R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'contributors()' for more information and
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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.
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+ ,dim=c(5
+ ,157)
+ ,dimnames=list(c('YT'
+ ,'X1'
+ ,'X2'
+ ,'X3'
+ ,'X4')
+ ,1:157))
> y <- array(NA,dim=c(5,157),dimnames=list(c('YT','X1','X2','X3','X4'),1:157))
> 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'
> #'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.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
YT X1 X2 X3 X4
1 4 2 5 4 3
2 4 2 4 3 2
3 5 4 4 2 2
4 3 2 4 2 2
5 4 3 2 2 2
6 3 4 5 2 2
7 4 3 5 3 2
8 3 3 4 2 1
9 2 3 3 1 2
10 4 2 4 2 2
11 2 4 4 2 2
12 2 3 3 2 2
13 1 3 3 2 2
14 4 4 4 2 2
15 4 4 5 1 1
16 2 3 4 2 2
17 2 3 2 2 1
18 3 3 4 3 2
19 3 4 4 4 2
20 3 2 4 4 2
21 4 5 4 4 4
22 3 4 4 4 2
23 2 2 4 4 4
24 2 3 5 2 2
25 4 4 4 2 2
26 4 4 4 4 2
27 3 3 4 2 2
28 4 4 4 3 2
29 2 4 4 2 2
30 4 1 4 4 2
31 4 4 4 3 3
32 5 5 2 4 2
33 5 2 4 2 2
34 4 4 4 2 2
35 4 3 5 4 3
36 4 2 5 5 4
37 2 4 4 2 1
38 4 5 3 4 2
39 4 4 4 4 3
40 4 4 5 5 3
41 3 4 4 3 2
42 2 3 4 2 2
43 3 4 5 3 2
44 4 2 4 2 2
45 3 2 5 1 2
46 2 4 4 2 2
47 4 2 4 4 4
48 4 4 4 4 4
49 3 4 3 4 2
50 4 1 4 4 3
51 3 4 4 2 2
52 4 2 4 2 2
53 2 1 2 1 1
54 4 4 3 4 3
55 4 3 5 2 4
56 4 2 4 4 2
57 4 4 4 2 2
58 3 3 5 2 1
59 1 2 3 1 2
60 3 2 5 2 2
61 3 3 4 2 2
62 4 2 5 2 2
63 2 1 4 2 2
64 3 3 4 1 1
65 5 2 5 5 2
66 4 3 4 3 3
67 4 3 4 2 2
68 3 3 5 1 1
69 4 2 4 2 2
70 2 3 3 4 4
71 3 2 4 2 2
72 4 4 5 5 3
73 3 4 5 4 4
74 4 4 5 3 2
75 4 2 4 2 4
76 3 3 4 2 1
77 3 4 5 2 2
78 2 3 5 2 2
79 4 4 4 4 4
80 3 2 5 2 3
81 2 3 3 2 2
82 2 3 4 4 2
83 3 4 4 4 2
84 2 2 4 2 3
85 2 4 4 2 2
86 4 2 4 3 2
87 4 2 5 2 1
88 4 4 4 4 2
89 2 3 4 2 2
90 2 4 4 4 4
91 4 2 5 1 1
92 2 2 3 2 2
93 3 3 3 3 2
94 3 3 5 2 2
95 5 5 5 4 4
96 3 2 4 2 4
97 4 3 4 3 3
98 3 4 4 2 2
99 2 3 4 2 3
100 4 4 4 2 2
101 3 3 4 2 1
102 3 3 4 2 2
103 3 2 4 2 2
104 4 3 5 3 2
105 1 2 2 2 4
106 3 3 4 2 2
107 2 2 2 4 3
108 3 4 4 3 3
109 2 2 5 2 2
110 2 4 3 1 1
111 2 4 4 2 4
112 4 1 3 2 2
113 5 5 4 5 2
114 5 2 4 1 1
115 3 3 4 2 2
116 4 4 2 2 2
117 4 1 1 2 2
118 3 5 4 2 3
119 2 3 3 2 1
120 4 3 4 5 3
121 2 3 3 2 2
122 3 3 3 2 3
123 2 2 5 2 1
124 2 2 4 2 2
125 2 4 3 2 3
126 4 4 4 2 1
127 4 3 4 3 2
128 4 3 4 2 2
129 4 3 4 4 3
130 3 4 3 4 2
131 2 3 4 2 2
132 4 4 4 4 2
133 3 4 4 4 2
134 2 2 4 2 2
135 4 4 4 4 2
136 3 2 3 3 3
137 3 4 4 2 2
138 3 3 4 2 2
139 3 3 2 3 3
140 3 2 2 4 2
141 4 2 4 4 2
142 5 5 2 5 1
143 2 2 4 2 1
144 4 3 4 3 4
145 3 3 3 5 3
146 3 3 2 2 3
147 1 3 2 2 2
148 2 4 4 2 2
149 4 4 3 2 2
150 4 4 4 2 4
151 5 4 4 5 3
152 2 4 2 3 3
153 4 5 5 2 2
154 3 3 4 2 2
155 2 3 4 3 2
156 4 4 4 3 3
157 2 4 3 2 5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1 X2 X3 X4
1.29605 0.06892 0.25292 0.36383 -0.11842
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.75239 -0.75239 -0.00531 0.68036 2.30902
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.29605 0.43198 3.000 0.00315 **
X1 0.06892 0.07362 0.936 0.35063
X2 0.25292 0.08260 3.062 0.00260 **
X3 0.36383 0.07244 5.022 1.42e-06 ***
X4 -0.11842 0.08978 -1.319 0.18916
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8666 on 152 degrees of freedom
Multiple R-squared: 0.2088, Adjusted R-squared: 0.1879
F-statistic: 10.03 on 4 and 152 DF, p-value: 3.151e-07
> 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,] 0.4722296 0.94445924 0.52777038
[2,] 0.4172436 0.83448722 0.58275639
