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(8.3,98.6,8.2,8.7,8.5,96.5,8.3,8.2,8.6,95.9,8.5,8.3,8.5,103.7,8.6,8.5,8.2,103.1,8.5,8.6,8.1,103.7,8.2,8.5,7.9,112.1,8.1,8.2,8.6,86.9,7.9,8.1,8.7,95,8.6,7.9,8.7,111.8,8.7,8.6,8.5,108.8,8.7,8.7,8.4,109.3,8.5,8.7,8.5,101.4,8.4,8.5,8.7,100.5,8.5,8.4,8.7,100.7,8.7,8.5,8.6,113.5,8.7,8.7,8.5,106.1,8.6,8.7,8.3,111.6,8.5,8.6,8,114.9,8.3,8.5,8.2,88.6,8,8.3,8.1,99.5,8.2,8,8.1,115.1,8.1,8.2,8,118,8.1,8.1,7.9,111.4,8,8.1,7.9,107.3,7.9,8,8,105.3,7.9,7.9,8,105.3,8,7.9,7.9,117.9,8,8,8,110.2,7.9,8,7.7,112.4,8,7.9,7.2,117.5,7.7,8,7.5,93,7.2,7.7,7.3,103.5,7.5,7.2,7,116.3,7.3,7.5,7,120,7,7.3,7,114.3,7,7,7.2,104.7,7,7,7.3,109.8,7.2,7,7.1,112.6,7.3,7.2,6.8,114.4,7.1,7.3,6.4,115.7,6.8,7.1,6.1,114.7,6.4,6.8,6.5,118.4,6.1,6.4,7.7,94.9,6.5,6.1,7.9,103.8,7.7,6.5,7.5,115.1,7.9,7.7,6.9,113.7,7.5,7.9,6.6,104,6.9,7.5,6.9,94.3,6.6,6.9,7.7,92.5,6.9,6.6,8,93.2,7.7,6.9,8,104.7,8,7.7,7.7,94,8,8,7.3,98.1,7.7,8,7.4,102.7,7.3,7.7,8.1,82.4,7.4,7.3),dim=c(4,56),dimnames=list(c('Y','X','Y1','Y2'),1:56))
> y <- array(NA,dim=c(4,56),dimnames=list(c('Y','X','Y1','Y2'),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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.3 98.6 8.2 8.7 1 0 0 0 0 0 0 0 0 0 0 1
2 8.5 96.5 8.3 8.2 0 1 0 0 0 0 0 0 0 0 0 2
3 8.6 95.9 8.5 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 8.5 103.7 8.6 8.5 0 0 0 1 0 0 0 0 0 0 0 4
5 8.2 103.1 8.5 8.6 0 0 0 0 1 0 0 0 0 0 0 5
6 8.1 103.7 8.2 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 7.9 112.1 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 7
8 8.6 86.9 7.9 8.1 0 0 0 0 0 0 0 1 0 0 0 8
9 8.7 95.0 8.6 7.9 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 111.8 8.7 8.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 108.8 8.7 8.7 0 0 0 0 0 0 0 0 0 0 1 11
12 8.4 109.3 8.5 8.7 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 101.4 8.4 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 100.5 8.5 8.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.7 100.7 8.7 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.6 113.5 8.7 8.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 106.1 8.6 8.7 0 0 0 0 1 0 0 0 0 0 0 17
18 8.3 111.6 8.5 8.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.0 114.9 8.3 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 88.6 8.0 8.3 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 99.5 8.2 8.0 0 0 0 0 0 0 0 0 1 0 0 21
22 8.1 115.1 8.1 8.2 0 0 0 0 0 0 0 0 0 1 0 22
23 8.0 118.0 8.1 8.1 0 0 0 0 0 0 0 0 0 0 1 23
24 7.9 111.4 8.0 8.1 0 0 0 0 0 0 0 0 0 0 0 24
25 7.9 107.3 7.9 8.0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 105.3 7.9 7.9 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 105.3 8.0 7.9 0 0 1 0 0 0 0 0 0 0 0 27
28 7.9 117.9 8.0 8.0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 110.2 7.9 8.0 0 0 0 0 1 0 0 0 0 0 0 29
30 7.7 112.4 8.0 7.9 0 0 0 0 0 1 0 0 0 0 0 30
31 7.2 117.5 7.7 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.5 93.0 7.2 7.7 0 0 0 0 0 0 0 1 0 0 0 32
33 7.3 103.5 7.5 7.2 0 0 0 0 0 0 0 0 1 0 0 33
34 7.0 116.3 7.3 7.5 0 0 0 0 0 0 0 0 0 1 0 34
35 7.0 120.0 7.0 7.3 0 0 0 0 0 0 0 0 0 0 1 35
36 7.0 114.3 7.0 7.0 0 0 0 0 0 0 0 0 0 0 0 36
37 7.2 104.7 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.3 109.8 7.2 7.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.1 112.6 7.3 7.2 0 0 1 0 0 0 0 0 0 0 0 39
