R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(114,106.3,113.8,107.2,113.6,107.8,113.7,109.2,114.2,109.7,114.8,108.7,115.2,109.3,115.3,110.4,114.9,111.1,115.1,110.1,116,109.5,116,109,116,108.5,115.9,108.8,115.6,109.8,116.6,110.7,116.9,110.6,117.9,111.2,117.9,112,117.7,111.1,117.4,111.6,117.3,110.2,119,111.5,119.1,110.6,119,110.6,118.5,110.3,117,111.7,117.5,113.8,118.2,113.9,118.2,114.3,118.3,113.8,118.2,114.3,117.9,116.4,117.8,115.6,118.6,115.2,118.9,113.6,120.8,115.5,121.8,115.6,121.3,115.3,121.9,117.3,122,118.7,121.9,118.3,122,120.6,122.2,119.3,123,121.8,123.1,120.8,124.9,121.6,125.4,121.6,124.7,121.1,124.4,122.4,124,121.9,125,125.1,125.1,124.5,125.4,123.5,125.7,124.9,126.4,125.2,125.7,125.7,125.4,124.5,126.4,124.7,126.2,122.9),dim=c(2,60),dimnames=list(c('x','y'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('x','y'),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 = '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
x y t
1 114.0 106.3 1
2 113.8 107.2 2
3 113.6 107.8 3
4 113.7 109.2 4
5 114.2 109.7 5
6 114.8 108.7 6
7 115.2 109.3 7
8 115.3 110.4 8
9 114.9 111.1 9
10 115.1 110.1 10
11 116.0 109.5 11
12 116.0 109.0 12
13 116.0 108.5 13
14 115.9 108.8 14
15 115.6 109.8 15
16 116.6 110.7 16
17 116.9 110.6 17
18 117.9 111.2 18
19 117.9 112.0 19
20 117.7 111.1 20
21 117.4 111.6 21
22 117.3 110.2 22
23 119.0 111.5 23
24 119.1 110.6 24
25 119.0 110.6 25
26 118.5 110.3 26
27 117.0 111.7 27
28 117.5 113.8 28
29 118.2 113.9 29
30 118.2 114.3 30
31 118.3 113.8 31
32 118.2 114.3 32
33 117.9 116.4 33
34 117.8 115.6 34
35 118.6 115.2 35
36 118.9 113.6 36
37 120.8 115.5 37
38 121.8 115.6 38
39 121.3 115.3 39
40 121.9 117.3 40
41 122.0 118.7 41
42 121.9 118.3 42
43 122.0 120.6 43
44 122.2 119.3 44
45 123.0 121.8 45
46 123.1 120.8 46
47 124.9 121.6 47
48 125.4 121.6 48
49 124.7 121.1 49
50 124.4 122.4 50
51 124.0 121.9 51
52 125.0 125.1 52
53 125.1 124.5 53
54 125.4 123.5 54
55 125.7 124.9 55
56 126.4 125.2 56
57 125.7 125.7 57
58 125.4 124.5 58
59 126.4 124.7 59
60 126.2 122.9 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) y t
88.8882 0.2274 0.1484
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.4251 -0.3872 0.1344 0.5151 1.7324
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 88.88822 7.61275 11.676 < 2e-16 ***
y 0.22743 0.07214 3.153 0.00258 **
t 0.14842 0.02389 6.212 6.44e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8913 on 57 degrees of freedom
Multiple R-squared: 0.9513, Adjusted R-squared: 0.9496
F-statistic: 557 on 2 and 57 DF, p-value: < 2.2e-16
> 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,] 0.0354089622 0.0708179244 0.964591038
[2,] 0.0109046934 0.0218093867 0.989095307
[3,] 0.0036018113 0.0072036226 0.996398189
[4,] 0.0017655042 0.0035310084 0.998234496
[5,] 0.0042213069 0.0084426138 0.995778693
[6,] 0.0013648754 0.0027297508 0.998635125
[7,] 0.0006113812 0.0012227625 0.999388619
[8,] 0.0003849975 0.0007699950 0.999615002
[9,] 0.0003010831 0.0006021663 0.999698917
[10,] 0.0005501418 0.0011002835 0.999449858
[11,] 0.0002252672 0.0004505343 0.999774733
[12,] 0.0000978705 0.0001957410 0.999902129
