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.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(8,0,8.1,0,7.7,0,7.5,0,7.6,0,7.8,0,7.8,0,7.8,0,7.5,0,7.5,0,7.1,0,7.5,0,7.5,0,7.6,0,7.7,0,7.7,0,7.9,0,8.1,0,8.2,0,8.2,0,8.2,0,7.9,0,7.3,0,6.9,0,6.6,0,6.7,0,6.9,0,7,0,7.1,0,7.2,0,7.1,0,6.9,0,7,0,6.8,0,6.4,0,6.7,0,6.6,0,6.4,0,6.3,0,6.2,0,6.5,0,6.8,1,6.8,1,6.4,1,6.1,1,5.8,1,6.1,1,7.2,1,7.3,1,6.9,1,6.1,1,5.8,1,6.2,1,7.1,1,7.7,1,7.9,1,7.7,1,7.4,1,7.5,1,8,1,8.1,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),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 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.0 0 1 0 0 0 0 0 0 0 0 0 0
2 8.1 0 0 1 0 0 0 0 0 0 0 0 0
3 7.7 0 0 0 1 0 0 0 0 0 0 0 0
4 7.5 0 0 0 0 1 0 0 0 0 0 0 0
5 7.6 0 0 0 0 0 1 0 0 0 0 0 0
6 7.8 0 0 0 0 0 0 1 0 0 0 0 0
7 7.8 0 0 0 0 0 0 0 1 0 0 0 0
8 7.8 0 0 0 0 0 0 0 0 1 0 0 0
9 7.5 0 0 0 0 0 0 0 0 0 1 0 0
10 7.5 0 0 0 0 0 0 0 0 0 0 1 0
11 7.1 0 0 0 0 0 0 0 0 0 0 0 1
12 7.5 0 0 0 0 0 0 0 0 0 0 0 0
13 7.5 0 1 0 0 0 0 0 0 0 0 0 0
14 7.6 0 0 1 0 0 0 0 0 0 0 0 0
15 7.7 0 0 0 1 0 0 0 0 0 0 0 0
16 7.7 0 0 0 0 1 0 0 0 0 0 0 0
17 7.9 0 0 0 0 0 1 0 0 0 0 0 0
18 8.1 0 0 0 0 0 0 1 0 0 0 0 0
19 8.2 0 0 0 0 0 0 0 1 0 0 0 0
20 8.2 0 0 0 0 0 0 0 0 1 0 0 0
21 8.2 0 0 0 0 0 0 0 0 0 1 0 0
22 7.9 0 0 0 0 0 0 0 0 0 0 1 0
23 7.3 0 0 0 0 0 0 0 0 0 0 0 1
24 6.9 0 0 0 0 0 0 0 0 0 0 0 0
25 6.6 0 1 0 0 0 0 0 0 0 0 0 0
26 6.7 0 0 1 0 0 0 0 0 0 0 0 0
27 6.9 0 0 0 1 0 0 0 0 0 0 0 0
28 7.0 0 0 0 0 1 0 0 0 0 0 0 0
29 7.1 0 0 0 0 0 1 0 0 0 0 0 0
30 7.2 0 0 0 0 0 0 1 0 0 0 0 0
31 7.1 0 0 0 0 0 0 0 1 0 0 0 0
32 6.9 0 0 0 0 0 0 0 0 1 0 0 0
33 7.0 0 0 0 0 0 0 0 0 0 1 0 0
34 6.8 0 0 0 0 0 0 0 0 0 0 1 0
35 6.4 0 0 0 0 0 0 0 0 0 0 0 1
36 6.7 0 0 0 0 0 0 0 0 0 0 0 0
37 6.6 0 1 0 0 0 0 0 0 0 0 0 0
38 6.4 0 0 1 0 0 0 0 0 0 0 0 0
39 6.3 0 0 0 1 0 0 0 0 0 0 0 0
40 6.2 0 0 0 0 1 0 0 0 0 0 0 0
41 6.5 0 0 0 0 0 1 0 0 0 0 0 0
42 6.8 1 0 0 0 0 0 1 0 0 0 0 0
43 6.8 1 0 0 0 0 0 0 1 0 0 0 0
44 6.4 1 0 0 0 0 0 0 0 1 0 0 0
45 6.1 1 0 0 0 0 0 0 0 0 1 0 0
46 5.8 1 0 0 0 0 0 0 0 0 0 1 0
47 6.1 1 0 0 0 0 0 0 0 0 0 0 1
48 7.2 1 0 0 0 0 0 0 0 0 0 0 0
49 7.3 1 1 0 0 0 0 0 0 0 0 0 0
50 6.9 1 0 1 0 0 0 0 0 0 0 0 0
51 6.1 1 0 0 1 0 0 0 0 0 0 0 0
52 5.8 1 0 0 0 1 0 0 0 0 0 0 0
53 6.2 1 0 0 0 0 1 0 0 0 0 0 0
54 7.1 1 0 0 0 0 0 1 0 0 0 0 0
55 7.7 1 0 0 0 0 0 0 1 0 0 0 0
56 7.9 1 0 0 0 0 0 0 0 1 0 0 0
57 7.7 1 0 0 0 0 0 0 0 0 1 0 0
58 7.4 1 0 0 0 0 0 0 0 0 0 1 0
59 7.5 1 0 0 0 0 0 0 0 0 0 0 1
60 8.0 1 0 0 0 0 0 0 0 0 0 0 0
61 8.1 1 1 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
7.43134 -0.42835 0.06144 -0.20567 -0.40567 -0.50567
M5 M6 M7 M8 M9 M10
-0.28567 0.14000 0.26000 0.18000 0.04000 -0.18000
M11
-0.38000
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.02299 -0.52567 0.06866 0.52866 1.03557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.43134 0.30567 24.312 <2e-16 ***
X -0.42835 0.18444 -2.322 0.0245 *
M1 0.06144 0.40183 0.153 0.8791
M2 -0.20567 0.42112 -0.488 0.6275
M3 -0.40567 0.42112 -0.963 0.3402
M4 -0.50567 0.42112 -1.201 0.2357
M5 -0.28567 0.42112 -0.678 0.5008
M6 0.14000 0.41950 0.334 0.7400
M7 0.26000 0.41950 0.620 0.5383
M8 0.18000 0.41950 0.429 0.6698
M9 0.04000 0.41950 0.095 0.9244
M10 -0.18000 0.41950 -0.429 0.6698
