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)
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.
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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(101645
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+ ,14
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+ ,27)
+ ,dim=c(6
+ ,133)
+ ,dimnames=list(c('time_in_rfc'
+ ,'logins'
+ ,'blogged_computations'
+ ,'feedback_messages_p120'
+ ,'totsize'
+ ,'tothyperlinks
')
+ ,1:133))
> y <- array(NA,dim=c(6,133),dimnames=list(c('time_in_rfc','logins','blogged_computations','feedback_messages_p120','totsize','tothyperlinks
'),1:133))
> 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'
> 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
time_in_rfc logins blogged_computations feedback_messages_p120 totsize
1 101645 63 20 38 17140
2 101011 34 30 39 27570
3 7176 17 0 0 1423
4 96560 76 42 38 22996
5 175824 107 57 77 39992
6 341570 168 94 78 117105
7 103597 43 27 49 23789
8 112611 41 46 73 26706
9 85574 34 37 36 24266
10 220801 75 51 63 44418
11 92661 61 40 41 35232
12 133328 55 56 56 40909
13 61361 77 27 25 13294
14 125930 75 37 65 32387
15 82316 32 27 38 21233
16 102010 53 28 44 44332
17 101523 42 59 87 61056
18 41566 35 0 27 13497
19 99923 66 44 80 32334
20 22648 19 12 28 44339
21 46698 45 14 33 10288
22 131698 65 60 59 65622
23 91735 35 7 49 16563
24 79863 37 29 49 29011
25 108043 62 45 38 34553
26 98866 18 25 39 23517
27 120445 118 36 56 51009
28 116048 64 50 50 33416
29 250047 81 41 61 83305
30 136084 30 27 41 27142
31 92499 32 25 55 21399
32 135781 31 45 44 24874
33 74408 67 29 21 34988
34 81240 66 58 50 45549
35 133368 36 37 57 32755
36 98146 40 15 48 27114
37 79619 43 42 32 20760
38 59194 31 7 68 37636
39 139942 42 54 87 65461
40 118612 46 54 43 30080
41 72880 33 14 67 24094
42 65475 18 16 46 69008
43 99643 55 33 46 54968
44 71965 35 32 56 46090
45 77272 59 21 48 27507
46 49289 19 15 44 10672
47 135131 66 38 60 34029
48 108446 60 22 65 46300
49 89746 36 28 55 24760
50 44296 25 10 38 18779
51 77648 47 31 52 21280
52 181528 54 32 60 40662
53 134019 53 32 54 28987
54 124064 40 43 86 22827
55 92630 40 27 24 18513
56 121848 39 37 52 30594
57 52915 14 20 49 24006
58 81872 45 32 61 27913
59 58981 36 0 61 42744
60 53515 28 5 81 12934
61 60812 44 26 43 22574
62 56375 30 10 40 41385
63 65490 22 27 40 18653
64 80949 17 11 56 18472
65 76302 31 29 68 30976
66 104011 55 25 79 63339
67 98104 54 55 47 25568
68 67989 21 23 57 33747
69 30989 14 5 41 4154
70 135458 81 43 29 19474
71 73504 35 23 3 35130
72 63123 43 34 60 39067
73 61254 46 36 30 13310
74 74914 30 35 79 65892
75 31774 23 0 47 4143
76 81437 38 37 40 28579
77 87186 54 28 48 51776
78 50090 20 16 36 21152
79 65745 53 26 42 38084
80 56653 45 38 49 27717
81 158399 39 23 57 32928
82 46455 20 22 12 11342
83 73624 24 30 40 19499
84 38395 31 16 43 16380
85 91899 35 18 33 36874
86 139526 151 28 77 48259
87 52164 52 32 43 16734
88 51567 30 21 45 28207
89 70551 31 23 47 30143
90 84856 29 29 43 41369
91 102538 57 50 45 45833
92 86678 40 12 50 29156
93 85709 44 21 35 35944
94 34662 25 18 7 36278
95 150580 77 27 71 45588
96 99611 35 41 67 45097
97 19349 11 13 0 3895
