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.
R is a collaborative project with many contributors.
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(22.680,1,22.052,1,21.467,1,21.383,1,21.777,1,21.928,1,21.814,1,22.937,1,23.595,1,20.830,1,19.650,1,19.195,1,19.644,0,18.483,0,18.079,0,19.178,0,18.391,0,18.441,0,18.584,0,20.108,0,20.148,0,19.394,0,17.745,0,17.696,0,17.032,0,16.438,0,15.683,0,15.594,0,15.713,0,15.937,0,16.171,0,15.928,0,16.348,0,15.579,0,15.305,0,15.648,0,14.954,0,15.137,0,15.839,0,16.050,0,15.168,0,17.064,0,16.005,0,14.886,0,14.931,0,14.544,0,13.812,0),dim=c(2,47),dimnames=list(c('gk','cr'),1:47))
> y <- array(NA,dim=c(2,47),dimnames=list(c('gk','cr'),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 = '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
gk cr M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 22.680 1 1 0 0 0 0 0 0 0 0 0 0
2 22.052 1 0 1 0 0 0 0 0 0 0 0 0
3 21.467 1 0 0 1 0 0 0 0 0 0 0 0
4 21.383 1 0 0 0 1 0 0 0 0 0 0 0
5 21.777 1 0 0 0 0 1 0 0 0 0 0 0
6 21.928 1 0 0 0 0 0 1 0 0 0 0 0
7 21.814 1 0 0 0 0 0 0 1 0 0 0 0
8 22.937 1 0 0 0 0 0 0 0 1 0 0 0
9 23.595 1 0 0 0 0 0 0 0 0 1 0 0
10 20.830 1 0 0 0 0 0 0 0 0 0 1 0
11 19.650 1 0 0 0 0 0 0 0 0 0 0 1
12 19.195 1 0 0 0 0 0 0 0 0 0 0 0
13 19.644 0 1 0 0 0 0 0 0 0 0 0 0
14 18.483 0 0 1 0 0 0 0 0 0 0 0 0
15 18.079 0 0 0 1 0 0 0 0 0 0 0 0
16 19.178 0 0 0 0 1 0 0 0 0 0 0 0
17 18.391 0 0 0 0 0 1 0 0 0 0 0 0
18 18.441 0 0 0 0 0 0 1 0 0 0 0 0
19 18.584 0 0 0 0 0 0 0 1 0 0 0 0
20 20.108 0 0 0 0 0 0 0 0 1 0 0 0
21 20.148 0 0 0 0 0 0 0 0 0 1 0 0
22 19.394 0 0 0 0 0 0 0 0 0 0 1 0
23 17.745 0 0 0 0 0 0 0 0 0 0 0 1
24 17.696 0 0 0 0 0 0 0 0 0 0 0 0
25 17.032 0 1 0 0 0 0 0 0 0 0 0 0
26 16.438 0 0 1 0 0 0 0 0 0 0 0 0
27 15.683 0 0 0 1 0 0 0 0 0 0 0 0
28 15.594 0 0 0 0 1 0 0 0 0 0 0 0
29 15.713 0 0 0 0 0 1 0 0 0 0 0 0
30 15.937 0 0 0 0 0 0 1 0 0 0 0 0
31 16.171 0 0 0 0 0 0 0 1 0 0 0 0
32 15.928 0 0 0 0 0 0 0 0 1 0 0 0
33 16.348 0 0 0 0 0 0 0 0 0 1 0 0
34 15.579 0 0 0 0 0 0 0 0 0 0 1 0
35 15.305 0 0 0 0 0 0 0 0 0 0 0 1
36 15.648 0 0 0 0 0 0 0 0 0 0 0 0
37 14.954 0 1 0 0 0 0 0 0 0 0 0 0
38 15.137 0 0 1 0 0 0 0 0 0 0 0 0
39 15.839 0 0 0 1 0 0 0 0 0 0 0 0
40 16.050 0 0 0 0 1 0 0 0 0 0 0 0
41 15.168 0 0 0 0 0 1 0 0 0 0 0 0
42 17.064 0 0 0 0 0 0 1 0 0 0 0 0
43 16.005 0 0 0 0 0 0 0 1 0 0 0 0
44 14.886 0 0 0 0 0 0 0 0 1 0 0 0
45 14.931 0 0 0 0 0 0 0 0 0 1 0 0
46 14.544 0 0 0 0 0 0 0 0 0 0 1 0
47 13.812 0 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) cr M1 M2 M3 M4
15.8798 4.8996 1.4728 0.9228 0.6623 0.9466
M5 M6 M7 M8 M9 M10
0.6576 1.2378 1.0388 1.3601 1.6508 0.4821
M11
-0.4767
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.5996 -1.0471 -0.3206 1.2441 3.0322
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.8798 1.0187 15.588 < 2e-16 ***
cr 4.8996 0.5801 8.446 7.31e-10 ***
M1 1.4728 1.3240 1.112 0.274
M2 0.9228 1.3240 0.697 0.491
M3 0.6623 1.3240 0.500 0.620
M4 0.9466 1.3240 0.715 0.480
M5 0.6576 1.3240 0.497 0.623
M6 1.2378 1.3240 0.935 0.356
M7 1.0388 1.3240 0.785 0.438
M8 1.3601 1.3240 1.027 0.312
M9 1.6508 1.3240 1.247 0.221
M10 0.4821 1.3240 0.364 0.718
M11 -0.4767 1.3240 -0.360 0.721
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.732 on 34 degrees of freedom
Multiple R-squared: 0.6913, Adjusted R-squared: 0.5823
F-statistic: 6.344 on 12 and 34 DF, p-value: 9.944e-06
> 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,] 1.420061e-02 0.0284012153 0.9857994
[2,] 3.318590e-03 0.0066371794 0.9966814
[3,] 7.380975e-04 0.0014761950 0.9992619
[4,] 1.399912e-04 0.0002799824 0.9998600
[5,] 7.322077e-05 0.0001464415 0.9999268
[6,] 6.321768e-05 0.0001264354 0.9999368
