R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1966
+ ,1
+ ,41
+ ,1966
+ ,2
+ ,39
+ ,1966
+ ,3
+ ,50
+ ,1966
+ ,4
+ ,40
+ ,1966
+ ,5
+ ,43
+ ,1966
+ ,6
+ ,38
+ ,1966
+ ,7
+ ,44
+ ,1966
+ ,8
+ ,35
+ ,1966
+ ,9
+ ,39
+ ,1966
+ ,10
+ ,35
+ ,1966
+ ,11
+ ,29
+ ,1966
+ ,12
+ ,49
+ ,1967
+ ,1
+ ,50
+ ,1967
+ ,2
+ ,59
+ ,1967
+ ,3
+ ,63
+ ,1967
+ ,4
+ ,32
+ ,1967
+ ,5
+ ,39
+ ,1967
+ ,6
+ ,47
+ ,1967
+ ,7
+ ,53
+ ,1967
+ ,8
+ ,60
+ ,1967
+ ,9
+ ,57
+ ,1967
+ ,10
+ ,52
+ ,1967
+ ,11
+ ,70
+ ,1967
+ ,12
+ ,90
+ ,1968
+ ,1
+ ,74
+ ,1968
+ ,2
+ ,62
+ ,1968
+ ,3
+ ,55
+ ,1968
+ ,4
+ ,84
+ ,1968
+ ,5
+ ,94
+ ,1968
+ ,6
+ ,70
+ ,1968
+ ,7
+ ,108
+ ,1968
+ ,8
+ ,139
+ ,1968
+ ,9
+ ,120
+ ,1968
+ ,10
+ ,97
+ ,1968
+ ,11
+ ,126
+ ,1968
+ ,12
+ ,149
+ ,1969
+ ,1
+ ,158
+ ,1969
+ ,2
+ ,124
+ ,1969
+ ,3
+ ,140
+ ,1969
+ ,4
+ ,109
+ ,1969
+ ,5
+ ,114
+ ,1969
+ ,6
+ ,77
+ ,1969
+ ,7
+ ,120
+ ,1969
+ ,8
+ ,133
+ ,1969
+ ,9
+ ,110
+ ,1969
+ ,10
+ ,92
+ ,1969
+ ,11
+ ,97
+ ,1969
+ ,12
+ ,78
+ ,1970
+ ,1
+ ,99
+ ,1970
+ ,2
+ ,107
+ ,1970
+ ,3
+ ,112
+ ,1970
+ ,4
+ ,90
+ ,1970
+ ,5
+ ,98
+ ,1970
+ ,6
+ ,125
+ ,1970
+ ,7
+ ,155
+ ,1970
+ ,8
+ ,190
+ ,1970
+ ,9
+ ,236
+ ,1970
+ ,10
+ ,189
+ ,1970
+ ,11
+ ,174
+ ,1970
+ ,12
+ ,178
+ ,1971
+ ,1
+ ,136
+ ,1971
+ ,2
+ ,161
+ ,1971
+ ,3
+ ,171
+ ,1971
+ ,4
+ ,149
+ ,1971
+ ,5
+ ,184
+ ,1971
+ ,6
+ ,155
+ ,1971
+ ,7
+ ,276
+ ,1971
+ ,8
+ ,224
+ ,1971
+ ,9
+ ,213
+ ,1971
+ ,10
+ ,279
+ ,1971
+ ,11
+ ,268
+ ,1971
+ ,12
+ ,287
+ ,1972
+ ,1
+ ,238
+ ,1972
+ ,2
+ ,213
+ ,1972
+ ,3
+ ,257
+ ,1972
+ ,4
+ ,293
+ ,1972
+ ,5
+ ,212
+ ,1972
+ ,6
+ ,246
+ ,1972
+ ,7
+ ,353
+ ,1972
+ ,8
+ ,339
+ ,1972
+ ,9
+ ,308
+ ,1972
+ ,10
+ ,247
+ ,1972
+ ,11
+ ,257
+ ,1972
+ ,12
+ ,322
+ ,1973
+ ,1
+ ,298
+ ,1973
+ ,2
+ ,273
+ ,1973
+ ,3
+ ,312
+ ,1973
+ ,4
+ ,249
+ ,1973
+ ,5
+ ,286
+ ,1973
+ ,6
+ ,279
+ ,1973
+ ,7
+ ,309
+ ,1973
+ ,8
+ ,401
+ ,1973
+ ,9
+ ,309
+ ,1973
+ ,10
+ ,328
+ ,1973
+ ,11
+ ,353
+ ,1973
+ ,12
+ ,354
+ ,1974
+ ,1
+ ,327
+ ,1974
+ ,2
+ ,324
+ ,1974
+ ,3
+ ,285
+ ,1974
+ ,4
+ ,243
+ ,1974
+ ,5
+ ,241
+ ,1974
+ ,6
+ ,287
+ ,1974
+ ,7
+ ,355
+ ,1974
+ ,8
+ ,460
+ ,1974
+ ,9
+ ,364
+ ,1974
+ ,10
+ ,487
+ ,1974
+ ,11
+ ,452
+ ,1974
+ ,12
+ ,391
+ ,1975
+ ,1
+ ,500
+ ,1975
+ ,2
+ ,451
+ ,1975
+ ,3
+ ,375
+ ,1975
+ ,4
+ ,372
+ ,1975
+ ,5
+ ,302
+ ,1975
+ ,6
+ ,316
+ ,1975
+ ,7
+ ,398
+ ,1975
+ ,8
+ ,394
+ ,1975
+ ,9
+ ,431
+ ,1975
+ ,10
+ ,431)
+ ,dim=c(3
+ ,118)
+ ,dimnames=list(c('year'
+ ,'month'
+ ,'robberies')
+ ,1:118))
> y <- array(NA,dim=c(3,118),dimnames=list(c('year','month','robberies'),1:118))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
year month robberies
1 1966 1 41
2 1966 2 39
3 1966 3 50
4 1966 4 40
5 1966 5 43
6 1966 6 38
7 1966 7 44
8 1966 8 35
9 1966 9 39
10 1966 10 35
11 1966 11 29
12 1966 12 49
13 1967 1 50
14 1967 2 59
15 1967 3 63
16 1967 4 32
17 1967 5 39
18 1967 6 47
19 1967 7 53
20 1967 8 60
21 1967 9 57
