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(6.3,4.5,1,6.6,42,2.1,69,2547,4603,624,9.1,27,10.55,179.5,180,15.8,19,0.023,0.3,35,5.2,30.4,160,169,392,10.9,28,3.3,25.6,63,8.3,50,52.16,440,230,11,7,0.425,6.4,112,3.2,30,465,423,281,6.3,3.5,0.075,1.2,42,8.6,50,3,25,28,6.6,6,0.785,3.5,42,9.5,10.4,0.2,5,120,3.3,20,27.66,115,148,11,3.9,0.12,1,16,4.7,41,85,325,310,10.4,9,0.101,4,28,7.4,7.6,1.04,5.5,68,2.1,46,521,655,336,7.7,2.6,0.005,0.14,21.5,17.9,24,0.01,0.25,50,6.1,100,62,1320,267,11.9,3.2,0.023,0.4,19,10.8,2,0.048,0.33,30,13.8,5,1.7,6.3,12,14.3,6.5,3.5,10.8,120,15.2,12,0.48,15.5,140,10,20.2,10,115,170,11.9,13,1.62,11.4,17,6.5,27,192,180,115,7.5,18,2.5,12.1,31,10.6,4.7,0.28,1.9,21,7.4,9.8,4.235,50.4,52,8.4,29,6.8,179,164,5.7,7,0.75,12.3,225,4.9,6,3.6,21,225,3.2,20,55.5,175,151,11,4.5,0.9,2.6,60,4.9,7.5,2,12.3,200,13.2,2.3,0.104,2.5,46,9.7,24,4.19,58,210,12.8,3,3.5,3.9,14),dim=c(5,42),dimnames=list(c('SWS','LS','BW','BRW','GT'),1:42))
> y <- array(NA,dim=c(5,42),dimnames=list(c('SWS','LS','BW','BRW','GT'),1:42))
> 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
SWS LS BW BRW GT
1 6.3 4.5 1.000 6.60 42.0
2 2.1 69.0 2547.000 4603.00 624.0
3 9.1 27.0 10.550 179.50 180.0
4 15.8 19.0 0.023 0.30 35.0
5 5.2 30.4 160.000 169.00 392.0
6 10.9 28.0 3.300 25.60 63.0
7 8.3 50.0 52.160 440.00 230.0
8 11.0 7.0 0.425 6.40 112.0
9 3.2 30.0 465.000 423.00 281.0
10 6.3 3.5 0.075 1.20 42.0
11 8.6 50.0 3.000 25.00 28.0
12 6.6 6.0 0.785 3.50 42.0
13 9.5 10.4 0.200 5.00 120.0
14 3.3 20.0 27.660 115.00 148.0
15 11.0 3.9 0.120 1.00 16.0
16 4.7 41.0 85.000 325.00 310.0
17 10.4 9.0 0.101 4.00 28.0
18 7.4 7.6 1.040 5.50 68.0
19 2.1 46.0 521.000 655.00 336.0
20 7.7 2.6 0.005 0.14 21.5
21 17.9 24.0 0.010 0.25 50.0
22 6.1 100.0 62.000 1320.00 267.0
23 11.9 3.2 0.023 0.40 19.0
24 10.8 2.0 0.048 0.33 30.0
25 13.8 5.0 1.700 6.30 12.0
26 14.3 6.5 3.500 10.80 120.0
27 15.2 12.0 0.480 15.50 140.0
28 10.0 20.2 10.000 115.00 170.0
29 11.9 13.0 1.620 11.40 17.0
30 6.5 27.0 192.000 180.00 115.0
31 7.5 18.0 2.500 12.10 31.0
32 10.6 4.7 0.280 1.90 21.0
33 7.4 9.8 4.235 50.40 52.0
34 8.4 29.0 6.800 179.00 164.0
35 5.7 7.0 0.750 12.30 225.0
36 4.9 6.0 3.600 21.00 225.0
37 3.2 20.0 55.500 175.00 151.0
38 11.0 4.5 0.900 2.60 60.0
39 4.9 7.5 2.000 12.30 200.0
40 13.2 2.3 0.104 2.50 46.0
41 9.7 24.0 4.190 58.00 210.0
42 12.8 3.0 3.500 3.90 14.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) LS BW BRW GT
11.387035 -0.014186 -0.002772 0.002209 -0.019801
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.146061 -2.065151 0.009003 1.637666 7.842967
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.387035 0.845591 13.466 7.77e-16 ***
LS -0.014186 0.043976 -0.323 0.74882
BW -0.002772 0.005501 -0.504 0.61728
BRW 0.002209 0.003256 0.678 0.50179
GT -0.019801 0.006521 -3.037 0.00437 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.184 on 37 degrees of freedom
Multiple R-squared: 0.3789, Adjusted R-squared: 0.3117
F-statistic: 5.642 on 4 and 37 DF, p-value: 0.001196
> 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,] 0.6668148 0.66637032 0.333185159
[2,] 0.6595712 0.68085759 0.340428797
[3,] 0.6656277 0.66874468 0.334372340
[4,] 0.6492760 0.70144798 0.350723990
[5,] 0.6246669 0.75066616 0.375333080
[6,] 0.5174996 0.96500073 0.482500367
[7,] 0.6800713 0.63985746 0.319928731
[8,] 0.5969320 0.80613594 0.403067969
[9,] 0.5140759 0.97184829 0.485924143
[10,] 0.4171503 0.83430061 0.582849693
