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(8.3,0,8.2,8.7,8.5,0,8.3,8.2,8.6,0,8.5,8.3,8.5,0,8.6,8.5,8.2,0,8.5,8.6,8.1,0,8.2,8.5,7.9,0,8.1,8.2,8.6,0,7.9,8.1,8.7,0,8.6,7.9,8.7,0,8.7,8.6,8.5,0,8.7,8.7,8.4,0,8.5,8.7,8.5,0,8.4,8.5,8.7,0,8.5,8.4,8.7,0,8.7,8.5,8.6,0,8.7,8.7,8.5,0,8.6,8.7,8.3,0,8.5,8.6,8,0,8.3,8.5,8.2,0,8,8.3,8.1,0,8.2,8,8.1,0,8.1,8.2,8,0,8.1,8.1,7.9,0,8,8.1,7.9,0,7.9,8,8,0,7.9,7.9,8,0,8,7.9,7.9,0,8,8,8,0,7.9,8,7.7,0,8,7.9,7.2,0,7.7,8,7.5,0,7.2,7.7,7.3,0,7.5,7.2,7,0,7.3,7.5,7,0,7,7.3,7,0,7,7,7.2,0,7,7,7.3,0,7.2,7,7.1,0,7.3,7.2,6.8,0,7.1,7.3,6.4,0,6.8,7.1,6.1,0,6.4,6.8,6.5,0,6.1,6.4,7.7,0,6.5,6.1,7.9,0,7.7,6.5,7.5,0,7.9,7.7,6.9,1,7.5,7.9,6.6,1,6.9,7.5,6.9,1,6.6,6.9,7.7,1,6.9,6.6,8,1,7.7,6.9,8,1,8,7.7,7.7,1,8,8,7.3,1,7.7,8,7.4,1,7.3,7.7,8.1,1,7.4,7.3,8.3,1,8.1,7.4,8.2,1,8.3,8.1),dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> 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
Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.3 0 8.2 8.7 1 0 0 0 0 0 0 0 0 0 0 1
2 8.5 0 8.3 8.2 0 1 0 0 0 0 0 0 0 0 0 2
3 8.6 0 8.5 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.5 0 8.6 8.5 0 0 0 1 0 0 0 0 0 0 0 4
5 8.2 0 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0 5
6 8.1 0 8.2 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 7.9 0 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 7
8 8.6 0 7.9 8.1 0 0 0 0 0 0 0 1 0 0 0 8
9 8.7 0 8.6 7.9 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 0 8.7 8.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 0 8.7 8.7 0 0 0 0 0 0 0 0 0 0 1 11
12 8.4 0 8.5 8.7 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 0 8.4 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 0 8.5 8.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.7 0 8.7 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.6 0 8.7 8.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 0 8.6 8.7 0 0 0 0 1 0 0 0 0 0 0 17
18 8.3 0 8.5 8.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.0 0 8.3 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 0 8.0 8.3 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 0 8.2 8.0 0 0 0 0 0 0 0 0 1 0 0 21
22 8.1 0 8.1 8.2 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 0 8.1 8.1 0 0 0 0 0 0 0 0 0 0 1 23
24 7.9 0 8.0 8.1 0 0 0 0 0 0 0 0 0 0 0 24
25 7.9 0 7.9 8.0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 0 7.9 7.9 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 0 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 0 8.0 8.0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 0 7.9 8.0 0 0 0 0 1 0 0 0 0 0 0 29
30 7.7 0 8.0 7.9 0 0 0 0 0 1 0 0 0 0 0 30
31 7.2 0 7.7 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.5 0 7.2 7.7 0 0 0 0 0 0 0 1 0 0 0 32
33 7.3 0 7.5 7.2 0 0 0 0 0 0 0 0 1 0 0 33
34 7.0 0 7.3 7.5 0 0 0 0 0 0 0 0 0 1 0 34
35 7.0 0 7.0 7.3 0 0 0 0 0 0 0 0 0 0 1 35
36 7.0 0 7.0 7.0 0 0 0 0 0 0 0 0 0 0 0 36
37 7.2 0 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.3 0 7.2 7.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.1 0 7.3 7.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 0 7.1 7.3 0 0 0 1 0 0 0 0 0 0 0 40
41 6.4 0 6.8 7.1 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 0 6.4 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.5 0 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 7.7 0 6.5 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 7.9 0 7.7 6.5 0 0 0 0 0 0 0 0 1 0 0 45
46 7.5 0 7.9 7.7 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 1 7.5 7.9 0 0 0 0 0 0 0 0 0 0 1 47
48 6.6 1 6.9 7.5 0 0 0 0 0 0 0 0 0 0 0 48
