R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2.06
+ ,0
+ ,2.08
+ ,2.05
+ ,2.09
+ ,2.11
+ ,2.06
+ ,0
+ ,2.06
+ ,2.08
+ ,2.05
+ ,2.09
+ ,2.08
+ ,0
+ ,2.06
+ ,2.06
+ ,2.08
+ ,2.05
+ ,2.07
+ ,0
+ ,2.08
+ ,2.06
+ ,2.06
+ ,2.08
+ ,2.06
+ ,0
+ ,2.07
+ ,2.08
+ ,2.06
+ ,2.06
+ ,2.07
+ ,0
+ ,2.06
+ ,2.07
+ ,2.08
+ ,2.06
+ ,2.06
+ ,0
+ ,2.07
+ ,2.06
+ ,2.07
+ ,2.08
+ ,2.09
+ ,0
+ ,2.06
+ ,2.07
+ ,2.06
+ ,2.07
+ ,2.07
+ ,0
+ ,2.09
+ ,2.06
+ ,2.07
+ ,2.06
+ ,2.09
+ ,0
+ ,2.07
+ ,2.09
+ ,2.06
+ ,2.07
+ ,2.28
+ ,0
+ ,2.09
+ ,2.07
+ ,2.09
+ ,2.06
+ ,2.33
+ ,0
+ ,2.28
+ ,2.09
+ ,2.07
+ ,2.09
+ ,2.35
+ ,0
+ ,2.33
+ ,2.28
+ ,2.09
+ ,2.07
+ ,2.52
+ ,0
+ ,2.35
+ ,2.33
+ ,2.28
+ ,2.09
+ ,2.63
+ ,0
+ ,2.52
+ ,2.35
+ ,2.33
+ ,2.28
+ ,2.58
+ ,0
+ ,2.63
+ ,2.52
+ ,2.35
+ ,2.33
+ ,2.70
+ ,0
+ ,2.58
+ ,2.63
+ ,2.52
+ ,2.35
+ ,2.81
+ ,0
+ ,2.70
+ ,2.58
+ ,2.63
+ ,2.52
+ ,2.97
+ ,0
+ ,2.81
+ ,2.70
+ ,2.58
+ ,2.63
+ ,3.04
+ ,0
+ ,2.97
+ ,2.81
+ ,2.70
+ ,2.58
+ ,3.28
+ ,0
+ ,3.04
+ ,2.97
+ ,2.81
+ ,2.70
+ ,3.33
+ ,0
+ ,3.28
+ ,3.04
+ ,2.97
+ ,2.81
+ ,3.50
+ ,0
+ ,3.33
+ ,3.28
+ ,3.04
+ ,2.97
+ ,3.56
+ ,0
+ ,3.50
+ ,3.33
+ ,3.28
+ ,3.04
+ ,3.57
+ ,0
+ ,3.56
+ ,3.50
+ ,3.33
+ ,3.28
+ ,3.69
+ ,0
+ ,3.57
+ ,3.56
+ ,3.50
+ ,3.33
+ ,3.82
+ ,0
+ ,3.69
+ ,3.57
+ ,3.56
+ ,3.50
+ ,3.79
+ ,0
+ ,3.82
+ ,3.69
+ ,3.57
+ ,3.56
+ ,3.96
+ ,0
+ ,3.79
+ ,3.82
+ ,3.69
+ ,3.57
+ ,4.06
+ ,0
+ ,3.96
+ ,3.79
+ ,3.82
+ ,3.69
+ ,4.05
+ ,0
+ ,4.06
+ ,3.96
+ ,3.79
+ ,3.82
+ ,4.03
+ ,0
+ ,4.05
+ ,4.06
+ ,3.96
+ ,3.79
+ ,3.94
+ ,0
+ ,4.03
+ ,4.05
+ ,4.06
+ ,3.96
+ ,4.02
+ ,0
+ ,3.94
+ ,4.03
+ ,4.05
+ ,4.06
+ ,3.88
+ ,0
+ ,4.02
+ ,3.94
+ ,4.03
+ ,4.05
+ ,4.02
+ ,0
+ ,3.88
+ ,4.02
+ ,3.94
+ ,4.03
+ ,4.03
+ ,0
+ ,4.02
+ ,3.88
+ ,4.02
+ ,3.94
+ ,4.09
+ ,0
+ ,4.03
+ ,4.02
+ ,3.88
+ ,4.02
+ ,3.99
+ ,0
+ ,4.09
+ ,4.03
+ ,4.02
+ ,3.88
+ ,4.01
+ ,0
+ ,3.99
+ ,4.09
+ ,4.03
+ ,4.02
+ ,4.01
+ ,0
+ ,4.01
+ ,3.99
+ ,4.09
+ ,4.03
+ ,4.19
+ ,0
+ ,4.01
+ ,4.01
+ ,3.99
+ ,4.09
+ ,4.30
+ ,0
+ ,4.19
+ ,4.01
+ ,4.01
+ ,3.99
+ ,4.27
+ ,0
+ ,4.30
+ ,4.19
+ ,4.01
+ ,4.01
+ ,3.82
+ ,0
+ ,4.27
+ ,4.30
+ ,4.19
+ ,4.01
+ ,3.15
+ ,1
+ ,3.82
+ ,4.27
+ ,4.30
+ ,4.19
+ ,2.49
+ ,1
+ ,3.15
+ ,3.82
+ ,4.27
+ ,4.30
+ ,1.81
+ ,1
+ ,2.49
+ ,3.15
+ ,3.82
+ ,4.27
+ ,1.26
+ ,1
+ ,1.81
+ ,2.49
+ ,3.15
+ ,3.82
+ ,1.06
+ ,1
+ ,1.26
+ ,1.81
+ ,2.49
+ ,3.15
+ ,0.84
+ ,1
+ ,1.06
+ ,1.26
+ ,1.81
+ ,2.49
+ ,0.78
+ ,1
+ ,0.84
+ ,1.06
+ ,1.26
+ ,1.81
+ ,0.70
+ ,1
+ ,0.78
+ ,0.84
+ ,1.06
+ ,1.26
+ ,0.36
+ ,1
+ ,0.70
+ ,0.78
+ ,0.84
+ ,1.06
+ ,0.35
+ ,1
+ ,0.36
+ ,0.70
+ ,0.78
+ ,0.84
+ ,0.36
+ ,1
+ ,0.35
+ ,0.36
+ ,0.70
+ ,0.78)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 t
1 2.06 0 2.08 2.05 2.09 2.11 1
2 2.06 0 2.06 2.08 2.05 2.09 2
3 2.08 0 2.06 2.06 2.08 2.05 3
4 2.07 0 2.08 2.06 2.06 2.08 4
5 2.06 0 2.07 2.08 2.06 2.06 5
6 2.07 0 2.06 2.07 2.08 2.06 6
7 2.06 0 2.07 2.06 2.07 2.08 7
8 2.09 0 2.06 2.07 2.06 2.07 8
