R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(7.2
+ ,2.4
+ ,7.5
+ ,8.3
+ ,8.9
+ ,7.4
+ ,2
+ ,7.2
+ ,7.5
+ ,8.8
+ ,8.8
+ ,2.1
+ ,7.4
+ ,7.2
+ ,8.3
+ ,9.3
+ ,2
+ ,8.8
+ ,7.4
+ ,7.5
+ ,9.3
+ ,1.8
+ ,9.3
+ ,8.8
+ ,7.2
+ ,8.7
+ ,2.7
+ ,9.3
+ ,9.3
+ ,7.4
+ ,8.2
+ ,2.3
+ ,8.7
+ ,9.3
+ ,8.8
+ ,8.3
+ ,1.9
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.5
+ ,2
+ ,8.3
+ ,8.2
+ ,9.3
+ ,8.6
+ ,2.3
+ ,8.5
+ ,8.3
+ ,8.7
+ ,8.5
+ ,2.8
+ ,8.6
+ ,8.5
+ ,8.2
+ ,8.2
+ ,2.4
+ ,8.5
+ ,8.6
+ ,8.3
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8.5
+ ,8.5
+ ,7.9
+ ,2.7
+ ,8.1
+ ,8.2
+ ,8.6
+ ,8.6
+ ,2.7
+ ,7.9
+ ,8.1
+ ,8.5
+ ,8.7
+ ,2.9
+ ,8.6
+ ,7.9
+ ,8.2
+ ,8.7
+ ,3
+ ,8.7
+ ,8.6
+ ,8.1
+ ,8.5
+ ,2.2
+ ,8.7
+ ,8.7
+ ,7.9
+ ,8.4
+ ,2.3
+ ,8.5
+ ,8.7
+ ,8.6
+ ,8.5
+ ,2.8
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.5
+ ,8.4
+ ,8.7
+ ,8.7
+ ,2.8
+ ,8.7
+ ,8.5
+ ,8.5
+ ,8.6
+ ,2.2
+ ,8.7
+ ,8.7
+ ,8.4
+ ,8.5
+ ,2.6
+ ,8.6
+ ,8.7
+ ,8.5
+ ,8.3
+ ,2.8
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8
+ ,2.5
+ ,8.3
+ ,8.5
+ ,8.7
+ ,8.2
+ ,2.4
+ ,8
+ ,8.3
+ ,8.6
+ ,8.1
+ ,2.3
+ ,8.2
+ ,8
+ ,8.5
+ ,8.1
+ ,1.9
+ ,8.1
+ ,8.2
+ ,8.3
+ ,8
+ ,1.7
+ ,8.1
+ ,8.1
+ ,8
+ ,7.9
+ ,2
+ ,8
+ ,8.1
+ ,8.2
+ ,7.9
+ ,2.1
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,1.7
+ ,7.9
+ ,7.9
+ ,8.1
+ ,8
+ ,1.8
+ ,8
+ ,7.9
+ ,8
+ ,7.9
+ ,1.8
+ ,8
+ ,8
+ ,7.9
+ ,8
+ ,1.8
+ ,7.9
+ ,8
+ ,7.9
+ ,7.7
+ ,1.3
+ ,8
+ ,7.9
+ ,8
+ ,7.2
+ ,1.3
+ ,7.7
+ ,8
+ ,8
+ ,7.5
+ ,1.3
+ ,7.2
+ ,7.7
+ ,7.9
+ ,7.3
+ ,1.2
+ ,7.5
+ ,7.2
+ ,8
+ ,7
+ ,1.4
+ ,7.3
+ ,7.5
+ ,7.7
+ ,7
+ ,2.2
+ ,7
+ ,7.3
+ ,7.2
+ ,7
+ ,2.9
+ ,7
+ ,7
+ ,7.5
+ ,7.2
+ ,3.1
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,3.5
+ ,7.2
+ ,7
+ ,7
+ ,7.1
+ ,3.6
+ ,7.3
+ ,7.2
+ ,7
+ ,6.8
+ ,4.4
+ ,7.1
+ ,7.3
+ ,7
+ ,6.4
+ ,4.1
+ ,6.8
+ ,7.1
+ ,7.2
+ ,6.1
+ ,5.1
+ ,6.4
+ ,6.8
+ ,7.3
+ ,6.5
+ ,5.8
+ ,6.1
+ ,6.4
+ ,7.1
+ ,7.7
+ ,5.9
+ ,6.5
+ ,6.1
+ ,6.8
+ ,7.9
+ ,5.4
+ ,7.7
+ ,6.5
+ ,6.4
+ ,7.5
+ ,5.5
+ ,7.9
+ ,7.7
+ ,6.1
+ ,6.9
+ ,4.8
+ ,7.5
+ ,7.9
+ ,6.5
+ ,6.6
+ ,3.2
+ ,6.9
+ ,7.5
+ ,7.7
+ ,6.9
+ ,2.7
+ ,6.6
+ ,6.9
+ ,7.9)
+ ,dim=c(5
+ ,56)
+ ,dimnames=list(c('Y(t)'
+ ,'X(t)'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-4)
')
+ ,1:56))
> y <- array(NA,dim=c(5,56),dimnames=list(c('Y(t)','X(t)','Y(t-1)','Y(t-2)','Y(t-4)
'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y(t) X(t) Y(t-1) Y(t-2) Y(t-4)\r t
1 7.2 2.4 7.5 8.3 8.9 1
2 7.4 2.0 7.2 7.5 8.8 2
3 8.8 2.1 7.4 7.2 8.3 3
4 9.3 2.0 8.8 7.4 7.5 4
5 9.3 1.8 9.3 8.8 7.2 5
6 8.7 2.7 9.3 9.3 7.4 6
7 8.2 2.3 8.7 9.3 8.8 7
8 8.3 1.9 8.2 8.7 9.3 8
9 8.5 2.0 8.3 8.2 9.3 9
10 8.6 2.3 8.5 8.3 8.7 10
11 8.5 2.8 8.6 8.5 8.2 11
12 8.2 2.4 8.5 8.6 8.3 12
13 8.1 2.3 8.2 8.5 8.5 13
14 7.9 2.7 8.1 8.2 8.6 14
