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.
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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.
Type 'q()' to quit R.
> x <- array(list(6.3,1000.00,3,2.1,2547000.00,4,9.1,10550.00,4,15.8,0.02,1,5.2,160000.00,4,10.9,3300.00,1,8.3,52160.00,1,11,0.43,4,3.2,465000.00,5,7.6,0.55,2,6.3,0.08,1,8.6,3000.00,2,6.6,0.79,2,9.5,0.20,2,4.8,1410.00,1,12,60000.00,1,3.3,27660.00,5,11,0.12,2,4.7,85000.00,1,10.4,0.10,3,7.4,1040.00,4,2.1,521000.00,5,7.7,0.01,4,17.9,0.01,1,6.1,62000.00,1,8.2,0.12,1,8.4,1350.00,3,11.9,0.02,3,10.8,0.05,3,13.8,1700.00,1,14.3,3500.00,1,15.2,0.48,2,10,10000.00,4,11.9,1620.00,2,6.5,192000.00,4,7.5,2500.00,5,10.6,0.28,3,7.4,4235.00,1,8.4,6800.00,2,5.7,0.75,2,4.9,3600.00,3,3.2,55500.00,5,8.1,0.06,2,11,0.90,2,4.9,2000.00,3,13.2,0.10,2,9.7,4190.00,4,12.8,3500.00,1),dim=c(3,48),dimnames=list(c('SWS','Body-Weight','Overall-danger
'),1:48))
> y <- array(NA,dim=c(3,48),dimnames=list(c('SWS','Body-Weight','Overall-danger
'),1:48))
> 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
SWS Body-Weight Overall-danger\r
1 6.3 1.000e+03 3
2 2.1 2.547e+06 4
3 9.1 1.055e+04 4
4 15.8 2.000e-02 1
5 5.2 1.600e+05 4
6 10.9 3.300e+03 1
7 8.3 5.216e+04 1
8 11.0 4.300e-01 4
9 3.2 4.650e+05 5
10 7.6 5.500e-01 2
11 6.3 8.000e-02 1
12 8.6 3.000e+03 2
13 6.6 7.900e-01 2
14 9.5 2.000e-01 2
15 4.8 1.410e+03 1
16 12.0 6.000e+04 1
17 3.3 2.766e+04 5
18 11.0 1.200e-01 2
19 4.7 8.500e+04 1
20 10.4 1.000e-01 3
21 7.4 1.040e+03 4
22 2.1 5.210e+05 5
23 7.7 1.000e-02 4
24 17.9 1.000e-02 1
25 6.1 6.200e+04 1
26 8.2 1.200e-01 1
27 8.4 1.350e+03 3
28 11.9 2.000e-02 3
29 10.8 5.000e-02 3
30 13.8 1.700e+03 1
31 14.3 3.500e+03 1
32 15.2 4.800e-01 2
33 10.0 1.000e+04 4
34 11.9 1.620e+03 2
35 6.5 1.920e+05 4
36 7.5 2.500e+03 5
37 10.6 2.800e-01 3
38 7.4 4.235e+03 1
39 8.4 6.800e+03 2
40 5.7 7.500e-01 2
41 4.9 3.600e+03 3
42 3.2 5.550e+04 5
43 8.1 6.000e-02 2
44 11.0 9.000e-01 2
45 4.9 2.000e+03 3
46 13.2 1.000e-01 2
47 9.7 4.190e+03 4
48 12.8 3.500e+03 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Body-Weight` `Overall-danger\r`
1.175e+01 -2.598e-06 -1.109e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.83453 -2.51575 0.03767 2.22879 7.26181
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.175e+01 9.767e-01 12.028 1.18e-15 ***
`Body-Weight` -2.598e-06 1.257e-06 -2.066 0.04464 *
`Overall-danger\r` -1.109e+00 3.467e-01 -3.199 0.00253 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.134 on 45 degrees of freedom
Multiple R-squared: 0.3004, Adjusted R-squared: 0.2694
F-statistic: 9.663 on 2 and 45 DF, p-value: 0.0003224
> 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,] 0.5480660 0.9038681 0.4519340
[2,] 0.5899462 0.8201076 0.4100538
[3,] 0.6110782 0.7778437 0.3889218
[4,] 0.5283314 0.9433372 0.4716686
[5,] 0.4703084 0.9406168 0.5296916
[6,] 0.5631377 0.8737245 0.4368623
[7,] 0.4558859 0.9117718 0.5441141
[8,] 0.4200069 0.8400138 0.5799931
[9,] 0.3238553 0.6477106 0.6761447
[10,] 0.4918485 0.9836971 0.5081515
[11,] 0.4573982 0.9147965 0.5426018
[12,] 0.4397488 0.8794976 0.5602512
[13,] 0.3853910 0.7707819 0.6146090
[14,] 0.5353891 0.9292218 0.4646109
[15,] 0.4926936 0.9853873 0.5073064
[16,] 0.4053555 0.8107110 0.5946445
[17,] 0.3732643 0.7465286 0.6267357
[18,] 0.2972627 0.5945254 0.7027373
[19,] 0.6724812 0.6550375 0.3275188
[20,] 0.7154081 0.5691839 0.2845919
[21,] 0.7020343 0.5959314 0.2979657
[22,] 0.6246755 0.7506490 0.3753245
[23,] 0.6299765 0.7400469 0.3700235
[24,] 0.5825957 0.8348087 0.4174043
[25,] 0.5605232 0.8789536 0.4394768
[26,] 0.5695783 0.8608435 0.4304217
