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(1
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+ ,0)
+ ,dim=c(3
+ ,154)
+ ,dimnames=list(c('Outcome'
+ ,'Used'
+ ,'CorrectAnalysis')
+ ,1:154))
> y <- array(NA,dim=c(3,154),dimnames=list(c('Outcome','Used','CorrectAnalysis'),1:154))
> 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
Outcome Used CorrectAnalysis
1 1 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 1 0 0
7 0 0 0
8 0 0 0
9 1 0 0
10 0 0 0
11 0 0 0
12 0 0 0
13 0 1 0
14 0 0 0
15 1 1 0
16 1 1 0
17 0 1 1
18 0 0 0
19 1 0 0
20 1 1 1
21 0 0 0
22 1 1 0
23 1 0 0
24 1 0 0
25 1 1 0
26 0 1 0
27 1 0 0
28 0 1 0
29 1 0 0
30 0 0 0
31 0 0 0
32 0 0 0
33 0 0 0
34 1 0 0
35 0 0 0
36 0 0 0
37 0 1 0
38 1 1 0
39 1 0 0
40 0 0 0
41 1 1 1
42 1 1 0
43 1 0 0
44 0 0 0
45 0 0 0
46 1 0 0
47 0 0 0
48 1 0 0
49 1 0 0
50 0 0 0
51 0 1 0
52 0 1 1
53 1 0 0
54 0 1 1
55 0 0 0
56 1 1 0
57 1 1 0
58 1 0 0
59 1 0 0
60 1 1 1
61 1 0 0
62 0 1 0
63 0 0 0
64 1 0 0
65 0 0 0
66 0 0 0
67 0 1 1
68 0 0 0
69 1 0 0
70 0 1 0
71 0 0 0
72 1 0 0
73 1 1 0
74 0 1 0
75 1 0 0
76 1 0 0
77 1 0 0
78 1 1 0
79 1 1 1
80 0 0 0
81 0 0 0
82 1 1 0
83 0 0 0
84 0 1 1
85 1 0 0
86 0 0 0
87 1 0 0
88 1 1 0
89 0 0 0
90 1 0 0
91 0 0 0
92 0 0 0
93 0 0 0
94 0 0 0
95 0 0 0
96 1 0 0
97 0 0 0
98 0 0 0
99 0 0 0
100 1 0 0
101 1 0 0
102 0 0 0
103 0 0 0
104 0 0 0
105 0 1 0
106 0 0 0
107 0 0 0
108 0 1 0
109 0 0 0
110 0 0 0
111 0 1 0
112 0 0 0
113 0 1 0
114 0 1 0
115 0 0 0
116 0 0 0
117 1 0 0
118 0 0 0
119 0 0 0
120 1 0 0
121 0 0 0
122 0 0 0
123 0 1 0
124 1 1 0
125 1 0 0
126 0 0 0
127 0 0 0
128 1 0 0
129 0 0 0
130 1 0 0
131 0 0 0
132 1 0 0
133 0 1 0
134 0 0 0
135 0 0 0
136 0 0 0
137 1 1 0
138 1 1 0
139 0 0 0
140 0 0 0
141 1 1 1
142 1 1 0
143 0 0 0
144 1 0 0
145 0 0 0
146 1 0 0
147 0 1 0
148 0 0 0
149 0 0 0
150 1 0 0
151 1 0 0
152 0 1 1
153 0 1 1
154 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Used CorrectAnalysis
0.36697 0.11788 -0.06818
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.4849 -0.3670 -0.3670 0.6330 0.6330
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.36697 0.04708 7.795 9.72e-13 ***
Used 0.11788 0.09766 1.207 0.229
CorrectAnalysis -0.06818 0.16569 -0.411 0.681
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4915 on 151 degrees of freedom
Multiple R-squared: 0.009704, Adjusted R-squared: -0.003413
F-statistic: 0.7398 on 2 and 151 DF, p-value: 0.4789
> 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,] 0.8564078 0.2871844 0.1435922
[2,] 0.7854206 0.4291587 0.2145794
[3,] 0.7021399 0.5957202 0.2978601
[4,] 0.7706171 0.4587658 0.2293829
[5,] 0.7108886 0.5782228 0.2891114
[6,] 0.6443878 0.7112243 0.3556122
[7,] 0.5734306 0.8531389 0.4265694
[8,] 0.4818148 0.9636297 0.5181852
[9,] 0.4131457 0.8262915 0.5868543
[10,] 0.5029543 0.9940914 0.4970457
[11,] 0.4784374 0.9568747 0.5215626
[12,] 0.4014487 0.8028974 0.5985513
[13,] 0.3436234 0.6872468 0.6563766
[14,] 0.4479511 0.8959021 0.5520489
[15,] 0.5209163 0.9581675 0.4790837
[16,] 0.4702748 0.9405497 0.5297252
