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(1.4,8.2,1.2,8.0,1.0,7.5,1.7,6.8,2.4,6.5,2.0,6.6,2.1,7.6,2.0,8.0,1.8,8.1,2.7,7.7,2.3,7.5,1.9,7.6,2.0,7.8,2.3,7.8,2.8,7.8,2.4,7.5,2.3,7.5,2.7,7.1,2.7,7.5,2.9,7.5,3.0,7.6,2.2,7.7,2.3,7.7,2.8,7.9,2.8,8.1,2.8,8.2,2.2,8.2,2.6,8.2,2.8,7.9,2.5,7.3,2.4,6.9,2.3,6.6,1.9,6.7,1.7,6.9,2.0,7.0,2.1,7.1,1.7,7.2,1.8,7.1,1.8,6.9,1.8,7.0,1.3,6.8,1.3,6.4,1.3,6.7,1.2,6.6,1.4,6.4,2.2,6.3,2.9,6.2,3.1,6.5,3.5,6.8,3.6,6.8,4.4,6.4,4.1,6.1,5.1,5.8,5.8,6.1,5.9,7.2,5.4,7.3,5.5,6.9,4.8,6.1,3.2,5.8,2.7,6.2,2.1,7.1,1.9,7.7,0.6,7.9,0.7,7.7),dim=c(2,64),dimnames=list(c('Y','X'),1:64))
> y <- array(NA,dim=c(2,64),dimnames=list(c('Y','X'),1:64))
> 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 1.4 8.2 1 0 0 0 0 0 0 0 0 0 0
2 1.2 8.0 0 1 0 0 0 0 0 0 0 0 0
3 1.0 7.5 0 0 1 0 0 0 0 0 0 0 0
4 1.7 6.8 0 0 0 1 0 0 0 0 0 0 0
5 2.4 6.5 0 0 0 0 1 0 0 0 0 0 0
6 2.0 6.6 0 0 0 0 0 1 0 0 0 0 0
7 2.1 7.6 0 0 0 0 0 0 1 0 0 0 0
8 2.0 8.0 0 0 0 0 0 0 0 1 0 0 0
9 1.8 8.1 0 0 0 0 0 0 0 0 1 0 0
10 2.7 7.7 0 0 0 0 0 0 0 0 0 1 0
11 2.3 7.5 0 0 0 0 0 0 0 0 0 0 1
12 1.9 7.6 0 0 0 0 0 0 0 0 0 0 0
13 2.0 7.8 1 0 0 0 0 0 0 0 0 0 0
14 2.3 7.8 0 1 0 0 0 0 0 0 0 0 0
15 2.8 7.8 0 0 1 0 0 0 0 0 0 0 0
16 2.4 7.5 0 0 0 1 0 0 0 0 0 0 0
17 2.3 7.5 0 0 0 0 1 0 0 0 0 0 0
18 2.7 7.1 0 0 0 0 0 1 0 0 0 0 0
19 2.7 7.5 0 0 0 0 0 0 1 0 0 0 0
20 2.9 7.5 0 0 0 0 0 0 0 1 0 0 0
21 3.0 7.6 0 0 0 0 0 0 0 0 1 0 0
22 2.2 7.7 0 0 0 0 0 0 0 0 0 1 0
23 2.3 7.7 0 0 0 0 0 0 0 0 0 0 1
24 2.8 7.9 0 0 0 0 0 0 0 0 0 0 0
25 2.8 8.1 1 0 0 0 0 0 0 0 0 0 0
26 2.8 8.2 0 1 0 0 0 0 0 0 0 0 0
27 2.2 8.2 0 0 1 0 0 0 0 0 0 0 0
28 2.6 8.2 0 0 0 1 0 0 0 0 0 0 0
29 2.8 7.9 0 0 0 0 1 0 0 0 0 0 0
30 2.5 7.3 0 0 0 0 0 1 0 0 0 0 0
31 2.4 6.9 0 0 0 0 0 0 1 0 0 0 0
32 2.3 6.6 0 0 0 0 0 0 0 1 0 0 0
33 1.9 6.7 0 0 0 0 0 0 0 0 1 0 0
34 1.7 6.9 0 0 0 0 0 0 0 0 0 1 0
35 2.0 7.0 0 0 0 0 0 0 0 0 0 0 1
36 2.1 7.1 0 0 0 0 0 0 0 0 0 0 0
37 1.7 7.2 1 0 0 0 0 0 0 0 0 0 0
38 1.8 7.1 0 1 0 0 0 0 0 0 0 0 0
39 1.8 6.9 0 0 1 0 0 0 0 0 0 0 0
40 1.8 7.0 0 0 0 1 0 0 0 0 0 0 0
41 1.3 6.8 0 0 0 0 1 0 0 0 0 0 0
42 1.3 6.4 0 0 0 0 0 1 0 0 0 0 0
43 1.3 6.7 0 0 0 0 0 0 1 0 0 0 0
44 1.2 6.6 0 0 0 0 0 0 0 1 0 0 0
45 1.4 6.4 0 0 0 0 0 0 0 0 1 0 0
46 2.2 6.3 0 0 0 0 0 0 0 0 0 1 0
47 2.9 6.2 0 0 0 0 0 0 0 0 0 0 1
48 3.1 6.5 0 0 0 0 0 0 0 0 0 0 0
49 3.5 6.8 1 0 0 0 0 0 0 0 0 0 0
50 3.6 6.8 0 1 0 0 0 0 0 0 0 0 0
51 4.4 6.4 0 0 1 0 0 0 0 0 0 0 0
52 4.1 6.1 0 0 0 1 0 0 0 0 0 0 0
53 5.1 5.8 0 0 0 0 1 0 0 0 0 0 0
54 5.8 6.1 0 0 0 0 0 1 0 0 0 0 0
55 5.9 7.2 0 0 0 0 0 0 1 0 0 0 0
56 5.4 7.3 0 0 0 0 0 0 0 1 0 0 0
57 5.5 6.9 0 0 0 0 0 0 0 0 1 0 0
58 4.8 6.1 0 0 0 0 0 0 0 0 0 1 0
59 3.2 5.8 0 0 0 0 0 0 0 0 0 0 1
60 2.7 6.2 0 0 0 0 0 0 0 0 0 0 0
61 2.1 7.1 1 0 0 0 0 0 0 0 0 0 0
62 1.9 7.7 0 1 0 0 0 0 0 0 0 0 0
