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(7.6,1.62,8.3,1.49,8.4,1.79,8.4,1.8,8.4,1.58,8.4,1.86,8.6,1.74,8.9,1.59,8.8,1.26,8.3,1.13,7.5,1.92,7.2,2.61,7.4,2.26,8.8,2.41,9.3,2.26,9.3,2.03,8.7,2.86,8.2,2.55,8.3,2.27,8.5,2.26,8.6,2.57,8.5,3.07,8.2,2.76,8.1,2.51,7.9,2.87,8.6,3.14,8.7,3.11,8.7,3.16,8.5,2.47,8.4,2.57,8.5,2.89,8.7,2.63,8.7,2.38,8.6,1.69,8.5,1.96,8.3,2.19,8,1.87,8.2,1.6,8.1,1.63,8.1,1.22,8,1.21,7.9,1.49,7.9,1.64,8,1.66,8,1.77,7.9,1.82,8,1.78,7.7,1.28,7.2,1.29,7.5,1.37,7.3,1.12,7,1.51,7,2.24,7,2.94,7.2,3.09,7.3,3.46,7.1,3.64,6.8,4.39,6.4,4.15,6.1,5.21,6.5,5.8,7.7,5.91,7.9,5.39,7.5,5.46,6.9,4.72,6.6,3.14,6.9,2.63,7.7,2.32,8,1.93,8,0.62,7.7,0.6,7.3,-0.37,7.4,-1.1),dim=c(2,73),dimnames=list(c('TWG','Infl'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('TWG','Infl'),1:73))
> 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
TWG Infl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 7.6 1.62 1 0 0 0 0 0 0 0 0 0 0
2 8.3 1.49 0 1 0 0 0 0 0 0 0 0 0
3 8.4 1.79 0 0 1 0 0 0 0 0 0 0 0
4 8.4 1.80 0 0 0 1 0 0 0 0 0 0 0
5 8.4 1.58 0 0 0 0 1 0 0 0 0 0 0
6 8.4 1.86 0 0 0 0 0 1 0 0 0 0 0
7 8.6 1.74 0 0 0 0 0 0 1 0 0 0 0
8 8.9 1.59 0 0 0 0 0 0 0 1 0 0 0
9 8.8 1.26 0 0 0 0 0 0 0 0 1 0 0
10 8.3 1.13 0 0 0 0 0 0 0 0 0 1 0
11 7.5 1.92 0 0 0 0 0 0 0 0 0 0 1
12 7.2 2.61 0 0 0 0 0 0 0 0 0 0 0
13 7.4 2.26 1 0 0 0 0 0 0 0 0 0 0
14 8.8 2.41 0 1 0 0 0 0 0 0 0 0 0
15 9.3 2.26 0 0 1 0 0 0 0 0 0 0 0
16 9.3 2.03 0 0 0 1 0 0 0 0 0 0 0
17 8.7 2.86 0 0 0 0 1 0 0 0 0 0 0
18 8.2 2.55 0 0 0 0 0 1 0 0 0 0 0
19 8.3 2.27 0 0 0 0 0 0 1 0 0 0 0
20 8.5 2.26 0 0 0 0 0 0 0 1 0 0 0
21 8.6 2.57 0 0 0 0 0 0 0 0 1 0 0
22 8.5 3.07 0 0 0 0 0 0 0 0 0 1 0
23 8.2 2.76 0 0 0 0 0 0 0 0 0 0 1
24 8.1 2.51 0 0 0 0 0 0 0 0 0 0 0
25 7.9 2.87 1 0 0 0 0 0 0 0 0 0 0
26 8.6 3.14 0 1 0 0 0 0 0 0 0 0 0
27 8.7 3.11 0 0 1 0 0 0 0 0 0 0 0
28 8.7 3.16 0 0 0 1 0 0 0 0 0 0 0
29 8.5 2.47 0 0 0 0 1 0 0 0 0 0 0
30 8.4 2.57 0 0 0 0 0 1 0 0 0 0 0
31 8.5 2.89 0 0 0 0 0 0 1 0 0 0 0
32 8.7 2.63 0 0 0 0 0 0 0 1 0 0 0
33 8.7 2.38 0 0 0 0 0 0 0 0 1 0 0
34 8.6 1.69 0 0 0 0 0 0 0 0 0 1 0
35 8.5 1.96 0 0 0 0 0 0 0 0 0 0 1
36 8.3 2.19 0 0 0 0 0 0 0 0 0 0 0
37 8.0 1.87 1 0 0 0 0 0 0 0 0 0 0
38 8.2 1.60 0 1 0 0 0 0 0 0 0 0 0
39 8.1 1.63 0 0 1 0 0 0 0 0 0 0 0
40 8.1 1.22 0 0 0 1 0 0 0 0 0 0 0
41 8.0 1.21 0 0 0 0 1 0 0 0 0 0 0
42 7.9 1.49 0 0 0 0 0 1 0 0 0 0 0
43 7.9 1.64 0 0 0 0 0 0 1 0 0 0 0
44 8.0 1.66 0 0 0 0 0 0 0 1 0 0 0
45 8.0 1.77 0 0 0 0 0 0 0 0 1 0 0
46 7.9 1.82 0 0 0 0 0 0 0 0 0 1 0
47 8.0 1.78 0 0 0 0 0 0 0 0 0 0 1
48 7.7 1.28 0 0 0 0 0 0 0 0 0 0 0
49 7.2 1.29 1 0 0 0 0 0 0 0 0 0 0
50 7.5 1.37 0 1 0 0 0 0 0 0 0 0 0
51 7.3 1.12 0 0 1 0 0 0 0 0 0 0 0
52 7.0 1.51 0 0 0 1 0 0 0 0 0 0 0
53 7.0 2.24 0 0 0 0 1 0 0 0 0 0 0
54 7.0 2.94 0 0 0 0 0 1 0 0 0 0 0
55 7.2 3.09 0 0 0 0 0 0 1 0 0 0 0
56 7.3 3.46 0 0 0 0 0 0 0 1 0 0 0
57 7.1 3.64 0 0 0 0 0 0 0 0 1 0 0
58 6.8 4.39 0 0 0 0 0 0 0 0 0 1 0
59 6.4 4.15 0 0 0 0 0 0 0 0 0 0 1
60 6.1 5.21 0 0 0 0 0 0 0 0 0 0 0
61 6.5 5.80 1 0 0 0 0 0 0 0 0 0 0
62 7.7 5.91 0 1 0 0 0 0 0 0 0 0 0
63 7.9 5.39 0 0 1 0 0 0 0 0 0 0 0
64 7.5 5.46 0 0 0 1 0 0 0 0 0 0 0
65 6.9 4.72 0 0 0 0 1 0 0 0 0 0 0
66 6.6 3.14 0 0 0 0 0 1 0 0 0 0 0
