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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(130,87.1,136.7,110.5,138.1,110.8,139.5,104.2,140.4,88.9,144.6,89.8,151.4,90,147.9,93.9,141.5,91.3,143.8,87.8,143.6,99.7,150.5,73.5,150.1,79.2,154.9,96.9,162.1,95.2,176.7,95.6,186.6,89.7,194.8,92.8,196.3,88,228.8,101.1,267.2,92.7,237.2,95.8,254.7,103.8,258.2,81.8,257.9,87.1,269.6,105.9,266.9,108.1,269.6,102.6,253.9,93.7,258.6,103.5,274.2,100.6,301.5,113.3,304.5,102.4,285.1,102.1,287.7,106.9,265.5,87.3,264.1,93.1,276.1,109.1,258.9,120.3,239.1,104.9,250.1,92.6,276.8,109.8,297.6,111.4,295.4,117.9,283,121.6,275.8,117.8,279.7,124.2,254.6,106.8,234.6,102.7,176.9,116.8,148.1,113.6,122.7,96.1,124.9,85,121.6,83.2,128.4,84.9,144.5,83,151.8,79.6,167.1,83.2,173.8,83.8,203.7,82.8,199.8,71.4),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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
1 130.0 87.1 1 0 0 0 0 0 0 0 0 0 0 1
2 136.7 110.5 0 1 0 0 0 0 0 0 0 0 0 2
3 138.1 110.8 0 0 1 0 0 0 0 0 0 0 0 3
4 139.5 104.2 0 0 0 1 0 0 0 0 0 0 0 4
5 140.4 88.9 0 0 0 0 1 0 0 0 0 0 0 5
6 144.6 89.8 0 0 0 0 0 1 0 0 0 0 0 6
7 151.4 90.0 0 0 0 0 0 0 1 0 0 0 0 7
8 147.9 93.9 0 0 0 0 0 0 0 1 0 0 0 8
9 141.5 91.3 0 0 0 0 0 0 0 0 1 0 0 9
10 143.8 87.8 0 0 0 0 0 0 0 0 0 1 0 10
11 143.6 99.7 0 0 0 0 0 0 0 0 0 0 1 11
12 150.5 73.5 0 0 0 0 0 0 0 0 0 0 0 12
13 150.1 79.2 1 0 0 0 0 0 0 0 0 0 0 13
14 154.9 96.9 0 1 0 0 0 0 0 0 0 0 0 14
15 162.1 95.2 0 0 1 0 0 0 0 0 0 0 0 15
16 176.7 95.6 0 0 0 1 0 0 0 0 0 0 0 16
17 186.6 89.7 0 0 0 0 1 0 0 0 0 0 0 17
18 194.8 92.8 0 0 0 0 0 1 0 0 0 0 0 18
19 196.3 88.0 0 0 0 0 0 0 1 0 0 0 0 19
20 228.8 101.1 0 0 0 0 0 0 0 1 0 0 0 20
21 267.2 92.7 0 0 0 0 0 0 0 0 1 0 0 21
22 237.2 95.8 0 0 0 0 0 0 0 0 0 1 0 22
23 254.7 103.8 0 0 0 0 0 0 0 0 0 0 1 23
24 258.2 81.8 0 0 0 0 0 0 0 0 0 0 0 24
25 257.9 87.1 1 0 0 0 0 0 0 0 0 0 0 25
26 269.6 105.9 0 1 0 0 0 0 0 0 0 0 0 26
27 266.9 108.1 0 0 1 0 0 0 0 0 0 0 0 27
28 269.6 102.6 0 0 0 1 0 0 0 0 0 0 0 28
