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(7.2
+ ,2.4
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,2
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,2.1
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,2
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,1.8
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,2.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,2.3
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,1.9
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,2
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,2.3
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,2.8
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,2.4
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,2.7
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,2.7
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,2.9
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,3
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,2.2
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,2.3
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,2.8
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,2.2
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,2.6
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,2.8
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8
+ ,2.5
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,2.4
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.1
+ ,1.9
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8
+ ,1.7
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,2
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,2.1
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,1.7
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,1.8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,7.9
+ ,1.8
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,1.8
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.7
+ ,1.3
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.2
+ ,1.3
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.5
+ ,1.3
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7.3
+ ,1.2
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,1.4
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,2.2
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7
+ ,2.9
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,3.1
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,3.5
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.1
+ ,3.6
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.8
+ ,4.4
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.4
+ ,4.1
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,5.9
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,5.4
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,5.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,4.8
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,3.2
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,2.7
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y(t)'
+ ,'X(t)'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-3)'
+ ,'Y(t-4)
')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y(t)','X(t)','Y(t-1)','Y(t-2)','Y(t-3)','Y(t-4)
'),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 = 'Do not include Seasonal 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(t) X(t) Y(t-1) Y(t-2) Y(t-3) Y(t-4)\r t
1 7.2 2.4 7.5 8.3 8.8 8.9 1
2 7.4 2.0 7.2 7.5 8.3 8.8 2
3 8.8 2.1 7.4 7.2 7.5 8.3 3
4 9.3 2.0 8.8 7.4 7.2 7.5 4
5 9.3 1.8 9.3 8.8 7.4 7.2 5
6 8.7 2.7 9.3 9.3 8.8 7.4 6
7 8.2 2.3 8.7 9.3 9.3 8.8 7
8 8.3 1.9 8.2 8.7 9.3 9.3 8
9 8.5 2.0 8.3 8.2 8.7 9.3 9
10 8.6 2.3 8.5 8.3 8.2 8.7 10
11 8.5 2.8 8.6 8.5 8.3 8.2 11
12 8.2 2.4 8.5 8.6 8.5 8.3 12
13 8.1 2.3 8.2 8.5 8.6 8.5 13
