R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(2,1,-8,-1,1,-1,2,2,1,-1,-2,-2,-1,-8,-4,-6,-3,-3,-7,-9,-11,-13,-11,-9,-17,-22,-25,-20,-24,-24,-22,-19,-18,-17,-11,-11,-12,-10,-15,-15,-15,-13,-8,-13,-9,-7,-4,-4,-2,0),dim=c(1,50),dimnames=list(c('Y'),1:50))
> y <- array(NA,dim=c(1,50),dimnames=list(c('Y'),1:50))
> 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
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2 1 0 0 0 0 0 0 0 0 0 0 1
2 1 0 1 0 0 0 0 0 0 0 0 0 2
3 -8 0 0 1 0 0 0 0 0 0 0 0 3
4 -1 0 0 0 1 0 0 0 0 0 0 0 4
5 1 0 0 0 0 1 0 0 0 0 0 0 5
6 -1 0 0 0 0 0 1 0 0 0 0 0 6
7 2 0 0 0 0 0 0 1 0 0 0 0 7
8 2 0 0 0 0 0 0 0 1 0 0 0 8
9 1 0 0 0 0 0 0 0 0 1 0 0 9
10 -1 0 0 0 0 0 0 0 0 0 1 0 10
11 -2 0 0 0 0 0 0 0 0 0 0 1 11
12 -2 0 0 0 0 0 0 0 0 0 0 0 12
13 -1 1 0 0 0 0 0 0 0 0 0 0 13
14 -8 0 1 0 0 0 0 0 0 0 0 0 14
15 -4 0 0 1 0 0 0 0 0 0 0 0 15
16 -6 0 0 0 1 0 0 0 0 0 0 0 16
17 -3 0 0 0 0 1 0 0 0 0 0 0 17
18 -3 0 0 0 0 0 1 0 0 0 0 0 18
19 -7 0 0 0 0 0 0 1 0 0 0 0 19
20 -9 0 0 0 0 0 0 0 1 0 0 0 20
21 -11 0 0 0 0 0 0 0 0 1 0 0 21
22 -13 0 0 0 0 0 0 0 0 0 1 0 22
23 -11 0 0 0 0 0 0 0 0 0 0 1 23
24 -9 0 0 0 0 0 0 0 0 0 0 0 24
25 -17 1 0 0 0 0 0 0 0 0 0 0 25
26 -22 0 1 0 0 0 0 0 0 0 0 0 26
27 -25 0 0 1 0 0 0 0 0 0 0 0 27
28 -20 0 0 0 1 0 0 0 0 0 0 0 28
29 -24 0 0 0 0 1 0 0 0 0 0 0 29
30 -24 0 0 0 0 0 1 0 0 0 0 0 30
31 -22 0 0 0 0 0 0 1 0 0 0 0 31
32 -19 0 0 0 0 0 0 0 1 0 0 0 32
33 -18 0 0 0 0 0 0 0 0 1 0 0 33
34 -17 0 0 0 0 0 0 0 0 0 1 0 34
35 -11 0 0 0 0 0 0 0 0 0 0 1 35
36 -11 0 0 0 0 0 0 0 0 0 0 0 36
37 -12 1 0 0 0 0 0 0 0 0 0 0 37
38 -10 0 1 0 0 0 0 0 0 0 0 0 38
39 -15 0 0 1 0 0 0 0 0 0 0 0 39
40 -15 0 0 0 1 0 0 0 0 0 0 0 40
41 -15 0 0 0 0 1 0 0 0 0 0 0 41
42 -13 0 0 0 0 0 1 0 0 0 0 0 42
43 -8 0 0 0 0 0 0 1 0 0 0 0 43
44 -13 0 0 0 0 0 0 0 1 0 0 0 44
45 -9 0 0 0 0 0 0 0 0 1 0 0 45
46 -7 0 0 0 0 0 0 0 0 0 1 0 46
47 -4 0 0 0 0 0 0 0 0 0 0 1 47
48 -4 0 0 0 0 0 0 0 0 0 0 0 48
49 -2 1 0 0 0 0 0 0 0 0 0 0 49
50 0 0 1 0 0 0 0 0 0 0 0 0 50
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
1.4107 -0.8185 -2.3548 -8.8732 -6.1095 -5.5958
M6 M7 M8 M9 M10 M11
-5.3321 -3.5685 -4.3048 -3.5411 -3.5274 -0.7637
t
-0.2637
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.2000 -3.9027 0.6089 5.3714 14.1286
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4107 4.3743 0.323 0.74889
M1 -0.8185 5.0515 -0.162 0.87217
M2 -2.3548 5.0466 -0.467 0.64352
M3 -8.8732 5.3527 -1.658 0.10583
M4 -6.1095 5.3438 -1.143 0.26026
M5 -5.5958 5.3359 -1.049 0.30112
M6 -5.3321 5.3291 -1.001 0.32354
M7 -3.5684 5.3233 -0.670 0.50680
M8 -4.3048 5.3186 -0.809 0.42347
M9 -3.5411 5.3149 -0.666 0.50938
M10 -3.5274 5.3123 -0.664 0.51081
M11 -0.7637 5.3107 -0.144 0.88644
t -0.2637 0.0748 -3.525 0.00115 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.51 on 37 degrees of freedom
Multiple R-squared: 0.2974, Adjusted R-squared: 0.06953
F-statistic: 1.305 on 12 and 37 DF, p-value: 0.2569
> 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,] 0.15497906 0.309958113 0.845020943
[2,] 0.09605544 0.192110882 0.903944559
[3,] 0.08606173 0.172123468 0.913938266
[4,] 0.12421538 0.248430766 0.875784617
[5,] 0.27062550 0.541251000 0.729374500
[6,] 0.46928318 0.938566351 0.530716825
[7,] 0.62954400 0.740912010 0.370456005
[8,] 0.69747888 0.605042250 0.302521125
[9,] 0.96854157 0.062916859 0.031458430
[10,] 0.98784353 0.024312936 0.012156468
[11,] 0.99286346 0.014273085 0.007136543
[12,] 0.98981363 0.020372741 0.010186370
[13,] 0.99216183 0.015676347 0.007838173
[14,] 0.98996546 0.020069080 0.010034540
[15,] 0.98460562 0.030788761 0.015394381
[16,] 0.99679060 0.006418806 0.003209403
[17,] 0.99628049 0.007439023 0.003719512
[18,] 0.98523135 0.029537293 0.014768647
[19,] 0.96130130 0.077397408 0.038698704