15 8.6 2.7 7.9 8.1 8.5 15
16 8.7 2.9 8.6 7.9 8.2 16
17 8.7 3.0 8.7 8.6 8.1 17
18 8.5 2.2 8.7 8.7 7.9 18
19 8.4 2.3 8.5 8.7 8.6 19
20 8.5 2.8 8.4 8.5 8.7 20
21 8.7 2.8 8.5 8.4 8.7 21
22 8.7 2.8 8.7 8.5 8.5 22
23 8.6 2.2 8.7 8.7 8.4 23
24 8.5 2.6 8.6 8.7 8.5 24
25 8.3 2.8 8.5 8.6 8.7 25
26 8.0 2.5 8.3 8.5 8.7 26
27 8.2 2.4 8.0 8.3 8.6 27
28 8.1 2.3 8.2 8.0 8.5 28
29 8.1 1.9 8.1 8.2 8.3 29
30 8.0 1.7 8.1 8.1 8.0 30
31 7.9 2.0 8.0 8.1 8.2 31
32 7.9 2.1 7.9 8.0 8.1 32
33 8.0 1.7 7.9 7.9 8.1 33
34 8.0 1.8 8.0 7.9 8.0 34
35 7.9 1.8 8.0 8.0 7.9 35
36 8.0 1.8 7.9 8.0 7.9 36
37 7.7 1.3 8.0 7.9 8.0 37
38 7.2 1.3 7.7 8.0 8.0 38
39 7.5 1.3 7.2 7.7 7.9 39
40 7.3 1.2 7.5 7.2 8.0 40
41 7.0 1.4 7.3 7.5 7.7 41
42 7.0 2.2 7.0 7.3 7.2 42
43 7.0 2.9 7.0 7.0 7.5 43
44 7.2 3.1 7.0 7.0 7.3 44
45 7.3 3.5 7.2 7.0 7.0 45
46 7.1 3.6 7.3 7.2 7.0 46
47 6.8 4.4 7.1 7.3 7.0 47
48 6.4 4.1 6.8 7.1 7.2 48
49 6.1 5.1 6.4 6.8 7.3 49
50 6.5 5.8 6.1 6.4 7.1 50
51 7.7 5.9 6.5 6.1 6.8 51
52 7.9 5.4 7.7 6.5 6.4 52
53 7.5 5.5 7.9 7.7 6.1 53
54 6.9 4.8 7.5 7.9 6.5 54
55 6.6 3.2 6.9 7.5 7.7 55
56 6.9 2.7 6.6 6.9 7.9 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `X(t)` `Y(t-1)` `Y(t-2)` `Y(t-4)\r` t
2.27327 0.02721 1.21402 -0.67019 0.19691 -0.01091
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.427519 -0.185148 0.003312 0.122321 0.709617
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.273266 1.061935 2.141 0.0372 *
`X(t)` 0.027206 0.041235 0.660 0.5124
`Y(t-1)` 1.214015 0.113851 10.663 1.77e-14 ***
`Y(t-2)` -0.670191 0.116490 -5.753 5.28e-07 ***
`Y(t-4)\r` 0.196911 0.088435 2.227 0.0305 *
t -0.010905 0.003926 -2.777 0.0077 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2642 on 50 degrees of freedom
Multiple R-squared: 0.8853, Adjusted R-squared: 0.8739
F-statistic: 77.22 on 5 and 50 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,] 0.83847089 0.32305823 0.16152911
[2,] 0.86465016 0.27069968 0.13534984
[3,] 0.83932589 0.32134822 0.16067411
[4,] 0.90251585 0.19496829 0.09748415
[5,] 0.86257853 0.27484294 0.13742147
[6,] 0.87326448 0.25347104 0.12673552
[7,] 0.95095926 0.09808149 0.04904074
[8,] 0.93880557 0.12238885 0.06119443
[9,] 0.91120865 0.17758269 0.08879135
[10,] 0.90126685 0.19746630 0.09873315
[11,] 0.85523391 0.28953218 0.14476609
[12,] 0.80628702 0.38742595 0.19371298
[13,] 0.75236292 0.49527417 0.24763708
[14,] 0.67644827 0.64710345 0.32355173
[15,] 0.60929199 0.78141601 0.39070801
[16,] 0.52932802 0.94134397 0.47067198
[17,] 0.46131186 0.92262372 0.53868814
[18,] 0.47662077 0.95324154 0.52337923
[19,] 0.42061404 0.84122808 0.57938596
[20,] 0.47062905 0.94125811 0.52937095
[21,] 0.39749510 0.79499020 0.60250490
[22,] 0.33994494 0.67988988 0.66005506
[23,] 0.27508203 0.55016407 0.72491797
[24,] 0.21081223 0.42162446 0.78918777
