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(6.3,2,6.2,1.8,6.1,2.7,6.3,2.3,6.5,1.9,6.6,2,6.5,2.3,6.2,2.8,6.2,2.4,5.9,2.3,6.1,2.7,6.1,2.7,6.1,2.9,6.1,3,6.1,2.2,6.4,2.3,6.7,2.8,6.9,2.8,7,2.8,7,2.2,6.8,2.6,6.4,2.8,5.9,2.5,5.5,2.4,5.5,2.3,5.6,1.9,5.8,1.7,5.9,2,6.1,2.1,6.1,1.7,6,1.8,6,1.8,5.9,1.8,5.5,1.3,5.6,1.3,5.4,1.3,5.2,1.2,5.2,1.4,5.2,2.2,5.5,2.9,5.8,3.1,5.8,3.5,5.5,3.6,5.3,4.4,5.1,4.1,5.2,5.1,5.8,5.8,5.8,5.9,5.5,5.4,5,5.5,4.9,4.8,5.3,3.2,6.1,2.7,6.5,2.1,6.8,1.9,6.6,0.6,6.4,0.7,6.4,-0.2,6.6,-1,6.7,-1.7),dim=c(2,60),dimnames=list(c('WMan>25','Infl'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WMan>25','Infl'),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
WMan>25 Infl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 6.3 2.0 1 0 0 0 0 0 0 0 0 0 0
2 6.2 1.8 0 1 0 0 0 0 0 0 0 0 0
3 6.1 2.7 0 0 1 0 0 0 0 0 0 0 0
4 6.3 2.3 0 0 0 1 0 0 0 0 0 0 0
5 6.5 1.9 0 0 0 0 1 0 0 0 0 0 0
6 6.6 2.0 0 0 0 0 0 1 0 0 0 0 0
7 6.5 2.3 0 0 0 0 0 0 1 0 0 0 0
8 6.2 2.8 0 0 0 0 0 0 0 1 0 0 0
9 6.2 2.4 0 0 0 0 0 0 0 0 1 0 0
10 5.9 2.3 0 0 0 0 0 0 0 0 0 1 0
11 6.1 2.7 0 0 0 0 0 0 0 0 0 0 1
12 6.1 2.7 0 0 0 0 0 0 0 0 0 0 0
13 6.1 2.9 1 0 0 0 0 0 0 0 0 0 0
14 6.1 3.0 0 1 0 0 0 0 0 0 0 0 0
15 6.1 2.2 0 0 1 0 0 0 0 0 0 0 0
16 6.4 2.3 0 0 0 1 0 0 0 0 0 0 0
17 6.7 2.8 0 0 0 0 1 0 0 0 0 0 0
18 6.9 2.8 0 0 0 0 0 1 0 0 0 0 0
19 7.0 2.8 0 0 0 0 0 0 1 0 0 0 0
20 7.0 2.2 0 0 0 0 0 0 0 1 0 0 0
21 6.8 2.6 0 0 0 0 0 0 0 0 1 0 0
22 6.4 2.8 0 0 0 0 0 0 0 0 0 1 0
23 5.9 2.5 0 0 0 0 0 0 0 0 0 0 1
24 5.5 2.4 0 0 0 0 0 0 0 0 0 0 0
25 5.5 2.3 1 0 0 0 0 0 0 0 0 0 0
26 5.6 1.9 0 1 0 0 0 0 0 0 0 0 0
27 5.8 1.7 0 0 1 0 0 0 0 0 0 0 0
28 5.9 2.0 0 0 0 1 0 0 0 0 0 0 0
29 6.1 2.1 0 0 0 0 1 0 0 0 0 0 0
30 6.1 1.7 0 0 0 0 0 1 0 0 0 0 0
31 6.0 1.8 0 0 0 0 0 0 1 0 0 0 0
32 6.0 1.8 0 0 0 0 0 0 0 1 0 0 0
33 5.9 1.8 0 0 0 0 0 0 0 0 1 0 0
34 5.5 1.3 0 0 0 0 0 0 0 0 0 1 0
35 5.6 1.3 0 0 0 0 0 0 0 0 0 0 1
36 5.4 1.3 0 0 0 0 0 0 0 0 0 0 0
37 5.2 1.2 1 0 0 0 0 0 0 0 0 0 0
38 5.2 1.4 0 1 0 0 0 0 0 0 0 0 0
39 5.2 2.2 0 0 1 0 0 0 0 0 0 0 0
40 5.5 2.9 0 0 0 1 0 0 0 0 0 0 0
41 5.8 3.1 0 0 0 0 1 0 0 0 0 0 0
42 5.8 3.5 0 0 0 0 0 1 0 0 0 0 0
43 5.5 3.6 0 0 0 0 0 0 1 0 0 0 0
44 5.3 4.4 0 0 0 0 0 0 0 1 0 0 0
45 5.1 4.1 0 0 0 0 0 0 0 0 1 0 0
46 5.2 5.1 0 0 0 0 0 0 0 0 0 1 0
47 5.8 5.8 0 0 0 0 0 0 0 0 0 0 1
48 5.8 5.9 0 0 0 0 0 0 0 0 0 0 0
49 5.5 5.4 1 0 0 0 0 0 0 0 0 0 0
50 5.0 5.5 0 1 0 0 0 0 0 0 0 0 0
51 4.9 4.8 0 0 1 0 0 0 0 0 0 0 0
52 5.3 3.2 0 0 0 1 0 0 0 0 0 0 0
53 6.1 2.7 0 0 0 0 1 0 0 0 0 0 0
54 6.5 2.1 0 0 0 0 0 1 0 0 0 0 0
55 6.8 1.9 0 0 0 0 0 0 1 0 0 0 0
56 6.6 0.6 0 0 0 0 0 0 0 1 0 0 0
57 6.4 0.7 0 0 0 0 0 0 0 0 1 0 0
58 6.4 -0.2 0 0 0 0 0 0 0 0 0 1 0
59 6.6 -1.0 0 0 0 0 0 0 0 0 0 0 1
60 6.7 -1.7 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) Infl M1 M2 M3 M4
6.223336 -0.152517 -0.082389 -0.188490 -0.188490 0.044057
M5 M6 M7 M8 M9 M10
0.401007 0.525755 0.514906 0.356604 0.210503 0.001352
M11
0.121352
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.75793 -0.35298 0.05915 0.35950 0.76270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.223336 0.227745 27.326 < 2e-16 ***
Infl -0.152517 0.043116 -3.537 0.000921 ***