[7,] 6.941342e-03 0.0138826830 0.9930587
[8,] 4.054691e-02 0.0810938225 0.9594531
[9,] 1.060714e-01 0.2121428157 0.8939286
[10,] 6.355343e-01 0.7289313368 0.3644657
[11,] 7.963184e-01 0.4073632248 0.2036816
[12,] 8.176558e-01 0.3646883197 0.1823442
[13,] 8.385264e-01 0.3229471174 0.1614736
[14,] 8.061797e-01 0.3876405833 0.1938203
[15,] 7.814511e-01 0.4370978391 0.2185489
[16,] 6.518236e-01 0.6963527547 0.3481764
> postscript(file="/var/www/html/rcomp/tmp/1p2601258788504.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2tw451258788504.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33l0v1258788504.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4axu51258788504.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5b0ng1258788504.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
0.427766355 0.349766355 0.025266355 -0.342983645 0.340016355 -0.089233645
7 8 9 10 11 12
-0.004233645 0.797516355 1.164766355 -0.431483645 -0.652733645 -1.584429907
13 14 15 16 17 18
2.291411215 1.680411215 1.536911215 2.351661215 1.853661215 1.323411215
19 20 21 22 23 24
1.665411215 2.868161215 2.617411215 3.032161215 2.341911215 1.816214953
25 26 27 28 29 30
-0.320588785 -0.364588785 -0.859088785 -1.232338785 -0.824338785 -1.180588785
31 32 33 34 35 36
-0.747588785 -1.311838785 -1.182588785 -0.782838785 -0.098088785 -0.231785047
37 38 39 40 41 42
-2.398588785 -1.665588785 -0.703088785 -0.776338785 -1.369338785 -0.053588785
43 44 45 46 47
-0.913588785 -2.353838785 -2.599588785 -1.817838785 -1.591088785
> postscript(file="/var/www/html/rcomp/tmp/6ar161258788504.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 0.427766355 NA
1 0.349766355 0.427766355
2 0.025266355 0.349766355
3 -0.342983645 0.025266355
4 0.340016355 -0.342983645
5 -0.089233645 0.340016355
6 -0.004233645 -0.089233645
7 0.797516355 -0.004233645
8 1.164766355 0.797516355
9 -0.431483645 1.164766355
10 -0.652733645 -0.431483645
11 -1.584429907 -0.652733645
12 2.291411215 -1.584429907
13 1.680411215 2.291411215
14 1.536911215 1.680411215
15 2.351661215 1.536911215
16 1.853661215 2.351661215
17 1.323411215 1.853661215
18 1.665411215 1.323411215
19 2.868161215 1.665411215
20 2.617411215 2.868161215
21 3.032161215 2.617411215
22 2.341911215 3.032161215
23 1.816214953 2.341911215
24 -0.320588785 1.816214953
25 -0.364588785 -0.320588785
26 -0.859088785 -0.364588785
27 -1.232338785 -0.859088785
28 -0.824338785 -1.232338785
29 -1.180588785 -0.824338785
30 -0.747588785 -1.180588785
31 -1.311838785 -0.747588785
32 -1.182588785 -1.311838785
33 -0.782838785 -1.182588785
34 -0.098088785 -0.782838785
35 -0.231785047 -0.098088785
36 -2.398588785 -0.231785047
37 -1.665588785 -2.398588785
38 -0.703088785 -1.665588785
39 -0.776338785 -0.703088785
40 -1.369338785 -0.776338785
41 -0.053588785 -1.369338785
42 -0.913588785 -0.053588785
43 -2.353838785 -0.913588785
44 -2.599588785 -2.353838785
45 -1.817838785 -2.599588785
46 -1.591088785 -1.817838785
47 NA -1.591088785
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.349766355 0.427766355
[2,] 0.025266355 0.349766355
[3,] -0.342983645 0.025266355
[4,] 0.340016355 -0.342983645
[5,] -0.089233645 0.340016355
[6,] -0.004233645 -0.089233645
[7,] 0.797516355 -0.004233645
[8,] 1.164766355 0.797516355
[9,] -0.431483645 1.164766355
[10,] -0.652733645 -0.431483645
[11,] -1.584429907 -0.652733645
[12,] 2.291411215 -1.584429907
[13,] 1.680411215 2.291411215
[14,] 1.536911215 1.680411215
[15,] 2.351661215 1.536911215
[16,] 1.853661215 2.351661215
[17,] 1.323411215 1.853661215
[18,] 1.665411215 1.323411215
[19,] 2.868161215 1.665411215
[20,] 2.617411215 2.868161215
