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(99.06
+ ,152.2
+ ,96.92
+ ,98.2
+ ,98.54
+ ,98.71
+ ,99.65
+ ,169.4
+ ,99.06
+ ,96.92
+ ,98.2
+ ,98.54
+ ,99.82
+ ,168.6
+ ,99.65
+ ,99.06
+ ,96.92
+ ,98.2
+ ,99.99
+ ,161.1
+ ,99.82
+ ,99.65
+ ,99.06
+ ,96.92
+ ,100.33
+ ,174.1
+ ,99.99
+ ,99.82
+ ,99.65
+ ,99.06
+ ,99.31
+ ,179
+ ,100.33
+ ,99.99
+ ,99.82
+ ,99.65
+ ,101.1
+ ,190.6
+ ,99.31
+ ,100.33
+ ,99.99
+ ,99.82
+ ,101.1
+ ,190
+ ,101.1
+ ,99.31
+ ,100.33
+ ,99.99
+ ,100.93
+ ,181.6
+ ,101.1
+ ,101.1
+ ,99.31
+ ,100.33
+ ,100.85
+ ,174.8
+ ,100.93
+ ,101.1
+ ,101.1
+ ,99.31
+ ,100.93
+ ,180.5
+ ,100.85
+ ,100.93
+ ,101.1
+ ,101.1
+ ,99.6
+ ,196.8
+ ,100.93
+ ,100.85
+ ,100.93
+ ,101.1
+ ,101.88
+ ,193.8
+ ,99.6
+ ,100.93
+ ,100.85
+ ,100.93
+ ,101.81
+ ,197
+ ,101.88
+ ,99.6
+ ,100.93
+ ,100.85
+ ,102.38
+ ,216.3
+ ,101.81
+ ,101.88
+ ,99.6
+ ,100.93
+ ,102.74
+ ,221.4
+ ,102.38
+ ,101.81
+ ,101.88
+ ,99.6
+ ,102.82
+ ,217.9
+ ,102.74
+ ,102.38
+ ,101.81
+ ,101.88
+ ,101.72
+ ,229.7
+ ,102.82
+ ,102.74
+ ,102.38
+ ,101.81
+ ,103.47
+ ,227.4
+ ,101.72
+ ,102.82
+ ,102.74
+ ,102.38
+ ,102.98
+ ,204.2
+ ,103.47
+ ,101.72
+ ,102.82
+ ,102.74
+ ,102.68
+ ,196.6
+ ,102.98
+ ,103.47
+ ,101.72
+ ,102.82
+ ,102.9
+ ,198.8
+ ,102.68
+ ,102.98
+ ,103.47
+ ,101.72
+ ,103.03
+ ,207.5
+ ,102.9
+ ,102.68
+ ,102.98
+ ,103.47
+ ,101.29
+ ,190.7
+ ,103.03
+ ,102.9
+ ,102.68
+ ,102.98
+ ,103.69
+ ,201.6
+ ,101.29
+ ,103.03
+ ,102.9
+ ,102.68
+ ,103.68
+ ,210.5
+ ,103.69
+ ,101.29
+ ,103.03
+ ,102.9
+ ,104.2
+ ,223.5
+ ,103.68
+ ,103.69
+ ,101.29
+ ,103.03
+ ,104.08
+ ,223.8
+ ,104.2
+ ,103.68
+ ,103.69
+ ,101.29
+ ,104.16
+ ,231.2
+ ,104.08
+ ,104.2
+ ,103.68
+ ,103.69
+ ,103.05
+ ,244
+ ,104.16
+ ,104.08
+ ,104.2
+ ,103.68
+ ,104.66
+ ,234.7
+ ,103.05
+ ,104.16
+ ,104.08
+ ,104.2
+ ,104.46
+ ,250.2
+ ,104.66
+ ,103.05
+ ,104.16
+ ,104.08
+ ,104.95
+ ,265.7
+ ,104.46
+ ,104.66
+ ,103.05
+ ,104.16
+ ,105.85
+ ,287.6
+ ,104.95
+ ,104.46
+ ,104.66
+ ,103.05
+ ,106.23
+ ,283.3
+ ,105.85
+ ,104.95
+ ,104.46
+ ,104.66
+ ,104.86
+ ,295.4
+ ,106.23
+ ,105.85
+ ,104.95
+ ,104.46
+ ,107.44
+ ,312.3
+ ,104.86
+ ,106.23
+ ,105.85
+ ,104.95
+ ,108.23
+ ,333.8
+ ,107.44
+ ,104.86
+ ,106.23
+ ,105.85
+ ,108.45
+ ,347.7
+ ,108.23
+ ,107.44
+ ,104.86
+ ,106.23
+ ,109.39
+ ,383.2
+ ,108.45
+ ,108.23
+ ,107.44
+ ,104.86
+ ,110.15
+ ,407.1
+ ,109.39
+ ,108.45
+ ,108.23
+ ,107.44
+ ,109.13
+ ,413.6
+ ,110.15
+ ,109.39
+ ,108.45
+ ,108.23
+ ,110.28
+ ,362.7
+ ,109.13
+ ,110.15
+ ,109.39
+ ,108.45
+ ,110.17
+ ,321.9
+ ,110.28
+ ,109.13
+ ,110.15
+ ,109.39
+ ,109.99
+ ,239.4
+ ,110.17
+ ,110.28
+ ,109.13
+ ,110.15
+ ,109.26
+ ,191
+ ,109.99
+ ,110.17
+ ,110.28
+ ,109.13
+ ,109.11
+ ,159.7
+ ,109.26
