R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(99.90,0,99.80,0,99.80,0,100.30,0,99.90,0,99.90,0,100.00,0,100.10,0,100.10,0,100.20,0,100.30,0,100.60,0,100.00,0,100.10,0,100.20,0,100.00,0,100.10,0,100.10,0,100.10,0,100.50,0,100.50,0,100.50,0,96.30,1,96.30,1,96.80,1,96.80,1,96.90,1,96.80,1,96.80,1,96.80,1,96.80,1,97.00,1,97.00,1,97.00,1,96.80,1,96.90,1,97.20,1,97.30,1,97.30,1,97.20,1,97.30,1,97.30,1,97.30,1,97.30,1,97.30,1,97.30,1,98.10,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.80,1,96.90,1,97.10,1,97.10,1),dim=c(2,61),dimnames=list(c('x','d'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('x','d'),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
x d M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 99.9 0 1 0 0 0 0 0 0 0 0 0 0 1
2 99.8 0 0 1 0 0 0 0 0 0 0 0 0 2
3 99.8 0 0 0 1 0 0 0 0 0 0 0 0 3
4 100.3 0 0 0 0 1 0 0 0 0 0 0 0 4
5 99.9 0 0 0 0 0 1 0 0 0 0 0 0 5
6 99.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 100.0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 100.1 0 0 0 0 0 0 0 0 1 0 0 0 8
9 100.1 0 0 0 0 0 0 0 0 0 1 0 0 9
10 100.2 0 0 0 0 0 0 0 0 0 0 1 0 10
11 100.3 0 0 0 0 0 0 0 0 0 0 0 1 11
12 100.6 0 0 0 0 0 0 0 0 0 0 0 0 12
13 100.0 0 1 0 0 0 0 0 0 0 0 0 0 13
14 100.1 0 0 1 0 0 0 0 0 0 0 0 0 14
15 100.2 0 0 0 1 0 0 0 0 0 0 0 0 15
16 100.0 0 0 0 0 1 0 0 0 0 0 0 0 16
17 100.1 0 0 0 0 0 1 0 0 0 0 0 0 17
18 100.1 0 0 0 0 0 0 1 0 0 0 0 0 18
19 100.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 100.5 0 0 0 0 0 0 0 0 1 0 0 0 20
21 100.5 0 0 0 0 0 0 0 0 0 1 0 0 21
22 100.5 0 0 0 0 0 0 0 0 0 0 1 0 22
23 96.3 1 0 0 0 0 0 0 0 0 0 0 1 23
24 96.3 1 0 0 0 0 0 0 0 0 0 0 0 24
25 96.8 1 1 0 0 0 0 0 0 0 0 0 0 25
26 96.8 1 0 1 0 0 0 0 0 0 0 0 0 26
27 96.9 1 0 0 1 0 0 0 0 0 0 0 0 27
28 96.8 1 0 0 0 1 0 0 0 0 0 0 0 28
29 96.8 1 0 0 0 0 1 0 0 0 0 0 0 29
30 96.8 1 0 0 0 0 0 1 0 0 0 0 0 30
31 96.8 1 0 0 0 0 0 0 1 0 0 0 0 31
32 97.0 1 0 0 0 0 0 0 0 1 0 0 0 32
33 97.0 1 0 0 0 0 0 0 0 0 1 0 0 33
34 97.0 1 0 0 0 0 0 0 0 0 0 1 0 34
35 96.8 1 0 0 0 0 0 0 0 0 0 0 1 35
36 96.9 1 0 0 0 0 0 0 0 0 0 0 0 36
37 97.2 1 1 0 0 0 0 0 0 0 0 0 0 37
38 97.3 1 0 1 0 0 0 0 0 0 0 0 0 38
39 97.3 1 0 0 1 0 0 0 0 0 0 0 0 39
40 97.2 1 0 0 0 1 0 0 0 0 0 0 0 40
41 97.3 1 0 0 0 0 1 0 0 0 0 0 0 41
42 97.3 1 0 0 0 0 0 1 0 0 0 0 0 42
43 97.3 1 0 0 0 0 0 0 1 0 0 0 0 43
44 97.3 1 0 0 0 0 0 0 0 1 0 0 0 44
45 97.3 1 0 0 0 0 0 0 0 0 1 0 0 45
46 97.3 1 0 0 0 0 0 0 0 0 0 1 0 46
47 98.1 1 0 0 0 0 0 0 0 0 0 0 1 47
48 96.8 1 0 0 0 0 0 0 0 0 0 0 0 48
49 96.8 1 1 0 0 0 0 0 0 0 0 0 0 49
50 96.8 1 0 1 0 0 0 0 0 0 0 0 0 50
51 96.8 1 0 0 1 0 0 0 0 0 0 0 0 51
52 96.8 1 0 0 0 1 0 0 0 0 0 0 0 52
53 96.8 1 0 0 0 0 1 0 0 0 0 0 0 53
54 96.8 1 0 0 0 0 0 1 0 0 0 0 0 54
55 96.8 1 0 0 0 0 0 0 1 0 0 0 0 55
56 96.8 1 0 0 0 0 0 0 0 1 0 0 0 56
57 96.8 1 0 0 0 0 0 0 0 0 1 0 0 57
58 96.8 1 0 0 0 0 0 0 0 0 0 1 0 58
59 96.9 1 0 0 0 0 0 0 0 0 0 0 1 59
60 97.1 1 0 0 0 0 0 0 0 0 0 0 0 60
61 97.1 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) d M1 M2 M3 M4
99.987186 -3.394582 0.011345 0.015661 0.048203 0.060746
M5 M6 M7 M8 M9 M10
0.013288 0.005830 0.018372 0.150915 0.143457 0.155999
M11 t
0.147458 0.007458
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.61159 -0.16938 -0.02217 0.19274 1.00942
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.987186 0.155868 641.484 <2e-16 ***
d -3.394582 0.147489 -23.016 <2e-16 ***
