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(2.3
+ ,0
+ ,2.0
+ ,1.9
+ ,2.3
+ ,2.7
+ ,2.8
+ ,0
+ ,2.3
+ ,2.0
+ ,1.9
+ ,2.3
+ ,2.4
+ ,0
+ ,2.8
+ ,2.3
+ ,2.0
+ ,1.9
+ ,2.3
+ ,0
+ ,2.4
+ ,2.8
+ ,2.3
+ ,2.0
+ ,2.7
+ ,0
+ ,2.3
+ ,2.4
+ ,2.8
+ ,2.3
+ ,2.7
+ ,0
+ ,2.7
+ ,2.3
+ ,2.4
+ ,2.8
+ ,2.9
+ ,0
+ ,2.7
+ ,2.7
+ ,2.3
+ ,2.4
+ ,3.0
+ ,0
+ ,2.9
+ ,2.7
+ ,2.7
+ ,2.3
+ ,2.2
+ ,0
+ ,3.0
+ ,2.9
+ ,2.7
+ ,2.7
+ ,2.3
+ ,0
+ ,2.2
+ ,3.0
+ ,2.9
+ ,2.7
+ ,2.8
+ ,0
+ ,2.3
+ ,2.2
+ ,3.0
+ ,2.9
+ ,2.8
+ ,0
+ ,2.8
+ ,2.3
+ ,2.2
+ ,3.0
+ ,2.8
+ ,0
+ ,2.8
+ ,2.8
+ ,2.3
+ ,2.2
+ ,2.2
+ ,0
+ ,2.8
+ ,2.8
+ ,2.8
+ ,2.3
+ ,2.6
+ ,0
+ ,2.2
+ ,2.8
+ ,2.8
+ ,2.8
+ ,2.8
+ ,0
+ ,2.6
+ ,2.2
+ ,2.8
+ ,2.8
+ ,2.5
+ ,0
+ ,2.8
+ ,2.6
+ ,2.2
+ ,2.8
+ ,2.4
+ ,0
+ ,2.5
+ ,2.8
+ ,2.6
+ ,2.2
+ ,2.3
+ ,0
+ ,2.4
+ ,2.5
+ ,2.8
+ ,2.6
+ ,1.9
+ ,0
+ ,2.3
+ ,2.4
+ ,2.5
+ ,2.8
+ ,1.7
+ ,0
+ ,1.9
+ ,2.3
+ ,2.4
+ ,2.5
+ ,2.0
+ ,0
+ ,1.7
+ ,1.9
+ ,2.3
+ ,2.4
+ ,2.1
+ ,0
+ ,2.0
+ ,1.7
+ ,1.9
+ ,2.3
+ ,1.7
+ ,0
+ ,2.1
+ ,2.0
+ ,1.7
+ ,1.9
+ ,1.8
+ ,0
+ ,1.7
+ ,2.1
+ ,2.0
+ ,1.7
+ ,1.8
+ ,0
+ ,1.8
+ ,1.7
+ ,2.1
+ ,2.0
+ ,1.8
+ ,0
+ ,1.8
+ ,1.8
+ ,1.7
+ ,2.1
+ ,1.3
+ ,0
+ ,1.8
+ ,1.8
+ ,1.8
+ ,1.7
+ ,1.3
+ ,0
+ ,1.3
+ ,1.8
+ ,1.8
+ ,1.8
+ ,1.3
+ ,1
+ ,1.3
+ ,1.3
+ ,1.8
+ ,1.8
+ ,1.2
+ ,1
+ ,1.3
+ ,1.3
+ ,1.3
+ ,1.8
+ ,1.4
+ ,1
+ ,1.2
+ ,1.3
+ ,1.3
+ ,1.3
+ ,2.2
+ ,1
+ ,1.4
+ ,1.2
+ ,1.3
+ ,1.3
+ ,2.9
+ ,1
+ ,2.2
+ ,1.4
+ ,1.2
+ ,1.3
+ ,3.1
+ ,1
+ ,2.9
+ ,2.2
+ ,1.4
+ ,1.2
+ ,3.5
+ ,1
+ ,3.1
+ ,2.9
+ ,2.2
+ ,1.4
+ ,3.6
+ ,1
+ ,3.5
+ ,3.1
+ ,2.9
+ ,2.2
+ ,4.4
+ ,1
+ ,3.6
+ ,3.5
+ ,3.1
+ ,2.9
+ ,4.1
+ ,1
+ ,4.4
+ ,3.6
+ ,3.5
+ ,3.1
+ ,5.1
+ ,1
+ ,4.1
+ ,4.4
+ ,3.6
+ ,3.5
+ ,5.8
+ ,1
+ ,5.1
+ ,4.1
+ ,4.4
+ ,3.6
+ ,5.9
+ ,1
+ ,5.8
+ ,5.1
+ ,4.1
+ ,4.4
+ ,5.4
+ ,1
+ ,5.9
+ ,5.8
+ ,5.1
+ ,4.1
+ ,5.5
+ ,1
+ ,5.4
+ ,5.9
+ ,5.8
+ ,5.1
+ ,4.8
+ ,1
+ ,5.5
+ ,5.4
+ ,5.9
+ ,5.8
+ ,3.2
+ ,1
+ ,4.8
