R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(8,0,-10,0,-24,0,-19,0,8,1,24,1,14,1,7,1,9,1,-26,0,19,0,15,0,-1,0,-10,0,-21,0,-14,0,-27,0,26,0,23,0,5,0,19,0,-19,0,24,1,17,1,1,1,-9,1,-16,1,-21,1,-14,1,31,1,27,1,10,1,12,1,-23,1,13,1,26,1,-1,1,4,1,-16,1,-5,1,9,1,23,1,9,1,2,1,10,1,-29,0,17,0,9,0,9,0,-10,0,-23,0,13,0,13,0,-9,0,9,0,5,0,8,0,-18,0,7,1,4,1),dim=c(2,60),dimnames=list(c('Woongebouwen','Conjunctuur'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Woongebouwen','Conjunctuur'),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
Woongebouwen Conjunctuur M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8 0 1 0 0 0 0 0 0 0 0 0 0 1
2 -10 0 0 1 0 0 0 0 0 0 0 0 0 2
3 -24 0 0 0 1 0 0 0 0 0 0 0 0 3
4 -19 0 0 0 0 1 0 0 0 0 0 0 0 4
5 8 1 0 0 0 0 1 0 0 0 0 0 0 5
6 24 1 0 0 0 0 0 1 0 0 0 0 0 6
7 14 1 0 0 0 0 0 0 1 0 0 0 0 7
8 7 1 0 0 0 0 0 0 0 1 0 0 0 8
9 9 1 0 0 0 0 0 0 0 0 1 0 0 9
10 -26 0 0 0 0 0 0 0 0 0 0 1 0 10
11 19 0 0 0 0 0 0 0 0 0 0 0 1 11
12 15 0 0 0 0 0 0 0 0 0 0 0 0 12
13 -1 0 1 0 0 0 0 0 0 0 0 0 0 13
14 -10 0 0 1 0 0 0 0 0 0 0 0 0 14
15 -21 0 0 0 1 0 0 0 0 0 0 0 0 15
16 -14 0 0 0 0 1 0 0 0 0 0 0 0 16
17 -27 0 0 0 0 0 1 0 0 0 0 0 0 17
18 26 0 0 0 0 0 0 1 0 0 0 0 0 18
19 23 0 0 0 0 0 0 0 1 0 0 0 0 19
20 5 0 0 0 0 0 0 0 0 1 0 0 0 20
21 19 0 0 0 0 0 0 0 0 0 1 0 0 21
22 -19 0 0 0 0 0 0 0 0 0 0 1 0 22
23 24 1 0 0 0 0 0 0 0 0 0 0 1 23
24 17 1 0 0 0 0 0 0 0 0 0 0 0 24
25 1 1 1 0 0 0 0 0 0 0 0 0 0 25
26 -9 1 0 1 0 0 0 0 0 0 0 0 0 26
27 -16 1 0 0 1 0 0 0 0 0 0 0 0 27
28 -21 1 0 0 0 1 0 0 0 0 0 0 0 28
29 -14 1 0 0 0 0 1 0 0 0 0 0 0 29
30 31 1 0 0 0 0 0 1 0 0 0 0 0 30
31 27 1 0 0 0 0 0 0 1 0 0 0 0 31
32 10 1 0 0 0 0 0 0 0 1 0 0 0 32
33 12 1 0 0 0 0 0 0 0 0 1 0 0 33
34 -23 1 0 0 0 0 0 0 0 0 0 1 0 34
35 13 1 0 0 0 0 0 0 0 0 0 0 1 35
36 26 1 0 0 0 0 0 0 0 0 0 0 0 36
37 -1 1 1 0 0 0 0 0 0 0 0 0 0 37
38 4 1 0 1 0 0 0 0 0 0 0 0 0 38
39 -16 1 0 0 1 0 0 0 0 0 0 0 0 39
40 -5 1 0 0 0 1 0 0 0 0 0 0 0 40
41 9 1 0 0 0 0 1 0 0 0 0 0 0 41
42 23 1 0 0 0 0 0 1 0 0 0 0 0 42
43 9 1 0 0 0 0 0 0 1 0 0 0 0 43
44 2 1 0 0 0 0 0 0 0 1 0 0 0 44
45 10 1 0 0 0 0 0 0 0 0 1 0 0 45
46 -29 0 0 0 0 0 0 0 0 0 0 1 0 46
47 17 0 0 0 0 0 0 0 0 0 0 0 1 47
48 9 0 0 0 0 0 0 0 0 0 0 0 0 48
49 9 0 1 0 0 0 0 0 0 0 0 0 0 49
50 -10 0 0 1 0 0 0 0 0 0 0 0 0 50
51 -23 0 0 0 1 0 0 0 0 0 0 0 0 51
52 13 0 0 0 0 1 0 0 0 0 0 0 0 52
53 13 0 0 0 0 0 1 0 0 0 0 0 0 53
54 -9 0 0 0 0 0 0 1 0 0 0 0 0 54
55 9 0 0 0 0 0 0 0 1 0 0 0 0 55
56 5 0 0 0 0 0 0 0 0 1 0 0 0 56
57 8 0 0 0 0 0 0 0 0 0 1 0 0 57
58 -18 0 0 0 0 0 0 0 0 0 0 1 0 58
59 7 1 0 0 0 0 0 0 0 0 0 0 1 59
60 4 1 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) Conjunctuur M1 M2 M3 M4
13.45205 2.34425 -10.73239 -20.91410 -33.89580 -23.07751
M5 M6 M7 M8 M9 M10
-16.52806 4.69023 2.10853 -8.47318 -2.65488 -36.29889
M11 t
1.78171 -0.01829
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-26.1544 -3.8170 -0.4098 4.5116 23.5768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.45205 5.18347 2.595 0.012645 *
Conjunctuur 2.34425 2.53582 0.924 0.360074
M1 -10.73239 6.07097 -1.768 0.083723 .
