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(6.3,2,6.2,1.8,6.1,2.7,6.3,2.3,6.5,1.9,6.6,2,6.5,2.3,6.2,2.8,6.2,2.4,5.9,2.3,6.1,2.7,6.1,2.7,6.1,2.9,6.1,3,6.1,2.2,6.4,2.3,6.7,2.8,6.9,2.8,7,2.8,7,2.2,6.8,2.6,6.4,2.8,5.9,2.5,5.5,2.4,5.5,2.3,5.6,1.9,5.8,1.7,5.9,2,6.1,2.1,6.1,1.7,6,1.8,6,1.8,5.9,1.8,5.5,1.3,5.6,1.3,5.4,1.3,5.2,1.2,5.2,1.4,5.2,2.2,5.5,2.9,5.8,3.1,5.8,3.5,5.5,3.6,5.3,4.4,5.1,4.1,5.2,5.1,5.8,5.8,5.8,5.9,5.5,5.4,5,5.5,4.9,4.8,5.3,3.2,6.1,2.7,6.5,2.1,6.8,1.9,6.6,0.6,6.4,0.7,6.4,-0.2,6.6,-1,6.7,-1.7),dim=c(2,60),dimnames=list(c('WMan>25','Infl'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WMan>25','Infl'),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
WMan>25 Infl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.3 2.0 1 0 0 0 0 0 0 0 0 0 0 1
2 6.2 1.8 0 1 0 0 0 0 0 0 0 0 0 2
3 6.1 2.7 0 0 1 0 0 0 0 0 0 0 0 3
4 6.3 2.3 0 0 0 1 0 0 0 0 0 0 0 4
5 6.5 1.9 0 0 0 0 1 0 0 0 0 0 0 5
6 6.6 2.0 0 0 0 0 0 1 0 0 0 0 0 6
7 6.5 2.3 0 0 0 0 0 0 1 0 0 0 0 7
8 6.2 2.8 0 0 0 0 0 0 0 1 0 0 0 8
9 6.2 2.4 0 0 0 0 0 0 0 0 1 0 0 9
10 5.9 2.3 0 0 0 0 0 0 0 0 0 1 0 10
11 6.1 2.7 0 0 0 0 0 0 0 0 0 0 1 11
12 6.1 2.7 0 0 0 0 0 0 0 0 0 0 0 12
13 6.1 2.9 1 0 0 0 0 0 0 0 0 0 0 13
14 6.1 3.0 0 1 0 0 0 0 0 0 0 0 0 14
15 6.1 2.2 0 0 1 0 0 0 0 0 0 0 0 15
16 6.4 2.3 0 0 0 1 0 0 0 0 0 0 0 16
17 6.7 2.8 0 0 0 0 1 0 0 0 0 0 0 17
18 6.9 2.8 0 0 0 0 0 1 0 0 0 0 0 18
19 7.0 2.8 0 0 0 0 0 0 1 0 0 0 0 19
20 7.0 2.2 0 0 0 0 0 0 0 1 0 0 0 20
21 6.8 2.6 0 0 0 0 0 0 0 0 1 0 0 21
22 6.4 2.8 0 0 0 0 0 0 0 0 0 1 0 22
23 5.9 2.5 0 0 0 0 0 0 0 0 0 0 1 23
24 5.5 2.4 0 0 0 0 0 0 0 0 0 0 0 24
25 5.5 2.3 1 0 0 0 0 0 0 0 0 0 0 25
26 5.6 1.9 0 1 0 0 0 0 0 0 0 0 0 26
27 5.8 1.7 0 0 1 0 0 0 0 0 0 0 0 27
28 5.9 2.0 0 0 0 1 0 0 0 0 0 0 0 28
29 6.1 2.1 0 0 0 0 1 0 0 0 0 0 0 29
30 6.1 1.7 0 0 0 0 0 1 0 0 0 0 0 30
31 6.0 1.8 0 0 0 0 0 0 1 0 0 0 0 31
32 6.0 1.8 0 0 0 0 0 0 0 1 0 0 0 32
33 5.9 1.8 0 0 0 0 0 0 0 0 1 0 0 33
34 5.5 1.3 0 0 0 0 0 0 0 0 0 1 0 34
35 5.6 1.3 0 0 0 0 0 0 0 0 0 0 1 35
36 5.4 1.3 0 0 0 0 0 0 0 0 0 0 0 36
37 5.2 1.2 1 0 0 0 0 0 0 0 0 0 0 37
38 5.2 1.4 0 1 0 0 0 0 0 0 0 0 0 38
39 5.2 2.2 0 0 1 0 0 0 0 0 0 0 0 39
40 5.5 2.9 0 0 0 1 0 0 0 0 0 0 0 40
41 5.8 3.1 0 0 0 0 1 0 0 0 0 0 0 41
42 5.8 3.5 0 0 0 0 0 1 0 0 0 0 0 42
43 5.5 3.6 0 0 0 0 0 0 1 0 0 0 0 43
44 5.3 4.4 0 0 0 0 0 0 0 1 0 0 0 44
45 5.1 4.1 0 0 0 0 0 0 0 0 1 0 0 45
46 5.2 5.1 0 0 0 0 0 0 0 0 0 1 0 46
47 5.8 5.8 0 0 0 0 0 0 0 0 0 0 1 47
48 5.8 5.9 0 0 0 0 0 0 0 0 0 0 0 48
49 5.5 5.4 1 0 0 0 0 0 0 0 0 0 0 49
50 5.0 5.5 0 1 0 0 0 0 0 0 0 0 0 50
51 4.9 4.8 0 0 1 0 0 0 0 0 0 0 0 51
52 5.3 3.2 0 0 0 1 0 0 0 0 0 0 0 52
53 6.1 2.7 0 0 0 0 1 0 0 0 0 0 0 53
54 6.5 2.1 0 0 0 0 0 1 0 0 0 0 0 54
55 6.8 1.9 0 0 0 0 0 0 1 0 0 0 0 55
56 6.6 0.6 0 0 0 0 0 0 0 1 0 0 0 56
57 6.4 0.7 0 0 0 0 0 0 0 0 1 0 0 57
58 6.4 -0.2 0 0 0 0 0 0 0 0 0 1 0 58
59 6.6 -1.0 0 0 0 0 0 0 0 0 0 0 1 59
60 6.7 -1.7 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) Infl M1 M2 M3 M4
6.59620 -0.14805 -0.20207 -0.29737 -0.28675 -0.04278
M5 M6 M7 M8 M9 M10
0.32488 0.46070 0.46020 0.31305 0.17775 -0.02051
M11 t
0.11011 -0.01062
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6235 -0.2648 -0.0882 0.3608 0.6340
