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(19,0,18,0,19,0,19,0,22,0,23,0,20,0,14,0,14,0,14,0,15,0,11,0,17,0,16,0,20,0,24,0,23,0,20,0,21,0,19,0,23,0,23,0,23,0,23,0,27,0,26,0,17,0,24,0,26,0,24,0,27,0,27,0,26,0,24,0,23,0,23,0,24,1,17,1,21,1,19,1,22,1,22,1,18,1,16,1,14,1,12,1,14,1,16,1,8,1,3,1,0,1,5,1,1,1,1,1,3,1,6,1,7,1,8,1,14,1,14,1,13,1,15,1),dim=c(2,62),dimnames=list(c('consumentenvertrouwen','financiële_crisis'),1:62))
> y <- array(NA,dim=c(2,62),dimnames=list(c('consumentenvertrouwen','financiële_crisis'),1:62))
> 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
consumentenvertrouwen financi\353le_crisis
1 19 0
2 18 0
3 19 0
4 19 0
5 22 0
6 23 0
7 20 0
8 14 0
9 14 0
10 14 0
11 15 0
12 11 0
13 17 0
14 16 0
15 20 0
16 24 0
17 23 0
18 20 0
19 21 0
20 19 0
21 23 0
22 23 0
23 23 0
24 23 0
25 27 0
26 26 0
27 17 0
28 24 0
29 26 0
30 24 0
31 27 0
32 27 0
33 26 0
34 24 0
35 23 0
36 23 0
37 24 1
38 17 1
39 21 1
40 19 1
41 22 1
42 22 1
43 18 1
44 16 1
45 14 1
46 12 1
47 14 1
48 16 1
49 8 1
50 3 1
51 0 1
52 5 1
53 1 1
54 1 1
55 3 1
56 6 1
57 7 1
58 8 1
59 14 1
60 14 1
61 13 1
62 15 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `financi\353le_crisis`
20.944 -8.906
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.038 -4.015 1.962 3.056 11.962
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.9444 0.9386 22.315 < 2e-16 ***
`financi\353le_crisis` -8.9060 1.4494 -6.145 7.03e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.632 on 60 degrees of freedom
Multiple R-squared: 0.3862, Adjusted R-squared: 0.376
F-statistic: 37.76 on 1 and 60 DF, p-value: 7.035e-08
> postscript(file="/var/www/html/rcomp/tmp/1q89v1260988781.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/2t21a1260988781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3mjm31260988781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4efon1260988781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5krvx1260988781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 62
Frequency = 1
1 2 3 4 5 6
-1.94444444 -2.94444444 -1.94444444 -1.94444444 1.05555556 2.05555556
7 8 9 10 11 12
-0.94444444 -6.94444444 -6.94444444 -6.94444444 -5.94444444 -9.94444444
13 14 15 16 17 18
-3.94444444 -4.94444444 -0.94444444 3.05555556 2.05555556 -0.94444444
19 20 21 22 23 24
0.05555556 -1.94444444 2.05555556 2.05555556 2.05555556 2.05555556
25 26 27 28 29 30
6.05555556 5.05555556 -3.94444444 3.05555556 5.05555556 3.05555556
31 32 33 34 35 36
6.05555556 6.05555556 5.05555556 3.05555556 2.05555556 2.05555556
37 38 39 40 41 42
11.96153846 4.96153846 8.96153846 6.96153846 9.96153846 9.96153846
43 44 45 46 47 48
5.96153846 3.96153846 1.96153846 -0.03846154 1.96153846 3.96153846
49 50 51 52 53 54
-4.03846154 -9.03846154 -12.03846154 -7.03846154 -11.03846154 -11.03846154
55 56 57 58 59 60
-9.03846154 -6.03846154 -5.03846154 -4.03846154 1.96153846 1.96153846
61 62
0.96153846 2.96153846
> postscript(file="/var/www/html/rcomp/tmp/6uefn1260988781.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 = 62
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.94444444 NA
1 -2.94444444 -1.94444444
2 -1.94444444 -2.94444444
3 -1.94444444 -1.94444444
