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(100.95,0,101.26,0,101.42,0,101.68,0,101.75,0,101.89,0,102.07,0,102.22,0,102.45,0,102.62,0,102.67,0,102.86,0,104.78,0,104.87,0,105.06,0,105.14,0,105.32,0,105.54,0,105.68,0,105.77,0,106.07,0,106.03,0,106.13,0,106.28,0,106.61,0,106.74,0,107.01,0,107.1,0,107.28,0,107.4,0,107.59,0,107.69,0,107.78,0,108.02,0,108,0,108.07,0,108.36,0,108.74,0,108.99,0,109.21,0,109.31,0,109.41,0,109.54,0,109.81,1,109.85,1,110.01,1,110.23,1),dim=c(2,47),dimnames=list(c('huur','dummy'),1:47))
> y <- array(NA,dim=c(2,47),dimnames=list(c('huur','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
huur dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 100.95 0 1 0 0 0 0 0 0 0 0 0 0 1
2 101.26 0 0 1 0 0 0 0 0 0 0 0 0 2
3 101.42 0 0 0 1 0 0 0 0 0 0 0 0 3
4 101.68 0 0 0 0 1 0 0 0 0 0 0 0 4
5 101.75 0 0 0 0 0 1 0 0 0 0 0 0 5
6 101.89 0 0 0 0 0 0 1 0 0 0 0 0 6
7 102.07 0 0 0 0 0 0 0 1 0 0 0 0 7
8 102.22 0 0 0 0 0 0 0 0 1 0 0 0 8
9 102.45 0 0 0 0 0 0 0 0 0 1 0 0 9
10 102.62 0 0 0 0 0 0 0 0 0 0 1 0 10
11 102.67 0 0 0 0 0 0 0 0 0 0 0 1 11
12 102.86 0 0 0 0 0 0 0 0 0 0 0 0 12
13 104.78 0 1 0 0 0 0 0 0 0 0 0 0 13
14 104.87 0 0 1 0 0 0 0 0 0 0 0 0 14
15 105.06 0 0 0 1 0 0 0 0 0 0 0 0 15
16 105.14 0 0 0 0 1 0 0 0 0 0 0 0 16
17 105.32 0 0 0 0 0 1 0 0 0 0 0 0 17
18 105.54 0 0 0 0 0 0 1 0 0 0 0 0 18
19 105.68 0 0 0 0 0 0 0 1 0 0 0 0 19
20 105.77 0 0 0 0 0 0 0 0 1 0 0 0 20
21 106.07 0 0 0 0 0 0 0 0 0 1 0 0 21
22 106.03 0 0 0 0 0 0 0 0 0 0 1 0 22
23 106.13 0 0 0 0 0 0 0 0 0 0 0 1 23
24 106.28 0 0 0 0 0 0 0 0 0 0 0 0 24
25 106.61 0 1 0 0 0 0 0 0 0 0 0 0 25
26 106.74 0 0 1 0 0 0 0 0 0 0 0 0 26
27 107.01 0 0 0 1 0 0 0 0 0 0 0 0 27
28 107.10 0 0 0 0 1 0 0 0 0 0 0 0 28
29 107.28 0 0 0 0 0 1 0 0 0 0 0 0 29
30 107.40 0 0 0 0 0 0 1 0 0 0 0 0 30
31 107.59 0 0 0 0 0 0 0 1 0 0 0 0 31
32 107.69 0 0 0 0 0 0 0 0 1 0 0 0 32
33 107.78 0 0 0 0 0 0 0 0 0 1 0 0 33
34 108.02 0 0 0 0 0 0 0 0 0 0 1 0 34
35 108.00 0 0 0 0 0 0 0 0 0 0 0 1 35
36 108.07 0 0 0 0 0 0 0 0 0 0 0 0 36
37 108.36 0 1 0 0 0 0 0 0 0 0 0 0 37
38 108.74 0 0 1 0 0 0 0 0 0 0 0 0 38
39 108.99 0 0 0 1 0 0 0 0 0 0 0 0 39
40 109.21 0 0 0 0 1 0 0 0 0 0 0 0 40
41 109.31 0 0 0 0 0 1 0 0 0 0 0 0 41
42 109.41 0 0 0 0 0 0 1 0 0 0 0 0 42
43 109.54 0 0 0 0 0 0 0 1 0 0 0 0 43
44 109.81 1 0 0 0 0 0 0 0 1 0 0 0 44
45 109.85 1 0 0 0 0 0 0 0 0 1 0 0 45
46 110.01 1 0 0 0 0 0 0 0 0 0 1 0 46
47 110.23 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
100.74911 -0.46672 0.47741 0.49709 0.50678 0.46146
M5 M6 M7 M8 M9 M10
0.38615 0.32333 0.27552 0.33688 0.29407 0.21875
M11 t
0.09844 0.20781
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.55567 -0.40250 -0.05062 0.33916 0.85189
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.74911 0.33319 302.378 <2e-16 ***
dummy -0.46672 0.33319 -1.401 0.171
M1 0.47741 0.39293 1.215 0.233
M2 0.49709 0.39246 1.267 0.214
M3 0.50678 0.39210 1.292 0.205
M4 0.46146 0.39184 1.178 0.247
M5 0.38615 0.39168 0.986 0.331
M6 0.32333 0.39163 0.826 0.415
M7 0.27552 0.39168 0.703 0.487
M8 0.33688 0.39938 0.844 0.405
M9 0.29407 0.39902 0.737 0.466
M10 0.21876 0.39877 0.549 0.587
M11 0.09844 0.39861 0.247 0.806
t 0.20781 0.00637 32.624 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5128 on 33 degrees of freedom
Multiple R-squared: 0.976, Adjusted R-squared: 0.9665
F-statistic: 103.1 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,] 0.8215131 3.569738e-01 1.784869e-01
[2,] 0.6916091 6.167819e-01 3.083909e-01
[3,] 0.5401631 9.196739e-01 4.598369e-01
