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.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(6.3
+ ,2.3
+ ,6.1
+ ,6.2
+ ,6.3
+ ,6.5
+ ,1.9
+ ,6.3
+ ,6.1
+ ,6.2
+ ,6.6
+ ,2
+ ,6.5
+ ,6.3
+ ,6.1
+ ,6.5
+ ,2.3
+ ,6.6
+ ,6.5
+ ,6.3
+ ,6.2
+ ,2.8
+ ,6.5
+ ,6.6
+ ,6.5
+ ,6.2
+ ,2.4
+ ,6.2
+ ,6.5
+ ,6.6
+ ,5.9
+ ,2.3
+ ,6.2
+ ,6.2
+ ,6.5
+ ,6.1
+ ,2.7
+ ,5.9
+ ,6.2
+ ,6.2
+ ,6.1
+ ,2.7
+ ,6.1
+ ,5.9
+ ,6.2
+ ,6.1
+ ,2.9
+ ,6.1
+ ,6.1
+ ,5.9
+ ,6.1
+ ,3
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.1
+ ,2.2
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.4
+ ,2.3
+ ,6.1
+ ,6.1
+ ,6.1
+ ,6.7
+ ,2.8
+ ,6.4
+ ,6.1
+ ,6.1
+ ,6.9
+ ,2.8
+ ,6.7
+ ,6.4
+ ,6.1
+ ,7
+ ,2.8
+ ,6.9
+ ,6.7
+ ,6.4
+ ,7
+ ,2.2
+ ,7
+ ,6.9
+ ,6.7
+ ,6.8
+ ,2.6
+ ,7
+ ,7
+ ,6.9
+ ,6.4
+ ,2.8
+ ,6.8
+ ,7
+ ,7
+ ,5.9
+ ,2.5
+ ,6.4
+ ,6.8
+ ,7
+ ,5.5
+ ,2.4
+ ,5.9
+ ,6.4
+ ,6.8
+ ,5.5
+ ,2.3
+ ,5.5
+ ,5.9
+ ,6.4
+ ,5.6
+ ,1.9
+ ,5.5
+ ,5.5
+ ,5.9
+ ,5.8
+ ,1.7
+ ,5.6
+ ,5.5
+ ,5.5
+ ,5.9
+ ,2
+ ,5.8
+ ,5.6
+ ,5.5
+ ,6.1
+ ,2.1
+ ,5.9
+ ,5.8
+ ,5.6
+ ,6.1
+ ,1.7
+ ,6.1
+ ,5.9
+ ,5.8
+ ,6
+ ,1.8
+ ,6.1
+ ,6.1
+ ,5.9
+ ,6
+ ,1.8
+ ,6
+ ,6.1
+ ,6.1
+ ,5.9
+ ,1.8
+ ,6
+ ,6
+ ,6.1
+ ,5.5
+ ,1.3
+ ,5.9
+ ,6
+ ,6
+ ,5.6
+ ,1.3
+ ,5.5
+ ,5.9
+ ,6
+ ,5.4
+ ,1.3
+ ,5.6
+ ,5.5
+ ,5.9
+ ,5.2
+ ,1.2
+ ,5.4
+ ,5.6
+ ,5.5
+ ,5.2
+ ,1.4
+ ,5.2
+ ,5.4
+ ,5.6
+ ,5.2
+ ,2.2
+ ,5.2
+ ,5.2
+ ,5.4
+ ,5.5
+ ,2.9
+ ,5.2
+ ,5.2
+ ,5.2
+ ,5.8
+ ,3.1
+ ,5.5
+ ,5.2
+ ,5.2
+ ,5.8
+ ,3.5
+ ,5.8
+ ,5.5
+ ,5.2
+ ,5.5
+ ,3.6
+ ,5.8
+ ,5.8
+ ,5.5
+ ,5.3
+ ,4.4
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.1
+ ,4.1
+ ,5.3
+ ,5.5
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5.1
+ ,5.3
+ ,5.5
+ ,5.8
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5.3
+ ,5.8
+ ,5.9
+ ,5.8
+ ,5.2
+ ,5.1
+ ,5.5
+ ,5.4
+ ,5.8
+ ,5.8
+ ,5.2
+ ,5
+ ,5.5
+ ,5.5
+ ,5.8
+ ,5.8
+ ,4.9
+ ,4.8
+ ,5
+ ,5.5
+ ,5.8
+ ,5.3
+ ,3.2
+ ,4.9
+ ,5
+ ,5.5
+ ,6.1
+ ,2.7
+ ,5.3
+ ,4.9
+ ,5
+ ,6.5
+ ,2.1
+ ,6.1
+ ,5.3
+ ,4.9
+ ,6.8
+ ,1.9
+ ,6.5
+ ,6.1
+ ,5.3
+ ,6.6
+ ,0.6
+ ,6.8
+ ,6.5
+ ,6.1
+ ,6.4
+ ,0.7
+ ,6.6
+ ,6.8
+ ,6.5
+ ,6.4
+ ,-0.2
+ ,6.4
+ ,6.6
+ ,6.8
+ ,6.6
+ ,-1
+ ,6.4
+ ,6.4
+ ,6.6
+ ,6.7
+ ,-1.7
+ ,6.6
+ ,6.4
+ ,6.4)
+ ,dim=c(5
+ ,57)
+ ,dimnames=list(c('WMan>25'
+ ,'Infl'
+ ,'Yt-1'
+ ,'Yt-2'
+ ,'Yt-3')
+ ,1:57))
> y <- array(NA,dim=c(5,57),dimnames=list(c('WMan>25','Infl','Yt-1','Yt-2','Yt-3'),1:57))
> 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
WMan>25 Infl Yt-1 Yt-2 Yt-3 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.3 2.3 6.1 6.2 6.3 1 0 0 0 0 0 0 0 0 0 0 1
