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(5560,543,3922,594,3759,611,4138,613,4634,611,3996,594,4308,595,4143,591,4429,589,5219,584,4929,573,5755,567,5592,569,4163,621,4962,629,5208,628,4755,612,4491,595,5732,597,5731,593,5040,590,6102,580,4904,574,5369,573,5578,573,4619,620,4731,626,5011,620,5299,588,4146,566,4625,557,4736,561,4219,549,5116,532,4205,526,4121,511,5103,499,4300,555,4578,565,3809,542,5526,527,4247,510,3830,514,4394,517,4826,508,4409,493,4569,490,4106,469,4794,478,3914,528,3793,534,4405,518,4022,506,4100,502,4788,516,3163,528,3585,533,3903,536,4178,537,3863,524,4187,536),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),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'
> 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 5560 543
2 3922 594
3 3759 611
4 4138 613
5 4634 611
6 3996 594
7 4308 595
8 4143 591
9 4429 589
10 5219 584
11 4929 573
12 5755 567
13 5592 569
14 4163 621
15 4962 629
16 5208 628
17 4755 612
18 4491 595
19 5732 597
20 5731 593
21 5040 590
22 6102 580
23 4904 574
24 5369 573
25 5578 573
26 4619 620
27 4731 626
28 5011 620
29 5299 588
30 4146 566
31 4625 557
32 4736 561
33 4219 549
34 5116 532
35 4205 526
36 4121 511
37 5103 499
38 4300 555
39 4578 565
40 3809 542
41 5526 527
42 4247 510
43 3830 514
44 4394 517
45 4826 508
46 4409 493
47 4569 490
48 4106 469
49 4794 478
50 3914 528
51 3793 534
52 4405 518
53 4022 506
54 4100 502
55 4788 516
56 3163 528
57 3585 533
58 3903 536
59 4178 537
60 3863 524
61 4187 536
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
2311.026 4.076
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1300.2 -428.3 -116.7 373.7 1426.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2311.026 1048.195 2.205 0.0314 *
X 4.076 1.874 2.175 0.0337 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 614.6 on 59 degrees of freedom
Multiple R-squared: 0.07422, Adjusted R-squared: 0.05853
F-statistic: 4.73 on 1 and 59 DF, p-value: 0.03366
> 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,] 0.41726113 0.83452226 0.58273887
[2,] 0.34094066 0.68188131 0.65905934
[3,] 0.21437009 0.42874017 0.78562991
[4,] 0.14951261 0.29902521 0.85048739
[5,] 0.08564288 0.17128577 0.91435712
[6,] 0.13802951 0.27605902 0.86197049
[7,] 0.08441715 0.16883430 0.91558285
[8,] 0.15221748 0.30443496 0.84778252
[9,] 0.16270648 0.32541295 0.83729352
[10,] 0.15906221 0.31812442 0.84093779
[11,] 0.39932968 0.79865936 0.60067032
[12,] 0.57141966 0.85716068 0.42858034
[13,] 0.50782507 0.98434985 0.49217493
[14,] 0.44217369 0.88434738 0.55782631
[15,] 0.59643119 0.80713763 0.40356881
[16,] 0.70322583 0.59354834 0.29677417
[17,] 0.63960485 0.72079030 0.36039515
[18,] 0.84314038 0.31371924 0.15685962
[19,] 0.80298593 0.39402813 0.19701407
[20,] 0.79946592 0.40106816 0.20053408
[21,] 0.84671729 0.30656542 0.15328271
[22,] 0.79910461 0.40179078 0.20089539
[23,] 0.75284492 0.49431016 0.24715508
[24,] 0.73720777 0.52558447 0.26279223
[25,] 0.79306928 0.41386144 0.20693072
[26,] 0.83215860 0.33568281 0.16784140
[27,] 0.82432106 0.35135789 0.17567894
[28,] 0.82013355 0.35973290 0.17986645
[29,] 0.82613572 0.34772855 0.17386428
[30,] 0.86230443 0.27539115 0.13769557
[31,] 0.86029890 0.27940220 0.13970110
[32,] 0.85308071 0.29383858 0.14691929
[33,] 0.86824463 0.26351073 0.13175537
[34,] 0.84209900 0.31580201 0.15790100
[35,] 0.85243633 0.29512735 0.14756367
[36,] 0.84752898 0.30494205 0.15247102
[37,] 0.98715341 0.02569318 0.01284659
[38,] 0.98013458 0.03973084 0.01986542
[39,] 0.97850950 0.04298099 0.02149050
[40,] 0.96857428 0.06285143 0.03142572
[41,] 0.97580664 0.04838672 0.02419336
[42,] 0.95916247 0.08167505 0.04083753
[43,] 0.93878268 0.12243465 0.06121732
[44,] 0.94190504 0.11618992 0.05809496
[45,] 0.90964706 0.18070589 0.09035294
[46,] 0.86676102 0.26647795 0.13323898