+ ,109.99
+ ,110.17
+ ,110.28
+ ,107.06
+ ,163.4
+ ,109.11
+ ,109.26
+ ,109.99
+ ,110.17
+ ,109.53
+ ,157.6
+ ,107.06
+ ,109.11
+ ,109.26
+ ,109.99
+ ,108.92
+ ,166.2
+ ,109.53
+ ,107.06
+ ,109.11
+ ,109.26
+ ,109.24
+ ,176.7
+ ,108.92
+ ,109.53
+ ,107.06
+ ,109.11
+ ,109.12
+ ,198.3
+ ,109.24
+ ,108.92
+ ,109.53
+ ,107.06
+ ,109
+ ,226.2
+ ,109.12
+ ,109.24
+ ,108.92
+ ,109.53
+ ,107.23
+ ,216.2
+ ,109
+ ,109.12
+ ,109.24
+ ,108.92
+ ,109.49
+ ,235.9
+ ,107.23
+ ,109
+ ,109.12
+ ,109.24
+ ,109.04
+ ,226.9
+ ,109.49
+ ,107.23
+ ,109
+ ,109.12)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:56))
> 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 Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 99.06 152.2 96.92 98.20 98.54 98.71 1 0 0 0 0 0 0 0 0 0 0
2 99.65 169.4 99.06 96.92 98.20 98.54 0 1 0 0 0 0 0 0 0 0 0
3 99.82 168.6 99.65 99.06 96.92 98.20 0 0 1 0 0 0 0 0 0 0 0
4 99.99 161.1 99.82 99.65 99.06 96.92 0 0 0 1 0 0 0 0 0 0 0
5 100.33 174.1 99.99 99.82 99.65 99.06 0 0 0 0 1 0 0 0 0 0 0
6 99.31 179.0 100.33 99.99 99.82 99.65 0 0 0 0 0 1 0 0 0 0 0
7 101.10 190.6 99.31 100.33 99.99 99.82 0 0 0 0 0 0 1 0 0 0 0
8 101.10 190.0 101.10 99.31 100.33 99.99 0 0 0 0 0 0 0 1 0 0 0
9 100.93 181.6 101.10 101.10 99.31 100.33 0 0 0 0 0 0 0 0 1 0 0
10 100.85 174.8 100.93 101.10 101.10 99.31 0 0 0 0 0 0 0 0 0 1 0
11 100.93 180.5 100.85 100.93 101.10 101.10 0 0 0 0 0 0 0 0 0 0 1
12 99.60 196.8 100.93 100.85 100.93 101.10 0 0 0 0 0 0 0 0 0 0 0
13 101.88 193.8 99.60 100.93 100.85 100.93 1 0 0 0 0 0 0 0 0 0 0
14 101.81 197.0 101.88 99.60 100.93 100.85 0 1 0 0 0 0 0 0 0 0 0
15 102.38 216.3 101.81 101.88 99.60 100.93 0 0 1 0 0 0 0 0 0 0 0
16 102.74 221.4 102.38 101.81 101.88 99.60 0 0 0 1 0 0 0 0 0 0 0
17 102.82 217.9 102.74 102.38 101.81 101.88 0 0 0 0 1 0 0 0 0 0 0
18 101.72 229.7 102.82 102.74 102.38 101.81 0 0 0 0 0 1 0 0 0 0 0
19 103.47 227.4 101.72 102.82 102.74 102.38 0 0 0 0 0 0 1 0 0 0 0
20 102.98 204.2 103.47 101.72 102.82 102.74 0 0 0 0 0 0 0 1 0 0 0
21 102.68 196.6 102.98 103.47 101.72 102.82 0 0 0 0 0 0 0 0 1 0 0
22 102.90 198.8 102.68 102.98 103.47 101.72 0 0 0 0 0 0 0 0 0 1 0
23 103.03 207.5 102.90 102.68 102.98 103.47 0 0 0 0 0 0 0 0 0 0 1
24 101.29 190.7 103.03 102.90 102.68 102.98 0 0 0 0 0 0 0 0 0 0 0
25 103.69 201.6 101.29 103.03 102.90 102.68 1 0 0 0 0 0 0 0 0 0 0
26 103.68 210.5 103.69 101.29 103.03 102.90 0 1 0 0 0 0 0 0 0 0 0
27 104.20 223.5 103.68 103.69 101.29 103.03 0 0 1 0 0 0 0 0 0 0 0
28 104.08 223.8 104.20 103.68 103.69 101.29 0 0 0 1 0 0 0 0 0 0 0
29 104.16 231.2 104.08 104.20 103.68 103.69 0 0 0 0 1 0 0 0 0 0 0
30 103.05 244.0 104.16 104.08 104.20 103.68 0 0 0 0 0 1 0 0 0 0 0
31 104.66 234.7 103.05 104.16 104.08 104.20 0 0 0 0 0 0 1 0 0 0 0