2 6.5 1.9 6.3 6.1 6.2 0 1 0 0 0 0 0 0 0 0 0 2
3 6.6 2.0 6.5 6.3 6.1 0 0 1 0 0 0 0 0 0 0 0 3
4 6.5 2.3 6.6 6.5 6.3 0 0 0 1 0 0 0 0 0 0 0 4
5 6.2 2.8 6.5 6.6 6.5 0 0 0 0 1 0 0 0 0 0 0 5
6 6.2 2.4 6.2 6.5 6.6 0 0 0 0 0 1 0 0 0 0 0 6
7 5.9 2.3 6.2 6.2 6.5 0 0 0 0 0 0 1 0 0 0 0 7
8 6.1 2.7 5.9 6.2 6.2 0 0 0 0 0 0 0 1 0 0 0 8
9 6.1 2.7 6.1 5.9 6.2 0 0 0 0 0 0 0 0 1 0 0 9
10 6.1 2.9 6.1 6.1 5.9 0 0 0 0 0 0 0 0 0 1 0 10
11 6.1 3.0 6.1 6.1 6.1 0 0 0 0 0 0 0 0 0 0 1 11
12 6.1 2.2 6.1 6.1 6.1 0 0 0 0 0 0 0 0 0 0 0 12
13 6.4 2.3 6.1 6.1 6.1 1 0 0 0 0 0 0 0 0 0 0 13
14 6.7 2.8 6.4 6.1 6.1 0 1 0 0 0 0 0 0 0 0 0 14
15 6.9 2.8 6.7 6.4 6.1 0 0 1 0 0 0 0 0 0 0 0 15
16 7.0 2.8 6.9 6.7 6.4 0 0 0 1 0 0 0 0 0 0 0 16
17 7.0 2.2 7.0 6.9 6.7 0 0 0 0 1 0 0 0 0 0 0 17
18 6.8 2.6 7.0 7.0 6.9 0 0 0 0 0 1 0 0 0 0 0 18
19 6.4 2.8 6.8 7.0 7.0 0 0 0 0 0 0 1 0 0 0 0 19
20 5.9 2.5 6.4 6.8 7.0 0 0 0 0 0 0 0 1 0 0 0 20
21 5.5 2.4 5.9 6.4 6.8 0 0 0 0 0 0 0 0 1 0 0 21
22 5.5 2.3 5.5 5.9 6.4 0 0 0 0 0 0 0 0 0 1 0 22
23 5.6 1.9 5.5 5.5 5.9 0 0 0 0 0 0 0 0 0 0 1 23
24 5.8 1.7 5.6 5.5 5.5 0 0 0 0 0 0 0 0 0 0 0 24
25 5.9 2.0 5.8 5.6 5.5 1 0 0 0 0 0 0 0 0 0 0 25
26 6.1 2.1 5.9 5.8 5.6 0 1 0 0 0 0 0 0 0 0 0 26
27 6.1 1.7 6.1 5.9 5.8 0 0 1 0 0 0 0 0 0 0 0 27
28 6.0 1.8 6.1 6.1 5.9 0 0 0 1 0 0 0 0 0 0 0 28
29 6.0 1.8 6.0 6.1 6.1 0 0 0 0 1 0 0 0 0 0 0 29
30 5.9 1.8 6.0 6.0 6.1 0 0 0 0 0 1 0 0 0 0 0 30
31 5.5 1.3 5.9 6.0 6.0 0 0 0 0 0 0 1 0 0 0 0 31
32 5.6 1.3 5.5 5.9 6.0 0 0 0 0 0 0 0 1 0 0 0 32
33 5.4 1.3 5.6 5.5 5.9 0 0 0 0 0 0 0 0 1 0 0 33
34 5.2 1.2 5.4 5.6 5.5 0 0 0 0 0 0 0 0 0 1 0 34
35 5.2 1.4 5.2 5.4 5.6 0 0 0 0 0 0 0 0 0 0 1 35
36 5.2 2.2 5.2 5.2 5.4 0 0 0 0 0 0 0 0 0 0 0 36
37 5.5 2.9 5.2 5.2 5.2 1 0 0 0 0 0 0 0 0 0 0 37
38 5.8 3.1 5.5 5.2 5.2 0 1 0 0 0 0 0 0 0 0 0 38
39 5.8 3.5 5.8 5.5 5.2 0 0 1 0 0 0 0 0 0 0 0 39
40 5.5 3.6 5.8 5.8 5.5 0 0 0 1 0 0 0 0 0 0 0 40
41 5.3 4.4 5.5 5.8 5.8 0 0 0 0 1 0 0 0 0 0 0 41
42 5.1 4.1 5.3 5.5 5.8 0 0 0 0 0 1 0 0 0 0 0 42
43 5.2 5.1 5.1 5.3 5.5 0 0 0 0 0 0 1 0 0 0 0 43
44 5.8 5.8 5.2 5.1 5.3 0 0 0 0 0 0 0 1 0 0 0 44
45 5.8 5.9 5.8 5.2 5.1 0 0 0 0 0 0 0 0 1 0 0 45
46 5.5 5.4 5.8 5.8 5.2 0 0 0 0 0 0 0 0 0 1 0 46
47 5.0 5.5 5.5 5.8 5.8 0 0 0 0 0 0 0 0 0 0 1 47
48 4.9 4.8 5.0 5.5 5.8 0 0 0 0 0 0 0 0 0 0 0 48
49 5.3 3.2 4.9 5.0 5.5 1 0 0 0 0 0 0 0 0 0 0 49
50 6.1 2.7 5.3 4.9 5.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.5 2.1 6.1 5.3 4.9 0 0 1 0 0 0 0 0 0 0 0 51
52 6.8 1.9 6.5 6.1 5.3 0 0 0 1 0 0 0 0 0 0 0 52