M1 0.011345 0.181756 0.062 0.9505
M2 0.015661 0.190796 0.082 0.9349
M3 0.048203 0.190508 0.253 0.8014
M4 0.060746 0.190304 0.319 0.7510
M5 0.013288 0.190186 0.070 0.9446
M6 0.005830 0.190153 0.031 0.9757
M7 0.018372 0.190206 0.097 0.9235
M8 0.150915 0.190344 0.793 0.4318
M9 0.143457 0.190567 0.753 0.4553
M10 0.155999 0.190876 0.817 0.4179
M11 0.147458 0.189507 0.778 0.4404
t 0.007458 0.004034 1.849 0.0708 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2996 on 47 degrees of freedom
Multiple R-squared: 0.9711, Adjusted R-squared: 0.9631
F-statistic: 121.5 on 13 and 47 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,] 0.261195548 0.522391095 0.7388045
[2,] 0.132563809 0.265127618 0.8674362
[3,] 0.062414564 0.124829128 0.9375854
[4,] 0.039780753 0.079561505 0.9602192
[5,] 0.023025205 0.046050411 0.9769748
[6,] 0.010010226 0.020020453 0.9899898
[7,] 0.007517080 0.015034159 0.9924829
[8,] 0.006744318 0.013488636 0.9932557
[9,] 0.079512934 0.159025868 0.9204871
[10,] 0.100989522 0.201979045 0.8990105
[11,] 0.097588288 0.195176576 0.9024117
[12,] 0.064942100 0.129884199 0.9350579
[13,] 0.049387862 0.098775725 0.9506121
[14,] 0.037146693 0.074293386 0.9628533
[15,] 0.027275256 0.054550512 0.9727247
[16,] 0.016313471 0.032626941 0.9836865
[17,] 0.009650888 0.019301776 0.9903491
[18,] 0.005737967 0.011475935 0.9942620
[19,] 0.038612739 0.077225479 0.9613873
[20,] 0.052144234 0.104288469 0.9478558
[21,] 0.052930903 0.105861805 0.9470691
[22,] 0.042101019 0.084202038 0.9578990
[23,] 0.027012340 0.054024681 0.9729877
[24,] 0.013656085 0.027312170 0.9863439
[25,] 0.007774463 0.015548926 0.9922255
[26,] 0.003976496 0.007952992 0.9960235
[27,] 0.001728575 0.003457150 0.9982714
[28,] 0.000624322 0.001248644 0.9993757
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ko9n1227811757.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ofa11227811757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3pmr11227811757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4mx7e1227811757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5cbri1227811757.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
-0.105988593 -0.217762991 -0.257762991 0.222237009 -0.137762991 -0.137762991
7 8 9 10 11 12
-0.057762991 -0.097762991 -0.097762991 -0.017762991 0.083320659 0.523320659
13 14 15 16 17 18
-0.095481622 -0.007256020 0.052743980 -0.167256020 -0.027256020 -0.027256020
19 20 21 22 23 24
-0.047256020 0.212743980 0.212743980 0.192743980 -0.611590621 -0.471590621
25 26 27 28 29 30
0.009607098 -0.002167300 0.057832700 -0.062167300 -0.022167300 -0.022167300
31 32 33 34 35 36
-0.042167300 0.017832700 0.017832700 -0.002167300 -0.201083650 0.038916350
37 38 39 40 41 42
0.320114068 0.408339670 0.368339670 0.248339670 0.388339670 0.388339670
43 44 45 46 47 48
0.368339670 0.228339670 0.228339670 0.208339670 1.009423321 -0.150576679
49 50 51 52 53 54
-0.169378961 -0.181153359 -0.221153359 -0.241153359 -0.201153359 -0.201153359
55 56 57 58 59 60
-0.221153359 -0.361153359 -0.361153359 -0.381153359 -0.280069708 0.059930292
61
0.041128010
> postscript(file="/var/www/html/freestat/rcomp/tmp/6f7ok1227811757.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 -0.105988593 NA
1 -0.217762991 -0.105988593
2 -0.257762991 -0.217762991
3 0.222237009 -0.257762991
4 -0.137762991 0.222237009
5 -0.137762991 -0.137762991
6 -0.057762991 -0.137762991
