R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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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.
> x <- array(list(99.9,98.8,98.6,100.5,107.2,110.4,95.7,96.4,93.7,101.9,106.7,106.2,86.7,81,95.3,94.7,99.3,101,101.8,109.4,96,102.3,91.7,90.7,95.3,96.2,96.6,96.1,107.2,106,108,103.1,98.4,102,103.1,104.7,81.1,86,96.6,92.1,103.7,106.9,106.6,112.6,97.6,101.7,87.6,92,99.4,97.4,98.5,97,105.2,105.4,104.6,102.7,97.5,98.1,108.9,104.5,86.8,87.4,88.9,89.9,110.3,109.8,114.8,111.7,94.6,98.6,92,96.9,93.8,95.1,93.8,97,107.6,112.7,101,102.9,95.4,97.4,96.5,111.4,89.2,87.4,87.1,96.8,110.5,114.1,110.8,110.3,104.2,103.9,88.9,101.6,89.8,94.6,90,95.9,93.9,104.7,91.3,102.8,87.8,98.1,99.7,113.9,73.5,80.9,79.2,95.7,96.9,113.2,95.2,105.9,95.6,108.8,89.7,102.3),dim=c(2,60),dimnames=list(c('ProdMetal','IndProd'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('ProdMetal','IndProd'),1:60))
> 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 = '2'
> #'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
IndProd ProdMetal
1 98.8 99.9
2 100.5 98.6
3 110.4 107.2
4 96.4 95.7
5 101.9 93.7
6 106.2 106.7
7 81.0 86.7
8 94.7 95.3
9 101.0 99.3
10 109.4 101.8
11 102.3 96.0
12 90.7 91.7
13 96.2 95.3
14 96.1 96.6
15 106.0 107.2
16 103.1 108.0
17 102.0 98.4
18 104.7 103.1
19 86.0 81.1
20 92.1 96.6
21 106.9 103.7
22 112.6 106.6
23 101.7 97.6
24 92.0 87.6
25 97.4 99.4
26 97.0 98.5
27 105.4 105.2
28 102.7 104.6
29 98.1 97.5
30 104.5 108.9
31 87.4 86.8
32 89.9 88.9
33 109.8 110.3
34 111.7 114.8
35 98.6 94.6
36 96.9 92.0
37 95.1 93.8
38 97.0 93.8
39 112.7 107.6
40 102.9 101.0
41 97.4 95.4
42 111.4 96.5
43 87.4 89.2
44 96.8 87.1
45 114.1 110.5
46 110.3 110.8
47 103.9 104.2
48 101.6 88.9
49 94.6 89.8
50 95.9 90.0
51 104.7 93.9
52 102.8 91.3
53 98.1 87.8
54 113.9 99.7
55 80.9 73.5
56 95.7 79.2
57 113.2 96.9
58 105.9 95.2
59 108.8 95.6
60 102.3 89.7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ProdMetal
29.9537 0.7293
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.180 -3.279 -1.056 3.577 12.582
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.95373 7.86296 3.809 0.000339 ***
ProdMetal 0.72925 0.08081 9.024 1.21e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.153 on 58 degrees of freedom
Multiple R-squared: 0.5841, Adjusted R-squared: 0.5769
F-statistic: 81.44 on 1 and 58 DF, p-value: 1.208e-12
> 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,] 0.339544752 0.679089505 0.6604552
[2,] 0.196535777 0.393071553 0.8034642
[3,] 0.428149642 0.856299285 0.5718504
[4,] 0.312406770 0.624813539 0.6875932
[5,] 0.209428695 0.418857391 0.7905713
[6,] 0.255296777 0.510593553 0.7447032
[7,] 0.258170408 0.516340817 0.7418296
[8,] 0.202517009 0.405034019 0.7974830
[9,] 0.141312006 0.282624012 0.8586880
[10,] 0.104879707 0.209759415 0.8951203
[11,] 0.097652502 0.195305004 0.9023475
[12,] 0.149543115 0.299086230 0.8504569
[13,] 0.113303332 0.226606665 0.8866967
[14,] 0.076136485 0.152272969 0.9238635