9 2.07 0 2.09 2.06 2.07 2.06 9
10 2.09 0 2.07 2.09 2.06 2.07 10
11 2.28 0 2.09 2.07 2.09 2.06 11
12 2.33 0 2.28 2.09 2.07 2.09 12
13 2.35 0 2.33 2.28 2.09 2.07 13
14 2.52 0 2.35 2.33 2.28 2.09 14
15 2.63 0 2.52 2.35 2.33 2.28 15
16 2.58 0 2.63 2.52 2.35 2.33 16
17 2.70 0 2.58 2.63 2.52 2.35 17
18 2.81 0 2.70 2.58 2.63 2.52 18
19 2.97 0 2.81 2.70 2.58 2.63 19
20 3.04 0 2.97 2.81 2.70 2.58 20
21 3.28 0 3.04 2.97 2.81 2.70 21
22 3.33 0 3.28 3.04 2.97 2.81 22
23 3.50 0 3.33 3.28 3.04 2.97 23
24 3.56 0 3.50 3.33 3.28 3.04 24
25 3.57 0 3.56 3.50 3.33 3.28 25
26 3.69 0 3.57 3.56 3.50 3.33 26
27 3.82 0 3.69 3.57 3.56 3.50 27
28 3.79 0 3.82 3.69 3.57 3.56 28
29 3.96 0 3.79 3.82 3.69 3.57 29
30 4.06 0 3.96 3.79 3.82 3.69 30
31 4.05 0 4.06 3.96 3.79 3.82 31
32 4.03 0 4.05 4.06 3.96 3.79 32
33 3.94 0 4.03 4.05 4.06 3.96 33
34 4.02 0 3.94 4.03 4.05 4.06 34
35 3.88 0 4.02 3.94 4.03 4.05 35
36 4.02 0 3.88 4.02 3.94 4.03 36
37 4.03 0 4.02 3.88 4.02 3.94 37
38 4.09 0 4.03 4.02 3.88 4.02 38
39 3.99 0 4.09 4.03 4.02 3.88 39
40 4.01 0 3.99 4.09 4.03 4.02 40
41 4.01 0 4.01 3.99 4.09 4.03 41
42 4.19 0 4.01 4.01 3.99 4.09 42
43 4.30 0 4.19 4.01 4.01 3.99 43
44 4.27 0 4.30 4.19 4.01 4.01 44
45 3.82 0 4.27 4.30 4.19 4.01 45
46 3.15 1 3.82 4.27 4.30 4.19 46
47 2.49 1 3.15 3.82 4.27 4.30 47
48 1.81 1 2.49 3.15 3.82 4.27 48
49 1.26 1 1.81 2.49 3.15 3.82 49
50 1.06 1 1.26 1.81 2.49 3.15 50
51 0.84 1 1.06 1.26 1.81 2.49 51
52 0.78 1 0.84 1.06 1.26 1.81 52
53 0.70 1 0.78 0.84 1.06 1.26 53
54 0.36 1 0.70 0.78 0.84 1.06 54
55 0.35 1 0.36 0.70 0.78 0.84 55
56 0.36 1 0.35 0.36 0.70 0.78 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
0.299710 -0.663823 1.080449 -0.087984 -0.188246 0.056074
t
0.007229
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.4762915 -0.0547050 -0.0001249 0.0767043 0.1862984
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.299710 0.072599 4.128 0.000142 ***
X -0.663823 0.155341 -4.273 8.85e-05 ***
Y1 1.080449 0.176589 6.118 1.54e-07 ***
Y2 -0.087984 0.238677 -0.369 0.713991
Y3 -0.188246 0.242498 -0.776 0.441316
Y4 0.056074 0.135941 0.412 0.681779
t 0.007229 0.002356 3.069 0.003498 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1177 on 49 degrees of freedom
Multiple R-squared: 0.9909, Adjusted R-squared: 0.9898
F-statistic: 887.9 on 6 and 49 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,] 0.0025948165 0.0051896330 0.9974052
[2,] 0.0867610025 0.1735220049 0.9132390
[3,] 0.0399106218 0.0798212436 0.9600894
[4,] 0.0298578834 0.0597157667 0.9701421
[5,] 0.0137399987 0.0274799974 0.9862600
[6,] 0.0055050365 0.0110100730 0.9944950
[7,] 0.0053106181 0.0106212361 0.9946894
[8,] 0.0022141342 0.0044282685 0.9977859
[9,] 0.0008949172 0.0017898344 0.9991051
[10,] 0.0053210202 0.0106420404 0.9946790
[11,] 0.0032365273 0.0064730546 0.9967635
[12,] 0.0048507923 0.0097015846 0.9951492
[13,] 0.0044946913 0.0089893827 0.9955053
