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(8.9,8.6,8.9,8.5,8.9,8.3,8.9,7.8,9,7.8,9,8,9,8.6,9,8.9,9,8.9,9,8.6,9,8.3,9.1,8.3,9,8.3,9.1,8.4,9.1,8.5,9,8.4,9,8.6,9,8.5,9,8.5,8.9,8.4,8.9,8.5,8.9,8.5,8.9,8.5,8.8,8.5,8.8,8.5,8.7,8.5,8.7,8.5,8.5,8.5,8.5,8.6,8.4,8.4,8.2,8.1,8.2,8,8.1,8,8.1,8,8,8,7.9,7.9,7.8,7.8,7.7,7.8,7.6,7.9,7.5,8.1,7.5,8,7.5,7.6,7.5,7.3,7.5,7,7.4,6.8,7.4,7,7.3,7.1,7.3,7.2,7.3,7.1,7.2,6.9,7.2,6.7,7.3,6.7,7.4,6.6,7.4,6.9,7.5,7.3,7.6,7.5,7.7,7.3,7.9,7.1,8,6.9,8.2,7.1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.9 8.6 1 0 0 0 0 0 0 0 0 0 0
2 8.9 8.5 0 1 0 0 0 0 0 0 0 0 0
3 8.9 8.3 0 0 1 0 0 0 0 0 0 0 0
4 8.9 7.8 0 0 0 1 0 0 0 0 0 0 0
5 9.0 7.8 0 0 0 0 1 0 0 0 0 0 0
6 9.0 8.0 0 0 0 0 0 1 0 0 0 0 0
7 9.0 8.6 0 0 0 0 0 0 1 0 0 0 0
8 9.0 8.9 0 0 0 0 0 0 0 1 0 0 0
9 9.0 8.9 0 0 0 0 0 0 0 0 1 0 0
10 9.0 8.6 0 0 0 0 0 0 0 0 0 1 0
11 9.0 8.3 0 0 0 0 0 0 0 0 0 0 1
12 9.1 8.3 0 0 0 0 0 0 0 0 0 0 0
13 9.0 8.3 1 0 0 0 0 0 0 0 0 0 0
14 9.1 8.4 0 1 0 0 0 0 0 0 0 0 0
15 9.1 8.5 0 0 1 0 0 0 0 0 0 0 0
16 9.0 8.4 0 0 0 1 0 0 0 0 0 0 0
17 9.0 8.6 0 0 0 0 1 0 0 0 0 0 0
18 9.0 8.5 0 0 0 0 0 1 0 0 0 0 0
19 9.0 8.5 0 0 0 0 0 0 1 0 0 0 0
20 8.9 8.4 0 0 0 0 0 0 0 1 0 0 0
21 8.9 8.5 0 0 0 0 0 0 0 0 1 0 0
22 8.9 8.5 0 0 0 0 0 0 0 0 0 1 0
23 8.9 8.5 0 0 0 0 0 0 0 0 0 0 1
24 8.8 8.5 0 0 0 0 0 0 0 0 0 0 0
25 8.8 8.5 1 0 0 0 0 0 0 0 0 0 0
26 8.7 8.5 0 1 0 0 0 0 0 0 0 0 0
27 8.7 8.5 0 0 1 0 0 0 0 0 0 0 0
28 8.5 8.5 0 0 0 1 0 0 0 0 0 0 0
29 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0
30 8.4 8.4 0 0 0 0 0 1 0 0 0 0 0
31 8.2 8.1 0 0 0 0 0 0 1 0 0 0 0
32 8.2 8.0 0 0 0 0 0 0 0 1 0 0 0
33 8.1 8.0 0 0 0 0 0 0 0 0 1 0 0
34 8.1 8.0 0 0 0 0 0 0 0 0 0 1 0
35 8.0 8.0 0 0 0 0 0 0 0 0 0 0 1
36 7.9 7.9 0 0 0 0 0 0 0 0 0 0 0
37 7.8 7.8 1 0 0 0 0 0 0 0 0 0 0
38 7.7 7.8 0 1 0 0 0 0 0 0 0 0 0
39 7.6 7.9 0 0 1 0 0 0 0 0 0 0 0
40 7.5 8.1 0 0 0 1 0 0 0 0 0 0 0
41 7.5 8.0 0 0 0 0 1 0 0 0 0 0 0
42 7.5 7.6 0 0 0 0 0 1 0 0 0 0 0
43 7.5 7.3 0 0 0 0 0 0 1 0 0 0 0
44 7.5 7.0 0 0 0 0 0 0 0 1 0 0 0
45 7.4 6.8 0 0 0 0 0 0 0 0 1 0 0
46 7.4 7.0 0 0 0 0 0 0 0 0 0 1 0
47 7.3 7.1 0 0 0 0 0 0 0 0 0 0 1
48 7.3 7.2 0 0 0 0 0 0 0 0 0 0 0
49 7.3 7.1 1 0 0 0 0 0 0 0 0 0 0
50 7.2 6.9 0 1 0 0 0 0 0 0 0 0 0
51 7.2 6.7 0 0 1 0 0 0 0 0 0 0 0
52 7.3 6.7 0 0 0 1 0 0 0 0 0 0 0
53 7.4 6.6 0 0 0 0 1 0 0 0 0 0 0
54 7.4 6.9 0 0 0 0 0 1 0 0 0 0 0
55 7.5 7.3 0 0 0 0 0 0 1 0 0 0 0
56 7.6 7.5 0 0 0 0 0 0 0 1 0 0 0
57 7.7 7.3 0 0 0 0 0 0 0 0 1 0 0
58 7.9 7.1 0 0 0 0 0 0 0 0 0 1 0
59 8.0 6.9 0 0 0 0 0 0 0 0 0 0 1
60 8.2 7.1 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) X M1 M2 M3 M4
1.29281 0.89323 -0.13224 -0.13651 -0.12078 -0.10932
M5 M6 M7 M8 M9 M10
