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(100.49
+ ,1.9
+ ,100.16
+ ,99.6
+ ,100.25
+ ,100.03
+ ,99.72
+ ,2
+ ,100.49
+ ,100.16
+ ,99.6
+ ,100.25
+ ,100.14
+ ,2.3
+ ,99.72
+ ,100.49
+ ,100.16
+ ,99.6
+ ,98.48
+ ,2.8
+ ,100.14
+ ,99.72
+ ,100.49
+ ,100.16
+ ,100.38
+ ,2.4
+ ,98.48
+ ,100.14
+ ,99.72
+ ,100.49
+ ,101.45
+ ,2.3
+ ,100.38
+ ,98.48
+ ,100.14
+ ,99.72
+ ,98.42
+ ,2.7
+ ,101.45
+ ,100.38
+ ,98.48
+ ,100.14
+ ,98.6
+ ,2.7
+ ,98.42
+ ,101.45
+ ,100.38
+ ,98.48
+ ,100.06
+ ,2.9
+ ,98.6
+ ,98.42
+ ,101.45
+ ,100.38
+ ,98.62
+ ,3
+ ,100.06
+ ,98.6
+ ,98.42
+ ,101.45
+ ,100.84
+ ,2.2
+ ,98.62
+ ,100.06
+ ,98.6
+ ,98.42
+ ,100.02
+ ,2.3
+ ,100.84
+ ,98.62
+ ,100.06
+ ,98.6
+ ,97.95
+ ,2.8
+ ,100.02
+ ,100.84
+ ,98.62
+ ,100.06
+ ,98.32
+ ,2.8
+ ,97.95
+ ,100.02
+ ,100.84
+ ,98.62
+ ,98.27
+ ,2.8
+ ,98.32
+ ,97.95
+ ,100.02
+ ,100.84
+ ,97.22
+ ,2.2
+ ,98.27
+ ,98.32
+ ,97.95
+ ,100.02
+ ,99.28
+ ,2.6
+ ,97.22
+ ,98.27
+ ,98.32
+ ,97.95
+ ,100.38
+ ,2.8
+ ,99.28
+ ,97.22
+ ,98.27
+ ,98.32
+ ,99.02
+ ,2.5
+ ,100.38
+ ,99.28
+ ,97.22
+ ,98.27
+ ,100.32
+ ,2.4
+ ,99.02
+ ,100.38
+ ,99.28
+ ,97.22
+ ,99.81
+ ,2.3
+ ,100.32
+ ,99.02
+ ,100.38
+ ,99.28
+ ,100.6
+ ,1.9
+ ,99.81
+ ,100.32
+ ,99.02
+ ,100.38
+ ,101.19
+ ,1.7
+ ,100.6
+ ,99.81
+ ,100.32
+ ,99.02
+ ,100.47
+ ,2
+ ,101.19
+ ,100.6
+ ,99.81
+ ,100.32
+ ,101.77
+ ,2.1
+ ,100.47
+ ,101.19
+ ,100.6
+ ,99.81
+ ,102.32
+ ,1.7
+ ,101.77
+ ,100.47
+ ,101.19
+ ,100.6
+ ,102.39
+ ,1.8
+ ,102.32
+ ,101.77
+ ,100.47
+ ,101.19
+ ,101.16
+ ,1.8
+ ,102.39
+ ,102.32
+ ,101.77
+ ,100.47
+ ,100.63
+ ,1.8
+ ,101.16
+ ,102.39
+ ,102.32
+ ,101.77
+ ,101.48
+ ,1.3
+ ,100.63
+ ,101.16
+ ,102.39
+ ,102.32
+ ,101.44
+ ,1.3
+ ,101.48
+ ,100.63
+ ,101.16
+ ,102.39
+ ,100.09
+ ,1.3
+ ,101.44
+ ,101.48
+ ,100.63
+ ,101.16
+ ,100.7
+ ,1.2
+ ,100.09
+ ,101.44
+ ,101.48
+ ,100.63
+ ,100.78
+ ,1.4
+ ,100.7
+ ,100.09
+ ,101.44
+ ,101.48
+ ,99.81
+ ,2.2
+ ,100.78
+ ,100.7
+ ,100.09
+ ,101.44
+ ,98.45
+ ,2.9
+ ,99.81
+ ,100.78
+ ,100.7
+ ,100.09
+ ,98.49
+ ,3.1
+ ,98.45
+ ,99.81
+ ,100.78
+ ,100.7
+ ,97.48
+ ,3.5
+ ,98.49
+ ,98.45
+ ,99.81
+ ,100.78
+ ,97.91
+ ,3.6
+ ,97.48
+ ,98.49
+ ,98.45
+ ,99.81
+ ,96.94
+ ,4.4
+ ,97.91
+ ,97.48
+ ,98.49
+ ,98.45
+ ,98.53
+ ,4.1
+ ,96.94
+ ,97.91
+ ,97.48
+ ,98.49
+ ,96.82
+ ,5.1
+ ,98.53
+ ,96.94
+ ,97.91
+ ,97.48
+ ,95.76
+ ,5.8
+ ,96.82
+ ,98.53
+ ,96.94
+ ,97.91
+ ,95.27
+ ,5.9
+ ,95.76
+ ,96.82
+ ,98.53
+ ,96.94
+ ,97.32
+ ,5.4
+ ,95.27
+ ,95.76
+ ,96.82
+ ,98.53
+ ,96.68
+ ,5.5
+ ,97.32
+ ,95.27
+ ,95.76
+ ,96.82
+ ,97.87
+ ,4.8
+ ,96.68
+ ,97.32
+ ,95.27
+ ,95.76
+ ,97.42
+ ,3.2
+ ,97.87
