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(9.3,4,9.3,3.8,8.7,4.7,8.2,4.3,8.3,3.9,8.5,4,8.6,4.3,8.5,4.8,8.2,4.4,8.1,4.3,7.9,4.7,8.6,4.7,8.7,4.9,8.7,5,8.5,4.2,8.4,4.3,8.5,4.8,8.7,4.8,8.7,4.8,8.6,4.2,8.5,4.6,8.3,4.8,8,4.5,8.2,4.4,8.1,4.3,8.1,3.9,8,3.7,7.9,4,7.9,4.1,8,3.7,8,3.8,7.9,3.8,8,3.8,7.7,3.3,7.2,3.3,7.5,3.3,7.3,3.2,7,3.4,7,4.2,7,4.9,7.2,5.1,7.3,5.5,7.1,5.6,6.8,6.4,6.4,6.1,6.1,7.1,6.5,7.8,7.7,7.9,7.9,7.4,7.5,7.5,6.9,6.8,6.6,5.2,6.9,4.7,7.7,4.1,8,3.9,8,2.6,7.7,2.7,7.3,1.8,7.4,1,8.1,0.3),dim=c(2,60),dimnames=list(c('werklh','inflatie'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('werklh','inflatie'),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 = '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
werklh inflatie
1 9.3 4.0
2 9.3 3.8
3 8.7 4.7
4 8.2 4.3
5 8.3 3.9
6 8.5 4.0
7 8.6 4.3
8 8.5 4.8
9 8.2 4.4
10 8.1 4.3
11 7.9 4.7
12 8.6 4.7
13 8.7 4.9
14 8.7 5.0
15 8.5 4.2
16 8.4 4.3
17 8.5 4.8
18 8.7 4.8
19 8.7 4.8
20 8.6 4.2
21 8.5 4.6
22 8.3 4.8
23 8.0 4.5
24 8.2 4.4
25 8.1 4.3
26 8.1 3.9
27 8.0 3.7
28 7.9 4.0
29 7.9 4.1
30 8.0 3.7
31 8.0 3.8
32 7.9 3.8
33 8.0 3.8
34 7.7 3.3
35 7.2 3.3
36 7.5 3.3
37 7.3 3.2
38 7.0 3.4
39 7.0 4.2
40 7.0 4.9
41 7.2 5.1
42 7.3 5.5
43 7.1 5.6
44 6.8 6.4
45 6.4 6.1
46 6.1 7.1
47 6.5 7.8
48 7.7 7.9
49 7.9 7.4
50 7.5 7.5
51 6.9 6.8
52 6.6 5.2
53 6.9 4.7
54 7.7 4.1
55 8.0 3.9
56 8.0 2.6
57 7.7 2.7
58 7.3 1.8
59 7.4 1.0
60 8.1 0.3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) inflatie
8.4797 -0.1387
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.39512 -0.58012 0.05381 0.53121 1.37501
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.47966 0.29349 28.893 <2e-16 ***
inflatie -0.13867 0.06279 -2.209 0.0312 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6859 on 58 degrees of freedom
Multiple R-squared: 0.07758, Adjusted R-squared: 0.06167
F-statistic: 4.878 on 1 and 58 DF, p-value: 0.03117
> 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,] 0.48575926 0.97151852 0.51424074
[2,] 0.35912778 0.71825557 0.64087222
[3,] 0.23504527 0.47009053 0.76495473
[4,] 0.14744712 0.29489425 0.85255288
[5,] 0.11334299 0.22668598 0.88665701
[6,] 0.10042107 0.20084213 0.89957893
[7,] 0.07793098 0.15586196 0.92206902
[8,] 0.06180784 0.12361568 0.93819216
[9,] 0.06184099 0.12368198 0.93815901
[10,] 0.05890007 0.11780015 0.94109993
[11,] 0.04134744 0.08269488 0.95865256
[12,] 0.02922897 0.05845795 0.97077103
[13,] 0.02173890 0.04347781 0.97826110
[14,] 0.02230082 0.04460163 0.97769918
[15,] 0.02480115 0.04960230 0.97519885
[16,] 0.02331584 0.04663169 0.97668416
[17,] 0.02308021 0.04616041 0.97691979
[18,] 0.02278340 0.04556680 0.97721660
[19,] 0.03076707 0.06153413 0.96923293
[20,] 0.03367325 0.06734651 0.96632675
[21,] 0.04021594 0.08043188 0.95978406
[22,] 0.04817720 0.09635440 0.95182280
[23,] 0.05572522 0.11145044 0.94427478
[24,] 0.06580754 0.13161509 0.93419246
[25,] 0.07490773 0.14981546 0.92509227
[26,] 0.07352479 0.14704958 0.92647521
