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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> x <- array(list(1.43,0,1.43,0,1.43,0,1.43,0,1.43,0,1.43,0,1.44,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.48,0,1.57,0,1.58,0,1.58,0,1.58,0,1.58,0,1.59,1,1.6,1,1.6,1,1.61,1,1.61,1,1.61,1,1.62,1,1.63,1,1.63,1,1.64,1,1.64,1,1.64,1,1.64,1,1.64,1,1.65,1,1.65,1,1.65,1,1.65,1),dim=c(2,60),dimnames=list(c('Broodprijzen','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Broodprijzen','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
Broodprijzen X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.43 0 1 0 0 0 0 0 0 0 0 0 0
2 1.43 0 0 1 0 0 0 0 0 0 0 0 0
3 1.43 0 0 0 1 0 0 0 0 0 0 0 0
4 1.43 0 0 0 0 1 0 0 0 0 0 0 0
5 1.43 0 0 0 0 0 1 0 0 0 0 0 0
6 1.43 0 0 0 0 0 0 1 0 0 0 0 0
7 1.44 0 0 0 0 0 0 0 1 0 0 0 0
8 1.48 0 0 0 0 0 0 0 0 1 0 0 0
9 1.48 0 0 0 0 0 0 0 0 0 1 0 0
10 1.48 0 0 0 0 0 0 0 0 0 0 1 0
11 1.48 0 0 0 0 0 0 0 0 0 0 0 1
12 1.48 0 0 0 0 0 0 0 0 0 0 0 0
13 1.48 0 1 0 0 0 0 0 0 0 0 0 0
14 1.48 0 0 1 0 0 0 0 0 0 0 0 0
15 1.48 0 0 0 1 0 0 0 0 0 0 0 0
16 1.48 0 0 0 0 1 0 0 0 0 0 0 0
17 1.48 0 0 0 0 0 1 0 0 0 0 0 0
18 1.48 0 0 0 0 0 0 1 0 0 0 0 0
19 1.48 0 0 0 0 0 0 0 1 0 0 0 0
20 1.48 0 0 0 0 0 0 0 0 1 0 0 0
21 1.48 0 0 0 0 0 0 0 0 0 1 0 0
22 1.48 0 0 0 0 0 0 0 0 0 0 1 0
23 1.48 0 0 0 0 0 0 0 0 0 0 0 1
24 1.48 0 0 0 0 0 0 0 0 0 0 0 0
25 1.48 0 1 0 0 0 0 0 0 0 0 0 0
26 1.48 0 0 1 0 0 0 0 0 0 0 0 0
27 1.48 0 0 0 1 0 0 0 0 0 0 0 0
28 1.48 0 0 0 0 1 0 0 0 0 0 0 0
29 1.48 0 0 0 0 0 1 0 0 0 0 0 0
30 1.48 0 0 0 0 0 0 1 0 0 0 0 0
31 1.48 0 0 0 0 0 0 0 1 0 0 0 0
32 1.48 0 0 0 0 0 0 0 0 1 0 0 0
33 1.48 0 0 0 0 0 0 0 0 0 1 0 0
34 1.48 0 0 0 0 0 0 0 0 0 0 1 0
35 1.48 0 0 0 0 0 0 0 0 0 0 0 1
36 1.48 0 0 0 0 0 0 0 0 0 0 0 0
37 1.48 0 1 0 0 0 0 0 0 0 0 0 0
38 1.57 0 0 1 0 0 0 0 0 0 0 0 0
39 1.58 0 0 0 1 0 0 0 0 0 0 0 0
40 1.58 0 0 0 0 1 0 0 0 0 0 0 0
41 1.58 0 0 0 0 0 1 0 0 0 0 0 0
42 1.58 0 0 0 0 0 0 1 0 0 0 0 0
43 1.59 1 0 0 0 0 0 0 1 0 0 0 0
44 1.60 1 0 0 0 0 0 0 0 1 0 0 0
45 1.60 1 0 0 0 0 0 0 0 0 1 0 0
46 1.61 1 0 0 0 0 0 0 0 0 0 1 0
47 1.61 1 0 0 0 0 0 0 0 0 0 0 1
48 1.61 1 0 0 0 0 0 0 0 0 0 0 0
49 1.62 1 1 0 0 0 0 0 0 0 0 0 0
50 1.63 1 0 1 0 0 0 0 0 0 0 0 0
51 1.63 1 0 0 1 0 0 0 0 0 0 0 0
52 1.64 1 0 0 0 1 0 0 0 0 0 0 0
53 1.64 1 0 0 0 0 1 0 0 0 0 0 0
54 1.64 1 0 0 0 0 0 1 0 0 0 0 0
55 1.64 1 0 0 0 0 0 0 1 0 0 0 0
56 1.64 1 0 0 0 0 0 0 0 1 0 0 0
57 1.65 1 0 0 0 0 0 0 0 0 1 0 0
58 1.65 1 0 0 0 0 0 0 0 0 0 1 0
59 1.65 1 0 0 0 0 0 0 0 0 0 0 1
60 1.65 1 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
1.481e+00 1.465e-01 -1.270e-02 7.300e-03 9.300e-03 1.130e-02
M5 M6 M7 M8 M9 M10