[15,] 0.063091046 0.126182091 0.9369090
[16,] 0.098754455 0.197508910 0.9012455
[17,] 0.072807939 0.145615879 0.9271921
[18,] 0.075320184 0.150640369 0.9246798
[19,] 0.055979199 0.111958397 0.9440208
[20,] 0.043465655 0.086931310 0.9565343
[21,] 0.041352016 0.082704033 0.9586480
[22,] 0.037474896 0.074949793 0.9625251
[23,] 0.025086238 0.050172475 0.9749138
[24,] 0.021040187 0.042080374 0.9789598
[25,] 0.015292034 0.030584069 0.9847080
[26,] 0.019397083 0.038794167 0.9806029
[27,] 0.020939842 0.041879685 0.9790602
[28,] 0.021578906 0.043157813 0.9784211
[29,] 0.014604567 0.029209134 0.9853954
[30,] 0.012758326 0.025516651 0.9872417
[31,] 0.009850867 0.019701734 0.9901491
[32,] 0.007838477 0.015676955 0.9921615
[33,] 0.007664416 0.015328831 0.9923356
[34,] 0.006365544 0.012731087 0.9936345
[35,] 0.005664191 0.011328383 0.9943358
[36,] 0.004488278 0.008976557 0.9955117
[37,] 0.004530759 0.009061517 0.9954692
[38,] 0.045429166 0.090858333 0.9545708
[39,] 0.170246937 0.340493874 0.8297531
[40,] 0.163904570 0.327809140 0.8360954
[41,] 0.127607571 0.255215142 0.8723924
[42,] 0.173448733 0.346897467 0.8265513
[43,] 0.575248294 0.849503412 0.4247517
[44,] 0.588437824 0.823124351 0.4115622
[45,] 0.735657152 0.528685696 0.2643428
[46,] 0.868990853 0.262018295 0.1310091
[47,] 0.849113094 0.301773812 0.1508869
[48,] 0.798515348 0.402969304 0.2014847
[49,] 0.739385918 0.521228164 0.2606141
[50,] 0.660084200 0.679831599 0.3399158
[51,] 0.805144691 0.389710618 0.1948553
> postscript(file="/var/www/html/rcomp/tmp/1gqww1260637767.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nxaw1260637767.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ln7s1260637767.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kzng1260637767.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5y8d21260637767.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 = 60
Frequency = 1
1 2 3 4 5 6
-4.0059797 -1.3579524 2.2704827 -3.3431224 3.6153811 -1.5648915
7 8 9 10 11 12
-12.1798568 -4.7514217 -1.3684286 5.2084420 2.3381021 -6.1261155
13 14 15 16 17 18
-3.2514217 -4.2994490 -2.1295173 -5.6129187 0.2878979 -0.4395852
19 20 21 22 23 24
-3.0960471 -8.2994490 1.3228637 4.9080337 0.5712993 -1.8361834
25 26 27 28 29 30
-5.0413538 -4.7850273 -1.2710139 -3.5334628 -2.9557755 -4.8692453
31 32 33 34 35 36
-5.8527820 -4.8842106 -0.5901977 -1.9718305 -0.3409455 -0.1448910
37 38 39 40 41 42
-3.2575441 -1.3575441 4.2787820 -0.7081566 -2.1243469 11.0734762
43 44 45 46 47 48
-7.6029861 3.3284425 3.5639519 -0.4548236 -2.0417621 6.8157894
49 50 51 52 53 54
-0.8405372 0.3136125 6.2695307 6.2655852 4.1179663 11.2398707
55 56 57 58 59 60
-2.6537339 7.9895312 12.5817755 6.5215035 9.1298028 6.9323880
> postscript(file="/var/www/html/rcomp/tmp/67cc81260637767.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.0059797 NA
1 -1.3579524 -4.0059797
2 2.2704827 -1.3579524
3 -3.3431224 2.2704827
4 3.6153811 -3.3431224
5 -1.5648915 3.6153811
6 -12.1798568 -1.5648915
7 -4.7514217 -12.1798568
8 -1.3684286 -4.7514217
