R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(3.2,1,1.9,1,0,1,0.6,1,0.2,1,0.9,1,2.4,1,4.7,1,9.4,1,12.5,1,15.8,1,18.2,1,16.8,0,17.3,0,19.3,0,17.9,0,20.2,0,18.7,0,20.1,0,18.2,0,18.4,0,18.2,0,18.9,0,19.9,0,21.3,0,20,0,19.5,0,19.6,0,20.9,0,21,0,19.9,0,19.6,0,20.9,0,21.7,0,22.9,0,21.5,0,21.3,0,23.5,0,21.6,0,24.5,0,22.2,0,23.5,0,20.9,0,20.7,0,18.1,0,17.1,0,14.8,0,13.8,0,15.2,0,16,0,17.6,0,15,0,15,0,16.3,0,19.4,0,21.3,0,20.5,0,21.1,0,21.6,0,22.6,0),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 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3.2 1 1 0 0 0 0 0 0 0 0 0 0
2 1.9 1 0 1 0 0 0 0 0 0 0 0 0
3 0.0 1 0 0 1 0 0 0 0 0 0 0 0
4 0.6 1 0 0 0 1 0 0 0 0 0 0 0
5 0.2 1 0 0 0 0 1 0 0 0 0 0 0
6 0.9 1 0 0 0 0 0 1 0 0 0 0 0
7 2.4 1 0 0 0 0 0 0 1 0 0 0 0
8 4.7 1 0 0 0 0 0 0 0 1 0 0 0
9 9.4 1 0 0 0 0 0 0 0 0 1 0 0
10 12.5 1 0 0 0 0 0 0 0 0 0 1 0
11 15.8 1 0 0 0 0 0 0 0 0 0 0 1
12 18.2 1 0 0 0 0 0 0 0 0 0 0 0
13 16.8 0 1 0 0 0 0 0 0 0 0 0 0
14 17.3 0 0 1 0 0 0 0 0 0 0 0 0
15 19.3 0 0 0 1 0 0 0 0 0 0 0 0
16 17.9 0 0 0 0 1 0 0 0 0 0 0 0
17 20.2 0 0 0 0 0 1 0 0 0 0 0 0
18 18.7 0 0 0 0 0 0 1 0 0 0 0 0
19 20.1 0 0 0 0 0 0 0 1 0 0 0 0
20 18.2 0 0 0 0 0 0 0 0 1 0 0 0
21 18.4 0 0 0 0 0 0 0 0 0 1 0 0
22 18.2 0 0 0 0 0 0 0 0 0 0 1 0
23 18.9 0 0 0 0 0 0 0 0 0 0 0 1
24 19.9 0 0 0 0 0 0 0 0 0 0 0 0
25 21.3 0 1 0 0 0 0 0 0 0 0 0 0
26 20.0 0 0 1 0 0 0 0 0 0 0 0 0
27 19.5 0 0 0 1 0 0 0 0 0 0 0 0
28 19.6 0 0 0 0 1 0 0 0 0 0 0 0
29 20.9 0 0 0 0 0 1 0 0 0 0 0 0
30 21.0 0 0 0 0 0 0 1 0 0 0 0 0
31 19.9 0 0 0 0 0 0 0 1 0 0 0 0
32 19.6 0 0 0 0 0 0 0 0 1 0 0 0
33 20.9 0 0 0 0 0 0 0 0 0 1 0 0
34 21.7 0 0 0 0 0 0 0 0 0 0 1 0
35 22.9 0 0 0 0 0 0 0 0 0 0 0 1
36 21.5 0 0 0 0 0 0 0 0 0 0 0 0
37 21.3 0 1 0 0 0 0 0 0 0 0 0 0
38 23.5 0 0 1 0 0 0 0 0 0 0 0 0
39 21.6 0 0 0 1 0 0 0 0 0 0 0 0
40 24.5 0 0 0 0 1 0 0 0 0 0 0 0
41 22.2 0 0 0 0 0 1 0 0 0 0 0 0
42 23.5 0 0 0 0 0 0 1 0 0 0 0 0
43 20.9 0 0 0 0 0 0 0 1 0 0 0 0
44 20.7 0 0 0 0 0 0 0 0 1 0 0 0
45 18.1 0 0 0 0 0 0 0 0 0 1 0 0
46 17.1 0 0 0 0 0 0 0 0 0 0 1 0
47 14.8 0 0 0 0 0 0 0 0 0 0 0 1
48 13.8 0 0 0 0 0 0 0 0 0 0 0 0
49 15.2 0 1 0 0 0 0 0 0 0 0 0 0
50 16.0 0 0 1 0 0 0 0 0 0 0 0 0
51 17.6 0 0 0 1 0 0 0 0 0 0 0 0
52 15.0 0 0 0 0 1 0 0 0 0 0 0 0
53 15.0 0 0 0 0 0 1 0 0 0 0 0 0
54 16.3 0 0 0 0 0 0 1 0 0 0 0 0
55 19.4 0 0 0 0 0 0 0 1 0 0 0 0
56 21.3 0 0 0 0 0 0 0 0 1 0 0 0
57 20.5 0 0 0 0 0 0 0 0 0 1 0 0
58 21.1 0 0 0 0 0 0 0 0 0 0 1 0
59 21.6 0 0 0 0 0 0 0 0 0 0 0 1
60 22.6 0 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
21.94 -13.69 -3.64 -3.46 -3.60 -3.68
M5 M6 M7 M8 M9 M10
-3.50 -3.12 -2.66 -2.30 -1.74 -1.08
M11
-0.40
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.13792 -2.48792 0.09208 1.63208 9.95167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.938 1.693 12.955 < 2e-16 ***
X -13.690 1.210 -11.317 5.08e-15 ***
M1 -3.640 2.370 -1.536 0.131
M2 -3.460 2.370 -1.460 0.151
M3 -3.600 2.370 -1.519 0.136
M4 -3.680 2.370 -1.553 0.127
M5 -3.500 2.370 -1.477 0.146
M6 -3.120 2.370 -1.316 0.194
M7 -2.660 2.370 -1.122 0.267
M8 -2.300 2.370 -0.970 0.337
