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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> 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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
1 9.3 96.8
2 9.3 114.1
3 8.7 110.3
4 8.2 103.9
5 8.3 101.6
6 8.5 94.6
7 8.6 95.9
8 8.5 104.7
9 8.2 102.8
10 8.1 98.1
11 7.9 113.9
12 8.6 80.9
13 8.7 95.7
14 8.7 113.2
15 8.5 105.9
16 8.4 108.8
17 8.5 102.3
18 8.7 99.0
19 8.7 100.7
20 8.6 115.5
21 8.5 100.7
22 8.3 109.9
23 8.0 114.6
24 8.2 85.4
25 8.1 100.5
26 8.1 114.8
27 8.0 116.5
28 7.9 112.9
29 7.9 102.0
30 8.0 106.0
31 8.0 105.3
32 7.9 118.8
33 8.0 106.1
34 7.7 109.3
35 7.2 117.2
36 7.5 92.5
37 7.3 104.2
38 7.0 112.5
39 7.0 122.4
40 7.0 113.3
41 7.2 100.0
42 7.3 110.7
43 7.1 112.8
44 6.8 109.8
45 6.4 117.3
46 6.1 109.1
47 6.5 115.9
48 7.7 96.0
49 7.9 99.8
50 7.5 116.8
51 6.9 115.7
52 6.6 99.4
53 6.9 94.3
54 7.7 91.0
55 8.0 93.2
56 8.0 103.1
57 7.7 94.1
58 7.3 91.8
59 7.4 102.7
60 8.1 82.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ecogr
9.96949 -0.02014
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.6723 -0.5020 0.1290 0.4678 1.6284
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.969488 0.993981 10.030 2.78e-14 ***
ecogr -0.020139 0.009459 -2.129 0.0375 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6878 on 58 degrees of freedom
Multiple R-squared: 0.07249, Adjusted R-squared: 0.0565
F-statistic: 4.533 on 1 and 58 DF, p-value: 0.0375
> 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,] 0.54552811 0.90894379 0.45447189
[2,] 0.38103350 0.76206700 0.61896650
[3,] 0.24961319 0.49922638 0.75038681
[4,] 0.17065767 0.34131533 0.82934233
[5,] 0.14555066 0.29110132 0.85444934
[6,] 0.11732023 0.23464046 0.88267977
[7,] 0.16289100 0.32578199 0.83710900
[8,] 0.10693952 0.21387905 0.89306048
[9,] 0.07676523 0.15353045 0.92323477
[10,] 0.06308999 0.12617998 0.93691001
[11,] 0.04436095 0.08872189 0.95563905
[12,] 0.03177406 0.06354811 0.96822594
[13,] 0.02219605 0.04439209 0.97780395
[14,] 0.01831090 0.03662180 0.98168910
[15,] 0.01645441 0.03290882 0.98354559
[16,] 0.01775810 0.03551621 0.98224190
[17,] 0.01522195 0.03044390 0.98477805
[18,] 0.01520081 0.03040163 0.98479919
[19,] 0.02021307 0.04042614 0.97978693
[20,] 0.01820387 0.03640773 0.98179613
[21,] 0.01890971 0.03781941 0.98109029
[22,] 0.02350941 0.04701882 0.97649059
[23,] 0.03196143 0.06392285 0.96803857
[24,] 0.04343414 0.08686827 0.95656586
[25,] 0.05299971 0.10599943 0.94700029
[26,] 0.06274882 0.12549763 0.93725118
[27,] 0.07545311 0.15090622 0.92454689
[28,] 0.12766973 0.25533946 0.87233027
[29,] 0.17349629 0.34699258 0.82650371
[30,] 0.23725011 0.47450022 0.76274989
[31,] 0.36119683 0.72239366 0.63880317
[32,] 0.44875850 0.89751700 0.55124150
[33,] 0.51433478 0.97133043 0.48566522
[34,] 0.59826707 0.80346586 0.40173293
[35,] 0.63738057 0.72523886 0.36261943
[36,] 0.65577956 0.68844088 0.34422044
[37,] 0.67412824 0.65174352 0.32587176
[38,] 0.65520355 0.68959290 0.34479645
[39,] 0.63373706 0.73252588 0.36626294
[40,] 0.64316301 0.71367399 0.35683699
[41,] 0.67773929 0.64452141 0.32226071
[42,] 0.89395068 0.21209864 0.10604932
[43,] 0.91477273 0.17045455 0.08522727
[44,] 0.87433920 0.25132159 0.12566080
[45,] 0.84564596 0.30870808 0.15435404
[46,] 0.81723204 0.36553592 0.18276796
[47,] 0.73733946 0.52532108 0.26266054
[48,] 0.87693235 0.24613530 0.12306765
[49,] 0.95713484 0.08573031 0.04286516
[50,] 0.90270646 0.19458708 0.09729354
[51,] 0.81938167 0.36123666 0.18061833
> postscript(file="/var/www/html/rcomp/tmp/1jbrv1261057258.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/286vr1261057258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32zf61261057258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fe7y1261057258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55l461261057258.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
