R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(15836.8,89.1,17570.4,82.6,18252.1,102.7,16196.7,91.8,16643,94.1,17729,103.1,16446.1,93.2,15993.8,91,16373.5,94.3,17842.2,99.4,22321.5,115.7,22786.7,116.8,18274.1,99.8,22392.9,96,23899.3,115.9,21343.5,109.1,22952.3,117.3,21374.4,109.8,21164.1,112.8,20906.5,110.7,17877.4,100,20664.3,113.3,22160,122.4,19813.6,112.5,17735.4,104.2,19640.2,92.5,20844.4,117.2,19823.1,109.3,18594.6,106.1,21350.6,118.8,18574.1,105.3,18924.2,106,17343.4,102,19961.2,112.9,19932.1,116.5,19464.6,114.8,16165.4,100.5,17574.9,85.4,19795.4,114.6,19439.5,109.9,17170,100.7,21072.4,115.5,17751.8,100.7,17515.5,99,18040.3,102.3,19090.1,108.8,17746.5,105.9,19202.1,113.2,15141.6,95.7,16258.1,80.9,18586.5,113.9,17209.4,98.1,17838.7,102.8,19123.5,104.7,16583.6,95.9,15991.2,94.6,16704.4,101.6,17420.4,103.9,17872,110.3,17823.2,114.1),dim=c(2,60),dimnames=list(c('uitvoer','indproc'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('uitvoer','indproc'),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 = '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
uitvoer indproc M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 15836.8 89.1 1 0 0 0 0 0 0 0 0 0 0 1
2 17570.4 82.6 0 1 0 0 0 0 0 0 0 0 0 2
3 18252.1 102.7 0 0 1 0 0 0 0 0 0 0 0 3
4 16196.7 91.8 0 0 0 1 0 0 0 0 0 0 0 4
5 16643.0 94.1 0 0 0 0 1 0 0 0 0 0 0 5
6 17729.0 103.1 0 0 0 0 0 1 0 0 0 0 0 6
7 16446.1 93.2 0 0 0 0 0 0 1 0 0 0 0 7
8 15993.8 91.0 0 0 0 0 0 0 0 1 0 0 0 8
9 16373.5 94.3 0 0 0 0 0 0 0 0 1 0 0 9
10 17842.2 99.4 0 0 0 0 0 0 0 0 0 1 0 10
11 22321.5 115.7 0 0 0 0 0 0 0 0 0 0 1 11
12 22786.7 116.8 0 0 0 0 0 0 0 0 0 0 0 12
13 18274.1 99.8 1 0 0 0 0 0 0 0 0 0 0 13
14 22392.9 96.0 0 1 0 0 0 0 0 0 0 0 0 14
15 23899.3 115.9 0 0 1 0 0 0 0 0 0 0 0 15
16 21343.5 109.1 0 0 0 1 0 0 0 0 0 0 0 16
17 22952.3 117.3 0 0 0 0 1 0 0 0 0 0 0 17
18 21374.4 109.8 0 0 0 0 0 1 0 0 0 0 0 18
19 21164.1 112.8 0 0 0 0 0 0 1 0 0 0 0 19
20 20906.5 110.7 0 0 0 0 0 0 0 1 0 0 0 20
21 17877.4 100.0 0 0 0 0 0 0 0 0 1 0 0 21
22 20664.3 113.3 0 0 0 0 0 0 0 0 0 1 0 22
23 22160.0 122.4 0 0 0 0 0 0 0 0 0 0 1 23
24 19813.6 112.5 0 0 0 0 0 0 0 0 0 0 0 24
25 17735.4 104.2 1 0 0 0 0 0 0 0 0 0 0 25
26 19640.2 92.5 0 1 0 0 0 0 0 0 0 0 0 26
27 20844.4 117.2 0 0 1 0 0 0 0 0 0 0 0 27
28 19823.1 109.3 0 0 0 1 0 0 0 0 0 0 0 28
29 18594.6 106.1 0 0 0 0 1 0 0 0 0 0 0 29
30 21350.6 118.8 0 0 0 0 0 1 0 0 0 0 0 30
31 18574.1 105.3 0 0 0 0 0 0 1 0 0 0 0 31
32 18924.2 106.0 0 0 0 0 0 0 0 1 0 0 0 32
33 17343.4 102.0 0 0 0 0 0 0 0 0 1 0 0 33
34 19961.2 112.9 0 0 0 0 0 0 0 0 0 1 0 34
35 19932.1 116.5 0 0 0 0 0 0 0 0 0 0 1 35
36 19464.6 114.8 0 0 0 0 0 0 0 0 0 0 0 36
37 16165.4 100.5 1 0 0 0 0 0 0 0 0 0 0 37
38 17574.9 85.4 0 1 0 0 0 0 0 0 0 0 0 38
39 19795.4 114.6 0 0 1 0 0 0 0 0 0 0 0 39
40 19439.5 109.9 0 0 0 1 0 0 0 0 0 0 0 40
41 17170.0 100.7 0 0 0 0 1 0 0 0 0 0 0 41
42 21072.4 115.5 0 0 0 0 0 1 0 0 0 0 0 42
43 17751.8 100.7 0 0 0 0 0 0 1 0 0 0 0 43
44 17515.5 99.0 0 0 0 0 0 0 0 1 0 0 0 44
45 18040.3 102.3 0 0 0 0 0 0 0 0 1 0 0 45
46 19090.1 108.8 0 0 0 0 0 0 0 0 0 1 0 46
47 17746.5 105.9 0 0 0 0 0 0 0 0 0 0 1 47
48 19202.1 113.2 0 0 0 0 0 0 0 0 0 0 0 48
49 15141.6 95.7 1 0 0 0 0 0 0 0 0 0 0 49
50 16258.1 80.9 0 1 0 0 0 0 0 0 0 0 0 50
51 18586.5 113.9 0 0 1 0 0 0 0 0 0 0 0 51
52 17209.4 98.1 0 0 0 1 0 0 0 0 0 0 0 52
53 17838.7 102.8 0 0 0 0 1 0 0 0 0 0 0 53
54 19123.5 104.7 0 0 0 0 0 1 0 0 0 0 0 54
