R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(8.00,96.80,8.10,114.10,7.70,110.30,7.50,103.90,7.60,101.60,7.80,94.60,7.80,95.90,7.80,104.70,7.50,102.80,7.50,98.10,7.10,113.90,7.50,80.90,7.50,95.70,7.60,113.20,7.70,105.90,7.70,108.80,7.90,102.30,8.10,99.00,8.20,100.70,8.20,115.50,8.20,100.70,7.90,109.90,7.30,114.60,6.90,85.40,6.60,100.50,6.70,114.80,6.90,116.50,7.00,112.90,7.10,102.00,7.20,106.00,7.10,105.30,6.90,118.80,7.00,106.10,6.80,109.30,6.40,117.20,6.70,92.50,6.60,104.20,6.40,112.50,6.30,122.40,6.20,113.30,6.50,100.00,6.80,110.70,6.80,112.80,6.40,109.80,6.10,117.30,5.80,109.10,6.10,115.90,7.20,96.00,7.30,99.80,6.90,116.80,6.10,115.70,5.80,99.40,6.20,94.30,7.10,91.00,7.70,93.20,7.90,103.10,7.70,94.10,7.40,91.80,7.50,102.70,8.00,82.60),dim=c(2,60),dimnames=list(c('Wman','Ecogr'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Wman','Ecogr'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Wman Ecogr M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.0 96.8 1 0 0 0 0 0 0 0 0 0 0
2 8.1 114.1 0 1 0 0 0 0 0 0 0 0 0
3 7.7 110.3 0 0 1 0 0 0 0 0 0 0 0
4 7.5 103.9 0 0 0 1 0 0 0 0 0 0 0
5 7.6 101.6 0 0 0 0 1 0 0 0 0 0 0
6 7.8 94.6 0 0 0 0 0 1 0 0 0 0 0
7 7.8 95.9 0 0 0 0 0 0 1 0 0 0 0
8 7.8 104.7 0 0 0 0 0 0 0 1 0 0 0
9 7.5 102.8 0 0 0 0 0 0 0 0 1 0 0
10 7.5 98.1 0 0 0 0 0 0 0 0 0 1 0
11 7.1 113.9 0 0 0 0 0 0 0 0 0 0 1
12 7.5 80.9 0 0 0 0 0 0 0 0 0 0 0
13 7.5 95.7 1 0 0 0 0 0 0 0 0 0 0
14 7.6 113.2 0 1 0 0 0 0 0 0 0 0 0
15 7.7 105.9 0 0 1 0 0 0 0 0 0 0 0
16 7.7 108.8 0 0 0 1 0 0 0 0 0 0 0
17 7.9 102.3 0 0 0 0 1 0 0 0 0 0 0
18 8.1 99.0 0 0 0 0 0 1 0 0 0 0 0
19 8.2 100.7 0 0 0 0 0 0 1 0 0 0 0
20 8.2 115.5 0 0 0 0 0 0 0 1 0 0 0
21 8.2 100.7 0 0 0 0 0 0 0 0 1 0 0
22 7.9 109.9 0 0 0 0 0 0 0 0 0 1 0
23 7.3 114.6 0 0 0 0 0 0 0 0 0 0 1
24 6.9 85.4 0 0 0 0 0 0 0 0 0 0 0
25 6.6 100.5 1 0 0 0 0 0 0 0 0 0 0
26 6.7 114.8 0 1 0 0 0 0 0 0 0 0 0
27 6.9 116.5 0 0 1 0 0 0 0 0 0 0 0
28 7.0 112.9 0 0 0 1 0 0 0 0 0 0 0
29 7.1 102.0 0 0 0 0 1 0 0 0 0 0 0
30 7.2 106.0 0 0 0 0 0 1 0 0 0 0 0
31 7.1 105.3 0 0 0 0 0 0 1 0 0 0 0
32 6.9 118.8 0 0 0 0 0 0 0 1 0 0 0
33 7.0 106.1 0 0 0 0 0 0 0 0 1 0 0
34 6.8 109.3 0 0 0 0 0 0 0 0 0 1 0
35 6.4 117.2 0 0 0 0 0 0 0 0 0 0 1
36 6.7 92.5 0 0 0 0 0 0 0 0 0 0 0
37 6.6 104.2 1 0 0 0 0 0 0 0 0 0 0
38 6.4 112.5 0 1 0 0 0 0 0 0 0 0 0
39 6.3 122.4 0 0 1 0 0 0 0 0 0 0 0
40 6.2 113.3 0 0 0 1 0 0 0 0 0 0 0
41 6.5 100.0 0 0 0 0 1 0 0 0 0 0 0
42 6.8 110.7 0 0 0 0 0 1 0 0 0 0 0
43 6.8 112.8 0 0 0 0 0 0 1 0 0 0 0
44 6.4 109.8 0 0 0 0 0 0 0 1 0 0 0
45 6.1 117.3 0 0 0 0 0 0 0 0 1 0 0
46 5.8 109.1 0 0 0 0 0 0 0 0 0 1 0
47 6.1 115.9 0 0 0 0 0 0 0 0 0 0 1
48 7.2 96.0 0 0 0 0 0 0 0 0 0 0 0
49 7.3 99.8 1 0 0 0 0 0 0 0 0 0 0
50 6.9 116.8 0 1 0 0 0 0 0 0 0 0 0
51 6.1 115.7 0 0 1 0 0 0 0 0 0 0 0
52 5.8 99.4 0 0 0 1 0 0 0 0 0 0 0
53 6.2 94.3 0 0 0 0 1 0 0 0 0 0 0
54 7.1 91.0 0 0 0 0 0 1 0 0 0 0 0
55 7.7 93.2 0 0 0 0 0 0 1 0 0 0 0
56 7.9 103.1 0 0 0 0 0 0 0 1 0 0 0
57 7.7 94.1 0 0 0 0 0 0 0 0 1 0 0
58 7.4 91.8 0 0 0 0 0 0 0 0 0 1 0
59 7.5 102.7 0 0 0 0 0 0 0 0 0 0 1
60 8.0 82.6 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) Ecogr M1 M2 M3 M4
11.19775 -0.04501 0.47656 1.08635 0.88095 0.48837
M5 M6 M7 M8 M9 M10
0.36537 0.71527 0.89469 1.21080 0.79262 0.54741