[13,] 0.0004458843 0.0008917685 0.999554116
[14,] 0.0004337615 0.0008675230 0.999566239
[15,] 0.0002258045 0.0004516090 0.999774195
[16,] 0.0001475391 0.0002950781 0.999852461
[17,] 0.0001334150 0.0002668300 0.999866585
[18,] 0.0005617879 0.0011235759 0.999438212
[19,] 0.0017155882 0.0034311765 0.998284412
[20,] 0.0034965293 0.0069930585 0.996503471
[21,] 0.0063879444 0.0127758887 0.993612056
[22,] 0.1020364242 0.2040728484 0.897963576
[23,] 0.1640026970 0.3280053940 0.835997303
[24,] 0.1413794693 0.2827589385 0.858620531
[25,] 0.1186411738 0.2372823475 0.881358826
[26,] 0.1012937240 0.2025874480 0.898706276
[27,] 0.0961715051 0.1923430102 0.903828495
[28,] 0.1443000596 0.2886001192 0.855699940
[29,] 0.3735165568 0.7470331136 0.626483443
[30,] 0.5465074630 0.9069850740 0.453492537
[31,] 0.7350883310 0.5298233379 0.264911669
[32,] 0.7753524973 0.4492950054 0.224647503
[33,] 0.8820328951 0.2359342099 0.117967105
[34,] 0.8646544723 0.2706910554 0.135345528
[35,] 0.8816403213 0.2367193573 0.118359679
[36,] 0.8771146420 0.2457707161 0.122885358
[37,] 0.8534084349 0.2931831302 0.146591565
[38,] 0.8782880239 0.2434239521 0.121711976
[39,] 0.9083523611 0.1832952778 0.091647639
[40,] 0.9270914089 0.1458171821 0.072908591
[41,] 0.9632751400 0.0734497201 0.036724860
[42,] 0.9681053512 0.0637892976 0.031894649
[43,] 0.9942719477 0.0114561045 0.005728052
[44,] 0.9958437328 0.0083125343 0.004156267
[45,] 0.9900808769 0.0198382461 0.009919123
[46,] 0.9823294473 0.0353411053 0.017670553
[47,] 0.9577690066 0.0844619868 0.042230993
[48,] 0.9119622434 0.1760755132 0.088037757
[49,] 0.8118893986 0.3762212027 0.188110601
> postscript(file="/var/www/html/rcomp/tmp/1h1xf1258649994.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/27qmi1258649994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3pqff1258649994.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49iid1258649995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yfmc1258649995.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
0.78779667 0.23469178 -0.25018480 -0.61700352 -0.37913734 0.29987035
7 8 9 10 11 12
0.41499377 0.11640335 -0.59121600 -0.31220831 0.57582831 0.54112217
13 14 15 16 17 18
0.50641602 0.18976774 -0.48607991 0.16081521 0.33513799 1.05026141
19 20 21 22 23 24
0.71989929 0.57616421 0.01403040 0.08400916 1.33993320 1.49619813
25 26 27 28 29 30
1.24777815 0.66758647 -1.29923225 -1.42525034 -0.89641309 -1.13580414
31 32 33 34 35 36
-1.07051029 -1.43264410 -2.35866219 -2.42514004 -1.68258895 -1.16712465
37 38 39 40 41 42
0.15234279 0.98118004 0.40098836 0.39771304 0.03089432 -0.12655459
43 44 45 46 47 48
-0.69805822 -0.35082223 -0.26781139 -0.08880370 1.38083419 1.73241421
49 50 51 52 53 54
0.99770806 0.25363211 -0.18107404 -0.05726257 0.03077405 0.40978175
55 56 57 58 59 60
0.24296302 0.72631474 -0.23581907 -0.41132585 0.39476864 0.45571846
> postscript(file="/var/www/html/rcomp/tmp/6y3np1258649995.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 0.78779667 NA
1 0.23469178 0.78779667
2 -0.25018480 0.23469178
3 -0.61700352 -0.25018480
4 -0.37913734 -0.61700352
