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(29.837,0,29.571,0,30.167,0,30.524,0,30.996,0,31.033,0,31.198,0,30.937,0,31.649,0,33.115,0,34.106,0,33.926,0,33.382,0,32.851,0,32.948,0,36.112,0,36.113,0,35.210,0,35.193,0,34.383,0,35.349,0,37.058,0,38.076,0,36.630,0,36.045,0,35.638,0,35.114,0,35.465,0,35.254,0,35.299,0,35.916,0,36.683,0,37.288,0,38.536,0,38.977,0,36.407,0,34.955,0,34.951,0,32.680,0,34.791,0,34.178,0,35.213,0,34.871,0,35.299,0,35.443,0,37.108,0,36.419,0,34.471,0,33.868,0,34.385,0,33.643,1,34.627,1,32.919,1,35.500,1,36.110,1,37.086,1,37.711,1,40.427,1,39.884,1,38.512,1,38.767,1),dim=c(2,61),dimnames=list(c('saldo_zichtrek','crisis'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('saldo_zichtrek','crisis'),1:61))
> 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
saldo_zichtrek crisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 29.837 0 1 0 0 0 0 0 0 0 0 0 0 1
2 29.571 0 0 1 0 0 0 0 0 0 0 0 0 2
3 30.167 0 0 0 1 0 0 0 0 0 0 0 0 3
4 30.524 0 0 0 0 1 0 0 0 0 0 0 0 4
5 30.996 0 0 0 0 0 1 0 0 0 0 0 0 5
6 31.033 0 0 0 0 0 0 1 0 0 0 0 0 6
7 31.198 0 0 0 0 0 0 0 1 0 0 0 0 7
8 30.937 0 0 0 0 0 0 0 0 1 0 0 0 8
9 31.649 0 0 0 0 0 0 0 0 0 1 0 0 9
10 33.115 0 0 0 0 0 0 0 0 0 0 1 0 10
11 34.106 0 0 0 0 0 0 0 0 0 0 0 1 11
12 33.926 0 0 0 0 0 0 0 0 0 0 0 0 12
13 33.382 0 1 0 0 0 0 0 0 0 0 0 0 13
14 32.851 0 0 1 0 0 0 0 0 0 0 0 0 14
15 32.948 0 0 0 1 0 0 0 0 0 0 0 0 15
16 36.112 0 0 0 0 1 0 0 0 0 0 0 0 16
17 36.113 0 0 0 0 0 1 0 0 0 0 0 0 17
18 35.210 0 0 0 0 0 0 1 0 0 0 0 0 18
19 35.193 0 0 0 0 0 0 0 1 0 0 0 0 19
20 34.383 0 0 0 0 0 0 0 0 1 0 0 0 20
21 35.349 0 0 0 0 0 0 0 0 0 1 0 0 21
22 37.058 0 0 0 0 0 0 0 0 0 0 1 0 22
23 38.076 0 0 0 0 0 0 0 0 0 0 0 1 23
24 36.630 0 0 0 0 0 0 0 0 0 0 0 0 24
25 36.045 0 1 0 0 0 0 0 0 0 0 0 0 25
26 35.638 0 0 1 0 0 0 0 0 0 0 0 0 26
27 35.114 0 0 0 1 0 0 0 0 0 0 0 0 27
28 35.465 0 0 0 0 1 0 0 0 0 0 0 0 28
29 35.254 0 0 0 0 0 1 0 0 0 0 0 0 29
30 35.299 0 0 0 0 0 0 1 0 0 0 0 0 30
31 35.916 0 0 0 0 0 0 0 1 0 0 0 0 31
32 36.683 0 0 0 0 0 0 0 0 1 0 0 0 32
33 37.288 0 0 0 0 0 0 0 0 0 1 0 0 33
34 38.536 0 0 0 0 0 0 0 0 0 0 1 0 34
35 38.977 0 0 0 0 0 0 0 0 0 0 0 1 35
36 36.407 0 0 0 0 0 0 0 0 0 0 0 0 36
37 34.955 0 1 0 0 0 0 0 0 0 0 0 0 37
38 34.951 0 0 1 0 0 0 0 0 0 0 0 0 38
39 32.680 0 0 0 1 0 0 0 0 0 0 0 0 39
40 34.791 0 0 0 0 1 0 0 0 0 0 0 0 40
41 34.178 0 0 0 0 0 1 0 0 0 0 0 0 41
42 35.213 0 0 0 0 0 0 1 0 0 0 0 0 42
43 34.871 0 0 0 0 0 0 0 1 0 0 0 0 43
44 35.299 0 0 0 0 0 0 0 0 1 0 0 0 44
45 35.443 0 0 0 0 0 0 0 0 0 1 0 0 45
46 37.108 0 0 0 0 0 0 0 0 0 0 1 0 46
47 36.419 0 0 0 0 0 0 0 0 0 0 0 1 47
48 34.471 0 0 0 0 0 0 0 0 0 0 0 0 48
49 33.868 0 1 0 0 0 0 0 0 0 0 0 0 49
50 34.385 0 0 1 0 0 0 0 0 0 0 0 0 50
51 33.643 1 0 0 1 0 0 0 0 0 0 0 0 51
52 34.627 1 0 0 0 1 0 0 0 0 0 0 0 52
53 32.919 1 0 0 0 0 1 0 0 0 0 0 0 53
54 35.500 1 0 0 0 0 0 1 0 0 0 0 0 54
55 36.110 1 0 0 0 0 0 0 1 0 0 0 0 55
56 37.086 1 0 0 0 0 0 0 0 1 0 0 0 56
57 37.711 1 0 0 0 0 0 0 0 0 1 0 0 57
58 40.427 1 0 0 0 0 0 0 0 0 0 1 0 58
59 39.884 1 0 0 0 0 0 0 0 0 0 0 1 59
60 38.512 1 0 0 0 0 0 0 0 0 0 0 0 60
61 38.767 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) crisis M1 M2 M3 M4
32.86170 -0.55318 -1.08223 -1.72116 -2.26927 -0.96581
M5 M6 M7 M8 M9 M10
