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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(15991.2
+ ,0
+ ,16704.4
+ ,17420.4
+ ,17872
+ ,17823.2
+ ,15583.6
+ ,0
+ ,15991.2
+ ,16704.4
+ ,17420.4
+ ,17872
+ ,19123.5
+ ,0
+ ,15583.6
+ ,15991.2
+ ,16704.4
+ ,17420.4
+ ,17838.7
+ ,0
+ ,19123.5
+ ,15583.6
+ ,15991.2
+ ,16704.4
+ ,17209.4
+ ,0
+ ,17838.7
+ ,19123.5
+ ,15583.6
+ ,15991.2
+ ,18586.5
+ ,0
+ ,17209.4
+ ,17838.7
+ ,19123.5
+ ,15583.6
+ ,16258.1
+ ,0
+ ,18586.5
+ ,17209.4
+ ,17838.7
+ ,19123.5
+ ,15141.6
+ ,0
+ ,16258.1
+ ,18586.5
+ ,17209.4
+ ,17838.7
+ ,19202.1
+ ,0
+ ,15141.6
+ ,16258.1
+ ,18586.5
+ ,17209.4
+ ,17746.5
+ ,0
+ ,19202.1
+ ,15141.6
+ ,16258.1
+ ,18586.5
+ ,19090.1
+ ,1
+ ,17746.5
+ ,19202.1
+ ,15141.6
+ ,16258.1
+ ,18040.3
+ ,1
+ ,19090.1
+ ,17746.5
+ ,19202.1
+ ,15141.6
+ ,17515.5
+ ,1
+ ,18040.3
+ ,19090.1
+ ,17746.5
+ ,19202.1
+ ,17751.8
+ ,1
+ ,17515.5
+ ,18040.3
+ ,19090.1
+ ,17746.5
+ ,21072.4
+ ,1
+ ,17751.8
+ ,17515.5
+ ,18040.3
+ ,19090.1
+ ,17170
+ ,1
+ ,21072.4
+ ,17751.8
+ ,17515.5
+ ,18040.3
+ ,19439.5
+ ,1
+ ,17170
+ ,21072.4
+ ,17751.8
+ ,17515.5
+ ,19795.4
+ ,1
+ ,19439.5
+ ,17170
+ ,21072.4
+ ,17751.8
+ ,17574.9
+ ,1
+ ,19795.4
+ ,19439.5
+ ,17170
+ ,21072.4
+ ,16165.4
+ ,1
+ ,17574.9
+ ,19795.4
+ ,19439.5
+ ,17170
+ ,19464.6
+ ,1
+ ,16165.4
+ ,17574.9
+ ,19795.4
+ ,19439.5
+ ,19932.1
+ ,1
+ ,19464.6
+ ,16165.4
+ ,17574.9
+ ,19795.4
+ ,19961.2
+ ,1
+ ,19932.1
+ ,19464.6
+ ,16165.4
+ ,17574.9
+ ,17343.4
+ ,1
+ ,19961.2
+ ,19932.1
+ ,19464.6
+ ,16165.4
+ ,18924.2
+ ,1
+ ,17343.4
+ ,19961.2
+ ,19932.1
+ ,19464.6
+ ,18574.1
+ ,1
+ ,18924.2
+ ,17343.4
+ ,19961.2
+ ,19932.1
+ ,21350.6
+ ,1
+ ,18574.1
+ ,18924.2
+ ,17343.4
+ ,19961.2
+ ,18594.6
+ ,1
+ ,21350.6
+ ,18574.1
+ ,18924.2
+ ,17343.4
+ ,19832.1
+ ,1
+ ,18594.6
+ ,21350.6
+ ,18574.1
+ ,18924.2
+ ,20844.4
+ ,1
+ ,19832.1
+ ,18594.6
+ ,21350.6
+ ,18574.1
+ ,19640.2
+ ,1
+ ,20844.4
+ ,19832.1
+ ,18594.6
+ ,21350.6
+ ,17735.4
+ ,1
+ ,19640.2
+ ,20844.4
+ ,19832.1
+ ,18594.6
+ ,19813.6
+ ,1
+ ,17735.4
+ ,19640.2
+ ,20844.4
+ ,19832.1
+ ,22160
+ ,1
+ ,19813.6
+ ,17735.4
+ ,19640.2
+ ,20844.4
+ ,20664.3
+ ,1
+ ,22160
+ ,19813.6
+ ,17735.4
+ ,19640.2
+ ,17877.4
+ ,1
+ ,20664.3
+ ,22160
+ ,19813.6
+ ,17735.4
+ ,20906.5
+ ,1
+ ,17877.4
+ ,20664.3
+ ,22160
+ ,19813.6
+ ,21164.1
+ ,1
+ ,20906.5
+ ,17877.4
+ ,20664.3
+ ,22160
+ ,21374.4
+ ,1
+ ,21164.1
+ ,20906.5
+ ,17877.4
+ ,20664.3
+ ,22952.3
+ ,1
+ ,21374.4
+ ,21164.1
+ ,20906.5
+ ,17877.4
+ ,21343.5
+ ,1
+ ,22952.3
+ ,21374.4
+ ,21164.1
+ ,20906.5
+ ,23899.3
+ ,1
+ ,21343.5
+ ,22952.3
+ ,21374.4
+ ,21164.1
+ ,22392.9
+ ,1
+ ,23899.3
+ ,21343.5
+ ,22952.3
+ ,21374.4
+ ,18274.1
+ ,1
+ ,22392.9
+ ,23899.3
+ ,21343.5
+ ,22952.3
+ ,22786.7
+ ,1
+ ,18274.1
+ ,22392.9
+ ,23899.3
