R version 2.7.0 (2008-04-22)
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.
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(2.2,0,2.3,0,2.1,0,2.8,0,3.1,0,2.9,0,2.6,0,2.7,0,2.3,0,2.3,0,2.1,0,2.2,0,2.9,0,2.6,0,2.7,0,1.8,1,1.3,1,0.9,1,1.3,1,1.3,1,1.3,1,1.3,1,1.1,1,1.4,1,1.2,1,1.7,1,1.8,1,1.5,1,1,1,1.6,1,1.5,1,1.8,1,1.8,1,1.6,1,1.9,1,1.7,1,1.6,1,1.3,1,1.1,1,1.9,0,2.6,0,2.3,0,2.4,0,2.2,0,2,0,2.9,0,2.6,0,2.3,0,2.3,0,2.6,0,3.1,0,2.8,0,2.5,0,2.9,0,3.1,0,3.1,0,3.2,0,2.5,0,2.6,0,2.9,0),dim=c(2,60),dimnames=list(c('Consumptieprijsindex','Dumivariabele'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Consumptieprijsindex','Dumivariabele'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = '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)
> 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
Consumptieprijsindex Dumivariabele
1 2.2 0
2 2.3 0
3 2.1 0
4 2.8 0
5 3.1 0
6 2.9 0
7 2.6 0
8 2.7 0
9 2.3 0
10 2.3 0
11 2.1 0
12 2.2 0
13 2.9 0
14 2.6 0
15 2.7 0
16 1.8 1
17 1.3 1
18 0.9 1
19 1.3 1
20 1.3 1
21 1.3 1
22 1.3 1
23 1.1 1
24 1.4 1
25 1.2 1
26 1.7 1
27 1.8 1
28 1.5 1
29 1.0 1
30 1.6 1
31 1.5 1
32 1.8 1
33 1.8 1
34 1.6 1
35 1.9 1
36 1.7 1
37 1.6 1
38 1.3 1
39 1.1 1
40 1.9 0
41 2.6 0
42 2.3 0
43 2.4 0
44 2.2 0
45 2.0 0
46 2.9 0
47 2.6 0
48 2.3 0
49 2.3 0
50 2.6 0
51 3.1 0
52 2.8 0
53 2.5 0
54 2.9 0
55 3.1 0
56 3.1 0
57 3.2 0
58 2.5 0
59 2.6 0
60 2.9 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dumivariabele
2.572 -1.122
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.67222 -0.27222 0.02778 0.26944 0.62778
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.57222 0.05428 47.39 <2e-16 ***
Dumivariabele -1.12222 0.08583 -13.07 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3257 on 58 degrees of freedom
Multiple R-squared: 0.7467, Adjusted R-squared: 0.7423
F-statistic: 171 on 1 and 58 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/14uxh1226775017.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/2wcm61226775017.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/377fs1226775017.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43k631226775017.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vquo1226775017.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
-0.37222222 -0.27222222 -0.47222222 0.22777778 0.52777778 0.32777778
7 8 9 10 11 12
0.02777778 0.12777778 -0.27222222 -0.27222222 -0.47222222 -0.37222222
13 14 15 16 17 18
0.32777778 0.02777778 0.12777778 0.35000000 -0.15000000 -0.55000000
19 20 21 22 23 24
-0.15000000 -0.15000000 -0.15000000 -0.15000000 -0.35000000 -0.05000000
25 26 27 28 29 30
-0.25000000 0.25000000 0.35000000 0.05000000 -0.45000000 0.15000000
31 32 33 34 35 36
0.05000000 0.35000000 0.35000000 0.15000000 0.45000000 0.25000000
37 38 39 40 41 42
0.15000000 -0.15000000 -0.35000000 -0.67222222 0.02777778 -0.27222222
43 44 45 46 47 48
-0.17222222 -0.37222222 -0.57222222 0.32777778 0.02777778 -0.27222222
49 50 51 52 53 54
-0.27222222 0.02777778 0.52777778 0.22777778 -0.07222222 0.32777778
55 56 57 58 59 60
0.52777778 0.52777778 0.62777778 -0.07222222 0.02777778 0.32777778