63 0.6 7.9 0 0 1 0 0 0 0 0 0 0 0
64 0.7 7.7 0 0 0 1 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
6.5555634 -0.5716095 0.0005618 0.0553358 -0.1637389 -0.2137812
M5 M6 M7 M8 M9 M10
0.1685425 0.1342206 0.4285931 0.3200253 0.2457288 0.1314069
M11
-0.1057541
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.9030 -0.7435 -0.1173 0.5551 3.0314
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.5555634 1.8874496 3.473 0.00106 **
X -0.5716095 0.2558513 -2.234 0.02988 *
M1 0.0005618 0.7510890 0.001 0.99941
M2 0.0553358 0.7540271 0.073 0.94179
M3 -0.1637389 0.7479473 -0.219 0.82759
M4 -0.2137812 0.7423445 -0.288 0.77453
M5 0.1685425 0.7753040 0.217 0.82877
M6 0.1342206 0.7796820 0.172 0.86400
M7 0.4285931 0.7748310 0.553 0.58258
M8 0.3200253 0.7750506 0.413 0.68140
M9 0.2457288 0.7744930 0.317 0.75233
M10 0.1314069 0.7748310 0.170 0.86600
M11 -0.1057541 0.7762659 -0.136 0.89217
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.224 on 51 degrees of freedom
Multiple R-squared: 0.1357, Adjusted R-squared: -0.06772
F-statistic: 0.667 on 12 and 51 DF, p-value: 0.7741
> 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,] 2.509996e-01 5.019992e-01 0.7490004
[2,] 1.342987e-01 2.685974e-01 0.8657013
[3,] 6.752702e-02 1.350540e-01 0.9324730
[4,] 3.413176e-02 6.826352e-02 0.9658682
[5,] 2.215409e-02 4.430819e-02 0.9778459
[6,] 1.678953e-02 3.357906e-02 0.9832105
[7,] 7.760637e-03 1.552127e-02 0.9922394
[8,] 3.087203e-03 6.174406e-03 0.9969128
[9,] 1.835015e-03 3.670029e-03 0.9981650
[10,] 1.554826e-03 3.109653e-03 0.9984452
[11,] 1.164328e-03 2.328656e-03 0.9988357
[12,] 4.776069e-04 9.552138e-04 0.9995224
[13,] 2.185562e-04 4.371125e-04 0.9997814
[14,] 9.073561e-05 1.814712e-04 0.9999093
[15,] 3.100138e-05 6.200276e-05 0.9999690
[16,] 1.180275e-05 2.360551e-05 0.9999882
[17,] 4.564106e-06 9.128212e-06 0.9999954
[18,] 1.703535e-06 3.407069e-06 0.9999983
[19,] 6.464618e-07 1.292924e-06 0.9999994
[20,] 1.810455e-07 3.620910e-07 0.9999998
[21,] 4.762250e-08 9.524499e-08 1.0000000
[22,] 1.265009e-08 2.530017e-08 1.0000000
[23,] 3.504410e-09 7.008821e-09 1.0000000
[24,] 9.236729e-10 1.847346e-09 1.0000000
[25,] 2.173540e-10 4.347080e-10 1.0000000
[26,] 3.211534e-10 6.423069e-10 1.0000000
[27,] 1.346985e-09 2.693971e-09 1.0000000
[28,] 3.819657e-08 7.639314e-08 1.0000000
[29,] 1.017799e-05 2.035599e-05 0.9999898
[30,] 8.045909e-02 1.609182e-01 0.9195409
[31,] 5.263625e-01 9.472750e-01 0.4736375
[32,] 5.197244e-01 9.605512e-01 0.4802756
[33,] 7.350163e-01 5.299674e-01 0.2649837
> postscript(file="/var/www/html/rcomp/tmp/1nw2r1258662443.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/224x31258662443.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/308yq1258662443.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lnyj1258662443.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ffxe1258662443.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 = 64