M1 -0.082389 0.296290 -0.278 0.782179
M2 -0.188490 0.296134 -0.637 0.527538
M3 -0.188490 0.296134 -0.637 0.527538
M4 0.044057 0.295557 0.149 0.882140
M5 0.401007 0.295506 1.357 0.181257
M6 0.525755 0.295285 1.780 0.081459 .
M7 0.514906 0.295410 1.743 0.087869 .
M8 0.356604 0.295183 1.208 0.233061
M9 0.210503 0.295128 0.713 0.479211
M10 0.001352 0.295064 0.005 0.996362
M11 0.121352 0.295064 0.411 0.682740
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4664 on 47 degrees of freedom
Multiple R-squared: 0.3951, Adjusted R-squared: 0.2407
F-statistic: 2.559 on 12 and 47 DF, p-value: 0.01080
> 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,] 0.003092116 0.006184232 0.9969079
[2,] 0.005347683 0.010695366 0.9946523
[3,] 0.005882999 0.011765998 0.9941170
[4,] 0.016365693 0.032731386 0.9836343
[5,] 0.120155446 0.240310893 0.8798446
[6,] 0.209880556 0.419761112 0.7901194
[7,] 0.254141935 0.508283871 0.7458581
[8,] 0.179664626 0.359329253 0.8203354
[9,] 0.201465522 0.402931044 0.7985345
[10,] 0.266658920 0.533317839 0.7333411
[11,] 0.254711955 0.509423910 0.7452880
[12,] 0.210011532 0.420023064 0.7899885
[13,] 0.187143470 0.374286939 0.8128565
[14,] 0.161454717 0.322909433 0.8385453
[15,] 0.141258312 0.282516625 0.8587417
[16,] 0.129517210 0.259034420 0.8704828
[17,] 0.094820880 0.189641761 0.9051791
[18,] 0.066136950 0.132273901 0.9338630
[19,] 0.048790004 0.097580007 0.9512100
[20,] 0.054482617 0.108965235 0.9455174
[21,] 0.100075186 0.200150373 0.8999248
[22,] 0.178115870 0.356231740 0.8218841
[23,] 0.186640467 0.373280934 0.8133595
[24,] 0.213659293 0.427318586 0.7863407
[25,] 0.260583466 0.521166932 0.7394165
[26,] 0.266544855 0.533089709 0.7334551
[27,] 0.304999913 0.609999825 0.6950001
[28,] 0.557887267 0.884225466 0.4421127
[29,] 0.624439820 0.751120360 0.3755602
> postscript(file="/var/www/html/rcomp/tmp/1halz1258812471.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2djlq1258812471.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3dm8k1258812471.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xiyw1258812471.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5iylr1258812471.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
0.46408695 0.43968420 0.47694966 0.38339588 0.16543935 0.15594279
7 8 9 10 11 12
0.11254691 0.04710756 0.13220137 0.02610069 0.16710756 0.28845996
13 14 15 16 17 18
0.40135240 0.52270481 0.40069107 0.48339588 0.50270481 0.57795653
19 20 21 22 23 24
0.68880550 0.75559725 0.76270481 0.60235927 -0.06339588 -0.35729519
25 26 27 28 29 30
-0.29015790 -0.14506408 0.02443248 -0.06235927 -0.20405721 -0.38981237
31 32 33 34 35 36
-0.46371168 -0.30540962 -0.25930893 -0.52641649 -0.54641649 -0.62506408
37 38 39 40 41 42
-0.75792679 -0.62132267 -0.49930893 -0.32509382 -0.35154004 -0.41528145
43 44 45 46 47 48
-0.68918076 -0.60886496 -0.70851943 -0.24685123 0.33991080 0.47651492
49 50 51 52 53 54
0.18264534 -0.19600226 -0.40276428 -0.47933867 -0.11254691 0.07119450
55 56 57 58 59 60
0.35154004 0.11156977 0.07292218 0.14480775 0.10279401 0.21738439
> postscript(file="/var/www/html/rcomp/tmp/6o79a1258812471.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 0.46408695 NA