[21,] 3.032161215 2.617411215
[22,] 2.341911215 3.032161215
[23,] 1.816214953 2.341911215
[24,] -0.320588785 1.816214953
[25,] -0.364588785 -0.320588785
[26,] -0.859088785 -0.364588785
[27,] -1.232338785 -0.859088785
[28,] -0.824338785 -1.232338785
[29,] -1.180588785 -0.824338785
[30,] -0.747588785 -1.180588785
[31,] -1.311838785 -0.747588785
[32,] -1.182588785 -1.311838785
[33,] -0.782838785 -1.182588785
[34,] -0.098088785 -0.782838785
[35,] -0.231785047 -0.098088785
[36,] -2.398588785 -0.231785047
[37,] -1.665588785 -2.398588785
[38,] -0.703088785 -1.665588785
[39,] -0.776338785 -0.703088785
[40,] -1.369338785 -0.776338785
[41,] -0.053588785 -1.369338785
[42,] -0.913588785 -0.053588785
[43,] -2.353838785 -0.913588785
[44,] -2.599588785 -2.353838785
[45,] -1.817838785 -2.599588785
[46,] -1.591088785 -1.817838785
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.349766355 0.427766355
2 0.025266355 0.349766355
3 -0.342983645 0.025266355
4 0.340016355 -0.342983645
5 -0.089233645 0.340016355
6 -0.004233645 -0.089233645
7 0.797516355 -0.004233645
8 1.164766355 0.797516355
9 -0.431483645 1.164766355
10 -0.652733645 -0.431483645
11 -1.584429907 -0.652733645
12 2.291411215 -1.584429907
13 1.680411215 2.291411215
14 1.536911215 1.680411215
15 2.351661215 1.536911215
16 1.853661215 2.351661215
17 1.323411215 1.853661215
18 1.665411215 1.323411215
19 2.868161215 1.665411215
20 2.617411215 2.868161215
21 3.032161215 2.617411215
22 2.341911215 3.032161215
23 1.816214953 2.341911215
24 -0.320588785 1.816214953
25 -0.364588785 -0.320588785
26 -0.859088785 -0.364588785
27 -1.232338785 -0.859088785
28 -0.824338785 -1.232338785
29 -1.180588785 -0.824338785
30 -0.747588785 -1.180588785
31 -1.311838785 -0.747588785
32 -1.182588785 -1.311838785
33 -0.782838785 -1.182588785
34 -0.098088785 -0.782838785
35 -0.231785047 -0.098088785
36 -2.398588785 -0.231785047
37 -1.665588785 -2.398588785
38 -0.703088785 -1.665588785
39 -0.776338785 -0.703088785
40 -1.369338785 -0.776338785
41 -0.053588785 -1.369338785
42 -0.913588785 -0.053588785
43 -2.353838785 -0.913588785
44 -2.599588785 -2.353838785
45 -1.817838785 -2.599588785
46 -1.591088785 -1.817838785
> 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/7f0be1258788504.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84dsu1258788504.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9odh71258788504.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k9031258788504.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fgt31258788504.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/12zvhr1258788504.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/13xcaf1258788504.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/140v7w1258788504.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/15phqb1258788504.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/1638s91258788504.tab")
+ }
>
> system("convert tmp/1p2601258788504.ps tmp/1p2601258788504.png")
> system("convert tmp/2tw451258788504.ps tmp/2tw451258788504.png")
> system("convert tmp/33l0v1258788504.ps tmp/33l0v1258788504.png")
> system("convert tmp/4axu51258788504.ps tmp/4axu51258788504.png")
> system("convert tmp/5b0ng1258788504.ps tmp/5b0ng1258788504.png")
> system("convert tmp/6ar161258788504.ps tmp/6ar161258788504.png")
> system("convert tmp/7f0be1258788504.ps tmp/7f0be1258788504.png")
> system("convert tmp/84dsu1258788504.ps tmp/84dsu1258788504.png")
> system("convert tmp/9odh71258788504.ps tmp/9odh71258788504.png")
> system("convert tmp/10k9031258788504.ps tmp/10k9031258788504.png")
>
>
> proc.time()
user system elapsed
2.241 1.528 3.366