R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(124.9,1487.6,132,1320.9,151.4,1514,108.9,1290.9,121.3,1392.5,123.4,1288.2,90.3,1304.4,79.3,1297.8,117.2,1211,116.9,1454,120.8,1405.7,96.1,1160.8,100.8,1492.1,105.3,1263,116.1,1376.3,112.8,1368.6,114.5,1427.6,117.2,1339.8,77.1,1248.3,80.1,1309.8,120.3,1424,133.4,1590.5,109.4,1423.1,93.2,1355.3,91.2,1515,99.2,1385.6,108.2,1430,101.5,1494.2,106.9,1580.9,104.4,1369.8,77.9,1407.5,60,1388.3,99.5,1478.5,95,1630.4,105.6,1413.5,102.5,1493.8,93.3,1641.3,97.3,1465,127,1725.1,111.7,1628.4,96.4,1679.8,133,1876,72.2,1669.4,95.8,1712.4,124.1,1768.8,127.6,1820.5,110.7,1776.2,104.6,1693.7,112.7,1799.1,115.3,1917.5,139.4,1887.2,119,1787.8,97.4,1803.8,154,2196.4,81.5,1759.5,88.8,2002.6,127.7,2056.8,105.1,1851.1,114.9,1984.3,106.4,1725.3,104.5,2096.6,121.6,1792.2,141.4,2029.9,99,1785.3,126.7,2026.5,134.1,1930.8,81.3,1845.5,88.6,1943.1,132.7,2066.8,132.9,2354.4,134.4,2190.7,103.7,1929.6),dim=c(2,72),dimnames=list(c('transport','Import'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('transport','Import'),1:72))
> 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
transport Import M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 124.9 1487.6 1 0 0 0 0 0 0 0 0 0 0
2 132.0 1320.9 0 1 0 0 0 0 0 0 0 0 0
3 151.4 1514.0 0 0 1 0 0 0 0 0 0 0 0
4 108.9 1290.9 0 0 0 1 0 0 0 0 0 0 0
5 121.3 1392.5 0 0 0 0 1 0 0 0 0 0 0
6 123.4 1288.2 0 0 0 0 0 1 0 0 0 0 0
7 90.3 1304.4 0 0 0 0 0 0 1 0 0 0 0
8 79.3 1297.8 0 0 0 0 0 0 0 1 0 0 0
9 117.2 1211.0 0 0 0 0 0 0 0 0 1 0 0
10 116.9 1454.0 0 0 0 0 0 0 0 0 0 1 0
11 120.8 1405.7 0 0 0 0 0 0 0 0 0 0 1
12 96.1 1160.8 0 0 0 0 0 0 0 0 0 0 0
13 100.8 1492.1 1 0 0 0 0 0 0 0 0 0 0
14 105.3 1263.0 0 1 0 0 0 0 0 0 0 0 0
15 116.1 1376.3 0 0 1 0 0 0 0 0 0 0 0
16 112.8 1368.6 0 0 0 1 0 0 0 0 0 0 0
17 114.5 1427.6 0 0 0 0 1 0 0 0 0 0 0
18 117.2 1339.8 0 0 0 0 0 1 0 0 0 0 0
19 77.1 1248.3 0 0 0 0 0 0 1 0 0 0 0
20 80.1 1309.8 0 0 0 0 0 0 0 1 0 0 0
21 120.3 1424.0 0 0 0 0 0 0 0 0 1 0 0
22 133.4 1590.5 0 0 0 0 0 0 0 0 0 1 0
23 109.4 1423.1 0 0 0 0 0 0 0 0 0 0 1
24 93.2 1355.3 0 0 0 0 0 0 0 0 0 0 0
25 91.2 1515.0 1 0 0 0 0 0 0 0 0 0 0
26 99.2 1385.6 0 1 0 0 0 0 0 0 0 0 0
27 108.2 1430.0 0 0 1 0 0 0 0 0 0 0 0
