R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(37,1,0,0,30,2,0,0,47,3,0,0,35,4,0,0,30,5,0,0,43,6,0,0,82,7,0,0,40,8,0,0,47,9,0,0,19,10,0,0,52,11,0,0,136,12,0,0,80,13,0,0,42,14,0,0,54,15,0,0,66,16,0,0,81,17,0,0,63,18,0,0,137,19,0,0,72,20,0,0,107,21,0,0,58,22,0,0,36,23,0,0,52,24,0,0,79,25,0,0,77,26,0,0,54,27,0,0,84,28,0,0,48,29,0,0,96,30,0,0,83,31,0,0,66,32,0,0,61,33,0,0,53,34,0,0,30,35,0,0,74,36,0,0,69,37,0,0,59,38,0,0,42,39,0,0,65,40,0,0,70,41,0,0,100,42,0,0,63,43,0,0,105,44,0,0,82,45,0,0,81,46,0,0,75,47,0,0,102,48,0,0,121,49,0,0,98,50,0,0,76,51,0,0,77,52,0,0,63,53,0,0,37,54,1,54,35,55,1,55,23,56,1,56,40,57,1,57,29,58,1,58,37,59,1,59,51,60,1,60,20,61,1,61,28,62,1,62,13,63,1,63,22,64,1,64,25,65,1,65,13,66,1,66,16,67,1,67,13,68,1,68,16,69,1,69,17,70,1,70,9,71,1,71,17,72,1,72,25,73,1,73,14,74,1,74,8,75,1,75,7,76,1,76,10,77,1,77,7,78,1,78,10,79,1,79,3,80,1,80),dim=c(4,80),dimnames=list(c('Y','t','D','tD'),1:80))
> y <- array(NA,dim=c(4,80),dimnames=list(c('Y','t','D','tD'),1:80))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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 t D tD
1 37 1 0 0
2 30 2 0 0
3 47 3 0 0
4 35 4 0 0
5 30 5 0 0
6 43 6 0 0
7 82 7 0 0
8 40 8 0 0
9 47 9 0 0
10 19 10 0 0
11 52 11 0 0
12 136 12 0 0
13 80 13 0 0
14 42 14 0 0
15 54 15 0 0
16 66 16 0 0
17 81 17 0 0
18 63 18 0 0
19 137 19 0 0
20 72 20 0 0
21 107 21 0 0
22 58 22 0 0
23 36 23 0 0
24 52 24 0 0
25 79 25 0 0
26 77 26 0 0
27 54 27 0 0
28 84 28 0 0
29 48 29 0 0
30 96 30 0 0
31 83 31 0 0
32 66 32 0 0
33 61 33 0 0
34 53 34 0 0
35 30 35 0 0
36 74 36 0 0
37 69 37 0 0
38 59 38 0 0
39 42 39 0 0
40 65 40 0 0
41 70 41 0 0
42 100 42 0 0
43 63 43 0 0
44 105 44 0 0
45 82 45 0 0
46 81 46 0 0
47 75 47 0 0
48 102 48 0 0
49 121 49 0 0
50 98 50 0 0
51 76 51 0 0
52 77 52 0 0
53 63 53 0 0
54 37 54 1 54
55 35 55 1 55
56 23 56 1 56
57 40 57 1 57
58 29 58 1 58
59 37 59 1 59
60 51 60 1 60
61 20 61 1 61
62 28 62 1 62
63 13 63 1 63
64 22 64 1 64
65 25 65 1 65
66 13 66 1 66
67 16 67 1 67
68 13 68 1 68
69 16 69 1 69
70 17 70 1 70
71 9 71 1 71
72 17 72 1 72
73 25 73 1 73
74 14 74 1 74
75 8 75 1 75
76 7 76 1 76
77 10 77 1 77
78 7 78 1 78
79 10 79 1 79
80 3 80 1 80
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) t D tD
49.2678 0.6903 51.9473 -1.8997
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-43.428 -10.628 -2.185 7.285 78.449
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.2678 5.6683 8.692 5.2e-13 ***
t 0.6903 0.1827 3.779 0.000311 ***
D 51.9473 34.3719 1.511 0.134852
tD -1.8997 0.5348 -3.552 0.000660 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20.34 on 76 degrees of freedom
Multiple R-squared: 0.6093, Adjusted R-squared: 0.5939
F-statistic: 39.51 on 3 and 76 DF, p-value: 1.71e-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,] 0.5964863 8.070274e-01 4.035137e-01
[2,] 0.5566464 8.867072e-01 4.433536e-01
[3,] 0.4347792 8.695584e-01 5.652208e-01
[4,] 0.6176159 7.647683e-01 3.823841e-01
[5,] 0.5205425 9.589151e-01 4.794575e-01
[6,] 0.9954226 9.154840e-03 4.577420e-03
[7,] 0.9918405 1.631903e-02 8.159513e-03
[8,] 0.9953391 9.321887e-03 4.660943e-03
[9,] 0.9938416 1.231675e-02 6.158376e-03
[10,] 0.9893671 2.126573e-02 1.063287e-02
