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(8.6,1.59,8.5,1.26,8.3,1.13,7.8,1.92,7.8,2.61,8,2.26,8.6,2.41,8.9,2.26,8.9,2.03,8.6,2.86,8.3,2.55,8.3,2.27,8.3,2.26,8.4,2.57,8.5,3.07,8.4,2.76,8.6,2.51,8.5,2.87,8.5,3.14,8.5,3.11,8.5,3.16,8.5,2.47,8.5,2.57,8.5,2.89,8.5,2.63,8.5,2.38,8.5,1.69,8.5,1.96,8.6,2.19,8.4,1.87,8.1,1.6,8,1.63,8,1.22,8,1.21,8,1.49,7.9,1.64,7.8,1.66,7.8,1.77,7.9,1.82,8.1,1.78,8,1.28,7.6,1.29,7.3,1.37,7,1.12,6.8,1.51,7,2.24,7.1,2.94,7.2,3.09,7.1,3.46,6.9,3.64,6.7,4.39,6.7,4.15,6.6,5.21,6.9,5.8,7.3,5.91,7.5,5.39,7.3,5.46,7.1,4.72,6.9,3.14,7.1,2.63),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No 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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.6 1.59 1 0 0 0 0 0 0 0 0 0 0
2 8.5 1.26 0 1 0 0 0 0 0 0 0 0 0
3 8.3 1.13 0 0 1 0 0 0 0 0 0 0 0
4 7.8 1.92 0 0 0 1 0 0 0 0 0 0 0
5 7.8 2.61 0 0 0 0 1 0 0 0 0 0 0
6 8.0 2.26 0 0 0 0 0 1 0 0 0 0 0
7 8.6 2.41 0 0 0 0 0 0 1 0 0 0 0
8 8.9 2.26 0 0 0 0 0 0 0 1 0 0 0
9 8.9 2.03 0 0 0 0 0 0 0 0 1 0 0
10 8.6 2.86 0 0 0 0 0 0 0 0 0 1 0
11 8.3 2.55 0 0 0 0 0 0 0 0 0 0 1
12 8.3 2.27 0 0 0 0 0 0 0 0 0 0 0
13 8.3 2.26 1 0 0 0 0 0 0 0 0 0 0
14 8.4 2.57 0 1 0 0 0 0 0 0 0 0 0
15 8.5 3.07 0 0 1 0 0 0 0 0 0 0 0
16 8.4 2.76 0 0 0 1 0 0 0 0 0 0 0
17 8.6 2.51 0 0 0 0 1 0 0 0 0 0 0
18 8.5 2.87 0 0 0 0 0 1 0 0 0 0 0
19 8.5 3.14 0 0 0 0 0 0 1 0 0 0 0
20 8.5 3.11 0 0 0 0 0 0 0 1 0 0 0
21 8.5 3.16 0 0 0 0 0 0 0 0 1 0 0
22 8.5 2.47 0 0 0 0 0 0 0 0 0 1 0
23 8.5 2.57 0 0 0 0 0 0 0 0 0 0 1
24 8.5 2.89 0 0 0 0 0 0 0 0 0 0 0
25 8.5 2.63 1 0 0 0 0 0 0 0 0 0 0
26 8.5 2.38 0 1 0 0 0 0 0 0 0 0 0
27 8.5 1.69 0 0 1 0 0 0 0 0 0 0 0
28 8.5 1.96 0 0 0 1 0 0 0 0 0 0 0
29 8.6 2.19 0 0 0 0 1 0 0 0 0 0 0
30 8.4 1.87 0 0 0 0 0 1 0 0 0 0 0
31 8.1 1.60 0 0 0 0 0 0 1 0 0 0 0
32 8.0 1.63 0 0 0 0 0 0 0 1 0 0 0
33 8.0 1.22 0 0 0 0 0 0 0 0 1 0 0
34 8.0 1.21 0 0 0 0 0 0 0 0 0 1 0
35 8.0 1.49 0 0 0 0 0 0 0 0 0 0 1
36 7.9 1.64 0 0 0 0 0 0 0 0 0 0 0
37 7.8 1.66 1 0 0 0 0 0 0 0 0 0 0
38 7.8 1.77 0 1 0 0 0 0 0 0 0 0 0
39 7.9 1.82 0 0 1 0 0 0 0 0 0 0 0
40 8.1 1.78 0 0 0 1 0 0 0 0 0 0 0
41 8.0 1.28 0 0 0 0 1 0 0 0 0 0 0
42 7.6 1.29 0 0 0 0 0 1 0 0 0 0 0
43 7.3 1.37 0 0 0 0 0 0 1 0 0 0 0
44 7.0 1.12 0 0 0 0 0 0 0 1 0 0 0
45 6.8 1.51 0 0 0 0 0 0 0 0 1 0 0
46 7.0 2.24 0 0 0 0 0 0 0 0 0 1 0
47 7.1 2.94 0 0 0 0 0 0 0 0 0 0 1
48 7.2 3.09 0 0 0 0 0 0 0 0 0 0 0
49 7.1 3.46 1 0 0 0 0 0 0 0 0 0 0
50 6.9 3.64 0 1 0 0 0 0 0 0 0 0 0
51 6.7 4.39 0 0 1 0 0 0 0 0 0 0 0
52 6.7 4.15 0 0 0 1 0 0 0 0 0 0 0
53 6.6 5.21 0 0 0 0 1 0 0 0 0 0 0
54 6.9 5.80 0 0 0 0 0 1 0 0 0 0 0
55 7.3 5.91 0 0 0 0 0 0 1 0 0 0 0
56 7.5 5.39 0 0 0 0 0 0 0 1 0 0 0
57 7.3 5.46 0 0 0 0 0 0 0 0 1 0 0
58 7.1 4.72 0 0 0 0 0 0 0 0 0 1 0
59 6.9 3.14 0 0 0 0 0 0 0 0 0 0 1
60 7.1 2.63 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
8.39940 -0.23938 0.21595 0.17691 0.15989 0.10239
M5 M6 M7 M8 M9 M10
0.18128 0.15516 0.25144 0.22740 0.14117 0.08692
M11
-0.03186
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.37911 -0.36896 0.01775 0.53026 0.84536
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.39940 0.34424 24.400 < 2e-16 ***
X -0.23938 0.07183 -3.332 0.00168 **
