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.
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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(3.1,6.3,3.5,7.1,6,7.5,5.7,7.4,4.7,7.1,4.2,6.8,3.6,6.9,4.4,7.2,2.5,7.4,-0.6,7.3,-1.9,6.9,-1.9,6.9,0.7,6.8,-0.9,7.1,-1.7,7.2,-3.1,7.1,-2.1,7,0.2,6.9,1.2,7.1,3.8,7.3,4,7.5,6.6,7.5,5.3,7.5,7.6,7.3,4.7,7,6.6,6.7,4.4,6.5,4.6,6.5,6,6.5,4.8,6.6,4,6.8,2.7,6.9,3,6.9,4.1,6.8,4,6.8,2.7,6.5,2.6,6.1,3.1,6.1,4.4,5.9,3,5.7,2,5.9,1.3,5.9,1.5,6.1,1.3,6.3,3.2,6.2,1.8,5.9,3.3,5.7,1,5.4,2.4,5.6,0.4,6.2,-0.1,6.3,1.3,6,-1.1,5.6,-4.4,5.5,-7.5,5.9,-12.2,6.5,-14.5,6.8,-16,6.8,-16.7,6.5,-16.3,6.2,-16.9,6.2),dim=c(2,61),dimnames=list(c('ip','wklh'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('ip','wklh'),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'
> #'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
ip wklh
1 3.1 6.3
2 3.5 7.1
3 6.0 7.5
4 5.7 7.4
5 4.7 7.1
6 4.2 6.8
7 3.6 6.9
8 4.4 7.2
9 2.5 7.4
10 -0.6 7.3
11 -1.9 6.9
12 -1.9 6.9
13 0.7 6.8
14 -0.9 7.1
15 -1.7 7.2
16 -3.1 7.1
17 -2.1 7.0
18 0.2 6.9
19 1.2 7.1
20 3.8 7.3
21 4.0 7.5
22 6.6 7.5
23 5.3 7.5
24 7.6 7.3
25 4.7 7.0
26 6.6 6.7
27 4.4 6.5
28 4.6 6.5
29 6.0 6.5
30 4.8 6.6
31 4.0 6.8
32 2.7 6.9
33 3.0 6.9
34 4.1 6.8
35 4.0 6.8
36 2.7 6.5
37 2.6 6.1
38 3.1 6.1
39 4.4 5.9
40 3.0 5.7
41 2.0 5.9
42 1.3 5.9
43 1.5 6.1
44 1.3 6.3
45 3.2 6.2
46 1.8 5.9
47 3.3 5.7
48 1.0 5.4
49 2.4 5.6
50 0.4 6.2
51 -0.1 6.3
52 1.3 6.0
53 -1.1 5.6
54 -4.4 5.5
55 -7.5 5.9
56 -12.2 6.5
57 -14.5 6.8
58 -16.0 6.8
59 -16.7 6.5
60 -16.3 6.2
61 -16.9 6.2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) wklh
-13.140 2.072
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.030 -0.959 2.213 3.334 5.855
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -13.140 8.862 -1.483 0.143
wklh 2.072 1.337 1.550 0.126
Residual standard error: 5.965 on 59 degrees of freedom
Multiple R-squared: 0.03914, Adjusted R-squared: 0.02285
F-statistic: 2.403 on 1 and 59 DF, p-value: 0.1264
> 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,] 2.580044e-03 5.160088e-03 0.99741996
[2,] 2.737347e-04 5.474695e-04 0.99972627
[3,] 3.823269e-05 7.646539e-05 0.99996177
[4,] 4.364790e-06 8.729580e-06 0.99999564
[5,] 3.179133e-05 6.358266e-05 0.99996821
[6,] 7.608296e-04 1.521659e-03 0.99923917
[7,] 2.554371e-03 5.108743e-03 0.99744563
[8,] 3.394972e-03 6.789945e-03 0.99660503
[9,] 1.480444e-03 2.960888e-03 0.99851956
[10,] 1.138380e-03 2.276761e-03 0.99886162
[11,] 1.134390e-03 2.268781e-03 0.99886561
[12,] 1.543160e-03 3.086320e-03 0.99845684
[13,] 1.164968e-03 2.329936e-03 0.99883503
[14,] 5.209201e-04 1.041840e-03 0.99947908
[15,] 2.158794e-04 4.317588e-04 0.99978412
[16,] 1.006843e-04 2.013687e-04 0.99989932
[17,] 4.301488e-05 8.602975e-05 0.99995699
[18,] 3.341512e-05 6.683025e-05 0.99996658
[19,] 1.640988e-05 3.281976e-05 0.99998359
[20,] 2.336389e-05 4.672779e-05 0.99997664
[21,] 1.557875e-05 3.115751e-05 0.99998442
[22,] 3.109818e-05 6.219635e-05 0.99996890
[23,] 2.326931e-05 4.653862e-05 0.99997673
[24,] 1.658069e-05 3.316139e-05 0.99998342