1.130e-02 1.130e-02 -1.400e-02 -4.000e-03 -2.000e-03 1.172e-17
M11
8.229e-18
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.0627 -0.0127 -0.0014 0.0113 0.0893
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.481e+00 1.738e-02 85.220 <2e-16 ***
X 1.465e-01 1.086e-02 13.484 <2e-16 ***
M1 -1.270e-02 2.390e-02 -0.531 0.598
M2 7.300e-03 2.390e-02 0.305 0.761
M3 9.300e-03 2.390e-02 0.389 0.699
M4 1.130e-02 2.390e-02 0.473 0.639
M5 1.130e-02 2.390e-02 0.473 0.639
M6 1.130e-02 2.390e-02 0.473 0.639
M7 -1.400e-02 2.380e-02 -0.588 0.559
M8 -4.000e-03 2.380e-02 -0.168 0.867
M9 -2.000e-03 2.380e-02 -0.084 0.933
M10 1.171e-17 2.380e-02 4.92e-16 1.000
M11 8.229e-18 2.380e-02 3.46e-16 1.000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03764 on 47 degrees of freedom
Multiple R-squared: 0.8, Adjusted R-squared: 0.749
F-statistic: 15.67 on 12 and 47 DF, p-value: 1.397e-12
> 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,] 0.75052379 0.4989524 0.2494762
[2,] 0.72841202 0.5431760 0.2715880
[3,] 0.71375028 0.5724994 0.2862497
[4,] 0.64922101 0.7015580 0.3507790
[5,] 0.52532506 0.9493499 0.4746749
[6,] 0.40466140 0.8093228 0.5953386
[7,] 0.29717609 0.5943522 0.7028239
[8,] 0.20765237 0.4153047 0.7923476
[9,] 0.13811400 0.2762280 0.8618860
[10,] 0.10073545 0.2014709 0.8992646
[11,] 0.08970013 0.1794003 0.9102999
[12,] 0.08712858 0.1742572 0.9128714
[13,] 0.09562437 0.1912487 0.9043756
[14,] 0.11493966 0.2298793 0.8850603
[15,] 0.15375847 0.3075169 0.8462415
[16,] 0.11844320 0.2368864 0.8815568
[17,] 0.08682439 0.1736488 0.9131756
[18,] 0.06907893 0.1381579 0.9309211
[19,] 0.06617575 0.1323515 0.9338242
[20,] 0.08109417 0.1621883 0.9189058
[21,] 0.16048038 0.3209608 0.8395196
[22,] 0.32671886 0.6534377 0.6732811
[23,] 0.61610101 0.7677980 0.3838990
[24,] 0.75549183 0.4890163 0.2445082
[25,] 0.79153126 0.4169375 0.2084687
[26,] 0.78262839 0.4347432 0.2173716
[27,] 0.74054457 0.5189109 0.2594554
[28,] 0.70018588 0.5996282 0.2998141
[29,] 0.60617151 0.7876570 0.3938285
> postscript(file="/var/www/html/rcomp/tmp/1jy0q1258738364.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29pyf1258738364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3s6u11258738364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wtqx1258738364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c40r1258738364.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 7 8 9 10
-0.0387 -0.0587 -0.0607 -0.0627 -0.0627 -0.0627 -0.0274 0.0026 0.0006 -0.0014
11 12 13 14 15 16 17 18 19 20
-0.0014 -0.0014 0.0113 -0.0087 -0.0107 -0.0127 -0.0127 -0.0127 0.0126 0.0026
21 22 23 24 25 26 27 28 29 30
0.0006 -0.0014 -0.0014 -0.0014 0.0113 -0.0087 -0.0107 -0.0127 -0.0127 -0.0127
