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.
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Type 'contributors()' for more information and
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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(1972
+ ,33907
+ ,71433
+ ,152
+ ,74272
+ ,99
+ ,765
+ ,1973
+ ,35981
+ ,53655
+ ,99
+ ,78867
+ ,128
+ ,1371
+ ,1974
+ ,36588
+ ,70556
+ ,92
+ ,80176
+ ,57
+ ,1880
+ ,1975
+ ,16967
+ ,74702
+ ,138
+ ,36541
+ ,95
+ ,232
+ ,1976
+ ,25333
+ ,61201
+ ,106
+ ,55107
+ ,205
+ ,230
+ ,1977
+ ,21027
+ ,686
+ ,95
+ ,45527
+ ,51
+ ,828
+ ,1978
+ ,21114
+ ,87586
+ ,145
+ ,46001
+ ,59
+ ,1833
+ ,1979
+ ,28777
+ ,6615
+ ,181
+ ,62854
+ ,194
+ ,906
+ ,1980
+ ,35612
+ ,89725
+ ,190
+ ,78112
+ ,27
+ ,1781
+ ,1981
+ ,24183
+ ,40420
+ ,150
+ ,52653
+ ,9
+ ,1264
+ ,1982
+ ,22262
+ ,49569
+ ,186
+ ,48467
+ ,24
+ ,1123
+ ,1983
+ ,20637
+ ,13963
+ ,174
+ ,44873
+ ,189
+ ,1461
+ ,1984
+ ,29948
+ ,62508
+ ,151
+ ,65605
+ ,37
+ ,820
+ ,1985
+ ,22093
+ ,90901
+ ,112
+ ,48016
+ ,81
+ ,107
+ ,1986
+ ,36997
+ ,89418
+ ,143
+ ,81110
+ ,72
+ ,1349
+ ,1987
+ ,31089
+ ,83237
+ ,120
+ ,68019
+ ,81
+ ,870
+ ,1988
+ ,19477
+ ,22183
+ ,169
+ ,42198
+ ,90
+ ,1471
+ ,1989
+ ,31301
+ ,24346
+ ,135
+ ,68531
+ ,216
+ ,731
+ ,1990
+ ,18497
+ ,74341
+ ,161
+ ,40071
+ ,216
+ ,1945
+ ,1991
+ ,30142
+ ,24188
+ ,98
+ ,65849
+ ,13
+ ,521
+ ,1992
+ ,21326
+ ,11781
+ ,142
+ ,46362
+ ,153
+ ,1920
+ ,1993
+ ,16779
+ ,23072
+ ,190
+ ,36313
+ ,185
+ ,1924
+ ,1994
+ ,38068
+ ,49119
+ ,169
+ ,83521
+ ,131
+ ,100
+ ,1995
+ ,29707
+ ,67776
+ ,130
+ ,64932
+ ,136
+ ,34
+ ,1996
+ ,35016
+ ,86910
+ ,160
+ ,76730
+ ,182
+ ,325
+ ,1997
+ ,26131
+ ,69358
+ ,176
+ ,56982
+ ,139
+ ,1677
+ ,1998
+ ,29251
+ ,16144
+ ,111
+ ,63793
+ ,42
+ ,1779
+ ,1999
+ ,22855
+ ,77863
+ ,165
+ ,49740
+ ,213
+ ,477
+ ,2000
+ ,31806
+ ,89070
+ ,117
+ ,69447
+ ,184
+ ,1007
+ ,2001
+ ,34124
+ ,34790
+ ,122
+ ,74708
+ ,44
+ ,1527)
+ ,dim=c(7
+ ,30)
+ ,dimnames=list(c('Jaar'
+ ,'Omzet_product'
+ ,'Uitgaven_voor_promotie'
+ ,'Prijs_product'
+ ,'Gem_budget'
+ ,'Index_cons_vertrouwen'
+ ,'uitgave_lok_prom')
+ ,1:30))
> y <- array(NA,dim=c(7,30),dimnames=list(c('Jaar','Omzet_product','Uitgaven_voor_promotie','Prijs_product','Gem_budget','Index_cons_vertrouwen','uitgave_lok_prom'),1:30))
> 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'
> 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
Jaar Omzet_product Uitgaven_voor_promotie Prijs_product Gem_budget
1 1972 33907 71433 152 74272
2 1973 35981 53655 99 78867
3 1974 36588 70556 92 80176
4 1975 16967 74702 138 36541
5 1976 25333 61201 106 55107
6 1977 21027 686 95 45527
7 1978 21114 87586 145 46001
8 1979 28777 6615 181 62854
9 1980 35612 89725 190 78112
10 1981 24183 40420 150 52653
11 1982 22262 49569 186 48467
12 1983 20637 13963 174 44873
13 1984 29948 62508 151 65605
14 1985 22093 90901 112 48016
15 1986 36997 89418 143 81110
16 1987 31089 83237 120 68019
17 1988 19477 22183 169 42198
18 1989 31301 24346 135 68531
19 1990 18497 74341 161 40071
20 1991 30142 24188 98 65849
21 1992 21326 11781 142 46362
22 1993 16779 23072 190 36313
23 1994 38068 49119 169 83521
24 1995 29707 67776 130 64932
25 1996 35016 86910 160 76730
26 1997 26131 69358 176 56982
27 1998 29251 16144 111 63793
28 1999 22855 77863 165 49740
29 2000 31806 89070 117 69447