7 -0.097762991 -0.057762991
8 -0.097762991 -0.097762991
9 -0.017762991 -0.097762991
10 0.083320659 -0.017762991
11 0.523320659 0.083320659
12 -0.095481622 0.523320659
13 -0.007256020 -0.095481622
14 0.052743980 -0.007256020
15 -0.167256020 0.052743980
16 -0.027256020 -0.167256020
17 -0.027256020 -0.027256020
18 -0.047256020 -0.027256020
19 0.212743980 -0.047256020
20 0.212743980 0.212743980
21 0.192743980 0.212743980
22 -0.611590621 0.192743980
23 -0.471590621 -0.611590621
24 0.009607098 -0.471590621
25 -0.002167300 0.009607098
26 0.057832700 -0.002167300
27 -0.062167300 0.057832700
28 -0.022167300 -0.062167300
29 -0.022167300 -0.022167300
30 -0.042167300 -0.022167300
31 0.017832700 -0.042167300
32 0.017832700 0.017832700
33 -0.002167300 0.017832700
34 -0.201083650 -0.002167300
35 0.038916350 -0.201083650
36 0.320114068 0.038916350
37 0.408339670 0.320114068
38 0.368339670 0.408339670
39 0.248339670 0.368339670
40 0.388339670 0.248339670
41 0.388339670 0.388339670
42 0.368339670 0.388339670
43 0.228339670 0.368339670
44 0.228339670 0.228339670
45 0.208339670 0.228339670
46 1.009423321 0.208339670
47 -0.150576679 1.009423321
48 -0.169378961 -0.150576679
49 -0.181153359 -0.169378961
50 -0.221153359 -0.181153359
51 -0.241153359 -0.221153359
52 -0.201153359 -0.241153359
53 -0.201153359 -0.201153359
54 -0.221153359 -0.201153359
55 -0.361153359 -0.221153359
56 -0.361153359 -0.361153359
57 -0.381153359 -0.361153359
58 -0.280069708 -0.381153359
59 0.059930292 -0.280069708
60 0.041128010 0.059930292
61 NA 0.041128010
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.217762991 -0.105988593
[2,] -0.257762991 -0.217762991
[3,] 0.222237009 -0.257762991
[4,] -0.137762991 0.222237009
[5,] -0.137762991 -0.137762991
[6,] -0.057762991 -0.137762991
[7,] -0.097762991 -0.057762991
[8,] -0.097762991 -0.097762991
[9,] -0.017762991 -0.097762991
[10,] 0.083320659 -0.017762991
[11,] 0.523320659 0.083320659
[12,] -0.095481622 0.523320659
[13,] -0.007256020 -0.095481622
[14,] 0.052743980 -0.007256020
[15,] -0.167256020 0.052743980
[16,] -0.027256020 -0.167256020
[17,] -0.027256020 -0.027256020
[18,] -0.047256020 -0.027256020
[19,] 0.212743980 -0.047256020
[20,] 0.212743980 0.212743980
[21,] 0.192743980 0.212743980
[22,] -0.611590621 0.192743980
[23,] -0.471590621 -0.611590621
[24,] 0.009607098 -0.471590621
[25,] -0.002167300 0.009607098
[26,] 0.057832700 -0.002167300
[27,] -0.062167300 0.057832700
[28,] -0.022167300 -0.062167300
[29,] -0.022167300 -0.022167300
[30,] -0.042167300 -0.022167300
[31,] 0.017832700 -0.042167300
[32,] 0.017832700 0.017832700
[33,] -0.002167300 0.017832700
[34,] -0.201083650 -0.002167300
[35,] 0.038916350 -0.201083650
[36,] 0.320114068 0.038916350
[37,] 0.408339670 0.320114068
[38,] 0.368339670 0.408339670
[39,] 0.248339670 0.368339670
[40,] 0.388339670 0.248339670
[41,] 0.388339670 0.388339670
[42,] 0.368339670 0.388339670
[43,] 0.228339670 0.368339670
[44,] 0.228339670 0.228339670
[45,] 0.208339670 0.228339670
[46,] 1.009423321 0.208339670
[47,] -0.150576679 1.009423321
[48,] -0.169378961 -0.150576679
[49,] -0.181153359 -0.169378961
[50,] -0.221153359 -0.181153359
[51,] -0.241153359 -0.221153359
[52,] -0.201153359 -0.241153359
[53,] -0.201153359 -0.201153359
[54,] -0.221153359 -0.201153359
[55,] -0.361153359 -0.221153359
[56,] -0.361153359 -0.361153359
[57,] -0.381153359 -0.361153359
