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(105.29,0,101.23,0,102.33,0,100.26,0,104.13,0,103.54,0,100.02,0,98.66,0,108.64,0,105.67,0,102.66,0,100.3,0,95.13,0,93.2,0,102.84,0,101.36,0,102.55,0,103.12,0,96.3,0,99.13,0,102.23,0,104.3,0,99.58,0,98.45,0,96.23,0,97.62,0,102.32,0,105.23,0,100.05,0,102.66,0,100.98,0,99.2,0,98.36,0,102.56,0,97.33,0,96.22,0,99.22,0,102.32,0,104.22,0,100.06,0,107.23,0,99.62,0,98.32,1,101.23,1,102.33,1,100.6,1,95.63,1,94.63,1,95.66,1,100.78,1,90.36,1,95.45,1,103.65,1,99.89,1,97.68,1,99.62,1,98.33,1,96.23,1,102.65,1,99.35,1,92.65,1,100.6,1,97.67,1),dim=c(2,63),dimnames=list(c('Y','X'),1:63))
> y <- array(NA,dim=c(2,63),dimnames=list(c('Y','X'),1:63))
> 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
1 105.29 0 1 0 0 0 0 0 0 0 0 0 0
2 101.23 0 0 1 0 0 0 0 0 0 0 0 0
3 102.33 0 0 0 1 0 0 0 0 0 0 0 0
4 100.26 0 0 0 0 1 0 0 0 0 0 0 0
5 104.13 0 0 0 0 0 1 0 0 0 0 0 0
6 103.54 0 0 0 0 0 0 1 0 0 0 0 0
7 100.02 0 0 0 0 0 0 0 1 0 0 0 0
8 98.66 0 0 0 0 0 0 0 0 1 0 0 0
9 108.64 0 0 0 0 0 0 0 0 0 1 0 0
10 105.67 0 0 0 0 0 0 0 0 0 0 1 0
11 102.66 0 0 0 0 0 0 0 0 0 0 0 1
12 100.30 0 0 0 0 0 0 0 0 0 0 0 0
13 95.13 0 1 0 0 0 0 0 0 0 0 0 0
14 93.20 0 0 1 0 0 0 0 0 0 0 0 0
15 102.84 0 0 0 1 0 0 0 0 0 0 0 0
16 101.36 0 0 0 0 1 0 0 0 0 0 0 0
17 102.55 0 0 0 0 0 1 0 0 0 0 0 0
18 103.12 0 0 0 0 0 0 1 0 0 0 0 0
19 96.30 0 0 0 0 0 0 0 1 0 0 0 0
20 99.13 0 0 0 0 0 0 0 0 1 0 0 0
21 102.23 0 0 0 0 0 0 0 0 0 1 0 0
22 104.30 0 0 0 0 0 0 0 0 0 0 1 0
23 99.58 0 0 0 0 0 0 0 0 0 0 0 1
24 98.45 0 0 0 0 0 0 0 0 0 0 0 0
25 96.23 0 1 0 0 0 0 0 0 0 0 0 0
26 97.62 0 0 1 0 0 0 0 0 0 0 0 0
27 102.32 0 0 0 1 0 0 0 0 0 0 0 0
28 105.23 0 0 0 0 1 0 0 0 0 0 0 0
29 100.05 0 0 0 0 0 1 0 0 0 0 0 0
30 102.66 0 0 0 0 0 0 1 0 0 0 0 0
31 100.98 0 0 0 0 0 0 0 1 0 0 0 0
32 99.20 0 0 0 0 0 0 0 0 1 0 0 0
33 98.36 0 0 0 0 0 0 0 0 0 1 0 0
34 102.56 0 0 0 0 0 0 0 0 0 0 1 0
35 97.33 0 0 0 0 0 0 0 0 0 0 0 1
36 96.22 0 0 0 0 0 0 0 0 0 0 0 0
37 99.22 0 1 0 0 0 0 0 0 0 0 0 0
38 102.32 0 0 1 0 0 0 0 0 0 0 0 0
39 104.22 0 0 0 1 0 0 0 0 0 0 0 0
40 100.06 0 0 0 0 1 0 0 0 0 0 0 0
41 107.23 0 0 0 0 0 1 0 0 0 0 0 0
42 99.62 0 0 0 0 0 0 1 0 0 0 0 0
43 98.32 1 0 0 0 0 0 0 1 0 0 0 0
44 101.23 1 0 0 0 0 0 0 0 1 0 0 0
45 102.33 1 0 0 0 0 0 0 0 0 1 0 0
46 100.60 1 0 0 0 0 0 0 0 0 0 1 0
47 95.63 1 0 0 0 0 0 0 0 0 0 0 1
48 94.63 1 0 0 0 0 0 0 0 0 0 0 0
49 95.66 1 1 0 0 0 0 0 0 0 0 0 0
50 100.78 1 0 1 0 0 0 0 0 0 0 0 0
51 90.36 1 0 0 1 0 0 0 0 0 0 0 0
52 95.45 1 0 0 0 1 0 0 0 0 0 0 0
53 103.65 1 0 0 0 0 1 0 0 0 0 0 0
54 99.89 1 0 0 0 0 0 1 0 0 0 0 0
55 97.68 1 0 0 0 0 0 0 1 0 0 0 0
56 99.62 1 0 0 0 0 0 0 0 1 0 0 0
57 98.33 1 0 0 0 0 0 0 0 0 1 0 0
58 96.23 1 0 0 0 0 0 0 0 0 0 1 0
59 102.65 1 0 0 0 0 0 0 0 0 0 0 1
60 99.35 1 0 0 0 0 0 0 0 0 0 0 0
61 92.65 1 1 0 0 0 0 0 0 0 0 0 0
62 100.60 1 0 1 0 0 0 0 0 0 0 0 0
63 97.67 1 0 0 1 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
98.806 -2.540 -0.596 1.332 1.997 2.174
M5 M6 M7 M8 M9 M10
5.224 3.468 0.870 1.778 4.188 4.082
M11
1.780
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.9031 -1.9521 0.2522 1.7262 7.0799
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 98.8062 1.4491 68.186 <2e-16 ***
X -2.5404 0.8539 -2.975 0.0045 **
M1 -0.5960 1.9076 -0.312 0.7560
M2 1.3323 1.9076 0.698 0.4882
M3 1.9973 1.9076 1.047 0.3001
M4 2.1739 1.9989 1.088 0.2820
M5 5.2239 1.9989 2.613 0.0118 *
