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(111.4,0,87.4,0,96.8,0,114.1,0,110.3,0,103.9,0,101.6,0,94.6,0,95.9,0,104.7,0,102.8,0,98.1,0,113.9,0,80.9,0,95.7,0,113.2,0,105.9,0,108.8,0,102.3,0,99,0,100.7,0,115.5,0,100.7,0,109.9,0,114.6,0,85.4,0,100.5,0,114.8,0,116.5,0,112.9,0,102,0,106,0,105.3,0,118.8,0,106.1,0,109.3,0,117.2,0,92.5,0,104.2,0,112.5,0,122.4,0,113.3,0,100,0,110.7,0,112.8,0,109.8,0,117.3,0,109.1,0,115.9,0,96,0,99.8,0,116.8,1,115.7,1,99.4,1,94.3,1,91,1,93.2,1,103.1,1,94.1,1,91.8,1,102.7,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 111.4 0 1 0 0 0 0 0 0 0 0 0 0
2 87.4 0 0 1 0 0 0 0 0 0 0 0 0
3 96.8 0 0 0 1 0 0 0 0 0 0 0 0
4 114.1 0 0 0 0 1 0 0 0 0 0 0 0
5 110.3 0 0 0 0 0 1 0 0 0 0 0 0
6 103.9 0 0 0 0 0 0 1 0 0 0 0 0
7 101.6 0 0 0 0 0 0 0 1 0 0 0 0
8 94.6 0 0 0 0 0 0 0 0 1 0 0 0
9 95.9 0 0 0 0 0 0 0 0 0 1 0 0
10 104.7 0 0 0 0 0 0 0 0 0 0 1 0
11 102.8 0 0 0 0 0 0 0 0 0 0 0 1
12 98.1 0 0 0 0 0 0 0 0 0 0 0 0
13 113.9 0 1 0 0 0 0 0 0 0 0 0 0
14 80.9 0 0 1 0 0 0 0 0 0 0 0 0
15 95.7 0 0 0 1 0 0 0 0 0 0 0 0
16 113.2 0 0 0 0 1 0 0 0 0 0 0 0
17 105.9 0 0 0 0 0 1 0 0 0 0 0 0
18 108.8 0 0 0 0 0 0 1 0 0 0 0 0
19 102.3 0 0 0 0 0 0 0 1 0 0 0 0
20 99.0 0 0 0 0 0 0 0 0 1 0 0 0
21 100.7 0 0 0 0 0 0 0 0 0 1 0 0
22 115.5 0 0 0 0 0 0 0 0 0 0 1 0
23 100.7 0 0 0 0 0 0 0 0 0 0 0 1
24 109.9 0 0 0 0 0 0 0 0 0 0 0 0
25 114.6 0 1 0 0 0 0 0 0 0 0 0 0
26 85.4 0 0 1 0 0 0 0 0 0 0 0 0
27 100.5 0 0 0 1 0 0 0 0 0 0 0 0
28 114.8 0 0 0 0 1 0 0 0 0 0 0 0
29 116.5 0 0 0 0 0 1 0 0 0 0 0 0
30 112.9 0 0 0 0 0 0 1 0 0 0 0 0
31 102.0 0 0 0 0 0 0 0 1 0 0 0 0
32 106.0 0 0 0 0 0 0 0 0 1 0 0 0
33 105.3 0 0 0 0 0 0 0 0 0 1 0 0
34 118.8 0 0 0 0 0 0 0 0 0 0 1 0
35 106.1 0 0 0 0 0 0 0 0 0 0 0 1
36 109.3 0 0 0 0 0 0 0 0 0 0 0 0
37 117.2 0 1 0 0 0 0 0 0 0 0 0 0
38 92.5 0 0 1 0 0 0 0 0 0 0 0 0
39 104.2 0 0 0 1 0 0 0 0 0 0 0 0
40 112.5 0 0 0 0 1 0 0 0 0 0 0 0
41 122.4 0 0 0 0 0 1 0 0 0 0 0 0
42 113.3 0 0 0 0 0 0 1 0 0 0 0 0
43 100.0 0 0 0 0 0 0 0 1 0 0 0 0
44 110.7 0 0 0 0 0 0 0 0 1 0 0 0
45 112.8 0 0 0 0 0 0 0 0 0 1 0 0
46 109.8 0 0 0 0 0 0 0 0 0 0 1 0
47 117.3 0 0 0 0 0 0 0 0 0 0 0 1
48 109.1 0 0 0 0 0 0 0 0 0 0 0 0
49 115.9 0 1 0 0 0 0 0 0 0 0 0 0
50 96.0 0 0 1 0 0 0 0 0 0 0 0 0
51 99.8 0 0 0 1 0 0 0 0 0 0 0 0
52 116.8 1 0 0 0 1 0 0 0 0 0 0 0
53 115.7 1 0 0 0 0 1 0 0 0 0 0 0
54 99.4 1 0 0 0 0 0 1 0 0 0 0 0
55 94.3 1 0 0 0 0 0 0 1 0 0 0 0
56 91.0 1 0 0 0 0 0 0 0 1 0 0 0
57 93.2 1 0 0 0 0 0 0 0 0 1 0 0
58 103.1 1 0 0 0 0 0 0 0 0 0 1 0
59 94.1 1 0 0 0 0 0 0 0 0 0 0 1
60 91.8 1 0 0 0 0 0 0 0 0 0 0 0
61 102.7 1 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
105.301 -8.305 8.700 -16.861 -5.901 10.640
M5 M6 M7 M8 M9 M10
10.520 4.020 -3.600 -3.380 -2.060 6.740
M11
0.560
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.921 -2.996 -0.521 3.579 11.439
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 105.301 2.424 43.436 < 2e-16 ***
X -8.305 1.889 -4.396 6.08e-05 ***
M1 8.700 3.243 2.683 0.00999 **
M2 -16.861 3.408 -4.948 9.62e-06 ***
M3 -5.901 3.408 -1.732 0.08975 .
