R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(10,24.1,9.2,24.1,9.2,24.1,9.5,21.3,9.6,21.3,9.5,21.3,9.1,19.1,8.9,19.1,9,19.1,10.1,26.2,10.3,26.2,10.2,26.2,9.6,21.7,9.2,21.7,9.3,21.7,9.4,19.4,9.4,19.4,9.2,19.4,9,19.5,9,19.5,9,19.5,9.8,28.7,10,28.7,9.8,28.7,9.3,21.8,9,21.8,9,21.8,9.1,20,9.1,20,9.1,20,9.2,22.6,8.8,22.6,8.3,22.6,8.4,22.4,8.1,22.4,7.7,22.4,7.9,18.6,7.9,18.6,8,18.6,7.9,16.2,7.6,16.2,7.1,16.2,6.8,13.8,6.5,13.8,6.9,13.8,8.2,24.1,8.7,24.1,8.3,24.1,7.9,19.9,7.5,19.9,7.8,19.9,8.3,22.3,8.4,22.3,8.2,22.3,7.7,20.9,7.2,20.9,7.3,20.9,8.1,25.5,8.5,25.5,8.4,25.5),dim=c(2,60),dimnames=list(c('TWV','WV-25'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('TWV','WV-25'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> 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
TWV WV-25 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 10.0 24.1 1 0 0 0 0 0 0 0 0 0 0 1
2 9.2 24.1 0 1 0 0 0 0 0 0 0 0 0 2
3 9.2 24.1 0 0 1 0 0 0 0 0 0 0 0 3
4 9.5 21.3 0 0 0 1 0 0 0 0 0 0 0 4
5 9.6 21.3 0 0 0 0 1 0 0 0 0 0 0 5
6 9.5 21.3 0 0 0 0 0 1 0 0 0 0 0 6
7 9.1 19.1 0 0 0 0 0 0 1 0 0 0 0 7
8 8.9 19.1 0 0 0 0 0 0 0 1 0 0 0 8
9 9.0 19.1 0 0 0 0 0 0 0 0 1 0 0 9
10 10.1 26.2 0 0 0 0 0 0 0 0 0 1 0 10
11 10.3 26.2 0 0 0 0 0 0 0 0 0 0 1 11
12 10.2 26.2 0 0 0 0 0 0 0 0 0 0 0 12
13 9.6 21.7 1 0 0 0 0 0 0 0 0 0 0 13
14 9.2 21.7 0 1 0 0 0 0 0 0 0 0 0 14
15 9.3 21.7 0 0 1 0 0 0 0 0 0 0 0 15
16 9.4 19.4 0 0 0 1 0 0 0 0 0 0 0 16
17 9.4 19.4 0 0 0 0 1 0 0 0 0 0 0 17
18 9.2 19.4 0 0 0 0 0 1 0 0 0 0 0 18
19 9.0 19.5 0 0 0 0 0 0 1 0 0 0 0 19
20 9.0 19.5 0 0 0 0 0 0 0 1 0 0 0 20
21 9.0 19.5 0 0 0 0 0 0 0 0 1 0 0 21
22 9.8 28.7 0 0 0 0 0 0 0 0 0 1 0 22
23 10.0 28.7 0 0 0 0 0 0 0 0 0 0 1 23
24 9.8 28.7 0 0 0 0 0 0 0 0 0 0 0 24
25 9.3 21.8 1 0 0 0 0 0 0 0 0 0 0 25
26 9.0 21.8 0 1 0 0 0 0 0 0 0 0 0 26
27 9.0 21.8 0 0 1 0 0 0 0 0 0 0 0 27
28 9.1 20.0 0 0 0 1 0 0 0 0 0 0 0 28
29 9.1 20.0 0 0 0 0 1 0 0 0 0 0 0 29
30 9.1 20.0 0 0 0 0 0 1 0 0 0 0 0 30
31 9.2 22.6 0 0 0 0 0 0 1 0 0 0 0 31
32 8.8 22.6 0 0 0 0 0 0 0 1 0 0 0 32
33 8.3 22.6 0 0 0 0 0 0 0 0 1 0 0 33
34 8.4 22.4 0 0 0 0 0 0 0 0 0 1 0 34
35 8.1 22.4 0 0 0 0 0 0 0 0 0 0 1 35
36 7.7 22.4 0 0 0 0 0 0 0 0 0 0 0 36
37 7.9 18.6 1 0 0 0 0 0 0 0 0 0 0 37
38 7.9 18.6 0 1 0 0 0 0 0 0 0 0 0 38
39 8.0 18.6 0 0 1 0 0 0 0 0 0 0 0 39
40 7.9 16.2 0 0 0 1 0 0 0 0 0 0 0 40
41 7.6 16.2 0 0 0 0 1 0 0 0 0 0 0 41
42 7.1 16.2 0 0 0 0 0 1 0 0 0 0 0 42
43 6.8 13.8 0 0 0 0 0 0 1 0 0 0 0 43
44 6.5 13.8 0 0 0 0 0 0 0 1 0 0 0 44
45 6.9 13.8 0 0 0 0 0 0 0 0 1 0 0 45
46 8.2 24.1 0 0 0 0 0 0 0 0 0 1 0 46
47 8.7 24.1 0 0 0 0 0 0 0 0 0 0 1 47
48 8.3 24.1 0 0 0 0 0 0 0 0 0 0 0 48
49 7.9 19.9 1 0 0 0 0 0 0 0 0 0 0 49
50 7.5 19.9 0 1 0 0 0 0 0 0 0 0 0 50
51 7.8 19.9 0 0 1 0 0 0 0 0 0 0 0 51
52 8.3 22.3 0 0 0 1 0 0 0 0 0 0 0 52
