R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(309365,159129,308347,157928,298427,147768,289231,137507,291975,136919,294912,136151,293488,133001,290555,125554,284736,119647,281818,114158,287854,116193,316263,152803,325412,161761,326011,160942,328282,149470,317480,139208,317539,134588,313737,130322,312276,126611,309391,122401,302950,117352,300316,112135,304035,112879,333476,148729,337698,157230,335932,157221,323931,146681,313927,136524,314485,132111,313218,125326,309664,122716,302963,116615,298989,113719,298423,110737,301631,112093,329765,143565,335083,149946,327616,149147,309119,134339,295916,122683,291413,115614,291542,116566,284678,111272,276475,104609,272566,101802,264981,94542,263290,93051,296806,124129,303598,130374,286994,123946,276427,114971,266424,105531,267153,104919,268381,104782,262522,101281,255542,94545,253158,93248,243803,84031,250741,87486,280445,115867,285257,120327),dim=c(2,61),dimnames=list(c('vrouwen','jonger_dan_25'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('vrouwen','jonger_dan_25'),1:61))
> 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
vrouwen jonger_dan_25
1 309365 159129
2 308347 157928
3 298427 147768
4 289231 137507
5 291975 136919
6 294912 136151
7 293488 133001
8 290555 125554
9 284736 119647
10 281818 114158
11 287854 116193
12 316263 152803
13 325412 161761
14 326011 160942
15 328282 149470
16 317480 139208
17 317539 134588
18 313737 130322
19 312276 126611
20 309391 122401
21 302950 117352
22 300316 112135
23 304035 112879
24 333476 148729
25 337698 157230
26 335932 157221
27 323931 146681
28 313927 136524
29 314485 132111
30 313218 125326
31 309664 122716
32 302963 116615
33 298989 113719
34 298423 110737
35 301631 112093
36 329765 143565
37 335083 149946
38 327616 149147
39 309119 134339
40 295916 122683
41 291413 115614
42 291542 116566
43 284678 111272
44 276475 104609
45 272566 101802
46 264981 94542
47 263290 93051
48 296806 124129
49 303598 130374
50 286994 123946
51 276427 114971
52 266424 105531
53 267153 104919
54 268381 104782
55 262522 101281
56 255542 94545
57 253158 93248
58 243803 84031
59 250741 87486
60 280445 115867
61 285257 120327
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) jonger_dan_25
170903.75 1.01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-22266.0 -8978.5 526.5 10695.5 19118.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.709e+05 9.386e+03 18.21 <2e-16 ***
jonger_dan_25 1.010e+00 7.415e-02 13.62 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11480 on 59 degrees of freedom
Multiple R-squared: 0.7588, Adjusted R-squared: 0.7547
F-statistic: 185.6 on 1 and 59 DF, p-value: < 2.2e-16
> 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,] 0.0047574378 9.514876e-03 9.952426e-01
[2,] 0.0098965195 1.979304e-02 9.901035e-01
[3,] 0.0070987049 1.419741e-02 9.929013e-01
[4,] 0.0053717814 1.074356e-02 9.946282e-01
[5,] 0.0017305390 3.461078e-03 9.982695e-01
[6,] 0.0005112163 1.022433e-03 9.994888e-01
[7,] 0.0005150512 1.030102e-03 9.994849e-01
[8,] 0.0126086675 2.521734e-02 9.873913e-01
[9,] 0.0657121538 1.314243e-01 9.342878e-01
[10,] 0.1471933670 2.943867e-01 8.528066e-01