[14,] 0.0021599384 0.0043198767 0.9978401
[15,] 0.0021034555 0.0042069110 0.9978965
[16,] 0.0039239862 0.0078479723 0.9960760
[17,] 0.0019616970 0.0039233939 0.9980383
[18,] 0.0009436964 0.0018873929 0.9990563
[19,] 0.0013717695 0.0027435390 0.9986282
[20,] 0.0007650509 0.0015301018 0.9992349
[21,] 0.0003619725 0.0007239450 0.9996380
[22,] 0.0002352116 0.0004704233 0.9997648
[23,] 0.0004729158 0.0009458317 0.9995271
[24,] 0.0011918417 0.0023836835 0.9988082
[25,] 0.0006377315 0.0012754630 0.9993623
[26,] 0.0022276299 0.0044552598 0.9977724
[27,] 0.0016964475 0.0033928949 0.9983036
[28,] 0.0009941623 0.0019883246 0.9990058
[29,] 0.0004646251 0.0009292502 0.9995354
[30,] 0.0033383163 0.0066766327 0.9966617
[31,] 0.0027304792 0.0054609584 0.9972695
[32,] 0.0148574088 0.0297148176 0.9851426
[33,] 0.0124231241 0.0248462482 0.9875769
[34,] 0.0061057459 0.0122114918 0.9938943
[35,] 0.0466559393 0.0933118786 0.9533441
[36,] 0.1893034194 0.3786068388 0.8106966
[37,] 0.2306910548 0.4613821097 0.7693089
> postscript(file="/var/www/html/rcomp/tmp/1ogsa1258656255.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hoy91258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/348wz1258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hni81258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tntd1258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 56
Frequency = 1
1 2 3 4 5 6
-0.038790182 -0.028178571 -0.009276430 -0.053561090 -0.057103975 -0.040642936
7 8 9 10 11 12
-0.072559739 -0.039425660 -0.097504303 -0.062927547 0.102683382 -0.063517966
13 14 15 16 17 18
-0.083165690 0.097041178 0.016654131 -0.143505413 0.063846992 0.043739789
19 20 21 22 23 24
0.072639409 -0.002389599 0.182805907 -0.003620429 0.130449939 0.045198006
25 26 27 28 29 30
-0.005945821 0.130498230 0.126257750 -0.042353154 0.186298393 0.110497031
31 32 33 34 35 36
-0.012756252 0.013302065 -0.053905371 0.106856965 -0.137930189 0.137322209
37 38 39 40 41 42
-0.003380552 0.020063765 -0.116907076 0.003220376 -0.023681488 0.128660616
43 44 45 46 47 48
0.046323577 -0.095038819 -0.476291454 0.004478800 0.009743086 -0.106366310
49 50 51 52 53 54
-0.087849756 0.152667536 0.002139818 0.089608745 0.041042495 -0.255228326
55 56
0.088898774 0.060865137
> postscript(file="/var/www/html/rcomp/tmp/6trc61258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.038790182 NA
1 -0.028178571 -0.038790182
2 -0.009276430 -0.028178571
3 -0.053561090 -0.009276430
4 -0.057103975 -0.053561090
5 -0.040642936 -0.057103975
6 -0.072559739 -0.040642936
7 -0.039425660 -0.072559739
8 -0.097504303 -0.039425660
9 -0.062927547 -0.097504303
10 0.102683382 -0.062927547
11 -0.063517966 0.102683382
12 -0.083165690 -0.063517966
13 0.097041178 -0.083165690
14 0.016654131 0.097041178
15 -0.143505413 0.016654131
16 0.063846992 -0.143505413
17 0.043739789 0.063846992
18 0.072639409 0.043739789
19 -0.002389599 0.072639409
