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(8,11.1,8.1,10.9,7.7,10,7.5,9.2,7.6,9.2,7.8,9.5,7.8,9.6,7.8,9.5,7.5,9.1,7.5,8.9,7.1,9,7.5,10.1,7.5,10.3,7.6,10.2,7.7,9.6,7.7,9.2,7.9,9.3,8.1,9.4,8.2,9.4,8.2,9.2,8.2,9,7.9,9,7.3,9,6.9,9.8,6.6,10,6.7,9.8,6.9,9.3,7,9,7.1,9,7.2,9.1,7.1,9.1,6.9,9.1,7,9.2,6.8,8.8,6.4,8.3,6.7,8.4,6.6,8.1,6.4,7.7,6.3,7.9,6.2,7.9,6.5,8,6.8,7.9,6.8,7.6,6.4,7.1,6.1,6.8,5.8,6.5,6.1,6.9,7.2,8.2,7.3,8.7,6.9,8.3,6.1,7.9,5.8,7.5,6.2,7.8,7.1,8.3,7.7,8.4,7.9,8.2,7.7,7.7,7.4,7.2,7.5,7.3,8,8.1),dim=c(2,60),dimnames=list(c('X','Y'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('X','Y'),1:60))
> 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 = '2'
> #'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
Y X
1 11.1 8.0
2 10.9 8.1
3 10.0 7.7
4 9.2 7.5
5 9.2 7.6
6 9.5 7.8
7 9.6 7.8
8 9.5 7.8
9 9.1 7.5
10 8.9 7.5
11 9.0 7.1
12 10.1 7.5
13 10.3 7.5
14 10.2 7.6
15 9.6 7.7
16 9.2 7.7
17 9.3 7.9
18 9.4 8.1
19 9.4 8.2
20 9.2 8.2
21 9.0 8.2
22 9.0 7.9
23 9.0 7.3
24 9.8 6.9
25 10.0 6.6
26 9.8 6.7
27 9.3 6.9
28 9.0 7.0
29 9.0 7.1
30 9.1 7.2
31 9.1 7.1
32 9.1 6.9
33 9.2 7.0
34 8.8 6.8
35 8.3 6.4
36 8.4 6.7
37 8.1 6.6
38 7.7 6.4
39 7.9 6.3
40 7.9 6.2
41 8.0 6.5
42 7.9 6.8
43 7.6 6.8
44 7.1 6.4
45 6.8 6.1
46 6.5 5.8
47 6.9 6.1
48 8.2 7.2
49 8.7 7.3
50 8.3 6.9
51 7.9 6.1
52 7.5 5.8
53 7.8 6.2
54 8.3 7.1
55 8.4 7.7
56 8.2 7.9
57 7.7 7.7
58 7.2 7.4
59 7.3 7.5
60 8.1 8.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
2.102 0.926
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.75477 -0.43723 0.03974 0.37577 1.78604
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1022 1.1401 1.844 0.0703 .