29 253.9 93.7 0 0 0 0 1 0 0 0 0 0 0 29
30 258.6 103.5 0 0 0 0 0 1 0 0 0 0 0 30
31 274.2 100.6 0 0 0 0 0 0 1 0 0 0 0 31
32 301.5 113.3 0 0 0 0 0 0 0 1 0 0 0 32
33 304.5 102.4 0 0 0 0 0 0 0 0 1 0 0 33
34 285.1 102.1 0 0 0 0 0 0 0 0 0 1 0 34
35 287.7 106.9 0 0 0 0 0 0 0 0 0 0 1 35
36 265.5 87.3 0 0 0 0 0 0 0 0 0 0 0 36
37 264.1 93.1 1 0 0 0 0 0 0 0 0 0 0 37
38 276.1 109.1 0 1 0 0 0 0 0 0 0 0 0 38
39 258.9 120.3 0 0 1 0 0 0 0 0 0 0 0 39
40 239.1 104.9 0 0 0 1 0 0 0 0 0 0 0 40
41 250.1 92.6 0 0 0 0 1 0 0 0 0 0 0 41
42 276.8 109.8 0 0 0 0 0 1 0 0 0 0 0 42
43 297.6 111.4 0 0 0 0 0 0 1 0 0 0 0 43
44 295.4 117.9 0 0 0 0 0 0 0 1 0 0 0 44
45 283.0 121.6 0 0 0 0 0 0 0 0 1 0 0 45
46 275.8 117.8 0 0 0 0 0 0 0 0 0 1 0 46
47 279.7 124.2 0 0 0 0 0 0 0 0 0 0 1 47
48 254.6 106.8 0 0 0 0 0 0 0 0 0 0 0 48
49 234.6 102.7 1 0 0 0 0 0 0 0 0 0 0 49
50 176.9 116.8 0 1 0 0 0 0 0 0 0 0 0 50
51 148.1 113.6 0 0 1 0 0 0 0 0 0 0 0 51
52 122.7 96.1 0 0 0 1 0 0 0 0 0 0 0 52
53 124.9 85.0 0 0 0 0 1 0 0 0 0 0 0 53
54 121.6 83.2 0 0 0 0 0 1 0 0 0 0 0 54
55 128.4 84.9 0 0 0 0 0 0 1 0 0 0 0 55
56 144.5 83.0 0 0 0 0 0 0 0 1 0 0 0 56
57 151.8 79.6 0 0 0 0 0 0 0 0 1 0 0 57
58 167.1 83.2 0 0 0 0 0 0 0 0 0 1 0 58
59 173.8 83.8 0 0 0 0 0 0 0 0 0 0 1 59
60 203.7 82.8 0 0 0 0 0 0 0 0 0 0 0 60
61 199.8 71.4 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
-118.1384 3.7837 -19.2118 -99.7497 -114.9173 -86.9547
M5 M6 M7 M8 M9 M10
-45.2971 -59.7822 -46.7921 -59.1965 -37.3590 -44.9661
M11 t
-63.3430 0.4882
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-73.571 -34.977 5.155 34.519 77.756
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -118.1384 58.2474 -2.028 0.04823 *
X 3.7837 0.6244 6.060 2.18e-07 ***
M1 -19.2118 29.6033 -0.649 0.51951
M2 -99.7497 33.9921 -2.934 0.00515 **
M3 -114.9173 34.3983 -3.341 0.00164 **
M4 -86.9547 32.3400 -2.689 0.00989 **
M5 -45.2971 31.0637 -1.458 0.15144
M6 -59.7822 31.5333 -1.896 0.06414 .
M7 -46.7921 31.4055 -1.490 0.14292
M8 -59.1965 32.4062 -1.827 0.07410 .