Frequency = 1
1 2 3 4 5 6
-0.468926973 -0.838022851 -1.104752856 -0.754837308 -0.608643816 -0.917160954
7 8 9 10 11 12
-0.539923993 -0.302712368 -0.371254841 0.414423251 0.137262297 -0.311330848
13 14 15 16 17 18
-0.097570789 0.147655241 0.866730006 0.345289370 -0.137034276 0.068643816
19 20 21 22 23 24
0.002915053 0.311482862 0.542940388 -0.085576749 0.251584205 0.760152014
25 26 27 28 29 30
0.873912073 0.876299057 0.495373822 0.945416048 0.591609540 -0.017034276
31 32 33 34 35 36
-0.640050671 -0.802965724 -1.071508198 -1.042864382 -0.448542474 -0.397135618
37 38 39 40 41 42
-0.740536513 -0.752471437 -0.647718580 -0.540515400 -1.537160954 -1.731482862
43 44 45 46 47 48
-1.854372579 -1.902965724 -1.742991060 -0.885830106 -0.005830106 0.259898658
49 50 51 52 53 54
0.830819671 0.876045701 1.666476649 1.245036013 1.691229506 2.597034276
55 56 57 58 59 60
3.031432191 2.697160954 2.642813710 1.599847986 0.065526078 -0.311584205
61 62 63 64
-0.397697467 -0.309505713 -1.276109040 -1.240388722
> postscript(file="/var/www/html/rcomp/tmp/6bzi31258662443.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 = 64
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.468926973 NA
1 -0.838022851 -0.468926973
2 -1.104752856 -0.838022851
3 -0.754837308 -1.104752856
4 -0.608643816 -0.754837308
5 -0.917160954 -0.608643816
6 -0.539923993 -0.917160954
7 -0.302712368 -0.539923993
8 -0.371254841 -0.302712368
9 0.414423251 -0.371254841
10 0.137262297 0.414423251
11 -0.311330848 0.137262297
12 -0.097570789 -0.311330848
13 0.147655241 -0.097570789
14 0.866730006 0.147655241
15 0.345289370 0.866730006
16 -0.137034276 0.345289370
17 0.068643816 -0.137034276
18 0.002915053 0.068643816
19 0.311482862 0.002915053
20 0.542940388 0.311482862
21 -0.085576749 0.542940388
22 0.251584205 -0.085576749
23 0.760152014 0.251584205
24 0.873912073 0.760152014
25 0.876299057 0.873912073
26 0.495373822 0.876299057
27 0.945416048 0.495373822
28 0.591609540 0.945416048
29 -0.017034276 0.591609540
30 -0.640050671 -0.017034276
31 -0.802965724 -0.640050671
32 -1.071508198 -0.802965724
33 -1.042864382 -1.071508198
34 -0.448542474 -1.042864382
35 -0.397135618 -0.448542474
36 -0.740536513 -0.397135618
37 -0.752471437 -0.740536513
38 -0.647718580 -0.752471437
39 -0.540515400 -0.647718580
40 -1.537160954 -0.540515400
41 -1.731482862 -1.537160954
42 -1.854372579 -1.731482862
43 -1.902965724 -1.854372579
44 -1.742991060 -1.902965724
45 -0.885830106 -1.742991060
46 -0.005830106 -0.885830106
47 0.259898658 -0.005830106
48 0.830819671 0.259898658
49 0.876045701 0.830819671
50 1.666476649 0.876045701
51 1.245036013 1.666476649
52 1.691229506 1.245036013
53 2.597034276 1.691229506