+ ,96.68
+ ,97.32
+ ,95.27
+ ,97.94
+ ,2.7
+ ,97.42
+ ,97.87
+ ,96.68
+ ,97.32
+ ,99.52
+ ,2.1
+ ,97.94
+ ,97.42
+ ,97.87
+ ,96.68
+ ,100.99
+ ,1.9
+ ,99.52
+ ,97.94
+ ,97.42
+ ,97.87
+ ,99.92
+ ,0.6
+ ,100.99
+ ,99.52
+ ,97.94
+ ,97.42
+ ,101.97
+ ,0.7
+ ,99.92
+ ,100.99
+ ,99.52
+ ,97.94
+ ,101.58
+ ,-0.2
+ ,101.97
+ ,99.92
+ ,100.99
+ ,99.52
+ ,99.54
+ ,-1
+ ,101.58
+ ,101.97
+ ,99.92
+ ,100.99
+ ,100.83
+ ,-1.7
+ ,99.54
+ ,101.58
+ ,101.97
+ ,99.92)
+ ,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
1 100.49 1.9 100.16 99.60 100.25 100.03 1 0 0 0 0 0 0 0 0 0 0
2 99.72 2.0 100.49 100.16 99.60 100.25 0 1 0 0 0 0 0 0 0 0 0
3 100.14 2.3 99.72 100.49 100.16 99.60 0 0 1 0 0 0 0 0 0 0 0
4 98.48 2.8 100.14 99.72 100.49 100.16 0 0 0 1 0 0 0 0 0 0 0
5 100.38 2.4 98.48 100.14 99.72 100.49 0 0 0 0 1 0 0 0 0 0 0
6 101.45 2.3 100.38 98.48 100.14 99.72 0 0 0 0 0 1 0 0 0 0 0
7 98.42 2.7 101.45 100.38 98.48 100.14 0 0 0 0 0 0 1 0 0 0 0
8 98.60 2.7 98.42 101.45 100.38 98.48 0 0 0 0 0 0 0 1 0 0 0
9 100.06 2.9 98.60 98.42 101.45 100.38 0 0 0 0 0 0 0 0 1 0 0
10 98.62 3.0 100.06 98.60 98.42 101.45 0 0 0 0 0 0 0 0 0 1 0
11 100.84 2.2 98.62 100.06 98.60 98.42 0 0 0 0 0 0 0 0 0 0 1
12 100.02 2.3 100.84 98.62 100.06 98.60 0 0 0 0 0 0 0 0 0 0 0
13 97.95 2.8 100.02 100.84 98.62 100.06 1 0 0 0 0 0 0 0 0 0 0
14 98.32 2.8 97.95 100.02 100.84 98.62 0 1 0 0 0 0 0 0 0 0 0
15 98.27 2.8 98.32 97.95 100.02 100.84 0 0 1 0 0 0 0 0 0 0 0
16 97.22 2.2 98.27 98.32 97.95 100.02 0 0 0 1 0 0 0 0 0 0 0
17 99.28 2.6 97.22 98.27 98.32 97.95 0 0 0 0 1 0 0 0 0 0 0
18 100.38 2.8 99.28 97.22 98.27 98.32 0 0 0 0 0 1 0 0 0 0 0
19 99.02 2.5 100.38 99.28 97.22 98.27 0 0 0 0 0 0 1 0 0 0 0
20 100.32 2.4 99.02 100.38 99.28 97.22 0 0 0 0 0 0 0 1 0 0 0
21 99.81 2.3 100.32 99.02 100.38 99.28 0 0 0 0 0 0 0 0 1 0 0
22 100.60 1.9 99.81 100.32 99.02 100.38 0 0 0 0 0 0 0 0 0 1 0
23 101.19 1.7 100.60 99.81 100.32 99.02 0 0 0 0 0 0 0 0 0 0 1
24 100.47 2.0 101.19 100.60 99.81 100.32 0 0 0 0 0 0 0 0 0 0 0
25 101.77 2.1 100.47 101.19 100.60 99.81 1 0 0 0 0 0 0 0 0 0 0
26 102.32 1.7 101.77 100.47 101.19 100.60 0 1 0 0 0 0 0 0 0 0 0
27 102.39 1.8 102.32 101.77 100.47 101.19 0 0 1 0 0 0 0 0 0 0 0
28 101.16 1.8 102.39 102.32 101.77 100.47 0 0 0 1 0 0 0 0 0 0 0
29 100.63 1.8 101.16 102.39 102.32 101.77 0 0 0 0 1 0 0 0 0 0 0
30 101.48 1.3 100.63 101.16 102.39 102.32 0 0 0 0 0 1 0 0 0 0 0
31 101.44 1.3 101.48 100.63 101.16 102.39 0 0 0 0 0 0 1 0 0 0 0
32 100.09 1.3 101.44 101.48 100.63 101.16 0 0 0 0 0 0 0 1 0 0 0
33 100.70 1.2 100.09 101.44 101.48 100.63 0 0 0 0 0 0 0 0 1 0 0
34 100.78 1.4 100.70 100.09 101.44 101.48 0 0 0 0 0 0 0 0 0 1 0