[17,] 0.4309022 0.8618044 0.5690978
[18,] 0.5079199 0.9841602 0.4920801
[19,] 0.5651804 0.8696392 0.4348196
[20,] 0.5226752 0.9546496 0.4773248
[21,] 0.5825866 0.8348268 0.4174134
[22,] 0.6222214 0.7555572 0.3777786
[23,] 0.6437500 0.7125000 0.3562500
[24,] 0.6713341 0.6573317 0.3286659
[25,] 0.6477084 0.7045832 0.3522916
[26,] 0.6216704 0.7566591 0.3783296
[27,] 0.5935729 0.8128543 0.4064271
[28,] 0.5637700 0.8724600 0.4362300
[29,] 0.6011232 0.7977537 0.3988768
[30,] 0.5736301 0.8527397 0.4263699
[31,] 0.5448088 0.9103824 0.4551912
[32,] 0.5493804 0.9012392 0.4506196
[33,] 0.5463115 0.9073770 0.4536885
[34,] 0.5825302 0.8349395 0.4174698
[35,] 0.5562462 0.8875077 0.4437538
[36,] 0.5514349 0.8971301 0.4485651
[37,] 0.5437216 0.9125567 0.4562784
[38,] 0.5776944 0.8446113 0.4223056
[39,] 0.5535472 0.8929056 0.4464528
[40,] 0.5284648 0.9430704 0.4715352
[41,] 0.5623374 0.8753251 0.4376626
[42,] 0.5384796 0.9230408 0.4615204
[43,] 0.5703753 0.8592493 0.4296247
[44,] 0.5989674 0.8020653 0.4010326
[45,] 0.5778827 0.8442346 0.4221173
[46,] 0.5849844 0.8300311 0.4150156
[47,] 0.5875853 0.8248294 0.4124147
[48,] 0.6140887 0.7718225 0.3859113
[49,] 0.6019487 0.7961026 0.3980513
[50,] 0.5814720 0.8370560 0.4185280
[51,] 0.5785184 0.8429632 0.4214816
[52,] 0.5746417 0.8507165 0.4253583
[53,] 0.6013933 0.7972134 0.3986067
[54,] 0.6265605 0.7468790 0.3734395
[55,] 0.6421489 0.7157022 0.3578511
[56,] 0.6659518 0.6680963 0.3340482
[57,] 0.6708650 0.6582699 0.3291350
[58,] 0.6536982 0.6926035 0.3463018
[59,] 0.6773841 0.6452319 0.3226159
[60,] 0.6603381 0.6793237 0.3396619
[61,] 0.6423950 0.7152101 0.3576050
[62,] 0.6299958 0.7400084 0.3700042
[63,] 0.6109908 0.7780185 0.3890092
[64,] 0.6371473 0.7257053 0.3628527
[65,] 0.6375707 0.7248587 0.3624293
[66,] 0.6185167 0.7629666 0.3814833
[67,] 0.6449943 0.7100114 0.3550057
[68,] 0.6481694 0.7036612 0.3518306
[69,] 0.6469211 0.7061579 0.3530789
[70,] 0.6736640 0.6526720 0.3263360
[71,] 0.7005440 0.5989121 0.2994560
[72,] 0.7276618 0.5446765 0.2723382
[73,] 0.7335291 0.5329419 0.2664709
[74,] 0.7547583 0.4904833 0.2452417
[75,] 0.7384700 0.5230599 0.2615300
[76,] 0.7212450 0.5575100 0.2787550
[77,] 0.7309069 0.5381861 0.2690931
[78,] 0.7130915 0.5738171 0.2869085
[79,] 0.6965292 0.6069416 0.3034708
[80,] 0.7272670 0.5454660 0.2727330
[81,] 0.7086237 0.5827526 0.2913763
[82,] 0.7405955 0.5188090 0.2594045
[83,] 0.7566997 0.4866006 0.2433003
[84,] 0.7384774 0.5230452 0.2615226
[85,] 0.7718046 0.4563907 0.2281954
[86,] 0.7535114 0.4929773 0.2464886
[87,] 0.7342355 0.5315290 0.2657645
[88,] 0.7140434 0.5719133 0.2859566
[89,] 0.6930184 0.6139632 0.3069816
[90,] 0.6712609 0.6574782 0.3287391
[91,] 0.7087600 0.5824799 0.2912400
[92,] 0.6866325 0.6267350 0.3133675
[93,] 0.6637950 0.6724099 0.3362050
[94,] 0.6403831 0.7192337 0.3596169
[95,] 0.6802369 0.6395262 0.3197631
[96,] 0.7222370 0.5555260 0.2777630
[97,] 0.6984751 0.6030499 0.3015249
[98,] 0.6738752 0.6522495 0.3261248