[3,] 0.5815136 0.83697276 0.41848638
[4,] 0.7734985 0.45300300 0.22650150
[5,] 0.8399074 0.32018523 0.16009261
[6,] 0.9585674 0.08286525 0.04143263
[7,] 0.9536793 0.09264133 0.04632066
[8,] 0.9447369 0.11052625 0.05526312
[9,] 0.9500541 0.09989178 0.04994589
[10,] 0.9376405 0.12471908 0.06235954
[11,] 0.9165927 0.16681451 0.08340726
[12,] 0.8967141 0.20657186 0.10328593
[13,] 0.8680597 0.26388066 0.13194033
[14,] 0.8327059 0.33458817 0.16729409
[15,] 0.7953581 0.40928388 0.20464194
[16,] 0.8396607 0.32067864 0.16033932
[17,] 0.8868690 0.22626196 0.11313098
[18,] 0.8809990 0.23800193 0.11900096
[19,] 0.8530517 0.29389655 0.14694828
[20,] 0.8126912 0.37461753 0.18730876
[21,] 0.7849274 0.43014516 0.21507258
[22,] 0.8137956 0.37240887 0.18620444
[23,] 0.7990037 0.40199252 0.20099626
[24,] 0.7862680 0.42746396 0.21373198
[25,] 0.8478407 0.30431858 0.15215929
[26,] 0.9532372 0.09352552 0.04676276
[27,] 0.9502236 0.09955278 0.04977639
[28,] 0.9350280 0.12994400 0.06497200
[29,] 0.9166627 0.16667451 0.08333726
[30,] 0.9348527 0.13029455 0.06514728
[31,] 0.9177114 0.16457718 0.08228859
[32,] 0.8968715 0.20625706 0.10312853
[33,] 0.8733891 0.25322186 0.12661093
[34,] 0.8521764 0.29564721 0.14782360
[35,] 0.8598521 0.28029578 0.14014789
[36,] 0.8443157 0.31136856 0.15568428
[37,] 0.8565779 0.28684423 0.14342211
[38,] 0.8267347 0.34653051 0.17326526
[39,] 0.8421922 0.31561566 0.15780783
[40,] 0.8188315 0.36233710 0.18116855
[41,] 0.7883265 0.42334695 0.21167347
[42,] 0.7659994 0.46800115 0.23400057
[43,] 0.7366663 0.52666731 0.26333366
[44,] 0.6943854 0.61122917 0.30561458
[45,] 0.7063250 0.58735000 0.29367500
[46,] 0.6691648 0.66167045 0.33083522
[47,] 0.6364854 0.72702914 0.36351457
[48,] 0.6273151 0.74536972 0.37268486
[49,] 0.5896551 0.82068979 0.41034489
[50,] 0.5902476 0.81950476 0.40975238
[51,] 0.5466507 0.90669869 0.45334934
[52,] 0.6306153 0.73876939 0.36938470
[53,] 0.5856568 0.82868646 0.41434323
[54,] 0.5381186 0.92376283 0.46188142
[55,] 0.5328273 0.93434533 0.46717266
[56,] 0.5323456 0.93530874 0.46765437
[57,] 0.4895596 0.97911916 0.51044042
[58,] 0.4823489 0.96469790 0.51765105
[59,] 0.4647520 0.92950397 0.53524801
[60,] 0.4754783 0.95095650 0.52452175
[61,] 0.4286588 0.85731760 0.57134120
[62,] 0.4491082 0.89821644 0.55089178
[63,] 0.5124172 0.97516553 0.48758276
[64,] 0.4660862 0.93217240 0.53391380
[65,] 0.4251353 0.85027054 0.57486473
[66,] 0.4213550 0.84270997 0.57864501
[67,] 0.3820352 0.76407036 0.61796482
[68,] 0.4310999 0.86219988 0.56890006
[69,] 0.3861947 0.77238933 0.61380534
[70,] 0.3483382 0.69667636 0.65166182
[71,] 0.3928492 0.78569843 0.60715078
[72,] 0.3592821 0.71856419 0.64071791
[73,] 0.3186276 0.63725518 0.68137241
[74,] 0.3110965 0.62219294 0.68890353
[75,] 0.4297237 0.85944740 0.57027630
[76,] 0.4207126 0.84142525 0.57928738
[77,] 0.4174349 0.83486975 0.58256512
[78,] 0.4431550 0.88631004 0.55684498
[79,] 0.4287067 0.85741348 0.57129326
[80,] 0.4149691 0.82993813 0.58503093
[81,] 0.3725990 0.74519799 0.62740100
[82,] 0.3861072 0.77221444 0.61389278
[83,] 0.4867031 0.97340630 0.51329685
[84,] 0.5168017 0.96639651 0.48319825
[85,] 0.4946233 0.98924650 0.50537675
[86,] 0.4481507 0.89630147 0.55184927
[87,] 0.4045036 0.80900720 0.59549640
[88,] 0.4286870 0.85737409 0.57131295
[89,] 0.3947200 0.78944009 0.60527996
[90,] 0.3879264 0.77585274 0.61207363
[91,] 0.3429541 0.68590811 0.65704594
[92,] 0.3376524 0.67530481 0.66234759