40 6.8 114.4 7.1 7.3 0 0 0 1 0 0 0 0 0 0 0 40
41 6.4 115.7 6.8 7.1 0 0 0 0 1 0 0 0 0 0 0 41
42 6.1 114.7 6.4 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 6.5 118.4 6.1 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 7.7 94.9 6.5 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 7.9 103.8 7.7 6.5 0 0 0 0 0 0 0 0 1 0 0 45
46 7.5 115.1 7.9 7.7 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 113.7 7.5 7.9 0 0 0 0 0 0 0 0 0 0 1 47
48 6.6 104.0 6.9 7.5 0 0 0 0 0 0 0 0 0 0 0 48
49 6.9 94.3 6.6 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.7 92.5 6.9 6.6 0 1 0 0 0 0 0 0 0 0 0 50
51 8.0 93.2 7.7 6.9 0 0 1 0 0 0 0 0 0 0 0 51
52 8.0 104.7 8.0 7.7 0 0 0 1 0 0 0 0 0 0 0 52
53 7.7 94.0 8.0 8.0 0 0 0 0 1 0 0 0 0 0 0 53
54 7.3 98.1 7.7 8.0 0 0 0 0 0 1 0 0 0 0 0 54
55 7.4 102.7 7.3 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 8.1 82.4 7.4 7.3 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
4.149990 -0.015244 1.341711 -0.632194 0.083574 0.052515
M3 M4 M5 M6 M7 M8
-0.176804 -0.023288 -0.105117 -0.129480 0.077852 0.310812
M9 M10 M11 t
-0.447341 -0.019303 0.007283 -0.008402
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.24985 -0.09759 -0.02653 0.11427 0.33670
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.149990 0.990631 4.189 0.000150 ***
X -0.015244 0.004999 -3.049 0.004058 **
Y1 1.341711 0.116031 11.563 2.49e-14 ***
Y2 -0.632194 0.118245 -5.346 3.90e-06 ***
M1 0.083574 0.126953 0.658 0.514113
M2 0.052515 0.133460 0.393 0.696049
M3 -0.176804 0.134934 -1.310 0.197569
M4 -0.023288 0.123139 -0.189 0.850957
M5 -0.105117 0.119340 -0.881 0.383680
M6 -0.129480 0.117649 -1.101 0.277666
M7 0.077852 0.118305 0.658 0.514266
M8 0.310812 0.161086 1.929 0.060786 .
M9 -0.447341 0.161306 -2.773 0.008390 **
M10 -0.019303 0.131030 -0.147 0.883622
M11 0.007283 0.128388 0.057 0.955048
t -0.008402 0.002576 -3.262 0.002269 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1748 on 40 degrees of freedom
Multiple R-squared: 0.9498, Adjusted R-squared: 0.931
F-statistic: 50.44 on 15 and 40 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,] 0.05795178 0.11590356 0.9420482
[2,] 0.63309533 0.73380934 0.3669047
[3,] 0.48914216 0.97828432 0.5108578
[4,] 0.37859587 0.75719174 0.6214041
[5,] 0.26144365 0.52288731 0.7385563
[6,] 0.16617075 0.33234150 0.8338292
[7,] 0.12366834 0.24733668 0.8763317
[8,] 0.07027432 0.14054864 0.9297257
[9,] 0.04308656 0.08617312 0.9569134
[10,] 0.03003588 0.06007177 0.9699641
[11,] 0.37023150 0.74046300 0.6297685
[12,] 0.40768255 0.81536509 0.5923175
[13,] 0.53234170 0.93531661 0.4676583
[14,] 0.44338706 0.88677413 0.5566129
[15,] 0.36182976 0.72365951 0.6381702
[16,] 0.28302830 0.56605660 0.7169717
[17,] 0.57929850 0.84140299 0.4207015
[18,] 0.45158813 0.90317625 0.5484119
[19,] 0.36261654 0.72523307 0.6373835
> postscript(file="/var/www/html/rcomp/tmp/162j51258751798.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26pke1258751798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fsa71258751798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46pca1258751798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55en31258751798.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
0.07593993 -0.16687901 -0.04342741 -0.17737137 -0.19889609 0.08230920
7 8 9 10 11 12
-0.24405970 0.05236004 -0.02324592 0.12157970 -0.07911602 0.11253283
13 14 15 16 17 18