[11,] 0.4843062064 9.686124e-01 5.156938e-01
[12,] 0.6304400314 7.391199e-01 3.695600e-01
[13,] 0.7791678639 4.416643e-01 2.208321e-01
[14,] 0.8460030485 3.079939e-01 1.539970e-01
[15,] 0.8939369468 2.121261e-01 1.060631e-01
[16,] 0.9233328920 1.533342e-01 7.666711e-02
[17,] 0.9289335356 1.421329e-01 7.106646e-02
[18,] 0.9459037191 1.081926e-01 5.409628e-02
[19,] 0.9735701187 5.285976e-02 2.642988e-02
[20,] 0.9846219217 3.075616e-02 1.537808e-02
[21,] 0.9884890597 2.302188e-02 1.151094e-02
[22,] 0.9895241456 2.095171e-02 1.047585e-02
[23,] 0.9868407569 2.631849e-02 1.315924e-02
[24,] 0.9804845542 3.903089e-02 1.951545e-02
[25,] 0.9740065094 5.198698e-02 2.599349e-02
[26,] 0.9782519392 4.349612e-02 2.174806e-02
[27,] 0.9814198424 3.716032e-02 1.858016e-02
[28,] 0.9861720925 2.765582e-02 1.382791e-02
[29,] 0.9906264513 1.874710e-02 9.373549e-03
[30,] 0.9975227499 4.954500e-03 2.477250e-03
[31,] 0.9999048787 1.902426e-04 9.512131e-05
[32,] 0.9999473622 1.052755e-04 5.263775e-05
[33,] 0.9999639707 7.205851e-05 3.602926e-05
[34,] 0.9999271637 1.456726e-04 7.283629e-05
[35,] 0.9998376751 3.246497e-04 1.623249e-04
[36,] 0.9996981416 6.037168e-04 3.018584e-04
[37,] 0.9997243168 5.513664e-04 2.756832e-04
[38,] 0.9997410279 5.179441e-04 2.589721e-04
[39,] 0.9997667435 4.665131e-04 2.332565e-04
[40,] 0.9998005121 3.989758e-04 1.994879e-04
[41,] 0.9998250728 3.498545e-04 1.749272e-04
[42,] 0.9999165595 1.668810e-04 8.344051e-05
[43,] 0.9999883888 2.322249e-05 1.161124e-05
[44,] 0.9999942387 1.152266e-05 5.761329e-06
[45,] 0.9999999535 9.301827e-08 4.650914e-08
[46,] 0.9999997247 5.505494e-07 2.752747e-07
[47,] 0.9999992827 1.434599e-06 7.172994e-07
[48,] 0.9999978834 4.233114e-06 2.116557e-06
[49,] 0.9999841812 3.163768e-05 1.581884e-05
[50,] 0.9998887200 2.225600e-04 1.112800e-04
[51,] 0.9993393220 1.321356e-03 6.606780e-04
[52,] 0.9953839104 9.232179e-03 4.616090e-03
> postscript(file="/var/www/html/rcomp/tmp/1ulpv1227714922.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ryi71227714922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xq9m1227714922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lbqj1227714922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fs6q1227714922.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 = 61
Frequency = 1
1 2 3 4 5 6
-22266.0132 -22070.9506 -21728.9055 -20560.8459 -17222.9402 -13510.2265
7 8 9 10 11 12
-11752.5885 -7163.7922 -7016.4634 -4390.3330 -409.7721 -8978.4761
13 14 15 16 17 18
-8877.4485 -7451.2226 6407.0000 5970.0696 10695.4720 11202.3189
19 20 21 22 23 24
13489.5915 14856.8759 13515.5871 16150.9857 19118.5131 12349.4424
25 26 27 28 29 30
7985.0600 6228.1504 4873.0122 5128.0272 10143.3505 15729.4978
31 32 33 34 35 36
14811.7121 14272.9894 13224.0763 15670.0269 17508.4075 13854.3087
37 38 39 40 41 42
12727.2191 6067.2441 2526.9729 1097.0436 3734.0433 2901.4816
43 44 45 46 47 48
1384.6535 -88.4246 -1162.2316 -1414.3135 -1599.3382 526.5202
49 50 51 52 53 54
1010.7966 -9100.6418 -10602.4986 -11070.6850 -9723.5382 -8357.1622
55 56 57 58 59 60
-10679.9988 -10856.3437 -11930.3168 -11975.7430 -8527.4444 -7489.4978
61
-7182.2932