+ ,5.5
+ ,5.4
+ ,5.9
+ ,2.7
+ ,1
+ ,3.2
+ ,4.8
+ ,5.5
+ ,5.4
+ ,2.1
+ ,1
+ ,2.7
+ ,3.2
+ ,4.8
+ ,5.5
+ ,1.9
+ ,1
+ ,2.1
+ ,2.7
+ ,3.2
+ ,4.8
+ ,0.6
+ ,1
+ ,1.9
+ ,2.1
+ ,2.7
+ ,3.2
+ ,0.7
+ ,1
+ ,0.6
+ ,1.9
+ ,2.1
+ ,2.7
+ ,-0.2
+ ,1
+ ,0.7
+ ,0.6
+ ,1.9
+ ,2.1
+ ,-1.0
+ ,1
+ ,-0.2
+ ,0.7
+ ,0.6
+ ,1.9
+ ,-1.7
+ ,1
+ ,-1.0
+ ,-0.2
+ ,0.7
+ ,0.6
+ ,-0.7
+ ,1
+ ,-1.7
+ ,-1.0
+ ,-0.2
+ ,0.7
+ ,-1.0
+ ,1
+ ,-0.7
+ ,-1.7
+ ,-1.0
+ ,-0.2)
+ ,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 t
1 2.3 0 2.0 1.9 2.3 2.7 1 0 0 0 0 0 0 0 0 0 0 1
2 2.8 0 2.3 2.0 1.9 2.3 0 1 0 0 0 0 0 0 0 0 0 2
3 2.4 0 2.8 2.3 2.0 1.9 0 0 1 0 0 0 0 0 0 0 0 3
4 2.3 0 2.4 2.8 2.3 2.0 0 0 0 1 0 0 0 0 0 0 0 4
5 2.7 0 2.3 2.4 2.8 2.3 0 0 0 0 1 0 0 0 0 0 0 5
6 2.7 0 2.7 2.3 2.4 2.8 0 0 0 0 0 1 0 0 0 0 0 6
7 2.9 0 2.7 2.7 2.3 2.4 0 0 0 0 0 0 1 0 0 0 0 7
8 3.0 0 2.9 2.7 2.7 2.3 0 0 0 0 0 0 0 1 0 0 0 8
9 2.2 0 3.0 2.9 2.7 2.7 0 0 0 0 0 0 0 0 1 0 0 9
10 2.3 0 2.2 3.0 2.9 2.7 0 0 0 0 0 0 0 0 0 1 0 10
11 2.8 0 2.3 2.2 3.0 2.9 0 0 0 0 0 0 0 0 0 0 1 11
12 2.8 0 2.8 2.3 2.2 3.0 0 0 0 0 0 0 0 0 0 0 0 12
13 2.8 0 2.8 2.8 2.3 2.2 1 0 0 0 0 0 0 0 0 0 0 13
14 2.2 0 2.8 2.8 2.8 2.3 0 1 0 0 0 0 0 0 0 0 0 14
15 2.6 0 2.2 2.8 2.8 2.8 0 0 1 0 0 0 0 0 0 0 0 15
16 2.8 0 2.6 2.2 2.8 2.8 0 0 0 1 0 0 0 0 0 0 0 16
17 2.5 0 2.8 2.6 2.2 2.8 0 0 0 0 1 0 0 0 0 0 0 17
18 2.4 0 2.5 2.8 2.6 2.2 0 0 0 0 0 1 0 0 0 0 0 18
19 2.3 0 2.4 2.5 2.8 2.6 0 0 0 0 0 0 1 0 0 0 0 19
20 1.9 0 2.3 2.4 2.5 2.8 0 0 0 0 0 0 0 1 0 0 0 20
21 1.7 0 1.9 2.3 2.4 2.5 0 0 0 0 0 0 0 0 1 0 0 21
22 2.0 0 1.7 1.9 2.3 2.4 0 0 0 0 0 0 0 0 0 1 0 22
23 2.1 0 2.0 1.7 1.9 2.3 0 0 0 0 0 0 0 0 0 0 1 23
24 1.7 0 2.1 2.0 1.7 1.9 0 0 0 0 0 0 0 0 0 0 0 24
25 1.8 0 1.7 2.1 2.0 1.7 1 0 0 0 0 0 0 0 0 0 0 25
26 1.8 0 1.8 1.7 2.1 2.0 0 1 0 0 0 0 0 0 0 0 0 26
27 1.8 0 1.8 1.8 1.7 2.1 0 0 1 0 0 0 0 0 0 0 0 27
28 1.3 0 1.8 1.8 1.8 1.7 0 0 0 1 0 0 0 0 0 0 0 28
