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(8.9,0,8.8,0,8.3,0,7.5,0,7.2,0,7.4,0,8.8,0,9.3,0,9.3,0,8.7,0,8.2,0,8.3,0,8.5,0,8.6,0,8.5,0,8.2,0,8.1,0,7.9,0,8.6,0,8.7,0,8.7,0,8.5,0,8.4,0,8.5,0,8.7,0,8.7,0,8.6,0,8.5,0,8.3,0,8,0,8.2,0,8.1,0,8.1,0,8,0,7.9,0,7.9,0,8,0,8,0,7.9,0,8,0,7.7,0,7.2,0,7.5,0,7.3,0,7,0,7,0,7,0,7.2,0,7.3,1,7.1,1,6.8,1,6.4,1,6.1,1,6.5,1,7.7,1,7.9,1,7.5,1,6.9,1,6.6,1,6.9,1),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 = '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 8.9 0 1 0 0 0 0 0 0 0 0 0 0
2 8.8 0 0 1 0 0 0 0 0 0 0 0 0
3 8.3 0 0 0 1 0 0 0 0 0 0 0 0
4 7.5 0 0 0 0 1 0 0 0 0 0 0 0
5 7.2 0 0 0 0 0 1 0 0 0 0 0 0
6 7.4 0 0 0 0 0 0 1 0 0 0 0 0
7 8.8 0 0 0 0 0 0 0 1 0 0 0 0
8 9.3 0 0 0 0 0 0 0 0 1 0 0 0
9 9.3 0 0 0 0 0 0 0 0 0 1 0 0
10 8.7 0 0 0 0 0 0 0 0 0 0 1 0
11 8.2 0 0 0 0 0 0 0 0 0 0 0 1
12 8.3 0 0 0 0 0 0 0 0 0 0 0 0
13 8.5 0 1 0 0 0 0 0 0 0 0 0 0
14 8.6 0 0 1 0 0 0 0 0 0 0 0 0
15 8.5 0 0 0 1 0 0 0 0 0 0 0 0
16 8.2 0 0 0 0 1 0 0 0 0 0 0 0
17 8.1 0 0 0 0 0 1 0 0 0 0 0 0
18 7.9 0 0 0 0 0 0 1 0 0 0 0 0
19 8.6 0 0 0 0 0 0 0 1 0 0 0 0
20 8.7 0 0 0 0 0 0 0 0 1 0 0 0
21 8.7 0 0 0 0 0 0 0 0 0 1 0 0
22 8.5 0 0 0 0 0 0 0 0 0 0 1 0
23 8.4 0 0 0 0 0 0 0 0 0 0 0 1
24 8.5 0 0 0 0 0 0 0 0 0 0 0 0
25 8.7 0 1 0 0 0 0 0 0 0 0 0 0
26 8.7 0 0 1 0 0 0 0 0 0 0 0 0
27 8.6 0 0 0 1 0 0 0 0 0 0 0 0
28 8.5 0 0 0 0 1 0 0 0 0 0 0 0
29 8.3 0 0 0 0 0 1 0 0 0 0 0 0
30 8.0 0 0 0 0 0 0 1 0 0 0 0 0
31 8.2 0 0 0 0 0 0 0 1 0 0 0 0
32 8.1 0 0 0 0 0 0 0 0 1 0 0 0
33 8.1 0 0 0 0 0 0 0 0 0 1 0 0
34 8.0 0 0 0 0 0 0 0 0 0 0 1 0
35 7.9 0 0 0 0 0 0 0 0 0 0 0 1
36 7.9 0 0 0 0 0 0 0 0 0 0 0 0
37 8.0 0 1 0 0 0 0 0 0 0 0 0 0
38 8.0 0 0 1 0 0 0 0 0 0 0 0 0
39 7.9 0 0 0 1 0 0 0 0 0 0 0 0
40 8.0 0 0 0 0 1 0 0 0 0 0 0 0
41 7.7 0 0 0 0 0 1 0 0 0 0 0 0
42 7.2 0 0 0 0 0 0 1 0 0 0 0 0
43 7.5 0 0 0 0 0 0 0 1 0 0 0 0
44 7.3 0 0 0 0 0 0 0 0 1 0 0 0
45 7.0 0 0 0 0 0 0 0 0 0 1 0 0
46 7.0 0 0 0 0 0 0 0 0 0 0 1 0
47 7.0 0 0 0 0 0 0 0 0 0 0 0 1
48 7.2 0 0 0 0 0 0 0 0 0 0 0 0
49 7.3 1 1 0 0 0 0 0 0 0 0 0 0
50 7.1 1 0 1 0 0 0 0 0 0 0 0 0
51 6.8 1 0 0 1 0 0 0 0 0 0 0 0
52 6.4 1 0 0 0 1 0 0 0 0 0 0 0
53 6.1 1 0 0 0 0 1 0 0 0 0 0 0
54 6.5 1 0 0 0 0 0 1 0 0 0 0 0
55 7.7 1 0 0 0 0 0 0 1 0 0 0 0
56 7.9 1 0 0 0 0 0 0 0 1 0 0 0
57 7.5 1 0 0 0 0 0 0 0 0 1 0 0
58 6.9 1 0 0 0 0 0 0 0 0 0 1 0
59 6.6 1 0 0 0 0 0 0 0 0 0 0 1
60 6.9 1 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
7.993 -1.165 0.520 0.480 0.260 -0.040
M5 M6 M7 M8 M9 M10
-0.280 -0.360 0.400 0.500 0.360 0.060
M11
-0.140
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.35292 -0.30448 0.04708 0.34708 0.94708
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.9929 0.2480 32.223 < 2e-16 ***
X -1.1646 0.1772 -6.573 3.64e-08 ***
M1 0.5200 0.3472 1.498 0.141
M2 0.4800 0.3472 1.383 0.173
M3 0.2600 0.3472 0.749 0.458
M4 -0.0400 0.3472 -0.115 0.909
M5 -0.2800 0.3472 -0.806 0.424
M6 -0.3600 0.3472 -1.037 0.305
M7 0.4000 0.3472 1.152 0.255
M8 0.5000 0.3472 1.440 0.156
M9 0.3600 0.3472 1.037 0.305
M10 0.0600 0.3472 0.173 0.864
M11 -0.1400 0.3472 -0.403 0.689
