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.
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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
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> x <- array(list(100.35,102.1,100.35,102.86,100.36,102.99,100.39,103.73,100.34,105.02,100.34,104.43,100.35,104.63,100.43,104.93,100.47,105.87,100.67,105.66,100.75,106.76,100.78,106,100.79,107.22,100.67,107.33,100.64,107.11,100.64,108.86,100.76,107.72,100.79,107.88,100.79,108.38,100.9,107.72,100.98,108.41,101.11,109.9,101.18,111.45,101.22,112.18,101.23,113.34,101.09,113.46,101.26,114.06,101.28,115.54,101.43,116.39,101.53,115.94,101.54,116.97,101.54,115.94,101.79,115.91,102.18,116.43,102.37,116.26,102.46,116.35,102.46,117.9,102.03,117.7,102.26,117.53,102.33,117.86,102.44,117.65,102.5,116.51,102.52,115.93,102.66,115.31,102.72,115),dim=c(2,45),dimnames=list(c('Ktot','Vmtot'),1:45))
> y <- array(NA,dim=c(2,45),dimnames=list(c('Ktot','Vmtot'),1:45))
> 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'
> #'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
Ktot Vmtot M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 100.35 102.10 1 0 0 0 0 0 0 0 0 0 0
2 100.35 102.86 0 1 0 0 0 0 0 0 0 0 0
3 100.36 102.99 0 0 1 0 0 0 0 0 0 0 0
4 100.39 103.73 0 0 0 1 0 0 0 0 0 0 0
5 100.34 105.02 0 0 0 0 1 0 0 0 0 0 0
6 100.34 104.43 0 0 0 0 0 1 0 0 0 0 0
7 100.35 104.63 0 0 0 0 0 0 1 0 0 0 0
8 100.43 104.93 0 0 0 0 0 0 0 1 0 0 0
9 100.47 105.87 0 0 0 0 0 0 0 0 1 0 0
10 100.67 105.66 0 0 0 0 0 0 0 0 0 1 0
11 100.75 106.76 0 0 0 0 0 0 0 0 0 0 1
12 100.78 106.00 0 0 0 0 0 0 0 0 0 0 0
13 100.79 107.22 1 0 0 0 0 0 0 0 0 0 0
14 100.67 107.33 0 1 0 0 0 0 0 0 0 0 0
15 100.64 107.11 0 0 1 0 0 0 0 0 0 0 0
16 100.64 108.86 0 0 0 1 0 0 0 0 0 0 0
17 100.76 107.72 0 0 0 0 1 0 0 0 0 0 0
18 100.79 107.88 0 0 0 0 0 1 0 0 0 0 0
19 100.79 108.38 0 0 0 0 0 0 1 0 0 0 0
20 100.90 107.72 0 0 0 0 0 0 0 1 0 0 0
21 100.98 108.41 0 0 0 0 0 0 0 0 1 0 0
22 101.11 109.90 0 0 0 0 0 0 0 0 0 1 0
23 101.18 111.45 0 0 0 0 0 0 0 0 0 0 1
24 101.22 112.18 0 0 0 0 0 0 0 0 0 0 0
25 101.23 113.34 1 0 0 0 0 0 0 0 0 0 0
26 101.09 113.46 0 1 0 0 0 0 0 0 0 0 0
27 101.26 114.06 0 0 1 0 0 0 0 0 0 0 0
28 101.28 115.54 0 0 0 1 0 0 0 0 0 0 0
29 101.43 116.39 0 0 0 0 1 0 0 0 0 0 0
30 101.53 115.94 0 0 0 0 0 1 0 0 0 0 0
31 101.54 116.97 0 0 0 0 0 0 1 0 0 0 0
32 101.54 115.94 0 0 0 0 0 0 0 1 0 0 0
33 101.79 115.91 0 0 0 0 0 0 0 0 1 0 0
34 102.18 116.43 0 0 0 0 0 0 0 0 0 1 0
35 102.37 116.26 0 0 0 0 0 0 0 0 0 0 1
36 102.46 116.35 0 0 0 0 0 0 0 0 0 0 0
37 102.46 117.90 1 0 0 0 0 0 0 0 0 0 0
38 102.03 117.70 0 1 0 0 0 0 0 0 0 0 0
39 102.26 117.53 0 0 1 0 0 0 0 0 0 0 0
40 102.33 117.86 0 0 0 1 0 0 0 0 0 0 0
41 102.44 117.65 0 0 0 0 1 0 0 0 0 0 0
42 102.50 116.51 0 0 0 0 0 1 0 0 0 0 0
43 102.52 115.93 0 0 0 0 0 0 1 0 0 0 0
44 102.66 115.31 0 0 0 0 0 0 0 1 0 0 0
45 102.72 115.00 0 0 0 0 0 0 0 0 1 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Vmtot M1 M2 M3 M4
86.33914 0.13584 -0.09307 -0.29239 -0.20894 -0.32497
M5 M6 M7 M8 M9 M10
-0.26930 -0.15320 -0.18225 -0.03149 0.03220 -0.05166
M11
-0.05062
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.51695 -0.24790 -0.02085 0.23465 0.72705
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 86.33914 1.22323 70.583 < 2e-16 ***
Vmtot 0.13584 0.01080 12.578 6.29e-14 ***
M1 -0.09307 0.28375 -0.328 0.745
M2 -0.29239 0.28364 -1.031 0.310
M3 -0.20894 0.28361 -0.737 0.467
M4 -0.32497 0.28336 -1.147 0.260
M5 -0.26930 0.28337 -0.950 0.349
M6 -0.15320 0.28338 -0.541 0.593
M7 -0.18225 0.28336 -0.643 0.525
M8 -0.03149 0.28342 -0.111 0.912
M9 0.03220 0.28337 0.114 0.910
M10 -0.05166 0.30306 -0.170 0.866
M11 -0.05062 0.30293 -0.167 0.868