29 1.3 0 1.3 1.8 1.8 1.8 0 0 0 0 1 0 0 0 0 0 0 29
30 1.3 1 1.3 1.3 1.8 1.8 0 0 0 0 0 1 0 0 0 0 0 30
31 1.2 1 1.3 1.3 1.3 1.8 0 0 0 0 0 0 1 0 0 0 0 31
32 1.4 1 1.2 1.3 1.3 1.3 0 0 0 0 0 0 0 1 0 0 0 32
33 2.2 1 1.4 1.2 1.3 1.3 0 0 0 0 0 0 0 0 1 0 0 33
34 2.9 1 2.2 1.4 1.2 1.3 0 0 0 0 0 0 0 0 0 1 0 34
35 3.1 1 2.9 2.2 1.4 1.2 0 0 0 0 0 0 0 0 0 0 1 35
36 3.5 1 3.1 2.9 2.2 1.4 0 0 0 0 0 0 0 0 0 0 0 36
37 3.6 1 3.5 3.1 2.9 2.2 1 0 0 0 0 0 0 0 0 0 0 37
38 4.4 1 3.6 3.5 3.1 2.9 0 1 0 0 0 0 0 0 0 0 0 38
39 4.1 1 4.4 3.6 3.5 3.1 0 0 1 0 0 0 0 0 0 0 0 39
40 5.1 1 4.1 4.4 3.6 3.5 0 0 0 1 0 0 0 0 0 0 0 40
41 5.8 1 5.1 4.1 4.4 3.6 0 0 0 0 1 0 0 0 0 0 0 41
42 5.9 1 5.8 5.1 4.1 4.4 0 0 0 0 0 1 0 0 0 0 0 42
43 5.4 1 5.9 5.8 5.1 4.1 0 0 0 0 0 0 1 0 0 0 0 43
44 5.5 1 5.4 5.9 5.8 5.1 0 0 0 0 0 0 0 1 0 0 0 44
45 4.8 1 5.5 5.4 5.9 5.8 0 0 0 0 0 0 0 0 1 0 0 45
46 3.2 1 4.8 5.5 5.4 5.9 0 0 0 0 0 0 0 0 0 1 0 46
47 2.7 1 3.2 4.8 5.5 5.4 0 0 0 0 0 0 0 0 0 0 1 47
48 2.1 1 2.7 3.2 4.8 5.5 0 0 0 0 0 0 0 0 0 0 0 48
49 1.9 1 2.1 2.7 3.2 4.8 1 0 0 0 0 0 0 0 0 0 0 49
50 0.6 1 1.9 2.1 2.7 3.2 0 1 0 0 0 0 0 0 0 0 0 50
51 0.7 1 0.6 1.9 2.1 2.7 0 0 1 0 0 0 0 0 0 0 0 51
52 -0.2 1 0.7 0.6 1.9 2.1 0 0 0 1 0 0 0 0 0 0 0 52
53 -1.0 1 -0.2 0.7 0.6 1.9 0 0 0 0 1 0 0 0 0 0 0 53
54 -1.7 1 -1.0 -0.2 0.7 0.6 0 0 0 0 0 1 0 0 0 0 0 54
55 -0.7 1 -1.7 -1.0 -0.2 0.7 0 0 0 0 0 0 1 0 0 0 0 55
56 -1.0 1 -0.7 -1.7 -1.0 -0.2 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
0.539091 0.573439 1.028299 0.026967 0.032818 -0.210413
M1 M2 M3 M4 M5 M6
0.136005 -0.060537 0.039992 0.021028 0.120045 -0.137284
M7 M8 M9 M10 M11 t
0.119909 -0.032093 -0.136439 -0.006291 0.196690 -0.019678
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.02099 -0.27025 0.04085 0.29608 1.07877
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.539091 0.375288 1.436 0.1591
X 0.573439 0.325110 1.764 0.0858 .
