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.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3.2,1,1.9,1,0,1,0.6,1,0.2,1,0.9,1,2.4,1,4.7,1,9.4,1,12.5,1,15.8,1,18.2,1,16.8,0,17.3,0,19.3,0,17.9,0,20.2,0,18.7,0,20.1,0,18.2,0,18.4,0,18.2,0,18.9,0,19.9,0,21.3,0,20,0,19.5,0,19.6,0,20.9,0,21,0,19.9,0,19.6,0,20.9,0,21.7,0,22.9,0,21.5,0,21.3,0,23.5,0,21.6,0,24.5,0,22.2,0,23.5,0,20.9,0,20.7,0,18.1,0,17.1,0,14.8,0,13.8,0,15.2,0,16,0,17.6,0,15,0,15,0,16.3,0,19.4,0,21.3,0,20.5,0,21.1,0,21.6,0,22.6,0),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','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 = '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 t
1 3.2 1 1 0 0 0 0 0 0 0 0 0 0 1
2 1.9 1 0 1 0 0 0 0 0 0 0 0 0 2
3 0.0 1 0 0 1 0 0 0 0 0 0 0 0 3
4 0.6 1 0 0 0 1 0 0 0 0 0 0 0 4
5 0.2 1 0 0 0 0 1 0 0 0 0 0 0 5
6 0.9 1 0 0 0 0 0 1 0 0 0 0 0 6
7 2.4 1 0 0 0 0 0 0 1 0 0 0 0 7
8 4.7 1 0 0 0 0 0 0 0 1 0 0 0 8
9 9.4 1 0 0 0 0 0 0 0 0 1 0 0 9
10 12.5 1 0 0 0 0 0 0 0 0 0 1 0 10
11 15.8 1 0 0 0 0 0 0 0 0 0 0 1 11
12 18.2 1 0 0 0 0 0 0 0 0 0 0 0 12
13 16.8 0 1 0 0 0 0 0 0 0 0 0 0 13
14 17.3 0 0 1 0 0 0 0 0 0 0 0 0 14
15 19.3 0 0 0 1 0 0 0 0 0 0 0 0 15
16 17.9 0 0 0 0 1 0 0 0 0 0 0 0 16
17 20.2 0 0 0 0 0 1 0 0 0 0 0 0 17
18 18.7 0 0 0 0 0 0 1 0 0 0 0 0 18
19 20.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 18.2 0 0 0 0 0 0 0 0 1 0 0 0 20
21 18.4 0 0 0 0 0 0 0 0 0 1 0 0 21
22 18.2 0 0 0 0 0 0 0 0 0 0 1 0 22
23 18.9 0 0 0 0 0 0 0 0 0 0 0 1 23
24 19.9 0 0 0 0 0 0 0 0 0 0 0 0 24
25 21.3 0 1 0 0 0 0 0 0 0 0 0 0 25
26 20.0 0 0 1 0 0 0 0 0 0 0 0 0 26
27 19.5 0 0 0 1 0 0 0 0 0 0 0 0 27
28 19.6 0 0 0 0 1 0 0 0 0 0 0 0 28
29 20.9 0 0 0 0 0 1 0 0 0 0 0 0 29
30 21.0 0 0 0 0 0 0 1 0 0 0 0 0 30
31 19.9 0 0 0 0 0 0 0 1 0 0 0 0 31
32 19.6 0 0 0 0 0 0 0 0 1 0 0 0 32
33 20.9 0 0 0 0 0 0 0 0 0 1 0 0 33
34 21.7 0 0 0 0 0 0 0 0 0 0 1 0 34
35 22.9 0 0 0 0 0 0 0 0 0 0 0 1 35
36 21.5 0 0 0 0 0 0 0 0 0 0 0 0 36
37 21.3 0 1 0 0 0 0 0 0 0 0 0 0 37
38 23.5 0 0 1 0 0 0 0 0 0 0 0 0 38
39 21.6 0 0 0 1 0 0 0 0 0 0 0 0 39
40 24.5 0 0 0 0 1 0 0 0 0 0 0 0 40
41 22.2 0 0 0 0 0 1 0 0 0 0 0 0 41
42 23.5 0 0 0 0 0 0 1 0 0 0 0 0 42
43 20.9 0 0 0 0 0 0 0 1 0 0 0 0 43
44 20.7 0 0 0 0 0 0 0 0 1 0 0 0 44
45 18.1 0 0 0 0 0 0 0 0 0 1 0 0 45
46 17.1 0 0 0 0 0 0 0 0 0 0 1 0 46
47 14.8 0 0 0 0 0 0 0 0 0 0 0 1 47
48 13.8 0 0 0 0 0 0 0 0 0 0 0 0 48
49 15.2 0 1 0 0 0 0 0 0 0 0 0 0 49
50 16.0 0 0 1 0 0 0 0 0 0 0 0 0 50
51 17.6 0 0 0 1 0 0 0 0 0 0 0 0 51
52 15.0 0 0 0 0 1 0 0 0 0 0 0 0 52
53 15.0 0 0 0 0 0 1 0 0 0 0 0 0 53
54 16.3 0 0 0 0 0 0 1 0 0 0 0 0 54
55 19.4 0 0 0 0 0 0 0 1 0 0 0 0 55
56 21.3 0 0 0 0 0 0 0 0 1 0 0 0 56
57 20.5 0 0 0 0 0 0 0 0 0 1 0 0 57
58 21.1 0 0 0 0 0 0 0 0 0 0 1 0 58
59 21.6 0 0 0 0 0 0 0 0 0 0 0 1 59
60 22.6 0 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
22.337500 -13.975000 -3.744653 -3.555139 -3.685625 -3.756111
M5 M6 M7 M8 M9 M10
-3.566597 -3.177083 -2.707569 -2.338056 -1.768542 -1.099028
M11 t
-0.409514 -0.009514
