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(2529,314,2196,318,3202,320,2718,323,2728,325,2354,327,2697,330,2651,331,2067,332,2641,334,2539,334,2294,334,2712,339,2314,345,3092,346,2677,352,2813,355,2668,358,2939,361,2617,363,2231,364,2481,365,2421,366,2408,370,2560,371,2100,371,3315,372,2801,373,2403,373,3024,374,2507,375,2980,375,2211,376,2471,376,2594,377,2452,377,2232,378,2373,379,3127,380,2802,384,2641,389,2787,390,2619,391,2806,392,2193,393,2323,394,2529,394,2412,395,2262,396,2154,397,3230,398,2295,399,2715,400,2733,400,2317,401,2730,401,1913,406,2390,407,2484,423,1960,427),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = '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
1 2529 314 1 0 0 0 0 0 0 0 0 0 0
2 2196 318 0 1 0 0 0 0 0 0 0 0 0
3 3202 320 0 0 1 0 0 0 0 0 0 0 0
4 2718 323 0 0 0 1 0 0 0 0 0 0 0
5 2728 325 0 0 0 0 1 0 0 0 0 0 0
6 2354 327 0 0 0 0 0 1 0 0 0 0 0
7 2697 330 0 0 0 0 0 0 1 0 0 0 0
8 2651 331 0 0 0 0 0 0 0 1 0 0 0
9 2067 332 0 0 0 0 0 0 0 0 1 0 0
10 2641 334 0 0 0 0 0 0 0 0 0 1 0
11 2539 334 0 0 0 0 0 0 0 0 0 0 1
12 2294 334 0 0 0 0 0 0 0 0 0 0 0
13 2712 339 1 0 0 0 0 0 0 0 0 0 0
14 2314 345 0 1 0 0 0 0 0 0 0 0 0
15 3092 346 0 0 1 0 0 0 0 0 0 0 0
16 2677 352 0 0 0 1 0 0 0 0 0 0 0
17 2813 355 0 0 0 0 1 0 0 0 0 0 0
18 2668 358 0 0 0 0 0 1 0 0 0 0 0
19 2939 361 0 0 0 0 0 0 1 0 0 0 0
20 2617 363 0 0 0 0 0 0 0 1 0 0 0
21 2231 364 0 0 0 0 0 0 0 0 1 0 0
22 2481 365 0 0 0 0 0 0 0 0 0 1 0
23 2421 366 0 0 0 0 0 0 0 0 0 0 1
24 2408 370 0 0 0 0 0 0 0 0 0 0 0
25 2560 371 1 0 0 0 0 0 0 0 0 0 0
26 2100 371 0 1 0 0 0 0 0 0 0 0 0
27 3315 372 0 0 1 0 0 0 0 0 0 0 0
28 2801 373 0 0 0 1 0 0 0 0 0 0 0
29 2403 373 0 0 0 0 1 0 0 0 0 0 0
30 3024 374 0 0 0 0 0 1 0 0 0 0 0
31 2507 375 0 0 0 0 0 0 1 0 0 0 0
32 2980 375 0 0 0 0 0 0 0 1 0 0 0
33 2211 376 0 0 0 0 0 0 0 0 1 0 0
34 2471 376 0 0 0 0 0 0 0 0 0 1 0
35 2594 377 0 0 0 0 0 0 0 0 0 0 1
36 2452 377 0 0 0 0 0 0 0 0 0 0 0
37 2232 378 1 0 0 0 0 0 0 0 0 0 0
38 2373 379 0 1 0 0 0 0 0 0 0 0 0
39 3127 380 0 0 1 0 0 0 0 0 0 0 0
40 2802 384 0 0 0 1 0 0 0 0 0 0 0
41 2641 389 0 0 0 0 1 0 0 0 0 0 0
42 2787 390 0 0 0 0 0 1 0 0 0 0 0
43 2619 391 0 0 0 0 0 0 1 0 0 0 0
44 2806 392 0 0 0 0 0 0 0 1 0 0 0
45 2193 393 0 0 0 0 0 0 0 0 1 0 0
46 2323 394 0 0 0 0 0 0 0 0 0 1 0
47 2529 394 0 0 0 0 0 0 0 0 0 0 1
48 2412 395 0 0 0 0 0 0 0 0 0 0 0
49 2262 396 1 0 0 0 0 0 0 0 0 0 0
50 2154 397 0 1 0 0 0 0 0 0 0 0 0
51 3230 398 0 0 1 0 0 0 0 0 0 0 0
52 2295 399 0 0 0 1 0 0 0 0 0 0 0
53 2715 400 0 0 0 0 1 0 0 0 0 0 0
54 2733 400 0 0 0 0 0 1 0 0 0 0 0
55 2317 401 0 0 0 0 0 0 1 0 0 0 0
56 2730 401 0 0 0 0 0 0 0 1 0 0 0
57 1913 406 0 0 0 0 0 0 0 0 1 0 0
58 2390 407 0 0 0 0 0 0 0 0 0 1 0
59 2484 423 0 0 0 0 0 0 0 0 0 0 1
60 1960 427 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
2798.646 -1.296 126.574 -101.915 865.441 334.730
M5 M6 M7 M8 M9 M10
338.983 393.998 298.932 440.969 -190.498 148.999
M11
205.866
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-414.69 -105.54 10.86 95.07 316.25
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2798.6458 310.5535 9.012 8.24e-12 ***
X -1.2965 0.7924 -1.636 0.108484
M1 126.5736 106.0949 1.193 0.238849
M2 -101.9148 105.8133 -0.963 0.340399
M3 865.4410 105.6851 8.189 1.33e-10 ***
M4 334.7305 105.4013 3.176 0.002639 **