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.596196 0.236136 27.934 < 2e-16 ***
Infl -0.148053 0.039238 -3.773 0.00046 ***
M1 -0.202068 0.271928 -0.743 0.46120
M2 -0.297370 0.271368 -1.096 0.27886
M3 -0.286750 0.270993 -1.058 0.29551
M4 -0.042779 0.270110 -0.158 0.87485
M5 0.324880 0.269764 1.204 0.23463
M6 0.460695 0.269296 1.711 0.09387 .
M7 0.460198 0.269197 1.710 0.09409 .
M8 0.313052 0.268802 1.165 0.25018
M9 0.177750 0.268610 0.662 0.51144
M10 -0.020513 0.268449 -0.076 0.93942
M11 0.110107 0.268389 0.410 0.68353
t -0.010620 0.003229 -3.289 0.00193 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4242 on 46 degrees of freedom
Multiple R-squared: 0.5103, Adjusted R-squared: 0.3719
F-statistic: 3.687 on 13 and 46 DF, p-value: 0.0005068
> 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,] 0.02282374 0.04564748 0.97717626
[2,] 0.01708702 0.03417404 0.98291298
[3,] 0.02637432 0.05274864 0.97362568
[4,] 0.05091011 0.10182022 0.94908989
[5,] 0.06809212 0.13618424 0.93190788
[6,] 0.08809843 0.17619685 0.91190157
[7,] 0.12849628 0.25699257 0.87150372
[8,] 0.27453468 0.54906936 0.72546532
[9,] 0.44532322 0.89064643 0.55467678
[10,] 0.45659820 0.91319640 0.54340180
[11,] 0.49387995 0.98775991 0.50612005
[12,] 0.55848319 0.88303362 0.44151681
[13,] 0.55433229 0.89133542 0.44566771
[14,] 0.50357104 0.99285791 0.49642896
[15,] 0.44996331 0.89992662 0.55003669
[16,] 0.44375846 0.88751693 0.55624154
[17,] 0.55145354 0.89709293 0.44854646
[18,] 0.48100785 0.96201570 0.51899215
[19,] 0.39499813 0.78999627 0.60500187
[20,] 0.33368875 0.66737750 0.66631125
[21,] 0.29607409 0.59214818 0.70392591
[22,] 0.21462328 0.42924657 0.78537672
[23,] 0.22898360 0.45796720 0.77101640
[24,] 0.50844977 0.98310047 0.49155023
[25,] 0.78722773 0.42554454 0.21277227
[26,] 0.98609118 0.02781764 0.01390882
[27,] 0.98074236 0.03851528 0.01925764
> postscript(file="/var/www/html/rcomp/tmp/1qjp81258813351.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zzf21258813351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3afe01258813351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4w70h1258813351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55n4u1258813351.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
0.21259692 0.18890849 0.22215588 0.12958430 -0.08667571 -0.09706518
7 8 9 10 11 12
-0.14153255 -0.20973990 -0.12303886 -0.22896096 -0.08973990 0.03098747
13 14 15 16 17 18
0.27328584 0.39401321 0.27557109 0.35702583 0.37401321 0.44881847
19 20 21 22 23 24
0.55993531 0.62887004 0.63401321 0.47250690 -0.19190890 -0.48598679
25 26 27 28 29 30
-0.28810422 -0.14140317 0.02898630 -0.05994843 -0.20218211 -0.38659791
31 32 33 34 35 36
-0.46067580 -0.30290948 -0.25698738 -0.52213054 -0.54213054 -0.62140317
37 38 39 40 41 42
-0.62352060 -0.48798796 -0.36954584 -0.19925951 -0.22668793 -0.29266161
43 44 45 46 47 48
-0.56673950 -0.49053106 -0.58902475 -0.13208895 0.45154791 0.58708054
49 50 51 52 53 54
0.42574206 0.04646943 -0.15716742 -0.22740218 0.14153255 0.32750622
55 56 57 58 59 60
0.60901253 0.37431041 0.33503778 0.41067356 0.37223144 0.48932195
> postscript(file="/var/www/html/rcomp/tmp/60mtu1258813351.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 0.21259692 NA