30 2001 34124 34790 122 74708
Index_cons_vertrouwen uitgave_lok_prom
1 99 765
2 128 1371
3 57 1880
4 95 232
5 205 230
6 51 828
7 59 1833
8 194 906
9 27 1781
10 9 1264
11 24 1123
12 189 1461
13 37 820
14 81 107
15 72 1349
16 81 870
17 90 1471
18 216 731
19 216 1945
20 13 521
21 153 1920
22 185 1924
23 131 100
24 136 34
25 182 325
26 139 1677
27 42 1779
28 213 477
29 184 1007
30 44 1527
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Omzet_product Uitgaven_voor_promotie
1.942e+03 5.636e-02 -1.118e-05
Prijs_product Gem_budget Index_cons_vertrouwen
6.789e-02 -2.529e-02 2.974e-02
uitgave_lok_prom
7.704e-04
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.226 -6.488 1.047 5.814 15.248
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.942e+03 4.036e+01 48.111 <2e-16 ***
Omzet_product 5.636e-02 6.673e-02 0.845 0.407
Uitgaven_voor_promotie -1.118e-05 6.303e-05 -0.177 0.861
Prijs_product 6.789e-02 7.473e-02 0.909 0.373
Gem_budget -2.529e-02 3.005e-02 -0.842 0.409
Index_cons_vertrouwen 2.974e-02 2.678e-02 1.110 0.278
uitgave_lok_prom 7.704e-04 2.971e-03 0.259 0.798
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.302 on 23 degrees of freedom
Multiple R-squared: 0.1145, Adjusted R-squared: -0.1165
F-statistic: 0.4956 on 6 and 23 DF, p-value: 0.805
> 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.15465026 0.3093005 0.84534974
[2,] 0.09113553 0.1822711 0.90886447
[3,] 0.05863648 0.1172730 0.94136352
[4,] 0.14821929 0.2964386 0.85178071
[5,] 0.26619819 0.5323964 0.73380181
[6,] 0.61841131 0.7631774 0.38158869
[7,] 0.76309930 0.4738014 0.23690070
[8,] 0.85291107 0.2941779 0.14708893
[9,] 0.80517689 0.3896462 0.19482311
[10,] 0.87077560 0.2584488 0.12922440
[11,] 0.90220162 0.1955968 0.09779838
> postscript(file="/var/wessaorg/rcomp/tmp/1gvn81321630072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/22afx1321630072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3deeb1321630072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4a2gr1321630072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5j4tw1321630072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 30
Frequency = 1
1 2 3 4 5 6
-15.2254958 -12.8247456 -10.5433381 -10.2978076 -12.4648524 -6.8961859
7 8 9 10 11 12
-2.2460046 -13.5206989 -7.2116065 -2.9078581 -2.1958985 -5.2640287
13 14 15 16 17 18
2.4590921 3.4960596 -1.2596424 3.1992713 0.8227065 0.6165446
19 20 21 22 23 24
1.2719433 8.8108248 5.4302705 4.4432086 4.3518134 9.1662295
25 26 27 28 29 30
5.9420894 7.1704867 11.2213990 10.2003131 9.0082561 15.2476546
> postscript(file="/var/wessaorg/rcomp/tmp/6zski1321630072.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 = 30
Frequency = 1
lag(myerror, k = 1) myerror
0 -15.2254958 NA
1 -12.8247456 -15.2254958
2 -10.5433381 -12.8247456
3 -10.2978076 -10.5433381
4 -12.4648524 -10.2978076
5 -6.8961859 -12.4648524
6 -2.2460046 -6.8961859
7 -13.5206989 -2.2460046
8 -7.2116065 -13.5206989
9 -2.9078581 -7.2116065
10 -2.1958985 -2.9078581
11 -5.2640287 -2.1958985
12 2.4590921 -5.2640287
13 3.4960596 2.4590921
14 -1.2596424 3.4960596
15 3.1992713 -1.2596424
16 0.8227065 3.1992713
17 0.6165446 0.8227065
18 1.2719433 0.6165446
19 8.8108248 1.2719433
20 5.4302705 8.8108248
21 4.4432086 5.4302705
22 4.3518134 4.4432086
23 9.1662295 4.3518134
24 5.9420894 9.1662295
25 7.1704867 5.9420894