67 6.9 2.63 0 0 0 0 0 0 1 0 0 0 0
68 7.7 2.32 0 0 0 0 0 0 0 1 0 0 0
69 8.0 1.93 0 0 0 0 0 0 0 0 1 0 0
70 8.0 0.62 0 0 0 0 0 0 0 0 0 1 0
71 7.7 0.60 0 0 0 0 0 0 0 0 0 0 1
72 7.3 -0.37 0 0 0 0 0 0 0 0 0 0 0
73 7.4 -1.10 1 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
7.86696 -0.18628 -0.04959 0.81064 0.89139 0.77100
M5 M6 M7 M8 M9 M10
0.51789 0.33477 0.47577 0.74855 0.75373 0.54462
M11
0.25859
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.356674 -0.354713 0.003629 0.530979 1.040192
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.86696 0.29610 26.569 < 2e-16 ***
Infl -0.18628 0.06013 -3.098 0.00296 **
M1 -0.04959 0.35954 -0.138 0.89075
M2 0.81064 0.37382 2.169 0.03410 *
M3 0.89139 0.37346 2.387 0.02016 *
M4 0.77100 0.37340 2.065 0.04327 *
M5 0.51789 0.37336 1.387 0.17053
M6 0.33477 0.37316 0.897 0.37323
M7 0.47577 0.37308 1.275 0.20714
M8 0.74855 0.37302 2.007 0.04929 *
M9 0.75373 0.37299 2.021 0.04777 *
M10 0.54462 0.37306 1.460 0.14954
M11 0.25859 0.37300 0.693 0.49081
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.646 on 60 degrees of freedom
Multiple R-squared: 0.2845, Adjusted R-squared: 0.1414
F-statistic: 1.988 on 12 and 60 DF, p-value: 0.04119
> 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,] 0.296154673 0.592309346 0.7038453
[2,] 0.191059252 0.382118504 0.8089407
[3,] 0.130449304 0.260898608 0.8695507
[4,] 0.088839819 0.177679638 0.9111602
[5,] 0.067118394 0.134236787 0.9328816
[6,] 0.042464956 0.084929912 0.9575350
[7,] 0.022880829 0.045761658 0.9771192
[8,] 0.019828464 0.039656928 0.9801715
[9,] 0.031199568 0.062399136 0.9688004
[10,] 0.020440422 0.040880843 0.9795596
[11,] 0.012441069 0.024882138 0.9875589
[12,] 0.009118201 0.018236402 0.9908818
[13,] 0.007832460 0.015664921 0.9921675
[14,] 0.005928021 0.011856041 0.9940720
[15,] 0.005086114 0.010172229 0.9949139
[16,] 0.004723487 0.009446974 0.9952765
[17,] 0.004001544 0.008003088 0.9959985
[18,] 0.003340933 0.006681866 0.9966591
[19,] 0.003120522 0.006241044 0.9968795
[20,] 0.008510643 0.017021287 0.9914894
[21,] 0.029270260 0.058540519 0.9707297
[22,] 0.041702880 0.083405759 0.9582971
[23,] 0.032135322 0.064270644 0.9678647
[24,] 0.036820369 0.073640738 0.9631796
[25,] 0.041114523 0.082229047 0.9588855
[26,] 0.043571135 0.087142269 0.9564289
[27,] 0.054930344 0.109860688 0.9450697
[28,] 0.063745709 0.127491418 0.9362543
[29,] 0.064391163 0.128782325 0.9356088
[30,] 0.064812011 0.129624022 0.9351880
[31,] 0.061044180 0.122088360 0.9389558
[32,] 0.078474733 0.156949466 0.9215253
[33,] 0.107222213 0.214444426 0.8927778
[34,] 0.077628093 0.155256186 0.9223719
[35,] 0.117074900 0.234149800 0.8829251
[36,] 0.376067656 0.752135312 0.6239323
[37,] 0.846794260 0.306411480 0.1532057
[38,] 0.882308310 0.235383380 0.1176917
[39,] 0.883450923 0.233098155 0.1165491
[40,] 0.869641672 0.260716657 0.1303583
[41,] 0.806516853 0.386966295 0.1934831
[42,] 0.788031694 0.423936612 0.2119683
> postscript(file="/var/www/html/rcomp/tmp/118kv1261068736.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ehrq1261068736.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37mgf1261068736.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4788d1261068736.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yw601261068736.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 = 73
