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(627,0,696,0,825,0,677,0,656,0,785,0,412,0,352,0,839,0,729,0,696,0,641,0,695,0,638,0,762,0,635,0,721,0,854,0,418,0,367,0,824,0,687,0,601,0,676,0,740,0,691,0,683,0,594,0,729,0,731,0,386,0,331,0,707,0,715,0,657,0,653,0,642,0,643,0,718,0,654,0,632,0,731,0,392,0,344,0,792,0,852,0,649,0,629,0,685,1,617,1,715,1,715,1,629,1,916,1,531,1,357,1,917,1,828,1,708,1,858,1),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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X
1 627 0
2 696 0
3 825 0
4 677 0
5 656 0
6 785 0
7 412 0
8 352 0
9 839 0
10 729 0
11 696 0
12 641 0
13 695 0
14 638 0
15 762 0
16 635 0
17 721 0
18 854 0
19 418 0
20 367 0
21 824 0
22 687 0
23 601 0
24 676 0
25 740 0
26 691 0
27 683 0
28 594 0
29 729 0
30 731 0
31 386 0
32 331 0
33 707 0
34 715 0
35 657 0
36 653 0
37 642 0
38 643 0
39 718 0
40 654 0
41 632 0
42 731 0
43 392 0
44 344 0
45 792 0
46 852 0
47 649 0
48 629 0
49 685 1
50 617 1
51 715 1
52 715 1
53 629 1
54 916 1
55 531 1
56 357 1
57 917 1
58 828 1
59 708 1
60 858 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
648.08 58.25
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-349.333 -21.146 8.792 81.417 210.667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 648.08 20.80 31.158 <2e-16 ***
X 58.25 46.51 1.252 0.215
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 144.1 on 58 degrees of freedom
Multiple R-squared: 0.02633, Adjusted R-squared: 0.009545
F-statistic: 1.569 on 1 and 58 DF, p-value: 0.2154
> 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,] 0.21309740 0.42619479 0.7869026
[2,] 0.14969205 0.29938410 0.8503080
[3,] 0.56866574 0.86266852 0.4313343
[4,] 0.83509520 0.32980960 0.1649048
[5,] 0.86469609 0.27060782 0.1353039
[6,] 0.81260599 0.37478801 0.1873940
[7,] 0.73919165 0.52161671 0.2608084
[8,] 0.65200814 0.69598372 0.3479919
[9,] 0.56312127 0.87375746 0.4368787
[10,] 0.46960525 0.93921049 0.5303948
[11,] 0.42197172 0.84394343 0.5780283
[12,] 0.33830945 0.67661891 0.6616905
[13,] 0.27365348 0.54730695 0.7263465
[14,] 0.33207007 0.66414013 0.6679299
[15,] 0.48251485 0.96502969 0.5174852
[16,] 0.69424064 0.61151873 0.3057594
[17,] 0.71714982 0.56570036 0.2828502
[18,] 0.65042799 0.69914402 0.3495720
[19,] 0.58367910 0.83264180 0.4163209
[20,] 0.50830905 0.98338191 0.4916910
[21,] 0.45944789 0.91889577 0.5405521
[22,] 0.38986077 0.77972154 0.6101392
[23,] 0.32234239 0.64468478 0.6776576
[24,] 0.26605050 0.53210101 0.7339495
[25,] 0.22494410 0.44988820 0.7750559
[26,] 0.18954659 0.37909318 0.8104534
[27,] 0.31938303 0.63876605 0.6806170
[28,] 0.58264427 0.83471147 0.4173557
[29,] 0.51838229 0.96323543 0.4816177
[30,] 0.45765652 0.91531305 0.5423435
[31,] 0.38243114 0.76486228 0.6175689
[32,] 0.31091842 0.62183684 0.6890816
[33,] 0.24544754 0.49089508 0.7545525
[34,] 0.18788247 0.37576494 0.8121175
[35,] 0.15067826 0.30135653 0.8493217
[36,] 0.10923270 0.21846541 0.8907673
[37,] 0.07622106 0.15244211 0.9237789
[38,] 0.06061081 0.12122163 0.9393892
[39,] 0.10991977 0.21983955 0.8900802
[40,] 0.33450827 0.66901654 0.6654917
[41,] 0.28981146 0.57962293 0.7101885
[42,] 0.32483943 0.64967886 0.6751606
[43,] 0.24584530 0.49169059 0.7541547
[44,] 0.17687806 0.35375611 0.8231219
[45,] 0.12082717 0.24165434 0.8791728
[46,] 0.08795368 0.17590736 0.9120463
[47,] 0.05344138 0.10688276 0.9465586
[48,] 0.02966145 0.05932290 0.9703386
[49,] 0.01755482 0.03510963 0.9824452
[50,] 0.02160134 0.04320268 0.9783987
[51,] 0.02037946 0.04075893 0.9796205
> postscript(file="/var/www/html/rcomp/tmp/1hf901259318410.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gf1g1259318410.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/312se1259318410.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4bcxu1259318410.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tpk91259318410.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 6
