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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> x <- array(list(8.7,0,8.2,0,8.3,0,8.5,0,8.6,0,8.5,0,8.2,0,8.1,0,7.9,0,8.6,0,8.7,0,8.7,0,8.5,0,8.4,0,8.5,0,8.7,0,8.7,0,8.6,0,8.5,0,8.3,0,8,0,8.2,0,8.1,0,8.1,0,8,0,7.9,0,7.9,0,8,0,8,0,7.9,0,8,0,7.7,0,7.2,0,7.5,0,7.3,0,7,0,7,0,7,0,7.2,0,7.3,0,7.1,0,6.8,0,6.4,0,6.1,0,6.5,0,7.7,0,7.9,0,7.5,0,6.9,1,6.6,1,6.9,1,7.7,1,8,1,8,1,7.7,1,7.3,1,7.4,1,8.1,1,8.3,1,8.2,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> 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
Y X
1 8.7 0
2 8.2 0
3 8.3 0
4 8.5 0
5 8.6 0
6 8.5 0
7 8.2 0
8 8.1 0
9 7.9 0
10 8.6 0
11 8.7 0
12 8.7 0
13 8.5 0
14 8.4 0
15 8.5 0
16 8.7 0
17 8.7 0
18 8.6 0
19 8.5 0
20 8.3 0
21 8.0 0
22 8.2 0
23 8.1 0
24 8.1 0
25 8.0 0
26 7.9 0
27 7.9 0
28 8.0 0
29 8.0 0
30 7.9 0
31 8.0 0
32 7.7 0
33 7.2 0
34 7.5 0
35 7.3 0
36 7.0 0
37 7.0 0
38 7.0 0
39 7.2 0
40 7.3 0
41 7.1 0
42 6.8 0
43 6.4 0
44 6.1 0
45 6.5 0
46 7.7 0
47 7.9 0
48 7.5 0
49 6.9 1
50 6.6 1
51 6.9 1
52 7.7 1
53 8.0 1
54 8.0 1
55 7.7 1
56 7.3 1
57 7.4 1
58 8.1 1
59 8.3 1
60 8.2 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
7.8854 -0.2937
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.7854 -0.4354 0.1146 0.5380 0.8146
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.88542 0.09448 83.465 <2e-16 ***
X -0.29375 0.21125 -1.391 0.170
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6545 on 58 degrees of freedom
Multiple R-squared: 0.03226, Adjusted R-squared: 0.01558
F-statistic: 1.934 on 1 and 58 DF, p-value: 0.1697
> 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,] 6.005695e-02 0.1201139037 0.93994305
[2,] 1.828408e-02 0.0365681615 0.98171592
[3,] 9.864838e-03 0.0197296769 0.99013516
[4,] 7.080882e-03 0.0141617641 0.99291912
[5,] 1.009677e-02 0.0201935450 0.98990323
[6,] 6.011916e-03 0.0120238310 0.99398808
[7,] 4.787560e-03 0.0095751209 0.99521244
[8,] 3.619315e-03 0.0072386307 0.99638068
[9,] 1.710482e-03 0.0034209631 0.99828952
[10,] 7.493980e-04 0.0014987960 0.99925060
[11,] 3.552744e-04 0.0007105489 0.99964473
[12,] 3.082212e-04 0.0006164424 0.99969178
[13,] 2.797673e-04 0.0005595346 0.99972023
[14,] 1.961113e-04 0.0003922225 0.99980389
[15,] 1.221743e-04 0.0002443485 0.99987783
[16,] 8.207048e-05 0.0001641410 0.99991793
[17,] 1.486322e-04 0.0002972643 0.99985137
[18,] 1.224874e-04 0.0002449748 0.99987751
[19,] 1.298770e-04 0.0002597541 0.99987012
[20,] 1.371432e-04 0.0002742863 0.99986286
[21,] 1.874577e-04 0.0003749154 0.99981254
[22,] 3.237524e-04 0.0006475048 0.99967625
[23,] 4.958348e-04 0.0009916697 0.99950417
[24,] 6.032842e-04 0.0012065685 0.99939672
[25,] 7.731814e-04 0.0015463628 0.99922682
[26,] 1.172414e-03 0.0023448286 0.99882759
[27,] 1.759475e-03 0.0035189502 0.99824052
[28,] 3.803296e-03 0.0076065921 0.99619670
[29,] 2.234573e-02 0.0446914640 0.97765427
[30,] 3.593658e-02 0.0718731573 0.96406342
[31,] 6.323533e-02 0.1264706639 0.93676467
[32,] 1.321499e-01 0.2642997567 0.86785012
[33,] 1.986441e-01 0.3972882742 0.80135586
[34,] 2.508655e-01 0.5017310272 0.74913449
[35,] 2.569570e-01 0.5139139447 0.74304303
[36,] 2.483067e-01 0.4966134200 0.75169329
[37,] 2.487744e-01 0.4975487842 0.75122561
[38,] 2.830425e-01 0.5660849631 0.71695752
[39,] 4.232091e-01 0.8464181342 0.57679093
[40,] 7.239151e-01 0.5521697555 0.27608488
[41,] 8.650207e-01 0.2699585956 0.13497930
[42,] 8.057747e-01 0.3884505834 0.19422529
[43,] 7.438153e-01 0.5123693698 0.25618468
[44,] 6.595107e-01 0.6809786760 0.34048934
[45,] 6.685857e-01 0.6628286545 0.33141433
[46,] 8.449986e-01 0.3100027345 0.15500137
[47,] 9.404871e-01 0.1190258413 0.05951292
[48,] 8.992576e-01 0.2014848621 0.10074243
[49,] 8.322424e-01 0.3355151943 0.16775760
[50,] 7.278543e-01 0.5442914803 0.27214574
[51,] 5.710889e-01 0.8578222295 0.42891111
