R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(627,0,696,0,825,0,677,0,656,0,785,0,412,0,352,0,839,0,729,0,696,0,641,0,695,0,638,0,762,0,635,0,721,0,854,0,418,0,367,0,824,0,687,0,601,0,676,0,740,0,691,0,683,0,594,0,729,0,731,0,386,0,331,0,707,0,715,0,657,0,653,0,642,0,643,0,718,0,654,0,632,0,731,0,392,1,344,1,792,1,852,1,649,1,629,1,685,1,617,1,715,1,715,1,629,1,916,1,531,1,357,1,917,1,828,1,708,1,858,1,775,1,785,1,1006,1,789,1,734,1,906,1,532,1,387,1,991,1,841,1),dim=c(2,70),dimnames=list(c('Y','X'),1:70))
> y <- array(NA,dim=c(2,70),dimnames=list(c('Y','X'),1:70))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 627 0 1 0 0 0 0 0 0 0 0 0 0 1
2 696 0 0 1 0 0 0 0 0 0 0 0 0 2
3 825 0 0 0 1 0 0 0 0 0 0 0 0 3
4 677 0 0 0 0 1 0 0 0 0 0 0 0 4
5 656 0 0 0 0 0 1 0 0 0 0 0 0 5
6 785 0 0 0 0 0 0 1 0 0 0 0 0 6
7 412 0 0 0 0 0 0 0 1 0 0 0 0 7
8 352 0 0 0 0 0 0 0 0 1 0 0 0 8
9 839 0 0 0 0 0 0 0 0 0 1 0 0 9
10 729 0 0 0 0 0 0 0 0 0 0 1 0 10
11 696 0 0 0 0 0 0 0 0 0 0 0 1 11
12 641 0 0 0 0 0 0 0 0 0 0 0 0 12
13 695 0 1 0 0 0 0 0 0 0 0 0 0 13
14 638 0 0 1 0 0 0 0 0 0 0 0 0 14
15 762 0 0 0 1 0 0 0 0 0 0 0 0 15
16 635 0 0 0 0 1 0 0 0 0 0 0 0 16
17 721 0 0 0 0 0 1 0 0 0 0 0 0 17
18 854 0 0 0 0 0 0 1 0 0 0 0 0 18
19 418 0 0 0 0 0 0 0 1 0 0 0 0 19
20 367 0 0 0 0 0 0 0 0 1 0 0 0 20
21 824 0 0 0 0 0 0 0 0 0 1 0 0 21
22 687 0 0 0 0 0 0 0 0 0 0 1 0 22
23 601 0 0 0 0 0 0 0 0 0 0 0 1 23
24 676 0 0 0 0 0 0 0 0 0 0 0 0 24
25 740 0 1 0 0 0 0 0 0 0 0 0 0 25
26 691 0 0 1 0 0 0 0 0 0 0 0 0 26
27 683 0 0 0 1 0 0 0 0 0 0 0 0 27
28 594 0 0 0 0 1 0 0 0 0 0 0 0 28
29 729 0 0 0 0 0 1 0 0 0 0 0 0 29
30 731 0 0 0 0 0 0 1 0 0 0 0 0 30
31 386 0 0 0 0 0 0 0 1 0 0 0 0 31
32 331 0 0 0 0 0 0 0 0 1 0 0 0 32
33 707 0 0 0 0 0 0 0 0 0 1 0 0 33
34 715 0 0 0 0 0 0 0 0 0 0 1 0 34
35 657 0 0 0 0 0 0 0 0 0 0 0 1 35
36 653 0 0 0 0 0 0 0 0 0 0 0 0 36
37 642 0 1 0 0 0 0 0 0 0 0 0 0 37