[93,] 0.3405009 0.68100171 0.65949914
[94,] 0.2976115 0.59522302 0.70238849
[95,] 0.2566845 0.51336901 0.74331550
[96,] 0.2202188 0.44043765 0.77978118
[97,] 0.1952064 0.39041281 0.80479360
[98,] 0.2191537 0.43830740 0.78084630
[99,] 0.1843094 0.36861884 0.81569058
[100,] 0.2036926 0.40738520 0.79630740
[101,] 0.1732357 0.34647145 0.82676427
[102,] 0.1875169 0.37503381 0.81248309
[103,] 0.1704654 0.34093090 0.82953455
[104,] 0.1660053 0.33201064 0.83399468
[105,] 0.2222081 0.44441612 0.77779194
[106,] 0.2076837 0.41536736 0.79231632
[107,] 0.5668592 0.86628165 0.43314083
[108,] 0.5163181 0.96736386 0.48368193
[109,] 0.5873186 0.82536284 0.41268142
[110,] 0.8790880 0.24182390 0.12091195
[111,] 0.8577064 0.28458722 0.14229361
[112,] 0.8365534 0.32689321 0.16344661
[113,] 0.8005421 0.39891583 0.19945791
[114,] 0.7723749 0.45525017 0.22762509
[115,] 0.7454239 0.50915214 0.25457607
[116,] 0.7499593 0.50008132 0.25004066
[117,] 0.7246898 0.55062045 0.27531022
[118,] 0.7158959 0.56820819 0.28410410
[119,] 0.7222724 0.55545520 0.27772760
[120,] 0.7105272 0.57894560 0.28947280
[121,] 0.7807181 0.43856372 0.21928186
[122,] 0.7347161 0.53056772 0.26528386
[123,] 0.7158743 0.56825148 0.28412574
[124,] 0.6979811 0.60403775 0.30201888
[125,] 0.6353725 0.72925505 0.36462752
[126,] 0.6784600 0.64307995 0.32153998
[127,] 0.6308752 0.73824966 0.36912483
[128,] 0.5638491 0.87230178 0.43615089
[129,] 0.5060108 0.98797831 0.49398916
[130,] 0.4311525 0.86230503 0.56884749
[131,] 0.3618461 0.72369221 0.63815390
[132,] 0.3079121 0.61582411 0.69208794
[133,] 0.2582399 0.51647985 0.74176008
[134,] 0.2509931 0.50198617 0.74900692
[135,] 0.2607728 0.52154561 0.73922719
[136,] 0.1991848 0.39836953 0.80081524
[137,] 0.1731594 0.34631889 0.82684056
[138,] 0.1257389 0.25147776 0.87426112
[139,] 0.2027312 0.40546240 0.79726880
[140,] 0.1418616 0.28372317 0.85813841
[141,] 0.1866466 0.37329312 0.81335344
[142,] 0.3670527 0.73410544 0.63294728
> postscript(file="/var/www/html/rcomp/tmp/1x1st1290525433.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qs9w1290525433.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3qs9w1290525433.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qs9w1290525433.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qs9w1290525433.ps",horizontal=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 = 157
Frequency = 1
1 2 3 4 5 6
0.201461330 0.699786286 1.925764933 0.063614662 1.500523230 -0.327151783
7 8 9 10 11 12
0.377944705 -0.123730338 -0.388565110 1.063614662 -1.074235067 -0.752393486
13 14 15 16 17 18
-1.752393486 0.925764933 0.918256457 -1.005310202 -0.617896906 -0.369138578
19 20 21 22 23 24
-0.801891818 -0.664042090 0.366023589 -0.801891818 -1.427201818 -1.258226919
25 26 27 28 29 30
0.925764933 0.198108182 -0.005310202 0.561936558 -1.074235067 0.404882774
31 32 33 34 35 36
0.680356694 1.635016750 2.063614662 0.925764933 0.132536466 -0.043946910
37 38 39 40 41 42
-1.192655203 0.382100034 0.316528318 -0.300216775 -0.438063442 -1.005310202
43 44 45 46 47 48
-0.690980159 1.063614662 0.174526322 -1.074235067 0.572798182 0.434948454
49 50 51 52 53 54
-0.548975102 0.523302910 -0.074235067 1.063614662 -0.116218801 0.569445034
55 56 57 58 59 60
0.978613354 0.335957910 0.925764933 -0.376647055 -1.319640246 -0.189302054
61 62 63 64 65 66
-0.005310202 0.810697946 -0.867460474 0.240098038 0.719212818 0.749281558
67 68 69 70 71 72
0.994689798 -0.012818679 1.063614662 -1.243209966 0.063614662 -0.300216775