M2 -20.91410 6.06210 -3.450 0.001211 **
M3 -33.89580 6.05408 -5.599 1.15e-06 ***
M4 -23.07751 6.04691 -3.816 0.000403 ***
M5 -16.52806 6.02030 -2.745 0.008594 **
M6 4.69023 6.01468 0.780 0.439504
M7 2.10853 6.00991 0.351 0.727309
M8 -8.47318 6.00601 -1.411 0.165036
M9 -2.65488 6.00297 -0.442 0.660372
M10 -36.29889 6.08534 -5.965 3.27e-07 ***
M11 1.78171 5.99950 0.297 0.767822
t -0.01829 0.07218 -0.253 0.801041
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.485 on 46 degrees of freedom
Multiple R-squared: 0.7352, Adjusted R-squared: 0.6604
F-statistic: 9.826 on 13 and 46 DF, p-value: 2.342e-09
> 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,] 0.10236306 0.20472611 0.8976369
[2,] 0.57593392 0.84813216 0.4240661
[3,] 0.66080886 0.67838229 0.3391911
[4,] 0.54664246 0.90671509 0.4533575
[5,] 0.51786296 0.96427409 0.4821370
[6,] 0.41304370 0.82608740 0.5869563
[7,] 0.31207469 0.62414938 0.6879253
[8,] 0.21979314 0.43958629 0.7802069
[9,] 0.15814418 0.31628837 0.8418558
[10,] 0.10605294 0.21210589 0.8939471
[11,] 0.06776439 0.13552878 0.9322356
[12,] 0.10329260 0.20658520 0.8967074
[13,] 0.22571386 0.45142772 0.7742861
[14,] 0.24656879 0.49313757 0.7534312
[15,] 0.24519055 0.49038110 0.7548094
[16,] 0.17526980 0.35053960 0.8247302
[17,] 0.12080721 0.24161442 0.8791928
[18,] 0.08058335 0.16116670 0.9194166
[19,] 0.06497445 0.12994890 0.9350255
[20,] 0.07934655 0.15869310 0.9206535
[21,] 0.06716844 0.13433687 0.9328316
[22,] 0.07772060 0.15544120 0.9222794
[23,] 0.04857445 0.09714889 0.9514256
[24,] 0.09482773 0.18965546 0.9051723
[25,] 0.10542968 0.21085935 0.8945703
[26,] 0.75843597 0.48312806 0.2415640
[27,] 0.64653909 0.70692183 0.3534609
> postscript(file="/var/www/html/rcomp/tmp/1zhym1227371171.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2znmx1227371171.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3uzfj1227371171.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jigd1227371171.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ahug1227371171.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
5.2986301 -2.5013699 -3.5013699 -9.3013699 8.8232281 3.6232281
7 8 9 10 11 12
-3.7767719 -0.1767719 -3.9767719 -2.9702204 3.9674806 1.7674806
13 14 15 16 17 18
-3.4818344 -2.2818344 -0.2818344 -4.0818344 -23.6129839 8.1870161
19 20 21 22 23 24
7.7870161 0.3870161 8.5870161 4.2493151 6.8427635 1.6427635
25 26 27 28 29 30
-3.6065515 -3.4065515 2.5934485 -13.2065515 -12.7377010 11.0622990
31 32 33 34 35 36
9.6622990 3.2622990 -0.5377010 -1.8754020 -3.9377010 10.8622990
37 38 39 40 41 42
-5.3870161 9.8129839 2.8129839 3.0129839 10.4818344 3.2818344
43 44 45 46 47 48
-8.1181656 -4.5181656 -2.3181656 -5.3116141 2.6260870 -3.5739130
49 50 51 52 53 54
7.1767719 -1.6232281 -1.6232281 23.5767719 17.0456224 -26.1543776
55 56 57 58 59 60
-5.5543776 1.0456224 -1.7543776 5.9079214 -9.4986301 -10.6986301
> postscript(file="/var/www/html/rcomp/tmp/6hnh01227371171.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 5.2986301 NA
1 -2.5013699 5.2986301
