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.
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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(96.38
+ ,108.3
+ ,98.30
+ ,95.62
+ ,93.11
+ ,96.96
+ ,100.82
+ ,113.2
+ ,96.38
+ ,98.30
+ ,95.62
+ ,93.11
+ ,99.06
+ ,105
+ ,100.82
+ ,96.38
+ ,98.30
+ ,95.62
+ ,94.03
+ ,104
+ ,99.06
+ ,100.82
+ ,96.38
+ ,98.30
+ ,102.07
+ ,109.8
+ ,94.03
+ ,99.06
+ ,100.82
+ ,96.38
+ ,99.31
+ ,98.6
+ ,102.07
+ ,94.03
+ ,99.06
+ ,100.82
+ ,98.64
+ ,93.5
+ ,99.31
+ ,102.07
+ ,94.03
+ ,99.06
+ ,101.82
+ ,98.2
+ ,98.64
+ ,99.31
+ ,102.07
+ ,94.03
+ ,99.14
+ ,88
+ ,101.82
+ ,98.64
+ ,99.31
+ ,102.07
+ ,97.63
+ ,85.3
+ ,99.14
+ ,101.82
+ ,98.64
+ ,99.31
+ ,100.06
+ ,96.8
+ ,97.63
+ ,99.14
+ ,101.82
+ ,98.64
+ ,101.32
+ ,98.8
+ ,100.06
+ ,97.63
+ ,99.14
+ ,101.82
+ ,101.49
+ ,110.3
+ ,101.32
+ ,100.06
+ ,97.63
+ ,99.14
+ ,105.43
+ ,111.6
+ ,101.49
+ ,101.32
+ ,100.06
+ ,97.63
+ ,105.09
+ ,111.2
+ ,105.43
+ ,101.49
+ ,101.32
+ ,100.06
+ ,99.48
+ ,106.9
+ ,105.09
+ ,105.43
+ ,101.49
+ ,101.32
+ ,108.53
+ ,117.6
+ ,99.48
+ ,105.09
+ ,105.43
+ ,101.49
+ ,104.34
+ ,97
+ ,108.53
+ ,99.48
+ ,105.09
+ ,105.43
+ ,106.10
+ ,97.3
+ ,104.34
+ ,108.53
+ ,99.48
+ ,105.09
+ ,107.35
+ ,98.4
+ ,106.10
+ ,104.34
+ ,108.53
+ ,99.48
+ ,103.00
+ ,87.6
+ ,107.35
+ ,106.10
+ ,104.34
+ ,108.53
+ ,104.50
+ ,87.4
+ ,103.00
+ ,107.35
+ ,106.10
+ ,104.34
+ ,105.17
+ ,94.7
+ ,104.50
+ ,103.00
+ ,107.35
+ ,106.10
+ ,104.84
+ ,101.5
+ ,105.17
+ ,104.50
+ ,103.00
+ ,107.35
+ ,106.18
+ ,110.4
+ ,104.84
+ ,105.17
+ ,104.50
+ ,103.00
+ ,108.86
+ ,108.4
+ ,106.18
+ ,104.84
+ ,105.17
+ ,104.50
+ ,107.77
+ ,109.7
+ ,108.86
+ ,106.18
+ ,104.84
+ ,105.17
+ ,102.74
+ ,105.2
+ ,107.77
+ ,108.86
+ ,106.18
+ ,104.84
+ ,112.63
+ ,111.1
+ ,102.74
+ ,107.77
+ ,108.86
+ ,106.18
+ ,106.26
+ ,96.2
+ ,112.63
+ ,102.74
+ ,107.77
+ ,108.86
+ ,108.86
+ ,97.3
+ ,106.26
+ ,112.63
+ ,102.74
+ ,107.77
+ ,111.38
+ ,98.9
+ ,108.86
+ ,106.26
+ ,112.63
+ ,102.74
+ ,106.85
+ ,91.7
+ ,111.38
+ ,108.86
+ ,106.26
+ ,112.63
+ ,107.86
+ ,90.9
+ ,106.85
+ ,111.38
+ ,108.86
+ ,106.26
+ ,107.94
+ ,98.8
+ ,107.86
+ ,106.85
+ ,111.38
+ ,108.86
+ ,111.38
+ ,111.5
+ ,107.94
+ ,107.86
+ ,106.85
+ ,111.38
+ ,111.29
+ ,119
+ ,111.38
+ ,107.94
+ ,107.86
+ ,106.85
+ ,113.72
+ ,115.3
+ ,111.29
+ ,111.38
+ ,107.94
+ ,107.86
+ ,111.88
+ ,116.3
+ ,113.72
+ ,111.29
+ ,111.38
+ ,107.94
+ ,109.87
+ ,113.6
+ ,111.88
+ ,113.72
+ ,111.29
+ ,111.38
+ ,113.72
+ ,115.1
+ ,109.87
+ ,111.88
+ ,113.72
+ ,111.29
+ ,111.71
+ ,109.7
+ ,113.72
+ ,109.87
+ ,111.88
+ ,113.72
+ ,114.81
+ ,97.6
+ ,111.71
+ ,113.72
+ ,109.87
+ ,111.88
+ ,112.05
+ ,100.8
+ ,114.81
+ ,111.71
+ ,113.72
+ ,109.87
+ ,111.54
+ ,94
+ ,112.05
+ ,114.81
+ ,111.71
