R version 2.6.1 (2007-11-26)
Copyright (C) 2007 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
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(8.2,0,8.0,0,8.1,0,8.3,0,8.2,0,8.1,0,7.7,0,7.6,0,7.7,0,8.2,0,8.4,0,8.4,0,8.6,0,8.4,0,8.5,0,8.7,0,8.7,0,8.6,0,7.4,0,7.3,0,7.4,0,9.0,0,9.2,0,9.2,0,8.5,0,8.3,0,8.3,0,8.6,0,8.6,0,8.5,0,8.1,0,8.1,0,8.0,0,8.6,0,8.7,0,8.7,0,8.6,0,8.4,0,8.4,0,8.7,0,8.7,0,8.5,0,8.3,0,8.3,0,8.3,0,8.1,0,8.2,0,8.1,0,8.1,0,7.9,0,7.7,0,8.1,0,8.0,0,7.7,1,7.8,1,7.6,1,7.4,1,7.7,1,7.8,1,7.5,1,7.2,1),dim=c(2,61),dimnames=list(c('y','x'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('y','x'),1:61))
> 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
y x
1 8.2 0
2 8.0 0
3 8.1 0
4 8.3 0
5 8.2 0
6 8.1 0
7 7.7 0
8 7.6 0
9 7.7 0
10 8.2 0
11 8.4 0
12 8.4 0
13 8.6 0
14 8.4 0
15 8.5 0
16 8.7 0
17 8.7 0
18 8.6 0
19 7.4 0
20 7.3 0
21 7.4 0
22 9.0 0
23 9.2 0
24 9.2 0
25 8.5 0
26 8.3 0
27 8.3 0
28 8.6 0
29 8.6 0
30 8.5 0
31 8.1 0
32 8.1 0
33 8.0 0
34 8.6 0
35 8.7 0
36 8.7 0
37 8.6 0
38 8.4 0
39 8.4 0
40 8.7 0
41 8.7 0
42 8.5 0
43 8.3 0
44 8.3 0
45 8.3 0
46 8.1 0
47 8.2 0
48 8.1 0
49 8.1 0
50 7.9 0
51 7.7 0
52 8.1 0
53 8.0 0
54 7.7 1
55 7.8 1
56 7.6 1
57 7.4 1
58 7.7 1
59 7.8 1
60 7.5 1
61 7.2 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x
8.2887 -0.7012
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.98868 -0.18868 0.01132 0.21250 0.91132
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.28868 0.05395 153.632 < 2e-16 ***
x -0.70118 0.14898 -4.707 1.57e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3928 on 59 degrees of freedom
Multiple R-Squared: 0.273, Adjusted R-squared: 0.2606
F-statistic: 22.15 on 1 and 59 DF, p-value: 1.567e-05
> postscript(file="/var/www/html/rcomp/tmp/10er01197196134.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/2pph71197196134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/31d961197196134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rvhv1197196134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mpx11197196134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
-0.08867925 -0.28867925 -0.18867925 0.01132075 -0.08867925 -0.18867925
7 8 9 10 11 12
-0.58867925 -0.68867925 -0.58867925 -0.08867925 0.11132075 0.11132075
13 14 15 16 17 18
0.31132075 0.11132075 0.21132075 0.41132075 0.41132075 0.31132075
19 20 21 22 23 24
-0.88867925 -0.98867925 -0.88867925 0.71132075 0.91132075 0.91132075
25 26 27 28 29 30
0.21132075 0.01132075 0.01132075 0.31132075 0.31132075 0.21132075
31 32 33 34 35 36
-0.18867925 -0.18867925 -0.28867925 0.31132075 0.41132075 0.41132075
37 38 39 40 41 42
0.31132075 0.11132075 0.11132075 0.41132075 0.41132075 0.21132075
43 44 45 46 47 48
0.01132075 0.01132075 0.01132075 -0.18867925 -0.08867925 -0.18867925
49 50 51 52 53 54
-0.18867925 -0.38867925 -0.58867925 -0.18867925 -0.28867925 0.11250000
55 56 57 58 59 60
0.21250000 0.01250000 -0.18750000 0.11250000 0.21250000 -0.08750000
61
-0.38750000
> postscript(file="/var/www/html/rcomp/tmp/60yxj1197196134.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.08867925 NA