[25,] 1.730107e-05 3.460215e-05 0.99998270
[26,] 1.217048e-05 2.434096e-05 0.99998783
[27,] 8.029844e-06 1.605969e-05 0.99999197
[28,] 5.150733e-06 1.030147e-05 0.99999485
[29,] 4.265548e-06 8.531095e-06 0.99999573
[30,] 6.029820e-06 1.205964e-05 0.99999397
[31,] 1.685899e-05 3.371798e-05 0.99998314
[32,] 2.718410e-05 5.436819e-05 0.99997282
[33,] 1.932304e-05 3.864607e-05 0.99998068
[34,] 1.627842e-05 3.255685e-05 0.99998372
[35,] 1.294982e-05 2.589965e-05 0.99998705
[36,] 5.918495e-06 1.183699e-05 0.99999408
[37,] 3.271485e-06 6.542969e-06 0.99999673
[38,] 1.778649e-06 3.557298e-06 0.99999822
[39,] 1.556813e-06 3.113627e-06 0.99999844
[40,] 3.391522e-06 6.783044e-06 0.99999661
[41,] 1.582610e-05 3.165220e-05 0.99998417
[42,] 1.512787e-05 3.025574e-05 0.99998487
[43,] 1.322183e-05 2.644366e-05 0.99998678
[44,] 5.500168e-06 1.100034e-05 0.99999450
[45,] 3.229275e-06 6.458550e-06 0.99999677
[46,] 2.424988e-05 4.849977e-05 0.99997575
[47,] 1.078864e-03 2.157728e-03 0.99892114
[48,] 4.844973e-02 9.689945e-02 0.95155027
[49,] 1.153637e-01 2.307274e-01 0.88463631
[50,] 1.687150e-01 3.374300e-01 0.83128499
[51,] 7.741108e-01 4.517785e-01 0.22588923
[52,] 9.844727e-01 3.105459e-02 0.01552730
> postscript(file="/var/www/html/rcomp/tmp/1mqm61260816827.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2frj71260816827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hwvt1260816827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41bpl1260816827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5l1nb1260816827.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 = 61
Frequency = 1
1 2 3 4 5 6
3.18432997 1.92651801 3.59761203 3.50483852 3.12651801 3.24819749
7 8 9 10 11 12
2.44097100 2.61929151 0.30483852 -2.58793498 -3.05902900 -3.05902900
13 14 15 16 17 18
-0.25180251 -2.47348199 -3.48070849 -4.67348199 -3.46625550 -0.95902900
19 20 21 22 23 24
-0.37348199 1.81206502 1.59761203 4.19761203 2.89761203 5.61206502
25 26 27 28 29 30
3.33374450 5.85542399 4.06987698 4.26987698 5.66987698 4.26265048
31 32 33 34 35 36
3.04819749 1.54097100 1.84097100 3.14819749 3.04819749 2.36987698
37 38 39 40 41 42
3.09878296 3.59878296 5.31323595 4.32768894 2.91323595 2.21323595
43 44 45 46 47 48
1.99878296 1.38432997 3.49155646 2.71323595 4.62768894 2.94936842
49 50 51 52 53 54
3.93491543 0.69155646 -0.01567003 2.00600945 0.43491543 -2.65785807
55 56 57 58 59 60
-6.58676405 -12.53012302 -15.45180251 -16.95180251 -17.03012302 -16.00844354
61
-16.60844354
> postscript(file="/var/www/html/rcomp/tmp/6fw5q1260816827.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 3.18432997 NA
1 1.92651801 3.18432997
2 3.59761203 1.92651801
3 3.50483852 3.59761203
4 3.12651801 3.50483852
5 3.24819749 3.12651801
6 2.44097100 3.24819749
7 2.61929151 2.44097100
8 0.30483852 2.61929151
9 -2.58793498 0.30483852
10 -3.05902900 -2.58793498
11 -3.05902900 -3.05902900
12 -0.25180251 -3.05902900
13 -2.47348199 -0.25180251
14 -3.48070849 -2.47348199
15 -4.67348199 -3.48070849
16 -3.46625550 -4.67348199
17 -0.95902900 -3.46625550
