R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-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.
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(9676,8642,9402,9610,9294,9448,10319,9548,9801,9596,8923,9746,9829,9125,9782,9441,9162,9915,10444,10209,9985,9842,9429,10132,9849,9172,10313,9819,9955,10048,10082,10541,10208,10233,9439,9963,10158,9225,10474,9757,10490,10281,10444,10640,10695,10786,9832,9747,10411,9511,10402,9701,10540,10112,10915,11183,10384,10834,9886,10216,10943,9867,10203,10837,10573,10647,11502,10656,10866,10835,9945,10331,10718,9462,10579,10633,10346,10757,11207,11013,11015,10765,10042,10661),dim=c(1,84),dimnames=list(c('Y'),1:84))
> y <- array(NA,dim=c(1,84),dimnames=list(c('Y'),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'
> 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, 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9676 1 0 0 0 0 0 0 0 0 0 0 1
2 8642 0 1 0 0 0 0 0 0 0 0 0 2
3 9402 0 0 1 0 0 0 0 0 0 0 0 3
4 9610 0 0 0 1 0 0 0 0 0 0 0 4
5 9294 0 0 0 0 1 0 0 0 0 0 0 5
6 9448 0 0 0 0 0 1 0 0 0 0 0 6
7 10319 0 0 0 0 0 0 1 0 0 0 0 7
8 9548 0 0 0 0 0 0 0 1 0 0 0 8
9 9801 0 0 0 0 0 0 0 0 1 0 0 9
10 9596 0 0 0 0 0 0 0 0 0 1 0 10
11 8923 0 0 0 0 0 0 0 0 0 0 1 11
12 9746 0 0 0 0 0 0 0 0 0 0 0 12
13 9829 1 0 0 0 0 0 0 0 0 0 0 13
14 9125 0 1 0 0 0 0 0 0 0 0 0 14
15 9782 0 0 1 0 0 0 0 0 0 0 0 15
16 9441 0 0 0 1 0 0 0 0 0 0 0 16
17 9162 0 0 0 0 1 0 0 0 0 0 0 17
18 9915 0 0 0 0 0 1 0 0 0 0 0 18
19 10444 0 0 0 0 0 0 1 0 0 0 0 19
20 10209 0 0 0 0 0 0 0 1 0 0 0 20
21 9985 0 0 0 0 0 0 0 0 1 0 0 21
22 9842 0 0 0 0 0 0 0 0 0 1 0 22
23 9429 0 0 0 0 0 0 0 0 0 0 1 23
24 10132 0 0 0 0 0 0 0 0 0 0 0 24
25 9849 1 0 0 0 0 0 0 0 0 0 0 25
26 9172 0 1 0 0 0 0 0 0 0 0 0 26
27 10313 0 0 1 0 0 0 0 0 0 0 0 27
28 9819 0 0 0 1 0 0 0 0 0 0 0 28
29 9955 0 0 0 0 1 0 0 0 0 0 0 29
30 10048 0 0 0 0 0 1 0 0 0 0 0 30
31 10082 0 0 0 0 0 0 1 0 0 0 0 31
32 10541 0 0 0 0 0 0 0 1 0 0 0 32
33 10208 0 0 0 0 0 0 0 0 1 0 0 33
34 10233 0 0 0 0 0 0 0 0 0 1 0 34
35 9439 0 0 0 0 0 0 0 0 0 0 1 35
36 9963 0 0 0 0 0 0 0 0 0 0 0 36
37 10158 1 0 0 0 0 0 0 0 0 0 0 37
38 9225 0 1 0 0 0 0 0 0 0 0 0 38
39 10474 0 0 1 0 0 0 0 0 0 0 0 39
40 9757 0 0 0 1 0 0 0 0 0 0 0 40
41 10490 0 0 0 0 1 0 0 0 0 0 0 41
42 10281 0 0 0 0 0 1 0 0 0 0 0 42
43 10444 0 0 0 0 0 0 1 0 0 0 0 43
44 10640 0 0 0 0 0 0 0 1 0 0 0 44
45 10695 0 0 0 0 0 0 0 0 1 0 0 45
46 10786 0 0 0 0 0 0 0 0 0 1 0 46
47 9832 0 0 0 0 0 0 0 0 0 0 1 47
48 9747 0 0 0 0 0 0 0 0 0 0 0 48
49 10411 1 0 0 0 0 0 0 0 0 0 0 49
50 9511 0 1 0 0 0 0 0 0 0 0 0 50
51 10402 0 0 1 0 0 0 0 0 0 0 0 51
52 9701 0 0 0 1 0 0 0 0 0 0 0 52
53 10540 0 0 0 0 1 0 0 0 0 0 0 53
54 10112 0 0 0 0 0 1 0 0 0 0 0 54
55 10915 0 0 0 0 0 0 1 0 0 0 0 55
56 11183 0 0 0 0 0 0 0 1 0 0 0 56
57 10384 0 0 0 0 0 0 0 0 1 0 0 57
58 10834 0 0 0 0 0 0 0 0 0 1 0 58
59 9886 0 0 0 0 0 0 0 0 0 0 1 59
60 10216 0 0 0 0 0 0 0 0 0 0 0 60
61 10943 1 0 0 0 0 0 0 0 0 0 0 61
62 9867 0 1 0 0 0 0 0 0 0 0 0 62
63 10203 0 0 1 0 0 0 0 0 0 0 0 63
64 10837 0 0 0 1 0 0 0 0 0 0 0 64
65 10573 0 0 0 0 1 0 0 0 0 0 0 65
66 10647 0 0 0 0 0 1 0 0 0 0 0 66
67 11502 0 0 0 0 0 0 1 0 0 0 0 67
68 10656 0 0 0 0 0 0 0 1 0 0 0 68
69 10866 0 0 0 0 0 0 0 0 1 0 0 69
70 10835 0 0 0 0 0 0 0 0 0 1 0 70
71 9945 0 0 0 0 0 0 0 0 0 0 1 71
72 10331 0 0 0 0 0 0 0 0 0 0 0 72
73 10718 1 0 0 0 0 0 0 0 0 0 0 73
