R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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(6
+ ,10
+ ,2
+ ,2
+ ,5
+ ,6
+ ,9
+ ,2
+ ,2
+ ,7
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+ ,3
+ ,5
+ ,5)
+ ,dim=c(8
+ ,61)
+ ,dimnames=list(c('yt'
+ ,'x2t'
+ ,'x3t'
+ ,'x4t'
+ ,'x5t'
+ ,'x6t'
+ ,'x7t'
+ ,'x8t
')
+ ,1:61))
> y <- array(NA,dim=c(8,61),dimnames=list(c('yt','x2t','x3t','x4t','x5t','x6t','x7t','x8t
'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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, 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
yt x2t x3t x4t x5t x6t x7t x8t\r
1 6 10 2 2 5 6 9 2
2 2 7 4 9 4 6 5 5
3 2 2 10 1 1 5 7 5
4 2 10 4 10 6 3 9 3
5 6 8 3 3 9 1 8 5
6 5 5 7 3 1 7 2 7
7 3 3 5 6 10 9 1 2
8 5 9 9 4 3 10 10 6
9 1 4 3 3 10 7 9 6
10 5 6 8 4 8 3 9 4
11 1 4 2 9 1 10 9 5
12 6 4 9 10 7 9 1 5
13 1 2 5 3 7 4 2 5
14 10 5 6 6 1 7 7 4
15 6 3 5 8 7 2 4 2
16 9 4 3 5 7 10 8 5
17 6 9 5 9 9 1 10 4
18 2 3 7 4 9 1 5 4
19 6 6 1 5 4 10 4 3
20 7 2 10 9 8 4 8 10
21 7 3 6 10 9 7 5 5
22 5 9 2 7 4 5 3 3
23 10 8 7 1 9 2 8 2
24 10 8 2 10 7 7 2 9
25 7 10 7 5 9 4 6 6
26 8 4 9 3 7 3 3 9
27 2 7 9 10 3 3 1 4
28 6 5 9 6 6 8 4 10
29 6 1 6 6 10 7 4 2
30 7 2 9 6 4 10 6 8
31 5 6 5 4 10 7 8 7
32 8 10 7 1 9 7 2 5
33 9 10 9 3 2 8 6 9
34 5 8 1 1 8 2 7 10
35 1 5 4 8 8 4 1 2
36 3 1 7 5 5 4 10 3
37 2 9 3 8 5 4 1 7
38 9 2 6 7 9 10 3 9
39 1 10 7 3 5 5 7 3
40 6 8 8 5 5 8 4 6
41 8 5 2 4 8 9 9 8
42 9 9 1 8 7 5 3 3
43 10 5 3 6 4 5 1 1
44 4 2 9 3 3 2 6 3
45 3 10 2 7 3 1 7 6
46 7 5 5 7 8 8 7 10
47 4 2 8 10 3 9 8 8
48 7 1 8 7 10 7 10 5
49 5 3 1 9 2 9 9 8
50 6 5 1 2 8 9 8 3
51 10 6 1 8 2 6 1 6
52 10 6 10 1 6 4 1 9
53 10 10 2 10 9 7 8 9
54 10 8 1 5 4 1 8 6
55 5 7 5 2 7 7 7 1
56 4 6 10 4 10 4 2 3
57 10 10 6 4 3 5 10 6
58 6 9 8 10 3 1 10 9
59 2 1 2 10 7 5 3 6
60 4 9 9 6 10 9 10 10
61 3 2 8 6 10 3 5 5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x2t x3t x4t x5t x6t
3.45630 0.19616 -0.07593 -0.11307 0.04782 0.13924
x7t `x8t\\r`
-0.07507 0.26781
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.0582 -2.1787 0.0263 1.9111 5.3888
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.45630 2.17867 1.586 0.1186
x2t 0.19616 0.13242 1.481 0.1444
x3t -0.07593 0.13247 -0.573 0.5689
x4t -0.11307 0.13423 -0.842 0.4034
x5t 0.04782 0.13037 0.367 0.7152
x6t 0.13924 0.13673 1.018 0.3131
x7t -0.07507 0.12173 -0.617 0.5401
`x8t\\r` 0.26781 0.15023 1.783 0.0804 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.825 on 53 degrees of freedom
Multiple R-squared: 0.1329, Adjusted R-squared: 0.01838
F-statistic: 1.16 on 7 and 53 DF, p-value: 0.341
> 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,] 0.2514256 0.5028512 0.74857441
[2,] 0.2848256 0.5696511 0.71517443
[3,] 0.2500287 0.5000574 0.74997129
[4,] 0.7802390 0.4395219 0.21976097
[5,] 0.7371917 0.5256165 0.26280826
[6,] 0.8854970 0.2290060 0.11450302
[7,] 0.8458847 0.3082306 0.15411532
[8,] 0.8065425 0.3869151 0.19345753
[9,] 0.7368236 0.5263529 0.26317645
[10,] 0.7509649 0.4980702 0.24903510
[11,] 0.7133145 0.5733710 0.28668551