53 6.6 0.6 6.8 6.5 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 0.7 6.6 6.8 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.4 -0.2 6.4 6.6 6.8 0 0 0 0 0 0 1 0 0 0 0 55
56 6.6 -1.0 6.4 6.4 6.6 0 0 0 0 0 0 0 1 0 0 0 56
57 6.7 -1.7 6.6 6.4 6.4 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl `Yt-1` `Yt-2` `Yt-3` M1
0.7350257 -0.0210343 1.5312746 -0.8788445 0.2337964 0.1898598
M2 M3 M4 M5 M6 M7
0.1745045 -0.0105410 0.0117569 -0.0614530 -0.0384071 -0.1438358
M8 M9 M10 M11 t
0.1919297 -0.2379883 -0.0344461 -0.0984259 0.0001828
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.43861 -0.10527 0.02097 0.10613 0.27072
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7350257 0.5792408 1.269 0.21179
Infl -0.0210343 0.0205979 -1.021 0.31330
`Yt-1` 1.5312746 0.1614500 9.485 8.69e-12 ***
`Yt-2` -0.8788445 0.2602624 -3.377 0.00164 **
`Yt-3` 0.2337964 0.1747675 1.338 0.18853
M1 0.1898598 0.1221224 1.555 0.12790
M2 0.1745045 0.1306473 1.336 0.18920
M3 -0.0105410 0.1385003 -0.076 0.93971
M4 0.0117569 0.1384977 0.085 0.93277
M5 -0.0614530 0.1339448 -0.459 0.64887
M6 -0.0384071 0.1329466 -0.289 0.77416
M7 -0.1438358 0.1317780 -1.092 0.28158
M8 0.1919297 0.1273435 1.507 0.13962
M9 -0.2379883 0.1353325 -1.759 0.08630 .
M10 -0.0344461 0.1327904 -0.259 0.79666
M11 -0.0984259 0.1283201 -0.767 0.44757
t 0.0001828 0.0019929 0.092 0.92739
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1804 on 40 degrees of freedom
Multiple R-squared: 0.9223, Adjusted R-squared: 0.8912
F-statistic: 29.67 on 16 and 40 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,] 0.4925795 0.9851591 0.5074205
[2,] 0.3285049 0.6570098 0.6714951
[3,] 0.4846086 0.9692173 0.5153914
[4,] 0.4851203 0.9702406 0.5148797
[5,] 0.4779503 0.9559006 0.5220497
[6,] 0.6404476 0.7191049 0.3595524
[7,] 0.5271900 0.9456199 0.4728100
[8,] 0.4671906 0.9343813 0.5328094
[9,] 0.3972571 0.7945141 0.6027429
[10,] 0.5922799 0.8154401 0.4077201
[11,] 0.8446817 0.3106365 0.1553183
[12,] 0.8207145 0.3585711 0.1792855
[13,] 0.8352123 0.3295754 0.1647877
[14,] 0.7428782 0.5142436 0.2571218
[15,] 0.6377556 0.7244888 0.3622444
[16,] 0.6233668 0.7532665 0.3766332
[17,] 0.5620528 0.8758945 0.4379472
[18,] 0.4140518 0.8281035 0.5859482
> postscript(file="/var/www/html/rcomp/tmp/1kxmb1259099467.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2a5d81259099467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3d0mf1259099467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4vii31259099467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vk1c1259099467.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 = 57