X 0.9260 0.1583 5.849 2.41e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7998 on 58 degrees of freedom
Multiple R-squared: 0.371, Adjusted R-squared: 0.3602
F-statistic: 34.21 on 1 and 58 DF, p-value: 2.414e-07
> 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,] 0.067560440 0.13512088 0.932439560
[2,] 0.092338805 0.18467761 0.907661195
[3,] 0.063215831 0.12643166 0.936784169
[4,] 0.047195248 0.09439050 0.952804752
[5,] 0.021882448 0.04376490 0.978117552
[6,] 0.009203016 0.01840603 0.990796984
[7,] 0.031294352 0.06258870 0.968705648
[8,] 0.052816000 0.10563200 0.947184000
[9,] 0.101258637 0.20251727 0.898741363
[10,] 0.106952443 0.21390489 0.893047557
[11,] 0.078446965 0.15689393 0.921553035
[12,] 0.073852937 0.14770587 0.926147063
[13,] 0.085806629 0.17161326 0.914193371
[14,] 0.101284056 0.20256811 0.898715944
[15,] 0.104231425 0.20846285 0.895768575
[16,] 0.105558154 0.21111631 0.894441846
[17,] 0.108721528 0.21744306 0.891278472
[18,] 0.094734980 0.18946996 0.905265020
[19,] 0.074395911 0.14879182 0.925604089
[20,] 0.093536268 0.18707254 0.906463732
[21,] 0.170810805 0.34162161 0.829189195
[22,] 0.255608436 0.51121687 0.744391564
[23,] 0.283604818 0.56720964 0.716395182
[24,] 0.297371259 0.59474252 0.702628741
[25,] 0.307970516 0.61594103 0.692029484
[26,] 0.325049287 0.65009857 0.674950713
[27,] 0.362803254 0.72560651 0.637196746
[28,] 0.441314690 0.88262938 0.558685310
[29,] 0.577073795 0.84585241 0.422926205
[30,] 0.671523806 0.65695239 0.328476194
[31,] 0.726783860 0.54643228 0.273216140
[32,] 0.769075521 0.46184896 0.230924479
[33,] 0.789230649 0.42153870 0.210769351
[34,] 0.799928271 0.40014346 0.200071729
[35,] 0.788631515 0.42273697 0.211368485
[36,] 0.775711739 0.44857652 0.224288261
[37,] 0.764909854 0.47018029 0.235090146
[38,] 0.747797876 0.50440425 0.252202124
[39,] 0.740267541 0.51946492 0.259732459
[40,] 0.754864632 0.49027074 0.245135368
[41,] 0.785789730 0.42842054 0.214210270
[42,] 0.874909244 0.25018151 0.125090756
[43,] 0.923064997 0.15387001 0.076935003
[44,] 0.893298614 0.21340277 0.106701386
[45,] 0.926794125 0.14641175 0.073205875
[46,] 0.913977379 0.17204524 0.086022621
[47,] 0.865069366 0.26986127 0.134930634
[48,] 0.781600178 0.43679964 0.218399822
[49,] 0.675095440 0.64980912 0.324904560
[50,] 0.899808475 0.20038305 0.100191525
[51,] 0.992517013 0.01496597 0.007482987
> postscript(file="/var/www/html/rcomp/tmp/179sh1258485936.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20uy21258485936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32y441258485936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4okjy1258485936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bvjb1258485936.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 = 60
Frequency = 1
1 2 3 4 5 6
1.58961543 1.29701367 0.76742071 0.15262422 0.06002246 0.17481895
7 8 9 10 11 12
0.27481895 0.17481895 0.05262422 -0.14737578 0.32303126 1.05262422
13 14 15 16 17 18
1.25262422 1.06002246 0.36742071 -0.03257929 -0.11778281 -0.20298633
19 20 21 22 23 24
-0.29558809 -0.49558809 -0.69558809 -0.41778281 0.13782774 1.30823478
25 26 27 28 29 30
1.78604006 1.49343830 0.80823478 0.41563302 0.32303126 0.33042950
31 32 33 34 35 36
0.42303126 0.60823478 0.61563302 0.40083654 0.27124358 0.09343830
37 38 39 40 41 42
-0.11395994 -0.32875642 -0.03615466 0.05644709 -0.12135818 -0.49916346
