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(611,0,594,0,595,0,591,0,589,0,584,0,573,0,567,0,569,0,621,0,629,0,628,0,612,0,595,0,597,0,593,0,590,0,580,0,574,0,573,0,573,0,620,0,626,0,620,0,588,0,566,0,557,0,561,0,549,0,532,0,526,0,511,0,499,0,555,0,565,0,542,0,527,0,510,0,514,0,517,0,508,0,493,0,490,0,469,0,478,0,528,0,534,0,518,1,506,1,502,1,516,1,528,1,533,1,536,1,537,1,524,1,536,1,587,1,597,1,581,1),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
1 611 0
2 594 0
3 595 0
4 591 0
5 589 0
6 584 0
7 573 0
8 567 0
9 569 0
10 621 0
11 629 0
12 628 0
13 612 0
14 595 0
15 597 0
16 593 0
17 590 0
18 580 0
19 574 0
20 573 0
21 573 0
22 620 0
23 626 0
24 620 0
25 588 0
26 566 0
27 557 0
28 561 0
29 549 0
30 532 0
31 526 0
32 511 0
33 499 0
34 555 0
35 565 0
36 542 0
37 527 0
38 510 0
39 514 0
40 517 0
41 508 0
42 493 0
43 490 0
44 469 0
45 478 0
46 528 0
47 534 0
48 518 1
49 506 1
50 502 1
51 516 1
52 528 1
53 533 1
54 536 1
55 537 1
56 524 1
57 536 1
58 587 1
59 597 1
60 581 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
562.19 -23.65
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-93.1915 -30.7782 0.8085 31.0585 66.8085
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 562.191 5.975 94.092 <2e-16 ***
X -23.653 12.836 -1.843 0.0705 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 40.96 on 58 degrees of freedom
Multiple R-squared: 0.05531, Adjusted R-squared: 0.03902
F-statistic: 3.396 on 1 and 58 DF, p-value: 0.07049
> 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,] 0.0191196987 0.038239397 0.98088030
[2,] 0.0071491394 0.014298279 0.99285086
[3,] 0.0067253592 0.013450718 0.99327464
[4,] 0.0067045929 0.013409186 0.99329541
[5,] 0.0040871491 0.008174298 0.99591285
[6,] 0.0092898619 0.018579724 0.99071014
[7,] 0.0211205696 0.042241139 0.97887943
[8,] 0.0314089671 0.062817934 0.96859103
[9,] 0.0235536961 0.047107392 0.97644630
[10,] 0.0138147727 0.027629545 0.98618523
[11,] 0.0081720306 0.016344061 0.99182797
[12,] 0.0047927468 0.009585494 0.99520725
[13,] 0.0028492445 0.005698489 0.99715076
[14,] 0.0019582529 0.003916506 0.99804175
[15,] 0.0015703436 0.003140687 0.99842966
[16,] 0.0012586674 0.002517335 0.99874133
[17,] 0.0009827965 0.001965593 0.99901720
[18,] 0.0022439492 0.004487898 0.99775605
[19,] 0.0089081617 0.017816323 0.99109184
[20,] 0.0290453157 0.058090631 0.97095468
[21,] 0.0382508045 0.076501609 0.96174920
[22,] 0.0550599785 0.110119957 0.94494002
[23,] 0.0864118040 0.172823608 0.91358820
[24,] 0.1216002431 0.243200486 0.87839976
[25,] 0.1818880568 0.363776114 0.81811194
[26,] 0.2964768612 0.592953722 0.70352314
[27,] 0.4179834286 0.835966857 0.58201657
[28,] 0.5791940383 0.841611923 0.42080596
[29,] 0.7386463120 0.522707376 0.26135369
[30,] 0.7563995467 0.487200907 0.24360045
[31,] 0.8192934695 0.361413061 0.18070653
[32,] 0.8394505761 0.321098848 0.16054942
[33,] 0.8504047728 0.299190454 0.14959523
[34,] 0.8644671488 0.271065702 0.13553285
[35,] 0.8658964968 0.268207006 0.13410350
[36,] 0.8615796290 0.276840742 0.13842037
[37,] 0.8558460600 0.288307880 0.14415394
[38,] 0.8596792004 0.280641599 0.14032080
[39,] 0.8600980449 0.279803910 0.13990196
[40,] 0.9103013996 0.179397201 0.08969860
[41,] 0.9429963290 0.114007342 0.05700367
[42,] 0.9130933942 0.173813212 0.08690661
[43,] 0.8686776021 0.262644796 0.13132240
[44,] 0.8236387892 0.352722422 0.17636121
[45,] 0.8086072904 0.382785419 0.19139271
[46,] 0.8265152797 0.346969441 0.17348472
[47,] 0.8068617618 0.386276476 0.19313824
[48,] 0.7499864820 0.500027036 0.25001352
[49,] 0.6700334773 0.659933045 0.32996652
[50,] 0.5707625230 0.858474954 0.42923748