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.0808 -2.3167 0.2633 1.6129 9.9517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.337500 2.419346 9.233 4.84e-12 ***
X -13.975000 1.728104 -8.087 2.20e-10 ***
M1 -3.744653 2.436089 -1.537 0.131
M2 -3.555139 2.428928 -1.464 0.150
M3 -3.685625 2.422430 -1.521 0.135
M4 -3.756111 2.416602 -1.554 0.127
M5 -3.566597 2.411447 -1.479 0.146
M6 -3.177083 2.406971 -1.320 0.193
M7 -2.707569 2.403177 -1.127 0.266
M8 -2.338056 2.400068 -0.974 0.335
M9 -1.768542 2.397648 -0.738 0.464
M10 -1.099028 2.395917 -0.459 0.649
M11 -0.409514 2.394878 -0.171 0.865
t -0.009514 0.040732 -0.234 0.816
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.786 on 46 degrees of freedom
Multiple R-squared: 0.7421, Adjusted R-squared: 0.6692
F-statistic: 10.18 on 13 and 46 DF, p-value: 1.344e-09
> 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,] 0.31988934 0.6397787 0.6801107
[2,] 0.17794019 0.3558804 0.8220598
[3,] 0.08824198 0.1764840 0.9117580
[4,] 0.09708238 0.1941648 0.9029176
[5,] 0.31446704 0.6289341 0.6855330
[6,] 0.64814488 0.7037102 0.3518551
[7,] 0.85744690 0.2851062 0.1425531
[8,] 0.93201330 0.1359734 0.0679867
[9,] 0.89168331 0.2166334 0.1083167
[10,] 0.83795191 0.3240962 0.1620481
[11,] 0.77321403 0.4535719 0.2267860
[12,] 0.69702287 0.6059543 0.3029771
[13,] 0.60230637 0.7953873 0.3976936
[14,] 0.50391713 0.9921657 0.4960829
[15,] 0.42368124 0.8473625 0.5763188
[16,] 0.37017971 0.7403594 0.6298203
[17,] 0.29685531 0.5937106 0.7031447
[18,] 0.23025765 0.4605153 0.7697424
[19,] 0.18096216 0.3619243 0.8190378
[20,] 0.16212144 0.3242429 0.8378786
[21,] 0.12290596 0.2458119 0.8770940
[22,] 0.11509065 0.2301813 0.8849093
[23,] 0.07710606 0.1542121 0.9228939
[24,] 0.13496987 0.2699397 0.8650301
[25,] 0.19829799 0.3965960 0.8017020
[26,] 0.49022490 0.9804498 0.5097751
[27,] 0.55465463 0.8906907 0.4453454
> postscript(file="/var/www/html/rcomp/tmp/15rlm1258741295.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2l7fd1258741295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r55x1258741295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4l5w01258741295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5i2e11258741295.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
-1.4083333 -2.8883333 -4.6483333 -3.9683333 -4.5483333 -4.2283333 -3.1883333
8 9 10 11 12 13 14
-1.2483333 2.8916667 5.3316667 7.9516667 9.9516667 -1.6691667 -1.3491667
15 16 17 18 19 20 21
0.7908333 -0.5291667 1.5908333 -0.2891667 0.6508333 -1.6091667 -1.9691667
22 23 24 25 26 27 28
-2.8291667 -2.8091667 -2.2091667 2.9450000 1.4650000 1.1050000 1.2850000
29 30 31 32 33 34 35
2.4050000 2.1250000 0.5650000 -0.0950000 0.6450000 0.7850000 1.3050000
36 37 38 39 40 41 42
-0.4950000 3.0591667 5.0791667 3.3191667 6.2991667 3.8191667 4.7391667
43 44 45 46 47 48 49
1.6791667 1.1191667 -2.0408333 -3.7008333 -6.6808333 -8.0808333 -2.9266667
50 51 52 53 54 55 56
-2.3066667 -0.5666667 -3.0866667 -3.2666667 -2.3466667 0.2933333 1.8333333
57 58 59 60
0.4733333 0.4133333 0.2333333 0.8333333