-0.08719 -0.07146 -0.16292 -0.16292 -0.12932 -0.03573
M11
0.01573
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.91865 -0.21427 0.01565 0.26965 0.82719
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.29281 0.65373 1.978 0.0539 .
X 0.89323 0.08058 11.086 1.04e-14 ***
M1 -0.13224 0.25532 -0.518 0.6069
M2 -0.13651 0.25507 -0.535 0.5950
M3 -0.12078 0.25487 -0.474 0.6378
M4 -0.10932 0.25458 -0.429 0.6696
M5 -0.08719 0.25464 -0.342 0.7336
M6 -0.07146 0.25454 -0.281 0.7801
M7 -0.16292 0.25478 -0.639 0.5256
M8 -0.16292 0.25478 -0.639 0.5256
M9 -0.12932 0.25458 -0.508 0.6138
M10 -0.03573 0.25447 -0.140 0.8889
M11 0.01573 0.25447 0.062 0.9510
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4023 on 47 degrees of freedom
Multiple R-squared: 0.7243, Adjusted R-squared: 0.6539
F-statistic: 10.29 on 12 and 47 DF, p-value: 1.659e-09
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 5.914364e-02 1.182873e-01 0.9408564
[2,] 1.957068e-02 3.914137e-02 0.9804293
[3,] 6.132970e-03 1.226594e-02 0.9938670
[4,] 1.882437e-03 3.764873e-03 0.9981176
[5,] 6.458449e-04 1.291690e-03 0.9993542
[6,] 2.038879e-04 4.077757e-04 0.9997961
[7,] 7.001721e-05 1.400344e-04 0.9999300
[8,] 2.975334e-05 5.950668e-05 0.9999702
[9,] 1.187751e-04 2.375502e-04 0.9998812
[10,] 1.069805e-04 2.139610e-04 0.9998930
[11,] 4.503940e-04 9.007880e-04 0.9995496
[12,] 1.593089e-03 3.186178e-03 0.9984069
[13,] 9.693146e-03 1.938629e-02 0.9903069
[14,] 2.969072e-02 5.938144e-02 0.9703093
[15,] 1.120753e-01 2.241506e-01 0.8879247
[16,] 4.255721e-01 8.511443e-01 0.5744279
[17,] 6.042560e-01 7.914880e-01 0.3957440
[18,] 6.786735e-01 6.426531e-01 0.3213265
[19,] 6.992144e-01 6.015712e-01 0.3007856
[20,] 7.207174e-01 5.585652e-01 0.2792826
[21,] 7.173055e-01 5.653890e-01 0.2826945
[22,] 7.206705e-01 5.586589e-01 0.2793295
[23,] 7.160040e-01 5.679920e-01 0.2839960
[24,] 7.056547e-01 5.886906e-01 0.2943453
[25,] 7.198911e-01 5.602179e-01 0.2801089
[26,] 6.706284e-01 6.587433e-01 0.3293716
[27,] 5.520333e-01 8.959334e-01 0.4479667
[28,] 3.994734e-01 7.989468e-01 0.6005266
[29,] 2.602536e-01 5.205072e-01 0.7397464
> postscript(file="/var/www/html/rcomp/tmp/1nrcd1258708272.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2usj61258708272.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3prj31258708272.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4uq3b1258708272.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5px961258708272.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
0.0576560244 0.1512497995 0.3141665330 0.7493229584 0.8271875501
6 7 8 9 10
0.6328124499 0.1883330659 -0.0796358094 -0.1132295845 0.0611455158
11 12 13 14 15