> postscript(file="/var/www/html/rcomp/tmp/1cc9w1260888837.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2o5c21260888837.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/320ma1260888837.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cscu1260888837.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53zdu1260888837.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 = 60
Frequency = 1
1 2 3 4 5 6
0.81458333 0.31458333 0.41458333 0.61458333 0.71458333 0.61458333
7 8 9 10 11 12
0.31458333 0.21458333 0.01458333 0.71458333 0.81458333 0.81458333
13 14 15 16 17 18
0.61458333 0.51458333 0.61458333 0.81458333 0.81458333 0.71458333
19 20 21 22 23 24
0.61458333 0.41458333 0.11458333 0.31458333 0.21458333 0.21458333
25 26 27 28 29 30
0.11458333 0.01458333 0.01458333 0.11458333 0.11458333 0.01458333
31 32 33 34 35 36
0.11458333 -0.18541667 -0.68541667 -0.38541667 -0.58541667 -0.88541667
37 38 39 40 41 42
-0.88541667 -0.88541667 -0.68541667 -0.58541667 -0.78541667 -1.08541667
43 44 45 46 47 48
-1.48541667 -1.78541667 -1.38541667 -0.18541667 0.01458333 -0.38541667
49 50 51 52 53 54
-0.69166667 -0.99166667 -0.69166667 0.10833333 0.40833333 0.40833333
55 56 57 58 59 60
0.10833333 -0.29166667 -0.19166667 0.50833333 0.70833333 0.60833333
> postscript(file="/var/www/html/rcomp/tmp/69al11260888837.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.81458333 NA
1 0.31458333 0.81458333
2 0.41458333 0.31458333
3 0.61458333 0.41458333
4 0.71458333 0.61458333
5 0.61458333 0.71458333
6 0.31458333 0.61458333
7 0.21458333 0.31458333
8 0.01458333 0.21458333
9 0.71458333 0.01458333
10 0.81458333 0.71458333
11 0.81458333 0.81458333
12 0.61458333 0.81458333
13 0.51458333 0.61458333
14 0.61458333 0.51458333
15 0.81458333 0.61458333
16 0.81458333 0.81458333
17 0.71458333 0.81458333
18 0.61458333 0.71458333
19 0.41458333 0.61458333
20 0.11458333 0.41458333
21 0.31458333 0.11458333
22 0.21458333 0.31458333
23 0.21458333 0.21458333
24 0.11458333 0.21458333
25 0.01458333 0.11458333
26 0.01458333 0.01458333
27 0.11458333 0.01458333
28 0.11458333 0.11458333
29 0.01458333 0.11458333
30 0.11458333 0.01458333
31 -0.18541667 0.11458333
32 -0.68541667 -0.18541667
33 -0.38541667 -0.68541667
34 -0.58541667 -0.38541667
35 -0.88541667 -0.58541667
36 -0.88541667 -0.88541667
37 -0.88541667 -0.88541667
38 -0.68541667 -0.88541667
39 -0.58541667 -0.68541667
40 -0.78541667 -0.58541667
41 -1.08541667 -0.78541667
42 -1.48541667 -1.08541667
43 -1.78541667 -1.48541667
44 -1.38541667 -1.78541667
45 -0.18541667 -1.38541667
46 0.01458333 -0.18541667
47 -0.38541667 0.01458333
48 -0.69166667 -0.38541667
49 -0.99166667 -0.69166667
50 -0.69166667 -0.99166667
51 0.10833333 -0.69166667
52 0.40833333 0.10833333
53 0.40833333 0.40833333
54 0.10833333 0.40833333
55 -0.29166667 0.10833333
56 -0.19166667 -0.29166667
57 0.50833333 -0.19166667
58 0.70833333 0.50833333
59 0.60833333 0.70833333
60 NA 0.60833333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.31458333 0.81458333
[2,] 0.41458333 0.31458333
[3,] 0.61458333 0.41458333
[4,] 0.71458333 0.61458333
[5,] 0.61458333 0.71458333
[6,] 0.31458333 0.61458333
[7,] 0.21458333 0.31458333
[8,] 0.01458333 0.21458333
[9,] 0.71458333 0.01458333
[10,] 0.81458333 0.71458333
[11,] 0.81458333 0.81458333
[12,] 0.61458333 0.81458333
[13,] 0.51458333 0.61458333
[14,] 0.61458333 0.51458333
[15,] 0.81458333 0.61458333
[16,] 0.81458333 0.81458333
[17,] 0.71458333 0.81458333
[18,] 0.61458333 0.71458333
[19,] 0.41458333 0.61458333
[20,] 0.11458333 0.41458333
[21,] 0.31458333 0.11458333
[22,] 0.21458333 0.31458333
[23,] 0.21458333 0.21458333
[24,] 0.11458333 0.21458333
[25,] 0.01458333 0.11458333
[26,] 0.01458333 0.01458333
[27,] 0.11458333 0.01458333
[28,] 0.11458333 0.11458333
[29,] 0.01458333 0.11458333
[30,] 0.11458333 0.01458333
[31,] -0.18541667 0.11458333
[32,] -0.68541667 -0.18541667
[33,] -0.38541667 -0.68541667
[34,] -0.58541667 -0.38541667
[35,] -0.88541667 -0.58541667