35 99.81 2.2 100.78 100.70 100.09 101.44 0 0 0 0 0 0 0 0 0 0 1
36 98.45 2.9 99.81 100.78 100.70 100.09 0 0 0 0 0 0 0 0 0 0 0
37 98.49 3.1 98.45 99.81 100.78 100.70 1 0 0 0 0 0 0 0 0 0 0
38 97.48 3.5 98.49 98.45 99.81 100.78 0 1 0 0 0 0 0 0 0 0 0
39 97.91 3.6 97.48 98.49 98.45 99.81 0 0 1 0 0 0 0 0 0 0 0
40 96.94 4.4 97.91 97.48 98.49 98.45 0 0 0 1 0 0 0 0 0 0 0
41 98.53 4.1 96.94 97.91 97.48 98.49 0 0 0 0 1 0 0 0 0 0 0
42 96.82 5.1 98.53 96.94 97.91 97.48 0 0 0 0 0 1 0 0 0 0 0
43 95.76 5.8 96.82 98.53 96.94 97.91 0 0 0 0 0 0 1 0 0 0 0
44 95.27 5.9 95.76 96.82 98.53 96.94 0 0 0 0 0 0 0 1 0 0 0
45 97.32 5.4 95.27 95.76 96.82 98.53 0 0 0 0 0 0 0 0 1 0 0
46 96.68 5.5 97.32 95.27 95.76 96.82 0 0 0 0 0 0 0 0 0 1 0
47 97.87 4.8 96.68 97.32 95.27 95.76 0 0 0 0 0 0 0 0 0 0 1
48 97.42 3.2 97.87 96.68 97.32 95.27 0 0 0 0 0 0 0 0 0 0 0
49 97.94 2.7 97.42 97.87 96.68 97.32 1 0 0 0 0 0 0 0 0 0 0
50 99.52 2.1 97.94 97.42 97.87 96.68 0 1 0 0 0 0 0 0 0 0 0
51 100.99 1.9 99.52 97.94 97.42 97.87 0 0 1 0 0 0 0 0 0 0 0
52 99.92 0.6 100.99 99.52 97.94 97.42 0 0 0 1 0 0 0 0 0 0 0
53 101.97 0.7 99.92 100.99 99.52 97.94 0 0 0 0 1 0 0 0 0 0 0
54 101.58 -0.2 101.97 99.92 100.99 99.52 0 0 0 0 0 1 0 0 0 0 0
55 99.54 -1.0 101.58 101.97 99.92 100.99 0 0 0 0 0 0 1 0 0 0 0
56 100.83 -1.7 99.54 101.58 101.97 99.92 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
52.923713 -0.456561 0.529785 0.001629 0.211227 -0.268926
M1 M2 M3 M4 M5 M6
0.779716 0.720845 1.394167 -0.251541 1.761477 1.119170
M7 M8 M9 M10 M11 t
-0.099745 0.207964 1.424104 1.029986 1.501842 -0.008383
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.4419 -0.4695 -0.1392 0.5648 1.7106
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.923713 13.880394 3.813 0.00049 ***
X -0.456561 0.136277 -3.350 0.00183 **
Y1 0.529785 0.150924 3.510 0.00117 **
Y2 0.001629 0.169852 0.010 0.99240
Y3 0.211227 0.165712 1.275 0.21017
Y4 -0.268926 0.139164 -1.932 0.06079 .
M1 0.779716 0.631015 1.236 0.22417
M2 0.720845 0.604075 1.193 0.24015
M3 1.394167 0.625470 2.229 0.03180 *
M4 -0.251541 0.599557 -0.420 0.67718
M5 1.761477 0.654977 2.689 0.01057 *
M6 1.119170 0.607961 1.841 0.07346 .
M7 -0.099745 0.669930 -0.149 0.88243
M8 0.207964 0.660939 0.315 0.75475
M9 1.424104 0.661141 2.154 0.03765 *
M10 1.029986 0.684813 1.504 0.14084
M11 1.501842 0.655248 2.292 0.02753 *
t -0.008383 0.008107 -1.034 0.30764
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8717 on 38 degrees of freedom
Multiple R-squared: 0.8174, Adjusted R-squared: 0.7357
F-statistic: 10.01 on 17 and 38 DF, p-value: 2.725e-09
> 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,] 0.5934757 0.8130487 0.4065243