-1.46756 -0.99851 -0.88186 -0.75181 -0.23136 1.43950
M11 t
1.59315 0.08995
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.7082 -1.3308 0.3072 1.1766 3.1897
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.86170 0.90880 36.159 < 2e-16 ***
crisis -0.55318 0.76150 -0.726 0.4712
M1 -1.08223 1.01917 -1.062 0.2937
M2 -1.72116 1.07000 -1.609 0.1144
M3 -2.26927 1.07262 -2.116 0.0397 *
M4 -0.96581 1.07041 -0.902 0.3715
M5 -1.46756 1.06845 -1.374 0.1761
M6 -0.99851 1.06675 -0.936 0.3540
M7 -0.88186 1.06531 -0.828 0.4120
M8 -0.75181 1.06413 -0.706 0.4834
M9 -0.23136 1.06322 -0.218 0.8287
M10 1.43950 1.06256 1.355 0.1820
M11 1.59315 1.06217 1.500 0.1403
t 0.08995 0.01670 5.385 2.26e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.679 on 47 degrees of freedom
Multiple R-squared: 0.6367, Adjusted R-squared: 0.5363
F-statistic: 6.337 on 13 and 47 DF, p-value: 1.076e-06
> 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,] 0.48989980 0.97979960 0.5101002
[2,] 0.31538735 0.63077470 0.6846127
[3,] 0.18683114 0.37366228 0.8131689
[4,] 0.13885837 0.27771675 0.8611416
[5,] 0.09301797 0.18603594 0.9069820
[6,] 0.07015613 0.14031225 0.9298439
[7,] 0.04161294 0.08322588 0.9583871
[8,] 0.04468042 0.08936083 0.9553196
[9,] 0.05666617 0.11333233 0.9433338
[10,] 0.05311801 0.10623601 0.9468820
[11,] 0.10368901 0.20737801 0.8963110
[12,] 0.29871832 0.59743663 0.7012817
[13,] 0.53194138 0.93611725 0.4680586
[14,] 0.55211351 0.89577299 0.4478865
[15,] 0.50235941 0.99528117 0.4976406
[16,] 0.40932084 0.81864169 0.5906792
[17,] 0.33340364 0.66680728 0.6665964
[18,] 0.25190679 0.50381359 0.7480932
[19,] 0.22493082 0.44986163 0.7750692
[20,] 0.27012922 0.54025844 0.7298708
[21,] 0.32461367 0.64922733 0.6753863
[22,] 0.29564546 0.59129091 0.7043545
[23,] 0.40863046 0.81726093 0.5913695
[24,] 0.47838306 0.95676612 0.5216169
[25,] 0.76456317 0.47087365 0.2354368
[26,] 0.84371460 0.31257080 0.1562854
[27,] 0.86656582 0.26686836 0.1334342
[28,] 0.86585034 0.26829932 0.1341497
> postscript(file="/var/www/html/rcomp/tmp/15wiw1259256087.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cpf61259256087.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bh8r1259256087.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ba7v1259256087.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50hkb1259256087.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 = 61
Frequency = 1
1 2 3 4 5 6
-2.03241989 -1.74944462 -0.69528156 -1.73168156 -0.84788156 -1.36988156
7 8 9 10 11 12
-1.41148156 -1.89248156 -1.79088156 -2.08568156 -1.33828156 -0.01508156
13 14 15 16 17 18
0.43320242 0.45117769 1.00634075 2.77694075 3.18974075 1.72774075
19 20 21 22 23 24
1.50414075 0.47414075 0.82974075 0.77794075 1.55234075 1.60954075
25 26 27 28 29 30
2.01682473 2.15880000 2.09296306 1.05056306 1.25136306 0.73736306
31 32 33 34 35 36
1.14776306 1.69476306 1.68936306 1.17656306 1.37396306 0.30716306
37 38 39 40 41 42
-0.15255296 0.39242231 -1.42041462 -0.70281462 -0.90401462 -0.42801462
43 44 45 46 47 48
-0.97661462 -0.76861462 -1.23501462 -1.33081462 -2.26341462 -2.70821462
49 50 51 52 53 54
-2.31893065 -1.25295538 -0.98360763 -1.39300763 -2.68920763 -0.66720763
55 56 57 58 59 60
-0.26380763 0.49219237 0.50679237 1.46199237 0.67539237 0.80659237
61
2.05387634