Frequency = 1
1 2 3 4 5 6
0.084408347 -0.100041190 -0.024907520 0.097347601 0.309470297 0.544750769
7 8 9 10 11 12
0.581400571 0.580680934 0.414028645 0.098914327 -0.267894169 -0.180765255
13 14 15 16 17 18
0.003628715 0.571338089 0.962644937 1.040192421 0.847911033 0.473285228
19 20 21 22 23 24
0.380129939 0.305489757 0.458057835 0.660301067 0.588582564 0.700606562
25 26 27 28 29 30
0.617260629 0.507323821 0.520984489 0.650690883 0.575261121 0.677010865
31 32 33 34 35 36
0.695624670 0.574414032 0.522664289 0.503232149 0.739557104 0.840996378
37 38 39 40 41 42
0.530978804 -0.179550189 -0.354712612 -0.310695857 -0.159453979 -0.024173506
43 44 45 46 47 48
-0.137227611 -0.306279338 -0.290967625 -0.172551214 0.206026376 0.071479918
49 50 51 52 53 54
-0.377064655 -0.922395009 -1.249716343 -1.356674128 -0.967583699 -0.654064860
55 56 57 58 59 60
-0.567118965 -0.670972053 -0.842620612 -0.793806924 -0.952485699 -0.796432510
61 62 63 64 65 66
-0.236933624 0.123324477 0.145707050 -0.120860920 -0.605604773 -1.016808495
67 68 69 70 71 72
-0.952808604 -0.483333333 -0.261162533 -0.296089404 -0.313786177 -0.635885093
73
-0.622278216
> postscript(file="/var/www/html/rcomp/tmp/66ekm1261068736.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 0.084408347 NA
1 -0.100041190 0.084408347
2 -0.024907520 -0.100041190
3 0.097347601 -0.024907520
4 0.309470297 0.097347601
5 0.544750769 0.309470297
6 0.581400571 0.544750769
7 0.580680934 0.581400571
8 0.414028645 0.580680934
9 0.098914327 0.414028645
10 -0.267894169 0.098914327
11 -0.180765255 -0.267894169
12 0.003628715 -0.180765255
13 0.571338089 0.003628715
14 0.962644937 0.571338089
15 1.040192421 0.962644937
16 0.847911033 1.040192421
17 0.473285228 0.847911033
18 0.380129939 0.473285228
19 0.305489757 0.380129939
20 0.458057835 0.305489757
21 0.660301067 0.458057835
22 0.588582564 0.660301067
23 0.700606562 0.588582564
24 0.617260629 0.700606562
25 0.507323821 0.617260629
26 0.520984489 0.507323821
27 0.650690883 0.520984489
28 0.575261121 0.650690883
29 0.677010865 0.575261121
30 0.695624670 0.677010865
31 0.574414032 0.695624670
32 0.522664289 0.574414032
33 0.503232149 0.522664289
34 0.739557104 0.503232149
35 0.840996378 0.739557104
36 0.530978804 0.840996378
37 -0.179550189 0.530978804
38 -0.354712612 -0.179550189
39 -0.310695857 -0.354712612
40 -0.159453979 -0.310695857
41 -0.024173506 -0.159453979
42 -0.137227611 -0.024173506
43 -0.306279338 -0.137227611
44 -0.290967625 -0.306279338
45 -0.172551214 -0.290967625
46 0.206026376 -0.172551214
47 0.071479918 0.206026376
48 -0.377064655 0.071479918
49 -0.922395009 -0.377064655
50 -1.249716343 -0.922395009
51 -1.356674128 -1.249716343
52 -0.967583699 -1.356674128
53 -0.654064860 -0.967583699
54 -0.567118965 -0.654064860
55 -0.670972053 -0.567118965
56 -0.842620612 -0.670972053
57 -0.793806924 -0.842620612
58 -0.952485699 -0.793806924
59 -0.796432510 -0.952485699
60 -0.236933624 -0.796432510
61 0.123324477 -0.236933624