[2,] 0.4734982 0.9469963 0.5265018
[3,] 0.4456636 0.8913271 0.5543364
[4,] 0.4168256 0.8336511 0.5831744
[5,] 0.5830741 0.8338519 0.4169259
[6,] 0.5836970 0.8326059 0.4163030
[7,] 0.4824131 0.9648261 0.5175869
[8,] 0.4189850 0.8379699 0.5810150
[9,] 0.6095565 0.7808869 0.3904435
[10,] 0.6145643 0.7708714 0.3854357
[11,] 0.7254677 0.5490646 0.2745323
[12,] 0.7778116 0.4443769 0.2221884
[13,] 0.7781745 0.4436511 0.2218255
[14,] 0.6747354 0.6505292 0.3252646
[15,] 0.4959186 0.9918372 0.5040814
> postscript(file="/var/www/html/rcomp/tmp/1vk791258703368.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2c1gn1258703368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vaiy1258703368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4eixo1258703368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fqtm1258703368.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
0.16211230 -0.47425753 -0.46792045 -0.38591078 0.45697825 0.83233696
7 8 9 10 11 12
-0.89411815 -0.25768880 0.28038606 -0.55746602 0.74145745 0.04357718
13 14 15 16 17 18
-1.44186838 -0.76280267 -0.90016937 -0.32740734 -0.16789314 0.69452710
19 20 21 22 23 24
0.04708130 1.00331383 -1.12496683 0.73607036 -0.28674901 0.78391437
25 26 27 28 29 30
1.43470015 1.26960925 0.73757743 0.65546599 -0.99421968 0.69410212
31 32 33 34 35 36
1.71058088 -0.13776719 -0.38797944 0.10190034 -0.73530077 -0.24362032
37 38 39 40 41 42
-0.01440758 -0.56710151 -0.19495619 0.25403335 0.43971694 -1.36625188
43 44 45 46 47 48
0.34450981 -0.43151359 1.23256020 -0.28050467 0.28059233 -0.58387123
49 50 51 52 53 54
-0.14053648 0.53455247 0.82546859 -0.19618122 0.26541762 -0.85471430
55 56
-1.20805384 -0.17634425
> postscript(file="/var/www/html/rcomp/tmp/6gkc11258703368.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 0.16211230 NA
1 -0.47425753 0.16211230
2 -0.46792045 -0.47425753
3 -0.38591078 -0.46792045
4 0.45697825 -0.38591078
5 0.83233696 0.45697825
6 -0.89411815 0.83233696
7 -0.25768880 -0.89411815
8 0.28038606 -0.25768880
9 -0.55746602 0.28038606
10 0.74145745 -0.55746602
11 0.04357718 0.74145745
12 -1.44186838 0.04357718
13 -0.76280267 -1.44186838
14 -0.90016937 -0.76280267
15 -0.32740734 -0.90016937
16 -0.16789314 -0.32740734
17 0.69452710 -0.16789314
18 0.04708130 0.69452710
19 1.00331383 0.04708130
20 -1.12496683 1.00331383
21 0.73607036 -1.12496683
22 -0.28674901 0.73607036
23 0.78391437 -0.28674901
24 1.43470015 0.78391437
25 1.26960925 1.43470015
26 0.73757743 1.26960925
27 0.65546599 0.73757743
28 -0.99421968 0.65546599
29 0.69410212 -0.99421968
30 1.71058088 0.69410212
