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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> 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('TotProd','ProdMetal'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('TotProd','ProdMetal'),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 = 'Linear Trend'
> par2 = 'Include Monthly 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
TotProd ProdMetal M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 99.9 98.8 1 0 0 0 0 0 0 0 0 0 0 1
2 98.6 100.5 0 1 0 0 0 0 0 0 0 0 0 2
3 107.2 110.4 0 0 1 0 0 0 0 0 0 0 0 3
4 95.7 96.4 0 0 0 1 0 0 0 0 0 0 0 4
5 93.7 101.9 0 0 0 0 1 0 0 0 0 0 0 5
6 106.7 106.2 0 0 0 0 0 1 0 0 0 0 0 6
7 86.7 81.0 0 0 0 0 0 0 1 0 0 0 0 7
8 95.3 94.7 0 0 0 0 0 0 0 1 0 0 0 8
9 99.3 101.0 0 0 0 0 0 0 0 0 1 0 0 9
10 101.8 109.4 0 0 0 0 0 0 0 0 0 1 0 10
11 96.0 102.3 0 0 0 0 0 0 0 0 0 0 1 11
12 91.7 90.7 0 0 0 0 0 0 0 0 0 0 0 12
13 95.3 96.2 1 0 0 0 0 0 0 0 0 0 0 13
14 96.6 96.1 0 1 0 0 0 0 0 0 0 0 0 14
15 107.2 106.0 0 0 1 0 0 0 0 0 0 0 0 15
16 108.0 103.1 0 0 0 1 0 0 0 0 0 0 0 16
17 98.4 102.0 0 0 0 0 1 0 0 0 0 0 0 17
18 103.1 104.7 0 0 0 0 0 1 0 0 0 0 0 18
19 81.1 86.0 0 0 0 0 0 0 1 0 0 0 0 19
20 96.6 92.1 0 0 0 0 0 0 0 1 0 0 0 20
21 103.7 106.9 0 0 0 0 0 0 0 0 1 0 0 21
22 106.6 112.6 0 0 0 0 0 0 0 0 0 1 0 22
23 97.6 101.7 0 0 0 0 0 0 0 0 0 0 1 23
24 87.6 92.0 0 0 0 0 0 0 0 0 0 0 0 24
25 99.4 97.4 1 0 0 0 0 0 0 0 0 0 0 25
26 98.5 97.0 0 1 0 0 0 0 0 0 0 0 0 26
27 105.2 105.4 0 0 1 0 0 0 0 0 0 0 0 27
28 104.6 102.7 0 0 0 1 0 0 0 0 0 0 0 28
29 97.5 98.1 0 0 0 0 1 0 0 0 0 0 0 29
30 108.9 104.5 0 0 0 0 0 1 0 0 0 0 0 30
31 86.8 87.4 0 0 0 0 0 0 1 0 0 0 0 31
32 88.9 89.9 0 0 0 0 0 0 0 1 0 0 0 32
33 110.3 109.8 0 0 0 0 0 0 0 0 1 0 0 33
34 114.8 111.7 0 0 0 0 0 0 0 0 0 1 0 34
35 94.6 98.6 0 0 0 0 0 0 0 0 0 0 1 35
36 92.0 96.9 0 0 0 0 0 0 0 0 0 0 0 36
37 93.8 95.1 1 0 0 0 0 0 0 0 0 0 0 37
38 93.8 97.0 0 1 0 0 0 0 0 0 0 0 0 38
39 107.6 112.7 0 0 1 0 0 0 0 0 0 0 0 39
40 101.0 102.9 0 0 0 1 0 0 0 0 0 0 0 40
41 95.4 97.4 0 0 0 0 1 0 0 0 0 0 0 41
42 96.5 111.4 0 0 0 0 0 1 0 0 0 0 0 42
43 89.2 87.4 0 0 0 0 0 0 1 0 0 0 0 43
44 87.1 96.8 0 0 0 0 0 0 0 1 0 0 0 44
45 110.5 114.1 0 0 0 0 0 0 0 0 1 0 0 45
46 110.8 110.3 0 0 0 0 0 0 0 0 0 1 0 46
47 104.2 103.9 0 0 0 0 0 0 0 0 0 0 1 47
48 88.9 101.6 0 0 0 0 0 0 0 0 0 0 0 48
49 89.8 94.6 1 0 0 0 0 0 0 0 0 0 0 49
50 90.0 95.9 0 1 0 0 0 0 0 0 0 0 0 50
51 93.9 104.7 0 0 1 0 0 0 0 0 0 0 0 51
52 91.3 102.8 0 0 0 1 0 0 0 0 0 0 0 52
53 87.8 98.1 0 0 0 0 1 0 0 0 0 0 0 53
54 99.7 113.9 0 0 0 0 0 1 0 0 0 0 0 54
55 73.5 80.9 0 0 0 0 0 0 1 0 0 0 0 55
56 79.2 95.7 0 0 0 0 0 0 0 1 0 0 0 56
57 96.9 113.2 0 0 0 0 0 0 0 0 1 0 0 57
58 95.2 105.9 0 0 0 0 0 0 0 0 0 1 0 58
59 95.6 108.8 0 0 0 0 0 0 0 0 0 0 1 59
60 89.7 102.3 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ProdMetal M1 M2 M3 M4
39.1033 0.5853 4.0763 3.5802 6.2903 6.0129
M5 M6 M7 M8 M9 M10
1.8292 5.3513 -0.1974 0.4784 6.4845 7.7698
M11 t
3.7388 -0.1589
