R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(94.6,95.9,104.7,102.8,98.1,113.9,80.9,95.7,113.2,105.9,108.8,102.3,99,100.7,115.5,100.7,109.9,114.6,85.4,100.5,114.8,116.5,112.9,102,106,105.3,118.8,106.1,109.3,117.2,92.5,104.2,112.5,122.4,113.3,100,110.7,112.8,109.8,117.3,109.1,115.9,96,99.8,116.8,115.7,99.4,94.3,91,93.2,103.1,94.1,91.8,102.7,82.6,89.1,104.5,105.1,95.1,88.7,86.3,91.8,111.5,99.7,97.5,111.7,86.2,95.4),dim=c(1,68),dimnames=list(c('productie'),1:68))
> y <- array(NA,dim=c(1,68),dimnames=list(c('productie'),1:68))
> 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
> 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
productie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 94.6 1 0 0 0 0 0 0 0 0 0 0
2 95.9 0 1 0 0 0 0 0 0 0 0 0
3 104.7 0 0 1 0 0 0 0 0 0 0 0
4 102.8 0 0 0 1 0 0 0 0 0 0 0
5 98.1 0 0 0 0 1 0 0 0 0 0 0
6 113.9 0 0 0 0 0 1 0 0 0 0 0
7 80.9 0 0 0 0 0 0 1 0 0 0 0
8 95.7 0 0 0 0 0 0 0 1 0 0 0
9 113.2 0 0 0 0 0 0 0 0 1 0 0
10 105.9 0 0 0 0 0 0 0 0 0 1 0
11 108.8 0 0 0 0 0 0 0 0 0 0 1
12 102.3 0 0 0 0 0 0 0 0 0 0 0
13 99.0 1 0 0 0 0 0 0 0 0 0 0
14 100.7 0 1 0 0 0 0 0 0 0 0 0
15 115.5 0 0 1 0 0 0 0 0 0 0 0
16 100.7 0 0 0 1 0 0 0 0 0 0 0
17 109.9 0 0 0 0 1 0 0 0 0 0 0
18 114.6 0 0 0 0 0 1 0 0 0 0 0
19 85.4 0 0 0 0 0 0 1 0 0 0 0
20 100.5 0 0 0 0 0 0 0 1 0 0 0
21 114.8 0 0 0 0 0 0 0 0 1 0 0
22 116.5 0 0 0 0 0 0 0 0 0 1 0
23 112.9 0 0 0 0 0 0 0 0 0 0 1
24 102.0 0 0 0 0 0 0 0 0 0 0 0
25 106.0 1 0 0 0 0 0 0 0 0 0 0
26 105.3 0 1 0 0 0 0 0 0 0 0 0
27 118.8 0 0 1 0 0 0 0 0 0 0 0
28 106.1 0 0 0 1 0 0 0 0 0 0 0
29 109.3 0 0 0 0 1 0 0 0 0 0 0
30 117.2 0 0 0 0 0 1 0 0 0 0 0
31 92.5 0 0 0 0 0 0 1 0 0 0 0
32 104.2 0 0 0 0 0 0 0 1 0 0 0
33 112.5 0 0 0 0 0 0 0 0 1 0 0
34 122.4 0 0 0 0 0 0 0 0 0 1 0
35 113.3 0 0 0 0 0 0 0 0 0 0 1
36 100.0 0 0 0 0 0 0 0 0 0 0 0
37 110.7 1 0 0 0 0 0 0 0 0 0 0
38 112.8 0 1 0 0 0 0 0 0 0 0 0
39 109.8 0 0 1 0 0 0 0 0 0 0 0
40 117.3 0 0 0 1 0 0 0 0 0 0 0
41 109.1 0 0 0 0 1 0 0 0 0 0 0
42 115.9 0 0 0 0 0 1 0 0 0 0 0
43 96.0 0 0 0 0 0 0 1 0 0 0 0
44 99.8 0 0 0 0 0 0 0 1 0 0 0
45 116.8 0 0 0 0 0 0 0 0 1 0 0
46 115.7 0 0 0 0 0 0 0 0 0 1 0
47 99.4 0 0 0 0 0 0 0 0 0 0 1
48 94.3 0 0 0 0 0 0 0 0 0 0 0
49 91.0 1 0 0 0 0 0 0 0 0 0 0
50 93.2 0 1 0 0 0 0 0 0 0 0 0
51 103.1 0 0 1 0 0 0 0 0 0 0 0
52 94.1 0 0 0 1 0 0 0 0 0 0 0
53 91.8 0 0 0 0 1 0 0 0 0 0 0
54 102.7 0 0 0 0 0 1 0 0 0 0 0
55 82.6 0 0 0 0 0 0 1 0 0 0 0
56 89.1 0 0 0 0 0 0 0 1 0 0 0
57 104.5 0 0 0 0 0 0 0 0 1 0 0
58 105.1 0 0 0 0 0 0 0 0 0 1 0
59 95.1 0 0 0 0 0 0 0 0 0 0 1
60 88.7 0 0 0 0 0 0 0 0 0 0 0
61 86.3 1 0 0 0 0 0 0 0 0 0 0
62 91.8 0 1 0 0 0 0 0 0 0 0 0
63 111.5 0 0 1 0 0 0 0 0 0 0 0
64 99.7 0 0 0 1 0 0 0 0 0 0 0
65 97.5 0 0 0 0 1 0 0 0 0 0 0
