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(1.4,0.0,1.6,0.0,1.7,0.0,2.0,0.0,2.0,0.0,2.1,0.0,2.5,0.0,2.5,0.0,2.6,0.0,2.7,0.0,3.7,0.0,4.0,0.0,5.0,0.0,5.1,0.0,5.1,0.0,5.0,0.0,5.1,0.0,4.7,0.0,4.5,0.0,4.5,0.0,4.6,0.0,4.6,0.0,4.6,0.0,4.6,0.0,5.3,0.0,5.4,0.0,5.3,0.0,5.2,0.0,5.0,0.0,4.2,0.0,4.3,0.0,4.3,0.0,4.3,0.0,4.0,0.0,4.0,0.0,4.1,0.0,4.4,0.0,3.6,0.0,3.7,0.0,3.8,0.0,3.3,0.0,3.3,0.0,3.3,0.0,3.5,0.0,3.3,0.0,3.3,0.0,3.4,0.0,3.4,0.0,5.2,0.0,5.3,0.0,4.8,1.0,5.0,1.0,4.6,1.0,4.6,1.0,3.5,1.0,3.5,1.0),dim=c(2,56),dimnames=list(c('IndGez','InvlMex'),1:56))
> y <- array(NA,dim=c(2,56),dimnames=list(c('IndGez','InvlMex'),1:56))
> 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
IndGez InvlMex
1 1.4 0
2 1.6 0
3 1.7 0
4 2.0 0
5 2.0 0
6 2.1 0
7 2.5 0
8 2.5 0
9 2.6 0
10 2.7 0
11 3.7 0
12 4.0 0
13 5.0 0
14 5.1 0
15 5.1 0
16 5.0 0
17 5.1 0
18 4.7 0
19 4.5 0
20 4.5 0
21 4.6 0
22 4.6 0
23 4.6 0
24 4.6 0
25 5.3 0
26 5.4 0
27 5.3 0
28 5.2 0
29 5.0 0
30 4.2 0
31 4.3 0
32 4.3 0
33 4.3 0
34 4.0 0
35 4.0 0
36 4.1 0
37 4.4 0
38 3.6 0
39 3.7 0
40 3.8 0
41 3.3 0
42 3.3 0
43 3.3 0
44 3.5 0
45 3.3 0
46 3.3 0
47 3.4 0
48 3.4 0
49 5.2 0
50 5.3 0
51 4.8 1
52 5.0 1
53 4.6 1
54 4.6 1
55 3.5 1
56 3.5 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) InvlMex
3.8880 0.4453
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.4880 -0.5880 0.2393 0.7120 1.5120
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.8880 0.1520 25.578 <2e-16 ***
InvlMex 0.4453 0.4644 0.959 0.342
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.075 on 54 degrees of freedom
Multiple R-squared: 0.01674, Adjusted R-squared: -0.001464
F-statistic: 0.9196 on 1 and 54 DF, p-value: 0.3418
> 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,] 0.05316148 1.063230e-01 9.468385e-01
[2,] 0.03401792 6.803585e-02 9.659821e-01
[3,] 0.06514575 1.302915e-01 9.348543e-01
[4,] 0.07873220 1.574644e-01 9.212678e-01
[5,] 0.10146416 2.029283e-01 8.985358e-01
[6,] 0.13892955 2.778591e-01 8.610704e-01
[7,] 0.51561451 9.687710e-01 4.843855e-01
[8,] 0.79611655 4.077669e-01 2.038834e-01
[9,] 0.98130443 3.739115e-02 1.869557e-02
[10,] 0.99752365 4.952701e-03 2.476351e-03
[11,] 0.99943391 1.132172e-03 5.660862e-04
[12,] 0.99976373 4.725426e-04 2.362713e-04
[13,] 0.99989603 2.079434e-04 1.039717e-04
[14,] 0.99989052 2.189601e-04 1.094800e-04
[15,] 0.99984342 3.131599e-04 1.565799e-04
[16,] 0.99976750 4.649903e-04 2.324952e-04
[17,] 0.99967580 6.484009e-04 3.242005e-04
[18,] 0.99953622 9.275519e-04 4.637760e-04
[19,] 0.99932571 1.348579e-03 6.742897e-04
[20,] 0.99901094 1.978113e-03 9.890565e-04
[21,] 0.99942597 1.148068e-03 5.740338e-04
[22,] 0.99974823 5.035367e-04 2.517684e-04
[23,] 0.99988099 2.380158e-04 1.190079e-04
[24,] 0.99994059 1.188136e-04 5.940678e-05
[25,] 0.99995999 8.001691e-05 4.000846e-05
[26,] 0.99991402 1.719617e-04 8.598083e-05
[27,] 0.99983486 3.302742e-04 1.651371e-04
[28,] 0.99969387 6.122614e-04 3.061307e-04
[29,] 0.99945304 1.093910e-03 5.469551e-04
[30,] 0.99886640 2.267207e-03 1.133604e-03
[31,] 0.99773695 4.526099e-03 2.263050e-03
[32,] 0.99582359 8.352825e-03 4.176412e-03
[33,] 0.99408914 1.182171e-02 5.910857e-03
[34,] 0.98903618 2.192764e-02 1.096382e-02
[35,] 0.98010838 3.978324e-02 1.989162e-02
[36,] 0.96527543 6.944914e-02 3.472457e-02
[37,] 0.94847193 1.030561e-01 5.152807e-02
[38,] 0.92652485 1.469503e-01 7.347515e-02
[39,] 0.89990249 2.001950e-01 1.000975e-01
[40,] 0.85665170 2.866966e-01 1.433483e-01
[41,] 0.82560779 3.487844e-01 1.743922e-01
[42,] 0.81012690 3.797462e-01 1.898731e-01
[43,] 0.81985102 3.602980e-01 1.801490e-01