2 -3.5013699 -2.5013699
3 -9.3013699 -3.5013699
4 8.8232281 -9.3013699
5 3.6232281 8.8232281
6 -3.7767719 3.6232281
7 -0.1767719 -3.7767719
8 -3.9767719 -0.1767719
9 -2.9702204 -3.9767719
10 3.9674806 -2.9702204
11 1.7674806 3.9674806
12 -3.4818344 1.7674806
13 -2.2818344 -3.4818344
14 -0.2818344 -2.2818344
15 -4.0818344 -0.2818344
16 -23.6129839 -4.0818344
17 8.1870161 -23.6129839
18 7.7870161 8.1870161
19 0.3870161 7.7870161
20 8.5870161 0.3870161
21 4.2493151 8.5870161
22 6.8427635 4.2493151
23 1.6427635 6.8427635
24 -3.6065515 1.6427635
25 -3.4065515 -3.6065515
26 2.5934485 -3.4065515
27 -13.2065515 2.5934485
28 -12.7377010 -13.2065515
29 11.0622990 -12.7377010
30 9.6622990 11.0622990
31 3.2622990 9.6622990
32 -0.5377010 3.2622990
33 -1.8754020 -0.5377010
34 -3.9377010 -1.8754020
35 10.8622990 -3.9377010
36 -5.3870161 10.8622990
37 9.8129839 -5.3870161
38 2.8129839 9.8129839
39 3.0129839 2.8129839
40 10.4818344 3.0129839
41 3.2818344 10.4818344
42 -8.1181656 3.2818344
43 -4.5181656 -8.1181656
44 -2.3181656 -4.5181656
45 -5.3116141 -2.3181656
46 2.6260870 -5.3116141
47 -3.5739130 2.6260870
48 7.1767719 -3.5739130
49 -1.6232281 7.1767719
50 -1.6232281 -1.6232281
51 23.5767719 -1.6232281
52 17.0456224 23.5767719
53 -26.1543776 17.0456224
54 -5.5543776 -26.1543776
55 1.0456224 -5.5543776
56 -1.7543776 1.0456224
57 5.9079214 -1.7543776
58 -9.4986301 5.9079214
59 -10.6986301 -9.4986301
60 NA -10.6986301
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.5013699 5.2986301
[2,] -3.5013699 -2.5013699
[3,] -9.3013699 -3.5013699
[4,] 8.8232281 -9.3013699
[5,] 3.6232281 8.8232281
[6,] -3.7767719 3.6232281
[7,] -0.1767719 -3.7767719
[8,] -3.9767719 -0.1767719
[9,] -2.9702204 -3.9767719
[10,] 3.9674806 -2.9702204
[11,] 1.7674806 3.9674806
[12,] -3.4818344 1.7674806
[13,] -2.2818344 -3.4818344
[14,] -0.2818344 -2.2818344
[15,] -4.0818344 -0.2818344
[16,] -23.6129839 -4.0818344
[17,] 8.1870161 -23.6129839
[18,] 7.7870161 8.1870161
[19,] 0.3870161 7.7870161
[20,] 8.5870161 0.3870161
[21,] 4.2493151 8.5870161
[22,] 6.8427635 4.2493151
[23,] 1.6427635 6.8427635
[24,] -3.6065515 1.6427635
[25,] -3.4065515 -3.6065515
[26,] 2.5934485 -3.4065515
[27,] -13.2065515 2.5934485
[28,] -12.7377010 -13.2065515
[29,] 11.0622990 -12.7377010
[30,] 9.6622990 11.0622990
[31,] 3.2622990 9.6622990
[32,] -0.5377010 3.2622990
[33,] -1.8754020 -0.5377010
[34,] -3.9377010 -1.8754020
[35,] 10.8622990 -3.9377010
[36,] -5.3870161 10.8622990
[37,] 9.8129839 -5.3870161
[38,] 2.8129839 9.8129839
[39,] 3.0129839 2.8129839
[40,] 10.4818344 3.0129839
[41,] 3.2818344 10.4818344
[42,] -8.1181656 3.2818344
[43,] -4.5181656 -8.1181656
[44,] -2.3181656 -4.5181656
[45,] -5.3116141 -2.3181656
[46,] 2.6260870 -5.3116141
[47,] -3.5739130 2.6260870
[48,] 7.1767719 -3.5739130
[49,] -1.6232281 7.1767719
[50,] -1.6232281 -1.6232281
[51,] 23.5767719 -1.6232281
[52,] 17.0456224 23.5767719
[53,] -26.1543776 17.0456224
[54,] -5.5543776 -26.1543776
[55,] 1.0456224 -5.5543776
[56,] -1.7543776 1.0456224
