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.
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(0.301029996
+ ,3.00
+ ,1.62324929
+ ,0.491361694
+ ,1.00
+ ,2.079181246
+ ,-0.15490196
+ ,4.00
+ ,2.255272505
+ ,0.591064607
+ ,1.00
+ ,1.544068044
+ ,0.556302501
+ ,1.00
+ ,1.799340549
+ ,0.146128036
+ ,1.00
+ ,2.361727836
+ ,0.176091259
+ ,4.00
+ ,2.049218023
+ ,-0.15490196
+ ,5.00
+ ,2.44870632
+ ,0.255272505
+ ,4.00
+ ,2.79518459
+ ,0.380211242
+ ,1.00
+ ,1.716003344
+ ,0.079181246
+ ,2.00
+ ,2.079181246
+ ,-0.301029996
+ ,5.00
+ ,2.170261715
+ ,-0.045757491
+ ,2.00
+ ,2.352182518
+ ,-0.096910013
+ ,4.00
+ ,1.832508913
+ ,0.531478917
+ ,2.00
+ ,1.204119983
+ ,0.612783857
+ ,2.00
+ ,1.62324929
+ ,-0.096910013
+ ,5.00
+ ,2.526339277
+ ,0.301029996
+ ,1.00
+ ,1.698970004
+ ,0.819543936
+ ,1.00
+ ,1.146128036
+ ,0.278753601
+ ,1.00
+ ,2.426511261
+ ,0.322219295
+ ,1.00
+ ,1.62324929
+ ,0.113943352
+ ,3.00
+ ,1.278753601
+ ,0.748188027
+ ,1.00
+ ,1.079181246
+ ,0.255272505
+ ,2.00
+ ,2.146128036
+ ,-0.045757491
+ ,4.00
+ ,2.230448921
+ ,0.255272505
+ ,2.00
+ ,1.230448921
+ ,0.278753601
+ ,4.00
+ ,2.06069784
+ ,-0.045757491
+ ,5.00
+ ,1.491361694
+ ,0.414973348
+ ,3.00
+ ,1.322219295
+ ,0.079181246
+ ,2.00
+ ,2.214843848
+ ,-0.301029996
+ ,3.00
+ ,2.352182518
+ ,0.176091259
+ ,1.00
+ ,2.491361694
+ ,-0.22184875
+ ,5.00
+ ,2.178976947
+ ,0.531478917
+ ,3.00
+ ,1.447158031
+ ,0
+ ,4.00
+ ,2.593286067
+ ,0.361727836
+ ,2.00
+ ,1.77815125
+ ,-0.301029996
+ ,3.00
+ ,2.301029996
+ ,0.414973348
+ ,2.00
+ ,1.662757832
+ ,-0.22184875
+ ,4.00
+ ,2.322219295)
+ ,dim=c(3
+ ,39)
+ ,dimnames=list(c('log(PS)'
+ ,'D'
+ ,'log(tg)')
+ ,1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('log(PS)','D','log(tg)'),1:39))
> 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
log(PS) D log(tg)
1 0.30103000 3 1.623249
2 0.49136169 1 2.079181
3 -0.15490196 4 2.255273
4 0.59106461 1 1.544068
5 0.55630250 1 1.799341
6 0.14612804 1 2.361728
7 0.17609126 4 2.049218
8 -0.15490196 5 2.448706
9 0.25527250 4 2.795185
10 0.38021124 1 1.716003
11 0.07918125 2 2.079181
12 -0.30103000 5 2.170262
13 -0.04575749 2 2.352183
14 -0.09691001 4 1.832509
15 0.53147892 2 1.204120
16 0.61278386 2 1.623249
17 -0.09691001 5 2.526339
18 0.30103000 1 1.698970
19 0.81954394 1 1.146128
20 0.27875360 1 2.426511
21 0.32221930 1 1.623249
22 0.11394335 3 1.278754
23 0.74818803 1 1.079181
24 0.25527250 2 2.146128
25 -0.04575749 4 2.230449
26 0.25527250 2 1.230449
27 0.27875360 4 2.060698
28 -0.04575749 5 1.491362
29 0.41497335 3 1.322219
30 0.07918125 2 2.214844
31 -0.30103000 3 2.352183
32 0.17609126 1 2.491362
33 -0.22184875 5 2.178977
34 0.53147892 3 1.447158
35 0.00000000 4 2.593286
36 0.36172784 2 1.778151
37 -0.30103000 3 2.301030
38 0.41497335 2 1.662758
39 -0.22184875 4 2.322219
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) D `log(tg)`
1.0745 -0.1105 -0.3035
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.34555 -0.14523 0.04349 0.12512 0.47125
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.07451 0.12875 8.346 6.16e-10 ***
D -0.11051 0.02219 -4.980 1.60e-05 ***
`log(tg)` -0.30354 0.06890 -4.405 9.09e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1818 on 36 degrees of freedom
Multiple R-squared: 0.6546, Adjusted R-squared: 0.6354
F-statistic: 34.12 on 2 and 36 DF, p-value: 4.888e-09
> 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,] 0.1260965 0.2521930 0.8739035
[2,] 0.1827711 0.3655422 0.8172289
[3,] 0.1134897 0.2269793 0.8865103
[4,] 0.6488481 0.7023037 0.3511519
