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(8,11.1,8.1,10.9,7.7,10,7.5,9.2,7.6,9.2,7.8,9.5,7.8,9.6,7.8,9.5,7.5,9.1,7.5,8.9,7.1,9,7.5,10.1,7.5,10.3,7.6,10.2,7.7,9.6,7.7,9.2,7.9,9.3,8.1,9.4,8.2,9.4,8.2,9.2,8.2,9,7.9,9,7.3,9,6.9,9.8,6.6,10,6.7,9.8,6.9,9.3,7,9,7.1,9,7.2,9.1,7.1,9.1,6.9,9.1,7,9.2,6.8,8.8,6.4,8.3,6.7,8.4,6.6,8.1,6.4,7.7,6.3,7.9,6.2,7.9,6.5,8,6.8,7.9,6.8,7.6,6.4,7.1,6.1,6.8,5.8,6.5,6.1,6.9,7.2,8.2,7.3,8.7,6.9,8.3,6.1,7.9,5.8,7.5,6.2,7.8,7.1,8.3,7.7,8.4,7.9,8.2,7.7,7.7,7.4,7.2,7.5,7.3,8,8.1),dim=c(2,60),dimnames=list(c('X','Y'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('X','Y'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '2'
> #'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 11.1 8.0 1 0 0 0 0 0 0 0 0 0 0 1
2 10.9 8.1 0 1 0 0 0 0 0 0 0 0 0 2
3 10.0 7.7 0 0 1 0 0 0 0 0 0 0 0 3
4 9.2 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 9.2 7.6 0 0 0 0 1 0 0 0 0 0 0 5
6 9.5 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 9.6 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 9.5 7.8 0 0 0 0 0 0 0 1 0 0 0 8
9 9.1 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 8.9 7.5 0 0 0 0 0 0 0 0 0 1 0 10
11 9.0 7.1 0 0 0 0 0 0 0 0 0 0 1 11
12 10.1 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 10.3 7.5 1 0 0 0 0 0 0 0 0 0 0 13
14 10.2 7.6 0 1 0 0 0 0 0 0 0 0 0 14
15 9.6 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 9.2 7.7 0 0 0 1 0 0 0 0 0 0 0 16
17 9.3 7.9 0 0 0 0 1 0 0 0 0 0 0 17
18 9.4 8.1 0 0 0 0 0 1 0 0 0 0 0 18
19 9.4 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 9.2 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 9.0 8.2 0 0 0 0 0 0 0 0 1 0 0 21
22 9.0 7.9 0 0 0 0 0 0 0 0 0 1 0 22
23 9.0 7.3 0 0 0 0 0 0 0 0 0 0 1 23
24 9.8 6.9 0 0 0 0 0 0 0 0 0 0 0 24
25 10.0 6.6 1 0 0 0 0 0 0 0 0 0 0 25
26 9.8 6.7 0 1 0 0 0 0 0 0 0 0 0 26
27 9.3 6.9 0 0 1 0 0 0 0 0 0 0 0 27
28 9.0 7.0 0 0 0 1 0 0 0 0 0 0 0 28
29 9.0 7.1 0 0 0 0 1 0 0 0 0 0 0 29
30 9.1 7.2 0 0 0 0 0 1 0 0 0 0 0 30
31 9.1 7.1 0 0 0 0 0 0 1 0 0 0 0 31
32 9.1 6.9 0 0 0 0 0 0 0 1 0 0 0 32
33 9.2 7.0 0 0 0 0 0 0 0 0 1 0 0 33
34 8.8 6.8 0 0 0 0 0 0 0 0 0 1 0 34
35 8.3 6.4 0 0 0 0 0 0 0 0 0 0 1 35
36 8.4 6.7 0 0 0 0 0 0 0 0 0 0 0 36
37 8.1 6.6 1 0 0 0 0 0 0 0 0 0 0 37
38 7.7 6.4 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 6.3 0 0 1 0 0 0 0 0 0 0 0 39
40 7.9 6.2 0 0 0 1 0 0 0 0 0 0 0 40
41 8.0 6.5 0 0 0 0 1 0 0 0 0 0 0 41
42 7.9 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 7.6 6.8 0 0 0 0 0 0 1 0 0 0 0 43
44 7.1 6.4 0 0 0 0 0 0 0 1 0 0 0 44
45 6.8 6.1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.5 5.8 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 6.1 0 0 0 0 0 0 0 0 0 0 1 47
48 8.2 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 8.7 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 8.3 6.9 0 1 0 0 0 0 0 0 0 0 0 50
51 7.9 6.1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.5 5.8 0 0 0 1 0 0 0 0 0 0 0 52
53 7.8 6.2 0 0 0 0 1 0 0 0 0 0 0 53
54 8.3 7.1 0 0 0 0 0 1 0 0 0 0 0 54
55 8.4 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 8.2 7.9 0 0 0 0 0 0 0 1 0 0 0 56
57 7.7 7.7 0 0 0 0 0 0 0 0 1 0 0 57
58 7.2 7.4 0 0 0 0 0 0 0 0 0 1 0 58
59 7.3 7.5 0 0 0 0 0 0 0 0 0 0 1 59
60 8.1 8.0 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
7.02041 0.43945 0.35196 0.15418 -0.16208 -0.46228