53 8.4 22.3 0 0 0 0 1 0 0 0 0 0 0 53
54 8.2 22.3 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 20.9 0 0 0 0 0 0 1 0 0 0 0 55
56 7.2 20.9 0 0 0 0 0 0 0 1 0 0 0 56
57 7.3 20.9 0 0 0 0 0 0 0 0 1 0 0 57
58 8.1 25.5 0 0 0 0 0 0 0 0 0 1 0 58
59 8.5 25.5 0 0 0 0 0 0 0 0 0 0 1 59
60 8.4 25.5 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `WV-25` M1 M2 M3 M4
5.80184 0.16948 0.39129 0.04526 0.17924 0.62709
M5 M6 M7 M8 M9 M10
0.64106 0.47504 0.36087 0.11484 0.16882 -0.02795
M11 t
0.20602 -0.03398
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.76350 -0.18420 0.03139 0.22562 0.45806
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.801841 0.546032 10.625 5.71e-14 ***
`WV-25` 0.169475 0.019263 8.798 2.03e-11 ***
M1 0.391286 0.225110 1.738 0.08887 .
M2 0.045261 0.224496 0.202 0.84111
M3 0.179237 0.223911 0.800 0.42755
M4 0.627088 0.234967 2.669 0.01048 *
M5 0.641063 0.234371 2.735 0.00882 **
M6 0.475039 0.233802 2.032 0.04797 *
M7 0.360868 0.239567 1.506 0.13882
M8 0.114844 0.239023 0.480 0.63317
M9 0.168819 0.238506 0.708 0.48263
M10 -0.027951 0.204914 -0.136 0.89210
M11 0.206024 0.204864 1.006 0.31984
t -0.033976 0.002601 -13.060 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3239 on 46 degrees of freedom
Multiple R-squared: 0.9006, Adjusted R-squared: 0.8725
F-statistic: 32.04 on 13 and 46 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,] 0.1727796268 0.3455592536 0.82722037
[2,] 0.0859977206 0.1719954412 0.91400228
[3,] 0.0421073979 0.0842147958 0.95789260
[4,] 0.0282338908 0.0564677816 0.97176611
[5,] 0.0151356531 0.0302713063 0.98486435
[6,] 0.0159286523 0.0318573046 0.98407135
[7,] 0.0090984088 0.0181968176 0.99090159
[8,] 0.0058603010 0.0117206020 0.99413970
[9,] 0.0064095578 0.0128191157 0.99359044
[10,] 0.0029577203 0.0059154407 0.99704228
[11,] 0.0011772978 0.0023545956 0.99882270
[12,] 0.0005096428 0.0010192857 0.99949036
[13,] 0.0002796264 0.0005592528 0.99972037
[14,] 0.0004453767 0.0008907534 0.99955462
[15,] 0.0053662795 0.0107325591 0.99463372
[16,] 0.0165592172 0.0331184344 0.98344078
[17,] 0.0478143150 0.0956286300 0.95218568
[18,] 0.5853348702 0.8293302596 0.41466513
[19,] 0.8813578982 0.2372842036 0.11864210
[20,] 0.9917263107 0.0165473786 0.00827369
[21,] 0.9856086191 0.0287827619 0.01439138
[22,] 0.9787970840 0.0424058321 0.02120292
[23,] 0.9573770144 0.0852459712 0.04262299
[24,] 0.9347178611 0.1305642777 0.06528214
[25,] 0.8868363789 0.2263272422 0.11316362
[26,] 0.9585416650 0.0829166699 0.04145833
[27,] 0.9529155233 0.0941689535 0.04708448
> postscript(file="/var/www/html/rcomp/tmp/1udt91258662976.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2yik71258662976.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3m4wn1258662976.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/480we1258662976.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lnor1258662976.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 = 60
