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.
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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(95.1
+ ,93.8
+ ,96.9
+ ,98.6
+ ,111.7
+ ,109.8
+ ,97
+ ,93.8
+ ,95.1
+ ,96.9
+ ,98.6
+ ,111.7
+ ,112.7
+ ,107.6
+ ,97
+ ,95.1
+ ,96.9
+ ,98.6
+ ,102.9
+ ,101
+ ,112.7
+ ,97
+ ,95.1
+ ,96.9
+ ,97.4
+ ,95.4
+ ,102.9
+ ,112.7
+ ,97
+ ,95.1
+ ,111.4
+ ,96.5
+ ,97.4
+ ,102.9
+ ,112.7
+ ,97
+ ,87.4
+ ,89.2
+ ,111.4
+ ,97.4
+ ,102.9
+ ,112.7
+ ,96.8
+ ,87.1
+ ,87.4
+ ,111.4
+ ,97.4
+ ,102.9
+ ,114.1
+ ,110.5
+ ,96.8
+ ,87.4
+ ,111.4
+ ,97.4
+ ,110.3
+ ,110.8
+ ,114.1
+ ,96.8
+ ,87.4
+ ,111.4
+ ,103.9
+ ,104.2
+ ,110.3
+ ,114.1
+ ,96.8
+ ,87.4
+ ,101.6
+ ,88.9
+ ,103.9
+ ,110.3
+ ,114.1
+ ,96.8
+ ,94.6
+ ,89.8
+ ,101.6
+ ,103.9
+ ,110.3
+ ,114.1
+ ,95.9
+ ,90
+ ,94.6
+ ,101.6
+ ,103.9
+ ,110.3
+ ,104.7
+ ,93.9
+ ,95.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,102.8
+ ,91.3
+ ,104.7
+ ,95.9
+ ,94.6
+ ,101.6
+ ,98.1
+ ,87.8
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,113.9
+ ,99.7
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,80.9
+ ,73.5
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.7
+ ,79.2
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,113.2
+ ,96.9
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,105.9
+ ,95.2
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,108.8
+ ,95.6
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,102.3
+ ,89.7
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,99
+ ,92.8
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,100.7
+ ,88
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,115.5
+ ,101.1
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,100.7
+ ,92.7
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,109.9
+ ,95.8
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,114.6
+ ,103.8
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,85.4
+ ,81.8
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.5
+ ,87.1
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,114.8
+ ,105.9
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,116.5
+ ,108.1
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,112.9
+ ,102.6
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,102
+ ,93.7
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,106
+ ,103.5
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,105.3
+ ,100.6
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,118.8
+ ,113.3
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,106.1
+ ,102.4
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,109.3
+ ,102.1
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,117.2
+ ,106.9
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,92.5
+ ,87.3
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,104.2
+ ,93.1
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,112.5
+ ,109.1
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,122.4
