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
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Type 'q()' to quit R.
> x <- array(list(10.9,0,10,0,9.2,0,9.2,0,9.5,0,9.6,0,9.5,0,9.1,0,8.9,0,9,0,10.1,0,10.3,0,10.2,0,9.6,0,9.2,0,9.3,0,9.4,0,9.4,0,9.2,0,9,0,9,0,9,0,9.8,0,10,0,9.8,0,9.3,0,9,0,9,0,9.1,0,9.1,0,9.1,0,9.2,0,8.8,0,8.3,0,8.4,0,8.1,0,7.7,1,7.9,1,7.9,1,8,1,7.9,1,7.6,1,7.1,1,6.8,1,6.5,1,6.9,1,8.2,1,8.7,1,8.3,1,7.9,1,7.5,1,7.8,1,8.3,1,8.4,1,8.2,1,7.7,1,7.2,1,7.3,1,8.1,1,8.5,1),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 10.9 0
2 10.0 0
3 9.2 0
4 9.2 0
5 9.5 0
6 9.6 0
7 9.5 0
8 9.1 0
9 8.9 0
10 9.0 0
11 10.1 0
12 10.3 0
13 10.2 0
14 9.6 0
15 9.2 0
16 9.3 0
17 9.4 0
18 9.4 0
19 9.2 0
20 9.0 0
21 9.0 0
22 9.0 0
23 9.8 0
24 10.0 0
25 9.8 0
26 9.3 0
27 9.0 0
28 9.0 0
29 9.1 0
30 9.1 0
31 9.1 0
32 9.2 0
33 8.8 0
34 8.3 0
35 8.4 0
36 8.1 0
37 7.7 1
38 7.9 1
39 7.9 1
40 8.0 1
41 7.9 1
42 7.6 1
43 7.1 1
44 6.8 1
45 6.5 1
46 6.9 1
47 8.2 1
48 8.7 1
49 8.3 1
50 7.9 1
51 7.5 1
52 7.8 1
53 8.3 1
54 8.4 1
55 8.2 1
56 7.7 1
57 7.2 1
58 7.3 1
59 8.1 1
60 8.5 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
9.322 -1.556
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.26667 -0.32222 -0.04444 0.35833 1.57778
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.32222 0.09422 98.94 < 2e-16 ***
X -1.55556 0.14897 -10.44 6.1e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5653 on 58 degrees of freedom
Multiple R-squared: 0.6528, Adjusted R-squared: 0.6468
F-statistic: 109 on 1 and 58 DF, p-value: 6.101e-15
> 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,] 0.9180074 0.1639853 0.08199263
[2,] 0.8506159 0.2987681 0.14938407
[3,] 0.7688026 0.4623948 0.23119738
[4,] 0.7421635 0.5156730 0.25783650
[5,] 0.7525790 0.4948420 0.24742101
[6,] 0.7184700 0.5630601 0.28153003
[7,] 0.7305137 0.5389726 0.26948632
[8,] 0.7990404 0.4019191 0.20095957
[9,] 0.8313299 0.3373403 0.16867015
[10,] 0.7799189 0.4401622 0.22008109
[11,] 0.7402044 0.5195913 0.25979563
[12,] 0.6837149 0.6325703 0.31628513
[13,] 0.6172320 0.7655359 0.38276797
[14,] 0.5486356 0.9027289 0.45136444
[15,] 0.4929863 0.9859726 0.50701369
[16,] 0.4673733 0.9347467 0.53262667
[17,] 0.4355681 0.8711363 0.56443186
[18,] 0.3992308 0.7984616 0.60076920
[19,] 0.3876049 0.7752099 0.61239506
[20,] 0.4516120 0.9032239 0.54838803
[21,] 0.4754061 0.9508123 0.52459385
[22,] 0.4317412 0.8634824 0.56825881
[23,] 0.4027477 0.8054954 0.59725231
[24,] 0.3715156 0.7430312 0.62848438
[25,] 0.3350587 0.6701173 0.66494135
[26,] 0.3035643 0.6071285 0.69643573
[27,] 0.2800151 0.5600302 0.71998492
[28,] 0.2799682 0.5599365 0.72003176
[29,] 0.2881517 0.5763033 0.71184835
[30,] 0.3604029 0.7208058 0.63959710
[31,] 0.3946303 0.7892606 0.60536972
[32,] 0.4678354 0.9356708 0.53216461
[33,] 0.3912305 0.7824610 0.60876950
[34,] 0.3214396 0.6428793 0.67856036
[35,] 0.2561688 0.5123375 0.74383123
[36,] 0.2031239 0.4062478 0.79687612
[37,] 0.1526617 0.3053234 0.84733830
[38,] 0.1132709 0.2265418 0.88672909
[39,] 0.1225101 0.2450201 0.87748994
[40,] 0.2038230 0.4076460 0.79617702
[41,] 0.5287436 0.9425129 0.47125645
[42,] 0.7341194 0.5317612 0.26588062
[43,] 0.6853264 0.6293472 0.31467358
[44,] 0.7932328 0.4135344 0.20676719
[45,] 0.7644920 0.4710160 0.23550801
[46,] 0.6705240 0.6589520 0.32947602
[47,] 0.6187425 0.7625149 0.38125746
[48,] 0.5036381 0.9927237 0.49636186
[49,] 0.4300814 0.8601629 0.56991857
[50,] 0.4026263 0.8052525 0.59737373
[51,] 0.3136035 0.6272070 0.68639651
> postscript(file="/var/www/html/rcomp/tmp/17op21258796727.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jm421258796727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36rf71258796727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/434991258796727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kqi71258796727.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