M1 0.21595 0.41530 0.520 0.60550
M2 0.17691 0.41529 0.426 0.67205
M3 0.15989 0.41513 0.385 0.70186
M4 0.10239 0.41509 0.247 0.80623
M5 0.18128 0.41549 0.436 0.66462
M6 0.15516 0.41570 0.373 0.71063
M7 0.25144 0.41599 0.604 0.54846
M8 0.22740 0.41533 0.548 0.58662
M9 0.14117 0.41527 0.340 0.73541
M10 0.08692 0.41532 0.209 0.83514
M11 -0.03186 0.41509 -0.077 0.93914
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6563 on 47 degrees of freedom
Multiple R-squared: 0.2049, Adjusted R-squared: 0.001947
F-statistic: 1.01 on 12 and 47 DF, p-value: 0.4555
> 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,] 0.074988035 0.149976070 0.92501196
[2,] 0.114766840 0.229533680 0.88523316
[3,] 0.074027554 0.148055107 0.92597245
[4,] 0.038215123 0.076430247 0.96178488
[5,] 0.028778138 0.057556277 0.97122186
[6,] 0.023010208 0.046020416 0.97698979
[7,] 0.013449748 0.026899495 0.98655025
[8,] 0.009307334 0.018614668 0.99069267
[9,] 0.007175036 0.014350072 0.99282496
[10,] 0.004723340 0.009446680 0.99527666
[11,] 0.003369141 0.006738283 0.99663086
[12,] 0.002226647 0.004453295 0.99777335
[13,] 0.002691155 0.005382311 0.99730884
[14,] 0.004413792 0.008827585 0.99558621
[15,] 0.003656189 0.007312377 0.99634381
[16,] 0.003788982 0.007577964 0.99621102
[17,] 0.006552126 0.013104252 0.99344787
[18,] 0.008469603 0.016939207 0.99153040
[19,] 0.007493823 0.014987646 0.99250618
[20,] 0.007897114 0.015794229 0.99210289
[21,] 0.007266780 0.014533560 0.99273322
[22,] 0.008524044 0.017048088 0.99147596
[23,] 0.012725995 0.025451990 0.98727401
[24,] 0.024483464 0.048966929 0.97551654
[25,] 0.080545502 0.161091004 0.91945450
[26,] 0.494402023 0.988804046 0.50559798
[27,] 0.931163640 0.137672721 0.06883636
[28,] 0.981553754 0.036892491 0.01844625
[29,] 0.955543003 0.088913994 0.04445700
> postscript(file="/var/www/html/rcomp/tmp/1vpai1258554847.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2snm91258554847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31ngb1258554847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4sjzd1258554847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ff1n1258554847.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 = 60
Frequency = 1
1 2 3 4 5 6
0.36525414 0.22530193 0.01120252 -0.24219047 -0.15590668 -0.01357286
7 8 9 10 11 12
0.52605613 0.81419497 0.84536188 0.79830046 0.54287253 0.44398557
13 14 15 16 17 18
0.22563733 0.43888696 0.67559563 0.55888696 0.62015553 0.63244765
19 20 21 22 23 24
0.60080198 0.61766618 0.71585890 0.60494309 0.74766009 0.79239986
25 26 27 28 29 30
0.51420715 0.49340516 0.34525414 0.46738465 0.54355460 0.29306976
31 32 33 34 35 36
-0.16783996 -0.23661310 -0.24853420 -0.19667305 -0.01086803 -0.10682250
37 38 39 40 41 42
-0.41798941 -0.35261535 -0.22362673 0.02429663 -0.27427927 -0.64576941
43 44 45 46 47 48
-1.02289688 -1.35869582 -1.37911462 -0.95011383 -0.56377009 -0.45972456
49 50 51 52 53 54
-0.68710921 -0.80497870 -0.80842556 -0.80837778 -0.73352417 -0.26617514
55 56 57 58 59 60
0.06387873 0.16344776 0.06642804 -0.25645667 -0.71589451 -0.66983839
> postscript(file="/var/www/html/rcomp/tmp/67txd1258554847.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.36525414 NA
