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.
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Type 'license()' or 'licence()' for distribution details.
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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
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> 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 = 'No 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)
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 100 0.309 2.99 1 0 0 0 0 0 0 0 0 0 0
2 83 0.333 3.45 0 1 0 0 0 0 0 0 0 0 0
3 83 0.317 2.99 0 0 1 0 0 0 0 0 0 0 0
4 83 0.305 3.26 0 0 0 1 0 0 0 0 0 0 0
5 82 0.314 3.26 0 0 0 0 1 0 0 0 0 0 0
6 71 0.310 3.42 0 0 0 0 0 1 0 0 0 0 0
7 82 0.317 3.39 0 0 0 0 0 0 1 0 0 0 0
8 86 0.317 2.94 0 0 0 0 0 0 0 1 0 0 0
9 64 0.311 3.77 0 0 0 0 0 0 0 0 1 0 0
10 66 0.314 3.87 0 0 0 0 0 0 0 0 0 1 0
11 63 0.312 3.84 0 0 0 0 0 0 0 0 0 0 1
12 67 0.319 3.85 0 0 0 0 0 0 0 0 0 0 0
13 41 0.309 3.55 1 0 0 0 0 0 0 0 0 0 0
14 65 0.305 3.88 0 1 0 0 0 0 0 0 0 0 0
15 68 0.298 3.68 0 0 1 0 0 0 0 0 0 0 0
16 90 0.320 3.60 0 0 0 1 0 0 0 0 0 0 0
17 98 0.323 3.11 0 0 0 0 1 0 0 0 0 0 0
18 108 0.338 3.11 0 0 0 0 0 1 0 0 0 0 0
19 92 0.338 3.84 0 0 0 0 0 0 1 0 0 0 0
20 100 0.324 2.91 0 0 0 0 0 0 0 1 0 0 0
21 87 0.310 3.29 0 0 0 0 0 0 0 0 1 0 0
22 91 0.322 3.42 0 0 0 0 0 0 0 0 0 1 0
23 77 0.317 3.56 0 0 0 0 0 0 0 0 0 0 1
24 72 0.309 3.66 0 0 0 0 0 0 0 0 0 0 0
25 59 0.305 4.05 1 0 0 0 0 0 0 0 0 0 0
26 55 0.310 4.13 0 1 0 0 0 0 0 0 0 0 0
27 69 0.327 3.88 0 0 1 0 0 0 0 0 0 0 0
28 71 0.323 4.22 0 0 0 1 0 0 0 0 0 0 0
29 88 0.329 3.95 0 0 0 0 1 0 0 0 0 0 0
30 88 0.328 3.77 0 0 0 0 0 1 0 0 0 0 0
31 97 0.361 4.27 0 0 0 0 0 0 1 0 0 0 0
32 94 0.346 4.16 0 0 0 0 0 0 0 1 0 0 0
33 82 0.323 4.07 0 0 0 0 0 0 0 0 1 0 0
34 75 0.322 3.89 0 0 0 0 0 0 0 0 0 1 0
35 66 0.314 4.48 0 0 0 0 0 0 0 0 0 0 1
36 71 0.317 4.09 0 0 0 0 0 0 0 0 0 0 0
37 83 0.322 3.76 1 0 0 0 0 0 0 0 0 0 0
38 97 0.334 4.14 0 1 0 0 0 0 0 0 0 0 0
39 88 0.342 4.26 0 0 1 0 0 0 0 0 0 0 0
40 89 0.340 4.07 0 0 0 1 0 0 0 0 0 0 0
41 70 0.335 4.45 0 0 0 0 1 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) OBP ERA M1 M2 M3
-117.3032 847.8061 -20.6265 -1.8289 1.0246 -1.4730
M4 M5 M6 M7 M8 M9
5.6824 2.2175 1.3012 -0.4190 0.5320 4.7178
M10 M11
0.7718 1.1570
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-28.616 -5.275 2.105 4.578 18.833
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -117.3032 44.8981 -2.613 0.0145 *
OBP 847.8061 159.6154 5.312 1.32e-05 ***
ERA -20.6265 4.4593 -4.625 8.33e-05 ***
M1 -1.8289 7.4518 -0.245 0.8080
M2 1.0246 7.4169 0.138 0.8912
M3 -1.4730 7.5151 -0.196 0.8461
M4 5.6824 7.4900 0.759 0.4546
M5 2.2175 7.6718 0.289 0.7747
M6 1.3012 8.4648 0.154 0.8790
M7 -0.4190 8.7718 -0.048 0.9623
M8 0.5320 8.8209 0.060 0.9523
M9 4.7178 7.9097 0.596 0.5558
M10 0.7718 7.9621 0.097 0.9235
M11 1.1570 7.8953 0.147 0.8846
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.652 on 27 degrees of freedom
Multiple R-squared: 0.6882, Adjusted R-squared: 0.5381