32 104.46 250.2 104.66 103.05 104.16 104.08 0 0 0 0 0 0 0 1 0 0 0
33 104.95 265.7 104.46 104.66 103.05 104.16 0 0 0 0 0 0 0 0 1 0 0
34 105.85 287.6 104.95 104.46 104.66 103.05 0 0 0 0 0 0 0 0 0 1 0
35 106.23 283.3 105.85 104.95 104.46 104.66 0 0 0 0 0 0 0 0 0 0 1
36 104.86 295.4 106.23 105.85 104.95 104.46 0 0 0 0 0 0 0 0 0 0 0
37 107.44 312.3 104.86 106.23 105.85 104.95 1 0 0 0 0 0 0 0 0 0 0
38 108.23 333.8 107.44 104.86 106.23 105.85 0 1 0 0 0 0 0 0 0 0 0
39 108.45 347.7 108.23 107.44 104.86 106.23 0 0 1 0 0 0 0 0 0 0 0
40 109.39 383.2 108.45 108.23 107.44 104.86 0 0 0 1 0 0 0 0 0 0 0
41 110.15 407.1 109.39 108.45 108.23 107.44 0 0 0 0 1 0 0 0 0 0 0
42 109.13 413.6 110.15 109.39 108.45 108.23 0 0 0 0 0 1 0 0 0 0 0
43 110.28 362.7 109.13 110.15 109.39 108.45 0 0 0 0 0 0 1 0 0 0 0
44 110.17 321.9 110.28 109.13 110.15 109.39 0 0 0 0 0 0 0 1 0 0 0
45 109.99 239.4 110.17 110.28 109.13 110.15 0 0 0 0 0 0 0 0 1 0 0
46 109.26 191.0 109.99 110.17 110.28 109.13 0 0 0 0 0 0 0 0 0 1 0
47 109.11 159.7 109.26 109.99 110.17 110.28 0 0 0 0 0 0 0 0 0 0 1
48 107.06 163.4 109.11 109.26 109.99 110.17 0 0 0 0 0 0 0 0 0 0 0
49 109.53 157.6 107.06 109.11 109.26 109.99 1 0 0 0 0 0 0 0 0 0 0
50 108.92 166.2 109.53 107.06 109.11 109.26 0 1 0 0 0 0 0 0 0 0 0
51 109.24 176.7 108.92 109.53 107.06 109.11 0 0 1 0 0 0 0 0 0 0 0
52 109.12 198.3 109.24 108.92 109.53 107.06 0 0 0 1 0 0 0 0 0 0 0
53 109.00 226.2 109.12 109.24 108.92 109.53 0 0 0 0 1 0 0 0 0 0 0
54 107.23 216.2 109.00 109.12 109.24 108.92 0 0 0 0 0 1 0 0 0 0 0
55 109.49 235.9 107.23 109.00 109.12 109.24 0 0 0 0 0 0 1 0 0 0 0
56 109.04 226.9 109.49 107.23 109.00 109.12 0 0 0 0 0 0 0 1 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
12.007511 0.007069 0.492628 0.141018 0.007787 0.210384
M1 M2 M3 M4 M5 M6
3.106969 2.189399 2.060312 2.325011 1.768215 0.333423
M7 M8 M9 M10 M11 t
2.557666 1.647499 1.541058 1.917512 1.687456 0.015187
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.463785 -0.188432 -0.001107 0.168242 0.448421
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.007511 4.458237 2.693 0.01047 *
X 0.007069 0.001277 5.535 2.47e-06 ***
Y1 0.492628 0.147233 3.346 0.00186 **
Y2 0.141018 0.168918 0.835 0.40903
Y3 0.007787 0.183195 0.043 0.96632
Y4 0.210384 0.151534 1.388 0.17311
M1 3.106969 0.309330 10.044 3.02e-12 ***
M2 2.189399 0.375757 5.827 9.84e-07 ***
M3 2.060312 0.401601 5.130 8.83e-06 ***
M4 2.325011 0.394889 5.888 8.12e-07 ***
M5 1.768215 0.182991 9.663 8.78e-12 ***
M6 0.333423 0.185596 1.796 0.08037 .
M7 2.557666 0.274398 9.321 2.32e-11 ***
M8 1.647499 0.303220 5.433 3.41e-06 ***
M9 1.541058 0.299573 5.144 8.46e-06 ***
M10 1.917512 0.296605 6.465 1.31e-07 ***
M11 1.687456 0.192941 8.746 1.23e-10 ***
t 0.015187 0.010331 1.470 0.14979