62 0.145707050 0.123324477
63 -0.120860920 0.145707050
64 -0.605604773 -0.120860920
65 -1.016808495 -0.605604773
66 -0.952808604 -1.016808495
67 -0.483333333 -0.952808604
68 -0.261162533 -0.483333333
69 -0.296089404 -0.261162533
70 -0.313786177 -0.296089404
71 -0.635885093 -0.313786177
72 -0.622278216 -0.635885093
73 NA -0.622278216
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.100041190 0.084408347
[2,] -0.024907520 -0.100041190
[3,] 0.097347601 -0.024907520
[4,] 0.309470297 0.097347601
[5,] 0.544750769 0.309470297
[6,] 0.581400571 0.544750769
[7,] 0.580680934 0.581400571
[8,] 0.414028645 0.580680934
[9,] 0.098914327 0.414028645
[10,] -0.267894169 0.098914327
[11,] -0.180765255 -0.267894169
[12,] 0.003628715 -0.180765255
[13,] 0.571338089 0.003628715
[14,] 0.962644937 0.571338089
[15,] 1.040192421 0.962644937
[16,] 0.847911033 1.040192421
[17,] 0.473285228 0.847911033
[18,] 0.380129939 0.473285228
[19,] 0.305489757 0.380129939
[20,] 0.458057835 0.305489757
[21,] 0.660301067 0.458057835
[22,] 0.588582564 0.660301067
[23,] 0.700606562 0.588582564
[24,] 0.617260629 0.700606562
[25,] 0.507323821 0.617260629
[26,] 0.520984489 0.507323821
[27,] 0.650690883 0.520984489
[28,] 0.575261121 0.650690883
[29,] 0.677010865 0.575261121
[30,] 0.695624670 0.677010865
[31,] 0.574414032 0.695624670
[32,] 0.522664289 0.574414032
[33,] 0.503232149 0.522664289
[34,] 0.739557104 0.503232149
[35,] 0.840996378 0.739557104
[36,] 0.530978804 0.840996378
[37,] -0.179550189 0.530978804
[38,] -0.354712612 -0.179550189
[39,] -0.310695857 -0.354712612
[40,] -0.159453979 -0.310695857
[41,] -0.024173506 -0.159453979
[42,] -0.137227611 -0.024173506
[43,] -0.306279338 -0.137227611
[44,] -0.290967625 -0.306279338
[45,] -0.172551214 -0.290967625
[46,] 0.206026376 -0.172551214
[47,] 0.071479918 0.206026376
[48,] -0.377064655 0.071479918
[49,] -0.922395009 -0.377064655
[50,] -1.249716343 -0.922395009
[51,] -1.356674128 -1.249716343
[52,] -0.967583699 -1.356674128
[53,] -0.654064860 -0.967583699
[54,] -0.567118965 -0.654064860
[55,] -0.670972053 -0.567118965
[56,] -0.842620612 -0.670972053
[57,] -0.793806924 -0.842620612
[58,] -0.952485699 -0.793806924
[59,] -0.796432510 -0.952485699
[60,] -0.236933624 -0.796432510
[61,] 0.123324477 -0.236933624
[62,] 0.145707050 0.123324477
[63,] -0.120860920 0.145707050
[64,] -0.605604773 -0.120860920
[65,] -1.016808495 -0.605604773
[66,] -0.952808604 -1.016808495
[67,] -0.483333333 -0.952808604
[68,] -0.261162533 -0.483333333
[69,] -0.296089404 -0.261162533
[70,] -0.313786177 -0.296089404
[71,] -0.635885093 -0.313786177
[72,] -0.622278216 -0.635885093
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.100041190 0.084408347
2 -0.024907520 -0.100041190
3 0.097347601 -0.024907520
4 0.309470297 0.097347601
5 0.544750769 0.309470297
6 0.581400571 0.544750769
7 0.580680934 0.581400571
8 0.414028645 0.580680934
9 0.098914327 0.414028645
10 -0.267894169 0.098914327
11 -0.180765255 -0.267894169
12 0.003628715 -0.180765255
13 0.571338089 0.003628715
14 0.962644937 0.571338089
15 1.040192421 0.962644937
16 0.847911033 1.040192421
17 0.473285228 0.847911033
18 0.380129939 0.473285228