> postscript(file="/var/www/html/rcomp/tmp/68ub31227714922.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -22266.0132 NA
1 -22070.9506 -22266.0132
2 -21728.9055 -22070.9506
3 -20560.8459 -21728.9055
4 -17222.9402 -20560.8459
5 -13510.2265 -17222.9402
6 -11752.5885 -13510.2265
7 -7163.7922 -11752.5885
8 -7016.4634 -7163.7922
9 -4390.3330 -7016.4634
10 -409.7721 -4390.3330
11 -8978.4761 -409.7721
12 -8877.4485 -8978.4761
13 -7451.2226 -8877.4485
14 6407.0000 -7451.2226
15 5970.0696 6407.0000
16 10695.4720 5970.0696
17 11202.3189 10695.4720
18 13489.5915 11202.3189
19 14856.8759 13489.5915
20 13515.5871 14856.8759
21 16150.9857 13515.5871
22 19118.5131 16150.9857
23 12349.4424 19118.5131
24 7985.0600 12349.4424
25 6228.1504 7985.0600
26 4873.0122 6228.1504
27 5128.0272 4873.0122
28 10143.3505 5128.0272
29 15729.4978 10143.3505
30 14811.7121 15729.4978
31 14272.9894 14811.7121
32 13224.0763 14272.9894
33 15670.0269 13224.0763
34 17508.4075 15670.0269
35 13854.3087 17508.4075
36 12727.2191 13854.3087
37 6067.2441 12727.2191
38 2526.9729 6067.2441
39 1097.0436 2526.9729
40 3734.0433 1097.0436
41 2901.4816 3734.0433
42 1384.6535 2901.4816
43 -88.4246 1384.6535
44 -1162.2316 -88.4246
45 -1414.3135 -1162.2316
46 -1599.3382 -1414.3135
47 526.5202 -1599.3382
48 1010.7966 526.5202
49 -9100.6418 1010.7966
50 -10602.4986 -9100.6418
51 -11070.6850 -10602.4986
52 -9723.5382 -11070.6850
53 -8357.1622 -9723.5382
54 -10679.9988 -8357.1622
55 -10856.3437 -10679.9988
56 -11930.3168 -10856.3437
57 -11975.7430 -11930.3168
58 -8527.4444 -11975.7430
59 -7489.4978 -8527.4444
60 -7182.2932 -7489.4978
61 NA -7182.2932
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -22070.9506 -22266.0132
[2,] -21728.9055 -22070.9506
[3,] -20560.8459 -21728.9055
[4,] -17222.9402 -20560.8459
[5,] -13510.2265 -17222.9402
[6,] -11752.5885 -13510.2265
[7,] -7163.7922 -11752.5885
[8,] -7016.4634 -7163.7922
[9,] -4390.3330 -7016.4634
[10,] -409.7721 -4390.3330
[11,] -8978.4761 -409.7721
[12,] -8877.4485 -8978.4761
[13,] -7451.2226 -8877.4485
[14,] 6407.0000 -7451.2226
[15,] 5970.0696 6407.0000
[16,] 10695.4720 5970.0696
[17,] 11202.3189 10695.4720
[18,] 13489.5915 11202.3189
[19,] 14856.8759 13489.5915
[20,] 13515.5871 14856.8759
[21,] 16150.9857 13515.5871
[22,] 19118.5131 16150.9857
[23,] 12349.4424 19118.5131
[24,] 7985.0600 12349.4424
[25,] 6228.1504 7985.0600
[26,] 4873.0122 6228.1504
[27,] 5128.0272 4873.0122
[28,] 10143.3505 5128.0272
[29,] 15729.4978 10143.3505
[30,] 14811.7121 15729.4978
[31,] 14272.9894 14811.7121
[32,] 13224.0763 14272.9894
[33,] 15670.0269 13224.0763
[34,] 17508.4075 15670.0269
[35,] 13854.3087 17508.4075
[36,] 12727.2191 13854.3087
[37,] 6067.2441 12727.2191
[38,] 2526.9729 6067.2441
[39,] 1097.0436 2526.9729
[40,] 3734.0433 1097.0436
[41,] 2901.4816 3734.0433
[42,] 1384.6535 2901.4816
[43,] -88.4246 1384.6535
[44,] -1162.2316 -88.4246
[45,] -1414.3135 -1162.2316
[46,] -1599.3382 -1414.3135
[47,] 526.5202 -1599.3382
[48,] 1010.7966 526.5202
[49,] -9100.6418 1010.7966
[50,] -10602.4986 -9100.6418
[51,] -11070.6850 -10602.4986
[52,] -9723.5382 -11070.6850
[53,] -8357.1622 -9723.5382
[54,] -10679.9988 -8357.1622
