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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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(8.9
+ ,426
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.8
+ ,428
+ ,8.9
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.3
+ ,430
+ ,8.8
+ ,8.9
+ ,8.6
+ ,8.4
+ ,7.5
+ ,424
+ ,8.3
+ ,8.8
+ ,8.9
+ ,8.6
+ ,7.2
+ ,423
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,7.4
+ ,427
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.8
+ ,441
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,449
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,9.3
+ ,452
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.7
+ ,462
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.2
+ ,455
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.3
+ ,461
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.5
+ ,461
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.6
+ ,463
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.5
+ ,462
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.2
+ ,456
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.1
+ ,455
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,7.9
+ ,456
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.6
+ ,472
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,472
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.7
+ ,471
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.5
+ ,465
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.4
+ ,459
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.5
+ ,465
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,468
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,467
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.6
+ ,463
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,460
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.3
+ ,462
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.00
+ ,461
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.2
+ ,476
+ ,8.00
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,476
+ ,8.2
+ ,8.00
+ ,8.3
+ ,8.5
+ ,8.1
+ ,471
+ ,8.1
+ ,8.2
+ ,8.00
+ ,8.3
+ ,8.00
+ ,453
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8.00
+ ,7.9
+ ,443
+ ,8.00
+ ,8.1
+ ,8.1
+ ,8.2
+ ,7.9
+ ,442
+ ,7.9
+ ,8.00
+ ,8.1
+ ,8.1
+ ,8.00
+ ,444
+ ,7.9
+ ,7.9
+ ,8.00
+ ,8.1
+ ,8.00
+ ,438
+ ,8.00
+ ,7.9
+ ,7.9
+ ,8.00
+ ,7.9
+ ,427
+ ,8.00
+ ,8.00
+ ,7.9
+ ,7.9
+ ,8.00
+ ,424
+ ,7.9
+ ,8.00
+ ,8.00
+ ,7.9
+ ,7.7
+ ,416
+ ,8.00
+ ,7.9
+ ,8.00
+ ,8.00
+ ,7.2
+ ,406
+ ,7.7
+ ,8.00
+ ,7.9
+ ,8.00
+ ,7.5
+ ,431
+ ,7.2
+ ,7.7
+ ,8.00
+ ,7.9
+ ,7.3
+ ,434
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8.00
+ ,7.00
+ ,418
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7.00
+ ,412
+ ,7.00
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.00
+ ,404
+ ,7.00
+ ,7.00
+ ,7.3
+ ,7.5
+ ,7.2
+ ,409
+ ,7.00
+ ,7.00
+ ,7.00
+ ,7.3
+ ,7.3
+ ,412
+ ,7.2
+ ,7.00
+ ,7.00
+ ,7.00
+ ,7.1
+ ,406
+ ,7.3
+ ,7.2
+ ,7.00
+ ,7.00
+ ,6.8
+ ,398
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7.00
+ ,6.4
+ ,397
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.1