[58,] -0.280069708 -0.381153359
[59,] 0.059930292 -0.280069708
[60,] 0.041128010 0.059930292
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.217762991 -0.105988593
2 -0.257762991 -0.217762991
3 0.222237009 -0.257762991
4 -0.137762991 0.222237009
5 -0.137762991 -0.137762991
6 -0.057762991 -0.137762991
7 -0.097762991 -0.057762991
8 -0.097762991 -0.097762991
9 -0.017762991 -0.097762991
10 0.083320659 -0.017762991
11 0.523320659 0.083320659
12 -0.095481622 0.523320659
13 -0.007256020 -0.095481622
14 0.052743980 -0.007256020
15 -0.167256020 0.052743980
16 -0.027256020 -0.167256020
17 -0.027256020 -0.027256020
18 -0.047256020 -0.027256020
19 0.212743980 -0.047256020
20 0.212743980 0.212743980
21 0.192743980 0.212743980
22 -0.611590621 0.192743980
23 -0.471590621 -0.611590621
24 0.009607098 -0.471590621
25 -0.002167300 0.009607098
26 0.057832700 -0.002167300
27 -0.062167300 0.057832700
28 -0.022167300 -0.062167300
29 -0.022167300 -0.022167300
30 -0.042167300 -0.022167300
31 0.017832700 -0.042167300
32 0.017832700 0.017832700
33 -0.002167300 0.017832700
34 -0.201083650 -0.002167300
35 0.038916350 -0.201083650
36 0.320114068 0.038916350
37 0.408339670 0.320114068
38 0.368339670 0.408339670
39 0.248339670 0.368339670
40 0.388339670 0.248339670
41 0.388339670 0.388339670
42 0.368339670 0.388339670
43 0.228339670 0.368339670
44 0.228339670 0.228339670
45 0.208339670 0.228339670
46 1.009423321 0.208339670
47 -0.150576679 1.009423321
48 -0.169378961 -0.150576679
49 -0.181153359 -0.169378961
50 -0.221153359 -0.181153359
51 -0.241153359 -0.221153359
52 -0.201153359 -0.241153359
53 -0.201153359 -0.201153359
54 -0.221153359 -0.201153359
55 -0.361153359 -0.221153359
56 -0.361153359 -0.361153359
57 -0.381153359 -0.361153359
58 -0.280069708 -0.381153359
59 0.059930292 -0.280069708
60 0.041128010 0.059930292
> 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/freestat/rcomp/tmp/7yslx1227811757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8us401227811757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9az7m1227811757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10q5n61227811757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11mh131227811757.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/freestat/rcomp/tmp/12jxwn1227811757.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/freestat/rcomp/tmp/13zko11227811757.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/freestat/rcomp/tmp/148osz1227811757.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/freestat/rcomp/tmp/159rdg1227811757.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/freestat/rcomp/tmp/1603yn1227811758.tab")
+ }
>
> system("convert tmp/1ko9n1227811757.ps tmp/1ko9n1227811757.png")
> system("convert tmp/2ofa11227811757.ps tmp/2ofa11227811757.png")
> system("convert tmp/3pmr11227811757.ps tmp/3pmr11227811757.png")
> system("convert tmp/4mx7e1227811757.ps tmp/4mx7e1227811757.png")
> system("convert tmp/5cbri1227811757.ps tmp/5cbri1227811757.png")
> system("convert tmp/6f7ok1227811757.ps tmp/6f7ok1227811757.png")
> system("convert tmp/7yslx1227811757.ps tmp/7yslx1227811757.png")
> system("convert tmp/8us401227811757.ps tmp/8us401227811757.png")
> system("convert tmp/9az7m1227811757.ps tmp/9az7m1227811757.png")
> system("convert tmp/10q5n61227811757.ps tmp/10q5n61227811757.png")
>
>
> proc.time()
user system elapsed
3.544 2.431 3.932