M6 3.4679 1.9989 1.735 0.0889 .
M7 0.8700 1.9916 0.437 0.6641
M8 1.7780 1.9916 0.893 0.3763
M9 4.1880 1.9916 2.103 0.0405 *
M10 4.0820 1.9916 2.050 0.0457 *
M11 1.7800 1.9916 0.894 0.3757
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.149 on 50 degrees of freedom
Multiple R-squared: 0.3634, Adjusted R-squared: 0.2107
F-statistic: 2.379 on 12 and 50 DF, p-value: 0.01626
> 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,] 0.9659788 0.06804243 0.03402122
[2,] 0.9312443 0.13751146 0.06875573
[3,] 0.8758079 0.24838414 0.12419207
[4,] 0.8505337 0.29893266 0.14946633
[5,] 0.7772666 0.44546686 0.22273343
[6,] 0.8004355 0.39912895 0.19956448
[7,] 0.7357258 0.52854848 0.26427424
[8,] 0.6696636 0.66067281 0.33033640
[9,] 0.5808418 0.83831645 0.41915822
[10,] 0.5467873 0.90642549 0.45321274
[11,] 0.5087262 0.98254762 0.49127381
[12,] 0.4384523 0.87690462 0.56154769
[13,] 0.5198836 0.96023285 0.48011643
[14,] 0.5818303 0.83633932 0.41816966
[15,] 0.4988035 0.99760702 0.50119649
[16,] 0.4353028 0.87060555 0.56469723
[17,] 0.3808436 0.76168726 0.61915637
[18,] 0.5206728 0.95865446 0.47932723
[19,] 0.4482162 0.89643243 0.55178379
[20,] 0.4846841 0.96936823 0.51531589
[21,] 0.5051925 0.98961508 0.49480754
[22,] 0.4103833 0.82076653 0.58961673
[23,] 0.4154678 0.83093565 0.58453218
[24,] 0.5157912 0.96841755 0.48420878
[25,] 0.4461919 0.89238374 0.55380813
[26,] 0.4250709 0.85014186 0.57492907
[27,] 0.3414570 0.68291397 0.65854302
[28,] 0.2426503 0.48530053 0.75734973
[29,] 0.1698690 0.33973808 0.83013096
[30,] 0.1349608 0.26992156 0.86503922
[31,] 0.1168156 0.23363127 0.88318436
[32,] 0.1863166 0.37263323 0.81368338
> postscript(file="/var/www/html/rcomp/tmp/14ewi1258990028.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qs241258990028.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/346r11258990028.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lozp1258990028.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/544py1258990028.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 = 63
Frequency = 1
1 2 3 4 5 6
7.079869281 1.091535948 1.526535948 -0.720078431 0.099921569 1.265921569
7 8 9 10 11 12
0.343843137 -1.924156863 5.645843137 2.781843137 2.073843137 1.493843137
13 14 15 16 17 18
-3.080130719 -6.938464052 2.036535948 0.379921569 -1.480078431 0.845921569
19 20 21 22 23 24
-3.376156863 -1.454156863 -0.764156863 1.411843137 -1.006156863 -0.356156863
25 26 27 28 29 30
-1.980130719 -2.518464052 1.516535948 4.249921569 -3.980078431 0.385921569
31 32 33 34 35 36
1.303843137 -1.384156863 -4.634156863 -0.328156863 -3.256156863 -2.586156863
37 38 39 40 41 42
1.009869281 2.181535948 3.416535948 -0.920078431 3.199921569 -2.654078431
43 44 45 46 47 48
1.184235294 3.186235294 1.876235294 0.252235294 -2.415764706 -1.635764706
49 50 51 52 53 54
-0.009738562 3.181928105 -7.903071895 -2.989686275 2.160313725 0.156313725
55 56 57 58 59 60
0.544235294 1.576235294 -2.123764706 -4.117764706 4.604235294 3.084235294
61 62 63
-3.019738562 3.001928105 -0.593071895
> postscript(file="/var/www/html/rcomp/tmp/61j2f1258990028.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 = 63
Frequency = 1
lag(myerror, k = 1) myerror
0 7.079869281 NA
1 1.091535948 7.079869281
2 1.526535948 1.091535948
3 -0.720078431 1.526535948
4 0.099921569 -0.720078431
5 1.265921569 0.099921569
6 0.343843137 1.265921569
7 -1.924156863 0.343843137
