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(9.3,98.3,9.3,112.3,8.7,113.9,8.2,106.2,8.3,98.6,8.5,96.5,8.6,95.9,8.5,103.7,8.2,103.1,8.1,103.7,7.9,112.1,8.6,86.9,8.7,95,8.7,111.8,8.5,108.8,8.4,109.3,8.5,101.4,8.7,100.5,8.7,100.7,8.6,113.5,8.5,106.1,8.3,111.6,8,114.9,8.2,88.6,8.1,99.5,8.1,115.1,8,118,7.9,111.4,7.9,107.3,8,105.3,8,105.3,7.9,117.9,8,110.2,7.7,112.4,7.2,117.5,7.5,93,7.3,103.5,7,116.3,7,120,7,114.3,7.2,104.7,7.3,109.8,7.1,112.6,6.8,114.4,6.4,115.7,6.1,114.7,6.5,118.4,7.7,94.9,7.9,103.8,7.5,115.1,6.9,113.7,6.6,104,6.9,94.3,7.7,92.5,8,93.2,8,104.7,7.7,94,7.3,98.1,7.4,102.7,8.1,82.4),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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
Y X
1 9.3 98.3
2 9.3 112.3
3 8.7 113.9
4 8.2 106.2
5 8.3 98.6
6 8.5 96.5
7 8.6 95.9
8 8.5 103.7
9 8.2 103.1
10 8.1 103.7
11 7.9 112.1
12 8.6 86.9
13 8.7 95.0
14 8.7 111.8
15 8.5 108.8
16 8.4 109.3
17 8.5 101.4
18 8.7 100.5
19 8.7 100.7
20 8.6 113.5
21 8.5 106.1
22 8.3 111.6
23 8.0 114.9
24 8.2 88.6
25 8.1 99.5
26 8.1 115.1
27 8.0 118.0
28 7.9 111.4
29 7.9 107.3
30 8.0 105.3
31 8.0 105.3
32 7.9 117.9
33 8.0 110.2
34 7.7 112.4
35 7.2 117.5
36 7.5 93.0
37 7.3 103.5
38 7.0 116.3
39 7.0 120.0
40 7.0 114.3
41 7.2 104.7
42 7.3 109.8
43 7.1 112.6
44 6.8 114.4
45 6.4 115.7
46 6.1 114.7
47 6.5 118.4
48 7.7 94.9
49 7.9 103.8
50 7.5 115.1
51 6.9 113.7
52 6.6 104.0
53 6.9 94.3
54 7.7 92.5
55 8.0 93.2
56 8.0 104.7
57 7.7 94.0
58 7.3 98.1
59 7.4 102.7
60 8.1 82.4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
10.55092 -0.02543
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.5337 -0.5089 0.1196 0.4794 1.6053
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.550923 1.045228 10.094 2.19e-14 ***
X -0.025434 0.009851 -2.582 0.0124 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6764 on 58 degrees of freedom
Multiple R-squared: 0.1031, Adjusted R-squared: 0.08762
F-statistic: 6.666 on 1 and 58 DF, p-value: 0.01237
> 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,] 0.56774928 0.86450144 0.43225072
[2,] 0.40734336 0.81468672 0.59265664
[3,] 0.27133628 0.54267256 0.72866372
[4,] 0.18467700 0.36935400 0.81532300
[5,] 0.15805645 0.31611290 0.84194355
[6,] 0.14589378 0.29178756 0.85410622
[7,] 0.17684267 0.35368534 0.82315733
[8,] 0.11739401 0.23478801 0.88260599
[9,] 0.08292656 0.16585312 0.91707344
[10,] 0.06904669 0.13809337 0.93095331
[11,] 0.04998958 0.09997915 0.95001042
[12,] 0.03624645 0.07249289 0.96375355
[13,] 0.02508427 0.05016854 0.97491573
[14,] 0.02073857 0.04147713 0.97926143
[15,] 0.01828716 0.03657432 0.98171284
[16,] 0.01951942 0.03903883 0.98048058
[17,] 0.01806303 0.03612605 0.98193697
[18,] 0.01855148 0.03710296 0.98144852
[19,] 0.02408641 0.04817283 0.97591359
[20,] 0.02276678 0.04553356 0.97723322
[21,] 0.02334280 0.04668560 0.97665720
[22,] 0.02942885 0.05885770 0.97057115
[23,] 0.04161446 0.08322891 0.95838554