[99,] 0.6486006 0.7027988 0.3513994
[100,] 0.6355892 0.7288216 0.3644108
[101,] 0.6091290 0.7817421 0.3908710
[102,] 0.5824287 0.8351426 0.4175713
[103,] 0.5683091 0.8633818 0.4316909
[104,] 0.5410521 0.9178959 0.4589479
[105,] 0.5140884 0.9718233 0.4859116
[106,] 0.5005585 0.9988830 0.4994415
[107,] 0.4737776 0.9475551 0.5262224
[108,] 0.4632001 0.9264001 0.5367999
[109,] 0.4582500 0.9164999 0.5417500
[110,] 0.4318163 0.8636326 0.5681837
[111,] 0.4065942 0.8131885 0.5934058
[112,] 0.4401396 0.8802791 0.5598604
[113,] 0.4131350 0.8262700 0.5868650
[114,] 0.3876705 0.7753411 0.6123295
[115,] 0.4208832 0.8417663 0.5791168
[116,] 0.3933244 0.7866488 0.6066756
[117,] 0.3677224 0.7354448 0.6322776
[118,] 0.3714063 0.7428127 0.6285937
[119,] 0.3650475 0.7300951 0.6349525
[120,] 0.3982173 0.7964345 0.6017827
[121,] 0.3692571 0.7385142 0.6307429
[122,] 0.3428963 0.6857925 0.6571037
[123,] 0.3748064 0.7496128 0.6251936
[124,] 0.3453044 0.6906088 0.6546956
[125,] 0.3825911 0.7651823 0.6174089
[126,] 0.3489764 0.6979529 0.6510236
[127,] 0.3941836 0.7883673 0.6058164
[128,] 0.4031472 0.8062944 0.5968528
[129,] 0.3627381 0.7254761 0.6372619
[130,] 0.3265660 0.6531320 0.6734340
[131,] 0.2958226 0.5916451 0.7041774
[132,] 0.2807799 0.5615597 0.7192201
[133,] 0.2944914 0.5889827 0.7055086
[134,] 0.2688162 0.5376324 0.7311838
[135,] 0.2528134 0.5056268 0.7471866
[136,] 0.3593426 0.7186853 0.6406574
[137,] 0.5059515 0.9880970 0.4940485
[138,] 0.5229262 0.9541476 0.4770738
[139,] 0.5162493 0.9675014 0.4837507
[140,] 0.5460088 0.9079825 0.4539912
[141,] 0.5308821 0.9382359 0.4691179
[142,] 0.3908153 0.7816306 0.6091847
[143,] 0.4309805 0.8619611 0.5690195
> postscript(file="/var/fisher/rcomp/tmp/1niha1356020865.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/251bc1356020865.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3fe8f1356020865.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ffej1356020865.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5zv9i1356020865.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 = 154
Frequency = 1
1 2 3 4 5 6 7
0.6330275 -0.3669725 -0.3669725 -0.3669725 -0.3669725 0.6330275 -0.3669725
8 9 10 11 12 13 14
-0.3669725 0.6330275 -0.3669725 -0.3669725 -0.3669725 -0.4848485 -0.3669725
15 16 17 18 19 20 21
0.5151515 0.5151515 -0.4166667 -0.3669725 0.6330275 0.5833333 -0.3669725
22 23 24 25 26 27 28
0.5151515 0.6330275 0.6330275 0.5151515 -0.4848485 0.6330275 -0.4848485
29 30 31 32 33 34 35
0.6330275 -0.3669725 -0.3669725 -0.3669725 -0.3669725 0.6330275 -0.3669725
36 37 38 39 40 41 42
-0.3669725 -0.4848485 0.5151515 0.6330275 -0.3669725 0.5833333 0.5151515
43 44 45 46 47 48 49
0.6330275 -0.3669725 -0.3669725 0.6330275 -0.3669725 0.6330275 0.6330275
50 51 52 53 54 55 56
-0.3669725 -0.4848485 -0.4166667 0.6330275 -0.4166667 -0.3669725 0.5151515
57 58 59 60 61 62 63
0.5151515 0.6330275 0.6330275 0.5833333 0.6330275 -0.4848485 -0.3669725
64 65 66 67 68 69 70
0.6330275 -0.3669725 -0.3669725 -0.4166667 -0.3669725 0.6330275 -0.4848485