66 111.7 0 0 0 0 0 1 0 0 0 0 0
67 86.2 0 0 0 0 0 0 1 0 0 0 0
68 95.4 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
97.4600 0.4733 2.4900 13.1067 5.9900 5.1567
M6 M7 M8 M9 M10 M11
15.2067 -10.1933 -0.0100 14.9000 15.6600 8.4400
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.633 -5.304 0.795 4.615 13.850
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.4600 3.1072 31.365 < 2e-16 ***
M1 0.4733 4.2072 0.113 0.910826
M2 2.4900 4.2072 0.592 0.556341
M3 13.1067 4.2072 3.115 0.002897 **
M4 5.9900 4.2072 1.424 0.160071
M5 5.1567 4.2072 1.226 0.225454
M6 15.2067 4.2072 3.614 0.000646 ***
M7 -10.1933 4.2072 -2.423 0.018660 *
M8 -0.0100 4.2072 -0.002 0.998112
M9 14.9000 4.3943 3.391 0.001284 **
M10 15.6600 4.3943 3.564 0.000756 ***
M11 8.4400 4.3943 1.921 0.059871 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.948 on 56 degrees of freedom
Multiple R-squared: 0.5924, Adjusted R-squared: 0.5124
F-statistic: 7.4 on 11 and 56 DF, p-value: 1.215e-07
> 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,] 0.279903946 0.559807892 0.7200961
[2,] 0.149490446 0.298980892 0.8505096
[3,] 0.225069076 0.450138153 0.7749309
[4,] 0.130239131 0.260478262 0.8697609
[5,] 0.082861794 0.165723587 0.9171382
[6,] 0.052729445 0.105458890 0.9472706
[7,] 0.027454843 0.054909685 0.9725452
[8,] 0.035761970 0.071523941 0.9642380
[9,] 0.024021388 0.048042776 0.9759786
[10,] 0.013144185 0.026288371 0.9868558
[11,] 0.019118079 0.038236158 0.9808819
[12,] 0.016971280 0.033942561 0.9830287
[13,] 0.021214498 0.042428996 0.9787855
[14,] 0.013670111 0.027340223 0.9863299
[15,] 0.011191616 0.022383231 0.9888084
[16,] 0.007090026 0.014180051 0.9929100
[17,] 0.008084295 0.016168590 0.9919157
[18,] 0.007075815 0.014151631 0.9929242
[19,] 0.003784346 0.007568692 0.9962157
[20,] 0.007170997 0.014341995 0.9928290
[21,] 0.007649126 0.015298252 0.9923509
[22,] 0.005171648 0.010343297 0.9948284
[23,] 0.027358624 0.054717247 0.9726414
[24,] 0.119511095 0.239022191 0.8804889
[25,] 0.086318395 0.172636790 0.9136816
[26,] 0.364523705 0.729047409 0.6354763
[27,] 0.504444226 0.991111547 0.4955558
[28,] 0.508005535 0.983988930 0.4919945
[29,] 0.663396262 0.673207475 0.3366037
[30,] 0.660297261 0.679405478 0.3397027
[31,] 0.765777349 0.468445301 0.2342227
[32,] 0.833076188 0.333847623 0.1669238
[33,] 0.829276755 0.341446491 0.1707232
[34,] 0.807723103 0.384553794 0.1922769
[35,] 0.789564117 0.420871767 0.2104359
[36,] 0.717245406 0.565509187 0.2827546
[37,] 0.750608491 0.498783018 0.2493915
[38,] 0.716205540 0.567588919 0.2837945
[39,] 0.669717308 0.660565383 0.3302827
