30.7 14.8 29.5
 [31] 24.7 16.1 28.2 20.2 21.0 32.0 12.6 28.6 42.5 11.8 15.8 32.1 11.7 11.6 43.6
 [46] 29.6 14.5 50.1 32.7 24.3 38.7 20.5 53.0 67.6 21.3 39.1 24.9 11.3 23.2 57.0
 [61] 37.5 14.4 53.2 10.4 15.4 35.9 22.7 29.4 78.9 28.5 25.1 41.0 19.9 38.7 54.0
 [76] 47.7 30.4 48.0 13.7 45.1 44.5 52.4 44.4 40.0 15.1 16.9 68.4 20.3 28.0 52.6
 [91] 22.7 25.9 52.9 49.8 20.9 64.1  8.5 13.9 65.4 25.9 24.5 52.5 21.4 17.4 75.0
[106] 20.3 30.5 54.0 30.0 16.0 50.0 15.0 12.0 41.0 38.0 18.0 47.0 25.0 34.0
> (lm1 <- lm(arr.sd~arr.mean))

Call:
lm(formula = arr.sd ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
    -7.8908       0.1539  

> (lnlm1 <- lm(log(arr.sd)~log(arr.mean)))

Call:
lm(formula = log(arr.sd) ~ log(arr.mean))

Coefficients:
  (Intercept)  log(arr.mean)  
       -4.419          1.399  

> (lm2 <- lm(arr.range~arr.mean))

Call:
lm(formula = arr.range ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
   -16.1677       0.3311  

> postscript(file="/var/www/html/rcomp/tmp/121ib1274903395.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/221ib1274903395.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
> dev.off()
null device 
          1 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Section',header=TRUE)
> a<-table.element(a,'Mean',header=TRUE)
> a<-table.element(a,'Standard Deviation',header=TRUE)
> a<-table.element(a,'Range',header=TRUE)
> a<-table.row.end(a)
> for (j in 1:np) {
+ a<-table.row.start(a)
+ a<-table.element(a,j,header=TRUE)
+ a<-table.element(a,arr.mean[j])
+ a<-table.element(a,arr.sd[j] )
+ a<-table.element(a,arr.range[j] )
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/36kgy1274903395.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lm1$coefficients[[1]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'beta',header=TRUE)
> a<-table.element(a,lm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4r2em1274903395.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$coefficients[[1]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'beta',header=TRUE)
> a<-table.element(a,lnlm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Lambda',header=TRUE)
> a<-table.element(a,1-lnlm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/55ccv1274903395.tab") 
> 
> try(system("convert tmp/121ib1274903395.ps tmp/121ib1274903395.png",intern=TRUE))
character(0)
> try(system("convert tmp/221ib1274903395.ps tmp/221ib1274903395.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.733   0.312   3.068