22 1967 10 52
23 1967 11 70
24 1967 12 90
25 1968 1 74
26 1968 2 62
27 1968 3 55
28 1968 4 84
29 1968 5 94
30 1968 6 70
31 1968 7 108
32 1968 8 139
33 1968 9 120
34 1968 10 97
35 1968 11 126
36 1968 12 149
37 1969 1 158
38 1969 2 124
39 1969 3 140
40 1969 4 109
41 1969 5 114
42 1969 6 77
43 1969 7 120
44 1969 8 133
45 1969 9 110
46 1969 10 92
47 1969 11 97
48 1969 12 78
49 1970 1 99
50 1970 2 107
51 1970 3 112
52 1970 4 90
53 1970 5 98
54 1970 6 125
55 1970 7 155
56 1970 8 190
57 1970 9 236
58 1970 10 189
59 1970 11 174
60 1970 12 178
61 1971 1 136
62 1971 2 161
63 1971 3 171
64 1971 4 149
65 1971 5 184
66 1971 6 155
67 1971 7 276
68 1971 8 224
69 1971 9 213
70 1971 10 279
71 1971 11 268
72 1971 12 287
73 1972 1 238
74 1972 2 213
75 1972 3 257
76 1972 4 293
77 1972 5 212
78 1972 6 246
79 1972 7 353
80 1972 8 339
81 1972 9 308
82 1972 10 247
83 1972 11 257
84 1972 12 322
85 1973 1 298
86 1973 2 273
87 1973 3 312
88 1973 4 249
89 1973 5 286
90 1973 6 279
91 1973 7 309
92 1973 8 401
93 1973 9 309
94 1973 10 328
95 1973 11 353
96 1973 12 354
97 1974 1 327
98 1974 2 324
99 1974 3 285
100 1974 4 243
101 1974 5 241
102 1974 6 287
103 1974 7 355
104 1974 8 460
105 1974 9 364
106 1974 10 487
107 1974 11 452
108 1974 12 391
109 1975 1 500
110 1975 2 451
111 1975 3 375
112 1975 4 372
113 1975 5 302
114 1975 6 316
115 1975 7 398
116 1975 8 394
117 1975 9 431
118 1975 10 431
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month robberies
1967.10365 -0.12590 0.02103
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.49232 -0.61260 0.03958 0.64792 2.45784
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.967e+03 2.266e-01 8682.475 < 2e-16 ***
month -1.259e-01 2.663e-02 -4.728 6.48e-06 ***
robberies 2.103e-02 7.139e-04 29.457 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9818 on 115 degrees of freedom
Multiple R-squared: 0.8832, Adjusted R-squared: 0.8811
F-statistic: 434.7 on 2 and 115 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 3.647200e-39 7.294401e-39 1.000000e+00
[2,] 3.589064e-56 7.178128e-56 1.000000e+00
[3,] 3.528266e-63 7.056531e-63 1.000000e+00
[4,] 8.907975e-78 1.781595e-77 1.000000e+00
[5,] 6.985235e-91 1.397047e-90 1.000000e+00
[6,] 2.715776e-108 5.431553e-108 1.000000e+00
[7,] 2.921237e-112 5.842473e-112 1.000000e+00
[8,] 8.326836e-05 1.665367e-04 9.999167e-01
[9,] 8.824840e-05 1.764968e-04 9.999118e-01
[10,] 3.709382e-05 7.418765e-05 9.999629e-01
[11,] 7.602752e-04 1.520550e-03 9.992397e-01
[12,] 1.467584e-03 2.935168e-03 9.985324e-01
[13,] 1.661496e-03 3.322992e-03 9.983385e-01
[14,] 1.353010e-03 2.706019e-03 9.986470e-01
[15,] 7.927303e-04 1.585461e-03 9.992073e-01
[16,] 4.657325e-04 9.314650e-04 9.995343e-01
[17,] 3.075150e-04 6.150301e-04 9.996925e-01
[18,] 1.524284e-04 3.048568e-04 9.998476e-01
[19,] 1.296807e-04 2.593614e-04 9.998703e-01
[20,] 1.743663e-04 3.487326e-04 9.998256e-01
[21,] 3.646490e-04 7.292979e-04 9.996354e-01