4 1.05555556 -1.94444444
5 2.05555556 1.05555556
6 -0.94444444 2.05555556
7 -6.94444444 -0.94444444
8 -6.94444444 -6.94444444
9 -6.94444444 -6.94444444
10 -5.94444444 -6.94444444
11 -9.94444444 -5.94444444
12 -3.94444444 -9.94444444
13 -4.94444444 -3.94444444
14 -0.94444444 -4.94444444
15 3.05555556 -0.94444444
16 2.05555556 3.05555556
17 -0.94444444 2.05555556
18 0.05555556 -0.94444444
19 -1.94444444 0.05555556
20 2.05555556 -1.94444444
21 2.05555556 2.05555556
22 2.05555556 2.05555556
23 2.05555556 2.05555556
24 6.05555556 2.05555556
25 5.05555556 6.05555556
26 -3.94444444 5.05555556
27 3.05555556 -3.94444444
28 5.05555556 3.05555556
29 3.05555556 5.05555556
30 6.05555556 3.05555556
31 6.05555556 6.05555556
32 5.05555556 6.05555556
33 3.05555556 5.05555556
34 2.05555556 3.05555556
35 2.05555556 2.05555556
36 11.96153846 2.05555556
37 4.96153846 11.96153846
38 8.96153846 4.96153846
39 6.96153846 8.96153846
40 9.96153846 6.96153846
41 9.96153846 9.96153846
42 5.96153846 9.96153846
43 3.96153846 5.96153846
44 1.96153846 3.96153846
45 -0.03846154 1.96153846
46 1.96153846 -0.03846154
47 3.96153846 1.96153846
48 -4.03846154 3.96153846
49 -9.03846154 -4.03846154
50 -12.03846154 -9.03846154
51 -7.03846154 -12.03846154
52 -11.03846154 -7.03846154
53 -11.03846154 -11.03846154
54 -9.03846154 -11.03846154
55 -6.03846154 -9.03846154
56 -5.03846154 -6.03846154
57 -4.03846154 -5.03846154
58 1.96153846 -4.03846154
59 1.96153846 1.96153846
60 0.96153846 1.96153846
61 2.96153846 0.96153846
62 NA 2.96153846
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.94444444 -1.94444444
[2,] -1.94444444 -2.94444444
[3,] -1.94444444 -1.94444444
[4,] 1.05555556 -1.94444444
[5,] 2.05555556 1.05555556
[6,] -0.94444444 2.05555556
[7,] -6.94444444 -0.94444444
[8,] -6.94444444 -6.94444444
[9,] -6.94444444 -6.94444444
[10,] -5.94444444 -6.94444444
[11,] -9.94444444 -5.94444444
[12,] -3.94444444 -9.94444444
[13,] -4.94444444 -3.94444444
[14,] -0.94444444 -4.94444444
[15,] 3.05555556 -0.94444444
[16,] 2.05555556 3.05555556
[17,] -0.94444444 2.05555556
[18,] 0.05555556 -0.94444444
[19,] -1.94444444 0.05555556
[20,] 2.05555556 -1.94444444
[21,] 2.05555556 2.05555556
[22,] 2.05555556 2.05555556
[23,] 2.05555556 2.05555556
[24,] 6.05555556 2.05555556
[25,] 5.05555556 6.05555556
[26,] -3.94444444 5.05555556
[27,] 3.05555556 -3.94444444
[28,] 5.05555556 3.05555556
[29,] 3.05555556 5.05555556
[30,] 6.05555556 3.05555556
[31,] 6.05555556 6.05555556
[32,] 5.05555556 6.05555556
[33,] 3.05555556 5.05555556
[34,] 2.05555556 3.05555556
[35,] 2.05555556 2.05555556
[36,] 11.96153846 2.05555556
[37,] 4.96153846 11.96153846
[38,] 8.96153846 4.96153846
[39,] 6.96153846 8.96153846
[40,] 9.96153846 6.96153846
[41,] 9.96153846 9.96153846
[42,] 5.96153846 9.96153846
[43,] 3.96153846 5.96153846
[44,] 1.96153846 3.96153846
[45,] -0.03846154 1.96153846
[46,] 1.96153846 -0.03846154
[47,] 3.96153846 1.96153846
[48,] -4.03846154 3.96153846
[49,] -9.03846154 -4.03846154
[50,] -12.03846154 -9.03846154
[51,] -7.03846154 -12.03846154
[52,] -11.03846154 -7.03846154
[53,] -11.03846154 -11.03846154
[54,] -9.03846154 -11.03846154
[55,] -6.03846154 -9.03846154
