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(9.3,96.8,9.3,114.1,8.7,110.3,8.2,103.9,8.3,101.6,8.5,94.6,8.6,95.9,8.5,104.7,8.2,102.8,8.1,98.1,7.9,113.9,8.6,80.9,8.7,95.7,8.7,113.2,8.5,105.9,8.4,108.8,8.5,102.3,8.7,99,8.7,100.7,8.6,115.5,8.5,100.7,8.3,109.9,8,114.6,8.2,85.4,8.1,100.5,8.1,114.8,8,116.5,7.9,112.9,7.9,102,8,106,8,105.3,7.9,118.8,8,106.1,7.7,109.3,7.2,117.2,7.5,92.5,7.3,104.2,7,112.5,7,122.4,7,113.3,7.2,100,7.3,110.7,7.1,112.8,6.8,109.8,6.4,117.3,6.1,109.1,6.5,115.9,7.7,96,7.9,99.8,7.5,116.8,6.9,115.7,6.6,99.4,6.9,94.3,7.7,91,8,93.2,8,103.1,7.7,94.1,7.3,91.8,7.4,102.7,8.1,82.6),dim=c(2,60),dimnames=list(c('werklh','ecogr'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('werklh','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 = '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
werklh ecogr M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.3 96.8 1 0 0 0 0 0 0 0 0 0 0 1
2 9.3 114.1 0 1 0 0 0 0 0 0 0 0 0 2
3 8.7 110.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.2 103.9 0 0 0 1 0 0 0 0 0 0 0 4
5 8.3 101.6 0 0 0 0 1 0 0 0 0 0 0 5
6 8.5 94.6 0 0 0 0 0 1 0 0 0 0 0 6
7 8.6 95.9 0 0 0 0 0 0 1 0 0 0 0 7
8 8.5 104.7 0 0 0 0 0 0 0 1 0 0 0 8
9 8.2 102.8 0 0 0 0 0 0 0 0 1 0 0 9
10 8.1 98.1 0 0 0 0 0 0 0 0 0 1 0 10
11 7.9 113.9 0 0 0 0 0 0 0 0 0 0 1 11
12 8.6 80.9 0 0 0 0 0 0 0 0 0 0 0 12
13 8.7 95.7 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 113.2 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 105.9 0 0 1 0 0 0 0 0 0 0 0 15
16 8.4 108.8 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 102.3 0 0 0 0 1 0 0 0 0 0 0 17
18 8.7 99.0 0 0 0 0 0 1 0 0 0 0 0 18
19 8.7 100.7 0 0 0 0 0 0 1 0 0 0 0 19
20 8.6 115.5 0 0 0 0 0 0 0 1 0 0 0 20
21 8.5 100.7 0 0 0 0 0 0 0 0 1 0 0 21
22 8.3 109.9 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 114.6 0 0 0 0 0 0 0 0 0 0 1 23
24 8.2 85.4 0 0 0 0 0 0 0 0 0 0 0 24
25 8.1 100.5 1 0 0 0 0 0 0 0 0 0 0 25
26 8.1 114.8 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 116.5 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 112.9 0 0 0 1 0 0 0 0 0 0 0 28
29 7.9 102.0 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 106.0 0 0 0 0 0 1 0 0 0 0 0 30
31 8.0 105.3 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 118.8 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 106.1 0 0 0 0 0 0 0 0 1 0 0 33
34 7.7 109.3 0 0 0 0 0 0 0 0 0 1 0 34
35 7.2 117.2 0 0 0 0 0 0 0 0 0 0 1 35
36 7.5 92.5 0 0 0 0 0 0 0 0 0 0 0 36
37 7.3 104.2 1 0 0 0 0 0 0 0 0 0 0 37
38 7.0 112.5 0 1 0 0 0 0 0 0 0 0 0 38
39 7.0 122.4 0 0 1 0 0 0 0 0 0 0 0 39
40 7.0 113.3 0 0 0 1 0 0 0 0 0 0 0 40
41 7.2 100.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.3 110.7 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 112.8 0 0 0 0 0 0 1 0 0 0 0 43
44 6.8 109.8 0 0 0 0 0 0 0 1 0 0 0 44
45 6.4 117.3 0 0 0 0 0 0 0 0 1 0 0 45
46 6.1 109.1 0 0 0 0 0 0 0 0 0 1 0 46
47 6.5 115.9 0 0 0 0 0 0 0 0 0 0 1 47
48 7.7 96.0 0 0 0 0 0 0 0 0 0 0 0 48
49 7.9 99.8 1 0 0 0 0 0 0 0 0 0 0 49
50 7.5 116.8 0 1 0 0 0 0 0 0 0 0 0 50
51 6.9 115.7 0 0 1 0 0 0 0 0 0 0 0 51
52 6.6 99.4 0 0 0 1 0 0 0 0 0 0 0 52
53 6.9 94.3 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 91.0 0 0 0 0 0 1 0 0 0 0 0 54
55 8.0 93.2 0 0 0 0 0 0 1 0 0 0 0 55
56 8.0 103.1 0 0 0 0 0 0 0 1 0 0 0 56
57 7.7 94.1 0 0 0 0 0 0 0 0 1 0 0 57
58 7.3 91.8 0 0 0 0 0 0 0 0 0 1 0 58
59 7.4 102.7 0 0 0 0 0 0 0 0 0 0 1 59
60 8.1 82.6 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) ecogr M1 M2 M3 M4
