R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
R is a collaborative project with many contributors.
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(101.76,0,101.76,0,101.76,0,101.76,0,101.76,0,101.76,0,101.76,0,101.76,0,101.76,0,103.36,0,103.36,0,103.36,0,104.85,0,104.85,0,104.85,0,104.85,0,104.85,0,104.85,0,104.85,0,104.85,0,104.85,0,107.35,0,107.35,0,107.35,0,107.35,0,107.35,0,107.35,0,107.35,0,107.35,1,107.35,1,107.35,1,107.35,1,107.35,1,109.47,1,109.47,1,109.47,1,109.47,1,109.47,1,109.47,1,109.47,1,109.47,1,109.47,1,109.47,1,109.47,1,109.47,1,111.29,1,111.29,1),dim=c(2,47),dimnames=list(c('Onderwijs','Dummy'),1:47))
> y <- array(NA,dim=c(2,47),dimnames=list(c('Onderwijs','Dummy'),1:47))
> 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
Onderwijs Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 101.76 0 1 0 0 0 0 0 0 0 0 0 0 1
2 101.76 0 0 1 0 0 0 0 0 0 0 0 0 2
3 101.76 0 0 0 1 0 0 0 0 0 0 0 0 3
4 101.76 0 0 0 0 1 0 0 0 0 0 0 0 4
5 101.76 0 0 0 0 0 1 0 0 0 0 0 0 5
6 101.76 0 0 0 0 0 0 1 0 0 0 0 0 6
7 101.76 0 0 0 0 0 0 0 1 0 0 0 0 7
8 101.76 0 0 0 0 0 0 0 0 1 0 0 0 8
9 101.76 0 0 0 0 0 0 0 0 0 1 0 0 9
10 103.36 0 0 0 0 0 0 0 0 0 0 1 0 10
11 103.36 0 0 0 0 0 0 0 0 0 0 0 1 11
12 103.36 0 0 0 0 0 0 0 0 0 0 0 0 12
13 104.85 0 1 0 0 0 0 0 0 0 0 0 0 13
14 104.85 0 0 1 0 0 0 0 0 0 0 0 0 14
15 104.85 0 0 0 1 0 0 0 0 0 0 0 0 15
16 104.85 0 0 0 0 1 0 0 0 0 0 0 0 16
17 104.85 0 0 0 0 0 1 0 0 0 0 0 0 17
18 104.85 0 0 0 0 0 0 1 0 0 0 0 0 18
19 104.85 0 0 0 0 0 0 0 1 0 0 0 0 19
20 104.85 0 0 0 0 0 0 0 0 1 0 0 0 20
21 104.85 0 0 0 0 0 0 0 0 0 1 0 0 21
22 107.35 0 0 0 0 0 0 0 0 0 0 1 0 22
23 107.35 0 0 0 0 0 0 0 0 0 0 0 1 23
24 107.35 0 0 0 0 0 0 0 0 0 0 0 0 24
25 107.35 0 1 0 0 0 0 0 0 0 0 0 0 25
26 107.35 0 0 1 0 0 0 0 0 0 0 0 0 26
27 107.35 0 0 0 1 0 0 0 0 0 0 0 0 27
28 107.35 0 0 0 0 1 0 0 0 0 0 0 0 28
29 107.35 1 0 0 0 0 1 0 0 0 0 0 0 29
30 107.35 1 0 0 0 0 0 1 0 0 0 0 0 30
31 107.35 1 0 0 0 0 0 0 1 0 0 0 0 31
32 107.35 1 0 0 0 0 0 0 0 1 0 0 0 32
33 107.35 1 0 0 0 0 0 0 0 0 1 0 0 33
34 109.47 1 0 0 0 0 0 0 0 0 0 1 0 34
35 109.47 1 0 0 0 0 0 0 0 0 0 0 1 35
36 109.47 1 0 0 0 0 0 0 0 0 0 0 0 36
37 109.47 1 1 0 0 0 0 0 0 0 0 0 0 37
38 109.47 1 0 1 0 0 0 0 0 0 0 0 0 38
39 109.47 1 0 0 1 0 0 0 0 0 0 0 0 39
40 109.47 1 0 0 0 1 0 0 0 0 0 0 0 40
41 109.47 1 0 0 0 0 1 0 0 0 0 0 0 41
42 109.47 1 0 0 0 0 0 1 0 0 0 0 0 42
43 109.47 1 0 0 0 0 0 0 1 0 0 0 0 43
44 109.47 1 0 0 0 0 0 0 0 1 0 0 0 44
45 109.47 1 0 0 0 0 0 0 0 0 1 0 0 45
46 111.29 1 0 0 0 0 0 0 0 0 0 1 0 46
47 111.29 1 0 0 0 0 0 0 0 0 0 0 1 47
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
101.34693 -0.51892 0.24441 0.01304 -0.21832 -0.44968
M5 M6 M7 M8 M9 M10
-0.55132 -0.78268 -1.01404 -1.24541 -1.47677 0.30187
M11 t
0.07050 0.23136
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.76328 -0.19243 -0.02541 0.25095 0.61122
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 101.346926 0.263623 384.439 < 2e-16 ***
Dummy -0.518922 0.227309 -2.283 0.029011 *
M1 0.244405 0.298365 0.819 0.418579
M2 0.013042 0.297795 0.044 0.965331
M3 -0.218321 0.297450 -0.734 0.468147
M4 -0.449684 0.297330 -1.512 0.139949
M5 -0.551317 0.300562 -1.834 0.075642 .