M11 -0.38000 0.41950 -0.906 0.3695
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6633 on 48 degrees of freedom
Multiple R-squared: 0.1992, Adjusted R-squared: -0.001059
F-statistic: 0.9947 on 12 and 48 DF, p-value: 0.468
> 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,] 0.095379367 0.19075873 0.9046206
[2,] 0.048354067 0.09670813 0.9516459
[3,] 0.024347517 0.04869503 0.9756525
[4,] 0.015589202 0.03117840 0.9844108
[5,] 0.010992795 0.02198559 0.9890072
[6,] 0.019614166 0.03922833 0.9803858
[7,] 0.017584242 0.03516848 0.9824158
[8,] 0.009769261 0.01953852 0.9902307
[9,] 0.009438043 0.01887609 0.9905620
[10,] 0.056759648 0.11351930 0.9432404
[11,] 0.119787632 0.23957526 0.8802124
[12,] 0.137556717 0.27511343 0.8624433
[13,] 0.151320016 0.30264003 0.8486800
[14,] 0.156793987 0.31358797 0.8432060
[15,] 0.152067506 0.30413501 0.8479325
[16,] 0.153091946 0.30618389 0.8469081
[17,] 0.173047314 0.34609463 0.8269527
[18,] 0.160767358 0.32153472 0.8392326
[19,] 0.156476176 0.31295235 0.8435238
[20,] 0.130028691 0.26005738 0.8699713
[21,] 0.105551646 0.21110329 0.8944484
[22,] 0.124369743 0.24873949 0.8756303
[23,] 0.131775862 0.26355172 0.8682241
[24,] 0.121113334 0.24222667 0.8788867
[25,] 0.108580411 0.21716082 0.8914196
[26,] 0.083678965 0.16735793 0.9163210
[27,] 0.048118269 0.09623654 0.9518817
[28,] 0.032709809 0.06541962 0.9672902
[29,] 0.039289855 0.07857971 0.9607101
[30,] 0.061263495 0.12252699 0.9387365
> postscript(file="/var/www/html/rcomp/tmp/1mop11258896063.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2o8rz1258896063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3paic1258896063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4t0e71258896063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/535fm1258896063.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
0.507216495 0.874329897 0.674329897 0.574329897 0.454329897 0.228659794
7 8 9 10 11 12
0.108659794 0.188659794 0.028659794 0.248659794 0.048659794 0.068659794
13 14 15 16 17 18
0.007216495 0.374329897 0.674329897 0.774329897 0.754329897 0.528659794
19 20 21 22 23 24
0.508659794 0.588659794 0.728659794 0.648659794 0.248659794 -0.531340206
25 26 27 28 29 30
-0.892783505 -0.525670103 -0.125670103 0.074329897 -0.045670103 -0.371340206
31 32 33 34 35 36
-0.591340206 -0.711340206 -0.471340206 -0.451340206 -0.651340206 -0.731340206
37 38 39 40 41 42
-0.892783505 -0.825670103 -0.725670103 -0.725670103 -0.645670103 -0.342989691
43 44 45 46 47 48
-0.462989691 -0.782989691 -0.942989691 -1.022989691 -0.522989691 0.197010309
49 50 51 52 53 54
0.235567010 0.102680412 -0.497319588 -0.697319588 -0.517319588 -0.042989691
55 56 57 58 59 60
0.437010309 0.717010309 0.657010309 0.577010309 0.877010309 0.997010309
61
1.035567010
> postscript(file="/var/www/html/rcomp/tmp/6oez41258896063.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 0.507216495 NA
1 0.874329897 0.507216495
2 0.674329897 0.874329897
3 0.574329897 0.674329897
4 0.454329897 0.574329897
5 0.228659794 0.454329897
6 0.108659794 0.228659794
7 0.188659794 0.108659794
8 0.028659794 0.188659794
9 0.248659794 0.028659794
10 0.048659794 0.248659794
11 0.068659794 0.048659794
12 0.007216495 0.068659794