74 9462 0 1 0 0 0 0 0 0 0 0 0 74
75 10579 0 0 1 0 0 0 0 0 0 0 0 75
76 10633 0 0 0 1 0 0 0 0 0 0 0 76
77 10346 0 0 0 0 1 0 0 0 0 0 0 77
78 10757 0 0 0 0 0 1 0 0 0 0 0 78
79 11207 0 0 0 0 0 0 1 0 0 0 0 79
80 11013 0 0 0 0 0 0 0 1 0 0 0 80
81 11015 0 0 0 0 0 0 0 0 1 0 0 81
82 10765 0 0 0 0 0 0 0 0 0 1 0 82
83 10042 0 0 0 0 0 0 0 0 0 0 1 83
84 10661 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) M1 M2 M3 M4 M5
9353.33 286.83 -669.02 193.86 -15.84 48.60
M6 M7 M8 M9 M10 M11
153.90 667.35 491.08 355.81 330.97 -455.59
t
15.84
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-509.24 -157.94 12.12 127.31 485.67
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9353.333 105.548 88.617 < 2e-16 ***
M1 286.825 129.834 2.209 0.030390 *
M2 -669.016 129.736 -5.157 2.18e-06 ***
M3 193.857 129.648 1.495 0.139278
M4 -15.841 129.568 -0.122 0.903037
M5 48.603 129.498 0.375 0.708542
M6 153.905 129.438 1.189 0.238391
M7 667.349 129.386 5.158 2.17e-06 ***
M8 491.079 129.344 3.797 0.000306 ***
M9 355.810 129.312 2.752 0.007522 **
M10 330.968 129.288 2.560 0.012599 *
M11 -455.587 129.274 -3.524 0.000748 ***
t 15.841 1.099 14.408 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 241.8 on 71 degrees of freedom
Multiple R-squared: 0.851, Adjusted R-squared: 0.8258
F-statistic: 33.79 on 12 and 71 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,] 0.42053259 0.8410652 0.57946741
[2,] 0.44637750 0.8927550 0.55362250
[3,] 0.41159004 0.8231801 0.58840996
[4,] 0.28913826 0.5782765 0.71086174
[5,] 0.37864558 0.7572912 0.62135442
[6,] 0.27794403 0.5558881 0.72205597
[7,] 0.20901654 0.4180331 0.79098346
[8,] 0.18029266 0.3605853 0.81970734
[9,] 0.14837808 0.2967562 0.85162192
[10,] 0.16220932 0.3244186 0.83779068
[11,] 0.11143763 0.2228753 0.88856237
[12,] 0.15288438 0.3057688 0.84711562
[13,] 0.10938179 0.2187636 0.89061821
[14,] 0.12275125 0.2455025 0.87724875
[15,] 0.08464547 0.1692909 0.91535453
[16,] 0.35293684 0.7058737 0.64706316
[17,] 0.33707898 0.6741580 0.66292102
[18,] 0.27509513 0.5501903 0.72490487
[19,] 0.23308436 0.4661687 0.76691564
[20,] 0.18680975 0.3736195 0.81319025
[21,] 0.18660122 0.3732024 0.81339878
[22,] 0.15478790 0.3095758 0.84521210
[23,] 0.13043663 0.2608733 0.86956337
[24,] 0.12796106 0.2559221 0.87203894
[25,] 0.14813300 0.2962660 0.85186700
[26,] 0.25273606 0.5054721 0.74726394
[27,] 0.19734906 0.3946981 0.80265094
[28,] 0.25710319 0.5142064 0.74289681
[29,] 0.20422016 0.4084403 0.79577984
[30,] 0.18344864 0.3668973 0.81655136
[31,] 0.20945513 0.4189103 0.79054487
[32,] 0.16890118 0.3378024 0.83109882
[33,] 0.31834664 0.6366933 0.68165336
[34,] 0.27395731 0.5479146 0.72604269
[35,] 0.21700590 0.4340118 0.78299410
[36,] 0.18223085 0.3644617 0.81776915
[37,] 0.57150349 0.8569930 0.42849651
[38,] 0.54832827 0.9033435 0.45167173
[39,] 0.65671153 0.6865769 0.34328847
[40,] 0.67379981 0.6524004 0.32620019
[41,] 0.78138466 0.4372307 0.21861534
[42,] 0.88774911 0.2245018 0.11225089
[43,] 0.84673124 0.3065375 0.15326876
[44,] 0.78492770 0.4301446 0.21507230
[45,] 0.77500342 0.4499932 0.22499658
[46,] 0.75215818 0.4956836 0.24784182
[47,] 0.82556127 0.3488775 0.17443873
[48,] 0.86628341 0.2674332 0.13371659
[49,] 0.86635861 0.2672828 0.13364139
[50,] 0.86858911 0.2628218 0.13141089
[51,] 0.77340633 0.4531873 0.22659367
[52,] 0.90910212 0.1817958 0.09089788