-21.0833333 47.9166667 176.9166667 28.9166667 7.9166667 136.9166667
7 8 9 10 11 12
-236.0833333 -296.0833333 190.9166667 80.9166667 47.9166667 -7.0833333
13 14 15 16 17 18
46.9166667 -10.0833333 113.9166667 -13.0833333 72.9166667 205.9166667
19 20 21 22 23 24
-230.0833333 -281.0833333 175.9166667 38.9166667 -47.0833333 27.9166667
25 26 27 28 29 30
91.9166667 42.9166667 34.9166667 -54.0833333 80.9166667 82.9166667
31 32 33 34 35 36
-262.0833333 -317.0833333 58.9166667 66.9166667 8.9166667 4.9166667
37 38 39 40 41 42
-6.0833333 -5.0833333 69.9166667 5.9166667 -16.0833333 82.9166667
43 44 45 46 47 48
-256.0833333 -304.0833333 143.9166667 203.9166667 0.9166667 -19.0833333
49 50 51 52 53 54
-21.3333333 -89.3333333 8.6666667 8.6666667 -77.3333333 209.6666667
55 56 57 58 59 60
-175.3333333 -349.3333333 210.6666667 121.6666667 1.6666667 151.6666667
> postscript(file="/var/www/html/rcomp/tmp/6f2lh1259318410.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 -21.0833333 NA
1 47.9166667 -21.0833333
2 176.9166667 47.9166667
3 28.9166667 176.9166667
4 7.9166667 28.9166667
5 136.9166667 7.9166667
6 -236.0833333 136.9166667
7 -296.0833333 -236.0833333
8 190.9166667 -296.0833333
9 80.9166667 190.9166667
10 47.9166667 80.9166667
11 -7.0833333 47.9166667
12 46.9166667 -7.0833333
13 -10.0833333 46.9166667
14 113.9166667 -10.0833333
15 -13.0833333 113.9166667
16 72.9166667 -13.0833333
17 205.9166667 72.9166667
18 -230.0833333 205.9166667
19 -281.0833333 -230.0833333
20 175.9166667 -281.0833333
21 38.9166667 175.9166667
22 -47.0833333 38.9166667
23 27.9166667 -47.0833333
24 91.9166667 27.9166667
25 42.9166667 91.9166667
26 34.9166667 42.9166667
27 -54.0833333 34.9166667
28 80.9166667 -54.0833333
29 82.9166667 80.9166667
30 -262.0833333 82.9166667
31 -317.0833333 -262.0833333
32 58.9166667 -317.0833333
33 66.9166667 58.9166667
34 8.9166667 66.9166667
35 4.9166667 8.9166667
36 -6.0833333 4.9166667
37 -5.0833333 -6.0833333
38 69.9166667 -5.0833333
39 5.9166667 69.9166667
40 -16.0833333 5.9166667
41 82.9166667 -16.0833333
42 -256.0833333 82.9166667
43 -304.0833333 -256.0833333
44 143.9166667 -304.0833333
45 203.9166667 143.9166667
46 0.9166667 203.9166667
47 -19.0833333 0.9166667
48 -21.3333333 -19.0833333
49 -89.3333333 -21.3333333
50 8.6666667 -89.3333333
51 8.6666667 8.6666667
52 -77.3333333 8.6666667
53 209.6666667 -77.3333333
54 -175.3333333 209.6666667
55 -349.3333333 -175.3333333
56 210.6666667 -349.3333333
57 121.6666667 210.6666667
58 1.6666667 121.6666667
59 151.6666667 1.6666667
60 NA 151.6666667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 47.9166667 -21.0833333
[2,] 176.9166667 47.9166667
[3,] 28.9166667 176.9166667
[4,] 7.9166667 28.9166667
[5,] 136.9166667 7.9166667
[6,] -236.0833333 136.9166667
[7,] -296.0833333 -236.0833333
[8,] 190.9166667 -296.0833333
[9,] 80.9166667 190.9166667
[10,] 47.9166667 80.9166667
[11,] -7.0833333 47.9166667
[12,] 46.9166667 -7.0833333
[13,] -10.0833333 46.9166667
[14,] 113.9166667 -10.0833333
[15,] -13.0833333 113.9166667
[16,] 72.9166667 -13.0833333
[17,] 205.9166667 72.9166667
[18,] -230.0833333 205.9166667
[19,] -281.0833333 -230.0833333
[20,] 175.9166667 -281.0833333
[21,] 38.9166667 175.9166667
[22,] -47.0833333 38.9166667
[23,] 27.9166667 -47.0833333
[24,] 91.9166667 27.9166667
[25,] 42.9166667 91.9166667
[26,] 34.9166667 42.9166667
[27,] -54.0833333 34.9166667
[28,] 80.9166667 -54.0833333
[29,] 82.9166667 80.9166667
[30,] -262.0833333 82.9166667
[31,] -317.0833333 -262.0833333
[32,] 58.9166667 -317.0833333
[33,] 66.9166667 58.9166667
[34,] 8.9166667 66.9166667
[35,] 4.9166667 8.9166667