+ ,385
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,6.5
+ ,390
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.7
+ ,413
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.9
+ ,413
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,7.5
+ ,401
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.9
+ ,397
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.6
+ ,397
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.9
+ ,409
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,419
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.5
+ ,8.00
+ ,424
+ ,7.7
+ ,6.9
+ ,6.6
+ ,6.9
+ ,8.00
+ ,428
+ ,8.00
+ ,7.7
+ ,6.9
+ ,6.6
+ ,7.7
+ ,430
+ ,8.00
+ ,8.00
+ ,7.7
+ ,6.9
+ ,7.3
+ ,424
+ ,7.7
+ ,8.00
+ ,8.00
+ ,7.7
+ ,7.4
+ ,433
+ ,7.3
+ ,7.7
+ ,8.00
+ ,8.00
+ ,8.1
+ ,456
+ ,7.4
+ ,7.3
+ ,7.7
+ ,8.00
+ ,8.3
+ ,459
+ ,8.1
+ ,7.4
+ ,7.3
+ ,7.7
+ ,8.2
+ ,446
+ ,8.3
+ ,8.1
+ ,7.4
+ ,7.3)
+ ,dim=c(6
+ ,69)
+ ,dimnames=list(c('wgb'
+ ,'nwwz'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:69))
> y <- array(NA,dim=c(6,69),dimnames=list(c('wgb','nwwz','Y1','Y2','Y3','Y4'),1:69))
> 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
wgb nwwz Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.9 426 8.6 8.4 8.4 8.4 1 0 0 0 0 0 0 0 0 0 0 1
2 8.8 428 8.9 8.6 8.4 8.4 0 1 0 0 0 0 0 0 0 0 0 2
3 8.3 430 8.8 8.9 8.6 8.4 0 0 1 0 0 0 0 0 0 0 0 3
4 7.5 424 8.3 8.8 8.9 8.6 0 0 0 1 0 0 0 0 0 0 0 4
5 7.2 423 7.5 8.3 8.8 8.9 0 0 0 0 1 0 0 0 0 0 0 5
6 7.4 427 7.2 7.5 8.3 8.8 0 0 0 0 0 1 0 0 0 0 0 6
7 8.8 441 7.4 7.2 7.5 8.3 0 0 0 0 0 0 1 0 0 0 0 7
8 9.3 449 8.8 7.4 7.2 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 9.3 452 9.3 8.8 7.4 7.2 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 462 9.3 9.3 8.8 7.4 0 0 0 0 0 0 0 0 0 1 0 10
11 8.2 455 8.7 9.3 9.3 8.8 0 0 0 0 0 0 0 0 0 0 1 11
12 8.3 461 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 461 8.3 8.2 8.7 9.3 1 0 0 0 0 0 0 0 0 0 0 13
14 8.6 463 8.5 8.3 8.2 8.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 462 8.6 8.5 8.3 8.2 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 456 8.5 8.6 8.5 8.3 0 0 0 1 0 0 0 0 0 0 0 16
17 8.1 455 8.2 8.5 8.6 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 7.9 456 8.1 8.2 8.5 8.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.6 472 7.9 8.1 8.2 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.7 472 8.6 7.9 8.1 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 471 8.7 8.6 7.9 8.1 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 465 8.7 8.7 8.6 7.9 0 0 0 0 0 0 0 0 0 1 0 22
23 8.4 459 8.5 8.7 8.7 8.6 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 465 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 0 0 0 0 24
25 8.7 468 8.5 8.4 8.5 8.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.7 467 8.7 8.5 8.4 8.5 0 1 0 0 0 0 0 0 0 0 0 26
27 8.6 463 8.7 8.7 8.5 8.4 0 0 1 0 0 0 0 0 0 0 0 27
28 8.5 460 8.6 8.7 8.7 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.3 462 8.5 8.6 8.7 8.7 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 461 8.3 8.5 8.6 8.7 0 0 0 0 0 1 0 0 0 0 0 30
31 8.2 476 8.0 8.3 8.5 8.6 0 0 0 0 0 0 1 0 0 0 0 31
