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(103.52,0,103.5,0,103.52,0,103.53,0,103.53,0,103.53,0,103.52,0,103.54,0,103.59,0,103.59,0,103.59,0,103.59,0,103.63,0,103.74,0,103.7,0,103.72,0,103.81,0,103.8,0,104.22,0,106.91,1,107.06,1,107.17,1,107.25,1,107.28,1,107.24,1,107.23,1,107.34,1,107.34,1,107.3,1,107.24,1,107.3,1,107.32,1,107.28,1,107.33,1,107.33,1,107.33,1,107.28,1,107.28,1,107.29,1,107.29,1,107.23,1,107.24,1,107.24,1,107.2,1,107.23,1,107.2,1,107.21,1,107.24,1,107.21,1,113.89,1,114.05,1,114.05,1,114.05,1,114.05,1,115.12,1,115.68,1,116.05,1,116.18,1,116.35,1,116.44,1,117,1,117.61,1,118.17,1,118.33,1,118.33,1,118.42,1,118.5,1,118.67,1,119.09,1,119.14,1,119.23,1,119.33,1),dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> 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
1 103.52 0 1 0 0 0 0 0 0 0 0 0 0
2 103.50 0 0 1 0 0 0 0 0 0 0 0 0
3 103.52 0 0 0 1 0 0 0 0 0 0 0 0
4 103.53 0 0 0 0 1 0 0 0 0 0 0 0
5 103.53 0 0 0 0 0 1 0 0 0 0 0 0
6 103.53 0 0 0 0 0 0 1 0 0 0 0 0
7 103.52 0 0 0 0 0 0 0 1 0 0 0 0
8 103.54 0 0 0 0 0 0 0 0 1 0 0 0
9 103.59 0 0 0 0 0 0 0 0 0 1 0 0
10 103.59 0 0 0 0 0 0 0 0 0 0 1 0
11 103.59 0 0 0 0 0 0 0 0 0 0 0 1
12 103.59 0 0 0 0 0 0 0 0 0 0 0 0
13 103.63 0 1 0 0 0 0 0 0 0 0 0 0
14 103.74 0 0 1 0 0 0 0 0 0 0 0 0
15 103.70 0 0 0 1 0 0 0 0 0 0 0 0
16 103.72 0 0 0 0 1 0 0 0 0 0 0 0
17 103.81 0 0 0 0 0 1 0 0 0 0 0 0
18 103.80 0 0 0 0 0 0 1 0 0 0 0 0
19 104.22 0 0 0 0 0 0 0 1 0 0 0 0
20 106.91 1 0 0 0 0 0 0 0 1 0 0 0
21 107.06 1 0 0 0 0 0 0 0 0 1 0 0
22 107.17 1 0 0 0 0 0 0 0 0 0 1 0
23 107.25 1 0 0 0 0 0 0 0 0 0 0 1
24 107.28 1 0 0 0 0 0 0 0 0 0 0 0
25 107.24 1 1 0 0 0 0 0 0 0 0 0 0
26 107.23 1 0 1 0 0 0 0 0 0 0 0 0
27 107.34 1 0 0 1 0 0 0 0 0 0 0 0
28 107.34 1 0 0 0 1 0 0 0 0 0 0 0
29 107.30 1 0 0 0 0 1 0 0 0 0 0 0
30 107.24 1 0 0 0 0 0 1 0 0 0 0 0
31 107.30 1 0 0 0 0 0 0 1 0 0 0 0
32 107.32 1 0 0 0 0 0 0 0 1 0 0 0
33 107.28 1 0 0 0 0 0 0 0 0 1 0 0
34 107.33 1 0 0 0 0 0 0 0 0 0 1 0
35 107.33 1 0 0 0 0 0 0 0 0 0 0 1
36 107.33 1 0 0 0 0 0 0 0 0 0 0 0
37 107.28 1 1 0 0 0 0 0 0 0 0 0 0
38 107.28 1 0 1 0 0 0 0 0 0 0 0 0
39 107.29 1 0 0 1 0 0 0 0 0 0 0 0
40 107.29 1 0 0 0 1 0 0 0 0 0 0 0
41 107.23 1 0 0 0 0 1 0 0 0 0 0 0
42 107.24 1 0 0 0 0 0 1 0 0 0 0 0
43 107.24 1 0 0 0 0 0 0 1 0 0 0 0
44 107.20 1 0 0 0 0 0 0 0 1 0 0 0
45 107.23 1 0 0 0 0 0 0 0 0 1 0 0
46 107.20 1 0 0 0 0 0 0 0 0 0 1 0
47 107.21 1 0 0 0 0 0 0 0 0 0 0 1
48 107.24 1 0 0 0 0 0 0 0 0 0 0 0
49 107.21 1 1 0 0 0 0 0 0 0 0 0 0
50 113.89 1 0 1 0 0 0 0 0 0 0 0 0
51 114.05 1 0 0 1 0 0 0 0 0 0 0 0
52 114.05 1 0 0 0 1 0 0 0 0 0 0 0
53 114.05 1 0 0 0 0 1 0 0 0 0 0 0
54 114.05 1 0 0 0 0 0 1 0 0 0 0 0
55 115.12 1 0 0 0 0 0 0 1 0 0 0 0
56 115.68 1 0 0 0 0 0 0 0 1 0 0 0
