R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(8.1,9.9,11.5,23.4,25.4,27.9,26.1,18.8,14.1,11.5,15.8,12.4,4.5,-2.2,-4.2,-9.4,-14.5,-17.9,-15.1,-15.2,-15.7,-18,-18.1,-13.5,-9.9,-4.8,-1.7,-0.1,2.2,10.2,7.6,10.8,3.8,11,10.8,20.1,14.9,13,10.9,9.6,4,-1.1,-7.7,-8.9,-8,-7.1,-5.3,-2.5,-2.4,-2.9,-4.8,-7.2,1.7,2.2,13.4,12.3,13.7,4.4,-2.5),dim=c(1,59),dimnames=list(c('registraties_personenwagens'),1:59))
> y <- array(NA,dim=c(1,59),dimnames=list(c('registraties_personenwagens'),1:59))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> 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
registraties_personenwagens M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.1 1 0 0 0 0 0 0 0 0 0 0
2 9.9 0 1 0 0 0 0 0 0 0 0 0
3 11.5 0 0 1 0 0 0 0 0 0 0 0
4 23.4 0 0 0 1 0 0 0 0 0 0 0
5 25.4 0 0 0 0 1 0 0 0 0 0 0
6 27.9 0 0 0 0 0 1 0 0 0 0 0
7 26.1 0 0 0 0 0 0 1 0 0 0 0
8 18.8 0 0 0 0 0 0 0 1 0 0 0
9 14.1 0 0 0 0 0 0 0 0 1 0 0
10 11.5 0 0 0 0 0 0 0 0 0 1 0
11 15.8 0 0 0 0 0 0 0 0 0 0 1
12 12.4 0 0 0 0 0 0 0 0 0 0 0
13 4.5 1 0 0 0 0 0 0 0 0 0 0
14 -2.2 0 1 0 0 0 0 0 0 0 0 0
15 -4.2 0 0 1 0 0 0 0 0 0 0 0
16 -9.4 0 0 0 1 0 0 0 0 0 0 0
17 -14.5 0 0 0 0 1 0 0 0 0 0 0
18 -17.9 0 0 0 0 0 1 0 0 0 0 0
19 -15.1 0 0 0 0 0 0 1 0 0 0 0
20 -15.2 0 0 0 0 0 0 0 1 0 0 0
21 -15.7 0 0 0 0 0 0 0 0 1 0 0
22 -18.0 0 0 0 0 0 0 0 0 0 1 0
23 -18.1 0 0 0 0 0 0 0 0 0 0 1
24 -13.5 0 0 0 0 0 0 0 0 0 0 0
25 -9.9 1 0 0 0 0 0 0 0 0 0 0
26 -4.8 0 1 0 0 0 0 0 0 0 0 0
27 -1.7 0 0 1 0 0 0 0 0 0 0 0
28 -0.1 0 0 0 1 0 0 0 0 0 0 0
29 2.2 0 0 0 0 1 0 0 0 0 0 0
30 10.2 0 0 0 0 0 1 0 0 0 0 0
31 7.6 0 0 0 0 0 0 1 0 0 0 0
32 10.8 0 0 0 0 0 0 0 1 0 0 0
33 3.8 0 0 0 0 0 0 0 0 1 0 0
34 11.0 0 0 0 0 0 0 0 0 0 1 0
35 10.8 0 0 0 0 0 0 0 0 0 0 1
36 20.1 0 0 0 0 0 0 0 0 0 0 0
37 14.9 1 0 0 0 0 0 0 0 0 0 0
38 13.0 0 1 0 0 0 0 0 0 0 0 0
39 10.9 0 0 1 0 0 0 0 0 0 0 0
40 9.6 0 0 0 1 0 0 0 0 0 0 0
41 4.0 0 0 0 0 1 0 0 0 0 0 0
42 -1.1 0 0 0 0 0 1 0 0 0 0 0
43 -7.7 0 0 0 0 0 0 1 0 0 0 0
44 -8.9 0 0 0 0 0 0 0 1 0 0 0
45 -8.0 0 0 0 0 0 0 0 0 1 0 0
46 -7.1 0 0 0 0 0 0 0 0 0 1 0
47 -5.3 0 0 0 0 0 0 0 0 0 0 1
48 -2.5 0 0 0 0 0 0 0 0 0 0 0
49 -2.4 1 0 0 0 0 0 0 0 0 0 0
50 -2.9 0 1 0 0 0 0 0 0 0 0 0
51 -4.8 0 0 1 0 0 0 0 0 0 0 0
52 -7.2 0 0 0 1 0 0 0 0 0 0 0
53 1.7 0 0 0 0 1 0 0 0 0 0 0
54 2.2 0 0 0 0 0 1 0 0 0 0 0
55 13.4 0 0 0 0 0 0 1 0 0 0 0
56 12.3 0 0 0 0 0 0 0 1 0 0 0
57 13.7 0 0 0 0 0 0 0 0 1 0 0
58 4.4 0 0 0 0 0 0 0 0 0 1 0
59 -2.5 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
4.125 -1.085 -1.525 -1.785 -0.865 -0.365
M6 M7 M8 M9 M10 M11
0.135 0.735 -0.565 -2.545 -3.765 -3.985
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-22.16 -7.43 -1.56 8.95 23.64
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.125 6.630 0.622 0.537
M1 -1.085 8.895 -0.122 0.903
M2 -1.525 8.895 -0.171 0.865
M3 -1.785 8.895 -0.201 0.842
M4 -0.865 8.895 -0.097 0.923
M5 -0.365 8.895 -0.041 0.967
M6 0.135 8.895 0.015 0.988
M7 0.735 8.895 0.083 0.934
M8 -0.565 8.895 -0.064 0.950
M9 -2.545 8.895 -0.286 0.776
M10 -3.765 8.895 -0.423 0.674
M11 -3.985 8.895 -0.448 0.656
Residual standard error: 13.26 on 47 degrees of freedom
Multiple R-squared: 0.0145, Adjusted R-squared: -0.2161
F-statistic: 0.06287 on 11 and 47 DF, p-value: 1
> 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.2937066 0.587413188 0.7062934059
[2,] 0.6948697 0.610260514 0.3051302568
[3,] 0.9113953 0.177209489 0.0886047446
[4,] 0.9831971 0.033605880 0.0168029402
[5,] 0.9943425 0.011314920 0.0056574599