13 0.374329897 0.007216495
14 0.674329897 0.374329897
15 0.774329897 0.674329897
16 0.754329897 0.774329897
17 0.528659794 0.754329897
18 0.508659794 0.528659794
19 0.588659794 0.508659794
20 0.728659794 0.588659794
21 0.648659794 0.728659794
22 0.248659794 0.648659794
23 -0.531340206 0.248659794
24 -0.892783505 -0.531340206
25 -0.525670103 -0.892783505
26 -0.125670103 -0.525670103
27 0.074329897 -0.125670103
28 -0.045670103 0.074329897
29 -0.371340206 -0.045670103
30 -0.591340206 -0.371340206
31 -0.711340206 -0.591340206
32 -0.471340206 -0.711340206
33 -0.451340206 -0.471340206
34 -0.651340206 -0.451340206
35 -0.731340206 -0.651340206
36 -0.892783505 -0.731340206
37 -0.825670103 -0.892783505
38 -0.725670103 -0.825670103
39 -0.725670103 -0.725670103
40 -0.645670103 -0.725670103
41 -0.342989691 -0.645670103
42 -0.462989691 -0.342989691
43 -0.782989691 -0.462989691
44 -0.942989691 -0.782989691
45 -1.022989691 -0.942989691
46 -0.522989691 -1.022989691
47 0.197010309 -0.522989691
48 0.235567010 0.197010309
49 0.102680412 0.235567010
50 -0.497319588 0.102680412
51 -0.697319588 -0.497319588
52 -0.517319588 -0.697319588
53 -0.042989691 -0.517319588
54 0.437010309 -0.042989691
55 0.717010309 0.437010309
56 0.657010309 0.717010309
57 0.577010309 0.657010309
58 0.877010309 0.577010309
59 0.997010309 0.877010309
60 1.035567010 0.997010309
61 NA 1.035567010
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.874329897 0.507216495
[2,] 0.674329897 0.874329897
[3,] 0.574329897 0.674329897
[4,] 0.454329897 0.574329897
[5,] 0.228659794 0.454329897
[6,] 0.108659794 0.228659794
[7,] 0.188659794 0.108659794
[8,] 0.028659794 0.188659794
[9,] 0.248659794 0.028659794
[10,] 0.048659794 0.248659794
[11,] 0.068659794 0.048659794
[12,] 0.007216495 0.068659794
[13,] 0.374329897 0.007216495
[14,] 0.674329897 0.374329897
[15,] 0.774329897 0.674329897
[16,] 0.754329897 0.774329897
[17,] 0.528659794 0.754329897
[18,] 0.508659794 0.528659794
[19,] 0.588659794 0.508659794
[20,] 0.728659794 0.588659794
[21,] 0.648659794 0.728659794
[22,] 0.248659794 0.648659794
[23,] -0.531340206 0.248659794
[24,] -0.892783505 -0.531340206
[25,] -0.525670103 -0.892783505
[26,] -0.125670103 -0.525670103
[27,] 0.074329897 -0.125670103
[28,] -0.045670103 0.074329897
[29,] -0.371340206 -0.045670103
[30,] -0.591340206 -0.371340206
[31,] -0.711340206 -0.591340206
[32,] -0.471340206 -0.711340206
[33,] -0.451340206 -0.471340206
[34,] -0.651340206 -0.451340206
[35,] -0.731340206 -0.651340206
[36,] -0.892783505 -0.731340206
[37,] -0.825670103 -0.892783505
[38,] -0.725670103 -0.825670103
[39,] -0.725670103 -0.725670103
[40,] -0.645670103 -0.725670103
[41,] -0.342989691 -0.645670103
[42,] -0.462989691 -0.342989691
[43,] -0.782989691 -0.462989691
[44,] -0.942989691 -0.782989691
[45,] -1.022989691 -0.942989691
[46,] -0.522989691 -1.022989691
[47,] 0.197010309 -0.522989691
[48,] 0.235567010 0.197010309
[49,] 0.102680412 0.235567010
[50,] -0.497319588 0.102680412
[51,] -0.697319588 -0.497319588
[52,] -0.517319588 -0.697319588
[53,] -0.042989691 -0.517319588
[54,] 0.437010309 -0.042989691
[55,] 0.717010309 0.437010309
[56,] 0.657010309 0.717010309
[57,] 0.577010309 0.657010309
[58,] 0.877010309 0.577010309