[5,] 0.5568719 0.8862563 0.4431281
[6,] 0.5744936 0.8510129 0.4255064
[7,] 0.6035194 0.7929613 0.3964806
[8,] 0.6517366 0.6965268 0.3482634
[9,] 0.6201985 0.7596030 0.3798015
[10,] 0.5412198 0.9175604 0.4587802
[11,] 0.6168780 0.7662440 0.3831220
[12,] 0.5780732 0.8438535 0.4219268
[13,] 0.5421867 0.9156267 0.4578133
[14,] 0.5556049 0.8887902 0.4443951
[15,] 0.4719064 0.9438129 0.5280936
[16,] 0.4314501 0.8629001 0.5685499
[17,] 0.4977289 0.9954578 0.5022711
[18,] 0.4296141 0.8592281 0.5703859
[19,] 0.3502365 0.7004730 0.6497635
[20,] 0.2622771 0.5245543 0.7377229
[21,] 0.3299413 0.6598825 0.6700587
[22,] 0.5156856 0.9686287 0.4843144
[23,] 0.4675215 0.9350430 0.5324785
[24,] 0.3671738 0.7343477 0.6328262
[25,] 0.2717529 0.5435058 0.7282471
[26,] 0.3902886 0.7805772 0.6097114
[27,] 0.2821189 0.5642378 0.7178811
[28,] 0.2109644 0.4219289 0.7890356
> postscript(file="/var/www/html/rcomp/tmp/1fcn01292936546.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/284431292936546.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/384431292936546.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/484431292936546.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/584431292936546.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 = 39
Frequency = 1
1 2 3 4 5 6
0.050773408 0.158477176 -0.102804437 0.095752433 0.138475455 -0.100992610
7 8 9 10 11 12
0.165643238 0.066420745 0.471254332 -0.062911885 -0.143192772 -0.164226052
13 14 15 16 17 18
-0.185265012 -0.173137672 0.043489793 0.252016769 0.147977311 -0.147263412
19 20 21 22 23 24
0.203441502 0.051297243 -0.149058293 -0.240881068 0.111764641 0.053219440
25 26 27 28 29 30
-0.001194890 -0.224724763 0.271790151 -0.115026091 0.073342456 -0.102013899
31 32 33 34 35 36
-0.330027017 -0.031680472 -0.082399394 0.227771787 0.154697777 0.047979514
37 38 39
-0.345553796 0.066198638 -0.149430274
> postscript(file="/var/www/html/rcomp/tmp/6jvlo1292936546.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 = 39
Frequency = 1
lag(myerror, k = 1) myerror
0 0.050773408 NA
1 0.158477176 0.050773408
2 -0.102804437 0.158477176
3 0.095752433 -0.102804437
4 0.138475455 0.095752433
5 -0.100992610 0.138475455
6 0.165643238 -0.100992610
7 0.066420745 0.165643238
8 0.471254332 0.066420745
9 -0.062911885 0.471254332
10 -0.143192772 -0.062911885
11 -0.164226052 -0.143192772
12 -0.185265012 -0.164226052
13 -0.173137672 -0.185265012
14 0.043489793 -0.173137672
15 0.252016769 0.043489793
16 0.147977311 0.252016769
17 -0.147263412 0.147977311
18 0.203441502 -0.147263412
19 0.051297243 0.203441502
20 -0.149058293 0.051297243
21 -0.240881068 -0.149058293
22 0.111764641 -0.240881068
23 0.053219440 0.111764641
24 -0.001194890 0.053219440
25 -0.224724763 -0.001194890
26 0.271790151 -0.224724763
27 -0.115026091 0.271790151
28 0.073342456 -0.115026091
29 -0.102013899 0.073342456
30 -0.330027017 -0.102013899
31 -0.031680472 -0.330027017
32 -0.082399394 -0.031680472
33 0.227771787 -0.082399394
34 0.154697777 0.227771787
35 0.047979514 0.154697777
36 -0.345553796 0.047979514
37 0.066198638 -0.345553796
38 -0.149430274 0.066198638
39 NA -0.149430274
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.158477176 0.050773408
[2,] -0.102804437 0.158477176
[3,] 0.095752433 -0.102804437
[4,] 0.138475455 0.095752433
[5,] -0.100992610 0.138475455
[6,] 0.165643238 -0.100992610
[7,] 0.066420745 0.165643238
[8,] 0.471254332 0.066420745
[9,] -0.062911885 0.471254332
[10,] -0.143192772 -0.062911885
[11,] -0.164226052 -0.143192772
[12,] -0.185265012 -0.164226052