5 0.29987035 -0.37913734
6 0.41499377 0.29987035
7 0.11640335 0.41499377
8 -0.59121600 0.11640335
9 -0.31220831 -0.59121600
10 0.57582831 -0.31220831
11 0.54112217 0.57582831
12 0.50641602 0.54112217
13 0.18976774 0.50641602
14 -0.48607991 0.18976774
15 0.16081521 -0.48607991
16 0.33513799 0.16081521
17 1.05026141 0.33513799
18 0.71989929 1.05026141
19 0.57616421 0.71989929
20 0.01403040 0.57616421
21 0.08400916 0.01403040
22 1.33993320 0.08400916
23 1.49619813 1.33993320
24 1.24777815 1.49619813
25 0.66758647 1.24777815
26 -1.29923225 0.66758647
27 -1.42525034 -1.29923225
28 -0.89641309 -1.42525034
29 -1.13580414 -0.89641309
30 -1.07051029 -1.13580414
31 -1.43264410 -1.07051029
32 -2.35866219 -1.43264410
33 -2.42514004 -2.35866219
34 -1.68258895 -2.42514004
35 -1.16712465 -1.68258895
36 0.15234279 -1.16712465
37 0.98118004 0.15234279
38 0.40098836 0.98118004
39 0.39771304 0.40098836
40 0.03089432 0.39771304
41 -0.12655459 0.03089432
42 -0.69805822 -0.12655459
43 -0.35082223 -0.69805822
44 -0.26781139 -0.35082223
45 -0.08880370 -0.26781139
46 1.38083419 -0.08880370
47 1.73241421 1.38083419
48 0.99770806 1.73241421
49 0.25363211 0.99770806
50 -0.18107404 0.25363211
51 -0.05726257 -0.18107404
52 0.03077405 -0.05726257
53 0.40978175 0.03077405
54 0.24296302 0.40978175
55 0.72631474 0.24296302
56 -0.23581907 0.72631474
57 -0.41132585 -0.23581907
58 0.39476864 -0.41132585
59 0.45571846 0.39476864
60 NA 0.45571846
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.23469178 0.78779667
[2,] -0.25018480 0.23469178
[3,] -0.61700352 -0.25018480
[4,] -0.37913734 -0.61700352
[5,] 0.29987035 -0.37913734
[6,] 0.41499377 0.29987035
[7,] 0.11640335 0.41499377
[8,] -0.59121600 0.11640335
[9,] -0.31220831 -0.59121600
[10,] 0.57582831 -0.31220831
[11,] 0.54112217 0.57582831
[12,] 0.50641602 0.54112217
[13,] 0.18976774 0.50641602
[14,] -0.48607991 0.18976774
[15,] 0.16081521 -0.48607991
[16,] 0.33513799 0.16081521
[17,] 1.05026141 0.33513799
[18,] 0.71989929 1.05026141
[19,] 0.57616421 0.71989929
[20,] 0.01403040 0.57616421
[21,] 0.08400916 0.01403040
[22,] 1.33993320 0.08400916
[23,] 1.49619813 1.33993320
[24,] 1.24777815 1.49619813
[25,] 0.66758647 1.24777815
[26,] -1.29923225 0.66758647
[27,] -1.42525034 -1.29923225
[28,] -0.89641309 -1.42525034
[29,] -1.13580414 -0.89641309
[30,] -1.07051029 -1.13580414
[31,] -1.43264410 -1.07051029
[32,] -2.35866219 -1.43264410
[33,] -2.42514004 -2.35866219
[34,] -1.68258895 -2.42514004
[35,] -1.16712465 -1.68258895
[36,] 0.15234279 -1.16712465
[37,] 0.98118004 0.15234279
[38,] 0.40098836 0.98118004
[39,] 0.39771304 0.40098836
[40,] 0.03089432 0.39771304
[41,] -0.12655459 0.03089432
[42,] -0.69805822 -0.12655459
[43,] -0.35082223 -0.69805822
[44,] -0.26781139 -0.35082223
[45,] -0.08880370 -0.26781139
[46,] 1.38083419 -0.08880370
[47,] 1.73241421 1.38083419
[48,] 0.99770806 1.73241421
[49,] 0.25363211 0.99770806
[50,] -0.18107404 0.25363211
[51,] -0.05726257 -0.18107404
[52,] 0.03077405 -0.05726257
[53,] 0.40978175 0.03077405
[54,] 0.24296302 0.40978175
[55,] 0.72631474 0.24296302
[56,] -0.23581907 0.72631474