1.28000421 1.62841555 0.95188589 0.32299384 0.37667325 0.43569757
7 8 9 10 11 12
0.56187877 0.63910534 0.30084051 0.10618541 0.22438767 0.25978801
13 14 15 16 17 18
0.65785089 1.01029010 0.66327260 0.62167682 0.59077082 0.72431086
19 20 21 22 23 24
0.75854781 0.95661069 0.55854781 0.54383014 0.33848524 -0.04958476
25 26 27 28 29 30
0.15451993 0.44251312 0.37675007 0.20424829 -0.01527099 0.16528654
31 32 33 34 35 36
0.15118897 0.32307065 0.16730048 -0.06825349 -0.40915236 -0.60659514
37 38 39 40 41 42
-0.57096435 -0.70380746 -0.50442757 -0.68769596 -0.75554976 -0.44005836
43 44 45 46 47 48
-0.59776565 -0.95818380 -1.20713842 -1.67228137 -1.13533356 -0.33610729
49 50 51 52 53 54
-0.05957764 -0.11720811 -0.73936144 -1.36763339 -1.17034425 -0.43680421
55 56 57 58 59 60
-0.09249757 0.10688233 -0.37437212 -0.82069271 -0.50117342 -0.20597504
> postscript(file="/var/www/html/rcomp/tmp/6q9s21261057258.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 1.28000421 NA
1 1.62841555 1.28000421
2 0.95188589 1.62841555
3 0.32299384 0.95188589
4 0.37667325 0.32299384
5 0.43569757 0.37667325
6 0.56187877 0.43569757
7 0.63910534 0.56187877
8 0.30084051 0.63910534
9 0.10618541 0.30084051
10 0.22438767 0.10618541
11 0.25978801 0.22438767
12 0.65785089 0.25978801
13 1.01029010 0.65785089
14 0.66327260 1.01029010
15 0.62167682 0.66327260
16 0.59077082 0.62167682
17 0.72431086 0.59077082
18 0.75854781 0.72431086
19 0.95661069 0.75854781
20 0.55854781 0.95661069
21 0.54383014 0.55854781
22 0.33848524 0.54383014
23 -0.04958476 0.33848524
24 0.15451993 -0.04958476
25 0.44251312 0.15451993
26 0.37675007 0.44251312
27 0.20424829 0.37675007
28 -0.01527099 0.20424829
29 0.16528654 -0.01527099
30 0.15118897 0.16528654
31 0.32307065 0.15118897
32 0.16730048 0.32307065
33 -0.06825349 0.16730048
34 -0.40915236 -0.06825349
35 -0.60659514 -0.40915236
36 -0.57096435 -0.60659514
37 -0.70380746 -0.57096435
38 -0.50442757 -0.70380746
39 -0.68769596 -0.50442757
40 -0.75554976 -0.68769596
41 -0.44005836 -0.75554976
42 -0.59776565 -0.44005836
43 -0.95818380 -0.59776565
44 -1.20713842 -0.95818380
45 -1.67228137 -1.20713842
46 -1.13533356 -1.67228137
47 -0.33610729 -1.13533356
48 -0.05957764 -0.33610729
49 -0.11720811 -0.05957764
50 -0.73936144 -0.11720811
51 -1.36763339 -0.73936144
52 -1.17034425 -1.36763339
53 -0.43680421 -1.17034425
54 -0.09249757 -0.43680421
55 0.10688233 -0.09249757
56 -0.37437212 0.10688233
57 -0.82069271 -0.37437212
58 -0.50117342 -0.82069271
59 -0.20597504 -0.50117342
60 NA -0.20597504
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.62841555 1.28000421
[2,] 0.95188589 1.62841555
[3,] 0.32299384 0.95188589
[4,] 0.37667325 0.32299384
[5,] 0.43569757 0.37667325
[6,] 0.56187877 0.43569757
[7,] 0.63910534 0.56187877
[8,] 0.30084051 0.63910534
[9,] 0.10618541 0.30084051
[10,] 0.22438767 0.10618541
[11,] 0.25978801 0.22438767
[12,] 0.65785089 0.25978801
[13,] 1.01029010 0.65785089
[14,] 0.66327260 1.01029010
[15,] 0.62167682 0.66327260
[16,] 0.59077082 0.62167682
[17,] 0.72431086 0.59077082
[18,] 0.75854781 0.72431086
[19,] 0.95661069 0.75854781
[20,] 0.55854781 0.95661069
[21,] 0.54383014 0.55854781
[22,] 0.33848524 0.54383014
[23,] -0.04958476 0.33848524
[24,] 0.15451993 -0.04958476
[25,] 0.44251312 0.15451993
[26,] 0.37675007 0.44251312
[27,] 0.20424829 0.37675007
[28,] -0.01527099 0.20424829
[29,] 0.16528654 -0.01527099
[30,] 0.15118897 0.16528654
[31,] 0.32307065 0.15118897
[32,] 0.16730048 0.32307065
[33,] -0.06825349 0.16730048
[34,] -0.40915236 -0.06825349
[35,] -0.60659514 -0.40915236
[36,] -0.57096435 -0.60659514
[37,] -0.70380746 -0.57096435
[38,] -0.50442757 -0.70380746
[39,] -0.68769596 -0.50442757
[40,] -0.75554976 -0.68769596