> postscript(file="/var/www/rcomp/tmp/1obx31290883615.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2obx31290883615.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3obx31290883615.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4y3eo1290883615.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5y3eo1290883615.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 = 68
Frequency = 1
1 2 3 4 5 6
-3.3333333 -4.0500000 -5.8666667 -0.6500000 -4.5166667 1.2333333
7 8 9 10 11 12
-6.3666667 -1.7500000 0.8400000 -7.2200000 2.9000000 4.8400000
13 14 15 16 17 18
1.0666667 0.7500000 4.9333333 -2.7500000 7.2833333 1.9333333
19 20 21 22 23 24
-1.8666667 3.0500000 2.4400000 3.3800000 7.0000000 4.5400000
25 26 27 28 29 30
8.0666667 5.3500000 8.2333333 2.6500000 6.6833333 4.5333333
31 32 33 34 35 36
5.2333333 6.7500000 0.1400000 9.2800000 7.4000000 2.5400000
37 38 39 40 41 42
12.7666667 12.8500000 -0.7666667 13.8500000 6.4833333 3.2333333
43 44 45 46 47 48
8.7333333 2.3500000 4.4400000 2.5800000 -6.5000000 -3.1600000
49 50 51 52 53 54
-6.9333333 -6.7500000 -7.4666667 -9.3500000 -10.8166667 -9.9666667
55 56 57 58 59 60
-4.6666667 -8.3500000 -7.8600000 -8.0200000 -10.8000000 -8.7600000
61 62 63 64 65 66
-11.6333333 -8.1500000 0.9333333 -3.7500000 -5.1166667 -0.9666667
67 68
-1.0666667 -2.0500000
> postscript(file="/var/www/rcomp/tmp/6y3eo1290883615.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.3333333 NA
1 -4.0500000 -3.3333333
2 -5.8666667 -4.0500000
3 -0.6500000 -5.8666667
4 -4.5166667 -0.6500000
5 1.2333333 -4.5166667
6 -6.3666667 1.2333333
7 -1.7500000 -6.3666667
8 0.8400000 -1.7500000
9 -7.2200000 0.8400000
10 2.9000000 -7.2200000
11 4.8400000 2.9000000
12 1.0666667 4.8400000
13 0.7500000 1.0666667
14 4.9333333 0.7500000
15 -2.7500000 4.9333333
16 7.2833333 -2.7500000
17 1.9333333 7.2833333
18 -1.8666667 1.9333333
19 3.0500000 -1.8666667
20 2.4400000 3.0500000
21 3.3800000 2.4400000
22 7.0000000 3.3800000
23 4.5400000 7.0000000
24 8.0666667 4.5400000
25 5.3500000 8.0666667
26 8.2333333 5.3500000
27 2.6500000 8.2333333
28 6.6833333 2.6500000
29 4.5333333 6.6833333
30 5.2333333 4.5333333
31 6.7500000 5.2333333
32 0.1400000 6.7500000
33 9.2800000 0.1400000
34 7.4000000 9.2800000
35 2.5400000 7.4000000
36 12.7666667 2.5400000
37 12.8500000 12.7666667
38 -0.7666667 12.8500000
39 13.8500000 -0.7666667
40 6.4833333 13.8500000
41 3.2333333 6.4833333
42 8.7333333 3.2333333
43 2.3500000 8.7333333
44 4.4400000 2.3500000
45 2.5800000 4.4400000
46 -6.5000000 2.5800000
47 -3.1600000 -6.5000000
48 -6.9333333 -3.1600000
49 -6.7500000 -6.9333333
50 -7.4666667 -6.7500000
51 -9.3500000 -7.4666667
52 -10.8166667 -9.3500000
53 -9.9666667 -10.8166667
54 -4.6666667 -9.9666667
55 -8.3500000 -4.6666667
56 -7.8600000 -8.3500000
57 -8.0200000 -7.8600000
58 -10.8000000 -8.0200000
59 -8.7600000 -10.8000000