Y1 1.028299 0.160039 6.425 1.49e-07 ***
Y2 0.026967 0.242223 0.111 0.9119
Y3 0.032818 0.254511 0.129 0.8981
Y4 -0.210413 0.175993 -1.196 0.2393
M1 0.136005 0.367371 0.370 0.7133
M2 -0.060537 0.366645 -0.165 0.8697
M3 0.039992 0.368788 0.108 0.9142
M4 0.021028 0.369717 0.057 0.9549
M5 0.120045 0.367397 0.327 0.7457
M6 -0.137284 0.369555 -0.371 0.7123
M7 0.119909 0.371871 0.322 0.7489
M8 -0.032093 0.369313 -0.087 0.9312
M9 -0.136439 0.386666 -0.353 0.7261
M10 -0.006291 0.388555 -0.016 0.9872
M11 0.196690 0.387853 0.507 0.6150
t -0.019678 0.010447 -1.884 0.0673 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5427 on 38 degrees of freedom
Multiple R-squared: 0.9199, Adjusted R-squared: 0.884
F-statistic: 25.66 on 17 and 38 DF, p-value: 1.007e-15
> 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,] 0.3851129391 0.7702258782 0.6148871
[2,] 0.2389822984 0.4779645968 0.7610177
[3,] 0.1315248439 0.2630496878 0.8684752
[4,] 0.0725129608 0.1450259217 0.9274870
[5,] 0.0330092435 0.0660184869 0.9669908
[6,] 0.0140635349 0.0281270698 0.9859365
[7,] 0.0054677273 0.0109354546 0.9945323
[8,] 0.0028084123 0.0056168246 0.9971916
[9,] 0.0009342180 0.0018684359 0.9990658
[10,] 0.0002794597 0.0005589193 0.9997205
[11,] 0.0001967524 0.0003935049 0.9998032
[12,] 0.0009998830 0.0019997659 0.9990001
[13,] 0.0167651152 0.0335302303 0.9832349
[14,] 0.0090827635 0.0181655270 0.9909172
[15,] 0.0068347396 0.0136694792 0.9931653
> postscript(file="/var/www/html/rcomp/tmp/1t2pg1259323372.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2es1u1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3us671259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hv6s1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c58f1259323372.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
0.029381297 0.363376170 -0.727161627 -0.379487511 0.101504603 0.088222222
7 8 9 10 11 12
-0.040962452 -0.009111438 -0.709145872 0.093763203 0.368004636 0.114821819
13 14 15 16 17 18
-0.186600940 -0.565749435 0.475584913 0.319088208 -0.257007111 0.083720459
19 20 21 22 23 24
-0.065272229 -0.136138396 0.142059190 0.530276837 0.135963320 -0.236190900
25 26 27 28 29 30
0.104176900 0.288195258 0.238815727 -0.309988936 0.145862088 -0.137086342
31 32 33 34 35 36
-0.458191685 -0.088889167 0.632171449 0.396951584 -0.355339329 0.052319598
37 38 39 40 41 42
-0.235362203 0.807965860 -0.369265576 1.037176075 0.632415074 0.440821375
43 44 45 46 47 48
-0.514341802 0.456230499 -0.065084767 -1.020991623 -0.148628627 0.069049484
49 50 51 52 53 54
0.288404947 -0.893787853 0.382026563 -0.666787837 -0.622774654 -0.475677714
55 56
1.078768168 -0.222091499
> postscript(file="/var/www/html/rcomp/tmp/6oaxb1259323372.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 0.029381297 NA
1 0.363376170 0.029381297
2 -0.727161627 0.363376170
3 -0.379487511 -0.727161627
4 0.101504603 -0.379487511
5 0.088222222 0.101504603
6 -0.040962452 0.088222222
7 -0.009111438 -0.040962452
8 -0.709145872 -0.009111438
9 0.093763203 -0.709145872