M9 -1.740 2.370 -0.734 0.467
M10 -1.080 2.370 -0.456 0.651
M11 -0.400 2.370 -0.169 0.867
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.748 on 47 degrees of freedom
Multiple R-squared: 0.7418, Adjusted R-squared: 0.6759
F-statistic: 11.25 on 12 and 47 DF, p-value: 3.967e-10
> 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,] 0.20120183 0.40240366 0.7987982
[2,] 0.18277735 0.36555470 0.8172227
[3,] 0.09365197 0.18730393 0.9063480
[4,] 0.04324974 0.08649947 0.9567503
[5,] 0.05083288 0.10166576 0.9491671
[6,] 0.21531572 0.43063145 0.7846843
[7,] 0.52983862 0.94032276 0.4701614
[8,] 0.77482994 0.45034011 0.2251701
[9,] 0.88307612 0.23384775 0.1169239
[10,] 0.86703127 0.26593746 0.1329687
[11,] 0.82567615 0.34864770 0.1743238
[12,] 0.76979898 0.46040205 0.2302010
[13,] 0.71013144 0.57973713 0.2898686
[14,] 0.65741007 0.68517986 0.3425899
[15,] 0.59824142 0.80351716 0.4017586
[16,] 0.50494342 0.99011316 0.4950566
[17,] 0.41242519 0.82485039 0.5875748
[18,] 0.32336144 0.64672288 0.6766386
[19,] 0.25079101 0.50158202 0.7492090
[20,] 0.21362406 0.42724813 0.7863759
[21,] 0.18438677 0.36877353 0.8156132
[22,] 0.17005029 0.34010058 0.8299497
[23,] 0.21212994 0.42425987 0.7878701
[24,] 0.17583701 0.35167402 0.8241630
[25,] 0.30779385 0.61558771 0.6922061
[26,] 0.34589638 0.69179276 0.6541036
[27,] 0.41628953 0.83257907 0.5837105
[28,] 0.28912030 0.57824061 0.7108797
[29,] 0.16943206 0.33886412 0.8305679
> postscript(file="/var/www/html/rcomp/tmp/1lrec1258739076.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mcck1258739076.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3pzqg1258739076.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46yeq1258739076.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lez51258739076.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
-1.40833333 -2.88833333 -4.64833333 -3.96833333 -4.54833333 -4.22833333
7 8 9 10 11 12
-3.18833333 -1.24833333 2.89166667 5.33166667 7.95166667 9.95166667
13 14 15 16 17 18
-1.49791667 -1.17791667 0.96208333 -0.35791667 1.76208333 -0.11791667
19 20 21 22 23 24
0.82208333 -1.43791667 -1.79791667 -2.65791667 -2.63791667 -2.03791667
25 26 27 28 29 30
3.00208333 1.52208333 1.16208333 1.34208333 2.46208333 2.18208333
31 32 33 34 35 36
0.62208333 -0.03791667 0.70208333 0.84208333 1.36208333 -0.43791667
37 38 39 40 41 42
3.00208333 5.02208333 3.26208333 6.24208333 3.76208333 4.68208333
43 44 45 46 47 48
1.62208333 1.06208333 -2.09791667 -3.75791667 -6.73791667 -8.13791667
49 50 51 52 53 54
-3.09791667 -2.47791667 -0.73791667 -3.25791667 -3.43791667 -2.51791667
55 56 57 58 59 60
0.12208333 1.66208333 0.30208333 0.24208333 0.06208333 0.66208333
> postscript(file="/var/www/html/rcomp/tmp/6ucrn1258739076.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 -1.40833333 NA
1 -2.88833333 -1.40833333
2 -4.64833333 -2.88833333
3 -3.96833333 -4.64833333
4 -4.54833333 -3.96833333
5 -4.22833333 -4.54833333
6 -3.18833333 -4.22833333
7 -1.24833333 -3.18833333
8 2.89166667 -1.24833333
9 5.33166667 2.89166667
