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 = '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 t
1 163414 0 1
2 163652 0 2
3 164603 0 3
4 165257 0 4
5 168731 0 5
6 171848 0 6
7 175032 0 7
8 179187 0 8
9 187369 0 9
10 194147 0 10
11 200145 0 11
12 203750 0 12
13 206464 0 13
14 205034 0 14
15 211782 0 15
16 244562 0 16
17 247059 0 17
18 255703 0 18
19 260218 0 19
20 268852 0 20
21 279436 0 21
22 281514 0 22
23 285458 1 23
24 288338 1 24
25 296369 1 25
26 302221 1 26
27 311016 1 27
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `ja/nee` t
138696 5173 6112
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-19236.2 -6063.4 -311.4 7276.4 18605.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 138695.8 4189.8 33.10 < 2e-16 ***
`ja/nee` 5173.2 6388.0 0.81 0.426
t 6112.5 318.6 19.19 4.59e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9534 on 24 degrees of freedom
Multiple R-Squared: 0.9674, Adjusted R-squared: 0.9647
F-statistic: 356.4 on 2 and 24 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1hseu1198923804.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/2muwd1198923804.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3hp461198923805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cx9b1198923805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fqo51198923805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
18605.7795 12731.3200 7569.8605 2111.4009 -527.0586 -3522.5181
7 8 9 10 11 12
-6450.9776 -8408.4371 -6338.8967 -5673.3562 -5787.8157 -8295.2752
13 14 15 16 17 18
-11693.7347 -19236.1943 -18600.6538 8066.8867 4451.4272 6982.9677
19 20 21 22 23 24
5385.5081 7907.0486 12378.5891 8344.1296 1002.5190 -2229.9405
25 26 27
-311.4000 -571.8595 2110.6810
> postscript(file="/var/www/html/rcomp/tmp/6581d1198923805.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 18605.7795 NA
1 12731.3200 18605.7795
2 7569.8605 12731.3200
3 2111.4009 7569.8605
4 -527.0586 2111.4009
5 -3522.5181 -527.0586
6 -6450.9776 -3522.5181
7 -8408.4371 -6450.9776
8 -6338.8967 -8408.4371
9 -5673.3562 -6338.8967
10 -5787.8157 -5673.3562
11 -8295.2752 -5787.8157
12 -11693.7347 -8295.2752
13 -19236.1943 -11693.7347
14 -18600.6538 -19236.1943
15 8066.8867 -18600.6538
16 4451.4272 8066.8867
17 6982.9677 4451.4272
18 5385.5081 6982.9677
19 7907.0486 5385.5081
20 12378.5891 7907.0486
21 8344.1296 12378.5891
22 1002.5190 8344.1296
23 -2229.9405 1002.5190
24 -311.4000 -2229.9405
25 -571.8595 -311.4000
26 2110.6810 -571.8595
27 NA 2110.6810
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 12731.3200 18605.7795
[2,] 7569.8605 12731.3200
[3,] 2111.4009 7569.8605
[4,] -527.0586 2111.4009
[5,] -3522.5181 -527.0586
[6,] -6450.9776 -3522.5181
[7,] -8408.4371 -6450.9776
[8,] -6338.8967 -8408.4371
[9,] -5673.3562 -6338.8967
[10,] -5787.8157 -5673.3562
[11,] -8295.2752 -5787.8157
[12,] -11693.7347 -8295.2752
[13,] -19236.1943 -11693.7347
[14,] -18600.6538 -19236.1943
[15,] 8066.8867 -18600.6538
[16,] 4451.4272 8066.8867
[17,] 6982.9677 4451.4272
[18,] 5385.5081 6982.9677
[19,] 7907.0486 5385.5081
[20,] 12378.5891 7907.0486
[21,] 8344.1296 12378.5891
[22,] 1002.5190 8344.1296
[23,] -2229.9405 1002.5190
[24,] -311.4000 -2229.9405
[25,] -571.8595 -311.4000
[26,] 2110.6810 -571.8595
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 12731.3200 18605.7795
2 7569.8605 12731.3200
3 2111.4009 7569.8605
4 -527.0586 2111.4009
5 -3522.5181 -527.0586
6 -6450.9776 -3522.5181
7 -8408.4371 -6450.9776
8 -6338.8967 -8408.4371
9 -5673.3562 -6338.8967
10 -5787.8157 -5673.3562
11 -8295.2752 -5787.8157
12 -11693.7347 -8295.2752
13 -19236.1943 -11693.7347
14 -18600.6538 -19236.1943
15 8066.8867 -18600.6538
16 4451.4272 8066.8867
17 6982.9677 4451.4272
18 5385.5081 6982.9677
19 7907.0486 5385.5081
20 12378.5891 7907.0486
21 8344.1296 12378.5891
22 1002.5190 8344.1296
23 -2229.9405 1002.5190
24 -311.4000 -2229.9405
25 -571.8595 -311.4000
26 2110.6810 -571.8595
> 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/7kz5y1198923805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87h271198923805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r3pq1198923805.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10teai1198923805.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/11qucj1198923805.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/12kqxo1198923805.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/13h7up1198923805.tab")
>
> system("convert tmp/1hseu1198923804.ps tmp/1hseu1198923804.png")
> system("convert tmp/2muwd1198923804.ps tmp/2muwd1198923804.png")
> system("convert tmp/3hp461198923805.ps tmp/3hp461198923805.png")
> system("convert tmp/4cx9b1198923805.ps tmp/4cx9b1198923805.png")
> system("convert tmp/5fqo51198923805.ps tmp/5fqo51198923805.png")
> system("convert tmp/6581d1198923805.ps tmp/6581d1198923805.png")
> system("convert tmp/7kz5y1198923805.ps tmp/7kz5y1198923805.png")
> system("convert tmp/87h271198923805.ps tmp/87h271198923805.png")
> system("convert tmp/9r3pq1198923805.ps tmp/9r3pq1198923805.png")
>
>
> proc.time()
user system elapsed
2.181 1.424 2.884