M5 M6 M7 M8 M9 M10
-0.42310 -0.35666 -0.39353 -0.52252 -0.68514 -0.83261
M11 t
-0.68887 -0.03586
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.9245 -0.2303 -0.1091 0.2945 0.9718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.020413 0.951326 7.380 2.46e-09 ***
X 0.439447 0.115365 3.809 0.000412 ***
M1 0.351957 0.296141 1.188 0.240744
M2 0.154179 0.296242 0.520 0.605246
M3 -0.162076 0.299140 -0.542 0.590567
M4 -0.462276 0.300707 -1.537 0.131072
M5 -0.423099 0.295482 -1.432 0.158935
M6 -0.356655 0.292618 -1.219 0.229118
M7 -0.393533 0.292964 -1.343 0.185770
M8 -0.522522 0.292417 -1.787 0.080543 .
M9 -0.685144 0.292048 -2.346 0.023342 *
M10 -0.832610 0.293032 -2.841 0.006673 **
M11 -0.688866 0.295493 -2.331 0.024177 *
t -0.035855 0.004192 -8.553 4.58e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4615 on 46 degrees of freedom
Multiple R-squared: 0.8339, Adjusted R-squared: 0.787
F-statistic: 17.77 on 13 and 46 DF, p-value: 9.862e-14
> 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,] 3.977505e-03 7.955009e-03 0.996022495
[2,] 1.105495e-03 2.210991e-03 0.998894505
[3,] 1.223250e-03 2.446499e-03 0.998776750
[4,] 1.149668e-03 2.299337e-03 0.998850332
[5,] 7.508289e-04 1.501658e-03 0.999249171
[6,] 4.377325e-04 8.754651e-04 0.999562267
[7,] 2.233632e-04 4.467265e-04 0.999776637
[8,] 2.989108e-04 5.978215e-04 0.999701089
[9,] 2.529171e-04 5.058343e-04 0.999747083
[10,] 1.372012e-04 2.744023e-04 0.999862799
[11,] 5.602285e-05 1.120457e-04 0.999943977
[12,] 9.680413e-05 1.936083e-04 0.999903196
[13,] 1.080179e-04 2.160358e-04 0.999891982
[14,] 5.598486e-05 1.119697e-04 0.999944015
[15,] 2.240912e-05 4.481825e-05 0.999977591
[16,] 3.573146e-05 7.146293e-05 0.999964269
[17,] 7.722237e-04 1.544447e-03 0.999227776
[18,] 6.048488e-03 1.209698e-02 0.993951512
[19,] 1.316629e-01 2.633257e-01 0.868337137
[20,] 8.331632e-01 3.336735e-01 0.166836759
[21,] 9.754866e-01 4.902682e-02 0.024513408
[22,] 9.914645e-01 1.707096e-02 0.008535482
[23,] 9.829580e-01 3.408405e-02 0.017042027
[24,] 9.624749e-01 7.505027e-02 0.037525137
[25,] 9.173920e-01 1.652161e-01 0.082608041
[26,] 8.669618e-01 2.660763e-01 0.133038169
[27,] 9.299966e-01 1.400067e-01 0.070003365
> postscript(file="/var/www/html/rcomp/tmp/1yfts1258486472.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jo4o1258486472.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31kg21258486472.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4agex1258486472.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dpaa1258486472.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
0.24791225 0.23760078 -0.13450989 -0.51056522 -0.55783162 -0.37630909
7 8 9 10 11 12
-0.20357549 -0.13873122 -0.20841976 -0.22509802 -0.05720869 0.21400238
13 14 15 16 17 18
0.09790079 0.18758932 -0.10424467 -0.16818934 -0.15940040 -0.17787787
19 20 21 22 23 24
-0.14908894 -0.18424467 -0.18576720 0.12938853 0.28516719 0.60793558
25 26 27 28 29 30
0.62366799 0.61335653 0.37757787 0.36968854 0.32242213 0.34788933
31 32 33 34 35 36
0.46456760 0.71730119 0.97183400 0.84304506 0.41093439 -0.27390987
37 38 39 40 41 42
-0.84606679 -0.92454426 -0.32848893 0.05151107 0.01635534 -0.24606679
43 44 45 46 47 48
-0.47333319 -0.63271026 -0.60239880 -0.58724307 -0.42696640 -0.26336799
49 50 51 52 53 54