[12,] 0.6370503 0.7258994 0.36294970
[13,] 0.7488716 0.5022567 0.25112836
[14,] 0.7813925 0.4372151 0.21860754
[15,] 0.7228434 0.5543132 0.27715658
[16,] 0.6767743 0.6464513 0.32322565
[17,] 0.6638655 0.6722691 0.33613454
[18,] 0.5930082 0.8139835 0.40699175
[19,] 0.5380806 0.9238389 0.46191943
[20,] 0.4640152 0.9280304 0.53598481
[21,] 0.4026412 0.8052824 0.59735882
[22,] 0.3243247 0.6486495 0.67567526
[23,] 0.2601022 0.5202045 0.73989777
[24,] 0.2501730 0.5003461 0.74982697
[25,] 0.2634596 0.5269192 0.73654040
[26,] 0.2000624 0.4001247 0.79993764
[27,] 0.3322674 0.6645348 0.66773261
[28,] 0.2910142 0.5820284 0.70898578
[29,] 0.4985965 0.9971930 0.50140349
[30,] 0.4328508 0.8657017 0.56714916
[31,] 0.3499994 0.6999987 0.65000065
[32,] 0.3319033 0.6638066 0.66809672
[33,] 0.4847928 0.9695855 0.51520724
[34,] 0.3866392 0.7732784 0.61336082
[35,] 0.5788585 0.8422830 0.42114149
[36,] 0.4612242 0.9224483 0.53877583
[37,] 0.3443757 0.6887514 0.65562432
[38,] 0.9228899 0.1542202 0.07711009
[39,] 0.8544679 0.2910643 0.14553214
[40,] 0.7173056 0.5653888 0.28269440
> postscript(file="/var/fisher/rcomp/tmp/14h9j1353431442.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2btdn1353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/316d71353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/40p5y1353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/59qsj1353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
0.02548072 -3.49857713 -2.53389069 -2.81601249 0.23320961 -1.31348169
7 8 9 10 11 12
-2.17871331 -1.47822176 -5.05817446 0.23047405 -4.17518023 0.72115384
13 14 15 16 17 18
-4.21044602 5.12857911 2.39080561 3.08645593 1.28527531 -2.32657989
19 20 21 22 23 24
-0.07889528 1.91113013 2.17263294 -0.74416285 4.97499196 2.68729124
25 26 27 28 29 30
0.53506604 1.84400349 -2.57275850 -0.85408816 1.79322994 1.23732474
31 32 33 34 35 36
-1.52845047 0.63260214 1.43514343 -2.65034921 -3.62896235 -0.40446460
37 38 39 40 41 42
-4.68513864 2.39046246 -4.76050574 -0.51248979 1.06433678 3.14950236
43 44 45 46 47 48
5.38883871 0.39896006 -2.83870010 0.08481436 -1.04912404 2.70512203
49 50 51 52 53 54
-0.76697406 0.02627313 3.88428859 3.05991720 2.64972450 4.27879720
55 56 57 58 59 60
-0.27545692 -1.11021752 3.79404218 0.57401034 -2.78257837 -3.51887106
61
-1.42247478
> postscript(file="/var/fisher/rcomp/tmp/63j2g1353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.02548072 NA
1 -3.49857713 0.02548072
2 -2.53389069 -3.49857713
3 -2.81601249 -2.53389069
4 0.23320961 -2.81601249
5 -1.31348169 0.23320961
6 -2.17871331 -1.31348169
7 -1.47822176 -2.17871331
8 -5.05817446 -1.47822176
9 0.23047405 -5.05817446
10 -4.17518023 0.23047405
11 0.72115384 -4.17518023
12 -4.21044602 0.72115384
13 5.12857911 -4.21044602
14 2.39080561 5.12857911
15 3.08645593 2.39080561
16 1.28527531 3.08645593
17 -2.32657989 1.28527531
18 -0.07889528 -2.32657989
19 1.91113013 -0.07889528
20 2.17263294 1.91113013
21 -0.74416285 2.17263294
22 4.97499196 -0.74416285
23 2.68729124 4.97499196
24 0.53506604 2.68729124
