R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
Natural language support but running in an English locale
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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(102.8
+ ,112.5
+ ,116.7
+ ,116.1
+ ,98.1
+ ,113
+ ,112.5
+ ,107.5
+ ,113.9
+ ,126.4
+ ,113
+ ,116.7
+ ,80.9
+ ,114.1
+ ,126.4
+ ,112.5
+ ,95.7
+ ,112.5
+ ,114.1
+ ,113
+ ,113.2
+ ,112.4
+ ,112.5
+ ,126.4
+ ,105.9
+ ,113.1
+ ,112.4
+ ,114.1
+ ,108.8
+ ,116.3
+ ,113.1
+ ,112.5
+ ,102.3
+ ,111.7
+ ,116.3
+ ,112.4
+ ,99
+ ,118.8
+ ,111.7
+ ,113.1
+ ,100.7
+ ,116.5
+ ,118.8
+ ,116.3
+ ,115.5
+ ,125.1
+ ,116.5
+ ,111.7
+ ,100.7
+ ,113.1
+ ,125.1
+ ,118.8
+ ,109.9
+ ,119.6
+ ,113.1
+ ,116.5
+ ,114.6
+ ,114.4
+ ,119.6
+ ,125.1
+ ,85.4
+ ,114
+ ,114.4
+ ,113.1
+ ,100.5
+ ,117.8
+ ,114
+ ,119.6
+ ,114.8
+ ,117
+ ,117.8
+ ,114.4
+ ,116.5
+ ,120.9
+ ,117
+ ,114
+ ,112.9
+ ,115
+ ,120.9
+ ,117.8
+ ,102
+ ,117.3
+ ,115
+ ,117
+ ,106
+ ,119.4
+ ,117.3
+ ,120.9
+ ,105.3
+ ,114.9
+ ,119.4
+ ,115
+ ,118.8
+ ,125.8
+ ,114.9
+ ,117.3
+ ,106.1
+ ,117.6
+ ,125.8
+ ,119.4
+ ,109.3
+ ,117.6
+ ,117.6
+ ,114.9
+ ,117.2
+ ,114.9
+ ,117.6
+ ,125.8
+ ,92.5
+ ,121.9
+ ,114.9
+ ,117.6
+ ,104.2
+ ,117
+ ,121.9
+ ,117.6
+ ,112.5
+ ,106.4
+ ,117
+ ,114.9
+ ,122.4
+ ,110.5
+ ,106.4
+ ,121.9
+ ,113.3
+ ,113.6
+ ,110.5
+ ,117
+ ,100
+ ,114.2
+ ,113.6
+ ,106.4
+ ,110.7
+ ,125.4
+ ,114.2
+ ,110.5
+ ,112.8
+ ,124.6
+ ,125.4
+ ,113.6
+ ,109.8
+ ,120.2
+ ,124.6
+ ,114.2
+ ,117.3
+ ,120.8
+ ,120.2
+ ,125.4
+ ,109.1
+ ,111.4
+ ,120.8
+ ,124.6
+ ,115.9
+ ,124.1
+ ,111.4
+ ,120.2
+ ,96
+ ,120.2
+ ,124.1
+ ,120.8
+ ,99.8
+ ,125.5
+ ,120.2
+ ,111.4
+ ,116.8
+ ,116
+ ,125.5
+ ,124.1
+ ,115.7
+ ,117
+ ,116
+ ,120.2
+ ,99.4
+ ,105.7
+ ,117
+ ,125.5
+ ,94.3
+ ,102
+ ,105.7
+ ,116
+ ,91
+ ,106.4
+ ,102
+ ,117
+ ,93.2
+ ,96.9
+ ,106.4
+ ,105.7
+ ,103.1
+ ,107.6
+ ,96.9
+ ,102
+ ,94.1
+ ,98.8
+ ,107.6
+ ,106.4
+ ,91.8
+ ,101.1
+ ,98.8
+ ,96.9
+ ,102.7
+ ,105.7
+ ,101.1
+ ,107.6
+ ,82.6
+ ,104.6
+ ,105.7
+ ,98.8
+ ,89.1
+ ,103.2
+ ,104.6
+ ,101.1
+ ,104.5
+ ,101.6
+ ,103.2
+ ,105.7
+ ,105.1
+ ,106.7
+ ,101.6
+ ,104.6
+ ,95.1
+ ,99.5
+ ,106.7
+ ,103.2
+ ,88.7
+ ,101
+ ,99.5
+ ,101.6)
+ ,dim=c(4
+ ,57)
+ ,dimnames=list(c('T.I.P.'