98 99373 63 12 62 28394
99 86230 44 21 54 18632
100 30837 19 8 4 2325
101 31706 13 26 25 25139
102 89806 42 27 40 27975
103 62088 38 13 38 14483
104 40151 29 16 19 13127
105 27634 20 2 17 5839
106 76990 27 42 67 24069
107 37460 20 5 14 3738
108 54157 19 37 30 18625
109 49862 37 17 54 36341
110 84337 26 38 35 24548
111 64175 42 37 59 21792
112 59382 49 29 24 26263
113 119308 30 32 58 23686
114 76702 49 35 42 49303
115 103425 67 17 46 25659
116 70344 28 20 61 28904
117 43410 19 7 3 2781
118 104838 49 46 52 29236
119 62215 27 24 25 19546
120 69304 30 40 40 22818
121 53117 22 3 32 32689
122 19764 12 10 4 5752
123 86680 31 37 49 22197
124 84105 20 17 63 20055
125 77945 20 28 67 25272
126 89113 39 19 32 82206
127 91005 29 29 23 32073
128 40248 16 8 7 5444
129 64187 27 10 54 20154
130 50857 21 15 37 36944
131 56613 19 15 35 8019
132 62792 35 28 51 30884
133 72535 14 17 39 19540
tothyperlinks\r
1 28
2 35
3 0
4 47
5 70
6 135
7 26
8 48
9 40
10 66
11 39
12 66
13 27
14 65
15 25
16 26
17 77
18 2
19 36
20 24
21 14
22 78
23 15
24 24
25 40
26 50
27 63
28 63
29 55
30 40
31 21
32 32
33 36
34 13
35 57
36 21
37 43
38 20
39 82
40 90
41 25
42 60
43 61
44 85
45 43
46 25
47 41
48 26
49 38
50 12
51 29
52 49
53 46
54 41
55 31
56 41
57 26
58 23
59 14
60 16
61 25
62 21
63 32
64 9
65 35
66 42
67 68
68 32
69 6
70 68
71 33
72 84
73 46
74 30
75 0
76 36
77 47
78 20
79 50
80 30
81 30
82 34
83 33
84 34
85 37
86 83
87 32
88 30
89 43
90 41
91 51
92 19
93 37
94 33
95 41
96 54
97 14
98 25
99 25
100 8
101 26
102 20
103 11
104 14
105 3
106 40
107 5
108 38
109 32
110 41
111 46
112 47
113 37
114 51
115 49
116 21
117 1
118 44
119 26
120 21
121 4
122 10
123 43
124 34
125 32
126 20
127 34
128 6
129 12
130 24
131 16
132 72
133 27
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logins blogged_computations
1248.5303 672.4887 830.7121
feedback_messages_p120 totsize `tothyperlinks\r`
388.2222 0.4785 40.6794
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-54310 -14746 -2529 11181 94486
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1248.5303 6206.2845 0.201 0.840886
logins 672.4887 122.9729 5.469 2.31e-07 ***
blogged_computations 830.7121 236.2986 3.516 0.000609 ***
feedback_messages_p120 388.2222 139.0698 2.792 0.006056 **
totsize 0.4785 0.1762 2.716 0.007529 **
`tothyperlinks\r` 40.6794 186.1188 0.219 0.827338
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 25410 on 127 degrees of freedom
Multiple R-squared: 0.6798, Adjusted R-squared: 0.6672
F-statistic: 53.93 on 5 and 127 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.04649216 9.298433e-02 9.535078e-01
[2,] 0.59900704 8.019859e-01 4.009930e-01
[3,] 0.46930073 9.386015e-01 5.306993e-01
[4,] 0.37829432 7.565886e-01 6.217057e-01
[5,] 0.27821089 5.564218e-01 7.217891e-01
[6,] 0.58984566 8.203087e-01 4.101543e-01
[7,] 0.48791374 9.758275e-01 5.120863e-01
[8,] 0.49125832 9.825166e-01 5.087417e-01
[9,] 0.91062265 1.787547e-01 8.937735e-02
[10,] 0.87297738 2.540452e-01 1.270226e-01
[11,] 0.84858859 3.028228e-01 1.514114e-01
[12,] 0.89347720 2.130456e-01 1.065228e-01
[13,] 0.85991894 2.801621e-01 1.400811e-01
[14,] 0.86048748 2.790250e-01 1.395125e-01
[15,] 0.88373918 2.325216e-01 1.162608e-01