73 74 75 76 77 78
-0.817968263 0.309019841 1.300454934 -0.123730338 -0.327151783 -1.258226919
79 80 81 82 83 84
0.434948454 -0.070881918 -0.752393486 -1.732966954 -0.801891818 -0.817965202
85 86 87 88 89 90
-1.074235067 0.699786286 0.692277810 0.198108182 -1.005310202 -1.565051546
91 92 93 94 95 96
1.056106186 -0.683468622 -0.116221862 -0.258226919 1.113106873 0.300454934
97 98 99 100 101 102
0.749281558 -0.074235067 -0.886890066 0.925764933 -0.123730338 -0.005310202
103 104 105 106 107 108
0.063614662 0.377944705 -1.193711633 -0.005310202 -1.039788521 -0.319643306
109 110 111 112 113 114
-1.189302054 -0.575910110 -0.837394794 1.385456243 0.765354941 2.309022902
115 116 117 118 119 120
-0.005310202 1.431598366 1.891289675 -0.024739795 -0.870813622 0.021624806
121 122 123 124 125 126
-0.752393486 0.366026650 -1.307722190 -0.936385338 -0.702898214 0.807344797
127 128 129 130 131 132
0.630861422 0.994689798 0.385453182 -0.548975102 -1.005310202 0.198108182
133 134 135 136 137 138
-0.801891818 -0.936385338 0.198108182 0.071123138 -0.074235067 -0.005310202
139 140 141 142 143 144
0.255114990 -0.158208657 0.335957910 1.152768238 -1.054805474 0.867701694
145 146 147 148 149 150
-0.725458478 0.618943366 -1.499476770 -1.074235067 1.178681650 1.162605206
151 152 153 154 155 156
0.952699942 -0.813809874 0.603923353 -0.005310202 -1.369138578 0.680356694
157
-0.466057942
> postscript(file="/var/www/html/rcomp/tmp/61j9h1290525433.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 157
Frequency = 1
lag(myerror, k = 1) myerror
0 0.201461330 NA
1 0.699786286 0.201461330
2 1.925764933 0.699786286
3 0.063614662 1.925764933
4 1.500523230 0.063614662
5 -0.327151783 1.500523230
6 0.377944705 -0.327151783
7 -0.123730338 0.377944705
8 -0.388565110 -0.123730338
9 1.063614662 -0.388565110
10 -1.074235067 1.063614662
11 -0.752393486 -1.074235067
12 -1.752393486 -0.752393486
13 0.925764933 -1.752393486
14 0.918256457 0.925764933
15 -1.005310202 0.918256457
16 -0.617896906 -1.005310202
17 -0.369138578 -0.617896906
18 -0.801891818 -0.369138578
19 -0.664042090 -0.801891818
20 0.366023589 -0.664042090
21 -0.801891818 0.366023589
22 -1.427201818 -0.801891818
23 -1.258226919 -1.427201818
24 0.925764933 -1.258226919
25 0.198108182 0.925764933
26 -0.005310202 0.198108182
27 0.561936558 -0.005310202
28 -1.074235067 0.561936558
29 0.404882774 -1.074235067
30 0.680356694 0.404882774
31 1.635016750 0.680356694
32 2.063614662 1.635016750
33 0.925764933 2.063614662
34 0.132536466 0.925764933
35 -0.043946910 0.132536466
36 -1.192655203 -0.043946910
37 0.382100034 -1.192655203
38 0.316528318 0.382100034
39 -0.300216775 0.316528318
40 -0.438063442 -0.300216775
41 -1.005310202 -0.438063442
42 -0.690980159 -1.005310202
43 1.063614662 -0.690980159
44 0.174526322 1.063614662
45 -1.074235067 0.174526322
46 0.572798182 -1.074235067
47 0.434948454 0.572798182
48 -0.548975102 0.434948454
49 0.523302910 -0.548975102
50 -0.074235067 0.523302910
51 1.063614662 -0.074235067
52 -0.116218801 1.063614662
53 0.569445034 -0.116218801
54 0.978613354 0.569445034
55 0.335957910 0.978613354
56 0.925764933 0.335957910
57 -0.376647055 0.925764933
58 -1.319640246 -0.376647055
59 -0.189302054 -1.319640246
60 -0.005310202 -0.189302054
61 0.810697946 -0.005310202
62 -0.867460474 0.810697946
63 0.240098038 -0.867460474
64 0.719212818 0.240098038
65 0.749281558 0.719212818
66 0.994689798 0.749281558
67 -0.012818679 0.994689798