[25,] 0.16328468 0.32656937 0.83671532
[26,] 0.12229755 0.24459510 0.87770245
[27,] 0.09253325 0.18506650 0.90746675
[28,] 0.14615964 0.29231927 0.85384036
[29,] 0.14320909 0.28641818 0.85679091
[30,] 0.13676241 0.27352483 0.86323759
[31,] 0.69482451 0.61035097 0.30517549
[32,] 0.70428821 0.59142359 0.29571179
[33,] 0.66262343 0.67475314 0.33737657
[34,] 0.61593067 0.76813866 0.38406933
[35,] 0.57411099 0.85177802 0.42588901
[36,] 0.52523486 0.94953028 0.47476514
[37,] 0.39856280 0.79712559 0.60143720
[38,] 0.28854784 0.57709568 0.71145216
[39,] 0.43638502 0.87277003 0.56361498
> postscript(file="/var/www/html/rcomp/tmp/1duts1258566575.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zhih1258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cv451258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4djc11258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cgin1258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 56
Frequency = 1
1 2 3 4 5 6
-0.422692885 -0.353162253 0.709617384 -0.184811173 0.221867933 -0.095999539
7 8 9 10 11 12
-0.121478136 0.106747122 -0.141565332 -0.096459580 -0.088065608 -0.197548503
13 14 15 16 17 18
-0.026119530 -0.325443817 0.580936282 -0.238375370 0.137232161 0.076303573
19 20 21 22 23 24
0.089453388 0.154427539 0.176912020 0.051415321 0.132373444 0.134106381
25 26 27 28 29 30
-0.045429574 -0.150578655 0.312904514 -0.197638937 0.118970513 0.027371047
31 32 33 34 35 36
0.012133530 0.094391483 0.149160019 0.055634028 0.053249257 0.285555831
37 38 39 40 41 42
-0.198047596 -0.255918929 0.480627548 -0.424737742 -0.216340438 0.101421363
43 44 45 46 47 48
-0.166848524 0.077997448 -0.005709782 -0.184888714 -0.185926650 -0.376075478
49 50 51 52 53 54
-0.427519057 0.099851961 0.680446399 -0.205022805 0.023660770 -0.005509884
55 56
-0.027034959 0.220181188
> postscript(file="/var/www/html/rcomp/tmp/6i34g1258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.422692885 NA
1 -0.353162253 -0.422692885
2 0.709617384 -0.353162253
3 -0.184811173 0.709617384
4 0.221867933 -0.184811173
5 -0.095999539 0.221867933
6 -0.121478136 -0.095999539
7 0.106747122 -0.121478136
8 -0.141565332 0.106747122
9 -0.096459580 -0.141565332
10 -0.088065608 -0.096459580
11 -0.197548503 -0.088065608
12 -0.026119530 -0.197548503
13 -0.325443817 -0.026119530
14 0.580936282 -0.325443817
15 -0.238375370 0.580936282
16 0.137232161 -0.238375370
17 0.076303573 0.137232161
18 0.089453388 0.076303573
19 0.154427539 0.089453388
20 0.176912020 0.154427539
21 0.051415321 0.176912020
22 0.132373444 0.051415321
23 0.134106381 0.132373444
24 -0.045429574 0.134106381
25 -0.150578655 -0.045429574
26 0.312904514 -0.150578655
27 -0.197638937 0.312904514
28 0.118970513 -0.197638937
29 0.027371047 0.118970513
