R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(0.301029996
+ ,3
+ ,1.62324929
+ ,0.255272505
+ ,4
+ ,2.79518459
+ ,-0.15490196
+ ,4
+ ,2.255272505
+ ,0.591064607
+ ,1
+ ,1.544068044
+ ,0
+ ,4
+ ,2.593286067
+ ,0.556302501
+ ,1
+ ,1.799340549
+ ,0.146128036
+ ,1
+ ,2.361727836
+ ,0.176091259
+ ,4
+ ,2.049218023
+ ,-0.15490196
+ ,5
+ ,2.44870632
+ ,0.322219295
+ ,1
+ ,1.62324929
+ ,0.612783857
+ ,2
+ ,1.62324929
+ ,0.079181246
+ ,2
+ ,2.079181246
+ ,-0.301029996
+ ,5
+ ,2.170261715
+ ,0.531478917
+ ,2
+ ,1.204119983
+ ,0.176091259
+ ,1
+ ,2.491361694
+ ,0.531478917
+ ,3
+ ,1.447158031
+ ,-0.096910013
+ ,4
+ ,1.832508913
+ ,-0.096910013
+ ,5
+ ,2.526339277
+ ,0.146128036
+ ,4
+ ,1.33243846
+ ,0.301029996
+ ,1
+ ,1.698970004
+ ,0.278753601
+ ,1
+ ,2.426511261
+ ,0.113943352
+ ,3
+ ,1.278753601
+ ,0.301029996
+ ,3
+ ,1.477121255
+ ,0.748188027
+ ,1
+ ,1.079181246
+ ,0.491361694
+ ,1
+ ,2.079181246
+ ,0.255272505
+ ,2
+ ,2.146128036
+ ,-0.045757491
+ ,4
+ ,2.230448921
+ ,0.255272505
+ ,2
+ ,1.230448921
+ ,0.278753601
+ ,4
+ ,2.06069784
+ ,-0.045757491
+ ,5
+ ,1.491361694
+ ,0.414973348
+ ,3
+ ,1.322219295
+ ,0.380211242
+ ,1
+ ,1.716003344
+ ,0.079181246
+ ,2
+ ,2.214843848
+ ,-0.045757491
+ ,2
+ ,2.352182518
+ ,-0.301029996
+ ,3
+ ,2.352182518
+ ,-0.22184875
+ ,5
+ ,2.178976947
+ ,0.361727836
+ ,2
+ ,1.77815125
+ ,-0.301029996
+ ,3
+ ,2.301029996
+ ,0.414973348
+ ,2
+ ,1.662757832
+ ,-0.22184875
+ ,4
+ ,2.322219295
+ ,0.819543936
+ ,1
+ ,1.146128036)
+ ,dim=c(3
+ ,41)
+ ,dimnames=list(c('
logPS'
+ ,'D'
+ ,'logG
')
+ ,1:41))
> y <- array(NA,dim=c(3,41),dimnames=list(c('
logPS','D','logG
'),1:41))
> 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)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> 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
\rlogPS D logG\r\r
1 0.30103000 3 1.623249
2 0.25527250 4 2.795185
3 -0.15490196 4 2.255273
4 0.59106461 1 1.544068
5 0.00000000 4 2.593286
6 0.55630250 1 1.799341
7 0.14612804 1 2.361728
8 0.17609126 4 2.049218
9 -0.15490196 5 2.448706
10 0.32221930 1 1.623249
11 0.61278386 2 1.623249
12 0.07918125 2 2.079181
13 -0.30103000 5 2.170262
14 0.53147892 2 1.204120
15 0.17609126 1 2.491362
16 0.53147892 3 1.447158
17 -0.09691001 4 1.832509
18 -0.09691001 5 2.526339
19 0.14612804 4 1.332438
20 0.30103000 1 1.698970
21 0.27875360 1 2.426511
22 0.11394335 3 1.278754
23 0.30103000 3 1.477121
24 0.74818803 1 1.079181
25 0.49136169 1 2.079181
26 0.25527250 2 2.146128
27 -0.04575749 4 2.230449
28 0.25527250 2 1.230449
29 0.27875360 4 2.060698
30 -0.04575749 5 1.491362
31 0.41497335 3 1.322219
32 0.38021124 1 1.716003
33 0.07918125 2 2.214844
34 -0.04575749 2 2.352183
35 -0.30103000 3 2.352183
36 -0.22184875 5 2.178977
37 0.36172784 2 1.778151
38 -0.30103000 3 2.301030
39 0.41497335 2 1.662758
40 -0.22184875 4 2.322219
41 0.81954394 1 1.146128
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) D `logG\r\r`
1.0646 -0.1125 -0.2964
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.34589 -0.14399 0.01194 0.11605 0.46943
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.06458 0.12116 8.787 1.09e-10 ***
D -0.11254 0.02100 -5.358 4.32e-06 ***
`logG\r\r` -0.29643 0.06382 -4.645 4.00e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1774 on 38 degrees of freedom
Multiple R-squared: 0.6543, Adjusted R-squared: 0.6361
F-statistic: 35.95 on 2 and 38 DF, p-value: 1.723e-09
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.6230976 0.75380488 0.37690244
[2,] 0.8301475 0.33970493 0.16985246
[3,] 0.7539182 0.49216360 0.24608180
[4,] 0.6896613 0.62067731 0.31033866
[5,] 0.6578402 0.68431955 0.34215978