+ ,21343.5
+ ,22321.5
+ ,1
+ ,22786.7
+ ,18274.1
+ ,22392.9
+ ,23899.3
+ ,17842.2
+ ,1
+ ,22321.5
+ ,22786.7
+ ,18274.1
+ ,22392.9
+ ,16373.5
+ ,1
+ ,17842.2
+ ,22321.5
+ ,22786.7
+ ,18274.1
+ ,15933.8
+ ,0
+ ,16373.5
+ ,17842.2
+ ,22321.5
+ ,22786.7
+ ,16446.1
+ ,0
+ ,15933.8
+ ,16373.5
+ ,17842.2
+ ,22321.5
+ ,17729
+ ,0
+ ,16446.1
+ ,15933.8
+ ,16373.5
+ ,17842.2
+ ,16643
+ ,0
+ ,17729
+ ,16446.1
+ ,15933.8
+ ,16373.5
+ ,16196.7
+ ,0
+ ,16643
+ ,17729
+ ,16446.1
+ ,15933.8
+ ,18252.1
+ ,0
+ ,16196.7
+ ,16643
+ ,17729
+ ,16446.1
+ ,17570.4
+ ,0
+ ,18252.1
+ ,16196.7
+ ,16643
+ ,17729
+ ,15836.8
+ ,0
+ ,17570.4
+ ,18252.1
+ ,16196.7
+ ,16643)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:56))
> 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
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 15991.2 0 16704.4 17420.4 17872.0 17823.2 1 0 0 0 0 0 0 0 0 0 0
2 15583.6 0 15991.2 16704.4 17420.4 17872.0 0 1 0 0 0 0 0 0 0 0 0
3 19123.5 0 15583.6 15991.2 16704.4 17420.4 0 0 1 0 0 0 0 0 0 0 0
4 17838.7 0 19123.5 15583.6 15991.2 16704.4 0 0 0 1 0 0 0 0 0 0 0
5 17209.4 0 17838.7 19123.5 15583.6 15991.2 0 0 0 0 1 0 0 0 0 0 0
6 18586.5 0 17209.4 17838.7 19123.5 15583.6 0 0 0 0 0 1 0 0 0 0 0
7 16258.1 0 18586.5 17209.4 17838.7 19123.5 0 0 0 0 0 0 1 0 0 0 0
8 15141.6 0 16258.1 18586.5 17209.4 17838.7 0 0 0 0 0 0 0 1 0 0 0
9 19202.1 0 15141.6 16258.1 18586.5 17209.4 0 0 0 0 0 0 0 0 1 0 0
10 17746.5 0 19202.1 15141.6 16258.1 18586.5 0 0 0 0 0 0 0 0 0 1 0
11 19090.1 1 17746.5 19202.1 15141.6 16258.1 0 0 0 0 0 0 0 0 0 0 1
12 18040.3 1 19090.1 17746.5 19202.1 15141.6 0 0 0 0 0 0 0 0 0 0 0
13 17515.5 1 18040.3 19090.1 17746.5 19202.1 1 0 0 0 0 0 0 0 0 0 0
14 17751.8 1 17515.5 18040.3 19090.1 17746.5 0 1 0 0 0 0 0 0 0 0 0
15 21072.4 1 17751.8 17515.5 18040.3 19090.1 0 0 1 0 0 0 0 0 0 0 0
16 17170.0 1 21072.4 17751.8 17515.5 18040.3 0 0 0 1 0 0 0 0 0 0 0
17 19439.5 1 17170.0 21072.4 17751.8 17515.5 0 0 0 0 1 0 0 0 0 0 0
18 19795.4 1 19439.5 17170.0 21072.4 17751.8 0 0 0 0 0 1 0 0 0 0 0
19 17574.9 1 19795.4 19439.5 17170.0 21072.4 0 0 0 0 0 0 1 0 0 0 0
20 16165.4 1 17574.9 19795.4 19439.5 17170.0 0 0 0 0 0 0 0 1 0 0 0
21 19464.6 1 16165.4 17574.9 19795.4 19439.5 0 0 0 0 0 0 0 0 1 0 0
22 19932.1 1 19464.6 16165.4 17574.9 19795.4 0 0 0 0 0 0 0 0 0 1 0
23 19961.2 1 19932.1 19464.6 16165.4 17574.9 0 0 0 0 0 0 0 0 0 0 1
24 17343.4 1 19961.2 19932.1 19464.6 16165.4 0 0 0 0 0 0 0 0 0 0 0
25 18924.2 1 17343.4 19961.2 19932.1 19464.6 1 0 0 0 0 0 0 0 0 0 0
26 18574.1 1 18924.2 17343.4 19961.2 19932.1 0 1 0 0 0 0 0 0 0 0 0
27 21350.6 1 18574.1 18924.2 17343.4 19961.2 0 0 1 0 0 0 0 0 0 0 0
28 18594.6 1 21350.6 18574.1 18924.2 17343.4 0 0 0 1 0 0 0 0 0 0 0
29 19832.1 1 18594.6 21350.6 18574.1 18924.2 0 0 0 0 1 0 0 0 0 0 0