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.549 on 47 degrees of freedom
Multiple R-squared: 0.5655, Adjusted R-squared: 0.4545
F-statistic: 5.097 on 12 and 47 DF, p-value: 2.291e-05
> 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,] 0.23098872 0.46197745 0.7690113
[2,] 0.32502520 0.65005040 0.6749748
[3,] 0.24922564 0.49845129 0.7507744
[4,] 0.15717176 0.31434352 0.8428282
[5,] 0.13992660 0.27985320 0.8600734
[6,] 0.13393216 0.26786432 0.8660678
[7,] 0.09790364 0.19580728 0.9020964
[8,] 0.07449636 0.14899272 0.9255036
[9,] 0.05660351 0.11320701 0.9433965
[10,] 0.03546711 0.07093423 0.9645329
[11,] 0.02321242 0.04642483 0.9767876
[12,] 0.01811004 0.03622008 0.9818900
[13,] 0.03272500 0.06545000 0.9672750
[14,] 0.06264672 0.12529343 0.9373533
[15,] 0.06462227 0.12924455 0.9353777
[16,] 0.05612957 0.11225913 0.9438704
[17,] 0.08312516 0.16625031 0.9168748
[18,] 0.12014043 0.24028086 0.8798596
[19,] 0.13925076 0.27850152 0.8607492
[20,] 0.15550645 0.31101290 0.8444935
[21,] 0.15121419 0.30242839 0.8487858
[22,] 0.13630228 0.27260456 0.8636977
[23,] 0.12728937 0.25457874 0.8727106
[24,] 0.12408482 0.24816964 0.8759152
[25,] 0.22894684 0.45789368 0.7710532
[26,] 0.66506758 0.66986483 0.3349324
[27,] 0.77155028 0.45689944 0.2284497
[28,] 0.70039192 0.59921616 0.2996081
[29,] 0.79220666 0.41558667 0.2077933
> postscript(file="/var/www/html/rcomp/tmp/1wl3d1261138801.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2dpsa1261138801.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3kbar1261138801.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qdpi1261138801.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5k1bl1261138801.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
0.38708333 0.32708333 0.04708333 -0.45291667 -0.51291667 -0.23291667
7 8 9 10 11 12
0.40708333 0.80708333 0.94708333 0.64708333 0.34708333 0.30708333
13 14 15 16 17 18
-0.01291667 0.12708333 0.24708333 0.24708333 0.38708333 0.26708333
19 20 21 22 23 24
0.20708333 0.20708333 0.34708333 0.44708333 0.54708333 0.50708333
25 26 27 28 29 30
0.18708333 0.22708333 0.34708333 0.54708333 0.58708333 0.36708333
31 32 33 34 35 36
-0.19291667 -0.39291667 -0.25291667 -0.05291667 0.04708333 -0.09291667
37 38 39 40 41 42
-0.51291667 -0.47291667 -0.35291667 0.04708333 -0.01291667 -0.43291667
43 44 45 46 47 48
-0.89291667 -1.19291667 -1.35291667 -1.05291667 -0.85291667 -0.79291667
49 50 51 52 53 54
-0.04833333 -0.20833333 -0.28833333 -0.38833333 -0.44833333 0.03166667
55 56 57 58 59 60
0.47166667 0.57166667 0.31166667 0.01166667 -0.08833333 0.07166667
> postscript(file="/var/www/html/rcomp/tmp/6cko31261138801.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 0.38708333 NA
1 0.32708333 0.38708333
2 0.04708333 0.32708333
3 -0.45291667 0.04708333
4 -0.51291667 -0.45291667
5 -0.23291667 -0.51291667
6 0.40708333 -0.23291667
7 0.80708333 0.40708333
8 0.94708333 0.80708333
9 0.64708333 0.94708333
10 0.34708333 0.64708333
11 0.30708333 0.34708333
12 -0.01291667 0.30708333
13 0.12708333 -0.01291667
14 0.24708333 0.12708333