10 0.368004636 0.093763203
11 0.114821819 0.368004636
12 -0.186600940 0.114821819
13 -0.565749435 -0.186600940
14 0.475584913 -0.565749435
15 0.319088208 0.475584913
16 -0.257007111 0.319088208
17 0.083720459 -0.257007111
18 -0.065272229 0.083720459
19 -0.136138396 -0.065272229
20 0.142059190 -0.136138396
21 0.530276837 0.142059190
22 0.135963320 0.530276837
23 -0.236190900 0.135963320
24 0.104176900 -0.236190900
25 0.288195258 0.104176900
26 0.238815727 0.288195258
27 -0.309988936 0.238815727
28 0.145862088 -0.309988936
29 -0.137086342 0.145862088
30 -0.458191685 -0.137086342
31 -0.088889167 -0.458191685
32 0.632171449 -0.088889167
33 0.396951584 0.632171449
34 -0.355339329 0.396951584
35 0.052319598 -0.355339329
36 -0.235362203 0.052319598
37 0.807965860 -0.235362203
38 -0.369265576 0.807965860
39 1.037176075 -0.369265576
40 0.632415074 1.037176075
41 0.440821375 0.632415074
42 -0.514341802 0.440821375
43 0.456230499 -0.514341802
44 -0.065084767 0.456230499
45 -1.020991623 -0.065084767
46 -0.148628627 -1.020991623
47 0.069049484 -0.148628627
48 0.288404947 0.069049484
49 -0.893787853 0.288404947
50 0.382026563 -0.893787853
51 -0.666787837 0.382026563
52 -0.622774654 -0.666787837
53 -0.475677714 -0.622774654
54 1.078768168 -0.475677714
55 -0.222091499 1.078768168
56 NA -0.222091499
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.363376170 0.029381297
[2,] -0.727161627 0.363376170
[3,] -0.379487511 -0.727161627
[4,] 0.101504603 -0.379487511
[5,] 0.088222222 0.101504603
[6,] -0.040962452 0.088222222
[7,] -0.009111438 -0.040962452
[8,] -0.709145872 -0.009111438
[9,] 0.093763203 -0.709145872
[10,] 0.368004636 0.093763203
[11,] 0.114821819 0.368004636
[12,] -0.186600940 0.114821819
[13,] -0.565749435 -0.186600940
[14,] 0.475584913 -0.565749435
[15,] 0.319088208 0.475584913
[16,] -0.257007111 0.319088208
[17,] 0.083720459 -0.257007111
[18,] -0.065272229 0.083720459
[19,] -0.136138396 -0.065272229
[20,] 0.142059190 -0.136138396
[21,] 0.530276837 0.142059190
[22,] 0.135963320 0.530276837
[23,] -0.236190900 0.135963320
[24,] 0.104176900 -0.236190900
[25,] 0.288195258 0.104176900
[26,] 0.238815727 0.288195258
[27,] -0.309988936 0.238815727
[28,] 0.145862088 -0.309988936
[29,] -0.137086342 0.145862088
[30,] -0.458191685 -0.137086342
[31,] -0.088889167 -0.458191685
[32,] 0.632171449 -0.088889167
[33,] 0.396951584 0.632171449
[34,] -0.355339329 0.396951584
[35,] 0.052319598 -0.355339329
[36,] -0.235362203 0.052319598
[37,] 0.807965860 -0.235362203
[38,] -0.369265576 0.807965860
[39,] 1.037176075 -0.369265576
[40,] 0.632415074 1.037176075
[41,] 0.440821375 0.632415074
[42,] -0.514341802 0.440821375
[43,] 0.456230499 -0.514341802
[44,] -0.065084767 0.456230499
[45,] -1.020991623 -0.065084767
[46,] -0.148628627 -1.020991623
[47,] 0.069049484 -0.148628627
[48,] 0.288404947 0.069049484
[49,] -0.893787853 0.288404947
[50,] 0.382026563 -0.893787853
[51,] -0.666787837 0.382026563
[52,] -0.622774654 -0.666787837
[53,] -0.475677714 -0.622774654
[54,] 1.078768168 -0.475677714