[4,] 0.4232285 8.464570e-01 5.767715e-01
[5,] 0.5195325 9.609350e-01 4.804675e-01
[6,] 0.6128312 7.743375e-01 3.871688e-01
[7,] 0.5593631 8.812737e-01 4.406369e-01
[8,] 0.6698054 6.603892e-01 3.301946e-01
[9,] 0.9999951 9.738145e-06 4.869072e-06
[10,] 0.9999943 1.133871e-05 5.669354e-06
[11,] 0.9999774 4.521608e-05 2.260804e-05
[12,] 0.9999294 1.411744e-04 7.058720e-05
[13,] 0.9995036 9.927271e-04 4.963636e-04
[14,] 0.9964326 7.134805e-03 3.567402e-03
> postscript(file="/var/www/html/freestat/rcomp/tmp/1j1c21229865437.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2sb651229865437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3rf7u1229865437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4coel1229865437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5nw5x1229865437.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
-0.48433333 -0.40183333 -0.45933333 -0.36183333 -0.42433333 -0.42933333
7 8 9 10 11 12
-0.40933333 -0.52851389 -0.46351389 -0.42601389 -0.46351389 -0.38288889
13 14 15 16 17 18
0.85188889 0.71438889 0.68688889 0.60438889 0.65188889 0.72688889
19 20 21 22 23 24
0.70688889 0.52770833 0.66270833 0.49020833 0.50270833 0.54333333
25 26 27 28 29 30
0.18811111 0.09061111 0.14311111 0.07061111 0.11811111 0.09311111
31 32 33 34 35 36
0.12311111 -0.04606944 -0.12106944 -0.01356944 -0.12106944 -0.16044444
37 38 39 40 41 42
-0.55566667 -0.40316667 -0.37066667 -0.31316667 -0.34566667 -0.39066667
43 44 45 46 47
-0.42066667 0.04687500 -0.07812500 -0.05062500 0.08187500
> postscript(file="/var/www/html/freestat/rcomp/tmp/62tet1229865437.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 -0.48433333 NA
1 -0.40183333 -0.48433333
2 -0.45933333 -0.40183333
3 -0.36183333 -0.45933333
4 -0.42433333 -0.36183333
5 -0.42933333 -0.42433333
6 -0.40933333 -0.42933333
7 -0.52851389 -0.40933333
8 -0.46351389 -0.52851389
9 -0.42601389 -0.46351389
10 -0.46351389 -0.42601389
11 -0.38288889 -0.46351389
12 0.85188889 -0.38288889
13 0.71438889 0.85188889
14 0.68688889 0.71438889
15 0.60438889 0.68688889
16 0.65188889 0.60438889
17 0.72688889 0.65188889
18 0.70688889 0.72688889
19 0.52770833 0.70688889
20 0.66270833 0.52770833
21 0.49020833 0.66270833
22 0.50270833 0.49020833
23 0.54333333 0.50270833
24 0.18811111 0.54333333
25 0.09061111 0.18811111
26 0.14311111 0.09061111
27 0.07061111 0.14311111
28 0.11811111 0.07061111
29 0.09311111 0.11811111
30 0.12311111 0.09311111
31 -0.04606944 0.12311111
32 -0.12106944 -0.04606944
33 -0.01356944 -0.12106944
34 -0.12106944 -0.01356944
35 -0.16044444 -0.12106944
36 -0.55566667 -0.16044444
37 -0.40316667 -0.55566667
38 -0.37066667 -0.40316667
39 -0.31316667 -0.37066667
40 -0.34566667 -0.31316667
41 -0.39066667 -0.34566667
42 -0.42066667 -0.39066667
43 0.04687500 -0.42066667
44 -0.07812500 0.04687500
45 -0.05062500 -0.07812500
46 0.08187500 -0.05062500
47 NA 0.08187500
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.40183333 -0.48433333
[2,] -0.45933333 -0.40183333
[3,] -0.36183333 -0.45933333
[4,] -0.42433333 -0.36183333
[5,] -0.42933333 -0.42433333
[6,] -0.40933333 -0.42933333
[7,] -0.52851389 -0.40933333
[8,] -0.46351389 -0.52851389
[9,] -0.42601389 -0.46351389
[10,] -0.46351389 -0.42601389
[11,] -0.38288889 -0.46351389
[12,] 0.85188889 -0.38288889
[13,] 0.71438889 0.85188889
[14,] 0.68688889 0.71438889
[15,] 0.60438889 0.68688889
[16,] 0.65188889 0.60438889
[17,] 0.72688889 0.65188889
[18,] 0.70688889 0.72688889
[19,] 0.52770833 0.70688889
[20,] 0.66270833 0.52770833
[21,] 0.49020833 0.66270833
[22,] 0.50270833 0.49020833