[47,] 0.81442789 0.37114421 0.18557211
[48,] 0.76147222 0.47705556 0.23852778
[49,] 0.66494814 0.67010372 0.33505186
[50,] 0.56163509 0.87672982 0.43836491
[51,] 0.82401094 0.35197813 0.17598906
[52,] 0.90835715 0.18328570 0.09164285
> postscript(file="/var/www/html/rcomp/tmp/1vyyk1258980468.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/2it2t1258980468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/32qgc1258980468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4au3e1258980468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55hpe1258980468.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1035.69173 -810.18560 -1042.47804 -671.63009 -167.47804 -736.18560
7 8 9 10 11 12
-428.26163 -576.95752 -282.80547 527.57466 282.41095 1132.86710
13 14 15 16 17 18
961.71505 -679.23830 87.15349 337.22952 -50.55407 -245.26163
19 20 21 22 23 24
987.58632 1002.89043 324.11850 1426.87876 253.33492 722.41095
25 26 27 28 29 30
931.41095 -219.16228 -131.61843 172.83772 591.27056 -472.05687
31 32 33 34 35 36
43.62736 138.32326 -329.76443 636.52801 -250.01583 -272.87544
37 38 39 40 41 42
758.03687 -273.22059 -35.98085 -711.23225 1066.90814 -142.79942
43 44 45 46 47 48
-576.10352 -24.33160 444.35264 88.49303 260.72110 -116.68235
49 50 51 52 53 54
534.63342 -549.16788 -694.62404 -17.40762 -351.49531 -257.19121
55 56 57 58 59 60
373.74443 -1300.16788 -898.54801 -592.77609 -321.85212 -583.86378
61
-308.77609
> postscript(file="/var/www/html/rcomp/tmp/6t8tk1258980468.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 1035.69173 NA
1 -810.18560 1035.69173
2 -1042.47804 -810.18560
3 -671.63009 -1042.47804
4 -167.47804 -671.63009
5 -736.18560 -167.47804
6 -428.26163 -736.18560
7 -576.95752 -428.26163
8 -282.80547 -576.95752
9 527.57466 -282.80547
10 282.41095 527.57466
11 1132.86710 282.41095
12 961.71505 1132.86710
13 -679.23830 961.71505
14 87.15349 -679.23830
15 337.22952 87.15349
16 -50.55407 337.22952
17 -245.26163 -50.55407
18 987.58632 -245.26163
19 1002.89043 987.58632
20 324.11850 1002.89043
21 1426.87876 324.11850
22 253.33492 1426.87876
23 722.41095 253.33492
24 931.41095 722.41095
25 -219.16228 931.41095
26 -131.61843 -219.16228
27 172.83772 -131.61843
28 591.27056 172.83772
29 -472.05687 591.27056
30 43.62736 -472.05687
31 138.32326 43.62736
32 -329.76443 138.32326
33 636.52801 -329.76443
34 -250.01583 636.52801
35 -272.87544 -250.01583
36 758.03687 -272.87544
37 -273.22059 758.03687
38 -35.98085 -273.22059
39 -711.23225 -35.98085
40 1066.90814 -711.23225
41 -142.79942 1066.90814
42 -576.10352 -142.79942
43 -24.33160 -576.10352
44 444.35264 -24.33160
45 88.49303 444.35264
46 260.72110 88.49303
47 -116.68235 260.72110
48 534.63342 -116.68235
49 -549.16788 534.63342
50 -694.62404 -549.16788
51 -17.40762 -694.62404
52 -351.49531 -17.40762
53 -257.19121 -351.49531
54 373.74443 -257.19121
55 -1300.16788 373.74443
56 -898.54801 -1300.16788
57 -592.77609 -898.54801
58 -321.85212 -592.77609
59 -583.86378 -321.85212
60 -308.77609 -583.86378
61 NA -308.77609
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -810.18560 1035.69173
[2,] -1042.47804 -810.18560
[3,] -671.63009 -1042.47804
[4,] -167.47804 -671.63009
[5,] -736.18560 -167.47804
[6,] -428.26163 -736.18560
[7,] -576.95752 -428.26163
[8,] -282.80547 -576.95752
[9,] 527.57466 -282.80547
[10,] 282.41095 527.57466
[11,] 1132.86710 282.41095
[12,] 961.71505 1132.86710
[13,] -679.23830 961.71505
[14,] 87.15349 -679.23830
[15,] 337.22952 87.15349
[16,] -50.55407 337.22952
[17,] -245.26163 -50.55407
[18,] 987.58632 -245.26163
[19,] 1002.89043 987.58632
[20,] 324.11850 1002.89043
[21,] 1426.87876 324.11850
[22,] 253.33492 1426.87876
[23,] 722.41095 253.33492
[24,] 931.41095 722.41095
[25,] -219.16228 931.41095
[26,] -131.61843 -219.16228
[27,] 172.83772 -131.61843
[28,] 591.27056 172.83772