[53,] 0.88764669 0.2247066 0.11235331
> postscript(file="/var/wessaorg/rcomp/tmp/1qyhx1356009951.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2yw6a1356009951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ioph1356009951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4tftk1356009951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5nkcn1356009951.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 = 84
Frequency = 1
1 2 3 4 5 6
20.000000 -74.000000 -192.714286 209.142857 -187.142857 -154.285714
7 8 9 10 11 12
187.428571 -423.142857 -50.714286 -246.714286 -149.000000 202.571429
13 14 15 16 17 18
-17.095238 218.904762 -2.809524 -149.952381 -509.238095 122.619048
19 20 21 22 23 24
122.333333 47.761905 -56.809524 -190.809524 166.904762 398.476190
25 26 27 28 29 30
-187.190476 75.809524 338.095238 37.952381 93.666667 65.523810
31 32 33 34 35 36
-429.761905 189.666667 -23.904762 10.095238 -13.190476 39.380952
37 38 39 40 41 42
-68.285714 -61.285714 309.000000 -214.142857 438.571429 108.428571
43 44 45 46 47 48
-257.857143 98.571429 273.000000 373.000000 189.714286 -366.714286
49 50 51 52 53 54
-5.380952 34.619048 46.904762 -460.238095 298.476190 -250.666667
55 56 57 58 59 60
23.047619 451.476190 -228.095238 230.904762 53.619048 -87.809524
61 62 63 64 65 66
336.523810 200.523810 -342.190476 485.666667 141.380952 94.238095
67 68 69 70 71 72
419.952381 -265.619048 63.809524 41.809524 -77.476190 -162.904762
73 74 75 76 77 78
-78.571429 -394.571429 -156.285714 91.571429 -275.714286 14.142857
79 80 81 82 83 84
-65.142857 -98.714286 22.714286 -218.285714 -170.571429 -23.000000
> postscript(file="/var/wessaorg/rcomp/tmp/6oqfl1356009951.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 = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 20.000000 NA
1 -74.000000 20.000000
2 -192.714286 -74.000000
3 209.142857 -192.714286
4 -187.142857 209.142857
5 -154.285714 -187.142857
6 187.428571 -154.285714
7 -423.142857 187.428571
8 -50.714286 -423.142857
9 -246.714286 -50.714286
10 -149.000000 -246.714286
11 202.571429 -149.000000
12 -17.095238 202.571429
13 218.904762 -17.095238
14 -2.809524 218.904762
15 -149.952381 -2.809524
16 -509.238095 -149.952381
17 122.619048 -509.238095
18 122.333333 122.619048
19 47.761905 122.333333
20 -56.809524 47.761905
21 -190.809524 -56.809524
22 166.904762 -190.809524
23 398.476190 166.904762
24 -187.190476 398.476190
25 75.809524 -187.190476
26 338.095238 75.809524
27 37.952381 338.095238
28 93.666667 37.952381
29 65.523810 93.666667
30 -429.761905 65.523810
31 189.666667 -429.761905
32 -23.904762 189.666667
33 10.095238 -23.904762
34 -13.190476 10.095238
35 39.380952 -13.190476
36 -68.285714 39.380952
37 -61.285714 -68.285714
38 309.000000 -61.285714
39 -214.142857 309.000000
40 438.571429 -214.142857
41 108.428571 438.571429
42 -257.857143 108.428571
43 98.571429 -257.857143
44 273.000000 98.571429
45 373.000000 273.000000
46 189.714286 373.000000
47 -366.714286 189.714286
48 -5.380952 -366.714286
49 34.619048 -5.380952
50 46.904762 34.619048
51 -460.238095 46.904762
52 298.476190 -460.238095
53 -250.666667 298.476190
54 23.047619 -250.666667
55 451.476190 23.047619
56 -228.095238 451.476190
57 230.904762 -228.095238
58 53.619048 230.904762
59 -87.809524 53.619048
60 336.523810 -87.809524
61 200.523810 336.523810
62 -342.190476 200.523810