+ ,'Y(t)'
+ ,'Y(t-1)'
+ ,'Y(t-3)')
+ ,1:57))
> y <- array(NA,dim=c(4,57),dimnames=list(c('T.I.P.','Y(t)','Y(t-1)','Y(t-3)'),1:57))
> 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 = '2'
> #'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) T.I.P. Y(t-1) Y(t-3) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 112.5 102.8 116.7 116.1 1 0 0 0 0 0 0 0 0 0 0 1
2 113.0 98.1 112.5 107.5 0 1 0 0 0 0 0 0 0 0 0 2
3 126.4 113.9 113.0 116.7 0 0 1 0 0 0 0 0 0 0 0 3
4 114.1 80.9 126.4 112.5 0 0 0 1 0 0 0 0 0 0 0 4
5 112.5 95.7 114.1 113.0 0 0 0 0 1 0 0 0 0 0 0 5
6 112.4 113.2 112.5 126.4 0 0 0 0 0 1 0 0 0 0 0 6
7 113.1 105.9 112.4 114.1 0 0 0 0 0 0 1 0 0 0 0 7
8 116.3 108.8 113.1 112.5 0 0 0 0 0 0 0 1 0 0 0 8
9 111.7 102.3 116.3 112.4 0 0 0 0 0 0 0 0 1 0 0 9
10 118.8 99.0 111.7 113.1 0 0 0 0 0 0 0 0 0 1 0 10
11 116.5 100.7 118.8 116.3 0 0 0 0 0 0 0 0 0 0 1 11
12 125.1 115.5 116.5 111.7 0 0 0 0 0 0 0 0 0 0 0 12
13 113.1 100.7 125.1 118.8 1 0 0 0 0 0 0 0 0 0 0 13
14 119.6 109.9 113.1 116.5 0 1 0 0 0 0 0 0 0 0 0 14
15 114.4 114.6 119.6 125.1 0 0 1 0 0 0 0 0 0 0 0 15
16 114.0 85.4 114.4 113.1 0 0 0 1 0 0 0 0 0 0 0 16
17 117.8 100.5 114.0 119.6 0 0 0 0 1 0 0 0 0 0 0 17
18 117.0 114.8 117.8 114.4 0 0 0 0 0 1 0 0 0 0 0 18
19 120.9 116.5 117.0 114.0 0 0 0 0 0 0 1 0 0 0 0 19
20 115.0 112.9 120.9 117.8 0 0 0 0 0 0 0 1 0 0 0 20
21 117.3 102.0 115.0 117.0 0 0 0 0 0 0 0 0 1 0 0 21
22 119.4 106.0 117.3 120.9 0 0 0 0 0 0 0 0 0 1 0 22
23 114.9 105.3 119.4 115.0 0 0 0 0 0 0 0 0 0 0 1 23
24 125.8 118.8 114.9 117.3 0 0 0 0 0 0 0 0 0 0 0 24
25 117.6 106.1 125.8 119.4 1 0 0 0 0 0 0 0 0 0 0 25
26 117.6 109.3 117.6 114.9 0 1 0 0 0 0 0 0 0 0 0 26
27 114.9 117.2 117.6 125.8 0 0 1 0 0 0 0 0 0 0 0 27
28 121.9 92.5 114.9 117.6 0 0 0 1 0 0 0 0 0 0 0 28
29 117.0 104.2 121.9 117.6 0 0 0 0 1 0 0 0 0 0 0 29
30 106.4 112.5 117.0 114.9 0 0 0 0 0 1 0 0 0 0 0 30
31 110.5 122.4 106.4 121.9 0 0 0 0 0 0 1 0 0 0 0 31
32 113.6 113.3 110.5 117.0 0 0 0 0 0 0 0 1 0 0 0 32
33 114.2 100.0 113.6 106.4 0 0 0 0 0 0 0 0 1 0 0 33
34 125.4 110.7 114.2 110.5 0 0 0 0 0 0 0 0 0 1 0 34
35 124.6 112.8 125.4 113.6 0 0 0 0 0 0 0 0 0 0 1 35
36 120.2 109.8 124.6 114.2 0 0 0 0 0 0 0 0 0 0 0 36
37 120.8 117.3 120.2 125.4 1 0 0 0 0 0 0 0 0 0 0 37
38 111.4 109.1 120.8 124.6 0 1 0 0 0 0 0 0 0 0 0 38
39 124.1 115.9 111.4 120.2 0 0 1 0 0 0 0 0 0 0 0 39
40 120.2 96.0 124.1 120.8 0 0 0 1 0 0 0 0 0 0 0 40
41 125.5 99.8 120.2 111.4 0 0 0 0 1 0 0 0 0 0 0 41
42 116.0 116.8 125.5 124.1 0 0 0 0 0 1 0 0 0 0 0 42
43 117.0 115.7 116.0 120.2 0 0 0 0 0 0 1 0 0 0 0 43
44 105.7 99.4 117.0 125.5 0 0 0 0 0 0 0 1 0 0 0 44
45 102.0 94.3 105.7 116.0 0 0 0 0 0 0 0 0 1 0 0 45
46 106.4 91.0 102.0 117.0 0 0 0 0 0 0 0 0 0 1 0 46
47 96.9 93.2 106.4 105.7 0 0 0 0 0 0 0 0 0 0 1 47
48 107.6 103.1 96.9 102.0 0 0 0 0 0 0 0 0 0 0 0 48
49 98.8 94.1 107.6 106.4 1 0 0 0 0 0 0 0 0 0 0 49
50 101.1 91.8 98.8 96.9 0 1 0 0 0 0 0 0 0 0 0 50
51 105.7 102.7 101.1 107.6 0 0 1 0 0 0 0 0 0 0 0 51
52 104.6 82.6 105.7 98.8 0 0 0 1 0 0 0 0 0 0 0 52
53 103.2 89.1 104.6 101.1 0 0 0 0 1 0 0 0 0 0 0 53
54 101.6 104.5 103.2 105.7 0 0 0 0 0 1 0 0 0 0 0 54
55 106.7 105.1 101.6 104.6 0 0 0 0 0 0 1 0 0 0 0 55
56 99.5 95.1 106.7 103.2 0 0 0 0 0 0 0 1 0 0 0 56