M11
0.76243
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.41181 -0.34651 0.04135 0.39975 1.10178
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.19775 1.28399 8.721 2.19e-11 ***
Ecogr -0.04501 0.01432 -3.144 0.00288 **
M1 0.47656 0.43538 1.095 0.27928
M2 1.08635 0.55465 1.959 0.05610 .
M3 0.88095 0.55346 1.592 0.11815
M4 0.48837 0.49386 0.989 0.32778
M5 0.36537 0.43905 0.832 0.40952
M6 0.71527 0.44035 1.624 0.11099
M7 0.89469 0.44853 1.995 0.05189 .
M8 1.21080 0.51760 2.339 0.02362 *
M9 0.79262 0.46661 1.699 0.09599 .
M10 0.54741 0.46255 1.183 0.24258
M11 0.76243 0.54079 1.410 0.16516
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6333 on 47 degrees of freedom
Multiple R-squared: 0.2614, Adjusted R-squared: 0.0728
F-statistic: 1.386 on 12 and 47 DF, p-value: 0.2062
> 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,] 0.090621455 0.181242910 0.90937854
[2,] 0.048450619 0.096901239 0.95154938
[3,] 0.019063501 0.038127002 0.98093650
[4,] 0.007572517 0.015145034 0.99242748
[5,] 0.004101843 0.008203687 0.99589816
[6,] 0.021210295 0.042420590 0.97878970
[7,] 0.019651113 0.039302226 0.98034889
[8,] 0.012237330 0.024474660 0.98776267
[9,] 0.027694877 0.055389755 0.97230512
[10,] 0.191691293 0.383382587 0.80830871
[11,] 0.341134295 0.682268589 0.65886571
[12,] 0.370354927 0.740709855 0.62964507
[13,] 0.481795752 0.963591503 0.51820425
[14,] 0.571941010 0.856117979 0.42805899
[15,] 0.544887194 0.910225612 0.45511281
[16,] 0.502840000 0.994320000 0.49716000
[17,] 0.483894945 0.967789890 0.51610505
[18,] 0.431344118 0.862688236 0.56865588
[19,] 0.437176036 0.874352072 0.56282396
[20,] 0.376368445 0.752736890 0.62363155
[21,] 0.365178718 0.730357435 0.63482128
[22,] 0.308219659 0.616439319 0.69178034
[23,] 0.397525721 0.795051442 0.60247428
[24,] 0.344145099 0.688290197 0.65585490
[25,] 0.526467417 0.947065166 0.47353258
[26,] 0.569721144 0.860557712 0.43027886
[27,] 0.701517996 0.596964009 0.29848200
[28,] 0.685379726 0.629240548 0.31462027
[29,] 0.919813542 0.160372916 0.08018646
> postscript(file="/var/www/html/rcomp/tmp/14pg91258561866.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25ehh1258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31e8y1258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4x4br1258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rjov1258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
0.68296583 0.95189763 0.58624927 0.49075058 0.61022050 0.14522561
7 8 9 10 11 12
0.02432535 0.10432535 0.13698160 0.17062719 0.26681367 -0.05618648
13 14 15 16 17 18
0.13345137 0.41138581 0.38819144 0.91131498 0.94172970 0.64328344
19 20 21 22 23 24
0.64038843 0.99046729 0.74245400 1.10178227 0.49832287 -0.45362734
25 26 27 28 29 30
-0.55048554 -0.41659317 0.06533075 0.39586887 0.12822576 0.05837544
31 32 33 34 35 36
-0.25255111 -0.16098934 -0.21447503 -0.02522561 -0.28464296 -0.33403402
37 38 39 40 41 42
-0.38393692 -0.82012339 -0.26909170 -0.38612588 -0.56180053 -0.13006279
43 44 45 46 47 48
-0.21495254 -1.06610762 -0.61032783 -1.03422824 -0.64316005 0.32351198
49 50 51 52 53 54
0.11800526 -0.12656688 -0.77067976 -1.41180856 -1.11837544 -0.71682170
55 56 57 58 59 60
-0.19721014 0.13230432 -0.05463274 -0.21295561 0.16266647 0.52033586