1 0.22530193 0.36525414
2 0.01120252 0.22530193
3 -0.24219047 0.01120252
4 -0.15590668 -0.24219047
5 -0.01357286 -0.15590668
6 0.52605613 -0.01357286
7 0.81419497 0.52605613
8 0.84536188 0.81419497
9 0.79830046 0.84536188
10 0.54287253 0.79830046
11 0.44398557 0.54287253
12 0.22563733 0.44398557
13 0.43888696 0.22563733
14 0.67559563 0.43888696
15 0.55888696 0.67559563
16 0.62015553 0.55888696
17 0.63244765 0.62015553
18 0.60080198 0.63244765
19 0.61766618 0.60080198
20 0.71585890 0.61766618
21 0.60494309 0.71585890
22 0.74766009 0.60494309
23 0.79239986 0.74766009
24 0.51420715 0.79239986
25 0.49340516 0.51420715
26 0.34525414 0.49340516
27 0.46738465 0.34525414
28 0.54355460 0.46738465
29 0.29306976 0.54355460
30 -0.16783996 0.29306976
31 -0.23661310 -0.16783996
32 -0.24853420 -0.23661310
33 -0.19667305 -0.24853420
34 -0.01086803 -0.19667305
35 -0.10682250 -0.01086803
36 -0.41798941 -0.10682250
37 -0.35261535 -0.41798941
38 -0.22362673 -0.35261535
39 0.02429663 -0.22362673
40 -0.27427927 0.02429663
41 -0.64576941 -0.27427927
42 -1.02289688 -0.64576941
43 -1.35869582 -1.02289688
44 -1.37911462 -1.35869582
45 -0.95011383 -1.37911462
46 -0.56377009 -0.95011383
47 -0.45972456 -0.56377009
48 -0.68710921 -0.45972456
49 -0.80497870 -0.68710921
50 -0.80842556 -0.80497870
51 -0.80837778 -0.80842556
52 -0.73352417 -0.80837778
53 -0.26617514 -0.73352417
54 0.06387873 -0.26617514
55 0.16344776 0.06387873
56 0.06642804 0.16344776
57 -0.25645667 0.06642804
58 -0.71589451 -0.25645667
59 -0.66983839 -0.71589451
60 NA -0.66983839
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.22530193 0.36525414
[2,] 0.01120252 0.22530193
[3,] -0.24219047 0.01120252
[4,] -0.15590668 -0.24219047
[5,] -0.01357286 -0.15590668
[6,] 0.52605613 -0.01357286
[7,] 0.81419497 0.52605613
[8,] 0.84536188 0.81419497
[9,] 0.79830046 0.84536188
[10,] 0.54287253 0.79830046
[11,] 0.44398557 0.54287253
[12,] 0.22563733 0.44398557
[13,] 0.43888696 0.22563733
[14,] 0.67559563 0.43888696
[15,] 0.55888696 0.67559563
[16,] 0.62015553 0.55888696
[17,] 0.63244765 0.62015553
[18,] 0.60080198 0.63244765
[19,] 0.61766618 0.60080198
[20,] 0.71585890 0.61766618
[21,] 0.60494309 0.71585890
[22,] 0.74766009 0.60494309
[23,] 0.79239986 0.74766009
[24,] 0.51420715 0.79239986
[25,] 0.49340516 0.51420715
[26,] 0.34525414 0.49340516
[27,] 0.46738465 0.34525414
[28,] 0.54355460 0.46738465
[29,] 0.29306976 0.54355460
[30,] -0.16783996 0.29306976
[31,] -0.23661310 -0.16783996
[32,] -0.24853420 -0.23661310
[33,] -0.19667305 -0.24853420
[34,] -0.01086803 -0.19667305
[35,] -0.10682250 -0.01086803
[36,] -0.41798941 -0.10682250
[37,] -0.35261535 -0.41798941
[38,] -0.22362673 -0.35261535
[39,] 0.02429663 -0.22362673
[40,] -0.27427927 0.02429663
[41,] -0.64576941 -0.27427927
[42,] -1.02289688 -0.64576941
[43,] -1.35869582 -1.02289688
[44,] -1.37911462 -1.35869582
[45,] -0.95011383 -1.37911462
[46,] -0.56377009 -0.95011383
[47,] -0.45972456 -0.56377009
[48,] -0.68710921 -0.45972456
[49,] -0.80497870 -0.68710921
[50,] -0.80842556 -0.80497870
[51,] -0.80837778 -0.80842556
[52,] -0.73352417 -0.80837778
[53,] -0.26617514 -0.73352417
[54,] 0.06387873 -0.26617514