[36,] -6.0833333 4.9166667
[37,] -5.0833333 -6.0833333
[38,] 69.9166667 -5.0833333
[39,] 5.9166667 69.9166667
[40,] -16.0833333 5.9166667
[41,] 82.9166667 -16.0833333
[42,] -256.0833333 82.9166667
[43,] -304.0833333 -256.0833333
[44,] 143.9166667 -304.0833333
[45,] 203.9166667 143.9166667
[46,] 0.9166667 203.9166667
[47,] -19.0833333 0.9166667
[48,] -21.3333333 -19.0833333
[49,] -89.3333333 -21.3333333
[50,] 8.6666667 -89.3333333
[51,] 8.6666667 8.6666667
[52,] -77.3333333 8.6666667
[53,] 209.6666667 -77.3333333
[54,] -175.3333333 209.6666667
[55,] -349.3333333 -175.3333333
[56,] 210.6666667 -349.3333333
[57,] 121.6666667 210.6666667
[58,] 1.6666667 121.6666667
[59,] 151.6666667 1.6666667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 47.9166667 -21.0833333
2 176.9166667 47.9166667
3 28.9166667 176.9166667
4 7.9166667 28.9166667
5 136.9166667 7.9166667
6 -236.0833333 136.9166667
7 -296.0833333 -236.0833333
8 190.9166667 -296.0833333
9 80.9166667 190.9166667
10 47.9166667 80.9166667
11 -7.0833333 47.9166667
12 46.9166667 -7.0833333
13 -10.0833333 46.9166667
14 113.9166667 -10.0833333
15 -13.0833333 113.9166667
16 72.9166667 -13.0833333
17 205.9166667 72.9166667
18 -230.0833333 205.9166667
19 -281.0833333 -230.0833333
20 175.9166667 -281.0833333
21 38.9166667 175.9166667
22 -47.0833333 38.9166667
23 27.9166667 -47.0833333
24 91.9166667 27.9166667
25 42.9166667 91.9166667
26 34.9166667 42.9166667
27 -54.0833333 34.9166667
28 80.9166667 -54.0833333
29 82.9166667 80.9166667
30 -262.0833333 82.9166667
31 -317.0833333 -262.0833333
32 58.9166667 -317.0833333
33 66.9166667 58.9166667
34 8.9166667 66.9166667
35 4.9166667 8.9166667
36 -6.0833333 4.9166667
37 -5.0833333 -6.0833333
38 69.9166667 -5.0833333
39 5.9166667 69.9166667
40 -16.0833333 5.9166667
41 82.9166667 -16.0833333
42 -256.0833333 82.9166667
43 -304.0833333 -256.0833333
44 143.9166667 -304.0833333
45 203.9166667 143.9166667
46 0.9166667 203.9166667
47 -19.0833333 0.9166667
48 -21.3333333 -19.0833333
49 -89.3333333 -21.3333333
50 8.6666667 -89.3333333
51 8.6666667 8.6666667
52 -77.3333333 8.6666667
53 209.6666667 -77.3333333
54 -175.3333333 209.6666667
55 -349.3333333 -175.3333333
56 210.6666667 -349.3333333
57 121.6666667 210.6666667
58 1.6666667 121.6666667
59 151.6666667 1.6666667
> 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/78ysh1259318410.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jsok1259318410.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9k2731259318410.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jnxq1259318410.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ot7s1259318410.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/12woei1259318411.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/13ut5x1259318411.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/1468pb1259318411.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/15kvpd1259318411.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/16q2v61259318411.tab")
+ }
>
> system("convert tmp/1hf901259318410.ps tmp/1hf901259318410.png")
> system("convert tmp/2gf1g1259318410.ps tmp/2gf1g1259318410.png")
> system("convert tmp/312se1259318410.ps tmp/312se1259318410.png")
> system("convert tmp/4bcxu1259318410.ps tmp/4bcxu1259318410.png")
> system("convert tmp/5tpk91259318410.ps tmp/5tpk91259318410.png")
> system("convert tmp/6f2lh1259318410.ps tmp/6f2lh1259318410.png")
> system("convert tmp/78ysh1259318410.ps tmp/78ysh1259318410.png")
> system("convert tmp/8jsok1259318410.ps tmp/8jsok1259318410.png")
> system("convert tmp/9k2731259318410.ps tmp/9k2731259318410.png")
> system("convert tmp/10jnxq1259318410.ps tmp/10jnxq1259318410.png")
>
>
> proc.time()
user system elapsed
2.448 1.572 2.888