+ ,120.3
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,113.3
+ ,104.9
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,100
+ ,92.6
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,110.7
+ ,109.8
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,112.8
+ ,111.4
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,109.8
+ ,117.9
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,117.3
+ ,121.6
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,109.1
+ ,117.8
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,115.9
+ ,124.2
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,96
+ ,106.8
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,99.8
+ ,102.7
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,116.8
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,115.7
+ ,113.6
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,99.4
+ ,96.1
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,94.3
+ ,85
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8)
+ ,dim=c(6
+ ,60)
+ ,dimnames=list(c('TIA'
+ ,'IAidM'
+ ,'TIA(t-1)'
+ ,'TIA(t-2)'
+ ,'TIA(t-3)'
+ ,'TIA(t-4)')
+ ,1:60))
> y <- array(NA,dim=c(6,60),dimnames=list(c('TIA','IAidM','TIA(t-1)','TIA(t-2)','TIA(t-3)','TIA(t-4)'),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
TIA IAidM TIA(t-1) TIA(t-2) TIA(t-3) TIA(t-4) M1 M2 M3 M4 M5 M6 M7 M8 M9
1 95.1 93.8 96.9 98.6 111.7 109.8 1 0 0 0 0 0 0 0 0
2 97.0 93.8 95.1 96.9 98.6 111.7 0 1 0 0 0 0 0 0 0
3 112.7 107.6 97.0 95.1 96.9 98.6 0 0 1 0 0 0 0 0 0
4 102.9 101.0 112.7 97.0 95.1 96.9 0 0 0 1 0 0 0 0 0
5 97.4 95.4 102.9 112.7 97.0 95.1 0 0 0 0 1 0 0 0 0
6 111.4 96.5 97.4 102.9 112.7 97.0 0 0 0 0 0 1 0 0 0
7 87.4 89.2 111.4 97.4 102.9 112.7 0 0 0 0 0 0 1 0 0
8 96.8 87.1 87.4 111.4 97.4 102.9 0 0 0 0 0 0 0 1 0
9 114.1 110.5 96.8 87.4 111.4 97.4 0 0 0 0 0 0 0 0 1
10 110.3 110.8 114.1 96.8 87.4 111.4 0 0 0 0 0 0 0 0 0
11 103.9 104.2 110.3 114.1 96.8 87.4 0 0 0 0 0 0 0 0 0
12 101.6 88.9 103.9 110.3 114.1 96.8 0 0 0 0 0 0 0 0 0
13 94.6 89.8 101.6 103.9 110.3 114.1 1 0 0 0 0 0 0 0 0
14 95.9 90.0 94.6 101.6 103.9 110.3 0 1 0 0 0 0 0 0 0
15 104.7 93.9 95.9 94.6 101.6 103.9 0 0 1 0 0 0 0 0 0
16 102.8 91.3 104.7 95.9 94.6 101.6 0 0 0 1 0 0 0 0 0
17 98.1 87.8 102.8 104.7 95.9 94.6 0 0 0 0 1 0 0 0 0
18 113.9 99.7 98.1 102.8 104.7 95.9 0 0 0 0 0 1 0 0 0
19 80.9 73.5 113.9 98.1 102.8 104.7 0 0 0 0 0 0 1 0 0
20 95.7 79.2 80.9 113.9 98.1 102.8 0 0 0 0 0 0 0 1 0
21 113.2 96.9 95.7 80.9 113.9 98.1 0 0 0 0 0 0 0 0 1
22 105.9 95.2 113.2 95.7 80.9 113.9 0 0 0 0 0 0 0 0 0
23 108.8 95.6 105.9 113.2 95.7 80.9 0 0 0 0 0 0 0 0 0
24 102.3 89.7 108.8 105.9 113.2 95.7 0 0 0 0 0 0 0 0 0
25 99.0 92.8 102.3 108.8 105.9 113.2 1 0 0 0 0 0 0 0 0
26 100.7 88.0 99.0 102.3 108.8 105.9 0 1 0 0 0 0 0 0 0
27 115.5 101.1 100.7 99.0 102.3 108.8 0 0 1 0 0 0 0 0 0
28 100.7 92.7 115.5 100.7 99.0 102.3 0 0 0 1 0 0 0 0 0
29 109.9 95.8 100.7 115.5 100.7 99.0 0 0 0 0 1 0 0 0 0