> postscript(file="/var/www/html/rcomp/tmp/68tpz1226775017.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.37222222 NA
1 -0.27222222 -0.37222222
2 -0.47222222 -0.27222222
3 0.22777778 -0.47222222
4 0.52777778 0.22777778
5 0.32777778 0.52777778
6 0.02777778 0.32777778
7 0.12777778 0.02777778
8 -0.27222222 0.12777778
9 -0.27222222 -0.27222222
10 -0.47222222 -0.27222222
11 -0.37222222 -0.47222222
12 0.32777778 -0.37222222
13 0.02777778 0.32777778
14 0.12777778 0.02777778
15 0.35000000 0.12777778
16 -0.15000000 0.35000000
17 -0.55000000 -0.15000000
18 -0.15000000 -0.55000000
19 -0.15000000 -0.15000000
20 -0.15000000 -0.15000000
21 -0.15000000 -0.15000000
22 -0.35000000 -0.15000000
23 -0.05000000 -0.35000000
24 -0.25000000 -0.05000000
25 0.25000000 -0.25000000
26 0.35000000 0.25000000
27 0.05000000 0.35000000
28 -0.45000000 0.05000000
29 0.15000000 -0.45000000
30 0.05000000 0.15000000
31 0.35000000 0.05000000
32 0.35000000 0.35000000
33 0.15000000 0.35000000
34 0.45000000 0.15000000
35 0.25000000 0.45000000
36 0.15000000 0.25000000
37 -0.15000000 0.15000000
38 -0.35000000 -0.15000000
39 -0.67222222 -0.35000000
40 0.02777778 -0.67222222
41 -0.27222222 0.02777778
42 -0.17222222 -0.27222222
43 -0.37222222 -0.17222222
44 -0.57222222 -0.37222222
45 0.32777778 -0.57222222
46 0.02777778 0.32777778
47 -0.27222222 0.02777778
48 -0.27222222 -0.27222222
49 0.02777778 -0.27222222
50 0.52777778 0.02777778
51 0.22777778 0.52777778
52 -0.07222222 0.22777778
53 0.32777778 -0.07222222
54 0.52777778 0.32777778
55 0.52777778 0.52777778
56 0.62777778 0.52777778
57 -0.07222222 0.62777778
58 0.02777778 -0.07222222
59 0.32777778 0.02777778
60 NA 0.32777778
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.27222222 -0.37222222
[2,] -0.47222222 -0.27222222
[3,] 0.22777778 -0.47222222
[4,] 0.52777778 0.22777778
[5,] 0.32777778 0.52777778
[6,] 0.02777778 0.32777778
[7,] 0.12777778 0.02777778
[8,] -0.27222222 0.12777778
[9,] -0.27222222 -0.27222222
[10,] -0.47222222 -0.27222222
[11,] -0.37222222 -0.47222222
[12,] 0.32777778 -0.37222222
[13,] 0.02777778 0.32777778
[14,] 0.12777778 0.02777778
[15,] 0.35000000 0.12777778
[16,] -0.15000000 0.35000000
[17,] -0.55000000 -0.15000000
[18,] -0.15000000 -0.55000000
[19,] -0.15000000 -0.15000000
[20,] -0.15000000 -0.15000000
[21,] -0.15000000 -0.15000000
[22,] -0.35000000 -0.15000000
[23,] -0.05000000 -0.35000000
[24,] -0.25000000 -0.05000000
[25,] 0.25000000 -0.25000000
[26,] 0.35000000 0.25000000
[27,] 0.05000000 0.35000000
[28,] -0.45000000 0.05000000
[29,] 0.15000000 -0.45000000
[30,] 0.05000000 0.15000000
[31,] 0.35000000 0.05000000
[32,] 0.35000000 0.35000000
[33,] 0.15000000 0.35000000
[34,] 0.45000000 0.15000000
[35,] 0.25000000 0.45000000
[36,] 0.15000000 0.25000000
[37,] -0.15000000 0.15000000
[38,] -0.35000000 -0.15000000
[39,] -0.67222222 -0.35000000
[40,] 0.02777778 -0.67222222
[41,] -0.27222222 0.02777778
[42,] -0.17222222 -0.27222222
[43,] -0.37222222 -0.17222222
[44,] -0.57222222 -0.37222222
[45,] 0.32777778 -0.57222222
[46,] 0.02777778 0.32777778
[47,] -0.27222222 0.02777778