14 7.9 2.7 8.1 8.2 8.5 8.6 14
15 8.6 2.7 7.9 8.1 8.2 8.5 15
16 8.7 2.9 8.6 7.9 8.1 8.2 16
17 8.7 3.0 8.7 8.6 7.9 8.1 17
18 8.5 2.2 8.7 8.7 8.6 7.9 18
19 8.4 2.3 8.5 8.7 8.7 8.6 19
20 8.5 2.8 8.4 8.5 8.7 8.7 20
21 8.7 2.8 8.5 8.4 8.5 8.7 21
22 8.7 2.8 8.7 8.5 8.4 8.5 22
23 8.6 2.2 8.7 8.7 8.5 8.4 23
24 8.5 2.6 8.6 8.7 8.7 8.5 24
25 8.3 2.8 8.5 8.6 8.7 8.7 25
26 8.0 2.5 8.3 8.5 8.6 8.7 26
27 8.2 2.4 8.0 8.3 8.5 8.6 27
28 8.1 2.3 8.2 8.0 8.3 8.5 28
29 8.1 1.9 8.1 8.2 8.0 8.3 29
30 8.0 1.7 8.1 8.1 8.2 8.0 30
31 7.9 2.0 8.0 8.1 8.1 8.2 31
32 7.9 2.1 7.9 8.0 8.1 8.1 32
33 8.0 1.7 7.9 7.9 8.0 8.1 33
34 8.0 1.8 8.0 7.9 7.9 8.0 34
35 7.9 1.8 8.0 8.0 7.9 7.9 35
36 8.0 1.8 7.9 8.0 8.0 7.9 36
37 7.7 1.3 8.0 7.9 8.0 8.0 37
38 7.2 1.3 7.7 8.0 7.9 8.0 38
39 7.5 1.3 7.2 7.7 8.0 7.9 39
40 7.3 1.2 7.5 7.2 7.7 8.0 40
41 7.0 1.4 7.3 7.5 7.2 7.7 41
42 7.0 2.2 7.0 7.3 7.5 7.2 42
43 7.0 2.9 7.0 7.0 7.3 7.5 43
44 7.2 3.1 7.0 7.0 7.0 7.3 44
45 7.3 3.5 7.2 7.0 7.0 7.0 45
46 7.1 3.6 7.3 7.2 7.0 7.0 46
47 6.8 4.4 7.1 7.3 7.2 7.0 47
48 6.4 4.1 6.8 7.1 7.3 7.2 48
49 6.1 5.1 6.4 6.8 7.1 7.3 49
50 6.5 5.8 6.1 6.4 6.8 7.1 50
51 7.7 5.9 6.5 6.1 6.4 6.8 51
52 7.9 5.4 7.7 6.5 6.1 6.4 52
53 7.5 5.5 7.9 7.7 6.5 6.1 53
54 6.9 4.8 7.5 7.9 7.7 6.5 54
55 6.6 3.2 6.9 7.5 7.9 7.7 55
56 6.9 2.7 6.6 6.9 7.5 7.9 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `X(t)` `Y(t-1)` `Y(t-2)` `Y(t-3)` `Y(t-4)\r`
2.20712 0.03007 1.14110 -0.46973 -0.24493 0.32106
t
-0.01078
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.44603 -0.17064 0.01203 0.12493 0.69762
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.207119 1.058326 2.085 0.04226 *
`X(t)` 0.030071 0.041108 0.732 0.46795
`Y(t-1)` 1.141099 0.128234 8.899 8.35e-12 ***
`Y(t-2)` -0.469731 0.201695 -2.329 0.02403 *
`Y(t-3)` -0.244935 0.201658 -1.215 0.23034
`Y(t-4)\r` 0.321061 0.134889 2.380 0.02124 *
t -0.010776 0.003909 -2.757 0.00818 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2629 on 49 degrees of freedom
Multiple R-squared: 0.8887, Adjusted R-squared: 0.8751
F-statistic: 65.2 on 6 and 49 DF, p-value: < 2.2e-16
> 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,] 0.92093934 0.15812132 0.07906066
[2,] 0.89549727 0.20900546 0.10450273
[3,] 0.92563957 0.14872086 0.07436043
[4,] 0.88157525 0.23684951 0.11842475
[5,] 0.88343880 0.23312240 0.11656120
[6,] 0.94499825 0.11000349 0.05500175
[7,] 0.92690530 0.14618940 0.07309470
[8,] 0.88594619 0.22810762 0.11405381
[9,] 0.83560399 0.32879202 0.16439601
[10,] 0.76950684 0.46098632 0.23049316
[11,] 0.70989607 0.58020786 0.29010393
[12,] 0.63907060 0.72185880 0.36092940
[13,] 0.55112285 0.89775430 0.44887715
[14,] 0.47992964 0.95985929 0.52007036
[15,] 0.39442756 0.78885512 0.60557244
[16,] 0.32942971 0.65885942 0.67057029
[17,] 0.35710791 0.71421582 0.64289209
[18,] 0.30428114 0.60856227 0.69571886
[19,] 0.32922465 0.65844931 0.67077535
[20,] 0.28633612 0.57267223 0.71366388
[21,] 0.21984786 0.43969572 0.78015214
[22,] 0.17094908 0.34189817 0.82905092
[23,] 0.12139655 0.24279310 0.87860345
[24,] 0.08785719 0.17571437 0.91214281
[25,] 0.06128854 0.12257707 0.93871146
[26,] 0.04395507 0.08791015 0.95604493
[27,] 0.07368488 0.14736976 0.92631512
[28,] 0.06603761 0.13207521 0.93396239
[29,] 0.07422895 0.14845791 0.92577105
[30,] 0.57133928 0.85732143 0.42866072
[31,] 0.58446000 0.83108001 0.41554000
[32,] 0.55935293 0.88129415 0.44064707
[33,] 0.49787449 0.99574899 0.50212551
[34,] 0.46094085 0.92188170 0.53905915
[35,] 0.38968344 0.77936688 0.61031656
[36,] 0.26584588 0.53169175 0.73415412
[37,] 0.16809915 0.33619831 0.83190085