0.02466663 0.05301790 0.08866460 0.16511103 0.17670867 0.16426668
19 20 21 22 23 24
-0.07923602 -0.22863283 0.14608008 0.22485829 0.08766255 0.03690890
25 26 27 28 29 30
-0.02981124 0.01594288 0.11949250 0.12967077 0.33669525 -0.09439366
31 32 33 34 35 36
-0.24984753 -0.06668260 -0.05867712 -0.12519123 0.18910200 -0.07176146
37 38 39 40 41 42
-0.09327452 -0.14441178 -0.07174057 -0.15785380 -0.17173119 -0.10718378
43 44 45 46 47 48
0.29992398 0.19079293 -0.06415704 -0.22124676 -0.19764854 -0.07768027
49 50 51 52 53 54
0.02247919 0.24233001 -0.09298913 0.04044337 -0.14277663 -0.04499844
55 56
0.27321927 0.05216246
> postscript(file="/var/www/html/rcomp/tmp/6d5551258751798.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 0.07593993 NA
1 -0.16687901 0.07593993
2 -0.04342741 -0.16687901
3 -0.17737137 -0.04342741
4 -0.19889609 -0.17737137
5 0.08230920 -0.19889609
6 -0.24405970 0.08230920
7 0.05236004 -0.24405970
8 -0.02324592 0.05236004
9 0.12157970 -0.02324592
10 -0.07911602 0.12157970
11 0.11253283 -0.07911602
12 0.02466663 0.11253283
13 0.05301790 0.02466663
14 0.08866460 0.05301790
15 0.16511103 0.08866460
16 0.17670867 0.16511103
17 0.16426668 0.17670867
18 -0.07923602 0.16426668
19 -0.22863283 -0.07923602
20 0.14608008 -0.22863283
21 0.22485829 0.14608008
22 0.08766255 0.22485829
23 0.03690890 0.08766255
24 -0.02981124 0.03690890
25 0.01594288 -0.02981124
26 0.11949250 0.01594288
27 0.12967077 0.11949250
28 0.33669525 0.12967077
29 -0.09439366 0.33669525
30 -0.24984753 -0.09439366
31 -0.06668260 -0.24984753
32 -0.05867712 -0.06668260
33 -0.12519123 -0.05867712
34 0.18910200 -0.12519123
35 -0.07176146 0.18910200
36 -0.09327452 -0.07176146
37 -0.14441178 -0.09327452
38 -0.07174057 -0.14441178
39 -0.15785380 -0.07174057
40 -0.17173119 -0.15785380
41 -0.10718378 -0.17173119
42 0.29992398 -0.10718378
43 0.19079293 0.29992398
44 -0.06415704 0.19079293
45 -0.22124676 -0.06415704
46 -0.19764854 -0.22124676
47 -0.07768027 -0.19764854
48 0.02247919 -0.07768027
49 0.24233001 0.02247919
50 -0.09298913 0.24233001
51 0.04044337 -0.09298913
52 -0.14277663 0.04044337
53 -0.04499844 -0.14277663
54 0.27321927 -0.04499844
55 0.05216246 0.27321927
56 NA 0.05216246
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.16687901 0.07593993
[2,] -0.04342741 -0.16687901
[3,] -0.17737137 -0.04342741
[4,] -0.19889609 -0.17737137
[5,] 0.08230920 -0.19889609
[6,] -0.24405970 0.08230920
[7,] 0.05236004 -0.24405970
[8,] -0.02324592 0.05236004
[9,] 0.12157970 -0.02324592
[10,] -0.07911602 0.12157970
[11,] 0.11253283 -0.07911602
[12,] 0.02466663 0.11253283
[13,] 0.05301790 0.02466663
[14,] 0.08866460 0.05301790
[15,] 0.16511103 0.08866460
[16,] 0.17670867 0.16511103
[17,] 0.16426668 0.17670867
[18,] -0.07923602 0.16426668
[19,] -0.22863283 -0.07923602
[20,] 0.14608008 -0.22863283
[21,] 0.22485829 0.14608008
[22,] 0.08766255 0.22485829
[23,] 0.03690890 0.08766255
[24,] -0.02981124 0.03690890
[25,] 0.01594288 -0.02981124
[26,] 0.11949250 0.01594288
[27,] 0.12967077 0.11949250
[28,] 0.33669525 0.12967077
[29,] -0.09439366 0.33669525
[30,] -0.24984753 -0.09439366
[31,] -0.06668260 -0.24984753
[32,] -0.05867712 -0.06668260
[33,] -0.12519123 -0.05867712
[34,] 0.18910200 -0.12519123
[35,] -0.07176146 0.18910200
[36,] -0.09327452 -0.07176146
[37,] -0.14441178 -0.09327452
[38,] -0.07174057 -0.14441178
[39,] -0.15785380 -0.07174057
