R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(101,0,98.7,1,105.1,0,98.4,1,101.7,0,102.9,0,92.2,1,94.9,1,92.8,1,98.5,1,94.3,1,87.4,1,103.4,0,101.2,0,109.6,0,111.9,0,108.9,0,105.6,0,107.8,0,97.5,1,102.4,0,105.6,0,99.8,1,96.2,1,113.1,0,107.4,0,116.8,0,112.9,0,105.3,0,109.3,0,107.9,0,101.1,0,114.7,0,116.2,0,108.4,0,113.4,0,108.7,0,112.6,0,124.2,0,114.9,0,110.5,0,121.5,0,118.1,0,111.7,0,132.7,0,119,0,116.7,0,120.1,0,113.4,0,106.6,0,116.3,0,112.6,0,111.6,0,125.1,0,110.7,0,109.6,0,114.2,0,113.4,0,116,0,109.6,0,117.8,0,115.8,0,125.3,0,113,0,120.5,0,116.6,0,111.8,0,115.2,0,118.6,0,122.4,0,116.4,0,114.5,0,119.8,0,115.8,0,127.8,0,118.8,0,119.7,0,118.6,0,120.8,0,115.9,0,109.7,0,114.8,0,116.2,0,112.2,0),dim=c(2,84),dimnames=list(c('y','x'),1:84))
> y <- array(NA,dim=c(2,84),dimnames=list(c('y','x'),1:84))
> 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)
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 101.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 98.7 1 0 1 0 0 0 0 0 0 0 0 0 2
3 105.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 98.4 1 0 0 0 1 0 0 0 0 0 0 0 4
5 101.7 0 0 0 0 0 1 0 0 0 0 0 0 5
6 102.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 92.2 1 0 0 0 0 0 0 1 0 0 0 0 7
8 94.9 1 0 0 0 0 0 0 0 1 0 0 0 8
9 92.8 1 0 0 0 0 0 0 0 0 1 0 0 9
10 98.5 1 0 0 0 0 0 0 0 0 0 1 0 10
11 94.3 1 0 0 0 0 0 0 0 0 0 0 1 11
12 87.4 1 0 0 0 0 0 0 0 0 0 0 0 12
13 103.4 0 1 0 0 0 0 0 0 0 0 0 0 13
14 101.2 0 0 1 0 0 0 0 0 0 0 0 0 14
15 109.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 111.9 0 0 0 0 1 0 0 0 0 0 0 0 16
17 108.9 0 0 0 0 0 1 0 0 0 0 0 0 17
18 105.6 0 0 0 0 0 0 1 0 0 0 0 0 18
19 107.8 0 0 0 0 0 0 0 1 0 0 0 0 19
20 97.5 1 0 0 0 0 0 0 0 1 0 0 0 20
21 102.4 0 0 0 0 0 0 0 0 0 1 0 0 21
22 105.6 0 0 0 0 0 0 0 0 0 0 1 0 22
23 99.8 1 0 0 0 0 0 0 0 0 0 0 1 23
24 96.2 1 0 0 0 0 0 0 0 0 0 0 0 24
25 113.1 0 1 0 0 0 0 0 0 0 0 0 0 25
26 107.4 0 0 1 0 0 0 0 0 0 0 0 0 26
27 116.8 0 0 0 1 0 0 0 0 0 0 0 0 27
28 112.9 0 0 0 0 1 0 0 0 0 0 0 0 28
29 105.3 0 0 0 0 0 1 0 0 0 0 0 0 29
30 109.3 0 0 0 0 0 0 1 0 0 0 0 0 30
31 107.9 0 0 0 0 0 0 0 1 0 0 0 0 31
32 101.1 0 0 0 0 0 0 0 0 1 0 0 0 32
33 114.7 0 0 0 0 0 0 0 0 0 1 0 0 33
34 116.2 0 0 0 0 0 0 0 0 0 0 1 0 34
35 108.4 0 0 0 0 0 0 0 0 0 0 0 1 35
36 113.4 0 0 0 0 0 0 0 0 0 0 0 0 36
37 108.7 0 1 0 0 0 0 0 0 0 0 0 0 37