26 11.2213990 7.1704867
27 10.2003131 11.2213990
28 9.0082561 10.2003131
29 15.2476546 9.0082561
30 NA 15.2476546
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -12.8247456 -15.2254958
[2,] -10.5433381 -12.8247456
[3,] -10.2978076 -10.5433381
[4,] -12.4648524 -10.2978076
[5,] -6.8961859 -12.4648524
[6,] -2.2460046 -6.8961859
[7,] -13.5206989 -2.2460046
[8,] -7.2116065 -13.5206989
[9,] -2.9078581 -7.2116065
[10,] -2.1958985 -2.9078581
[11,] -5.2640287 -2.1958985
[12,] 2.4590921 -5.2640287
[13,] 3.4960596 2.4590921
[14,] -1.2596424 3.4960596
[15,] 3.1992713 -1.2596424
[16,] 0.8227065 3.1992713
[17,] 0.6165446 0.8227065
[18,] 1.2719433 0.6165446
[19,] 8.8108248 1.2719433
[20,] 5.4302705 8.8108248
[21,] 4.4432086 5.4302705
[22,] 4.3518134 4.4432086
[23,] 9.1662295 4.3518134
[24,] 5.9420894 9.1662295
[25,] 7.1704867 5.9420894
[26,] 11.2213990 7.1704867
[27,] 10.2003131 11.2213990
[28,] 9.0082561 10.2003131
[29,] 15.2476546 9.0082561
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -12.8247456 -15.2254958
2 -10.5433381 -12.8247456
3 -10.2978076 -10.5433381
4 -12.4648524 -10.2978076
5 -6.8961859 -12.4648524
6 -2.2460046 -6.8961859
7 -13.5206989 -2.2460046
8 -7.2116065 -13.5206989
9 -2.9078581 -7.2116065
10 -2.1958985 -2.9078581
11 -5.2640287 -2.1958985
12 2.4590921 -5.2640287
13 3.4960596 2.4590921
14 -1.2596424 3.4960596
15 3.1992713 -1.2596424
16 0.8227065 3.1992713
17 0.6165446 0.8227065
18 1.2719433 0.6165446
19 8.8108248 1.2719433
20 5.4302705 8.8108248
21 4.4432086 5.4302705
22 4.3518134 4.4432086
23 9.1662295 4.3518134
24 5.9420894 9.1662295
25 7.1704867 5.9420894
26 11.2213990 7.1704867
27 10.2003131 11.2213990
28 9.0082561 10.2003131
29 15.2476546 9.0082561
> 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/7l2is1321630072.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/8xwyd1321630072.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/9smlu1321630072.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/10w0sa1321630072.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11f7lp1321630072.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1291ht1321630072.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/135aq51321630072.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/143aoo1321630072.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/15d6ha1321630072.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/163gnv1321630072.tab")
+ }
>
> try(system("convert tmp/1gvn81321630072.ps tmp/1gvn81321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/22afx1321630072.ps tmp/22afx1321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/3deeb1321630072.ps tmp/3deeb1321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a2gr1321630072.ps tmp/4a2gr1321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j4tw1321630072.ps tmp/5j4tw1321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zski1321630072.ps tmp/6zski1321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/7l2is1321630072.ps tmp/7l2is1321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xwyd1321630072.ps tmp/8xwyd1321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/9smlu1321630072.ps tmp/9smlu1321630072.png",intern=TRUE))
character(0)
> try(system("convert tmp/10w0sa1321630072.ps tmp/10w0sa1321630072.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.868 0.477 3.399