R version 2.7.0 (2008-04-22)
Copyright (C) 2008 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(100.30,0,100.60,0,100.00,0,100.10,0,100.20,0,100.00,0,100.10,0,100.10,0,100.10,0,100.50,0,100.50,0,100.50,0,96.30,1,96.30,1,96.80,1,96.80,1,96.90,1,96.80,1,96.80,1,96.80,1,96.80,1,97.00,1,97.00,1,97.00,1,96.80,1,96.90,1,97.20,1,97.30,1,97.30,1,97.20,1,97.30,1,97.30,1,97.30,1,97.30,1,97.30,1,97.30,1,98.10,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1),dim=c(2,48),dimnames=list(c('x','d'),1:48))
> y <- array(NA,dim=c(2,48),dimnames=list(c('x','d'),1:48))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
x d M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 100.3 0 1 0 0 0 0 0 0 0 0 0 0 1
2 100.6 0 0 1 0 0 0 0 0 0 0 0 0 2
3 100.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 100.1 0 0 0 0 1 0 0 0 0 0 0 0 4
5 100.2 0 0 0 0 0 1 0 0 0 0 0 0 5
6 100.0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 100.1 0 0 0 0 0 0 0 1 0 0 0 0 7
8 100.1 0 0 0 0 0 0 0 0 1 0 0 0 8
9 100.1 0 0 0 0 0 0 0 0 0 1 0 0 9
10 100.5 0 0 0 0 0 0 0 0 0 0 1 0 10
11 100.5 0 0 0 0 0 0 0 0 0 0 0 1 11
12 100.5 0 0 0 0 0 0 0 0 0 0 0 0 12
13 96.3 1 1 0 0 0 0 0 0 0 0 0 0 13
14 96.3 1 0 1 0 0 0 0 0 0 0 0 0 14
15 96.8 1 0 0 1 0 0 0 0 0 0 0 0 15
16 96.8 1 0 0 0 1 0 0 0 0 0 0 0 16
17 96.9 1 0 0 0 0 1 0 0 0 0 0 0 17
18 96.8 1 0 0 0 0 0 1 0 0 0 0 0 18
19 96.8 1 0 0 0 0 0 0 1 0 0 0 0 19
20 96.8 1 0 0 0 0 0 0 0 1 0 0 0 20
21 96.8 1 0 0 0 0 0 0 0 0 1 0 0 21
22 97.0 1 0 0 0 0 0 0 0 0 0 1 0 22
23 97.0 1 0 0 0 0 0 0 0 0 0 0 1 23
24 97.0 1 0 0 0 0 0 0 0 0 0 0 0 24
25 96.8 1 1 0 0 0 0 0 0 0 0 0 0 25
26 96.9 1 0 1 0 0 0 0 0 0 0 0 0 26
27 97.2 1 0 0 1 0 0 0 0 0 0 0 0 27
28 97.3 1 0 0 0 1 0 0 0 0 0 0 0 28
29 97.3 1 0 0 0 0 1 0 0 0 0 0 0 29
30 97.2 1 0 0 0 0 0 1 0 0 0 0 0 30
31 97.3 1 0 0 0 0 0 0 1 0 0 0 0 31
32 97.3 1 0 0 0 0 0 0 0 1 0 0 0 32
33 97.3 1 0 0 0 0 0 0 0 0 1 0 0 33
34 97.3 1 0 0 0 0 0 0 0 0 0 1 0 34
35 97.3 1 0 0 0 0 0 0 0 0 0 0 1 35
36 97.3 1 0 0 0 0 0 0 0 0 0 0 0 36
37 98.1 1 1 0 0 0 0 0 0 0 0 0 0 37
38 96.8 1 0 1 0 0 0 0 0 0 0 0 0 38
39 96.8 1 0 0 1 0 0 0 0 0 0 0 0 39
40 96.8 1 0 0 0 1 0 0 0 0 0 0 0 40
41 96.8 1 0 0 0 0 1 0 0 0 0 0 0 41
42 96.8 1 0 0 0 0 0 1 0 0 0 0 0 42
43 96.8 1 0 0 0 0 0 0 1 0 0 0 0 43
44 96.8 1 0 0 0 0 0 0 0 1 0 0 0 44
45 96.8 1 0 0 0 0 0 0 0 0 1 0 0 45
46 96.8 1 0 0 0 0 0 0 0 0 0 1 0 46
47 96.8 1 0 0 0 0 0 0 0 0 0 0 1 47
48 96.8 1 0 0 0 0 0 0 0 0 0 0 0 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) d M1 M2 M3 M4
100.297917 -3.419444 0.036111 -0.194444 -0.150000 -0.105556
M5 M6 M7 M8 M9 M10
-0.061111 -0.166667 -0.122222 -0.127778 -0.133333 0.011111
M11 t
0.005556 0.005556
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6868 -0.1646 -0.0618 0.1569 0.9799
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.297917 0.198013 506.521 <2e-16 ***
d -3.419444 0.175364 -19.499 <2e-16 ***
M1 0.036111 0.243373 0.148 0.883
M2 -0.194444 0.241987 -0.804 0.427
M3 -0.150000 0.240726 -0.623 0.537
M4 -0.105556 0.239592 -0.441 0.662
M5 -0.061111 0.238588 -0.256 0.799
M6 -0.166667 0.237713 -0.701 0.488
M7 -0.122222 0.236971 -0.516 0.609
M8 -0.127778 0.236362 -0.541 0.592
M9 -0.133333 0.235887 -0.565 0.576
M10 0.011111 0.235547 0.047 0.963
M11 0.005556 0.235343 0.024 0.981
t 0.005556 0.005660 0.982 0.333
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3327 on 34 degrees of freedom
Multiple R-squared: 0.9629, Adjusted R-squared: 0.9487
F-statistic: 67.84 on 13 and 34 DF, p-value: < 2.2e-16
> 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,] 0.69634085 6.073183e-01 3.036592e-01
[2,] 0.60455795 7.908841e-01 3.954421e-01