38 112.6 0 0 1 0 0 0 0 0 0 0 0 0 38
39 124.2 0 0 0 1 0 0 0 0 0 0 0 0 39
40 114.9 0 0 0 0 1 0 0 0 0 0 0 0 40
41 110.5 0 0 0 0 0 1 0 0 0 0 0 0 41
42 121.5 0 0 0 0 0 0 1 0 0 0 0 0 42
43 118.1 0 0 0 0 0 0 0 1 0 0 0 0 43
44 111.7 0 0 0 0 0 0 0 0 1 0 0 0 44
45 132.7 0 0 0 0 0 0 0 0 0 1 0 0 45
46 119.0 0 0 0 0 0 0 0 0 0 0 1 0 46
47 116.7 0 0 0 0 0 0 0 0 0 0 0 1 47
48 120.1 0 0 0 0 0 0 0 0 0 0 0 0 48
49 113.4 0 1 0 0 0 0 0 0 0 0 0 0 49
50 106.6 0 0 1 0 0 0 0 0 0 0 0 0 50
51 116.3 0 0 0 1 0 0 0 0 0 0 0 0 51
52 112.6 0 0 0 0 1 0 0 0 0 0 0 0 52
53 111.6 0 0 0 0 0 1 0 0 0 0 0 0 53
54 125.1 0 0 0 0 0 0 1 0 0 0 0 0 54
55 110.7 0 0 0 0 0 0 0 1 0 0 0 0 55
56 109.6 0 0 0 0 0 0 0 0 1 0 0 0 56
57 114.2 0 0 0 0 0 0 0 0 0 1 0 0 57
58 113.4 0 0 0 0 0 0 0 0 0 0 1 0 58
59 116.0 0 0 0 0 0 0 0 0 0 0 0 1 59
60 109.6 0 0 0 0 0 0 0 0 0 0 0 0 60
61 117.8 0 1 0 0 0 0 0 0 0 0 0 0 61
62 115.8 0 0 1 0 0 0 0 0 0 0 0 0 62
63 125.3 0 0 0 1 0 0 0 0 0 0 0 0 63
64 113.0 0 0 0 0 1 0 0 0 0 0 0 0 64
65 120.5 0 0 0 0 0 1 0 0 0 0 0 0 65
66 116.6 0 0 0 0 0 0 1 0 0 0 0 0 66
67 111.8 0 0 0 0 0 0 0 1 0 0 0 0 67
68 115.2 0 0 0 0 0 0 0 0 1 0 0 0 68
69 118.6 0 0 0 0 0 0 0 0 0 1 0 0 69
70 122.4 0 0 0 0 0 0 0 0 0 0 1 0 70
71 116.4 0 0 0 0 0 0 0 0 0 0 0 1 71
72 114.5 0 0 0 0 0 0 0 0 0 0 0 0 72
73 119.8 0 1 0 0 0 0 0 0 0 0 0 0 73
74 115.8 0 0 1 0 0 0 0 0 0 0 0 0 74
75 127.8 0 0 0 1 0 0 0 0 0 0 0 0 75
76 118.8 0 0 0 0 1 0 0 0 0 0 0 0 76
77 119.7 0 0 0 0 0 1 0 0 0 0 0 0 77
78 118.6 0 0 0 0 0 0 1 0 0 0 0 0 78
79 120.8 0 0 0 0 0 0 0 1 0 0 0 0 79
80 115.9 0 0 0 0 0 0 0 0 1 0 0 0 80
81 109.7 0 0 0 0 0 0 0 0 0 1 0 0 81
82 114.8 0 0 0 0 0 0 0 0 0 0 1 0 82
83 116.2 0 0 0 0 0 0 0 0 0 0 0 1 83
84 112.2 0 0 0 0 0 0 0 0 0 0 0 0 84
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
100.9408 -9.5079 2.8386 1.2724 9.2896 4.3663
M5 M6 M7 M8 M9 M10
2.1978 5.0590 1.8928 -0.2877 3.7581 4.2479
M11 t
2.2531 0.1959
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.8687 -3.0055 -0.4871 2.4321 19.1846
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.94078 2.44636 41.262 < 2e-16 ***
x -9.50788 1.97408 -4.816 8.18e-06 ***
M1 2.83862 2.67304 1.062 0.29191
M2 1.27240 2.60627 0.488 0.62693
M3 9.28963 2.66166 3.490 0.00084 ***
M4 4.36626 2.59832 1.680 0.09733 .
M5 2.19778 2.65126 0.829 0.40995
M6 5.05900 2.64644 1.912 0.06002 .