[6,] 0.9969210 0.006157932 0.0030789660
[7,] 0.9978416 0.004316871 0.0021584354
[8,] 0.9986857 0.002628614 0.0013143071
[9,] 0.9992835 0.001433080 0.0007165402
[10,] 0.9995782 0.000843632 0.0004218160
[11,] 0.9995340 0.000932010 0.0004660050
[12,] 0.9991586 0.001682797 0.0008413983
[13,] 0.9982123 0.003575497 0.0017877484
[14,] 0.9962742 0.007451532 0.0037257662
[15,] 0.9924191 0.015161737 0.0075808685
[16,] 0.9876180 0.024763915 0.0123819576
[17,] 0.9776794 0.044641204 0.0223206020
[18,] 0.9660261 0.067947767 0.0339738833
[19,] 0.9419684 0.116063272 0.0580316360
[20,] 0.9271077 0.145784534 0.0728922669
[21,] 0.9147832 0.170433544 0.0852167720
[22,] 0.9300410 0.139917939 0.0699589695
[23,] 0.9228336 0.154332870 0.0771664352
[24,] 0.9086854 0.182629132 0.0913145659
[25,] 0.8897501 0.220499827 0.1102499136
[26,] 0.8729212 0.254157651 0.1270788254
[27,] 0.7870807 0.425838683 0.2129193416
[28,] 0.6718622 0.656275590 0.3281377952
[29,] 0.6867380 0.626524023 0.3132620116
[30,] 0.7239399 0.552120266 0.2760601331
> postscript(file="/var/www/html/rcomp/tmp/1ml9u1292954500.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xcqx1292954500.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3xcqx1292954500.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xcqx1292954500.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xcqx1292954500.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 59
Frequency = 1
1 2 3 4 5 6 7 8 9 10
5.060 7.300 9.160 20.140 21.640 23.640 21.240 15.240 12.520 11.140
11 12 13 14 15 16 17 18 19 20
15.660 8.275 1.460 -4.800 -6.540 -12.660 -18.260 -22.160 -19.960 -18.760
21 22 23 24 25 26 27 28 29 30
-17.280 -18.360 -18.240 -17.625 -12.940 -7.400 -4.040 -3.360 -1.560 5.940
31 32 33 34 35 36 37 38 39 40
2.740 7.240 2.220 10.640 10.660 15.975 11.860 10.400 8.560 6.340
41 42 43 44 45 46 47 48 49 50
0.240 -5.360 -12.560 -12.460 -9.580 -7.460 -5.440 -6.625 -5.440 -5.500
51 52 53 54 55 56 57 58 59
-7.140 -10.460 -2.060 -2.060 8.540 8.740 12.120 4.040 -2.640
> postscript(file="/var/www/html/rcomp/tmp/6q38i1292954500.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 5.060 NA
1 7.300 5.060
2 9.160 7.300
3 20.140 9.160
4 21.640 20.140
5 23.640 21.640
6 21.240 23.640
7 15.240 21.240
8 12.520 15.240
9 11.140 12.520
10 15.660 11.140
11 8.275 15.660
12 1.460 8.275
13 -4.800 1.460
14 -6.540 -4.800
15 -12.660 -6.540
16 -18.260 -12.660
17 -22.160 -18.260
18 -19.960 -22.160
19 -18.760 -19.960
20 -17.280 -18.760
21 -18.360 -17.280
22 -18.240 -18.360
23 -17.625 -18.240
24 -12.940 -17.625
25 -7.400 -12.940
26 -4.040 -7.400
27 -3.360 -4.040
28 -1.560 -3.360
29 5.940 -1.560
30 2.740 5.940
31 7.240 2.740
32 2.220 7.240
33 10.640 2.220
34 10.660 10.640
35 15.975 10.660
36 11.860 15.975
37 10.400 11.860
38 8.560 10.400
39 6.340 8.560
40 0.240 6.340
41 -5.360 0.240
42 -12.560 -5.360
43 -12.460 -12.560
44 -9.580 -12.460
45 -7.460 -9.580
46 -5.440 -7.460
47 -6.625 -5.440
48 -5.440 -6.625
49 -5.500 -5.440
50 -7.140 -5.500
51 -10.460 -7.140
52 -2.060 -10.460
53 -2.060 -2.060
54 8.540 -2.060
55 8.740 8.540
56 12.120 8.740
57 4.040 12.120
58 -2.640 4.040
59 NA -2.640
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.300 5.060
[2,] 9.160 7.300
[3,] 20.140 9.160
[4,] 21.640 20.140
[5,] 23.640 21.640
[6,] 21.240 23.640
[7,] 15.240 21.240
[8,] 12.520 15.240
[9,] 11.140 12.520
[10,] 15.660 11.140
[11,] 8.275 15.660
[12,] 1.460 8.275
[13,] -4.800 1.460
[14,] -6.540 -4.800
[15,] -12.660 -6.540
[16,] -18.260 -12.660
[17,] -22.160 -18.260