[55,] -10856.3437 -10679.9988
[56,] -11930.3168 -10856.3437
[57,] -11975.7430 -11930.3168
[58,] -8527.4444 -11975.7430
[59,] -7489.4978 -8527.4444
[60,] -7182.2932 -7489.4978
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -22070.9506 -22266.0132
2 -21728.9055 -22070.9506
3 -20560.8459 -21728.9055
4 -17222.9402 -20560.8459
5 -13510.2265 -17222.9402
6 -11752.5885 -13510.2265
7 -7163.7922 -11752.5885
8 -7016.4634 -7163.7922
9 -4390.3330 -7016.4634
10 -409.7721 -4390.3330
11 -8978.4761 -409.7721
12 -8877.4485 -8978.4761
13 -7451.2226 -8877.4485
14 6407.0000 -7451.2226
15 5970.0696 6407.0000
16 10695.4720 5970.0696
17 11202.3189 10695.4720
18 13489.5915 11202.3189
19 14856.8759 13489.5915
20 13515.5871 14856.8759
21 16150.9857 13515.5871
22 19118.5131 16150.9857
23 12349.4424 19118.5131
24 7985.0600 12349.4424
25 6228.1504 7985.0600
26 4873.0122 6228.1504
27 5128.0272 4873.0122
28 10143.3505 5128.0272
29 15729.4978 10143.3505
30 14811.7121 15729.4978
31 14272.9894 14811.7121
32 13224.0763 14272.9894
33 15670.0269 13224.0763
34 17508.4075 15670.0269
35 13854.3087 17508.4075
36 12727.2191 13854.3087
37 6067.2441 12727.2191
38 2526.9729 6067.2441
39 1097.0436 2526.9729
40 3734.0433 1097.0436
41 2901.4816 3734.0433
42 1384.6535 2901.4816
43 -88.4246 1384.6535
44 -1162.2316 -88.4246
45 -1414.3135 -1162.2316
46 -1599.3382 -1414.3135
47 526.5202 -1599.3382
48 1010.7966 526.5202
49 -9100.6418 1010.7966
50 -10602.4986 -9100.6418
51 -11070.6850 -10602.4986
52 -9723.5382 -11070.6850
53 -8357.1622 -9723.5382
54 -10679.9988 -8357.1622
55 -10856.3437 -10679.9988
56 -11930.3168 -10856.3437
57 -11975.7430 -11930.3168
58 -8527.4444 -11975.7430
59 -7489.4978 -8527.4444
60 -7182.2932 -7489.4978
> 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/71yhk1227714922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8a9ao1227714922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/988l91227714922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dqg81227714922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ixt61227714923.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/12ns761227714923.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/1358pb1227714923.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/1440xv1227714923.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/154aln1227714923.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/16z2011227714923.tab")
+ }
>
> system("convert tmp/1ulpv1227714922.ps tmp/1ulpv1227714922.png")
> system("convert tmp/2ryi71227714922.ps tmp/2ryi71227714922.png")
> system("convert tmp/3xq9m1227714922.ps tmp/3xq9m1227714922.png")
> system("convert tmp/4lbqj1227714922.ps tmp/4lbqj1227714922.png")
> system("convert tmp/5fs6q1227714922.ps tmp/5fs6q1227714922.png")
> system("convert tmp/68ub31227714922.ps tmp/68ub31227714922.png")
> system("convert tmp/71yhk1227714922.ps tmp/71yhk1227714922.png")
> system("convert tmp/8a9ao1227714922.ps tmp/8a9ao1227714922.png")
> system("convert tmp/988l91227714922.ps tmp/988l91227714922.png")
> system("convert tmp/10dqg81227714922.ps tmp/10dqg81227714922.png")
>
>
> proc.time()
user system elapsed
2.467 1.566 4.859