55 16583.6 95.9 0 0 0 0 0 0 1 0 0 0 0 55
56 15991.2 94.6 0 0 0 0 0 0 0 1 0 0 0 56
57 16704.4 101.6 0 0 0 0 0 0 0 0 1 0 0 57
58 17420.4 103.9 0 0 0 0 0 0 0 0 0 1 0 58
59 17872.0 110.3 0 0 0 0 0 0 0 0 0 0 1 59
60 17823.2 114.1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) indproc M1 M2 M3 M4
-8033.87 255.27 600.65 5343.72 489.80 1407.01
M5 M6 M7 M8 M9 M10
1138.03 1087.38 1344.44 1480.38 974.79 794.14
M11 t
182.33 -36.69
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1423.1 -434.5 -138.6 417.6 2407.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8033.868 1983.559 -4.050 0.000195 ***
indproc 255.273 17.076 14.950 < 2e-16 ***
M1 600.651 564.907 1.063 0.293208
M2 5343.716 669.638 7.980 3.17e-10 ***
M3 489.804 491.152 0.997 0.323858
M4 1407.012 521.931 2.696 0.009776 **
M5 1138.026 518.307 2.196 0.033198 *
M6 1087.383 493.385 2.204 0.032573 *
M7 1344.437 534.117 2.517 0.015380 *
M8 1480.385 543.435 2.724 0.009084 **
M9 974.792 544.989 1.789 0.080259 .
M10 794.136 500.759 1.586 0.119622
M11 182.326 487.853 0.374 0.710322
t -36.687 5.873 -6.246 1.24e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 771.3 on 46 degrees of freedom
Multiple R-squared: 0.892, Adjusted R-squared: 0.8615
F-statistic: 29.23 on 13 and 46 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.9893879 0.0212242102 0.0106121051
[2,] 0.9819855 0.0360290798 0.0180145399
[3,] 0.9825939 0.0348122971 0.0174061486
[4,] 0.9785540 0.0428920114 0.0214460057
[5,] 0.9899805 0.0200390123 0.0100195062
[6,] 0.9937428 0.0125143612 0.0062571806
[7,] 0.9997948 0.0004104169 0.0002052085
[8,] 0.9997943 0.0004113147 0.0002056574
[9,] 0.9998197 0.0003606596 0.0001803298
[10,] 0.9997498 0.0005003950 0.0002501975
[11,] 0.9997935 0.0004129946 0.0002064973
[12,] 0.9994659 0.0010682951 0.0005341475
[13,] 0.9988750 0.0022500810 0.0011250405
[14,] 0.9990985 0.0018030379 0.0009015189
[15,] 0.9986360 0.0027279518 0.0013639759
[16,] 0.9968867 0.0062266942 0.0031133471
[17,] 0.9985680 0.0028639593 0.0014319796
[18,] 0.9966961 0.0066078342 0.0033039171
[19,] 0.9922388 0.0155223870 0.0077611935
[20,] 0.9849335 0.0301330231 0.0150665116
[21,] 0.9727919 0.0544161890 0.0272080945
[22,] 0.9581846 0.0836308346 0.0418154173
[23,] 0.9294080 0.1411840324 0.0705920162
[24,] 0.8694617 0.2610765697 0.1305382849
[25,] 0.9685392 0.0629215381 0.0314607690
[26,] 0.9384017 0.1231966683 0.0615983341
[27,] 0.9191999 0.1616002833 0.0808001416
> postscript(file="/var/www/html/rcomp/tmp/1u3od1258479779.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/294qr1258479779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3o6sx1258479779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44ovi1258479779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58vk01258479779.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
561.843474 -751.657366 -310.353749 -463.794416 -298.950153 -1423.081176
7 8 9 10 11 12
-399.140423 -389.099820 -309.522231 74.626768 1041.467327 1444.879383
13 14 15 16 17 18
707.963771 1090.424718 2407.483017 707.021359 528.252489 952.232770
19 20 21 22 23 24
-244.253496 -65.040234 179.565127 -211.327855 -390.118727 9.700865
25 26 27 28 29 30
-513.693434 -328.572530 -539.026610 -424.187514 -530.139483 -928.782132
31 32 33 34 35 36
-479.457094 -407.309387 -424.735888 -372.072681 -671.659785 -486.182174
37 38 39 40 41 42