> postscript(file="/var/www/html/rcomp/tmp/6xjuw1258741295.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 -1.4083333 NA
1 -2.8883333 -1.4083333
2 -4.6483333 -2.8883333
3 -3.9683333 -4.6483333
4 -4.5483333 -3.9683333
5 -4.2283333 -4.5483333
6 -3.1883333 -4.2283333
7 -1.2483333 -3.1883333
8 2.8916667 -1.2483333
9 5.3316667 2.8916667
10 7.9516667 5.3316667
11 9.9516667 7.9516667
12 -1.6691667 9.9516667
13 -1.3491667 -1.6691667
14 0.7908333 -1.3491667
15 -0.5291667 0.7908333
16 1.5908333 -0.5291667
17 -0.2891667 1.5908333
18 0.6508333 -0.2891667
19 -1.6091667 0.6508333
20 -1.9691667 -1.6091667
21 -2.8291667 -1.9691667
22 -2.8091667 -2.8291667
23 -2.2091667 -2.8091667
24 2.9450000 -2.2091667
25 1.4650000 2.9450000
26 1.1050000 1.4650000
27 1.2850000 1.1050000
28 2.4050000 1.2850000
29 2.1250000 2.4050000
30 0.5650000 2.1250000
31 -0.0950000 0.5650000
32 0.6450000 -0.0950000
33 0.7850000 0.6450000
34 1.3050000 0.7850000
35 -0.4950000 1.3050000
36 3.0591667 -0.4950000
37 5.0791667 3.0591667
38 3.3191667 5.0791667
39 6.2991667 3.3191667
40 3.8191667 6.2991667
41 4.7391667 3.8191667
42 1.6791667 4.7391667
43 1.1191667 1.6791667
44 -2.0408333 1.1191667
45 -3.7008333 -2.0408333
46 -6.6808333 -3.7008333
47 -8.0808333 -6.6808333
48 -2.9266667 -8.0808333
49 -2.3066667 -2.9266667
50 -0.5666667 -2.3066667
51 -3.0866667 -0.5666667
52 -3.2666667 -3.0866667
53 -2.3466667 -3.2666667
54 0.2933333 -2.3466667
55 1.8333333 0.2933333
56 0.4733333 1.8333333
57 0.4133333 0.4733333
58 0.2333333 0.4133333
59 0.8333333 0.2333333
60 NA 0.8333333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.8883333 -1.4083333
[2,] -4.6483333 -2.8883333
[3,] -3.9683333 -4.6483333
[4,] -4.5483333 -3.9683333
[5,] -4.2283333 -4.5483333
[6,] -3.1883333 -4.2283333
[7,] -1.2483333 -3.1883333
[8,] 2.8916667 -1.2483333
[9,] 5.3316667 2.8916667
[10,] 7.9516667 5.3316667
[11,] 9.9516667 7.9516667
[12,] -1.6691667 9.9516667
[13,] -1.3491667 -1.6691667
[14,] 0.7908333 -1.3491667
[15,] -0.5291667 0.7908333
[16,] 1.5908333 -0.5291667
[17,] -0.2891667 1.5908333
[18,] 0.6508333 -0.2891667
[19,] -1.6091667 0.6508333
[20,] -1.9691667 -1.6091667
[21,] -2.8291667 -1.9691667
[22,] -2.8091667 -2.8291667
[23,] -2.2091667 -2.8091667
[24,] 2.9450000 -2.2091667
[25,] 1.4650000 2.9450000
[26,] 1.1050000 1.4650000
[27,] 1.2850000 1.1050000
[28,] 2.4050000 1.2850000
[29,] 2.1250000 2.4050000
[30,] 0.5650000 2.1250000
[31,] -0.0950000 0.5650000
[32,] 0.6450000 -0.0950000
[33,] 0.7850000 0.6450000
[34,] 1.3050000 0.7850000
[35,] -0.4950000 1.3050000
[36,] 3.0591667 -0.4950000
[37,] 5.0791667 3.0591667
[38,] 3.3191667 5.0791667
[39,] 6.2991667 3.3191667
[40,] 3.8191667 6.2991667
[41,] 4.7391667 3.8191667
[42,] 1.6791667 4.7391667
[43,] 1.1191667 1.6791667
[44,] -2.0408333 1.1191667
[45,] -3.7008333 -2.0408333
[46,] -6.6808333 -3.7008333
[47,] -8.0808333 -6.6808333
[48,] -2.9266667 -8.0808333
[49,] -2.3066667 -2.9266667
[50,] -0.5666667 -2.3066667
[51,] -3.0866667 -0.5666667
[52,] -3.2666667 -3.0866667
[53,] -2.3466667 -3.2666667
[54,] 0.2933333 -2.3466667