Frequency = 1
1 2 3 4 5 6
0.058454350 -0.105546661 0.074313107 -0.065975018 -0.088178140 0.228297744
7 8 9 10 11 12
-0.208833493 0.193153347 0.052980234 0.099369964 0.118511108 0.003075031
13 14 15 16 17 18
0.115135934 -0.018556850 0.170576840 0.135355777 0.148264809 -0.025424984
19 20 21 22 23 24
-0.033096917 -0.438614446 0.049876075 0.110653832 0.031397470 0.068973061
25 26 27 28 29 30
-0.233129648 -0.016591957 -0.105272707 -0.073260592 0.106134680 -0.104978467
31 32 33 34 35 36
-0.233742602 0.054934614 -0.196615852 -0.114786302 0.060323929 -0.150466893
37 38 39 40 41 42
0.020973869 -0.119029207 -0.121481795 -0.248344501 0.030753505 -0.156183914
43 44 45 46 47 48
0.270721240 0.267359973 -0.084922476 -0.095237493 -0.210232508 0.078418801
49 50 51 52 53 54
0.038565494 0.259724675 -0.018135445 0.252224334 -0.196974854 0.058289621
55 56 57
0.204951772 -0.076833489 0.178682019
> postscript(file="/var/www/html/rcomp/tmp/6jsey1259099467.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 0.058454350 NA
1 -0.105546661 0.058454350
2 0.074313107 -0.105546661
3 -0.065975018 0.074313107
4 -0.088178140 -0.065975018
5 0.228297744 -0.088178140
6 -0.208833493 0.228297744
7 0.193153347 -0.208833493
8 0.052980234 0.193153347
9 0.099369964 0.052980234
10 0.118511108 0.099369964
11 0.003075031 0.118511108
12 0.115135934 0.003075031
13 -0.018556850 0.115135934
14 0.170576840 -0.018556850
15 0.135355777 0.170576840
16 0.148264809 0.135355777
17 -0.025424984 0.148264809
18 -0.033096917 -0.025424984
19 -0.438614446 -0.033096917
20 0.049876075 -0.438614446
21 0.110653832 0.049876075
22 0.031397470 0.110653832
23 0.068973061 0.031397470
24 -0.233129648 0.068973061
25 -0.016591957 -0.233129648
26 -0.105272707 -0.016591957
27 -0.073260592 -0.105272707
28 0.106134680 -0.073260592
29 -0.104978467 0.106134680
30 -0.233742602 -0.104978467
31 0.054934614 -0.233742602
32 -0.196615852 0.054934614
33 -0.114786302 -0.196615852
34 0.060323929 -0.114786302
35 -0.150466893 0.060323929
36 0.020973869 -0.150466893
37 -0.119029207 0.020973869
38 -0.121481795 -0.119029207
39 -0.248344501 -0.121481795
40 0.030753505 -0.248344501
41 -0.156183914 0.030753505
42 0.270721240 -0.156183914
43 0.267359973 0.270721240
44 -0.084922476 0.267359973
45 -0.095237493 -0.084922476
46 -0.210232508 -0.095237493
47 0.078418801 -0.210232508
48 0.038565494 0.078418801
49 0.259724675 0.038565494
50 -0.018135445 0.259724675