54 3.031432191 2.597034276
55 2.697160954 3.031432191
56 2.642813710 2.697160954
57 1.599847986 2.642813710
58 0.065526078 1.599847986
59 -0.311584205 0.065526078
60 -0.397697467 -0.311584205
61 -0.309505713 -0.397697467
62 -1.276109040 -0.309505713
63 -1.240388722 -1.276109040
64 NA -1.240388722
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.838022851 -0.468926973
[2,] -1.104752856 -0.838022851
[3,] -0.754837308 -1.104752856
[4,] -0.608643816 -0.754837308
[5,] -0.917160954 -0.608643816
[6,] -0.539923993 -0.917160954
[7,] -0.302712368 -0.539923993
[8,] -0.371254841 -0.302712368
[9,] 0.414423251 -0.371254841
[10,] 0.137262297 0.414423251
[11,] -0.311330848 0.137262297
[12,] -0.097570789 -0.311330848
[13,] 0.147655241 -0.097570789
[14,] 0.866730006 0.147655241
[15,] 0.345289370 0.866730006
[16,] -0.137034276 0.345289370
[17,] 0.068643816 -0.137034276
[18,] 0.002915053 0.068643816
[19,] 0.311482862 0.002915053
[20,] 0.542940388 0.311482862
[21,] -0.085576749 0.542940388
[22,] 0.251584205 -0.085576749
[23,] 0.760152014 0.251584205
[24,] 0.873912073 0.760152014
[25,] 0.876299057 0.873912073
[26,] 0.495373822 0.876299057
[27,] 0.945416048 0.495373822
[28,] 0.591609540 0.945416048
[29,] -0.017034276 0.591609540
[30,] -0.640050671 -0.017034276
[31,] -0.802965724 -0.640050671
[32,] -1.071508198 -0.802965724
[33,] -1.042864382 -1.071508198
[34,] -0.448542474 -1.042864382
[35,] -0.397135618 -0.448542474
[36,] -0.740536513 -0.397135618
[37,] -0.752471437 -0.740536513
[38,] -0.647718580 -0.752471437
[39,] -0.540515400 -0.647718580
[40,] -1.537160954 -0.540515400
[41,] -1.731482862 -1.537160954
[42,] -1.854372579 -1.731482862
[43,] -1.902965724 -1.854372579
[44,] -1.742991060 -1.902965724
[45,] -0.885830106 -1.742991060
[46,] -0.005830106 -0.885830106
[47,] 0.259898658 -0.005830106
[48,] 0.830819671 0.259898658
[49,] 0.876045701 0.830819671
[50,] 1.666476649 0.876045701
[51,] 1.245036013 1.666476649
[52,] 1.691229506 1.245036013
[53,] 2.597034276 1.691229506
[54,] 3.031432191 2.597034276
[55,] 2.697160954 3.031432191
[56,] 2.642813710 2.697160954
[57,] 1.599847986 2.642813710
[58,] 0.065526078 1.599847986
[59,] -0.311584205 0.065526078
[60,] -0.397697467 -0.311584205
[61,] -0.309505713 -0.397697467
[62,] -1.276109040 -0.309505713
[63,] -1.240388722 -1.276109040
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.838022851 -0.468926973
2 -1.104752856 -0.838022851
3 -0.754837308 -1.104752856
4 -0.608643816 -0.754837308
5 -0.917160954 -0.608643816
6 -0.539923993 -0.917160954
7 -0.302712368 -0.539923993
8 -0.371254841 -0.302712368
9 0.414423251 -0.371254841
10 0.137262297 0.414423251
11 -0.311330848 0.137262297
12 -0.097570789 -0.311330848
13 0.147655241 -0.097570789
14 0.866730006 0.147655241
15 0.345289370 0.866730006
