R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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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.
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> x <- array(list(100.29,0,101.12,0,102.65,0,102.71,0,103.39,0,102.8,0,102.07,0,102.15,0,101.21,0,101.27,0,101.86,0,101.65,0,101.94,0,102.62,0,102.71,0,103.39,0,104.51,0,104.09,0,104.29,0,104.57,0,105.39,0,105.15,0,106.13,0,105.46,0,106.47,0,106.62,0,106.52,0,108.04,0,107.15,0,107.32,0,107.76,0,107.26,0,107.89,0,109.08,0,110.4,0,111.03,0,112.05,0,112.28,0,112.8,0,114.17,0,114.92,0,114.65,0,115.49,0,114.67,1,114.71,1,115.15,1,115.03,1),dim=c(2,47),dimnames=list(c('voedingsmiddelen','dummy'),1:47))
> y <- array(NA,dim=c(2,47),dimnames=list(c('voedingsmiddelen','dummy'),1:47))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
voedingsmiddelen dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 100.29 0 1 0 0 0 0 0 0 0 0 0 0 1
2 101.12 0 0 1 0 0 0 0 0 0 0 0 0 2
3 102.65 0 0 0 1 0 0 0 0 0 0 0 0 3
4 102.71 0 0 0 0 1 0 0 0 0 0 0 0 4
5 103.39 0 0 0 0 0 1 0 0 0 0 0 0 5
6 102.80 0 0 0 0 0 0 1 0 0 0 0 0 6
7 102.07 0 0 0 0 0 0 0 1 0 0 0 0 7
8 102.15 0 0 0 0 0 0 0 0 1 0 0 0 8
9 101.21 0 0 0 0 0 0 0 0 0 1 0 0 9
10 101.27 0 0 0 0 0 0 0 0 0 0 1 0 10
11 101.86 0 0 0 0 0 0 0 0 0 0 0 1 11
12 101.65 0 0 0 0 0 0 0 0 0 0 0 0 12
13 101.94 0 1 0 0 0 0 0 0 0 0 0 0 13
14 102.62 0 0 1 0 0 0 0 0 0 0 0 0 14
15 102.71 0 0 0 1 0 0 0 0 0 0 0 0 15
16 103.39 0 0 0 0 1 0 0 0 0 0 0 0 16
17 104.51 0 0 0 0 0 1 0 0 0 0 0 0 17
18 104.09 0 0 0 0 0 0 1 0 0 0 0 0 18
19 104.29 0 0 0 0 0 0 0 1 0 0 0 0 19
20 104.57 0 0 0 0 0 0 0 0 1 0 0 0 20
21 105.39 0 0 0 0 0 0 0 0 0 1 0 0 21
22 105.15 0 0 0 0 0 0 0 0 0 0 1 0 22
23 106.13 0 0 0 0 0 0 0 0 0 0 0 1 23
24 105.46 0 0 0 0 0 0 0 0 0 0 0 0 24
25 106.47 0 1 0 0 0 0 0 0 0 0 0 0 25
26 106.62 0 0 1 0 0 0 0 0 0 0 0 0 26
27 106.52 0 0 0 1 0 0 0 0 0 0 0 0 27
28 108.04 0 0 0 0 1 0 0 0 0 0 0 0 28
29 107.15 0 0 0 0 0 1 0 0 0 0 0 0 29
30 107.32 0 0 0 0 0 0 1 0 0 0 0 0 30
31 107.76 0 0 0 0 0 0 0 1 0 0 0 0 31
32 107.26 0 0 0 0 0 0 0 0 1 0 0 0 32
33 107.89 0 0 0 0 0 0 0 0 0 1 0 0 33
34 109.08 0 0 0 0 0 0 0 0 0 0 1 0 34
35 110.40 0 0 0 0 0 0 0 0 0 0 0 1 35
36 111.03 0 0 0 0 0 0 0 0 0 0 0 0 36
37 112.05 0 1 0 0 0 0 0 0 0 0 0 0 37
38 112.28 0 0 1 0 0 0 0 0 0 0 0 0 38
39 112.80 0 0 0 1 0 0 0 0 0 0 0 0 39
40 114.17 0 0 0 0 1 0 0 0 0 0 0 0 40
41 114.92 0 0 0 0 0 1 0 0 0 0 0 0 41
42 114.65 0 0 0 0 0 0 1 0 0 0 0 0 42
43 115.49 0 0 0 0 0 0 0 1 0 0 0 0 43
44 114.67 1 0 0 0 0 0 0 0 1 0 0 0 44
45 114.71 1 0 0 0 0 0 0 0 0 1 0 0 45
46 115.15 1 0 0 0 0 0 0 0 0 0 1 0 46
47 115.03 1 0 0 0 0 0 0 0 0 0 0 1 47
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy M1 M2 M3 M4
98.37200 2.01867 0.73972 0.89244 1.08267 1.67039
M5 M6 M7 M8 M9 M10
1.76561 1.16833 1.03606 -0.02839 -0.21067 -0.16794
M11 t
0.20478 0.31978
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.2612 -1.0112 0.0035 0.9902 2.3315
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 98.37200 0.91026 108.070 <2e-16 ***
dummy 2.01867 0.91026 2.218 0.0336 *
M1 0.73972 1.07346 0.689 0.4956
M2 0.89244 1.07219 0.832 0.4112
M3 1.08267 1.07120 1.011 0.3195
M4 1.67039 1.07049 1.560 0.1282
M5 1.76561 1.07007 1.650 0.1084
M6 1.16833 1.06993 1.092 0.2828
M7 1.03606 1.07007 0.968 0.3400
M8 -0.02839 1.09109 -0.026 0.9794
M9 -0.21067 1.09012 -0.193 0.8479
M10 -0.16794 1.08942 -0.154 0.8784
M11 0.20478 1.08900 0.188 0.8520
t 0.31978 0.01740 18.376 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.401 on 33 degrees of freedom
Multiple R-squared: 0.9381, Adjusted R-squared: 0.9137
