R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(5,12,6,17,5,10,6,14,7,14,8,14,5,18,5,17,10,16,5,11,5,13,5,14,5,16,5,9,5,15,5,13,5,15,5,16,6,12,5,13,5,14,5,11,5,12,6,12,5,8,7,13,5,10,6,12,5,15,5,14,6,15,6,13,5,15,7,13,6,15,5,16,4,16,5,15,5,17,7,15,6,12,6,11,5,9,7,15,6,10,6,14,8,16,6,17,5,13,5,9,4,15,5,15,5,11,7,14,5,13,5,16,5,15,5,11,5,12,7,9,10,16),dim=c(2,61),dimnames=list(c('Leeftijd','Happiness'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Leeftijd','Happiness'),1:61))
> 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 = '2'
> #'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
> 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
Happiness Leeftijd
1 12 5
2 17 6
3 10 5
4 14 6
5 14 7
6 14 8
7 18 5
8 17 5
9 16 10
10 11 5
11 13 5
12 14 5
13 16 5
14 9 5
15 15 5
16 13 5
17 15 5
18 16 5
19 12 6
20 13 5
21 14 5
22 11 5
23 12 5
24 12 6
25 8 5
26 13 7
27 10 5
28 12 6
29 15 5
30 14 5
31 15 6
32 13 6
33 15 5
34 13 7
35 15 6
36 16 5
37 16 4
38 15 5
39 17 5
40 15 7
41 12 6
42 11 6
43 9 5
44 15 7
45 10 6
46 14 6
47 16 8
48 17 6
49 13 5
50 9 5
51 15 4
52 15 5
53 11 5
54 14 7
55 13 5
56 16 5
57 15 5
58 11 5
59 12 5
60 9 7
61 16 10
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Leeftijd
11.7356 0.3096
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.2837 -1.5933 0.0971 1.7163 4.7163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.7356 1.5158 7.742 1.49e-10 ***
Leeftijd 0.3096 0.2617 1.183 0.242
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.391 on 59 degrees of freedom
Multiple R-squared: 0.02317, Adjusted R-squared: 0.006614
F-statistic: 1.399 on 1 and 59 DF, p-value: 0.2416
> 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,] 0.5844520 0.8310959 0.4155480
[2,] 0.5310968 0.9378064 0.4689032
[3,] 0.8202034 0.3595932 0.1797966
[4,] 0.8341372 0.3317256 0.1658628
[5,] 0.7662579 0.4674842 0.2337421
[6,] 0.7906355 0.4187291 0.2093645
[7,] 0.7144114 0.5711771 0.2855886
[8,] 0.6249368 0.7501263 0.3750632
[9,] 0.6091321 0.7817359 0.3908679
[10,] 0.7974277 0.4051445 0.2025723
[11,] 0.7539919 0.4920162 0.2460081
[12,] 0.6831096 0.6337809 0.3168904
[13,] 0.6322717 0.7354567 0.3677283
[14,] 0.6302549 0.7394902 0.3697451
[15,] 0.6006265 0.7987470 0.3993735
[16,] 0.5246042 0.9507917 0.4753958
[17,] 0.4479780 0.8959561 0.5520220
[18,] 0.4515405 0.9030810 0.5484595
[19,] 0.3999096 0.7998191 0.6000904
[20,] 0.3630850 0.7261700 0.6369150
[21,] 0.6415035 0.7169930 0.3584965
[22,] 0.5791045 0.8417911 0.4208955
[23,] 0.6348490 0.7303020 0.3651510
[24,] 0.5925709 0.8148582 0.4074291
[25,] 0.5562454 0.8875093 0.4437546
[26,] 0.4876750 0.9753500 0.5123250
[27,] 0.4366087 0.8732173 0.5633913
[28,] 0.3677121 0.7354242 0.6322879
[29,] 0.3314309 0.6628617 0.6685691
[30,] 0.2742433 0.5484865 0.7257567
[31,] 0.2326672 0.4653345 0.7673328
[32,] 0.2449311 0.4898621 0.7550689
[33,] 0.2799725 0.5599450 0.7200275
[34,] 0.2519836 0.5039672 0.7480164
[35,] 0.3610953 0.7221907 0.6389047
[36,] 0.3068129 0.6136258 0.6931871
[37,] 0.2610157 0.5220313 0.7389843
[38,] 0.2569187 0.5138373 0.7430813
[39,] 0.3792959 0.7585917 0.6207041
[40,] 0.3175846 0.6351693 0.6824154
[41,] 0.3962841 0.7925681 0.6037159
[42,] 0.3159083 0.6318166 0.6840917
[43,] 0.2774666 0.5549333 0.7225334
[44,] 0.3696994 0.7393989 0.6303006
[45,] 0.2827094 0.5654188 0.7172906
[46,] 0.4372412 0.8744825 0.5627588
[47,] 0.4059864 0.8119729 0.5940136
[48,] 0.3754705 0.7509411 0.6245295
[49,] 0.3221402 0.6442804 0.6778598
[50,] 0.2200367 0.4400733 0.7799633
[51,] 0.1322506 0.2645012 0.8677494
[52,] 0.1813798 0.3627595 0.8186202
> postscript(file="/var/wessaorg/rcomp/tmp/19s321321996089.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2w4iz1321996089.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3fnwe1321996089.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4y3o21321996089.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5xaat1321996089.ps",horizontal=F,onefile=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 = 61