68 1.063614662 -0.012818679
69 -1.243209966 1.063614662
70 0.063614662 -1.243209966
71 -0.300216775 0.063614662
72 -0.817968263 -0.300216775
73 0.309019841 -0.817968263
74 1.300454934 0.309019841
75 -0.123730338 1.300454934
76 -0.327151783 -0.123730338
77 -1.258226919 -0.327151783
78 0.434948454 -1.258226919
79 -0.070881918 0.434948454
80 -0.752393486 -0.070881918
81 -1.732966954 -0.752393486
82 -0.801891818 -1.732966954
83 -0.817965202 -0.801891818
84 -1.074235067 -0.817965202
85 0.699786286 -1.074235067
86 0.692277810 0.699786286
87 0.198108182 0.692277810
88 -1.005310202 0.198108182
89 -1.565051546 -1.005310202
90 1.056106186 -1.565051546
91 -0.683468622 1.056106186
92 -0.116221862 -0.683468622
93 -0.258226919 -0.116221862
94 1.113106873 -0.258226919
95 0.300454934 1.113106873
96 0.749281558 0.300454934
97 -0.074235067 0.749281558
98 -0.886890066 -0.074235067
99 0.925764933 -0.886890066
100 -0.123730338 0.925764933
101 -0.005310202 -0.123730338
102 0.063614662 -0.005310202
103 0.377944705 0.063614662
104 -1.193711633 0.377944705
105 -0.005310202 -1.193711633
106 -1.039788521 -0.005310202
107 -0.319643306 -1.039788521
108 -1.189302054 -0.319643306
109 -0.575910110 -1.189302054
110 -0.837394794 -0.575910110
111 1.385456243 -0.837394794
112 0.765354941 1.385456243
113 2.309022902 0.765354941
114 -0.005310202 2.309022902
115 1.431598366 -0.005310202
116 1.891289675 1.431598366
117 -0.024739795 1.891289675
118 -0.870813622 -0.024739795
119 0.021624806 -0.870813622
120 -0.752393486 0.021624806
121 0.366026650 -0.752393486
122 -1.307722190 0.366026650
123 -0.936385338 -1.307722190
124 -0.702898214 -0.936385338
125 0.807344797 -0.702898214
126 0.630861422 0.807344797
127 0.994689798 0.630861422
128 0.385453182 0.994689798
129 -0.548975102 0.385453182
130 -1.005310202 -0.548975102
131 0.198108182 -1.005310202
132 -0.801891818 0.198108182
133 -0.936385338 -0.801891818
134 0.198108182 -0.936385338
135 0.071123138 0.198108182
136 -0.074235067 0.071123138
137 -0.005310202 -0.074235067
138 0.255114990 -0.005310202
139 -0.158208657 0.255114990
140 0.335957910 -0.158208657
141 1.152768238 0.335957910
142 -1.054805474 1.152768238
143 0.867701694 -1.054805474
144 -0.725458478 0.867701694
145 0.618943366 -0.725458478
146 -1.499476770 0.618943366
147 -1.074235067 -1.499476770
148 1.178681650 -1.074235067
149 1.162605206 1.178681650
150 0.952699942 1.162605206
151 -0.813809874 0.952699942
152 0.603923353 -0.813809874
153 -0.005310202 0.603923353
154 -1.369138578 -0.005310202
155 0.680356694 -1.369138578
156 -0.466057942 0.680356694
157 NA -0.466057942
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.699786286 0.201461330
[2,] 1.925764933 0.699786286
[3,] 0.063614662 1.925764933
[4,] 1.500523230 0.063614662
[5,] -0.327151783 1.500523230
[6,] 0.377944705 -0.327151783
[7,] -0.123730338 0.377944705
[8,] -0.388565110 -0.123730338
[9,] 1.063614662 -0.388565110
[10,] -1.074235067 1.063614662
[11,] -0.752393486 -1.074235067
[12,] -1.752393486 -0.752393486
[13,] 0.925764933 -1.752393486
[14,] 0.918256457 0.925764933
[15,] -1.005310202 0.918256457
[16,] -0.617896906 -1.005310202
[17,] -0.369138578 -0.617896906
[18,] -0.801891818 -0.369138578
[19,] -0.664042090 -0.801891818
[20,] 0.366023589 -0.664042090
[21,] -0.801891818 0.366023589
[22,] -1.427201818 -0.801891818
[23,] -1.258226919 -1.427201818
[24,] 0.925764933 -1.258226919
[25,] 0.198108182 0.925764933
[26,] -0.005310202 0.198108182
