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(48
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+ ,dim=c(9
+ ,56)
+ ,dimnames=list(c('Sat'
+ ,'Age'
+ ,'MF'
+ ,'Rm'
+ ,'PubPr'
+ ,'LOS'
+ ,'OorG'
+ ,'type'
+ ,'wr
')
+ ,1:56))
> y <- array(NA,dim=c(9,56),dimnames=list(c('Sat','Age','MF','Rm','PubPr','LOS','OorG','type','wr
'),1:56))
> 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'
> 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
> 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
Sat Age MF Rm PubPr LOS OorG type wr\r
1 48 81 0 4 1 6 0 1 6
2 56 38 1 4 1 3 0 1 6
3 47 89 0 1 1 2 0 1 6
4 48 80 1 2 1 2 0 2 6
5 45 79 1 2 1 2 0 1 6
6 56 61 1 2 1 1 0 1 6
7 48 42 0 2 1 2 0 1 6
8 45 67 0 2 2 2 0 1 6
9 52 47 1 2 1 2 0 1 6
10 47 55 1 2 2 3 0 1 7
11 52 85 1 4 1 2 0 1 7
12 60 75 0 4 1 3 0 1 7
13 45 66 0 4 1 2 0 1 7
14 48 53 1 4 1 2 0 2 7
15 56 67 0 1 1 1 0 1 7
16 60 63 1 2 1 4 0 2 7
17 52 51 1 4 1 2 0 2 7
18 60 41 1 4 1 1 0 2 7
19 47 59 1 1 1 1 0 2 7
20 52 44 0 1 1 5 0 1 7
21 47 48 0 1 1 1 0 1 7
22 59 60 1 2 1 2 0 2 8
23 48 84 1 2 1 2 0 1 8
24 61 67 0 2 2 1 0 1 8
25 60 38 1 2 1 7 0 1 8
26 45 46 0 4 1 2 0 1 8
27 63 36 0 2 1 1 0 1 8
28 41 79 0 4 1 2 0 1 8
29 49 32 1 1 1 1 0 2 8
30 63 65 0 4 1 3 1 1 7
31 54 79 1 4 1 4 1 1 7
32 55 67 1 4 1 4 1 2 7
33 70 73 1 2 2 1 1 1 7
34 43 74 1 4 1 3 1 2 7
35 58 61 0 4 1 4 1 2 7
36 56 69 1 4 1 2 1 2 7
37 50 55 0 4 1 4 1 2 7
38 32 75 1 4 1 2 1 2 7
39 59 29 0 2 1 5 1 1 7
40 58 66 0 1 1 3 1 1 7
41 56 48 0 2 1 3 1 1 7
42 50 64 1 4 1 1 1 1 7
43 32 52 1 4 1 1 1 1 7
44 36 31 0 2 1 1 1 1 10
45 46 85 1 4 1 5 1 1 10
46 47 58 1 2 3 3 1 1 10
47 67 75 0 4 2 6 1 1 10
48 61 46 0 1 3 5 1 1 10
49 49 53 1 1 1 4 1 1 10
50 49 80 1 4 1 3 1 2 10
51 56 67 1 4 1 1 1 2 10
52 56 58 1 4 1 5 1 2 10
53 52 70 1 4 1 4 1 2 10
54 49 77 1 4 1 1 1 2 10
55 55 58 0 4 1 9 1 1 10
56 56 65 0 4 1 4 1 2 10
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Age MF Rm PubPr LOS
48.77164 -0.03293 -1.54457 -0.46201 3.31334 1.18533
OorG type `wr\r`
-0.18841 2.14874 -0.33310
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-18.3709 -4.3366 0.3717 4.9809 18.6599
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.77164 8.49514 5.741 6.62e-07 ***
Age -0.03293 0.07281 -0.452 0.6532
MF -1.54457 2.37937 -0.649 0.5194
Rm -0.46201 1.04295 -0.443 0.6598
PubPr 3.31334 2.59325 1.278 0.2076
LOS 1.18533 0.66997 1.769 0.0833 .