20 0.182805907 -0.002389599
21 -0.003620429 0.182805907
22 0.130449939 -0.003620429
23 0.045198006 0.130449939
24 -0.005945821 0.045198006
25 0.130498230 -0.005945821
26 0.126257750 0.130498230
27 -0.042353154 0.126257750
28 0.186298393 -0.042353154
29 0.110497031 0.186298393
30 -0.012756252 0.110497031
31 0.013302065 -0.012756252
32 -0.053905371 0.013302065
33 0.106856965 -0.053905371
34 -0.137930189 0.106856965
35 0.137322209 -0.137930189
36 -0.003380552 0.137322209
37 0.020063765 -0.003380552
38 -0.116907076 0.020063765
39 0.003220376 -0.116907076
40 -0.023681488 0.003220376
41 0.128660616 -0.023681488
42 0.046323577 0.128660616
43 -0.095038819 0.046323577
44 -0.476291454 -0.095038819
45 0.004478800 -0.476291454
46 0.009743086 0.004478800
47 -0.106366310 0.009743086
48 -0.087849756 -0.106366310
49 0.152667536 -0.087849756
50 0.002139818 0.152667536
51 0.089608745 0.002139818
52 0.041042495 0.089608745
53 -0.255228326 0.041042495
54 0.088898774 -0.255228326
55 0.060865137 0.088898774
56 NA 0.060865137
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.028178571 -0.038790182
[2,] -0.009276430 -0.028178571
[3,] -0.053561090 -0.009276430
[4,] -0.057103975 -0.053561090
[5,] -0.040642936 -0.057103975
[6,] -0.072559739 -0.040642936
[7,] -0.039425660 -0.072559739
[8,] -0.097504303 -0.039425660
[9,] -0.062927547 -0.097504303
[10,] 0.102683382 -0.062927547
[11,] -0.063517966 0.102683382
[12,] -0.083165690 -0.063517966
[13,] 0.097041178 -0.083165690
[14,] 0.016654131 0.097041178
[15,] -0.143505413 0.016654131
[16,] 0.063846992 -0.143505413
[17,] 0.043739789 0.063846992
[18,] 0.072639409 0.043739789
[19,] -0.002389599 0.072639409
[20,] 0.182805907 -0.002389599
[21,] -0.003620429 0.182805907
[22,] 0.130449939 -0.003620429
[23,] 0.045198006 0.130449939
[24,] -0.005945821 0.045198006
[25,] 0.130498230 -0.005945821
[26,] 0.126257750 0.130498230
[27,] -0.042353154 0.126257750
[28,] 0.186298393 -0.042353154
[29,] 0.110497031 0.186298393
[30,] -0.012756252 0.110497031
[31,] 0.013302065 -0.012756252
[32,] -0.053905371 0.013302065
[33,] 0.106856965 -0.053905371
[34,] -0.137930189 0.106856965
[35,] 0.137322209 -0.137930189
[36,] -0.003380552 0.137322209
[37,] 0.020063765 -0.003380552
[38,] -0.116907076 0.020063765
[39,] 0.003220376 -0.116907076
[40,] -0.023681488 0.003220376
[41,] 0.128660616 -0.023681488
[42,] 0.046323577 0.128660616
[43,] -0.095038819 0.046323577
[44,] -0.476291454 -0.095038819
[45,] 0.004478800 -0.476291454
[46,] 0.009743086 0.004478800
[47,] -0.106366310 0.009743086
[48,] -0.087849756 -0.106366310
[49,] 0.152667536 -0.087849756
[50,] 0.002139818 0.152667536
[51,] 0.089608745 0.002139818
[52,] 0.041042495 0.089608745
[53,] -0.255228326 0.041042495
[54,] 0.088898774 -0.255228326
[55,] 0.060865137 0.088898774
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.028178571 -0.038790182
2 -0.009276430 -0.028178571