[27,] 0.7487594 0.5024811 0.2512406
[28,] 0.7264208 0.5471584 0.2735792
[29,] 0.6992656 0.6014688 0.3007344
[30,] 0.6826758 0.6346484 0.3173242
[31,] 0.5843510 0.8312980 0.4156490
[32,] 0.5328191 0.9343617 0.4671809
[33,] 0.5140202 0.9719597 0.4859798
[34,] 0.3989117 0.7978233 0.6010883
[35,] 0.4640904 0.9281808 0.5359096
[36,] 0.4889896 0.9779792 0.5110104
[37,] 0.3568482 0.7136963 0.6431518
> postscript(file="/var/www/html/rcomp/tmp/1seko1292365156.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2seko1292365156.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3l6js1292365156.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4l6js1292365156.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5l6js1292365156.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 = 48
Frequency = 1
1 2 3 4 5 6
-2.11738811 1.40520533 1.81652347 5.16180533 -1.69526525 0.27037736
7 8 9 10 11 12
-2.20270392 3.68911991 -1.79389283 -1.92908879 -4.33819451 -0.92129742
13 14 15 16 17 18
-2.92908816 -0.02908970 -5.83453210 1.51766126 -2.82992710 1.47091010
19 20 21 22 23 24
-5.71739874 1.98001455 0.09182030 -2.74842724 0.38911882 7.26180531
25 26 27 28 29 30
-4.37714354 -2.43819441 -0.01647895 3.48001434 2.38001442 3.16622120
31 32 33 34 35 36
3.67089688 5.67091103 2.71509479 2.37511790 -0.31214206 1.30471730
37 38 39 40 41 42
2.18001502 -3.22719389 -1.11142654 -3.82908827 -3.51063435 -2.85760992
43 44 45 46 47 48
-1.42909006 1.47091212 -3.51479051 3.67091004 2.40000273 2.17089688
> postscript(file="/var/www/html/rcomp/tmp/6wx1v1292365156.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 = 48
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.11738811 NA
1 1.40520533 -2.11738811
2 1.81652347 1.40520533
3 5.16180533 1.81652347
4 -1.69526525 5.16180533
5 0.27037736 -1.69526525
6 -2.20270392 0.27037736
7 3.68911991 -2.20270392
8 -1.79389283 3.68911991
9 -1.92908879 -1.79389283
10 -4.33819451 -1.92908879
11 -0.92129742 -4.33819451
12 -2.92908816 -0.92129742
13 -0.02908970 -2.92908816
14 -5.83453210 -0.02908970
15 1.51766126 -5.83453210
16 -2.82992710 1.51766126
17 1.47091010 -2.82992710
18 -5.71739874 1.47091010
19 1.98001455 -5.71739874
20 0.09182030 1.98001455
21 -2.74842724 0.09182030
22 0.38911882 -2.74842724
23 7.26180531 0.38911882
24 -4.37714354 7.26180531
25 -2.43819441 -4.37714354
26 -0.01647895 -2.43819441
27 3.48001434 -0.01647895
28 2.38001442 3.48001434
29 3.16622120 2.38001442
30 3.67089688 3.16622120
31 5.67091103 3.67089688
32 2.71509479 5.67091103
33 2.37511790 2.71509479
34 -0.31214206 2.37511790
35 1.30471730 -0.31214206
36 2.18001502 1.30471730
37 -3.22719389 2.18001502
38 -1.11142654 -3.22719389
39 -3.82908827 -1.11142654
40 -3.51063435 -3.82908827
41 -2.85760992 -3.51063435
42 -1.42909006 -2.85760992
43 1.47091212 -1.42909006
44 -3.51479051 1.47091212
45 3.67091004 -3.51479051
46 2.40000273 3.67091004
47 2.17089688 2.40000273
48 NA 2.17089688
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.40520533 -2.11738811
[2,] 1.81652347 1.40520533
[3,] 5.16180533 1.81652347
[4,] -1.69526525 5.16180533
[5,] 0.27037736 -1.69526525
[6,] -2.20270392 0.27037736
[7,] 3.68911991 -2.20270392
[8,] -1.79389283 3.68911991
[9,] -1.92908879 -1.79389283
[10,] -4.33819451 -1.92908879
[11,] -0.92129742 -4.33819451
[12,] -2.92908816 -0.92129742
[13,] -0.02908970 -2.92908816
[14,] -5.83453210 -0.02908970
[15,] 1.51766126 -5.83453210
[16,] -2.82992710 1.51766126
[17,] 1.47091010 -2.82992710
[18,] -5.71739874 1.47091010
[19,] 1.98001455 -5.71739874
[20,] 0.09182030 1.98001455
[21,] -2.74842724 0.09182030
[22,] 0.38911882 -2.74842724