[27,] 0.561936558 -0.005310202
[28,] -1.074235067 0.561936558
[29,] 0.404882774 -1.074235067
[30,] 0.680356694 0.404882774
[31,] 1.635016750 0.680356694
[32,] 2.063614662 1.635016750
[33,] 0.925764933 2.063614662
[34,] 0.132536466 0.925764933
[35,] -0.043946910 0.132536466
[36,] -1.192655203 -0.043946910
[37,] 0.382100034 -1.192655203
[38,] 0.316528318 0.382100034
[39,] -0.300216775 0.316528318
[40,] -0.438063442 -0.300216775
[41,] -1.005310202 -0.438063442
[42,] -0.690980159 -1.005310202
[43,] 1.063614662 -0.690980159
[44,] 0.174526322 1.063614662
[45,] -1.074235067 0.174526322
[46,] 0.572798182 -1.074235067
[47,] 0.434948454 0.572798182
[48,] -0.548975102 0.434948454
[49,] 0.523302910 -0.548975102
[50,] -0.074235067 0.523302910
[51,] 1.063614662 -0.074235067
[52,] -0.116218801 1.063614662
[53,] 0.569445034 -0.116218801
[54,] 0.978613354 0.569445034
[55,] 0.335957910 0.978613354
[56,] 0.925764933 0.335957910
[57,] -0.376647055 0.925764933
[58,] -1.319640246 -0.376647055
[59,] -0.189302054 -1.319640246
[60,] -0.005310202 -0.189302054
[61,] 0.810697946 -0.005310202
[62,] -0.867460474 0.810697946
[63,] 0.240098038 -0.867460474
[64,] 0.719212818 0.240098038
[65,] 0.749281558 0.719212818
[66,] 0.994689798 0.749281558
[67,] -0.012818679 0.994689798
[68,] 1.063614662 -0.012818679
[69,] -1.243209966 1.063614662
[70,] 0.063614662 -1.243209966
[71,] -0.300216775 0.063614662
[72,] -0.817968263 -0.300216775
[73,] 0.309019841 -0.817968263
[74,] 1.300454934 0.309019841
[75,] -0.123730338 1.300454934
[76,] -0.327151783 -0.123730338
[77,] -1.258226919 -0.327151783
[78,] 0.434948454 -1.258226919
[79,] -0.070881918 0.434948454
[80,] -0.752393486 -0.070881918
[81,] -1.732966954 -0.752393486
[82,] -0.801891818 -1.732966954
[83,] -0.817965202 -0.801891818
[84,] -1.074235067 -0.817965202
[85,] 0.699786286 -1.074235067
[86,] 0.692277810 0.699786286
[87,] 0.198108182 0.692277810
[88,] -1.005310202 0.198108182
[89,] -1.565051546 -1.005310202
[90,] 1.056106186 -1.565051546
[91,] -0.683468622 1.056106186
[92,] -0.116221862 -0.683468622
[93,] -0.258226919 -0.116221862
[94,] 1.113106873 -0.258226919
[95,] 0.300454934 1.113106873
[96,] 0.749281558 0.300454934
[97,] -0.074235067 0.749281558
[98,] -0.886890066 -0.074235067
[99,] 0.925764933 -0.886890066
[100,] -0.123730338 0.925764933
[101,] -0.005310202 -0.123730338
[102,] 0.063614662 -0.005310202
[103,] 0.377944705 0.063614662
[104,] -1.193711633 0.377944705
[105,] -0.005310202 -1.193711633
[106,] -1.039788521 -0.005310202
[107,] -0.319643306 -1.039788521
[108,] -1.189302054 -0.319643306
[109,] -0.575910110 -1.189302054
[110,] -0.837394794 -0.575910110
[111,] 1.385456243 -0.837394794
[112,] 0.765354941 1.385456243
[113,] 2.309022902 0.765354941
[114,] -0.005310202 2.309022902
[115,] 1.431598366 -0.005310202
[116,] 1.891289675 1.431598366
[117,] -0.024739795 1.891289675
[118,] -0.870813622 -0.024739795
[119,] 0.021624806 -0.870813622
[120,] -0.752393486 0.021624806
[121,] 0.366026650 -0.752393486
[122,] -1.307722190 0.366026650
[123,] -0.936385338 -1.307722190
[124,] -0.702898214 -0.936385338
[125,] 0.807344797 -0.702898214
[126,] 0.630861422 0.807344797
[127,] 0.994689798 0.630861422
[128,] 0.385453182 0.994689798
[129,] -0.548975102 0.385453182
[130,] -1.005310202 -0.548975102
[131,] 0.198108182 -1.005310202
[132,] -0.801891818 0.198108182
[133,] -0.936385338 -0.801891818
[134,] 0.198108182 -0.936385338
[135,] 0.071123138 0.198108182