38 643 0 0 1 0 0 0 0 0 0 0 0 0 38
39 718 0 0 0 1 0 0 0 0 0 0 0 0 39
40 654 0 0 0 0 1 0 0 0 0 0 0 0 40
41 632 0 0 0 0 0 1 0 0 0 0 0 0 41
42 731 0 0 0 0 0 0 1 0 0 0 0 0 42
43 392 1 0 0 0 0 0 0 1 0 0 0 0 43
44 344 1 0 0 0 0 0 0 0 1 0 0 0 44
45 792 1 0 0 0 0 0 0 0 0 1 0 0 45
46 852 1 0 0 0 0 0 0 0 0 0 1 0 46
47 649 1 0 0 0 0 0 0 0 0 0 0 1 47
48 629 1 0 0 0 0 0 0 0 0 0 0 0 48
49 685 1 1 0 0 0 0 0 0 0 0 0 0 49
50 617 1 0 1 0 0 0 0 0 0 0 0 0 50
51 715 1 0 0 1 0 0 0 0 0 0 0 0 51
52 715 1 0 0 0 1 0 0 0 0 0 0 0 52
53 629 1 0 0 0 0 1 0 0 0 0 0 0 53
54 916 1 0 0 0 0 0 1 0 0 0 0 0 54
55 531 1 0 0 0 0 0 0 1 0 0 0 0 55
56 357 1 0 0 0 0 0 0 0 1 0 0 0 56
57 917 1 0 0 0 0 0 0 0 0 1 0 0 57
58 828 1 0 0 0 0 0 0 0 0 0 1 0 58
59 708 1 0 0 0 0 0 0 0 0 0 0 1 59
60 858 1 0 0 0 0 0 0 0 0 0 0 0 60
61 775 1 1 0 0 0 0 0 0 0 0 0 0 61
62 785 1 0 1 0 0 0 0 0 0 0 0 0 62
63 1006 1 0 0 1 0 0 0 0 0 0 0 0 63
64 789 1 0 0 0 1 0 0 0 0 0 0 0 64
65 734 1 0 0 0 0 1 0 0 0 0 0 0 65
66 906 1 0 0 0 0 0 1 0 0 0 0 0 66
67 532 1 0 0 0 0 0 0 1 0 0 0 0 67
68 387 1 0 0 0 0 0 0 0 1 0 0 0 68
69 991 1 0 0 0 0 0 0 0 0 1 0 0 69
70 841 1 0 0 0 0 0 0 0 0 0 1 0 70
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
653.402 62.233 8.569 -7.462 98.674 -9.190
M5 M6 M7 M8 M9 M10
-3.387 133.249 -252.821 -342.018 146.285 76.254
M11 t
-28.836 0.364
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-117.874 -34.861 1.944 37.287 168.757
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 653.4020 33.0565 19.766 < 2e-16 ***
X 62.2332 29.6488 2.099 0.040337 *
M1 8.5690 38.5024 0.223 0.824690
M2 -7.4617 38.4736 -0.194 0.846922
M3 98.6743 38.4581 2.566 0.012999 *
M4 -9.1897 38.4560 -0.239 0.812003
M5 -3.3871 38.4672 -0.088 0.930150
M6 133.2489 38.4918 3.462 0.001035 **
M7 -252.8207 38.5150 -6.564 1.81e-08 ***
M8 -342.0180 38.4882 -8.886 2.75e-12 ***
M9 146.2846 38.4747 3.802 0.000356 ***
M10 76.2539 38.4745 1.982 0.052403 .
