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(9.9,8.2,9.8,8,9.3,7.5,8.3,6.8,8,6.5,8.5,6.6,10.4,7.6,11.1,8,10.9,8.1,10,7.7,9.2,7.5,9.2,7.6,9.5,7.8,9.6,7.8,9.5,7.8,9.1,7.5,8.9,7.5,9,7.1,10.1,7.5,10.3,7.5,10.2,7.6,9.6,7.7,9.2,7.7,9.3,7.9,9.4,8.1,9.4,8.2,9.2,8.2,9,8.2,9,7.9,9,7.3,9.8,6.9,10,6.6,9.8,6.7,9.3,6.9,9,7,9,7.1,9.1,7.2,9.1,7.1,9.1,6.9,9.2,7,8.8,6.8,8.3,6.4,8.4,6.7,8.1,6.6,7.7,6.4,7.9,6.3,7.9,6.2,8,6.5,7.9,6.8,7.6,6.8,7.1,6.4,6.8,6.1,6.5,5.8,6.9,6.1,8.2,7.2,8.7,7.3,8.3,6.9,7.9,6.1,7.5,5.8,7.8,6.2),dim=c(2,60),dimnames=list(c('WLVrouw','WLMan'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WLVrouw','WLMan'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = '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
WLVrouw WLMan M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 9.9 8.2 1 0 0 0 0 0 0 0 0 0 0
2 9.8 8.0 0 1 0 0 0 0 0 0 0 0 0
3 9.3 7.5 0 0 1 0 0 0 0 0 0 0 0
4 8.3 6.8 0 0 0 1 0 0 0 0 0 0 0
5 8.0 6.5 0 0 0 0 1 0 0 0 0 0 0
6 8.5 6.6 0 0 0 0 0 1 0 0 0 0 0
7 10.4 7.6 0 0 0 0 0 0 1 0 0 0 0
8 11.1 8.0 0 0 0 0 0 0 0 1 0 0 0
9 10.9 8.1 0 0 0 0 0 0 0 0 1 0 0
10 10.0 7.7 0 0 0 0 0 0 0 0 0 1 0
11 9.2 7.5 0 0 0 0 0 0 0 0 0 0 1
12 9.2 7.6 0 0 0 0 0 0 0 0 0 0 0
13 9.5 7.8 1 0 0 0 0 0 0 0 0 0 0
14 9.6 7.8 0 1 0 0 0 0 0 0 0 0 0
15 9.5 7.8 0 0 1 0 0 0 0 0 0 0 0
16 9.1 7.5 0 0 0 1 0 0 0 0 0 0 0
17 8.9 7.5 0 0 0 0 1 0 0 0 0 0 0
18 9.0 7.1 0 0 0 0 0 1 0 0 0 0 0
19 10.1 7.5 0 0 0 0 0 0 1 0 0 0 0
20 10.3 7.5 0 0 0 0 0 0 0 1 0 0 0
21 10.2 7.6 0 0 0 0 0 0 0 0 1 0 0
22 9.6 7.7 0 0 0 0 0 0 0 0 0 1 0
23 9.2 7.7 0 0 0 0 0 0 0 0 0 0 1
24 9.3 7.9 0 0 0 0 0 0 0 0 0 0 0
25 9.4 8.1 1 0 0 0 0 0 0 0 0 0 0
26 9.4 8.2 0 1 0 0 0 0 0 0 0 0 0
27 9.2 8.2 0 0 1 0 0 0 0 0 0 0 0
28 9.0 8.2 0 0 0 1 0 0 0 0 0 0 0
29 9.0 7.9 0 0 0 0 1 0 0 0 0 0 0
30 9.0 7.3 0 0 0 0 0 1 0 0 0 0 0
31 9.8 6.9 0 0 0 0 0 0 1 0 0 0 0
32 10.0 6.6 0 0 0 0 0 0 0 1 0 0 0
33 9.8 6.7 0 0 0 0 0 0 0 0 1 0 0
34 9.3 6.9 0 0 0 0 0 0 0 0 0 1 0
35 9.0 7.0 0 0 0 0 0 0 0 0 0 0 1
36 9.0 7.1 0 0 0 0 0 0 0 0 0 0 0
37 9.1 7.2 1 0 0 0 0 0 0 0 0 0 0
38 9.1 7.1 0 1 0 0 0 0 0 0 0 0 0
39 9.1 6.9 0 0 1 0 0 0 0 0 0 0 0
40 9.2 7.0 0 0 0 1 0 0 0 0 0 0 0
41 8.8 6.8 0 0 0 0 1 0 0 0 0 0 0
42 8.3 6.4 0 0 0 0 0 1 0 0 0 0 0
43 8.4 6.7 0 0 0 0 0 0 1 0 0 0 0
44 8.1 6.6 0 0 0 0 0 0 0 1 0 0 0
45 7.7 6.4 0 0 0 0 0 0 0 0 1 0 0
46 7.9 6.3 0 0 0 0 0 0 0 0 0 1 0
47 7.9 6.2 0 0 0 0 0 0 0 0 0 0 1
48 8.0 6.5 0 0 0 0 0 0 0 0 0 0 0
49 7.9 6.8 1 0 0 0 0 0 0 0 0 0 0
50 7.6 6.8 0 1 0 0 0 0 0 0 0 0 0
51 7.1 6.4 0 0 1 0 0 0 0 0 0 0 0
52 6.8 6.1 0 0 0 1 0 0 0 0 0 0 0
53 6.5 5.8 0 0 0 0 1 0 0 0 0 0 0
54 6.9 6.1 0 0 0 0 0 1 0 0 0 0 0
55 8.2 7.2 0 0 0 0 0 0 1 0 0 0 0
56 8.7 7.3 0 0 0 0 0 0 0 1 0 0 0
57 8.3 6.9 0 0 0 0 0 0 0 0 1 0 0
58 7.9 6.1 0 0 0 0 0 0 0 0 0 1 0
59 7.5 5.8 0 0 0 0 0 0 0 0 0 0 1
60 7.8 6.2 0 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) WLMan M1 M2 M3 M4
0.2966 1.1846 -0.1634 -0.1760 -0.1754 -0.2511
M5 M6 M7 M8 M9 M10
-0.2305 0.1065 0.5778 0.8142 0.6252 0.4222
M11
0.1606
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.2037 -0.3869 0.1328 0.2968 1.0708
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2966 0.8912 0.333 0.7408
WLMan 1.1846 0.1210 9.793 6.27e-13 ***
M1 -0.1634 0.3662 -0.446 0.6576
M2 -0.1760 0.3654 -0.482 0.6323
M3 -0.1754 0.3617 -0.485 0.6301