[16,] 0.84787477 3.042505e-01 1.521252e-01
[17,] 0.80331138 3.933772e-01 1.966886e-01
[18,] 0.80415211 3.916958e-01 1.958479e-01
[19,] 0.90955777 1.808845e-01 9.044223e-02
[20,] 0.88920105 2.215979e-01 1.107990e-01
[21,] 0.99667830 6.643403e-03 3.321701e-03
[22,] 0.99943438 1.131232e-03 5.656161e-04
[23,] 0.99922501 1.549980e-03 7.749898e-04
[24,] 0.99979072 4.185519e-04 2.092759e-04
[25,] 0.99975969 4.806286e-04 2.403143e-04
[26,] 0.99987984 2.403228e-04 1.201614e-04
[27,] 0.99990829 1.834104e-04 9.170522e-05
[28,] 0.99988583 2.283311e-04 1.141656e-04
[29,] 0.99981093 3.781348e-04 1.890674e-04
[30,] 0.99982479 3.504229e-04 1.752115e-04
[31,] 0.99982635 3.473037e-04 1.736519e-04
[32,] 0.99978044 4.391244e-04 2.195622e-04
[33,] 0.99964511 7.097893e-04 3.548947e-04
[34,] 0.99975385 4.923056e-04 2.461528e-04
[35,] 0.99968374 6.325197e-04 3.162598e-04
[36,] 0.99974359 5.128142e-04 2.564071e-04
[37,] 0.99964445 7.111055e-04 3.555527e-04
[38,] 0.99942906 1.141872e-03 5.709360e-04
[39,] 0.99927933 1.441344e-03 7.206718e-04
[40,] 0.99887972 2.240557e-03 1.120279e-03
[41,] 0.99832843 3.343145e-03 1.671572e-03
[42,] 0.99757546 4.849077e-03 2.424538e-03
[43,] 0.99669744 6.605117e-03 3.302558e-03
[44,] 0.99992109 1.578131e-04 7.890653e-05
[45,] 0.99996085 7.830270e-05 3.915135e-05
[46,] 0.99994929 1.014100e-04 5.070502e-05
[47,] 0.99994899 1.020136e-04 5.100679e-05
[48,] 0.99996717 6.565610e-05 3.282805e-05
[49,] 0.99994590 1.081937e-04 5.409684e-05
[50,] 0.99992034 1.593292e-04 7.966461e-05
[51,] 0.99988620 2.275984e-04 1.137992e-04
[52,] 0.99984316 3.136777e-04 1.568389e-04
[53,] 0.99981462 3.707573e-04 1.853787e-04
[54,] 0.99972063 5.587358e-04 2.793679e-04
[55,] 0.99955405 8.919016e-04 4.459508e-04
[56,] 0.99956810 8.637965e-04 4.318982e-04
[57,] 0.99937706 1.245870e-03 6.229350e-04
[58,] 0.99915554 1.688920e-03 8.444599e-04
[59,] 0.99889385 2.212299e-03 1.106149e-03
[60,] 0.99832925 3.341499e-03 1.670749e-03
[61,] 0.99767776 4.644486e-03 2.322243e-03
[62,] 0.99864029 2.719411e-03 1.359706e-03
[63,] 0.99837389 3.252227e-03 1.626113e-03
[64,] 0.99891224 2.175524e-03 1.087762e-03
[65,] 0.99855215 2.895701e-03 1.447850e-03
[66,] 0.99931351 1.372973e-03 6.864864e-04
[67,] 0.99928060 1.438794e-03 7.193968e-04
[68,] 0.99888186 2.236288e-03 1.118144e-03
[69,] 0.99847203 3.055930e-03 1.527965e-03
[70,] 0.99775753 4.484940e-03 2.242470e-03
[71,] 0.99767028 4.659435e-03 2.329718e-03
[72,] 0.99871834 2.563318e-03 1.281659e-03
[73,] 0.99999289 1.422628e-05 7.113141e-06
[74,] 0.99998744 2.511486e-05 1.255743e-05
[75,] 0.99997839 4.321139e-05 2.160570e-05
[76,] 0.99998177 3.645976e-05 1.822988e-05
[77,] 0.99998602 2.795937e-05 1.397969e-05
[78,] 0.99998965 2.069782e-05 1.034891e-05
[79,] 0.99999773 4.537069e-06 2.268535e-06
[80,] 0.99999765 4.696502e-06 2.348251e-06
[81,] 0.99999517 9.665320e-06 4.832660e-06
[82,] 0.99999201 1.597108e-05 7.985538e-06
[83,] 0.99998606 2.787360e-05 1.393680e-05
[84,] 0.99997521 4.958665e-05 2.479332e-05
[85,] 0.99995808 8.384239e-05 4.192119e-05
[86,] 0.99993126 1.374870e-04 6.874352e-05
[87,] 0.99995399 9.201185e-05 4.600593e-05
[88,] 0.99991336 1.732865e-04 8.664326e-05
[89,] 0.99984491 3.101841e-04 1.550920e-04