[11,] 0.9833243 3.335135e-02 1.667567e-02
[12,] 0.9760684 4.786311e-02 2.393156e-02
[13,] 0.9995694 8.611979e-04 4.305990e-04
[14,] 0.9994046 1.190880e-03 5.954398e-04
[15,] 0.9998540 2.920461e-04 1.460231e-04
[16,] 0.9998971 2.058893e-04 1.029446e-04
[17,] 0.9999847 3.063532e-05 1.531766e-05
[18,] 0.9999853 2.937758e-05 1.468879e-05
[19,] 0.9999776 4.472447e-05 2.236223e-05
[20,] 0.9999667 6.660745e-05 3.330373e-05
[21,] 0.9999613 7.733633e-05 3.866817e-05
[22,] 0.9999587 8.266766e-05 4.133383e-05
[23,] 0.9999646 7.071236e-05 3.535618e-05
[24,] 0.9999887 2.262320e-05 1.131160e-05
[25,] 0.9999922 1.568214e-05 7.841071e-06
[26,] 0.9999889 2.224341e-05 1.112171e-05
[27,] 0.9999832 3.356819e-05 1.678410e-05
[28,] 0.9999774 4.524289e-05 2.262144e-05
[29,] 0.9999974 5.174519e-06 2.587260e-06
[30,] 0.9999949 1.021176e-05 5.105878e-06
[31,] 0.9999892 2.157421e-05 1.078711e-05
[32,] 0.9999826 3.473840e-05 1.736920e-05
[33,] 0.9999976 4.895122e-06 2.447561e-06
[34,] 0.9999975 4.905988e-06 2.452994e-06
[35,] 0.9999978 4.496290e-06 2.248145e-06
[36,] 0.9999970 5.950333e-06 2.975166e-06
[37,] 0.9999995 9.545321e-07 4.772661e-07
[38,] 0.9999994 1.250355e-06 6.251773e-07
[39,] 0.9999991 1.823216e-06 9.116078e-07
[40,] 0.9999994 1.273962e-06 6.369808e-07
[41,] 1.0000000 5.478248e-09 2.739124e-09
[42,] 1.0000000 1.358599e-09 6.792994e-10
[43,] 1.0000000 1.092617e-09 5.463086e-10
[44,] 1.0000000 2.450229e-09 1.225114e-09
[45,] 1.0000000 6.115074e-09 3.057537e-09
[46,] 1.0000000 2.085405e-08 1.042703e-08
[47,] 1.0000000 5.932145e-08 2.966072e-08
[48,] 0.9999999 1.978966e-07 9.894832e-08
[49,] 0.9999997 6.455441e-07 3.227720e-07
[50,] 0.9999996 8.482483e-07 4.241241e-07
[51,] 0.9999991 1.879063e-06 9.395317e-07
[52,] 0.9999972 5.691038e-06 2.845519e-06
[53,] 0.9999938 1.246793e-05 6.233966e-06
[54,] 1.0000000 5.066272e-08 2.533136e-08
[55,] 0.9999999 1.932647e-07 9.663237e-08
[56,] 0.9999998 4.178043e-07 2.089022e-07
[57,] 0.9999997 6.488542e-07 3.244271e-07
[58,] 0.9999985 2.951970e-06 1.475985e-06
[59,] 0.9999966 6.823612e-06 3.411806e-06
[60,] 0.9999897 2.066781e-05 1.033390e-05
[61,] 0.9999560 8.794405e-05 4.397203e-05
[62,] 0.9998748 2.504463e-04 1.252231e-04
[63,] 0.9995046 9.907785e-04 4.953893e-04
[64,] 0.9980076 3.984786e-03 1.992393e-03
[65,] 0.9977265 4.546994e-03 2.273497e-03
[66,] 0.9906780 1.864399e-02 9.321994e-03
[67,] 0.9932653 1.346937e-02 6.734683e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1m7s31322752947.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/207sm1322752947.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3yn5l1322752947.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4zysq1322752947.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5le2h1322752947.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 80
Frequency = 1
1 2 3 4 5 6
-12.95807128 -20.64836317 -4.33865506 -17.02894694 -22.71923883 -10.40953072
7 8 9 10 11 12
27.90017739 -14.79011450 -8.48040639 -37.17069827 -4.86099016 78.44871795
13 14 15 16 17 18
21.75842606 -16.93186583 -5.62215772 5.68755040 19.99725851 1.30696662
19 20 21 22 23 24
74.61667473 8.92638284 43.23609095 -6.45420094 -29.14449282 -13.83478471