32 8.1 476 8.2 8.0 8.3 8.5 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 471 8.1 8.2 8.0 8.3 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 453 8.1 8.1 8.2 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 443 8.0 8.1 8.1 8.2 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 442 7.9 8.0 8.1 8.1 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 444 7.9 7.9 8.0 8.1 1 0 0 0 0 0 0 0 0 0 0 37
38 8.0 438 8.0 7.9 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 427 8.0 8.0 7.9 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 8.0 424 7.9 8.0 8.0 7.9 0 0 0 1 0 0 0 0 0 0 0 40
41 7.7 416 8.0 7.9 8.0 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.2 406 7.7 8.0 7.9 8.0 0 0 0 0 0 1 0 0 0 0 0 42
43 7.5 431 7.2 7.7 8.0 7.9 0 0 0 0 0 0 1 0 0 0 0 43
44 7.3 434 7.5 7.2 7.7 8.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.0 418 7.3 7.5 7.2 7.7 0 0 0 0 0 0 0 0 1 0 0 45
46 7.0 412 7.0 7.3 7.5 7.2 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 404 7.0 7.0 7.3 7.5 0 0 0 0 0 0 0 0 0 0 1 47
48 7.2 409 7.0 7.0 7.0 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 7.3 412 7.2 7.0 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0 49
50 7.1 406 7.3 7.2 7.0 7.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.8 398 7.1 7.3 7.2 7.0 0 0 1 0 0 0 0 0 0 0 0 51
52 6.4 397 6.8 7.1 7.3 7.2 0 0 0 1 0 0 0 0 0 0 0 52
53 6.1 385 6.4 6.8 7.1 7.3 0 0 0 0 1 0 0 0 0 0 0 53
54 6.5 390 6.1 6.4 6.8 7.1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 413 6.5 6.1 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 55
56 7.9 413 7.7 6.5 6.1 6.4 0 0 0 0 0 0 0 1 0 0 0 56
57 7.5 401 7.9 7.7 6.5 6.1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.9 397 7.5 7.9 7.7 6.5 0 0 0 0 0 0 0 0 0 1 0 58
59 6.6 397 6.9 7.5 7.9 7.7 0 0 0 0 0 0 0 0 0 0 1 59
60 6.9 409 6.6 6.9 7.5 7.9 0 0 0 0 0 0 0 0 0 0 0 60
61 7.7 419 6.9 6.6 6.9 7.5 1 0 0 0 0 0 0 0 0 0 0 61
62 8.0 424 7.7 6.9 6.6 6.9 0 1 0 0 0 0 0 0 0 0 0 62
63 8.0 428 8.0 7.7 6.9 6.6 0 0 1 0 0 0 0 0 0 0 0 63
64 7.7 430 8.0 8.0 7.7 6.9 0 0 0 1 0 0 0 0 0 0 0 64
65 7.3 424 7.7 8.0 8.0 7.7 0 0 0 0 1 0 0 0 0 0 0 65
66 7.4 433 7.3 7.7 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 66
67 8.1 456 7.4 7.3 7.7 8.0 0 0 0 0 0 0 1 0 0 0 0 67
68 8.3 459 8.1 7.4 7.3 7.7 0 0 0 0 0 0 0 1 0 0 0 68
69 8.2 446 8.3 8.1 7.4 7.3 0 0 0 0 0 0 0 0 1 0 0 69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) nwwz Y1 Y2 Y3 Y4
0.451954 0.005873 1.388090 -0.788797 -0.114052 0.167309
M1 M2 M3 M4 M5 M6
-0.052024 -0.280147 -0.197064 -0.202127 -0.213586 -0.158147
M7 M8 M9 M10 M11 t
0.337283 -0.617033 -0.235285 -0.148948 -0.118844 -0.003449
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.304781 -0.106323 -0.001841 0.080600 0.333305
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.451954 0.493516 0.916 0.36409
nwwz 0.005873 0.002115 2.777 0.00766 **
Y1 1.388090 0.144931 9.578 5.50e-13 ***
Y2 -0.788797 0.244577 -3.225 0.00220 **
Y3 -0.114052 0.242952 -0.469 0.64075
Y4 0.167309 0.139028 1.203 0.23437
M1 -0.052024 0.103645 -0.502 0.61787
M2 -0.280147 0.110452 -2.536 0.01430 *
M3 -0.197064 0.110807 -1.778 0.08129 .
M4 -0.202127 0.105266 -1.920 0.06044 .
