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(2529,330,2196,331,3202,332,2718,334,2728,334,2354,334,2697,339,2651,345,2067,346,2641,352,2539,355,2294,358,2712,361,2314,363,3092,364,2677,365,2813,366,2668,370,2939,371,2617,371,2231,372,2481,373,2421,373,2408,374,2560,375,2100,375,3315,376,2801,376,2403,377,3024,377,2507,378,2980,379,2211,380,2471,384,2594,389,2452,390,2232,391,2373,392,3127,393,2802,394,2641,394,2787,395,2619,396,2806,397,2193,398,2323,399,2529,400,2412,400,2262,401,2154,401,3230,406,2295,407,2715,423,2733,427),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 2529 330 1 0 0 0 0 0 0 0 0 0 0 1
2 2196 331 0 1 0 0 0 0 0 0 0 0 0 2
3 3202 332 0 0 1 0 0 0 0 0 0 0 0 3
4 2718 334 0 0 0 1 0 0 0 0 0 0 0 4
5 2728 334 0 0 0 0 1 0 0 0 0 0 0 5
6 2354 334 0 0 0 0 0 1 0 0 0 0 0 6
7 2697 339 0 0 0 0 0 0 1 0 0 0 0 7
8 2651 345 0 0 0 0 0 0 0 1 0 0 0 8
9 2067 346 0 0 0 0 0 0 0 0 1 0 0 9
10 2641 352 0 0 0 0 0 0 0 0 0 1 0 10
11 2539 355 0 0 0 0 0 0 0 0 0 0 1 11
12 2294 358 0 0 0 0 0 0 0 0 0 0 0 12
13 2712 361 1 0 0 0 0 0 0 0 0 0 0 13
14 2314 363 0 1 0 0 0 0 0 0 0 0 0 14
15 3092 364 0 0 1 0 0 0 0 0 0 0 0 15
16 2677 365 0 0 0 1 0 0 0 0 0 0 0 16
17 2813 366 0 0 0 0 1 0 0 0 0 0 0 17
18 2668 370 0 0 0 0 0 1 0 0 0 0 0 18
19 2939 371 0 0 0 0 0 0 1 0 0 0 0 19
20 2617 371 0 0 0 0 0 0 0 1 0 0 0 20
21 2231 372 0 0 0 0 0 0 0 0 1 0 0 21
22 2481 373 0 0 0 0 0 0 0 0 0 1 0 22
23 2421 373 0 0 0 0 0 0 0 0 0 0 1 23
24 2408 374 0 0 0 0 0 0 0 0 0 0 0 24
25 2560 375 1 0 0 0 0 0 0 0 0 0 0 25
26 2100 375 0 1 0 0 0 0 0 0 0 0 0 26
27 3315 376 0 0 1 0 0 0 0 0 0 0 0 27
28 2801 376 0 0 0 1 0 0 0 0 0 0 0 28
29 2403 377 0 0 0 0 1 0 0 0 0 0 0 29
30 3024 377 0 0 0 0 0 1 0 0 0 0 0 30
31 2507 378 0 0 0 0 0 0 1 0 0 0 0 31
32 2980 379 0 0 0 0 0 0 0 1 0 0 0 32
33 2211 380 0 0 0 0 0 0 0 0 1 0 0 33
34 2471 384 0 0 0 0 0 0 0 0 0 1 0 34
35 2594 389 0 0 0 0 0 0 0 0 0 0 1 35
36 2452 390 0 0 0 0 0 0 0 0 0 0 0 36
37 2232 391 1 0 0 0 0 0 0 0 0 0 0 37
38 2373 392 0 1 0 0 0 0 0 0 0 0 0 38
39 3127 393 0 0 1 0 0 0 0 0 0 0 0 39
40 2802 394 0 0 0 1 0 0 0 0 0 0 0 40
41 2641 394 0 0 0 0 1 0 0 0 0 0 0 41
42 2787 395 0 0 0 0 0 1 0 0 0 0 0 42
43 2619 396 0 0 0 0 0 0 1 0 0 0 0 43
44 2806 397 0 0 0 0 0 0 0 1 0 0 0 44
45 2193 398 0 0 0 0 0 0 0 0 1 0 0 45
46 2323 399 0 0 0 0 0 0 0 0 0 1 0 46
47 2529 400 0 0 0 0 0 0 0 0 0 0 1 47
48 2412 400 0 0 0 0 0 0 0 0 0 0 0 48
49 2262 401 1 0 0 0 0 0 0 0 0 0 0 49
50 2154 401 0 1 0 0 0 0 0 0 0 0 0 50
51 3230 406 0 0 1 0 0 0 0 0 0 0 0 51
52 2295 407 0 0 0 1 0 0 0 0 0 0 0 52
53 2715 423 0 0 0 0 1 0 0 0 0 0 0 53
54 2733 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
157.408 6.718 73.619 -152.621 811.820 281.237
M5 M6 M7 M8 M9 M10
269.186 321.028 309.150 379.448 -204.536 89.545
M11 t
126.913 -10.734
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-319.61 -108.64 12.53 82.23 334.98
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 157.408 1372.401 0.115 0.90926
X 6.718 4.071 1.650 0.10671
M1 73.619 104.558 0.704 0.48545
M2 -152.621 104.679 -1.458 0.15265
M3 811.820 104.508 7.768 1.64e-09 ***
M4 281.237 104.621 2.688 0.01042 *
M5 269.186 104.156 2.584 0.01351 *
M6 321.028 104.143 3.083 0.00371 **
M7 309.150 110.338 2.802 0.00779 **
M8 379.448 110.111 3.446 0.00135 **
M9 -204.536 110.183 -1.856 0.07078 .