M11 -28.8360 40.1567 -0.718 0.475689
t 0.3640 0.7165 0.508 0.613424
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 63.48 on 56 degrees of freedom
Multiple R-squared: 0.8659, Adjusted R-squared: 0.8348
F-statistic: 27.82 on 13 and 56 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,] 0.408441754 0.816883509 0.5915582
[2,] 0.363053789 0.726107577 0.6369462
[3,] 0.231306040 0.462612080 0.7686940
[4,] 0.155798615 0.311597230 0.8442014
[5,] 0.100414443 0.200828885 0.8995856
[6,] 0.067594180 0.135188361 0.9324058
[7,] 0.084441196 0.168882391 0.9155588
[8,] 0.059368222 0.118736444 0.9406318
[9,] 0.095228105 0.190456211 0.9047719
[10,] 0.079513326 0.159026651 0.9204867
[11,] 0.150210817 0.300421634 0.8497892
[12,] 0.122079875 0.244159750 0.8779201
[13,] 0.211878624 0.423757248 0.7881214
[14,] 0.212712585 0.425425170 0.7872874
[15,] 0.155632147 0.311264294 0.8443679
[16,] 0.144785212 0.289570423 0.8552148
[17,] 0.202236548 0.404473096 0.7977635
[18,] 0.152118915 0.304237830 0.8478811
[19,] 0.132041396 0.264082792 0.8679586
[20,] 0.091631686 0.183263373 0.9083683
[21,] 0.062061542 0.124123084 0.9379385
[22,] 0.044170186 0.088340372 0.9558298
[23,] 0.028195523 0.056391046 0.9718045
[24,] 0.020063449 0.040126898 0.9799366
[25,] 0.020945087 0.041890174 0.9790549
[26,] 0.012736240 0.025472480 0.9872638
[27,] 0.007339215 0.014678431 0.9926608
[28,] 0.006010403 0.012020805 0.9939896
[29,] 0.003605741 0.007211483 0.9963943
[30,] 0.028332536 0.056665072 0.9716675
[31,] 0.017742163 0.035484326 0.9822578
[32,] 0.034275506 0.068551013 0.9657245
[33,] 0.018339506 0.036679012 0.9816605
[34,] 0.019382034 0.038764069 0.9806180
[35,] 0.686882899 0.626234202 0.3131171
[36,] 0.622328941 0.755342119 0.3776711
[37,] 0.772988575 0.454022850 0.2270114
> postscript(file="/var/www/html/rcomp/tmp/1qo151260388488.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2guyk1260388488.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3rrw81260388488.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u4kn1260388488.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rho01260388488.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 = 70
Frequency = 1
1 2 3 4 5 6
-35.3350112 49.3316555 71.8316555 31.3316555 4.1649888 -3.8350112
7 8 9 10 11 12
8.8705257 37.7038591 36.0371924 -4.2961409 67.4297539 -16.7702461
13 14 15 16 17 18
28.2967562 -13.0365772 4.4634228 -15.0365772 64.7967562 60.7967562
19 20 21 22 23 24
10.5022931 48.3356264 16.6689597 -50.6643736 -31.9384787 13.8615213
25 26 27 28 29 30
68.9285235 35.5951902 -78.9048098 -60.4048098 68.4285235 -66.5714765
31 32 33 34 35 36
-25.8659396 7.9673937 -104.6992729 -27.0326063 19.6932886 -13.5067114
37 38 39 40 41 42
-33.4397092 -16.7730425 -48.2730425 -4.7730425 -32.9397092 -70.9397092
43 44 45 46 47 48
-86.4673937 -45.6340604 -86.3007271 43.3659396 -54.9081655 -104.1081655
49 50 51 52 53 54
-57.0411633 -109.3744966 -117.8744966 -10.3744966 -102.5411633 47.4588367
55 56 57 58 59 60
48.1643736 -37.0022931 34.3310403 14.9977069 -0.2763982 120.5236018
61 62 63 64 65 66
28.5906040 54.2572707 168.7572707 59.2572707 -1.9093960 33.0906040
67 68 69 70
44.7961409 -11.3705257 103.9628076 23.6294743
> postscript(file="/var/www/html/rcomp/tmp/6ux531260388488.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 = 70
Frequency = 1
lag(myerror, k = 1) myerror
0 -35.3350112 NA
1 49.3316555 -35.3350112
2 71.8316555 49.3316555
3 31.3316555 71.8316555
4 4.1649888 31.3316555
5 -3.8350112 4.1649888
6 8.8705257 -3.8350112