1 -0.28867925 -0.08867925
2 -0.18867925 -0.28867925
3 0.01132075 -0.18867925
4 -0.08867925 0.01132075
5 -0.18867925 -0.08867925
6 -0.58867925 -0.18867925
7 -0.68867925 -0.58867925
8 -0.58867925 -0.68867925
9 -0.08867925 -0.58867925
10 0.11132075 -0.08867925
11 0.11132075 0.11132075
12 0.31132075 0.11132075
13 0.11132075 0.31132075
14 0.21132075 0.11132075
15 0.41132075 0.21132075
16 0.41132075 0.41132075
17 0.31132075 0.41132075
18 -0.88867925 0.31132075
19 -0.98867925 -0.88867925
20 -0.88867925 -0.98867925
21 0.71132075 -0.88867925
22 0.91132075 0.71132075
23 0.91132075 0.91132075
24 0.21132075 0.91132075
25 0.01132075 0.21132075
26 0.01132075 0.01132075
27 0.31132075 0.01132075
28 0.31132075 0.31132075
29 0.21132075 0.31132075
30 -0.18867925 0.21132075
31 -0.18867925 -0.18867925
32 -0.28867925 -0.18867925
33 0.31132075 -0.28867925
34 0.41132075 0.31132075
35 0.41132075 0.41132075
36 0.31132075 0.41132075
37 0.11132075 0.31132075
38 0.11132075 0.11132075
39 0.41132075 0.11132075
40 0.41132075 0.41132075
41 0.21132075 0.41132075
42 0.01132075 0.21132075
43 0.01132075 0.01132075
44 0.01132075 0.01132075
45 -0.18867925 0.01132075
46 -0.08867925 -0.18867925
47 -0.18867925 -0.08867925
48 -0.18867925 -0.18867925
49 -0.38867925 -0.18867925
50 -0.58867925 -0.38867925
51 -0.18867925 -0.58867925
52 -0.28867925 -0.18867925
53 0.11250000 -0.28867925
54 0.21250000 0.11250000
55 0.01250000 0.21250000
56 -0.18750000 0.01250000
57 0.11250000 -0.18750000
58 0.21250000 0.11250000
59 -0.08750000 0.21250000
60 -0.38750000 -0.08750000
61 NA -0.38750000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.28867925 -0.08867925
[2,] -0.18867925 -0.28867925
[3,] 0.01132075 -0.18867925
[4,] -0.08867925 0.01132075
[5,] -0.18867925 -0.08867925
[6,] -0.58867925 -0.18867925
[7,] -0.68867925 -0.58867925
[8,] -0.58867925 -0.68867925
[9,] -0.08867925 -0.58867925
[10,] 0.11132075 -0.08867925
[11,] 0.11132075 0.11132075
[12,] 0.31132075 0.11132075
[13,] 0.11132075 0.31132075
[14,] 0.21132075 0.11132075
[15,] 0.41132075 0.21132075
[16,] 0.41132075 0.41132075
[17,] 0.31132075 0.41132075
[18,] -0.88867925 0.31132075
[19,] -0.98867925 -0.88867925
[20,] -0.88867925 -0.98867925
[21,] 0.71132075 -0.88867925
[22,] 0.91132075 0.71132075
[23,] 0.91132075 0.91132075
[24,] 0.21132075 0.91132075
[25,] 0.01132075 0.21132075
[26,] 0.01132075 0.01132075
[27,] 0.31132075 0.01132075
[28,] 0.31132075 0.31132075
[29,] 0.21132075 0.31132075
[30,] -0.18867925 0.21132075
[31,] -0.18867925 -0.18867925
[32,] -0.28867925 -0.18867925
[33,] 0.31132075 -0.28867925
[34,] 0.41132075 0.31132075
[35,] 0.41132075 0.41132075
[36,] 0.31132075 0.41132075
[37,] 0.11132075 0.31132075
[38,] 0.11132075 0.11132075
[39,] 0.41132075 0.11132075
[40,] 0.41132075 0.41132075
[41,] 0.21132075 0.41132075
[42,] 0.01132075 0.21132075
[43,] 0.01132075 0.01132075
[44,] 0.01132075 0.01132075
[45,] -0.18867925 0.01132075
[46,] -0.08867925 -0.18867925
[47,] -0.18867925 -0.08867925
[48,] -0.18867925 -0.18867925
[49,] -0.38867925 -0.18867925
[50,] -0.58867925 -0.38867925