[44,] 0.93410214 1.317957e-01 6.589786e-02
[45,] 0.87677284 2.464543e-01 1.232272e-01
[46,] 0.78102313 4.379537e-01 2.189769e-01
[47,] 0.66765853 6.646829e-01 3.323415e-01
> postscript(file="/var/www/html/rcomp/tmp/14wi51258729844.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hspe1258729844.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xkig1258729844.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4v0oc1258729844.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50vr21258729844.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 = 56
Frequency = 1
1 2 3 4 5 6 7
-2.4880000 -2.2880000 -2.1880000 -1.8880000 -1.8880000 -1.7880000 -1.3880000
8 9 10 11 12 13 14
-1.3880000 -1.2880000 -1.1880000 -0.1880000 0.1120000 1.1120000 1.2120000
15 16 17 18 19 20 21
1.2120000 1.1120000 1.2120000 0.8120000 0.6120000 0.6120000 0.7120000
22 23 24 25 26 27 28
0.7120000 0.7120000 0.7120000 1.4120000 1.5120000 1.4120000 1.3120000
29 30 31 32 33 34 35
1.1120000 0.3120000 0.4120000 0.4120000 0.4120000 0.1120000 0.1120000
36 37 38 39 40 41 42
0.2120000 0.5120000 -0.2880000 -0.1880000 -0.0880000 -0.5880000 -0.5880000
43 44 45 46 47 48 49
-0.5880000 -0.3880000 -0.5880000 -0.5880000 -0.4880000 -0.4880000 1.3120000
50 51 52 53 54 55 56
1.4120000 0.4666667 0.6666667 0.2666667 0.2666667 -0.8333333 -0.8333333
> postscript(file="/var/www/html/rcomp/tmp/6tmpo1258729844.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.4880000 NA
1 -2.2880000 -2.4880000
2 -2.1880000 -2.2880000
3 -1.8880000 -2.1880000
4 -1.8880000 -1.8880000
5 -1.7880000 -1.8880000
6 -1.3880000 -1.7880000
7 -1.3880000 -1.3880000
8 -1.2880000 -1.3880000
9 -1.1880000 -1.2880000
10 -0.1880000 -1.1880000
11 0.1120000 -0.1880000
12 1.1120000 0.1120000
13 1.2120000 1.1120000
14 1.2120000 1.2120000
15 1.1120000 1.2120000
16 1.2120000 1.1120000
17 0.8120000 1.2120000
18 0.6120000 0.8120000
19 0.6120000 0.6120000
20 0.7120000 0.6120000
21 0.7120000 0.7120000
22 0.7120000 0.7120000
23 0.7120000 0.7120000
24 1.4120000 0.7120000
25 1.5120000 1.4120000
26 1.4120000 1.5120000
27 1.3120000 1.4120000
28 1.1120000 1.3120000
29 0.3120000 1.1120000
30 0.4120000 0.3120000
31 0.4120000 0.4120000
32 0.4120000 0.4120000
33 0.1120000 0.4120000
34 0.1120000 0.1120000
35 0.2120000 0.1120000
36 0.5120000 0.2120000
37 -0.2880000 0.5120000
38 -0.1880000 -0.2880000
39 -0.0880000 -0.1880000
40 -0.5880000 -0.0880000
41 -0.5880000 -0.5880000
42 -0.5880000 -0.5880000
43 -0.3880000 -0.5880000
44 -0.5880000 -0.3880000
45 -0.5880000 -0.5880000
46 -0.4880000 -0.5880000
47 -0.4880000 -0.4880000
48 1.3120000 -0.4880000
49 1.4120000 1.3120000
50 0.4666667 1.4120000
51 0.6666667 0.4666667
52 0.2666667 0.6666667
53 0.2666667 0.2666667
54 -0.8333333 0.2666667
55 -0.8333333 -0.8333333
56 NA -0.8333333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.2880000 -2.4880000
[2,] -2.1880000 -2.2880000
[3,] -1.8880000 -2.1880000
[4,] -1.8880000 -1.8880000
[5,] -1.7880000 -1.8880000
[6,] -1.3880000 -1.7880000
[7,] -1.3880000 -1.3880000
[8,] -1.2880000 -1.3880000
[9,] -1.1880000 -1.2880000
[10,] -0.1880000 -1.1880000
[11,] 0.1120000 -0.1880000
[12,] 1.1120000 0.1120000
[13,] 1.2120000 1.1120000
[14,] 1.2120000 1.2120000
[15,] 1.1120000 1.2120000
[16,] 1.2120000 1.1120000
[17,] 0.8120000 1.2120000
[18,] 0.6120000 0.8120000
[19,] 0.6120000 0.6120000
[20,] 0.7120000 0.6120000
[21,] 0.7120000 0.7120000
[22,] 0.7120000 0.7120000
[23,] 0.7120000 0.7120000
[24,] 1.4120000 0.7120000
[25,] 1.5120000 1.4120000
[26,] 1.4120000 1.5120000
[27,] 1.3120000 1.4120000