25 26 27 28 29 30
12.47492340 9.78463151 -13.90566038 15.40404773 -21.28624415 26.02346396
31 32 33 34 35 36
12.33317207 -5.35711982 -11.04741171 -19.73770360 -43.42799548 -0.11828737
37 38 39 40 41 42
-5.80857926 -16.49887115 -34.18916304 -11.87945493 -7.56974682 21.73996130
43 44 45 46 47 48
-15.95033059 25.35937752 1.66908563 -0.02120626 -6.71149815 19.59820997
49 50 51 52 53 54
37.90791808 14.21762619 -8.47266570 -8.16295759 -22.85324948 1.09259259
55 56 57 58 59 60
0.30199430 -10.48860399 7.72079772 -2.06980057 7.13960114 22.34900285
61 62 63 64 65 66
-7.44159544 1.76780627 -12.02279202 -1.81339031 2.39601140 -8.39458689
67 68 69 70 71 72
-4.18518519 -5.97578348 -1.76638177 0.44301994 -6.34757835 2.86182336
73 74 75 76 77 78
12.07122507 2.28062678 -2.50997151 -2.30056980 1.90883191 0.11823362
79 80
4.32763533 -1.46296296
> postscript(file="/var/wessaorg/rcomp/tmp/68awv1322752947.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 80
Frequency = 1
lag(myerror, k = 1) myerror
0 -12.95807128 NA
1 -20.64836317 -12.95807128
2 -4.33865506 -20.64836317
3 -17.02894694 -4.33865506
4 -22.71923883 -17.02894694
5 -10.40953072 -22.71923883
6 27.90017739 -10.40953072
7 -14.79011450 27.90017739
8 -8.48040639 -14.79011450
9 -37.17069827 -8.48040639
10 -4.86099016 -37.17069827
11 78.44871795 -4.86099016
12 21.75842606 78.44871795
13 -16.93186583 21.75842606
14 -5.62215772 -16.93186583
15 5.68755040 -5.62215772
16 19.99725851 5.68755040
17 1.30696662 19.99725851
18 74.61667473 1.30696662
19 8.92638284 74.61667473
20 43.23609095 8.92638284
21 -6.45420094 43.23609095
22 -29.14449282 -6.45420094
23 -13.83478471 -29.14449282
24 12.47492340 -13.83478471
25 9.78463151 12.47492340
26 -13.90566038 9.78463151
27 15.40404773 -13.90566038
28 -21.28624415 15.40404773
29 26.02346396 -21.28624415
30 12.33317207 26.02346396
31 -5.35711982 12.33317207
32 -11.04741171 -5.35711982
33 -19.73770360 -11.04741171
34 -43.42799548 -19.73770360
35 -0.11828737 -43.42799548
36 -5.80857926 -0.11828737
37 -16.49887115 -5.80857926
38 -34.18916304 -16.49887115
39 -11.87945493 -34.18916304
40 -7.56974682 -11.87945493
41 21.73996130 -7.56974682
42 -15.95033059 21.73996130
43 25.35937752 -15.95033059
44 1.66908563 25.35937752
45 -0.02120626 1.66908563
46 -6.71149815 -0.02120626
47 19.59820997 -6.71149815
48 37.90791808 19.59820997
49 14.21762619 37.90791808
50 -8.47266570 14.21762619
51 -8.16295759 -8.47266570
52 -22.85324948 -8.16295759
53 1.09259259 -22.85324948
54 0.30199430 1.09259259
55 -10.48860399 0.30199430
56 7.72079772 -10.48860399
57 -2.06980057 7.72079772
58 7.13960114 -2.06980057
59 22.34900285 7.13960114
60 -7.44159544 22.34900285
61 1.76780627 -7.44159544
62 -12.02279202 1.76780627
63 -1.81339031 -12.02279202
64 2.39601140 -1.81339031
65 -8.39458689 2.39601140
66 -4.18518519 -8.39458689
67 -5.97578348 -4.18518519
68 -1.76638177 -5.97578348
69 0.44301994 -1.76638177
70 -6.34757835 0.44301994
71 2.86182336 -6.34757835
72 12.07122507 2.86182336
73 2.28062678 12.07122507
74 -2.50997151 2.28062678
75 -2.30056980 -2.50997151
76 1.90883191 -2.30056980
77 0.11823362 1.90883191
78 4.32763533 0.11823362
79 -1.46296296 4.32763533
80 NA -1.46296296