57 116.05 1 0 0 0 0 0 0 0 0 1 0 0
58 116.18 1 0 0 0 0 0 0 0 0 0 1 0
59 116.35 1 0 0 0 0 0 0 0 0 0 0 1
60 116.44 1 0 0 0 0 0 0 0 0 0 0 0
61 117.00 1 1 0 0 0 0 0 0 0 0 0 0
62 117.61 1 0 1 0 0 0 0 0 0 0 0 0
63 118.17 1 0 0 1 0 0 0 0 0 0 0 0
64 118.33 1 0 0 0 1 0 0 0 0 0 0 0
65 118.33 1 0 0 0 0 1 0 0 0 0 0 0
66 118.42 1 0 0 0 0 0 1 0 0 0 0 0
67 118.50 1 0 0 0 0 0 0 1 0 0 0 0
68 118.67 1 0 0 0 0 0 0 0 1 0 0 0
69 119.09 1 0 0 0 0 0 0 0 0 1 0 0
70 119.14 1 0 0 0 0 0 0 0 0 0 1 0
71 119.23 1 0 0 0 0 0 0 0 0 0 0 1
72 119.33 1 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
103.70825 7.79210 -1.25632 -0.02798 0.10868 0.14035
M5 M6 M7 M8 M9 M10
0.13868 0.14368 0.41368 -0.31500 -0.15167 -0.10000
M11
-0.04167
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.6740 -4.2024 -0.1544 2.6322 7.8297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 103.70825 2.17140 47.761 < 2e-16 ***
X 7.79210 1.26543 6.158 7.07e-08 ***
M1 -1.25632 2.69266 -0.467 0.643
M2 -0.02798 2.69266 -0.010 0.992
M3 0.10868 2.69266 0.040 0.968
M4 0.14035 2.69266 0.052 0.959
M5 0.13868 2.69266 0.052 0.959
M6 0.14368 2.69266 0.053 0.958
M7 0.41368 2.69266 0.154 0.878
M8 -0.31500 2.68439 -0.117 0.907
M9 -0.15167 2.68439 -0.056 0.955
M10 -0.10000 2.68439 -0.037 0.970
M11 -0.04167 2.68439 -0.016 0.988
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.649 on 59 degrees of freedom
Multiple R-squared: 0.4019, Adjusted R-squared: 0.2803
F-statistic: 3.304 on 12 and 59 DF, p-value: 0.001062
> 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,] 3.057616e-05 6.115231e-05 0.9999694
[2,] 1.676163e-06 3.352326e-06 0.9999983
[3,] 8.650712e-08 1.730142e-07 0.9999999
[4,] 4.421908e-08 8.843815e-08 1.0000000
[5,] 1.865617e-09 3.731235e-09 1.0000000
[6,] 7.642470e-11 1.528494e-10 1.0000000
[7,] 3.205608e-12 6.411217e-12 1.0000000
[8,] 1.385810e-13 2.771619e-13 1.0000000
[9,] 5.809151e-15 1.161830e-14 1.0000000
[10,] 2.079122e-16 4.158243e-16 1.0000000
[11,] 7.244353e-18 1.448871e-17 1.0000000
[12,] 2.986160e-19 5.972320e-19 1.0000000
[13,] 1.132906e-20 2.265812e-20 1.0000000
[14,] 3.786625e-22 7.573250e-22 1.0000000
[15,] 1.280990e-23 2.561981e-23 1.0000000
[16,] 6.441300e-25 1.288260e-24 1.0000000
[17,] 6.586275e-26 1.317255e-25 1.0000000
[18,] 3.282570e-27 6.565141e-27 1.0000000
[19,] 1.634068e-28 3.268136e-28 1.0000000
[20,] 7.451623e-30 1.490325e-29 1.0000000
[21,] 3.394375e-31 6.788751e-31 1.0000000
[22,] 1.120018e-32 2.240035e-32 1.0000000
[23,] 5.890019e-34 1.178004e-33 1.0000000
[24,] 3.528631e-35 7.057262e-35 1.0000000
[25,] 2.378965e-36 4.757931e-36 1.0000000
[26,] 2.045525e-37 4.091051e-37 1.0000000
[27,] 1.943761e-38 3.887522e-38 1.0000000
[28,] 5.486347e-39 1.097269e-38 1.0000000
[29,] 1.449656e-39 2.899313e-39 1.0000000