[22,] 9.775865e-04 1.955173e-03 9.990224e-01
[23,] 6.220992e-04 1.244198e-03 9.993779e-01
[24,] 4.020232e-04 8.040465e-04 9.995980e-01
[25,] 4.294451e-04 8.588902e-04 9.995706e-01
[26,] 3.680908e-04 7.361816e-04 9.996319e-01
[27,] 9.747422e-04 1.949484e-03 9.990253e-01
[28,] 7.129577e-04 1.425915e-03 9.992870e-01
[29,] 5.339317e-04 1.067863e-03 9.994661e-01
[30,] 3.883662e-04 7.767323e-04 9.996116e-01
[31,] 4.856021e-04 9.712041e-04 9.995144e-01
[32,] 7.130421e-04 1.426084e-03 9.992870e-01
[33,] 7.779854e-04 1.555971e-03 9.992220e-01
[34,] 7.976487e-04 1.595297e-03 9.992024e-01
[35,] 1.292395e-03 2.584790e-03 9.987076e-01
[36,] 1.721244e-03 3.442489e-03 9.982788e-01
[37,] 1.030801e-02 2.061602e-02 9.896920e-01
[38,] 1.067052e-02 2.134104e-02 9.893295e-01
[39,] 9.438025e-03 1.887605e-02 9.905620e-01
[40,] 1.221637e-02 2.443274e-02 9.877836e-01
[41,] 2.585400e-02 5.170799e-02 9.741460e-01
[42,] 3.898149e-02 7.796297e-02 9.610185e-01
[43,] 8.596721e-02 1.719344e-01 9.140328e-01
[44,] 1.574986e-01 3.149972e-01 8.425014e-01
[45,] 2.067810e-01 4.135621e-01 7.932190e-01
[46,] 2.378105e-01 4.756209e-01 7.621895e-01
[47,] 3.431802e-01 6.863605e-01 6.568198e-01
[48,] 4.132699e-01 8.265398e-01 5.867301e-01
[49,] 3.986425e-01 7.972849e-01 6.013575e-01
[50,] 3.571967e-01 7.143933e-01 6.428033e-01
[51,] 3.604531e-01 7.209062e-01 6.395469e-01
[52,] 5.036595e-01 9.926810e-01 4.963405e-01
[53,] 4.718461e-01 9.436921e-01 5.281539e-01
[54,] 4.278786e-01 8.557571e-01 5.721214e-01
[55,] 3.829670e-01 7.659340e-01 6.170330e-01
[56,] 4.045521e-01 8.091042e-01 5.954479e-01
[57,] 3.839914e-01 7.679829e-01 6.160086e-01
[58,] 3.565725e-01 7.131450e-01 6.434275e-01
[59,] 3.516218e-01 7.032436e-01 6.483782e-01
[60,] 3.172147e-01 6.344294e-01 6.827853e-01
[61,] 3.077104e-01 6.154208e-01 6.922896e-01
[62,] 4.697085e-01 9.394171e-01 5.302915e-01
[63,] 4.457584e-01 8.915168e-01 5.542416e-01
[64,] 4.063941e-01 8.127882e-01 5.936059e-01
[65,] 4.844551e-01 9.689102e-01 5.155449e-01
[66,] 5.095501e-01 9.808998e-01 4.904499e-01
[67,] 5.731215e-01 8.537570e-01 4.268785e-01
[68,] 5.684957e-01 8.630085e-01 4.315043e-01
[69,] 5.495619e-01 9.008762e-01 4.504381e-01
[70,] 5.645754e-01 8.708493e-01 4.354246e-01
[71,] 6.461180e-01 7.077640e-01 3.538820e-01
[72,] 6.376843e-01 7.246315e-01 3.623157e-01
[73,] 6.266282e-01 7.467437e-01 3.733718e-01
[74,] 8.055839e-01 3.888322e-01 1.944161e-01
[75,] 8.846488e-01 2.307024e-01 1.153512e-01
[76,] 9.051699e-01 1.896602e-01 9.483010e-02
[77,] 9.003980e-01 1.992039e-01 9.960195e-02
[78,] 8.958831e-01 2.082339e-01 1.041169e-01
[79,] 9.215045e-01 1.569909e-01 7.849547e-02
[80,] 9.312180e-01 1.375640e-01 6.878200e-02
[81,] 9.344248e-01 1.311504e-01 6.557519e-02
[82,] 9.500611e-01 9.987780e-02 4.993890e-02
[83,] 9.526029e-01 9.479415e-02 4.739708e-02
[84,] 9.564577e-01 8.708467e-02 4.354233e-02
[85,] 9.583840e-01 8.323206e-02 4.161603e-02