F-statistic: 38.47 on 13 and 33 DF, p-value: 3.792e-16
> 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,] 0.12001560 0.24003120 0.8799844
[2,] 0.04863705 0.09727411 0.9513629
[3,] 0.04926228 0.09852456 0.9507377
[4,] 0.05707052 0.11414104 0.9429295
[5,] 0.37070247 0.74140495 0.6292975
[6,] 0.53712463 0.92575074 0.4628754
[7,] 0.87394481 0.25211038 0.1260552
[8,] 0.86817374 0.26365251 0.1318263
[9,] 0.88450491 0.23099019 0.1154951
[10,] 0.87791152 0.24417696 0.1220885
[11,] 0.82379484 0.35241032 0.1762052
[12,] 0.82905984 0.34188032 0.1709402
[13,] 0.71002147 0.57995705 0.2899785
[14,] 0.53746041 0.92507917 0.4625396
> postscript(file="/var/www/html/rcomp/tmp/1k11a1229926098.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25r201229926098.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37s9i1229926098.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4n8e11229926098.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5d72q1229926098.ps",horizontal=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 = 47
Frequency = 1
1 2 3 4 5 6
0.85850000 1.21600000 2.23600000 1.38850000 1.65350000 1.34100000
7 8 9 10 11 12
0.42350000 1.24816667 0.17066667 -0.13183333 -0.23433333 -0.55933333
13 14 15 16 17 18
-1.32883333 -1.12133333 -1.54133333 -1.76883333 -1.06383333 -1.20633333
19 20 21 22 23 24
-1.19383333 -0.16916667 0.51333333 -0.08916667 0.19833333 -0.58666667
25 26 27 28 29 30
-0.63616667 -0.95866667 -1.56866667 -0.95616667 -2.26116667 -1.81366667
31 32 33 34 35 36
-1.56116667 -1.31650000 -0.82400000 0.00350000 0.63100000 1.14600000
37 38 39 40 41 42
1.10650000 0.86400000 0.87400000 1.33650000 1.67150000 1.67900000
43 44 45 46 47
2.33150000 0.23750000 0.14000000 0.21750000 -0.59500000
> postscript(file="/var/www/html/rcomp/tmp/6tkb51229926098.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 47
Frequency = 1
lag(myerror, k = 1) myerror
0 0.85850000 NA
1 1.21600000 0.85850000
2 2.23600000 1.21600000
3 1.38850000 2.23600000
4 1.65350000 1.38850000
5 1.34100000 1.65350000
6 0.42350000 1.34100000
7 1.24816667 0.42350000
8 0.17066667 1.24816667
9 -0.13183333 0.17066667
10 -0.23433333 -0.13183333
11 -0.55933333 -0.23433333
12 -1.32883333 -0.55933333
13 -1.12133333 -1.32883333
14 -1.54133333 -1.12133333
15 -1.76883333 -1.54133333
16 -1.06383333 -1.76883333
17 -1.20633333 -1.06383333
18 -1.19383333 -1.20633333
19 -0.16916667 -1.19383333
20 0.51333333 -0.16916667
21 -0.08916667 0.51333333
22 0.19833333 -0.08916667
23 -0.58666667 0.19833333
24 -0.63616667 -0.58666667
25 -0.95866667 -0.63616667
26 -1.56866667 -0.95866667
27 -0.95616667 -1.56866667
28 -2.26116667 -0.95616667
29 -1.81366667 -2.26116667
30 -1.56116667 -1.81366667
31 -1.31650000 -1.56116667
32 -0.82400000 -1.31650000
33 0.00350000 -0.82400000
34 0.63100000 0.00350000
35 1.14600000 0.63100000
36 1.10650000 1.14600000
37 0.86400000 1.10650000
38 0.87400000 0.86400000
39 1.33650000 0.87400000
40 1.67150000 1.33650000
41 1.67900000 1.67150000
42 2.33150000 1.67900000
43 0.23750000 2.33150000
44 0.14000000 0.23750000
45 0.21750000 0.14000000
46 -0.59500000 0.21750000
47 NA -0.59500000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.21600000 0.85850000
[2,] 2.23600000 1.21600000
[3,] 1.38850000 2.23600000
[4,] 1.65350000 1.38850000
[5,] 1.34100000 1.65350000
[6,] 0.42350000 1.34100000
[7,] 1.24816667 0.42350000
[8,] 0.17066667 1.24816667
[9,] -0.13183333 0.17066667
[10,] -0.23433333 -0.13183333
[11,] -0.55933333 -0.23433333
[12,] -1.32883333 -0.55933333
[13,] -1.12133333 -1.32883333
[14,] -1.54133333 -1.12133333
[15,] -1.76883333 -1.54133333
