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)
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Type 'q()' to quit R.
> x <- array(list(1
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+ ,3)
+ ,dim=c(13
+ ,82)
+ ,dimnames=list(c('maand'
+ ,'X_1t'
+ ,'Yt'
+ ,'X_2t'
+ ,'X_3t'
+ ,'X_4t'
+ ,'X_5t'
+ ,'X_6t'
+ ,'X_7t'
+ ,'X_8t'
+ ,'X_9t'
+ ,'X_10t'
+ ,'X_11t')
+ ,1:82))
> y <- array(NA,dim=c(13,82),dimnames=list(c('maand','X_1t','Yt','X_2t','X_3t','X_4t','X_5t','X_6t','X_7t','X_8t','X_9t','X_10t','X_11t'),1:82))
> 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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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
Yt maand X_1t X_2t X_3t X_4t X_5t X_6t X_7t X_8t X_9t X_10t X_11t
1 -3 1 -19 53 14 24 20 -9 -2 20 6 -29 17
2 -4 2 -20 50 16 24 19 -12 -4 21 6 -29 13
3 -7 3 -21 50 19 31 21 -10 -5 20 5 -27 12
4 -7 4 -19 51 18 25 17 -10 -2 21 5 -29 13
5 -7 5 -17 53 19 28 15 -11 -4 19 3 -24 10
6 -3 6 -16 49 20 24 18 -11 -4 22 5 -29 14
7 0 7 -10 54 20 25 19 -10 -5 20 5 -21 13
8 -5 8 -16 57 24 16 16 -13 -7 18 5 -20 10
9 -3 9 -10 58 18 17 21 -10 -5 16 3 -26 11
10 3 10 -8 56 15 11 26 -6 -6 17 6 -19 12
11 2 11 -7 60 25 12 23 -9 -4 18 6 -22 7
12 -7 12 -15 55 23 39 24 -8 -2 19 4 -22 11
13 -1 13 -7 54 20 19 23 -12 -3 18 6 -15 9
14 0 14 -6 52 20 14 19 -10 0 20 5 -16 13
15 -3 15 -6 55 22 15 25 -11 -4 21 4 -22 12
16 4 16 2 56 25 7 21 -13 -3 18 5 -21 5
17 2 17 -4 54 22 12 19 -10 -3 19 5 -11 13
18 3 18 -4 53 26 12 20 -10 -3 19 4 -10 11
19 0 19 -8 59 27 14 20 -11 -4 19 3 -6 8
20 -10 20 -10 62 41 9 17 -11 -5 21 2 -8 8
21 -10 21 -16 63 29 8 25 -11 -5 19 3 -15 8
22 -9 22 -14 64 33 4 19 -10 -6 19 2 -16 8
23 -22 23 -30 75 39 7 13 -13 -10 17 -1 -24 0
24 -16 24 -33 77 27 3 15 -12 -11 16 0 -27 3
25 -18 25 -40 79 27 5 15 -13 -13 16 -2 -33 0
26 -14 26 -38 77 25 0 13 -15 -12 17 1 -29 -1
27 -12 27 -39 82 19 -2 11 -16 -13 16 -2 -34 -1
28 -17 28 -46 83 15 6 9 -18 -12 15 -2 -37 -4
29 -23 29 -50 81 19 11 2 -17 -15 16 -2 -31 1
30 -28 30 -55 78 23 9 -2 -18 -14 16 -6 -33 -1
31 -31 31 -66 79 23 17 -4 -20 -16 16 -4 -25 0
32 -21 32 -63 79 7 21 -2 -22 -16 18 -2 -27 -1
33 -19 33 -56 73 1 21 1 -17 -12 19 0 -21 6
34 -22 34 -66 72 7 41 -13 -19 -16 16 -5 -32 0
35 -22 35 -63 67 4 57 -11 -18 -15 16 -4 -31 -3
36 -25 36 -69 67 -8 65 -14 -26 -17 16 -5 -32 -3
37 -16 37 -69 50 -14 68 -4 -19 -15 18 -1 -30 4
38 -22 38 -72 45 -10 73 -9 -23 -14 16 -2 -34 1