Frequency = 1
1 2 3 4 5 6
-1.28369352 3.40667976 -3.28369352 0.40667976 0.09705305 -0.21257367
7 8 9 10 11 12
4.71630648 3.71630648 1.16817289 -2.28369352 -0.28369352 0.71630648
13 14 15 16 17 18
2.71630648 -4.28369352 1.71630648 -0.28369352 1.71630648 2.71630648
19 20 21 22 23 24
-1.59332024 -0.28369352 0.71630648 -2.28369352 -1.28369352 -1.59332024
25 26 27 28 29 30
-5.28369352 -0.90294695 -3.28369352 -1.59332024 1.71630648 0.71630648
31 32 33 34 35 36
1.40667976 -0.59332024 1.71630648 -0.90294695 1.40667976 2.71630648
37 38 39 40 41 42
3.02593320 1.71630648 3.71630648 1.09705305 -1.59332024 -2.59332024
43 44 45 46 47 48
-4.28369352 1.09705305 -3.59332024 0.40667976 1.78742633 3.40667976
49 50 51 52 53 54
-0.28369352 -4.28369352 2.02593320 1.71630648 -2.28369352 0.09705305
55 56 57 58 59 60
-0.28369352 2.71630648 1.71630648 -2.28369352 -1.28369352 -4.90294695
61
1.16817289
> postscript(file="/var/wessaorg/rcomp/tmp/6rvtd1321996089.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.28369352 NA
1 3.40667976 -1.28369352
2 -3.28369352 3.40667976
3 0.40667976 -3.28369352
4 0.09705305 0.40667976
5 -0.21257367 0.09705305
6 4.71630648 -0.21257367
7 3.71630648 4.71630648
8 1.16817289 3.71630648
9 -2.28369352 1.16817289
10 -0.28369352 -2.28369352
11 0.71630648 -0.28369352
12 2.71630648 0.71630648
13 -4.28369352 2.71630648
14 1.71630648 -4.28369352
15 -0.28369352 1.71630648
16 1.71630648 -0.28369352
17 2.71630648 1.71630648
18 -1.59332024 2.71630648
19 -0.28369352 -1.59332024
20 0.71630648 -0.28369352
21 -2.28369352 0.71630648
22 -1.28369352 -2.28369352
23 -1.59332024 -1.28369352
24 -5.28369352 -1.59332024
25 -0.90294695 -5.28369352
26 -3.28369352 -0.90294695
27 -1.59332024 -3.28369352
28 1.71630648 -1.59332024
29 0.71630648 1.71630648
30 1.40667976 0.71630648
31 -0.59332024 1.40667976
32 1.71630648 -0.59332024
33 -0.90294695 1.71630648
34 1.40667976 -0.90294695
35 2.71630648 1.40667976
36 3.02593320 2.71630648
37 1.71630648 3.02593320
38 3.71630648 1.71630648
39 1.09705305 3.71630648
40 -1.59332024 1.09705305
41 -2.59332024 -1.59332024
42 -4.28369352 -2.59332024
43 1.09705305 -4.28369352
44 -3.59332024 1.09705305
45 0.40667976 -3.59332024
46 1.78742633 0.40667976
47 3.40667976 1.78742633
48 -0.28369352 3.40667976
49 -4.28369352 -0.28369352
50 2.02593320 -4.28369352
51 1.71630648 2.02593320
52 -2.28369352 1.71630648
53 0.09705305 -2.28369352
54 -0.28369352 0.09705305
55 2.71630648 -0.28369352
56 1.71630648 2.71630648
57 -2.28369352 1.71630648
58 -1.28369352 -2.28369352
59 -4.90294695 -1.28369352
60 1.16817289 -4.90294695
61 NA 1.16817289
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.40667976 -1.28369352
[2,] -3.28369352 3.40667976
[3,] 0.40667976 -3.28369352
[4,] 0.09705305 0.40667976
[5,] -0.21257367 0.09705305
[6,] 4.71630648 -0.21257367
[7,] 3.71630648 4.71630648
[8,] 1.16817289 3.71630648
[9,] -2.28369352 1.16817289
[10,] -0.28369352 -2.28369352
[11,] 0.71630648 -0.28369352
[12,] 2.71630648 0.71630648
[13,] -4.28369352 2.71630648
[14,] 1.71630648 -4.28369352
[15,] -0.28369352 1.71630648
[16,] 1.71630648 -0.28369352
[17,] 2.71630648 1.71630648
[18,] -1.59332024 2.71630648
[19,] -0.28369352 -1.59332024
[20,] 0.71630648 -0.28369352
[21,] -2.28369352 0.71630648
[22,] -1.28369352 -2.28369352
[23,] -1.59332024 -1.28369352
[24,] -5.28369352 -1.59332024
[25,] -0.90294695 -5.28369352
[26,] -3.28369352 -0.90294695
[27,] -1.59332024 -3.28369352
[28,] 1.71630648 -1.59332024
[29,] 0.71630648 1.71630648
[30,] 1.40667976 0.71630648
[31,] -0.59332024 1.40667976
[32,] 1.71630648 -0.59332024
[33,] -0.90294695 1.71630648
[34,] 1.40667976 -0.90294695
[35,] 2.71630648 1.40667976
[36,] 3.02593320 2.71630648
[37,] 1.71630648 3.02593320
[38,] 3.71630648 1.71630648
[39,] 1.09705305 3.71630648
[40,] -1.59332024 1.09705305
[41,] -2.59332024 -1.59332024