[11,] 0.3612070 0.72241391 0.638793045
[12,] 0.3678004 0.73560084 0.632199580
[13,] 0.3423223 0.68464465 0.657677676
[14,] 0.6980899 0.60382025 0.301910124
[15,] 0.6901962 0.61960768 0.309803842
[16,] 0.6059503 0.78809936 0.394049680
[17,] 0.5079573 0.98408543 0.492042714
[18,] 0.4627356 0.92547130 0.537264351
[19,] 0.6131255 0.77374902 0.386874509
[20,] 0.8608784 0.27824314 0.139121572
[21,] 0.8500828 0.29983438 0.149917188
[22,] 0.7702090 0.45958195 0.229790977
[23,] 0.7992416 0.40151689 0.200758443
[24,] 0.9062595 0.18748104 0.093740519
[25,] 0.8570396 0.28592088 0.142960439
[26,] 0.9733364 0.05332730 0.026663648
[27,] 0.9931631 0.01367387 0.006836935
> postscript(file="/var/www/html/rcomp/tmp/1z75f1292367021.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2z75f1292367021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3z75f1292367021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ry4h1292367021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ry4h1292367021.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 = 42
Frequency = 1
1 2 3 4 5 6
-4.20335886 0.94054012 1.29292607 5.37494500 2.07647178 1.11024989
7 8 9 10 11 12
1.34918363 1.91702790 -1.84268708 -4.20818119 -1.57019149 -3.87582749
13 14 15 16 17 18
0.62613887 -5.05010359 -0.01676716 -0.44934448 -0.31348342 -2.54201314
19 20 21 22 23 24
-1.98388307 -3.22472288 7.84296715 -1.32545396 0.93376194 0.03477361
25 26 27 28 29 30
2.71230630 5.36714799 6.72244021 2.03940051 1.01331549 -2.09224028
31 32 33 34 35 36
-3.03764458 -0.30795732 -2.91794448 0.29519119 -1.15758629 -1.98308996
37 38 39 40 41 42
-5.14606088 0.86161847 -2.44205393 2.75120802 2.69515452 1.73382688
> postscript(file="/var/www/html/rcomp/tmp/6ry4h1292367021.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 = 42
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.20335886 NA
1 0.94054012 -4.20335886
2 1.29292607 0.94054012
3 5.37494500 1.29292607
4 2.07647178 5.37494500
5 1.11024989 2.07647178
6 1.34918363 1.11024989
7 1.91702790 1.34918363
8 -1.84268708 1.91702790
9 -4.20818119 -1.84268708
10 -1.57019149 -4.20818119
11 -3.87582749 -1.57019149
12 0.62613887 -3.87582749
13 -5.05010359 0.62613887
14 -0.01676716 -5.05010359
15 -0.44934448 -0.01676716
16 -0.31348342 -0.44934448
17 -2.54201314 -0.31348342
18 -1.98388307 -2.54201314
19 -3.22472288 -1.98388307
20 7.84296715 -3.22472288
21 -1.32545396 7.84296715
22 0.93376194 -1.32545396
23 0.03477361 0.93376194
24 2.71230630 0.03477361
25 5.36714799 2.71230630
26 6.72244021 5.36714799
27 2.03940051 6.72244021
28 1.01331549 2.03940051
29 -2.09224028 1.01331549
30 -3.03764458 -2.09224028
31 -0.30795732 -3.03764458
32 -2.91794448 -0.30795732
33 0.29519119 -2.91794448
34 -1.15758629 0.29519119
35 -1.98308996 -1.15758629
36 -5.14606088 -1.98308996
37 0.86161847 -5.14606088
38 -2.44205393 0.86161847
39 2.75120802 -2.44205393
40 2.69515452 2.75120802
41 1.73382688 2.69515452
42 NA 1.73382688
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.94054012 -4.20335886
[2,] 1.29292607 0.94054012
[3,] 5.37494500 1.29292607
[4,] 2.07647178 5.37494500
[5,] 1.11024989 2.07647178
[6,] 1.34918363 1.11024989
[7,] 1.91702790 1.34918363
[8,] -1.84268708 1.91702790
[9,] -4.20818119 -1.84268708
[10,] -1.57019149 -4.20818119
[11,] -3.87582749 -1.57019149
[12,] 0.62613887 -3.87582749
[13,] -5.05010359 0.62613887
[14,] -0.01676716 -5.05010359
[15,] -0.44934448 -0.01676716
[16,] -0.31348342 -0.44934448