> postscript(file="/var/www/html/rcomp/tmp/60rja1258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.68296583 NA
1 0.95189763 0.68296583
2 0.58624927 0.95189763
3 0.49075058 0.58624927
4 0.61022050 0.49075058
5 0.14522561 0.61022050
6 0.02432535 0.14522561
7 0.10432535 0.02432535
8 0.13698160 0.10432535
9 0.17062719 0.13698160
10 0.26681367 0.17062719
11 -0.05618648 0.26681367
12 0.13345137 -0.05618648
13 0.41138581 0.13345137
14 0.38819144 0.41138581
15 0.91131498 0.38819144
16 0.94172970 0.91131498
17 0.64328344 0.94172970
18 0.64038843 0.64328344
19 0.99046729 0.64038843
20 0.74245400 0.99046729
21 1.10178227 0.74245400
22 0.49832287 1.10178227
23 -0.45362734 0.49832287
24 -0.55048554 -0.45362734
25 -0.41659317 -0.55048554
26 0.06533075 -0.41659317
27 0.39586887 0.06533075
28 0.12822576 0.39586887
29 0.05837544 0.12822576
30 -0.25255111 0.05837544
31 -0.16098934 -0.25255111
32 -0.21447503 -0.16098934
33 -0.02522561 -0.21447503
34 -0.28464296 -0.02522561
35 -0.33403402 -0.28464296
36 -0.38393692 -0.33403402
37 -0.82012339 -0.38393692
38 -0.26909170 -0.82012339
39 -0.38612588 -0.26909170
40 -0.56180053 -0.38612588
41 -0.13006279 -0.56180053
42 -0.21495254 -0.13006279
43 -1.06610762 -0.21495254
44 -0.61032783 -1.06610762
45 -1.03422824 -0.61032783
46 -0.64316005 -1.03422824
47 0.32351198 -0.64316005
48 0.11800526 0.32351198
49 -0.12656688 0.11800526
50 -0.77067976 -0.12656688
51 -1.41180856 -0.77067976
52 -1.11837544 -1.41180856
53 -0.71682170 -1.11837544
54 -0.19721014 -0.71682170
55 0.13230432 -0.19721014
56 -0.05463274 0.13230432
57 -0.21295561 -0.05463274
58 0.16266647 -0.21295561
59 0.52033586 0.16266647
60 NA 0.52033586
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.95189763 0.68296583
[2,] 0.58624927 0.95189763
[3,] 0.49075058 0.58624927
[4,] 0.61022050 0.49075058
[5,] 0.14522561 0.61022050
[6,] 0.02432535 0.14522561
[7,] 0.10432535 0.02432535
[8,] 0.13698160 0.10432535
[9,] 0.17062719 0.13698160
[10,] 0.26681367 0.17062719
[11,] -0.05618648 0.26681367
[12,] 0.13345137 -0.05618648
[13,] 0.41138581 0.13345137
[14,] 0.38819144 0.41138581
[15,] 0.91131498 0.38819144
[16,] 0.94172970 0.91131498
[17,] 0.64328344 0.94172970
[18,] 0.64038843 0.64328344
[19,] 0.99046729 0.64038843
[20,] 0.74245400 0.99046729
[21,] 1.10178227 0.74245400
[22,] 0.49832287 1.10178227
[23,] -0.45362734 0.49832287
[24,] -0.55048554 -0.45362734
[25,] -0.41659317 -0.55048554
[26,] 0.06533075 -0.41659317
[27,] 0.39586887 0.06533075
[28,] 0.12822576 0.39586887
[29,] 0.05837544 0.12822576
[30,] -0.25255111 0.05837544
[31,] -0.16098934 -0.25255111
[32,] -0.21447503 -0.16098934
[33,] -0.02522561 -0.21447503
[34,] -0.28464296 -0.02522561
[35,] -0.33403402 -0.28464296
[36,] -0.38393692 -0.33403402
[37,] -0.82012339 -0.38393692
[38,] -0.26909170 -0.82012339
[39,] -0.38612588 -0.26909170
[40,] -0.56180053 -0.38612588
[41,] -0.13006279 -0.56180053
[42,] -0.21495254 -0.13006279
[43,] -1.06610762 -0.21495254
[44,] -0.61032783 -1.06610762
[45,] -1.03422824 -0.61032783
[46,] -0.64316005 -1.03422824
[47,] 0.32351198 -0.64316005
[48,] 0.11800526 0.32351198
[49,] -0.12656688 0.11800526
[50,] -0.77067976 -0.12656688
[51,] -1.41180856 -0.77067976