OorG -0.18841 2.63124 -0.072 0.9432
type 2.14874 2.62247 0.819 0.4167
`wr\r` -0.33310 0.93670 -0.356 0.7237
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.798 on 47 degrees of freedom
Multiple R-squared: 0.1254, Adjusted R-squared: -0.02351
F-statistic: 0.8421 on 8 and 47 DF, p-value: 0.5709
> 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,] 0.26657147 0.53314295 0.7334285
[2,] 0.39962240 0.79924480 0.6003776
[3,] 0.27914593 0.55829185 0.7208541
[4,] 0.21762081 0.43524162 0.7823792
[5,] 0.18342542 0.36685085 0.8165746
[6,] 0.10996520 0.21993040 0.8900348
[7,] 0.11508691 0.23017383 0.8849131
[8,] 0.11129323 0.22258647 0.8887068
[9,] 0.08191926 0.16383851 0.9180807
[10,] 0.06705190 0.13410380 0.9329481
[11,] 0.04588257 0.09176515 0.9541174
[12,] 0.03862329 0.07724658 0.9613767
[13,] 0.05319505 0.10639010 0.9468050
[14,] 0.03414065 0.06828130 0.9658593
[15,] 0.04605998 0.09211996 0.9539400
[16,] 0.07809380 0.15618761 0.9219062
[17,] 0.11895905 0.23791810 0.8810409
[18,] 0.10717568 0.21435136 0.8928243
[19,] 0.08674545 0.17349091 0.9132545
[20,] 0.06797954 0.13595908 0.9320205
[21,] 0.04754603 0.09509206 0.9524540
[22,] 0.14880367 0.29760734 0.8511963
[23,] 0.20921947 0.41843894 0.7907805
[24,] 0.14957006 0.29914012 0.8504299
[25,] 0.13660403 0.27320806 0.8633960
[26,] 0.10781404 0.21562809 0.8921860
[27,] 0.68619966 0.62760068 0.3138003
[28,] 0.62019436 0.75961127 0.3798056
[29,] 0.52814744 0.94370511 0.4718526
[30,] 0.40337445 0.80674890 0.5966256
[31,] 0.49862067 0.99724135 0.5013793
[32,] 0.46335818 0.92671635 0.5366418
[33,] 0.53106436 0.93787127 0.4689356
> postscript(file="/var/wessaorg/rcomp/tmp/17yiy1345792332.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ijib1345792332.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3f4pl1345792332.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4rq8v1345792332.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5x8u81345792332.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 = 56
Frequency = 1
1 2 3 4 5 6
-6.83204824 4.85269255 -4.21335449 -3.65184763 -4.53603506 7.05662726
7 8 9 10 11 12
-4.29886377 -9.78905504 1.41033432 -7.49182656 3.91864326 8.85948584
13 14 15 16 17 18
-5.25151823 -3.28372588 5.58070521 5.75085322 0.65042220 9.50649218
19 20 21 22 23 24
-4.28687267 -3.91790996 -4.04488797 7.35583564 -0.70520287 8.06247690
25 26 27 28 29 30
3.85355541 -5.57693616 12.35511244 -8.49037959 -2.84277230 11.71864037
31 32 33 34 35 36
3.53884252 1.99499252 18.65991384 -8.58919624 3.25286847 5.43150351
37 38 39 40 41 42
-4.94468727 -18.37094075 3.23862546 5.36553426 3.23487772 2.60094180
43 44 45 46 47 48
-15.79416968 -13.95490086 -4.44962767 -9.51867119 10.17787472 -0.29102074
49 50 51 52 53 54
-2.70396081 -1.39233690 7.55028474 2.51263297 0.09307399 0.87954431
55 56
-2.62451498 2.38387590
> postscript(file="/var/wessaorg/rcomp/tmp/64yu11345792332.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -6.83204824 NA
1 4.85269255 -6.83204824
2 -4.21335449 4.85269255
3 -3.65184763 -4.21335449
4 -4.53603506 -3.65184763
5 7.05662726 -4.53603506