39 -21 39 -69 39 -11 71 -5 -21 -15 15 -4 -35 0
40 -10 40 -67 39 -10 71 -4 -27 -14 15 -1 -37 -4
41 -7 41 -64 37 -8 70 -8 -27 -16 16 1 -32 -2
42 -5 42 -61 30 -8 69 -1 -21 -11 18 1 -28 3
43 -4 43 -58 24 -7 65 -2 -22 -14 16 -2 -26 2
44 7 44 -47 27 -8 57 -1 -24 -12 19 1 -24 5
45 6 45 -44 19 -4 57 8 -21 -11 19 1 -27 6
46 3 46 -42 19 3 57 8 -21 -13 18 3 -26 6
47 10 47 -34 25 -5 55 6 -22 -12 17 3 -27 3
48 0 48 -38 16 -4 65 7 -25 -12 19 1 -27 4
49 -2 49 -41 20 5 65 2 -21 -10 22 1 -24 7
50 -1 50 -38 25 3 64 3 -26 -12 19 0 -28 5
51 2 51 -37 34 6 60 0 -27 -11 19 2 -23 6
52 8 52 -22 39 10 43 5 -22 -10 16 2 -23 1
53 -6 53 -37 40 16 47 -1 -22 -12 18 -1 -29 3
54 -4 54 -36 38 11 40 3 -20 -12 20 1 -25 6
55 4 55 -25 42 10 31 4 -21 -11 17 0 -24 0
56 7 56 -15 46 21 27 8 -16 -12 17 1 -20 3
57 3 57 -17 48 18 24 10 -17 -9 17 1 -22 4
58 3 58 -19 51 20 23 14 -19 -6 20 3 -24 7
59 8 59 -12 55 18 17 15 -20 -7 21 2 -27 6
60 3 60 -17 52 23 16 9 -20 -7 19 0 -25 6
61 -3 61 -21 55 28 15 8 -20 -10 18 0 -26 6
62 4 62 -10 58 31 8 10 -19 -8 20 3 -24 6
63 -5 63 -19 72 38 5 5 -20 -11 17 -2 -26 2
64 -1 64 -14 70 27 6 4 -25 -12 15 0 -22 2
65 5 65 -8 70 21 5 8 -25 -11 17 1 -20 2
66 0 66 -16 63 31 12 8 -22 -11 18 -1 -26 3
67 -6 67 -14 66 31 8 10 -19 -9 20 -2 -22 -1
68 -13 68 -30 65 29 17 8 -20 -9 19 -1 -29 -4
69 -15 69 -33 55 24 22 10 -18 -12 20 -1 -30 4
70 -8 70 -37 57 27 24 -8 -17 -10 22 1 -26 5
71 -20 71 -47 60 36 36 -6 -17 -10 20 -2 -30 3
72 -10 72 -48 63 35 31 -10 -21 -13 21 -5 -33 -1
73 -22 73 -50 65 44 34 -15 -17 -13 19 -5 -33 -4
74 -25 74 -56 61 39 47 -21 -22 -12 22 -6 -31 0
75 -10 75 -47 65 26 33 -24 -24 -14 19 -4 -36 -1
76 -8 76 -37 63 27 35 -15 -18 -9 21 -3 -43 -1
77 -9 77 -35 59 17 31 -12 -20 -12 19 -3 -40 3
78 -5 78 -29 56 20 35 -11 -21 -10 21 -1 -38 2
79 -7 79 -28 54 22 39 -11 -17 -13 18 -2 -41 -4
80 -11 80 -29 56 32 46 -13 -17 -11 18 -3 -38 -3
81 -11 81 -33 54 28 40 -10 -17 -11 20 -3 -40 -1
82 -16 82 -41 58 30 50 -9 -21 -11 19 -3 -41 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) maand X_1t X_2t X_3t X_4t
31.25234 0.06926 0.39249 -0.33041 -0.28196 -0.19717
X_5t X_6t X_7t X_8t X_9t X_10t
-0.21757 -0.23029 -0.10110 -0.14327 1.01434 0.02255
X_11t
-0.16966
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7496 -2.1080 -0.0473 1.9025 9.7261
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.25234 9.11300 3.429 0.001025 **
maand 0.06926 0.04060 1.706 0.092496 .