[3,] 0.48873085 9.774617e-01 5.112692e-01
[4,] 0.38529408 7.705882e-01 6.147059e-01
[5,] 0.30295858 6.059172e-01 6.970414e-01
[6,] 0.20800744 4.160149e-01 7.919926e-01
[7,] 0.14072963 2.814593e-01 8.592704e-01
[8,] 0.09931968 1.986394e-01 9.006803e-01
[9,] 0.99658136 6.837272e-03 3.418636e-03
[10,] 0.99999708 5.848059e-06 2.924029e-06
[11,] 0.99999766 4.673725e-06 2.336862e-06
[12,] 0.99998158 3.683170e-05 1.841585e-05
[13,] 0.99984706 3.058770e-04 1.529385e-04
[14,] 1.00000000 5.267790e-53 2.633895e-53
[15,] 1.00000000 2.423268e-41 1.211634e-41
> postscript(file="/var/www/html/rcomp/tmp/1hswd1227812284.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bi7v1227812284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tefc1227812284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4q3rw1227812284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54ms61227812284.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 = 48
Frequency = 1
1 2 3 4 5 6
-0.03958333 0.48541667 -0.16458333 -0.11458333 -0.06458333 -0.16458333
7 8 9 10 11 12
-0.11458333 -0.11458333 -0.11458333 0.13541667 0.13541667 0.13541667
13 14 15 16 17 18
-0.68680556 -0.46180556 -0.01180556 -0.06180556 -0.01180556 -0.01180556
19 20 21 22 23 24
-0.06180556 -0.06180556 -0.06180556 -0.01180556 -0.01180556 -0.01180556
25 26 27 28 29 30
-0.25347222 0.07152778 0.32152778 0.37152778 0.32152778 0.32152778
31 32 33 34 35 36
0.37152778 0.37152778 0.37152778 0.22152778 0.22152778 0.22152778
37 38 39 40 41 42
0.97986111 -0.09513889 -0.14513889 -0.19513889 -0.24513889 -0.14513889
43 44 45 46 47 48
-0.19513889 -0.19513889 -0.19513889 -0.34513889 -0.34513889 -0.34513889
> postscript(file="/var/www/html/rcomp/tmp/63mqg1227812284.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 = 48
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.03958333 NA
1 0.48541667 -0.03958333
2 -0.16458333 0.48541667
3 -0.11458333 -0.16458333
4 -0.06458333 -0.11458333
5 -0.16458333 -0.06458333
6 -0.11458333 -0.16458333
7 -0.11458333 -0.11458333
8 -0.11458333 -0.11458333
9 0.13541667 -0.11458333
10 0.13541667 0.13541667
11 0.13541667 0.13541667
12 -0.68680556 0.13541667
13 -0.46180556 -0.68680556
14 -0.01180556 -0.46180556
15 -0.06180556 -0.01180556
16 -0.01180556 -0.06180556
17 -0.01180556 -0.01180556
18 -0.06180556 -0.01180556
19 -0.06180556 -0.06180556
20 -0.06180556 -0.06180556
21 -0.01180556 -0.06180556
22 -0.01180556 -0.01180556
23 -0.01180556 -0.01180556
24 -0.25347222 -0.01180556
25 0.07152778 -0.25347222
26 0.32152778 0.07152778
27 0.37152778 0.32152778
28 0.32152778 0.37152778
29 0.32152778 0.32152778
30 0.37152778 0.32152778
31 0.37152778 0.37152778
32 0.37152778 0.37152778
33 0.22152778 0.37152778
34 0.22152778 0.22152778
35 0.22152778 0.22152778
36 0.97986111 0.22152778
37 -0.09513889 0.97986111
38 -0.14513889 -0.09513889
39 -0.19513889 -0.14513889
40 -0.24513889 -0.19513889
41 -0.14513889 -0.24513889
42 -0.19513889 -0.14513889
43 -0.19513889 -0.19513889
44 -0.19513889 -0.19513889
45 -0.34513889 -0.19513889
46 -0.34513889 -0.34513889
47 -0.34513889 -0.34513889
48 NA -0.34513889
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.48541667 -0.03958333
[2,] -0.16458333 0.48541667
[3,] -0.11458333 -0.16458333
[4,] -0.06458333 -0.11458333
[5,] -0.16458333 -0.06458333
[6,] -0.11458333 -0.16458333
[7,] -0.11458333 -0.11458333
[8,] -0.11458333 -0.11458333
[9,] 0.13541667 -0.11458333
[10,] 0.13541667 0.13541667
[11,] 0.13541667 0.13541667
[12,] -0.68680556 0.13541667
[13,] -0.46180556 -0.68680556
[14,] -0.01180556 -0.46180556
[15,] -0.06180556 -0.01180556
[16,] -0.01180556 -0.06180556
[17,] -0.01180556 -0.01180556