Frequency = 1
1 2 3 4 5
-0.2435008489 -0.6635008489 -0.7635008489 -0.4028462083 -0.2828462083
6 7 8 9 10
-0.1828462083 -0.0618545557 0.0181454443 0.0981454443 0.2256178551
11 12 13 14 15
0.2256178551 0.3656178551 0.1709456706 0.1509456706 0.1509456706
16 17 18 19 20
0.2268627617 0.2468627617 0.2468627617 0.1780616865 0.4580616865
21 22 23 24 25
0.4380616865 -0.0903636106 -0.0903636106 -0.0503636106 0.2617044426
26 27 28 29 30
0.3417044426 0.2417044426 0.2328839842 0.2528839842 0.4528839842
31 32 33 34 35
0.2603951613 0.1403951613 -0.3796048387 -0.0149642049 -0.5149642049
36 37 38 39 40
-0.6749642049 -0.1882689587 0.1917310413 0.1917310413 0.0845956423
41 42 43 44 45
-0.1954043577 -0.4954043577 -0.2405176853 -0.2605176853 0.1194823147
46 47 48 49 50
-0.0953655914 0.2046344086 0.0446344086 -0.0008803056 -0.0208803056
51 52 53 54 55
0.1791196944 -0.1414961800 -0.0214961800 -0.0214961800 -0.1360846067
56 57 58 59 60
-0.3560846067 -0.2760846067 -0.0249244482 0.1750755518 0.3150755518
> postscript(file="/var/www/html/rcomp/tmp/6gyi21258662976.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.2435008489 NA
1 -0.6635008489 -0.2435008489
2 -0.7635008489 -0.6635008489
3 -0.4028462083 -0.7635008489
4 -0.2828462083 -0.4028462083
5 -0.1828462083 -0.2828462083
6 -0.0618545557 -0.1828462083
7 0.0181454443 -0.0618545557
8 0.0981454443 0.0181454443
9 0.2256178551 0.0981454443
10 0.2256178551 0.2256178551
11 0.3656178551 0.2256178551
12 0.1709456706 0.3656178551
13 0.1509456706 0.1709456706
14 0.1509456706 0.1509456706
15 0.2268627617 0.1509456706
16 0.2468627617 0.2268627617
17 0.2468627617 0.2468627617
18 0.1780616865 0.2468627617
19 0.4580616865 0.1780616865
20 0.4380616865 0.4580616865
21 -0.0903636106 0.4380616865
22 -0.0903636106 -0.0903636106
23 -0.0503636106 -0.0903636106
24 0.2617044426 -0.0503636106
25 0.3417044426 0.2617044426
26 0.2417044426 0.3417044426
27 0.2328839842 0.2417044426
28 0.2528839842 0.2328839842
29 0.4528839842 0.2528839842
30 0.2603951613 0.4528839842
31 0.1403951613 0.2603951613
32 -0.3796048387 0.1403951613
33 -0.0149642049 -0.3796048387
34 -0.5149642049 -0.0149642049
35 -0.6749642049 -0.5149642049
36 -0.1882689587 -0.6749642049
37 0.1917310413 -0.1882689587
38 0.1917310413 0.1917310413
39 0.0845956423 0.1917310413
40 -0.1954043577 0.0845956423
41 -0.4954043577 -0.1954043577
42 -0.2405176853 -0.4954043577
43 -0.2605176853 -0.2405176853
44 0.1194823147 -0.2605176853
45 -0.0953655914 0.1194823147
46 0.2046344086 -0.0953655914
47 0.0446344086 0.2046344086
48 -0.0008803056 0.0446344086
49 -0.0208803056 -0.0008803056
50 0.1791196944 -0.0208803056