57 101.0 88.7 99.5 101.6 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T.I.P. `Y(t-1)` `Y(t-3)` M1 M2
27.5647 0.7192 0.3516 -0.2255 -2.8753 -1.2585
M3 M4 M5 M6 M7 M8
-1.6439 11.4974 5.1202 -8.7844 -5.1637 -4.4770
M9 M10 M11 t
1.1676 6.5019 -1.4200 -0.1005
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7448 -1.8238 0.4007 1.8021 8.0116
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 27.56465 11.15424 2.471 0.01771 *
T.I.P. 0.71924 0.11058 6.504 8.3e-08 ***
`Y(t-1)` 0.35162 0.11157 3.151 0.00303 **
`Y(t-3)` -0.22548 0.11191 -2.015 0.05050 .
M1 -2.87532 2.81047 -1.023 0.31227
M2 -1.25845 2.60687 -0.483 0.63184
M3 -1.64389 2.58531 -0.636 0.52840
M4 11.49739 3.79326 3.031 0.00421 **
M5 5.12018 2.95082 1.735 0.09022 .
M6 -8.78442 2.49878 -3.515 0.00109 **
M7 -5.16373 2.50390 -2.062 0.04556 *
M8 -4.47697 2.60633 -1.718 0.09339 .
M9 1.16760 2.84131 0.411 0.68326
M10 6.50191 2.90250 2.240 0.03057 *
M11 -1.42000 2.85439 -0.497 0.62151
t -0.10053 0.03439 -2.923 0.00562 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.629 on 41 degrees of freedom
Multiple R-squared: 0.8341, Adjusted R-squared: 0.7733
F-statistic: 13.74 on 15 and 41 DF, p-value: 1.752e-11
> 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,] 0.6816132 0.6367736 0.3183868
[2,] 0.5310043 0.9379914 0.4689957
[3,] 0.6284292 0.7431417 0.3715708
[4,] 0.4990386 0.9980772 0.5009614
[5,] 0.4476663 0.8953325 0.5523337
[6,] 0.3365006 0.6730012 0.6634994
[7,] 0.2707139 0.5414278 0.7292861
[8,] 0.1918916 0.3837832 0.8081084
[9,] 0.2457544 0.4915088 0.7542456
[10,] 0.4225300 0.8450600 0.5774700
[11,] 0.3666108 0.7332216 0.6333892
[12,] 0.3847918 0.7695835 0.6152082
[13,] 0.6607695 0.6784611 0.3392305
[14,] 0.5754092 0.8491816 0.4245908
[15,] 0.4810182 0.9620364 0.5189818
[16,] 0.7157539 0.5684921 0.2842461
[17,] 0.5968694 0.8062612 0.4031306
[18,] 0.5153156 0.9693687 0.4846844
[19,] 0.3810356 0.7620711 0.6189644
[20,] 0.2874165 0.5748331 0.7125835
> postscript(file="/var/www/html/freestat/rcomp/tmp/17mjt1292675149.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/27mjt1292675149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3id0w1292675149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4id0w1292675149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5id0w1292675149.ps",horizontal=F,onefile=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 = 57
Frequency = 1
1 2 3 4 5 6
-0.88155361 1.02017422 5.44081053 -1.82376469 -3.15312772 1.74939215
7 8 9 10 11 12
1.44140979 1.36249018 -5.25422086 0.76074890 3.48556057 -0.10712244
13 14 15 16 17 18
0.09046968 2.15793364 -6.28287646 0.40074104 1.42426967 1.83566194
19 20 21 22 23 24
1.18390617 -3.22753747 3.26226366 -2.67780841 -0.72063227 1.25107325
25 26 27 28 29 30
1.80214068 -0.14716982 -5.58542922 5.23941050 -4.05924209 -5.50966183
31 32 33 34 35 36
-6.74476534 -0.23241739 0.90928946 -0.10683141 2.36611218 -0.77905922
37 38 39 40 41 42
1.47501516 -3.93492275 6.67329546 -0.28484862 8.01155893 1.28971118
43 44 45 46 47 48
2.02169185 2.70247467 -1.04226918 2.02389091 -5.13104048 -0.36489159
49 50 51 52 53 54
-2.48607191 0.90398471 -0.24580032 -3.53153823 -2.22345880 0.63489656
55 56 57
2.09775753 -0.60500998 2.12493692