15 0.24708333 0.24708333
16 0.38708333 0.24708333
17 0.26708333 0.38708333
18 0.20708333 0.26708333
19 0.20708333 0.20708333
20 0.34708333 0.20708333
21 0.44708333 0.34708333
22 0.54708333 0.44708333
23 0.50708333 0.54708333
24 0.18708333 0.50708333
25 0.22708333 0.18708333
26 0.34708333 0.22708333
27 0.54708333 0.34708333
28 0.58708333 0.54708333
29 0.36708333 0.58708333
30 -0.19291667 0.36708333
31 -0.39291667 -0.19291667
32 -0.25291667 -0.39291667
33 -0.05291667 -0.25291667
34 0.04708333 -0.05291667
35 -0.09291667 0.04708333
36 -0.51291667 -0.09291667
37 -0.47291667 -0.51291667
38 -0.35291667 -0.47291667
39 0.04708333 -0.35291667
40 -0.01291667 0.04708333
41 -0.43291667 -0.01291667
42 -0.89291667 -0.43291667
43 -1.19291667 -0.89291667
44 -1.35291667 -1.19291667
45 -1.05291667 -1.35291667
46 -0.85291667 -1.05291667
47 -0.79291667 -0.85291667
48 -0.04833333 -0.79291667
49 -0.20833333 -0.04833333
50 -0.28833333 -0.20833333
51 -0.38833333 -0.28833333
52 -0.44833333 -0.38833333
53 0.03166667 -0.44833333
54 0.47166667 0.03166667
55 0.57166667 0.47166667
56 0.31166667 0.57166667
57 0.01166667 0.31166667
58 -0.08833333 0.01166667
59 0.07166667 -0.08833333
60 NA 0.07166667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.32708333 0.38708333
[2,] 0.04708333 0.32708333
[3,] -0.45291667 0.04708333
[4,] -0.51291667 -0.45291667
[5,] -0.23291667 -0.51291667
[6,] 0.40708333 -0.23291667
[7,] 0.80708333 0.40708333
[8,] 0.94708333 0.80708333
[9,] 0.64708333 0.94708333
[10,] 0.34708333 0.64708333
[11,] 0.30708333 0.34708333
[12,] -0.01291667 0.30708333
[13,] 0.12708333 -0.01291667
[14,] 0.24708333 0.12708333
[15,] 0.24708333 0.24708333
[16,] 0.38708333 0.24708333
[17,] 0.26708333 0.38708333
[18,] 0.20708333 0.26708333
[19,] 0.20708333 0.20708333
[20,] 0.34708333 0.20708333
[21,] 0.44708333 0.34708333
[22,] 0.54708333 0.44708333
[23,] 0.50708333 0.54708333
[24,] 0.18708333 0.50708333
[25,] 0.22708333 0.18708333
[26,] 0.34708333 0.22708333
[27,] 0.54708333 0.34708333
[28,] 0.58708333 0.54708333
[29,] 0.36708333 0.58708333
[30,] -0.19291667 0.36708333
[31,] -0.39291667 -0.19291667
[32,] -0.25291667 -0.39291667
[33,] -0.05291667 -0.25291667
[34,] 0.04708333 -0.05291667
[35,] -0.09291667 0.04708333
[36,] -0.51291667 -0.09291667
[37,] -0.47291667 -0.51291667
[38,] -0.35291667 -0.47291667
[39,] 0.04708333 -0.35291667
[40,] -0.01291667 0.04708333
[41,] -0.43291667 -0.01291667
[42,] -0.89291667 -0.43291667
[43,] -1.19291667 -0.89291667
[44,] -1.35291667 -1.19291667
[45,] -1.05291667 -1.35291667
[46,] -0.85291667 -1.05291667
[47,] -0.79291667 -0.85291667
[48,] -0.04833333 -0.79291667
[49,] -0.20833333 -0.04833333
[50,] -0.28833333 -0.20833333
[51,] -0.38833333 -0.28833333
[52,] -0.44833333 -0.38833333
[53,] 0.03166667 -0.44833333
[54,] 0.47166667 0.03166667
[55,] 0.57166667 0.47166667
[56,] 0.31166667 0.57166667
[57,] 0.01166667 0.31166667
[58,] -0.08833333 0.01166667
[59,] 0.07166667 -0.08833333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.32708333 0.38708333
2 0.04708333 0.32708333
3 -0.45291667 0.04708333
4 -0.51291667 -0.45291667