30 0.012133530 0.027371047
31 0.094391483 0.012133530
32 0.149160019 0.094391483
33 0.055634028 0.149160019
34 0.053249257 0.055634028
35 0.285555831 0.053249257
36 -0.198047596 0.285555831
37 -0.255918929 -0.198047596
38 0.480627548 -0.255918929
39 -0.424737742 0.480627548
40 -0.216340438 -0.424737742
41 0.101421363 -0.216340438
42 -0.166848524 0.101421363
43 0.077997448 -0.166848524
44 -0.005709782 0.077997448
45 -0.184888714 -0.005709782
46 -0.185926650 -0.184888714
47 -0.376075478 -0.185926650
48 -0.427519057 -0.376075478
49 0.099851961 -0.427519057
50 0.680446399 0.099851961
51 -0.205022805 0.680446399
52 0.023660770 -0.205022805
53 -0.005509884 0.023660770
54 -0.027034959 -0.005509884
55 0.220181188 -0.027034959
56 NA 0.220181188
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.353162253 -0.422692885
[2,] 0.709617384 -0.353162253
[3,] -0.184811173 0.709617384
[4,] 0.221867933 -0.184811173
[5,] -0.095999539 0.221867933
[6,] -0.121478136 -0.095999539
[7,] 0.106747122 -0.121478136
[8,] -0.141565332 0.106747122
[9,] -0.096459580 -0.141565332
[10,] -0.088065608 -0.096459580
[11,] -0.197548503 -0.088065608
[12,] -0.026119530 -0.197548503
[13,] -0.325443817 -0.026119530
[14,] 0.580936282 -0.325443817
[15,] -0.238375370 0.580936282
[16,] 0.137232161 -0.238375370
[17,] 0.076303573 0.137232161
[18,] 0.089453388 0.076303573
[19,] 0.154427539 0.089453388
[20,] 0.176912020 0.154427539
[21,] 0.051415321 0.176912020
[22,] 0.132373444 0.051415321
[23,] 0.134106381 0.132373444
[24,] -0.045429574 0.134106381
[25,] -0.150578655 -0.045429574
[26,] 0.312904514 -0.150578655
[27,] -0.197638937 0.312904514
[28,] 0.118970513 -0.197638937
[29,] 0.027371047 0.118970513
[30,] 0.012133530 0.027371047
[31,] 0.094391483 0.012133530
[32,] 0.149160019 0.094391483
[33,] 0.055634028 0.149160019
[34,] 0.053249257 0.055634028
[35,] 0.285555831 0.053249257
[36,] -0.198047596 0.285555831
[37,] -0.255918929 -0.198047596
[38,] 0.480627548 -0.255918929
[39,] -0.424737742 0.480627548
[40,] -0.216340438 -0.424737742
[41,] 0.101421363 -0.216340438
[42,] -0.166848524 0.101421363
[43,] 0.077997448 -0.166848524
[44,] -0.005709782 0.077997448
[45,] -0.184888714 -0.005709782
[46,] -0.185926650 -0.184888714
[47,] -0.376075478 -0.185926650
[48,] -0.427519057 -0.376075478
[49,] 0.099851961 -0.427519057
[50,] 0.680446399 0.099851961
[51,] -0.205022805 0.680446399
[52,] 0.023660770 -0.205022805
[53,] -0.005509884 0.023660770
[54,] -0.027034959 -0.005509884
[55,] 0.220181188 -0.027034959
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.353162253 -0.422692885
2 0.709617384 -0.353162253
3 -0.184811173 0.709617384
4 0.221867933 -0.184811173
5 -0.095999539 0.221867933
6 -0.121478136 -0.095999539
7 0.106747122 -0.121478136
8 -0.141565332 0.106747122