[24,] 0.05538096 0.11076193 0.94461904
[25,] 0.06907720 0.13815440 0.93092280
[26,] 0.08077751 0.16155502 0.91922249
[27,] 0.09629966 0.19259932 0.90370034
[28,] 0.15966827 0.31933655 0.84033173
[29,] 0.24197019 0.48394038 0.75802981
[30,] 0.34214590 0.68429180 0.65785410
[31,] 0.48330304 0.96660607 0.51669696
[32,] 0.60196779 0.79606442 0.39803221
[33,] 0.66953651 0.66092698 0.33046349
[34,] 0.73243884 0.53512232 0.26756116
[35,] 0.76482738 0.47034524 0.23517262
[36,] 0.77713274 0.44573451 0.22286726
[37,] 0.77866230 0.44267541 0.22133770
[38,] 0.76173250 0.47653501 0.23826750
[39,] 0.74262832 0.51474336 0.25737168
[40,] 0.72953212 0.54093575 0.27046788
[41,] 0.77081786 0.45836427 0.22918214
[42,] 0.90250727 0.19498546 0.09749273
[43,] 0.90773199 0.18453602 0.09226801
[44,] 0.86832602 0.26334796 0.13167398
[45,] 0.85418179 0.29163643 0.14581821
[46,] 0.83930386 0.32139228 0.16069614
[47,] 0.76636127 0.46727747 0.23363873
[48,] 0.86806998 0.26386005 0.13193002
[49,] 0.96536074 0.06927851 0.03463926
[50,] 0.91973532 0.16052936 0.08026468
[51,] 0.84012917 0.31974166 0.15987083
> postscript(file="/var/www/html/rcomp/tmp/1k8u81258749926.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/209ze1258749926.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3kkh31258749926.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40wui1258749926.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5s7n01258749926.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
1.24923211 1.60530705 1.04600133 0.35016011 0.25686228 0.40345104
7 8 9 10 11 12
0.48819069 0.58657530 0.27131494 0.18657530 0.20022026 0.25928537
13 14 15 16 17 18
0.56530015 0.99259008 0.71628831 0.62900527 0.52807727 0.70518674
19 20 21 22 23 24
0.71027352 0.93582776 0.64761672 0.58750330 0.37143525 -0.09747696
25 26 27 28 29 30
0.07975281 0.47652204 0.45028042 0.18241652 0.07813743 0.12726958
31 32 33 34 35 36
0.12726958 0.34773702 0.25189581 0.00785044 -0.36243655 -0.68556769
37 38 39 40 41 42
-0.61851149 -0.59295726 -0.49885174 -0.64382510 -0.68799078 -0.45827776
43 44 45 46 47 48
-0.58706278 -0.84128171 -1.20821761 -1.53365153 -1.03954601 -0.43724324
49 50 51 52 53 54
-0.01088131 -0.12347796 -0.75908546 -1.30579453 -1.25250359 -0.49828466
55 56 57 58 59 60
-0.18048091 0.11200922 -0.46013377 -0.75585468 -0.53885863 -0.35516729
> postscript(file="/var/www/html/rcomp/tmp/6fh9k1258749926.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 1.24923211 NA
1 1.60530705 1.24923211
2 1.04600133 1.60530705
3 0.35016011 1.04600133
4 0.25686228 0.35016011
5 0.40345104 0.25686228
6 0.48819069 0.40345104
7 0.58657530 0.48819069
8 0.27131494 0.58657530
9 0.18657530 0.27131494
10 0.20022026 0.18657530
11 0.25928537 0.20022026
12 0.56530015 0.25928537
13 0.99259008 0.56530015
14 0.71628831 0.99259008
15 0.62900527 0.71628831
16 0.52807727 0.62900527
17 0.70518674 0.52807727
18 0.71027352 0.70518674
19 0.93582776 0.71027352