51 0.252224334 -0.018135445
52 -0.196974854 0.252224334
53 0.058289621 -0.196974854
54 0.204951772 0.058289621
55 -0.076833489 0.204951772
56 0.178682019 -0.076833489
57 NA 0.178682019
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.105546661 0.058454350
[2,] 0.074313107 -0.105546661
[3,] -0.065975018 0.074313107
[4,] -0.088178140 -0.065975018
[5,] 0.228297744 -0.088178140
[6,] -0.208833493 0.228297744
[7,] 0.193153347 -0.208833493
[8,] 0.052980234 0.193153347
[9,] 0.099369964 0.052980234
[10,] 0.118511108 0.099369964
[11,] 0.003075031 0.118511108
[12,] 0.115135934 0.003075031
[13,] -0.018556850 0.115135934
[14,] 0.170576840 -0.018556850
[15,] 0.135355777 0.170576840
[16,] 0.148264809 0.135355777
[17,] -0.025424984 0.148264809
[18,] -0.033096917 -0.025424984
[19,] -0.438614446 -0.033096917
[20,] 0.049876075 -0.438614446
[21,] 0.110653832 0.049876075
[22,] 0.031397470 0.110653832
[23,] 0.068973061 0.031397470
[24,] -0.233129648 0.068973061
[25,] -0.016591957 -0.233129648
[26,] -0.105272707 -0.016591957
[27,] -0.073260592 -0.105272707
[28,] 0.106134680 -0.073260592
[29,] -0.104978467 0.106134680
[30,] -0.233742602 -0.104978467
[31,] 0.054934614 -0.233742602
[32,] -0.196615852 0.054934614
[33,] -0.114786302 -0.196615852
[34,] 0.060323929 -0.114786302
[35,] -0.150466893 0.060323929
[36,] 0.020973869 -0.150466893
[37,] -0.119029207 0.020973869
[38,] -0.121481795 -0.119029207
[39,] -0.248344501 -0.121481795
[40,] 0.030753505 -0.248344501
[41,] -0.156183914 0.030753505
[42,] 0.270721240 -0.156183914
[43,] 0.267359973 0.270721240
[44,] -0.084922476 0.267359973
[45,] -0.095237493 -0.084922476
[46,] -0.210232508 -0.095237493
[47,] 0.078418801 -0.210232508
[48,] 0.038565494 0.078418801
[49,] 0.259724675 0.038565494
[50,] -0.018135445 0.259724675
[51,] 0.252224334 -0.018135445
[52,] -0.196974854 0.252224334
[53,] 0.058289621 -0.196974854
[54,] 0.204951772 0.058289621
[55,] -0.076833489 0.204951772
[56,] 0.178682019 -0.076833489
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.105546661 0.058454350
2 0.074313107 -0.105546661
3 -0.065975018 0.074313107
4 -0.088178140 -0.065975018
5 0.228297744 -0.088178140
6 -0.208833493 0.228297744
7 0.193153347 -0.208833493
8 0.052980234 0.193153347
9 0.099369964 0.052980234
10 0.118511108 0.099369964
11 0.003075031 0.118511108
12 0.115135934 0.003075031
13 -0.018556850 0.115135934
14 0.170576840 -0.018556850
15 0.135355777 0.170576840
16 0.148264809 0.135355777
17 -0.025424984 0.148264809
18 -0.033096917 -0.025424984
