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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(588261,1,596397,1,576612,0,538141,0,491481,0,469740,0,474427,0,507632,0,541047,0,570046,0,588251,0,596872,0,588676,0,549738,0,472907,0,429496,0,402790,0,419304,0,459425,0,500845,0,516761,0,557423,0,595042,0,589496,0,535029,0),dim=c(2,25),dimnames=list(c('y','x'),1:25))
> y <- array(NA,dim=c(2,25),dimnames=list(c('y','x'),1:25))
> 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 = 'Include Monthly 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 588261 1 1 0 0 0 0 0 0 0 0 0 0 1
2 596397 1 0 1 0 0 0 0 0 0 0 0 0 2
3 576612 0 0 0 1 0 0 0 0 0 0 0 0 3
4 538141 0 0 0 0 1 0 0 0 0 0 0 0 4
5 491481 0 0 0 0 0 1 0 0 0 0 0 0 5
6 469740 0 0 0 0 0 0 1 0 0 0 0 0 6
7 474427 0 0 0 0 0 0 0 1 0 0 0 0 7
8 507632 0 0 0 0 0 0 0 0 1 0 0 0 8
9 541047 0 0 0 0 0 0 0 0 0 1 0 0 9
10 570046 0 0 0 0 0 0 0 0 0 0 1 0 10
11 588251 0 0 0 0 0 0 0 0 0 0 0 1 11
12 596872 0 0 0 0 0 0 0 0 0 0 0 0 12
13 588676 0 1 0 0 0 0 0 0 0 0 0 0 13
14 549738 0 0 1 0 0 0 0 0 0 0 0 0 14
15 472907 0 0 0 1 0 0 0 0 0 0 0 0 15
16 429496 0 0 0 0 1 0 0 0 0 0 0 0 16
17 402790 0 0 0 0 0 1 0 0 0 0 0 0 17
18 419304 0 0 0 0 0 0 1 0 0 0 0 0 18
19 459425 0 0 0 0 0 0 0 1 0 0 0 0 19
20 500845 0 0 0 0 0 0 0 0 1 0 0 0 20
21 516761 0 0 0 0 0 0 0 0 0 1 0 0 21
22 557423 0 0 0 0 0 0 0 0 0 0 1 0 22
23 595042 0 0 0 0 0 0 0 0 0 0 0 1 23
24 589496 0 0 0 0 0 0 0 0 0 0 0 0 24
25 535029 0 1 0 0 0 0 0 0 0 0 0 0 25
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
654921 -17831 -33734 -45500 -99293 -136804
M5 M6 M7 M8 M9 M10
-170058 -169241 -143407 -102665 -74570 -36309
M11 t
-4967 -3430
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-33743.3 -14267.7 -411.5 14267.7 33743.3
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 654922 27439 23.869 7.96e-11 ***
x -17830 31002 -0.575 0.576770
M1 -33734 27812 -1.213 0.250552
M2 -45500 31889 -1.427 0.181391
M3 -99293 30283 -3.279 0.007349 **
M4 -136804 29990 -4.562 0.000814 ***
M5 -170058 29730 -5.720 0.000134 ***
M6 -169241 29502 -5.737 0.000131 ***
M7 -143407 29308 -4.893 0.000477 ***
M8 -102665 29148 -3.522 0.004780 **
M9 -74570 29023 -2.569 0.026080 *
M10 -36309 28933 -1.255 0.235509
M11 -4967 28879 -0.172 0.866558
t -3430 1019 -3.366 0.006296 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 28860 on 11 degrees of freedom
Multiple R-Squared: 0.8961, Adjusted R-squared: 0.7732
F-statistic: 7.296 on 13 and 11 DF, p-value: 0.001138
> postscript(file="/var/www/html/rcomp/tmp/1uzy01195649654.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/2vazq1195649654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3u8nn1195649654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nhon1195649654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kku01195649654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 25
Frequency = 1
1 2 3 4 5 6
-11665.6154 11665.6154 31273.3462 33743.3462 23766.3462 4638.8462
7 8 9 10 11 12
-13078.1538 -17185.6538 -8436.1538 -14267.6538 -23974.6538 -16891.1538
13 14 15 16 17 18
12077.1538 -11665.6154 -31273.3462 -33743.3462 -23766.3462 -4638.8462
19 20 21 22 23 24
13078.1538 17185.6538 8436.1538 14267.6538 23974.6538 16891.1538
25
-411.5385
