R version 2.6.0 (2007-10-03)
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.
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(163414,0,163652,0,164603,0,165257,0,168731,0,171848,0,175032,0,179187,0,187369,0,194147,0,200145,0,203750,0,206464,0,205034,0,211782,0,244562,0,247059,0,255703,0,260218,0,268852,0,279436,0,281514,0,285458,1,288338,1,296369,1,302221,1,311016,1),dim=c(2,27),dimnames=list(c('BBP','Ja/nee'),1:27))
> y <- array(NA,dim=c(2,27),dimnames=list(c('BBP','Ja/nee'),1:27))
> 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
BBP Ja/nee
1 163414 0
2 163652 0
3 164603 0
4 165257 0
5 168731 0
6 171848 0
7 175032 0
8 179187 0
9 187369 0
10 194147 0
11 200145 0
12 203750 0
13 206464 0
14 205034 0
15 211782 0
16 244562 0
17 247059 0
18 255703 0
19 260218 0
20 268852 0
21 279436 0
22 281514 0
23 285458 1
24 288338 1
25 296369 1
26 302221 1
27 311016 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Ja/nee`
208989 87691
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-45575 -31880 -5239 24954 72525
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 208989 8050 25.962 < 2e-16 ***
`Ja/nee` 87691 18706 4.688 8.37e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 37760 on 25 degrees of freedom
Multiple R-Squared: 0.4678, Adjusted R-squared: 0.4465
F-statistic: 21.98 on 1 and 25 DF, p-value: 8.367e-05
> postscript(file="/var/www/html/rcomp/tmp/1hwkt1198923222.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/2fqoq1198923222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3w69i1198923222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4f2l21198923222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5etsn1198923222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 27
Frequency = 1
1 2 3 4 5 6 7
-45575.045 -45337.045 -44386.045 -43732.045 -40258.045 -37141.045 -33957.045
8 9 10 11 12 13 14
-29802.045 -21620.045 -14842.045 -8844.045 -5239.045 -2525.045 -3955.045
15 16 17 18 19 20 21
2792.955 35572.955 38069.955 46713.955 51228.955 59862.955 70446.955
22 23 24 25 26 27
72524.955 -11222.400 -8342.400 -311.400 5540.600 14335.600
> postscript(file="/var/www/html/rcomp/tmp/6l04p1198923222.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 = 27
Frequency = 1
lag(myerror, k = 1) myerror
0 -45575.045 NA
1 -45337.045 -45575.045
2 -44386.045 -45337.045
3 -43732.045 -44386.045
4 -40258.045 -43732.045
5 -37141.045 -40258.045
6 -33957.045 -37141.045
7 -29802.045 -33957.045
8 -21620.045 -29802.045
9 -14842.045 -21620.045
10 -8844.045 -14842.045
11 -5239.045 -8844.045
12 -2525.045 -5239.045
13 -3955.045 -2525.045
14 2792.955 -3955.045
15 35572.955 2792.955
16 38069.955 35572.955
17 46713.955 38069.955
18 51228.955 46713.955
19 59862.955 51228.955
20 70446.955 59862.955
21 72524.955 70446.955
22 -11222.400 72524.955
23 -8342.400 -11222.400
24 -311.400 -8342.400
25 5540.600 -311.400
26 14335.600 5540.600
27 NA 14335.600
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -45337.045 -45575.045
[2,] -44386.045 -45337.045
[3,] -43732.045 -44386.045
[4,] -40258.045 -43732.045
[5,] -37141.045 -40258.045
[6,] -33957.045 -37141.045
[7,] -29802.045 -33957.045
[8,] -21620.045 -29802.045
[9,] -14842.045 -21620.045
[10,] -8844.045 -14842.045
[11,] -5239.045 -8844.045
[12,] -2525.045 -5239.045
[13,] -3955.045 -2525.045
[14,] 2792.955 -3955.045
[15,] 35572.955 2792.955
[16,] 38069.955 35572.955
[17,] 46713.955 38069.955
[18,] 51228.955 46713.955
[19,] 59862.955 51228.955
[20,] 70446.955 59862.955
[21,] 72524.955 70446.955
[22,] -11222.400 72524.955
[23,] -8342.400 -11222.400
[24,] -311.400 -8342.400
[25,] 5540.600 -311.400
[26,] 14335.600 5540.600
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -45337.045 -45575.045
2 -44386.045 -45337.045
3 -43732.045 -44386.045
4 -40258.045 -43732.045
5 -37141.045 -40258.045
6 -33957.045 -37141.045
7 -29802.045 -33957.045
8 -21620.045 -29802.045
9 -14842.045 -21620.045
10 -8844.045 -14842.045
11 -5239.045 -8844.045
12 -2525.045 -5239.045
13 -3955.045 -2525.045
14 2792.955 -3955.045
15 35572.955 2792.955
16 38069.955 35572.955
17 46713.955 38069.955
18 51228.955 46713.955
19 59862.955 51228.955
20 70446.955 59862.955
21 72524.955 70446.955
22 -11222.400 72524.955
23 -8342.400 -11222.400
24 -311.400 -8342.400
25 5540.600 -311.400
26 14335.600 5540.600
> 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/79gr11198923222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/82bjo1198923222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vm4u1198923222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10m7it1198923222.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/11167w1198923222.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/12r7m61198923223.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/137s6p1198923223.tab")
>
> system("convert tmp/1hwkt1198923222.ps tmp/1hwkt1198923222.png")
> system("convert tmp/2fqoq1198923222.ps tmp/2fqoq1198923222.png")
> system("convert tmp/3w69i1198923222.ps tmp/3w69i1198923222.png")
> system("convert tmp/4f2l21198923222.ps tmp/4f2l21198923222.png")
> system("convert tmp/5etsn1198923222.ps tmp/5etsn1198923222.png")
> system("convert tmp/6l04p1198923222.ps tmp/6l04p1198923222.png")
> system("convert tmp/79gr11198923222.ps tmp/79gr11198923222.png")
> system("convert tmp/82bjo1198923222.ps tmp/82bjo1198923222.png")
> system("convert tmp/9vm4u1198923222.ps tmp/9vm4u1198923222.png")
>
>
> proc.time()
user system elapsed
2.155 1.410 2.797