M5 338.9828 105.2269 3.221 0.002318 **
M6 393.9979 105.1307 3.748 0.000488 ***
M7 298.9315 105.0243 2.846 0.006536 **
M8 440.9687 104.9831 4.200 0.000118 ***
M9 -190.4976 104.9045 -1.816 0.075765 .
M10 148.9989 104.8692 1.421 0.161974
M11 205.8663 104.7916 1.965 0.055396 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 165.7 on 47 degrees of freedom
Multiple R-squared: 0.7751, Adjusted R-squared: 0.7177
F-statistic: 13.5 on 12 and 47 DF, p-value: 1.899e-11
> 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,] 0.2250678 0.4501356 0.7749322
[2,] 0.1105709 0.2211418 0.8894291
[3,] 0.1998674 0.3997348 0.8001326
[4,] 0.1834408 0.3668816 0.8165592
[5,] 0.2076089 0.4152178 0.7923911
[6,] 0.1328268 0.2656535 0.8671732
[7,] 0.1919810 0.3839619 0.8080190
[8,] 0.2432108 0.4864217 0.7567892
[9,] 0.1687958 0.3375916 0.8312042
[10,] 0.1736606 0.3473213 0.8263394
[11,] 0.2474398 0.4948796 0.7525602
[12,] 0.2089800 0.4179601 0.7910200
[13,] 0.1654790 0.3309579 0.8345210
[14,] 0.6565441 0.6869118 0.3434559
[15,] 0.8667212 0.2665576 0.1332788
[16,] 0.8922443 0.2155114 0.1077557
[17,] 0.8928206 0.2143588 0.1071794
[18,] 0.8380313 0.3239374 0.1619687
[19,] 0.7806035 0.4387930 0.2193965
[20,] 0.7399531 0.5200938 0.2600469
[21,] 0.6498453 0.7003094 0.3501547
[22,] 0.7185812 0.5628375 0.2814188
[23,] 0.6345977 0.7308047 0.3654023
[24,] 0.6551048 0.6897905 0.3448952
[25,] 0.7992245 0.4015510 0.2007755
[26,] 0.7597637 0.4804726 0.2402363
[27,] 0.6395829 0.7208341 0.3604171
[28,] 0.6315303 0.7369395 0.3684697
[29,] 0.4551566 0.9103133 0.5448434
> postscript(file="/var/www/html/rcomp/tmp/10ma21258718922.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26c6c1258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3am6y1258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jwe11258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58hcm1258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5
10.87984847 -88.44576025 -47.20856461 3.39143539 11.73213648
6 7 8 9 10
-414.68996678 27.26582668 -159.47487441 -110.71207005 126.38442450
11 12 13 14 15
-32.48295589 -71.61664608 226.29221225 64.55959263 -123.49970628
16 17 18 19 20
-0.01022262 135.62697302 -60.49863570 309.45715776 -151.98704878
21 22 23 24 25
94.77575558 6.57575558 -108.99513025 89.05715776 115.78003788
26 27 28 29 30
-115.73154904 133.20915205 151.21616295 -251.03612507 316.24527711
31 32 33 34 35
-104.39191853 226.57088583 90.33369019 10.83719564 78.26630981
36 37 38 39 40
142.13261962 -203.14450026 167.64040737 -44.41889154 166.47760301
41 42 43 44 45
7.70778775 99.98918993 28.35199429 74.61129320 94.37409756
46 47 48 49 50
-113.82590244 35.30671718 125.46952154 -149.80759834 -28.02269071
51 52 53 54 55
81.91801038 -321.07497873 95.96922781 58.95413544 -260.68306020
56 57 58 59 60
10.27974416 -168.77147328 -29.97147328 27.90505916 -285.04265283
> postscript(file="/var/www/html/rcomp/tmp/6ehwl1258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 10.87984847 NA
1 -88.44576025 10.87984847
2 -47.20856461 -88.44576025
3 3.39143539 -47.20856461
4 11.73213648 3.39143539
5 -414.68996678 11.73213648
6 27.26582668 -414.68996678
7 -159.47487441 27.26582668
8 -110.71207005 -159.47487441
9 126.38442450 -110.71207005
10 -32.48295589 126.38442450