M9 -37.3590 31.6721 -1.180 0.24411
M10 -44.9661 31.6277 -1.422 0.16171
M11 -63.3430 32.6990 -1.937 0.05875 .
t 0.4882 0.3626 1.346 0.18472
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 48.79 on 47 degrees of freedom
Multiple R-squared: 0.4961, Adjusted R-squared: 0.3567
F-statistic: 3.559 on 13 and 47 DF, p-value: 0.0006659
> 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,] 0.0067649898 0.0135299796 0.9932350
[2,] 0.0010821720 0.0021643441 0.9989178
[3,] 0.0001947932 0.0003895864 0.9998052
[4,] 0.0004419106 0.0008838212 0.9995581
[5,] 0.0634593325 0.1269186650 0.9365407
[6,] 0.0426346456 0.0852692911 0.9573654
[7,] 0.0574275799 0.1148551598 0.9425724
[8,] 0.0387378436 0.0774756872 0.9612622
[9,] 0.0327091883 0.0654183767 0.9672908
[10,] 0.0194971395 0.0389942790 0.9805029
[11,] 0.0099903006 0.0199806012 0.9900097
[12,] 0.0048236199 0.0096472399 0.9951764
[13,] 0.0046313473 0.0092626945 0.9953687
[14,] 0.0140344279 0.0280688559 0.9859656
[15,] 0.0114451387 0.0228902773 0.9885549
[16,] 0.0076776846 0.0153553692 0.9923223
[17,] 0.0044066155 0.0088132309 0.9955934
[18,] 0.0028209772 0.0056419544 0.9971790
[19,] 0.0017434600 0.0034869200 0.9982565
[20,] 0.0055894333 0.0111788665 0.9944106
[21,] 0.0777919475 0.1555838949 0.9222081
[22,] 0.0628941930 0.1257883860 0.9371058
[23,] 0.0949283886 0.1898567773 0.9050716
[24,] 0.1153512863 0.2307025727 0.8846487
[25,] 0.0933042305 0.1866084610 0.9066958
[26,] 0.1131448050 0.2262896099 0.8868552
[27,] 0.4355265680 0.8710531361 0.5644734
[28,] 0.6819693651 0.6360612698 0.3180306
> postscript(file="/var/www/html/rcomp/tmp/1e7fh1258731992.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mpp01258731992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38wi31258731992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jmob1258731992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/51qrw1258731992.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 = 61
Frequency = 1
1 2 3 4 5 6
-62.6994075 -64.4885469 -49.5443252 -51.6225404 -34.9774586 -20.1859141
7 8 9 10 11 12
-27.6209761 -33.9611843 -52.8491714 -30.1872428 -57.5246896 -15.3226171
13 14 15 16 17 18
-18.5661367 -0.6881094 27.6235391 12.2593298 2.3375052 12.8048801
19 20 21 22 23 24
18.9883853 13.8380134 61.6955643 27.0849842 32.2040198 55.1144959
25 26 27 28 29 30
53.4844616 74.1004041 77.7555702 72.8152702 48.6445860 30.2610807
31 32 33 34 35 36
43.3555304 34.5186439 56.4354784 45.2895240 47.6164427 35.7460064
37 38 39 40 41 42
31.1241154 62.6344556 17.7362007 27.7546638 43.1486053 18.7656205
43 44 45 46 47 48
20.0333597 5.1554966 -43.5698853 -29.2728426 -31.6998654 -54.7944712
49 50 51 52 53 54
-40.5575991 -71.5582034 -73.5709847 -61.2067233 -59.1532379 -41.6456672
55 56 57 58 59 60
-54.7562994 -19.5509696 -21.7119859 -12.9144228 9.4040924 -20.7434140
61
37.2145663
> postscript(file="/var/www/html/rcomp/tmp/6wcj21258731992.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -62.6994075 NA
1 -64.4885469 -62.6994075
2 -49.5443252 -64.4885469
3 -51.6225404 -49.5443252
4 -34.9774586 -51.6225404
5 -20.1859141 -34.9774586
6 -27.6209761 -20.1859141