7 37.7038591 8.8705257
8 36.0371924 37.7038591
9 -4.2961409 36.0371924
10 67.4297539 -4.2961409
11 -16.7702461 67.4297539
12 28.2967562 -16.7702461
13 -13.0365772 28.2967562
14 4.4634228 -13.0365772
15 -15.0365772 4.4634228
16 64.7967562 -15.0365772
17 60.7967562 64.7967562
18 10.5022931 60.7967562
19 48.3356264 10.5022931
20 16.6689597 48.3356264
21 -50.6643736 16.6689597
22 -31.9384787 -50.6643736
23 13.8615213 -31.9384787
24 68.9285235 13.8615213
25 35.5951902 68.9285235
26 -78.9048098 35.5951902
27 -60.4048098 -78.9048098
28 68.4285235 -60.4048098
29 -66.5714765 68.4285235
30 -25.8659396 -66.5714765
31 7.9673937 -25.8659396
32 -104.6992729 7.9673937
33 -27.0326063 -104.6992729
34 19.6932886 -27.0326063
35 -13.5067114 19.6932886
36 -33.4397092 -13.5067114
37 -16.7730425 -33.4397092
38 -48.2730425 -16.7730425
39 -4.7730425 -48.2730425
40 -32.9397092 -4.7730425
41 -70.9397092 -32.9397092
42 -86.4673937 -70.9397092
43 -45.6340604 -86.4673937
44 -86.3007271 -45.6340604
45 43.3659396 -86.3007271
46 -54.9081655 43.3659396
47 -104.1081655 -54.9081655
48 -57.0411633 -104.1081655
49 -109.3744966 -57.0411633
50 -117.8744966 -109.3744966
51 -10.3744966 -117.8744966
52 -102.5411633 -10.3744966
53 47.4588367 -102.5411633
54 48.1643736 47.4588367
55 -37.0022931 48.1643736
56 34.3310403 -37.0022931
57 14.9977069 34.3310403
58 -0.2763982 14.9977069
59 120.5236018 -0.2763982
60 28.5906040 120.5236018
61 54.2572707 28.5906040
62 168.7572707 54.2572707
63 59.2572707 168.7572707
64 -1.9093960 59.2572707
65 33.0906040 -1.9093960
66 44.7961409 33.0906040
67 -11.3705257 44.7961409
68 103.9628076 -11.3705257
69 23.6294743 103.9628076
70 NA 23.6294743
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 49.3316555 -35.3350112
[2,] 71.8316555 49.3316555
[3,] 31.3316555 71.8316555
[4,] 4.1649888 31.3316555
[5,] -3.8350112 4.1649888
[6,] 8.8705257 -3.8350112
[7,] 37.7038591 8.8705257
[8,] 36.0371924 37.7038591
[9,] -4.2961409 36.0371924
[10,] 67.4297539 -4.2961409
[11,] -16.7702461 67.4297539
[12,] 28.2967562 -16.7702461
[13,] -13.0365772 28.2967562
[14,] 4.4634228 -13.0365772
[15,] -15.0365772 4.4634228
[16,] 64.7967562 -15.0365772
[17,] 60.7967562 64.7967562
[18,] 10.5022931 60.7967562
[19,] 48.3356264 10.5022931
[20,] 16.6689597 48.3356264
[21,] -50.6643736 16.6689597
[22,] -31.9384787 -50.6643736
[23,] 13.8615213 -31.9384787
[24,] 68.9285235 13.8615213
[25,] 35.5951902 68.9285235
[26,] -78.9048098 35.5951902
[27,] -60.4048098 -78.9048098
[28,] 68.4285235 -60.4048098
[29,] -66.5714765 68.4285235
[30,] -25.8659396 -66.5714765
[31,] 7.9673937 -25.8659396
[32,] -104.6992729 7.9673937
[33,] -27.0326063 -104.6992729
[34,] 19.6932886 -27.0326063
[35,] -13.5067114 19.6932886
[36,] -33.4397092 -13.5067114
[37,] -16.7730425 -33.4397092
[38,] -48.2730425 -16.7730425
[39,] -4.7730425 -48.2730425
[40,] -32.9397092 -4.7730425
[41,] -70.9397092 -32.9397092
[42,] -86.4673937 -70.9397092
[43,] -45.6340604 -86.4673937
[44,] -86.3007271 -45.6340604
[45,] 43.3659396 -86.3007271
[46,] -54.9081655 43.3659396
[47,] -104.1081655 -54.9081655
[48,] -57.0411633 -104.1081655
[49,] -109.3744966 -57.0411633
[50,] -117.8744966 -109.3744966
[51,] -10.3744966 -117.8744966
[52,] -102.5411633 -10.3744966
[53,] 47.4588367 -102.5411633
[54,] 48.1643736 47.4588367
[55,] -37.0022931 48.1643736
[56,] 34.3310403 -37.0022931
[57,] 14.9977069 34.3310403
[58,] -0.2763982 14.9977069
[59,] 120.5236018 -0.2763982
[60,] 28.5906040 120.5236018
[61,] 54.2572707 28.5906040
[62,] 168.7572707 54.2572707