71 72 73 74 75 76 77
-0.3669725 0.6330275 0.5151515 -0.4848485 0.6330275 0.6330275 0.6330275
78 79 80 81 82 83 84
0.5151515 0.5833333 -0.3669725 -0.3669725 0.5151515 -0.3669725 -0.4166667
85 86 87 88 89 90 91
0.6330275 -0.3669725 0.6330275 0.5151515 -0.3669725 0.6330275 -0.3669725
92 93 94 95 96 97 98
-0.3669725 -0.3669725 -0.3669725 -0.3669725 0.6330275 -0.3669725 -0.3669725
99 100 101 102 103 104 105
-0.3669725 0.6330275 0.6330275 -0.3669725 -0.3669725 -0.3669725 -0.4848485
106 107 108 109 110 111 112
-0.3669725 -0.3669725 -0.4848485 -0.3669725 -0.3669725 -0.4848485 -0.3669725
113 114 115 116 117 118 119
-0.4848485 -0.4848485 -0.3669725 -0.3669725 0.6330275 -0.3669725 -0.3669725
120 121 122 123 124 125 126
0.6330275 -0.3669725 -0.3669725 -0.4848485 0.5151515 0.6330275 -0.3669725
127 128 129 130 131 132 133
-0.3669725 0.6330275 -0.3669725 0.6330275 -0.3669725 0.6330275 -0.4848485
134 135 136 137 138 139 140
-0.3669725 -0.3669725 -0.3669725 0.5151515 0.5151515 -0.3669725 -0.3669725
141 142 143 144 145 146 147
0.5833333 0.5151515 -0.3669725 0.6330275 -0.3669725 0.6330275 -0.4848485
148 149 150 151 152 153 154
-0.3669725 -0.3669725 0.6330275 0.6330275 -0.4166667 -0.4166667 -0.4848485
> postscript(file="/var/fisher/rcomp/tmp/6ae3q1356020865.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 0.6330275 NA
1 -0.3669725 0.6330275
2 -0.3669725 -0.3669725
3 -0.3669725 -0.3669725
4 -0.3669725 -0.3669725
5 0.6330275 -0.3669725
6 -0.3669725 0.6330275
7 -0.3669725 -0.3669725
8 0.6330275 -0.3669725
9 -0.3669725 0.6330275
10 -0.3669725 -0.3669725
11 -0.3669725 -0.3669725
12 -0.4848485 -0.3669725
13 -0.3669725 -0.4848485
14 0.5151515 -0.3669725
15 0.5151515 0.5151515
16 -0.4166667 0.5151515
17 -0.3669725 -0.4166667
18 0.6330275 -0.3669725
19 0.5833333 0.6330275
20 -0.3669725 0.5833333
21 0.5151515 -0.3669725
22 0.6330275 0.5151515
23 0.6330275 0.6330275
24 0.5151515 0.6330275
25 -0.4848485 0.5151515
26 0.6330275 -0.4848485
27 -0.4848485 0.6330275
28 0.6330275 -0.4848485
29 -0.3669725 0.6330275
30 -0.3669725 -0.3669725
31 -0.3669725 -0.3669725
32 -0.3669725 -0.3669725
33 0.6330275 -0.3669725
34 -0.3669725 0.6330275
35 -0.3669725 -0.3669725
36 -0.4848485 -0.3669725
37 0.5151515 -0.4848485
38 0.6330275 0.5151515
39 -0.3669725 0.6330275
40 0.5833333 -0.3669725
41 0.5151515 0.5833333
42 0.6330275 0.5151515
43 -0.3669725 0.6330275
44 -0.3669725 -0.3669725
45 0.6330275 -0.3669725
46 -0.3669725 0.6330275
47 0.6330275 -0.3669725
48 0.6330275 0.6330275
49 -0.3669725 0.6330275
50 -0.4848485 -0.3669725
51 -0.4166667 -0.4848485
52 0.6330275 -0.4166667
53 -0.4166667 0.6330275
54 -0.3669725 -0.4166667
55 0.5151515 -0.3669725
56 0.5151515 0.5151515
57 0.6330275 0.5151515
58 0.6330275 0.6330275
59 0.5833333 0.6330275
60 0.6330275 0.5833333
61 -0.4848485 0.6330275
62 -0.3669725 -0.4848485
63 0.6330275 -0.3669725
64 -0.3669725 0.6330275
65 -0.3669725 -0.3669725
66 -0.4166667 -0.3669725
67 -0.3669725 -0.4166667
68 0.6330275 -0.3669725