[18,] -0.06180556 -0.01180556
[19,] -0.06180556 -0.06180556
[20,] -0.06180556 -0.06180556
[21,] -0.01180556 -0.06180556
[22,] -0.01180556 -0.01180556
[23,] -0.01180556 -0.01180556
[24,] -0.25347222 -0.01180556
[25,] 0.07152778 -0.25347222
[26,] 0.32152778 0.07152778
[27,] 0.37152778 0.32152778
[28,] 0.32152778 0.37152778
[29,] 0.32152778 0.32152778
[30,] 0.37152778 0.32152778
[31,] 0.37152778 0.37152778
[32,] 0.37152778 0.37152778
[33,] 0.22152778 0.37152778
[34,] 0.22152778 0.22152778
[35,] 0.22152778 0.22152778
[36,] 0.97986111 0.22152778
[37,] -0.09513889 0.97986111
[38,] -0.14513889 -0.09513889
[39,] -0.19513889 -0.14513889
[40,] -0.24513889 -0.19513889
[41,] -0.14513889 -0.24513889
[42,] -0.19513889 -0.14513889
[43,] -0.19513889 -0.19513889
[44,] -0.19513889 -0.19513889
[45,] -0.34513889 -0.19513889
[46,] -0.34513889 -0.34513889
[47,] -0.34513889 -0.34513889
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.48541667 -0.03958333
2 -0.16458333 0.48541667
3 -0.11458333 -0.16458333
4 -0.06458333 -0.11458333
5 -0.16458333 -0.06458333
6 -0.11458333 -0.16458333
7 -0.11458333 -0.11458333
8 -0.11458333 -0.11458333
9 0.13541667 -0.11458333
10 0.13541667 0.13541667
11 0.13541667 0.13541667
12 -0.68680556 0.13541667
13 -0.46180556 -0.68680556
14 -0.01180556 -0.46180556
15 -0.06180556 -0.01180556
16 -0.01180556 -0.06180556
17 -0.01180556 -0.01180556
18 -0.06180556 -0.01180556
19 -0.06180556 -0.06180556
20 -0.06180556 -0.06180556
21 -0.01180556 -0.06180556
22 -0.01180556 -0.01180556
23 -0.01180556 -0.01180556
24 -0.25347222 -0.01180556
25 0.07152778 -0.25347222
26 0.32152778 0.07152778
27 0.37152778 0.32152778
28 0.32152778 0.37152778
29 0.32152778 0.32152778
30 0.37152778 0.32152778
31 0.37152778 0.37152778
32 0.37152778 0.37152778
33 0.22152778 0.37152778
34 0.22152778 0.22152778
35 0.22152778 0.22152778
36 0.97986111 0.22152778
37 -0.09513889 0.97986111
38 -0.14513889 -0.09513889
39 -0.19513889 -0.14513889
40 -0.24513889 -0.19513889
41 -0.14513889 -0.24513889
42 -0.19513889 -0.14513889
43 -0.19513889 -0.19513889
44 -0.19513889 -0.19513889
45 -0.34513889 -0.19513889
46 -0.34513889 -0.34513889
47 -0.34513889 -0.34513889
> 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/7rygq1227812284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8krrw1227812284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9a1ro1227812284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1080a31227812284.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11flth1227812284.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/12gdi21227812284.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/13aied1227812284.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/14pdur1227812284.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/158p981227812284.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/16pd5e1227812284.tab")
+ }
>
> system("convert tmp/1hswd1227812284.ps tmp/1hswd1227812284.png")
> system("convert tmp/2bi7v1227812284.ps tmp/2bi7v1227812284.png")
> system("convert tmp/3tefc1227812284.ps tmp/3tefc1227812284.png")
> system("convert tmp/4q3rw1227812284.ps tmp/4q3rw1227812284.png")
> system("convert tmp/54ms61227812284.ps tmp/54ms61227812284.png")
> system("convert tmp/63mqg1227812284.ps tmp/63mqg1227812284.png")
> system("convert tmp/7rygq1227812284.ps tmp/7rygq1227812284.png")
> system("convert tmp/8krrw1227812284.ps tmp/8krrw1227812284.png")
> system("convert tmp/9a1ro1227812284.ps tmp/9a1ro1227812284.png")
> system("convert tmp/1080a31227812284.ps tmp/1080a31227812284.png")
>
>
> proc.time()
user system elapsed
4.575 2.643 4.972