[28,] 1.1120000 1.3120000
[29,] 0.3120000 1.1120000
[30,] 0.4120000 0.3120000
[31,] 0.4120000 0.4120000
[32,] 0.4120000 0.4120000
[33,] 0.1120000 0.4120000
[34,] 0.1120000 0.1120000
[35,] 0.2120000 0.1120000
[36,] 0.5120000 0.2120000
[37,] -0.2880000 0.5120000
[38,] -0.1880000 -0.2880000
[39,] -0.0880000 -0.1880000
[40,] -0.5880000 -0.0880000
[41,] -0.5880000 -0.5880000
[42,] -0.5880000 -0.5880000
[43,] -0.3880000 -0.5880000
[44,] -0.5880000 -0.3880000
[45,] -0.5880000 -0.5880000
[46,] -0.4880000 -0.5880000
[47,] -0.4880000 -0.4880000
[48,] 1.3120000 -0.4880000
[49,] 1.4120000 1.3120000
[50,] 0.4666667 1.4120000
[51,] 0.6666667 0.4666667
[52,] 0.2666667 0.6666667
[53,] 0.2666667 0.2666667
[54,] -0.8333333 0.2666667
[55,] -0.8333333 -0.8333333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.2880000 -2.4880000
2 -2.1880000 -2.2880000
3 -1.8880000 -2.1880000
4 -1.8880000 -1.8880000
5 -1.7880000 -1.8880000
6 -1.3880000 -1.7880000
7 -1.3880000 -1.3880000
8 -1.2880000 -1.3880000
9 -1.1880000 -1.2880000
10 -0.1880000 -1.1880000
11 0.1120000 -0.1880000
12 1.1120000 0.1120000
13 1.2120000 1.1120000
14 1.2120000 1.2120000
15 1.1120000 1.2120000
16 1.2120000 1.1120000
17 0.8120000 1.2120000
18 0.6120000 0.8120000
19 0.6120000 0.6120000
20 0.7120000 0.6120000
21 0.7120000 0.7120000
22 0.7120000 0.7120000
23 0.7120000 0.7120000
24 1.4120000 0.7120000
25 1.5120000 1.4120000
26 1.4120000 1.5120000
27 1.3120000 1.4120000
28 1.1120000 1.3120000
29 0.3120000 1.1120000
30 0.4120000 0.3120000
31 0.4120000 0.4120000
32 0.4120000 0.4120000
33 0.1120000 0.4120000
34 0.1120000 0.1120000
35 0.2120000 0.1120000
36 0.5120000 0.2120000
37 -0.2880000 0.5120000
38 -0.1880000 -0.2880000
39 -0.0880000 -0.1880000
40 -0.5880000 -0.0880000
41 -0.5880000 -0.5880000
42 -0.5880000 -0.5880000
43 -0.3880000 -0.5880000
44 -0.5880000 -0.3880000
45 -0.5880000 -0.5880000
46 -0.4880000 -0.5880000
47 -0.4880000 -0.4880000
48 1.3120000 -0.4880000
49 1.4120000 1.3120000
50 0.4666667 1.4120000
51 0.6666667 0.4666667
52 0.2666667 0.6666667
53 0.2666667 0.2666667
54 -0.8333333 0.2666667
55 -0.8333333 -0.8333333
> 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/7hsb51258729844.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8f8kc1258729844.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ldmo1258729844.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nnn91258729844.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110nr61258729844.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/1224pa1258729844.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/13q3551258729844.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/14yc5j1258729844.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/1558441258729844.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/16s1q91258729844.tab")
+ }
>
> system("convert tmp/14wi51258729844.ps tmp/14wi51258729844.png")
> system("convert tmp/2hspe1258729844.ps tmp/2hspe1258729844.png")
> system("convert tmp/3xkig1258729844.ps tmp/3xkig1258729844.png")
> system("convert tmp/4v0oc1258729844.ps tmp/4v0oc1258729844.png")
> system("convert tmp/50vr21258729844.ps tmp/50vr21258729844.png")
> system("convert tmp/6tmpo1258729844.ps tmp/6tmpo1258729844.png")
> system("convert tmp/7hsb51258729844.ps tmp/7hsb51258729844.png")
> system("convert tmp/8f8kc1258729844.ps tmp/8f8kc1258729844.png")
> system("convert tmp/9ldmo1258729844.ps tmp/9ldmo1258729844.png")
> system("convert tmp/10nnn91258729844.ps tmp/10nnn91258729844.png")
>
>
> proc.time()
user system elapsed
2.428 1.560 5.058