31 32 33 34 35 36 37 38 39 40
0.0126 0.0026 0.0006 -0.0014 -0.0014 -0.0014 0.0113 0.0813 0.0893 0.0873
41 42 43 44 45 46 47 48 49 50
0.0873 0.0873 -0.0239 -0.0239 -0.0259 -0.0179 -0.0179 -0.0179 0.0048 -0.0052
51 52 53 54 55 56 57 58 59 60
-0.0072 0.0008 0.0008 0.0008 0.0261 0.0161 0.0241 0.0221 0.0221 0.0221
> postscript(file="/var/www/html/rcomp/tmp/6l5491258738364.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 -0.0387 NA
1 -0.0587 -0.0387
2 -0.0607 -0.0587
3 -0.0627 -0.0607
4 -0.0627 -0.0627
5 -0.0627 -0.0627
6 -0.0274 -0.0627
7 0.0026 -0.0274
8 0.0006 0.0026
9 -0.0014 0.0006
10 -0.0014 -0.0014
11 -0.0014 -0.0014
12 0.0113 -0.0014
13 -0.0087 0.0113
14 -0.0107 -0.0087
15 -0.0127 -0.0107
16 -0.0127 -0.0127
17 -0.0127 -0.0127
18 0.0126 -0.0127
19 0.0026 0.0126
20 0.0006 0.0026
21 -0.0014 0.0006
22 -0.0014 -0.0014
23 -0.0014 -0.0014
24 0.0113 -0.0014
25 -0.0087 0.0113
26 -0.0107 -0.0087
27 -0.0127 -0.0107
28 -0.0127 -0.0127
29 -0.0127 -0.0127
30 0.0126 -0.0127
31 0.0026 0.0126
32 0.0006 0.0026
33 -0.0014 0.0006
34 -0.0014 -0.0014
35 -0.0014 -0.0014
36 0.0113 -0.0014
37 0.0813 0.0113
38 0.0893 0.0813
39 0.0873 0.0893
40 0.0873 0.0873
41 0.0873 0.0873
42 -0.0239 0.0873
43 -0.0239 -0.0239
44 -0.0259 -0.0239
45 -0.0179 -0.0259
46 -0.0179 -0.0179
47 -0.0179 -0.0179
48 0.0048 -0.0179
49 -0.0052 0.0048
50 -0.0072 -0.0052
51 0.0008 -0.0072
52 0.0008 0.0008
53 0.0008 0.0008
54 0.0261 0.0008
55 0.0161 0.0261
56 0.0241 0.0161
57 0.0221 0.0241
58 0.0221 0.0221
59 0.0221 0.0221
60 NA 0.0221
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0587 -0.0387
[2,] -0.0607 -0.0587
[3,] -0.0627 -0.0607
[4,] -0.0627 -0.0627
[5,] -0.0627 -0.0627
[6,] -0.0274 -0.0627
[7,] 0.0026 -0.0274
[8,] 0.0006 0.0026
[9,] -0.0014 0.0006
[10,] -0.0014 -0.0014
[11,] -0.0014 -0.0014
[12,] 0.0113 -0.0014
[13,] -0.0087 0.0113
[14,] -0.0107 -0.0087
[15,] -0.0127 -0.0107
[16,] -0.0127 -0.0127
[17,] -0.0127 -0.0127
[18,] 0.0126 -0.0127
[19,] 0.0026 0.0126
[20,] 0.0006 0.0026
[21,] -0.0014 0.0006
[22,] -0.0014 -0.0014
[23,] -0.0014 -0.0014
[24,] 0.0113 -0.0014
[25,] -0.0087 0.0113
[26,] -0.0107 -0.0087
[27,] -0.0127 -0.0107
[28,] -0.0127 -0.0127
[29,] -0.0127 -0.0127
[30,] 0.0126 -0.0127
[31,] 0.0026 0.0126
[32,] 0.0006 0.0026
[33,] -0.0014 0.0006
[34,] -0.0014 -0.0014
[35,] -0.0014 -0.0014
[36,] 0.0113 -0.0014
[37,] 0.0813 0.0113
[38,] 0.0893 0.0813
[39,] 0.0873 0.0893
[40,] 0.0873 0.0873
[41,] 0.0873 0.0873
[42,] -0.0239 0.0873
[43,] -0.0239 -0.0239
[44,] -0.0259 -0.0239
[45,] -0.0179 -0.0259
[46,] -0.0179 -0.0179
[47,] -0.0179 -0.0179
[48,] 0.0048 -0.0179
[49,] -0.0052 0.0048
[50,] -0.0072 -0.0052
[51,] 0.0008 -0.0072
[52,] 0.0008 0.0008
[53,] 0.0008 0.0008