19 -0.438614446 -0.033096917
20 0.049876075 -0.438614446
21 0.110653832 0.049876075
22 0.031397470 0.110653832
23 0.068973061 0.031397470
24 -0.233129648 0.068973061
25 -0.016591957 -0.233129648
26 -0.105272707 -0.016591957
27 -0.073260592 -0.105272707
28 0.106134680 -0.073260592
29 -0.104978467 0.106134680
30 -0.233742602 -0.104978467
31 0.054934614 -0.233742602
32 -0.196615852 0.054934614
33 -0.114786302 -0.196615852
34 0.060323929 -0.114786302
35 -0.150466893 0.060323929
36 0.020973869 -0.150466893
37 -0.119029207 0.020973869
38 -0.121481795 -0.119029207
39 -0.248344501 -0.121481795
40 0.030753505 -0.248344501
41 -0.156183914 0.030753505
42 0.270721240 -0.156183914
43 0.267359973 0.270721240
44 -0.084922476 0.267359973
45 -0.095237493 -0.084922476
46 -0.210232508 -0.095237493
47 0.078418801 -0.210232508
48 0.038565494 0.078418801
49 0.259724675 0.038565494
50 -0.018135445 0.259724675
51 0.252224334 -0.018135445
52 -0.196974854 0.252224334
53 0.058289621 -0.196974854
54 0.204951772 0.058289621
55 -0.076833489 0.204951772
56 0.178682019 -0.076833489
> 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/7yiaz1259099467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/82lmi1259099467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9t7ya1259099467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108hr71259099467.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i10g1259099467.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/125mby1259099467.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/13a1ul1259099467.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/1403441259099467.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/15xkw51259099467.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/16p0491259099467.tab")
+ }
>
> system("convert tmp/1kxmb1259099467.ps tmp/1kxmb1259099467.png")
> system("convert tmp/2a5d81259099467.ps tmp/2a5d81259099467.png")
> system("convert tmp/3d0mf1259099467.ps tmp/3d0mf1259099467.png")
> system("convert tmp/4vii31259099467.ps tmp/4vii31259099467.png")
> system("convert tmp/5vk1c1259099467.ps tmp/5vk1c1259099467.png")
> system("convert tmp/6jsey1259099467.ps tmp/6jsey1259099467.png")
> system("convert tmp/7yiaz1259099467.ps tmp/7yiaz1259099467.png")
> system("convert tmp/82lmi1259099467.ps tmp/82lmi1259099467.png")
> system("convert tmp/9t7ya1259099467.ps tmp/9t7ya1259099467.png")
> system("convert tmp/108hr71259099467.ps tmp/108hr71259099467.png")
>
>
> proc.time()
user system elapsed
2.383 1.559 2.900