[57,] 5.9079214 -1.7543776
[58,] -9.4986301 5.9079214
[59,] -10.6986301 -9.4986301
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.5013699 5.2986301
2 -3.5013699 -2.5013699
3 -9.3013699 -3.5013699
4 8.8232281 -9.3013699
5 3.6232281 8.8232281
6 -3.7767719 3.6232281
7 -0.1767719 -3.7767719
8 -3.9767719 -0.1767719
9 -2.9702204 -3.9767719
10 3.9674806 -2.9702204
11 1.7674806 3.9674806
12 -3.4818344 1.7674806
13 -2.2818344 -3.4818344
14 -0.2818344 -2.2818344
15 -4.0818344 -0.2818344
16 -23.6129839 -4.0818344
17 8.1870161 -23.6129839
18 7.7870161 8.1870161
19 0.3870161 7.7870161
20 8.5870161 0.3870161
21 4.2493151 8.5870161
22 6.8427635 4.2493151
23 1.6427635 6.8427635
24 -3.6065515 1.6427635
25 -3.4065515 -3.6065515
26 2.5934485 -3.4065515
27 -13.2065515 2.5934485
28 -12.7377010 -13.2065515
29 11.0622990 -12.7377010
30 9.6622990 11.0622990
31 3.2622990 9.6622990
32 -0.5377010 3.2622990
33 -1.8754020 -0.5377010
34 -3.9377010 -1.8754020
35 10.8622990 -3.9377010
36 -5.3870161 10.8622990
37 9.8129839 -5.3870161
38 2.8129839 9.8129839
39 3.0129839 2.8129839
40 10.4818344 3.0129839
41 3.2818344 10.4818344
42 -8.1181656 3.2818344
43 -4.5181656 -8.1181656
44 -2.3181656 -4.5181656
45 -5.3116141 -2.3181656
46 2.6260870 -5.3116141
47 -3.5739130 2.6260870
48 7.1767719 -3.5739130
49 -1.6232281 7.1767719
50 -1.6232281 -1.6232281
51 23.5767719 -1.6232281
52 17.0456224 23.5767719
53 -26.1543776 17.0456224
54 -5.5543776 -26.1543776
55 1.0456224 -5.5543776
56 -1.7543776 1.0456224
57 5.9079214 -1.7543776
58 -9.4986301 5.9079214
59 -10.6986301 -9.4986301
> 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/7kk921227371171.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8w1cf1227371171.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hkns1227371171.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fbst1227371171.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bxls1227371171.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/1219on1227371171.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/13sm1w1227371172.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/14gysu1227371172.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/155xtf1227371172.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/166obs1227371172.tab")
+ }
>
> system("convert tmp/1zhym1227371171.ps tmp/1zhym1227371171.png")
> system("convert tmp/2znmx1227371171.ps tmp/2znmx1227371171.png")
> system("convert tmp/3uzfj1227371171.ps tmp/3uzfj1227371171.png")
> system("convert tmp/4jigd1227371171.ps tmp/4jigd1227371171.png")
> system("convert tmp/5ahug1227371171.ps tmp/5ahug1227371171.png")
> system("convert tmp/6hnh01227371171.ps tmp/6hnh01227371171.png")
> system("convert tmp/7kk921227371171.ps tmp/7kk921227371171.png")
> system("convert tmp/8w1cf1227371171.ps tmp/8w1cf1227371171.png")
> system("convert tmp/9hkns1227371171.ps tmp/9hkns1227371171.png")
> system("convert tmp/10fbst1227371171.ps tmp/10fbst1227371171.png")
>
>
> proc.time()
user system elapsed
2.418 1.594 3.295