F-statistic: 4.585 on 13 and 27 DF, p-value: 0.0004117
> 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,] 0.9926221 0.01475576 0.007377882
[2,] 0.9862099 0.02758026 0.013790130
[3,] 0.9826451 0.03470972 0.017354859
[4,] 0.9622855 0.07542904 0.037714522
[5,] 0.9250406 0.14991887 0.074959435
[6,] 0.8661513 0.26769734 0.133848671
[7,] 0.8128048 0.37439035 0.187195174
[8,] 0.6476587 0.70468255 0.352341276
> postscript(file="/var/www/html/rcomp/tmp/1hm961259931249.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/215421259931249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tmz11259931249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jmt31259931249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xs2p1259931249.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
18.8332492 -11.8794863 -5.3051339 3.2822415 -1.8831290 -5.2753265
7 8 9 10 11 12
0.8913975 -5.3414927 -9.3204563 -3.8552406 -6.1636286 -6.7349987
13 14 15 16 17 18
-28.6159289 2.7284647 10.0354441 4.5781495 3.3926463 1.5918986
19 20 21 22 23 24
2.3693805 2.1050708 4.6266453 5.0804004 -2.1780699 2.8240331
25 26 27 28 29 30
3.0885292 -6.3539487 -9.4256384 -4.1768588 5.6320427 3.6834279
31 32 33 34 35 36
-3.2607780 3.2364218 4.6938111 -1.2251598 8.3416985 3.9109656
37 38 39 40 41
6.6941504 15.5049703 4.6953282 -3.6835322 -7.1415599
> postscript(file="/var/www/html/rcomp/tmp/63k7w1259931249.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 18.8332492 NA
1 -11.8794863 18.8332492
2 -5.3051339 -11.8794863
3 3.2822415 -5.3051339
4 -1.8831290 3.2822415
5 -5.2753265 -1.8831290
6 0.8913975 -5.2753265
7 -5.3414927 0.8913975
8 -9.3204563 -5.3414927
9 -3.8552406 -9.3204563
10 -6.1636286 -3.8552406
11 -6.7349987 -6.1636286
12 -28.6159289 -6.7349987
13 2.7284647 -28.6159289
14 10.0354441 2.7284647
15 4.5781495 10.0354441
16 3.3926463 4.5781495
17 1.5918986 3.3926463
18 2.3693805 1.5918986
19 2.1050708 2.3693805
20 4.6266453 2.1050708
21 5.0804004 4.6266453
22 -2.1780699 5.0804004
23 2.8240331 -2.1780699
24 3.0885292 2.8240331
25 -6.3539487 3.0885292
26 -9.4256384 -6.3539487
27 -4.1768588 -9.4256384
28 5.6320427 -4.1768588
29 3.6834279 5.6320427
30 -3.2607780 3.6834279
31 3.2364218 -3.2607780
32 4.6938111 3.2364218
33 -1.2251598 4.6938111
34 8.3416985 -1.2251598
35 3.9109656 8.3416985
36 6.6941504 3.9109656
37 15.5049703 6.6941504
38 4.6953282 15.5049703
39 -3.6835322 4.6953282
40 -7.1415599 -3.6835322
41 NA -7.1415599
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -11.8794863 18.8332492
[2,] -5.3051339 -11.8794863
[3,] 3.2822415 -5.3051339
[4,] -1.8831290 3.2822415
[5,] -5.2753265 -1.8831290
[6,] 0.8913975 -5.2753265
[7,] -5.3414927 0.8913975
[8,] -9.3204563 -5.3414927
[9,] -3.8552406 -9.3204563
[10,] -6.1636286 -3.8552406
[11,] -6.7349987 -6.1636286
[12,] -28.6159289 -6.7349987
[13,] 2.7284647 -28.6159289
[14,] 10.0354441 2.7284647
[15,] 4.5781495 10.0354441
[16,] 3.3926463 4.5781495
[17,] 1.5918986 3.3926463
[18,] 2.3693805 1.5918986
[19,] 2.1050708 2.3693805