-0.12341424 -0.11400238 0.18966562 0.25755495 0.37845455 0.45236442
55 56 57 58 59 60
0.36143003 0.23838497 0.02475177 -0.16009250 -0.21192650 -0.28466010
> postscript(file="/var/www/html/rcomp/tmp/6ezot1258486472.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 0.24791225 NA
1 0.23760078 0.24791225
2 -0.13450989 0.23760078
3 -0.51056522 -0.13450989
4 -0.55783162 -0.51056522
5 -0.37630909 -0.55783162
6 -0.20357549 -0.37630909
7 -0.13873122 -0.20357549
8 -0.20841976 -0.13873122
9 -0.22509802 -0.20841976
10 -0.05720869 -0.22509802
11 0.21400238 -0.05720869
12 0.09790079 0.21400238
13 0.18758932 0.09790079
14 -0.10424467 0.18758932
15 -0.16818934 -0.10424467
16 -0.15940040 -0.16818934
17 -0.17787787 -0.15940040
18 -0.14908894 -0.17787787
19 -0.18424467 -0.14908894
20 -0.18576720 -0.18424467
21 0.12938853 -0.18576720
22 0.28516719 0.12938853
23 0.60793558 0.28516719
24 0.62366799 0.60793558
25 0.61335653 0.62366799
26 0.37757787 0.61335653
27 0.36968854 0.37757787
28 0.32242213 0.36968854
29 0.34788933 0.32242213
30 0.46456760 0.34788933
31 0.71730119 0.46456760
32 0.97183400 0.71730119
33 0.84304506 0.97183400
34 0.41093439 0.84304506
35 -0.27390987 0.41093439
36 -0.84606679 -0.27390987
37 -0.92454426 -0.84606679
38 -0.32848893 -0.92454426
39 0.05151107 -0.32848893
40 0.01635534 0.05151107
41 -0.24606679 0.01635534
42 -0.47333319 -0.24606679
43 -0.63271026 -0.47333319
44 -0.60239880 -0.63271026
45 -0.58724307 -0.60239880
46 -0.42696640 -0.58724307
47 -0.26336799 -0.42696640
48 -0.12341424 -0.26336799
49 -0.11400238 -0.12341424
50 0.18966562 -0.11400238
51 0.25755495 0.18966562
52 0.37845455 0.25755495
53 0.45236442 0.37845455
54 0.36143003 0.45236442
55 0.23838497 0.36143003
56 0.02475177 0.23838497
57 -0.16009250 0.02475177
58 -0.21192650 -0.16009250
59 -0.28466010 -0.21192650
60 NA -0.28466010
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.23760078 0.24791225
[2,] -0.13450989 0.23760078
[3,] -0.51056522 -0.13450989
[4,] -0.55783162 -0.51056522
[5,] -0.37630909 -0.55783162
[6,] -0.20357549 -0.37630909
[7,] -0.13873122 -0.20357549
[8,] -0.20841976 -0.13873122
[9,] -0.22509802 -0.20841976
[10,] -0.05720869 -0.22509802
[11,] 0.21400238 -0.05720869
[12,] 0.09790079 0.21400238
[13,] 0.18758932 0.09790079
[14,] -0.10424467 0.18758932
[15,] -0.16818934 -0.10424467
[16,] -0.15940040 -0.16818934
[17,] -0.17787787 -0.15940040
[18,] -0.14908894 -0.17787787
[19,] -0.18424467 -0.14908894
[20,] -0.18576720 -0.18424467
[21,] 0.12938853 -0.18576720
[22,] 0.28516719 0.12938853
[23,] 0.60793558 0.28516719
[24,] 0.62366799 0.60793558
[25,] 0.61335653 0.62366799
[26,] 0.37757787 0.61335653
[27,] 0.36968854 0.37757787
[28,] 0.32242213 0.36968854
[29,] 0.34788933 0.32242213
[30,] 0.46456760 0.34788933
[31,] 0.71730119 0.46456760
[32,] 0.97183400 0.71730119
[33,] 0.84304506 0.97183400
[34,] 0.41093439 0.84304506
[35,] -0.27390987 0.41093439
[36,] -0.84606679 -0.27390987
[37,] -0.92454426 -0.84606679
[38,] -0.32848893 -0.92454426
[39,] 0.05151107 -0.32848893
[40,] 0.01635534 0.05151107
[41,] -0.24606679 0.01635534
[42,] -0.47333319 -0.24606679
[43,] -0.63271026 -0.47333319
[44,] -0.60239880 -0.63271026
[45,] -0.58724307 -0.60239880
[46,] -0.42696640 -0.58724307
[47,] -0.26336799 -0.42696640
[48,] -0.12341424 -0.26336799
[49,] -0.11400238 -0.12341424