60 -11.6333333 -8.7600000
61 -8.1500000 -11.6333333
62 0.9333333 -8.1500000
63 -3.7500000 0.9333333
64 -5.1166667 -3.7500000
65 -0.9666667 -5.1166667
66 -1.0666667 -0.9666667
67 -2.0500000 -1.0666667
68 NA -2.0500000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.0500000 -3.3333333
[2,] -5.8666667 -4.0500000
[3,] -0.6500000 -5.8666667
[4,] -4.5166667 -0.6500000
[5,] 1.2333333 -4.5166667
[6,] -6.3666667 1.2333333
[7,] -1.7500000 -6.3666667
[8,] 0.8400000 -1.7500000
[9,] -7.2200000 0.8400000
[10,] 2.9000000 -7.2200000
[11,] 4.8400000 2.9000000
[12,] 1.0666667 4.8400000
[13,] 0.7500000 1.0666667
[14,] 4.9333333 0.7500000
[15,] -2.7500000 4.9333333
[16,] 7.2833333 -2.7500000
[17,] 1.9333333 7.2833333
[18,] -1.8666667 1.9333333
[19,] 3.0500000 -1.8666667
[20,] 2.4400000 3.0500000
[21,] 3.3800000 2.4400000
[22,] 7.0000000 3.3800000
[23,] 4.5400000 7.0000000
[24,] 8.0666667 4.5400000
[25,] 5.3500000 8.0666667
[26,] 8.2333333 5.3500000
[27,] 2.6500000 8.2333333
[28,] 6.6833333 2.6500000
[29,] 4.5333333 6.6833333
[30,] 5.2333333 4.5333333
[31,] 6.7500000 5.2333333
[32,] 0.1400000 6.7500000
[33,] 9.2800000 0.1400000
[34,] 7.4000000 9.2800000
[35,] 2.5400000 7.4000000
[36,] 12.7666667 2.5400000
[37,] 12.8500000 12.7666667
[38,] -0.7666667 12.8500000
[39,] 13.8500000 -0.7666667
[40,] 6.4833333 13.8500000
[41,] 3.2333333 6.4833333
[42,] 8.7333333 3.2333333
[43,] 2.3500000 8.7333333
[44,] 4.4400000 2.3500000
[45,] 2.5800000 4.4400000
[46,] -6.5000000 2.5800000
[47,] -3.1600000 -6.5000000
[48,] -6.9333333 -3.1600000
[49,] -6.7500000 -6.9333333
[50,] -7.4666667 -6.7500000
[51,] -9.3500000 -7.4666667
[52,] -10.8166667 -9.3500000
[53,] -9.9666667 -10.8166667
[54,] -4.6666667 -9.9666667
[55,] -8.3500000 -4.6666667
[56,] -7.8600000 -8.3500000
[57,] -8.0200000 -7.8600000
[58,] -10.8000000 -8.0200000
[59,] -8.7600000 -10.8000000
[60,] -11.6333333 -8.7600000
[61,] -8.1500000 -11.6333333
[62,] 0.9333333 -8.1500000
[63,] -3.7500000 0.9333333
[64,] -5.1166667 -3.7500000
[65,] -0.9666667 -5.1166667
[66,] -1.0666667 -0.9666667
[67,] -2.0500000 -1.0666667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.0500000 -3.3333333
2 -5.8666667 -4.0500000
3 -0.6500000 -5.8666667
4 -4.5166667 -0.6500000
5 1.2333333 -4.5166667
6 -6.3666667 1.2333333
7 -1.7500000 -6.3666667
8 0.8400000 -1.7500000
9 -7.2200000 0.8400000
10 2.9000000 -7.2200000
11 4.8400000 2.9000000
12 1.0666667 4.8400000
13 0.7500000 1.0666667
14 4.9333333 0.7500000
15 -2.7500000 4.9333333
16 7.2833333 -2.7500000
17 1.9333333 7.2833333
18 -1.8666667 1.9333333
19 3.0500000 -1.8666667
20 2.4400000 3.0500000
21 3.3800000 2.4400000
22 7.0000000 3.3800000
23 4.5400000 7.0000000
24 8.0666667 4.5400000
25 5.3500000 8.0666667