[16,] -1.06383333 -1.76883333
[17,] -1.20633333 -1.06383333
[18,] -1.19383333 -1.20633333
[19,] -0.16916667 -1.19383333
[20,] 0.51333333 -0.16916667
[21,] -0.08916667 0.51333333
[22,] 0.19833333 -0.08916667
[23,] -0.58666667 0.19833333
[24,] -0.63616667 -0.58666667
[25,] -0.95866667 -0.63616667
[26,] -1.56866667 -0.95866667
[27,] -0.95616667 -1.56866667
[28,] -2.26116667 -0.95616667
[29,] -1.81366667 -2.26116667
[30,] -1.56116667 -1.81366667
[31,] -1.31650000 -1.56116667
[32,] -0.82400000 -1.31650000
[33,] 0.00350000 -0.82400000
[34,] 0.63100000 0.00350000
[35,] 1.14600000 0.63100000
[36,] 1.10650000 1.14600000
[37,] 0.86400000 1.10650000
[38,] 0.87400000 0.86400000
[39,] 1.33650000 0.87400000
[40,] 1.67150000 1.33650000
[41,] 1.67900000 1.67150000
[42,] 2.33150000 1.67900000
[43,] 0.23750000 2.33150000
[44,] 0.14000000 0.23750000
[45,] 0.21750000 0.14000000
[46,] -0.59500000 0.21750000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.21600000 0.85850000
2 2.23600000 1.21600000
3 1.38850000 2.23600000
4 1.65350000 1.38850000
5 1.34100000 1.65350000
6 0.42350000 1.34100000
7 1.24816667 0.42350000
8 0.17066667 1.24816667
9 -0.13183333 0.17066667
10 -0.23433333 -0.13183333
11 -0.55933333 -0.23433333
12 -1.32883333 -0.55933333
13 -1.12133333 -1.32883333
14 -1.54133333 -1.12133333
15 -1.76883333 -1.54133333
16 -1.06383333 -1.76883333
17 -1.20633333 -1.06383333
18 -1.19383333 -1.20633333
19 -0.16916667 -1.19383333
20 0.51333333 -0.16916667
21 -0.08916667 0.51333333
22 0.19833333 -0.08916667
23 -0.58666667 0.19833333
24 -0.63616667 -0.58666667
25 -0.95866667 -0.63616667
26 -1.56866667 -0.95866667
27 -0.95616667 -1.56866667
28 -2.26116667 -0.95616667
29 -1.81366667 -2.26116667
30 -1.56116667 -1.81366667
31 -1.31650000 -1.56116667
32 -0.82400000 -1.31650000
33 0.00350000 -0.82400000
34 0.63100000 0.00350000
35 1.14600000 0.63100000
36 1.10650000 1.14600000
37 0.86400000 1.10650000
38 0.87400000 0.86400000
39 1.33650000 0.87400000
40 1.67150000 1.33650000
41 1.67900000 1.67150000
42 2.33150000 1.67900000
43 0.23750000 2.33150000
44 0.14000000 0.23750000
45 0.21750000 0.14000000
46 -0.59500000 0.21750000
> 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/7rk5f1229926098.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8a8a61229926098.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m6g11229926099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109d7d1229926099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11prr31229926099.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/124c2e1229926099.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/136fkm1229926099.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/14i0j01229926099.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/15wmfb1229926099.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/16fw091229926099.tab")
+ }
>
> system("convert tmp/1k11a1229926098.ps tmp/1k11a1229926098.png")
> system("convert tmp/25r201229926098.ps tmp/25r201229926098.png")
> system("convert tmp/37s9i1229926098.ps tmp/37s9i1229926098.png")
> system("convert tmp/4n8e11229926098.ps tmp/4n8e11229926098.png")
> system("convert tmp/5d72q1229926098.ps tmp/5d72q1229926098.png")
> system("convert tmp/6tkb51229926098.ps tmp/6tkb51229926098.png")
> system("convert tmp/7rk5f1229926098.ps tmp/7rk5f1229926098.png")
> system("convert tmp/8a8a61229926098.ps tmp/8a8a61229926098.png")
> system("convert tmp/9m6g11229926099.ps tmp/9m6g11229926099.png")
> system("convert tmp/109d7d1229926099.ps tmp/109d7d1229926099.png")
>
>
> proc.time()
user system elapsed
2.279 1.545 5.962