7 -33.9611843 -27.6209761
8 -52.8491714 -33.9611843
9 -30.1872428 -52.8491714
10 -57.5246896 -30.1872428
11 -15.3226171 -57.5246896
12 -18.5661367 -15.3226171
13 -0.6881094 -18.5661367
14 27.6235391 -0.6881094
15 12.2593298 27.6235391
16 2.3375052 12.2593298
17 12.8048801 2.3375052
18 18.9883853 12.8048801
19 13.8380134 18.9883853
20 61.6955643 13.8380134
21 27.0849842 61.6955643
22 32.2040198 27.0849842
23 55.1144959 32.2040198
24 53.4844616 55.1144959
25 74.1004041 53.4844616
26 77.7555702 74.1004041
27 72.8152702 77.7555702
28 48.6445860 72.8152702
29 30.2610807 48.6445860
30 43.3555304 30.2610807
31 34.5186439 43.3555304
32 56.4354784 34.5186439
33 45.2895240 56.4354784
34 47.6164427 45.2895240
35 35.7460064 47.6164427
36 31.1241154 35.7460064
37 62.6344556 31.1241154
38 17.7362007 62.6344556
39 27.7546638 17.7362007
40 43.1486053 27.7546638
41 18.7656205 43.1486053
42 20.0333597 18.7656205
43 5.1554966 20.0333597
44 -43.5698853 5.1554966
45 -29.2728426 -43.5698853
46 -31.6998654 -29.2728426
47 -54.7944712 -31.6998654
48 -40.5575991 -54.7944712
49 -71.5582034 -40.5575991
50 -73.5709847 -71.5582034
51 -61.2067233 -73.5709847
52 -59.1532379 -61.2067233
53 -41.6456672 -59.1532379
54 -54.7562994 -41.6456672
55 -19.5509696 -54.7562994
56 -21.7119859 -19.5509696
57 -12.9144228 -21.7119859
58 9.4040924 -12.9144228
59 -20.7434140 9.4040924
60 37.2145663 -20.7434140
61 NA 37.2145663
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -64.4885469 -62.6994075
[2,] -49.5443252 -64.4885469
[3,] -51.6225404 -49.5443252
[4,] -34.9774586 -51.6225404
[5,] -20.1859141 -34.9774586
[6,] -27.6209761 -20.1859141
[7,] -33.9611843 -27.6209761
[8,] -52.8491714 -33.9611843
[9,] -30.1872428 -52.8491714
[10,] -57.5246896 -30.1872428
[11,] -15.3226171 -57.5246896
[12,] -18.5661367 -15.3226171
[13,] -0.6881094 -18.5661367
[14,] 27.6235391 -0.6881094
[15,] 12.2593298 27.6235391
[16,] 2.3375052 12.2593298
[17,] 12.8048801 2.3375052
[18,] 18.9883853 12.8048801
[19,] 13.8380134 18.9883853
[20,] 61.6955643 13.8380134
[21,] 27.0849842 61.6955643
[22,] 32.2040198 27.0849842
[23,] 55.1144959 32.2040198
[24,] 53.4844616 55.1144959
[25,] 74.1004041 53.4844616
[26,] 77.7555702 74.1004041
[27,] 72.8152702 77.7555702
[28,] 48.6445860 72.8152702
[29,] 30.2610807 48.6445860
[30,] 43.3555304 30.2610807
[31,] 34.5186439 43.3555304
[32,] 56.4354784 34.5186439
[33,] 45.2895240 56.4354784
[34,] 47.6164427 45.2895240
[35,] 35.7460064 47.6164427
[36,] 31.1241154 35.7460064
[37,] 62.6344556 31.1241154
[38,] 17.7362007 62.6344556
[39,] 27.7546638 17.7362007
[40,] 43.1486053 27.7546638
[41,] 18.7656205 43.1486053
[42,] 20.0333597 18.7656205
[43,] 5.1554966 20.0333597
[44,] -43.5698853 5.1554966
[45,] -29.2728426 -43.5698853
[46,] -31.6998654 -29.2728426
[47,] -54.7944712 -31.6998654
[48,] -40.5575991 -54.7944712
[49,] -71.5582034 -40.5575991
[50,] -73.5709847 -71.5582034
[51,] -61.2067233 -73.5709847
[52,] -59.1532379 -61.2067233
[53,] -41.6456672 -59.1532379
[54,] -54.7562994 -41.6456672
[55,] -19.5509696 -54.7562994
[56,] -21.7119859 -19.5509696
[57,] -12.9144228 -21.7119859