M5 -0.213586 0.101665 -2.101 0.04061 *
M6 -0.158147 0.099475 -1.590 0.11806
M7 0.337283 0.108874 3.098 0.00317 **
M8 -0.617033 0.131268 -4.701 2.01e-05 ***
M9 -0.235285 0.154413 -1.524 0.13375
M10 -0.148948 0.135101 -1.102 0.27542
M11 -0.118844 0.106089 -1.120 0.26786
t -0.003449 0.001895 -1.820 0.07457 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1627 on 51 degrees of freedom
Multiple R-squared: 0.9598, Adjusted R-squared: 0.9464
F-statistic: 71.67 on 17 and 51 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,] 0.20062495 0.4012499 0.7993751
[2,] 0.11591872 0.2318374 0.8840813
[3,] 0.16711351 0.3342270 0.8328865
[4,] 0.24869819 0.4973964 0.7513018
[5,] 0.15458373 0.3091675 0.8454163
[6,] 0.09396957 0.1879391 0.9060304
[7,] 0.13082894 0.2616579 0.8691711
[8,] 0.26812344 0.5362469 0.7318766
[9,] 0.22828524 0.4565705 0.7717148
[10,] 0.22092722 0.4418544 0.7790728
[11,] 0.50127844 0.9974431 0.4987216
[12,] 0.42266389 0.8453278 0.5773361
[13,] 0.36109811 0.7221962 0.6389019
[14,] 0.35750250 0.7150050 0.6424975
[15,] 0.35630838 0.7126168 0.6436916
[16,] 0.30941624 0.6188325 0.6905838
[17,] 0.23483197 0.4696639 0.7651680
[18,] 0.19731871 0.3946374 0.8026813
[19,] 0.15402501 0.3080500 0.8459750
[20,] 0.72741948 0.5451610 0.2725805
[21,] 0.82420722 0.3515856 0.1757928
[22,] 0.74621670 0.5075666 0.2537833
[23,] 0.83577591 0.3284482 0.1642241
[24,] 0.78540643 0.4291871 0.2145936
[25,] 0.72041692 0.5591662 0.2795831
[26,] 0.69951384 0.6009723 0.3004862
[27,] 0.59434923 0.8113015 0.4056508
[28,] 0.67351093 0.6529781 0.3264891
> postscript(file="/var/www/html/rcomp/tmp/1lbsd1258626342.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24z4a1258626342.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r5gs1258626342.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cdqy1258626342.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5zigd1258626342.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 = 69
Frequency = 1
1 2 3 4 5 6
0.24276186 0.10392139 -0.08919991 -0.22953238 0.14572402 0.01533802
7 8 9 10 11 12
0.31929766 0.04414655 0.13150334 -0.08949994 0.08059967 0.16708230
13 14 15 16 17 18
-0.17908328 -0.01463493 -0.07438678 -0.10687036 0.12940097 -0.25442798
19 20 21 22 23 24
0.04088445 0.00801419 0.04285684 -0.01261698 0.06786957 -0.01844148
25 26 27 28 29 30
-0.02108511 0.03967813 0.06942922 0.14044828 -0.02992141 -0.18870591
31 32 33 34 35 36
-0.30478106 0.03264706 -0.02047460 -0.10353224 -0.07752022 -0.11038175
37 38 39 40 41 42
-0.05693853 0.07638596 0.05696010 0.33330486 -0.13922629 -0.14858934
43 44 45 46 47 48
-0.00184125 -0.12346634 -0.20038061 0.12850553 -0.16081180 -0.10632288
49 50 51 52 53 54
-0.19589255 -0.11013443 -0.06348037 -0.21248390 -0.14804943 0.27075210
55 56 57 58 59 60
0.05639967 -0.10331730 -0.04639377 0.07714363 0.08986277 0.06806380
61 62 63 64 65 66
0.21023762 -0.09521612 0.10067774 0.07513350 0.04207214 0.30563311
67 68 69
-0.10995948 0.14197584 0.09288881
> postscript(file="/var/www/html/rcomp/tmp/6bvv01258626342.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 = 69
Frequency = 1
lag(myerror, k = 1) myerror
0 0.24276186 NA
1 0.10392139 0.24276186
2 -0.08919991 0.10392139
3 -0.22953238 -0.08919991
4 0.14572402 -0.22953238
5 0.01533802 0.14572402
6 0.31929766 0.01533802
7 0.04414655 0.31929766
8 0.13150334 0.04414655
9 -0.08949994 0.13150334
10 0.08059967 -0.08949994
11 0.16708230 0.08059967