[86,] 9.624223e-01 7.515549e-02 3.757775e-02
[87,] 9.852069e-01 2.958618e-02 1.479309e-02
[88,] 9.858599e-01 2.828012e-02 1.414006e-02
[89,] 9.875882e-01 2.482357e-02 1.241178e-02
[90,] 9.914976e-01 1.700478e-02 8.502390e-03
[91,] 9.959846e-01 8.030892e-03 4.015446e-03
[92,] 9.956441e-01 8.711860e-03 4.355930e-03
[93,] 9.956620e-01 8.675922e-03 4.337961e-03
[94,] 9.956570e-01 8.685918e-03 4.342959e-03
[95,] 9.962881e-01 7.423842e-03 3.711921e-03
[96,] 9.973217e-01 5.356635e-03 2.678317e-03
[97,] 9.984000e-01 3.200035e-03 1.600018e-03
[98,] 9.990341e-01 1.931714e-03 9.658570e-04
[99,] 9.991008e-01 1.798463e-03 8.992314e-04
[100,] 9.994162e-01 1.167505e-03 5.837525e-04
[101,] 9.993018e-01 1.396482e-03 6.982410e-04
[102,] 9.995028e-01 9.944446e-04 4.972223e-04
[103,] 1.000000e+00 1.148043e-95 5.740217e-96
[104,] 1.000000e+00 1.776377e-78 8.881887e-79
[105,] 1.000000e+00 3.240242e-64 1.620121e-64
[106,] 1.000000e+00 1.386228e-59 6.931142e-60
[107,] 1.000000e+00 4.682369e-38 2.341185e-38
> postscript(file="/var/wessaorg/rcomp/tmp/1it5l1354888648.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2wbbo1354888648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35wb01354888648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4twz11354888648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5r6591354888648.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 = 118
Frequency = 1
1 2 3 4 5 6
-1.83994534 -1.67198492 -1.77740329 -1.44120976 -1.37839502 -1.14734719
7 8 9 10 11 12
-1.14761987 -0.83245548 -0.79066988 -0.58065118 -0.32857420 -0.62325481
13 14 15 16 17 18
-1.02920758 -1.09256768 -1.05078208 -0.27297666 -0.29427847 -0.33660943
19 20 21 22 23 24
-0.33688211 -0.35818392 -0.16919436 0.06185348 -0.19076886 -0.48544947
25 26 27 28 29 30
-0.53390689 -0.15565509 0.11745102 -0.36649183 -0.45088106 0.17972040
31 32 33 34 35 36
-0.49348469 -1.01948582 -0.49403005 0.11554227 -0.36840058 -0.72616860
37 38 39 40 41 42
-1.30035447 -0.45946164 -0.67002569 0.10777973 0.12853619 1.03251644
43 44 45 46 47 48
0.25416566 0.10668901 0.71626133 1.22068796 1.24144442 1.76690019
49 50 51 52 53 54
0.94036466 0.89803371 0.91879017 1.50733335 1.46500239 1.02311782
55 56 57 58 59 60
0.51814583 -0.09197185 -0.93341004 0.18086159 0.62220080 0.66398640
61 62 63 64 65 66
1.16228656 0.76246026 0.67807103 1.26661421 0.65649654 1.39224368
67 68 69 70 71 72
-1.02637985 0.19303747 0.55026013 -0.71176082 -0.35453816 -0.62818963
73 74 75 76 77 78
0.01731450 0.66894509 -0.13043483 -0.76158164 1.06768067 0.47859214
79 80 81 82 83 84
-1.64562347 -1.22531339 -0.44750797 0.96117159 0.87678236 -0.36420945
85 86 87 88 89 90
-0.24443378 0.40719682 -0.28703741 1.16370042 0.51152447 0.78463059
91 92 93 94 95 96
0.27965860 -1.52911994 0.53146289 0.25781142 -0.14201488 -0.03714187
97 98 99 100 101 102
0.14572123 0.33471079 1.28074931 2.28987525 2.45783568 1.61639748
103 104 105 106 107 108