M6 -0.782680 0.299569 -2.613 0.013421 *
M7 -1.014043 0.298798 -3.394 0.001808 **
M8 -1.245406 0.298249 -4.176 0.000204 ***
M9 -1.476769 0.297926 -4.957 2.09e-05 ***
M10 0.301868 0.297827 1.014 0.318163
M11 0.070505 0.297954 0.237 0.814407
t 0.231363 0.008194 28.235 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3891 on 33 degrees of freedom
Multiple R-squared: 0.9879, Adjusted R-squared: 0.9832
F-statistic: 207.8 on 13 and 33 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,] 3.050428e-42 6.100856e-42 1.0000000000
[2,] 1.599236e-55 3.198471e-55 1.0000000000
[3,] 8.713493e-66 1.742699e-65 1.0000000000
[4,] 1.099489e-76 2.198979e-76 1.0000000000
[5,] 1.358824e-89 2.717649e-89 1.0000000000
[6,] 9.980455e-01 3.908990e-03 0.0019544952
[7,] 9.996463e-01 7.074199e-04 0.0003537099
[8,] 9.996315e-01 7.370643e-04 0.0003685321
[9,] 9.996747e-01 6.505251e-04 0.0003252626
[10,] 9.992839e-01 1.432214e-03 0.0007161068
[11,] 9.978700e-01 4.260082e-03 0.0021300409
[12,] 9.930154e-01 1.396924e-02 0.0069846177
[13,] 9.773879e-01 4.522413e-02 0.0226120657
[14,] 9.346198e-01 1.307605e-01 0.0653802329
> postscript(file="/var/www/html/rcomp/tmp/15fw41229963711.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2opjz1229963711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3p48x1229963711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lo0q1229963711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ns0i1229963711.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 = 47
Frequency = 1
1 2 3 4 5 6
-0.06269461 -0.06269461 -0.06269461 -0.06269461 -0.19242515 -0.19242515
7 8 9 10 11 12
-0.19242515 -0.19242515 -0.19242515 -0.60242515 -0.60242515 -0.76328343
13 14 15 16 17 18
0.25094810 0.25094810 0.25094810 0.25094810 0.12121756 0.12121756
19 20 21 22 23 24
0.12121756 0.12121756 0.12121756 0.61121756 0.61121756 0.45035928
25 26 27 28 29 30
-0.02540918 -0.02540918 -0.02540918 -0.02540918 0.36378244 0.36378244
31 32 33 34 35 36
0.36378244 0.36378244 0.36378244 0.47378244 0.47378244 0.31292415
37 38 39 40 41 42
-0.16284431 -0.16284431 -0.16284431 -0.16284431 -0.29257485 -0.29257485
43 44 45 46 47
-0.29257485 -0.29257485 -0.29257485 -0.48257485 -0.48257485
> postscript(file="/var/www/html/rcomp/tmp/63lh81229963711.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 = 47
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.06269461 NA
1 -0.06269461 -0.06269461
2 -0.06269461 -0.06269461
3 -0.06269461 -0.06269461
4 -0.19242515 -0.06269461
5 -0.19242515 -0.19242515
6 -0.19242515 -0.19242515
7 -0.19242515 -0.19242515
8 -0.19242515 -0.19242515
9 -0.60242515 -0.19242515
10 -0.60242515 -0.60242515
11 -0.76328343 -0.60242515
12 0.25094810 -0.76328343
13 0.25094810 0.25094810
14 0.25094810 0.25094810
15 0.25094810 0.25094810
16 0.12121756 0.25094810
17 0.12121756 0.12121756
18 0.12121756 0.12121756
19 0.12121756 0.12121756
20 0.12121756 0.12121756
21 0.61121756 0.12121756
22 0.61121756 0.61121756
23 0.45035928 0.61121756
24 -0.02540918 0.45035928
25 -0.02540918 -0.02540918
26 -0.02540918 -0.02540918
27 -0.02540918 -0.02540918
28 0.36378244 -0.02540918
29 0.36378244 0.36378244
30 0.36378244 0.36378244
31 0.36378244 0.36378244
32 0.36378244 0.36378244
33 0.47378244 0.36378244
34 0.47378244 0.47378244
35 0.31292415 0.47378244
36 -0.16284431 0.31292415
37 -0.16284431 -0.16284431
38 -0.16284431 -0.16284431
39 -0.16284431 -0.16284431
40 -0.29257485 -0.16284431
41 -0.29257485 -0.29257485
42 -0.29257485 -0.29257485
43 -0.29257485 -0.29257485
44 -0.29257485 -0.29257485
45 -0.48257485 -0.29257485
46 -0.48257485 -0.48257485
47 NA -0.48257485
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.06269461 -0.06269461
[2,] -0.06269461 -0.06269461
[3,] -0.06269461 -0.06269461
[4,] -0.19242515 -0.06269461
[5,] -0.19242515 -0.19242515
[6,] -0.19242515 -0.19242515
[7,] -0.19242515 -0.19242515