[27,] 0.07434835 0.14869671 0.92565165
[28,] 0.07511189 0.15022378 0.92488811
[29,] 0.07932373 0.15864747 0.92067627
[30,] 0.06909213 0.13818425 0.93090787
[31,] 0.09160604 0.18321208 0.90839396
[32,] 0.07350017 0.14700034 0.92649983
[33,] 0.06249588 0.12499176 0.93750412
[34,] 0.08868373 0.17736746 0.91131627
[35,] 0.18874920 0.37749840 0.81125080
[36,] 0.38331727 0.76663453 0.61668273
[37,] 0.46832878 0.93665755 0.53167122
[38,] 0.50481830 0.99036340 0.49518170
[39,] 0.52694134 0.94611731 0.47305866
[40,] 0.56138789 0.87722422 0.43861211
[41,] 0.68151295 0.63697410 0.31848705
[42,] 0.84650197 0.30699606 0.15349803
[43,] 0.87903927 0.24192145 0.12096073
[44,] 0.85849616 0.28300768 0.14150384
[45,] 0.89402722 0.21194557 0.10597278
[46,] 0.89548495 0.20903009 0.10451505
[47,] 0.83093137 0.33813726 0.16906863
[48,] 0.87963939 0.24072122 0.12036061
[49,] 0.94207946 0.11584109 0.05792054
[50,] 0.87756149 0.24487702 0.12243851
[51,] 0.77876226 0.44247548 0.22123774
> postscript(file="/var/www/html/rcomp/tmp/1nqvr1261057648.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2a49p1261057648.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3n5ty1261057648.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pue81261057648.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59wns1261057648.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
1.37500824 1.34727462 0.87207590 0.31660867 0.36114143 0.57500824
7 8 9 10 11 12
0.71660867 0.68594271 0.33047547 0.21660867 0.07207590 0.77207590
13 14 15 16 17 18
0.89980952 0.91367633 0.60274186 0.51660867 0.68594271 0.88594271
19 20 21 22 23 24
0.88594271 0.70274186 0.65820909 0.48594271 0.14434228 0.33047547
25 26 27 28 29 30
0.21660867 0.16114143 0.03340781 -0.02499176 -0.01112495 0.03340781
31 32 33 34 35 36
0.04727462 -0.05272538 0.04727462 -0.32205942 -0.82205942 -0.52205942
37 38 39 40 41 42
-0.73592623 -1.00819261 -0.89725814 -0.80019048 -0.57245686 -0.41698963
43 44 45 46 47 48
-0.60312282 -0.79218835 -1.23378877 -1.39512068 -0.89805302 0.31581379
49 50 51 52 53 54
0.44647974 0.06034655 -0.63672111 -1.15859005 -0.92792410 -0.21112495
55 56 57 58 59 60
0.06114143 -0.11912709 -0.40526028 -0.93006156 -0.94099603 -0.33806369
> postscript(file="/var/www/html/rcomp/tmp/6wm0w1261057648.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 1.37500824 NA
1 1.34727462 1.37500824
2 0.87207590 1.34727462
3 0.31660867 0.87207590
4 0.36114143 0.31660867
5 0.57500824 0.36114143
6 0.71660867 0.57500824
7 0.68594271 0.71660867
8 0.33047547 0.68594271
9 0.21660867 0.33047547
10 0.07207590 0.21660867
11 0.77207590 0.07207590
12 0.89980952 0.77207590
13 0.91367633 0.89980952
14 0.60274186 0.91367633
15 0.51660867 0.60274186
16 0.68594271 0.51660867
17 0.88594271 0.68594271
18 0.88594271 0.88594271
19 0.70274186 0.88594271
20 0.65820909 0.70274186
21 0.48594271 0.65820909
22 0.14434228 0.48594271
23 0.33047547 0.14434228
24 0.21660867 0.33047547