[51,] -0.18867925 -0.58867925
[52,] -0.28867925 -0.18867925
[53,] 0.11250000 -0.28867925
[54,] 0.21250000 0.11250000
[55,] 0.01250000 0.21250000
[56,] -0.18750000 0.01250000
[57,] 0.11250000 -0.18750000
[58,] 0.21250000 0.11250000
[59,] -0.08750000 0.21250000
[60,] -0.38750000 -0.08750000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.28867925 -0.08867925
2 -0.18867925 -0.28867925
3 0.01132075 -0.18867925
4 -0.08867925 0.01132075
5 -0.18867925 -0.08867925
6 -0.58867925 -0.18867925
7 -0.68867925 -0.58867925
8 -0.58867925 -0.68867925
9 -0.08867925 -0.58867925
10 0.11132075 -0.08867925
11 0.11132075 0.11132075
12 0.31132075 0.11132075
13 0.11132075 0.31132075
14 0.21132075 0.11132075
15 0.41132075 0.21132075
16 0.41132075 0.41132075
17 0.31132075 0.41132075
18 -0.88867925 0.31132075
19 -0.98867925 -0.88867925
20 -0.88867925 -0.98867925
21 0.71132075 -0.88867925
22 0.91132075 0.71132075
23 0.91132075 0.91132075
24 0.21132075 0.91132075
25 0.01132075 0.21132075
26 0.01132075 0.01132075
27 0.31132075 0.01132075
28 0.31132075 0.31132075
29 0.21132075 0.31132075
30 -0.18867925 0.21132075
31 -0.18867925 -0.18867925
32 -0.28867925 -0.18867925
33 0.31132075 -0.28867925
34 0.41132075 0.31132075
35 0.41132075 0.41132075
36 0.31132075 0.41132075
37 0.11132075 0.31132075
38 0.11132075 0.11132075
39 0.41132075 0.11132075
40 0.41132075 0.41132075
41 0.21132075 0.41132075
42 0.01132075 0.21132075
43 0.01132075 0.01132075
44 0.01132075 0.01132075
45 -0.18867925 0.01132075
46 -0.08867925 -0.18867925
47 -0.18867925 -0.08867925
48 -0.18867925 -0.18867925
49 -0.38867925 -0.18867925
50 -0.58867925 -0.38867925
51 -0.18867925 -0.58867925
52 -0.28867925 -0.18867925
53 0.11250000 -0.28867925
54 0.21250000 0.11250000
55 0.01250000 0.21250000
56 -0.18750000 0.01250000
57 0.11250000 -0.18750000
58 0.21250000 0.11250000
59 -0.08750000 0.21250000
60 -0.38750000 -0.08750000
> 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/7fvdd1197196134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8uuk01197196134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rvpu1197196134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10j2vm1197196134.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/11f5ye1197196134.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/12uvpc1197196135.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/13t28q1197196135.tab")
>
> system("convert tmp/10er01197196134.ps tmp/10er01197196134.png")
> system("convert tmp/2pph71197196134.ps tmp/2pph71197196134.png")
> system("convert tmp/31d961197196134.ps tmp/31d961197196134.png")
> system("convert tmp/4rvhv1197196134.ps tmp/4rvhv1197196134.png")
> system("convert tmp/5mpx11197196134.ps tmp/5mpx11197196134.png")
> system("convert tmp/60yxj1197196134.ps tmp/60yxj1197196134.png")
> system("convert tmp/7fvdd1197196134.ps tmp/7fvdd1197196134.png")
> system("convert tmp/8uuk01197196134.ps tmp/8uuk01197196134.png")
> system("convert tmp/9rvpu1197196134.ps tmp/9rvpu1197196134.png")
>
>
> proc.time()
user system elapsed
4.091 2.485 4.393