M4 -0.2511 0.3600 -0.697 0.4890
M5 -0.2305 0.3604 -0.639 0.5257
M6 0.1065 0.3625 0.294 0.7703
M7 0.5778 0.3602 1.604 0.1154
M8 0.8142 0.3603 2.260 0.0285 *
M9 0.6252 0.3601 1.736 0.0890 .
M10 0.4222 0.3602 1.172 0.2471
M11 0.1606 0.3609 0.445 0.6583
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5691 on 47 degrees of freedom
Multiple R-squared: 0.7347, Adjusted R-squared: 0.667
F-statistic: 10.85 on 12 and 47 DF, p-value: 7.155e-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,] 7.995218e-04 1.599044e-03 0.99920048
[2,] 1.272739e-04 2.545478e-04 0.99987273
[3,] 9.711197e-06 1.942239e-05 0.99999029
[4,] 1.015153e-05 2.030306e-05 0.99998985
[5,] 1.715364e-05 3.430728e-05 0.99998285
[6,] 4.875305e-06 9.750610e-06 0.99999512
[7,] 1.041666e-05 2.083333e-05 0.99998958
[8,] 3.456646e-06 6.913291e-06 0.99999654
[9,] 1.139912e-06 2.279824e-06 0.99999886
[10,] 2.027242e-06 4.054484e-06 0.99999797
[11,] 1.086670e-05 2.173340e-05 0.99998913
[12,] 4.402663e-05 8.805325e-05 0.99995597
[13,] 4.726188e-05 9.452375e-05 0.99995274
[14,] 2.271396e-05 4.542793e-05 0.99997729
[15,] 7.129128e-06 1.425826e-05 0.99999287
[16,] 7.921548e-06 1.584310e-05 0.99999208
[17,] 9.287854e-05 1.857571e-04 0.99990712
[18,] 8.894642e-04 1.778928e-03 0.99911054
[19,] 3.725107e-04 7.450215e-04 0.99962749
[20,] 2.866929e-04 5.733858e-04 0.99971331
[21,] 1.625631e-04 3.251262e-04 0.99983744
[22,] 7.178408e-05 1.435682e-04 0.99992822
[23,] 8.748319e-05 1.749664e-04 0.99991252
[24,] 4.019494e-04 8.038988e-04 0.99959805
[25,] 4.911420e-03 9.822840e-03 0.99508858
[26,] 3.823435e-02 7.646869e-02 0.96176565
[27,] 6.004845e-01 7.990310e-01 0.39951550
[28,] 9.882052e-01 2.358966e-02 0.01179483
[29,] 9.826062e-01 3.478758e-02 0.01739379
> postscript(file="/var/www/html/rcomp/tmp/1nt9w1258727779.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dq8i1258727779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3d5nn1258727779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40iw81258727779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5maw71258727779.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
0.052920658 0.202459787 0.294153262 0.199078258 0.233847822 0.278461956
7 8 9 10 11 12
0.522459787 0.512304356 0.382765227 0.159689138 -0.141848907 -0.099694560
13 14 15 16 17 18
0.126768480 0.239383698 0.138767396 0.169844569 -0.050771733 0.186152178
19 20 21 22 23 24
0.340921742 0.304614133 0.275075005 -0.240310862 -0.378772818 -0.355080427
25 26 27 28 29 30
-0.328617387 -0.434464124 -0.635080427 -0.759389120 -0.424619555 -0.050771733
31 32 33 34 35 36
0.751693476 1.070771733 0.941232604 0.407384782 0.250460871 0.292615218
37 38 39 40 41 42
0.437540213 0.568617387 0.804924995 0.862154347 0.678461956 0.315385867
43 44 45 46 47 48
-0.411382613 -0.829228267 -0.803381529 -0.281843485 0.098156515 0.003386951
49 50 51 52 53 54
-0.288611965 -0.575996747 -0.602765227 -0.471688053 -0.436918489 -0.729228267
55 56 57 58 59 60
-1.203692391 -1.058461956 -0.795691307 -0.044919573 0.172004338 0.158772818
> postscript(file="/var/www/html/rcomp/tmp/644rt1258727779.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 0.052920658 NA
1 0.202459787 0.052920658
2 0.294153262 0.202459787
3 0.199078258 0.294153262
4 0.233847822 0.199078258