30 114.6 103.8 109.9 100.7 115.5 100.7 0 0 0 0 0 1 0 0 0
31 85.4 81.8 114.6 109.9 100.7 115.5 0 0 0 0 0 0 1 0 0
32 100.5 87.1 85.4 114.6 109.9 100.7 0 0 0 0 0 0 0 1 0
33 114.8 105.9 100.5 85.4 114.6 109.9 0 0 0 0 0 0 0 0 1
34 116.5 108.1 114.8 100.5 85.4 114.6 0 0 0 0 0 0 0 0 0
35 112.9 102.6 116.5 114.8 100.5 85.4 0 0 0 0 0 0 0 0 0
36 102.0 93.7 112.9 116.5 114.8 100.5 0 0 0 0 0 0 0 0 0
37 106.0 103.5 102.0 112.9 116.5 114.8 1 0 0 0 0 0 0 0 0
38 105.3 100.6 106.0 102.0 112.9 116.5 0 1 0 0 0 0 0 0 0
39 118.8 113.3 105.3 106.0 102.0 112.9 0 0 1 0 0 0 0 0 0
40 106.1 102.4 118.8 105.3 106.0 102.0 0 0 0 1 0 0 0 0 0
41 109.3 102.1 106.1 118.8 105.3 106.0 0 0 0 0 1 0 0 0 0
42 117.2 106.9 109.3 106.1 118.8 105.3 0 0 0 0 0 1 0 0 0
43 92.5 87.3 117.2 109.3 106.1 118.8 0 0 0 0 0 0 1 0 0
44 104.2 93.1 92.5 117.2 109.3 106.1 0 0 0 0 0 0 0 1 0
45 112.5 109.1 104.2 92.5 117.2 109.3 0 0 0 0 0 0 0 0 1
46 122.4 120.3 112.5 104.2 92.5 117.2 0 0 0 0 0 0 0 0 0
47 113.3 104.9 122.4 112.5 104.2 92.5 0 0 0 0 0 0 0 0 0
48 100.0 92.6 113.3 122.4 112.5 104.2 0 0 0 0 0 0 0 0 0
49 110.7 109.8 100.0 113.3 122.4 112.5 1 0 0 0 0 0 0 0 0
50 112.8 111.4 110.7 100.0 113.3 122.4 0 1 0 0 0 0 0 0 0
51 109.8 117.9 112.8 110.7 100.0 113.3 0 0 1 0 0 0 0 0 0
52 117.3 121.6 109.8 112.8 110.7 100.0 0 0 0 1 0 0 0 0 0
53 109.1 117.8 117.3 109.8 112.8 110.7 0 0 0 0 1 0 0 0 0
54 115.9 124.2 109.1 117.3 109.8 112.8 0 0 0 0 0 1 0 0 0
55 96.0 106.8 115.9 109.1 117.3 109.8 0 0 0 0 0 0 1 0 0
56 99.8 102.7 96.0 115.9 109.1 117.3 0 0 0 0 0 0 0 1 0
57 116.8 116.8 99.8 96.0 115.9 109.1 0 0 0 0 0 0 0 0 1
58 115.7 113.6 116.8 99.8 96.0 115.9 0 0 0 0 0 0 0 0 0
59 99.4 96.1 115.7 116.8 99.8 96.0 0 0 0 0 0 0 0 0 0
60 94.3 85.0 99.4 115.7 116.8 99.8 0 0 0 0 0 0 0 0 0
M10 M11 t
1 0 0 1
2 0 0 2
3 0 0 3
4 0 0 4
5 0 0 5
6 0 0 6
7 0 0 7
8 0 0 8
9 0 0 9
10 1 0 10
11 0 1 11
12 0 0 12
13 0 0 13
14 0 0 14
15 0 0 15
16 0 0 16
17 0 0 17
18 0 0 18
19 0 0 19
20 0 0 20
21 0 0 21
22 1 0 22
23 0 1 23
24 0 0 24
25 0 0 25
26 0 0 26
27 0 0 27
28 0 0 28
29 0 0 29
30 0 0 30
31 0 0 31
32 0 0 32
33 0 0 33
34 1 0 34
35 0 1 35
36 0 0 36
37 0 0 37
38 0 0 38
39 0 0 39
40 0 0 40
41 0 0 41
42 0 0 42
43 0 0 43
44 0 0 44
45 0 0 45
46 1 0 46
47 0 1 47
48 0 0 48
49 0 0 49
50 0 0 50
51 0 0 51
52 0 0 52
53 0 0 53
54 0 0 54
55 0 0 55
56 0 0 56
57 0 0 57
58 1 0 58
59 0 1 59
60 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) IAidM `TIA(t-1)` `TIA(t-2)` `TIA(t-3)` `TIA(t-4)`
49.38518 0.32823 -0.05692 0.05504 0.31532 -0.15972
M1 M2 M3 M4 M5 M6
1.07936 5.04024 12.99101 7.48174 5.69905 10.74423
M7 M8 M9 M10 M11 t
-5.15003 2.42700 10.14582 19.48964 7.43513 0.02241
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.2643 -2.3977 0.1846 2.1578 6.1557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.38518 34.91299 1.415 0.164580
IAidM 0.32823 0.09199 3.568 0.000915 ***
`TIA(t-1)` -0.05692 0.14603 -0.390 0.698695
`TIA(t-2)` 0.05504 0.16080 0.342 0.733835
`TIA(t-3)` 0.31532 0.17445 1.807 0.077860 .
