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(2354,330,2697,331,2651,332,2067,334,2641,334,2539,334,2294,339,2712,345,2314,346,3092,352,2677,355,2813,358,2668,361,2939,363,2617,364,2231,365,2481,366,2421,370,2408,371,2560,371,2100,372,3315,373,2801,373,2403,374,3024,375,2507,375,2980,376,2211,376,2471,377,2594,377,2452,378,2232,379,2373,380,3127,384,2802,389,2641,390,2787,391,2619,392,2806,393,2193,394,2323,394,2529,395,2412,396,2262,397,2154,398,3230,399,2295,400,2715,400,2733,401,2317,401,2730,406,1913,407,2390,423,2484,427),dim=c(2,54),dimnames=list(c('Y','X'),1:54))
> y <- array(NA,dim=c(2,54),dimnames=list(c('Y','X'),1:54))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2354 330 1 0 0 0 0 0 0 0 0 0 0 1
2 2697 331 0 1 0 0 0 0 0 0 0 0 0 2
3 2651 332 0 0 1 0 0 0 0 0 0 0 0 3
4 2067 334 0 0 0 1 0 0 0 0 0 0 0 4
5 2641 334 0 0 0 0 1 0 0 0 0 0 0 5
6 2539 334 0 0 0 0 0 1 0 0 0 0 0 6
7 2294 339 0 0 0 0 0 0 1 0 0 0 0 7
8 2712 345 0 0 0 0 0 0 0 1 0 0 0 8
9 2314 346 0 0 0 0 0 0 0 0 1 0 0 9
10 3092 352 0 0 0 0 0 0 0 0 0 1 0 10
11 2677 355 0 0 0 0 0 0 0 0 0 0 1 11
12 2813 358 0 0 0 0 0 0 0 0 0 0 0 12
13 2668 361 1 0 0 0 0 0 0 0 0 0 0 13
14 2939 363 0 1 0 0 0 0 0 0 0 0 0 14
15 2617 364 0 0 1 0 0 0 0 0 0 0 0 15
16 2231 365 0 0 0 1 0 0 0 0 0 0 0 16
17 2481 366 0 0 0 0 1 0 0 0 0 0 0 17
18 2421 370 0 0 0 0 0 1 0 0 0 0 0 18
19 2408 371 0 0 0 0 0 0 1 0 0 0 0 19
20 2560 371 0 0 0 0 0 0 0 1 0 0 0 20
21 2100 372 0 0 0 0 0 0 0 0 1 0 0 21
22 3315 373 0 0 0 0 0 0 0 0 0 1 0 22
23 2801 373 0 0 0 0 0 0 0 0 0 0 1 23
24 2403 374 0 0 0 0 0 0 0 0 0 0 0 24
25 3024 375 1 0 0 0 0 0 0 0 0 0 0 25
26 2507 375 0 1 0 0 0 0 0 0 0 0 0 26
27 2980 376 0 0 1 0 0 0 0 0 0 0 0 27
28 2211 376 0 0 0 1 0 0 0 0 0 0 0 28
29 2471 377 0 0 0 0 1 0 0 0 0 0 0 29
30 2594 377 0 0 0 0 0 1 0 0 0 0 0 30
31 2452 378 0 0 0 0 0 0 1 0 0 0 0 31
32 2232 379 0 0 0 0 0 0 0 1 0 0 0 32
33 2373 380 0 0 0 0 0 0 0 0 1 0 0 33
34 3127 384 0 0 0 0 0 0 0 0 0 1 0 34
35 2802 389 0 0 0 0 0 0 0 0 0 0 1 35
36 2641 390 0 0 0 0 0 0 0 0 0 0 0 36
37 2787 391 1 0 0 0 0 0 0 0 0 0 0 37
38 2619 392 0 1 0 0 0 0 0 0 0 0 0 38
39 2806 393 0 0 1 0 0 0 0 0 0 0 0 39
40 2193 394 0 0 0 1 0 0 0 0 0 0 0 40
41 2323 394 0 0 0 0 1 0 0 0 0 0 0 41
42 2529 395 0 0 0 0 0 1 0 0 0 0 0 42
43 2412 396 0 0 0 0 0 0 1 0 0 0 0 43
44 2262 397 0 0 0 0 0 0 0 1 0 0 0 44
45 2154 398 0 0 0 0 0 0 0 0 1 0 0 45
46 3230 399 0 0 0 0 0 0 0 0 0 1 0 46
47 2295 400 0 0 0 0 0 0 0 0 0 0 1 47
48 2715 400 0 0 0 0 0 0 0 0 0 0 0 48
49 2733 401 1 0 0 0 0 0 0 0 0 0 0 49
50 2317 401 0 1 0 0 0 0 0 0 0 0 0 50
51 2730 406 0 0 1 0 0 0 0 0 0 0 0 51
52 1913 407 0 0 0 1 0 0 0 0 0 0 0 52
53 2390 423 0 0 0 0 1 0 0 0 0 0 0 53
54 2484 427 0 0 0 0 0 1 0 0 0 0 0 54
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
632.245 6.149 70.092 -21.260 119.638 -509.344
M5 M6 M7 M8 M9 M10
-182.314 -130.215 -247.919 -199.250 -400.682 547.588
M11 t
-2.531 -10.967