69 -0.4848485 0.6330275
70 -0.3669725 -0.4848485
71 0.6330275 -0.3669725
72 0.5151515 0.6330275
73 -0.4848485 0.5151515
74 0.6330275 -0.4848485
75 0.6330275 0.6330275
76 0.6330275 0.6330275
77 0.5151515 0.6330275
78 0.5833333 0.5151515
79 -0.3669725 0.5833333
80 -0.3669725 -0.3669725
81 0.5151515 -0.3669725
82 -0.3669725 0.5151515
83 -0.4166667 -0.3669725
84 0.6330275 -0.4166667
85 -0.3669725 0.6330275
86 0.6330275 -0.3669725
87 0.5151515 0.6330275
88 -0.3669725 0.5151515
89 0.6330275 -0.3669725
90 -0.3669725 0.6330275
91 -0.3669725 -0.3669725
92 -0.3669725 -0.3669725
93 -0.3669725 -0.3669725
94 -0.3669725 -0.3669725
95 0.6330275 -0.3669725
96 -0.3669725 0.6330275
97 -0.3669725 -0.3669725
98 -0.3669725 -0.3669725
99 0.6330275 -0.3669725
100 0.6330275 0.6330275
101 -0.3669725 0.6330275
102 -0.3669725 -0.3669725
103 -0.3669725 -0.3669725
104 -0.4848485 -0.3669725
105 -0.3669725 -0.4848485
106 -0.3669725 -0.3669725
107 -0.4848485 -0.3669725
108 -0.3669725 -0.4848485
109 -0.3669725 -0.3669725
110 -0.4848485 -0.3669725
111 -0.3669725 -0.4848485
112 -0.4848485 -0.3669725
113 -0.4848485 -0.4848485
114 -0.3669725 -0.4848485
115 -0.3669725 -0.3669725
116 0.6330275 -0.3669725
117 -0.3669725 0.6330275
118 -0.3669725 -0.3669725
119 0.6330275 -0.3669725
120 -0.3669725 0.6330275
121 -0.3669725 -0.3669725
122 -0.4848485 -0.3669725
123 0.5151515 -0.4848485
124 0.6330275 0.5151515
125 -0.3669725 0.6330275
126 -0.3669725 -0.3669725
127 0.6330275 -0.3669725
128 -0.3669725 0.6330275
129 0.6330275 -0.3669725
130 -0.3669725 0.6330275
131 0.6330275 -0.3669725
132 -0.4848485 0.6330275
133 -0.3669725 -0.4848485
134 -0.3669725 -0.3669725
135 -0.3669725 -0.3669725
136 0.5151515 -0.3669725
137 0.5151515 0.5151515
138 -0.3669725 0.5151515
139 -0.3669725 -0.3669725
140 0.5833333 -0.3669725
141 0.5151515 0.5833333
142 -0.3669725 0.5151515
143 0.6330275 -0.3669725
144 -0.3669725 0.6330275
145 0.6330275 -0.3669725
146 -0.4848485 0.6330275
147 -0.3669725 -0.4848485
148 -0.3669725 -0.3669725
149 0.6330275 -0.3669725
150 0.6330275 0.6330275
151 -0.4166667 0.6330275
152 -0.4166667 -0.4166667
153 -0.4848485 -0.4166667
154 NA -0.4848485
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3669725 0.6330275
[2,] -0.3669725 -0.3669725
[3,] -0.3669725 -0.3669725
[4,] -0.3669725 -0.3669725
[5,] 0.6330275 -0.3669725
[6,] -0.3669725 0.6330275
[7,] -0.3669725 -0.3669725
[8,] 0.6330275 -0.3669725
[9,] -0.3669725 0.6330275
[10,] -0.3669725 -0.3669725
[11,] -0.3669725 -0.3669725
[12,] -0.4848485 -0.3669725
[13,] -0.3669725 -0.4848485
[14,] 0.5151515 -0.3669725
[15,] 0.5151515 0.5151515
[16,] -0.4166667 0.5151515
[17,] -0.3669725 -0.4166667
[18,] 0.6330275 -0.3669725
[19,] 0.5833333 0.6330275
[20,] -0.3669725 0.5833333
[21,] 0.5151515 -0.3669725
[22,] 0.6330275 0.5151515
[23,] 0.6330275 0.6330275
[24,] 0.5151515 0.6330275
[25,] -0.4848485 0.5151515
[26,] 0.6330275 -0.4848485
[27,] -0.4848485 0.6330275
[28,] 0.6330275 -0.4848485
[29,] -0.3669725 0.6330275