43 44 45 46 47 48
-0.79916346 -0.92875642 -0.95095115 -0.97314587 -0.85095115 -0.56957050
49 50 51 52 53 54
-0.16217226 -0.19176522 0.14904885 0.02685413 -0.04355291 -0.37696874
55 56 57 58 59 60
-0.83257929 -1.21778281 -1.53257929 -1.75477402 -1.74737578 -1.41038457
> postscript(file="/var/www/html/rcomp/tmp/6njrg1258485936.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 1.58961543 NA
1 1.29701367 1.58961543
2 0.76742071 1.29701367
3 0.15262422 0.76742071
4 0.06002246 0.15262422
5 0.17481895 0.06002246
6 0.27481895 0.17481895
7 0.17481895 0.27481895
8 0.05262422 0.17481895
9 -0.14737578 0.05262422
10 0.32303126 -0.14737578
11 1.05262422 0.32303126
12 1.25262422 1.05262422
13 1.06002246 1.25262422
14 0.36742071 1.06002246
15 -0.03257929 0.36742071
16 -0.11778281 -0.03257929
17 -0.20298633 -0.11778281
18 -0.29558809 -0.20298633
19 -0.49558809 -0.29558809
20 -0.69558809 -0.49558809
21 -0.41778281 -0.69558809
22 0.13782774 -0.41778281
23 1.30823478 0.13782774
24 1.78604006 1.30823478
25 1.49343830 1.78604006
26 0.80823478 1.49343830
27 0.41563302 0.80823478
28 0.32303126 0.41563302
29 0.33042950 0.32303126
30 0.42303126 0.33042950
31 0.60823478 0.42303126
32 0.61563302 0.60823478
33 0.40083654 0.61563302
34 0.27124358 0.40083654
35 0.09343830 0.27124358
36 -0.11395994 0.09343830
37 -0.32875642 -0.11395994
38 -0.03615466 -0.32875642
39 0.05644709 -0.03615466
40 -0.12135818 0.05644709
41 -0.49916346 -0.12135818
42 -0.79916346 -0.49916346
43 -0.92875642 -0.79916346
44 -0.95095115 -0.92875642
45 -0.97314587 -0.95095115
46 -0.85095115 -0.97314587
47 -0.56957050 -0.85095115
48 -0.16217226 -0.56957050
49 -0.19176522 -0.16217226
50 0.14904885 -0.19176522
51 0.02685413 0.14904885
52 -0.04355291 0.02685413
53 -0.37696874 -0.04355291
54 -0.83257929 -0.37696874
55 -1.21778281 -0.83257929
56 -1.53257929 -1.21778281
57 -1.75477402 -1.53257929
58 -1.74737578 -1.75477402
59 -1.41038457 -1.74737578
60 NA -1.41038457
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.29701367 1.58961543
[2,] 0.76742071 1.29701367
[3,] 0.15262422 0.76742071
[4,] 0.06002246 0.15262422
[5,] 0.17481895 0.06002246
[6,] 0.27481895 0.17481895
[7,] 0.17481895 0.27481895
[8,] 0.05262422 0.17481895
[9,] -0.14737578 0.05262422
[10,] 0.32303126 -0.14737578
[11,] 1.05262422 0.32303126
[12,] 1.25262422 1.05262422
[13,] 1.06002246 1.25262422
[14,] 0.36742071 1.06002246
[15,] -0.03257929 0.36742071
[16,] -0.11778281 -0.03257929
[17,] -0.20298633 -0.11778281
[18,] -0.29558809 -0.20298633
[19,] -0.49558809 -0.29558809
[20,] -0.69558809 -0.49558809
[21,] -0.41778281 -0.69558809
[22,] 0.13782774 -0.41778281
[23,] 1.30823478 0.13782774
[24,] 1.78604006 1.30823478
[25,] 1.49343830 1.78604006
[26,] 0.80823478 1.49343830
[27,] 0.41563302 0.80823478
[28,] 0.32303126 0.41563302
[29,] 0.33042950 0.32303126
[30,] 0.42303126 0.33042950
[31,] 0.60823478 0.42303126
[32,] 0.61563302 0.60823478
[33,] 0.40083654 0.61563302
[34,] 0.27124358 0.40083654
[35,] 0.09343830 0.27124358
[36,] -0.11395994 0.09343830
[37,] -0.32875642 -0.11395994
[38,] -0.03615466 -0.32875642
[39,] 0.05644709 -0.03615466
[40,] -0.12135818 0.05644709
[41,] -0.49916346 -0.12135818
[42,] -0.79916346 -0.49916346
[43,] -0.92875642 -0.79916346
[44,] -0.95095115 -0.92875642
[45,] -0.97314587 -0.95095115
[46,] -0.85095115 -0.97314587
[47,] -0.56957050 -0.85095115
[48,] -0.16217226 -0.56957050
