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(100,.309,2.99,83,.333,3.45,83,.317,2.99,83,.305,3.26,82,.314,3.26,71,.310,3.42,82,.317,3.39,86,.317,2.94,64,.311,3.77,66,.314,3.87,63,.312,3.84,67,.319,3.85,41,.309,3.55,65,.305,3.88,68,.298,3.68,90,.320,3.60,98,.323,3.11,108,.338,3.11,92,.338,3.84,100,.324,2.91,87,.310,3.29,91,.322,3.42,77,.317,3.56,72,.309,3.66,59,.305,4.05,55,.310,4.13,69,.327,3.88,71,.323,4.22,88,.329,3.95,88,.328,3.77,97,.361,4.27,94,.346,4.16,82,.323,4.07,75,.322,3.89,66,.314,4.48,71,.317,4.09,83,.322,3.76,97,.334,4.14,88,.342,4.26,89,.340,4.07,70,.335,4.45),dim=c(3,41),dimnames=list(c('WINS','OBP','ERA'),1:41))
> y <- array(NA,dim=c(3,41),dimnames=list(c('WINS','OBP','ERA'),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 = '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
WINS OBP ERA t
1 100 0.309 2.99 1
2 83 0.333 3.45 2
3 83 0.317 2.99 3
4 83 0.305 3.26 4
5 82 0.314 3.26 5
6 71 0.310 3.42 6
7 82 0.317 3.39 7
8 86 0.317 2.94 8
9 64 0.311 3.77 9
10 66 0.314 3.87 10
11 63 0.312 3.84 11
12 67 0.319 3.85 12
13 41 0.309 3.55 13
14 65 0.305 3.88 14
15 68 0.298 3.68 15
16 90 0.320 3.60 16
17 98 0.323 3.11 17
18 108 0.338 3.11 18
19 92 0.338 3.84 19
20 100 0.324 2.91 20
21 87 0.310 3.29 21
22 91 0.322 3.42 22
23 77 0.317 3.56 23
24 72 0.309 3.66 24
25 59 0.305 4.05 25
26 55 0.310 4.13 26
27 69 0.327 3.88 27
28 71 0.323 4.22 28
29 88 0.329 3.95 29
30 88 0.328 3.77 30
31 97 0.361 4.27 31
32 94 0.346 4.16 32
33 82 0.323 4.07 33
34 75 0.322 3.89 34
35 66 0.314 4.48 35
36 71 0.317 4.09 36
37 83 0.322 3.76 37
38 97 0.334 4.14 38
39 88 0.342 4.26 39
40 89 0.340 4.07 40
41 70 0.335 4.45 41
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) OBP ERA t
-65.0728 740.3088 -27.3151 0.3942
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-30.8385 -3.1842 0.3833 4.1657 17.5953
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -65.0728 38.2811 -1.700 0.0975 .
OBP 740.3088 112.3517 6.589 1.01e-07 ***
ERA -27.3151 4.3409 -6.292 2.53e-07 ***
t 0.3942 0.1690 2.333 0.0252 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.999 on 37 degrees of freedom
Multiple R-squared: 0.7066, Adjusted R-squared: 0.6828
F-statistic: 29.7 on 3 and 37 DF, p-value: 5.921e-10
> 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,] 0.5468646 0.906270737 0.453135368
[2,] 0.3974293 0.794858697 0.602570651
[3,] 0.2526781 0.505356264 0.747321868
[4,] 0.1778163 0.355632576 0.822183712
[5,] 0.1012000 0.202399945 0.898800028
[6,] 0.0734641 0.146928192 0.926535904
[7,] 0.9403767 0.119246542 0.059623271
[8,] 0.9529675 0.094064998 0.047032499
[9,] 0.9581694 0.083661166 0.041830583
[10,] 0.9966398 0.006720307 0.003360153
[11,] 0.9948106 0.010378774 0.005189387
[12,] 0.9905241 0.018951892 0.009475946
[13,] 0.9867901 0.026419738 0.013209869
[14,] 0.9766093 0.046781499 0.023390750
[15,] 0.9678255 0.064348975 0.032174488
[16,] 0.9548358 0.090328413 0.045164206
[17,] 0.9271250 0.145750081 0.072875041
[18,] 0.8854705 0.229059062 0.114529531
[19,] 0.8253792 0.349241562 0.174620781