[63,] 59.2572707 168.7572707
[64,] -1.9093960 59.2572707
[65,] 33.0906040 -1.9093960
[66,] 44.7961409 33.0906040
[67,] -11.3705257 44.7961409
[68,] 103.9628076 -11.3705257
[69,] 23.6294743 103.9628076
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 49.3316555 -35.3350112
2 71.8316555 49.3316555
3 31.3316555 71.8316555
4 4.1649888 31.3316555
5 -3.8350112 4.1649888
6 8.8705257 -3.8350112
7 37.7038591 8.8705257
8 36.0371924 37.7038591
9 -4.2961409 36.0371924
10 67.4297539 -4.2961409
11 -16.7702461 67.4297539
12 28.2967562 -16.7702461
13 -13.0365772 28.2967562
14 4.4634228 -13.0365772
15 -15.0365772 4.4634228
16 64.7967562 -15.0365772
17 60.7967562 64.7967562
18 10.5022931 60.7967562
19 48.3356264 10.5022931
20 16.6689597 48.3356264
21 -50.6643736 16.6689597
22 -31.9384787 -50.6643736
23 13.8615213 -31.9384787
24 68.9285235 13.8615213
25 35.5951902 68.9285235
26 -78.9048098 35.5951902
27 -60.4048098 -78.9048098
28 68.4285235 -60.4048098
29 -66.5714765 68.4285235
30 -25.8659396 -66.5714765
31 7.9673937 -25.8659396
32 -104.6992729 7.9673937
33 -27.0326063 -104.6992729
34 19.6932886 -27.0326063
35 -13.5067114 19.6932886
36 -33.4397092 -13.5067114
37 -16.7730425 -33.4397092
38 -48.2730425 -16.7730425
39 -4.7730425 -48.2730425
40 -32.9397092 -4.7730425
41 -70.9397092 -32.9397092
42 -86.4673937 -70.9397092
43 -45.6340604 -86.4673937
44 -86.3007271 -45.6340604
45 43.3659396 -86.3007271
46 -54.9081655 43.3659396
47 -104.1081655 -54.9081655
48 -57.0411633 -104.1081655
49 -109.3744966 -57.0411633
50 -117.8744966 -109.3744966
51 -10.3744966 -117.8744966
52 -102.5411633 -10.3744966
53 47.4588367 -102.5411633
54 48.1643736 47.4588367
55 -37.0022931 48.1643736
56 34.3310403 -37.0022931
57 14.9977069 34.3310403
58 -0.2763982 14.9977069
59 120.5236018 -0.2763982
60 28.5906040 120.5236018
61 54.2572707 28.5906040
62 168.7572707 54.2572707
63 59.2572707 168.7572707
64 -1.9093960 59.2572707
65 33.0906040 -1.9093960
66 44.7961409 33.0906040
67 -11.3705257 44.7961409
68 103.9628076 -11.3705257
69 23.6294743 103.9628076
> 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/7hmbx1260388488.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/825u11260388488.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w01l1260388488.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104olp1260388488.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h5ec1260388489.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/12ra3v1260388489.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/13i6bl1260388489.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/14gmlf1260388489.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/15p5fi1260388489.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/16fuww1260388489.tab")
+ }
>
> system("convert tmp/1qo151260388488.ps tmp/1qo151260388488.png")
> system("convert tmp/2guyk1260388488.ps tmp/2guyk1260388488.png")
> system("convert tmp/3rrw81260388488.ps tmp/3rrw81260388488.png")
> system("convert tmp/4u4kn1260388488.ps tmp/4u4kn1260388488.png")
> system("convert tmp/5rho01260388488.ps tmp/5rho01260388488.png")
> system("convert tmp/6ux531260388488.ps tmp/6ux531260388488.png")
> system("convert tmp/7hmbx1260388488.ps tmp/7hmbx1260388488.png")
> system("convert tmp/825u11260388488.ps tmp/825u11260388488.png")
> system("convert tmp/9w01l1260388488.ps tmp/9w01l1260388488.png")
> system("convert tmp/104olp1260388488.ps tmp/104olp1260388488.png")
>
>
> proc.time()
user system elapsed
2.566 1.595 3.413