X_1t 0.39249 0.06098 6.436 1.38e-08 ***
X_2t -0.33041 0.06118 -5.401 8.88e-07 ***
X_3t -0.28196 0.05984 -4.712 1.23e-05 ***
X_4t -0.19717 0.05614 -3.512 0.000788 ***
X_5t -0.21757 0.08544 -2.547 0.013115 *
X_6t -0.23029 0.14596 -1.578 0.119196
X_7t -0.10110 0.27384 -0.369 0.713111
X_8t -0.14327 0.31403 -0.456 0.649651
X_9t 1.01434 0.31690 3.201 0.002072 **
X_10t 0.02255 0.06655 0.339 0.735727
X_11t -0.16966 0.16236 -1.045 0.299708
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.984 on 69 degrees of freedom
Multiple R-squared: 0.9195, Adjusted R-squared: 0.9055
F-statistic: 65.65 on 12 and 69 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,] 0.067780900 0.13556180 0.9322191
[2,] 0.025395561 0.05079112 0.9746044
[3,] 0.023699852 0.04739970 0.9763001
[4,] 0.051549231 0.10309846 0.9484508
[5,] 0.149687819 0.29937564 0.8503122
[6,] 0.090516940 0.18103388 0.9094831
[7,] 0.054010921 0.10802184 0.9459891
[8,] 0.037869639 0.07573928 0.9621304
[9,] 0.028778429 0.05755686 0.9712216
[10,] 0.040980862 0.08196172 0.9590191
[11,] 0.023352604 0.04670521 0.9766474
[12,] 0.019215268 0.03843054 0.9807847
[13,] 0.013173802 0.02634760 0.9868262
[14,] 0.021289623 0.04257925 0.9787104
[15,] 0.015279115 0.03055823 0.9847209
[16,] 0.014749932 0.02949986 0.9852501
[17,] 0.009191006 0.01838201 0.9908090
[18,] 0.018552559 0.03710512 0.9814474
[19,] 0.020934680 0.04186936 0.9790653
[20,] 0.014930867 0.02986173 0.9850691
[21,] 0.013460115 0.02692023 0.9865399
[22,] 0.012200002 0.02440000 0.9878000
[23,] 0.011177388 0.02235478 0.9888226
[24,] 0.020596712 0.04119342 0.9794033
[25,] 0.090060524 0.18012105 0.9099395
[26,] 0.102137055 0.20427411 0.8978629
[27,] 0.131795577 0.26359115 0.8682044
[28,] 0.127772731 0.25554546 0.8722273
[29,] 0.175319271 0.35063854 0.8246807
[30,] 0.218215385 0.43643077 0.7817846
[31,] 0.220327713 0.44065543 0.7796723
[32,] 0.249960133 0.49992027 0.7500399
[33,] 0.322816390 0.64563278 0.6771836
[34,] 0.271893279 0.54378656 0.7281067
[35,] 0.225799327 0.45159865 0.7742007
[36,] 0.201966427 0.40393285 0.7980336
[37,] 0.178664666 0.35732933 0.8213353
[38,] 0.224649344 0.44929869 0.7753507
[39,] 0.420634186 0.84126837 0.5793658
[40,] 0.362797539 0.72559508 0.6372025
[41,] 0.300453229 0.60090646 0.6995468
[42,] 0.249274843 0.49854969 0.7507252
[43,] 0.204255031 0.40851006 0.7957450
[44,] 0.175556209 0.35111242 0.8244438
[45,] 0.253773683 0.50754737 0.7462263
[46,] 0.264959257 0.52991851 0.7350407
[47,] 0.185972941 0.37194588 0.8140271
[48,] 0.183237694 0.36647539 0.8167623
[49,] 0.133307873 0.26661575 0.8666921
[50,] 0.076320616 0.15264123 0.9236794
[51,] 0.038957413 0.07791483 0.9610426
> postscript(file="/var/fisher/rcomp/tmp/1o2ee1352141631.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2je951352141631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3c6gc1352141631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4adjp1352141631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5bpih1352141631.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 = 82
Frequency = 1
1 2 3 4 5 6
1.72119241 -1.02886910 -0.02865918 -2.22639560 -1.29355356 0.26154457
7 8 9 10 11 12
2.39669715 -2.33688229 0.10271535 1.57864515 2.67662883 3.35551240