[90,] 0.99971523 5.695361e-04 2.847681e-04
[91,] 0.99948821 1.023578e-03 5.117888e-04
[92,] 0.99908225 1.835509e-03 9.177544e-04
[93,] 0.99917060 1.658805e-03 8.294025e-04
[94,] 0.99873904 2.521919e-03 1.260959e-03
[95,] 0.99775147 4.497057e-03 2.248528e-03
[96,] 0.99676346 6.473081e-03 3.236541e-03
[97,] 0.99536544 9.269125e-03 4.634562e-03
[98,] 0.99358728 1.282544e-02 6.412718e-03
[99,] 0.98944497 2.111006e-02 1.055503e-02
[100,] 0.98551547 2.896906e-02 1.448453e-02
[101,] 0.99055113 1.889774e-02 9.448868e-03
[102,] 0.98613754 2.772492e-02 1.386246e-02
[103,] 0.99222435 1.555131e-02 7.775653e-03
[104,] 0.99091741 1.816518e-02 9.082588e-03
[105,] 0.99813669 3.726616e-03 1.863308e-03
[106,] 0.99826145 3.477106e-03 1.738553e-03
[107,] 0.99697784 6.044327e-03 3.022163e-03
[108,] 0.99431447 1.137105e-02 5.685527e-03
[109,] 0.98976653 2.046693e-02 1.023347e-02
[110,] 0.98072632 3.854737e-02 1.927368e-02
[111,] 0.96124250 7.751501e-02 3.875750e-02
[112,] 0.96331001 7.337998e-02 3.668999e-02
[113,] 0.92519847 1.496031e-01 7.480153e-02
[114,] 0.92401344 1.519731e-01 7.598656e-02
[115,] 0.84985762 3.002848e-01 1.501424e-01
[116,] 0.87183248 2.563350e-01 1.281675e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1lak81324653465.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/2sq251324653465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/36uu11324653465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/43n641324653465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ufzc1324653465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 133
Frequency = 1
1 2 3 4 5 6
17322.3010 22219.5094 -6185.7590 -18355.7854 3391.3256 57447.3974
7 8 9 10 11 12
19538.3888 2505.7411 3509.7930 78352.1776 -17200.3167 4572.0402
13 14 15 16 17 18
-31263.6063 132.3424 11188.9610 2506.8583 -43105.6563 -241.4499
19 20 21 22 23 24
-30255.5119 -34409.5677 -14746.2524 -20583.9462 33575.7314 -4239.5264
25 26 27 28 29 30
-5195.4701 36317.1010 -38774.4250 -7739.2347 94486.4449 61699.5615
31 32 33 34 35 36
16516.8931 46017.2859 -22347.1774 -54309.7056 37052.5233 25073.9719
37 38 39 40 41 42
-9542.6575 -13938.5851 -2844.3319 6822.2354 -747.7324 -14489.7630
43 44 45 46 47 48
-12648.3424 -26656.1676 -14644.5885 -402.9238 16686.7318 125.3353
49 50 51 52 53 54
6281.9776 -6298.4148 -12509.5364 72638.4730 33839.9437 14217.3762
55 56 57 58 59 60
22615.6339 27141.1003 -5930.2907 -14195.1545 -11181.6440 -9002.6979
61 62 63 64 65 66
-20136.9850 -9541.6271 1261.2049 28184.7245 -12529.5626 -17678.6801
67 68 69 70 71 72
-18395.2879 -6066.8733 -1976.8517 20674.1045 10294.8231 -40691.1313
73 74 75 76 77 78
-20721.5414 -39004.0675 -5170.6825 -6771.1489 -18958.8198 -2810.7405
79 80 81 82 83 84
-29306.8457 -39930.7289 72711.5889 2011.9957 5112.5919 -22906.7314
85 86 87 88 89 90
20199.4780 -42890.2062 -36639.4217 -19488.8740 -5070.4603 1857.7327
91 92 93 94 95 96
-20054.2151 14425.9041 5133.5137 -19770.9481 24074.6416 -9020.7971
97 98 99 100 101 102
-2529.4741 7115.5460 7050.4200 7174.6261 -22675.8946 8154.9086
103 104 105 106 107 108
2355.4564 -8118.2381 1758.4327 -16460.9676 11180.9548 -12709.9064
109 110 111 112 113 114