25 0.16114143 0.21660867
26 0.03340781 0.16114143
27 -0.02499176 0.03340781
28 -0.01112495 -0.02499176
29 0.03340781 -0.01112495
30 0.04727462 0.03340781
31 -0.05272538 0.04727462
32 0.04727462 -0.05272538
33 -0.32205942 0.04727462
34 -0.82205942 -0.32205942
35 -0.52205942 -0.82205942
36 -0.73592623 -0.52205942
37 -1.00819261 -0.73592623
38 -0.89725814 -1.00819261
39 -0.80019048 -0.89725814
40 -0.57245686 -0.80019048
41 -0.41698963 -0.57245686
42 -0.60312282 -0.41698963
43 -0.79218835 -0.60312282
44 -1.23378877 -0.79218835
45 -1.39512068 -1.23378877
46 -0.89805302 -1.39512068
47 0.31581379 -0.89805302
48 0.44647974 0.31581379
49 0.06034655 0.44647974
50 -0.63672111 0.06034655
51 -1.15859005 -0.63672111
52 -0.92792410 -1.15859005
53 -0.21112495 -0.92792410
54 0.06114143 -0.21112495
55 -0.11912709 0.06114143
56 -0.40526028 -0.11912709
57 -0.93006156 -0.40526028
58 -0.94099603 -0.93006156
59 -0.33806369 -0.94099603
60 NA -0.33806369
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.34727462 1.37500824
[2,] 0.87207590 1.34727462
[3,] 0.31660867 0.87207590
[4,] 0.36114143 0.31660867
[5,] 0.57500824 0.36114143
[6,] 0.71660867 0.57500824
[7,] 0.68594271 0.71660867
[8,] 0.33047547 0.68594271
[9,] 0.21660867 0.33047547
[10,] 0.07207590 0.21660867
[11,] 0.77207590 0.07207590
[12,] 0.89980952 0.77207590
[13,] 0.91367633 0.89980952
[14,] 0.60274186 0.91367633
[15,] 0.51660867 0.60274186
[16,] 0.68594271 0.51660867
[17,] 0.88594271 0.68594271
[18,] 0.88594271 0.88594271
[19,] 0.70274186 0.88594271
[20,] 0.65820909 0.70274186
[21,] 0.48594271 0.65820909
[22,] 0.14434228 0.48594271
[23,] 0.33047547 0.14434228
[24,] 0.21660867 0.33047547
[25,] 0.16114143 0.21660867
[26,] 0.03340781 0.16114143
[27,] -0.02499176 0.03340781
[28,] -0.01112495 -0.02499176
[29,] 0.03340781 -0.01112495
[30,] 0.04727462 0.03340781
[31,] -0.05272538 0.04727462
[32,] 0.04727462 -0.05272538
[33,] -0.32205942 0.04727462
[34,] -0.82205942 -0.32205942
[35,] -0.52205942 -0.82205942
[36,] -0.73592623 -0.52205942
[37,] -1.00819261 -0.73592623
[38,] -0.89725814 -1.00819261
[39,] -0.80019048 -0.89725814
[40,] -0.57245686 -0.80019048
[41,] -0.41698963 -0.57245686
[42,] -0.60312282 -0.41698963
[43,] -0.79218835 -0.60312282
[44,] -1.23378877 -0.79218835
[45,] -1.39512068 -1.23378877
[46,] -0.89805302 -1.39512068
[47,] 0.31581379 -0.89805302
[48,] 0.44647974 0.31581379
[49,] 0.06034655 0.44647974
[50,] -0.63672111 0.06034655
[51,] -1.15859005 -0.63672111
[52,] -0.92792410 -1.15859005
[53,] -0.21112495 -0.92792410
[54,] 0.06114143 -0.21112495
[55,] -0.11912709 0.06114143
[56,] -0.40526028 -0.11912709
[57,] -0.93006156 -0.40526028
[58,] -0.94099603 -0.93006156
[59,] -0.33806369 -0.94099603
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.34727462 1.37500824
2 0.87207590 1.34727462
3 0.31660867 0.87207590
4 0.36114143 0.31660867
5 0.57500824 0.36114143
6 0.71660867 0.57500824
7 0.68594271 0.71660867
8 0.33047547 0.68594271