5 0.278461956 0.233847822
6 0.522459787 0.278461956
7 0.512304356 0.522459787
8 0.382765227 0.512304356
9 0.159689138 0.382765227
10 -0.141848907 0.159689138
11 -0.099694560 -0.141848907
12 0.126768480 -0.099694560
13 0.239383698 0.126768480
14 0.138767396 0.239383698
15 0.169844569 0.138767396
16 -0.050771733 0.169844569
17 0.186152178 -0.050771733
18 0.340921742 0.186152178
19 0.304614133 0.340921742
20 0.275075005 0.304614133
21 -0.240310862 0.275075005
22 -0.378772818 -0.240310862
23 -0.355080427 -0.378772818
24 -0.328617387 -0.355080427
25 -0.434464124 -0.328617387
26 -0.635080427 -0.434464124
27 -0.759389120 -0.635080427
28 -0.424619555 -0.759389120
29 -0.050771733 -0.424619555
30 0.751693476 -0.050771733
31 1.070771733 0.751693476
32 0.941232604 1.070771733
33 0.407384782 0.941232604
34 0.250460871 0.407384782
35 0.292615218 0.250460871
36 0.437540213 0.292615218
37 0.568617387 0.437540213
38 0.804924995 0.568617387
39 0.862154347 0.804924995
40 0.678461956 0.862154347
41 0.315385867 0.678461956
42 -0.411382613 0.315385867
43 -0.829228267 -0.411382613
44 -0.803381529 -0.829228267
45 -0.281843485 -0.803381529
46 0.098156515 -0.281843485
47 0.003386951 0.098156515
48 -0.288611965 0.003386951
49 -0.575996747 -0.288611965
50 -0.602765227 -0.575996747
51 -0.471688053 -0.602765227
52 -0.436918489 -0.471688053
53 -0.729228267 -0.436918489
54 -1.203692391 -0.729228267
55 -1.058461956 -1.203692391
56 -0.795691307 -1.058461956
57 -0.044919573 -0.795691307
58 0.172004338 -0.044919573
59 0.158772818 0.172004338
60 NA 0.158772818
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.202459787 0.052920658
[2,] 0.294153262 0.202459787
[3,] 0.199078258 0.294153262
[4,] 0.233847822 0.199078258
[5,] 0.278461956 0.233847822
[6,] 0.522459787 0.278461956
[7,] 0.512304356 0.522459787
[8,] 0.382765227 0.512304356
[9,] 0.159689138 0.382765227
[10,] -0.141848907 0.159689138
[11,] -0.099694560 -0.141848907
[12,] 0.126768480 -0.099694560
[13,] 0.239383698 0.126768480
[14,] 0.138767396 0.239383698
[15,] 0.169844569 0.138767396
[16,] -0.050771733 0.169844569
[17,] 0.186152178 -0.050771733
[18,] 0.340921742 0.186152178
[19,] 0.304614133 0.340921742
[20,] 0.275075005 0.304614133
[21,] -0.240310862 0.275075005
[22,] -0.378772818 -0.240310862
[23,] -0.355080427 -0.378772818
[24,] -0.328617387 -0.355080427
[25,] -0.434464124 -0.328617387
[26,] -0.635080427 -0.434464124
[27,] -0.759389120 -0.635080427
[28,] -0.424619555 -0.759389120
[29,] -0.050771733 -0.424619555
[30,] 0.751693476 -0.050771733
[31,] 1.070771733 0.751693476
[32,] 0.941232604 1.070771733
[33,] 0.407384782 0.941232604
[34,] 0.250460871 0.407384782
[35,] 0.292615218 0.250460871
[36,] 0.437540213 0.292615218
[37,] 0.568617387 0.437540213
[38,] 0.804924995 0.568617387
[39,] 0.862154347 0.804924995
[40,] 0.678461956 0.862154347
[41,] 0.315385867 0.678461956
[42,] -0.411382613 0.315385867
[43,] -0.829228267 -0.411382613
[44,] -0.803381529 -0.829228267
[45,] -0.281843485 -0.803381529
[46,] 0.098156515 -0.281843485
[47,] 0.003386951 0.098156515
[48,] -0.288611965 0.003386951
[49,] -0.575996747 -0.288611965
[50,] -0.602765227 -0.575996747
[51,] -0.471688053 -0.602765227
[52,] -0.436918489 -0.471688053
[53,] -0.729228267 -0.436918489
[54,] -1.203692391 -0.729228267
[55,] -1.058461956 -1.203692391