25 1.84400349 0.53506604
26 -2.57275850 1.84400349
27 -0.85408816 -2.57275850
28 1.79322994 -0.85408816
29 1.23732474 1.79322994
30 -1.52845047 1.23732474
31 0.63260214 -1.52845047
32 1.43514343 0.63260214
33 -2.65034921 1.43514343
34 -3.62896235 -2.65034921
35 -0.40446460 -3.62896235
36 -4.68513864 -0.40446460
37 2.39046246 -4.68513864
38 -4.76050574 2.39046246
39 -0.51248979 -4.76050574
40 1.06433678 -0.51248979
41 3.14950236 1.06433678
42 5.38883871 3.14950236
43 0.39896006 5.38883871
44 -2.83870010 0.39896006
45 0.08481436 -2.83870010
46 -1.04912404 0.08481436
47 2.70512203 -1.04912404
48 -0.76697406 2.70512203
49 0.02627313 -0.76697406
50 3.88428859 0.02627313
51 3.05991720 3.88428859
52 2.64972450 3.05991720
53 4.27879720 2.64972450
54 -0.27545692 4.27879720
55 -1.11021752 -0.27545692
56 3.79404218 -1.11021752
57 0.57401034 3.79404218
58 -2.78257837 0.57401034
59 -3.51887106 -2.78257837
60 -1.42247478 -3.51887106
61 NA -1.42247478
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.49857713 0.02548072
[2,] -2.53389069 -3.49857713
[3,] -2.81601249 -2.53389069
[4,] 0.23320961 -2.81601249
[5,] -1.31348169 0.23320961
[6,] -2.17871331 -1.31348169
[7,] -1.47822176 -2.17871331
[8,] -5.05817446 -1.47822176
[9,] 0.23047405 -5.05817446
[10,] -4.17518023 0.23047405
[11,] 0.72115384 -4.17518023
[12,] -4.21044602 0.72115384
[13,] 5.12857911 -4.21044602
[14,] 2.39080561 5.12857911
[15,] 3.08645593 2.39080561
[16,] 1.28527531 3.08645593
[17,] -2.32657989 1.28527531
[18,] -0.07889528 -2.32657989
[19,] 1.91113013 -0.07889528
[20,] 2.17263294 1.91113013
[21,] -0.74416285 2.17263294
[22,] 4.97499196 -0.74416285
[23,] 2.68729124 4.97499196
[24,] 0.53506604 2.68729124
[25,] 1.84400349 0.53506604
[26,] -2.57275850 1.84400349
[27,] -0.85408816 -2.57275850
[28,] 1.79322994 -0.85408816
[29,] 1.23732474 1.79322994
[30,] -1.52845047 1.23732474
[31,] 0.63260214 -1.52845047
[32,] 1.43514343 0.63260214
[33,] -2.65034921 1.43514343
[34,] -3.62896235 -2.65034921
[35,] -0.40446460 -3.62896235
[36,] -4.68513864 -0.40446460
[37,] 2.39046246 -4.68513864
[38,] -4.76050574 2.39046246
[39,] -0.51248979 -4.76050574
[40,] 1.06433678 -0.51248979
[41,] 3.14950236 1.06433678
[42,] 5.38883871 3.14950236
[43,] 0.39896006 5.38883871
[44,] -2.83870010 0.39896006
[45,] 0.08481436 -2.83870010
[46,] -1.04912404 0.08481436
[47,] 2.70512203 -1.04912404
[48,] -0.76697406 2.70512203
[49,] 0.02627313 -0.76697406
[50,] 3.88428859 0.02627313
[51,] 3.05991720 3.88428859
[52,] 2.64972450 3.05991720
[53,] 4.27879720 2.64972450
[54,] -0.27545692 4.27879720
[55,] -1.11021752 -0.27545692
[56,] 3.79404218 -1.11021752
[57,] 0.57401034 3.79404218
[58,] -2.78257837 0.57401034
[59,] -3.51887106 -2.78257837
[60,] -1.42247478 -3.51887106
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.49857713 0.02548072
2 -2.53389069 -3.49857713
3 -2.81601249 -2.53389069
4 0.23320961 -2.81601249
5 -1.31348169 0.23320961
6 -2.17871331 -1.31348169
7 -1.47822176 -2.17871331
8 -5.05817446 -1.47822176
9 0.23047405 -5.05817446