6 -4.29886377 7.05662726
7 -9.78905504 -4.29886377
8 1.41033432 -9.78905504
9 -7.49182656 1.41033432
10 3.91864326 -7.49182656
11 8.85948584 3.91864326
12 -5.25151823 8.85948584
13 -3.28372588 -5.25151823
14 5.58070521 -3.28372588
15 5.75085322 5.58070521
16 0.65042220 5.75085322
17 9.50649218 0.65042220
18 -4.28687267 9.50649218
19 -3.91790996 -4.28687267
20 -4.04488797 -3.91790996
21 7.35583564 -4.04488797
22 -0.70520287 7.35583564
23 8.06247690 -0.70520287
24 3.85355541 8.06247690
25 -5.57693616 3.85355541
26 12.35511244 -5.57693616
27 -8.49037959 12.35511244
28 -2.84277230 -8.49037959
29 11.71864037 -2.84277230
30 3.53884252 11.71864037
31 1.99499252 3.53884252
32 18.65991384 1.99499252
33 -8.58919624 18.65991384
34 3.25286847 -8.58919624
35 5.43150351 3.25286847
36 -4.94468727 5.43150351
37 -18.37094075 -4.94468727
38 3.23862546 -18.37094075
39 5.36553426 3.23862546
40 3.23487772 5.36553426
41 2.60094180 3.23487772
42 -15.79416968 2.60094180
43 -13.95490086 -15.79416968
44 -4.44962767 -13.95490086
45 -9.51867119 -4.44962767
46 10.17787472 -9.51867119
47 -0.29102074 10.17787472
48 -2.70396081 -0.29102074
49 -1.39233690 -2.70396081
50 7.55028474 -1.39233690
51 2.51263297 7.55028474
52 0.09307399 2.51263297
53 0.87954431 0.09307399
54 -2.62451498 0.87954431
55 2.38387590 -2.62451498
56 NA 2.38387590
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.85269255 -6.83204824
[2,] -4.21335449 4.85269255
[3,] -3.65184763 -4.21335449
[4,] -4.53603506 -3.65184763
[5,] 7.05662726 -4.53603506
[6,] -4.29886377 7.05662726
[7,] -9.78905504 -4.29886377
[8,] 1.41033432 -9.78905504
[9,] -7.49182656 1.41033432
[10,] 3.91864326 -7.49182656
[11,] 8.85948584 3.91864326
[12,] -5.25151823 8.85948584
[13,] -3.28372588 -5.25151823
[14,] 5.58070521 -3.28372588
[15,] 5.75085322 5.58070521
[16,] 0.65042220 5.75085322
[17,] 9.50649218 0.65042220
[18,] -4.28687267 9.50649218
[19,] -3.91790996 -4.28687267
[20,] -4.04488797 -3.91790996
[21,] 7.35583564 -4.04488797
[22,] -0.70520287 7.35583564
[23,] 8.06247690 -0.70520287
[24,] 3.85355541 8.06247690
[25,] -5.57693616 3.85355541
[26,] 12.35511244 -5.57693616
[27,] -8.49037959 12.35511244
[28,] -2.84277230 -8.49037959
[29,] 11.71864037 -2.84277230
[30,] 3.53884252 11.71864037
[31,] 1.99499252 3.53884252
[32,] 18.65991384 1.99499252
[33,] -8.58919624 18.65991384
[34,] 3.25286847 -8.58919624
[35,] 5.43150351 3.25286847
[36,] -4.94468727 5.43150351
[37,] -18.37094075 -4.94468727
[38,] 3.23862546 -18.37094075
[39,] 5.36553426 3.23862546
[40,] 3.23487772 5.36553426
[41,] 2.60094180 3.23487772
[42,] -15.79416968 2.60094180
[43,] -13.95490086 -15.79416968
[44,] -4.44962767 -13.95490086
[45,] -9.51867119 -4.44962767
[46,] 10.17787472 -9.51867119
[47,] -0.29102074 10.17787472
[48,] -2.70396081 -0.29102074
[49,] -1.39233690 -2.70396081
[50,] 7.55028474 -1.39233690
[51,] 2.51263297 7.55028474
[52,] 0.09307399 2.51263297
[53,] 0.87954431 0.09307399
[54,] -2.62451498 0.87954431
[55,] 2.38387590 -2.62451498
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.85269255 -6.83204824
2 -4.21335449 4.85269255