[8,] -0.19242515 -0.19242515
[9,] -0.60242515 -0.19242515
[10,] -0.60242515 -0.60242515
[11,] -0.76328343 -0.60242515
[12,] 0.25094810 -0.76328343
[13,] 0.25094810 0.25094810
[14,] 0.25094810 0.25094810
[15,] 0.25094810 0.25094810
[16,] 0.12121756 0.25094810
[17,] 0.12121756 0.12121756
[18,] 0.12121756 0.12121756
[19,] 0.12121756 0.12121756
[20,] 0.12121756 0.12121756
[21,] 0.61121756 0.12121756
[22,] 0.61121756 0.61121756
[23,] 0.45035928 0.61121756
[24,] -0.02540918 0.45035928
[25,] -0.02540918 -0.02540918
[26,] -0.02540918 -0.02540918
[27,] -0.02540918 -0.02540918
[28,] 0.36378244 -0.02540918
[29,] 0.36378244 0.36378244
[30,] 0.36378244 0.36378244
[31,] 0.36378244 0.36378244
[32,] 0.36378244 0.36378244
[33,] 0.47378244 0.36378244
[34,] 0.47378244 0.47378244
[35,] 0.31292415 0.47378244
[36,] -0.16284431 0.31292415
[37,] -0.16284431 -0.16284431
[38,] -0.16284431 -0.16284431
[39,] -0.16284431 -0.16284431
[40,] -0.29257485 -0.16284431
[41,] -0.29257485 -0.29257485
[42,] -0.29257485 -0.29257485
[43,] -0.29257485 -0.29257485
[44,] -0.29257485 -0.29257485
[45,] -0.48257485 -0.29257485
[46,] -0.48257485 -0.48257485
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.06269461 -0.06269461
2 -0.06269461 -0.06269461
3 -0.06269461 -0.06269461
4 -0.19242515 -0.06269461
5 -0.19242515 -0.19242515
6 -0.19242515 -0.19242515
7 -0.19242515 -0.19242515
8 -0.19242515 -0.19242515
9 -0.60242515 -0.19242515
10 -0.60242515 -0.60242515
11 -0.76328343 -0.60242515
12 0.25094810 -0.76328343
13 0.25094810 0.25094810
14 0.25094810 0.25094810
15 0.25094810 0.25094810
16 0.12121756 0.25094810
17 0.12121756 0.12121756
18 0.12121756 0.12121756
19 0.12121756 0.12121756
20 0.12121756 0.12121756
21 0.61121756 0.12121756
22 0.61121756 0.61121756
23 0.45035928 0.61121756
24 -0.02540918 0.45035928
25 -0.02540918 -0.02540918
26 -0.02540918 -0.02540918
27 -0.02540918 -0.02540918
28 0.36378244 -0.02540918
29 0.36378244 0.36378244
30 0.36378244 0.36378244
31 0.36378244 0.36378244
32 0.36378244 0.36378244
33 0.47378244 0.36378244
34 0.47378244 0.47378244
35 0.31292415 0.47378244
36 -0.16284431 0.31292415
37 -0.16284431 -0.16284431
38 -0.16284431 -0.16284431
39 -0.16284431 -0.16284431
40 -0.29257485 -0.16284431
41 -0.29257485 -0.29257485
42 -0.29257485 -0.29257485
43 -0.29257485 -0.29257485
44 -0.29257485 -0.29257485
45 -0.48257485 -0.29257485
46 -0.48257485 -0.48257485
> 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/7ctdd1229963711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8n7i01229963711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9myiz1229963711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nkdd1229963711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116zzw1229963711.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/12oosy1229963711.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/130roj1229963711.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/14ebej1229963711.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/15v2u61229963711.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/169a3r1229963711.tab")
+ }
>
> system("convert tmp/15fw41229963711.ps tmp/15fw41229963711.png")
> system("convert tmp/2opjz1229963711.ps tmp/2opjz1229963711.png")
> system("convert tmp/3p48x1229963711.ps tmp/3p48x1229963711.png")
> system("convert tmp/4lo0q1229963711.ps tmp/4lo0q1229963711.png")
> system("convert tmp/5ns0i1229963711.ps tmp/5ns0i1229963711.png")
> system("convert tmp/63lh81229963711.ps tmp/63lh81229963711.png")
> system("convert tmp/7ctdd1229963711.ps tmp/7ctdd1229963711.png")
> system("convert tmp/8n7i01229963711.ps tmp/8n7i01229963711.png")
> system("convert tmp/9myiz1229963711.ps tmp/9myiz1229963711.png")
> system("convert tmp/10nkdd1229963711.ps tmp/10nkdd1229963711.png")
>
>
> proc.time()
user system elapsed
2.343 1.621 6.478