10 -4.17518023 0.23047405
11 0.72115384 -4.17518023
12 -4.21044602 0.72115384
13 5.12857911 -4.21044602
14 2.39080561 5.12857911
15 3.08645593 2.39080561
16 1.28527531 3.08645593
17 -2.32657989 1.28527531
18 -0.07889528 -2.32657989
19 1.91113013 -0.07889528
20 2.17263294 1.91113013
21 -0.74416285 2.17263294
22 4.97499196 -0.74416285
23 2.68729124 4.97499196
24 0.53506604 2.68729124
25 1.84400349 0.53506604
26 -2.57275850 1.84400349
27 -0.85408816 -2.57275850
28 1.79322994 -0.85408816
29 1.23732474 1.79322994
30 -1.52845047 1.23732474
31 0.63260214 -1.52845047
32 1.43514343 0.63260214
33 -2.65034921 1.43514343
34 -3.62896235 -2.65034921
35 -0.40446460 -3.62896235
36 -4.68513864 -0.40446460
37 2.39046246 -4.68513864
38 -4.76050574 2.39046246
39 -0.51248979 -4.76050574
40 1.06433678 -0.51248979
41 3.14950236 1.06433678
42 5.38883871 3.14950236
43 0.39896006 5.38883871
44 -2.83870010 0.39896006
45 0.08481436 -2.83870010
46 -1.04912404 0.08481436
47 2.70512203 -1.04912404
48 -0.76697406 2.70512203
49 0.02627313 -0.76697406
50 3.88428859 0.02627313
51 3.05991720 3.88428859
52 2.64972450 3.05991720
53 4.27879720 2.64972450
54 -0.27545692 4.27879720
55 -1.11021752 -0.27545692
56 3.79404218 -1.11021752
57 0.57401034 3.79404218
58 -2.78257837 0.57401034
59 -3.51887106 -2.78257837
60 -1.42247478 -3.51887106
> 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/fisher/rcomp/tmp/7lwut1353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/88ae11353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9p5mi1353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/1008c51353431442.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11v3u61353431442.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/fisher/rcomp/tmp/124xnt1353431442.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/fisher/rcomp/tmp/13rpie1353431442.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/fisher/rcomp/tmp/14wjbz1353431442.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/fisher/rcomp/tmp/15mt8z1353431442.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/fisher/rcomp/tmp/16phpz1353431442.tab")
+ }
>
> try(system("convert tmp/14h9j1353431442.ps tmp/14h9j1353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/2btdn1353431442.ps tmp/2btdn1353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/316d71353431442.ps tmp/316d71353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/40p5y1353431442.ps tmp/40p5y1353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/59qsj1353431442.ps tmp/59qsj1353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/63j2g1353431442.ps tmp/63j2g1353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lwut1353431442.ps tmp/7lwut1353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/88ae11353431442.ps tmp/88ae11353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/9p5mi1353431442.ps tmp/9p5mi1353431442.png",intern=TRUE))
character(0)
> try(system("convert tmp/1008c51353431442.ps tmp/1008c51353431442.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.044 1.341 7.386