[30,] 7.678106e-40 1.535621e-39 1.0000000
[31,] 1.060833e-39 2.121666e-39 1.0000000
[32,] 7.667575e-39 1.533515e-38 1.0000000
[33,] 1.274269e-36 2.548539e-36 1.0000000
[34,] 4.816022e-34 9.632044e-34 1.0000000
[35,] 4.117403e-06 8.234806e-06 0.9999959
[36,] 2.524443e-03 5.048885e-03 0.9974756
[37,] 3.284371e-02 6.568743e-02 0.9671563
[38,] 1.205977e-01 2.411954e-01 0.8794023
[39,] 2.645493e-01 5.290985e-01 0.7354507
[40,] 3.692430e-01 7.384861e-01 0.6307570
[41,] 4.251770e-01 8.503540e-01 0.5748230
> postscript(file="/var/www/html/rcomp/tmp/15opq1259057119.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2n75z1259057119.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3aocl1259057119.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pr1d1259057119.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5itc31259057119.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 = 72
Frequency = 1
1 2 3 4 5 6
1.06806584 -0.18026749 -0.29693416 -0.31860082 -0.31693416 -0.32193416
7 8 9 10 11 12
-0.60193416 0.14674897 0.03341564 -0.01825103 -0.07658436 -0.11825103
13 14 15 16 17 18
1.17806584 0.05973251 -0.11693416 -0.12860082 -0.03693416 -0.05193416
19 20 21 22 23 24
0.09806584 -4.27534979 -4.28868313 -4.23034979 -4.20868313 -4.22034979
25 26 27 28 29 30
-3.00403292 -4.24236626 -4.26903292 -4.30069959 -4.33903292 -4.40403292
31 32 33 34 35 36
-4.61403292 -3.86534979 -4.06868313 -4.07034979 -4.12868313 -4.17034979
37 38 39 40 41 42
-2.96403292 -4.19236626 -4.31903292 -4.35069959 -4.40903292 -4.40403292
43 44 45 46 47 48
-4.67403292 -3.98534979 -4.11868313 -4.20034979 -4.24868313 -4.26034979
49 50 51 52 53 54
-3.03403292 2.41763374 2.44096708 2.40930041 2.41096708 2.40596708
55 56 57 58 59 60
3.20596708 4.49465021 4.70131687 4.77965021 4.89131687 4.93965021
61 62 63 64 65 66
6.75596708 6.13763374 6.56096708 6.68930041 6.69096708 6.77596708
67 68 69 70 71 72
6.58596708 7.48465021 7.74131687 7.73965021 7.77131687 7.82965021
> postscript(file="/var/www/html/rcomp/tmp/6fqg01259057119.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 1.06806584 NA
1 -0.18026749 1.06806584
2 -0.29693416 -0.18026749
3 -0.31860082 -0.29693416
4 -0.31693416 -0.31860082
5 -0.32193416 -0.31693416
6 -0.60193416 -0.32193416
7 0.14674897 -0.60193416
8 0.03341564 0.14674897
9 -0.01825103 0.03341564
10 -0.07658436 -0.01825103
11 -0.11825103 -0.07658436
12 1.17806584 -0.11825103
13 0.05973251 1.17806584
14 -0.11693416 0.05973251
15 -0.12860082 -0.11693416
16 -0.03693416 -0.12860082
17 -0.05193416 -0.03693416
18 0.09806584 -0.05193416
19 -4.27534979 0.09806584
20 -4.28868313 -4.27534979
21 -4.23034979 -4.28868313
22 -4.20868313 -4.23034979
23 -4.22034979 -4.20868313