28 101.5 1494.2 0 0 0 1 0 0 0 0 0 0 0
29 106.9 1580.9 0 0 0 0 1 0 0 0 0 0 0
30 104.4 1369.8 0 0 0 0 0 1 0 0 0 0 0
31 77.9 1407.5 0 0 0 0 0 0 1 0 0 0 0
32 60.0 1388.3 0 0 0 0 0 0 0 1 0 0 0
33 99.5 1478.5 0 0 0 0 0 0 0 0 1 0 0
34 95.0 1630.4 0 0 0 0 0 0 0 0 0 1 0
35 105.6 1413.5 0 0 0 0 0 0 0 0 0 0 1
36 102.5 1493.8 0 0 0 0 0 0 0 0 0 0 0
37 93.3 1641.3 1 0 0 0 0 0 0 0 0 0 0
38 97.3 1465.0 0 1 0 0 0 0 0 0 0 0 0
39 127.0 1725.1 0 0 1 0 0 0 0 0 0 0 0
40 111.7 1628.4 0 0 0 1 0 0 0 0 0 0 0
41 96.4 1679.8 0 0 0 0 1 0 0 0 0 0 0
42 133.0 1876.0 0 0 0 0 0 1 0 0 0 0 0
43 72.2 1669.4 0 0 0 0 0 0 1 0 0 0 0
44 95.8 1712.4 0 0 0 0 0 0 0 1 0 0 0
45 124.1 1768.8 0 0 0 0 0 0 0 0 1 0 0
46 127.6 1820.5 0 0 0 0 0 0 0 0 0 1 0
47 110.7 1776.2 0 0 0 0 0 0 0 0 0 0 1
48 104.6 1693.7 0 0 0 0 0 0 0 0 0 0 0
49 112.7 1799.1 1 0 0 0 0 0 0 0 0 0 0
50 115.3 1917.5 0 1 0 0 0 0 0 0 0 0 0
51 139.4 1887.2 0 0 1 0 0 0 0 0 0 0 0
52 119.0 1787.8 0 0 0 1 0 0 0 0 0 0 0
53 97.4 1803.8 0 0 0 0 1 0 0 0 0 0 0
54 154.0 2196.4 0 0 0 0 0 1 0 0 0 0 0
55 81.5 1759.5 0 0 0 0 0 0 1 0 0 0 0
56 88.8 2002.6 0 0 0 0 0 0 0 1 0 0 0
57 127.7 2056.8 0 0 0 0 0 0 0 0 1 0 0
58 105.1 1851.1 0 0 0 0 0 0 0 0 0 1 0
59 114.9 1984.3 0 0 0 0 0 0 0 0 0 0 1
60 106.4 1725.3 0 0 0 0 0 0 0 0 0 0 0
61 104.5 2096.6 1 0 0 0 0 0 0 0 0 0 0
62 121.6 1792.2 0 1 0 0 0 0 0 0 0 0 0
63 141.4 2029.9 0 0 1 0 0 0 0 0 0 0 0
64 99.0 1785.3 0 0 0 1 0 0 0 0 0 0 0
65 126.7 2026.5 0 0 0 0 1 0 0 0 0 0 0
66 134.1 1930.8 0 0 0 0 0 1 0 0 0 0 0
67 81.3 1845.5 0 0 0 0 0 0 1 0 0 0 0
68 88.6 1943.1 0 0 0 0 0 0 0 1 0 0 0
69 132.7 2066.8 0 0 0 0 0 0 0 0 1 0 0
70 132.9 2354.4 0 0 0 0 0 0 0 0 0 1 0
71 134.4 2190.7 0 0 0 0 0 0 0 0 0 0 1
72 103.7 1929.6 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) Import M1 M2 M3 M4
71.85189 0.01874 1.38058 11.36937 27.61340 7.74364
M5 M6 M7 M8 M9 M10
7.72394 24.59314 -20.64633 -19.90633 17.14450 13.20699
M11
12.27520
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-20.614 -6.178 1.201 5.005 24.024
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 71.851891 8.897680 8.075 4.08e-11 ***
Import 0.018741 0.004905 3.821 0.000322 ***
M1 1.380581 6.449238 0.214 0.831231
M2 11.369370 6.428105 1.769 0.082114 .