[51,] 0.4640697446 0.928139489 0.53593026
> postscript(file="/var/www/html/rcomp/tmp/1nkl21258656375.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26vz11258656375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hacy1258656375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4b7am1258656375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5b9bv1258656375.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 7
48.808511 31.808511 32.808511 28.808511 26.808511 21.808511 10.808511
8 9 10 11 12 13 14
4.808511 6.808511 58.808511 66.808511 65.808511 49.808511 32.808511
15 16 17 18 19 20 21
34.808511 30.808511 27.808511 17.808511 11.808511 10.808511 10.808511
22 23 24 25 26 27 28
57.808511 63.808511 57.808511 25.808511 3.808511 -5.191489 -1.191489
29 30 31 32 33 34 35
-13.191489 -30.191489 -36.191489 -51.191489 -63.191489 -7.191489 2.808511
36 37 38 39 40 41 42
-20.191489 -35.191489 -52.191489 -48.191489 -45.191489 -54.191489 -69.191489
43 44 45 46 47 48 49
-72.191489 -93.191489 -84.191489 -34.191489 -28.191489 -20.538462 -32.538462
50 51 52 53 54 55 56
-36.538462 -22.538462 -10.538462 -5.538462 -2.538462 -1.538462 -14.538462
57 58 59 60
-2.538462 48.461538 58.461538 42.461538
> postscript(file="/var/www/html/rcomp/tmp/6wuv01258656375.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 48.808511 NA
1 31.808511 48.808511
2 32.808511 31.808511
3 28.808511 32.808511
4 26.808511 28.808511
5 21.808511 26.808511
6 10.808511 21.808511
7 4.808511 10.808511
8 6.808511 4.808511
9 58.808511 6.808511
10 66.808511 58.808511
11 65.808511 66.808511
12 49.808511 65.808511
13 32.808511 49.808511
14 34.808511 32.808511
15 30.808511 34.808511
16 27.808511 30.808511
17 17.808511 27.808511
18 11.808511 17.808511
19 10.808511 11.808511
20 10.808511 10.808511
21 57.808511 10.808511
22 63.808511 57.808511
23 57.808511 63.808511
24 25.808511 57.808511
25 3.808511 25.808511
26 -5.191489 3.808511
27 -1.191489 -5.191489
28 -13.191489 -1.191489
29 -30.191489 -13.191489
30 -36.191489 -30.191489
31 -51.191489 -36.191489
32 -63.191489 -51.191489
33 -7.191489 -63.191489
34 2.808511 -7.191489
35 -20.191489 2.808511
36 -35.191489 -20.191489
37 -52.191489 -35.191489
38 -48.191489 -52.191489
39 -45.191489 -48.191489
40 -54.191489 -45.191489
41 -69.191489 -54.191489
42 -72.191489 -69.191489
43 -93.191489 -72.191489
44 -84.191489 -93.191489
45 -34.191489 -84.191489
46 -28.191489 -34.191489
47 -20.538462 -28.191489
48 -32.538462 -20.538462
49 -36.538462 -32.538462
50 -22.538462 -36.538462
51 -10.538462 -22.538462
52 -5.538462 -10.538462
53 -2.538462 -5.538462
54 -1.538462 -2.538462
55 -14.538462 -1.538462
56 -2.538462 -14.538462
57 48.461538 -2.538462
58 58.461538 48.461538
59 42.461538 58.461538
60 NA 42.461538
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 31.808511 48.808511
[2,] 32.808511 31.808511
[3,] 28.808511 32.808511
[4,] 26.808511 28.808511
[5,] 21.808511 26.808511
[6,] 10.808511 21.808511
[7,] 4.808511 10.808511
[8,] 6.808511 4.808511
[9,] 58.808511 6.808511
[10,] 66.808511 58.808511
[11,] 65.808511 66.808511
[12,] 49.808511 65.808511
[13,] 32.808511 49.808511
[14,] 34.808511 32.808511
[15,] 30.808511 34.808511
[16,] 27.808511 30.808511
[17,] 17.808511 27.808511
[18,] 11.808511 17.808511
[19,] 10.808511 11.808511
[20,] 10.808511 10.808511
[21,] 57.808511 10.808511
[22,] 63.808511 57.808511
[23,] 57.808511 63.808511
[24,] 25.808511 57.808511
[25,] 3.808511 25.808511
[26,] -5.191489 3.808511
[27,] -1.191489 -5.191489
[28,] -13.191489 -1.191489
[29,] -30.191489 -13.191489
[30,] -36.191489 -30.191489
[31,] -51.191489 -36.191489
[32,] -63.191489 -51.191489