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2678 on 38 degrees of freedom
Multiple R-squared: 0.9962, Adjusted R-squared: 0.9944
F-statistic: 578.5 on 17 and 38 DF, p-value: < 2.2e-16
> 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,] 0.331941144 0.66388229 0.6680589
[2,] 0.189451503 0.37890301 0.8105485
[3,] 0.133656320 0.26731264 0.8663437
[4,] 0.069097637 0.13819527 0.9309024
[5,] 0.036009972 0.07201994 0.9639900
[6,] 0.016625809 0.03325162 0.9833742
[7,] 0.006616157 0.01323231 0.9933838
[8,] 0.022253485 0.04450697 0.9777465
[9,] 0.053262103 0.10652421 0.9467379
[10,] 0.053961490 0.10792298 0.9460385
[11,] 0.033630683 0.06726137 0.9663693
[12,] 0.023159706 0.04631941 0.9768403
[13,] 0.024756815 0.04951363 0.9752432
[14,] 0.018107646 0.03621529 0.9818924
[15,] 0.008435620 0.01687124 0.9915644
> postscript(file="/var/www/html/rcomp/tmp/1zp0x1258722692.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2po611258722692.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32ugf1258722692.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hwxs1258722692.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5zmqz1258722692.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 = 56
Frequency = 1
1 2 3 4 5 6
-0.27342156 0.26206616 0.04068883 0.06950241 0.29667717 0.34472669
7 8 9 10 11 12
0.23074162 0.35358520 0.01820738 -0.12096241 -0.17959077 0.02064721
13 14 15 16 17 18
-0.11999896 -0.22966620 0.02428811 0.05948139 -0.03102560 0.12527617
19 20 21 22 23 24
0.05999307 -0.15436429 -0.32304600 -0.07555454 -0.22261541 -0.16122759
25 26 27 28 29 30
-0.06019321 -0.21495659 -0.02027378 -0.32965549 -0.27941622 -0.08472841
31 32 33 34 35 36
-0.22134500 -0.24791279 0.08706826 0.44842141 0.22406248 0.16494183
37 38 39 40 41 42
0.01453637 0.28484490 -0.30179737 0.15571962 0.24534055 -0.07587463
43 44 45 46 47 48
-0.46378538 -0.01674939 0.21777035 -0.25190446 0.17814370 -0.02436145
49 50 51 52 53 54
0.43907736 -0.10228828 0.25709422 0.04495207 -0.23157591 -0.30939981
55 56
0.39439569 0.06544127
> postscript(file="/var/www/html/rcomp/tmp/68tyt1258722692.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.27342156 NA
1 0.26206616 -0.27342156
2 0.04068883 0.26206616
3 0.06950241 0.04068883
4 0.29667717 0.06950241
5 0.34472669 0.29667717
6 0.23074162 0.34472669
7 0.35358520 0.23074162
8 0.01820738 0.35358520
9 -0.12096241 0.01820738
10 -0.17959077 -0.12096241
11 0.02064721 -0.17959077
12 -0.11999896 0.02064721
13 -0.22966620 -0.11999896
14 0.02428811 -0.22966620
15 0.05948139 0.02428811
16 -0.03102560 0.05948139
17 0.12527617 -0.03102560
18 0.05999307 0.12527617
19 -0.15436429 0.05999307
20 -0.32304600 -0.15436429
21 -0.07555454 -0.32304600