10 7.95166667 5.33166667
11 9.95166667 7.95166667
12 -1.49791667 9.95166667
13 -1.17791667 -1.49791667
14 0.96208333 -1.17791667
15 -0.35791667 0.96208333
16 1.76208333 -0.35791667
17 -0.11791667 1.76208333
18 0.82208333 -0.11791667
19 -1.43791667 0.82208333
20 -1.79791667 -1.43791667
21 -2.65791667 -1.79791667
22 -2.63791667 -2.65791667
23 -2.03791667 -2.63791667
24 3.00208333 -2.03791667
25 1.52208333 3.00208333
26 1.16208333 1.52208333
27 1.34208333 1.16208333
28 2.46208333 1.34208333
29 2.18208333 2.46208333
30 0.62208333 2.18208333
31 -0.03791667 0.62208333
32 0.70208333 -0.03791667
33 0.84208333 0.70208333
34 1.36208333 0.84208333
35 -0.43791667 1.36208333
36 3.00208333 -0.43791667
37 5.02208333 3.00208333
38 3.26208333 5.02208333
39 6.24208333 3.26208333
40 3.76208333 6.24208333
41 4.68208333 3.76208333
42 1.62208333 4.68208333
43 1.06208333 1.62208333
44 -2.09791667 1.06208333
45 -3.75791667 -2.09791667
46 -6.73791667 -3.75791667
47 -8.13791667 -6.73791667
48 -3.09791667 -8.13791667
49 -2.47791667 -3.09791667
50 -0.73791667 -2.47791667
51 -3.25791667 -0.73791667
52 -3.43791667 -3.25791667
53 -2.51791667 -3.43791667
54 0.12208333 -2.51791667
55 1.66208333 0.12208333
56 0.30208333 1.66208333
57 0.24208333 0.30208333
58 0.06208333 0.24208333
59 0.66208333 0.06208333
60 NA 0.66208333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.88833333 -1.40833333
[2,] -4.64833333 -2.88833333
[3,] -3.96833333 -4.64833333
[4,] -4.54833333 -3.96833333
[5,] -4.22833333 -4.54833333
[6,] -3.18833333 -4.22833333
[7,] -1.24833333 -3.18833333
[8,] 2.89166667 -1.24833333
[9,] 5.33166667 2.89166667
[10,] 7.95166667 5.33166667
[11,] 9.95166667 7.95166667
[12,] -1.49791667 9.95166667
[13,] -1.17791667 -1.49791667
[14,] 0.96208333 -1.17791667
[15,] -0.35791667 0.96208333
[16,] 1.76208333 -0.35791667
[17,] -0.11791667 1.76208333
[18,] 0.82208333 -0.11791667
[19,] -1.43791667 0.82208333
[20,] -1.79791667 -1.43791667
[21,] -2.65791667 -1.79791667
[22,] -2.63791667 -2.65791667
[23,] -2.03791667 -2.63791667
[24,] 3.00208333 -2.03791667
[25,] 1.52208333 3.00208333
[26,] 1.16208333 1.52208333
[27,] 1.34208333 1.16208333
[28,] 2.46208333 1.34208333
[29,] 2.18208333 2.46208333
[30,] 0.62208333 2.18208333
[31,] -0.03791667 0.62208333
[32,] 0.70208333 -0.03791667
[33,] 0.84208333 0.70208333
[34,] 1.36208333 0.84208333
[35,] -0.43791667 1.36208333
[36,] 3.00208333 -0.43791667
[37,] 5.02208333 3.00208333
[38,] 3.26208333 5.02208333
[39,] 6.24208333 3.26208333
[40,] 3.76208333 6.24208333
[41,] 4.68208333 3.76208333
[42,] 1.62208333 4.68208333
[43,] 1.06208333 1.62208333
[44,] -2.09791667 1.06208333
[45,] -3.75791667 -2.09791667
[46,] -6.73791667 -3.75791667
[47,] -8.13791667 -6.73791667
[48,] -3.09791667 -8.13791667
[49,] -2.47791667 -3.09791667
[50,] -0.73791667 -2.47791667
[51,] -3.25791667 -0.73791667
[52,] -3.43791667 -3.25791667
[53,] -2.51791667 -3.43791667
[54,] 0.12208333 -2.51791667
[55,] 1.66208333 0.12208333
[56,] 0.30208333 1.66208333
[57,] 0.24208333 0.30208333
[58,] 0.06208333 0.24208333