8 5.645843137 -1.924156863
9 2.781843137 5.645843137
10 2.073843137 2.781843137
11 1.493843137 2.073843137
12 -3.080130719 1.493843137
13 -6.938464052 -3.080130719
14 2.036535948 -6.938464052
15 0.379921569 2.036535948
16 -1.480078431 0.379921569
17 0.845921569 -1.480078431
18 -3.376156863 0.845921569
19 -1.454156863 -3.376156863
20 -0.764156863 -1.454156863
21 1.411843137 -0.764156863
22 -1.006156863 1.411843137
23 -0.356156863 -1.006156863
24 -1.980130719 -0.356156863
25 -2.518464052 -1.980130719
26 1.516535948 -2.518464052
27 4.249921569 1.516535948
28 -3.980078431 4.249921569
29 0.385921569 -3.980078431
30 1.303843137 0.385921569
31 -1.384156863 1.303843137
32 -4.634156863 -1.384156863
33 -0.328156863 -4.634156863
34 -3.256156863 -0.328156863
35 -2.586156863 -3.256156863
36 1.009869281 -2.586156863
37 2.181535948 1.009869281
38 3.416535948 2.181535948
39 -0.920078431 3.416535948
40 3.199921569 -0.920078431
41 -2.654078431 3.199921569
42 1.184235294 -2.654078431
43 3.186235294 1.184235294
44 1.876235294 3.186235294
45 0.252235294 1.876235294
46 -2.415764706 0.252235294
47 -1.635764706 -2.415764706
48 -0.009738562 -1.635764706
49 3.181928105 -0.009738562
50 -7.903071895 3.181928105
51 -2.989686275 -7.903071895
52 2.160313725 -2.989686275
53 0.156313725 2.160313725
54 0.544235294 0.156313725
55 1.576235294 0.544235294
56 -2.123764706 1.576235294
57 -4.117764706 -2.123764706
58 4.604235294 -4.117764706
59 3.084235294 4.604235294
60 -3.019738562 3.084235294
61 3.001928105 -3.019738562
62 -0.593071895 3.001928105
63 NA -0.593071895
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.091535948 7.079869281
[2,] 1.526535948 1.091535948
[3,] -0.720078431 1.526535948
[4,] 0.099921569 -0.720078431
[5,] 1.265921569 0.099921569
[6,] 0.343843137 1.265921569
[7,] -1.924156863 0.343843137
[8,] 5.645843137 -1.924156863
[9,] 2.781843137 5.645843137
[10,] 2.073843137 2.781843137
[11,] 1.493843137 2.073843137
[12,] -3.080130719 1.493843137
[13,] -6.938464052 -3.080130719
[14,] 2.036535948 -6.938464052
[15,] 0.379921569 2.036535948
[16,] -1.480078431 0.379921569
[17,] 0.845921569 -1.480078431
[18,] -3.376156863 0.845921569
[19,] -1.454156863 -3.376156863
[20,] -0.764156863 -1.454156863
[21,] 1.411843137 -0.764156863
[22,] -1.006156863 1.411843137
[23,] -0.356156863 -1.006156863
[24,] -1.980130719 -0.356156863
[25,] -2.518464052 -1.980130719
[26,] 1.516535948 -2.518464052
[27,] 4.249921569 1.516535948
[28,] -3.980078431 4.249921569
[29,] 0.385921569 -3.980078431
[30,] 1.303843137 0.385921569
[31,] -1.384156863 1.303843137
[32,] -4.634156863 -1.384156863
[33,] -0.328156863 -4.634156863
[34,] -3.256156863 -0.328156863
[35,] -2.586156863 -3.256156863
[36,] 1.009869281 -2.586156863
[37,] 2.181535948 1.009869281
[38,] 3.416535948 2.181535948
[39,] -0.920078431 3.416535948
[40,] 3.199921569 -0.920078431
[41,] -2.654078431 3.199921569
[42,] 1.184235294 -2.654078431
[43,] 3.186235294 1.184235294
[44,] 1.876235294 3.186235294
[45,] 0.252235294 1.876235294
[46,] -2.415764706 0.252235294
[47,] -1.635764706 -2.415764706
[48,] -0.009738562 -1.635764706
[49,] 3.181928105 -0.009738562
[50,] -7.903071895 3.181928105
[51,] -2.989686275 -7.903071895
[52,] 2.160313725 -2.989686275
[53,] 0.156313725 2.160313725
[54,] 0.544235294 0.156313725
[55,] 1.576235294 0.544235294
[56,] -2.123764706 1.576235294
[57,] -4.117764706 -2.123764706