[30,] -0.3669725 -0.3669725
[31,] -0.3669725 -0.3669725
[32,] -0.3669725 -0.3669725
[33,] 0.6330275 -0.3669725
[34,] -0.3669725 0.6330275
[35,] -0.3669725 -0.3669725
[36,] -0.4848485 -0.3669725
[37,] 0.5151515 -0.4848485
[38,] 0.6330275 0.5151515
[39,] -0.3669725 0.6330275
[40,] 0.5833333 -0.3669725
[41,] 0.5151515 0.5833333
[42,] 0.6330275 0.5151515
[43,] -0.3669725 0.6330275
[44,] -0.3669725 -0.3669725
[45,] 0.6330275 -0.3669725
[46,] -0.3669725 0.6330275
[47,] 0.6330275 -0.3669725
[48,] 0.6330275 0.6330275
[49,] -0.3669725 0.6330275
[50,] -0.4848485 -0.3669725
[51,] -0.4166667 -0.4848485
[52,] 0.6330275 -0.4166667
[53,] -0.4166667 0.6330275
[54,] -0.3669725 -0.4166667
[55,] 0.5151515 -0.3669725
[56,] 0.5151515 0.5151515
[57,] 0.6330275 0.5151515
[58,] 0.6330275 0.6330275
[59,] 0.5833333 0.6330275
[60,] 0.6330275 0.5833333
[61,] -0.4848485 0.6330275
[62,] -0.3669725 -0.4848485
[63,] 0.6330275 -0.3669725
[64,] -0.3669725 0.6330275
[65,] -0.3669725 -0.3669725
[66,] -0.4166667 -0.3669725
[67,] -0.3669725 -0.4166667
[68,] 0.6330275 -0.3669725
[69,] -0.4848485 0.6330275
[70,] -0.3669725 -0.4848485
[71,] 0.6330275 -0.3669725
[72,] 0.5151515 0.6330275
[73,] -0.4848485 0.5151515
[74,] 0.6330275 -0.4848485
[75,] 0.6330275 0.6330275
[76,] 0.6330275 0.6330275
[77,] 0.5151515 0.6330275
[78,] 0.5833333 0.5151515
[79,] -0.3669725 0.5833333
[80,] -0.3669725 -0.3669725
[81,] 0.5151515 -0.3669725
[82,] -0.3669725 0.5151515
[83,] -0.4166667 -0.3669725
[84,] 0.6330275 -0.4166667
[85,] -0.3669725 0.6330275
[86,] 0.6330275 -0.3669725
[87,] 0.5151515 0.6330275
[88,] -0.3669725 0.5151515
[89,] 0.6330275 -0.3669725
[90,] -0.3669725 0.6330275
[91,] -0.3669725 -0.3669725
[92,] -0.3669725 -0.3669725
[93,] -0.3669725 -0.3669725
[94,] -0.3669725 -0.3669725
[95,] 0.6330275 -0.3669725
[96,] -0.3669725 0.6330275
[97,] -0.3669725 -0.3669725
[98,] -0.3669725 -0.3669725
[99,] 0.6330275 -0.3669725
[100,] 0.6330275 0.6330275
[101,] -0.3669725 0.6330275
[102,] -0.3669725 -0.3669725
[103,] -0.3669725 -0.3669725
[104,] -0.4848485 -0.3669725
[105,] -0.3669725 -0.4848485
[106,] -0.3669725 -0.3669725
[107,] -0.4848485 -0.3669725
[108,] -0.3669725 -0.4848485
[109,] -0.3669725 -0.3669725
[110,] -0.4848485 -0.3669725
[111,] -0.3669725 -0.4848485
[112,] -0.4848485 -0.3669725
[113,] -0.4848485 -0.4848485
[114,] -0.3669725 -0.4848485
[115,] -0.3669725 -0.3669725
[116,] 0.6330275 -0.3669725
[117,] -0.3669725 0.6330275
[118,] -0.3669725 -0.3669725
[119,] 0.6330275 -0.3669725
[120,] -0.3669725 0.6330275
[121,] -0.3669725 -0.3669725
[122,] -0.4848485 -0.3669725
[123,] 0.5151515 -0.4848485
[124,] 0.6330275 0.5151515
[125,] -0.3669725 0.6330275
[126,] -0.3669725 -0.3669725
[127,] 0.6330275 -0.3669725
[128,] -0.3669725 0.6330275
[129,] 0.6330275 -0.3669725
[130,] -0.3669725 0.6330275
[131,] 0.6330275 -0.3669725
[132,] -0.4848485 0.6330275
[133,] -0.3669725 -0.4848485
[134,] -0.3669725 -0.3669725
[135,] -0.3669725 -0.3669725
[136,] 0.5151515 -0.3669725
[137,] 0.5151515 0.5151515