1.57777778 0.67777778 -0.12222222 -0.12222222 0.17777778 0.27777778
7 8 9 10 11 12
0.17777778 -0.22222222 -0.42222222 -0.32222222 0.77777778 0.97777778
13 14 15 16 17 18
0.87777778 0.27777778 -0.12222222 -0.02222222 0.07777778 0.07777778
19 20 21 22 23 24
-0.12222222 -0.32222222 -0.32222222 -0.32222222 0.47777778 0.67777778
25 26 27 28 29 30
0.47777778 -0.02222222 -0.32222222 -0.32222222 -0.22222222 -0.22222222
31 32 33 34 35 36
-0.22222222 -0.12222222 -0.52222222 -1.02222222 -0.92222222 -1.22222222
37 38 39 40 41 42
-0.06666667 0.13333333 0.13333333 0.23333333 0.13333333 -0.16666667
43 44 45 46 47 48
-0.66666667 -0.96666667 -1.26666667 -0.86666667 0.43333333 0.93333333
49 50 51 52 53 54
0.53333333 0.13333333 -0.26666667 0.03333333 0.53333333 0.63333333
55 56 57 58 59 60
0.43333333 -0.06666667 -0.56666667 -0.46666667 0.33333333 0.73333333
> postscript(file="/var/www/html/rcomp/tmp/6o4b41258796727.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 1.57777778 NA
1 0.67777778 1.57777778
2 -0.12222222 0.67777778
3 -0.12222222 -0.12222222
4 0.17777778 -0.12222222
5 0.27777778 0.17777778
6 0.17777778 0.27777778
7 -0.22222222 0.17777778
8 -0.42222222 -0.22222222
9 -0.32222222 -0.42222222
10 0.77777778 -0.32222222
11 0.97777778 0.77777778
12 0.87777778 0.97777778
13 0.27777778 0.87777778
14 -0.12222222 0.27777778
15 -0.02222222 -0.12222222
16 0.07777778 -0.02222222
17 0.07777778 0.07777778
18 -0.12222222 0.07777778
19 -0.32222222 -0.12222222
20 -0.32222222 -0.32222222
21 -0.32222222 -0.32222222
22 0.47777778 -0.32222222
23 0.67777778 0.47777778
24 0.47777778 0.67777778
25 -0.02222222 0.47777778
26 -0.32222222 -0.02222222
27 -0.32222222 -0.32222222
28 -0.22222222 -0.32222222
29 -0.22222222 -0.22222222
30 -0.22222222 -0.22222222
31 -0.12222222 -0.22222222
32 -0.52222222 -0.12222222
33 -1.02222222 -0.52222222
34 -0.92222222 -1.02222222
35 -1.22222222 -0.92222222
36 -0.06666667 -1.22222222
37 0.13333333 -0.06666667
38 0.13333333 0.13333333
39 0.23333333 0.13333333
40 0.13333333 0.23333333
41 -0.16666667 0.13333333
42 -0.66666667 -0.16666667
43 -0.96666667 -0.66666667
44 -1.26666667 -0.96666667
45 -0.86666667 -1.26666667
46 0.43333333 -0.86666667
47 0.93333333 0.43333333
48 0.53333333 0.93333333
49 0.13333333 0.53333333
50 -0.26666667 0.13333333
51 0.03333333 -0.26666667
52 0.53333333 0.03333333
53 0.63333333 0.53333333
54 0.43333333 0.63333333
55 -0.06666667 0.43333333
56 -0.56666667 -0.06666667
57 -0.46666667 -0.56666667
58 0.33333333 -0.46666667
59 0.73333333 0.33333333
60 NA 0.73333333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.67777778 1.57777778
[2,] -0.12222222 0.67777778
[3,] -0.12222222 -0.12222222
[4,] 0.17777778 -0.12222222
[5,] 0.27777778 0.17777778
[6,] 0.17777778 0.27777778
[7,] -0.22222222 0.17777778
[8,] -0.42222222 -0.22222222
[9,] -0.32222222 -0.42222222
[10,] 0.77777778 -0.32222222
[11,] 0.97777778 0.77777778
[12,] 0.87777778 0.97777778
[13,] 0.27777778 0.87777778
[14,] -0.12222222 0.27777778
[15,] -0.02222222 -0.12222222
[16,] 0.07777778 -0.02222222
[17,] 0.07777778 0.07777778
[18,] -0.12222222 0.07777778
[19,] -0.32222222 -0.12222222
[20,] -0.32222222 -0.32222222
[21,] -0.32222222 -0.32222222
[22,] 0.47777778 -0.32222222
[23,] 0.67777778 0.47777778
[24,] 0.47777778 0.67777778
[25,] -0.02222222 0.47777778
[26,] -0.32222222 -0.02222222
[27,] -0.32222222 -0.32222222
[28,] -0.22222222 -0.32222222
[29,] -0.22222222 -0.22222222
[30,] -0.22222222 -0.22222222
[31,] -0.12222222 -0.22222222
[32,] -0.52222222 -0.12222222
[33,] -1.02222222 -0.52222222
[34,] -0.92222222 -1.02222222
[35,] -1.22222222 -0.92222222