[17,] -2.54201314 -0.31348342
[18,] -1.98388307 -2.54201314
[19,] -3.22472288 -1.98388307
[20,] 7.84296715 -3.22472288
[21,] -1.32545396 7.84296715
[22,] 0.93376194 -1.32545396
[23,] 0.03477361 0.93376194
[24,] 2.71230630 0.03477361
[25,] 5.36714799 2.71230630
[26,] 6.72244021 5.36714799
[27,] 2.03940051 6.72244021
[28,] 1.01331549 2.03940051
[29,] -2.09224028 1.01331549
[30,] -3.03764458 -2.09224028
[31,] -0.30795732 -3.03764458
[32,] -2.91794448 -0.30795732
[33,] 0.29519119 -2.91794448
[34,] -1.15758629 0.29519119
[35,] -1.98308996 -1.15758629
[36,] -5.14606088 -1.98308996
[37,] 0.86161847 -5.14606088
[38,] -2.44205393 0.86161847
[39,] 2.75120802 -2.44205393
[40,] 2.69515452 2.75120802
[41,] 1.73382688 2.69515452
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.94054012 -4.20335886
2 1.29292607 0.94054012
3 5.37494500 1.29292607
4 2.07647178 5.37494500
5 1.11024989 2.07647178
6 1.34918363 1.11024989
7 1.91702790 1.34918363
8 -1.84268708 1.91702790
9 -4.20818119 -1.84268708
10 -1.57019149 -4.20818119
11 -3.87582749 -1.57019149
12 0.62613887 -3.87582749
13 -5.05010359 0.62613887
14 -0.01676716 -5.05010359
15 -0.44934448 -0.01676716
16 -0.31348342 -0.44934448
17 -2.54201314 -0.31348342
18 -1.98388307 -2.54201314
19 -3.22472288 -1.98388307
20 7.84296715 -3.22472288
21 -1.32545396 7.84296715
22 0.93376194 -1.32545396
23 0.03477361 0.93376194
24 2.71230630 0.03477361
25 5.36714799 2.71230630
26 6.72244021 5.36714799
27 2.03940051 6.72244021
28 1.01331549 2.03940051
29 -2.09224028 1.01331549
30 -3.03764458 -2.09224028
31 -0.30795732 -3.03764458
32 -2.91794448 -0.30795732
33 0.29519119 -2.91794448
34 -1.15758629 0.29519119
35 -1.98308996 -1.15758629
36 -5.14606088 -1.98308996
37 0.86161847 -5.14606088
38 -2.44205393 0.86161847
39 2.75120802 -2.44205393
40 2.69515452 2.75120802
41 1.73382688 2.69515452
> 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/7kq3k1292367021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dhlo1292367021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dhlo1292367021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105q2q1292367021.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11r9iw1292367021.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/12urhk1292367021.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/1381fb1292367021.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/14u2vh1292367021.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/15f2u51292367021.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/161lab1292367021.tab")
+ }
>
> try(system("convert tmp/1z75f1292367021.ps tmp/1z75f1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z75f1292367021.ps tmp/2z75f1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z75f1292367021.ps tmp/3z75f1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ry4h1292367021.ps tmp/4ry4h1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ry4h1292367021.ps tmp/5ry4h1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ry4h1292367021.ps tmp/6ry4h1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kq3k1292367021.ps tmp/7kq3k1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dhlo1292367021.ps tmp/8dhlo1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dhlo1292367021.ps tmp/9dhlo1292367021.png",intern=TRUE))
character(0)
> try(system("convert tmp/105q2q1292367021.ps tmp/105q2q1292367021.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.387 1.678 7.350