[136,] -0.074235067 0.071123138
[137,] -0.005310202 -0.074235067
[138,] 0.255114990 -0.005310202
[139,] -0.158208657 0.255114990
[140,] 0.335957910 -0.158208657
[141,] 1.152768238 0.335957910
[142,] -1.054805474 1.152768238
[143,] 0.867701694 -1.054805474
[144,] -0.725458478 0.867701694
[145,] 0.618943366 -0.725458478
[146,] -1.499476770 0.618943366
[147,] -1.074235067 -1.499476770
[148,] 1.178681650 -1.074235067
[149,] 1.162605206 1.178681650
[150,] 0.952699942 1.162605206
[151,] -0.813809874 0.952699942
[152,] 0.603923353 -0.813809874
[153,] -0.005310202 0.603923353
[154,] -1.369138578 -0.005310202
[155,] 0.680356694 -1.369138578
[156,] -0.466057942 0.680356694
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.699786286 0.201461330
2 1.925764933 0.699786286
3 0.063614662 1.925764933
4 1.500523230 0.063614662
5 -0.327151783 1.500523230
6 0.377944705 -0.327151783
7 -0.123730338 0.377944705
8 -0.388565110 -0.123730338
9 1.063614662 -0.388565110
10 -1.074235067 1.063614662
11 -0.752393486 -1.074235067
12 -1.752393486 -0.752393486
13 0.925764933 -1.752393486
14 0.918256457 0.925764933
15 -1.005310202 0.918256457
16 -0.617896906 -1.005310202
17 -0.369138578 -0.617896906
18 -0.801891818 -0.369138578
19 -0.664042090 -0.801891818
20 0.366023589 -0.664042090
21 -0.801891818 0.366023589
22 -1.427201818 -0.801891818
23 -1.258226919 -1.427201818
24 0.925764933 -1.258226919
25 0.198108182 0.925764933
26 -0.005310202 0.198108182
27 0.561936558 -0.005310202
28 -1.074235067 0.561936558
29 0.404882774 -1.074235067
30 0.680356694 0.404882774
31 1.635016750 0.680356694
32 2.063614662 1.635016750
33 0.925764933 2.063614662
34 0.132536466 0.925764933
35 -0.043946910 0.132536466
36 -1.192655203 -0.043946910
37 0.382100034 -1.192655203
38 0.316528318 0.382100034
39 -0.300216775 0.316528318
40 -0.438063442 -0.300216775
41 -1.005310202 -0.438063442
42 -0.690980159 -1.005310202
43 1.063614662 -0.690980159
44 0.174526322 1.063614662
45 -1.074235067 0.174526322
46 0.572798182 -1.074235067
47 0.434948454 0.572798182
48 -0.548975102 0.434948454
49 0.523302910 -0.548975102
50 -0.074235067 0.523302910
51 1.063614662 -0.074235067
52 -0.116218801 1.063614662
53 0.569445034 -0.116218801
54 0.978613354 0.569445034
55 0.335957910 0.978613354
56 0.925764933 0.335957910
57 -0.376647055 0.925764933
58 -1.319640246 -0.376647055
59 -0.189302054 -1.319640246
60 -0.005310202 -0.189302054
61 0.810697946 -0.005310202
62 -0.867460474 0.810697946
63 0.240098038 -0.867460474
64 0.719212818 0.240098038
65 0.749281558 0.719212818
66 0.994689798 0.749281558
67 -0.012818679 0.994689798
68 1.063614662 -0.012818679
69 -1.243209966 1.063614662
70 0.063614662 -1.243209966
71 -0.300216775 0.063614662
72 -0.817968263 -0.300216775
73 0.309019841 -0.817968263
74 1.300454934 0.309019841
75 -0.123730338 1.300454934
76 -0.327151783 -0.123730338
77 -1.258226919 -0.327151783
78 0.434948454 -1.258226919
79 -0.070881918 0.434948454
80 -0.752393486 -0.070881918
81 -1.732966954 -0.752393486
82 -0.801891818 -1.732966954
83 -0.817965202 -0.801891818
84 -1.074235067 -0.817965202
85 0.699786286 -1.074235067
86 0.692277810 0.699786286
87 0.198108182 0.692277810
88 -1.005310202 0.198108182
89 -1.565051546 -1.005310202
90 1.056106186 -1.565051546
91 -0.683468622 1.056106186
92 -0.116221862 -0.683468622
93 -0.258226919 -0.116221862
94 1.113106873 -0.258226919
95 0.300454934 1.113106873
96 0.749281558 0.300454934
97 -0.074235067 0.749281558