[36,] -0.06666667 -1.22222222
[37,] 0.13333333 -0.06666667
[38,] 0.13333333 0.13333333
[39,] 0.23333333 0.13333333
[40,] 0.13333333 0.23333333
[41,] -0.16666667 0.13333333
[42,] -0.66666667 -0.16666667
[43,] -0.96666667 -0.66666667
[44,] -1.26666667 -0.96666667
[45,] -0.86666667 -1.26666667
[46,] 0.43333333 -0.86666667
[47,] 0.93333333 0.43333333
[48,] 0.53333333 0.93333333
[49,] 0.13333333 0.53333333
[50,] -0.26666667 0.13333333
[51,] 0.03333333 -0.26666667
[52,] 0.53333333 0.03333333
[53,] 0.63333333 0.53333333
[54,] 0.43333333 0.63333333
[55,] -0.06666667 0.43333333
[56,] -0.56666667 -0.06666667
[57,] -0.46666667 -0.56666667
[58,] 0.33333333 -0.46666667
[59,] 0.73333333 0.33333333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.67777778 1.57777778
2 -0.12222222 0.67777778
3 -0.12222222 -0.12222222
4 0.17777778 -0.12222222
5 0.27777778 0.17777778
6 0.17777778 0.27777778
7 -0.22222222 0.17777778
8 -0.42222222 -0.22222222
9 -0.32222222 -0.42222222
10 0.77777778 -0.32222222
11 0.97777778 0.77777778
12 0.87777778 0.97777778
13 0.27777778 0.87777778
14 -0.12222222 0.27777778
15 -0.02222222 -0.12222222
16 0.07777778 -0.02222222
17 0.07777778 0.07777778
18 -0.12222222 0.07777778
19 -0.32222222 -0.12222222
20 -0.32222222 -0.32222222
21 -0.32222222 -0.32222222
22 0.47777778 -0.32222222
23 0.67777778 0.47777778
24 0.47777778 0.67777778
25 -0.02222222 0.47777778
26 -0.32222222 -0.02222222
27 -0.32222222 -0.32222222
28 -0.22222222 -0.32222222
29 -0.22222222 -0.22222222
30 -0.22222222 -0.22222222
31 -0.12222222 -0.22222222
32 -0.52222222 -0.12222222
33 -1.02222222 -0.52222222
34 -0.92222222 -1.02222222
35 -1.22222222 -0.92222222
36 -0.06666667 -1.22222222
37 0.13333333 -0.06666667
38 0.13333333 0.13333333
39 0.23333333 0.13333333
40 0.13333333 0.23333333
41 -0.16666667 0.13333333
42 -0.66666667 -0.16666667
43 -0.96666667 -0.66666667
44 -1.26666667 -0.96666667
45 -0.86666667 -1.26666667
46 0.43333333 -0.86666667
47 0.93333333 0.43333333
48 0.53333333 0.93333333
49 0.13333333 0.53333333
50 -0.26666667 0.13333333
51 0.03333333 -0.26666667
52 0.53333333 0.03333333
53 0.63333333 0.53333333
54 0.43333333 0.63333333
55 -0.06666667 0.43333333
56 -0.56666667 -0.06666667
57 -0.46666667 -0.56666667
58 0.33333333 -0.46666667
59 0.73333333 0.33333333
> 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/7lbt41258796727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8a5891258796727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9oxdn1258796727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ykm41258796727.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h6pd1258796727.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/12b2b71258796727.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/13d5mu1258796727.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/14eb231258796727.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/15ct6e1258796727.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/16ie5y1258796727.tab")
+ }
>
> system("convert tmp/17op21258796727.ps tmp/17op21258796727.png")
> system("convert tmp/2jm421258796727.ps tmp/2jm421258796727.png")
> system("convert tmp/36rf71258796727.ps tmp/36rf71258796727.png")
> system("convert tmp/434991258796727.ps tmp/434991258796727.png")
> system("convert tmp/5kqi71258796727.ps tmp/5kqi71258796727.png")
> system("convert tmp/6o4b41258796727.ps tmp/6o4b41258796727.png")
> system("convert tmp/7lbt41258796727.ps tmp/7lbt41258796727.png")
> system("convert tmp/8a5891258796727.ps tmp/8a5891258796727.png")
> system("convert tmp/9oxdn1258796727.ps tmp/9oxdn1258796727.png")
> system("convert tmp/10ykm41258796727.ps tmp/10ykm41258796727.png")
>
>
> proc.time()
user system elapsed
2.422 1.534 3.567