> postscript(file="/var/www/rcomp/tmp/1qh261292250610.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2qh261292250610.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3j82r1292250610.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4j82r1292250610.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5j82r1292250610.ps",horizontal=F,onefile=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 = 50
Frequency = 1
1 2 3 4 5
1.671428571 2.471428571 0.253571429 4.753571429 6.503571429
6 7 8 9 10
4.503571429 6.003571429 7.003571429 5.503571429 3.753571429
11 12 13 14 15
0.253571429 -0.246428571 1.835714286 -3.364285714 7.417857143
16 17 18 19 20
2.917857143 5.667857143 5.667857143 0.167857143 -0.832142857
21 22 23 24 25
-3.332142857 -5.082142857 -5.582142857 -4.082142857 -11.000000000
26 27 28 29 30
-14.200000000 -10.417857143 -7.917857143 -12.167857143 -12.167857143
31 32 33 34 35
-11.667857143 -7.667857143 -7.167857143 -5.917857143 -2.417857143
36 37 38 39 40
-2.917857143 -2.835714286 0.964285714 2.746428571 0.246428571
41 42 43 44 45
-0.003571429 1.996428571 5.496428571 1.496428571 4.996428571
46 47 48 49 50
7.246428571 7.746428571 7.246428571 10.328571429 14.128571429
> postscript(file="/var/www/rcomp/tmp/6bh1u1292250610.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 50
Frequency = 1
lag(myerror, k = 1) myerror
0 1.671428571 NA
1 2.471428571 1.671428571
2 0.253571429 2.471428571
3 4.753571429 0.253571429
4 6.503571429 4.753571429
5 4.503571429 6.503571429
6 6.003571429 4.503571429
7 7.003571429 6.003571429
8 5.503571429 7.003571429
9 3.753571429 5.503571429
10 0.253571429 3.753571429
11 -0.246428571 0.253571429
12 1.835714286 -0.246428571
13 -3.364285714 1.835714286
14 7.417857143 -3.364285714
15 2.917857143 7.417857143
16 5.667857143 2.917857143
17 5.667857143 5.667857143
18 0.167857143 5.667857143
19 -0.832142857 0.167857143
20 -3.332142857 -0.832142857
21 -5.082142857 -3.332142857
22 -5.582142857 -5.082142857
23 -4.082142857 -5.582142857
24 -11.000000000 -4.082142857
25 -14.200000000 -11.000000000
26 -10.417857143 -14.200000000
27 -7.917857143 -10.417857143
28 -12.167857143 -7.917857143
29 -12.167857143 -12.167857143
30 -11.667857143 -12.167857143
31 -7.667857143 -11.667857143
32 -7.167857143 -7.667857143
33 -5.917857143 -7.167857143
34 -2.417857143 -5.917857143
35 -2.917857143 -2.417857143
36 -2.835714286 -2.917857143
37 0.964285714 -2.835714286
38 2.746428571 0.964285714
39 0.246428571 2.746428571
40 -0.003571429 0.246428571
41 1.996428571 -0.003571429
42 5.496428571 1.996428571
43 1.496428571 5.496428571
44 4.996428571 1.496428571
45 7.246428571 4.996428571
46 7.746428571 7.246428571
47 7.246428571 7.746428571
48 10.328571429 7.246428571
49 14.128571429 10.328571429
50 NA 14.128571429
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.471428571 1.671428571
[2,] 0.253571429 2.471428571
[3,] 4.753571429 0.253571429
[4,] 6.503571429 4.753571429
[5,] 4.503571429 6.503571429
[6,] 6.003571429 4.503571429
[7,] 7.003571429 6.003571429
[8,] 5.503571429 7.003571429
[9,] 3.753571429 5.503571429
[10,] 0.253571429 3.753571429
[11,] -0.246428571 0.253571429
[12,] 1.835714286 -0.246428571
[13,] -3.364285714 1.835714286
[14,] 7.417857143 -3.364285714
[15,] 2.917857143 7.417857143
[16,] 5.667857143 2.917857143
[17,] 5.667857143 5.667857143
[18,] 0.167857143 5.667857143
[19,] -0.832142857 0.167857143
[20,] -3.332142857 -0.832142857
[21,] -5.082142857 -3.332142857
[22,] -5.582142857 -5.082142857
[23,] -4.082142857 -5.582142857
[24,] -11.000000000 -4.082142857
[25,] -14.200000000 -11.000000000
[26,] -10.417857143 -14.200000000
[27,] -7.917857143 -10.417857143
[28,] -12.167857143 -7.917857143
[29,] -12.167857143 -12.167857143