+ ,113.72
+ ,110.87
+ ,87.2
+ ,111.54
+ ,112.05
+ ,114.81
+ ,111.71
+ ,110.87
+ ,102.9
+ ,110.87
+ ,111.54
+ ,112.05
+ ,114.81
+ ,115.48
+ ,111.3
+ ,110.87
+ ,110.87
+ ,111.54
+ ,112.05
+ ,111.63
+ ,106.6
+ ,115.48
+ ,110.87
+ ,110.87
+ ,111.54
+ ,116.24
+ ,108.9
+ ,111.63
+ ,115.48
+ ,110.87
+ ,110.87
+ ,113.56
+ ,108.3
+ ,116.24
+ ,111.63
+ ,115.48
+ ,110.87
+ ,106.01
+ ,100.5
+ ,113.56
+ ,116.24
+ ,111.63
+ ,115.48
+ ,110.45
+ ,104
+ ,106.01
+ ,113.56
+ ,116.24
+ ,111.63
+ ,107.77
+ ,89.9
+ ,110.45
+ ,106.01
+ ,113.56
+ ,116.24
+ ,108.61
+ ,86.8
+ ,107.77
+ ,110.45
+ ,106.01
+ ,113.56
+ ,108.19
+ ,91.2
+ ,108.61
+ ,107.77
+ ,110.45
+ ,106.01)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('BESTC'
+ ,'INDUSTR'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('BESTC','INDUSTR','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
BESTC INDUSTR Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 96.38 108.3 98.30 95.62 93.11 96.96 1 0 0 0 0 0 0 0 0 0
2 100.82 113.2 96.38 98.30 95.62 93.11 0 1 0 0 0 0 0 0 0 0
3 99.06 105.0 100.82 96.38 98.30 95.62 0 0 1 0 0 0 0 0 0 0
4 94.03 104.0 99.06 100.82 96.38 98.30 0 0 0 1 0 0 0 0 0 0
5 102.07 109.8 94.03 99.06 100.82 96.38 0 0 0 0 1 0 0 0 0 0
6 99.31 98.6 102.07 94.03 99.06 100.82 0 0 0 0 0 1 0 0 0 0
7 98.64 93.5 99.31 102.07 94.03 99.06 0 0 0 0 0 0 1 0 0 0
8 101.82 98.2 98.64 99.31 102.07 94.03 0 0 0 0 0 0 0 1 0 0
9 99.14 88.0 101.82 98.64 99.31 102.07 0 0 0 0 0 0 0 0 1 0
10 97.63 85.3 99.14 101.82 98.64 99.31 0 0 0 0 0 0 0 0 0 1
11 100.06 96.8 97.63 99.14 101.82 98.64 0 0 0 0 0 0 0 0 0 0
12 101.32 98.8 100.06 97.63 99.14 101.82 0 0 0 0 0 0 0 0 0 0
13 101.49 110.3 101.32 100.06 97.63 99.14 1 0 0 0 0 0 0 0 0 0
14 105.43 111.6 101.49 101.32 100.06 97.63 0 1 0 0 0 0 0 0 0 0
15 105.09 111.2 105.43 101.49 101.32 100.06 0 0 1 0 0 0 0 0 0 0
16 99.48 106.9 105.09 105.43 101.49 101.32 0 0 0 1 0 0 0 0 0 0
17 108.53 117.6 99.48 105.09 105.43 101.49 0 0 0 0 1 0 0 0 0 0
18 104.34 97.0 108.53 99.48 105.09 105.43 0 0 0 0 0 1 0 0 0 0
19 106.10 97.3 104.34 108.53 99.48 105.09 0 0 0 0 0 0 1 0 0 0
20 107.35 98.4 106.10 104.34 108.53 99.48 0 0 0 0 0 0 0 1 0 0
21 103.00 87.6 107.35 106.10 104.34 108.53 0 0 0 0 0 0 0 0 1 0
22 104.50 87.4 103.00 107.35 106.10 104.34 0 0 0 0 0 0 0 0 0 1
23 105.17 94.7 104.50 103.00 107.35 106.10 0 0 0 0 0 0 0 0 0 0
24 104.84 101.5 105.17 104.50 103.00 107.35 0 0 0 0 0 0 0 0 0 0
25 106.18 110.4 104.84 105.17 104.50 103.00 1 0 0 0 0 0 0 0 0 0
26 108.86 108.4 106.18 104.84 105.17 104.50 0 1 0 0 0 0 0 0 0 0
27 107.77 109.7 108.86 106.18 104.84 105.17 0 0 1 0 0 0 0 0 0 0
28 102.74 105.2 107.77 108.86 106.18 104.84 0 0 0 1 0 0 0 0 0 0
29 112.63 111.1 102.74 107.77 108.86 106.18 0 0 0 0 1 0 0 0 0 0