[20,] 4.6266453 2.1050708
[21,] 5.0804004 4.6266453
[22,] -2.1780699 5.0804004
[23,] 2.8240331 -2.1780699
[24,] 3.0885292 2.8240331
[25,] -6.3539487 3.0885292
[26,] -9.4256384 -6.3539487
[27,] -4.1768588 -9.4256384
[28,] 5.6320427 -4.1768588
[29,] 3.6834279 5.6320427
[30,] -3.2607780 3.6834279
[31,] 3.2364218 -3.2607780
[32,] 4.6938111 3.2364218
[33,] -1.2251598 4.6938111
[34,] 8.3416985 -1.2251598
[35,] 3.9109656 8.3416985
[36,] 6.6941504 3.9109656
[37,] 15.5049703 6.6941504
[38,] 4.6953282 15.5049703
[39,] -3.6835322 4.6953282
[40,] -7.1415599 -3.6835322
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -11.8794863 18.8332492
2 -5.3051339 -11.8794863
3 3.2822415 -5.3051339
4 -1.8831290 3.2822415
5 -5.2753265 -1.8831290
6 0.8913975 -5.2753265
7 -5.3414927 0.8913975
8 -9.3204563 -5.3414927
9 -3.8552406 -9.3204563
10 -6.1636286 -3.8552406
11 -6.7349987 -6.1636286
12 -28.6159289 -6.7349987
13 2.7284647 -28.6159289
14 10.0354441 2.7284647
15 4.5781495 10.0354441
16 3.3926463 4.5781495
17 1.5918986 3.3926463
18 2.3693805 1.5918986
19 2.1050708 2.3693805
20 4.6266453 2.1050708
21 5.0804004 4.6266453
22 -2.1780699 5.0804004
23 2.8240331 -2.1780699
24 3.0885292 2.8240331
25 -6.3539487 3.0885292
26 -9.4256384 -6.3539487
27 -4.1768588 -9.4256384
28 5.6320427 -4.1768588
29 3.6834279 5.6320427
30 -3.2607780 3.6834279
31 3.2364218 -3.2607780
32 4.6938111 3.2364218
33 -1.2251598 4.6938111
34 8.3416985 -1.2251598
35 3.9109656 8.3416985
36 6.6941504 3.9109656
37 15.5049703 6.6941504
38 4.6953282 15.5049703
39 -3.6835322 4.6953282
40 -7.1415599 -3.6835322
> 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/71sw91259931249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wpiz1259931249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9so5y1259931249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vr0t1259931249.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11j1h41259931249.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/12sarb1259931249.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/138nxe1259931249.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/14lbif1259931249.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/156gf51259931249.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/16vq0v1259931249.tab")
+ }
>
> system("convert tmp/1hm961259931249.ps tmp/1hm961259931249.png")
> system("convert tmp/215421259931249.ps tmp/215421259931249.png")
> system("convert tmp/3tmz11259931249.ps tmp/3tmz11259931249.png")
> system("convert tmp/4jmt31259931249.ps tmp/4jmt31259931249.png")
> system("convert tmp/5xs2p1259931249.ps tmp/5xs2p1259931249.png")
> system("convert tmp/63k7w1259931249.ps tmp/63k7w1259931249.png")
> system("convert tmp/71sw91259931249.ps tmp/71sw91259931249.png")
> system("convert tmp/8wpiz1259931249.ps tmp/8wpiz1259931249.png")
> system("convert tmp/9so5y1259931249.ps tmp/9so5y1259931249.png")
> system("convert tmp/10vr0t1259931249.ps tmp/10vr0t1259931249.png")
>
>
> proc.time()
user system elapsed
2.265 1.614 2.903