9 0.21660867 0.33047547
10 0.07207590 0.21660867
11 0.77207590 0.07207590
12 0.89980952 0.77207590
13 0.91367633 0.89980952
14 0.60274186 0.91367633
15 0.51660867 0.60274186
16 0.68594271 0.51660867
17 0.88594271 0.68594271
18 0.88594271 0.88594271
19 0.70274186 0.88594271
20 0.65820909 0.70274186
21 0.48594271 0.65820909
22 0.14434228 0.48594271
23 0.33047547 0.14434228
24 0.21660867 0.33047547
25 0.16114143 0.21660867
26 0.03340781 0.16114143
27 -0.02499176 0.03340781
28 -0.01112495 -0.02499176
29 0.03340781 -0.01112495
30 0.04727462 0.03340781
31 -0.05272538 0.04727462
32 0.04727462 -0.05272538
33 -0.32205942 0.04727462
34 -0.82205942 -0.32205942
35 -0.52205942 -0.82205942
36 -0.73592623 -0.52205942
37 -1.00819261 -0.73592623
38 -0.89725814 -1.00819261
39 -0.80019048 -0.89725814
40 -0.57245686 -0.80019048
41 -0.41698963 -0.57245686
42 -0.60312282 -0.41698963
43 -0.79218835 -0.60312282
44 -1.23378877 -0.79218835
45 -1.39512068 -1.23378877
46 -0.89805302 -1.39512068
47 0.31581379 -0.89805302
48 0.44647974 0.31581379
49 0.06034655 0.44647974
50 -0.63672111 0.06034655
51 -1.15859005 -0.63672111
52 -0.92792410 -1.15859005
53 -0.21112495 -0.92792410
54 0.06114143 -0.21112495
55 -0.11912709 0.06114143
56 -0.40526028 -0.11912709
57 -0.93006156 -0.40526028
58 -0.94099603 -0.93006156
59 -0.33806369 -0.94099603
> 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/71dcd1261057648.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8apey1261057648.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9niah1261057648.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hhpi1261057648.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kje81261057648.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/12619e1261057648.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/13t1h41261057648.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/14ey3k1261057648.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/156eai1261057648.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/161cgz1261057649.tab")
+ }
>
> try(system("convert tmp/1nqvr1261057648.ps tmp/1nqvr1261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a49p1261057648.ps tmp/2a49p1261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/3n5ty1261057648.ps tmp/3n5ty1261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pue81261057648.ps tmp/4pue81261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/59wns1261057648.ps tmp/59wns1261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wm0w1261057648.ps tmp/6wm0w1261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/71dcd1261057648.ps tmp/71dcd1261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/8apey1261057648.ps tmp/8apey1261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/9niah1261057648.ps tmp/9niah1261057648.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hhpi1261057648.ps tmp/10hhpi1261057648.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.522 1.610 3.971