[138,] -0.3669725 0.5151515
[139,] -0.3669725 -0.3669725
[140,] 0.5833333 -0.3669725
[141,] 0.5151515 0.5833333
[142,] -0.3669725 0.5151515
[143,] 0.6330275 -0.3669725
[144,] -0.3669725 0.6330275
[145,] 0.6330275 -0.3669725
[146,] -0.4848485 0.6330275
[147,] -0.3669725 -0.4848485
[148,] -0.3669725 -0.3669725
[149,] 0.6330275 -0.3669725
[150,] 0.6330275 0.6330275
[151,] -0.4166667 0.6330275
[152,] -0.4166667 -0.4166667
[153,] -0.4848485 -0.4166667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3669725 0.6330275
2 -0.3669725 -0.3669725
3 -0.3669725 -0.3669725
4 -0.3669725 -0.3669725
5 0.6330275 -0.3669725
6 -0.3669725 0.6330275
7 -0.3669725 -0.3669725
8 0.6330275 -0.3669725
9 -0.3669725 0.6330275
10 -0.3669725 -0.3669725
11 -0.3669725 -0.3669725
12 -0.4848485 -0.3669725
13 -0.3669725 -0.4848485
14 0.5151515 -0.3669725
15 0.5151515 0.5151515
16 -0.4166667 0.5151515
17 -0.3669725 -0.4166667
18 0.6330275 -0.3669725
19 0.5833333 0.6330275
20 -0.3669725 0.5833333
21 0.5151515 -0.3669725
22 0.6330275 0.5151515
23 0.6330275 0.6330275
24 0.5151515 0.6330275
25 -0.4848485 0.5151515
26 0.6330275 -0.4848485
27 -0.4848485 0.6330275
28 0.6330275 -0.4848485
29 -0.3669725 0.6330275
30 -0.3669725 -0.3669725
31 -0.3669725 -0.3669725
32 -0.3669725 -0.3669725
33 0.6330275 -0.3669725
34 -0.3669725 0.6330275
35 -0.3669725 -0.3669725
36 -0.4848485 -0.3669725
37 0.5151515 -0.4848485
38 0.6330275 0.5151515
39 -0.3669725 0.6330275
40 0.5833333 -0.3669725
41 0.5151515 0.5833333
42 0.6330275 0.5151515
43 -0.3669725 0.6330275
44 -0.3669725 -0.3669725
45 0.6330275 -0.3669725
46 -0.3669725 0.6330275
47 0.6330275 -0.3669725
48 0.6330275 0.6330275
49 -0.3669725 0.6330275
50 -0.4848485 -0.3669725
51 -0.4166667 -0.4848485
52 0.6330275 -0.4166667
53 -0.4166667 0.6330275
54 -0.3669725 -0.4166667
55 0.5151515 -0.3669725
56 0.5151515 0.5151515
57 0.6330275 0.5151515
58 0.6330275 0.6330275
59 0.5833333 0.6330275
60 0.6330275 0.5833333
61 -0.4848485 0.6330275
62 -0.3669725 -0.4848485
63 0.6330275 -0.3669725
64 -0.3669725 0.6330275
65 -0.3669725 -0.3669725
66 -0.4166667 -0.3669725
67 -0.3669725 -0.4166667
68 0.6330275 -0.3669725
69 -0.4848485 0.6330275
70 -0.3669725 -0.4848485
71 0.6330275 -0.3669725
72 0.5151515 0.6330275
73 -0.4848485 0.5151515
74 0.6330275 -0.4848485
75 0.6330275 0.6330275
76 0.6330275 0.6330275
77 0.5151515 0.6330275
78 0.5833333 0.5151515
79 -0.3669725 0.5833333
80 -0.3669725 -0.3669725
81 0.5151515 -0.3669725
82 -0.3669725 0.5151515
83 -0.4166667 -0.3669725
84 0.6330275 -0.4166667
85 -0.3669725 0.6330275
86 0.6330275 -0.3669725
87 0.5151515 0.6330275
88 -0.3669725 0.5151515
89 0.6330275 -0.3669725
90 -0.3669725 0.6330275
91 -0.3669725 -0.3669725
92 -0.3669725 -0.3669725
93 -0.3669725 -0.3669725
94 -0.3669725 -0.3669725
95 0.6330275 -0.3669725
96 -0.3669725 0.6330275
97 -0.3669725 -0.3669725
98 -0.3669725 -0.3669725
99 0.6330275 -0.3669725
100 0.6330275 0.6330275
101 -0.3669725 0.6330275
102 -0.3669725 -0.3669725
103 -0.3669725 -0.3669725