M7 1.89278 2.58833 0.731 0.46705
M8 -0.28773 2.56365 -0.112 0.91096
M9 3.75807 2.58299 1.455 0.15016
M10 4.24786 2.58072 1.646 0.10425
M11 2.25307 2.56163 0.880 0.38212
t 0.19592 0.02627 7.457 1.82e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.792 on 70 degrees of freedom
Multiple R-squared: 0.7453, Adjusted R-squared: 0.698
F-statistic: 15.75 on 13 and 70 DF, p-value: 5.984e-16
> postscript(file="/var/www/html/rcomp/tmp/1hwpt1227723438.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/2fl1v1227723438.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3zv4o1227723438.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4o9op1227723438.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5sy6d1227723438.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 84
Frequency = 1
1 2 3 4 5 6
-2.97532468 5.60285714 -5.71818182 1.81714286 -2.41818182 -4.27532468
7 8 9 10 11 12
-2.49714286 2.18744589 -4.15428571 0.86000000 -1.54112554 -6.38398268
13 14 15 16 17 18
-2.92640693 -3.75610390 -3.56926407 3.45818182 2.43073593 -3.92640693
19 20 21 22 23 24
1.24389610 2.43636364 -6.41324675 -3.89896104 1.60779221 0.06493506
25 26 27 28 29 30
4.42251082 0.09281385 1.27965368 2.10709957 -3.52034632 -2.57748918
31 32 33 34 35 36
-1.00718615 -5.82259740 3.53567100 4.34995671 -1.65116883 5.40597403
37 38 39 40 41 42
-2.32857143 2.94173160 6.32857143 1.75601732 -0.67142857 7.27142857
43 44 45 46 47 48
6.84173160 2.42632035 19.18458874 4.79887446 4.29774892 9.75489177
49 50 51 52 53 54
0.02034632 -5.40935065 -3.92251082 -2.89506494 -1.92251082 8.52034632
55 56 57 58 59 60
-2.90935065 -2.02476190 -1.66649351 -3.15220779 1.24666667 -3.09619048
61 62 63 64 65 66
2.06926407 1.43956710 2.72640693 -4.84614719 4.62640693 -2.33073593
67 68 69 70 71 72
-4.16043290 1.22415584 0.38242424 3.49670996 -0.70441558 -0.54727273
73 74 75 76 77 78
1.71818182 -0.91151515 2.87532468 -1.39722944 1.47532468 -2.68181818
79 80 81 82 83 84
2.48848485 -0.42692641 -10.86865801 -6.45437229 -3.25549784 -5.19835498
> postscript(file="/var/www/html/rcomp/tmp/634or1227723438.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 = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.97532468 NA
1 5.60285714 -2.97532468
2 -5.71818182 5.60285714
3 1.81714286 -5.71818182
4 -2.41818182 1.81714286
5 -4.27532468 -2.41818182
6 -2.49714286 -4.27532468
7 2.18744589 -2.49714286
8 -4.15428571 2.18744589
9 0.86000000 -4.15428571
10 -1.54112554 0.86000000
11 -6.38398268 -1.54112554
12 -2.92640693 -6.38398268
13 -3.75610390 -2.92640693
14 -3.56926407 -3.75610390
15 3.45818182 -3.56926407
16 2.43073593 3.45818182
17 -3.92640693 2.43073593
18 1.24389610 -3.92640693
19 2.43636364 1.24389610
20 -6.41324675 2.43636364
21 -3.89896104 -6.41324675
22 1.60779221 -3.89896104
23 0.06493506 1.60779221
24 4.42251082 0.06493506
25 0.09281385 4.42251082
26 1.27965368 0.09281385
27 2.10709957 1.27965368
28 -3.52034632 2.10709957
29 -2.57748918 -3.52034632
30 -1.00718615 -2.57748918
31 -5.82259740 -1.00718615
32 3.53567100 -5.82259740
33 4.34995671 3.53567100
34 -1.65116883 4.34995671
35 5.40597403 -1.65116883
36 -2.32857143 5.40597403
37 2.94173160 -2.32857143
38 6.32857143 2.94173160
39 1.75601732 6.32857143
40 -0.67142857 1.75601732
41 7.27142857 -0.67142857
42 6.84173160 7.27142857
43 2.42632035 6.84173160
44 19.18458874 2.42632035
45 4.79887446 19.18458874
46 4.29774892 4.79887446
47 9.75489177 4.29774892