5 -0.23291667 -0.51291667
6 0.40708333 -0.23291667
7 0.80708333 0.40708333
8 0.94708333 0.80708333
9 0.64708333 0.94708333
10 0.34708333 0.64708333
11 0.30708333 0.34708333
12 -0.01291667 0.30708333
13 0.12708333 -0.01291667
14 0.24708333 0.12708333
15 0.24708333 0.24708333
16 0.38708333 0.24708333
17 0.26708333 0.38708333
18 0.20708333 0.26708333
19 0.20708333 0.20708333
20 0.34708333 0.20708333
21 0.44708333 0.34708333
22 0.54708333 0.44708333
23 0.50708333 0.54708333
24 0.18708333 0.50708333
25 0.22708333 0.18708333
26 0.34708333 0.22708333
27 0.54708333 0.34708333
28 0.58708333 0.54708333
29 0.36708333 0.58708333
30 -0.19291667 0.36708333
31 -0.39291667 -0.19291667
32 -0.25291667 -0.39291667
33 -0.05291667 -0.25291667
34 0.04708333 -0.05291667
35 -0.09291667 0.04708333
36 -0.51291667 -0.09291667
37 -0.47291667 -0.51291667
38 -0.35291667 -0.47291667
39 0.04708333 -0.35291667
40 -0.01291667 0.04708333
41 -0.43291667 -0.01291667
42 -0.89291667 -0.43291667
43 -1.19291667 -0.89291667
44 -1.35291667 -1.19291667
45 -1.05291667 -1.35291667
46 -0.85291667 -1.05291667
47 -0.79291667 -0.85291667
48 -0.04833333 -0.79291667
49 -0.20833333 -0.04833333
50 -0.28833333 -0.20833333
51 -0.38833333 -0.28833333
52 -0.44833333 -0.38833333
53 0.03166667 -0.44833333
54 0.47166667 0.03166667
55 0.57166667 0.47166667
56 0.31166667 0.57166667
57 0.01166667 0.31166667
58 -0.08833333 0.01166667
59 0.07166667 -0.08833333
> 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/7gay61261138801.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86z0l1261138801.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r7ge1261138801.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10m09e1261138801.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112ys01261138802.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/12t7oc1261138802.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/13j3x81261138802.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/14xvkg1261138802.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/154ssv1261138802.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/16u1d61261138802.tab")
+ }
>
> try(system("convert tmp/1wl3d1261138801.ps tmp/1wl3d1261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dpsa1261138801.ps tmp/2dpsa1261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kbar1261138801.ps tmp/3kbar1261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qdpi1261138801.ps tmp/4qdpi1261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/5k1bl1261138801.ps tmp/5k1bl1261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cko31261138801.ps tmp/6cko31261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gay61261138801.ps tmp/7gay61261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/86z0l1261138801.ps tmp/86z0l1261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/9r7ge1261138801.ps tmp/9r7ge1261138801.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m09e1261138801.ps tmp/10m09e1261138801.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.469 1.570 4.284