[23,] 0.54333333 0.50270833
[24,] 0.18811111 0.54333333
[25,] 0.09061111 0.18811111
[26,] 0.14311111 0.09061111
[27,] 0.07061111 0.14311111
[28,] 0.11811111 0.07061111
[29,] 0.09311111 0.11811111
[30,] 0.12311111 0.09311111
[31,] -0.04606944 0.12311111
[32,] -0.12106944 -0.04606944
[33,] -0.01356944 -0.12106944
[34,] -0.12106944 -0.01356944
[35,] -0.16044444 -0.12106944
[36,] -0.55566667 -0.16044444
[37,] -0.40316667 -0.55566667
[38,] -0.37066667 -0.40316667
[39,] -0.31316667 -0.37066667
[40,] -0.34566667 -0.31316667
[41,] -0.39066667 -0.34566667
[42,] -0.42066667 -0.39066667
[43,] 0.04687500 -0.42066667
[44,] -0.07812500 0.04687500
[45,] -0.05062500 -0.07812500
[46,] 0.08187500 -0.05062500
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.40183333 -0.48433333
2 -0.45933333 -0.40183333
3 -0.36183333 -0.45933333
4 -0.42433333 -0.36183333
5 -0.42933333 -0.42433333
6 -0.40933333 -0.42933333
7 -0.52851389 -0.40933333
8 -0.46351389 -0.52851389
9 -0.42601389 -0.46351389
10 -0.46351389 -0.42601389
11 -0.38288889 -0.46351389
12 0.85188889 -0.38288889
13 0.71438889 0.85188889
14 0.68688889 0.71438889
15 0.60438889 0.68688889
16 0.65188889 0.60438889
17 0.72688889 0.65188889
18 0.70688889 0.72688889
19 0.52770833 0.70688889
20 0.66270833 0.52770833
21 0.49020833 0.66270833
22 0.50270833 0.49020833
23 0.54333333 0.50270833
24 0.18811111 0.54333333
25 0.09061111 0.18811111
26 0.14311111 0.09061111
27 0.07061111 0.14311111
28 0.11811111 0.07061111
29 0.09311111 0.11811111
30 0.12311111 0.09311111
31 -0.04606944 0.12311111
32 -0.12106944 -0.04606944
33 -0.01356944 -0.12106944
34 -0.12106944 -0.01356944
35 -0.16044444 -0.12106944
36 -0.55566667 -0.16044444
37 -0.40316667 -0.55566667
38 -0.37066667 -0.40316667
39 -0.31316667 -0.37066667
40 -0.34566667 -0.31316667
41 -0.39066667 -0.34566667
42 -0.42066667 -0.39066667
43 0.04687500 -0.42066667
44 -0.07812500 0.04687500
45 -0.05062500 -0.07812500
46 0.08187500 -0.05062500
> 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/freestat/rcomp/tmp/76nb91229865437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/83lue1229865437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9k7d31229865437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10qbc51229865437.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11ktz01229865437.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/freestat/rcomp/tmp/123gu31229865437.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/freestat/rcomp/tmp/13wnpm1229865437.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/freestat/rcomp/tmp/14k23w1229865437.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/freestat/rcomp/tmp/15dkmm1229865437.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/freestat/rcomp/tmp/16buhs1229865437.tab")
+ }
>
> system("convert tmp/1j1c21229865437.ps tmp/1j1c21229865437.png")
> system("convert tmp/2sb651229865437.ps tmp/2sb651229865437.png")
> system("convert tmp/3rf7u1229865437.ps tmp/3rf7u1229865437.png")
> system("convert tmp/4coel1229865437.ps tmp/4coel1229865437.png")
> system("convert tmp/5nw5x1229865437.ps tmp/5nw5x1229865437.png")
> system("convert tmp/62tet1229865437.ps tmp/62tet1229865437.png")
> system("convert tmp/76nb91229865437.ps tmp/76nb91229865437.png")
> system("convert tmp/83lue1229865437.ps tmp/83lue1229865437.png")
> system("convert tmp/9k7d31229865437.ps tmp/9k7d31229865437.png")
> system("convert tmp/10qbc51229865437.ps tmp/10qbc51229865437.png")
>
>
> proc.time()
user system elapsed
3.490 2.518 3.942