12.12811 -0.03479 0.32937 0.73662 0.46202 0.06546
M5 M6 M7 M8 M9 M10
-0.03006 0.28716 0.40266 0.61839 0.23296 -0.01695
M11 t
0.23339 -0.02957
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.85515 -0.27109 0.03683 0.30022 0.66289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.128108 0.858555 14.126 < 2e-16 ***
ecogr -0.034790 0.009502 -3.661 0.000646 ***
M1 0.329371 0.291257 1.131 0.263975
M2 0.736619 0.369783 1.992 0.052323 .
M3 0.462019 0.368707 1.253 0.216509
M4 0.065460 0.328953 0.199 0.843144
M5 -0.030064 0.292363 -0.103 0.918545
M6 0.287165 0.292981 0.980 0.332140
M7 0.402662 0.298202 1.350 0.183526
M8 0.618387 0.343869 1.798 0.078690 .
M9 0.232961 0.309896 0.752 0.456037
M10 -0.016946 0.307097 -0.055 0.956232
M11 0.233391 0.358960 0.650 0.518806
t -0.029575 0.003198 -9.249 4.59e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4203 on 46 degrees of freedom
Multiple R-squared: 0.7253, Adjusted R-squared: 0.6477
F-statistic: 9.342 on 13 and 46 DF, p-value: 5.097e-09
> 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,] 0.232798536 0.46559707 0.7672015
[2,] 0.111478298 0.22295660 0.8885217
[3,] 0.048142432 0.09628486 0.9518576
[4,] 0.031377778 0.06275556 0.9686222
[5,] 0.037795812 0.07559162 0.9622042
[6,] 0.021610684 0.04322137 0.9783893
[7,] 0.010844560 0.02168912 0.9891554
[8,] 0.012532711 0.02506542 0.9874673
[9,] 0.048504213 0.09700843 0.9514958
[10,] 0.053357418 0.10671484 0.9466426
[11,] 0.040888656 0.08177731 0.9591113
[12,] 0.034721794 0.06944359 0.9652782
[13,] 0.023395938 0.04679188 0.9766041
[14,] 0.016329722 0.03265944 0.9836703
[15,] 0.010044024 0.02008805 0.9899560
[16,] 0.009388348 0.01877670 0.9906117
[17,] 0.007835311 0.01567062 0.9921647
[18,] 0.035379631 0.07075926 0.9646204
[19,] 0.051756802 0.10351360 0.9482432
[20,] 0.044996730 0.08999346 0.9550033
[21,] 0.064191691 0.12838338 0.9358083
[22,] 0.115895538 0.23179108 0.8841045
[23,] 0.119364852 0.23872970 0.8806351
[24,] 0.274358600 0.54871720 0.7256414
[25,] 0.532463514 0.93507297 0.4675365
[26,] 0.619650560 0.76069888 0.3803494
[27,] 0.487709818 0.97541964 0.5122902
> postscript(file="/var/www/html/rcomp/tmp/1otyu1261058326.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2e2kq1261058326.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34gxr1261058326.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4w5ps1261058326.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54wbd1261058326.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
0.239750100 0.463941464 0.035914927 -0.260606091 -0.115524249 -0.446706749
7 8 9 10 11 12
-0.387402546 -0.367402546 -0.318502116 -0.302531971 -0.173614956 -0.358713384
13 14 15 16 17 18
-0.043620518 0.187528810 0.037737903 0.464762211 0.463726809 0.261266643
19 20 21 22 23 24
0.234486774 0.463225694 0.263337446 0.662886089 0.305636103 -0.247261010
25 26 27 28 29 30
-0.121731198 -0.001909294 0.261408179 0.462298657 0.208188047 0.159693567
31 32 33 34 35 36
0.049418130 0.232930284 0.306100658 0.396910381 -0.049012181 -0.345355104
37 38 39 40 41 42
-0.438110680 -0.827027695 -0.178433699 -0.068887231 -0.206493409 -0.021896095
43 44 45 46 47 48
-0.234760036 -0.825279911 -0.549355174 -0.855149399 -0.439340763 0.331307450
49 50 51 52 53 54
0.363712296 0.177466715 -0.156627309 -0.597567545 -0.349897198 0.047642635
55 56 57 58 59 60
0.338257677 0.496526479 0.298419186 0.097884900 0.356331797 0.620022047