51 -0.1414961800 0.1791196944
52 -0.0214961800 -0.1414961800
53 -0.0214961800 -0.0214961800
54 -0.1360846067 -0.0214961800
55 -0.3560846067 -0.1360846067
56 -0.2760846067 -0.3560846067
57 -0.0249244482 -0.2760846067
58 0.1750755518 -0.0249244482
59 0.3150755518 0.1750755518
60 NA 0.3150755518
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.6635008489 -0.2435008489
[2,] -0.7635008489 -0.6635008489
[3,] -0.4028462083 -0.7635008489
[4,] -0.2828462083 -0.4028462083
[5,] -0.1828462083 -0.2828462083
[6,] -0.0618545557 -0.1828462083
[7,] 0.0181454443 -0.0618545557
[8,] 0.0981454443 0.0181454443
[9,] 0.2256178551 0.0981454443
[10,] 0.2256178551 0.2256178551
[11,] 0.3656178551 0.2256178551
[12,] 0.1709456706 0.3656178551
[13,] 0.1509456706 0.1709456706
[14,] 0.1509456706 0.1509456706
[15,] 0.2268627617 0.1509456706
[16,] 0.2468627617 0.2268627617
[17,] 0.2468627617 0.2468627617
[18,] 0.1780616865 0.2468627617
[19,] 0.4580616865 0.1780616865
[20,] 0.4380616865 0.4580616865
[21,] -0.0903636106 0.4380616865
[22,] -0.0903636106 -0.0903636106
[23,] -0.0503636106 -0.0903636106
[24,] 0.2617044426 -0.0503636106
[25,] 0.3417044426 0.2617044426
[26,] 0.2417044426 0.3417044426
[27,] 0.2328839842 0.2417044426
[28,] 0.2528839842 0.2328839842
[29,] 0.4528839842 0.2528839842
[30,] 0.2603951613 0.4528839842
[31,] 0.1403951613 0.2603951613
[32,] -0.3796048387 0.1403951613
[33,] -0.0149642049 -0.3796048387
[34,] -0.5149642049 -0.0149642049
[35,] -0.6749642049 -0.5149642049
[36,] -0.1882689587 -0.6749642049
[37,] 0.1917310413 -0.1882689587
[38,] 0.1917310413 0.1917310413
[39,] 0.0845956423 0.1917310413
[40,] -0.1954043577 0.0845956423
[41,] -0.4954043577 -0.1954043577
[42,] -0.2405176853 -0.4954043577
[43,] -0.2605176853 -0.2405176853
[44,] 0.1194823147 -0.2605176853
[45,] -0.0953655914 0.1194823147
[46,] 0.2046344086 -0.0953655914
[47,] 0.0446344086 0.2046344086
[48,] -0.0008803056 0.0446344086
[49,] -0.0208803056 -0.0008803056
[50,] 0.1791196944 -0.0208803056
[51,] -0.1414961800 0.1791196944
[52,] -0.0214961800 -0.1414961800
[53,] -0.0214961800 -0.0214961800
[54,] -0.1360846067 -0.0214961800
[55,] -0.3560846067 -0.1360846067
[56,] -0.2760846067 -0.3560846067
[57,] -0.0249244482 -0.2760846067
[58,] 0.1750755518 -0.0249244482
[59,] 0.3150755518 0.1750755518
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.6635008489 -0.2435008489
2 -0.7635008489 -0.6635008489
3 -0.4028462083 -0.7635008489
4 -0.2828462083 -0.4028462083
5 -0.1828462083 -0.2828462083
6 -0.0618545557 -0.1828462083
7 0.0181454443 -0.0618545557
8 0.0981454443 0.0181454443
9 0.2256178551 0.0981454443
10 0.2256178551 0.2256178551
11 0.3656178551 0.2256178551
12 0.1709456706 0.3656178551
13 0.1509456706 0.1709456706
14 0.1509456706 0.1509456706
15 0.2268627617 0.1509456706