[49,] -0.19176522 -0.16217226
[50,] 0.14904885 -0.19176522
[51,] 0.02685413 0.14904885
[52,] -0.04355291 0.02685413
[53,] -0.37696874 -0.04355291
[54,] -0.83257929 -0.37696874
[55,] -1.21778281 -0.83257929
[56,] -1.53257929 -1.21778281
[57,] -1.75477402 -1.53257929
[58,] -1.74737578 -1.75477402
[59,] -1.41038457 -1.74737578
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.29701367 1.58961543
2 0.76742071 1.29701367
3 0.15262422 0.76742071
4 0.06002246 0.15262422
5 0.17481895 0.06002246
6 0.27481895 0.17481895
7 0.17481895 0.27481895
8 0.05262422 0.17481895
9 -0.14737578 0.05262422
10 0.32303126 -0.14737578
11 1.05262422 0.32303126
12 1.25262422 1.05262422
13 1.06002246 1.25262422
14 0.36742071 1.06002246
15 -0.03257929 0.36742071
16 -0.11778281 -0.03257929
17 -0.20298633 -0.11778281
18 -0.29558809 -0.20298633
19 -0.49558809 -0.29558809
20 -0.69558809 -0.49558809
21 -0.41778281 -0.69558809
22 0.13782774 -0.41778281
23 1.30823478 0.13782774
24 1.78604006 1.30823478
25 1.49343830 1.78604006
26 0.80823478 1.49343830
27 0.41563302 0.80823478
28 0.32303126 0.41563302
29 0.33042950 0.32303126
30 0.42303126 0.33042950
31 0.60823478 0.42303126
32 0.61563302 0.60823478
33 0.40083654 0.61563302
34 0.27124358 0.40083654
35 0.09343830 0.27124358
36 -0.11395994 0.09343830
37 -0.32875642 -0.11395994
38 -0.03615466 -0.32875642
39 0.05644709 -0.03615466
40 -0.12135818 0.05644709
41 -0.49916346 -0.12135818
42 -0.79916346 -0.49916346
43 -0.92875642 -0.79916346
44 -0.95095115 -0.92875642
45 -0.97314587 -0.95095115
46 -0.85095115 -0.97314587
47 -0.56957050 -0.85095115
48 -0.16217226 -0.56957050
49 -0.19176522 -0.16217226
50 0.14904885 -0.19176522
51 0.02685413 0.14904885
52 -0.04355291 0.02685413
53 -0.37696874 -0.04355291
54 -0.83257929 -0.37696874
55 -1.21778281 -0.83257929
56 -1.53257929 -1.21778281
57 -1.75477402 -1.53257929
58 -1.74737578 -1.75477402
59 -1.41038457 -1.74737578
> 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/74dz11258485936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ny9e1258485936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9u6mn1258485936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10b7oh1258485936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110ocv1258485936.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/12a9y41258485936.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/139g5d1258485936.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/14oiau1258485936.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/15ntqz1258485936.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/16cmgw1258485936.tab")
+ }
>
> system("convert tmp/179sh1258485936.ps tmp/179sh1258485936.png")
> system("convert tmp/20uy21258485936.ps tmp/20uy21258485936.png")
> system("convert tmp/32y441258485936.ps tmp/32y441258485936.png")
> system("convert tmp/4okjy1258485936.ps tmp/4okjy1258485936.png")
> system("convert tmp/5bvjb1258485936.ps tmp/5bvjb1258485936.png")
> system("convert tmp/6njrg1258485936.ps tmp/6njrg1258485936.png")
> system("convert tmp/74dz11258485936.ps tmp/74dz11258485936.png")
> system("convert tmp/8ny9e1258485936.ps tmp/8ny9e1258485936.png")
> system("convert tmp/9u6mn1258485936.ps tmp/9u6mn1258485936.png")
> system("convert tmp/10b7oh1258485936.ps tmp/10b7oh1258485936.png")
>
>
> proc.time()
user system elapsed
2.473 1.577 3.128