> postscript(file="/var/www/html/rcomp/tmp/6uj8r1261058326.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 0.239750100 NA
1 0.463941464 0.239750100
2 0.035914927 0.463941464
3 -0.260606091 0.035914927
4 -0.115524249 -0.260606091
5 -0.446706749 -0.115524249
6 -0.387402546 -0.446706749
7 -0.367402546 -0.387402546
8 -0.318502116 -0.367402546
9 -0.302531971 -0.318502116
10 -0.173614956 -0.302531971
11 -0.358713384 -0.173614956
12 -0.043620518 -0.358713384
13 0.187528810 -0.043620518
14 0.037737903 0.187528810
15 0.464762211 0.037737903
16 0.463726809 0.464762211
17 0.261266643 0.463726809
18 0.234486774 0.261266643
19 0.463225694 0.234486774
20 0.263337446 0.463225694
21 0.662886089 0.263337446
22 0.305636103 0.662886089
23 -0.247261010 0.305636103
24 -0.121731198 -0.247261010
25 -0.001909294 -0.121731198
26 0.261408179 -0.001909294
27 0.462298657 0.261408179
28 0.208188047 0.462298657
29 0.159693567 0.208188047
30 0.049418130 0.159693567
31 0.232930284 0.049418130
32 0.306100658 0.232930284
33 0.396910381 0.306100658
34 -0.049012181 0.396910381
35 -0.345355104 -0.049012181
36 -0.438110680 -0.345355104
37 -0.827027695 -0.438110680
38 -0.178433699 -0.827027695
39 -0.068887231 -0.178433699
40 -0.206493409 -0.068887231
41 -0.021896095 -0.206493409
42 -0.234760036 -0.021896095
43 -0.825279911 -0.234760036
44 -0.549355174 -0.825279911
45 -0.855149399 -0.549355174
46 -0.439340763 -0.855149399
47 0.331307450 -0.439340763
48 0.363712296 0.331307450
49 0.177466715 0.363712296
50 -0.156627309 0.177466715
51 -0.597567545 -0.156627309
52 -0.349897198 -0.597567545
53 0.047642635 -0.349897198
54 0.338257677 0.047642635
55 0.496526479 0.338257677
56 0.298419186 0.496526479
57 0.097884900 0.298419186
58 0.356331797 0.097884900
59 0.620022047 0.356331797
60 NA 0.620022047
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.463941464 0.239750100
[2,] 0.035914927 0.463941464
[3,] -0.260606091 0.035914927
[4,] -0.115524249 -0.260606091
[5,] -0.446706749 -0.115524249
[6,] -0.387402546 -0.446706749
[7,] -0.367402546 -0.387402546
[8,] -0.318502116 -0.367402546
[9,] -0.302531971 -0.318502116
[10,] -0.173614956 -0.302531971
[11,] -0.358713384 -0.173614956
[12,] -0.043620518 -0.358713384
[13,] 0.187528810 -0.043620518
[14,] 0.037737903 0.187528810
[15,] 0.464762211 0.037737903
[16,] 0.463726809 0.464762211
[17,] 0.261266643 0.463726809
[18,] 0.234486774 0.261266643
[19,] 0.463225694 0.234486774
[20,] 0.263337446 0.463225694
[21,] 0.662886089 0.263337446
[22,] 0.305636103 0.662886089
[23,] -0.247261010 0.305636103
[24,] -0.121731198 -0.247261010
[25,] -0.001909294 -0.121731198
[26,] 0.261408179 -0.001909294
[27,] 0.462298657 0.261408179
[28,] 0.208188047 0.462298657
[29,] 0.159693567 0.208188047
[30,] 0.049418130 0.159693567
[31,] 0.232930284 0.049418130
[32,] 0.306100658 0.232930284
[33,] 0.396910381 0.306100658
[34,] -0.049012181 0.396910381
[35,] -0.345355104 -0.049012181
[36,] -0.438110680 -0.345355104
[37,] -0.827027695 -0.438110680
[38,] -0.178433699 -0.827027695
[39,] -0.068887231 -0.178433699
[40,] -0.206493409 -0.068887231
[41,] -0.021896095 -0.206493409
[42,] -0.234760036 -0.021896095
[43,] -0.825279911 -0.234760036
[44,] -0.549355174 -0.825279911
[45,] -0.855149399 -0.549355174
[46,] -0.439340763 -0.855149399
[47,] 0.331307450 -0.439340763
[48,] 0.363712296 0.331307450
[49,] 0.177466715 0.363712296
[50,] -0.156627309 0.177466715
[51,] -0.597567545 -0.156627309
[52,] -0.349897198 -0.597567545
[53,] 0.047642635 -0.349897198
[54,] 0.338257677 0.047642635