> postscript(file="/var/www/html/rcomp/tmp/6a9w71259256087.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.03241989 NA
1 -1.74944462 -2.03241989
2 -0.69528156 -1.74944462
3 -1.73168156 -0.69528156
4 -0.84788156 -1.73168156
5 -1.36988156 -0.84788156
6 -1.41148156 -1.36988156
7 -1.89248156 -1.41148156
8 -1.79088156 -1.89248156
9 -2.08568156 -1.79088156
10 -1.33828156 -2.08568156
11 -0.01508156 -1.33828156
12 0.43320242 -0.01508156
13 0.45117769 0.43320242
14 1.00634075 0.45117769
15 2.77694075 1.00634075
16 3.18974075 2.77694075
17 1.72774075 3.18974075
18 1.50414075 1.72774075
19 0.47414075 1.50414075
20 0.82974075 0.47414075
21 0.77794075 0.82974075
22 1.55234075 0.77794075
23 1.60954075 1.55234075
24 2.01682473 1.60954075
25 2.15880000 2.01682473
26 2.09296306 2.15880000
27 1.05056306 2.09296306
28 1.25136306 1.05056306
29 0.73736306 1.25136306
30 1.14776306 0.73736306
31 1.69476306 1.14776306
32 1.68936306 1.69476306
33 1.17656306 1.68936306
34 1.37396306 1.17656306
35 0.30716306 1.37396306
36 -0.15255296 0.30716306
37 0.39242231 -0.15255296
38 -1.42041462 0.39242231
39 -0.70281462 -1.42041462
40 -0.90401462 -0.70281462
41 -0.42801462 -0.90401462
42 -0.97661462 -0.42801462
43 -0.76861462 -0.97661462
44 -1.23501462 -0.76861462
45 -1.33081462 -1.23501462
46 -2.26341462 -1.33081462
47 -2.70821462 -2.26341462
48 -2.31893065 -2.70821462
49 -1.25295538 -2.31893065
50 -0.98360763 -1.25295538
51 -1.39300763 -0.98360763
52 -2.68920763 -1.39300763
53 -0.66720763 -2.68920763
54 -0.26380763 -0.66720763
55 0.49219237 -0.26380763
56 0.50679237 0.49219237
57 1.46199237 0.50679237
58 0.67539237 1.46199237
59 0.80659237 0.67539237
60 2.05387634 0.80659237
61 NA 2.05387634
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.74944462 -2.03241989
[2,] -0.69528156 -1.74944462
[3,] -1.73168156 -0.69528156
[4,] -0.84788156 -1.73168156
[5,] -1.36988156 -0.84788156
[6,] -1.41148156 -1.36988156
[7,] -1.89248156 -1.41148156
[8,] -1.79088156 -1.89248156
[9,] -2.08568156 -1.79088156
[10,] -1.33828156 -2.08568156
[11,] -0.01508156 -1.33828156
[12,] 0.43320242 -0.01508156
[13,] 0.45117769 0.43320242
[14,] 1.00634075 0.45117769
[15,] 2.77694075 1.00634075
[16,] 3.18974075 2.77694075
[17,] 1.72774075 3.18974075
[18,] 1.50414075 1.72774075
[19,] 0.47414075 1.50414075
[20,] 0.82974075 0.47414075
[21,] 0.77794075 0.82974075
[22,] 1.55234075 0.77794075
[23,] 1.60954075 1.55234075
[24,] 2.01682473 1.60954075
[25,] 2.15880000 2.01682473
[26,] 2.09296306 2.15880000
[27,] 1.05056306 2.09296306
[28,] 1.25136306 1.05056306
[29,] 0.73736306 1.25136306
[30,] 1.14776306 0.73736306
[31,] 1.69476306 1.14776306
[32,] 1.68936306 1.69476306
[33,] 1.17656306 1.68936306
[34,] 1.37396306 1.17656306
[35,] 0.30716306 1.37396306
[36,] -0.15255296 0.30716306
[37,] 0.39242231 -0.15255296
[38,] -1.42041462 0.39242231
[39,] -0.70281462 -1.42041462
[40,] -0.90401462 -0.70281462
[41,] -0.42801462 -0.90401462
[42,] -0.97661462 -0.42801462
[43,] -0.76861462 -0.97661462
[44,] -1.23501462 -0.76861462
[45,] -1.33081462 -1.23501462
[46,] -2.26341462 -1.33081462
[47,] -2.70821462 -2.26341462
[48,] -2.31893065 -2.70821462
[49,] -1.25295538 -2.31893065
[50,] -0.98360763 -1.25295538
[51,] -1.39300763 -0.98360763
[52,] -2.68920763 -1.39300763
[53,] -0.66720763 -2.68920763