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.371 on 32 degrees of freedom
Multiple R-squared: 0.837, Adjusted R-squared: 0.7758
F-statistic: 13.69 on 12 and 32 DF, p-value: 2.206e-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,] 2.364328e-03 4.728656e-03 0.9976357
[2,] 3.165983e-03 6.331965e-03 0.9968340
[3,] 1.317605e-03 2.635209e-03 0.9986824
[4,] 3.638281e-04 7.276562e-04 0.9996362
[5,] 2.224135e-04 4.448270e-04 0.9997776
[6,] 1.698906e-04 3.397811e-04 0.9998301
[7,] 4.037038e-05 8.074077e-05 0.9999596
[8,] 7.392088e-06 1.478418e-05 0.9999926
[9,] 1.829876e-06 3.659752e-06 0.9999982
[10,] 3.957021e-07 7.914042e-07 0.9999996
[11,] 8.596165e-08 1.719233e-07 0.9999999
[12,] 1.173299e-08 2.346598e-08 1.0000000
[13,] 4.352621e-09 8.705243e-09 1.0000000
[14,] 6.784876e-08 1.356975e-07 0.9999999
> postscript(file="/var/www/html/rcomp/tmp/1y5qi1258758056.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ko4q1258758056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bz4z1258758056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4n6bn1258758056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5drp11258758056.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 = 45
Frequency = 1
1 2 3 4 5 6
0.234654500 0.330744437 0.239631632 0.285138069 0.004232747 -0.031720843
7 8 9 10 11 12
-0.019834834 -0.131346523 -0.282727793 0.029653360 -0.040809604 0.041812350
13 14 15 16 17 18
-0.020846873 0.043539137 -0.040029629 -0.161721705 0.057464445 -0.050369230
19 20 21 22 23 24
-0.089235253 -0.040340436 -0.117761677 -0.106308715 -0.247899729 -0.357679542
25 26 27 28 29 30
-0.412188358 -0.369160750 -0.364118407 -0.429133652 -0.450269326 -0.405240532
31 32 33 34 35 36
-0.506101815 -0.516946156 -0.326562516 0.076655355 0.288709333 0.315867192
37 38 39 40 41 42
0.198380731 -0.005122824 0.164516404 0.305717288 0.388572133 0.487330605
43 44 45
0.615171902 0.688633115 0.727051986
> postscript(file="/var/www/html/rcomp/tmp/6oeom1258758056.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 = 45
Frequency = 1
lag(myerror, k = 1) myerror
0 0.234654500 NA
1 0.330744437 0.234654500
2 0.239631632 0.330744437
3 0.285138069 0.239631632
4 0.004232747 0.285138069
5 -0.031720843 0.004232747
6 -0.019834834 -0.031720843
7 -0.131346523 -0.019834834
8 -0.282727793 -0.131346523
9 0.029653360 -0.282727793
10 -0.040809604 0.029653360
11 0.041812350 -0.040809604
12 -0.020846873 0.041812350
13 0.043539137 -0.020846873
14 -0.040029629 0.043539137
15 -0.161721705 -0.040029629
16 0.057464445 -0.161721705
17 -0.050369230 0.057464445
18 -0.089235253 -0.050369230
19 -0.040340436 -0.089235253
20 -0.117761677 -0.040340436
21 -0.106308715 -0.117761677
22 -0.247899729 -0.106308715
23 -0.357679542 -0.247899729
24 -0.412188358 -0.357679542
25 -0.369160750 -0.412188358
26 -0.364118407 -0.369160750
27 -0.429133652 -0.364118407
28 -0.450269326 -0.429133652
29 -0.405240532 -0.450269326
30 -0.506101815 -0.405240532
31 -0.516946156 -0.506101815
32 -0.326562516 -0.516946156
33 0.076655355 -0.326562516
34 0.288709333 0.076655355
35 0.315867192 0.288709333
36 0.198380731 0.315867192
37 -0.005122824 0.198380731
38 0.164516404 -0.005122824
39 0.305717288 0.164516404
40 0.388572133 0.305717288
41 0.487330605 0.388572133
42 0.615171902 0.487330605
43 0.688633115 0.615171902
44 0.727051986 0.688633115
45 NA 0.727051986
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.330744437 0.234654500
[2,] 0.239631632 0.330744437
[3,] 0.285138069 0.239631632
[4,] 0.004232747 0.285138069
[5,] -0.031720843 0.004232747
[6,] -0.019834834 -0.031720843
[7,] -0.131346523 -0.019834834
[8,] -0.282727793 -0.131346523
[9,] 0.029653360 -0.282727793
[10,] -0.040809604 0.029653360
[11,] 0.041812350 -0.040809604
[12,] -0.020846873 0.041812350