-30046.0099 7034.5933 -31258.4684 -22705.5164 35945.9947 -28545.3890
115 116 117 118 119 120
10868.0055 -4715.1463 21033.1156 -3542.4167 2755.9971 -12649.4847
121 122 123 124 125 126
6353.7229 -2573.5916 2454.3708 19846.9625 581.2100 -6719.2565
127 128 129 130 131 132
20504.1664 16027.3102 5378.1008 -9993.0956 12050.6792 -22760.1443
133
22160.4161
> postscript(file="/var/wessaorg/rcomp/tmp/626ag1324653465.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 = 133
Frequency = 1
lag(myerror, k = 1) myerror
0 17322.3010 NA
1 22219.5094 17322.3010
2 -6185.7590 22219.5094
3 -18355.7854 -6185.7590
4 3391.3256 -18355.7854
5 57447.3974 3391.3256
6 19538.3888 57447.3974
7 2505.7411 19538.3888
8 3509.7930 2505.7411
9 78352.1776 3509.7930
10 -17200.3167 78352.1776
11 4572.0402 -17200.3167
12 -31263.6063 4572.0402
13 132.3424 -31263.6063
14 11188.9610 132.3424
15 2506.8583 11188.9610
16 -43105.6563 2506.8583
17 -241.4499 -43105.6563
18 -30255.5119 -241.4499
19 -34409.5677 -30255.5119
20 -14746.2524 -34409.5677
21 -20583.9462 -14746.2524
22 33575.7314 -20583.9462
23 -4239.5264 33575.7314
24 -5195.4701 -4239.5264
25 36317.1010 -5195.4701
26 -38774.4250 36317.1010
27 -7739.2347 -38774.4250
28 94486.4449 -7739.2347
29 61699.5615 94486.4449
30 16516.8931 61699.5615
31 46017.2859 16516.8931
32 -22347.1774 46017.2859
33 -54309.7056 -22347.1774
34 37052.5233 -54309.7056
35 25073.9719 37052.5233
36 -9542.6575 25073.9719
37 -13938.5851 -9542.6575
38 -2844.3319 -13938.5851
39 6822.2354 -2844.3319
40 -747.7324 6822.2354
41 -14489.7630 -747.7324
42 -12648.3424 -14489.7630
43 -26656.1676 -12648.3424
44 -14644.5885 -26656.1676
45 -402.9238 -14644.5885
46 16686.7318 -402.9238
47 125.3353 16686.7318
48 6281.9776 125.3353
49 -6298.4148 6281.9776
50 -12509.5364 -6298.4148
51 72638.4730 -12509.5364
52 33839.9437 72638.4730
53 14217.3762 33839.9437
54 22615.6339 14217.3762
55 27141.1003 22615.6339
56 -5930.2907 27141.1003
57 -14195.1545 -5930.2907
58 -11181.6440 -14195.1545
59 -9002.6979 -11181.6440
60 -20136.9850 -9002.6979
61 -9541.6271 -20136.9850
62 1261.2049 -9541.6271
63 28184.7245 1261.2049
64 -12529.5626 28184.7245
65 -17678.6801 -12529.5626
66 -18395.2879 -17678.6801
67 -6066.8733 -18395.2879
68 -1976.8517 -6066.8733
69 20674.1045 -1976.8517
70 10294.8231 20674.1045
71 -40691.1313 10294.8231
72 -20721.5414 -40691.1313
73 -39004.0675 -20721.5414
74 -5170.6825 -39004.0675
75 -6771.1489 -5170.6825
76 -18958.8198 -6771.1489
77 -2810.7405 -18958.8198
78 -29306.8457 -2810.7405
79 -39930.7289 -29306.8457
80 72711.5889 -39930.7289
81 2011.9957 72711.5889
82 5112.5919 2011.9957
83 -22906.7314 5112.5919
84 20199.4780 -22906.7314
85 -42890.2062 20199.4780
86 -36639.4217 -42890.2062
87 -19488.8740 -36639.4217
88 -5070.4603 -19488.8740
89 1857.7327 -5070.4603
90 -20054.2151 1857.7327
91 14425.9041 -20054.2151
92 5133.5137 14425.9041
93 -19770.9481 5133.5137
94 24074.6416 -19770.9481
95 -9020.7971 24074.6416
96 -2529.4741 -9020.7971
97 7115.5460 -2529.4741
98 7050.4200 7115.5460
99 7174.6261 7050.4200
100 -22675.8946 7174.6261
101 8154.9086 -22675.8946
102 2355.4564 8154.9086
103 -8118.2381 2355.4564
104 1758.4327 -8118.2381
105 -16460.9676 1758.4327
106 11180.9548 -16460.9676