13 14 15 16 17 18
-2.88230502 -2.09505008 -1.61800652 -2.11229107 -0.81674887 1.78147035
19 20 21 22 23 24
3.02472068 -1.71450102 -2.08162766 -1.40560176 -3.09820715 0.48192205
25 26 27 28 29 30
3.43779758 0.41873269 4.55390721 1.63280525 -2.15336044 -2.75417504
31 32 33 34 35 36
-2.73563043 0.40277552 -2.71277250 3.40875457 2.03943025 -2.14137349
37 38 39 40 41 42
1.43361038 -4.59537667 -4.53284112 1.15527796 -0.11685399 2.58115630
43 44 45 46 47 48
1.63580647 5.19091576 4.41609738 0.13887498 2.06811337 -5.13964120
49 50 51 52 53 54
-1.26547659 -1.42188841 1.39360222 1.92726992 -1.14716556 -3.05228828
55 56 57 58 59 60
-0.54136918 3.42402611 0.08608319 1.82799281 4.52779847 2.03403918
61 62 63 64 65 66
-0.90297309 0.23333471 3.09144554 -2.38104919 -0.49573932 2.62963337
67 68 69 70 71 72
-2.16180773 -4.24517583 -6.74957860 -1.49422180 -0.80113758 9.72614354
73 74 75 76 77 78
1.26965201 0.10903441 2.57224900 3.86893257 -2.70201448 -2.25031356
79 80 81 82
-3.76821015 -1.70093455 -1.84826484 -0.06600918
> postscript(file="/var/fisher/rcomp/tmp/6oo3v1352141631.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 = 82
Frequency = 1
lag(myerror, k = 1) myerror
0 1.72119241 NA
1 -1.02886910 1.72119241
2 -0.02865918 -1.02886910
3 -2.22639560 -0.02865918
4 -1.29355356 -2.22639560
5 0.26154457 -1.29355356
6 2.39669715 0.26154457
7 -2.33688229 2.39669715
8 0.10271535 -2.33688229
9 1.57864515 0.10271535
10 2.67662883 1.57864515
11 3.35551240 2.67662883
12 -2.88230502 3.35551240
13 -2.09505008 -2.88230502
14 -1.61800652 -2.09505008
15 -2.11229107 -1.61800652
16 -0.81674887 -2.11229107
17 1.78147035 -0.81674887
18 3.02472068 1.78147035
19 -1.71450102 3.02472068
20 -2.08162766 -1.71450102
21 -1.40560176 -2.08162766
22 -3.09820715 -1.40560176
23 0.48192205 -3.09820715
24 3.43779758 0.48192205
25 0.41873269 3.43779758
26 4.55390721 0.41873269
27 1.63280525 4.55390721
28 -2.15336044 1.63280525
29 -2.75417504 -2.15336044
30 -2.73563043 -2.75417504
31 0.40277552 -2.73563043
32 -2.71277250 0.40277552
33 3.40875457 -2.71277250
34 2.03943025 3.40875457
35 -2.14137349 2.03943025
36 1.43361038 -2.14137349
37 -4.59537667 1.43361038
38 -4.53284112 -4.59537667
39 1.15527796 -4.53284112
40 -0.11685399 1.15527796
41 2.58115630 -0.11685399
42 1.63580647 2.58115630
43 5.19091576 1.63580647
44 4.41609738 5.19091576
45 0.13887498 4.41609738
46 2.06811337 0.13887498
47 -5.13964120 2.06811337
48 -1.26547659 -5.13964120
49 -1.42188841 -1.26547659
50 1.39360222 -1.42188841
51 1.92726992 1.39360222
52 -1.14716556 1.92726992
53 -3.05228828 -1.14716556
54 -0.54136918 -3.05228828
55 3.42402611 -0.54136918
56 0.08608319 3.42402611
57 1.82799281 0.08608319
58 4.52779847 1.82799281
59 2.03403918 4.52779847
60 -0.90297309 2.03403918
61 0.23333471 -0.90297309
62 3.09144554 0.23333471
63 -2.38104919 3.09144554
64 -0.49573932 -2.38104919
65 2.62963337 -0.49573932
66 -2.16180773 2.62963337
67 -4.24517583 -2.16180773
68 -6.74957860 -4.24517583
69 -1.49422180 -6.74957860
70 -0.80113758 -1.49422180
71 9.72614354 -0.80113758
72 1.26965201 9.72614354
73 0.10903441 1.26965201
74 2.57224900 0.10903441
75 3.86893257 2.57224900
76 -2.70201448 3.86893257
77 -2.25031356 -2.70201448