> postscript(file="/var/www/html/rcomp/tmp/6cu1j1195649654.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 = 25
Frequency = 1
lag(myerror, k = 1) myerror
0 -11665.6154 NA
1 11665.6154 -11665.6154
2 31273.3462 11665.6154
3 33743.3462 31273.3462
4 23766.3462 33743.3462
5 4638.8462 23766.3462
6 -13078.1538 4638.8462
7 -17185.6538 -13078.1538
8 -8436.1538 -17185.6538
9 -14267.6538 -8436.1538
10 -23974.6538 -14267.6538
11 -16891.1538 -23974.6538
12 12077.1538 -16891.1538
13 -11665.6154 12077.1538
14 -31273.3462 -11665.6154
15 -33743.3462 -31273.3462
16 -23766.3462 -33743.3462
17 -4638.8462 -23766.3462
18 13078.1538 -4638.8462
19 17185.6538 13078.1538
20 8436.1538 17185.6538
21 14267.6538 8436.1538
22 23974.6538 14267.6538
23 16891.1538 23974.6538
24 -411.5385 16891.1538
25 NA -411.5385
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 11665.6154 -11665.615
[2,] 31273.3462 11665.615
[3,] 33743.3462 31273.346
[4,] 23766.3462 33743.346
[5,] 4638.8462 23766.346
[6,] -13078.1538 4638.846
[7,] -17185.6538 -13078.154
[8,] -8436.1538 -17185.654
[9,] -14267.6538 -8436.154
[10,] -23974.6538 -14267.654
[11,] -16891.1538 -23974.654
[12,] 12077.1538 -16891.154
[13,] -11665.6154 12077.154
[14,] -31273.3462 -11665.615
[15,] -33743.3462 -31273.346
[16,] -23766.3462 -33743.346
[17,] -4638.8462 -23766.346
[18,] 13078.1538 -4638.846
[19,] 17185.6538 13078.154
[20,] 8436.1538 17185.654
[21,] 14267.6538 8436.154
[22,] 23974.6538 14267.654
[23,] 16891.1538 23974.654
[24,] -411.5385 16891.154
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 11665.6154 -11665.615
2 31273.3462 11665.615
3 33743.3462 31273.346
4 23766.3462 33743.346
5 4638.8462 23766.346
6 -13078.1538 4638.846
7 -17185.6538 -13078.154
8 -8436.1538 -17185.654
9 -14267.6538 -8436.154
10 -23974.6538 -14267.654
11 -16891.1538 -23974.654
12 12077.1538 -16891.154
13 -11665.6154 12077.154
14 -31273.3462 -11665.615
15 -33743.3462 -31273.346
16 -23766.3462 -33743.346
17 -4638.8462 -23766.346
18 13078.1538 -4638.846
19 17185.6538 13078.154
20 8436.1538 17185.654
21 14267.6538 8436.154
22 23974.6538 14267.654
23 16891.1538 23974.654
24 -411.5385 16891.154
> 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/7ivje1195649654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wb281195649654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/983g91195649654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10psn51195649654.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/11sk3l1195649655.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/12ii981195649655.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/130vkc1195649655.tab")
>
> system("convert tmp/1uzy01195649654.ps tmp/1uzy01195649654.png")
> system("convert tmp/2vazq1195649654.ps tmp/2vazq1195649654.png")
> system("convert tmp/3u8nn1195649654.ps tmp/3u8nn1195649654.png")
> system("convert tmp/4nhon1195649654.ps tmp/4nhon1195649654.png")
> system("convert tmp/5kku01195649654.ps tmp/5kku01195649654.png")
> system("convert tmp/6cu1j1195649654.ps tmp/6cu1j1195649654.png")
> system("convert tmp/7ivje1195649654.ps tmp/7ivje1195649654.png")
> system("convert tmp/8wb281195649654.ps tmp/8wb281195649654.png")
> system("convert tmp/983g91195649654.ps tmp/983g91195649654.png")
>
>
> proc.time()
user system elapsed
2.286 1.515 2.949