20 0.64761672 0.93582776
21 0.58750330 0.64761672
22 0.37143525 0.58750330
23 -0.09747696 0.37143525
24 0.07975281 -0.09747696
25 0.47652204 0.07975281
26 0.45028042 0.47652204
27 0.18241652 0.45028042
28 0.07813743 0.18241652
29 0.12726958 0.07813743
30 0.12726958 0.12726958
31 0.34773702 0.12726958
32 0.25189581 0.34773702
33 0.00785044 0.25189581
34 -0.36243655 0.00785044
35 -0.68556769 -0.36243655
36 -0.61851149 -0.68556769
37 -0.59295726 -0.61851149
38 -0.49885174 -0.59295726
39 -0.64382510 -0.49885174
40 -0.68799078 -0.64382510
41 -0.45827776 -0.68799078
42 -0.58706278 -0.45827776
43 -0.84128171 -0.58706278
44 -1.20821761 -0.84128171
45 -1.53365153 -1.20821761
46 -1.03954601 -1.53365153
47 -0.43724324 -1.03954601
48 -0.01088131 -0.43724324
49 -0.12347796 -0.01088131
50 -0.75908546 -0.12347796
51 -1.30579453 -0.75908546
52 -1.25250359 -1.30579453
53 -0.49828466 -1.25250359
54 -0.18048091 -0.49828466
55 0.11200922 -0.18048091
56 -0.46013377 0.11200922
57 -0.75585468 -0.46013377
58 -0.53885863 -0.75585468
59 -0.35516729 -0.53885863
60 NA -0.35516729
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.60530705 1.24923211
[2,] 1.04600133 1.60530705
[3,] 0.35016011 1.04600133
[4,] 0.25686228 0.35016011
[5,] 0.40345104 0.25686228
[6,] 0.48819069 0.40345104
[7,] 0.58657530 0.48819069
[8,] 0.27131494 0.58657530
[9,] 0.18657530 0.27131494
[10,] 0.20022026 0.18657530
[11,] 0.25928537 0.20022026
[12,] 0.56530015 0.25928537
[13,] 0.99259008 0.56530015
[14,] 0.71628831 0.99259008
[15,] 0.62900527 0.71628831
[16,] 0.52807727 0.62900527
[17,] 0.70518674 0.52807727
[18,] 0.71027352 0.70518674
[19,] 0.93582776 0.71027352
[20,] 0.64761672 0.93582776
[21,] 0.58750330 0.64761672
[22,] 0.37143525 0.58750330
[23,] -0.09747696 0.37143525
[24,] 0.07975281 -0.09747696
[25,] 0.47652204 0.07975281
[26,] 0.45028042 0.47652204
[27,] 0.18241652 0.45028042
[28,] 0.07813743 0.18241652
[29,] 0.12726958 0.07813743
[30,] 0.12726958 0.12726958
[31,] 0.34773702 0.12726958
[32,] 0.25189581 0.34773702
[33,] 0.00785044 0.25189581
[34,] -0.36243655 0.00785044
[35,] -0.68556769 -0.36243655
[36,] -0.61851149 -0.68556769
[37,] -0.59295726 -0.61851149
[38,] -0.49885174 -0.59295726
[39,] -0.64382510 -0.49885174
[40,] -0.68799078 -0.64382510
[41,] -0.45827776 -0.68799078
[42,] -0.58706278 -0.45827776
[43,] -0.84128171 -0.58706278
[44,] -1.20821761 -0.84128171
[45,] -1.53365153 -1.20821761
[46,] -1.03954601 -1.53365153
[47,] -0.43724324 -1.03954601
[48,] -0.01088131 -0.43724324
[49,] -0.12347796 -0.01088131
[50,] -0.75908546 -0.12347796
[51,] -1.30579453 -0.75908546
[52,] -1.25250359 -1.30579453
[53,] -0.49828466 -1.25250359
[54,] -0.18048091 -0.49828466
[55,] 0.11200922 -0.18048091
[56,] -0.46013377 0.11200922
[57,] -0.75585468 -0.46013377
[58,] -0.53885863 -0.75585468
[59,] -0.35516729 -0.53885863
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.60530705 1.24923211
2 1.04600133 1.60530705