30 106.26 96.2 112.63 102.74 107.77 108.86 0 0 0 0 0 1 0 0 0 0
31 108.86 97.3 106.26 112.63 102.74 107.77 0 0 0 0 0 0 1 0 0 0
32 111.38 98.9 108.86 106.26 112.63 102.74 0 0 0 0 0 0 0 1 0 0
33 106.85 91.7 111.38 108.86 106.26 112.63 0 0 0 0 0 0 0 0 1 0
34 107.86 90.9 106.85 111.38 108.86 106.26 0 0 0 0 0 0 0 0 0 1
35 107.94 98.8 107.86 106.85 111.38 108.86 0 0 0 0 0 0 0 0 0 0
36 111.38 111.5 107.94 107.86 106.85 111.38 0 0 0 0 0 0 0 0 0 0
37 111.29 119.0 111.38 107.94 107.86 106.85 1 0 0 0 0 0 0 0 0 0
38 113.72 115.3 111.29 111.38 107.94 107.86 0 1 0 0 0 0 0 0 0 0
39 111.88 116.3 113.72 111.29 111.38 107.94 0 0 1 0 0 0 0 0 0 0
40 109.87 113.6 111.88 113.72 111.29 111.38 0 0 0 1 0 0 0 0 0 0
41 113.72 115.1 109.87 111.88 113.72 111.29 0 0 0 0 1 0 0 0 0 0
42 111.71 109.7 113.72 109.87 111.88 113.72 0 0 0 0 0 1 0 0 0 0
43 114.81 97.6 111.71 113.72 109.87 111.88 0 0 0 0 0 0 1 0 0 0
44 112.05 100.8 114.81 111.71 113.72 109.87 0 0 0 0 0 0 0 1 0 0
45 111.54 94.0 112.05 114.81 111.71 113.72 0 0 0 0 0 0 0 0 1 0
46 110.87 87.2 111.54 112.05 114.81 111.71 0 0 0 0 0 0 0 0 0 1
47 110.87 102.9 110.87 111.54 112.05 114.81 0 0 0 0 0 0 0 0 0 0
48 115.48 111.3 110.87 110.87 111.54 112.05 0 0 0 0 0 0 0 0 0 0
49 111.63 106.6 115.48 110.87 110.87 111.54 1 0 0 0 0 0 0 0 0 0
50 116.24 108.9 111.63 115.48 110.87 110.87 0 1 0 0 0 0 0 0 0 0
51 113.56 108.3 116.24 111.63 115.48 110.87 0 0 1 0 0 0 0 0 0 0
52 106.01 100.5 113.56 116.24 111.63 115.48 0 0 0 1 0 0 0 0 0 0
53 110.45 104.0 106.01 113.56 116.24 111.63 0 0 0 0 1 0 0 0 0 0
54 107.77 89.9 110.45 106.01 113.56 116.24 0 0 0 0 0 1 0 0 0 0
55 108.61 86.8 107.77 110.45 106.01 113.56 0 0 0 0 0 0 1 0 0 0
56 108.19 91.2 108.61 107.77 110.45 106.01 0 0 0 0 0 0 0 1 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) INDUSTR Y1 Y2 Y3 Y4
-6.926052 0.259802 0.155336 0.064915 0.580834 0.028575
M1 M2 M3 M4 M5 M6
-2.621253 0.187869 -2.896746 -6.453004 -2.024826 -2.171303
M7 M8 M9 M10 M11 t
3.405031 -0.584882 0.271970 0.552451 -1.888590 0.008277
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.60217 -0.38486 0.05628 0.39830 3.25798
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.926052 12.419377 -0.558 0.58033
INDUSTR 0.259802 0.050456 5.149 8.33e-06 ***
Y1 0.155336 0.127793 1.216 0.23166
Y2 0.064915 0.134293 0.483 0.63160
Y3 0.580834 0.121949 4.763 2.78e-05 ***
Y4 0.028575 0.157421 0.182 0.85692
M1 -2.621253 0.959351 -2.732 0.00949 **
M2 0.187869 1.028426 0.183 0.85602
M3 -2.896746 1.065543 -2.719 0.00983 **
M4 -6.453004 1.002550 -6.437 1.44e-07 ***
M5 -2.024826 1.086113 -1.864 0.07002 .
M6 -2.171303 0.954236 -2.275 0.02861 *
M7 3.405031 1.356839 2.510 0.01647 *
M8 -0.584882 1.429526 -0.409 0.68473
M9 0.271970 1.273798 0.214 0.83207
M10 0.552451 1.559290 0.354 0.72508