0.31231826 -1.76983907 0.37486031 -2.08582150 -1.22389953 0.18478003
109 110 111 112 113 114
-2.49231963 -1.33598972 0.38812690 0.57711647 2.17505827 2.00655248
115 116 117 118
0.40806533 0.61808403 -0.03409193 0.09181022
> postscript(file="/var/wessaorg/rcomp/tmp/618kk1354888648.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 = 118
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.83994534 NA
1 -1.67198492 -1.83994534
2 -1.77740329 -1.67198492
3 -1.44120976 -1.77740329
4 -1.37839502 -1.44120976
5 -1.14734719 -1.37839502
6 -1.14761987 -1.14734719
7 -0.83245548 -1.14761987
8 -0.79066988 -0.83245548
9 -0.58065118 -0.79066988
10 -0.32857420 -0.58065118
11 -0.62325481 -0.32857420
12 -1.02920758 -0.62325481
13 -1.09256768 -1.02920758
14 -1.05078208 -1.09256768
15 -0.27297666 -1.05078208
16 -0.29427847 -0.27297666
17 -0.33660943 -0.29427847
18 -0.33688211 -0.33660943
19 -0.35818392 -0.33688211
20 -0.16919436 -0.35818392
21 0.06185348 -0.16919436
22 -0.19076886 0.06185348
23 -0.48544947 -0.19076886
24 -0.53390689 -0.48544947
25 -0.15565509 -0.53390689
26 0.11745102 -0.15565509
27 -0.36649183 0.11745102
28 -0.45088106 -0.36649183
29 0.17972040 -0.45088106
30 -0.49348469 0.17972040
31 -1.01948582 -0.49348469
32 -0.49403005 -1.01948582
33 0.11554227 -0.49403005
34 -0.36840058 0.11554227
35 -0.72616860 -0.36840058
36 -1.30035447 -0.72616860
37 -0.45946164 -1.30035447
38 -0.67002569 -0.45946164
39 0.10777973 -0.67002569
40 0.12853619 0.10777973
41 1.03251644 0.12853619
42 0.25416566 1.03251644
43 0.10668901 0.25416566
44 0.71626133 0.10668901
45 1.22068796 0.71626133
46 1.24144442 1.22068796
47 1.76690019 1.24144442
48 0.94036466 1.76690019
49 0.89803371 0.94036466
50 0.91879017 0.89803371
51 1.50733335 0.91879017
52 1.46500239 1.50733335
53 1.02311782 1.46500239
54 0.51814583 1.02311782
55 -0.09197185 0.51814583
56 -0.93341004 -0.09197185
57 0.18086159 -0.93341004
58 0.62220080 0.18086159
59 0.66398640 0.62220080
60 1.16228656 0.66398640
61 0.76246026 1.16228656
62 0.67807103 0.76246026
63 1.26661421 0.67807103
64 0.65649654 1.26661421
65 1.39224368 0.65649654
66 -1.02637985 1.39224368
67 0.19303747 -1.02637985
68 0.55026013 0.19303747
69 -0.71176082 0.55026013
70 -0.35453816 -0.71176082
71 -0.62818963 -0.35453816
72 0.01731450 -0.62818963
73 0.66894509 0.01731450
74 -0.13043483 0.66894509
75 -0.76158164 -0.13043483
76 1.06768067 -0.76158164
77 0.47859214 1.06768067
78 -1.64562347 0.47859214
79 -1.22531339 -1.64562347
80 -0.44750797 -1.22531339
81 0.96117159 -0.44750797
82 0.87678236 0.96117159
83 -0.36420945 0.87678236
84 -0.24443378 -0.36420945
85 0.40719682 -0.24443378
86 -0.28703741 0.40719682
87 1.16370042 -0.28703741
88 0.51152447 1.16370042
89 0.78463059 0.51152447
90 0.27965860 0.78463059
91 -1.52911994 0.27965860
92 0.53146289 -1.52911994
93 0.25781142 0.53146289
94 -0.14201488 0.25781142
95 -0.03714187 -0.14201488
96 0.14572123 -0.03714187
97 0.33471079 0.14572123