78 -3.76821015 -2.25031356
79 -1.70093455 -3.76821015
80 -1.84826484 -1.70093455
81 -0.06600918 -1.84826484
82 NA -0.06600918
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.02886910 1.72119241
[2,] -0.02865918 -1.02886910
[3,] -2.22639560 -0.02865918
[4,] -1.29355356 -2.22639560
[5,] 0.26154457 -1.29355356
[6,] 2.39669715 0.26154457
[7,] -2.33688229 2.39669715
[8,] 0.10271535 -2.33688229
[9,] 1.57864515 0.10271535
[10,] 2.67662883 1.57864515
[11,] 3.35551240 2.67662883
[12,] -2.88230502 3.35551240
[13,] -2.09505008 -2.88230502
[14,] -1.61800652 -2.09505008
[15,] -2.11229107 -1.61800652
[16,] -0.81674887 -2.11229107
[17,] 1.78147035 -0.81674887
[18,] 3.02472068 1.78147035
[19,] -1.71450102 3.02472068
[20,] -2.08162766 -1.71450102
[21,] -1.40560176 -2.08162766
[22,] -3.09820715 -1.40560176
[23,] 0.48192205 -3.09820715
[24,] 3.43779758 0.48192205
[25,] 0.41873269 3.43779758
[26,] 4.55390721 0.41873269
[27,] 1.63280525 4.55390721
[28,] -2.15336044 1.63280525
[29,] -2.75417504 -2.15336044
[30,] -2.73563043 -2.75417504
[31,] 0.40277552 -2.73563043
[32,] -2.71277250 0.40277552
[33,] 3.40875457 -2.71277250
[34,] 2.03943025 3.40875457
[35,] -2.14137349 2.03943025
[36,] 1.43361038 -2.14137349
[37,] -4.59537667 1.43361038
[38,] -4.53284112 -4.59537667
[39,] 1.15527796 -4.53284112
[40,] -0.11685399 1.15527796
[41,] 2.58115630 -0.11685399
[42,] 1.63580647 2.58115630
[43,] 5.19091576 1.63580647
[44,] 4.41609738 5.19091576
[45,] 0.13887498 4.41609738
[46,] 2.06811337 0.13887498
[47,] -5.13964120 2.06811337
[48,] -1.26547659 -5.13964120
[49,] -1.42188841 -1.26547659
[50,] 1.39360222 -1.42188841
[51,] 1.92726992 1.39360222
[52,] -1.14716556 1.92726992
[53,] -3.05228828 -1.14716556
[54,] -0.54136918 -3.05228828
[55,] 3.42402611 -0.54136918
[56,] 0.08608319 3.42402611
[57,] 1.82799281 0.08608319
[58,] 4.52779847 1.82799281
[59,] 2.03403918 4.52779847
[60,] -0.90297309 2.03403918
[61,] 0.23333471 -0.90297309
[62,] 3.09144554 0.23333471
[63,] -2.38104919 3.09144554
[64,] -0.49573932 -2.38104919
[65,] 2.62963337 -0.49573932
[66,] -2.16180773 2.62963337
[67,] -4.24517583 -2.16180773
[68,] -6.74957860 -4.24517583
[69,] -1.49422180 -6.74957860
[70,] -0.80113758 -1.49422180
[71,] 9.72614354 -0.80113758
[72,] 1.26965201 9.72614354
[73,] 0.10903441 1.26965201
[74,] 2.57224900 0.10903441
[75,] 3.86893257 2.57224900
[76,] -2.70201448 3.86893257
[77,] -2.25031356 -2.70201448
[78,] -3.76821015 -2.25031356
[79,] -1.70093455 -3.76821015
[80,] -1.84826484 -1.70093455
[81,] -0.06600918 -1.84826484
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.02886910 1.72119241
2 -0.02865918 -1.02886910
3 -2.22639560 -0.02865918
4 -1.29355356 -2.22639560
5 0.26154457 -1.29355356
6 2.39669715 0.26154457
7 -2.33688229 2.39669715
8 0.10271535 -2.33688229
9 1.57864515 0.10271535
10 2.67662883 1.57864515
11 3.35551240 2.67662883
12 -2.88230502 3.35551240
13 -2.09505008 -2.88230502
14 -1.61800652 -2.09505008
15 -2.11229107 -1.61800652
16 -0.81674887 -2.11229107
17 1.78147035 -0.81674887
18 3.02472068 1.78147035
19 -1.71450102 3.02472068
20 -2.08162766 -1.71450102
21 -1.40560176 -2.08162766
22 -3.09820715 -1.40560176
23 0.48192205 -3.09820715
24 3.43779758 0.48192205