> postscript(file="/var/www/html/rcomp/tmp/1tncj1258565132.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2szxz1258565132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3h45j1258565132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4h5pi1258565132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5qxjw1258565132.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
-0.430010893 -0.331022632 0.672189915 -0.134251345 0.114917277 0.012191364
7 8 9 10 11 12
-0.107363088 0.143621845 -0.144545424 -0.153868229 -0.093267411 -0.192499311
13 14 15 16 17 18
-0.023078333 -0.307739571 0.542908814 -0.173220093 0.032369384 0.149841690
19 20 21 22 23 24
0.085581039 0.169379264 0.170085253 0.039333162 0.118697312 0.148435707
25 26 27 28 29 30
-0.043877889 -0.167327405 0.302451902 -0.169784941 0.051807136 0.066929522
31 32 33 34 35 36
-0.005911586 0.101100260 0.152437983 0.053709556 0.043564675 0.292944026
37 38 39 40 41 42
-0.174433723 -0.298848449 0.498157539 -0.370841066 -0.323089355 0.146024656
43 44 45 46 47 48
-0.150473475 0.045020081 0.011866218 -0.200528729 -0.189629561 -0.361167477
49 50 51 52 53 54
-0.446034927 0.088860763 0.697615180 -0.203056283 -0.065538276 0.082170407
55 56
0.001540481 0.225657062
> postscript(file="/var/www/html/rcomp/tmp/62y7h1258565132.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 -0.430010893 NA
1 -0.331022632 -0.430010893
2 0.672189915 -0.331022632
3 -0.134251345 0.672189915
4 0.114917277 -0.134251345
5 0.012191364 0.114917277
6 -0.107363088 0.012191364
7 0.143621845 -0.107363088
8 -0.144545424 0.143621845
9 -0.153868229 -0.144545424
10 -0.093267411 -0.153868229
11 -0.192499311 -0.093267411
12 -0.023078333 -0.192499311
13 -0.307739571 -0.023078333
14 0.542908814 -0.307739571
15 -0.173220093 0.542908814
16 0.032369384 -0.173220093
17 0.149841690 0.032369384
18 0.085581039 0.149841690
19 0.169379264 0.085581039
20 0.170085253 0.169379264
21 0.039333162 0.170085253
22 0.118697312 0.039333162
23 0.148435707 0.118697312
24 -0.043877889 0.148435707
25 -0.167327405 -0.043877889
26 0.302451902 -0.167327405
27 -0.169784941 0.302451902
28 0.051807136 -0.169784941
29 0.066929522 0.051807136
30 -0.005911586 0.066929522
31 0.101100260 -0.005911586
32 0.152437983 0.101100260
33 0.053709556 0.152437983
34 0.043564675 0.053709556
35 0.292944026 0.043564675
36 -0.174433723 0.292944026
37 -0.298848449 -0.174433723
38 0.498157539 -0.298848449
39 -0.370841066 0.498157539
40 -0.323089355 -0.370841066
41 0.146024656 -0.323089355
42 -0.150473475 0.146024656
43 0.045020081 -0.150473475
44 0.011866218 0.045020081
45 -0.200528729 0.011866218
46 -0.189629561 -0.200528729
47 -0.361167477 -0.189629561
48 -0.446034927 -0.361167477
49 0.088860763 -0.446034927
50 0.697615180 0.088860763
51 -0.203056283 0.697615180
52 -0.065538276 -0.203056283
53 0.082170407 -0.065538276
54 0.001540481 0.082170407
55 0.225657062 0.001540481
56 NA 0.225657062
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.331022632 -0.430010893
[2,] 0.672189915 -0.331022632
[3,] -0.134251345 0.672189915
[4,] 0.114917277 -0.134251345
[5,] 0.012191364 0.114917277
[6,] -0.107363088 0.012191364
[7,] 0.143621845 -0.107363088
[8,] -0.144545424 0.143621845
[9,] -0.153868229 -0.144545424
[10,] -0.093267411 -0.153868229
[11,] -0.192499311 -0.093267411
[12,] -0.023078333 -0.192499311
[13,] -0.307739571 -0.023078333
[14,] 0.542908814 -0.307739571
[15,] -0.173220093 0.542908814
[16,] 0.032369384 -0.173220093
[17,] 0.149841690 0.032369384
[18,] 0.085581039 0.149841690
[19,] 0.169379264 0.085581039
[20,] 0.170085253 0.169379264
[21,] 0.039333162 0.170085253
[22,] 0.118697312 0.039333162
[23,] 0.148435707 0.118697312
[24,] -0.043877889 0.148435707
[25,] -0.167327405 -0.043877889
[26,] 0.302451902 -0.167327405
[27,] -0.169784941 0.302451902
[28,] 0.051807136 -0.169784941
[29,] 0.066929522 0.051807136
[30,] -0.005911586 0.066929522
[31,] 0.101100260 -0.005911586
[32,] 0.152437983 0.101100260