3 -0.053561090 -0.009276430
4 -0.057103975 -0.053561090
5 -0.040642936 -0.057103975
6 -0.072559739 -0.040642936
7 -0.039425660 -0.072559739
8 -0.097504303 -0.039425660
9 -0.062927547 -0.097504303
10 0.102683382 -0.062927547
11 -0.063517966 0.102683382
12 -0.083165690 -0.063517966
13 0.097041178 -0.083165690
14 0.016654131 0.097041178
15 -0.143505413 0.016654131
16 0.063846992 -0.143505413
17 0.043739789 0.063846992
18 0.072639409 0.043739789
19 -0.002389599 0.072639409
20 0.182805907 -0.002389599
21 -0.003620429 0.182805907
22 0.130449939 -0.003620429
23 0.045198006 0.130449939
24 -0.005945821 0.045198006
25 0.130498230 -0.005945821
26 0.126257750 0.130498230
27 -0.042353154 0.126257750
28 0.186298393 -0.042353154
29 0.110497031 0.186298393
30 -0.012756252 0.110497031
31 0.013302065 -0.012756252
32 -0.053905371 0.013302065
33 0.106856965 -0.053905371
34 -0.137930189 0.106856965
35 0.137322209 -0.137930189
36 -0.003380552 0.137322209
37 0.020063765 -0.003380552
38 -0.116907076 0.020063765
39 0.003220376 -0.116907076
40 -0.023681488 0.003220376
41 0.128660616 -0.023681488
42 0.046323577 0.128660616
43 -0.095038819 0.046323577
44 -0.476291454 -0.095038819
45 0.004478800 -0.476291454
46 0.009743086 0.004478800
47 -0.106366310 0.009743086
48 -0.087849756 -0.106366310
49 0.152667536 -0.087849756
50 0.002139818 0.152667536
51 0.089608745 0.002139818
52 0.041042495 0.089608745
53 -0.255228326 0.041042495
54 0.088898774 -0.255228326
55 0.060865137 0.088898774
> 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/7n9zj1258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8182p1258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9t2t11258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dw611258656255.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pmzp1258656255.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/12swsh1258656255.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/139iei1258656255.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/14vy9o1258656255.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/15xnjx1258656255.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/16ak2s1258656255.tab")
+ }
>
> system("convert tmp/1ogsa1258656255.ps tmp/1ogsa1258656255.png")
> system("convert tmp/2hoy91258656255.ps tmp/2hoy91258656255.png")
> system("convert tmp/348wz1258656255.ps tmp/348wz1258656255.png")
> system("convert tmp/4hni81258656255.ps tmp/4hni81258656255.png")
> system("convert tmp/5tntd1258656255.ps tmp/5tntd1258656255.png")
> system("convert tmp/6trc61258656255.ps tmp/6trc61258656255.png")
> system("convert tmp/7n9zj1258656255.ps tmp/7n9zj1258656255.png")
> system("convert tmp/8182p1258656255.ps tmp/8182p1258656255.png")
> system("convert tmp/9t2t11258656255.ps tmp/9t2t11258656255.png")
> system("convert tmp/10dw611258656255.ps tmp/10dw611258656255.png")
>
>
> proc.time()
user system elapsed
2.435 1.550 3.311