16 0.2468627617 0.2268627617
17 0.2468627617 0.2468627617
18 0.1780616865 0.2468627617
19 0.4580616865 0.1780616865
20 0.4380616865 0.4580616865
21 -0.0903636106 0.4380616865
22 -0.0903636106 -0.0903636106
23 -0.0503636106 -0.0903636106
24 0.2617044426 -0.0503636106
25 0.3417044426 0.2617044426
26 0.2417044426 0.3417044426
27 0.2328839842 0.2417044426
28 0.2528839842 0.2328839842
29 0.4528839842 0.2528839842
30 0.2603951613 0.4528839842
31 0.1403951613 0.2603951613
32 -0.3796048387 0.1403951613
33 -0.0149642049 -0.3796048387
34 -0.5149642049 -0.0149642049
35 -0.6749642049 -0.5149642049
36 -0.1882689587 -0.6749642049
37 0.1917310413 -0.1882689587
38 0.1917310413 0.1917310413
39 0.0845956423 0.1917310413
40 -0.1954043577 0.0845956423
41 -0.4954043577 -0.1954043577
42 -0.2405176853 -0.4954043577
43 -0.2605176853 -0.2405176853
44 0.1194823147 -0.2605176853
45 -0.0953655914 0.1194823147
46 0.2046344086 -0.0953655914
47 0.0446344086 0.2046344086
48 -0.0008803056 0.0446344086
49 -0.0208803056 -0.0008803056
50 0.1791196944 -0.0208803056
51 -0.1414961800 0.1791196944
52 -0.0214961800 -0.1414961800
53 -0.0214961800 -0.0214961800
54 -0.1360846067 -0.0214961800
55 -0.3560846067 -0.1360846067
56 -0.2760846067 -0.3560846067
57 -0.0249244482 -0.2760846067
58 0.1750755518 -0.0249244482
59 0.3150755518 0.1750755518
> 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/75mw11258662976.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kk2x1258662976.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wh2v1258662976.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dn7s1258662976.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qraz1258662976.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/12gprh1258662976.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/130e8p1258662976.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/14l9yj1258662976.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/158j9v1258662976.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/16kv7t1258662976.tab")
+ }
>
> system("convert tmp/1udt91258662976.ps tmp/1udt91258662976.png")
> system("convert tmp/2yik71258662976.ps tmp/2yik71258662976.png")
> system("convert tmp/3m4wn1258662976.ps tmp/3m4wn1258662976.png")
> system("convert tmp/480we1258662976.ps tmp/480we1258662976.png")
> system("convert tmp/5lnor1258662976.ps tmp/5lnor1258662976.png")
> system("convert tmp/6gyi21258662976.ps tmp/6gyi21258662976.png")
> system("convert tmp/75mw11258662976.ps tmp/75mw11258662976.png")
> system("convert tmp/8kk2x1258662976.ps tmp/8kk2x1258662976.png")
> system("convert tmp/9wh2v1258662976.ps tmp/9wh2v1258662976.png")
> system("convert tmp/10dn7s1258662976.ps tmp/10dn7s1258662976.png")
>
>
> proc.time()
user system elapsed
2.364 1.575 3.204