[30,] -11.667857143 -12.167857143
[31,] -7.667857143 -11.667857143
[32,] -7.167857143 -7.667857143
[33,] -5.917857143 -7.167857143
[34,] -2.417857143 -5.917857143
[35,] -2.917857143 -2.417857143
[36,] -2.835714286 -2.917857143
[37,] 0.964285714 -2.835714286
[38,] 2.746428571 0.964285714
[39,] 0.246428571 2.746428571
[40,] -0.003571429 0.246428571
[41,] 1.996428571 -0.003571429
[42,] 5.496428571 1.996428571
[43,] 1.496428571 5.496428571
[44,] 4.996428571 1.496428571
[45,] 7.246428571 4.996428571
[46,] 7.746428571 7.246428571
[47,] 7.246428571 7.746428571
[48,] 10.328571429 7.246428571
[49,] 14.128571429 10.328571429
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.471428571 1.671428571
2 0.253571429 2.471428571
3 4.753571429 0.253571429
4 6.503571429 4.753571429
5 4.503571429 6.503571429
6 6.003571429 4.503571429
7 7.003571429 6.003571429
8 5.503571429 7.003571429
9 3.753571429 5.503571429
10 0.253571429 3.753571429
11 -0.246428571 0.253571429
12 1.835714286 -0.246428571
13 -3.364285714 1.835714286
14 7.417857143 -3.364285714
15 2.917857143 7.417857143
16 5.667857143 2.917857143
17 5.667857143 5.667857143
18 0.167857143 5.667857143
19 -0.832142857 0.167857143
20 -3.332142857 -0.832142857
21 -5.082142857 -3.332142857
22 -5.582142857 -5.082142857
23 -4.082142857 -5.582142857
24 -11.000000000 -4.082142857
25 -14.200000000 -11.000000000
26 -10.417857143 -14.200000000
27 -7.917857143 -10.417857143
28 -12.167857143 -7.917857143
29 -12.167857143 -12.167857143
30 -11.667857143 -12.167857143
31 -7.667857143 -11.667857143
32 -7.167857143 -7.667857143
33 -5.917857143 -7.167857143
34 -2.417857143 -5.917857143
35 -2.917857143 -2.417857143
36 -2.835714286 -2.917857143
37 0.964285714 -2.835714286
38 2.746428571 0.964285714
39 0.246428571 2.746428571
40 -0.003571429 0.246428571
41 1.996428571 -0.003571429
42 5.496428571 1.996428571
43 1.496428571 5.496428571
44 4.996428571 1.496428571
45 7.246428571 4.996428571
46 7.746428571 7.246428571
47 7.246428571 7.746428571
48 10.328571429 7.246428571
49 14.128571429 10.328571429
> 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/rcomp/tmp/74qif1292250610.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/84qif1292250610.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/94qif1292250610.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10fiz01292250610.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11i0y61292250610.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/rcomp/tmp/124jfc1292250610.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/rcomp/tmp/13skbn1292250610.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/rcomp/tmp/143bbq1292250610.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/rcomp/tmp/15z39z1292250610.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/rcomp/tmp/16k3p51292250610.tab")
+ }
>
> try(system("convert tmp/1qh261292250610.ps tmp/1qh261292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qh261292250610.ps tmp/2qh261292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j82r1292250610.ps tmp/3j82r1292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j82r1292250610.ps tmp/4j82r1292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j82r1292250610.ps tmp/5j82r1292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bh1u1292250610.ps tmp/6bh1u1292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/74qif1292250610.ps tmp/74qif1292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/84qif1292250610.ps tmp/84qif1292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/94qif1292250610.ps tmp/94qif1292250610.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fiz01292250610.ps tmp/10fiz01292250610.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.070 1.510 4.604