[56,] -5.03846154 -6.03846154
[57,] -4.03846154 -5.03846154
[58,] 1.96153846 -4.03846154
[59,] 1.96153846 1.96153846
[60,] 0.96153846 1.96153846
[61,] 2.96153846 0.96153846
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.94444444 -1.94444444
2 -1.94444444 -2.94444444
3 -1.94444444 -1.94444444
4 1.05555556 -1.94444444
5 2.05555556 1.05555556
6 -0.94444444 2.05555556
7 -6.94444444 -0.94444444
8 -6.94444444 -6.94444444
9 -6.94444444 -6.94444444
10 -5.94444444 -6.94444444
11 -9.94444444 -5.94444444
12 -3.94444444 -9.94444444
13 -4.94444444 -3.94444444
14 -0.94444444 -4.94444444
15 3.05555556 -0.94444444
16 2.05555556 3.05555556
17 -0.94444444 2.05555556
18 0.05555556 -0.94444444
19 -1.94444444 0.05555556
20 2.05555556 -1.94444444
21 2.05555556 2.05555556
22 2.05555556 2.05555556
23 2.05555556 2.05555556
24 6.05555556 2.05555556
25 5.05555556 6.05555556
26 -3.94444444 5.05555556
27 3.05555556 -3.94444444
28 5.05555556 3.05555556
29 3.05555556 5.05555556
30 6.05555556 3.05555556
31 6.05555556 6.05555556
32 5.05555556 6.05555556
33 3.05555556 5.05555556
34 2.05555556 3.05555556
35 2.05555556 2.05555556
36 11.96153846 2.05555556
37 4.96153846 11.96153846
38 8.96153846 4.96153846
39 6.96153846 8.96153846
40 9.96153846 6.96153846
41 9.96153846 9.96153846
42 5.96153846 9.96153846
43 3.96153846 5.96153846
44 1.96153846 3.96153846
45 -0.03846154 1.96153846
46 1.96153846 -0.03846154
47 3.96153846 1.96153846
48 -4.03846154 3.96153846
49 -9.03846154 -4.03846154
50 -12.03846154 -9.03846154
51 -7.03846154 -12.03846154
52 -11.03846154 -7.03846154
53 -11.03846154 -11.03846154
54 -9.03846154 -11.03846154
55 -6.03846154 -9.03846154
56 -5.03846154 -6.03846154
57 -4.03846154 -5.03846154
58 1.96153846 -4.03846154
59 1.96153846 1.96153846
60 0.96153846 1.96153846
61 2.96153846 0.96153846
> 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/76nje1260988781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zbbg1260988781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cese1260988781.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/100v391260988781.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/11tuxw1260988782.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/12urnf1260988782.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/130ctt1260988782.tab")
> try(system("convert tmp/1q89v1260988781.ps tmp/1q89v1260988781.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t21a1260988781.ps tmp/2t21a1260988781.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mjm31260988781.ps tmp/3mjm31260988781.png",intern=TRUE))
character(0)
> try(system("convert tmp/4efon1260988781.ps tmp/4efon1260988781.png",intern=TRUE))
character(0)
> try(system("convert tmp/5krvx1260988781.ps tmp/5krvx1260988781.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uefn1260988781.ps tmp/6uefn1260988781.png",intern=TRUE))
character(0)
> try(system("convert tmp/76nje1260988781.ps tmp/76nje1260988781.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zbbg1260988781.ps tmp/8zbbg1260988781.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cese1260988781.ps tmp/9cese1260988781.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
1.915 1.410 2.348