107 -12709.9064 11180.9548
108 -30046.0099 -12709.9064
109 7034.5933 -30046.0099
110 -31258.4684 7034.5933
111 -22705.5164 -31258.4684
112 35945.9947 -22705.5164
113 -28545.3890 35945.9947
114 10868.0055 -28545.3890
115 -4715.1463 10868.0055
116 21033.1156 -4715.1463
117 -3542.4167 21033.1156
118 2755.9971 -3542.4167
119 -12649.4847 2755.9971
120 6353.7229 -12649.4847
121 -2573.5916 6353.7229
122 2454.3708 -2573.5916
123 19846.9625 2454.3708
124 581.2100 19846.9625
125 -6719.2565 581.2100
126 20504.1664 -6719.2565
127 16027.3102 20504.1664
128 5378.1008 16027.3102
129 -9993.0956 5378.1008
130 12050.6792 -9993.0956
131 -22760.1443 12050.6792
132 22160.4161 -22760.1443
133 NA 22160.4161
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 22219.5094 17322.3010
[2,] -6185.7590 22219.5094
[3,] -18355.7854 -6185.7590
[4,] 3391.3256 -18355.7854
[5,] 57447.3974 3391.3256
[6,] 19538.3888 57447.3974
[7,] 2505.7411 19538.3888
[8,] 3509.7930 2505.7411
[9,] 78352.1776 3509.7930
[10,] -17200.3167 78352.1776
[11,] 4572.0402 -17200.3167
[12,] -31263.6063 4572.0402
[13,] 132.3424 -31263.6063
[14,] 11188.9610 132.3424
[15,] 2506.8583 11188.9610
[16,] -43105.6563 2506.8583
[17,] -241.4499 -43105.6563
[18,] -30255.5119 -241.4499
[19,] -34409.5677 -30255.5119
[20,] -14746.2524 -34409.5677
[21,] -20583.9462 -14746.2524
[22,] 33575.7314 -20583.9462
[23,] -4239.5264 33575.7314
[24,] -5195.4701 -4239.5264
[25,] 36317.1010 -5195.4701
[26,] -38774.4250 36317.1010
[27,] -7739.2347 -38774.4250
[28,] 94486.4449 -7739.2347
[29,] 61699.5615 94486.4449
[30,] 16516.8931 61699.5615
[31,] 46017.2859 16516.8931
[32,] -22347.1774 46017.2859
[33,] -54309.7056 -22347.1774
[34,] 37052.5233 -54309.7056
[35,] 25073.9719 37052.5233
[36,] -9542.6575 25073.9719
[37,] -13938.5851 -9542.6575
[38,] -2844.3319 -13938.5851
[39,] 6822.2354 -2844.3319
[40,] -747.7324 6822.2354
[41,] -14489.7630 -747.7324
[42,] -12648.3424 -14489.7630
[43,] -26656.1676 -12648.3424
[44,] -14644.5885 -26656.1676
[45,] -402.9238 -14644.5885
[46,] 16686.7318 -402.9238
[47,] 125.3353 16686.7318
[48,] 6281.9776 125.3353
[49,] -6298.4148 6281.9776
[50,] -12509.5364 -6298.4148
[51,] 72638.4730 -12509.5364
[52,] 33839.9437 72638.4730
[53,] 14217.3762 33839.9437
[54,] 22615.6339 14217.3762
[55,] 27141.1003 22615.6339
[56,] -5930.2907 27141.1003
[57,] -14195.1545 -5930.2907
[58,] -11181.6440 -14195.1545
[59,] -9002.6979 -11181.6440
[60,] -20136.9850 -9002.6979
[61,] -9541.6271 -20136.9850
[62,] 1261.2049 -9541.6271
[63,] 28184.7245 1261.2049
[64,] -12529.5626 28184.7245
[65,] -17678.6801 -12529.5626
[66,] -18395.2879 -17678.6801
[67,] -6066.8733 -18395.2879
[68,] -1976.8517 -6066.8733
[69,] 20674.1045 -1976.8517
[70,] 10294.8231 20674.1045
[71,] -40691.1313 10294.8231
[72,] -20721.5414 -40691.1313
[73,] -39004.0675 -20721.5414
[74,] -5170.6825 -39004.0675
[75,] -6771.1489 -5170.6825
[76,] -18958.8198 -6771.1489
[77,] -2810.7405 -18958.8198
[78,] -29306.8457 -2810.7405
[79,] -39930.7289 -29306.8457
[80,] 72711.5889 -39930.7289
[81,] 2011.9957 72711.5889
[82,] 5112.5919 2011.9957
[83,] -22906.7314 5112.5919
[84,] 20199.4780 -22906.7314
[85,] -42890.2062 20199.4780
[86,] -36639.4217 -42890.2062