26 8.2333333 5.3500000
27 2.6500000 8.2333333
28 6.6833333 2.6500000
29 4.5333333 6.6833333
30 5.2333333 4.5333333
31 6.7500000 5.2333333
32 0.1400000 6.7500000
33 9.2800000 0.1400000
34 7.4000000 9.2800000
35 2.5400000 7.4000000
36 12.7666667 2.5400000
37 12.8500000 12.7666667
38 -0.7666667 12.8500000
39 13.8500000 -0.7666667
40 6.4833333 13.8500000
41 3.2333333 6.4833333
42 8.7333333 3.2333333
43 2.3500000 8.7333333
44 4.4400000 2.3500000
45 2.5800000 4.4400000
46 -6.5000000 2.5800000
47 -3.1600000 -6.5000000
48 -6.9333333 -3.1600000
49 -6.7500000 -6.9333333
50 -7.4666667 -6.7500000
51 -9.3500000 -7.4666667
52 -10.8166667 -9.3500000
53 -9.9666667 -10.8166667
54 -4.6666667 -9.9666667
55 -8.3500000 -4.6666667
56 -7.8600000 -8.3500000
57 -8.0200000 -7.8600000
58 -10.8000000 -8.0200000
59 -8.7600000 -10.8000000
60 -11.6333333 -8.7600000
61 -8.1500000 -11.6333333
62 0.9333333 -8.1500000
63 -3.7500000 0.9333333
64 -5.1166667 -3.7500000
65 -0.9666667 -5.1166667
66 -1.0666667 -0.9666667
67 -2.0500000 -1.0666667
> 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/rcomp/tmp/79ce91290883615.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/89ce91290883615.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/92lvc1290883615.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/102lvc1290883615.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11n3t01290883615.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/rcomp/tmp/12r4a61290883615.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/rcomp/tmp/138xru1290883616.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/rcomp/tmp/14tg8i1290883616.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/rcomp/tmp/15fy661290883616.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/rcomp/tmp/160h5c1290883616.tab")
+ }
>
> try(system("convert tmp/1obx31290883615.ps tmp/1obx31290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/2obx31290883615.ps tmp/2obx31290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/3obx31290883615.ps tmp/3obx31290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y3eo1290883615.ps tmp/4y3eo1290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/5y3eo1290883615.ps tmp/5y3eo1290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y3eo1290883615.ps tmp/6y3eo1290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/79ce91290883615.ps tmp/79ce91290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/89ce91290883615.ps tmp/89ce91290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/92lvc1290883615.ps tmp/92lvc1290883615.png",intern=TRUE))
character(0)
> try(system("convert tmp/102lvc1290883615.ps tmp/102lvc1290883615.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.82 1.02 4.84