1 0.18890849 0.21259692
2 0.22215588 0.18890849
3 0.12958430 0.22215588
4 -0.08667571 0.12958430
5 -0.09706518 -0.08667571
6 -0.14153255 -0.09706518
7 -0.20973990 -0.14153255
8 -0.12303886 -0.20973990
9 -0.22896096 -0.12303886
10 -0.08973990 -0.22896096
11 0.03098747 -0.08973990
12 0.27328584 0.03098747
13 0.39401321 0.27328584
14 0.27557109 0.39401321
15 0.35702583 0.27557109
16 0.37401321 0.35702583
17 0.44881847 0.37401321
18 0.55993531 0.44881847
19 0.62887004 0.55993531
20 0.63401321 0.62887004
21 0.47250690 0.63401321
22 -0.19190890 0.47250690
23 -0.48598679 -0.19190890
24 -0.28810422 -0.48598679
25 -0.14140317 -0.28810422
26 0.02898630 -0.14140317
27 -0.05994843 0.02898630
28 -0.20218211 -0.05994843
29 -0.38659791 -0.20218211
30 -0.46067580 -0.38659791
31 -0.30290948 -0.46067580
32 -0.25698738 -0.30290948
33 -0.52213054 -0.25698738
34 -0.54213054 -0.52213054
35 -0.62140317 -0.54213054
36 -0.62352060 -0.62140317
37 -0.48798796 -0.62352060
38 -0.36954584 -0.48798796
39 -0.19925951 -0.36954584
40 -0.22668793 -0.19925951
41 -0.29266161 -0.22668793
42 -0.56673950 -0.29266161
43 -0.49053106 -0.56673950
44 -0.58902475 -0.49053106
45 -0.13208895 -0.58902475
46 0.45154791 -0.13208895
47 0.58708054 0.45154791
48 0.42574206 0.58708054
49 0.04646943 0.42574206
50 -0.15716742 0.04646943
51 -0.22740218 -0.15716742
52 0.14153255 -0.22740218
53 0.32750622 0.14153255
54 0.60901253 0.32750622
55 0.37431041 0.60901253
56 0.33503778 0.37431041
57 0.41067356 0.33503778
58 0.37223144 0.41067356
59 0.48932195 0.37223144
60 NA 0.48932195
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.18890849 0.21259692
[2,] 0.22215588 0.18890849
[3,] 0.12958430 0.22215588
[4,] -0.08667571 0.12958430
[5,] -0.09706518 -0.08667571
[6,] -0.14153255 -0.09706518
[7,] -0.20973990 -0.14153255
[8,] -0.12303886 -0.20973990
[9,] -0.22896096 -0.12303886
[10,] -0.08973990 -0.22896096
[11,] 0.03098747 -0.08973990
[12,] 0.27328584 0.03098747
[13,] 0.39401321 0.27328584
[14,] 0.27557109 0.39401321
[15,] 0.35702583 0.27557109
[16,] 0.37401321 0.35702583
[17,] 0.44881847 0.37401321
[18,] 0.55993531 0.44881847
[19,] 0.62887004 0.55993531
[20,] 0.63401321 0.62887004
[21,] 0.47250690 0.63401321
[22,] -0.19190890 0.47250690
[23,] -0.48598679 -0.19190890
[24,] -0.28810422 -0.48598679
[25,] -0.14140317 -0.28810422
[26,] 0.02898630 -0.14140317
[27,] -0.05994843 0.02898630
[28,] -0.20218211 -0.05994843
[29,] -0.38659791 -0.20218211
[30,] -0.46067580 -0.38659791
[31,] -0.30290948 -0.46067580
[32,] -0.25698738 -0.30290948
[33,] -0.52213054 -0.25698738
[34,] -0.54213054 -0.52213054
[35,] -0.62140317 -0.54213054
[36,] -0.62352060 -0.62140317
[37,] -0.48798796 -0.62352060
[38,] -0.36954584 -0.48798796
[39,] -0.19925951 -0.36954584
[40,] -0.22668793 -0.19925951
[41,] -0.29266161 -0.22668793
[42,] -0.56673950 -0.29266161
[43,] -0.49053106 -0.56673950
[44,] -0.58902475 -0.49053106
[45,] -0.13208895 -0.58902475
[46,] 0.45154791 -0.13208895
[47,] 0.58708054 0.45154791
[48,] 0.42574206 0.58708054
[49,] 0.04646943 0.42574206
[50,] -0.15716742 0.04646943
[51,] -0.22740218 -0.15716742
[52,] 0.14153255 -0.22740218
[53,] 0.32750622 0.14153255