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.51335 -2.60922 0.06143 2.41654 8.05040
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 39.10326 18.93174 2.065 0.04454 *
ProdMetal 0.58527 0.19820 2.953 0.00494 **
M1 4.07634 2.92656 1.393 0.17035
M2 3.58017 2.93028 1.222 0.22801
M3 6.29025 3.71809 1.692 0.09745 .
M4 6.01293 3.09853 1.941 0.05845 .
M5 1.82917 2.97832 0.614 0.54214
M6 5.35127 3.72766 1.436 0.15789
M7 -0.19741 3.74112 -0.053 0.95815
M8 0.47841 2.95082 0.162 0.87191
M9 6.48453 3.81130 1.701 0.09562 .
M10 7.76983 3.93192 1.976 0.05416 .
M11 3.73879 3.16716 1.180 0.24388
t -0.15887 0.03651 -4.351 7.46e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.586 on 46 degrees of freedom
Multiple R-squared: 0.762, Adjusted R-squared: 0.6948
F-statistic: 11.33 on 13 and 46 DF, p-value: 2.42e-10
> 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,] 0.1233225 0.2466449 0.8766775
[2,] 0.1137249 0.2274497 0.8862751
[3,] 0.5502614 0.8994772 0.4497386
[4,] 0.4529614 0.9059228 0.5470386
[5,] 0.3558673 0.7117347 0.6441327
[6,] 0.4034990 0.8069981 0.5965010
[7,] 0.4034009 0.8068017 0.5965991
[8,] 0.4640094 0.9280187 0.5359906
[9,] 0.3749494 0.7498988 0.6250506
[10,] 0.2887376 0.5774753 0.7112624
[11,] 0.2120110 0.4240219 0.7879890
[12,] 0.1436783 0.2873566 0.8563217
[13,] 0.1170690 0.2341380 0.8829310
[14,] 0.2119323 0.4238646 0.7880677
[15,] 0.2514165 0.5028329 0.7485835
[16,] 0.3394385 0.6788770 0.6605615
[17,] 0.3099977 0.6199953 0.6900023
[18,] 0.4130881 0.8261762 0.5869119
[19,] 0.3939797 0.7879594 0.6060203
[20,] 0.3267411 0.6534822 0.6732589
[21,] 0.3019532 0.6039064 0.6980468
[22,] 0.3052086 0.6104172 0.6947914
[23,] 0.2590531 0.5181062 0.7409469
[24,] 0.1954777 0.3909554 0.8045223
[25,] 0.1201111 0.2402222 0.8798889
[26,] 0.3789907 0.7579814 0.6210093
[27,] 0.2431061 0.4862121 0.7568939
> postscript(file="/var/www/html/rcomp/tmp/1ge3y1258984713.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2krhg1258984714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39zc71258984714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pbqm1258984714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bivu1258984714.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 7
-0.9457575 -2.5856818 -2.3311067 -5.2010897 -6.0774629 1.0426241 1.4990618
8 9 10 11 12 13 14
1.5638583 -3.9706183 -7.5133478 -4.9679986 1.4188346 -2.1176431 -0.1040750
15 16 17 18 19 20 21
2.1505000 5.0839810 0.4704130 0.2269377 -5.1209026 6.2919727 -1.1173289
22 23 24 25 26 27 28
-2.6798199 -1.1104313 -1.5356177 3.1864319 3.1755821 2.4080674 3.8244937
29 30 31 32 33 34 35
3.7593829 8.0503956 1.6661177 1.7859777 5.6917812 7.9533295 -0.3896801
36 37 38 39 40 41 42
1.9029453 0.8389643 0.3819853 2.4419739 2.0138422 3.9754776 -6.4815884
43 44 45 46 47 48 49
5.9725210 -2.1460064 5.2815083 6.6791157 8.0147735 -2.0414370 -0.9619957
50 51 52 53 54 55 56
-0.8678106 -4.6694347 -5.7212272 -2.1278106 -2.8383690 -4.0167979 -7.4958023
57 58 59 60
-5.8853423 -4.4392775 -1.5466635 0.2552748