12 -0.17908328 0.16708230
13 -0.01463493 -0.17908328
14 -0.07438678 -0.01463493
15 -0.10687036 -0.07438678
16 0.12940097 -0.10687036
17 -0.25442798 0.12940097
18 0.04088445 -0.25442798
19 0.00801419 0.04088445
20 0.04285684 0.00801419
21 -0.01261698 0.04285684
22 0.06786957 -0.01261698
23 -0.01844148 0.06786957
24 -0.02108511 -0.01844148
25 0.03967813 -0.02108511
26 0.06942922 0.03967813
27 0.14044828 0.06942922
28 -0.02992141 0.14044828
29 -0.18870591 -0.02992141
30 -0.30478106 -0.18870591
31 0.03264706 -0.30478106
32 -0.02047460 0.03264706
33 -0.10353224 -0.02047460
34 -0.07752022 -0.10353224
35 -0.11038175 -0.07752022
36 -0.05693853 -0.11038175
37 0.07638596 -0.05693853
38 0.05696010 0.07638596
39 0.33330486 0.05696010
40 -0.13922629 0.33330486
41 -0.14858934 -0.13922629
42 -0.00184125 -0.14858934
43 -0.12346634 -0.00184125
44 -0.20038061 -0.12346634
45 0.12850553 -0.20038061
46 -0.16081180 0.12850553
47 -0.10632288 -0.16081180
48 -0.19589255 -0.10632288
49 -0.11013443 -0.19589255
50 -0.06348037 -0.11013443
51 -0.21248390 -0.06348037
52 -0.14804943 -0.21248390
53 0.27075210 -0.14804943
54 0.05639967 0.27075210
55 -0.10331730 0.05639967
56 -0.04639377 -0.10331730
57 0.07714363 -0.04639377
58 0.08986277 0.07714363
59 0.06806380 0.08986277
60 0.21023762 0.06806380
61 -0.09521612 0.21023762
62 0.10067774 -0.09521612
63 0.07513350 0.10067774
64 0.04207214 0.07513350
65 0.30563311 0.04207214
66 -0.10995948 0.30563311
67 0.14197584 -0.10995948
68 0.09288881 0.14197584
69 NA 0.09288881
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.10392139 0.24276186
[2,] -0.08919991 0.10392139
[3,] -0.22953238 -0.08919991
[4,] 0.14572402 -0.22953238
[5,] 0.01533802 0.14572402
[6,] 0.31929766 0.01533802
[7,] 0.04414655 0.31929766
[8,] 0.13150334 0.04414655
[9,] -0.08949994 0.13150334
[10,] 0.08059967 -0.08949994
[11,] 0.16708230 0.08059967
[12,] -0.17908328 0.16708230
[13,] -0.01463493 -0.17908328
[14,] -0.07438678 -0.01463493
[15,] -0.10687036 -0.07438678
[16,] 0.12940097 -0.10687036
[17,] -0.25442798 0.12940097
[18,] 0.04088445 -0.25442798
[19,] 0.00801419 0.04088445
[20,] 0.04285684 0.00801419
[21,] -0.01261698 0.04285684
[22,] 0.06786957 -0.01261698
[23,] -0.01844148 0.06786957
[24,] -0.02108511 -0.01844148
[25,] 0.03967813 -0.02108511
[26,] 0.06942922 0.03967813
[27,] 0.14044828 0.06942922
[28,] -0.02992141 0.14044828
[29,] -0.18870591 -0.02992141
[30,] -0.30478106 -0.18870591
[31,] 0.03264706 -0.30478106
[32,] -0.02047460 0.03264706
[33,] -0.10353224 -0.02047460
[34,] -0.07752022 -0.10353224
[35,] -0.11038175 -0.07752022
[36,] -0.05693853 -0.11038175
[37,] 0.07638596 -0.05693853
[38,] 0.05696010 0.07638596
[39,] 0.33330486 0.05696010
[40,] -0.13922629 0.33330486
[41,] -0.14858934 -0.13922629
[42,] -0.00184125 -0.14858934
[43,] -0.12346634 -0.00184125
[44,] -0.20038061 -0.12346634
[45,] 0.12850553 -0.20038061
[46,] -0.16081180 0.12850553
[47,] -0.10632288 -0.16081180
[48,] -0.19589255 -0.10632288
[49,] -0.11013443 -0.19589255
[50,] -0.06348037 -0.11013443
[51,] -0.21248390 -0.06348037
[52,] -0.14804943 -0.21248390
[53,] 0.27075210 -0.14804943
[54,] 0.05639967 0.27075210
[55,] -0.10331730 0.05639967
[56,] -0.04639377 -0.10331730
[57,] 0.07714363 -0.04639377
[58,] 0.08986277 0.07714363
[59,] 0.06806380 0.08986277
[60,] 0.21023762 0.06806380
[61,] -0.09521612 0.21023762
[62,] 0.10067774 -0.09521612
[63,] 0.07513350 0.10067774