30 20844.4 1 19832.1 18594.6 21350.6 18574.1 0 0 0 0 0 1 0 0 0 0 0
31 19640.2 1 20844.4 19832.1 18594.6 21350.6 0 0 0 0 0 0 1 0 0 0 0
32 17735.4 1 19640.2 20844.4 19832.1 18594.6 0 0 0 0 0 0 0 1 0 0 0
33 19813.6 1 17735.4 19640.2 20844.4 19832.1 0 0 0 0 0 0 0 0 1 0 0
34 22160.0 1 19813.6 17735.4 19640.2 20844.4 0 0 0 0 0 0 0 0 0 1 0
35 20664.3 1 22160.0 19813.6 17735.4 19640.2 0 0 0 0 0 0 0 0 0 0 1
36 17877.4 1 20664.3 22160.0 19813.6 17735.4 0 0 0 0 0 0 0 0 0 0 0
37 20906.5 1 17877.4 20664.3 22160.0 19813.6 1 0 0 0 0 0 0 0 0 0 0
38 21164.1 1 20906.5 17877.4 20664.3 22160.0 0 1 0 0 0 0 0 0 0 0 0
39 21374.4 1 21164.1 20906.5 17877.4 20664.3 0 0 1 0 0 0 0 0 0 0 0
40 22952.3 1 21374.4 21164.1 20906.5 17877.4 0 0 0 1 0 0 0 0 0 0 0
41 21343.5 1 22952.3 21374.4 21164.1 20906.5 0 0 0 0 1 0 0 0 0 0 0
42 23899.3 1 21343.5 22952.3 21374.4 21164.1 0 0 0 0 0 1 0 0 0 0 0
43 22392.9 1 23899.3 21343.5 22952.3 21374.4 0 0 0 0 0 0 1 0 0 0 0
44 18274.1 1 22392.9 23899.3 21343.5 22952.3 0 0 0 0 0 0 0 1 0 0 0
45 22786.7 1 18274.1 22392.9 23899.3 21343.5 0 0 0 0 0 0 0 0 1 0 0
46 22321.5 1 22786.7 18274.1 22392.9 23899.3 0 0 0 0 0 0 0 0 0 1 0
47 17842.2 1 22321.5 22786.7 18274.1 22392.9 0 0 0 0 0 0 0 0 0 0 1
48 16373.5 1 17842.2 22321.5 22786.7 18274.1 0 0 0 0 0 0 0 0 0 0 0
49 15933.8 0 16373.5 17842.2 22321.5 22786.7 1 0 0 0 0 0 0 0 0 0 0
50 16446.1 0 15933.8 16373.5 17842.2 22321.5 0 1 0 0 0 0 0 0 0 0 0
51 17729.0 0 16446.1 15933.8 16373.5 17842.2 0 0 1 0 0 0 0 0 0 0 0
52 16643.0 0 17729.0 16446.1 15933.8 16373.5 0 0 0 1 0 0 0 0 0 0 0
53 16196.7 0 16643.0 17729.0 16446.1 15933.8 0 0 0 0 1 0 0 0 0 0 0
54 18252.1 0 16196.7 16643.0 17729.0 16446.1 0 0 0 0 0 1 0 0 0 0 0
55 17570.4 0 18252.1 16196.7 16643.0 17729.0 0 0 0 0 0 0 1 0 0 0 0
56 15836.8 0 17570.4 18252.1 16196.7 16643.0 0 0 0 0 0 0 0 1 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
3086.0701 1029.8777 0.3491 0.2444 0.3356 -0.3311
M1 M2 M3 M4 M5 M6
3112.6595 3772.7513 6074.5867 3017.5201 3327.1813 4366.0652
M7 M8 M9 M10 M11 t
3452.4689 1033.4568 5183.7650 5750.9558 3520.3457 8.6493
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2278.78 -694.07 18.68 703.16 1829.62
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3086.0701 2743.3672 1.125 0.267678
X 1029.8777 530.1116 1.943 0.059482 .
Y1 0.3491 0.1542 2.264 0.029398 *
Y2 0.2444 0.1580 1.547 0.130167
Y3 0.3356 0.1498 2.240 0.031004 *
Y4 -0.3311 0.1584 -2.090 0.043368 *
M1 3112.6595 1032.3437 3.015 0.004560 **
M2 3772.7513 1128.1042 3.344 0.001864 **
M3 6074.5867 1043.8123 5.820 1.01e-06 ***
M4 3017.5201 915.1617 3.297 0.002124 **
M5 3327.1813 860.2241 3.868 0.000417 ***
M6 4366.0652 832.9423 5.242 6.23e-06 ***
M7 3452.4689 1027.1517 3.361 0.001779 **
M8 1033.4568 861.2548 1.200 0.237589
M9 5183.7650 1064.6990 4.869 2.00e-05 ***