25 0.41873269 3.43779758
26 4.55390721 0.41873269
27 1.63280525 4.55390721
28 -2.15336044 1.63280525
29 -2.75417504 -2.15336044
30 -2.73563043 -2.75417504
31 0.40277552 -2.73563043
32 -2.71277250 0.40277552
33 3.40875457 -2.71277250
34 2.03943025 3.40875457
35 -2.14137349 2.03943025
36 1.43361038 -2.14137349
37 -4.59537667 1.43361038
38 -4.53284112 -4.59537667
39 1.15527796 -4.53284112
40 -0.11685399 1.15527796
41 2.58115630 -0.11685399
42 1.63580647 2.58115630
43 5.19091576 1.63580647
44 4.41609738 5.19091576
45 0.13887498 4.41609738
46 2.06811337 0.13887498
47 -5.13964120 2.06811337
48 -1.26547659 -5.13964120
49 -1.42188841 -1.26547659
50 1.39360222 -1.42188841
51 1.92726992 1.39360222
52 -1.14716556 1.92726992
53 -3.05228828 -1.14716556
54 -0.54136918 -3.05228828
55 3.42402611 -0.54136918
56 0.08608319 3.42402611
57 1.82799281 0.08608319
58 4.52779847 1.82799281
59 2.03403918 4.52779847
60 -0.90297309 2.03403918
61 0.23333471 -0.90297309
62 3.09144554 0.23333471
63 -2.38104919 3.09144554
64 -0.49573932 -2.38104919
65 2.62963337 -0.49573932
66 -2.16180773 2.62963337
67 -4.24517583 -2.16180773
68 -6.74957860 -4.24517583
69 -1.49422180 -6.74957860
70 -0.80113758 -1.49422180
71 9.72614354 -0.80113758
72 1.26965201 9.72614354
73 0.10903441 1.26965201
74 2.57224900 0.10903441
75 3.86893257 2.57224900
76 -2.70201448 3.86893257
77 -2.25031356 -2.70201448
78 -3.76821015 -2.25031356
79 -1.70093455 -3.76821015
80 -1.84826484 -1.70093455
81 -0.06600918 -1.84826484
> 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/fisher/rcomp/tmp/7zidr1352141631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8uc1o1352141631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9hg9k1352141631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10gsp51352141631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11gdbt1352141631.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/fisher/rcomp/tmp/12817f1352141631.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/fisher/rcomp/tmp/13ae0t1352141631.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/fisher/rcomp/tmp/1415bc1352141631.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/fisher/rcomp/tmp/15eoy91352141631.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/fisher/rcomp/tmp/16sy201352141631.tab")
+ }
>
> try(system("convert tmp/1o2ee1352141631.ps tmp/1o2ee1352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/2je951352141631.ps tmp/2je951352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/3c6gc1352141631.ps tmp/3c6gc1352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/4adjp1352141631.ps tmp/4adjp1352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bpih1352141631.ps tmp/5bpih1352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oo3v1352141631.ps tmp/6oo3v1352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zidr1352141631.ps tmp/7zidr1352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uc1o1352141631.ps tmp/8uc1o1352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hg9k1352141631.ps tmp/9hg9k1352141631.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gsp51352141631.ps tmp/10gsp51352141631.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.455 1.086 7.542