R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(7.8
+ ,9.5
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.7
+ ,8.1
+ ,7.5
+ ,9.1
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.7
+ ,7.5
+ ,8.9
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,7.1
+ ,9
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.6
+ ,7.5
+ ,10.1
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.8
+ ,7.5
+ ,10.3
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.8
+ ,7.6
+ ,10.2
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.8
+ ,7.7
+ ,9.6
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.5
+ ,7.7
+ ,9.2
+ ,7.7
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.1
+ ,7.5
+ ,7.9
+ ,9.3
+ ,7.7
+ ,7.7
+ ,7.6
+ ,7.5
+ ,7.5
+ ,7.1
+ ,8.1
+ ,9.4
+ ,7.9
+ ,7.7
+ ,7.7
+ ,7.6
+ ,7.5
+ ,7.5
+ ,8.2
+ ,9.4
+ ,8.1
+ ,7.9
+ ,7.7
+ ,7.7
+ ,7.6
+ ,7.5
+ ,8.2
+ ,9.2
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.7
+ ,7.7
+ ,7.6
+ ,8.2
+ ,9
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.7
+ ,7.7
+ ,7.9
+ ,9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.9
+ ,7.7
+ ,7.3
+ ,9
+ ,7.9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,8.1
+ ,7.9
+ ,6.9
+ ,9.8
+ ,7.3
+ ,7.9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,8.1
+ ,6.6
+ ,10
+ ,6.9
+ ,7.3
+ ,7.9
+ ,8.2
+ ,8.2
+ ,8.2
+ ,6.7
+ ,9.8
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7.9
+ ,8.2
+ ,8.2
+ ,6.9
+ ,9.3
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7.9
+ ,8.2
+ ,7
+ ,9
+ ,6.9
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7.9
+ ,7.1
+ ,9
+ ,7
+ ,6.9
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.3
+ ,7.2
+ ,9.1
+ ,7.1
+ ,7
+ ,6.9
+ ,6.7
+ ,6.6
+ ,6.9
+ ,7.1
+ ,9.1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.9
+ ,6.7
+ ,6.6
+ ,6.9
+ ,9.1
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.9
+ ,6.7
+ ,7
+ ,9.2
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.9
+ ,6.8
+ ,8.8
+ ,7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,7
+ ,6.4
+ ,8.3
+ ,6.8
+ ,7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,7.1
+ ,6.7
+ ,8.4
+ ,6.4
+ ,6.8
+ ,7
+ ,6.9
+ ,7.1
+ ,7.2
+ ,6.6
+ ,8.1
+ ,6.7
+ ,6.4
+ ,6.8
+ ,7
+ ,6.9
+ ,7.1
+ ,6.4
+ ,7.7
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.8
+ ,7
+ ,6.9
+ ,6.3
+ ,7.9
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.8
+ ,7
+ ,6.2
+ ,7.9
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.8
+ ,6.5
+ ,8
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.4
+ ,6.8
+ ,7.9
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.7
+ ,6.8
+ ,7.6
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.6
+ ,6.4
+ ,7.1
+ ,6.8
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.3
+ ,6.4
+ ,6.1
+ ,6.8
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.3
+ ,5.8
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.5
+ ,6.2
+ ,6.1
+ ,6.9
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,6.8
+ ,6.5
+ ,7.2
+ ,8.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,6.8