M10 5750.9558 1254.2653 4.585 4.81e-05 ***
M11 3520.3457 1065.2627 3.305 0.002081 **
t 8.6493 11.2984 0.766 0.448682
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1119 on 38 degrees of freedom
Multiple R-squared: 0.8104, Adjusted R-squared: 0.7256
F-statistic: 9.554 on 17 and 38 DF, p-value: 5.257e-09
> 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,] 0.42066495 0.84132990 0.5793350
[2,] 0.26287297 0.52574594 0.7371270
[3,] 0.15147147 0.30294293 0.8485285
[4,] 0.11560091 0.23120183 0.8843991
[5,] 0.05982307 0.11964614 0.9401769
[6,] 0.05893040 0.11786080 0.9410696
[7,] 0.04683915 0.09367830 0.9531609
[8,] 0.06594033 0.13188067 0.9340597
[9,] 0.03762956 0.07525913 0.9623704
[10,] 0.02868472 0.05736944 0.9713153
[11,] 0.04323941 0.08647882 0.9567606
[12,] 0.04700125 0.09400249 0.9529988
[13,] 0.08502063 0.17004125 0.9149794
[14,] 0.16691444 0.33382887 0.8330856
[15,] 0.11695035 0.23390070 0.8830497
> postscript(file="/var/www/html/rcomp/tmp/1cmt71259248140.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2j14r1259248140.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/341231259248140.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pesa1259248140.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wfwr1259248140.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 = 56
Frequency = 1
1 2 3 4 5 6
-400.215646 -884.915856 751.801274 1381.467732 -82.019746 -541.589633
7 8 9 10 11 12
-688.788842 867.163058 1056.999634 -881.865629 773.411220 1389.745540
13 14 15 16 17 18
-385.286237 -1310.801033 542.211397 -1700.254105 548.781600 -1017.531006
19 20 21 22 23 24
-602.958204 -967.553503 -160.598545 -213.330632 806.041058 1.775318
25 26 27 28 29 30
303.577241 -482.398213 607.593961 -1380.955308 462.786587 -378.580483
31 32 33 34 35 36
510.487717 -138.724090 -1190.172115 1059.605457 699.324865 44.846497
37 38 39 40 41 42
1191.829000 1683.048531 -807.362886 1743.384846 130.568562 1829.618310
43 44 45 46 47 48
269.222083 -475.570764 293.771026 35.590804 -2278.777143 -1436.367356
49 50 51 52 53 54
-709.904358 995.066570 -1094.243746 -43.643166 -1060.117003 108.082812
55 56
512.037246 714.685298
> postscript(file="/var/www/html/rcomp/tmp/6r5mz1259248140.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -400.215646 NA
1 -884.915856 -400.215646
2 751.801274 -884.915856
3 1381.467732 751.801274
4 -82.019746 1381.467732
5 -541.589633 -82.019746
6 -688.788842 -541.589633
7 867.163058 -688.788842
8 1056.999634 867.163058
9 -881.865629 1056.999634
10 773.411220 -881.865629
11 1389.745540 773.411220
12 -385.286237 1389.745540
13 -1310.801033 -385.286237
14 542.211397 -1310.801033
15 -1700.254105 542.211397
16 548.781600 -1700.254105
17 -1017.531006 548.781600
18 -602.958204 -1017.531006
19 -967.553503 -602.958204