[13,] 0.043539137 -0.020846873
[14,] -0.040029629 0.043539137
[15,] -0.161721705 -0.040029629
[16,] 0.057464445 -0.161721705
[17,] -0.050369230 0.057464445
[18,] -0.089235253 -0.050369230
[19,] -0.040340436 -0.089235253
[20,] -0.117761677 -0.040340436
[21,] -0.106308715 -0.117761677
[22,] -0.247899729 -0.106308715
[23,] -0.357679542 -0.247899729
[24,] -0.412188358 -0.357679542
[25,] -0.369160750 -0.412188358
[26,] -0.364118407 -0.369160750
[27,] -0.429133652 -0.364118407
[28,] -0.450269326 -0.429133652
[29,] -0.405240532 -0.450269326
[30,] -0.506101815 -0.405240532
[31,] -0.516946156 -0.506101815
[32,] -0.326562516 -0.516946156
[33,] 0.076655355 -0.326562516
[34,] 0.288709333 0.076655355
[35,] 0.315867192 0.288709333
[36,] 0.198380731 0.315867192
[37,] -0.005122824 0.198380731
[38,] 0.164516404 -0.005122824
[39,] 0.305717288 0.164516404
[40,] 0.388572133 0.305717288
[41,] 0.487330605 0.388572133
[42,] 0.615171902 0.487330605
[43,] 0.688633115 0.615171902
[44,] 0.727051986 0.688633115
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.330744437 0.234654500
2 0.239631632 0.330744437
3 0.285138069 0.239631632
4 0.004232747 0.285138069
5 -0.031720843 0.004232747
6 -0.019834834 -0.031720843
7 -0.131346523 -0.019834834
8 -0.282727793 -0.131346523
9 0.029653360 -0.282727793
10 -0.040809604 0.029653360
11 0.041812350 -0.040809604
12 -0.020846873 0.041812350
13 0.043539137 -0.020846873
14 -0.040029629 0.043539137
15 -0.161721705 -0.040029629
16 0.057464445 -0.161721705
17 -0.050369230 0.057464445
18 -0.089235253 -0.050369230
19 -0.040340436 -0.089235253
20 -0.117761677 -0.040340436
21 -0.106308715 -0.117761677
22 -0.247899729 -0.106308715
23 -0.357679542 -0.247899729
24 -0.412188358 -0.357679542
25 -0.369160750 -0.412188358
26 -0.364118407 -0.369160750
27 -0.429133652 -0.364118407
28 -0.450269326 -0.429133652
29 -0.405240532 -0.450269326
30 -0.506101815 -0.405240532
31 -0.516946156 -0.506101815
32 -0.326562516 -0.516946156
33 0.076655355 -0.326562516
34 0.288709333 0.076655355
35 0.315867192 0.288709333
36 0.198380731 0.315867192
37 -0.005122824 0.198380731
38 0.164516404 -0.005122824
39 0.305717288 0.164516404
40 0.388572133 0.305717288
41 0.487330605 0.388572133
42 0.615171902 0.487330605
43 0.688633115 0.615171902
44 0.727051986 0.688633115
> 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/774b01258758056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8f6kv1258758056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vldt1258758056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105xdc1258758056.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11auzh1258758056.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/1275u81258758056.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/13zyps1258758056.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/14zg5e1258758056.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/15ngwt1258758056.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/169vun1258758056.tab")
+ }
>
> system("convert tmp/1y5qi1258758056.ps tmp/1y5qi1258758056.png")
> system("convert tmp/2ko4q1258758056.ps tmp/2ko4q1258758056.png")
> system("convert tmp/3bz4z1258758056.ps tmp/3bz4z1258758056.png")
> system("convert tmp/4n6bn1258758056.ps tmp/4n6bn1258758056.png")
> system("convert tmp/5drp11258758056.ps tmp/5drp11258758056.png")
> system("convert tmp/6oeom1258758056.ps tmp/6oeom1258758056.png")
> system("convert tmp/774b01258758056.ps tmp/774b01258758056.png")
> system("convert tmp/8f6kv1258758056.ps tmp/8f6kv1258758056.png")
> system("convert tmp/9vldt1258758056.ps tmp/9vldt1258758056.png")
> system("convert tmp/105xdc1258758056.ps tmp/105xdc1258758056.png")
>
>
> proc.time()
user system elapsed
2.241 1.554 4.053