31 -0.13776719 1.71058088
32 -0.38797944 -0.13776719
33 0.10190034 -0.38797944
34 -0.73530077 0.10190034
35 -0.24362032 -0.73530077
36 -0.01440758 -0.24362032
37 -0.56710151 -0.01440758
38 -0.19495619 -0.56710151
39 0.25403335 -0.19495619
40 0.43971694 0.25403335
41 -1.36625188 0.43971694
42 0.34450981 -1.36625188
43 -0.43151359 0.34450981
44 1.23256020 -0.43151359
45 -0.28050467 1.23256020
46 0.28059233 -0.28050467
47 -0.58387123 0.28059233
48 -0.14053648 -0.58387123
49 0.53455247 -0.14053648
50 0.82546859 0.53455247
51 -0.19618122 0.82546859
52 0.26541762 -0.19618122
53 -0.85471430 0.26541762
54 -1.20805384 -0.85471430
55 -0.17634425 -1.20805384
56 NA -0.17634425
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.47425753 0.16211230
[2,] -0.46792045 -0.47425753
[3,] -0.38591078 -0.46792045
[4,] 0.45697825 -0.38591078
[5,] 0.83233696 0.45697825
[6,] -0.89411815 0.83233696
[7,] -0.25768880 -0.89411815
[8,] 0.28038606 -0.25768880
[9,] -0.55746602 0.28038606
[10,] 0.74145745 -0.55746602
[11,] 0.04357718 0.74145745
[12,] -1.44186838 0.04357718
[13,] -0.76280267 -1.44186838
[14,] -0.90016937 -0.76280267
[15,] -0.32740734 -0.90016937
[16,] -0.16789314 -0.32740734
[17,] 0.69452710 -0.16789314
[18,] 0.04708130 0.69452710
[19,] 1.00331383 0.04708130
[20,] -1.12496683 1.00331383
[21,] 0.73607036 -1.12496683
[22,] -0.28674901 0.73607036
[23,] 0.78391437 -0.28674901
[24,] 1.43470015 0.78391437
[25,] 1.26960925 1.43470015
[26,] 0.73757743 1.26960925
[27,] 0.65546599 0.73757743
[28,] -0.99421968 0.65546599
[29,] 0.69410212 -0.99421968
[30,] 1.71058088 0.69410212
[31,] -0.13776719 1.71058088
[32,] -0.38797944 -0.13776719
[33,] 0.10190034 -0.38797944
[34,] -0.73530077 0.10190034
[35,] -0.24362032 -0.73530077
[36,] -0.01440758 -0.24362032
[37,] -0.56710151 -0.01440758
[38,] -0.19495619 -0.56710151
[39,] 0.25403335 -0.19495619
[40,] 0.43971694 0.25403335
[41,] -1.36625188 0.43971694
[42,] 0.34450981 -1.36625188
[43,] -0.43151359 0.34450981
[44,] 1.23256020 -0.43151359
[45,] -0.28050467 1.23256020
[46,] 0.28059233 -0.28050467
[47,] -0.58387123 0.28059233
[48,] -0.14053648 -0.58387123
[49,] 0.53455247 -0.14053648
[50,] 0.82546859 0.53455247
[51,] -0.19618122 0.82546859
[52,] 0.26541762 -0.19618122
[53,] -0.85471430 0.26541762
[54,] -1.20805384 -0.85471430
[55,] -0.17634425 -1.20805384
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.47425753 0.16211230
2 -0.46792045 -0.47425753
3 -0.38591078 -0.46792045
4 0.45697825 -0.38591078
5 0.83233696 0.45697825
6 -0.89411815 0.83233696
7 -0.25768880 -0.89411815
8 0.28038606 -0.25768880
9 -0.55746602 0.28038606
10 0.74145745 -0.55746602