M10 89.545 109.834 0.815 0.41974
M11 126.913 109.788 1.156 0.25454
t -10.734 6.130 -1.751 0.08758 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 155.2 on 40 degrees of freedom
Multiple R-squared: 0.7991, Adjusted R-squared: 0.7338
F-statistic: 12.24 on 13 and 40 DF, p-value: 4.008e-10
> 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,] 0.21537416 0.4307483 0.7846258
[2,] 0.14874590 0.2974918 0.8512541
[3,] 0.15338147 0.3067629 0.8466185
[4,] 0.13622678 0.2724536 0.8637732
[5,] 0.14751042 0.2950208 0.8524896
[6,] 0.08801648 0.1760330 0.9119835
[7,] 0.06384862 0.1276972 0.9361514
[8,] 0.10162365 0.2032473 0.8983764
[9,] 0.07380084 0.1476017 0.9261992
[10,] 0.07918244 0.1583649 0.9208176
[11,] 0.11464608 0.2292922 0.8853539
[12,] 0.10512726 0.2102545 0.8948727
[13,] 0.44214625 0.8842925 0.5578537
[14,] 0.80755162 0.3848968 0.1924484
[15,] 0.82643369 0.3471326 0.1735663
[16,] 0.82806380 0.3438724 0.1719362
[17,] 0.73772238 0.5245552 0.2622776
[18,] 0.62603931 0.7479214 0.3739607
[19,] 0.49292114 0.9858423 0.5070789
[20,] 0.36328453 0.7265691 0.6367155
[21,] 0.37640847 0.7528169 0.6235915
> postscript(file="/var/www/html/rcomp/tmp/1r1r71258720757.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2phm41258720757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xvxd1258720757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44pcf1258720757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/571cy1258720757.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
91.847481 -10.896070 34.678133 78.560379 111.344295 -303.763748
7 8 9 10 11 12
28.259814 -117.611204 -113.611204 136.735533 -12.052782 -139.558853
13 14 15 16 17 18
195.402646 20.941341 -161.484456 -41.884456 110.181705 -102.797354
19 20 21 22 23 24
184.097225 -197.467267 4.532733 -35.531758 -122.166810 -4.237372
25 26 27 28 29 30
78.159635 -144.866161 109.708042 137.025797 -244.908042 334.983916
31 32 33 34 35 36
-166.121505 240.596249 59.596249 9.378495 72.154670 61.084109
37 38 39 40 41 42
-228.518884 142.737565 -63.688232 145.911768 7.695684 105.869888
43 44 45 46 47 48
-46.235533 74.482221 49.482221 -110.582270 62.064923 82.712116
49 50 51 52 53 54
-136.890877 -7.916674 80.786512 -319.613488 15.686358 -34.292702
> postscript(file="/var/www/html/rcomp/tmp/6nhaq1258720757.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 91.847481 NA
1 -10.896070 91.847481
2 34.678133 -10.896070
3 78.560379 34.678133
4 111.344295 78.560379
5 -303.763748 111.344295
6 28.259814 -303.763748
7 -117.611204 28.259814
8 -113.611204 -117.611204
9 136.735533 -113.611204
10 -12.052782 136.735533
11 -139.558853 -12.052782
12 195.402646 -139.558853
13 20.941341 195.402646
14 -161.484456 20.941341
15 -41.884456 -161.484456
16 110.181705 -41.884456
17 -102.797354 110.181705
18 184.097225 -102.797354
19 -197.467267 184.097225
20 4.532733 -197.467267
21 -35.531758 4.532733
22 -122.166810 -35.531758
23 -4.237372 -122.166810
24 78.159635 -4.237372
25 -144.866161 78.159635
26 109.708042 -144.866161
27 137.025797 109.708042
28 -244.908042 137.025797
29 334.983916 -244.908042
30 -166.121505 334.983916
31 240.596249 -166.121505
32 59.596249 240.596249
33 9.378495 59.596249
34 72.154670 9.378495
35 61.084109 72.154670
36 -228.518884 61.084109
37 142.737565 -228.518884
38 -63.688232 142.737565
39 145.911768 -63.688232
40 7.695684 145.911768
41 105.869888 7.695684
42 -46.235533 105.869888
43 74.482221 -46.235533
44 49.482221 74.482221
45 -110.582270 49.482221
46 62.064923 -110.582270
47 82.712116 62.064923
48 -136.890877 82.712116
49 -7.916674 -136.890877
50 80.786512 -7.916674
51 -319.613488 80.786512
52 15.686358 -319.613488
53 -34.292702 15.686358
54 NA -34.292702
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -10.896070 91.847481
[2,] 34.678133 -10.896070
[3,] 78.560379 34.678133
[4,] 111.344295 78.560379
[5,] -303.763748 111.344295
[6,] 28.259814 -303.763748
[7,] -117.611204 28.259814
[8,] -113.611204 -117.611204
[9,] 136.735533 -113.611204
[10,] -12.052782 136.735533