[20,] 0.7996397 0.400720537 0.200360269
[21,] 0.9175683 0.164863458 0.082431729
[22,] 0.8895378 0.220924417 0.110462208
[23,] 0.8422194 0.315561290 0.157780645
[24,] 0.7545795 0.490841026 0.245420513
[25,] 0.6879010 0.624198058 0.312099029
[26,] 0.6034303 0.793139332 0.396569666
[27,] 0.4604739 0.920947845 0.539526078
[28,] 0.6744003 0.651199411 0.325599705
> postscript(file="/var/www/html/rcomp/tmp/1qqjs1259933513.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wyh41259933513.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31a3k1259933513.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47qpu1259933513.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58q9k1259933513.ps",horizontal=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
17.5953395 -5.0013129 -6.1155222 9.7490700 1.6920951 -2.3704462
7 8 9 10 11 12
2.2337429 -6.4522562 -1.7330502 0.3833396 -2.3496919 -3.6528980
13 14 15 16 17 18
-30.8385412 4.7424877 7.0674300 10.2012311 2.2017008 0.7028730
19 20 21 22 23 24
4.2487142 -3.1842186 4.1656543 2.4387184 -4.4298165 -1.1700297
25 26 27 28 29 30
-0.9500937 -6.8606238 -12.6688489 1.1853311 5.9742006 1.4035924
31 32 33 34 35 36
-0.7632345 3.9425390 6.1170853 -5.4535229 7.1906721 -1.0773463
37 38 39 40 41
-2.1870751 12.9147687 0.8759169 -2.2275337 -7.5404401
> postscript(file="/var/www/html/rcomp/tmp/6tjj81259933513.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 = 41
Frequency = 1
lag(myerror, k = 1) myerror
0 17.5953395 NA
1 -5.0013129 17.5953395
2 -6.1155222 -5.0013129
3 9.7490700 -6.1155222
4 1.6920951 9.7490700
5 -2.3704462 1.6920951
6 2.2337429 -2.3704462
7 -6.4522562 2.2337429
8 -1.7330502 -6.4522562
9 0.3833396 -1.7330502
10 -2.3496919 0.3833396
11 -3.6528980 -2.3496919
12 -30.8385412 -3.6528980
13 4.7424877 -30.8385412
14 7.0674300 4.7424877
15 10.2012311 7.0674300
16 2.2017008 10.2012311
17 0.7028730 2.2017008
18 4.2487142 0.7028730
19 -3.1842186 4.2487142
20 4.1656543 -3.1842186
21 2.4387184 4.1656543
22 -4.4298165 2.4387184
23 -1.1700297 -4.4298165
24 -0.9500937 -1.1700297
25 -6.8606238 -0.9500937
26 -12.6688489 -6.8606238
27 1.1853311 -12.6688489
28 5.9742006 1.1853311
29 1.4035924 5.9742006
30 -0.7632345 1.4035924
31 3.9425390 -0.7632345
32 6.1170853 3.9425390
33 -5.4535229 6.1170853
34 7.1906721 -5.4535229
35 -1.0773463 7.1906721
36 -2.1870751 -1.0773463
37 12.9147687 -2.1870751
38 0.8759169 12.9147687
39 -2.2275337 0.8759169
40 -7.5404401 -2.2275337
41 NA -7.5404401
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.0013129 17.5953395
[2,] -6.1155222 -5.0013129
[3,] 9.7490700 -6.1155222
[4,] 1.6920951 9.7490700
[5,] -2.3704462 1.6920951
[6,] 2.2337429 -2.3704462
[7,] -6.4522562 2.2337429
[8,] -1.7330502 -6.4522562
[9,] 0.3833396 -1.7330502
[10,] -2.3496919 0.3833396
[11,] -3.6528980 -2.3496919
[12,] -30.8385412 -3.6528980
[13,] 4.7424877 -30.8385412
[14,] 7.0674300 4.7424877
[15,] 10.2012311 7.0674300
[16,] 2.2017008 10.2012311
[17,] 0.7028730 2.2017008
[18,] 4.2487142 0.7028730
[19,] -3.1842186 4.2487142