[54,] 0.0261 0.0008
[55,] 0.0161 0.0261
[56,] 0.0241 0.0161
[57,] 0.0221 0.0241
[58,] 0.0221 0.0221
[59,] 0.0221 0.0221
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0587 -0.0387
2 -0.0607 -0.0587
3 -0.0627 -0.0607
4 -0.0627 -0.0627
5 -0.0627 -0.0627
6 -0.0274 -0.0627
7 0.0026 -0.0274
8 0.0006 0.0026
9 -0.0014 0.0006
10 -0.0014 -0.0014
11 -0.0014 -0.0014
12 0.0113 -0.0014
13 -0.0087 0.0113
14 -0.0107 -0.0087
15 -0.0127 -0.0107
16 -0.0127 -0.0127
17 -0.0127 -0.0127
18 0.0126 -0.0127
19 0.0026 0.0126
20 0.0006 0.0026
21 -0.0014 0.0006
22 -0.0014 -0.0014
23 -0.0014 -0.0014
24 0.0113 -0.0014
25 -0.0087 0.0113
26 -0.0107 -0.0087
27 -0.0127 -0.0107
28 -0.0127 -0.0127
29 -0.0127 -0.0127
30 0.0126 -0.0127
31 0.0026 0.0126
32 0.0006 0.0026
33 -0.0014 0.0006
34 -0.0014 -0.0014
35 -0.0014 -0.0014
36 0.0113 -0.0014
37 0.0813 0.0113
38 0.0893 0.0813
39 0.0873 0.0893
40 0.0873 0.0873
41 0.0873 0.0873
42 -0.0239 0.0873
43 -0.0239 -0.0239
44 -0.0259 -0.0239
45 -0.0179 -0.0259
46 -0.0179 -0.0179
47 -0.0179 -0.0179
48 0.0048 -0.0179
49 -0.0052 0.0048
50 -0.0072 -0.0052
51 0.0008 -0.0072
52 0.0008 0.0008
53 0.0008 0.0008
54 0.0261 0.0008
55 0.0161 0.0261
56 0.0241 0.0161
57 0.0221 0.0241
58 0.0221 0.0221
59 0.0221 0.0221
> 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/7tcd61258738364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8s8361258738364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91s161258738364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10urqt1258738364.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116btp1258738364.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/12nkfv1258738364.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/13n1uf1258738364.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/14rg2t1258738364.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/15i3r51258738364.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/162zzx1258738365.tab")
+ }
>
> system("convert tmp/1jy0q1258738364.ps tmp/1jy0q1258738364.png")
> system("convert tmp/29pyf1258738364.ps tmp/29pyf1258738364.png")
> system("convert tmp/3s6u11258738364.ps tmp/3s6u11258738364.png")
> system("convert tmp/4wtqx1258738364.ps tmp/4wtqx1258738364.png")
> system("convert tmp/5c40r1258738364.ps tmp/5c40r1258738364.png")
> system("convert tmp/6l5491258738364.ps tmp/6l5491258738364.png")
> system("convert tmp/7tcd61258738364.ps tmp/7tcd61258738364.png")
> system("convert tmp/8s8361258738364.ps tmp/8s8361258738364.png")
> system("convert tmp/91s161258738364.ps tmp/91s161258738364.png")
> system("convert tmp/10urqt1258738364.ps tmp/10urqt1258738364.png")
>
>
> proc.time()
user system elapsed
2.390 1.524 3.292