[11,] -139.558853 -12.052782
[12,] 195.402646 -139.558853
[13,] 20.941341 195.402646
[14,] -161.484456 20.941341
[15,] -41.884456 -161.484456
[16,] 110.181705 -41.884456
[17,] -102.797354 110.181705
[18,] 184.097225 -102.797354
[19,] -197.467267 184.097225
[20,] 4.532733 -197.467267
[21,] -35.531758 4.532733
[22,] -122.166810 -35.531758
[23,] -4.237372 -122.166810
[24,] 78.159635 -4.237372
[25,] -144.866161 78.159635
[26,] 109.708042 -144.866161
[27,] 137.025797 109.708042
[28,] -244.908042 137.025797
[29,] 334.983916 -244.908042
[30,] -166.121505 334.983916
[31,] 240.596249 -166.121505
[32,] 59.596249 240.596249
[33,] 9.378495 59.596249
[34,] 72.154670 9.378495
[35,] 61.084109 72.154670
[36,] -228.518884 61.084109
[37,] 142.737565 -228.518884
[38,] -63.688232 142.737565
[39,] 145.911768 -63.688232
[40,] 7.695684 145.911768
[41,] 105.869888 7.695684
[42,] -46.235533 105.869888
[43,] 74.482221 -46.235533
[44,] 49.482221 74.482221
[45,] -110.582270 49.482221
[46,] 62.064923 -110.582270
[47,] 82.712116 62.064923
[48,] -136.890877 82.712116
[49,] -7.916674 -136.890877
[50,] 80.786512 -7.916674
[51,] -319.613488 80.786512
[52,] 15.686358 -319.613488
[53,] -34.292702 15.686358
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -10.896070 91.847481
2 34.678133 -10.896070
3 78.560379 34.678133
4 111.344295 78.560379
5 -303.763748 111.344295
6 28.259814 -303.763748
7 -117.611204 28.259814
8 -113.611204 -117.611204
9 136.735533 -113.611204
10 -12.052782 136.735533
11 -139.558853 -12.052782
12 195.402646 -139.558853
13 20.941341 195.402646
14 -161.484456 20.941341
15 -41.884456 -161.484456
16 110.181705 -41.884456
17 -102.797354 110.181705
18 184.097225 -102.797354
19 -197.467267 184.097225
20 4.532733 -197.467267
21 -35.531758 4.532733
22 -122.166810 -35.531758
23 -4.237372 -122.166810
24 78.159635 -4.237372
25 -144.866161 78.159635
26 109.708042 -144.866161
27 137.025797 109.708042
28 -244.908042 137.025797
29 334.983916 -244.908042
30 -166.121505 334.983916
31 240.596249 -166.121505
32 59.596249 240.596249
33 9.378495 59.596249
34 72.154670 9.378495
35 61.084109 72.154670
36 -228.518884 61.084109
37 142.737565 -228.518884
38 -63.688232 142.737565
39 145.911768 -63.688232
40 7.695684 145.911768
41 105.869888 7.695684
42 -46.235533 105.869888
43 74.482221 -46.235533
44 49.482221 74.482221
45 -110.582270 49.482221
46 62.064923 -110.582270
47 82.712116 62.064923
48 -136.890877 82.712116
49 -7.916674 -136.890877
50 80.786512 -7.916674
51 -319.613488 80.786512
52 15.686358 -319.613488
53 -34.292702 15.686358
> 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/7jx0s1258720757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jtxg1258720757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/922k11258720757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ow391258720757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110nmw1258720757.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/12iyzy1258720757.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/13jw2q1258720757.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/14zme21258720757.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/15a0fo1258720757.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/16lf6b1258720757.tab")
+ }
> system("convert tmp/1r1r71258720757.ps tmp/1r1r71258720757.png")
> system("convert tmp/2phm41258720757.ps tmp/2phm41258720757.png")
> system("convert tmp/3xvxd1258720757.ps tmp/3xvxd1258720757.png")
> system("convert tmp/44pcf1258720757.ps tmp/44pcf1258720757.png")
> system("convert tmp/571cy1258720757.ps tmp/571cy1258720757.png")
> system("convert tmp/6nhaq1258720757.ps tmp/6nhaq1258720757.png")
> system("convert tmp/7jx0s1258720757.ps tmp/7jx0s1258720757.png")
> system("convert tmp/8jtxg1258720757.ps tmp/8jtxg1258720757.png")
> system("convert tmp/922k11258720757.ps tmp/922k11258720757.png")
> system("convert tmp/10ow391258720757.ps tmp/10ow391258720757.png")
>
>
> proc.time()
user system elapsed
2.349 1.545 3.322