[55,] -0.222091499 1.078768168
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.363376170 0.029381297
2 -0.727161627 0.363376170
3 -0.379487511 -0.727161627
4 0.101504603 -0.379487511
5 0.088222222 0.101504603
6 -0.040962452 0.088222222
7 -0.009111438 -0.040962452
8 -0.709145872 -0.009111438
9 0.093763203 -0.709145872
10 0.368004636 0.093763203
11 0.114821819 0.368004636
12 -0.186600940 0.114821819
13 -0.565749435 -0.186600940
14 0.475584913 -0.565749435
15 0.319088208 0.475584913
16 -0.257007111 0.319088208
17 0.083720459 -0.257007111
18 -0.065272229 0.083720459
19 -0.136138396 -0.065272229
20 0.142059190 -0.136138396
21 0.530276837 0.142059190
22 0.135963320 0.530276837
23 -0.236190900 0.135963320
24 0.104176900 -0.236190900
25 0.288195258 0.104176900
26 0.238815727 0.288195258
27 -0.309988936 0.238815727
28 0.145862088 -0.309988936
29 -0.137086342 0.145862088
30 -0.458191685 -0.137086342
31 -0.088889167 -0.458191685
32 0.632171449 -0.088889167
33 0.396951584 0.632171449
34 -0.355339329 0.396951584
35 0.052319598 -0.355339329
36 -0.235362203 0.052319598
37 0.807965860 -0.235362203
38 -0.369265576 0.807965860
39 1.037176075 -0.369265576
40 0.632415074 1.037176075
41 0.440821375 0.632415074
42 -0.514341802 0.440821375
43 0.456230499 -0.514341802
44 -0.065084767 0.456230499
45 -1.020991623 -0.065084767
46 -0.148628627 -1.020991623
47 0.069049484 -0.148628627
48 0.288404947 0.069049484
49 -0.893787853 0.288404947
50 0.382026563 -0.893787853
51 -0.666787837 0.382026563
52 -0.622774654 -0.666787837
53 -0.475677714 -0.622774654
54 1.078768168 -0.475677714
55 -0.222091499 1.078768168
> 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/779hv1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jec41259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9d69w1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cano1259323372.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qzdy1259323372.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/12yt5y1259323372.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/13fies1259323372.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/14flhp1259323372.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/15qyhj1259323372.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/16ddhm1259323372.tab")
+ }
>
> system("convert tmp/1t2pg1259323372.ps tmp/1t2pg1259323372.png")
> system("convert tmp/2es1u1259323372.ps tmp/2es1u1259323372.png")
> system("convert tmp/3us671259323372.ps tmp/3us671259323372.png")
> system("convert tmp/4hv6s1259323372.ps tmp/4hv6s1259323372.png")
> system("convert tmp/5c58f1259323372.ps tmp/5c58f1259323372.png")
> system("convert tmp/6oaxb1259323372.ps tmp/6oaxb1259323372.png")
> system("convert tmp/779hv1259323372.ps tmp/779hv1259323372.png")
> system("convert tmp/8jec41259323372.ps tmp/8jec41259323372.png")
> system("convert tmp/9d69w1259323372.ps tmp/9d69w1259323372.png")
> system("convert tmp/10cano1259323372.ps tmp/10cano1259323372.png")
>
>
> proc.time()
user system elapsed
2.305 1.526 3.289