49 6.9 1 6.6 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.7 1 6.9 6.6 0 1 0 0 0 0 0 0 0 0 0 50
51 8.0 1 7.7 6.9 0 0 1 0 0 0 0 0 0 0 0 51
52 8.0 1 8.0 7.7 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 1 8.0 8.0 0 0 0 0 1 0 0 0 0 0 0 53
54 7.3 1 7.7 8.0 0 0 0 0 0 1 0 0 0 0 0 54
55 7.4 1 7.3 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 8.1 1 7.4 7.3 0 0 0 0 0 0 0 1 0 0 0 56
57 8.3 1 8.1 7.4 0 0 0 0 0 0 0 0 1 0 0 57
58 8.2 1 8.3 8.1 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
2.43219 0.30534 1.39865 -0.67644 0.20722 0.16846
M3 M4 M5 M6 M7 M8
-0.07612 -0.05033 -0.04309 -0.09220 0.04850 0.64483
M9 M10 M11 t
-0.23764 -0.02185 -0.08366 -0.01234
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.319995 -0.114032 -0.003644 0.109625 0.347283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.432189 0.622279 3.909 0.000332 ***
X 0.305336 0.100615 3.035 0.004121 **
Y1 1.398653 0.113901 12.280 1.74e-15 ***
Y2 -0.676439 0.121961 -5.546 1.78e-06 ***
M1 0.207220 0.119015 1.741 0.088984 .
M2 0.168459 0.126392 1.333 0.189773
M3 -0.076124 0.131337 -0.580 0.565277
M4 -0.050329 0.123219 -0.408 0.685020
M5 -0.043094 0.120444 -0.358 0.722287
M6 -0.092198 0.118936 -0.775 0.442570
M7 0.048502 0.118346 0.410 0.684013
M8 0.644831 0.119648 5.389 2.98e-06 ***
M9 -0.237639 0.151134 -1.572 0.123368
M10 -0.021855 0.126701 -0.172 0.863880
M11 -0.083658 0.125100 -0.669 0.507328
t -0.012338 0.003569 -3.457 0.001262 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1761 on 42 degrees of freedom
Multiple R-squared: 0.9474, Adjusted R-squared: 0.9286
F-statistic: 50.4 on 15 and 42 DF, p-value: < 2.2e-16
> 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,] 0.05066589 0.10133177 0.9493341
[2,] 0.51127536 0.97744929 0.4887246
[3,] 0.35774239 0.71548477 0.6422576
[4,] 0.23594342 0.47188683 0.7640566
[5,] 0.14314130 0.28628259 0.8568587
[6,] 0.08918953 0.17837907 0.9108105
[7,] 0.05740493 0.11480987 0.9425951
[8,] 0.02950050 0.05900101 0.9704995
[9,] 0.02092799 0.04185598 0.9790720
[10,] 0.01181583 0.02363166 0.9881842
[11,] 0.12010666 0.24021332 0.8798933
[12,] 0.13613148 0.27226297 0.8638685
[13,] 0.17034988 0.34069975 0.8296501
[14,] 0.11737019 0.23474038 0.8826298
[15,] 0.08634929 0.17269859 0.9136507
[16,] 0.07279745 0.14559490 0.9272025
[17,] 0.43592702 0.87185405 0.5640730
[18,] 0.42719374 0.85438748 0.5728063
[19,] 0.52782398 0.94435205 0.4721760
[20,] 0.39837794 0.79675588 0.6016221
[21,] 0.44985717 0.89971433 0.5501428
> postscript(file="/var/www/html/rcomp/tmp/1j2221260889951.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bi021260889951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35eww1260889951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ra571260889951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5jg6m1260889951.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 = 58
Frequency = 1
1 2 3 4 5 6
0.088996840 -0.137988741 0.006845902 -0.111188363 -0.198575077 0.114818953
7 8 9 10 11 12
-0.276609343 0.051486138 -0.068049956 0.062146008 0.003931682 0.112342183
13 14 15 16 17 18
0.022037634 0.065627628 0.110462271 0.132293257 0.177262649 0.110926177
19 20 21 22 23 24
-0.105349080 -0.205032242 0.107114025 0.178821023 0.085318909 0.053864159
25 26 27 28 29 30
-0.068796497 0.014658749 0.131714749 0.085901841 0.330871233 -0.115195741
31 32 33 34 35 36
-0.256317959 -0.043914513 -0.106921284 -0.127705142 0.230744603 -0.043507079
37 38 39 40 41 42
-0.038389092 -0.167020455 -0.114676666 -0.080759073 -0.191346966 -0.073375473