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-366.61 -107.15 29.99 86.08 289.89
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 632.245 1461.420 0.433 0.66761
X 6.149 4.335 1.419 0.16376
M1 70.092 111.340 0.630 0.53258
M2 -21.260 111.468 -0.191 0.84970
M3 119.638 111.287 1.075 0.28880
M4 -509.344 111.408 -4.572 4.58e-05 ***
M5 -182.314 110.911 -1.644 0.10806
M6 -130.215 110.898 -1.174 0.24726
M7 -247.919 117.495 -2.110 0.04116 *
M8 -199.250 117.253 -1.699 0.09703 .
M9 -400.682 117.329 -3.415 0.00148 **
M10 547.588 116.958 4.682 3.24e-05 ***
M11 -2.531 116.909 -0.022 0.98284
t -10.967 6.527 -1.680 0.10070
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 165.3 on 40 degrees of freedom
Multiple R-squared: 0.7712, Adjusted R-squared: 0.6968
F-statistic: 10.37 on 13 and 40 DF, p-value: 4.511e-09
> 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,] 0.4872672 0.97453444 0.51273278
[2,] 0.4876856 0.97537113 0.51231444
[3,] 0.3535186 0.70703723 0.64648139
[4,] 0.5839489 0.83210219 0.41605110
[5,] 0.6445591 0.71088181 0.35544091
[6,] 0.6514227 0.69715467 0.34857734
[7,] 0.5555472 0.88890569 0.44445284
[8,] 0.9232061 0.15358774 0.07679387
[9,] 0.9763519 0.04729621 0.02364810
[10,] 0.9862801 0.02743985 0.01371993
[11,] 0.9822939 0.03541215 0.01770608
[12,] 0.9647626 0.07047480 0.03523740
[13,] 0.9403822 0.11923565 0.05961783
[14,] 0.8963750 0.20725002 0.10362501
[15,] 0.8327776 0.33444473 0.16722236
[16,] 0.8439060 0.31218804 0.15609402
[17,] 0.7829499 0.43410023 0.21705011
[18,] 0.7668270 0.46634600 0.23317300
[19,] 0.8891629 0.22167412 0.11083706
[20,] 0.9188902 0.16221957 0.08110978
[21,] 0.9033474 0.19330511 0.09665255
> postscript(file="/var/www/html/rcomp/tmp/1ic8l1258721230.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26du41258721230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3i6wj1258721230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4dvt11258721230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v83c1258721230.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 = 54
Frequency = 1
1 2 3 4 5 6
-366.605642 72.564518 -109.516122 -65.865323 192.071801 48.940363
7 8 9 10 11 12
-98.134871 245.268325 53.518325 -142.679278 -15.041179 110.947719
13 14 15 16 17 18
-111.624672 249.396287 -208.684352 39.115648 -33.096430 -158.824672
19 20 21 22 23 24
-49.303102 64.995301 -188.754699 82.793703 129.879405 -265.833295
25 26 27 28 29 30
289.892716 -124.787923 212.131438 83.080639 20.868562 102.737124
31 32 33 34 35 36
83.258694 -180.592105 166.657895 -41.241306 164.098391 5.385691
37 38 39 40 41 42
86.111703 14.281862 65.201223 86.001223 -100.061653 58.657708
43 44 45 46 47 48
64.179278 -129.671521 -31.421521 101.126881 -278.936617 149.499884
49 50 51 52 53 54
102.225895 -211.454744 40.867813 -142.332187 -79.782281 -51.510523
> postscript(file="/var/www/html/rcomp/tmp/6c0w71258721230.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 = 54