`TIA(t-4)` -0.15972 0.16860 -0.947 0.348911
M1 1.07936 4.00449 0.270 0.788837
M2 5.04024 4.50496 1.119 0.269575
M3 12.99101 5.10573 2.544 0.014712 *
M4 7.48174 4.44599 1.683 0.099834 .
M5 5.69905 3.36571 1.693 0.097811 .
M6 10.74423 3.41014 3.151 0.003000 **
M7 -5.15003 3.33108 -1.546 0.129595
M8 2.42700 4.13539 0.587 0.560423
M9 10.14582 5.90111 1.719 0.092921 .
M10 19.48964 6.83273 2.852 0.006705 **
M11 7.43513 5.02651 1.479 0.146553
t 0.02241 0.06782 0.330 0.742721
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.579 on 42 degrees of freedom
Multiple R-squared: 0.8874, Adjusted R-squared: 0.8419
F-statistic: 19.48 on 17 and 42 DF, p-value: 1.17e-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,] 0.25336745 0.50673490 0.7466326
[2,] 0.13986947 0.27973895 0.8601305
[3,] 0.21991089 0.43982177 0.7800891
[4,] 0.13297191 0.26594381 0.8670281
[5,] 0.09679228 0.19358455 0.9032077
[6,] 0.10434263 0.20868527 0.8956574
[7,] 0.13125681 0.26251363 0.8687432
[8,] 0.11641378 0.23282755 0.8835862
[9,] 0.12320167 0.24640335 0.8767983
[10,] 0.08534424 0.17068849 0.9146558
[11,] 0.06565658 0.13131315 0.9343434
[12,] 0.08405828 0.16811656 0.9159417
[13,] 0.05677225 0.11354449 0.9432278
[14,] 0.03086388 0.06172775 0.9691361
[15,] 0.02245114 0.04490228 0.9775489
[16,] 0.02189442 0.04378884 0.9781056
[17,] 0.02055761 0.04111522 0.9794424
[18,] 0.07442313 0.14884625 0.9255769
[19,] 0.04024514 0.08049027 0.9597549
> postscript(file="/var/www/html/rcomp/tmp/1n1711258745755.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22gzg1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/359l01258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4312q1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56afl1258745755.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
-3.77029127 -1.42837686 0.41988604 -0.64190984 -4.85206589 -0.70132340
7 8 9 10 11 12
0.26375131 0.78606797 -0.77252037 -3.76637133 -3.93362361 2.09223757
13 14 15 16 17 18
-2.12228113 -3.73194226 -4.02288881 2.68652832 -1.22482683 2.87162839
19 20 21 22 23 24
-2.49444310 1.26590789 2.14112401 -0.85678692 2.62801663 2.88991476
25 26 27 28 29 30
2.03792097 -0.58028985 4.73788639 -1.06698511 6.15574071 0.10542306
31 32 33 34 35 36
0.78983847 -0.63451165 2.20763243 3.75994788 3.88198180 0.92016434
37 38 39 40 41 42
1.92746557 0.43028341 4.39058410 -1.44016698 3.01228945 0.78180734
43 44 45 46 47 48
4.82106102 2.13988130 -2.50744488 3.22865835 3.68787602 0.02651226
49 50 51 52 53 54
1.92718584 5.31032557 -5.52546772 0.46253361 -3.09113744 -3.05753539
55 56 57 58 59 60
-3.38020771 -3.55734552 -1.06879119 -2.36544798 -6.26425083 -5.92882893
> postscript(file="/var/www/html/rcomp/tmp/6appa1258745755.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 -3.77029127 NA
1 -1.42837686 -3.77029127
2 0.41988604 -1.42837686
3 -0.64190984 0.41988604
4 -4.85206589 -0.64190984
5 -0.70132340 -4.85206589
6 0.26375131 -0.70132340
7 0.78606797 0.26375131
8 -0.77252037 0.78606797
9 -3.76637133 -0.77252037
10 -3.93362361 -3.76637133