[56,] -0.795691307 -1.058461956
[57,] -0.044919573 -0.795691307
[58,] 0.172004338 -0.044919573
[59,] 0.158772818 0.172004338
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.202459787 0.052920658
2 0.294153262 0.202459787
3 0.199078258 0.294153262
4 0.233847822 0.199078258
5 0.278461956 0.233847822
6 0.522459787 0.278461956
7 0.512304356 0.522459787
8 0.382765227 0.512304356
9 0.159689138 0.382765227
10 -0.141848907 0.159689138
11 -0.099694560 -0.141848907
12 0.126768480 -0.099694560
13 0.239383698 0.126768480
14 0.138767396 0.239383698
15 0.169844569 0.138767396
16 -0.050771733 0.169844569
17 0.186152178 -0.050771733
18 0.340921742 0.186152178
19 0.304614133 0.340921742
20 0.275075005 0.304614133
21 -0.240310862 0.275075005
22 -0.378772818 -0.240310862
23 -0.355080427 -0.378772818
24 -0.328617387 -0.355080427
25 -0.434464124 -0.328617387
26 -0.635080427 -0.434464124
27 -0.759389120 -0.635080427
28 -0.424619555 -0.759389120
29 -0.050771733 -0.424619555
30 0.751693476 -0.050771733
31 1.070771733 0.751693476
32 0.941232604 1.070771733
33 0.407384782 0.941232604
34 0.250460871 0.407384782
35 0.292615218 0.250460871
36 0.437540213 0.292615218
37 0.568617387 0.437540213
38 0.804924995 0.568617387
39 0.862154347 0.804924995
40 0.678461956 0.862154347
41 0.315385867 0.678461956
42 -0.411382613 0.315385867
43 -0.829228267 -0.411382613
44 -0.803381529 -0.829228267
45 -0.281843485 -0.803381529
46 0.098156515 -0.281843485
47 0.003386951 0.098156515
48 -0.288611965 0.003386951
49 -0.575996747 -0.288611965
50 -0.602765227 -0.575996747
51 -0.471688053 -0.602765227
52 -0.436918489 -0.471688053
53 -0.729228267 -0.436918489
54 -1.203692391 -0.729228267
55 -1.058461956 -1.203692391
56 -0.795691307 -1.058461956
57 -0.044919573 -0.795691307
58 0.172004338 -0.044919573
59 0.158772818 0.172004338
> 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/7ir7j1258727779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8aq8t1258727779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9snqr1258727779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cnjc1258727779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ccth1258727779.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/12b3eh1258727779.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/13d3991258727779.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/14l3y01258727780.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/154y8x1258727780.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/16c85q1258727780.tab")
+ }
>
> system("convert tmp/1nt9w1258727779.ps tmp/1nt9w1258727779.png")
> system("convert tmp/2dq8i1258727779.ps tmp/2dq8i1258727779.png")
> system("convert tmp/3d5nn1258727779.ps tmp/3d5nn1258727779.png")
> system("convert tmp/40iw81258727779.ps tmp/40iw81258727779.png")
> system("convert tmp/5maw71258727779.ps tmp/5maw71258727779.png")
> system("convert tmp/644rt1258727779.ps tmp/644rt1258727779.png")
> system("convert tmp/7ir7j1258727779.ps tmp/7ir7j1258727779.png")
> system("convert tmp/8aq8t1258727779.ps tmp/8aq8t1258727779.png")
> system("convert tmp/9snqr1258727779.ps tmp/9snqr1258727779.png")
> system("convert tmp/10cnjc1258727779.ps tmp/10cnjc1258727779.png")
>
>
> proc.time()
user system elapsed
2.459 1.581 4.967