[57,] -0.41132585 -0.23581907
[58,] 0.39476864 -0.41132585
[59,] 0.45571846 0.39476864
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.23469178 0.78779667
2 -0.25018480 0.23469178
3 -0.61700352 -0.25018480
4 -0.37913734 -0.61700352
5 0.29987035 -0.37913734
6 0.41499377 0.29987035
7 0.11640335 0.41499377
8 -0.59121600 0.11640335
9 -0.31220831 -0.59121600
10 0.57582831 -0.31220831
11 0.54112217 0.57582831
12 0.50641602 0.54112217
13 0.18976774 0.50641602
14 -0.48607991 0.18976774
15 0.16081521 -0.48607991
16 0.33513799 0.16081521
17 1.05026141 0.33513799
18 0.71989929 1.05026141
19 0.57616421 0.71989929
20 0.01403040 0.57616421
21 0.08400916 0.01403040
22 1.33993320 0.08400916
23 1.49619813 1.33993320
24 1.24777815 1.49619813
25 0.66758647 1.24777815
26 -1.29923225 0.66758647
27 -1.42525034 -1.29923225
28 -0.89641309 -1.42525034
29 -1.13580414 -0.89641309
30 -1.07051029 -1.13580414
31 -1.43264410 -1.07051029
32 -2.35866219 -1.43264410
33 -2.42514004 -2.35866219
34 -1.68258895 -2.42514004
35 -1.16712465 -1.68258895
36 0.15234279 -1.16712465
37 0.98118004 0.15234279
38 0.40098836 0.98118004
39 0.39771304 0.40098836
40 0.03089432 0.39771304
41 -0.12655459 0.03089432
42 -0.69805822 -0.12655459
43 -0.35082223 -0.69805822
44 -0.26781139 -0.35082223
45 -0.08880370 -0.26781139
46 1.38083419 -0.08880370
47 1.73241421 1.38083419
48 0.99770806 1.73241421
49 0.25363211 0.99770806
50 -0.18107404 0.25363211
51 -0.05726257 -0.18107404
52 0.03077405 -0.05726257
53 0.40978175 0.03077405
54 0.24296302 0.40978175
55 0.72631474 0.24296302
56 -0.23581907 0.72631474
57 -0.41132585 -0.23581907
58 0.39476864 -0.41132585
59 0.45571846 0.39476864
> 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/7lk1j1258649995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8pvwx1258649995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bwzb1258649995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zt241258649995.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11m1et1258649995.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/12p3831258649995.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/13c6h41258649995.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/14993p1258649995.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/15pvjf1258649995.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/166lpk1258649995.tab")
+ }
>
> system("convert tmp/1h1xf1258649994.ps tmp/1h1xf1258649994.png")
> system("convert tmp/27qmi1258649994.ps tmp/27qmi1258649994.png")
> system("convert tmp/3pqff1258649994.ps tmp/3pqff1258649994.png")
> system("convert tmp/49iid1258649995.ps tmp/49iid1258649995.png")
> system("convert tmp/5yfmc1258649995.ps tmp/5yfmc1258649995.png")
> system("convert tmp/6y3np1258649995.ps tmp/6y3np1258649995.png")
> system("convert tmp/7lk1j1258649995.ps tmp/7lk1j1258649995.png")
> system("convert tmp/8pvwx1258649995.ps tmp/8pvwx1258649995.png")
> system("convert tmp/9bwzb1258649995.ps tmp/9bwzb1258649995.png")
> system("convert tmp/10zt241258649995.ps tmp/10zt241258649995.png")
>
>
> proc.time()
user system elapsed
2.480 1.554 3.287