M4 10.640 3.387 3.142 0.00287 **
M5 10.520 3.387 3.106 0.00318 **
M6 4.020 3.387 1.187 0.24105
M7 -3.600 3.387 -1.063 0.29309
M8 -3.380 3.387 -0.998 0.32326
M9 -2.060 3.387 -0.608 0.54587
M10 6.740 3.387 1.990 0.05228 .
M11 0.560 3.387 0.165 0.86936
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.355 on 48 degrees of freedom
Multiple R-squared: 0.7323, Adjusted R-squared: 0.6654
F-statistic: 10.94 on 12 and 48 DF, p-value: 4.934e-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,] 0.18500641 0.3700128 0.8149936
[2,] 0.19509246 0.3901849 0.8049075
[3,] 0.17203816 0.3440763 0.8279618
[4,] 0.09221686 0.1844337 0.9077831
[5,] 0.08454759 0.1690952 0.9154524
[6,] 0.08400939 0.1680188 0.9159906
[7,] 0.23144661 0.4628932 0.7685534
[8,] 0.20757583 0.4151517 0.7924242
[9,] 0.38286888 0.7657378 0.6171311
[10,] 0.29578877 0.5915775 0.7042112
[11,] 0.27859936 0.5571987 0.7214006
[12,] 0.23439245 0.4687849 0.7656075
[13,] 0.18983805 0.3796761 0.8101620
[14,] 0.30779433 0.6155887 0.6922057
[15,] 0.29589324 0.5917865 0.7041068
[16,] 0.21874803 0.4374961 0.7812520
[17,] 0.25671844 0.5134369 0.7432816
[18,] 0.25421154 0.5084231 0.7457885
[19,] 0.30427204 0.6085441 0.6957280
[20,] 0.28229820 0.5645964 0.7177018
[21,] 0.23666026 0.4733205 0.7633397
[22,] 0.18084886 0.3616977 0.8191511
[23,] 0.17137042 0.3427408 0.8286296
[24,] 0.14855790 0.2971158 0.8514421
[25,] 0.44366201 0.8873240 0.5563380
[26,] 0.56113527 0.8777295 0.4388647
[27,] 0.45067667 0.9013533 0.5493233
[28,] 0.58044528 0.8391094 0.4195547
[29,] 0.52309044 0.9538191 0.4769096
[30,] 0.45960342 0.9192068 0.5403966
> postscript(file="/var/www/html/rcomp/tmp/16jlm1258734706.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hm6y1258734706.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3rxxa1258734706.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4f99k1258734706.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5m6aq1258734706.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 = 61
Frequency = 1
1 2 3 4 5 6 7
-2.6008299 -1.0400000 -2.6000000 -1.8409959 -5.5209959 -5.4209959 -0.1009959
8 9 10 11 12 13 14
-7.3209959 -7.3409959 -7.3409959 -3.0609959 -7.2009959 -0.1008299 -7.5400000
15 16 17 18 19 20 21
-3.7000000 -2.7409959 -9.9209959 -0.5209959 0.5990041 -2.9209959 -2.5409959
22 23 24 25 26 27 28
3.4590041 -5.1609959 4.5990041 0.5991701 -3.0400000 1.1000000 -1.1409959
29 30 31 32 33 34 35
0.6790041 3.5790041 0.2990041 4.0790041 2.0590041 6.7590041 0.2390041
36 37 38 39 40 41 42
3.9990041 3.1991701 4.0600000 4.8000000 -3.4409959 6.5790041 3.9790041
43 44 45 46 47 48 49
-1.7009959 8.7790041 9.5590041 -2.2409959 11.4390041 3.7990041 1.8991701
50 51 52 53 54 55 56
7.5600000 0.4000000 9.1639834 8.1839834 -1.6160166 0.9039834 -2.6160166
57 58 59 60 61
-1.7360166 -0.6360166 -3.4560166 -5.1960166 -2.9958506
> postscript(file="/var/www/html/rcomp/tmp/6g30a1258734706.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.6008299 NA
1 -1.0400000 -2.6008299
2 -2.6000000 -1.0400000