[23,] 7.26180531 0.38911882
[24,] -4.37714354 7.26180531
[25,] -2.43819441 -4.37714354
[26,] -0.01647895 -2.43819441
[27,] 3.48001434 -0.01647895
[28,] 2.38001442 3.48001434
[29,] 3.16622120 2.38001442
[30,] 3.67089688 3.16622120
[31,] 5.67091103 3.67089688
[32,] 2.71509479 5.67091103
[33,] 2.37511790 2.71509479
[34,] -0.31214206 2.37511790
[35,] 1.30471730 -0.31214206
[36,] 2.18001502 1.30471730
[37,] -3.22719389 2.18001502
[38,] -1.11142654 -3.22719389
[39,] -3.82908827 -1.11142654
[40,] -3.51063435 -3.82908827
[41,] -2.85760992 -3.51063435
[42,] -1.42909006 -2.85760992
[43,] 1.47091212 -1.42909006
[44,] -3.51479051 1.47091212
[45,] 3.67091004 -3.51479051
[46,] 2.40000273 3.67091004
[47,] 2.17089688 2.40000273
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.40520533 -2.11738811
2 1.81652347 1.40520533
3 5.16180533 1.81652347
4 -1.69526525 5.16180533
5 0.27037736 -1.69526525
6 -2.20270392 0.27037736
7 3.68911991 -2.20270392
8 -1.79389283 3.68911991
9 -1.92908879 -1.79389283
10 -4.33819451 -1.92908879
11 -0.92129742 -4.33819451
12 -2.92908816 -0.92129742
13 -0.02908970 -2.92908816
14 -5.83453210 -0.02908970
15 1.51766126 -5.83453210
16 -2.82992710 1.51766126
17 1.47091010 -2.82992710
18 -5.71739874 1.47091010
19 1.98001455 -5.71739874
20 0.09182030 1.98001455
21 -2.74842724 0.09182030
22 0.38911882 -2.74842724
23 7.26180531 0.38911882
24 -4.37714354 7.26180531
25 -2.43819441 -4.37714354
26 -0.01647895 -2.43819441
27 3.48001434 -0.01647895
28 2.38001442 3.48001434
29 3.16622120 2.38001442
30 3.67089688 3.16622120
31 5.67091103 3.67089688
32 2.71509479 5.67091103
33 2.37511790 2.71509479
34 -0.31214206 2.37511790
35 1.30471730 -0.31214206
36 2.18001502 1.30471730
37 -3.22719389 2.18001502
38 -1.11142654 -3.22719389
39 -3.82908827 -1.11142654
40 -3.51063435 -3.82908827
41 -2.85760992 -3.51063435
42 -1.42909006 -2.85760992
43 1.47091212 -1.42909006
44 -3.51479051 1.47091212
45 3.67091004 -3.51479051
46 2.40000273 3.67091004
47 2.17089688 2.40000273
> 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/7o6if1292365156.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8o6if1292365156.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9o6if1292365156.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hghj1292365156.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kgyo1292365156.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/12ozwu1292365156.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/13kqc31292365156.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/1459sr1292365156.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/1599rf1292365156.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/16caq31292365156.tab")
+ }
>
> try(system("convert tmp/1seko1292365156.ps tmp/1seko1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/2seko1292365156.ps tmp/2seko1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/3l6js1292365156.ps tmp/3l6js1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/4l6js1292365156.ps tmp/4l6js1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/5l6js1292365156.ps tmp/5l6js1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wx1v1292365156.ps tmp/6wx1v1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/7o6if1292365156.ps tmp/7o6if1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/8o6if1292365156.ps tmp/8o6if1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/9o6if1292365156.ps tmp/9o6if1292365156.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hghj1292365156.ps tmp/10hghj1292365156.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.394 1.698 6.194