[41,] -0.44005836 -0.75554976
[42,] -0.59776565 -0.44005836
[43,] -0.95818380 -0.59776565
[44,] -1.20713842 -0.95818380
[45,] -1.67228137 -1.20713842
[46,] -1.13533356 -1.67228137
[47,] -0.33610729 -1.13533356
[48,] -0.05957764 -0.33610729
[49,] -0.11720811 -0.05957764
[50,] -0.73936144 -0.11720811
[51,] -1.36763339 -0.73936144
[52,] -1.17034425 -1.36763339
[53,] -0.43680421 -1.17034425
[54,] -0.09249757 -0.43680421
[55,] 0.10688233 -0.09249757
[56,] -0.37437212 0.10688233
[57,] -0.82069271 -0.37437212
[58,] -0.50117342 -0.82069271
[59,] -0.20597504 -0.50117342
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.62841555 1.28000421
2 0.95188589 1.62841555
3 0.32299384 0.95188589
4 0.37667325 0.32299384
5 0.43569757 0.37667325
6 0.56187877 0.43569757
7 0.63910534 0.56187877
8 0.30084051 0.63910534
9 0.10618541 0.30084051
10 0.22438767 0.10618541
11 0.25978801 0.22438767
12 0.65785089 0.25978801
13 1.01029010 0.65785089
14 0.66327260 1.01029010
15 0.62167682 0.66327260
16 0.59077082 0.62167682
17 0.72431086 0.59077082
18 0.75854781 0.72431086
19 0.95661069 0.75854781
20 0.55854781 0.95661069
21 0.54383014 0.55854781
22 0.33848524 0.54383014
23 -0.04958476 0.33848524
24 0.15451993 -0.04958476
25 0.44251312 0.15451993
26 0.37675007 0.44251312
27 0.20424829 0.37675007
28 -0.01527099 0.20424829
29 0.16528654 -0.01527099
30 0.15118897 0.16528654
31 0.32307065 0.15118897
32 0.16730048 0.32307065
33 -0.06825349 0.16730048
34 -0.40915236 -0.06825349
35 -0.60659514 -0.40915236
36 -0.57096435 -0.60659514
37 -0.70380746 -0.57096435
38 -0.50442757 -0.70380746
39 -0.68769596 -0.50442757
40 -0.75554976 -0.68769596
41 -0.44005836 -0.75554976
42 -0.59776565 -0.44005836
43 -0.95818380 -0.59776565
44 -1.20713842 -0.95818380
45 -1.67228137 -1.20713842
46 -1.13533356 -1.67228137
47 -0.33610729 -1.13533356
48 -0.05957764 -0.33610729
49 -0.11720811 -0.05957764
50 -0.73936144 -0.11720811
51 -1.36763339 -0.73936144
52 -1.17034425 -1.36763339
53 -0.43680421 -1.17034425
54 -0.09249757 -0.43680421
55 0.10688233 -0.09249757
56 -0.37437212 0.10688233
57 -0.82069271 -0.37437212
58 -0.50117342 -0.82069271
59 -0.20597504 -0.50117342
> 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/7jox01261057258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87cek1261057258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9924g1261057258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10w2xd1261057258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vv5f1261057258.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/12r1091261057258.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/13dnl41261057258.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/149d891261057258.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/15d6cr1261057258.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/16ey4x1261057258.tab")
+ }
>
> try(system("convert tmp/1jbrv1261057258.ps tmp/1jbrv1261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/286vr1261057258.ps tmp/286vr1261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/32zf61261057258.ps tmp/32zf61261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fe7y1261057258.ps tmp/4fe7y1261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/55l461261057258.ps tmp/55l461261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q9s21261057258.ps tmp/6q9s21261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jox01261057258.ps tmp/7jox01261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/87cek1261057258.ps tmp/87cek1261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/9924g1261057258.ps tmp/9924g1261057258.png",intern=TRUE))
character(0)
> try(system("convert tmp/10w2xd1261057258.ps tmp/10w2xd1261057258.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.497 1.572 3.269