M3 27.613395 6.444658 4.285 6.85e-05 ***
M4 7.743641 6.425718 1.205 0.232976
M5 7.723944 6.441575 1.199 0.235292
M6 24.593140 6.447146 3.815 0.000329 ***
M7 -20.646329 6.426515 -3.213 0.002132 **
M8 -19.906333 6.430256 -3.096 0.003002 **
M9 17.144501 6.447473 2.659 0.010070 *
M10 13.206990 6.518742 2.026 0.047294 *
M11 12.275196 6.461868 1.900 0.062372 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.13 on 59 degrees of freedom
Multiple R-squared: 0.7136, Adjusted R-squared: 0.6553
F-statistic: 12.25 on 12 and 59 DF, p-value: 5.716e-12
> 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,] 0.8574558 0.28508834 0.142544168
[2,] 0.8575258 0.28494831 0.142474153
[3,] 0.8531770 0.29364598 0.146822990
[4,] 0.7891544 0.42169122 0.210845608
[5,] 0.7155268 0.56894641 0.284473204
[6,] 0.8492061 0.30158781 0.150793905
[7,] 0.9106000 0.17879999 0.089399995
[8,] 0.9080239 0.18395229 0.091976145
[9,] 0.9174483 0.16510344 0.082551718
[10,] 0.9547249 0.09055029 0.045275144
[11,] 0.9737843 0.05243145 0.026215724
[12,] 0.9863201 0.02735981 0.013679905
[13,] 0.9829199 0.03416013 0.017080063
[14,] 0.9821202 0.03575954 0.017879769
[15,] 0.9829972 0.03400561 0.017002805
[16,] 0.9817076 0.03658480 0.018292400
[17,] 0.9854495 0.02910098 0.014550489
[18,] 0.9864374 0.02712526 0.013562628
[19,] 0.9939507 0.01209866 0.006049328
[20,] 0.9906506 0.01869888 0.009349442
[21,] 0.9887516 0.02249674 0.011248371
[22,] 0.9828058 0.03438842 0.017194209
[23,] 0.9776240 0.04475194 0.022375970
[24,] 0.9686466 0.06270684 0.031353419
[25,] 0.9582141 0.08357178 0.041785888
[26,] 0.9498014 0.10039718 0.050198592
[27,] 0.9509690 0.09806199 0.049030995
[28,] 0.9297494 0.14050119 0.070250595
[29,] 0.9489042 0.10219154 0.051095772
[30,] 0.9241130 0.15177396 0.075886980
[31,] 0.9531829 0.09363426 0.046817132
[32,] 0.9226989 0.15460227 0.077301133
[33,] 0.8855084 0.22898326 0.114491628
[34,] 0.9400992 0.11980153 0.059900763
[35,] 0.9358187 0.12836269 0.064181346
[36,] 0.9031477 0.19370465 0.096852327
[37,] 0.9515481 0.09690375 0.048451873
[38,] 0.9770812 0.04583769 0.022918847
[39,] 0.9617469 0.07650617 0.038253083
[40,] 0.9170962 0.16580769 0.082903846
[41,] 0.8169789 0.36604229 0.183021145
> postscript(file="/var/www/html/rcomp/tmp/1x12w1229557011.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gdda1229557011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38d821229557011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pwja1229557011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5uv6f1229557011.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 = 72
Frequency = 1
1 2 3 4 5 6
23.7882564 24.0236102 23.5606771 5.1115724 15.6271728 2.8126745
7 8 9 10 11 12
14.6485379 3.0322326 5.5081266 4.5915491 10.3285383 2.4934314
13 14 15 16 17 18
-0.3960785 -1.5912797 -9.1586724 7.5553884 8.1693599 -4.3543666
19 20 21 22 23 24
2.4999140 3.6073393 4.6162708 18.5333880 -1.3975570 -4.0517140
25 26 27 28 29 30
-10.4252499 -9.9889394 -18.0650699 -6.0984947 -2.3036518 -17.7165998
31 32 33 34 35 36
0.3163297 -17.9638376 -17.2051196 -20.6143822 -5.0176423 2.6526427
37 38 39 40 41 42
-10.6922517 -13.3769833 -4.7955706 1.5864487 -14.6571473 1.3966518
43 44 45 46 47 48
-10.2919663 11.7621695 1.9543370 8.4229333 -6.7150419 1.0062954
49 50 51 52 53 54
5.7504016 -3.8573343 4.5664959 5.8991162 -15.9810446 16.3920010
55 56 57 58 59 60
-2.6805400 -0.6764998 0.1568981 -14.6505445 -6.4150663 2.2140764
61 62 63 64 65 66
-8.0250778 4.7909264 3.8921399 -14.0540310 9.1453109 1.4696391
67 68 69 70 71 72
-4.4922752 0.2385961 4.9694871 3.7170562 9.2167692 -4.3147318