+ ,7.3
+ ,8.7
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.8
+ ,6.9
+ ,8.3
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,6.4
+ ,6.1
+ ,7.9
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.1
+ ,5.8
+ ,7.5
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.1
+ ,5.8
+ ,6.2
+ ,7.8
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.2
+ ,6.1
+ ,7.1
+ ,8.3
+ ,6.2
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.2
+ ,7.7
+ ,8.4
+ ,7.1
+ ,6.2
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.3
+ ,7.9
+ ,8.2
+ ,7.7
+ ,7.1
+ ,6.2
+ ,5.8
+ ,6.1
+ ,6.9
+ ,7.7
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,6.2
+ ,5.8
+ ,6.1
+ ,7.4
+ ,7.2
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,6.2
+ ,5.8
+ ,7.5
+ ,7.3
+ ,7.4
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,6.2
+ ,8
+ ,8.1
+ ,7.5
+ ,7.4
+ ,7.7
+ ,7.9
+ ,7.7
+ ,7.1
+ ,8.1
+ ,8.5
+ ,8
+ ,7.5
+ ,7.4
+ ,7.7
+ ,7.9
+ ,7.7)
+ ,dim=c(8
+ ,54)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y-1'
+ ,'Y-2'
+ ,'Y-3'
+ ,'Y-4'
+ ,'Y-5'
+ ,'Y-6')
+ ,1:54))
> y <- array(NA,dim=c(8,54),dimnames=list(c('Y','X','Y-1','Y-2','Y-3','Y-4','Y-5','Y-6'),1:54))
> 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 Y-1 Y-2 Y-3 Y-4 Y-5 Y-6 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.8 9.5 7.8 7.8 7.6 7.5 7.7 8.1 1 0 0 0 0 0 0 0 0 0 0 1
2 7.5 9.1 7.8 7.8 7.8 7.6 7.5 7.7 0 1 0 0 0 0 0 0 0 0 0 2
3 7.5 8.9 7.5 7.8 7.8 7.8 7.6 7.5 0 0 1 0 0 0 0 0 0 0 0 3
4 7.1 9.0 7.5 7.5 7.8 7.8 7.8 7.6 0 0 0 1 0 0 0 0 0 0 0 4
5 7.5 10.1 7.1 7.5 7.5 7.8 7.8 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 7.5 10.3 7.5 7.1 7.5 7.5 7.8 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 7.6 10.2 7.5 7.5 7.1 7.5 7.5 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 7.7 9.6 7.6 7.5 7.5 7.1 7.5 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 7.7 9.2 7.7 7.6 7.5 7.5 7.1 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 7.9 9.3 7.7 7.7 7.6 7.5 7.5 7.1 0 0 0 0 0 0 0 0 0 1 0 10
11 8.1 9.4 7.9 7.7 7.7 7.6 7.5 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 9.4 8.1 7.9 7.7 7.7 7.6 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 8.2 9.2 8.2 8.1 7.9 7.7 7.7 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 8.2 9.0 8.2 8.2 8.1 7.9 7.7 7.7 0 1 0 0 0 0 0 0 0 0 0 14
15 7.9 9.0 8.2 8.2 8.2 8.1 7.9 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 7.3 9.0 7.9 8.2 8.2 8.2 8.1 7.9 0 0 0 1 0 0 0 0 0 0 0 16
17 6.9 9.8 7.3 7.9 8.2 8.2 8.2 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 6.6 10.0 6.9 7.3 7.9 8.2 8.2 8.2 0 0 0 0 0 1 0 0 0 0 0 18
19 6.7 9.8 6.6 6.9 7.3 7.9 8.2 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 6.9 9.3 6.7 6.6 6.9 7.3 7.9 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 7.0 9.0 6.9 6.7 6.6 6.9 7.3 7.9 0 0 0 0 0 0 0 0 1 0 0 21
22 7.1 9.0 7.0 6.9 6.7 6.6 6.9 7.3 0 0 0 0 0 0 0 0 0 1 0 22
23 7.2 9.1 7.1 7.0 6.9 6.7 6.6 6.9 0 0 0 0 0 0 0 0 0 0 1 23
24 7.1 9.1 7.2 7.1 7.0 6.9 6.7 6.6 0 0 0 0 0 0 0 0 0 0 0 24