[6,] 0.7366781 0.52664382 0.26332191
[7,] 0.7416296 0.51674080 0.25837040
[8,] 0.7879098 0.42418033 0.21209016
[9,] 0.7119046 0.57619072 0.28809536
[10,] 0.6340920 0.73181601 0.36590801
[11,] 0.6673624 0.66527528 0.33263764
[12,] 0.6869213 0.62615742 0.31307871
[13,] 0.6948189 0.61036218 0.30518109
[14,] 0.6242554 0.75148924 0.37574462
[15,] 0.6040829 0.79183424 0.39591712
[16,] 0.5218457 0.95630867 0.47815433
[17,] 0.6075393 0.78492141 0.39246071
[18,] 0.5115238 0.97695235 0.48847618
[19,] 0.4517216 0.90344313 0.54827844
[20,] 0.4636135 0.92722700 0.53638650
[21,] 0.4162805 0.83256109 0.58371945
[22,] 0.3524116 0.70482316 0.64758842
[23,] 0.5459024 0.90819524 0.45409762
[24,] 0.9511931 0.09761381 0.04880691
[25,] 0.9638973 0.07220542 0.03610271
[26,] 0.9544147 0.09117055 0.04558527
[27,] 0.9222827 0.15543461 0.07771730
[28,] 0.9025797 0.19484056 0.09742028
[29,] 0.9295518 0.14089645 0.07044822
[30,] 0.8709560 0.25808807 0.12904403
> postscript(file="/var/www/html/rcomp/tmp/1o6jg1292892647.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2o6jg1292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yf1j1292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4yf1j1292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yf1j1292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 41
Frequency = 1
1 2 3 4 5 6
0.055254603 0.469433230 -0.100785679 0.096731732 0.154312580 0.137639253
7 8 9 10 11 12
-0.105828514 0.169127457 0.069096316 -0.148642132 0.254465447 -0.143986686
13 14 15 16 17 18
-0.159570180 0.048919322 -0.037438333 0.233505345 -0.168112216 0.150100757
19 20 21 22 23 24
-0.073308482 -0.147385777 0.046000598 -0.233949817 0.011938328 0.116050218
25 26 27 28 29 30
0.155650746 0.051949400 0.001000413 -0.219482485 0.275192725 -0.105541877
31 32 33 34 35 36
0.079964578 -0.063155392 -0.103772647 -0.188000513 -0.330730001 -0.077805505
37 38 39 40 41
0.049326528 -0.345892983 0.068366331 -0.147887642 0.207250954
> postscript(file="/var/www/html/rcomp/tmp/6yf1j1292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 41
Frequency = 1
lag(myerror, k = 1) myerror
0 0.055254603 NA
1 0.469433230 0.055254603
2 -0.100785679 0.469433230
3 0.096731732 -0.100785679
4 0.154312580 0.096731732
5 0.137639253 0.154312580
6 -0.105828514 0.137639253
7 0.169127457 -0.105828514
8 0.069096316 0.169127457
9 -0.148642132 0.069096316
10 0.254465447 -0.148642132
11 -0.143986686 0.254465447
12 -0.159570180 -0.143986686
13 0.048919322 -0.159570180
14 -0.037438333 0.048919322
15 0.233505345 -0.037438333
16 -0.168112216 0.233505345
17 0.150100757 -0.168112216
18 -0.073308482 0.150100757
19 -0.147385777 -0.073308482
20 0.046000598 -0.147385777
21 -0.233949817 0.046000598
22 0.011938328 -0.233949817
23 0.116050218 0.011938328
24 0.155650746 0.116050218
25 0.051949400 0.155650746
26 0.001000413 0.051949400
27 -0.219482485 0.001000413
28 0.275192725 -0.219482485
29 -0.105541877 0.275192725
30 0.079964578 -0.105541877
31 -0.063155392 0.079964578
32 -0.103772647 -0.063155392
33 -0.188000513 -0.103772647
34 -0.330730001 -0.188000513
35 -0.077805505 -0.330730001
36 0.049326528 -0.077805505
37 -0.345892983 0.049326528
38 0.068366331 -0.345892983
39 -0.147887642 0.068366331
40 0.207250954 -0.147887642
41 NA 0.207250954
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.469433230 0.055254603
[2,] -0.100785679 0.469433230
[3,] 0.096731732 -0.100785679
[4,] 0.154312580 0.096731732
[5,] 0.137639253 0.154312580
[6,] -0.105828514 0.137639253
[7,] 0.169127457 -0.105828514
[8,] 0.069096316 0.169127457
[9,] -0.148642132 0.069096316
[10,] 0.254465447 -0.148642132
[11,] -0.143986686 0.254465447
[12,] -0.159570180 -0.143986686