104 -0.4848485 -0.3669725
105 -0.3669725 -0.4848485
106 -0.3669725 -0.3669725
107 -0.4848485 -0.3669725
108 -0.3669725 -0.4848485
109 -0.3669725 -0.3669725
110 -0.4848485 -0.3669725
111 -0.3669725 -0.4848485
112 -0.4848485 -0.3669725
113 -0.4848485 -0.4848485
114 -0.3669725 -0.4848485
115 -0.3669725 -0.3669725
116 0.6330275 -0.3669725
117 -0.3669725 0.6330275
118 -0.3669725 -0.3669725
119 0.6330275 -0.3669725
120 -0.3669725 0.6330275
121 -0.3669725 -0.3669725
122 -0.4848485 -0.3669725
123 0.5151515 -0.4848485
124 0.6330275 0.5151515
125 -0.3669725 0.6330275
126 -0.3669725 -0.3669725
127 0.6330275 -0.3669725
128 -0.3669725 0.6330275
129 0.6330275 -0.3669725
130 -0.3669725 0.6330275
131 0.6330275 -0.3669725
132 -0.4848485 0.6330275
133 -0.3669725 -0.4848485
134 -0.3669725 -0.3669725
135 -0.3669725 -0.3669725
136 0.5151515 -0.3669725
137 0.5151515 0.5151515
138 -0.3669725 0.5151515
139 -0.3669725 -0.3669725
140 0.5833333 -0.3669725
141 0.5151515 0.5833333
142 -0.3669725 0.5151515
143 0.6330275 -0.3669725
144 -0.3669725 0.6330275
145 0.6330275 -0.3669725
146 -0.4848485 0.6330275
147 -0.3669725 -0.4848485
148 -0.3669725 -0.3669725
149 0.6330275 -0.3669725
150 0.6330275 0.6330275
151 -0.4166667 0.6330275
152 -0.4166667 -0.4166667
153 -0.4848485 -0.4166667
> 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/fisher/rcomp/tmp/7os3j1356020865.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8e72c1356020865.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9xxxq1356020865.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10q9qo1356020865.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11op4q1356020865.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/fisher/rcomp/tmp/12fqhv1356020865.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/fisher/rcomp/tmp/13hl5k1356020865.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/fisher/rcomp/tmp/14kn9i1356020865.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/fisher/rcomp/tmp/153mr81356020865.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/fisher/rcomp/tmp/16t1mv1356020865.tab")
+ }
>
> try(system("convert tmp/1niha1356020865.ps tmp/1niha1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/251bc1356020865.ps tmp/251bc1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fe8f1356020865.ps tmp/3fe8f1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ffej1356020865.ps tmp/4ffej1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zv9i1356020865.ps tmp/5zv9i1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ae3q1356020865.ps tmp/6ae3q1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/7os3j1356020865.ps tmp/7os3j1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/8e72c1356020865.ps tmp/8e72c1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xxxq1356020865.ps tmp/9xxxq1356020865.png",intern=TRUE))
character(0)
> try(system("convert tmp/10q9qo1356020865.ps tmp/10q9qo1356020865.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.444 1.714 9.192