11 2.09223757 -3.93362361
12 -2.12228113 2.09223757
13 -3.73194226 -2.12228113
14 -4.02288881 -3.73194226
15 2.68652832 -4.02288881
16 -1.22482683 2.68652832
17 2.87162839 -1.22482683
18 -2.49444310 2.87162839
19 1.26590789 -2.49444310
20 2.14112401 1.26590789
21 -0.85678692 2.14112401
22 2.62801663 -0.85678692
23 2.88991476 2.62801663
24 2.03792097 2.88991476
25 -0.58028985 2.03792097
26 4.73788639 -0.58028985
27 -1.06698511 4.73788639
28 6.15574071 -1.06698511
29 0.10542306 6.15574071
30 0.78983847 0.10542306
31 -0.63451165 0.78983847
32 2.20763243 -0.63451165
33 3.75994788 2.20763243
34 3.88198180 3.75994788
35 0.92016434 3.88198180
36 1.92746557 0.92016434
37 0.43028341 1.92746557
38 4.39058410 0.43028341
39 -1.44016698 4.39058410
40 3.01228945 -1.44016698
41 0.78180734 3.01228945
42 4.82106102 0.78180734
43 2.13988130 4.82106102
44 -2.50744488 2.13988130
45 3.22865835 -2.50744488
46 3.68787602 3.22865835
47 0.02651226 3.68787602
48 1.92718584 0.02651226
49 5.31032557 1.92718584
50 -5.52546772 5.31032557
51 0.46253361 -5.52546772
52 -3.09113744 0.46253361
53 -3.05753539 -3.09113744
54 -3.38020771 -3.05753539
55 -3.55734552 -3.38020771
56 -1.06879119 -3.55734552
57 -2.36544798 -1.06879119
58 -6.26425083 -2.36544798
59 -5.92882893 -6.26425083
60 NA -5.92882893
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.42837686 -3.77029127
[2,] 0.41988604 -1.42837686
[3,] -0.64190984 0.41988604
[4,] -4.85206589 -0.64190984
[5,] -0.70132340 -4.85206589
[6,] 0.26375131 -0.70132340
[7,] 0.78606797 0.26375131
[8,] -0.77252037 0.78606797
[9,] -3.76637133 -0.77252037
[10,] -3.93362361 -3.76637133
[11,] 2.09223757 -3.93362361
[12,] -2.12228113 2.09223757
[13,] -3.73194226 -2.12228113
[14,] -4.02288881 -3.73194226
[15,] 2.68652832 -4.02288881
[16,] -1.22482683 2.68652832
[17,] 2.87162839 -1.22482683
[18,] -2.49444310 2.87162839
[19,] 1.26590789 -2.49444310
[20,] 2.14112401 1.26590789
[21,] -0.85678692 2.14112401
[22,] 2.62801663 -0.85678692
[23,] 2.88991476 2.62801663
[24,] 2.03792097 2.88991476
[25,] -0.58028985 2.03792097
[26,] 4.73788639 -0.58028985
[27,] -1.06698511 4.73788639
[28,] 6.15574071 -1.06698511
[29,] 0.10542306 6.15574071
[30,] 0.78983847 0.10542306
[31,] -0.63451165 0.78983847
[32,] 2.20763243 -0.63451165
[33,] 3.75994788 2.20763243
[34,] 3.88198180 3.75994788
[35,] 0.92016434 3.88198180
[36,] 1.92746557 0.92016434
[37,] 0.43028341 1.92746557
[38,] 4.39058410 0.43028341
[39,] -1.44016698 4.39058410
[40,] 3.01228945 -1.44016698
[41,] 0.78180734 3.01228945
[42,] 4.82106102 0.78180734
[43,] 2.13988130 4.82106102
[44,] -2.50744488 2.13988130
[45,] 3.22865835 -2.50744488
[46,] 3.68787602 3.22865835
[47,] 0.02651226 3.68787602
[48,] 1.92718584 0.02651226
[49,] 5.31032557 1.92718584
[50,] -5.52546772 5.31032557
[51,] 0.46253361 -5.52546772
[52,] -3.09113744 0.46253361
[53,] -3.05753539 -3.09113744
[54,] -3.38020771 -3.05753539
[55,] -3.55734552 -3.38020771
[56,] -1.06879119 -3.55734552
[57,] -2.36544798 -1.06879119
[58,] -6.26425083 -2.36544798