[50,] 0.18966562 -0.11400238
[51,] 0.25755495 0.18966562
[52,] 0.37845455 0.25755495
[53,] 0.45236442 0.37845455
[54,] 0.36143003 0.45236442
[55,] 0.23838497 0.36143003
[56,] 0.02475177 0.23838497
[57,] -0.16009250 0.02475177
[58,] -0.21192650 -0.16009250
[59,] -0.28466010 -0.21192650
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.23760078 0.24791225
2 -0.13450989 0.23760078
3 -0.51056522 -0.13450989
4 -0.55783162 -0.51056522
5 -0.37630909 -0.55783162
6 -0.20357549 -0.37630909
7 -0.13873122 -0.20357549
8 -0.20841976 -0.13873122
9 -0.22509802 -0.20841976
10 -0.05720869 -0.22509802
11 0.21400238 -0.05720869
12 0.09790079 0.21400238
13 0.18758932 0.09790079
14 -0.10424467 0.18758932
15 -0.16818934 -0.10424467
16 -0.15940040 -0.16818934
17 -0.17787787 -0.15940040
18 -0.14908894 -0.17787787
19 -0.18424467 -0.14908894
20 -0.18576720 -0.18424467
21 0.12938853 -0.18576720
22 0.28516719 0.12938853
23 0.60793558 0.28516719
24 0.62366799 0.60793558
25 0.61335653 0.62366799
26 0.37757787 0.61335653
27 0.36968854 0.37757787
28 0.32242213 0.36968854
29 0.34788933 0.32242213
30 0.46456760 0.34788933
31 0.71730119 0.46456760
32 0.97183400 0.71730119
33 0.84304506 0.97183400
34 0.41093439 0.84304506
35 -0.27390987 0.41093439
36 -0.84606679 -0.27390987
37 -0.92454426 -0.84606679
38 -0.32848893 -0.92454426
39 0.05151107 -0.32848893
40 0.01635534 0.05151107
41 -0.24606679 0.01635534
42 -0.47333319 -0.24606679
43 -0.63271026 -0.47333319
44 -0.60239880 -0.63271026
45 -0.58724307 -0.60239880
46 -0.42696640 -0.58724307
47 -0.26336799 -0.42696640
48 -0.12341424 -0.26336799
49 -0.11400238 -0.12341424
50 0.18966562 -0.11400238
51 0.25755495 0.18966562
52 0.37845455 0.25755495
53 0.45236442 0.37845455
54 0.36143003 0.45236442
55 0.23838497 0.36143003
56 0.02475177 0.23838497
57 -0.16009250 0.02475177
58 -0.21192650 -0.16009250
59 -0.28466010 -0.21192650
> 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/714h41258486472.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89s0c1258486472.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9oqor1258486472.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10o5du1258486472.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1148ti1258486472.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/120slj1258486472.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/13pe9r1258486472.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/145exq1258486472.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/15gj7h1258486472.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/16q4zn1258486472.tab")
+ }
>
> system("convert tmp/1yfts1258486472.ps tmp/1yfts1258486472.png")
> system("convert tmp/2jo4o1258486472.ps tmp/2jo4o1258486472.png")
> system("convert tmp/31kg21258486472.ps tmp/31kg21258486472.png")
> system("convert tmp/4agex1258486472.ps tmp/4agex1258486472.png")
> system("convert tmp/5dpaa1258486472.ps tmp/5dpaa1258486472.png")
> system("convert tmp/6ezot1258486472.ps tmp/6ezot1258486472.png")
> system("convert tmp/714h41258486472.ps tmp/714h41258486472.png")
> system("convert tmp/89s0c1258486472.ps tmp/89s0c1258486472.png")
> system("convert tmp/9oqor1258486472.ps tmp/9oqor1258486472.png")
> system("convert tmp/10o5du1258486472.ps tmp/10o5du1258486472.png")
>
>
> proc.time()
user system elapsed
2.381 1.542 2.817