Frequency = 1
lag(myerror, k = 1) myerror
0 -366.605642 NA
1 72.564518 -366.605642
2 -109.516122 72.564518
3 -65.865323 -109.516122
4 192.071801 -65.865323
5 48.940363 192.071801
6 -98.134871 48.940363
7 245.268325 -98.134871
8 53.518325 245.268325
9 -142.679278 53.518325
10 -15.041179 -142.679278
11 110.947719 -15.041179
12 -111.624672 110.947719
13 249.396287 -111.624672
14 -208.684352 249.396287
15 39.115648 -208.684352
16 -33.096430 39.115648
17 -158.824672 -33.096430
18 -49.303102 -158.824672
19 64.995301 -49.303102
20 -188.754699 64.995301
21 82.793703 -188.754699
22 129.879405 82.793703
23 -265.833295 129.879405
24 289.892716 -265.833295
25 -124.787923 289.892716
26 212.131438 -124.787923
27 83.080639 212.131438
28 20.868562 83.080639
29 102.737124 20.868562
30 83.258694 102.737124
31 -180.592105 83.258694
32 166.657895 -180.592105
33 -41.241306 166.657895
34 164.098391 -41.241306
35 5.385691 164.098391
36 86.111703 5.385691
37 14.281862 86.111703
38 65.201223 14.281862
39 86.001223 65.201223
40 -100.061653 86.001223
41 58.657708 -100.061653
42 64.179278 58.657708
43 -129.671521 64.179278
44 -31.421521 -129.671521
45 101.126881 -31.421521
46 -278.936617 101.126881
47 149.499884 -278.936617
48 102.225895 149.499884
49 -211.454744 102.225895
50 40.867813 -211.454744
51 -142.332187 40.867813
52 -79.782281 -142.332187
53 -51.510523 -79.782281
54 NA -51.510523
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 72.564518 -366.605642
[2,] -109.516122 72.564518
[3,] -65.865323 -109.516122
[4,] 192.071801 -65.865323
[5,] 48.940363 192.071801
[6,] -98.134871 48.940363
[7,] 245.268325 -98.134871
[8,] 53.518325 245.268325
[9,] -142.679278 53.518325
[10,] -15.041179 -142.679278
[11,] 110.947719 -15.041179
[12,] -111.624672 110.947719
[13,] 249.396287 -111.624672
[14,] -208.684352 249.396287
[15,] 39.115648 -208.684352
[16,] -33.096430 39.115648
[17,] -158.824672 -33.096430
[18,] -49.303102 -158.824672
[19,] 64.995301 -49.303102
[20,] -188.754699 64.995301
[21,] 82.793703 -188.754699
[22,] 129.879405 82.793703
[23,] -265.833295 129.879405
[24,] 289.892716 -265.833295
[25,] -124.787923 289.892716
[26,] 212.131438 -124.787923
[27,] 83.080639 212.131438
[28,] 20.868562 83.080639
[29,] 102.737124 20.868562
[30,] 83.258694 102.737124
[31,] -180.592105 83.258694
[32,] 166.657895 -180.592105
[33,] -41.241306 166.657895
[34,] 164.098391 -41.241306
[35,] 5.385691 164.098391
[36,] 86.111703 5.385691
[37,] 14.281862 86.111703
[38,] 65.201223 14.281862
[39,] 86.001223 65.201223
[40,] -100.061653 86.001223
[41,] 58.657708 -100.061653
[42,] 64.179278 58.657708
[43,] -129.671521 64.179278
[44,] -31.421521 -129.671521
[45,] 101.126881 -31.421521
[46,] -278.936617 101.126881
[47,] 149.499884 -278.936617
[48,] 102.225895 149.499884
[49,] -211.454744 102.225895
[50,] 40.867813 -211.454744