[54,] 0.60901253 0.32750622
[55,] 0.37431041 0.60901253
[56,] 0.33503778 0.37431041
[57,] 0.41067356 0.33503778
[58,] 0.37223144 0.41067356
[59,] 0.48932195 0.37223144
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.18890849 0.21259692
2 0.22215588 0.18890849
3 0.12958430 0.22215588
4 -0.08667571 0.12958430
5 -0.09706518 -0.08667571
6 -0.14153255 -0.09706518
7 -0.20973990 -0.14153255
8 -0.12303886 -0.20973990
9 -0.22896096 -0.12303886
10 -0.08973990 -0.22896096
11 0.03098747 -0.08973990
12 0.27328584 0.03098747
13 0.39401321 0.27328584
14 0.27557109 0.39401321
15 0.35702583 0.27557109
16 0.37401321 0.35702583
17 0.44881847 0.37401321
18 0.55993531 0.44881847
19 0.62887004 0.55993531
20 0.63401321 0.62887004
21 0.47250690 0.63401321
22 -0.19190890 0.47250690
23 -0.48598679 -0.19190890
24 -0.28810422 -0.48598679
25 -0.14140317 -0.28810422
26 0.02898630 -0.14140317
27 -0.05994843 0.02898630
28 -0.20218211 -0.05994843
29 -0.38659791 -0.20218211
30 -0.46067580 -0.38659791
31 -0.30290948 -0.46067580
32 -0.25698738 -0.30290948
33 -0.52213054 -0.25698738
34 -0.54213054 -0.52213054
35 -0.62140317 -0.54213054
36 -0.62352060 -0.62140317
37 -0.48798796 -0.62352060
38 -0.36954584 -0.48798796
39 -0.19925951 -0.36954584
40 -0.22668793 -0.19925951
41 -0.29266161 -0.22668793
42 -0.56673950 -0.29266161
43 -0.49053106 -0.56673950
44 -0.58902475 -0.49053106
45 -0.13208895 -0.58902475
46 0.45154791 -0.13208895
47 0.58708054 0.45154791
48 0.42574206 0.58708054
49 0.04646943 0.42574206
50 -0.15716742 0.04646943
51 -0.22740218 -0.15716742
52 0.14153255 -0.22740218
53 0.32750622 0.14153255
54 0.60901253 0.32750622
55 0.37431041 0.60901253
56 0.33503778 0.37431041
57 0.41067356 0.33503778
58 0.37223144 0.41067356
59 0.48932195 0.37223144
> 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/7g8h11258813351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89o0q1258813351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9u84d1258813351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k45v1258813351.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11j7j91258813351.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/1229pk1258813351.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/13dmfd1258813351.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/14vv791258813351.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/15g51a1258813351.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/16fhbb1258813351.tab")
+ }
>
> system("convert tmp/1qjp81258813351.ps tmp/1qjp81258813351.png")
> system("convert tmp/2zzf21258813351.ps tmp/2zzf21258813351.png")
> system("convert tmp/3afe01258813351.ps tmp/3afe01258813351.png")
> system("convert tmp/4w70h1258813351.ps tmp/4w70h1258813351.png")
> system("convert tmp/55n4u1258813351.ps tmp/55n4u1258813351.png")
> system("convert tmp/60mtu1258813351.ps tmp/60mtu1258813351.png")
> system("convert tmp/7g8h11258813351.ps tmp/7g8h11258813351.png")
> system("convert tmp/89o0q1258813351.ps tmp/89o0q1258813351.png")
> system("convert tmp/9u84d1258813351.ps tmp/9u84d1258813351.png")
> system("convert tmp/10k45v1258813351.ps tmp/10k45v1258813351.png")
>
>
> proc.time()
user system elapsed
2.408 1.589 3.119