11 0.04357718 0.74145745
12 -1.44186838 0.04357718
13 -0.76280267 -1.44186838
14 -0.90016937 -0.76280267
15 -0.32740734 -0.90016937
16 -0.16789314 -0.32740734
17 0.69452710 -0.16789314
18 0.04708130 0.69452710
19 1.00331383 0.04708130
20 -1.12496683 1.00331383
21 0.73607036 -1.12496683
22 -0.28674901 0.73607036
23 0.78391437 -0.28674901
24 1.43470015 0.78391437
25 1.26960925 1.43470015
26 0.73757743 1.26960925
27 0.65546599 0.73757743
28 -0.99421968 0.65546599
29 0.69410212 -0.99421968
30 1.71058088 0.69410212
31 -0.13776719 1.71058088
32 -0.38797944 -0.13776719
33 0.10190034 -0.38797944
34 -0.73530077 0.10190034
35 -0.24362032 -0.73530077
36 -0.01440758 -0.24362032
37 -0.56710151 -0.01440758
38 -0.19495619 -0.56710151
39 0.25403335 -0.19495619
40 0.43971694 0.25403335
41 -1.36625188 0.43971694
42 0.34450981 -1.36625188
43 -0.43151359 0.34450981
44 1.23256020 -0.43151359
45 -0.28050467 1.23256020
46 0.28059233 -0.28050467
47 -0.58387123 0.28059233
48 -0.14053648 -0.58387123
49 0.53455247 -0.14053648
50 0.82546859 0.53455247
51 -0.19618122 0.82546859
52 0.26541762 -0.19618122
53 -0.85471430 0.26541762
54 -1.20805384 -0.85471430
55 -0.17634425 -1.20805384
> 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/7pd6x1258703368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8o9g11258703368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uniy1258703368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10m61l1258703368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11243k1258703368.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/1289z81258703368.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/13fun01258703368.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/14zfj21258703368.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/157ob41258703368.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/16bf5b1258703368.tab")
+ }
>
> system("convert tmp/1vk791258703368.ps tmp/1vk791258703368.png")
> system("convert tmp/2c1gn1258703368.ps tmp/2c1gn1258703368.png")
> system("convert tmp/3vaiy1258703368.ps tmp/3vaiy1258703368.png")
> system("convert tmp/4eixo1258703368.ps tmp/4eixo1258703368.png")
> system("convert tmp/5fqtm1258703368.ps tmp/5fqtm1258703368.png")
> system("convert tmp/6gkc11258703368.ps tmp/6gkc11258703368.png")
> system("convert tmp/7pd6x1258703368.ps tmp/7pd6x1258703368.png")
> system("convert tmp/8o9g11258703368.ps tmp/8o9g11258703368.png")
> system("convert tmp/9uniy1258703368.ps tmp/9uniy1258703368.png")
> system("convert tmp/10m61l1258703368.ps tmp/10m61l1258703368.png")
>
>
> proc.time()
user system elapsed
2.351 1.572 3.018