[13,] 0.048919322 -0.159570180
[14,] -0.037438333 0.048919322
[15,] 0.233505345 -0.037438333
[16,] -0.168112216 0.233505345
[17,] 0.150100757 -0.168112216
[18,] -0.073308482 0.150100757
[19,] -0.147385777 -0.073308482
[20,] 0.046000598 -0.147385777
[21,] -0.233949817 0.046000598
[22,] 0.011938328 -0.233949817
[23,] 0.116050218 0.011938328
[24,] 0.155650746 0.116050218
[25,] 0.051949400 0.155650746
[26,] 0.001000413 0.051949400
[27,] -0.219482485 0.001000413
[28,] 0.275192725 -0.219482485
[29,] -0.105541877 0.275192725
[30,] 0.079964578 -0.105541877
[31,] -0.063155392 0.079964578
[32,] -0.103772647 -0.063155392
[33,] -0.188000513 -0.103772647
[34,] -0.330730001 -0.188000513
[35,] -0.077805505 -0.330730001
[36,] 0.049326528 -0.077805505
[37,] -0.345892983 0.049326528
[38,] 0.068366331 -0.345892983
[39,] -0.147887642 0.068366331
[40,] 0.207250954 -0.147887642
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.469433230 0.055254603
2 -0.100785679 0.469433230
3 0.096731732 -0.100785679
4 0.154312580 0.096731732
5 0.137639253 0.154312580
6 -0.105828514 0.137639253
7 0.169127457 -0.105828514
8 0.069096316 0.169127457
9 -0.148642132 0.069096316
10 0.254465447 -0.148642132
11 -0.143986686 0.254465447
12 -0.159570180 -0.143986686
13 0.048919322 -0.159570180
14 -0.037438333 0.048919322
15 0.233505345 -0.037438333
16 -0.168112216 0.233505345
17 0.150100757 -0.168112216
18 -0.073308482 0.150100757
19 -0.147385777 -0.073308482
20 0.046000598 -0.147385777
21 -0.233949817 0.046000598
22 0.011938328 -0.233949817
23 0.116050218 0.011938328
24 0.155650746 0.116050218
25 0.051949400 0.155650746
26 0.001000413 0.051949400
27 -0.219482485 0.001000413
28 0.275192725 -0.219482485
29 -0.105541877 0.275192725
30 0.079964578 -0.105541877
31 -0.063155392 0.079964578
32 -0.103772647 -0.063155392
33 -0.188000513 -0.103772647
34 -0.330730001 -0.188000513
35 -0.077805505 -0.330730001
36 0.049326528 -0.077805505
37 -0.345892983 0.049326528
38 0.068366331 -0.345892983
39 -0.147887642 0.068366331
40 0.207250954 -0.147887642
> 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/7ro041292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kfzp1292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kfzp1292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kfzp1292892647.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11y7xf1292892647.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/12rgwi1292892647.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/13yzbc1292892647.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/141isi1292892647.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15408o1292892647.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/167jot1292892647.tab")
+ }
>
> try(system("convert tmp/1o6jg1292892647.ps tmp/1o6jg1292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/2o6jg1292892647.ps tmp/2o6jg1292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yf1j1292892647.ps tmp/3yf1j1292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/4yf1j1292892647.ps tmp/4yf1j1292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yf1j1292892647.ps tmp/5yf1j1292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yf1j1292892647.ps tmp/6yf1j1292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ro041292892647.ps tmp/7ro041292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kfzp1292892647.ps tmp/8kfzp1292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kfzp1292892647.ps tmp/9kfzp1292892647.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kfzp1292892647.ps tmp/10kfzp1292892647.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.368 1.633 8.833