[48,] -0.27222222 -0.27222222
[49,] 0.02777778 -0.27222222
[50,] 0.52777778 0.02777778
[51,] 0.22777778 0.52777778
[52,] -0.07222222 0.22777778
[53,] 0.32777778 -0.07222222
[54,] 0.52777778 0.32777778
[55,] 0.52777778 0.52777778
[56,] 0.62777778 0.52777778
[57,] -0.07222222 0.62777778
[58,] 0.02777778 -0.07222222
[59,] 0.32777778 0.02777778
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.27222222 -0.37222222
2 -0.47222222 -0.27222222
3 0.22777778 -0.47222222
4 0.52777778 0.22777778
5 0.32777778 0.52777778
6 0.02777778 0.32777778
7 0.12777778 0.02777778
8 -0.27222222 0.12777778
9 -0.27222222 -0.27222222
10 -0.47222222 -0.27222222
11 -0.37222222 -0.47222222
12 0.32777778 -0.37222222
13 0.02777778 0.32777778
14 0.12777778 0.02777778
15 0.35000000 0.12777778
16 -0.15000000 0.35000000
17 -0.55000000 -0.15000000
18 -0.15000000 -0.55000000
19 -0.15000000 -0.15000000
20 -0.15000000 -0.15000000
21 -0.15000000 -0.15000000
22 -0.35000000 -0.15000000
23 -0.05000000 -0.35000000
24 -0.25000000 -0.05000000
25 0.25000000 -0.25000000
26 0.35000000 0.25000000
27 0.05000000 0.35000000
28 -0.45000000 0.05000000
29 0.15000000 -0.45000000
30 0.05000000 0.15000000
31 0.35000000 0.05000000
32 0.35000000 0.35000000
33 0.15000000 0.35000000
34 0.45000000 0.15000000
35 0.25000000 0.45000000
36 0.15000000 0.25000000
37 -0.15000000 0.15000000
38 -0.35000000 -0.15000000
39 -0.67222222 -0.35000000
40 0.02777778 -0.67222222
41 -0.27222222 0.02777778
42 -0.17222222 -0.27222222
43 -0.37222222 -0.17222222
44 -0.57222222 -0.37222222
45 0.32777778 -0.57222222
46 0.02777778 0.32777778
47 -0.27222222 0.02777778
48 -0.27222222 -0.27222222
49 0.02777778 -0.27222222
50 0.52777778 0.02777778
51 0.22777778 0.52777778
52 -0.07222222 0.22777778
53 0.32777778 -0.07222222
54 0.52777778 0.32777778
55 0.52777778 0.52777778
56 0.62777778 0.52777778
57 -0.07222222 0.62777778
58 0.02777778 -0.07222222
59 0.32777778 0.02777778
> 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/7m5ak1226775017.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81c3a1226775017.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91b0d1226775017.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10wh3z1226775017.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/11wnro1226775017.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/122hpj1226775017.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/13y2jk1226775017.tab")
>
> system("convert tmp/14uxh1226775017.ps tmp/14uxh1226775017.png")
> system("convert tmp/2wcm61226775017.ps tmp/2wcm61226775017.png")
> system("convert tmp/377fs1226775017.ps tmp/377fs1226775017.png")
> system("convert tmp/43k631226775017.ps tmp/43k631226775017.png")
> system("convert tmp/5vquo1226775017.ps tmp/5vquo1226775017.png")
> system("convert tmp/68tpz1226775017.ps tmp/68tpz1226775017.png")
> system("convert tmp/7m5ak1226775017.ps tmp/7m5ak1226775017.png")
> system("convert tmp/81c3a1226775017.ps tmp/81c3a1226775017.png")
> system("convert tmp/91b0d1226775017.ps tmp/91b0d1226775017.png")
>
>
> proc.time()
user system elapsed
3.998 2.495 4.337