3 -3.65184763 -4.21335449
4 -4.53603506 -3.65184763
5 7.05662726 -4.53603506
6 -4.29886377 7.05662726
7 -9.78905504 -4.29886377
8 1.41033432 -9.78905504
9 -7.49182656 1.41033432
10 3.91864326 -7.49182656
11 8.85948584 3.91864326
12 -5.25151823 8.85948584
13 -3.28372588 -5.25151823
14 5.58070521 -3.28372588
15 5.75085322 5.58070521
16 0.65042220 5.75085322
17 9.50649218 0.65042220
18 -4.28687267 9.50649218
19 -3.91790996 -4.28687267
20 -4.04488797 -3.91790996
21 7.35583564 -4.04488797
22 -0.70520287 7.35583564
23 8.06247690 -0.70520287
24 3.85355541 8.06247690
25 -5.57693616 3.85355541
26 12.35511244 -5.57693616
27 -8.49037959 12.35511244
28 -2.84277230 -8.49037959
29 11.71864037 -2.84277230
30 3.53884252 11.71864037
31 1.99499252 3.53884252
32 18.65991384 1.99499252
33 -8.58919624 18.65991384
34 3.25286847 -8.58919624
35 5.43150351 3.25286847
36 -4.94468727 5.43150351
37 -18.37094075 -4.94468727
38 3.23862546 -18.37094075
39 5.36553426 3.23862546
40 3.23487772 5.36553426
41 2.60094180 3.23487772
42 -15.79416968 2.60094180
43 -13.95490086 -15.79416968
44 -4.44962767 -13.95490086
45 -9.51867119 -4.44962767
46 10.17787472 -9.51867119
47 -0.29102074 10.17787472
48 -2.70396081 -0.29102074
49 -1.39233690 -2.70396081
50 7.55028474 -1.39233690
51 2.51263297 7.55028474
52 0.09307399 2.51263297
53 0.87954431 0.09307399
54 -2.62451498 0.87954431
55 2.38387590 -2.62451498
> 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/7p0o51345792333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8errm1345792333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9qak81345792333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10t2j91345792333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1178vy1345792333.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/12z3uw1345792333.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/13bo5g1345792333.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/14m0ib1345792333.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/1506hf1345792333.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/16y91o1345792333.tab")
+ }
>
> try(system("convert tmp/17yiy1345792332.ps tmp/17yiy1345792332.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ijib1345792332.ps tmp/2ijib1345792332.png",intern=TRUE))
character(0)
> try(system("convert tmp/3f4pl1345792332.ps tmp/3f4pl1345792332.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rq8v1345792332.ps tmp/4rq8v1345792332.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x8u81345792332.ps tmp/5x8u81345792332.png",intern=TRUE))
character(0)
> try(system("convert tmp/64yu11345792332.ps tmp/64yu11345792332.png",intern=TRUE))
character(0)
> try(system("convert tmp/7p0o51345792333.ps tmp/7p0o51345792333.png",intern=TRUE))
character(0)
> try(system("convert tmp/8errm1345792333.ps tmp/8errm1345792333.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qak81345792333.ps tmp/9qak81345792333.png",intern=TRUE))
character(0)
> try(system("convert tmp/10t2j91345792333.ps tmp/10t2j91345792333.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.535 0.739 5.286