25 6.9 9.1 7.1 7.2 7.1 7.0 6.9 6.7 1 0 0 0 0 0 0 0 0 0 0 25
26 7.0 9.2 6.9 7.1 7.2 7.1 7.0 6.9 0 1 0 0 0 0 0 0 0 0 0 26
27 6.8 8.8 7.0 6.9 7.1 7.2 7.1 7.0 0 0 1 0 0 0 0 0 0 0 0 27
28 6.4 8.3 6.8 7.0 6.9 7.1 7.2 7.1 0 0 0 1 0 0 0 0 0 0 0 28
29 6.7 8.4 6.4 6.8 7.0 6.9 7.1 7.2 0 0 0 0 1 0 0 0 0 0 0 29
30 6.6 8.1 6.7 6.4 6.8 7.0 6.9 7.1 0 0 0 0 0 1 0 0 0 0 0 30
31 6.4 7.7 6.6 6.7 6.4 6.8 7.0 6.9 0 0 0 0 0 0 1 0 0 0 0 31
32 6.3 7.9 6.4 6.6 6.7 6.4 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 32
33 6.2 7.9 6.3 6.4 6.6 6.7 6.4 6.8 0 0 0 0 0 0 0 0 1 0 0 33
34 6.5 8.0 6.2 6.3 6.4 6.6 6.7 6.4 0 0 0 0 0 0 0 0 0 1 0 34
35 6.8 7.9 6.5 6.2 6.3 6.4 6.6 6.7 0 0 0 0 0 0 0 0 0 0 1 35
36 6.8 7.6 6.8 6.5 6.2 6.3 6.4 6.6 0 0 0 0 0 0 0 0 0 0 0 36
37 6.4 7.1 6.8 6.8 6.5 6.2 6.3 6.4 1 0 0 0 0 0 0 0 0 0 0 37
38 6.1 6.8 6.4 6.8 6.8 6.5 6.2 6.3 0 1 0 0 0 0 0 0 0 0 0 38
39 5.8 6.5 6.1 6.4 6.8 6.8 6.5 6.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.1 6.9 5.8 6.1 6.4 6.8 6.8 6.5 0 0 0 1 0 0 0 0 0 0 0 40
41 7.2 8.2 6.1 5.8 6.1 6.4 6.8 6.8 0 0 0 0 1 0 0 0 0 0 0 41
42 7.3 8.7 7.2 6.1 5.8 6.1 6.4 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.9 8.3 7.3 7.2 6.1 5.8 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 6.1 7.9 6.9 7.3 7.2 6.1 5.8 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 5.8 7.5 6.1 6.9 7.3 7.2 6.1 5.8 0 0 0 0 0 0 0 0 1 0 0 45
46 6.2 7.8 5.8 6.1 6.9 7.3 7.2 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.1 8.3 6.2 5.8 6.1 6.9 7.3 7.2 0 0 0 0 0 0 0 0 0 0 1 47
48 7.7 8.4 7.1 6.2 5.8 6.1 6.9 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 7.9 8.2 7.7 7.1 6.2 5.8 6.1 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.7 7.7 7.9 7.7 7.1 6.2 5.8 6.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.4 7.2 7.7 7.9 7.7 7.1 6.2 5.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.5 7.3 7.4 7.7 7.9 7.7 7.1 6.2 0 0 0 1 0 0 0 0 0 0 0 52
53 8.0 8.1 7.5 7.4 7.7 7.9 7.7 7.1 0 0 0 0 1 0 0 0 0 0 0 53
54 8.1 8.5 8.0 7.5 7.4 7.7 7.9 7.7 0 0 0 0 0 1 0 0 0 0 0 54
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X `Y-1` `Y-2` `Y-3` `Y-4`
0.059146 0.066045 1.525127 -0.620545 -0.441318 0.498374
`Y-5` `Y-6` M1 M2 M3 M4
0.162625 -0.231594 0.111512 0.193313 -0.021941 -0.076259
M5 M6 M7 M8 M9 M10
0.453206 -0.292373 -0.089215 0.181115 0.079670 0.213161
M11 t
0.225130 0.002196
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.45352 -0.10004 0.02757 0.10205 0.34815
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.059146 0.786612 0.075 0.9405
X 0.066045 0.077628 0.851 0.4008
`Y-1` 1.525127 0.170155 8.963 1.78e-10 ***
`Y-2` -0.620545 0.313336 -1.980 0.0558 .
`Y-3` -0.441318 0.337024 -1.309 0.1992
`Y-4` 0.498374 0.342229 1.456 0.1545
`Y-5` 0.162625 0.333675 0.487 0.6291
`Y-6` -0.231594 0.201016 -1.152 0.2573
M1 0.111512 0.153467 0.727 0.4724
M2 0.193313 0.163651 1.181 0.2457
M3 -0.021941 0.163436 -0.134 0.8940
M4 -0.076259 0.165896 -0.460 0.6487
M5 0.453206 0.167597 2.704 0.0106 *
M6 -0.292373 0.169298 -1.727 0.0932 .