43 44 45 46 47 48
0.347282840 0.200899027 -0.112099960 -0.183549778 -0.319995194 -0.122699263
49 50 51 52 53 54
-0.003848886 0.224722819 -0.134346257 -0.026247662 -0.118211839 -0.037173915
55 56 57 58
0.290993542 -0.003438411 0.179957176 0.070287889
> postscript(file="/var/www/html/rcomp/tmp/6bs3v1260889951.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 0.088996840 NA
1 -0.137988741 0.088996840
2 0.006845902 -0.137988741
3 -0.111188363 0.006845902
4 -0.198575077 -0.111188363
5 0.114818953 -0.198575077
6 -0.276609343 0.114818953
7 0.051486138 -0.276609343
8 -0.068049956 0.051486138
9 0.062146008 -0.068049956
10 0.003931682 0.062146008
11 0.112342183 0.003931682
12 0.022037634 0.112342183
13 0.065627628 0.022037634
14 0.110462271 0.065627628
15 0.132293257 0.110462271
16 0.177262649 0.132293257
17 0.110926177 0.177262649
18 -0.105349080 0.110926177
19 -0.205032242 -0.105349080
20 0.107114025 -0.205032242
21 0.178821023 0.107114025
22 0.085318909 0.178821023
23 0.053864159 0.085318909
24 -0.068796497 0.053864159
25 0.014658749 -0.068796497
26 0.131714749 0.014658749
27 0.085901841 0.131714749
28 0.330871233 0.085901841
29 -0.115195741 0.330871233
30 -0.256317959 -0.115195741
31 -0.043914513 -0.256317959
32 -0.106921284 -0.043914513
33 -0.127705142 -0.106921284
34 0.230744603 -0.127705142
35 -0.043507079 0.230744603
36 -0.038389092 -0.043507079
37 -0.167020455 -0.038389092
38 -0.114676666 -0.167020455
39 -0.080759073 -0.114676666
40 -0.191346966 -0.080759073
41 -0.073375473 -0.191346966
42 0.347282840 -0.073375473
43 0.200899027 0.347282840
44 -0.112099960 0.200899027
45 -0.183549778 -0.112099960
46 -0.319995194 -0.183549778
47 -0.122699263 -0.319995194
48 -0.003848886 -0.122699263
49 0.224722819 -0.003848886
50 -0.134346257 0.224722819
51 -0.026247662 -0.134346257
52 -0.118211839 -0.026247662
53 -0.037173915 -0.118211839
54 0.290993542 -0.037173915
55 -0.003438411 0.290993542
56 0.179957176 -0.003438411
57 0.070287889 0.179957176
58 NA 0.070287889
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.137988741 0.088996840
[2,] 0.006845902 -0.137988741
[3,] -0.111188363 0.006845902
[4,] -0.198575077 -0.111188363
[5,] 0.114818953 -0.198575077
[6,] -0.276609343 0.114818953
[7,] 0.051486138 -0.276609343
[8,] -0.068049956 0.051486138
[9,] 0.062146008 -0.068049956
[10,] 0.003931682 0.062146008
[11,] 0.112342183 0.003931682
[12,] 0.022037634 0.112342183
[13,] 0.065627628 0.022037634
[14,] 0.110462271 0.065627628
[15,] 0.132293257 0.110462271
[16,] 0.177262649 0.132293257
[17,] 0.110926177 0.177262649
[18,] -0.105349080 0.110926177
[19,] -0.205032242 -0.105349080
[20,] 0.107114025 -0.205032242
[21,] 0.178821023 0.107114025
[22,] 0.085318909 0.178821023
[23,] 0.053864159 0.085318909
[24,] -0.068796497 0.053864159
[25,] 0.014658749 -0.068796497
[26,] 0.131714749 0.014658749
[27,] 0.085901841 0.131714749
[28,] 0.330871233 0.085901841
[29,] -0.115195741 0.330871233
[30,] -0.256317959 -0.115195741
[31,] -0.043914513 -0.256317959
[32,] -0.106921284 -0.043914513
[33,] -0.127705142 -0.106921284
[34,] 0.230744603 -0.127705142
[35,] -0.043507079 0.230744603
[36,] -0.038389092 -0.043507079
[37,] -0.167020455 -0.038389092
[38,] -0.114676666 -0.167020455
[39,] -0.080759073 -0.114676666
[40,] -0.191346966 -0.080759073
[41,] -0.073375473 -0.191346966
[42,] 0.347282840 -0.073375473
[43,] 0.200899027 0.347282840
[44,] -0.112099960 0.200899027
[45,] -0.183549778 -0.112099960
[46,] -0.319995194 -0.183549778
[47,] -0.122699263 -0.319995194
[48,] -0.003848886 -0.122699263