20 -160.598545 -967.553503
21 -213.330632 -160.598545
22 806.041058 -213.330632
23 1.775318 806.041058
24 303.577241 1.775318
25 -482.398213 303.577241
26 607.593961 -482.398213
27 -1380.955308 607.593961
28 462.786587 -1380.955308
29 -378.580483 462.786587
30 510.487717 -378.580483
31 -138.724090 510.487717
32 -1190.172115 -138.724090
33 1059.605457 -1190.172115
34 699.324865 1059.605457
35 44.846497 699.324865
36 1191.829000 44.846497
37 1683.048531 1191.829000
38 -807.362886 1683.048531
39 1743.384846 -807.362886
40 130.568562 1743.384846
41 1829.618310 130.568562
42 269.222083 1829.618310
43 -475.570764 269.222083
44 293.771026 -475.570764
45 35.590804 293.771026
46 -2278.777143 35.590804
47 -1436.367356 -2278.777143
48 -709.904358 -1436.367356
49 995.066570 -709.904358
50 -1094.243746 995.066570
51 -43.643166 -1094.243746
52 -1060.117003 -43.643166
53 108.082812 -1060.117003
54 512.037246 108.082812
55 714.685298 512.037246
56 NA 714.685298
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -884.915856 -400.215646
[2,] 751.801274 -884.915856
[3,] 1381.467732 751.801274
[4,] -82.019746 1381.467732
[5,] -541.589633 -82.019746
[6,] -688.788842 -541.589633
[7,] 867.163058 -688.788842
[8,] 1056.999634 867.163058
[9,] -881.865629 1056.999634
[10,] 773.411220 -881.865629
[11,] 1389.745540 773.411220
[12,] -385.286237 1389.745540
[13,] -1310.801033 -385.286237
[14,] 542.211397 -1310.801033
[15,] -1700.254105 542.211397
[16,] 548.781600 -1700.254105
[17,] -1017.531006 548.781600
[18,] -602.958204 -1017.531006
[19,] -967.553503 -602.958204
[20,] -160.598545 -967.553503
[21,] -213.330632 -160.598545
[22,] 806.041058 -213.330632
[23,] 1.775318 806.041058
[24,] 303.577241 1.775318
[25,] -482.398213 303.577241
[26,] 607.593961 -482.398213
[27,] -1380.955308 607.593961
[28,] 462.786587 -1380.955308
[29,] -378.580483 462.786587
[30,] 510.487717 -378.580483
[31,] -138.724090 510.487717
[32,] -1190.172115 -138.724090
[33,] 1059.605457 -1190.172115
[34,] 699.324865 1059.605457
[35,] 44.846497 699.324865
[36,] 1191.829000 44.846497
[37,] 1683.048531 1191.829000
[38,] -807.362886 1683.048531
[39,] 1743.384846 -807.362886
[40,] 130.568562 1743.384846
[41,] 1829.618310 130.568562
[42,] 269.222083 1829.618310
[43,] -475.570764 269.222083
[44,] 293.771026 -475.570764
[45,] 35.590804 293.771026
[46,] -2278.777143 35.590804
[47,] -1436.367356 -2278.777143
[48,] -709.904358 -1436.367356
[49,] 995.066570 -709.904358
[50,] -1094.243746 995.066570
[51,] -43.643166 -1094.243746
[52,] -1060.117003 -43.643166
[53,] 108.082812 -1060.117003
[54,] 512.037246 108.082812
[55,] 714.685298 512.037246
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -884.915856 -400.215646
2 751.801274 -884.915856
3 1381.467732 751.801274
4 -82.019746 1381.467732