[20,] 4.1656543 -3.1842186
[21,] 2.4387184 4.1656543
[22,] -4.4298165 2.4387184
[23,] -1.1700297 -4.4298165
[24,] -0.9500937 -1.1700297
[25,] -6.8606238 -0.9500937
[26,] -12.6688489 -6.8606238
[27,] 1.1853311 -12.6688489
[28,] 5.9742006 1.1853311
[29,] 1.4035924 5.9742006
[30,] -0.7632345 1.4035924
[31,] 3.9425390 -0.7632345
[32,] 6.1170853 3.9425390
[33,] -5.4535229 6.1170853
[34,] 7.1906721 -5.4535229
[35,] -1.0773463 7.1906721
[36,] -2.1870751 -1.0773463
[37,] 12.9147687 -2.1870751
[38,] 0.8759169 12.9147687
[39,] -2.2275337 0.8759169
[40,] -7.5404401 -2.2275337
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.0013129 17.5953395
2 -6.1155222 -5.0013129
3 9.7490700 -6.1155222
4 1.6920951 9.7490700
5 -2.3704462 1.6920951
6 2.2337429 -2.3704462
7 -6.4522562 2.2337429
8 -1.7330502 -6.4522562
9 0.3833396 -1.7330502
10 -2.3496919 0.3833396
11 -3.6528980 -2.3496919
12 -30.8385412 -3.6528980
13 4.7424877 -30.8385412
14 7.0674300 4.7424877
15 10.2012311 7.0674300
16 2.2017008 10.2012311
17 0.7028730 2.2017008
18 4.2487142 0.7028730
19 -3.1842186 4.2487142
20 4.1656543 -3.1842186
21 2.4387184 4.1656543
22 -4.4298165 2.4387184
23 -1.1700297 -4.4298165
24 -0.9500937 -1.1700297
25 -6.8606238 -0.9500937
26 -12.6688489 -6.8606238
27 1.1853311 -12.6688489
28 5.9742006 1.1853311
29 1.4035924 5.9742006
30 -0.7632345 1.4035924
31 3.9425390 -0.7632345
32 6.1170853 3.9425390
33 -5.4535229 6.1170853
34 7.1906721 -5.4535229
35 -1.0773463 7.1906721
36 -2.1870751 -1.0773463
37 12.9147687 -2.1870751
38 0.8759169 12.9147687
39 -2.2275337 0.8759169
40 -7.5404401 -2.2275337
> 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/787q01259933513.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vaqm1259933513.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90ug31259933514.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xf971259933514.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zwle1259933514.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/12o37z1259933514.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/134rl21259933514.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/14rule1259933514.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/15ofs41259933514.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/16nwjx1259933514.tab")
+ }
>
> system("convert tmp/1qqjs1259933513.ps tmp/1qqjs1259933513.png")
> system("convert tmp/2wyh41259933513.ps tmp/2wyh41259933513.png")
> system("convert tmp/31a3k1259933513.ps tmp/31a3k1259933513.png")
> system("convert tmp/47qpu1259933513.ps tmp/47qpu1259933513.png")
> system("convert tmp/58q9k1259933513.ps tmp/58q9k1259933513.png")
> system("convert tmp/6tjj81259933513.ps tmp/6tjj81259933513.png")
> system("convert tmp/787q01259933513.ps tmp/787q01259933513.png")
> system("convert tmp/8vaqm1259933513.ps tmp/8vaqm1259933513.png")
> system("convert tmp/90ug31259933514.ps tmp/90ug31259933514.png")
> system("convert tmp/10xf971259933514.ps tmp/10xf971259933514.png")
>
>
> proc.time()
user system elapsed
2.264 1.548 2.693