18 -0.37348199 -0.95902900
19 1.81206502 -0.37348199
20 1.59761203 1.81206502
21 4.19761203 1.59761203
22 2.89761203 4.19761203
23 5.61206502 2.89761203
24 3.33374450 5.61206502
25 5.85542399 3.33374450
26 4.06987698 5.85542399
27 4.26987698 4.06987698
28 5.66987698 4.26987698
29 4.26265048 5.66987698
30 3.04819749 4.26265048
31 1.54097100 3.04819749
32 1.84097100 1.54097100
33 3.14819749 1.84097100
34 3.04819749 3.14819749
35 2.36987698 3.04819749
36 3.09878296 2.36987698
37 3.59878296 3.09878296
38 5.31323595 3.59878296
39 4.32768894 5.31323595
40 2.91323595 4.32768894
41 2.21323595 2.91323595
42 1.99878296 2.21323595
43 1.38432997 1.99878296
44 3.49155646 1.38432997
45 2.71323595 3.49155646
46 4.62768894 2.71323595
47 2.94936842 4.62768894
48 3.93491543 2.94936842
49 0.69155646 3.93491543
50 -0.01567003 0.69155646
51 2.00600945 -0.01567003
52 0.43491543 2.00600945
53 -2.65785807 0.43491543
54 -6.58676405 -2.65785807
55 -12.53012302 -6.58676405
56 -15.45180251 -12.53012302
57 -16.95180251 -15.45180251
58 -17.03012302 -16.95180251
59 -16.00844354 -17.03012302
60 -16.60844354 -16.00844354
61 NA -16.60844354
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.92651801 3.18432997
[2,] 3.59761203 1.92651801
[3,] 3.50483852 3.59761203
[4,] 3.12651801 3.50483852
[5,] 3.24819749 3.12651801
[6,] 2.44097100 3.24819749
[7,] 2.61929151 2.44097100
[8,] 0.30483852 2.61929151
[9,] -2.58793498 0.30483852
[10,] -3.05902900 -2.58793498
[11,] -3.05902900 -3.05902900
[12,] -0.25180251 -3.05902900
[13,] -2.47348199 -0.25180251
[14,] -3.48070849 -2.47348199
[15,] -4.67348199 -3.48070849
[16,] -3.46625550 -4.67348199
[17,] -0.95902900 -3.46625550
[18,] -0.37348199 -0.95902900
[19,] 1.81206502 -0.37348199
[20,] 1.59761203 1.81206502
[21,] 4.19761203 1.59761203
[22,] 2.89761203 4.19761203
[23,] 5.61206502 2.89761203
[24,] 3.33374450 5.61206502
[25,] 5.85542399 3.33374450
[26,] 4.06987698 5.85542399
[27,] 4.26987698 4.06987698
[28,] 5.66987698 4.26987698
[29,] 4.26265048 5.66987698
[30,] 3.04819749 4.26265048
[31,] 1.54097100 3.04819749
[32,] 1.84097100 1.54097100
[33,] 3.14819749 1.84097100
[34,] 3.04819749 3.14819749
[35,] 2.36987698 3.04819749
[36,] 3.09878296 2.36987698
[37,] 3.59878296 3.09878296
[38,] 5.31323595 3.59878296
[39,] 4.32768894 5.31323595
[40,] 2.91323595 4.32768894
[41,] 2.21323595 2.91323595
[42,] 1.99878296 2.21323595
[43,] 1.38432997 1.99878296
[44,] 3.49155646 1.38432997
[45,] 2.71323595 3.49155646
[46,] 4.62768894 2.71323595
[47,] 2.94936842 4.62768894
[48,] 3.93491543 2.94936842
[49,] 0.69155646 3.93491543
[50,] -0.01567003 0.69155646
[51,] 2.00600945 -0.01567003
[52,] 0.43491543 2.00600945
[53,] -2.65785807 0.43491543
[54,] -6.58676405 -2.65785807
[55,] -12.53012302 -6.58676405
[56,] -15.45180251 -12.53012302
[57,] -16.95180251 -15.45180251
[58,] -17.03012302 -16.95180251
[59,] -16.00844354 -17.03012302
[60,] -16.60844354 -16.00844354
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.92651801 3.18432997
2 3.59761203 1.92651801
3 3.50483852 3.59761203
4 3.12651801 3.50483852
5 3.24819749 3.12651801