5 -541.589633 -82.019746
6 -688.788842 -541.589633
7 867.163058 -688.788842
8 1056.999634 867.163058
9 -881.865629 1056.999634
10 773.411220 -881.865629
11 1389.745540 773.411220
12 -385.286237 1389.745540
13 -1310.801033 -385.286237
14 542.211397 -1310.801033
15 -1700.254105 542.211397
16 548.781600 -1700.254105
17 -1017.531006 548.781600
18 -602.958204 -1017.531006
19 -967.553503 -602.958204
20 -160.598545 -967.553503
21 -213.330632 -160.598545
22 806.041058 -213.330632
23 1.775318 806.041058
24 303.577241 1.775318
25 -482.398213 303.577241
26 607.593961 -482.398213
27 -1380.955308 607.593961
28 462.786587 -1380.955308
29 -378.580483 462.786587
30 510.487717 -378.580483
31 -138.724090 510.487717
32 -1190.172115 -138.724090
33 1059.605457 -1190.172115
34 699.324865 1059.605457
35 44.846497 699.324865
36 1191.829000 44.846497
37 1683.048531 1191.829000
38 -807.362886 1683.048531
39 1743.384846 -807.362886
40 130.568562 1743.384846
41 1829.618310 130.568562
42 269.222083 1829.618310
43 -475.570764 269.222083
44 293.771026 -475.570764
45 35.590804 293.771026
46 -2278.777143 35.590804
47 -1436.367356 -2278.777143
48 -709.904358 -1436.367356
49 995.066570 -709.904358
50 -1094.243746 995.066570
51 -43.643166 -1094.243746
52 -1060.117003 -43.643166
53 108.082812 -1060.117003
54 512.037246 108.082812
55 714.685298 512.037246
> 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/7uent1259248140.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nxwz1259248140.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w4l91259248140.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10radi1259248140.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114xm91259248140.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/12ese51259248140.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/13t6x41259248140.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/14nq0b1259248140.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/15epl81259248140.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/16ffsi1259248140.tab")
+ }
> system("convert tmp/1cmt71259248140.ps tmp/1cmt71259248140.png")
> system("convert tmp/2j14r1259248140.ps tmp/2j14r1259248140.png")
> system("convert tmp/341231259248140.ps tmp/341231259248140.png")
> system("convert tmp/4pesa1259248140.ps tmp/4pesa1259248140.png")
> system("convert tmp/5wfwr1259248140.ps tmp/5wfwr1259248140.png")
> system("convert tmp/6r5mz1259248140.ps tmp/6r5mz1259248140.png")
> system("convert tmp/7uent1259248140.ps tmp/7uent1259248140.png")
> system("convert tmp/8nxwz1259248140.ps tmp/8nxwz1259248140.png")
> system("convert tmp/9w4l91259248140.ps tmp/9w4l91259248140.png")
> system("convert tmp/10radi1259248140.ps tmp/10radi1259248140.png")
>
>
> proc.time()
user system elapsed
2.381 1.550 3.511