[59,] -5.92882893 -6.26425083
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.42837686 -3.77029127
2 0.41988604 -1.42837686
3 -0.64190984 0.41988604
4 -4.85206589 -0.64190984
5 -0.70132340 -4.85206589
6 0.26375131 -0.70132340
7 0.78606797 0.26375131
8 -0.77252037 0.78606797
9 -3.76637133 -0.77252037
10 -3.93362361 -3.76637133
11 2.09223757 -3.93362361
12 -2.12228113 2.09223757
13 -3.73194226 -2.12228113
14 -4.02288881 -3.73194226
15 2.68652832 -4.02288881
16 -1.22482683 2.68652832
17 2.87162839 -1.22482683
18 -2.49444310 2.87162839
19 1.26590789 -2.49444310
20 2.14112401 1.26590789
21 -0.85678692 2.14112401
22 2.62801663 -0.85678692
23 2.88991476 2.62801663
24 2.03792097 2.88991476
25 -0.58028985 2.03792097
26 4.73788639 -0.58028985
27 -1.06698511 4.73788639
28 6.15574071 -1.06698511
29 0.10542306 6.15574071
30 0.78983847 0.10542306
31 -0.63451165 0.78983847
32 2.20763243 -0.63451165
33 3.75994788 2.20763243
34 3.88198180 3.75994788
35 0.92016434 3.88198180
36 1.92746557 0.92016434
37 0.43028341 1.92746557
38 4.39058410 0.43028341
39 -1.44016698 4.39058410
40 3.01228945 -1.44016698
41 0.78180734 3.01228945
42 4.82106102 0.78180734
43 2.13988130 4.82106102
44 -2.50744488 2.13988130
45 3.22865835 -2.50744488
46 3.68787602 3.22865835
47 0.02651226 3.68787602
48 1.92718584 0.02651226
49 5.31032557 1.92718584
50 -5.52546772 5.31032557
51 0.46253361 -5.52546772
52 -3.09113744 0.46253361
53 -3.05753539 -3.09113744
54 -3.38020771 -3.05753539
55 -3.55734552 -3.38020771
56 -1.06879119 -3.55734552
57 -2.36544798 -1.06879119
58 -6.26425083 -2.36544798
59 -5.92882893 -6.26425083
> 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/7ts951258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8frqx1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99woy1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mykf1258745755.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111al01258745755.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/12vzw21258745755.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/13uc611258745755.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/14zjnw1258745755.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/159nz11258745755.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/16i0u51258745755.tab")
+ }
>
> system("convert tmp/1n1711258745755.ps tmp/1n1711258745755.png")
> system("convert tmp/22gzg1258745755.ps tmp/22gzg1258745755.png")
> system("convert tmp/359l01258745755.ps tmp/359l01258745755.png")
> system("convert tmp/4312q1258745755.ps tmp/4312q1258745755.png")
> system("convert tmp/56afl1258745755.ps tmp/56afl1258745755.png")
> system("convert tmp/6appa1258745755.ps tmp/6appa1258745755.png")
> system("convert tmp/7ts951258745755.ps tmp/7ts951258745755.png")
> system("convert tmp/8frqx1258745755.ps tmp/8frqx1258745755.png")
> system("convert tmp/99woy1258745755.ps tmp/99woy1258745755.png")
> system("convert tmp/10mykf1258745755.ps tmp/10mykf1258745755.png")
>
>
> proc.time()
user system elapsed
2.425 1.607 2.813