M7 -0.089215 0.181994 -0.490 0.6271
M8 0.181115 0.204158 0.887 0.3812
M9 0.079670 0.194521 0.410 0.6847
M10 0.213161 0.170468 1.250 0.2197
M11 0.225130 0.157307 1.431 0.1615
t 0.002196 0.003890 0.565 0.5761
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2114 on 34 degrees of freedom
Multiple R-squared: 0.9347, Adjusted R-squared: 0.8982
F-statistic: 25.61 on 19 and 34 DF, p-value: 7.73e-15
> 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,] 0.5971932 0.8056135 0.40280676
[2,] 0.4769813 0.9539626 0.52301871
[3,] 0.3501474 0.7002948 0.64985261
[4,] 0.3662101 0.7324203 0.63378987
[5,] 0.2370939 0.4741879 0.76290605
[6,] 0.4152351 0.8304701 0.58476493
[7,] 0.8837420 0.2325160 0.11625798
[8,] 0.9112821 0.1774358 0.08871791
[9,] 0.9227023 0.1545953 0.07729767
> postscript(file="/var/www/html/rcomp/tmp/1ufml1259260155.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vzzz1259260155.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34n171259260155.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47avb1259260155.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53tq31259260155.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 = 54
Frequency = 1
1 2 3 4 5 6
0.183882345 -0.195382727 0.326167462 -0.223844845 0.095819031 0.117235807
7 8 9 10 11 12
0.138965517 0.159952163 0.060862174 0.067070137 0.028207103 0.104124671
13 14 15 16 17 18
0.070382390 0.073397192 -0.101611848 -0.227995893 -0.453523850 0.105137478
19 20 21 22 23 24
0.107034582 -0.099860557 -0.028717165 0.026930712 0.010279299 -0.098528683
25 26 27 28 29 30
-0.212740825 0.063979141 -0.260237754 -0.239542863 0.191362281 0.020067171
31 32 33 34 35 36
-0.159625401 0.085039224 -0.062220264 0.006095957 -0.079773478 -0.093329267
37 38 39 40 41 42
-0.235674614 -0.013820820 -0.093087278 0.348154092 0.323365188 -0.275584009
43 44 45 46 47 48
-0.086374698 -0.145130829 0.030075254 -0.100096806 0.041287076 0.087733278
49 50 51 52 53 54
0.194150704 0.071827214 0.128769418 0.343229510 -0.157022649 0.033143553
> postscript(file="/var/www/html/rcomp/tmp/6l34k1259260155.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 = 54
Frequency = 1
lag(myerror, k = 1) myerror
0 0.183882345 NA
1 -0.195382727 0.183882345
2 0.326167462 -0.195382727
3 -0.223844845 0.326167462
4 0.095819031 -0.223844845
5 0.117235807 0.095819031
6 0.138965517 0.117235807
7 0.159952163 0.138965517
8 0.060862174 0.159952163
9 0.067070137 0.060862174
10 0.028207103 0.067070137
11 0.104124671 0.028207103
12 0.070382390 0.104124671
13 0.073397192 0.070382390
14 -0.101611848 0.073397192
15 -0.227995893 -0.101611848
16 -0.453523850 -0.227995893
17 0.105137478 -0.453523850
18 0.107034582 0.105137478
19 -0.099860557 0.107034582
20 -0.028717165 -0.099860557
21 0.026930712 -0.028717165
22 0.010279299 0.026930712
23 -0.098528683 0.010279299
24 -0.212740825 -0.098528683
25 0.063979141 -0.212740825
26 -0.260237754 0.063979141
27 -0.239542863 -0.260237754
28 0.191362281 -0.239542863
29 0.020067171 0.191362281
30 -0.159625401 0.020067171
31 0.085039224 -0.159625401
32 -0.062220264 0.085039224
33 0.006095957 -0.062220264
34 -0.079773478 0.006095957
35 -0.093329267 -0.079773478
36 -0.235674614 -0.093329267
37 -0.013820820 -0.235674614
38 -0.093087278 -0.013820820
39 0.348154092 -0.093087278
40 0.323365188 0.348154092
41 -0.275584009 0.323365188
42 -0.086374698 -0.275584009
43 -0.145130829 -0.086374698
44 0.030075254 -0.145130829
45 -0.100096806 0.030075254
46 0.041287076 -0.100096806
47 0.087733278 0.041287076
48 0.194150704 0.087733278
49 0.071827214 0.194150704
50 0.128769418 0.071827214
51 0.343229510 0.128769418
52 -0.157022649 0.343229510
53 0.033143553 -0.157022649
54 NA 0.033143553
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.195382727 0.183882345
[2,] 0.326167462 -0.195382727
[3,] -0.223844845 0.326167462
[4,] 0.095819031 -0.223844845
[5,] 0.117235807 0.095819031
[6,] 0.138965517 0.117235807
[7,] 0.159952163 0.138965517
[8,] 0.060862174 0.159952163
[9,] 0.067070137 0.060862174
[10,] 0.028207103 0.067070137
[11,] 0.104124671 0.028207103
[12,] 0.070382390 0.104124671
[13,] 0.073397192 0.070382390
[14,] -0.101611848 0.073397192
[15,] -0.227995893 -0.101611848