[36,] -0.88541667 -0.88541667
[37,] -0.88541667 -0.88541667
[38,] -0.68541667 -0.88541667
[39,] -0.58541667 -0.68541667
[40,] -0.78541667 -0.58541667
[41,] -1.08541667 -0.78541667
[42,] -1.48541667 -1.08541667
[43,] -1.78541667 -1.48541667
[44,] -1.38541667 -1.78541667
[45,] -0.18541667 -1.38541667
[46,] 0.01458333 -0.18541667
[47,] -0.38541667 0.01458333
[48,] -0.69166667 -0.38541667
[49,] -0.99166667 -0.69166667
[50,] -0.69166667 -0.99166667
[51,] 0.10833333 -0.69166667
[52,] 0.40833333 0.10833333
[53,] 0.40833333 0.40833333
[54,] 0.10833333 0.40833333
[55,] -0.29166667 0.10833333
[56,] -0.19166667 -0.29166667
[57,] 0.50833333 -0.19166667
[58,] 0.70833333 0.50833333
[59,] 0.60833333 0.70833333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.31458333 0.81458333
2 0.41458333 0.31458333
3 0.61458333 0.41458333
4 0.71458333 0.61458333
5 0.61458333 0.71458333
6 0.31458333 0.61458333
7 0.21458333 0.31458333
8 0.01458333 0.21458333
9 0.71458333 0.01458333
10 0.81458333 0.71458333
11 0.81458333 0.81458333
12 0.61458333 0.81458333
13 0.51458333 0.61458333
14 0.61458333 0.51458333
15 0.81458333 0.61458333
16 0.81458333 0.81458333
17 0.71458333 0.81458333
18 0.61458333 0.71458333
19 0.41458333 0.61458333
20 0.11458333 0.41458333
21 0.31458333 0.11458333
22 0.21458333 0.31458333
23 0.21458333 0.21458333
24 0.11458333 0.21458333
25 0.01458333 0.11458333
26 0.01458333 0.01458333
27 0.11458333 0.01458333
28 0.11458333 0.11458333
29 0.01458333 0.11458333
30 0.11458333 0.01458333
31 -0.18541667 0.11458333
32 -0.68541667 -0.18541667
33 -0.38541667 -0.68541667
34 -0.58541667 -0.38541667
35 -0.88541667 -0.58541667
36 -0.88541667 -0.88541667
37 -0.88541667 -0.88541667
38 -0.68541667 -0.88541667
39 -0.58541667 -0.68541667
40 -0.78541667 -0.58541667
41 -1.08541667 -0.78541667
42 -1.48541667 -1.08541667
43 -1.78541667 -1.48541667
44 -1.38541667 -1.78541667
45 -0.18541667 -1.38541667
46 0.01458333 -0.18541667
47 -0.38541667 0.01458333
48 -0.69166667 -0.38541667
49 -0.99166667 -0.69166667
50 -0.69166667 -0.99166667
51 0.10833333 -0.69166667
52 0.40833333 0.10833333
53 0.40833333 0.40833333
54 0.10833333 0.40833333
55 -0.29166667 0.10833333
56 -0.19166667 -0.29166667
57 0.50833333 -0.19166667
58 0.70833333 0.50833333
59 0.60833333 0.70833333
> 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/7vkfq1260888837.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ag7g1260888837.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91ejf1260888837.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10phwn1260888837.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117dlf1260888838.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/12lvqd1260888838.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/136y0h1260888838.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/14lbd81260888838.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/158yka1260888838.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/16xnfs1260888838.tab")
+ }
>
> try(system("convert tmp/1cc9w1260888837.ps tmp/1cc9w1260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/2o5c21260888837.ps tmp/2o5c21260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/320ma1260888837.ps tmp/320ma1260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cscu1260888837.ps tmp/4cscu1260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/53zdu1260888837.ps tmp/53zdu1260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/69al11260888837.ps tmp/69al11260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vkfq1260888837.ps tmp/7vkfq1260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ag7g1260888837.ps tmp/8ag7g1260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/91ejf1260888837.ps tmp/91ejf1260888837.png",intern=TRUE))
character(0)
> try(system("convert tmp/10phwn1260888837.ps tmp/10phwn1260888837.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.427 1.538 3.226