R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
Natural language support but running in an English locale
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'
+ ,'LogTg')
+ ,1:41))
> y <- array(NA,dim=c(3,41),dimnames=list(c('LogPS','D','LogTg'),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
LogPS D LogTg
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 LogTg
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 ***
LogTg -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/freestat/rcomp/tmp/1f7d21292339809.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2f7d21292339809.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3f7d21292339809.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/47yu51292339809.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/57yu51292339809.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/freestat/rcomp/tmp/67yu51292339809.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/freestat/rcomp/tmp/70qtq1292339809.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8bzbt1292339809.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9bzbt1292339809.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10bzbt1292339809.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/117rq21292339809.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/freestat/rcomp/tmp/12zi851292339809.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/freestat/rcomp/tmp/136jny1292339809.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/freestat/rcomp/tmp/144vt81292339809.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/freestat/rcomp/tmp/15ktkp1292339809.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/freestat/rcomp/tmp/16hlig1292339809.tab")
+ }
>
> try(system("convert tmp/1f7d21292339809.ps tmp/1f7d21292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/2f7d21292339809.ps tmp/2f7d21292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/3f7d21292339809.ps tmp/3f7d21292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/47yu51292339809.ps tmp/47yu51292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/57yu51292339809.ps tmp/57yu51292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/67yu51292339809.ps tmp/67yu51292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/70qtq1292339809.ps tmp/70qtq1292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bzbt1292339809.ps tmp/8bzbt1292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bzbt1292339809.ps tmp/9bzbt1292339809.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bzbt1292339809.ps tmp/10bzbt1292339809.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.586 2.400 3.904