3 0.35016011 1.04600133
4 0.25686228 0.35016011
5 0.40345104 0.25686228
6 0.48819069 0.40345104
7 0.58657530 0.48819069
8 0.27131494 0.58657530
9 0.18657530 0.27131494
10 0.20022026 0.18657530
11 0.25928537 0.20022026
12 0.56530015 0.25928537
13 0.99259008 0.56530015
14 0.71628831 0.99259008
15 0.62900527 0.71628831
16 0.52807727 0.62900527
17 0.70518674 0.52807727
18 0.71027352 0.70518674
19 0.93582776 0.71027352
20 0.64761672 0.93582776
21 0.58750330 0.64761672
22 0.37143525 0.58750330
23 -0.09747696 0.37143525
24 0.07975281 -0.09747696
25 0.47652204 0.07975281
26 0.45028042 0.47652204
27 0.18241652 0.45028042
28 0.07813743 0.18241652
29 0.12726958 0.07813743
30 0.12726958 0.12726958
31 0.34773702 0.12726958
32 0.25189581 0.34773702
33 0.00785044 0.25189581
34 -0.36243655 0.00785044
35 -0.68556769 -0.36243655
36 -0.61851149 -0.68556769
37 -0.59295726 -0.61851149
38 -0.49885174 -0.59295726
39 -0.64382510 -0.49885174
40 -0.68799078 -0.64382510
41 -0.45827776 -0.68799078
42 -0.58706278 -0.45827776
43 -0.84128171 -0.58706278
44 -1.20821761 -0.84128171
45 -1.53365153 -1.20821761
46 -1.03954601 -1.53365153
47 -0.43724324 -1.03954601
48 -0.01088131 -0.43724324
49 -0.12347796 -0.01088131
50 -0.75908546 -0.12347796
51 -1.30579453 -0.75908546
52 -1.25250359 -1.30579453
53 -0.49828466 -1.25250359
54 -0.18048091 -0.49828466
55 0.11200922 -0.18048091
56 -0.46013377 0.11200922
57 -0.75585468 -0.46013377
58 -0.53885863 -0.75585468
59 -0.35516729 -0.53885863
> 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/72mrz1258749926.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xwas1258749926.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uo871258749926.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vc3v1258749926.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11g4gm1258749926.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/122fpv1258749926.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/131mpl1258749926.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/14ubml1258749926.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/15qw4l1258749926.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/16jaiv1258749926.tab")
+ }
>
> system("convert tmp/1k8u81258749926.ps tmp/1k8u81258749926.png")
> system("convert tmp/209ze1258749926.ps tmp/209ze1258749926.png")
> system("convert tmp/3kkh31258749926.ps tmp/3kkh31258749926.png")
> system("convert tmp/40wui1258749926.ps tmp/40wui1258749926.png")
> system("convert tmp/5s7n01258749926.ps tmp/5s7n01258749926.png")
> system("convert tmp/6fh9k1258749926.ps tmp/6fh9k1258749926.png")
> system("convert tmp/72mrz1258749926.ps tmp/72mrz1258749926.png")
> system("convert tmp/8xwas1258749926.ps tmp/8xwas1258749926.png")
> system("convert tmp/9uo871258749926.ps tmp/9uo871258749926.png")
> system("convert tmp/10vc3v1258749926.ps tmp/10vc3v1258749926.png")
>
>
> proc.time()
user system elapsed
2.394 1.556 3.628