[87,] -19488.8740 -36639.4217
[88,] -5070.4603 -19488.8740
[89,] 1857.7327 -5070.4603
[90,] -20054.2151 1857.7327
[91,] 14425.9041 -20054.2151
[92,] 5133.5137 14425.9041
[93,] -19770.9481 5133.5137
[94,] 24074.6416 -19770.9481
[95,] -9020.7971 24074.6416
[96,] -2529.4741 -9020.7971
[97,] 7115.5460 -2529.4741
[98,] 7050.4200 7115.5460
[99,] 7174.6261 7050.4200
[100,] -22675.8946 7174.6261
[101,] 8154.9086 -22675.8946
[102,] 2355.4564 8154.9086
[103,] -8118.2381 2355.4564
[104,] 1758.4327 -8118.2381
[105,] -16460.9676 1758.4327
[106,] 11180.9548 -16460.9676
[107,] -12709.9064 11180.9548
[108,] -30046.0099 -12709.9064
[109,] 7034.5933 -30046.0099
[110,] -31258.4684 7034.5933
[111,] -22705.5164 -31258.4684
[112,] 35945.9947 -22705.5164
[113,] -28545.3890 35945.9947
[114,] 10868.0055 -28545.3890
[115,] -4715.1463 10868.0055
[116,] 21033.1156 -4715.1463
[117,] -3542.4167 21033.1156
[118,] 2755.9971 -3542.4167
[119,] -12649.4847 2755.9971
[120,] 6353.7229 -12649.4847
[121,] -2573.5916 6353.7229
[122,] 2454.3708 -2573.5916
[123,] 19846.9625 2454.3708
[124,] 581.2100 19846.9625
[125,] -6719.2565 581.2100
[126,] 20504.1664 -6719.2565
[127,] 16027.3102 20504.1664
[128,] 5378.1008 16027.3102
[129,] -9993.0956 5378.1008
[130,] 12050.6792 -9993.0956
[131,] -22760.1443 12050.6792
[132,] 22160.4161 -22760.1443
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 22219.5094 17322.3010
2 -6185.7590 22219.5094
3 -18355.7854 -6185.7590
4 3391.3256 -18355.7854
5 57447.3974 3391.3256
6 19538.3888 57447.3974
7 2505.7411 19538.3888
8 3509.7930 2505.7411
9 78352.1776 3509.7930
10 -17200.3167 78352.1776
11 4572.0402 -17200.3167
12 -31263.6063 4572.0402
13 132.3424 -31263.6063
14 11188.9610 132.3424
15 2506.8583 11188.9610
16 -43105.6563 2506.8583
17 -241.4499 -43105.6563
18 -30255.5119 -241.4499
19 -34409.5677 -30255.5119
20 -14746.2524 -34409.5677
21 -20583.9462 -14746.2524
22 33575.7314 -20583.9462
23 -4239.5264 33575.7314
24 -5195.4701 -4239.5264
25 36317.1010 -5195.4701
26 -38774.4250 36317.1010
27 -7739.2347 -38774.4250
28 94486.4449 -7739.2347
29 61699.5615 94486.4449
30 16516.8931 61699.5615
31 46017.2859 16516.8931
32 -22347.1774 46017.2859
33 -54309.7056 -22347.1774
34 37052.5233 -54309.7056
35 25073.9719 37052.5233
36 -9542.6575 25073.9719
37 -13938.5851 -9542.6575
38 -2844.3319 -13938.5851
39 6822.2354 -2844.3319
40 -747.7324 6822.2354
41 -14489.7630 -747.7324
42 -12648.3424 -14489.7630
43 -26656.1676 -12648.3424
44 -14644.5885 -26656.1676
45 -402.9238 -14644.5885
46 16686.7318 -402.9238
47 125.3353 16686.7318
48 6281.9776 125.3353
49 -6298.4148 6281.9776
50 -12509.5364 -6298.4148
51 72638.4730 -12509.5364
52 33839.9437 72638.4730
53 14217.3762 33839.9437
54 22615.6339 14217.3762
55 27141.1003 22615.6339
56 -5930.2907 27141.1003
57 -14195.1545 -5930.2907
58 -11181.6440 -14195.1545
59 -9002.6979 -11181.6440
60 -20136.9850 -9002.6979
61 -9541.6271 -20136.9850
62 1261.2049 -9541.6271
63 28184.7245 1261.2049
64 -12529.5626 28184.7245
65 -17678.6801 -12529.5626
66 -18395.2879 -17678.6801
67 -6066.8733 -18395.2879
68 -1976.8517 -6066.8733
69 20674.1045 -1976.8517
70 10294.8231 20674.1045
71 -40691.1313 10294.8231
72 -20721.5414 -40691.1313