[16,] -0.453523850 -0.227995893
[17,] 0.105137478 -0.453523850
[18,] 0.107034582 0.105137478
[19,] -0.099860557 0.107034582
[20,] -0.028717165 -0.099860557
[21,] 0.026930712 -0.028717165
[22,] 0.010279299 0.026930712
[23,] -0.098528683 0.010279299
[24,] -0.212740825 -0.098528683
[25,] 0.063979141 -0.212740825
[26,] -0.260237754 0.063979141
[27,] -0.239542863 -0.260237754
[28,] 0.191362281 -0.239542863
[29,] 0.020067171 0.191362281
[30,] -0.159625401 0.020067171
[31,] 0.085039224 -0.159625401
[32,] -0.062220264 0.085039224
[33,] 0.006095957 -0.062220264
[34,] -0.079773478 0.006095957
[35,] -0.093329267 -0.079773478
[36,] -0.235674614 -0.093329267
[37,] -0.013820820 -0.235674614
[38,] -0.093087278 -0.013820820
[39,] 0.348154092 -0.093087278
[40,] 0.323365188 0.348154092
[41,] -0.275584009 0.323365188
[42,] -0.086374698 -0.275584009
[43,] -0.145130829 -0.086374698
[44,] 0.030075254 -0.145130829
[45,] -0.100096806 0.030075254
[46,] 0.041287076 -0.100096806
[47,] 0.087733278 0.041287076
[48,] 0.194150704 0.087733278
[49,] 0.071827214 0.194150704
[50,] 0.128769418 0.071827214
[51,] 0.343229510 0.128769418
[52,] -0.157022649 0.343229510
[53,] 0.033143553 -0.157022649
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.195382727 0.183882345
2 0.326167462 -0.195382727
3 -0.223844845 0.326167462
4 0.095819031 -0.223844845
5 0.117235807 0.095819031
6 0.138965517 0.117235807
7 0.159952163 0.138965517
8 0.060862174 0.159952163
9 0.067070137 0.060862174
10 0.028207103 0.067070137
11 0.104124671 0.028207103
12 0.070382390 0.104124671
13 0.073397192 0.070382390
14 -0.101611848 0.073397192
15 -0.227995893 -0.101611848
16 -0.453523850 -0.227995893
17 0.105137478 -0.453523850
18 0.107034582 0.105137478
19 -0.099860557 0.107034582
20 -0.028717165 -0.099860557
21 0.026930712 -0.028717165
22 0.010279299 0.026930712
23 -0.098528683 0.010279299
24 -0.212740825 -0.098528683
25 0.063979141 -0.212740825
26 -0.260237754 0.063979141
27 -0.239542863 -0.260237754
28 0.191362281 -0.239542863
29 0.020067171 0.191362281
30 -0.159625401 0.020067171
31 0.085039224 -0.159625401
32 -0.062220264 0.085039224
33 0.006095957 -0.062220264
34 -0.079773478 0.006095957
35 -0.093329267 -0.079773478
36 -0.235674614 -0.093329267
37 -0.013820820 -0.235674614
38 -0.093087278 -0.013820820
39 0.348154092 -0.093087278
40 0.323365188 0.348154092
41 -0.275584009 0.323365188
42 -0.086374698 -0.275584009
43 -0.145130829 -0.086374698
44 0.030075254 -0.145130829
45 -0.100096806 0.030075254
46 0.041287076 -0.100096806
47 0.087733278 0.041287076
48 0.194150704 0.087733278
49 0.071827214 0.194150704
50 0.128769418 0.071827214
51 0.343229510 0.128769418
52 -0.157022649 0.343229510
53 0.033143553 -0.157022649
> 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/718pv1259260155.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8opb71259260155.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9c9xz1259260155.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107ii51259260155.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mi9i1259260155.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/12i9881259260155.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/13763p1259260155.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/1449v31259260155.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/15qvq41259260155.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/16q3741259260155.tab")
+ }
>
> system("convert tmp/1ufml1259260155.ps tmp/1ufml1259260155.png")
> system("convert tmp/2vzzz1259260155.ps tmp/2vzzz1259260155.png")
> system("convert tmp/34n171259260155.ps tmp/34n171259260155.png")
> system("convert tmp/47avb1259260155.ps tmp/47avb1259260155.png")
> system("convert tmp/53tq31259260155.ps tmp/53tq31259260155.png")
> system("convert tmp/6l34k1259260155.ps tmp/6l34k1259260155.png")
> system("convert tmp/718pv1259260155.ps tmp/718pv1259260155.png")
> system("convert tmp/8opb71259260155.ps tmp/8opb71259260155.png")
> system("convert tmp/9c9xz1259260155.ps tmp/9c9xz1259260155.png")
> system("convert tmp/107ii51259260155.ps tmp/107ii51259260155.png")
>
>
> proc.time()
user system elapsed
2.275 1.555 3.023