[59,] 0.66208333 0.06208333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.88833333 -1.40833333
2 -4.64833333 -2.88833333
3 -3.96833333 -4.64833333
4 -4.54833333 -3.96833333
5 -4.22833333 -4.54833333
6 -3.18833333 -4.22833333
7 -1.24833333 -3.18833333
8 2.89166667 -1.24833333
9 5.33166667 2.89166667
10 7.95166667 5.33166667
11 9.95166667 7.95166667
12 -1.49791667 9.95166667
13 -1.17791667 -1.49791667
14 0.96208333 -1.17791667
15 -0.35791667 0.96208333
16 1.76208333 -0.35791667
17 -0.11791667 1.76208333
18 0.82208333 -0.11791667
19 -1.43791667 0.82208333
20 -1.79791667 -1.43791667
21 -2.65791667 -1.79791667
22 -2.63791667 -2.65791667
23 -2.03791667 -2.63791667
24 3.00208333 -2.03791667
25 1.52208333 3.00208333
26 1.16208333 1.52208333
27 1.34208333 1.16208333
28 2.46208333 1.34208333
29 2.18208333 2.46208333
30 0.62208333 2.18208333
31 -0.03791667 0.62208333
32 0.70208333 -0.03791667
33 0.84208333 0.70208333
34 1.36208333 0.84208333
35 -0.43791667 1.36208333
36 3.00208333 -0.43791667
37 5.02208333 3.00208333
38 3.26208333 5.02208333
39 6.24208333 3.26208333
40 3.76208333 6.24208333
41 4.68208333 3.76208333
42 1.62208333 4.68208333
43 1.06208333 1.62208333
44 -2.09791667 1.06208333
45 -3.75791667 -2.09791667
46 -6.73791667 -3.75791667
47 -8.13791667 -6.73791667
48 -3.09791667 -8.13791667
49 -2.47791667 -3.09791667
50 -0.73791667 -2.47791667
51 -3.25791667 -0.73791667
52 -3.43791667 -3.25791667
53 -2.51791667 -3.43791667
54 0.12208333 -2.51791667
55 1.66208333 0.12208333
56 0.30208333 1.66208333
57 0.24208333 0.30208333
58 0.06208333 0.24208333
59 0.66208333 0.06208333
> 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/7oix61258739076.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tpxz1258739076.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9um9q1258739076.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109z811258739076.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112tgn1258739076.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/12v2fj1258739076.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/13633s1258739077.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/14wybb1258739077.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/156qa01258739077.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/16n4q61258739077.tab")
+ }
> system("convert tmp/1lrec1258739076.ps tmp/1lrec1258739076.png")
> system("convert tmp/2mcck1258739076.ps tmp/2mcck1258739076.png")
> system("convert tmp/3pzqg1258739076.ps tmp/3pzqg1258739076.png")
> system("convert tmp/46yeq1258739076.ps tmp/46yeq1258739076.png")
> system("convert tmp/5lez51258739076.ps tmp/5lez51258739076.png")
> system("convert tmp/6ucrn1258739076.ps tmp/6ucrn1258739076.png")
> system("convert tmp/7oix61258739076.ps tmp/7oix61258739076.png")
> system("convert tmp/8tpxz1258739076.ps tmp/8tpxz1258739076.png")
> system("convert tmp/9um9q1258739076.ps tmp/9um9q1258739076.png")
> system("convert tmp/109z811258739076.ps tmp/109z811258739076.png")
>
>
> proc.time()
user system elapsed
2.430 1.562 5.720