3 -1.8409959 -2.6000000
4 -5.5209959 -1.8409959
5 -5.4209959 -5.5209959
6 -0.1009959 -5.4209959
7 -7.3209959 -0.1009959
8 -7.3409959 -7.3209959
9 -7.3409959 -7.3409959
10 -3.0609959 -7.3409959
11 -7.2009959 -3.0609959
12 -0.1008299 -7.2009959
13 -7.5400000 -0.1008299
14 -3.7000000 -7.5400000
15 -2.7409959 -3.7000000
16 -9.9209959 -2.7409959
17 -0.5209959 -9.9209959
18 0.5990041 -0.5209959
19 -2.9209959 0.5990041
20 -2.5409959 -2.9209959
21 3.4590041 -2.5409959
22 -5.1609959 3.4590041
23 4.5990041 -5.1609959
24 0.5991701 4.5990041
25 -3.0400000 0.5991701
26 1.1000000 -3.0400000
27 -1.1409959 1.1000000
28 0.6790041 -1.1409959
29 3.5790041 0.6790041
30 0.2990041 3.5790041
31 4.0790041 0.2990041
32 2.0590041 4.0790041
33 6.7590041 2.0590041
34 0.2390041 6.7590041
35 3.9990041 0.2390041
36 3.1991701 3.9990041
37 4.0600000 3.1991701
38 4.8000000 4.0600000
39 -3.4409959 4.8000000
40 6.5790041 -3.4409959
41 3.9790041 6.5790041
42 -1.7009959 3.9790041
43 8.7790041 -1.7009959
44 9.5590041 8.7790041
45 -2.2409959 9.5590041
46 11.4390041 -2.2409959
47 3.7990041 11.4390041
48 1.8991701 3.7990041
49 7.5600000 1.8991701
50 0.4000000 7.5600000
51 9.1639834 0.4000000
52 8.1839834 9.1639834
53 -1.6160166 8.1839834
54 0.9039834 -1.6160166
55 -2.6160166 0.9039834
56 -1.7360166 -2.6160166
57 -0.6360166 -1.7360166
58 -3.4560166 -0.6360166
59 -5.1960166 -3.4560166
60 -2.9958506 -5.1960166
61 NA -2.9958506
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.0400000 -2.6008299
[2,] -2.6000000 -1.0400000
[3,] -1.8409959 -2.6000000
[4,] -5.5209959 -1.8409959
[5,] -5.4209959 -5.5209959
[6,] -0.1009959 -5.4209959
[7,] -7.3209959 -0.1009959
[8,] -7.3409959 -7.3209959
[9,] -7.3409959 -7.3409959
[10,] -3.0609959 -7.3409959
[11,] -7.2009959 -3.0609959
[12,] -0.1008299 -7.2009959
[13,] -7.5400000 -0.1008299
[14,] -3.7000000 -7.5400000
[15,] -2.7409959 -3.7000000
[16,] -9.9209959 -2.7409959
[17,] -0.5209959 -9.9209959
[18,] 0.5990041 -0.5209959
[19,] -2.9209959 0.5990041
[20,] -2.5409959 -2.9209959
[21,] 3.4590041 -2.5409959
[22,] -5.1609959 3.4590041
[23,] 4.5990041 -5.1609959
[24,] 0.5991701 4.5990041
[25,] -3.0400000 0.5991701
[26,] 1.1000000 -3.0400000
[27,] -1.1409959 1.1000000
[28,] 0.6790041 -1.1409959
[29,] 3.5790041 0.6790041
[30,] 0.2990041 3.5790041
[31,] 4.0790041 0.2990041
[32,] 2.0590041 4.0790041
[33,] 6.7590041 2.0590041
[34,] 0.2390041 6.7590041
[35,] 3.9990041 0.2390041
[36,] 3.1991701 3.9990041
[37,] 4.0600000 3.1991701
[38,] 4.8000000 4.0600000
[39,] -3.4409959 4.8000000
[40,] 6.5790041 -3.4409959
[41,] 3.9790041 6.5790041
[42,] -1.7009959 3.9790041
[43,] 8.7790041 -1.7009959
[44,] 9.5590041 8.7790041
[45,] -2.2409959 9.5590041
[46,] 11.4390041 -2.2409959
[47,] 3.7990041 11.4390041
[48,] 1.8991701 3.7990041
[49,] 7.5600000 1.8991701
[50,] 0.4000000 7.5600000
[51,] 9.1639834 0.4000000
[52,] 8.1839834 9.1639834
[53,] -1.6160166 8.1839834
[54,] 0.9039834 -1.6160166
[55,] -2.6160166 0.9039834
[56,] -1.7360166 -2.6160166
[57,] -0.6360166 -1.7360166
[58,] -3.4560166 -0.6360166