[33,] 0.053709556 0.152437983
[34,] 0.043564675 0.053709556
[35,] 0.292944026 0.043564675
[36,] -0.174433723 0.292944026
[37,] -0.298848449 -0.174433723
[38,] 0.498157539 -0.298848449
[39,] -0.370841066 0.498157539
[40,] -0.323089355 -0.370841066
[41,] 0.146024656 -0.323089355
[42,] -0.150473475 0.146024656
[43,] 0.045020081 -0.150473475
[44,] 0.011866218 0.045020081
[45,] -0.200528729 0.011866218
[46,] -0.189629561 -0.200528729
[47,] -0.361167477 -0.189629561
[48,] -0.446034927 -0.361167477
[49,] 0.088860763 -0.446034927
[50,] 0.697615180 0.088860763
[51,] -0.203056283 0.697615180
[52,] -0.065538276 -0.203056283
[53,] 0.082170407 -0.065538276
[54,] 0.001540481 0.082170407
[55,] 0.225657062 0.001540481
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.331022632 -0.430010893
2 0.672189915 -0.331022632
3 -0.134251345 0.672189915
4 0.114917277 -0.134251345
5 0.012191364 0.114917277
6 -0.107363088 0.012191364
7 0.143621845 -0.107363088
8 -0.144545424 0.143621845
9 -0.153868229 -0.144545424
10 -0.093267411 -0.153868229
11 -0.192499311 -0.093267411
12 -0.023078333 -0.192499311
13 -0.307739571 -0.023078333
14 0.542908814 -0.307739571
15 -0.173220093 0.542908814
16 0.032369384 -0.173220093
17 0.149841690 0.032369384
18 0.085581039 0.149841690
19 0.169379264 0.085581039
20 0.170085253 0.169379264
21 0.039333162 0.170085253
22 0.118697312 0.039333162
23 0.148435707 0.118697312
24 -0.043877889 0.148435707
25 -0.167327405 -0.043877889
26 0.302451902 -0.167327405
27 -0.169784941 0.302451902
28 0.051807136 -0.169784941
29 0.066929522 0.051807136
30 -0.005911586 0.066929522
31 0.101100260 -0.005911586
32 0.152437983 0.101100260
33 0.053709556 0.152437983
34 0.043564675 0.053709556
35 0.292944026 0.043564675
36 -0.174433723 0.292944026
37 -0.298848449 -0.174433723
38 0.498157539 -0.298848449
39 -0.370841066 0.498157539
40 -0.323089355 -0.370841066
41 0.146024656 -0.323089355
42 -0.150473475 0.146024656
43 0.045020081 -0.150473475
44 0.011866218 0.045020081
45 -0.200528729 0.011866218
46 -0.189629561 -0.200528729
47 -0.361167477 -0.189629561
48 -0.446034927 -0.361167477
49 0.088860763 -0.446034927
50 0.697615180 0.088860763
51 -0.203056283 0.697615180
52 -0.065538276 -0.203056283
53 0.082170407 -0.065538276
54 0.001540481 0.082170407
55 0.225657062 0.001540481
> 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/7fi3h1258565132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81p7o1258565132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vyc81258565132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xx2h1258565132.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qxfz1258565132.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/129bci1258565132.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/138wbs1258565132.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/14m1wi1258565132.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/15ybmo1258565132.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/16qv0y1258565132.tab")
+ }
>
> system("convert tmp/1tncj1258565132.ps tmp/1tncj1258565132.png")
> system("convert tmp/2szxz1258565132.ps tmp/2szxz1258565132.png")
> system("convert tmp/3h45j1258565132.ps tmp/3h45j1258565132.png")
> system("convert tmp/4h5pi1258565132.ps tmp/4h5pi1258565132.png")
> system("convert tmp/5qxjw1258565132.ps tmp/5qxjw1258565132.png")
> system("convert tmp/62y7h1258565132.ps tmp/62y7h1258565132.png")
> system("convert tmp/7fi3h1258565132.ps tmp/7fi3h1258565132.png")
> system("convert tmp/81p7o1258565132.ps tmp/81p7o1258565132.png")
> system("convert tmp/9vyc81258565132.ps tmp/9vyc81258565132.png")
> system("convert tmp/10xx2h1258565132.ps tmp/10xx2h1258565132.png")
>
>
> proc.time()
user system elapsed
2.437 1.574 2.833