> postscript(file="/var/www/html/rcomp/tmp/64y581258984714.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 -0.9457575 NA
1 -2.5856818 -0.9457575
2 -2.3311067 -2.5856818
3 -5.2010897 -2.3311067
4 -6.0774629 -5.2010897
5 1.0426241 -6.0774629
6 1.4990618 1.0426241
7 1.5638583 1.4990618
8 -3.9706183 1.5638583
9 -7.5133478 -3.9706183
10 -4.9679986 -7.5133478
11 1.4188346 -4.9679986
12 -2.1176431 1.4188346
13 -0.1040750 -2.1176431
14 2.1505000 -0.1040750
15 5.0839810 2.1505000
16 0.4704130 5.0839810
17 0.2269377 0.4704130
18 -5.1209026 0.2269377
19 6.2919727 -5.1209026
20 -1.1173289 6.2919727
21 -2.6798199 -1.1173289
22 -1.1104313 -2.6798199
23 -1.5356177 -1.1104313
24 3.1864319 -1.5356177
25 3.1755821 3.1864319
26 2.4080674 3.1755821
27 3.8244937 2.4080674
28 3.7593829 3.8244937
29 8.0503956 3.7593829
30 1.6661177 8.0503956
31 1.7859777 1.6661177
32 5.6917812 1.7859777
33 7.9533295 5.6917812
34 -0.3896801 7.9533295
35 1.9029453 -0.3896801
36 0.8389643 1.9029453
37 0.3819853 0.8389643
38 2.4419739 0.3819853
39 2.0138422 2.4419739
40 3.9754776 2.0138422
41 -6.4815884 3.9754776
42 5.9725210 -6.4815884
43 -2.1460064 5.9725210
44 5.2815083 -2.1460064
45 6.6791157 5.2815083
46 8.0147735 6.6791157
47 -2.0414370 8.0147735
48 -0.9619957 -2.0414370
49 -0.8678106 -0.9619957
50 -4.6694347 -0.8678106
51 -5.7212272 -4.6694347
52 -2.1278106 -5.7212272
53 -2.8383690 -2.1278106
54 -4.0167979 -2.8383690
55 -7.4958023 -4.0167979
56 -5.8853423 -7.4958023
57 -4.4392775 -5.8853423
58 -1.5466635 -4.4392775
59 0.2552748 -1.5466635
60 NA 0.2552748
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.5856818 -0.9457575
[2,] -2.3311067 -2.5856818
[3,] -5.2010897 -2.3311067
[4,] -6.0774629 -5.2010897
[5,] 1.0426241 -6.0774629
[6,] 1.4990618 1.0426241
[7,] 1.5638583 1.4990618
[8,] -3.9706183 1.5638583
[9,] -7.5133478 -3.9706183
[10,] -4.9679986 -7.5133478
[11,] 1.4188346 -4.9679986
[12,] -2.1176431 1.4188346
[13,] -0.1040750 -2.1176431
[14,] 2.1505000 -0.1040750
[15,] 5.0839810 2.1505000
[16,] 0.4704130 5.0839810
[17,] 0.2269377 0.4704130
[18,] -5.1209026 0.2269377
[19,] 6.2919727 -5.1209026
[20,] -1.1173289 6.2919727
[21,] -2.6798199 -1.1173289
[22,] -1.1104313 -2.6798199
[23,] -1.5356177 -1.1104313
[24,] 3.1864319 -1.5356177
[25,] 3.1755821 3.1864319
[26,] 2.4080674 3.1755821
[27,] 3.8244937 2.4080674
[28,] 3.7593829 3.8244937
[29,] 8.0503956 3.7593829
[30,] 1.6661177 8.0503956
[31,] 1.7859777 1.6661177
[32,] 5.6917812 1.7859777
[33,] 7.9533295 5.6917812
[34,] -0.3896801 7.9533295
[35,] 1.9029453 -0.3896801
[36,] 0.8389643 1.9029453
[37,] 0.3819853 0.8389643
[38,] 2.4419739 0.3819853
[39,] 2.0138422 2.4419739
[40,] 3.9754776 2.0138422
[41,] -6.4815884 3.9754776
[42,] 5.9725210 -6.4815884
[43,] -2.1460064 5.9725210
[44,] 5.2815083 -2.1460064
[45,] 6.6791157 5.2815083
[46,] 8.0147735 6.6791157
[47,] -2.0414370 8.0147735
[48,] -0.9619957 -2.0414370
[49,] -0.8678106 -0.9619957
[50,] -4.6694347 -0.8678106
[51,] -5.7212272 -4.6694347
[52,] -2.1278106 -5.7212272
[53,] -2.8383690 -2.1278106
[54,] -4.0167979 -2.8383690
[55,] -7.4958023 -4.0167979