98 1.28074931 0.33471079
99 2.28987525 1.28074931
100 2.45783568 2.28987525
101 1.61639748 2.45783568
102 0.31231826 1.61639748
103 -1.76983907 0.31231826
104 0.37486031 -1.76983907
105 -2.08582150 0.37486031
106 -1.22389953 -2.08582150
107 0.18478003 -1.22389953
108 -2.49231963 0.18478003
109 -1.33598972 -2.49231963
110 0.38812690 -1.33598972
111 0.57711647 0.38812690
112 2.17505827 0.57711647
113 2.00655248 2.17505827
114 0.40806533 2.00655248
115 0.61808403 0.40806533
116 -0.03409193 0.61808403
117 0.09181022 -0.03409193
118 NA 0.09181022
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.67198492 -1.83994534
[2,] -1.77740329 -1.67198492
[3,] -1.44120976 -1.77740329
[4,] -1.37839502 -1.44120976
[5,] -1.14734719 -1.37839502
[6,] -1.14761987 -1.14734719
[7,] -0.83245548 -1.14761987
[8,] -0.79066988 -0.83245548
[9,] -0.58065118 -0.79066988
[10,] -0.32857420 -0.58065118
[11,] -0.62325481 -0.32857420
[12,] -1.02920758 -0.62325481
[13,] -1.09256768 -1.02920758
[14,] -1.05078208 -1.09256768
[15,] -0.27297666 -1.05078208
[16,] -0.29427847 -0.27297666
[17,] -0.33660943 -0.29427847
[18,] -0.33688211 -0.33660943
[19,] -0.35818392 -0.33688211
[20,] -0.16919436 -0.35818392
[21,] 0.06185348 -0.16919436
[22,] -0.19076886 0.06185348
[23,] -0.48544947 -0.19076886
[24,] -0.53390689 -0.48544947
[25,] -0.15565509 -0.53390689
[26,] 0.11745102 -0.15565509
[27,] -0.36649183 0.11745102
[28,] -0.45088106 -0.36649183
[29,] 0.17972040 -0.45088106
[30,] -0.49348469 0.17972040
[31,] -1.01948582 -0.49348469
[32,] -0.49403005 -1.01948582
[33,] 0.11554227 -0.49403005
[34,] -0.36840058 0.11554227
[35,] -0.72616860 -0.36840058
[36,] -1.30035447 -0.72616860
[37,] -0.45946164 -1.30035447
[38,] -0.67002569 -0.45946164
[39,] 0.10777973 -0.67002569
[40,] 0.12853619 0.10777973
[41,] 1.03251644 0.12853619
[42,] 0.25416566 1.03251644
[43,] 0.10668901 0.25416566
[44,] 0.71626133 0.10668901
[45,] 1.22068796 0.71626133
[46,] 1.24144442 1.22068796
[47,] 1.76690019 1.24144442
[48,] 0.94036466 1.76690019
[49,] 0.89803371 0.94036466
[50,] 0.91879017 0.89803371
[51,] 1.50733335 0.91879017
[52,] 1.46500239 1.50733335
[53,] 1.02311782 1.46500239
[54,] 0.51814583 1.02311782
[55,] -0.09197185 0.51814583
[56,] -0.93341004 -0.09197185
[57,] 0.18086159 -0.93341004
[58,] 0.62220080 0.18086159
[59,] 0.66398640 0.62220080
[60,] 1.16228656 0.66398640
[61,] 0.76246026 1.16228656
[62,] 0.67807103 0.76246026
[63,] 1.26661421 0.67807103
[64,] 0.65649654 1.26661421
[65,] 1.39224368 0.65649654
[66,] -1.02637985 1.39224368
[67,] 0.19303747 -1.02637985
[68,] 0.55026013 0.19303747
[69,] -0.71176082 0.55026013
[70,] -0.35453816 -0.71176082
[71,] -0.62818963 -0.35453816
[72,] 0.01731450 -0.62818963
[73,] 0.66894509 0.01731450
[74,] -0.13043483 0.66894509
[75,] -0.76158164 -0.13043483
[76,] 1.06768067 -0.76158164
[77,] 0.47859214 1.06768067
[78,] -1.64562347 0.47859214
[79,] -1.22531339 -1.64562347
[80,] -0.44750797 -1.22531339