> postscript(file="/var/www/html/rcomp/tmp/69req1229557011.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 23.7882564 NA
1 24.0236102 23.7882564
2 23.5606771 24.0236102
3 5.1115724 23.5606771
4 15.6271728 5.1115724
5 2.8126745 15.6271728
6 14.6485379 2.8126745
7 3.0322326 14.6485379
8 5.5081266 3.0322326
9 4.5915491 5.5081266
10 10.3285383 4.5915491
11 2.4934314 10.3285383
12 -0.3960785 2.4934314
13 -1.5912797 -0.3960785
14 -9.1586724 -1.5912797
15 7.5553884 -9.1586724
16 8.1693599 7.5553884
17 -4.3543666 8.1693599
18 2.4999140 -4.3543666
19 3.6073393 2.4999140
20 4.6162708 3.6073393
21 18.5333880 4.6162708
22 -1.3975570 18.5333880
23 -4.0517140 -1.3975570
24 -10.4252499 -4.0517140
25 -9.9889394 -10.4252499
26 -18.0650699 -9.9889394
27 -6.0984947 -18.0650699
28 -2.3036518 -6.0984947
29 -17.7165998 -2.3036518
30 0.3163297 -17.7165998
31 -17.9638376 0.3163297
32 -17.2051196 -17.9638376
33 -20.6143822 -17.2051196
34 -5.0176423 -20.6143822
35 2.6526427 -5.0176423
36 -10.6922517 2.6526427
37 -13.3769833 -10.6922517
38 -4.7955706 -13.3769833
39 1.5864487 -4.7955706
40 -14.6571473 1.5864487
41 1.3966518 -14.6571473
42 -10.2919663 1.3966518
43 11.7621695 -10.2919663
44 1.9543370 11.7621695
45 8.4229333 1.9543370
46 -6.7150419 8.4229333
47 1.0062954 -6.7150419
48 5.7504016 1.0062954
49 -3.8573343 5.7504016
50 4.5664959 -3.8573343
51 5.8991162 4.5664959
52 -15.9810446 5.8991162
53 16.3920010 -15.9810446
54 -2.6805400 16.3920010
55 -0.6764998 -2.6805400
56 0.1568981 -0.6764998
57 -14.6505445 0.1568981
58 -6.4150663 -14.6505445
59 2.2140764 -6.4150663
60 -8.0250778 2.2140764
61 4.7909264 -8.0250778
62 3.8921399 4.7909264
63 -14.0540310 3.8921399
64 9.1453109 -14.0540310
65 1.4696391 9.1453109
66 -4.4922752 1.4696391
67 0.2385961 -4.4922752
68 4.9694871 0.2385961
69 3.7170562 4.9694871
70 9.2167692 3.7170562
71 -4.3147318 9.2167692
72 NA -4.3147318
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 24.0236102 23.7882564
[2,] 23.5606771 24.0236102
[3,] 5.1115724 23.5606771
[4,] 15.6271728 5.1115724
[5,] 2.8126745 15.6271728
[6,] 14.6485379 2.8126745
[7,] 3.0322326 14.6485379
[8,] 5.5081266 3.0322326
[9,] 4.5915491 5.5081266
[10,] 10.3285383 4.5915491
[11,] 2.4934314 10.3285383
[12,] -0.3960785 2.4934314
[13,] -1.5912797 -0.3960785
[14,] -9.1586724 -1.5912797
[15,] 7.5553884 -9.1586724
[16,] 8.1693599 7.5553884
[17,] -4.3543666 8.1693599
[18,] 2.4999140 -4.3543666
[19,] 3.6073393 2.4999140
[20,] 4.6162708 3.6073393
[21,] 18.5333880 4.6162708
[22,] -1.3975570 18.5333880
[23,] -4.0517140 -1.3975570
[24,] -10.4252499 -4.0517140
[25,] -9.9889394 -10.4252499
[26,] -18.0650699 -9.9889394
[27,] -6.0984947 -18.0650699
[28,] -2.3036518 -6.0984947
[29,] -17.7165998 -2.3036518
[30,] 0.3163297 -17.7165998
[31,] -17.9638376 0.3163297
[32,] -17.2051196 -17.9638376
[33,] -20.6143822 -17.2051196
[34,] -5.0176423 -20.6143822
[35,] 2.6526427 -5.0176423
[36,] -10.6922517 2.6526427
[37,] -13.3769833 -10.6922517
[38,] -4.7955706 -13.3769833
[39,] 1.5864487 -4.7955706
[40,] -14.6571473 1.5864487
[41,] 1.3966518 -14.6571473
[42,] -10.2919663 1.3966518
[43,] 11.7621695 -10.2919663
[44,] 1.9543370 11.7621695
[45,] 8.4229333 1.9543370
[46,] -6.7150419 8.4229333
[47,] 1.0062954 -6.7150419
[48,] 5.7504016 1.0062954
[49,] -3.8573343 5.7504016
[50,] 4.5664959 -3.8573343
[51,] 5.8991162 4.5664959
[52,] -15.9810446 5.8991162
[53,] 16.3920010 -15.9810446
[54,] -2.6805400 16.3920010
[55,] -0.6764998 -2.6805400
[56,] 0.1568981 -0.6764998
[57,] -14.6505445 0.1568981
[58,] -6.4150663 -14.6505445
[59,] 2.2140764 -6.4150663