M11 -1.888590 0.950698 -1.987 0.05422 .
t 0.008277 0.046890 0.177 0.86082
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.071 on 38 degrees of freedom
Multiple R-squared: 0.972, Adjusted R-squared: 0.9595
F-statistic: 77.67 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,] 0.086565766 0.173131533 0.9134342
[2,] 0.040618547 0.081237093 0.9593815
[3,] 0.013949363 0.027898725 0.9860506
[4,] 0.013192067 0.026384134 0.9868079
[5,] 0.010023795 0.020047590 0.9899762
[6,] 0.007723423 0.015446846 0.9922766
[7,] 0.002898300 0.005796600 0.9971017
[8,] 0.001431683 0.002863366 0.9985683
[9,] 0.255251755 0.510503510 0.7447482
[10,] 0.206268414 0.412536829 0.7937316
[11,] 0.124843804 0.249687609 0.8751562
[12,] 0.122995823 0.245991645 0.8770042
[13,] 0.079396458 0.158792916 0.9206035
[14,] 0.061267898 0.122535795 0.9387321
[15,] 0.068294875 0.136589750 0.9317051
> postscript(file="/var/www/html/rcomp/tmp/15dc51258752759.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2abtq1258752759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3u1fy1258752759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4eqkj1258752759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ri7q1258752759.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
-0.54637398 -1.42040794 -0.16710930 -0.36553883 0.10271088 0.36370480
7 8 9 10 11 12
-1.68722041 0.01041721 0.03814720 -0.38124823 0.07441800 0.10426778
13 14 15 16 17 18
0.49969430 -0.19192559 1.22398380 -0.05858567 0.37524513 0.71864128
19 20 21 22 23 24
0.14765952 -0.00412343 -0.54672556 0.40850887 0.88776044 -0.81629899
25 26 27 28 29 30
0.08525888 -0.15128687 1.16655052 0.08009590 3.25798473 0.24400678
31 32 33 34 35 36
0.27383592 0.76871064 0.10123029 0.24225745 -0.69823463 0.02659147
37 38 39 40 41 42
-0.39569433 -0.10648249 -1.50192587 0.81957132 -1.13377027 -1.07090881
43 44 45 46 47 48
0.87043952 -1.26912807 0.40734807 -0.26951809 -0.26394380 0.68543974
49 50 51 52 53 54
0.35711513 1.87010289 -0.72149916 -0.47554271 -2.60217046 -0.25544405
55 56
0.39528545 0.49412365
> postscript(file="/var/www/html/rcomp/tmp/6avwz1258752759.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 -0.54637398 NA
1 -1.42040794 -0.54637398
2 -0.16710930 -1.42040794
3 -0.36553883 -0.16710930
4 0.10271088 -0.36553883
5 0.36370480 0.10271088
6 -1.68722041 0.36370480
7 0.01041721 -1.68722041
8 0.03814720 0.01041721
9 -0.38124823 0.03814720
10 0.07441800 -0.38124823
11 0.10426778 0.07441800
12 0.49969430 0.10426778
13 -0.19192559 0.49969430
14 1.22398380 -0.19192559
15 -0.05858567 1.22398380
16 0.37524513 -0.05858567
17 0.71864128 0.37524513
18 0.14765952 0.71864128
19 -0.00412343 0.14765952
20 -0.54672556 -0.00412343
21 0.40850887 -0.54672556