[81,] 0.96117159 -0.44750797
[82,] 0.87678236 0.96117159
[83,] -0.36420945 0.87678236
[84,] -0.24443378 -0.36420945
[85,] 0.40719682 -0.24443378
[86,] -0.28703741 0.40719682
[87,] 1.16370042 -0.28703741
[88,] 0.51152447 1.16370042
[89,] 0.78463059 0.51152447
[90,] 0.27965860 0.78463059
[91,] -1.52911994 0.27965860
[92,] 0.53146289 -1.52911994
[93,] 0.25781142 0.53146289
[94,] -0.14201488 0.25781142
[95,] -0.03714187 -0.14201488
[96,] 0.14572123 -0.03714187
[97,] 0.33471079 0.14572123
[98,] 1.28074931 0.33471079
[99,] 2.28987525 1.28074931
[100,] 2.45783568 2.28987525
[101,] 1.61639748 2.45783568
[102,] 0.31231826 1.61639748
[103,] -1.76983907 0.31231826
[104,] 0.37486031 -1.76983907
[105,] -2.08582150 0.37486031
[106,] -1.22389953 -2.08582150
[107,] 0.18478003 -1.22389953
[108,] -2.49231963 0.18478003
[109,] -1.33598972 -2.49231963
[110,] 0.38812690 -1.33598972
[111,] 0.57711647 0.38812690
[112,] 2.17505827 0.57711647
[113,] 2.00655248 2.17505827
[114,] 0.40806533 2.00655248
[115,] 0.61808403 0.40806533
[116,] -0.03409193 0.61808403
[117,] 0.09181022 -0.03409193
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.67198492 -1.83994534
2 -1.77740329 -1.67198492
3 -1.44120976 -1.77740329
4 -1.37839502 -1.44120976
5 -1.14734719 -1.37839502
6 -1.14761987 -1.14734719
7 -0.83245548 -1.14761987
8 -0.79066988 -0.83245548
9 -0.58065118 -0.79066988
10 -0.32857420 -0.58065118
11 -0.62325481 -0.32857420
12 -1.02920758 -0.62325481
13 -1.09256768 -1.02920758
14 -1.05078208 -1.09256768
15 -0.27297666 -1.05078208
16 -0.29427847 -0.27297666
17 -0.33660943 -0.29427847
18 -0.33688211 -0.33660943
19 -0.35818392 -0.33688211
20 -0.16919436 -0.35818392
21 0.06185348 -0.16919436
22 -0.19076886 0.06185348
23 -0.48544947 -0.19076886
24 -0.53390689 -0.48544947
25 -0.15565509 -0.53390689
26 0.11745102 -0.15565509
27 -0.36649183 0.11745102
28 -0.45088106 -0.36649183
29 0.17972040 -0.45088106
30 -0.49348469 0.17972040
31 -1.01948582 -0.49348469
32 -0.49403005 -1.01948582
33 0.11554227 -0.49403005
34 -0.36840058 0.11554227
35 -0.72616860 -0.36840058
36 -1.30035447 -0.72616860
37 -0.45946164 -1.30035447
38 -0.67002569 -0.45946164
39 0.10777973 -0.67002569
40 0.12853619 0.10777973
41 1.03251644 0.12853619
42 0.25416566 1.03251644
43 0.10668901 0.25416566
44 0.71626133 0.10668901
45 1.22068796 0.71626133
46 1.24144442 1.22068796
47 1.76690019 1.24144442
48 0.94036466 1.76690019
49 0.89803371 0.94036466
50 0.91879017 0.89803371
51 1.50733335 0.91879017
52 1.46500239 1.50733335
53 1.02311782 1.46500239
54 0.51814583 1.02311782
55 -0.09197185 0.51814583
56 -0.93341004 -0.09197185
57 0.18086159 -0.93341004
58 0.62220080 0.18086159
59 0.66398640 0.62220080
60 1.16228656 0.66398640
61 0.76246026 1.16228656
62 0.67807103 0.76246026
63 1.26661421 0.67807103
64 0.65649654 1.26661421
65 1.39224368 0.65649654
66 -1.02637985 1.39224368
67 0.19303747 -1.02637985
68 0.55026013 0.19303747