1 0.43968420 0.46408695
2 0.47694966 0.43968420
3 0.38339588 0.47694966
4 0.16543935 0.38339588
5 0.15594279 0.16543935
6 0.11254691 0.15594279
7 0.04710756 0.11254691
8 0.13220137 0.04710756
9 0.02610069 0.13220137
10 0.16710756 0.02610069
11 0.28845996 0.16710756
12 0.40135240 0.28845996
13 0.52270481 0.40135240
14 0.40069107 0.52270481
15 0.48339588 0.40069107
16 0.50270481 0.48339588
17 0.57795653 0.50270481
18 0.68880550 0.57795653
19 0.75559725 0.68880550
20 0.76270481 0.75559725
21 0.60235927 0.76270481
22 -0.06339588 0.60235927
23 -0.35729519 -0.06339588
24 -0.29015790 -0.35729519
25 -0.14506408 -0.29015790
26 0.02443248 -0.14506408
27 -0.06235927 0.02443248
28 -0.20405721 -0.06235927
29 -0.38981237 -0.20405721
30 -0.46371168 -0.38981237
31 -0.30540962 -0.46371168
32 -0.25930893 -0.30540962
33 -0.52641649 -0.25930893
34 -0.54641649 -0.52641649
35 -0.62506408 -0.54641649
36 -0.75792679 -0.62506408
37 -0.62132267 -0.75792679
38 -0.49930893 -0.62132267
39 -0.32509382 -0.49930893
40 -0.35154004 -0.32509382
41 -0.41528145 -0.35154004
42 -0.68918076 -0.41528145
43 -0.60886496 -0.68918076
44 -0.70851943 -0.60886496
45 -0.24685123 -0.70851943
46 0.33991080 -0.24685123
47 0.47651492 0.33991080
48 0.18264534 0.47651492
49 -0.19600226 0.18264534
50 -0.40276428 -0.19600226
51 -0.47933867 -0.40276428
52 -0.11254691 -0.47933867
53 0.07119450 -0.11254691
54 0.35154004 0.07119450
55 0.11156977 0.35154004
56 0.07292218 0.11156977
57 0.14480775 0.07292218
58 0.10279401 0.14480775
59 0.21738439 0.10279401
60 NA 0.21738439
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.43968420 0.46408695
[2,] 0.47694966 0.43968420
[3,] 0.38339588 0.47694966
[4,] 0.16543935 0.38339588
[5,] 0.15594279 0.16543935
[6,] 0.11254691 0.15594279
[7,] 0.04710756 0.11254691
[8,] 0.13220137 0.04710756
[9,] 0.02610069 0.13220137
[10,] 0.16710756 0.02610069
[11,] 0.28845996 0.16710756
[12,] 0.40135240 0.28845996
[13,] 0.52270481 0.40135240
[14,] 0.40069107 0.52270481
[15,] 0.48339588 0.40069107
[16,] 0.50270481 0.48339588
[17,] 0.57795653 0.50270481
[18,] 0.68880550 0.57795653
[19,] 0.75559725 0.68880550
[20,] 0.76270481 0.75559725
[21,] 0.60235927 0.76270481
[22,] -0.06339588 0.60235927
[23,] -0.35729519 -0.06339588
[24,] -0.29015790 -0.35729519
[25,] -0.14506408 -0.29015790
[26,] 0.02443248 -0.14506408
[27,] -0.06235927 0.02443248
[28,] -0.20405721 -0.06235927
[29,] -0.38981237 -0.20405721
[30,] -0.46371168 -0.38981237
[31,] -0.30540962 -0.46371168
[32,] -0.25930893 -0.30540962
[33,] -0.52641649 -0.25930893
[34,] -0.54641649 -0.52641649
[35,] -0.62506408 -0.54641649
[36,] -0.75792679 -0.62506408
[37,] -0.62132267 -0.75792679
[38,] -0.49930893 -0.62132267
[39,] -0.32509382 -0.49930893
[40,] -0.35154004 -0.32509382
[41,] -0.41528145 -0.35154004
[42,] -0.68918076 -0.41528145
[43,] -0.60886496 -0.68918076
[44,] -0.70851943 -0.60886496
[45,] -0.24685123 -0.70851943
[46,] 0.33991080 -0.24685123
[47,] 0.47651492 0.33991080
[48,] 0.18264534 0.47651492
[49,] -0.19600226 0.18264534
[50,] -0.40276428 -0.19600226
[51,] -0.47933867 -0.40276428
[52,] -0.11254691 -0.47933867
[53,] 0.07119450 -0.11254691
[54,] 0.35154004 0.07119450