[55,] 1.8333333 0.2933333
[56,] 0.4733333 1.8333333
[57,] 0.4133333 0.4733333
[58,] 0.2333333 0.4133333
[59,] 0.8333333 0.2333333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.8883333 -1.4083333
2 -4.6483333 -2.8883333
3 -3.9683333 -4.6483333
4 -4.5483333 -3.9683333
5 -4.2283333 -4.5483333
6 -3.1883333 -4.2283333
7 -1.2483333 -3.1883333
8 2.8916667 -1.2483333
9 5.3316667 2.8916667
10 7.9516667 5.3316667
11 9.9516667 7.9516667
12 -1.6691667 9.9516667
13 -1.3491667 -1.6691667
14 0.7908333 -1.3491667
15 -0.5291667 0.7908333
16 1.5908333 -0.5291667
17 -0.2891667 1.5908333
18 0.6508333 -0.2891667
19 -1.6091667 0.6508333
20 -1.9691667 -1.6091667
21 -2.8291667 -1.9691667
22 -2.8091667 -2.8291667
23 -2.2091667 -2.8091667
24 2.9450000 -2.2091667
25 1.4650000 2.9450000
26 1.1050000 1.4650000
27 1.2850000 1.1050000
28 2.4050000 1.2850000
29 2.1250000 2.4050000
30 0.5650000 2.1250000
31 -0.0950000 0.5650000
32 0.6450000 -0.0950000
33 0.7850000 0.6450000
34 1.3050000 0.7850000
35 -0.4950000 1.3050000
36 3.0591667 -0.4950000
37 5.0791667 3.0591667
38 3.3191667 5.0791667
39 6.2991667 3.3191667
40 3.8191667 6.2991667
41 4.7391667 3.8191667
42 1.6791667 4.7391667
43 1.1191667 1.6791667
44 -2.0408333 1.1191667
45 -3.7008333 -2.0408333
46 -6.6808333 -3.7008333
47 -8.0808333 -6.6808333
48 -2.9266667 -8.0808333
49 -2.3066667 -2.9266667
50 -0.5666667 -2.3066667
51 -3.0866667 -0.5666667
52 -3.2666667 -3.0866667
53 -2.3466667 -3.2666667
54 0.2933333 -2.3466667
55 1.8333333 0.2933333
56 0.4733333 1.8333333
57 0.4133333 0.4733333
58 0.2333333 0.4133333
59 0.8333333 0.2333333
> 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/79tmy1258741295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mb2l1258741295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9eny11258741295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wvxv1258741295.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gfvd1258741295.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/12rof11258741295.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/137b0w1258741296.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/14q5sy1258741296.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/15cgvg1258741296.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/164mpl1258741296.tab")
+ }
> system("convert tmp/15rlm1258741295.ps tmp/15rlm1258741295.png")
> system("convert tmp/2l7fd1258741295.ps tmp/2l7fd1258741295.png")
> system("convert tmp/3r55x1258741295.ps tmp/3r55x1258741295.png")
> system("convert tmp/4l5w01258741295.ps tmp/4l5w01258741295.png")
> system("convert tmp/5i2e11258741295.ps tmp/5i2e11258741295.png")
> system("convert tmp/6xjuw1258741295.ps tmp/6xjuw1258741295.png")
> system("convert tmp/79tmy1258741295.ps tmp/79tmy1258741295.png")
> system("convert tmp/8mb2l1258741295.ps tmp/8mb2l1258741295.png")
> system("convert tmp/9eny11258741295.ps tmp/9eny11258741295.png")
> system("convert tmp/10wvxv1258741295.ps tmp/10wvxv1258741295.png")
>
>
> proc.time()
user system elapsed
2.395 1.542 2.768