[49,] 0.224722819 -0.003848886
[50,] -0.134346257 0.224722819
[51,] -0.026247662 -0.134346257
[52,] -0.118211839 -0.026247662
[53,] -0.037173915 -0.118211839
[54,] 0.290993542 -0.037173915
[55,] -0.003438411 0.290993542
[56,] 0.179957176 -0.003438411
[57,] 0.070287889 0.179957176
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.137988741 0.088996840
2 0.006845902 -0.137988741
3 -0.111188363 0.006845902
4 -0.198575077 -0.111188363
5 0.114818953 -0.198575077
6 -0.276609343 0.114818953
7 0.051486138 -0.276609343
8 -0.068049956 0.051486138
9 0.062146008 -0.068049956
10 0.003931682 0.062146008
11 0.112342183 0.003931682
12 0.022037634 0.112342183
13 0.065627628 0.022037634
14 0.110462271 0.065627628
15 0.132293257 0.110462271
16 0.177262649 0.132293257
17 0.110926177 0.177262649
18 -0.105349080 0.110926177
19 -0.205032242 -0.105349080
20 0.107114025 -0.205032242
21 0.178821023 0.107114025
22 0.085318909 0.178821023
23 0.053864159 0.085318909
24 -0.068796497 0.053864159
25 0.014658749 -0.068796497
26 0.131714749 0.014658749
27 0.085901841 0.131714749
28 0.330871233 0.085901841
29 -0.115195741 0.330871233
30 -0.256317959 -0.115195741
31 -0.043914513 -0.256317959
32 -0.106921284 -0.043914513
33 -0.127705142 -0.106921284
34 0.230744603 -0.127705142
35 -0.043507079 0.230744603
36 -0.038389092 -0.043507079
37 -0.167020455 -0.038389092
38 -0.114676666 -0.167020455
39 -0.080759073 -0.114676666
40 -0.191346966 -0.080759073
41 -0.073375473 -0.191346966
42 0.347282840 -0.073375473
43 0.200899027 0.347282840
44 -0.112099960 0.200899027
45 -0.183549778 -0.112099960
46 -0.319995194 -0.183549778
47 -0.122699263 -0.319995194
48 -0.003848886 -0.122699263
49 0.224722819 -0.003848886
50 -0.134346257 0.224722819
51 -0.026247662 -0.134346257
52 -0.118211839 -0.026247662
53 -0.037173915 -0.118211839
54 0.290993542 -0.037173915
55 -0.003438411 0.290993542
56 0.179957176 -0.003438411
57 0.070287889 0.179957176
> 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/7rdac1260889951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zzfd1260889951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uied1260889951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108bm11260889951.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1147yh1260889951.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/12sh901260889951.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/1307eo1260889951.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/14b7231260889951.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/15wc0r1260889951.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/16ygah1260889951.tab")
+ }
>
> try(system("convert tmp/1j2221260889951.ps tmp/1j2221260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bi021260889951.ps tmp/2bi021260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/35eww1260889951.ps tmp/35eww1260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ra571260889951.ps tmp/4ra571260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jg6m1260889951.ps tmp/5jg6m1260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bs3v1260889951.ps tmp/6bs3v1260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rdac1260889951.ps tmp/7rdac1260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zzfd1260889951.ps tmp/8zzfd1260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/9uied1260889951.ps tmp/9uied1260889951.png",intern=TRUE))
character(0)
> try(system("convert tmp/108bm11260889951.ps tmp/108bm11260889951.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.399 1.558 2.871