9 -0.096459580 -0.141565332
10 -0.088065608 -0.096459580
11 -0.197548503 -0.088065608
12 -0.026119530 -0.197548503
13 -0.325443817 -0.026119530
14 0.580936282 -0.325443817
15 -0.238375370 0.580936282
16 0.137232161 -0.238375370
17 0.076303573 0.137232161
18 0.089453388 0.076303573
19 0.154427539 0.089453388
20 0.176912020 0.154427539
21 0.051415321 0.176912020
22 0.132373444 0.051415321
23 0.134106381 0.132373444
24 -0.045429574 0.134106381
25 -0.150578655 -0.045429574
26 0.312904514 -0.150578655
27 -0.197638937 0.312904514
28 0.118970513 -0.197638937
29 0.027371047 0.118970513
30 0.012133530 0.027371047
31 0.094391483 0.012133530
32 0.149160019 0.094391483
33 0.055634028 0.149160019
34 0.053249257 0.055634028
35 0.285555831 0.053249257
36 -0.198047596 0.285555831
37 -0.255918929 -0.198047596
38 0.480627548 -0.255918929
39 -0.424737742 0.480627548
40 -0.216340438 -0.424737742
41 0.101421363 -0.216340438
42 -0.166848524 0.101421363
43 0.077997448 -0.166848524
44 -0.005709782 0.077997448
45 -0.184888714 -0.005709782
46 -0.185926650 -0.184888714
47 -0.376075478 -0.185926650
48 -0.427519057 -0.376075478
49 0.099851961 -0.427519057
50 0.680446399 0.099851961
51 -0.205022805 0.680446399
52 0.023660770 -0.205022805
53 -0.005509884 0.023660770
54 -0.027034959 -0.005509884
55 0.220181188 -0.027034959
> 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/7zgvv1258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8fy081258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gl4i1258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10eihf1258566575.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11ruw41258566575.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/12vx911258566575.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/136t6z1258566575.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/14etbn1258566575.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15jdmv1258566575.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/161oaq1258566575.tab")
+ }
>
> system("convert tmp/1duts1258566575.ps tmp/1duts1258566575.png")
> system("convert tmp/2zhih1258566575.ps tmp/2zhih1258566575.png")
> system("convert tmp/3cv451258566575.ps tmp/3cv451258566575.png")
> system("convert tmp/4djc11258566575.ps tmp/4djc11258566575.png")
> system("convert tmp/5cgin1258566575.ps tmp/5cgin1258566575.png")
> system("convert tmp/6i34g1258566575.ps tmp/6i34g1258566575.png")
> system("convert tmp/7zgvv1258566575.ps tmp/7zgvv1258566575.png")
> system("convert tmp/8fy081258566575.ps tmp/8fy081258566575.png")
> system("convert tmp/9gl4i1258566575.ps tmp/9gl4i1258566575.png")
> system("convert tmp/10eihf1258566575.ps tmp/10eihf1258566575.png")
>
>
> proc.time()
user system elapsed
2.439 1.578 2.858