[64,] 0.04207214 0.07513350
[65,] 0.30563311 0.04207214
[66,] -0.10995948 0.30563311
[67,] 0.14197584 -0.10995948
[68,] 0.09288881 0.14197584
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.10392139 0.24276186
2 -0.08919991 0.10392139
3 -0.22953238 -0.08919991
4 0.14572402 -0.22953238
5 0.01533802 0.14572402
6 0.31929766 0.01533802
7 0.04414655 0.31929766
8 0.13150334 0.04414655
9 -0.08949994 0.13150334
10 0.08059967 -0.08949994
11 0.16708230 0.08059967
12 -0.17908328 0.16708230
13 -0.01463493 -0.17908328
14 -0.07438678 -0.01463493
15 -0.10687036 -0.07438678
16 0.12940097 -0.10687036
17 -0.25442798 0.12940097
18 0.04088445 -0.25442798
19 0.00801419 0.04088445
20 0.04285684 0.00801419
21 -0.01261698 0.04285684
22 0.06786957 -0.01261698
23 -0.01844148 0.06786957
24 -0.02108511 -0.01844148
25 0.03967813 -0.02108511
26 0.06942922 0.03967813
27 0.14044828 0.06942922
28 -0.02992141 0.14044828
29 -0.18870591 -0.02992141
30 -0.30478106 -0.18870591
31 0.03264706 -0.30478106
32 -0.02047460 0.03264706
33 -0.10353224 -0.02047460
34 -0.07752022 -0.10353224
35 -0.11038175 -0.07752022
36 -0.05693853 -0.11038175
37 0.07638596 -0.05693853
38 0.05696010 0.07638596
39 0.33330486 0.05696010
40 -0.13922629 0.33330486
41 -0.14858934 -0.13922629
42 -0.00184125 -0.14858934
43 -0.12346634 -0.00184125
44 -0.20038061 -0.12346634
45 0.12850553 -0.20038061
46 -0.16081180 0.12850553
47 -0.10632288 -0.16081180
48 -0.19589255 -0.10632288
49 -0.11013443 -0.19589255
50 -0.06348037 -0.11013443
51 -0.21248390 -0.06348037
52 -0.14804943 -0.21248390
53 0.27075210 -0.14804943
54 0.05639967 0.27075210
55 -0.10331730 0.05639967
56 -0.04639377 -0.10331730
57 0.07714363 -0.04639377
58 0.08986277 0.07714363
59 0.06806380 0.08986277
60 0.21023762 0.06806380
61 -0.09521612 0.21023762
62 0.10067774 -0.09521612
63 0.07513350 0.10067774
64 0.04207214 0.07513350
65 0.30563311 0.04207214
66 -0.10995948 0.30563311
67 0.14197584 -0.10995948
68 0.09288881 0.14197584
> 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/7x6911258626342.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sp0j1258626342.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9o79i1258626342.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mzfu1258626342.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1108fd1258626342.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/1244et1258626342.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/13iisf1258626342.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/14rfr11258626342.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/15581q1258626342.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/16gq041258626342.tab")
+ }
>
> system("convert tmp/1lbsd1258626342.ps tmp/1lbsd1258626342.png")
> system("convert tmp/24z4a1258626342.ps tmp/24z4a1258626342.png")
> system("convert tmp/3r5gs1258626342.ps tmp/3r5gs1258626342.png")
> system("convert tmp/4cdqy1258626342.ps tmp/4cdqy1258626342.png")
> system("convert tmp/5zigd1258626342.ps tmp/5zigd1258626342.png")
> system("convert tmp/6bvv01258626342.ps tmp/6bvv01258626342.png")
> system("convert tmp/7x6911258626342.ps tmp/7x6911258626342.png")
> system("convert tmp/8sp0j1258626342.ps tmp/8sp0j1258626342.png")
> system("convert tmp/9o79i1258626342.ps tmp/9o79i1258626342.png")
> system("convert tmp/10mzfu1258626342.ps tmp/10mzfu1258626342.png")
>
>
> proc.time()
user system elapsed
2.511 1.542 4.238