-698.936000 -141.185493 -484.069928 -520.705752 -136.017248 75.665938
43 44 45 46 47 48
312.746412 411.150309 635.827897 243.694119 288.884195 100.001094
49 50 51 52 53 54
-57.177811 130.990672 -1074.032731 701.666323 436.854396 1323.964600
55 56 57 58 59 60
810.104600 450.299132 -81.134905 265.079649 -268.573010 -1068.399168
> postscript(file="/var/www/html/rcomp/tmp/6wyxq1258479779.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
0 561.843474 NA
1 -751.657366 561.843474
2 -310.353749 -751.657366
3 -463.794416 -310.353749
4 -298.950153 -463.794416
5 -1423.081176 -298.950153
6 -399.140423 -1423.081176
7 -389.099820 -399.140423
8 -309.522231 -389.099820
9 74.626768 -309.522231
10 1041.467327 74.626768
11 1444.879383 1041.467327
12 707.963771 1444.879383
13 1090.424718 707.963771
14 2407.483017 1090.424718
15 707.021359 2407.483017
16 528.252489 707.021359
17 952.232770 528.252489
18 -244.253496 952.232770
19 -65.040234 -244.253496
20 179.565127 -65.040234
21 -211.327855 179.565127
22 -390.118727 -211.327855
23 9.700865 -390.118727
24 -513.693434 9.700865
25 -328.572530 -513.693434
26 -539.026610 -328.572530
27 -424.187514 -539.026610
28 -530.139483 -424.187514
29 -928.782132 -530.139483
30 -479.457094 -928.782132
31 -407.309387 -479.457094
32 -424.735888 -407.309387
33 -372.072681 -424.735888
34 -671.659785 -372.072681
35 -486.182174 -671.659785
36 -698.936000 -486.182174
37 -141.185493 -698.936000
38 -484.069928 -141.185493
39 -520.705752 -484.069928
40 -136.017248 -520.705752
41 75.665938 -136.017248
42 312.746412 75.665938
43 411.150309 312.746412
44 635.827897 411.150309
45 243.694119 635.827897
46 288.884195 243.694119
47 100.001094 288.884195
48 -57.177811 100.001094
49 130.990672 -57.177811
50 -1074.032731 130.990672
51 701.666323 -1074.032731
52 436.854396 701.666323
53 1323.964600 436.854396
54 810.104600 1323.964600
55 450.299132 810.104600
56 -81.134905 450.299132
57 265.079649 -81.134905
58 -268.573010 265.079649
59 -1068.399168 -268.573010
60 NA -1068.399168
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -751.657366 561.843474
[2,] -310.353749 -751.657366
[3,] -463.794416 -310.353749
[4,] -298.950153 -463.794416
[5,] -1423.081176 -298.950153
[6,] -399.140423 -1423.081176
[7,] -389.099820 -399.140423
[8,] -309.522231 -389.099820
[9,] 74.626768 -309.522231
[10,] 1041.467327 74.626768
[11,] 1444.879383 1041.467327
[12,] 707.963771 1444.879383
[13,] 1090.424718 707.963771
[14,] 2407.483017 1090.424718
[15,] 707.021359 2407.483017
[16,] 528.252489 707.021359
[17,] 952.232770 528.252489
[18,] -244.253496 952.232770
[19,] -65.040234 -244.253496
[20,] 179.565127 -65.040234
[21,] -211.327855 179.565127
[22,] -390.118727 -211.327855
[23,] 9.700865 -390.118727
[24,] -513.693434 9.700865
[25,] -328.572530 -513.693434
[26,] -539.026610 -328.572530
[27,] -424.187514 -539.026610
[28,] -530.139483 -424.187514
[29,] -928.782132 -530.139483
[30,] -479.457094 -928.782132
[31,] -407.309387 -479.457094
[32,] -424.735888 -407.309387
[33,] -372.072681 -424.735888
[34,] -671.659785 -372.072681
[35,] -486.182174 -671.659785
[36,] -698.936000 -486.182174
[37,] -141.185493 -698.936000
[38,] -484.069928 -141.185493
[39,] -520.705752 -484.069928
[40,] -136.017248 -520.705752
[41,] 75.665938 -136.017248
[42,] 312.746412 75.665938
[43,] 411.150309 312.746412
[44,] 635.827897 411.150309
[45,] 243.694119 635.827897
[46,] 288.884195 243.694119
[47,] 100.001094 288.884195