[55,] 0.11156977 0.35154004
[56,] 0.07292218 0.11156977
[57,] 0.14480775 0.07292218
[58,] 0.10279401 0.14480775
[59,] 0.21738439 0.10279401
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.43968420 0.46408695
2 0.47694966 0.43968420
3 0.38339588 0.47694966
4 0.16543935 0.38339588
5 0.15594279 0.16543935
6 0.11254691 0.15594279
7 0.04710756 0.11254691
8 0.13220137 0.04710756
9 0.02610069 0.13220137
10 0.16710756 0.02610069
11 0.28845996 0.16710756
12 0.40135240 0.28845996
13 0.52270481 0.40135240
14 0.40069107 0.52270481
15 0.48339588 0.40069107
16 0.50270481 0.48339588
17 0.57795653 0.50270481
18 0.68880550 0.57795653
19 0.75559725 0.68880550
20 0.76270481 0.75559725
21 0.60235927 0.76270481
22 -0.06339588 0.60235927
23 -0.35729519 -0.06339588
24 -0.29015790 -0.35729519
25 -0.14506408 -0.29015790
26 0.02443248 -0.14506408
27 -0.06235927 0.02443248
28 -0.20405721 -0.06235927
29 -0.38981237 -0.20405721
30 -0.46371168 -0.38981237
31 -0.30540962 -0.46371168
32 -0.25930893 -0.30540962
33 -0.52641649 -0.25930893
34 -0.54641649 -0.52641649
35 -0.62506408 -0.54641649
36 -0.75792679 -0.62506408
37 -0.62132267 -0.75792679
38 -0.49930893 -0.62132267
39 -0.32509382 -0.49930893
40 -0.35154004 -0.32509382
41 -0.41528145 -0.35154004
42 -0.68918076 -0.41528145
43 -0.60886496 -0.68918076
44 -0.70851943 -0.60886496
45 -0.24685123 -0.70851943
46 0.33991080 -0.24685123
47 0.47651492 0.33991080
48 0.18264534 0.47651492
49 -0.19600226 0.18264534
50 -0.40276428 -0.19600226
51 -0.47933867 -0.40276428
52 -0.11254691 -0.47933867
53 0.07119450 -0.11254691
54 0.35154004 0.07119450
55 0.11156977 0.35154004
56 0.07292218 0.11156977
57 0.14480775 0.07292218
58 0.10279401 0.14480775
59 0.21738439 0.10279401
> 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/7h78n1258812471.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84hvn1258812471.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m7rw1258812471.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1049k91258812471.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11744v1258812471.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/12j1kv1258812471.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/13djya1258812471.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/14vkjr1258812471.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/15lopa1258812471.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/16sdbz1258812471.tab")
+ }
>
> system("convert tmp/1halz1258812471.ps tmp/1halz1258812471.png")
> system("convert tmp/2djlq1258812471.ps tmp/2djlq1258812471.png")
> system("convert tmp/3dm8k1258812471.ps tmp/3dm8k1258812471.png")
> system("convert tmp/4xiyw1258812471.ps tmp/4xiyw1258812471.png")
> system("convert tmp/5iylr1258812471.ps tmp/5iylr1258812471.png")
> system("convert tmp/6o79a1258812471.ps tmp/6o79a1258812471.png")
> system("convert tmp/7h78n1258812471.ps tmp/7h78n1258812471.png")
> system("convert tmp/84hvn1258812471.ps tmp/84hvn1258812471.png")
> system("convert tmp/9m7rw1258812471.ps tmp/9m7rw1258812471.png")
> system("convert tmp/1049k91258812471.ps tmp/1049k91258812471.png")
>
>
> proc.time()
user system elapsed
2.417 1.557 3.189