[51,] -142.332187 40.867813
[52,] -79.782281 -142.332187
[53,] -51.510523 -79.782281
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 72.564518 -366.605642
2 -109.516122 72.564518
3 -65.865323 -109.516122
4 192.071801 -65.865323
5 48.940363 192.071801
6 -98.134871 48.940363
7 245.268325 -98.134871
8 53.518325 245.268325
9 -142.679278 53.518325
10 -15.041179 -142.679278
11 110.947719 -15.041179
12 -111.624672 110.947719
13 249.396287 -111.624672
14 -208.684352 249.396287
15 39.115648 -208.684352
16 -33.096430 39.115648
17 -158.824672 -33.096430
18 -49.303102 -158.824672
19 64.995301 -49.303102
20 -188.754699 64.995301
21 82.793703 -188.754699
22 129.879405 82.793703
23 -265.833295 129.879405
24 289.892716 -265.833295
25 -124.787923 289.892716
26 212.131438 -124.787923
27 83.080639 212.131438
28 20.868562 83.080639
29 102.737124 20.868562
30 83.258694 102.737124
31 -180.592105 83.258694
32 166.657895 -180.592105
33 -41.241306 166.657895
34 164.098391 -41.241306
35 5.385691 164.098391
36 86.111703 5.385691
37 14.281862 86.111703
38 65.201223 14.281862
39 86.001223 65.201223
40 -100.061653 86.001223
41 58.657708 -100.061653
42 64.179278 58.657708
43 -129.671521 64.179278
44 -31.421521 -129.671521
45 101.126881 -31.421521
46 -278.936617 101.126881
47 149.499884 -278.936617
48 102.225895 149.499884
49 -211.454744 102.225895
50 40.867813 -211.454744
51 -142.332187 40.867813
52 -79.782281 -142.332187
53 -51.510523 -79.782281
> 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/72l4t1258721230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8310x1258721230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9c4961258721230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lits1258721230.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11f8qw1258721230.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/12cx811258721230.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/13mf2i1258721231.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/14adoc1258721231.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/15is071258721231.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/16v8gp1258721231.tab")
+ }
> system("convert tmp/1ic8l1258721230.ps tmp/1ic8l1258721230.png")
> system("convert tmp/26du41258721230.ps tmp/26du41258721230.png")
> system("convert tmp/3i6wj1258721230.ps tmp/3i6wj1258721230.png")
> system("convert tmp/4dvt11258721230.ps tmp/4dvt11258721230.png")
> system("convert tmp/5v83c1258721230.ps tmp/5v83c1258721230.png")
> system("convert tmp/6c0w71258721230.ps tmp/6c0w71258721230.png")
> system("convert tmp/72l4t1258721230.ps tmp/72l4t1258721230.png")
> system("convert tmp/8310x1258721230.ps tmp/8310x1258721230.png")
> system("convert tmp/9c4961258721230.ps tmp/9c4961258721230.png")
> system("convert tmp/10lits1258721230.ps tmp/10lits1258721230.png")
>
>
> proc.time()
user system elapsed
2.307 1.571 2.810