[13,] -0.173137672 -0.185265012
[14,] 0.043489793 -0.173137672
[15,] 0.252016769 0.043489793
[16,] 0.147977311 0.252016769
[17,] -0.147263412 0.147977311
[18,] 0.203441502 -0.147263412
[19,] 0.051297243 0.203441502
[20,] -0.149058293 0.051297243
[21,] -0.240881068 -0.149058293
[22,] 0.111764641 -0.240881068
[23,] 0.053219440 0.111764641
[24,] -0.001194890 0.053219440
[25,] -0.224724763 -0.001194890
[26,] 0.271790151 -0.224724763
[27,] -0.115026091 0.271790151
[28,] 0.073342456 -0.115026091
[29,] -0.102013899 0.073342456
[30,] -0.330027017 -0.102013899
[31,] -0.031680472 -0.330027017
[32,] -0.082399394 -0.031680472
[33,] 0.227771787 -0.082399394
[34,] 0.154697777 0.227771787
[35,] 0.047979514 0.154697777
[36,] -0.345553796 0.047979514
[37,] 0.066198638 -0.345553796
[38,] -0.149430274 0.066198638
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.158477176 0.050773408
2 -0.102804437 0.158477176
3 0.095752433 -0.102804437
4 0.138475455 0.095752433
5 -0.100992610 0.138475455
6 0.165643238 -0.100992610
7 0.066420745 0.165643238
8 0.471254332 0.066420745
9 -0.062911885 0.471254332
10 -0.143192772 -0.062911885
11 -0.164226052 -0.143192772
12 -0.185265012 -0.164226052
13 -0.173137672 -0.185265012
14 0.043489793 -0.173137672
15 0.252016769 0.043489793
16 0.147977311 0.252016769
17 -0.147263412 0.147977311
18 0.203441502 -0.147263412
19 0.051297243 0.203441502
20 -0.149058293 0.051297243
21 -0.240881068 -0.149058293
22 0.111764641 -0.240881068
23 0.053219440 0.111764641
24 -0.001194890 0.053219440
25 -0.224724763 -0.001194890
26 0.271790151 -0.224724763
27 -0.115026091 0.271790151
28 0.073342456 -0.115026091
29 -0.102013899 0.073342456
30 -0.330027017 -0.102013899
31 -0.031680472 -0.330027017
32 -0.082399394 -0.031680472
33 0.227771787 -0.082399394
34 0.154697777 0.227771787
35 0.047979514 0.154697777
36 -0.345553796 0.047979514
37 0.066198638 -0.345553796
38 -0.149430274 0.066198638
> 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/7bm3r1292936546.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8bm3r1292936546.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bm3r1292936546.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mv2c1292936546.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pei01292936546.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/12twzn1292936546.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/137ofw1292936546.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/14apdk1292936546.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/15d7uq1292936546.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/16hqsw1292936546.tab")
+ }
>
> try(system("convert tmp/1fcn01292936546.ps tmp/1fcn01292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/284431292936546.ps tmp/284431292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/384431292936546.ps tmp/384431292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/484431292936546.ps tmp/484431292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/584431292936546.ps tmp/584431292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jvlo1292936546.ps tmp/6jvlo1292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/7bm3r1292936546.ps tmp/7bm3r1292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bm3r1292936546.ps tmp/8bm3r1292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bm3r1292936546.ps tmp/9bm3r1292936546.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mv2c1292936546.ps tmp/10mv2c1292936546.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.313 1.593 5.221