> postscript(file="/var/www/html/freestat/rcomp/tmp/6amih1292675149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.88155361 NA
1 1.02017422 -0.88155361
2 5.44081053 1.02017422
3 -1.82376469 5.44081053
4 -3.15312772 -1.82376469
5 1.74939215 -3.15312772
6 1.44140979 1.74939215
7 1.36249018 1.44140979
8 -5.25422086 1.36249018
9 0.76074890 -5.25422086
10 3.48556057 0.76074890
11 -0.10712244 3.48556057
12 0.09046968 -0.10712244
13 2.15793364 0.09046968
14 -6.28287646 2.15793364
15 0.40074104 -6.28287646
16 1.42426967 0.40074104
17 1.83566194 1.42426967
18 1.18390617 1.83566194
19 -3.22753747 1.18390617
20 3.26226366 -3.22753747
21 -2.67780841 3.26226366
22 -0.72063227 -2.67780841
23 1.25107325 -0.72063227
24 1.80214068 1.25107325
25 -0.14716982 1.80214068
26 -5.58542922 -0.14716982
27 5.23941050 -5.58542922
28 -4.05924209 5.23941050
29 -5.50966183 -4.05924209
30 -6.74476534 -5.50966183
31 -0.23241739 -6.74476534
32 0.90928946 -0.23241739
33 -0.10683141 0.90928946
34 2.36611218 -0.10683141
35 -0.77905922 2.36611218
36 1.47501516 -0.77905922
37 -3.93492275 1.47501516
38 6.67329546 -3.93492275
39 -0.28484862 6.67329546
40 8.01155893 -0.28484862
41 1.28971118 8.01155893
42 2.02169185 1.28971118
43 2.70247467 2.02169185
44 -1.04226918 2.70247467
45 2.02389091 -1.04226918
46 -5.13104048 2.02389091
47 -0.36489159 -5.13104048
48 -2.48607191 -0.36489159
49 0.90398471 -2.48607191
50 -0.24580032 0.90398471
51 -3.53153823 -0.24580032
52 -2.22345880 -3.53153823
53 0.63489656 -2.22345880
54 2.09775753 0.63489656
55 -0.60500998 2.09775753
56 2.12493692 -0.60500998
57 NA 2.12493692
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.02017422 -0.88155361
[2,] 5.44081053 1.02017422
[3,] -1.82376469 5.44081053
[4,] -3.15312772 -1.82376469
[5,] 1.74939215 -3.15312772
[6,] 1.44140979 1.74939215
[7,] 1.36249018 1.44140979
[8,] -5.25422086 1.36249018
[9,] 0.76074890 -5.25422086
[10,] 3.48556057 0.76074890
[11,] -0.10712244 3.48556057
[12,] 0.09046968 -0.10712244
[13,] 2.15793364 0.09046968
[14,] -6.28287646 2.15793364
[15,] 0.40074104 -6.28287646
[16,] 1.42426967 0.40074104
[17,] 1.83566194 1.42426967
[18,] 1.18390617 1.83566194
[19,] -3.22753747 1.18390617
[20,] 3.26226366 -3.22753747
[21,] -2.67780841 3.26226366
[22,] -0.72063227 -2.67780841
[23,] 1.25107325 -0.72063227
[24,] 1.80214068 1.25107325
[25,] -0.14716982 1.80214068
[26,] -5.58542922 -0.14716982
[27,] 5.23941050 -5.58542922
[28,] -4.05924209 5.23941050
[29,] -5.50966183 -4.05924209
[30,] -6.74476534 -5.50966183
[31,] -0.23241739 -6.74476534
[32,] 0.90928946 -0.23241739
[33,] -0.10683141 0.90928946
[34,] 2.36611218 -0.10683141
[35,] -0.77905922 2.36611218
[36,] 1.47501516 -0.77905922
[37,] -3.93492275 1.47501516
[38,] 6.67329546 -3.93492275
[39,] -0.28484862 6.67329546
[40,] 8.01155893 -0.28484862
[41,] 1.28971118 8.01155893
[42,] 2.02169185 1.28971118
[43,] 2.70247467 2.02169185
[44,] -1.04226918 2.70247467
[45,] 2.02389091 -1.04226918
[46,] -5.13104048 2.02389091
[47,] -0.36489159 -5.13104048
[48,] -2.48607191 -0.36489159
[49,] 0.90398471 -2.48607191
[50,] -0.24580032 0.90398471
[51,] -3.53153823 -0.24580032
[52,] -2.22345880 -3.53153823
[53,] 0.63489656 -2.22345880
[54,] 2.09775753 0.63489656
[55,] -0.60500998 2.09775753
[56,] 2.12493692 -0.60500998