9 5.2084420 -1.3684286
10 2.3381021 5.2084420
11 -6.1261155 2.3381021
12 -3.2514217 -6.1261155
13 -4.2994490 -3.2514217
14 -2.1295173 -4.2994490
15 -5.6129187 -2.1295173
16 0.2878979 -5.6129187
17 -0.4395852 0.2878979
18 -3.0960471 -0.4395852
19 -8.2994490 -3.0960471
20 1.3228637 -8.2994490
21 4.9080337 1.3228637
22 0.5712993 4.9080337
23 -1.8361834 0.5712993
24 -5.0413538 -1.8361834
25 -4.7850273 -5.0413538
26 -1.2710139 -4.7850273
27 -3.5334628 -1.2710139
28 -2.9557755 -3.5334628
29 -4.8692453 -2.9557755
30 -5.8527820 -4.8692453
31 -4.8842106 -5.8527820
32 -0.5901977 -4.8842106
33 -1.9718305 -0.5901977
34 -0.3409455 -1.9718305
35 -0.1448910 -0.3409455
36 -3.2575441 -0.1448910
37 -1.3575441 -3.2575441
38 4.2787820 -1.3575441
39 -0.7081566 4.2787820
40 -2.1243469 -0.7081566
41 11.0734762 -2.1243469
42 -7.6029861 11.0734762
43 3.3284425 -7.6029861
44 3.5639519 3.3284425
45 -0.4548236 3.5639519
46 -2.0417621 -0.4548236
47 6.8157894 -2.0417621
48 -0.8405372 6.8157894
49 0.3136125 -0.8405372
50 6.2695307 0.3136125
51 6.2655852 6.2695307
52 4.1179663 6.2655852
53 11.2398707 4.1179663
54 -2.6537339 11.2398707
55 7.9895312 -2.6537339
56 12.5817755 7.9895312
57 6.5215035 12.5817755
58 9.1298028 6.5215035
59 6.9323880 9.1298028
60 NA 6.9323880
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.3579524 -4.0059797
[2,] 2.2704827 -1.3579524
[3,] -3.3431224 2.2704827
[4,] 3.6153811 -3.3431224
[5,] -1.5648915 3.6153811
[6,] -12.1798568 -1.5648915
[7,] -4.7514217 -12.1798568
[8,] -1.3684286 -4.7514217
[9,] 5.2084420 -1.3684286
[10,] 2.3381021 5.2084420
[11,] -6.1261155 2.3381021
[12,] -3.2514217 -6.1261155
[13,] -4.2994490 -3.2514217
[14,] -2.1295173 -4.2994490
[15,] -5.6129187 -2.1295173
[16,] 0.2878979 -5.6129187
[17,] -0.4395852 0.2878979
[18,] -3.0960471 -0.4395852
[19,] -8.2994490 -3.0960471
[20,] 1.3228637 -8.2994490
[21,] 4.9080337 1.3228637
[22,] 0.5712993 4.9080337
[23,] -1.8361834 0.5712993
[24,] -5.0413538 -1.8361834
[25,] -4.7850273 -5.0413538
[26,] -1.2710139 -4.7850273
[27,] -3.5334628 -1.2710139
[28,] -2.9557755 -3.5334628
[29,] -4.8692453 -2.9557755
[30,] -5.8527820 -4.8692453
[31,] -4.8842106 -5.8527820
[32,] -0.5901977 -4.8842106
[33,] -1.9718305 -0.5901977
[34,] -0.3409455 -1.9718305
[35,] -0.1448910 -0.3409455
[36,] -3.2575441 -0.1448910
[37,] -1.3575441 -3.2575441
[38,] 4.2787820 -1.3575441
[39,] -0.7081566 4.2787820
[40,] -2.1243469 -0.7081566
[41,] 11.0734762 -2.1243469
[42,] -7.6029861 11.0734762
[43,] 3.3284425 -7.6029861
[44,] 3.5639519 3.3284425
[45,] -0.4548236 3.5639519
[46,] -2.0417621 -0.4548236
[47,] 6.8157894 -2.0417621
[48,] -0.8405372 6.8157894
[49,] 0.3136125 -0.8405372
[50,] 6.2695307 0.3136125
[51,] 6.2655852 6.2695307
[52,] 4.1179663 6.2655852
[53,] 11.2398707 4.1179663
[54,] -2.6537339 11.2398707
[55,] 7.9895312 -2.6537339
[56,] 12.5817755 7.9895312
[57,] 6.5215035 12.5817755
[58,] 9.1298028 6.5215035
[59,] 6.9323880 9.1298028
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.3579524 -4.0059797
2 2.2704827 -1.3579524