6 2.44097100 3.24819749
7 2.61929151 2.44097100
8 0.30483852 2.61929151
9 -2.58793498 0.30483852
10 -3.05902900 -2.58793498
11 -3.05902900 -3.05902900
12 -0.25180251 -3.05902900
13 -2.47348199 -0.25180251
14 -3.48070849 -2.47348199
15 -4.67348199 -3.48070849
16 -3.46625550 -4.67348199
17 -0.95902900 -3.46625550
18 -0.37348199 -0.95902900
19 1.81206502 -0.37348199
20 1.59761203 1.81206502
21 4.19761203 1.59761203
22 2.89761203 4.19761203
23 5.61206502 2.89761203
24 3.33374450 5.61206502
25 5.85542399 3.33374450
26 4.06987698 5.85542399
27 4.26987698 4.06987698
28 5.66987698 4.26987698
29 4.26265048 5.66987698
30 3.04819749 4.26265048
31 1.54097100 3.04819749
32 1.84097100 1.54097100
33 3.14819749 1.84097100
34 3.04819749 3.14819749
35 2.36987698 3.04819749
36 3.09878296 2.36987698
37 3.59878296 3.09878296
38 5.31323595 3.59878296
39 4.32768894 5.31323595
40 2.91323595 4.32768894
41 2.21323595 2.91323595
42 1.99878296 2.21323595
43 1.38432997 1.99878296
44 3.49155646 1.38432997
45 2.71323595 3.49155646
46 4.62768894 2.71323595
47 2.94936842 4.62768894
48 3.93491543 2.94936842
49 0.69155646 3.93491543
50 -0.01567003 0.69155646
51 2.00600945 -0.01567003
52 0.43491543 2.00600945
53 -2.65785807 0.43491543
54 -6.58676405 -2.65785807
55 -12.53012302 -6.58676405
56 -15.45180251 -12.53012302
57 -16.95180251 -15.45180251
58 -17.03012302 -16.95180251
59 -16.00844354 -17.03012302
60 -16.60844354 -16.00844354
> 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/7kt9b1260816827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87w9e1260816827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xs241260816827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106y451260816827.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qpkz1260816827.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/12uqhj1260816827.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/13y7it1260816827.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/14se3a1260816827.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/1589zb1260816827.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/16vwx91260816827.tab")
+ }
>
> try(system("convert tmp/1mqm61260816827.ps tmp/1mqm61260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/2frj71260816827.ps tmp/2frj71260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hwvt1260816827.ps tmp/3hwvt1260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/41bpl1260816827.ps tmp/41bpl1260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/5l1nb1260816827.ps tmp/5l1nb1260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fw5q1260816827.ps tmp/6fw5q1260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kt9b1260816827.ps tmp/7kt9b1260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/87w9e1260816827.ps tmp/87w9e1260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xs241260816827.ps tmp/9xs241260816827.png",intern=TRUE))
character(0)
> try(system("convert tmp/106y451260816827.ps tmp/106y451260816827.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.497 1.586 3.679