[55,] 0.16344776 0.06387873
[56,] 0.06642804 0.16344776
[57,] -0.25645667 0.06642804
[58,] -0.71589451 -0.25645667
[59,] -0.66983839 -0.71589451
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.22530193 0.36525414
2 0.01120252 0.22530193
3 -0.24219047 0.01120252
4 -0.15590668 -0.24219047
5 -0.01357286 -0.15590668
6 0.52605613 -0.01357286
7 0.81419497 0.52605613
8 0.84536188 0.81419497
9 0.79830046 0.84536188
10 0.54287253 0.79830046
11 0.44398557 0.54287253
12 0.22563733 0.44398557
13 0.43888696 0.22563733
14 0.67559563 0.43888696
15 0.55888696 0.67559563
16 0.62015553 0.55888696
17 0.63244765 0.62015553
18 0.60080198 0.63244765
19 0.61766618 0.60080198
20 0.71585890 0.61766618
21 0.60494309 0.71585890
22 0.74766009 0.60494309
23 0.79239986 0.74766009
24 0.51420715 0.79239986
25 0.49340516 0.51420715
26 0.34525414 0.49340516
27 0.46738465 0.34525414
28 0.54355460 0.46738465
29 0.29306976 0.54355460
30 -0.16783996 0.29306976
31 -0.23661310 -0.16783996
32 -0.24853420 -0.23661310
33 -0.19667305 -0.24853420
34 -0.01086803 -0.19667305
35 -0.10682250 -0.01086803
36 -0.41798941 -0.10682250
37 -0.35261535 -0.41798941
38 -0.22362673 -0.35261535
39 0.02429663 -0.22362673
40 -0.27427927 0.02429663
41 -0.64576941 -0.27427927
42 -1.02289688 -0.64576941
43 -1.35869582 -1.02289688
44 -1.37911462 -1.35869582
45 -0.95011383 -1.37911462
46 -0.56377009 -0.95011383
47 -0.45972456 -0.56377009
48 -0.68710921 -0.45972456
49 -0.80497870 -0.68710921
50 -0.80842556 -0.80497870
51 -0.80837778 -0.80842556
52 -0.73352417 -0.80837778
53 -0.26617514 -0.73352417
54 0.06387873 -0.26617514
55 0.16344776 0.06387873
56 0.06642804 0.16344776
57 -0.25645667 0.06642804
58 -0.71589451 -0.25645667
59 -0.66983839 -0.71589451
> 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/72g331258554847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/890cg1258554847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/92c1z1258554847.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1040io1258554848.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dyem1258554848.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/128egn1258554848.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/133tzd1258554848.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/14oues1258554848.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/15ujcd1258554848.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/16lr3b1258554848.tab")
+ }
>
> system("convert tmp/1vpai1258554847.ps tmp/1vpai1258554847.png")
> system("convert tmp/2snm91258554847.ps tmp/2snm91258554847.png")
> system("convert tmp/31ngb1258554847.ps tmp/31ngb1258554847.png")
> system("convert tmp/4sjzd1258554847.ps tmp/4sjzd1258554847.png")
> system("convert tmp/5ff1n1258554847.ps tmp/5ff1n1258554847.png")
> system("convert tmp/67txd1258554847.ps tmp/67txd1258554847.png")
> system("convert tmp/72g331258554847.ps tmp/72g331258554847.png")
> system("convert tmp/890cg1258554847.ps tmp/890cg1258554847.png")
> system("convert tmp/92c1z1258554847.ps tmp/92c1z1258554847.png")
> system("convert tmp/1040io1258554848.ps tmp/1040io1258554848.png")
>
>
> proc.time()
user system elapsed
2.363 1.585 2.868