0.2776560244 0.3933852078 0.4256248997 0.4405727579 0.3355206161
16 17 18 19 20
0.3133852078 0.1126038826 0.1861976576 0.2776560244 0.2669789828
21 22 23 24 25
0.1440622493 0.0504684742 -0.0009898925 -0.0852607091 0.0469789828
26 27 28 29 30
-0.0487502005 -0.0644793839 -0.2759377507 -0.3873961174 -0.3244793839
31 32 33 34 35
-0.1650521418 -0.0757291834 -0.2093229584 -0.3029167335 -0.4543751003
36 37 38 39 40
-0.4493229584 -0.3277603080 -0.4234894914 -0.6285416332 -0.9186459169
41 42 43 44 45
-0.8514583668 -0.5098957163 -0.1504684742 0.1175004011 0.1625525429
46 47 48 49 50
-0.1096871490 -0.3504684742 -0.4240622493 -0.2024995989 -0.1195828654
51 52 53 54 55
0.0433338681 0.1318755014 0.2990630515 0.0153649928 -0.1504684742
56 57 58 59 60
-0.2291143911 0.0159377507 0.3009898925 0.5281774426 0.5652607091
> postscript(file="/var/www/html/rcomp/tmp/62dp41258708272.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 0.0576560244 NA
1 0.1512497995 0.0576560244
2 0.3141665330 0.1512497995
3 0.7493229584 0.3141665330
4 0.8271875501 0.7493229584
5 0.6328124499 0.8271875501
6 0.1883330659 0.6328124499
7 -0.0796358094 0.1883330659
8 -0.1132295845 -0.0796358094
9 0.0611455158 -0.1132295845
10 0.2776560244 0.0611455158
11 0.3933852078 0.2776560244
12 0.4256248997 0.3933852078
13 0.4405727579 0.4256248997
14 0.3355206161 0.4405727579
15 0.3133852078 0.3355206161
16 0.1126038826 0.3133852078
17 0.1861976576 0.1126038826
18 0.2776560244 0.1861976576
19 0.2669789828 0.2776560244
20 0.1440622493 0.2669789828
21 0.0504684742 0.1440622493
22 -0.0009898925 0.0504684742
23 -0.0852607091 -0.0009898925
24 0.0469789828 -0.0852607091
25 -0.0487502005 0.0469789828
26 -0.0644793839 -0.0487502005
27 -0.2759377507 -0.0644793839
28 -0.3873961174 -0.2759377507
29 -0.3244793839 -0.3873961174
30 -0.1650521418 -0.3244793839
31 -0.0757291834 -0.1650521418
32 -0.2093229584 -0.0757291834
33 -0.3029167335 -0.2093229584
34 -0.4543751003 -0.3029167335
35 -0.4493229584 -0.4543751003
36 -0.3277603080 -0.4493229584
37 -0.4234894914 -0.3277603080
38 -0.6285416332 -0.4234894914
39 -0.9186459169 -0.6285416332
40 -0.8514583668 -0.9186459169
41 -0.5098957163 -0.8514583668
42 -0.1504684742 -0.5098957163
43 0.1175004011 -0.1504684742
44 0.1625525429 0.1175004011
45 -0.1096871490 0.1625525429
46 -0.3504684742 -0.1096871490
47 -0.4240622493 -0.3504684742
48 -0.2024995989 -0.4240622493
49 -0.1195828654 -0.2024995989
50 0.0433338681 -0.1195828654
51 0.1318755014 0.0433338681
52 0.2990630515 0.1318755014
53 0.0153649928 0.2990630515
54 -0.1504684742 0.0153649928
55 -0.2291143911 -0.1504684742
56 0.0159377507 -0.2291143911
57 0.3009898925 0.0159377507