[54,] -0.26380763 -0.66720763
[55,] 0.49219237 -0.26380763
[56,] 0.50679237 0.49219237
[57,] 1.46199237 0.50679237
[58,] 0.67539237 1.46199237
[59,] 0.80659237 0.67539237
[60,] 2.05387634 0.80659237
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.74944462 -2.03241989
2 -0.69528156 -1.74944462
3 -1.73168156 -0.69528156
4 -0.84788156 -1.73168156
5 -1.36988156 -0.84788156
6 -1.41148156 -1.36988156
7 -1.89248156 -1.41148156
8 -1.79088156 -1.89248156
9 -2.08568156 -1.79088156
10 -1.33828156 -2.08568156
11 -0.01508156 -1.33828156
12 0.43320242 -0.01508156
13 0.45117769 0.43320242
14 1.00634075 0.45117769
15 2.77694075 1.00634075
16 3.18974075 2.77694075
17 1.72774075 3.18974075
18 1.50414075 1.72774075
19 0.47414075 1.50414075
20 0.82974075 0.47414075
21 0.77794075 0.82974075
22 1.55234075 0.77794075
23 1.60954075 1.55234075
24 2.01682473 1.60954075
25 2.15880000 2.01682473
26 2.09296306 2.15880000
27 1.05056306 2.09296306
28 1.25136306 1.05056306
29 0.73736306 1.25136306
30 1.14776306 0.73736306
31 1.69476306 1.14776306
32 1.68936306 1.69476306
33 1.17656306 1.68936306
34 1.37396306 1.17656306
35 0.30716306 1.37396306
36 -0.15255296 0.30716306
37 0.39242231 -0.15255296
38 -1.42041462 0.39242231
39 -0.70281462 -1.42041462
40 -0.90401462 -0.70281462
41 -0.42801462 -0.90401462
42 -0.97661462 -0.42801462
43 -0.76861462 -0.97661462
44 -1.23501462 -0.76861462
45 -1.33081462 -1.23501462
46 -2.26341462 -1.33081462
47 -2.70821462 -2.26341462
48 -2.31893065 -2.70821462
49 -1.25295538 -2.31893065
50 -0.98360763 -1.25295538
51 -1.39300763 -0.98360763
52 -2.68920763 -1.39300763
53 -0.66720763 -2.68920763
54 -0.26380763 -0.66720763
55 0.49219237 -0.26380763
56 0.50679237 0.49219237
57 1.46199237 0.50679237
58 0.67539237 1.46199237
59 0.80659237 0.67539237
60 2.05387634 0.80659237
> 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/71hsq1259256087.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ipgy1259256087.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rhvf1259256087.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10sljs1259256087.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11svjw1259256087.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/126ws71259256087.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/13rmza1259256087.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/14ew9e1259256087.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/15t37g1259256087.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/16nezi1259256087.tab")
+ }
>
> system("convert tmp/15wiw1259256087.ps tmp/15wiw1259256087.png")
> system("convert tmp/2cpf61259256087.ps tmp/2cpf61259256087.png")
> system("convert tmp/3bh8r1259256087.ps tmp/3bh8r1259256087.png")
> system("convert tmp/4ba7v1259256087.ps tmp/4ba7v1259256087.png")
> system("convert tmp/50hkb1259256087.ps tmp/50hkb1259256087.png")
> system("convert tmp/6a9w71259256087.ps tmp/6a9w71259256087.png")
> system("convert tmp/71hsq1259256087.ps tmp/71hsq1259256087.png")
> system("convert tmp/8ipgy1259256087.ps tmp/8ipgy1259256087.png")
> system("convert tmp/9rhvf1259256087.ps tmp/9rhvf1259256087.png")
> system("convert tmp/10sljs1259256087.ps tmp/10sljs1259256087.png")
>
>
> proc.time()
user system elapsed
2.422 1.565 2.853