11 -71.61664608 -32.48295589
12 226.29221225 -71.61664608
13 64.55959263 226.29221225
14 -123.49970628 64.55959263
15 -0.01022262 -123.49970628
16 135.62697302 -0.01022262
17 -60.49863570 135.62697302
18 309.45715776 -60.49863570
19 -151.98704878 309.45715776
20 94.77575558 -151.98704878
21 6.57575558 94.77575558
22 -108.99513025 6.57575558
23 89.05715776 -108.99513025
24 115.78003788 89.05715776
25 -115.73154904 115.78003788
26 133.20915205 -115.73154904
27 151.21616295 133.20915205
28 -251.03612507 151.21616295
29 316.24527711 -251.03612507
30 -104.39191853 316.24527711
31 226.57088583 -104.39191853
32 90.33369019 226.57088583
33 10.83719564 90.33369019
34 78.26630981 10.83719564
35 142.13261962 78.26630981
36 -203.14450026 142.13261962
37 167.64040737 -203.14450026
38 -44.41889154 167.64040737
39 166.47760301 -44.41889154
40 7.70778775 166.47760301
41 99.98918993 7.70778775
42 28.35199429 99.98918993
43 74.61129320 28.35199429
44 94.37409756 74.61129320
45 -113.82590244 94.37409756
46 35.30671718 -113.82590244
47 125.46952154 35.30671718
48 -149.80759834 125.46952154
49 -28.02269071 -149.80759834
50 81.91801038 -28.02269071
51 -321.07497873 81.91801038
52 95.96922781 -321.07497873
53 58.95413544 95.96922781
54 -260.68306020 58.95413544
55 10.27974416 -260.68306020
56 -168.77147328 10.27974416
57 -29.97147328 -168.77147328
58 27.90505916 -29.97147328
59 -285.04265283 27.90505916
60 NA -285.04265283
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -88.44576025 10.87984847
[2,] -47.20856461 -88.44576025
[3,] 3.39143539 -47.20856461
[4,] 11.73213648 3.39143539
[5,] -414.68996678 11.73213648
[6,] 27.26582668 -414.68996678
[7,] -159.47487441 27.26582668
[8,] -110.71207005 -159.47487441
[9,] 126.38442450 -110.71207005
[10,] -32.48295589 126.38442450
[11,] -71.61664608 -32.48295589
[12,] 226.29221225 -71.61664608
[13,] 64.55959263 226.29221225
[14,] -123.49970628 64.55959263
[15,] -0.01022262 -123.49970628
[16,] 135.62697302 -0.01022262
[17,] -60.49863570 135.62697302
[18,] 309.45715776 -60.49863570
[19,] -151.98704878 309.45715776
[20,] 94.77575558 -151.98704878
[21,] 6.57575558 94.77575558
[22,] -108.99513025 6.57575558
[23,] 89.05715776 -108.99513025
[24,] 115.78003788 89.05715776
[25,] -115.73154904 115.78003788
[26,] 133.20915205 -115.73154904
[27,] 151.21616295 133.20915205
[28,] -251.03612507 151.21616295
[29,] 316.24527711 -251.03612507
[30,] -104.39191853 316.24527711
[31,] 226.57088583 -104.39191853
[32,] 90.33369019 226.57088583
[33,] 10.83719564 90.33369019
[34,] 78.26630981 10.83719564
[35,] 142.13261962 78.26630981
[36,] -203.14450026 142.13261962
[37,] 167.64040737 -203.14450026
[38,] -44.41889154 167.64040737
[39,] 166.47760301 -44.41889154
[40,] 7.70778775 166.47760301
[41,] 99.98918993 7.70778775
[42,] 28.35199429 99.98918993
[43,] 74.61129320 28.35199429
[44,] 94.37409756 74.61129320
[45,] -113.82590244 94.37409756
[46,] 35.30671718 -113.82590244
[47,] 125.46952154 35.30671718
[48,] -149.80759834 125.46952154
[49,] -28.02269071 -149.80759834
[50,] 81.91801038 -28.02269071
[51,] -321.07497873 81.91801038
[52,] 95.96922781 -321.07497873
[53,] 58.95413544 95.96922781
[54,] -260.68306020 58.95413544
[55,] 10.27974416 -260.68306020
[56,] -168.77147328 10.27974416