48 0.02034632 9.75489177
49 -5.40935065 0.02034632
50 -3.92251082 -5.40935065
51 -2.89506494 -3.92251082
52 -1.92251082 -2.89506494
53 8.52034632 -1.92251082
54 -2.90935065 8.52034632
55 -2.02476190 -2.90935065
56 -1.66649351 -2.02476190
57 -3.15220779 -1.66649351
58 1.24666667 -3.15220779
59 -3.09619048 1.24666667
60 2.06926407 -3.09619048
61 1.43956710 2.06926407
62 2.72640693 1.43956710
63 -4.84614719 2.72640693
64 4.62640693 -4.84614719
65 -2.33073593 4.62640693
66 -4.16043290 -2.33073593
67 1.22415584 -4.16043290
68 0.38242424 1.22415584
69 3.49670996 0.38242424
70 -0.70441558 3.49670996
71 -0.54727273 -0.70441558
72 1.71818182 -0.54727273
73 -0.91151515 1.71818182
74 2.87532468 -0.91151515
75 -1.39722944 2.87532468
76 1.47532468 -1.39722944
77 -2.68181818 1.47532468
78 2.48848485 -2.68181818
79 -0.42692641 2.48848485
80 -10.86865801 -0.42692641
81 -6.45437229 -10.86865801
82 -3.25549784 -6.45437229
83 -5.19835498 -3.25549784
84 NA -5.19835498
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.60285714 -2.97532468
[2,] -5.71818182 5.60285714
[3,] 1.81714286 -5.71818182
[4,] -2.41818182 1.81714286
[5,] -4.27532468 -2.41818182
[6,] -2.49714286 -4.27532468
[7,] 2.18744589 -2.49714286
[8,] -4.15428571 2.18744589
[9,] 0.86000000 -4.15428571
[10,] -1.54112554 0.86000000
[11,] -6.38398268 -1.54112554
[12,] -2.92640693 -6.38398268
[13,] -3.75610390 -2.92640693
[14,] -3.56926407 -3.75610390
[15,] 3.45818182 -3.56926407
[16,] 2.43073593 3.45818182
[17,] -3.92640693 2.43073593
[18,] 1.24389610 -3.92640693
[19,] 2.43636364 1.24389610
[20,] -6.41324675 2.43636364
[21,] -3.89896104 -6.41324675
[22,] 1.60779221 -3.89896104
[23,] 0.06493506 1.60779221
[24,] 4.42251082 0.06493506
[25,] 0.09281385 4.42251082
[26,] 1.27965368 0.09281385
[27,] 2.10709957 1.27965368
[28,] -3.52034632 2.10709957
[29,] -2.57748918 -3.52034632
[30,] -1.00718615 -2.57748918
[31,] -5.82259740 -1.00718615
[32,] 3.53567100 -5.82259740
[33,] 4.34995671 3.53567100
[34,] -1.65116883 4.34995671
[35,] 5.40597403 -1.65116883
[36,] -2.32857143 5.40597403
[37,] 2.94173160 -2.32857143
[38,] 6.32857143 2.94173160
[39,] 1.75601732 6.32857143
[40,] -0.67142857 1.75601732
[41,] 7.27142857 -0.67142857
[42,] 6.84173160 7.27142857
[43,] 2.42632035 6.84173160
[44,] 19.18458874 2.42632035
[45,] 4.79887446 19.18458874
[46,] 4.29774892 4.79887446
[47,] 9.75489177 4.29774892
[48,] 0.02034632 9.75489177
[49,] -5.40935065 0.02034632
[50,] -3.92251082 -5.40935065
[51,] -2.89506494 -3.92251082
[52,] -1.92251082 -2.89506494
[53,] 8.52034632 -1.92251082
[54,] -2.90935065 8.52034632
[55,] -2.02476190 -2.90935065
[56,] -1.66649351 -2.02476190
[57,] -3.15220779 -1.66649351
[58,] 1.24666667 -3.15220779
[59,] -3.09619048 1.24666667
[60,] 2.06926407 -3.09619048
[61,] 1.43956710 2.06926407
[62,] 2.72640693 1.43956710
[63,] -4.84614719 2.72640693
[64,] 4.62640693 -4.84614719
[65,] -2.33073593 4.62640693
[66,] -4.16043290 -2.33073593
[67,] 1.22415584 -4.16043290
[68,] 0.38242424 1.22415584
[69,] 3.49670996 0.38242424
[70,] -0.70441558 3.49670996
[71,] -0.54727273 -0.70441558
[72,] 1.71818182 -0.54727273
[73,] -0.91151515 1.71818182
[74,] 2.87532468 -0.91151515
[75,] -1.39722944 2.87532468
[76,] 1.47532468 -1.39722944
[77,] -2.68181818 1.47532468
[78,] 2.48848485 -2.68181818
[79,] -0.42692641 2.48848485
[80,] -10.86865801 -0.42692641
[81,] -6.45437229 -10.86865801
[82,] -3.25549784 -6.45437229