22 -0.22261541 -0.07555454
23 -0.16122759 -0.22261541
24 -0.06019321 -0.16122759
25 -0.21495659 -0.06019321
26 -0.02027378 -0.21495659
27 -0.32965549 -0.02027378
28 -0.27941622 -0.32965549
29 -0.08472841 -0.27941622
30 -0.22134500 -0.08472841
31 -0.24791279 -0.22134500
32 0.08706826 -0.24791279
33 0.44842141 0.08706826
34 0.22406248 0.44842141
35 0.16494183 0.22406248
36 0.01453637 0.16494183
37 0.28484490 0.01453637
38 -0.30179737 0.28484490
39 0.15571962 -0.30179737
40 0.24534055 0.15571962
41 -0.07587463 0.24534055
42 -0.46378538 -0.07587463
43 -0.01674939 -0.46378538
44 0.21777035 -0.01674939
45 -0.25190446 0.21777035
46 0.17814370 -0.25190446
47 -0.02436145 0.17814370
48 0.43907736 -0.02436145
49 -0.10228828 0.43907736
50 0.25709422 -0.10228828
51 0.04495207 0.25709422
52 -0.23157591 0.04495207
53 -0.30939981 -0.23157591
54 0.39439569 -0.30939981
55 0.06544127 0.39439569
56 NA 0.06544127
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.26206616 -0.27342156
[2,] 0.04068883 0.26206616
[3,] 0.06950241 0.04068883
[4,] 0.29667717 0.06950241
[5,] 0.34472669 0.29667717
[6,] 0.23074162 0.34472669
[7,] 0.35358520 0.23074162
[8,] 0.01820738 0.35358520
[9,] -0.12096241 0.01820738
[10,] -0.17959077 -0.12096241
[11,] 0.02064721 -0.17959077
[12,] -0.11999896 0.02064721
[13,] -0.22966620 -0.11999896
[14,] 0.02428811 -0.22966620
[15,] 0.05948139 0.02428811
[16,] -0.03102560 0.05948139
[17,] 0.12527617 -0.03102560
[18,] 0.05999307 0.12527617
[19,] -0.15436429 0.05999307
[20,] -0.32304600 -0.15436429
[21,] -0.07555454 -0.32304600
[22,] -0.22261541 -0.07555454
[23,] -0.16122759 -0.22261541
[24,] -0.06019321 -0.16122759
[25,] -0.21495659 -0.06019321
[26,] -0.02027378 -0.21495659
[27,] -0.32965549 -0.02027378
[28,] -0.27941622 -0.32965549
[29,] -0.08472841 -0.27941622
[30,] -0.22134500 -0.08472841
[31,] -0.24791279 -0.22134500
[32,] 0.08706826 -0.24791279
[33,] 0.44842141 0.08706826
[34,] 0.22406248 0.44842141
[35,] 0.16494183 0.22406248
[36,] 0.01453637 0.16494183
[37,] 0.28484490 0.01453637
[38,] -0.30179737 0.28484490
[39,] 0.15571962 -0.30179737
[40,] 0.24534055 0.15571962
[41,] -0.07587463 0.24534055
[42,] -0.46378538 -0.07587463
[43,] -0.01674939 -0.46378538
[44,] 0.21777035 -0.01674939
[45,] -0.25190446 0.21777035
[46,] 0.17814370 -0.25190446
[47,] -0.02436145 0.17814370
[48,] 0.43907736 -0.02436145
[49,] -0.10228828 0.43907736
[50,] 0.25709422 -0.10228828
[51,] 0.04495207 0.25709422
[52,] -0.23157591 0.04495207
[53,] -0.30939981 -0.23157591
[54,] 0.39439569 -0.30939981
[55,] 0.06544127 0.39439569
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.26206616 -0.27342156
2 0.04068883 0.26206616
3 0.06950241 0.04068883
4 0.29667717 0.06950241
5 0.34472669 0.29667717
6 0.23074162 0.34472669