[33,] -7.191489 -63.191489
[34,] 2.808511 -7.191489
[35,] -20.191489 2.808511
[36,] -35.191489 -20.191489
[37,] -52.191489 -35.191489
[38,] -48.191489 -52.191489
[39,] -45.191489 -48.191489
[40,] -54.191489 -45.191489
[41,] -69.191489 -54.191489
[42,] -72.191489 -69.191489
[43,] -93.191489 -72.191489
[44,] -84.191489 -93.191489
[45,] -34.191489 -84.191489
[46,] -28.191489 -34.191489
[47,] -20.538462 -28.191489
[48,] -32.538462 -20.538462
[49,] -36.538462 -32.538462
[50,] -22.538462 -36.538462
[51,] -10.538462 -22.538462
[52,] -5.538462 -10.538462
[53,] -2.538462 -5.538462
[54,] -1.538462 -2.538462
[55,] -14.538462 -1.538462
[56,] -2.538462 -14.538462
[57,] 48.461538 -2.538462
[58,] 58.461538 48.461538
[59,] 42.461538 58.461538
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 31.808511 48.808511
2 32.808511 31.808511
3 28.808511 32.808511
4 26.808511 28.808511
5 21.808511 26.808511
6 10.808511 21.808511
7 4.808511 10.808511
8 6.808511 4.808511
9 58.808511 6.808511
10 66.808511 58.808511
11 65.808511 66.808511
12 49.808511 65.808511
13 32.808511 49.808511
14 34.808511 32.808511
15 30.808511 34.808511
16 27.808511 30.808511
17 17.808511 27.808511
18 11.808511 17.808511
19 10.808511 11.808511
20 10.808511 10.808511
21 57.808511 10.808511
22 63.808511 57.808511
23 57.808511 63.808511
24 25.808511 57.808511
25 3.808511 25.808511
26 -5.191489 3.808511
27 -1.191489 -5.191489
28 -13.191489 -1.191489
29 -30.191489 -13.191489
30 -36.191489 -30.191489
31 -51.191489 -36.191489
32 -63.191489 -51.191489
33 -7.191489 -63.191489
34 2.808511 -7.191489
35 -20.191489 2.808511
36 -35.191489 -20.191489
37 -52.191489 -35.191489
38 -48.191489 -52.191489
39 -45.191489 -48.191489
40 -54.191489 -45.191489
41 -69.191489 -54.191489
42 -72.191489 -69.191489
43 -93.191489 -72.191489
44 -84.191489 -93.191489
45 -34.191489 -84.191489
46 -28.191489 -34.191489
47 -20.538462 -28.191489
48 -32.538462 -20.538462
49 -36.538462 -32.538462
50 -22.538462 -36.538462
51 -10.538462 -22.538462
52 -5.538462 -10.538462
53 -2.538462 -5.538462
54 -1.538462 -2.538462
55 -14.538462 -1.538462
56 -2.538462 -14.538462
57 48.461538 -2.538462
58 58.461538 48.461538
59 42.461538 58.461538
> 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/7j86z1258656375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81nax1258656375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ckag1258656375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10r6rc1258656375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11y7871258656375.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/12gsn81258656375.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/135dxh1258656375.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/14rxnn1258656375.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/15tnoe1258656375.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/16e3hg1258656375.tab")
+ }
> system("convert tmp/1nkl21258656375.ps tmp/1nkl21258656375.png")
> system("convert tmp/26vz11258656375.ps tmp/26vz11258656375.png")
> system("convert tmp/3hacy1258656375.ps tmp/3hacy1258656375.png")
> system("convert tmp/4b7am1258656375.ps tmp/4b7am1258656375.png")
> system("convert tmp/5b9bv1258656375.ps tmp/5b9bv1258656375.png")
> system("convert tmp/6wuv01258656375.ps tmp/6wuv01258656375.png")
> system("convert tmp/7j86z1258656375.ps tmp/7j86z1258656375.png")
> system("convert tmp/81nax1258656375.ps tmp/81nax1258656375.png")
> system("convert tmp/9ckag1258656375.ps tmp/9ckag1258656375.png")
> system("convert tmp/10r6rc1258656375.ps tmp/10r6rc1258656375.png")
>
>
> proc.time()
user system elapsed
2.421 1.523 3.296