3 -3.3431224 2.2704827
4 3.6153811 -3.3431224
5 -1.5648915 3.6153811
6 -12.1798568 -1.5648915
7 -4.7514217 -12.1798568
8 -1.3684286 -4.7514217
9 5.2084420 -1.3684286
10 2.3381021 5.2084420
11 -6.1261155 2.3381021
12 -3.2514217 -6.1261155
13 -4.2994490 -3.2514217
14 -2.1295173 -4.2994490
15 -5.6129187 -2.1295173
16 0.2878979 -5.6129187
17 -0.4395852 0.2878979
18 -3.0960471 -0.4395852
19 -8.2994490 -3.0960471
20 1.3228637 -8.2994490
21 4.9080337 1.3228637
22 0.5712993 4.9080337
23 -1.8361834 0.5712993
24 -5.0413538 -1.8361834
25 -4.7850273 -5.0413538
26 -1.2710139 -4.7850273
27 -3.5334628 -1.2710139
28 -2.9557755 -3.5334628
29 -4.8692453 -2.9557755
30 -5.8527820 -4.8692453
31 -4.8842106 -5.8527820
32 -0.5901977 -4.8842106
33 -1.9718305 -0.5901977
34 -0.3409455 -1.9718305
35 -0.1448910 -0.3409455
36 -3.2575441 -0.1448910
37 -1.3575441 -3.2575441
38 4.2787820 -1.3575441
39 -0.7081566 4.2787820
40 -2.1243469 -0.7081566
41 11.0734762 -2.1243469
42 -7.6029861 11.0734762
43 3.3284425 -7.6029861
44 3.5639519 3.3284425
45 -0.4548236 3.5639519
46 -2.0417621 -0.4548236
47 6.8157894 -2.0417621
48 -0.8405372 6.8157894
49 0.3136125 -0.8405372
50 6.2695307 0.3136125
51 6.2655852 6.2695307
52 4.1179663 6.2655852
53 11.2398707 4.1179663
54 -2.6537339 11.2398707
55 7.9895312 -2.6537339
56 12.5817755 7.9895312
57 6.5215035 12.5817755
58 9.1298028 6.5215035
59 6.9323880 9.1298028
> 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/7ot5y1260637767.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8klwv1260637767.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zcat1260637767.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qutg1260637767.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1196vz1260637767.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/12lrbq1260637767.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/13oeem1260637767.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/14jthx1260637767.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/15v8dw1260637767.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/16frdu1260637767.tab")
+ }
> try(system("convert tmp/1gqww1260637767.ps tmp/1gqww1260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nxaw1260637767.ps tmp/2nxaw1260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ln7s1260637767.ps tmp/3ln7s1260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kzng1260637767.ps tmp/4kzng1260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/5y8d21260637767.ps tmp/5y8d21260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/67cc81260637767.ps tmp/67cc81260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ot5y1260637767.ps tmp/7ot5y1260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/8klwv1260637767.ps tmp/8klwv1260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zcat1260637767.ps tmp/9zcat1260637767.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qutg1260637767.ps tmp/10qutg1260637767.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.483 1.570 3.054