7 0.35358520 0.23074162
8 0.01820738 0.35358520
9 -0.12096241 0.01820738
10 -0.17959077 -0.12096241
11 0.02064721 -0.17959077
12 -0.11999896 0.02064721
13 -0.22966620 -0.11999896
14 0.02428811 -0.22966620
15 0.05948139 0.02428811
16 -0.03102560 0.05948139
17 0.12527617 -0.03102560
18 0.05999307 0.12527617
19 -0.15436429 0.05999307
20 -0.32304600 -0.15436429
21 -0.07555454 -0.32304600
22 -0.22261541 -0.07555454
23 -0.16122759 -0.22261541
24 -0.06019321 -0.16122759
25 -0.21495659 -0.06019321
26 -0.02027378 -0.21495659
27 -0.32965549 -0.02027378
28 -0.27941622 -0.32965549
29 -0.08472841 -0.27941622
30 -0.22134500 -0.08472841
31 -0.24791279 -0.22134500
32 0.08706826 -0.24791279
33 0.44842141 0.08706826
34 0.22406248 0.44842141
35 0.16494183 0.22406248
36 0.01453637 0.16494183
37 0.28484490 0.01453637
38 -0.30179737 0.28484490
39 0.15571962 -0.30179737
40 0.24534055 0.15571962
41 -0.07587463 0.24534055
42 -0.46378538 -0.07587463
43 -0.01674939 -0.46378538
44 0.21777035 -0.01674939
45 -0.25190446 0.21777035
46 0.17814370 -0.25190446
47 -0.02436145 0.17814370
48 0.43907736 -0.02436145
49 -0.10228828 0.43907736
50 0.25709422 -0.10228828
51 0.04495207 0.25709422
52 -0.23157591 0.04495207
53 -0.30939981 -0.23157591
54 0.39439569 -0.30939981
55 0.06544127 0.39439569
> 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/78b3d1258722692.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jbpf1258722692.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vucp1258722692.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lnb81258722692.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kxaw1258722692.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/12voeh1258722692.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/13k4f41258722692.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/147atn1258722692.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/1528ho1258722692.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/16zate1258722692.tab")
+ }
>
> system("convert tmp/1zp0x1258722692.ps tmp/1zp0x1258722692.png")
> system("convert tmp/2po611258722692.ps tmp/2po611258722692.png")
> system("convert tmp/32ugf1258722692.ps tmp/32ugf1258722692.png")
> system("convert tmp/4hwxs1258722692.ps tmp/4hwxs1258722692.png")
> system("convert tmp/5zmqz1258722692.ps tmp/5zmqz1258722692.png")
> system("convert tmp/68tyt1258722692.ps tmp/68tyt1258722692.png")
> system("convert tmp/78b3d1258722692.ps tmp/78b3d1258722692.png")
> system("convert tmp/8jbpf1258722692.ps tmp/8jbpf1258722692.png")
> system("convert tmp/9vucp1258722692.ps tmp/9vucp1258722692.png")
> system("convert tmp/10lnb81258722692.ps tmp/10lnb81258722692.png")
>
>
> proc.time()
user system elapsed
2.372 1.577 5.590