[58,] 4.604235294 -4.117764706
[59,] 3.084235294 4.604235294
[60,] -3.019738562 3.084235294
[61,] 3.001928105 -3.019738562
[62,] -0.593071895 3.001928105
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.091535948 7.079869281
2 1.526535948 1.091535948
3 -0.720078431 1.526535948
4 0.099921569 -0.720078431
5 1.265921569 0.099921569
6 0.343843137 1.265921569
7 -1.924156863 0.343843137
8 5.645843137 -1.924156863
9 2.781843137 5.645843137
10 2.073843137 2.781843137
11 1.493843137 2.073843137
12 -3.080130719 1.493843137
13 -6.938464052 -3.080130719
14 2.036535948 -6.938464052
15 0.379921569 2.036535948
16 -1.480078431 0.379921569
17 0.845921569 -1.480078431
18 -3.376156863 0.845921569
19 -1.454156863 -3.376156863
20 -0.764156863 -1.454156863
21 1.411843137 -0.764156863
22 -1.006156863 1.411843137
23 -0.356156863 -1.006156863
24 -1.980130719 -0.356156863
25 -2.518464052 -1.980130719
26 1.516535948 -2.518464052
27 4.249921569 1.516535948
28 -3.980078431 4.249921569
29 0.385921569 -3.980078431
30 1.303843137 0.385921569
31 -1.384156863 1.303843137
32 -4.634156863 -1.384156863
33 -0.328156863 -4.634156863
34 -3.256156863 -0.328156863
35 -2.586156863 -3.256156863
36 1.009869281 -2.586156863
37 2.181535948 1.009869281
38 3.416535948 2.181535948
39 -0.920078431 3.416535948
40 3.199921569 -0.920078431
41 -2.654078431 3.199921569
42 1.184235294 -2.654078431
43 3.186235294 1.184235294
44 1.876235294 3.186235294
45 0.252235294 1.876235294
46 -2.415764706 0.252235294
47 -1.635764706 -2.415764706
48 -0.009738562 -1.635764706
49 3.181928105 -0.009738562
50 -7.903071895 3.181928105
51 -2.989686275 -7.903071895
52 2.160313725 -2.989686275
53 0.156313725 2.160313725
54 0.544235294 0.156313725
55 1.576235294 0.544235294
56 -2.123764706 1.576235294
57 -4.117764706 -2.123764706
58 4.604235294 -4.117764706
59 3.084235294 4.604235294
60 -3.019738562 3.084235294
61 3.001928105 -3.019738562
62 -0.593071895 3.001928105
> 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/7uu4o1258990028.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8306e1258990028.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96n0i1258990028.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10g1tf1258990028.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kb471258990028.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/12ij6g1258990028.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/13lhmg1258990028.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/14p8j81258990028.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/15sevy1258990028.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/160dlb1258990028.tab")
+ }
>
> system("convert tmp/14ewi1258990028.ps tmp/14ewi1258990028.png")
> system("convert tmp/2qs241258990028.ps tmp/2qs241258990028.png")
> system("convert tmp/346r11258990028.ps tmp/346r11258990028.png")
> system("convert tmp/4lozp1258990028.ps tmp/4lozp1258990028.png")
> system("convert tmp/544py1258990028.ps tmp/544py1258990028.png")
> system("convert tmp/61j2f1258990028.ps tmp/61j2f1258990028.png")
> system("convert tmp/7uu4o1258990028.ps tmp/7uu4o1258990028.png")
> system("convert tmp/8306e1258990028.ps tmp/8306e1258990028.png")
> system("convert tmp/96n0i1258990028.ps tmp/96n0i1258990028.png")
> system("convert tmp/10g1tf1258990028.ps tmp/10g1tf1258990028.png")
>
>
> proc.time()
user system elapsed
2.424 1.564 2.937