[56,] -5.8853423 -7.4958023
[57,] -4.4392775 -5.8853423
[58,] -1.5466635 -4.4392775
[59,] 0.2552748 -1.5466635
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.5856818 -0.9457575
2 -2.3311067 -2.5856818
3 -5.2010897 -2.3311067
4 -6.0774629 -5.2010897
5 1.0426241 -6.0774629
6 1.4990618 1.0426241
7 1.5638583 1.4990618
8 -3.9706183 1.5638583
9 -7.5133478 -3.9706183
10 -4.9679986 -7.5133478
11 1.4188346 -4.9679986
12 -2.1176431 1.4188346
13 -0.1040750 -2.1176431
14 2.1505000 -0.1040750
15 5.0839810 2.1505000
16 0.4704130 5.0839810
17 0.2269377 0.4704130
18 -5.1209026 0.2269377
19 6.2919727 -5.1209026
20 -1.1173289 6.2919727
21 -2.6798199 -1.1173289
22 -1.1104313 -2.6798199
23 -1.5356177 -1.1104313
24 3.1864319 -1.5356177
25 3.1755821 3.1864319
26 2.4080674 3.1755821
27 3.8244937 2.4080674
28 3.7593829 3.8244937
29 8.0503956 3.7593829
30 1.6661177 8.0503956
31 1.7859777 1.6661177
32 5.6917812 1.7859777
33 7.9533295 5.6917812
34 -0.3896801 7.9533295
35 1.9029453 -0.3896801
36 0.8389643 1.9029453
37 0.3819853 0.8389643
38 2.4419739 0.3819853
39 2.0138422 2.4419739
40 3.9754776 2.0138422
41 -6.4815884 3.9754776
42 5.9725210 -6.4815884
43 -2.1460064 5.9725210
44 5.2815083 -2.1460064
45 6.6791157 5.2815083
46 8.0147735 6.6791157
47 -2.0414370 8.0147735
48 -0.9619957 -2.0414370
49 -0.8678106 -0.9619957
50 -4.6694347 -0.8678106
51 -5.7212272 -4.6694347
52 -2.1278106 -5.7212272
53 -2.8383690 -2.1278106
54 -4.0167979 -2.8383690
55 -7.4958023 -4.0167979
56 -5.8853423 -7.4958023
57 -4.4392775 -5.8853423
58 -1.5466635 -4.4392775
59 0.2552748 -1.5466635
> 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/72tbd1258984714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8olyc1258984714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yiqz1258984714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10381h1258984714.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11e0bm1258984714.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/12y3ac1258984714.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/13xf391258984714.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/14yy8r1258984714.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/15pzml1258984714.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/16k5u61258984714.tab")
+ }
>
> system("convert tmp/1ge3y1258984713.ps tmp/1ge3y1258984713.png")
> system("convert tmp/2krhg1258984714.ps tmp/2krhg1258984714.png")
> system("convert tmp/39zc71258984714.ps tmp/39zc71258984714.png")
> system("convert tmp/4pbqm1258984714.ps tmp/4pbqm1258984714.png")
> system("convert tmp/5bivu1258984714.ps tmp/5bivu1258984714.png")
> system("convert tmp/64y581258984714.ps tmp/64y581258984714.png")
> system("convert tmp/72tbd1258984714.ps tmp/72tbd1258984714.png")
> system("convert tmp/8olyc1258984714.ps tmp/8olyc1258984714.png")
> system("convert tmp/9yiqz1258984714.ps tmp/9yiqz1258984714.png")
> system("convert tmp/10381h1258984714.ps tmp/10381h1258984714.png")
>
>
> proc.time()
user system elapsed
2.434 1.559 3.285