[18,] -19.960 -22.160
[19,] -18.760 -19.960
[20,] -17.280 -18.760
[21,] -18.360 -17.280
[22,] -18.240 -18.360
[23,] -17.625 -18.240
[24,] -12.940 -17.625
[25,] -7.400 -12.940
[26,] -4.040 -7.400
[27,] -3.360 -4.040
[28,] -1.560 -3.360
[29,] 5.940 -1.560
[30,] 2.740 5.940
[31,] 7.240 2.740
[32,] 2.220 7.240
[33,] 10.640 2.220
[34,] 10.660 10.640
[35,] 15.975 10.660
[36,] 11.860 15.975
[37,] 10.400 11.860
[38,] 8.560 10.400
[39,] 6.340 8.560
[40,] 0.240 6.340
[41,] -5.360 0.240
[42,] -12.560 -5.360
[43,] -12.460 -12.560
[44,] -9.580 -12.460
[45,] -7.460 -9.580
[46,] -5.440 -7.460
[47,] -6.625 -5.440
[48,] -5.440 -6.625
[49,] -5.500 -5.440
[50,] -7.140 -5.500
[51,] -10.460 -7.140
[52,] -2.060 -10.460
[53,] -2.060 -2.060
[54,] 8.540 -2.060
[55,] 8.740 8.540
[56,] 12.120 8.740
[57,] 4.040 12.120
[58,] -2.640 4.040
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.300 5.060
2 9.160 7.300
3 20.140 9.160
4 21.640 20.140
5 23.640 21.640
6 21.240 23.640
7 15.240 21.240
8 12.520 15.240
9 11.140 12.520
10 15.660 11.140
11 8.275 15.660
12 1.460 8.275
13 -4.800 1.460
14 -6.540 -4.800
15 -12.660 -6.540
16 -18.260 -12.660
17 -22.160 -18.260
18 -19.960 -22.160
19 -18.760 -19.960
20 -17.280 -18.760
21 -18.360 -17.280
22 -18.240 -18.360
23 -17.625 -18.240
24 -12.940 -17.625
25 -7.400 -12.940
26 -4.040 -7.400
27 -3.360 -4.040
28 -1.560 -3.360
29 5.940 -1.560
30 2.740 5.940
31 7.240 2.740
32 2.220 7.240
33 10.640 2.220
34 10.660 10.640
35 15.975 10.660
36 11.860 15.975
37 10.400 11.860
38 8.560 10.400
39 6.340 8.560
40 0.240 6.340
41 -5.360 0.240
42 -12.560 -5.360
43 -12.460 -12.560
44 -9.580 -12.460
45 -7.460 -9.580
46 -5.440 -7.460
47 -6.625 -5.440
48 -5.440 -6.625
49 -5.500 -5.440
50 -7.140 -5.500
51 -10.460 -7.140
52 -2.060 -10.460
53 -2.060 -2.060
54 8.540 -2.060
55 8.740 8.540
56 12.120 8.740
57 4.040 12.120
58 -2.640 4.040
> 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/70up21292954500.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/80up21292954500.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/90up21292954500.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/10bmo61292954500.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11em5b1292954500.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12i54z1292954500.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13wf1q1292954500.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14zfie1292954500.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/153yyk1292954500.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/16oyf81292954500.tab")
+ }
>
> try(system("convert tmp/1ml9u1292954500.ps tmp/1ml9u1292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xcqx1292954500.ps tmp/2xcqx1292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xcqx1292954500.ps tmp/3xcqx1292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xcqx1292954500.ps tmp/4xcqx1292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xcqx1292954500.ps tmp/5xcqx1292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q38i1292954500.ps tmp/6q38i1292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/70up21292954500.ps tmp/70up21292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/80up21292954500.ps tmp/80up21292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/90up21292954500.ps tmp/90up21292954500.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bmo61292954500.ps tmp/10bmo61292954500.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.442 1.605 5.645