[42,] -4.28369352 -2.59332024
[43,] 1.09705305 -4.28369352
[44,] -3.59332024 1.09705305
[45,] 0.40667976 -3.59332024
[46,] 1.78742633 0.40667976
[47,] 3.40667976 1.78742633
[48,] -0.28369352 3.40667976
[49,] -4.28369352 -0.28369352
[50,] 2.02593320 -4.28369352
[51,] 1.71630648 2.02593320
[52,] -2.28369352 1.71630648
[53,] 0.09705305 -2.28369352
[54,] -0.28369352 0.09705305
[55,] 2.71630648 -0.28369352
[56,] 1.71630648 2.71630648
[57,] -2.28369352 1.71630648
[58,] -1.28369352 -2.28369352
[59,] -4.90294695 -1.28369352
[60,] 1.16817289 -4.90294695
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.40667976 -1.28369352
2 -3.28369352 3.40667976
3 0.40667976 -3.28369352
4 0.09705305 0.40667976
5 -0.21257367 0.09705305
6 4.71630648 -0.21257367
7 3.71630648 4.71630648
8 1.16817289 3.71630648
9 -2.28369352 1.16817289
10 -0.28369352 -2.28369352
11 0.71630648 -0.28369352
12 2.71630648 0.71630648
13 -4.28369352 2.71630648
14 1.71630648 -4.28369352
15 -0.28369352 1.71630648
16 1.71630648 -0.28369352
17 2.71630648 1.71630648
18 -1.59332024 2.71630648
19 -0.28369352 -1.59332024
20 0.71630648 -0.28369352
21 -2.28369352 0.71630648
22 -1.28369352 -2.28369352
23 -1.59332024 -1.28369352
24 -5.28369352 -1.59332024
25 -0.90294695 -5.28369352
26 -3.28369352 -0.90294695
27 -1.59332024 -3.28369352
28 1.71630648 -1.59332024
29 0.71630648 1.71630648
30 1.40667976 0.71630648
31 -0.59332024 1.40667976
32 1.71630648 -0.59332024
33 -0.90294695 1.71630648
34 1.40667976 -0.90294695
35 2.71630648 1.40667976
36 3.02593320 2.71630648
37 1.71630648 3.02593320
38 3.71630648 1.71630648
39 1.09705305 3.71630648
40 -1.59332024 1.09705305
41 -2.59332024 -1.59332024
42 -4.28369352 -2.59332024
43 1.09705305 -4.28369352
44 -3.59332024 1.09705305
45 0.40667976 -3.59332024
46 1.78742633 0.40667976
47 3.40667976 1.78742633
48 -0.28369352 3.40667976
49 -4.28369352 -0.28369352
50 2.02593320 -4.28369352
51 1.71630648 2.02593320
52 -2.28369352 1.71630648
53 0.09705305 -2.28369352
54 -0.28369352 0.09705305
55 2.71630648 -0.28369352
56 1.71630648 2.71630648
57 -2.28369352 1.71630648
58 -1.28369352 -2.28369352
59 -4.90294695 -1.28369352
60 1.16817289 -4.90294695
> 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/wessaorg/rcomp/tmp/7i1c01321996089.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8sn121321996089.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9g55c1321996089.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/109nbp1321996089.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11a8391321996089.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/wessaorg/rcomp/tmp/12bsc91321996089.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/wessaorg/rcomp/tmp/133m2v1321996089.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/wessaorg/rcomp/tmp/14iw801321996089.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/wessaorg/rcomp/tmp/15yk1q1321996089.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/wessaorg/rcomp/tmp/1682to1321996090.tab")
+ }
>
> try(system("convert tmp/19s321321996089.ps tmp/19s321321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w4iz1321996089.ps tmp/2w4iz1321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fnwe1321996089.ps tmp/3fnwe1321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y3o21321996089.ps tmp/4y3o21321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xaat1321996089.ps tmp/5xaat1321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rvtd1321996089.ps tmp/6rvtd1321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/7i1c01321996089.ps tmp/7i1c01321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/8sn121321996089.ps tmp/8sn121321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/9g55c1321996089.ps tmp/9g55c1321996089.png",intern=TRUE))
character(0)
> try(system("convert tmp/109nbp1321996089.ps tmp/109nbp1321996089.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.376 0.489 3.901