[52,] -1.11837544 -1.41180856
[53,] -0.71682170 -1.11837544
[54,] -0.19721014 -0.71682170
[55,] 0.13230432 -0.19721014
[56,] -0.05463274 0.13230432
[57,] -0.21295561 -0.05463274
[58,] 0.16266647 -0.21295561
[59,] 0.52033586 0.16266647
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.95189763 0.68296583
2 0.58624927 0.95189763
3 0.49075058 0.58624927
4 0.61022050 0.49075058
5 0.14522561 0.61022050
6 0.02432535 0.14522561
7 0.10432535 0.02432535
8 0.13698160 0.10432535
9 0.17062719 0.13698160
10 0.26681367 0.17062719
11 -0.05618648 0.26681367
12 0.13345137 -0.05618648
13 0.41138581 0.13345137
14 0.38819144 0.41138581
15 0.91131498 0.38819144
16 0.94172970 0.91131498
17 0.64328344 0.94172970
18 0.64038843 0.64328344
19 0.99046729 0.64038843
20 0.74245400 0.99046729
21 1.10178227 0.74245400
22 0.49832287 1.10178227
23 -0.45362734 0.49832287
24 -0.55048554 -0.45362734
25 -0.41659317 -0.55048554
26 0.06533075 -0.41659317
27 0.39586887 0.06533075
28 0.12822576 0.39586887
29 0.05837544 0.12822576
30 -0.25255111 0.05837544
31 -0.16098934 -0.25255111
32 -0.21447503 -0.16098934
33 -0.02522561 -0.21447503
34 -0.28464296 -0.02522561
35 -0.33403402 -0.28464296
36 -0.38393692 -0.33403402
37 -0.82012339 -0.38393692
38 -0.26909170 -0.82012339
39 -0.38612588 -0.26909170
40 -0.56180053 -0.38612588
41 -0.13006279 -0.56180053
42 -0.21495254 -0.13006279
43 -1.06610762 -0.21495254
44 -0.61032783 -1.06610762
45 -1.03422824 -0.61032783
46 -0.64316005 -1.03422824
47 0.32351198 -0.64316005
48 0.11800526 0.32351198
49 -0.12656688 0.11800526
50 -0.77067976 -0.12656688
51 -1.41180856 -0.77067976
52 -1.11837544 -1.41180856
53 -0.71682170 -1.11837544
54 -0.19721014 -0.71682170
55 0.13230432 -0.19721014
56 -0.05463274 0.13230432
57 -0.21295561 -0.05463274
58 0.16266647 -0.21295561
59 0.52033586 0.16266647
> 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/7isbz1258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85ki81258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jfq61258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10uhg41258561866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117c3b1258561866.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/121cmw1258561866.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/13nsoc1258561866.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/14hfjo1258561866.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/155pvf1258561866.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/16z7e31258561866.tab")
+ }
>
> system("convert tmp/14pg91258561866.ps tmp/14pg91258561866.png")
> system("convert tmp/25ehh1258561866.ps tmp/25ehh1258561866.png")
> system("convert tmp/31e8y1258561866.ps tmp/31e8y1258561866.png")
> system("convert tmp/4x4br1258561866.ps tmp/4x4br1258561866.png")
> system("convert tmp/5rjov1258561866.ps tmp/5rjov1258561866.png")
> system("convert tmp/60rja1258561866.ps tmp/60rja1258561866.png")
> system("convert tmp/7isbz1258561866.ps tmp/7isbz1258561866.png")
> system("convert tmp/85ki81258561866.ps tmp/85ki81258561866.png")
> system("convert tmp/9jfq61258561866.ps tmp/9jfq61258561866.png")
> system("convert tmp/10uhg41258561866.ps tmp/10uhg41258561866.png")
>
>
> proc.time()
user system elapsed
2.461 1.610 3.905