22 0.88776044 0.40850887
23 -0.81629899 0.88776044
24 0.08525888 -0.81629899
25 -0.15128687 0.08525888
26 1.16655052 -0.15128687
27 0.08009590 1.16655052
28 3.25798473 0.08009590
29 0.24400678 3.25798473
30 0.27383592 0.24400678
31 0.76871064 0.27383592
32 0.10123029 0.76871064
33 0.24225745 0.10123029
34 -0.69823463 0.24225745
35 0.02659147 -0.69823463
36 -0.39569433 0.02659147
37 -0.10648249 -0.39569433
38 -1.50192587 -0.10648249
39 0.81957132 -1.50192587
40 -1.13377027 0.81957132
41 -1.07090881 -1.13377027
42 0.87043952 -1.07090881
43 -1.26912807 0.87043952
44 0.40734807 -1.26912807
45 -0.26951809 0.40734807
46 -0.26394380 -0.26951809
47 0.68543974 -0.26394380
48 0.35711513 0.68543974
49 1.87010289 0.35711513
50 -0.72149916 1.87010289
51 -0.47554271 -0.72149916
52 -2.60217046 -0.47554271
53 -0.25544405 -2.60217046
54 0.39528545 -0.25544405
55 0.49412365 0.39528545
56 NA 0.49412365
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.42040794 -0.54637398
[2,] -0.16710930 -1.42040794
[3,] -0.36553883 -0.16710930
[4,] 0.10271088 -0.36553883
[5,] 0.36370480 0.10271088
[6,] -1.68722041 0.36370480
[7,] 0.01041721 -1.68722041
[8,] 0.03814720 0.01041721
[9,] -0.38124823 0.03814720
[10,] 0.07441800 -0.38124823
[11,] 0.10426778 0.07441800
[12,] 0.49969430 0.10426778
[13,] -0.19192559 0.49969430
[14,] 1.22398380 -0.19192559
[15,] -0.05858567 1.22398380
[16,] 0.37524513 -0.05858567
[17,] 0.71864128 0.37524513
[18,] 0.14765952 0.71864128
[19,] -0.00412343 0.14765952
[20,] -0.54672556 -0.00412343
[21,] 0.40850887 -0.54672556
[22,] 0.88776044 0.40850887
[23,] -0.81629899 0.88776044
[24,] 0.08525888 -0.81629899
[25,] -0.15128687 0.08525888
[26,] 1.16655052 -0.15128687
[27,] 0.08009590 1.16655052
[28,] 3.25798473 0.08009590
[29,] 0.24400678 3.25798473
[30,] 0.27383592 0.24400678
[31,] 0.76871064 0.27383592
[32,] 0.10123029 0.76871064
[33,] 0.24225745 0.10123029
[34,] -0.69823463 0.24225745
[35,] 0.02659147 -0.69823463
[36,] -0.39569433 0.02659147
[37,] -0.10648249 -0.39569433
[38,] -1.50192587 -0.10648249
[39,] 0.81957132 -1.50192587
[40,] -1.13377027 0.81957132
[41,] -1.07090881 -1.13377027
[42,] 0.87043952 -1.07090881
[43,] -1.26912807 0.87043952
[44,] 0.40734807 -1.26912807
[45,] -0.26951809 0.40734807
[46,] -0.26394380 -0.26951809
[47,] 0.68543974 -0.26394380
[48,] 0.35711513 0.68543974
[49,] 1.87010289 0.35711513
[50,] -0.72149916 1.87010289
[51,] -0.47554271 -0.72149916
[52,] -2.60217046 -0.47554271
[53,] -0.25544405 -2.60217046
[54,] 0.39528545 -0.25544405
[55,] 0.49412365 0.39528545
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.42040794 -0.54637398
2 -0.16710930 -1.42040794
3 -0.36553883 -0.16710930
4 0.10271088 -0.36553883
5 0.36370480 0.10271088