[57,] -29.97147328 -168.77147328
[58,] 27.90505916 -29.97147328
[59,] -285.04265283 27.90505916
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -88.44576025 10.87984847
2 -47.20856461 -88.44576025
3 3.39143539 -47.20856461
4 11.73213648 3.39143539
5 -414.68996678 11.73213648
6 27.26582668 -414.68996678
7 -159.47487441 27.26582668
8 -110.71207005 -159.47487441
9 126.38442450 -110.71207005
10 -32.48295589 126.38442450
11 -71.61664608 -32.48295589
12 226.29221225 -71.61664608
13 64.55959263 226.29221225
14 -123.49970628 64.55959263
15 -0.01022262 -123.49970628
16 135.62697302 -0.01022262
17 -60.49863570 135.62697302
18 309.45715776 -60.49863570
19 -151.98704878 309.45715776
20 94.77575558 -151.98704878
21 6.57575558 94.77575558
22 -108.99513025 6.57575558
23 89.05715776 -108.99513025
24 115.78003788 89.05715776
25 -115.73154904 115.78003788
26 133.20915205 -115.73154904
27 151.21616295 133.20915205
28 -251.03612507 151.21616295
29 316.24527711 -251.03612507
30 -104.39191853 316.24527711
31 226.57088583 -104.39191853
32 90.33369019 226.57088583
33 10.83719564 90.33369019
34 78.26630981 10.83719564
35 142.13261962 78.26630981
36 -203.14450026 142.13261962
37 167.64040737 -203.14450026
38 -44.41889154 167.64040737
39 166.47760301 -44.41889154
40 7.70778775 166.47760301
41 99.98918993 7.70778775
42 28.35199429 99.98918993
43 74.61129320 28.35199429
44 94.37409756 74.61129320
45 -113.82590244 94.37409756
46 35.30671718 -113.82590244
47 125.46952154 35.30671718
48 -149.80759834 125.46952154
49 -28.02269071 -149.80759834
50 81.91801038 -28.02269071
51 -321.07497873 81.91801038
52 95.96922781 -321.07497873
53 58.95413544 95.96922781
54 -260.68306020 58.95413544
55 10.27974416 -260.68306020
56 -168.77147328 10.27974416
57 -29.97147328 -168.77147328
58 27.90505916 -29.97147328
59 -285.04265283 27.90505916
> 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/7m14g1258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vs2d1258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9o18l1258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10n4wz1258718922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zhch1258718922.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/1268p61258718922.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/136ht71258718922.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/14llwu1258718922.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/1574l81258718922.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/16e9t31258718922.tab")
+ }
> system("convert tmp/10ma21258718922.ps tmp/10ma21258718922.png")
> system("convert tmp/26c6c1258718922.ps tmp/26c6c1258718922.png")
> system("convert tmp/3am6y1258718922.ps tmp/3am6y1258718922.png")
> system("convert tmp/4jwe11258718922.ps tmp/4jwe11258718922.png")
> system("convert tmp/58hcm1258718922.ps tmp/58hcm1258718922.png")
> system("convert tmp/6ehwl1258718922.ps tmp/6ehwl1258718922.png")
> system("convert tmp/7m14g1258718922.ps tmp/7m14g1258718922.png")
> system("convert tmp/8vs2d1258718922.ps tmp/8vs2d1258718922.png")
> system("convert tmp/9o18l1258718922.ps tmp/9o18l1258718922.png")
> system("convert tmp/10n4wz1258718922.ps tmp/10n4wz1258718922.png")
>
>
> proc.time()
user system elapsed
2.470 1.590 8.028