[55,] 0.496526479 0.338257677
[56,] 0.298419186 0.496526479
[57,] 0.097884900 0.298419186
[58,] 0.356331797 0.097884900
[59,] 0.620022047 0.356331797
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.463941464 0.239750100
2 0.035914927 0.463941464
3 -0.260606091 0.035914927
4 -0.115524249 -0.260606091
5 -0.446706749 -0.115524249
6 -0.387402546 -0.446706749
7 -0.367402546 -0.387402546
8 -0.318502116 -0.367402546
9 -0.302531971 -0.318502116
10 -0.173614956 -0.302531971
11 -0.358713384 -0.173614956
12 -0.043620518 -0.358713384
13 0.187528810 -0.043620518
14 0.037737903 0.187528810
15 0.464762211 0.037737903
16 0.463726809 0.464762211
17 0.261266643 0.463726809
18 0.234486774 0.261266643
19 0.463225694 0.234486774
20 0.263337446 0.463225694
21 0.662886089 0.263337446
22 0.305636103 0.662886089
23 -0.247261010 0.305636103
24 -0.121731198 -0.247261010
25 -0.001909294 -0.121731198
26 0.261408179 -0.001909294
27 0.462298657 0.261408179
28 0.208188047 0.462298657
29 0.159693567 0.208188047
30 0.049418130 0.159693567
31 0.232930284 0.049418130
32 0.306100658 0.232930284
33 0.396910381 0.306100658
34 -0.049012181 0.396910381
35 -0.345355104 -0.049012181
36 -0.438110680 -0.345355104
37 -0.827027695 -0.438110680
38 -0.178433699 -0.827027695
39 -0.068887231 -0.178433699
40 -0.206493409 -0.068887231
41 -0.021896095 -0.206493409
42 -0.234760036 -0.021896095
43 -0.825279911 -0.234760036
44 -0.549355174 -0.825279911
45 -0.855149399 -0.549355174
46 -0.439340763 -0.855149399
47 0.331307450 -0.439340763
48 0.363712296 0.331307450
49 0.177466715 0.363712296
50 -0.156627309 0.177466715
51 -0.597567545 -0.156627309
52 -0.349897198 -0.597567545
53 0.047642635 -0.349897198
54 0.338257677 0.047642635
55 0.496526479 0.338257677
56 0.298419186 0.496526479
57 0.097884900 0.298419186
58 0.356331797 0.097884900
59 0.620022047 0.356331797
> 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/7g4da1261058326.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8d30r1261058326.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9861h1261058326.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10s1t71261058326.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zxhy1261058326.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/12uh2c1261058326.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/13jp6v1261058326.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/143kr21261058326.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/15cc2k1261058326.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/16xup41261058326.tab")
+ }
>
> try(system("convert tmp/1otyu1261058326.ps tmp/1otyu1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/2e2kq1261058326.ps tmp/2e2kq1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/34gxr1261058326.ps tmp/34gxr1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/4w5ps1261058326.ps tmp/4w5ps1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/54wbd1261058326.ps tmp/54wbd1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uj8r1261058326.ps tmp/6uj8r1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/7g4da1261058326.ps tmp/7g4da1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d30r1261058326.ps tmp/8d30r1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/9861h1261058326.ps tmp/9861h1261058326.png",intern=TRUE))
character(0)
> try(system("convert tmp/10s1t71261058326.ps tmp/10s1t71261058326.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.348 1.521 3.547