[48,] -57.177811 100.001094
[49,] 130.990672 -57.177811
[50,] -1074.032731 130.990672
[51,] 701.666323 -1074.032731
[52,] 436.854396 701.666323
[53,] 1323.964600 436.854396
[54,] 810.104600 1323.964600
[55,] 450.299132 810.104600
[56,] -81.134905 450.299132
[57,] 265.079649 -81.134905
[58,] -268.573010 265.079649
[59,] -1068.399168 -268.573010
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -751.657366 561.843474
2 -310.353749 -751.657366
3 -463.794416 -310.353749
4 -298.950153 -463.794416
5 -1423.081176 -298.950153
6 -399.140423 -1423.081176
7 -389.099820 -399.140423
8 -309.522231 -389.099820
9 74.626768 -309.522231
10 1041.467327 74.626768
11 1444.879383 1041.467327
12 707.963771 1444.879383
13 1090.424718 707.963771
14 2407.483017 1090.424718
15 707.021359 2407.483017
16 528.252489 707.021359
17 952.232770 528.252489
18 -244.253496 952.232770
19 -65.040234 -244.253496
20 179.565127 -65.040234
21 -211.327855 179.565127
22 -390.118727 -211.327855
23 9.700865 -390.118727
24 -513.693434 9.700865
25 -328.572530 -513.693434
26 -539.026610 -328.572530
27 -424.187514 -539.026610
28 -530.139483 -424.187514
29 -928.782132 -530.139483
30 -479.457094 -928.782132
31 -407.309387 -479.457094
32 -424.735888 -407.309387
33 -372.072681 -424.735888
34 -671.659785 -372.072681
35 -486.182174 -671.659785
36 -698.936000 -486.182174
37 -141.185493 -698.936000
38 -484.069928 -141.185493
39 -520.705752 -484.069928
40 -136.017248 -520.705752
41 75.665938 -136.017248
42 312.746412 75.665938
43 411.150309 312.746412
44 635.827897 411.150309
45 243.694119 635.827897
46 288.884195 243.694119
47 100.001094 288.884195
48 -57.177811 100.001094
49 130.990672 -57.177811
50 -1074.032731 130.990672
51 701.666323 -1074.032731
52 436.854396 701.666323
53 1323.964600 436.854396
54 810.104600 1323.964600
55 450.299132 810.104600
56 -81.134905 450.299132
57 265.079649 -81.134905
58 -268.573010 265.079649
59 -1068.399168 -268.573010
> 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/7yued1258479779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8czua1258479779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/930321258479779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rxio1258479779.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11d67i1258479779.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/12rbrd1258479779.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/135d1x1258479779.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/14kz4k1258479779.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/154dv01258479779.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/1614de1258479779.tab")
+ }
>
> system("convert tmp/1u3od1258479779.ps tmp/1u3od1258479779.png")
> system("convert tmp/294qr1258479779.ps tmp/294qr1258479779.png")
> system("convert tmp/3o6sx1258479779.ps tmp/3o6sx1258479779.png")
> system("convert tmp/44ovi1258479779.ps tmp/44ovi1258479779.png")
> system("convert tmp/58vk01258479779.ps tmp/58vk01258479779.png")
> system("convert tmp/6wyxq1258479779.ps tmp/6wyxq1258479779.png")
> system("convert tmp/7yued1258479779.ps tmp/7yued1258479779.png")
> system("convert tmp/8czua1258479779.ps tmp/8czua1258479779.png")
> system("convert tmp/930321258479779.ps tmp/930321258479779.png")
> system("convert tmp/10rxio1258479779.ps tmp/10rxio1258479779.png")
>
>
> proc.time()
user system elapsed
2.427 1.561 3.225