16 -0.137034276 0.345289370
17 0.068643816 -0.137034276
18 0.002915053 0.068643816
19 0.311482862 0.002915053
20 0.542940388 0.311482862
21 -0.085576749 0.542940388
22 0.251584205 -0.085576749
23 0.760152014 0.251584205
24 0.873912073 0.760152014
25 0.876299057 0.873912073
26 0.495373822 0.876299057
27 0.945416048 0.495373822
28 0.591609540 0.945416048
29 -0.017034276 0.591609540
30 -0.640050671 -0.017034276
31 -0.802965724 -0.640050671
32 -1.071508198 -0.802965724
33 -1.042864382 -1.071508198
34 -0.448542474 -1.042864382
35 -0.397135618 -0.448542474
36 -0.740536513 -0.397135618
37 -0.752471437 -0.740536513
38 -0.647718580 -0.752471437
39 -0.540515400 -0.647718580
40 -1.537160954 -0.540515400
41 -1.731482862 -1.537160954
42 -1.854372579 -1.731482862
43 -1.902965724 -1.854372579
44 -1.742991060 -1.902965724
45 -0.885830106 -1.742991060
46 -0.005830106 -0.885830106
47 0.259898658 -0.005830106
48 0.830819671 0.259898658
49 0.876045701 0.830819671
50 1.666476649 0.876045701
51 1.245036013 1.666476649
52 1.691229506 1.245036013
53 2.597034276 1.691229506
54 3.031432191 2.597034276
55 2.697160954 3.031432191
56 2.642813710 2.697160954
57 1.599847986 2.642813710
58 0.065526078 1.599847986
59 -0.311584205 0.065526078
60 -0.397697467 -0.311584205
61 -0.309505713 -0.397697467
62 -1.276109040 -0.309505713
63 -1.240388722 -1.276109040
> 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/7id5l1258662443.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86h5k1258662443.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vv7z1258662443.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hwx71258662443.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ftfr1258662443.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/1272nu1258662443.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/13d5pd1258662443.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/14920k1258662443.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/15b0em1258662443.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/1684471258662443.tab")
+ }
>
> system("convert tmp/1nw2r1258662443.ps tmp/1nw2r1258662443.png")
> system("convert tmp/224x31258662443.ps tmp/224x31258662443.png")
> system("convert tmp/308yq1258662443.ps tmp/308yq1258662443.png")
> system("convert tmp/4lnyj1258662443.ps tmp/4lnyj1258662443.png")
> system("convert tmp/5ffxe1258662443.ps tmp/5ffxe1258662443.png")
> system("convert tmp/6bzi31258662443.ps tmp/6bzi31258662443.png")
> system("convert tmp/7id5l1258662443.ps tmp/7id5l1258662443.png")
> system("convert tmp/86h5k1258662443.ps tmp/86h5k1258662443.png")
> system("convert tmp/9vv7z1258662443.ps tmp/9vv7z1258662443.png")
> system("convert tmp/10hwx71258662443.ps tmp/10hwx71258662443.png")
>
>
> proc.time()
user system elapsed
2.428 1.569 3.044