73 -39004.0675 -20721.5414
74 -5170.6825 -39004.0675
75 -6771.1489 -5170.6825
76 -18958.8198 -6771.1489
77 -2810.7405 -18958.8198
78 -29306.8457 -2810.7405
79 -39930.7289 -29306.8457
80 72711.5889 -39930.7289
81 2011.9957 72711.5889
82 5112.5919 2011.9957
83 -22906.7314 5112.5919
84 20199.4780 -22906.7314
85 -42890.2062 20199.4780
86 -36639.4217 -42890.2062
87 -19488.8740 -36639.4217
88 -5070.4603 -19488.8740
89 1857.7327 -5070.4603
90 -20054.2151 1857.7327
91 14425.9041 -20054.2151
92 5133.5137 14425.9041
93 -19770.9481 5133.5137
94 24074.6416 -19770.9481
95 -9020.7971 24074.6416
96 -2529.4741 -9020.7971
97 7115.5460 -2529.4741
98 7050.4200 7115.5460
99 7174.6261 7050.4200
100 -22675.8946 7174.6261
101 8154.9086 -22675.8946
102 2355.4564 8154.9086
103 -8118.2381 2355.4564
104 1758.4327 -8118.2381
105 -16460.9676 1758.4327
106 11180.9548 -16460.9676
107 -12709.9064 11180.9548
108 -30046.0099 -12709.9064
109 7034.5933 -30046.0099
110 -31258.4684 7034.5933
111 -22705.5164 -31258.4684
112 35945.9947 -22705.5164
113 -28545.3890 35945.9947
114 10868.0055 -28545.3890
115 -4715.1463 10868.0055
116 21033.1156 -4715.1463
117 -3542.4167 21033.1156
118 2755.9971 -3542.4167
119 -12649.4847 2755.9971
120 6353.7229 -12649.4847
121 -2573.5916 6353.7229
122 2454.3708 -2573.5916
123 19846.9625 2454.3708
124 581.2100 19846.9625
125 -6719.2565 581.2100
126 20504.1664 -6719.2565
127 16027.3102 20504.1664
128 5378.1008 16027.3102
129 -9993.0956 5378.1008
130 12050.6792 -9993.0956
131 -22760.1443 12050.6792
132 22160.4161 -22760.1443
> 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/7q0nv1324653465.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/8uk1g1324653465.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/9vmon1324653465.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/10drsj1324653465.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11lmlp1324653466.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12aru41324653466.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/136uh91324653466.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/141rqr1324653466.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/15sdnm1324653466.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/16xo2s1324653466.tab")
+ }
>
> try(system("convert tmp/1lak81324653465.ps tmp/1lak81324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sq251324653465.ps tmp/2sq251324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/36uu11324653465.ps tmp/36uu11324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/43n641324653465.ps tmp/43n641324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ufzc1324653465.ps tmp/5ufzc1324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/626ag1324653465.ps tmp/626ag1324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/7q0nv1324653465.ps tmp/7q0nv1324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uk1g1324653465.ps tmp/8uk1g1324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vmon1324653465.ps tmp/9vmon1324653465.png",intern=TRUE))
character(0)
> try(system("convert tmp/10drsj1324653465.ps tmp/10drsj1324653465.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.345 0.601 4.979