69 -0.71176082 0.55026013
70 -0.35453816 -0.71176082
71 -0.62818963 -0.35453816
72 0.01731450 -0.62818963
73 0.66894509 0.01731450
74 -0.13043483 0.66894509
75 -0.76158164 -0.13043483
76 1.06768067 -0.76158164
77 0.47859214 1.06768067
78 -1.64562347 0.47859214
79 -1.22531339 -1.64562347
80 -0.44750797 -1.22531339
81 0.96117159 -0.44750797
82 0.87678236 0.96117159
83 -0.36420945 0.87678236
84 -0.24443378 -0.36420945
85 0.40719682 -0.24443378
86 -0.28703741 0.40719682
87 1.16370042 -0.28703741
88 0.51152447 1.16370042
89 0.78463059 0.51152447
90 0.27965860 0.78463059
91 -1.52911994 0.27965860
92 0.53146289 -1.52911994
93 0.25781142 0.53146289
94 -0.14201488 0.25781142
95 -0.03714187 -0.14201488
96 0.14572123 -0.03714187
97 0.33471079 0.14572123
98 1.28074931 0.33471079
99 2.28987525 1.28074931
100 2.45783568 2.28987525
101 1.61639748 2.45783568
102 0.31231826 1.61639748
103 -1.76983907 0.31231826
104 0.37486031 -1.76983907
105 -2.08582150 0.37486031
106 -1.22389953 -2.08582150
107 0.18478003 -1.22389953
108 -2.49231963 0.18478003
109 -1.33598972 -2.49231963
110 0.38812690 -1.33598972
111 0.57711647 0.38812690
112 2.17505827 0.57711647
113 2.00655248 2.17505827
114 0.40806533 2.00655248
115 0.61808403 0.40806533
116 -0.03409193 0.61808403
117 0.09181022 -0.03409193
> 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/7fi0h1354888648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8s07c1354888648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9begw1354888648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10hmia1354888648.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112d8w1354888648.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/12k9w21354888648.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/13s4gs1354888648.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/14k2fq1354888648.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/15odme1354888648.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/1653sl1354888648.tab")
+ }
>
> try(system("convert tmp/1it5l1354888648.ps tmp/1it5l1354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wbbo1354888648.ps tmp/2wbbo1354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/35wb01354888648.ps tmp/35wb01354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/4twz11354888648.ps tmp/4twz11354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/5r6591354888648.ps tmp/5r6591354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/618kk1354888648.ps tmp/618kk1354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fi0h1354888648.ps tmp/7fi0h1354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/8s07c1354888648.ps tmp/8s07c1354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/9begw1354888648.ps tmp/9begw1354888648.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hmia1354888648.ps tmp/10hmia1354888648.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.697 1.160 7.906