[60,] -8.0250778 2.2140764
[61,] 4.7909264 -8.0250778
[62,] 3.8921399 4.7909264
[63,] -14.0540310 3.8921399
[64,] 9.1453109 -14.0540310
[65,] 1.4696391 9.1453109
[66,] -4.4922752 1.4696391
[67,] 0.2385961 -4.4922752
[68,] 4.9694871 0.2385961
[69,] 3.7170562 4.9694871
[70,] 9.2167692 3.7170562
[71,] -4.3147318 9.2167692
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 24.0236102 23.7882564
2 23.5606771 24.0236102
3 5.1115724 23.5606771
4 15.6271728 5.1115724
5 2.8126745 15.6271728
6 14.6485379 2.8126745
7 3.0322326 14.6485379
8 5.5081266 3.0322326
9 4.5915491 5.5081266
10 10.3285383 4.5915491
11 2.4934314 10.3285383
12 -0.3960785 2.4934314
13 -1.5912797 -0.3960785
14 -9.1586724 -1.5912797
15 7.5553884 -9.1586724
16 8.1693599 7.5553884
17 -4.3543666 8.1693599
18 2.4999140 -4.3543666
19 3.6073393 2.4999140
20 4.6162708 3.6073393
21 18.5333880 4.6162708
22 -1.3975570 18.5333880
23 -4.0517140 -1.3975570
24 -10.4252499 -4.0517140
25 -9.9889394 -10.4252499
26 -18.0650699 -9.9889394
27 -6.0984947 -18.0650699
28 -2.3036518 -6.0984947
29 -17.7165998 -2.3036518
30 0.3163297 -17.7165998
31 -17.9638376 0.3163297
32 -17.2051196 -17.9638376
33 -20.6143822 -17.2051196
34 -5.0176423 -20.6143822
35 2.6526427 -5.0176423
36 -10.6922517 2.6526427
37 -13.3769833 -10.6922517
38 -4.7955706 -13.3769833
39 1.5864487 -4.7955706
40 -14.6571473 1.5864487
41 1.3966518 -14.6571473
42 -10.2919663 1.3966518
43 11.7621695 -10.2919663
44 1.9543370 11.7621695
45 8.4229333 1.9543370
46 -6.7150419 8.4229333
47 1.0062954 -6.7150419
48 5.7504016 1.0062954
49 -3.8573343 5.7504016
50 4.5664959 -3.8573343
51 5.8991162 4.5664959
52 -15.9810446 5.8991162
53 16.3920010 -15.9810446
54 -2.6805400 16.3920010
55 -0.6764998 -2.6805400
56 0.1568981 -0.6764998
57 -14.6505445 0.1568981
58 -6.4150663 -14.6505445
59 2.2140764 -6.4150663
60 -8.0250778 2.2140764
61 4.7909264 -8.0250778
62 3.8921399 4.7909264
63 -14.0540310 3.8921399
64 9.1453109 -14.0540310
65 1.4696391 9.1453109
66 -4.4922752 1.4696391
67 0.2385961 -4.4922752
68 4.9694871 0.2385961
69 3.7170562 4.9694871
70 9.2167692 3.7170562
71 -4.3147318 9.2167692
> 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/7vmjz1229557011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ko221229557011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9af201229557011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10iawf1229557011.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ix581229557011.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/12owny1229557011.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/13eda61229557011.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/145mw51229557011.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/15ss5d1229557011.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/16un5e1229557012.tab")
+ }
>
> system("convert tmp/1x12w1229557011.ps tmp/1x12w1229557011.png")
> system("convert tmp/2gdda1229557011.ps tmp/2gdda1229557011.png")
> system("convert tmp/38d821229557011.ps tmp/38d821229557011.png")
> system("convert tmp/4pwja1229557011.ps tmp/4pwja1229557011.png")
> system("convert tmp/5uv6f1229557011.ps tmp/5uv6f1229557011.png")
> system("convert tmp/69req1229557011.ps tmp/69req1229557011.png")
> system("convert tmp/7vmjz1229557011.ps tmp/7vmjz1229557011.png")
> system("convert tmp/8ko221229557011.ps tmp/8ko221229557011.png")
> system("convert tmp/9af201229557011.ps tmp/9af201229557011.png")
> system("convert tmp/10iawf1229557011.ps tmp/10iawf1229557011.png")
>
>
> proc.time()
user system elapsed
2.587 1.583 3.287