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -20.64836317 -12.95807128
[2,] -4.33865506 -20.64836317
[3,] -17.02894694 -4.33865506
[4,] -22.71923883 -17.02894694
[5,] -10.40953072 -22.71923883
[6,] 27.90017739 -10.40953072
[7,] -14.79011450 27.90017739
[8,] -8.48040639 -14.79011450
[9,] -37.17069827 -8.48040639
[10,] -4.86099016 -37.17069827
[11,] 78.44871795 -4.86099016
[12,] 21.75842606 78.44871795
[13,] -16.93186583 21.75842606
[14,] -5.62215772 -16.93186583
[15,] 5.68755040 -5.62215772
[16,] 19.99725851 5.68755040
[17,] 1.30696662 19.99725851
[18,] 74.61667473 1.30696662
[19,] 8.92638284 74.61667473
[20,] 43.23609095 8.92638284
[21,] -6.45420094 43.23609095
[22,] -29.14449282 -6.45420094
[23,] -13.83478471 -29.14449282
[24,] 12.47492340 -13.83478471
[25,] 9.78463151 12.47492340
[26,] -13.90566038 9.78463151
[27,] 15.40404773 -13.90566038
[28,] -21.28624415 15.40404773
[29,] 26.02346396 -21.28624415
[30,] 12.33317207 26.02346396
[31,] -5.35711982 12.33317207
[32,] -11.04741171 -5.35711982
[33,] -19.73770360 -11.04741171
[34,] -43.42799548 -19.73770360
[35,] -0.11828737 -43.42799548
[36,] -5.80857926 -0.11828737
[37,] -16.49887115 -5.80857926
[38,] -34.18916304 -16.49887115
[39,] -11.87945493 -34.18916304
[40,] -7.56974682 -11.87945493
[41,] 21.73996130 -7.56974682
[42,] -15.95033059 21.73996130
[43,] 25.35937752 -15.95033059
[44,] 1.66908563 25.35937752
[45,] -0.02120626 1.66908563
[46,] -6.71149815 -0.02120626
[47,] 19.59820997 -6.71149815
[48,] 37.90791808 19.59820997
[49,] 14.21762619 37.90791808
[50,] -8.47266570 14.21762619
[51,] -8.16295759 -8.47266570
[52,] -22.85324948 -8.16295759
[53,] 1.09259259 -22.85324948
[54,] 0.30199430 1.09259259
[55,] -10.48860399 0.30199430
[56,] 7.72079772 -10.48860399
[57,] -2.06980057 7.72079772
[58,] 7.13960114 -2.06980057
[59,] 22.34900285 7.13960114
[60,] -7.44159544 22.34900285
[61,] 1.76780627 -7.44159544
[62,] -12.02279202 1.76780627
[63,] -1.81339031 -12.02279202
[64,] 2.39601140 -1.81339031
[65,] -8.39458689 2.39601140
[66,] -4.18518519 -8.39458689
[67,] -5.97578348 -4.18518519
[68,] -1.76638177 -5.97578348
[69,] 0.44301994 -1.76638177
[70,] -6.34757835 0.44301994
[71,] 2.86182336 -6.34757835
[72,] 12.07122507 2.86182336
[73,] 2.28062678 12.07122507
[74,] -2.50997151 2.28062678
[75,] -2.30056980 -2.50997151
[76,] 1.90883191 -2.30056980
[77,] 0.11823362 1.90883191
[78,] 4.32763533 0.11823362
[79,] -1.46296296 4.32763533
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -20.64836317 -12.95807128
2 -4.33865506 -20.64836317
3 -17.02894694 -4.33865506
4 -22.71923883 -17.02894694
5 -10.40953072 -22.71923883
6 27.90017739 -10.40953072
7 -14.79011450 27.90017739
8 -8.48040639 -14.79011450
9 -37.17069827 -8.48040639
10 -4.86099016 -37.17069827
11 78.44871795 -4.86099016
12 21.75842606 78.44871795
13 -16.93186583 21.75842606
14 -5.62215772 -16.93186583
15 5.68755040 -5.62215772
16 19.99725851 5.68755040
17 1.30696662 19.99725851
18 74.61667473 1.30696662
19 8.92638284 74.61667473
20 43.23609095 8.92638284
21 -6.45420094 43.23609095
22 -29.14449282 -6.45420094
23 -13.83478471 -29.14449282
24 12.47492340 -13.83478471
25 9.78463151 12.47492340
26 -13.90566038 9.78463151
27 15.40404773 -13.90566038