19 0.305489757 0.380129939
20 0.458057835 0.305489757
21 0.660301067 0.458057835
22 0.588582564 0.660301067
23 0.700606562 0.588582564
24 0.617260629 0.700606562
25 0.507323821 0.617260629
26 0.520984489 0.507323821
27 0.650690883 0.520984489
28 0.575261121 0.650690883
29 0.677010865 0.575261121
30 0.695624670 0.677010865
31 0.574414032 0.695624670
32 0.522664289 0.574414032
33 0.503232149 0.522664289
34 0.739557104 0.503232149
35 0.840996378 0.739557104
36 0.530978804 0.840996378
37 -0.179550189 0.530978804
38 -0.354712612 -0.179550189
39 -0.310695857 -0.354712612
40 -0.159453979 -0.310695857
41 -0.024173506 -0.159453979
42 -0.137227611 -0.024173506
43 -0.306279338 -0.137227611
44 -0.290967625 -0.306279338
45 -0.172551214 -0.290967625
46 0.206026376 -0.172551214
47 0.071479918 0.206026376
48 -0.377064655 0.071479918
49 -0.922395009 -0.377064655
50 -1.249716343 -0.922395009
51 -1.356674128 -1.249716343
52 -0.967583699 -1.356674128
53 -0.654064860 -0.967583699
54 -0.567118965 -0.654064860
55 -0.670972053 -0.567118965
56 -0.842620612 -0.670972053
57 -0.793806924 -0.842620612
58 -0.952485699 -0.793806924
59 -0.796432510 -0.952485699
60 -0.236933624 -0.796432510
61 0.123324477 -0.236933624
62 0.145707050 0.123324477
63 -0.120860920 0.145707050
64 -0.605604773 -0.120860920
65 -1.016808495 -0.605604773
66 -0.952808604 -1.016808495
67 -0.483333333 -0.952808604
68 -0.261162533 -0.483333333
69 -0.296089404 -0.261162533
70 -0.313786177 -0.296089404
71 -0.635885093 -0.313786177
72 -0.622278216 -0.635885093
> 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/7duk41261068736.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8twu41261068736.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wtwp1261068736.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10b1rd1261068736.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qll21261068736.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/12vu0z1261068736.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/138maf1261068736.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/14kcy61261068736.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/15x4dv1261068736.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/16iuw51261068736.tab")
+ }
>
> try(system("convert tmp/118kv1261068736.ps tmp/118kv1261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ehrq1261068736.ps tmp/2ehrq1261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/37mgf1261068736.ps tmp/37mgf1261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/4788d1261068736.ps tmp/4788d1261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yw601261068736.ps tmp/5yw601261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/66ekm1261068736.ps tmp/66ekm1261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/7duk41261068736.ps tmp/7duk41261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/8twu41261068736.ps tmp/8twu41261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wtwp1261068736.ps tmp/9wtwp1261068736.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b1rd1261068736.ps tmp/10b1rd1261068736.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.571 1.574 3.639