[83,] -5.19835498 -3.25549784
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.60285714 -2.97532468
2 -5.71818182 5.60285714
3 1.81714286 -5.71818182
4 -2.41818182 1.81714286
5 -4.27532468 -2.41818182
6 -2.49714286 -4.27532468
7 2.18744589 -2.49714286
8 -4.15428571 2.18744589
9 0.86000000 -4.15428571
10 -1.54112554 0.86000000
11 -6.38398268 -1.54112554
12 -2.92640693 -6.38398268
13 -3.75610390 -2.92640693
14 -3.56926407 -3.75610390
15 3.45818182 -3.56926407
16 2.43073593 3.45818182
17 -3.92640693 2.43073593
18 1.24389610 -3.92640693
19 2.43636364 1.24389610
20 -6.41324675 2.43636364
21 -3.89896104 -6.41324675
22 1.60779221 -3.89896104
23 0.06493506 1.60779221
24 4.42251082 0.06493506
25 0.09281385 4.42251082
26 1.27965368 0.09281385
27 2.10709957 1.27965368
28 -3.52034632 2.10709957
29 -2.57748918 -3.52034632
30 -1.00718615 -2.57748918
31 -5.82259740 -1.00718615
32 3.53567100 -5.82259740
33 4.34995671 3.53567100
34 -1.65116883 4.34995671
35 5.40597403 -1.65116883
36 -2.32857143 5.40597403
37 2.94173160 -2.32857143
38 6.32857143 2.94173160
39 1.75601732 6.32857143
40 -0.67142857 1.75601732
41 7.27142857 -0.67142857
42 6.84173160 7.27142857
43 2.42632035 6.84173160
44 19.18458874 2.42632035
45 4.79887446 19.18458874
46 4.29774892 4.79887446
47 9.75489177 4.29774892
48 0.02034632 9.75489177
49 -5.40935065 0.02034632
50 -3.92251082 -5.40935065
51 -2.89506494 -3.92251082
52 -1.92251082 -2.89506494
53 8.52034632 -1.92251082
54 -2.90935065 8.52034632
55 -2.02476190 -2.90935065
56 -1.66649351 -2.02476190
57 -3.15220779 -1.66649351
58 1.24666667 -3.15220779
59 -3.09619048 1.24666667
60 2.06926407 -3.09619048
61 1.43956710 2.06926407
62 2.72640693 1.43956710
63 -4.84614719 2.72640693
64 4.62640693 -4.84614719
65 -2.33073593 4.62640693
66 -4.16043290 -2.33073593
67 1.22415584 -4.16043290
68 0.38242424 1.22415584
69 3.49670996 0.38242424
70 -0.70441558 3.49670996
71 -0.54727273 -0.70441558
72 1.71818182 -0.54727273
73 -0.91151515 1.71818182
74 2.87532468 -0.91151515
75 -1.39722944 2.87532468
76 1.47532468 -1.39722944
77 -2.68181818 1.47532468
78 2.48848485 -2.68181818
79 -0.42692641 2.48848485
80 -10.86865801 -0.42692641
81 -6.45437229 -10.86865801
82 -3.25549784 -6.45437229
83 -5.19835498 -3.25549784
> 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/71ake1227723438.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rnm71227723438.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gx1u1227723438.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/102bq91227723438.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/1112sz1227723438.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/120sp91227723438.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/134gzy1227723438.tab")
> system("convert tmp/1hwpt1227723438.ps tmp/1hwpt1227723438.png")
> system("convert tmp/2fl1v1227723438.ps tmp/2fl1v1227723438.png")
> system("convert tmp/3zv4o1227723438.ps tmp/3zv4o1227723438.png")
> system("convert tmp/4o9op1227723438.ps tmp/4o9op1227723438.png")
> system("convert tmp/5sy6d1227723438.ps tmp/5sy6d1227723438.png")
> system("convert tmp/634or1227723438.ps tmp/634or1227723438.png")
> system("convert tmp/71ake1227723438.ps tmp/71ake1227723438.png")
> system("convert tmp/8rnm71227723438.ps tmp/8rnm71227723438.png")
> system("convert tmp/9gx1u1227723438.ps tmp/9gx1u1227723438.png")
>
>
> proc.time()
user system elapsed
1.991 1.415 2.514