6 -1.68722041 0.36370480
7 0.01041721 -1.68722041
8 0.03814720 0.01041721
9 -0.38124823 0.03814720
10 0.07441800 -0.38124823
11 0.10426778 0.07441800
12 0.49969430 0.10426778
13 -0.19192559 0.49969430
14 1.22398380 -0.19192559
15 -0.05858567 1.22398380
16 0.37524513 -0.05858567
17 0.71864128 0.37524513
18 0.14765952 0.71864128
19 -0.00412343 0.14765952
20 -0.54672556 -0.00412343
21 0.40850887 -0.54672556
22 0.88776044 0.40850887
23 -0.81629899 0.88776044
24 0.08525888 -0.81629899
25 -0.15128687 0.08525888
26 1.16655052 -0.15128687
27 0.08009590 1.16655052
28 3.25798473 0.08009590
29 0.24400678 3.25798473
30 0.27383592 0.24400678
31 0.76871064 0.27383592
32 0.10123029 0.76871064
33 0.24225745 0.10123029
34 -0.69823463 0.24225745
35 0.02659147 -0.69823463
36 -0.39569433 0.02659147
37 -0.10648249 -0.39569433
38 -1.50192587 -0.10648249
39 0.81957132 -1.50192587
40 -1.13377027 0.81957132
41 -1.07090881 -1.13377027
42 0.87043952 -1.07090881
43 -1.26912807 0.87043952
44 0.40734807 -1.26912807
45 -0.26951809 0.40734807
46 -0.26394380 -0.26951809
47 0.68543974 -0.26394380
48 0.35711513 0.68543974
49 1.87010289 0.35711513
50 -0.72149916 1.87010289
51 -0.47554271 -0.72149916
52 -2.60217046 -0.47554271
53 -0.25544405 -2.60217046
54 0.39528545 -0.25544405
55 0.49412365 0.39528545
> 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/7wo091258752759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8aqt31258752759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95ts11258752759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109p0k1258752759.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bn921258752759.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/12z7lx1258752759.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/13nx2o1258752759.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/1485ch1258752759.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/156f8b1258752759.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/164pz31258752759.tab")
+ }
> system("convert tmp/15dc51258752759.ps tmp/15dc51258752759.png")
> system("convert tmp/2abtq1258752759.ps tmp/2abtq1258752759.png")
> system("convert tmp/3u1fy1258752759.ps tmp/3u1fy1258752759.png")
> system("convert tmp/4eqkj1258752759.ps tmp/4eqkj1258752759.png")
> system("convert tmp/5ri7q1258752759.ps tmp/5ri7q1258752759.png")
> system("convert tmp/6avwz1258752759.ps tmp/6avwz1258752759.png")
> system("convert tmp/7wo091258752759.ps tmp/7wo091258752759.png")
> system("convert tmp/8aqt31258752759.ps tmp/8aqt31258752759.png")
> system("convert tmp/95ts11258752759.ps tmp/95ts11258752759.png")
> system("convert tmp/109p0k1258752759.ps tmp/109p0k1258752759.png")
>
>
> proc.time()
user system elapsed
2.382 1.589 3.274