63 485.666667 -342.190476
64 141.380952 485.666667
65 94.238095 141.380952
66 419.952381 94.238095
67 -265.619048 419.952381
68 63.809524 -265.619048
69 41.809524 63.809524
70 -77.476190 41.809524
71 -162.904762 -77.476190
72 -78.571429 -162.904762
73 -394.571429 -78.571429
74 -156.285714 -394.571429
75 91.571429 -156.285714
76 -275.714286 91.571429
77 14.142857 -275.714286
78 -65.142857 14.142857
79 -98.714286 -65.142857
80 22.714286 -98.714286
81 -218.285714 22.714286
82 -170.571429 -218.285714
83 -23.000000 -170.571429
84 NA -23.000000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -74.000000 20.000000
[2,] -192.714286 -74.000000
[3,] 209.142857 -192.714286
[4,] -187.142857 209.142857
[5,] -154.285714 -187.142857
[6,] 187.428571 -154.285714
[7,] -423.142857 187.428571
[8,] -50.714286 -423.142857
[9,] -246.714286 -50.714286
[10,] -149.000000 -246.714286
[11,] 202.571429 -149.000000
[12,] -17.095238 202.571429
[13,] 218.904762 -17.095238
[14,] -2.809524 218.904762
[15,] -149.952381 -2.809524
[16,] -509.238095 -149.952381
[17,] 122.619048 -509.238095
[18,] 122.333333 122.619048
[19,] 47.761905 122.333333
[20,] -56.809524 47.761905
[21,] -190.809524 -56.809524
[22,] 166.904762 -190.809524
[23,] 398.476190 166.904762
[24,] -187.190476 398.476190
[25,] 75.809524 -187.190476
[26,] 338.095238 75.809524
[27,] 37.952381 338.095238
[28,] 93.666667 37.952381
[29,] 65.523810 93.666667
[30,] -429.761905 65.523810
[31,] 189.666667 -429.761905
[32,] -23.904762 189.666667
[33,] 10.095238 -23.904762
[34,] -13.190476 10.095238
[35,] 39.380952 -13.190476
[36,] -68.285714 39.380952
[37,] -61.285714 -68.285714
[38,] 309.000000 -61.285714
[39,] -214.142857 309.000000
[40,] 438.571429 -214.142857
[41,] 108.428571 438.571429
[42,] -257.857143 108.428571
[43,] 98.571429 -257.857143
[44,] 273.000000 98.571429
[45,] 373.000000 273.000000
[46,] 189.714286 373.000000
[47,] -366.714286 189.714286
[48,] -5.380952 -366.714286
[49,] 34.619048 -5.380952
[50,] 46.904762 34.619048
[51,] -460.238095 46.904762
[52,] 298.476190 -460.238095
[53,] -250.666667 298.476190
[54,] 23.047619 -250.666667
[55,] 451.476190 23.047619
[56,] -228.095238 451.476190
[57,] 230.904762 -228.095238
[58,] 53.619048 230.904762
[59,] -87.809524 53.619048
[60,] 336.523810 -87.809524
[61,] 200.523810 336.523810
[62,] -342.190476 200.523810
[63,] 485.666667 -342.190476
[64,] 141.380952 485.666667
[65,] 94.238095 141.380952
[66,] 419.952381 94.238095
[67,] -265.619048 419.952381
[68,] 63.809524 -265.619048
[69,] 41.809524 63.809524
[70,] -77.476190 41.809524
[71,] -162.904762 -77.476190
[72,] -78.571429 -162.904762
[73,] -394.571429 -78.571429
[74,] -156.285714 -394.571429
[75,] 91.571429 -156.285714
[76,] -275.714286 91.571429
[77,] 14.142857 -275.714286
[78,] -65.142857 14.142857
[79,] -98.714286 -65.142857
[80,] 22.714286 -98.714286
[81,] -218.285714 22.714286
[82,] -170.571429 -218.285714
[83,] -23.000000 -170.571429
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -74.000000 20.000000
2 -192.714286 -74.000000
3 209.142857 -192.714286
4 -187.142857 209.142857
5 -154.285714 -187.142857
6 187.428571 -154.285714
7 -423.142857 187.428571
8 -50.714286 -423.142857
9 -246.714286 -50.714286
10 -149.000000 -246.714286
11 202.571429 -149.000000
12 -17.095238 202.571429
13 218.904762 -17.095238
14 -2.809524 218.904762