98 -0.886890066 -0.074235067
99 0.925764933 -0.886890066
100 -0.123730338 0.925764933
101 -0.005310202 -0.123730338
102 0.063614662 -0.005310202
103 0.377944705 0.063614662
104 -1.193711633 0.377944705
105 -0.005310202 -1.193711633
106 -1.039788521 -0.005310202
107 -0.319643306 -1.039788521
108 -1.189302054 -0.319643306
109 -0.575910110 -1.189302054
110 -0.837394794 -0.575910110
111 1.385456243 -0.837394794
112 0.765354941 1.385456243
113 2.309022902 0.765354941
114 -0.005310202 2.309022902
115 1.431598366 -0.005310202
116 1.891289675 1.431598366
117 -0.024739795 1.891289675
118 -0.870813622 -0.024739795
119 0.021624806 -0.870813622
120 -0.752393486 0.021624806
121 0.366026650 -0.752393486
122 -1.307722190 0.366026650
123 -0.936385338 -1.307722190
124 -0.702898214 -0.936385338
125 0.807344797 -0.702898214
126 0.630861422 0.807344797
127 0.994689798 0.630861422
128 0.385453182 0.994689798
129 -0.548975102 0.385453182
130 -1.005310202 -0.548975102
131 0.198108182 -1.005310202
132 -0.801891818 0.198108182
133 -0.936385338 -0.801891818
134 0.198108182 -0.936385338
135 0.071123138 0.198108182
136 -0.074235067 0.071123138
137 -0.005310202 -0.074235067
138 0.255114990 -0.005310202
139 -0.158208657 0.255114990
140 0.335957910 -0.158208657
141 1.152768238 0.335957910
142 -1.054805474 1.152768238
143 0.867701694 -1.054805474
144 -0.725458478 0.867701694
145 0.618943366 -0.725458478
146 -1.499476770 0.618943366
147 -1.074235067 -1.499476770
148 1.178681650 -1.074235067
149 1.162605206 1.178681650
150 0.952699942 1.162605206
151 -0.813809874 0.952699942
152 0.603923353 -0.813809874
153 -0.005310202 0.603923353
154 -1.369138578 -0.005310202
155 0.680356694 -1.369138578
156 -0.466057942 0.680356694
> 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/www/html/rcomp/tmp/7bb821290525433.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8bb821290525433.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bb821290525433.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mkpn1290525433.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/11pk6t1290525433.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/www/html/rcomp/tmp/12tl4h1290525433.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/www/html/rcomp/tmp/137v2p1290525433.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/www/html/rcomp/tmp/14sdjv1290525433.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/www/html/rcomp/tmp/15vwzj1290525433.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/www/html/rcomp/tmp/16sofa1290525433.tab")
+ }
>
> try(system("convert tmp/1x1st1290525433.ps tmp/1x1st1290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qs9w1290525433.ps tmp/2qs9w1290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qs9w1290525433.ps tmp/3qs9w1290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qs9w1290525433.ps tmp/4qs9w1290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qs9w1290525433.ps tmp/5qs9w1290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/61j9h1290525433.ps tmp/61j9h1290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/7bb821290525433.ps tmp/7bb821290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bb821290525433.ps tmp/8bb821290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bb821290525433.ps tmp/9bb821290525433.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mkpn1290525433.ps tmp/10mkpn1290525433.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.887 1.682 22.005