[40,] -0.17173119 -0.15785380
[41,] -0.10718378 -0.17173119
[42,] 0.29992398 -0.10718378
[43,] 0.19079293 0.29992398
[44,] -0.06415704 0.19079293
[45,] -0.22124676 -0.06415704
[46,] -0.19764854 -0.22124676
[47,] -0.07768027 -0.19764854
[48,] 0.02247919 -0.07768027
[49,] 0.24233001 0.02247919
[50,] -0.09298913 0.24233001
[51,] 0.04044337 -0.09298913
[52,] -0.14277663 0.04044337
[53,] -0.04499844 -0.14277663
[54,] 0.27321927 -0.04499844
[55,] 0.05216246 0.27321927
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.16687901 0.07593993
2 -0.04342741 -0.16687901
3 -0.17737137 -0.04342741
4 -0.19889609 -0.17737137
5 0.08230920 -0.19889609
6 -0.24405970 0.08230920
7 0.05236004 -0.24405970
8 -0.02324592 0.05236004
9 0.12157970 -0.02324592
10 -0.07911602 0.12157970
11 0.11253283 -0.07911602
12 0.02466663 0.11253283
13 0.05301790 0.02466663
14 0.08866460 0.05301790
15 0.16511103 0.08866460
16 0.17670867 0.16511103
17 0.16426668 0.17670867
18 -0.07923602 0.16426668
19 -0.22863283 -0.07923602
20 0.14608008 -0.22863283
21 0.22485829 0.14608008
22 0.08766255 0.22485829
23 0.03690890 0.08766255
24 -0.02981124 0.03690890
25 0.01594288 -0.02981124
26 0.11949250 0.01594288
27 0.12967077 0.11949250
28 0.33669525 0.12967077
29 -0.09439366 0.33669525
30 -0.24984753 -0.09439366
31 -0.06668260 -0.24984753
32 -0.05867712 -0.06668260
33 -0.12519123 -0.05867712
34 0.18910200 -0.12519123
35 -0.07176146 0.18910200
36 -0.09327452 -0.07176146
37 -0.14441178 -0.09327452
38 -0.07174057 -0.14441178
39 -0.15785380 -0.07174057
40 -0.17173119 -0.15785380
41 -0.10718378 -0.17173119
42 0.29992398 -0.10718378
43 0.19079293 0.29992398
44 -0.06415704 0.19079293
45 -0.22124676 -0.06415704
46 -0.19764854 -0.22124676
47 -0.07768027 -0.19764854
48 0.02247919 -0.07768027
49 0.24233001 0.02247919
50 -0.09298913 0.24233001
51 0.04044337 -0.09298913
52 -0.14277663 0.04044337
53 -0.04499844 -0.14277663
54 0.27321927 -0.04499844
55 0.05216246 0.27321927
> 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/7r64b1258751798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8o1e11258751798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jg311258751798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zjpr1258751798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hi451258751798.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/12v69v1258751798.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/1331ln1258751798.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/14srh41258751798.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/15ig0d1258751798.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/16iyiz1258751798.tab")
+ }
>
> system("convert tmp/162j51258751798.ps tmp/162j51258751798.png")
> system("convert tmp/26pke1258751798.ps tmp/26pke1258751798.png")
> system("convert tmp/3fsa71258751798.ps tmp/3fsa71258751798.png")
> system("convert tmp/46pca1258751798.ps tmp/46pca1258751798.png")
> system("convert tmp/55en31258751798.ps tmp/55en31258751798.png")
> system("convert tmp/6d5551258751798.ps tmp/6d5551258751798.png")
> system("convert tmp/7r64b1258751798.ps tmp/7r64b1258751798.png")
> system("convert tmp/8o1e11258751798.ps tmp/8o1e11258751798.png")
> system("convert tmp/9jg311258751798.ps tmp/9jg311258751798.png")
> system("convert tmp/10zjpr1258751798.ps tmp/10zjpr1258751798.png")
>
>
> proc.time()
user system elapsed
2.334 1.565 2.741