[59,] -5.1960166 -3.4560166
[60,] -2.9958506 -5.1960166
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.0400000 -2.6008299
2 -2.6000000 -1.0400000
3 -1.8409959 -2.6000000
4 -5.5209959 -1.8409959
5 -5.4209959 -5.5209959
6 -0.1009959 -5.4209959
7 -7.3209959 -0.1009959
8 -7.3409959 -7.3209959
9 -7.3409959 -7.3409959
10 -3.0609959 -7.3409959
11 -7.2009959 -3.0609959
12 -0.1008299 -7.2009959
13 -7.5400000 -0.1008299
14 -3.7000000 -7.5400000
15 -2.7409959 -3.7000000
16 -9.9209959 -2.7409959
17 -0.5209959 -9.9209959
18 0.5990041 -0.5209959
19 -2.9209959 0.5990041
20 -2.5409959 -2.9209959
21 3.4590041 -2.5409959
22 -5.1609959 3.4590041
23 4.5990041 -5.1609959
24 0.5991701 4.5990041
25 -3.0400000 0.5991701
26 1.1000000 -3.0400000
27 -1.1409959 1.1000000
28 0.6790041 -1.1409959
29 3.5790041 0.6790041
30 0.2990041 3.5790041
31 4.0790041 0.2990041
32 2.0590041 4.0790041
33 6.7590041 2.0590041
34 0.2390041 6.7590041
35 3.9990041 0.2390041
36 3.1991701 3.9990041
37 4.0600000 3.1991701
38 4.8000000 4.0600000
39 -3.4409959 4.8000000
40 6.5790041 -3.4409959
41 3.9790041 6.5790041
42 -1.7009959 3.9790041
43 8.7790041 -1.7009959
44 9.5590041 8.7790041
45 -2.2409959 9.5590041
46 11.4390041 -2.2409959
47 3.7990041 11.4390041
48 1.8991701 3.7990041
49 7.5600000 1.8991701
50 0.4000000 7.5600000
51 9.1639834 0.4000000
52 8.1839834 9.1639834
53 -1.6160166 8.1839834
54 0.9039834 -1.6160166
55 -2.6160166 0.9039834
56 -1.7360166 -2.6160166
57 -0.6360166 -1.7360166
58 -3.4560166 -0.6360166
59 -5.1960166 -3.4560166
60 -2.9958506 -5.1960166
> 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/7eopr1258734706.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8s5p61258734706.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ak641258734706.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cwzu1258734706.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rtq21258734706.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/12yygf1258734706.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/13k8tp1258734706.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/143a5x1258734706.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/153zhu1258734706.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/16qwhu1258734706.tab")
+ }
>
> system("convert tmp/16jlm1258734706.ps tmp/16jlm1258734706.png")
> system("convert tmp/2hm6y1258734706.ps tmp/2hm6y1258734706.png")
> system("convert tmp/3rxxa1258734706.ps tmp/3rxxa1258734706.png")
> system("convert tmp/4f99k1258734706.ps tmp/4f99k1258734706.png")
> system("convert tmp/5m6aq1258734706.ps tmp/5m6aq1258734706.png")
> system("convert tmp/6g30a1258734706.ps tmp/6g30a1258734706.png")
> system("convert tmp/7eopr1258734706.ps tmp/7eopr1258734706.png")
> system("convert tmp/8s5p61258734706.ps tmp/8s5p61258734706.png")
> system("convert tmp/9ak641258734706.ps tmp/9ak641258734706.png")
> system("convert tmp/10cwzu1258734706.ps tmp/10cwzu1258734706.png")
>
>
> proc.time()
user system elapsed
2.414 1.552 2.824