R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> 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 = 'Include Quarterly 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 Q1 Q2 Q3
1 163414 0 1 0 0
2 163652 0 0 1 0
3 164603 0 0 0 1
4 165257 0 0 0 0
5 168731 0 1 0 0
6 171848 0 0 1 0
7 175032 0 0 0 1
8 179187 0 0 0 0
9 187369 0 1 0 0
10 194147 0 0 1 0
11 200145 0 0 0 1
12 203750 0 0 0 0
13 206464 0 1 0 0
14 205034 0 0 1 0
15 211782 0 0 0 1
16 244562 0 0 0 0
17 247059 0 1 0 0
18 255703 0 0 1 0
19 260218 0 0 0 1
20 268852 0 0 0 0
21 279436 0 1 0 0
22 281514 0 0 1 0
23 285458 1 0 0 1
24 288338 1 0 0 0
25 296369 1 1 0 0
26 302221 1 0 1 0
27 311016 1 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `ja/nee` Q1 Q2 Q3
210204 88721 -1615 1996 -5802
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-48548 -30193 -6454 26125 70847
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 210204 16720 12.572 1.62e-11 ***
`ja/nee` 88721 20121 4.409 0.000222 ***
Q1 -1615 22327 -0.072 0.942974
Q2 1996 22327 0.089 0.929590
Q3 -5802 22450 -0.258 0.798452
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 40120 on 22 degrees of freedom
Multiple R-Squared: 0.4712, Adjusted R-squared: 0.375
F-statistic: 4.9 on 4 and 22 DF, p-value: 0.005594
> postscript(file="/var/www/html/rcomp/tmp/1666q1198925038.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/2es031198925038.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/32bry1198925038.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nb1o1198925038.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ft401198925038.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-45174.6792 -48547.6792 -39798.6441 -44947.1257 -39857.6792 -40351.6792
7 8 9 10 11 12
-29369.6441 -31017.1257 -21219.6792 -18052.6792 -4256.6441 -6454.1257
13 14 15 16 17 18
-2124.6792 -7165.6792 7380.3559 34357.8743 38470.3208 43503.3208
19 20 21 22 23 24
55816.3559 58647.8743 70847.3208 69314.3208 -7664.8896 -10587.3713
25 26 27
-940.9247 1300.0753 17893.1104
> postscript(file="/var/www/html/rcomp/tmp/6qp221198925038.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 -45174.6792 NA
1 -48547.6792 -45174.6792
2 -39798.6441 -48547.6792
3 -44947.1257 -39798.6441
4 -39857.6792 -44947.1257
5 -40351.6792 -39857.6792
6 -29369.6441 -40351.6792
7 -31017.1257 -29369.6441
8 -21219.6792 -31017.1257
9 -18052.6792 -21219.6792
10 -4256.6441 -18052.6792
11 -6454.1257 -4256.6441
12 -2124.6792 -6454.1257
13 -7165.6792 -2124.6792
14 7380.3559 -7165.6792
15 34357.8743 7380.3559
16 38470.3208 34357.8743
17 43503.3208 38470.3208
18 55816.3559 43503.3208
19 58647.8743 55816.3559
20 70847.3208 58647.8743
21 69314.3208 70847.3208
22 -7664.8896 69314.3208
23 -10587.3713 -7664.8896
24 -940.9247 -10587.3713
25 1300.0753 -940.9247
26 17893.1104 1300.0753
27 NA 17893.1104
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -48547.6792 -45174.6792
[2,] -39798.6441 -48547.6792
[3,] -44947.1257 -39798.6441
[4,] -39857.6792 -44947.1257
[5,] -40351.6792 -39857.6792
[6,] -29369.6441 -40351.6792
[7,] -31017.1257 -29369.6441
[8,] -21219.6792 -31017.1257
[9,] -18052.6792 -21219.6792
[10,] -4256.6441 -18052.6792
[11,] -6454.1257 -4256.6441
[12,] -2124.6792 -6454.1257
[13,] -7165.6792 -2124.6792
[14,] 7380.3559 -7165.6792
[15,] 34357.8743 7380.3559
[16,] 38470.3208 34357.8743
[17,] 43503.3208 38470.3208
[18,] 55816.3559 43503.3208
[19,] 58647.8743 55816.3559
[20,] 70847.3208 58647.8743
[21,] 69314.3208 70847.3208
[22,] -7664.8896 69314.3208
[23,] -10587.3713 -7664.8896
[24,] -940.9247 -10587.3713
[25,] 1300.0753 -940.9247
[26,] 17893.1104 1300.0753
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -48547.6792 -45174.6792
2 -39798.6441 -48547.6792
3 -44947.1257 -39798.6441
4 -39857.6792 -44947.1257
5 -40351.6792 -39857.6792
6 -29369.6441 -40351.6792
7 -31017.1257 -29369.6441
8 -21219.6792 -31017.1257
9 -18052.6792 -21219.6792
10 -4256.6441 -18052.6792
11 -6454.1257 -4256.6441
12 -2124.6792 -6454.1257
13 -7165.6792 -2124.6792
14 7380.3559 -7165.6792
15 34357.8743 7380.3559
16 38470.3208 34357.8743
17 43503.3208 38470.3208
18 55816.3559 43503.3208
19 58647.8743 55816.3559
20 70847.3208 58647.8743
21 69314.3208 70847.3208
22 -7664.8896 69314.3208
23 -10587.3713 -7664.8896
24 -940.9247 -10587.3713
25 1300.0753 -940.9247
26 17893.1104 1300.0753
> 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/7baud1198925038.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rb3i1198925038.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yjoi1198925038.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10zrs21198925038.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/115j7t1198925038.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/12698l1198925038.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/13hvwe1198925038.tab")
>
> system("convert tmp/1666q1198925038.ps tmp/1666q1198925038.png")
> system("convert tmp/2es031198925038.ps tmp/2es031198925038.png")
> system("convert tmp/32bry1198925038.ps tmp/32bry1198925038.png")
> system("convert tmp/4nb1o1198925038.ps tmp/4nb1o1198925038.png")
> system("convert tmp/5ft401198925038.ps tmp/5ft401198925038.png")
> system("convert tmp/6qp221198925038.ps tmp/6qp221198925038.png")
> system("convert tmp/7baud1198925038.ps tmp/7baud1198925038.png")
> system("convert tmp/8rb3i1198925038.ps tmp/8rb3i1198925038.png")
> system("convert tmp/9yjoi1198925038.ps tmp/9yjoi1198925038.png")
>
>
> proc.time()
user system elapsed
2.126 1.405 2.750