28 -21.28624415 15.40404773
29 26.02346396 -21.28624415
30 12.33317207 26.02346396
31 -5.35711982 12.33317207
32 -11.04741171 -5.35711982
33 -19.73770360 -11.04741171
34 -43.42799548 -19.73770360
35 -0.11828737 -43.42799548
36 -5.80857926 -0.11828737
37 -16.49887115 -5.80857926
38 -34.18916304 -16.49887115
39 -11.87945493 -34.18916304
40 -7.56974682 -11.87945493
41 21.73996130 -7.56974682
42 -15.95033059 21.73996130
43 25.35937752 -15.95033059
44 1.66908563 25.35937752
45 -0.02120626 1.66908563
46 -6.71149815 -0.02120626
47 19.59820997 -6.71149815
48 37.90791808 19.59820997
49 14.21762619 37.90791808
50 -8.47266570 14.21762619
51 -8.16295759 -8.47266570
52 -22.85324948 -8.16295759
53 1.09259259 -22.85324948
54 0.30199430 1.09259259
55 -10.48860399 0.30199430
56 7.72079772 -10.48860399
57 -2.06980057 7.72079772
58 7.13960114 -2.06980057
59 22.34900285 7.13960114
60 -7.44159544 22.34900285
61 1.76780627 -7.44159544
62 -12.02279202 1.76780627
63 -1.81339031 -12.02279202
64 2.39601140 -1.81339031
65 -8.39458689 2.39601140
66 -4.18518519 -8.39458689
67 -5.97578348 -4.18518519
68 -1.76638177 -5.97578348
69 0.44301994 -1.76638177
70 -6.34757835 0.44301994
71 2.86182336 -6.34757835
72 12.07122507 2.86182336
73 2.28062678 12.07122507
74 -2.50997151 2.28062678
75 -2.30056980 -2.50997151
76 1.90883191 -2.30056980
77 0.11823362 1.90883191
78 4.32763533 0.11823362
79 -1.46296296 4.32763533
> 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/wessaorg/rcomp/tmp/76x4b1322752947.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/8pdrb1322752947.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/9ljel1322752947.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/10q78p1322752947.ps",horizontal=F,onefile=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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11vebv1322752947.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12c3my1322752947.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13hofq1322752947.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14zbm51322752947.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/wessaorg/rcomp/tmp/159qhz1322752947.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/wessaorg/rcomp/tmp/1648bp1322752947.tab")
+ }
>
> try(system("convert tmp/1m7s31322752947.ps tmp/1m7s31322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/207sm1322752947.ps tmp/207sm1322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yn5l1322752947.ps tmp/3yn5l1322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zysq1322752947.ps tmp/4zysq1322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/5le2h1322752947.ps tmp/5le2h1322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/68awv1322752947.ps tmp/68awv1322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/76x4b1322752947.ps tmp/76x4b1322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pdrb1322752947.ps tmp/8pdrb1322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ljel1322752947.ps tmp/9ljel1322752947.png",intern=TRUE))
character(0)
> try(system("convert tmp/10q78p1322752947.ps tmp/10q78p1322752947.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.379 0.469 3.861