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.02017422 -0.88155361
2 5.44081053 1.02017422
3 -1.82376469 5.44081053
4 -3.15312772 -1.82376469
5 1.74939215 -3.15312772
6 1.44140979 1.74939215
7 1.36249018 1.44140979
8 -5.25422086 1.36249018
9 0.76074890 -5.25422086
10 3.48556057 0.76074890
11 -0.10712244 3.48556057
12 0.09046968 -0.10712244
13 2.15793364 0.09046968
14 -6.28287646 2.15793364
15 0.40074104 -6.28287646
16 1.42426967 0.40074104
17 1.83566194 1.42426967
18 1.18390617 1.83566194
19 -3.22753747 1.18390617
20 3.26226366 -3.22753747
21 -2.67780841 3.26226366
22 -0.72063227 -2.67780841
23 1.25107325 -0.72063227
24 1.80214068 1.25107325
25 -0.14716982 1.80214068
26 -5.58542922 -0.14716982
27 5.23941050 -5.58542922
28 -4.05924209 5.23941050
29 -5.50966183 -4.05924209
30 -6.74476534 -5.50966183
31 -0.23241739 -6.74476534
32 0.90928946 -0.23241739
33 -0.10683141 0.90928946
34 2.36611218 -0.10683141
35 -0.77905922 2.36611218
36 1.47501516 -0.77905922
37 -3.93492275 1.47501516
38 6.67329546 -3.93492275
39 -0.28484862 6.67329546
40 8.01155893 -0.28484862
41 1.28971118 8.01155893
42 2.02169185 1.28971118
43 2.70247467 2.02169185
44 -1.04226918 2.70247467
45 2.02389091 -1.04226918
46 -5.13104048 2.02389091
47 -0.36489159 -5.13104048
48 -2.48607191 -0.36489159
49 0.90398471 -2.48607191
50 -0.24580032 0.90398471
51 -3.53153823 -0.24580032
52 -2.22345880 -3.53153823
53 0.63489656 -2.22345880
54 2.09775753 0.63489656
55 -0.60500998 2.09775753
56 2.12493692 -0.60500998
> 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/freestat/rcomp/tmp/73vz11292675149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/83vz11292675149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/93vz11292675149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10w5y41292675149.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11h5es1292675149.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/freestat/rcomp/tmp/123odg1292675149.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/freestat/rcomp/tmp/13hyb71292675149.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/freestat/rcomp/tmp/14rpas1292675149.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/freestat/rcomp/tmp/15d7rg1292675149.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/freestat/rcomp/tmp/16rz7p1292675149.tab")
+ }
>
> try(system("convert tmp/17mjt1292675149.ps tmp/17mjt1292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/27mjt1292675149.ps tmp/27mjt1292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/3id0w1292675149.ps tmp/3id0w1292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/4id0w1292675149.ps tmp/4id0w1292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/5id0w1292675149.ps tmp/5id0w1292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/6amih1292675149.ps tmp/6amih1292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/73vz11292675149.ps tmp/73vz11292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/83vz11292675149.ps tmp/83vz11292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/93vz11292675149.ps tmp/93vz11292675149.png",intern=TRUE))
character(0)
> try(system("convert tmp/10w5y41292675149.ps tmp/10w5y41292675149.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.686 2.428 3.987