15 -149.952381 -2.809524
16 -509.238095 -149.952381
17 122.619048 -509.238095
18 122.333333 122.619048
19 47.761905 122.333333
20 -56.809524 47.761905
21 -190.809524 -56.809524
22 166.904762 -190.809524
23 398.476190 166.904762
24 -187.190476 398.476190
25 75.809524 -187.190476
26 338.095238 75.809524
27 37.952381 338.095238
28 93.666667 37.952381
29 65.523810 93.666667
30 -429.761905 65.523810
31 189.666667 -429.761905
32 -23.904762 189.666667
33 10.095238 -23.904762
34 -13.190476 10.095238
35 39.380952 -13.190476
36 -68.285714 39.380952
37 -61.285714 -68.285714
38 309.000000 -61.285714
39 -214.142857 309.000000
40 438.571429 -214.142857
41 108.428571 438.571429
42 -257.857143 108.428571
43 98.571429 -257.857143
44 273.000000 98.571429
45 373.000000 273.000000
46 189.714286 373.000000
47 -366.714286 189.714286
48 -5.380952 -366.714286
49 34.619048 -5.380952
50 46.904762 34.619048
51 -460.238095 46.904762
52 298.476190 -460.238095
53 -250.666667 298.476190
54 23.047619 -250.666667
55 451.476190 23.047619
56 -228.095238 451.476190
57 230.904762 -228.095238
58 53.619048 230.904762
59 -87.809524 53.619048
60 336.523810 -87.809524
61 200.523810 336.523810
62 -342.190476 200.523810
63 485.666667 -342.190476
64 141.380952 485.666667
65 94.238095 141.380952
66 419.952381 94.238095
67 -265.619048 419.952381
68 63.809524 -265.619048
69 41.809524 63.809524
70 -77.476190 41.809524
71 -162.904762 -77.476190
72 -78.571429 -162.904762
73 -394.571429 -78.571429
74 -156.285714 -394.571429
75 91.571429 -156.285714
76 -275.714286 91.571429
77 14.142857 -275.714286
78 -65.142857 14.142857
79 -98.714286 -65.142857
80 22.714286 -98.714286
81 -218.285714 22.714286
82 -170.571429 -218.285714
83 -23.000000 -170.571429
> 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/7zzd51356009951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ha0v1356009951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ia2n1356009951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10fyc21356009951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kg8u1356009951.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/12hwdh1356009951.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/136izb1356009951.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/14izom1356009951.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/155hf51356009951.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/16shwc1356009951.tab")
+ }
>
> try(system("convert tmp/1qyhx1356009951.ps tmp/1qyhx1356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yw6a1356009951.ps tmp/2yw6a1356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ioph1356009951.ps tmp/3ioph1356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tftk1356009951.ps tmp/4tftk1356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nkcn1356009951.ps tmp/5nkcn1356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oqfl1356009951.ps tmp/6oqfl1356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zzd51356009951.ps tmp/7zzd51356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ha0v1356009951.ps tmp/8ha0v1356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ia2n1356009951.ps tmp/9ia2n1356009951.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fyc21356009951.ps tmp/10fyc21356009951.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.832 1.283 8.194