58 0.5281774426 0.3009898925
59 0.5652607091 0.5281774426
60 NA 0.5652607091
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.1512497995 0.0576560244
[2,] 0.3141665330 0.1512497995
[3,] 0.7493229584 0.3141665330
[4,] 0.8271875501 0.7493229584
[5,] 0.6328124499 0.8271875501
[6,] 0.1883330659 0.6328124499
[7,] -0.0796358094 0.1883330659
[8,] -0.1132295845 -0.0796358094
[9,] 0.0611455158 -0.1132295845
[10,] 0.2776560244 0.0611455158
[11,] 0.3933852078 0.2776560244
[12,] 0.4256248997 0.3933852078
[13,] 0.4405727579 0.4256248997
[14,] 0.3355206161 0.4405727579
[15,] 0.3133852078 0.3355206161
[16,] 0.1126038826 0.3133852078
[17,] 0.1861976576 0.1126038826
[18,] 0.2776560244 0.1861976576
[19,] 0.2669789828 0.2776560244
[20,] 0.1440622493 0.2669789828
[21,] 0.0504684742 0.1440622493
[22,] -0.0009898925 0.0504684742
[23,] -0.0852607091 -0.0009898925
[24,] 0.0469789828 -0.0852607091
[25,] -0.0487502005 0.0469789828
[26,] -0.0644793839 -0.0487502005
[27,] -0.2759377507 -0.0644793839
[28,] -0.3873961174 -0.2759377507
[29,] -0.3244793839 -0.3873961174
[30,] -0.1650521418 -0.3244793839
[31,] -0.0757291834 -0.1650521418
[32,] -0.2093229584 -0.0757291834
[33,] -0.3029167335 -0.2093229584
[34,] -0.4543751003 -0.3029167335
[35,] -0.4493229584 -0.4543751003
[36,] -0.3277603080 -0.4493229584
[37,] -0.4234894914 -0.3277603080
[38,] -0.6285416332 -0.4234894914
[39,] -0.9186459169 -0.6285416332
[40,] -0.8514583668 -0.9186459169
[41,] -0.5098957163 -0.8514583668
[42,] -0.1504684742 -0.5098957163
[43,] 0.1175004011 -0.1504684742
[44,] 0.1625525429 0.1175004011
[45,] -0.1096871490 0.1625525429
[46,] -0.3504684742 -0.1096871490
[47,] -0.4240622493 -0.3504684742
[48,] -0.2024995989 -0.4240622493
[49,] -0.1195828654 -0.2024995989
[50,] 0.0433338681 -0.1195828654
[51,] 0.1318755014 0.0433338681
[52,] 0.2990630515 0.1318755014
[53,] 0.0153649928 0.2990630515
[54,] -0.1504684742 0.0153649928
[55,] -0.2291143911 -0.1504684742
[56,] 0.0159377507 -0.2291143911
[57,] 0.3009898925 0.0159377507
[58,] 0.5281774426 0.3009898925
[59,] 0.5652607091 0.5281774426
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.1512497995 0.0576560244
2 0.3141665330 0.1512497995
3 0.7493229584 0.3141665330
4 0.8271875501 0.7493229584
5 0.6328124499 0.8271875501
6 0.1883330659 0.6328124499
7 -0.0796358094 0.1883330659
8 -0.1132295845 -0.0796358094
9 0.0611455158 -0.1132295845
10 0.2776560244 0.0611455158
11 0.3933852078 0.2776560244
12 0.4256248997 0.3933852078
13 0.4405727579 0.4256248997
14 0.3355206161 0.4405727579
15 0.3133852078 0.3355206161
16 0.1126038826 0.3133852078
17 0.1861976576 0.1126038826
18 0.2776560244 0.1861976576
19 0.2669789828 0.2776560244