[58,] 9.4040924 -12.9144228
[59,] -20.7434140 9.4040924
[60,] 37.2145663 -20.7434140
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -64.4885469 -62.6994075
2 -49.5443252 -64.4885469
3 -51.6225404 -49.5443252
4 -34.9774586 -51.6225404
5 -20.1859141 -34.9774586
6 -27.6209761 -20.1859141
7 -33.9611843 -27.6209761
8 -52.8491714 -33.9611843
9 -30.1872428 -52.8491714
10 -57.5246896 -30.1872428
11 -15.3226171 -57.5246896
12 -18.5661367 -15.3226171
13 -0.6881094 -18.5661367
14 27.6235391 -0.6881094
15 12.2593298 27.6235391
16 2.3375052 12.2593298
17 12.8048801 2.3375052
18 18.9883853 12.8048801
19 13.8380134 18.9883853
20 61.6955643 13.8380134
21 27.0849842 61.6955643
22 32.2040198 27.0849842
23 55.1144959 32.2040198
24 53.4844616 55.1144959
25 74.1004041 53.4844616
26 77.7555702 74.1004041
27 72.8152702 77.7555702
28 48.6445860 72.8152702
29 30.2610807 48.6445860
30 43.3555304 30.2610807
31 34.5186439 43.3555304
32 56.4354784 34.5186439
33 45.2895240 56.4354784
34 47.6164427 45.2895240
35 35.7460064 47.6164427
36 31.1241154 35.7460064
37 62.6344556 31.1241154
38 17.7362007 62.6344556
39 27.7546638 17.7362007
40 43.1486053 27.7546638
41 18.7656205 43.1486053
42 20.0333597 18.7656205
43 5.1554966 20.0333597
44 -43.5698853 5.1554966
45 -29.2728426 -43.5698853
46 -31.6998654 -29.2728426
47 -54.7944712 -31.6998654
48 -40.5575991 -54.7944712
49 -71.5582034 -40.5575991
50 -73.5709847 -71.5582034
51 -61.2067233 -73.5709847
52 -59.1532379 -61.2067233
53 -41.6456672 -59.1532379
54 -54.7562994 -41.6456672
55 -19.5509696 -54.7562994
56 -21.7119859 -19.5509696
57 -12.9144228 -21.7119859
58 9.4040924 -12.9144228
59 -20.7434140 9.4040924
60 37.2145663 -20.7434140
> 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/7d4f81258731992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8y10t1258731992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z75g1258731992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rpi71258731992.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kwcf1258731993.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/129j8u1258731993.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/13fae61258731993.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/14cyto1258731993.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/157zpu1258731993.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/16rseo1258731993.tab")
+ }
>
> system("convert tmp/1e7fh1258731992.ps tmp/1e7fh1258731992.png")
> system("convert tmp/2mpp01258731992.ps tmp/2mpp01258731992.png")
> system("convert tmp/38wi31258731992.ps tmp/38wi31258731992.png")
> system("convert tmp/4jmob1258731992.ps tmp/4jmob1258731992.png")
> system("convert tmp/51qrw1258731992.ps tmp/51qrw1258731992.png")
> system("convert tmp/6wcj21258731992.ps tmp/6wcj21258731992.png")
> system("convert tmp/7d4f81258731992.ps tmp/7d4f81258731992.png")
> system("convert tmp/8y10t1258731992.ps tmp/8y10t1258731992.png")
> system("convert tmp/9z75g1258731992.ps tmp/9z75g1258731992.png")
> system("convert tmp/10rpi71258731992.ps tmp/10rpi71258731992.png")
>
>
> proc.time()
user system elapsed
2.388 1.529 2.906