[59,] 0.997010309 0.877010309
[60,] 1.035567010 0.997010309
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.874329897 0.507216495
2 0.674329897 0.874329897
3 0.574329897 0.674329897
4 0.454329897 0.574329897
5 0.228659794 0.454329897
6 0.108659794 0.228659794
7 0.188659794 0.108659794
8 0.028659794 0.188659794
9 0.248659794 0.028659794
10 0.048659794 0.248659794
11 0.068659794 0.048659794
12 0.007216495 0.068659794
13 0.374329897 0.007216495
14 0.674329897 0.374329897
15 0.774329897 0.674329897
16 0.754329897 0.774329897
17 0.528659794 0.754329897
18 0.508659794 0.528659794
19 0.588659794 0.508659794
20 0.728659794 0.588659794
21 0.648659794 0.728659794
22 0.248659794 0.648659794
23 -0.531340206 0.248659794
24 -0.892783505 -0.531340206
25 -0.525670103 -0.892783505
26 -0.125670103 -0.525670103
27 0.074329897 -0.125670103
28 -0.045670103 0.074329897
29 -0.371340206 -0.045670103
30 -0.591340206 -0.371340206
31 -0.711340206 -0.591340206
32 -0.471340206 -0.711340206
33 -0.451340206 -0.471340206
34 -0.651340206 -0.451340206
35 -0.731340206 -0.651340206
36 -0.892783505 -0.731340206
37 -0.825670103 -0.892783505
38 -0.725670103 -0.825670103
39 -0.725670103 -0.725670103
40 -0.645670103 -0.725670103
41 -0.342989691 -0.645670103
42 -0.462989691 -0.342989691
43 -0.782989691 -0.462989691
44 -0.942989691 -0.782989691
45 -1.022989691 -0.942989691
46 -0.522989691 -1.022989691
47 0.197010309 -0.522989691
48 0.235567010 0.197010309
49 0.102680412 0.235567010
50 -0.497319588 0.102680412
51 -0.697319588 -0.497319588
52 -0.517319588 -0.697319588
53 -0.042989691 -0.517319588
54 0.437010309 -0.042989691
55 0.717010309 0.437010309
56 0.657010309 0.717010309
57 0.577010309 0.657010309
58 0.877010309 0.577010309
59 0.997010309 0.877010309
60 1.035567010 0.997010309
> 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/74udq1258896063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ums71258896063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jymh1258896063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ynkc1258896063.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nhr41258896063.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/12fljx1258896063.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/13x2h31258896063.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/149r5b1258896063.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/150dra1258896063.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/16ujca1258896063.tab")
+ }
>
> system("convert tmp/1mop11258896063.ps tmp/1mop11258896063.png")
> system("convert tmp/2o8rz1258896063.ps tmp/2o8rz1258896063.png")
> system("convert tmp/3paic1258896063.ps tmp/3paic1258896063.png")
> system("convert tmp/4t0e71258896063.ps tmp/4t0e71258896063.png")
> system("convert tmp/535fm1258896063.ps tmp/535fm1258896063.png")
> system("convert tmp/6oez41258896063.ps tmp/6oez41258896063.png")
> system("convert tmp/74udq1258896063.ps tmp/74udq1258896063.png")
> system("convert tmp/8ums71258896063.ps tmp/8ums71258896063.png")
> system("convert tmp/9jymh1258896063.ps tmp/9jymh1258896063.png")
> system("convert tmp/10ynkc1258896063.ps tmp/10ynkc1258896063.png")
>
>
> proc.time()
user system elapsed
2.388 1.518 2.952