20 0.1440622493 0.2669789828
21 0.0504684742 0.1440622493
22 -0.0009898925 0.0504684742
23 -0.0852607091 -0.0009898925
24 0.0469789828 -0.0852607091
25 -0.0487502005 0.0469789828
26 -0.0644793839 -0.0487502005
27 -0.2759377507 -0.0644793839
28 -0.3873961174 -0.2759377507
29 -0.3244793839 -0.3873961174
30 -0.1650521418 -0.3244793839
31 -0.0757291834 -0.1650521418
32 -0.2093229584 -0.0757291834
33 -0.3029167335 -0.2093229584
34 -0.4543751003 -0.3029167335
35 -0.4493229584 -0.4543751003
36 -0.3277603080 -0.4493229584
37 -0.4234894914 -0.3277603080
38 -0.6285416332 -0.4234894914
39 -0.9186459169 -0.6285416332
40 -0.8514583668 -0.9186459169
41 -0.5098957163 -0.8514583668
42 -0.1504684742 -0.5098957163
43 0.1175004011 -0.1504684742
44 0.1625525429 0.1175004011
45 -0.1096871490 0.1625525429
46 -0.3504684742 -0.1096871490
47 -0.4240622493 -0.3504684742
48 -0.2024995989 -0.4240622493
49 -0.1195828654 -0.2024995989
50 0.0433338681 -0.1195828654
51 0.1318755014 0.0433338681
52 0.2990630515 0.1318755014
53 0.0153649928 0.2990630515
54 -0.1504684742 0.0153649928
55 -0.2291143911 -0.1504684742
56 0.0159377507 -0.2291143911
57 0.3009898925 0.0159377507
58 0.5281774426 0.3009898925
59 0.5652607091 0.5281774426
> 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/7omhh1258708272.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vn511258708272.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9grhl1258708272.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107vhr1258708272.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11arl31258708272.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/12r0mt1258708272.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/13qdg11258708272.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/14dugi1258708272.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/153cnp1258708272.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/166u2p1258708272.tab")
+ }
>
> system("convert tmp/1nrcd1258708272.ps tmp/1nrcd1258708272.png")
> system("convert tmp/2usj61258708272.ps tmp/2usj61258708272.png")
> system("convert tmp/3prj31258708272.ps tmp/3prj31258708272.png")
> system("convert tmp/4uq3b1258708272.ps tmp/4uq3b1258708272.png")
> system("convert tmp/5px961258708272.ps tmp/5px961258708272.png")
> system("convert tmp/62dp41258708272.ps tmp/62dp41258708272.png")
> system("convert tmp/7omhh1258708272.ps tmp/7omhh1258708272.png")
> system("convert tmp/8vn511258708272.ps tmp/8vn511258708272.png")
> system("convert tmp/9grhl1258708272.ps tmp/9grhl1258708272.png")
> system("convert tmp/107vhr1258708272.ps tmp/107vhr1258708272.png")
>
>
> proc.time()
user system elapsed
2.311 1.487 2.846