[29,] -472.05687 591.27056
[30,] 43.62736 -472.05687
[31,] 138.32326 43.62736
[32,] -329.76443 138.32326
[33,] 636.52801 -329.76443
[34,] -250.01583 636.52801
[35,] -272.87544 -250.01583
[36,] 758.03687 -272.87544
[37,] -273.22059 758.03687
[38,] -35.98085 -273.22059
[39,] -711.23225 -35.98085
[40,] 1066.90814 -711.23225
[41,] -142.79942 1066.90814
[42,] -576.10352 -142.79942
[43,] -24.33160 -576.10352
[44,] 444.35264 -24.33160
[45,] 88.49303 444.35264
[46,] 260.72110 88.49303
[47,] -116.68235 260.72110
[48,] 534.63342 -116.68235
[49,] -549.16788 534.63342
[50,] -694.62404 -549.16788
[51,] -17.40762 -694.62404
[52,] -351.49531 -17.40762
[53,] -257.19121 -351.49531
[54,] 373.74443 -257.19121
[55,] -1300.16788 373.74443
[56,] -898.54801 -1300.16788
[57,] -592.77609 -898.54801
[58,] -321.85212 -592.77609
[59,] -583.86378 -321.85212
[60,] -308.77609 -583.86378
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -810.18560 1035.69173
2 -1042.47804 -810.18560
3 -671.63009 -1042.47804
4 -167.47804 -671.63009
5 -736.18560 -167.47804
6 -428.26163 -736.18560
7 -576.95752 -428.26163
8 -282.80547 -576.95752
9 527.57466 -282.80547
10 282.41095 527.57466
11 1132.86710 282.41095
12 961.71505 1132.86710
13 -679.23830 961.71505
14 87.15349 -679.23830
15 337.22952 87.15349
16 -50.55407 337.22952
17 -245.26163 -50.55407
18 987.58632 -245.26163
19 1002.89043 987.58632
20 324.11850 1002.89043
21 1426.87876 324.11850
22 253.33492 1426.87876
23 722.41095 253.33492
24 931.41095 722.41095
25 -219.16228 931.41095
26 -131.61843 -219.16228
27 172.83772 -131.61843
28 591.27056 172.83772
29 -472.05687 591.27056
30 43.62736 -472.05687
31 138.32326 43.62736
32 -329.76443 138.32326
33 636.52801 -329.76443
34 -250.01583 636.52801
35 -272.87544 -250.01583
36 758.03687 -272.87544
37 -273.22059 758.03687
38 -35.98085 -273.22059
39 -711.23225 -35.98085
40 1066.90814 -711.23225
41 -142.79942 1066.90814
42 -576.10352 -142.79942
43 -24.33160 -576.10352
44 444.35264 -24.33160
45 88.49303 444.35264
46 260.72110 88.49303
47 -116.68235 260.72110
48 534.63342 -116.68235
49 -549.16788 534.63342
50 -694.62404 -549.16788
51 -17.40762 -694.62404
52 -351.49531 -17.40762
53 -257.19121 -351.49531
54 373.74443 -257.19121
55 -1300.16788 373.74443
56 -898.54801 -1300.16788
57 -592.77609 -898.54801
58 -321.85212 -592.77609
59 -583.86378 -321.85212
60 -308.77609 -583.86378
> 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/7uz3l1258980468.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/8822k1258980468.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/96z8d1258980468.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/10gvzj1258980468.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/116p9l1258980468.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12v0s31258980468.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1308db1258980469.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14issf1258980469.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/151bzy1258980469.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/16fvhc1258980469.tab")
+ }
>
> system("convert tmp/1vyyk1258980468.ps tmp/1vyyk1258980468.png")
> system("convert tmp/2it2t1258980468.ps tmp/2it2t1258980468.png")
> system("convert tmp/32qgc1258980468.ps tmp/32qgc1258980468.png")
> system("convert tmp/4au3e1258980468.ps tmp/4au3e1258980468.png")
> system("convert tmp/55hpe1258980468.ps tmp/55hpe1258980468.png")
> system("convert tmp/6t8tk1258980468.ps tmp/6t8tk1258980468.png")
> system("convert tmp/7uz3l1258980468.ps tmp/7uz3l1258980468.png")
> system("convert tmp/8822k1258980468.ps tmp/8822k1258980468.png")
> system("convert tmp/96z8d1258980468.ps tmp/96z8d1258980468.png")
> system("convert tmp/10gvzj1258980468.ps tmp/10gvzj1258980468.png")
>
>
> proc.time()
user system elapsed
2.485 1.565 3.018