24 -3.00403292 -4.22034979
25 -4.24236626 -3.00403292
26 -4.26903292 -4.24236626
27 -4.30069959 -4.26903292
28 -4.33903292 -4.30069959
29 -4.40403292 -4.33903292
30 -4.61403292 -4.40403292
31 -3.86534979 -4.61403292
32 -4.06868313 -3.86534979
33 -4.07034979 -4.06868313
34 -4.12868313 -4.07034979
35 -4.17034979 -4.12868313
36 -2.96403292 -4.17034979
37 -4.19236626 -2.96403292
38 -4.31903292 -4.19236626
39 -4.35069959 -4.31903292
40 -4.40903292 -4.35069959
41 -4.40403292 -4.40903292
42 -4.67403292 -4.40403292
43 -3.98534979 -4.67403292
44 -4.11868313 -3.98534979
45 -4.20034979 -4.11868313
46 -4.24868313 -4.20034979
47 -4.26034979 -4.24868313
48 -3.03403292 -4.26034979
49 2.41763374 -3.03403292
50 2.44096708 2.41763374
51 2.40930041 2.44096708
52 2.41096708 2.40930041
53 2.40596708 2.41096708
54 3.20596708 2.40596708
55 4.49465021 3.20596708
56 4.70131687 4.49465021
57 4.77965021 4.70131687
58 4.89131687 4.77965021
59 4.93965021 4.89131687
60 6.75596708 4.93965021
61 6.13763374 6.75596708
62 6.56096708 6.13763374
63 6.68930041 6.56096708
64 6.69096708 6.68930041
65 6.77596708 6.69096708
66 6.58596708 6.77596708
67 7.48465021 6.58596708
68 7.74131687 7.48465021
69 7.73965021 7.74131687
70 7.77131687 7.73965021
71 7.82965021 7.77131687
72 NA 7.82965021
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.18026749 1.06806584
[2,] -0.29693416 -0.18026749
[3,] -0.31860082 -0.29693416
[4,] -0.31693416 -0.31860082
[5,] -0.32193416 -0.31693416
[6,] -0.60193416 -0.32193416
[7,] 0.14674897 -0.60193416
[8,] 0.03341564 0.14674897
[9,] -0.01825103 0.03341564
[10,] -0.07658436 -0.01825103
[11,] -0.11825103 -0.07658436
[12,] 1.17806584 -0.11825103
[13,] 0.05973251 1.17806584
[14,] -0.11693416 0.05973251
[15,] -0.12860082 -0.11693416
[16,] -0.03693416 -0.12860082
[17,] -0.05193416 -0.03693416
[18,] 0.09806584 -0.05193416
[19,] -4.27534979 0.09806584
[20,] -4.28868313 -4.27534979
[21,] -4.23034979 -4.28868313
[22,] -4.20868313 -4.23034979
[23,] -4.22034979 -4.20868313
[24,] -3.00403292 -4.22034979
[25,] -4.24236626 -3.00403292
[26,] -4.26903292 -4.24236626
[27,] -4.30069959 -4.26903292
[28,] -4.33903292 -4.30069959
[29,] -4.40403292 -4.33903292
[30,] -4.61403292 -4.40403292
[31,] -3.86534979 -4.61403292
[32,] -4.06868313 -3.86534979
[33,] -4.07034979 -4.06868313
[34,] -4.12868313 -4.07034979
[35,] -4.17034979 -4.12868313
[36,] -2.96403292 -4.17034979
[37,] -4.19236626 -2.96403292
[38,] -4.31903292 -4.19236626
[39,] -4.35069959 -4.31903292
[40,] -4.40903292 -4.35069959
[41,] -4.40403292 -4.40903292
[42,] -4.67403292 -4.40403292
[43,] -3.98534979 -4.67403292
[44,] -4.11868313 -3.98534979
[45,] -4.20034979 -4.11868313
[46,] -4.24868313 -4.20034979
[47,] -4.26034979 -4.24868313
[48,] -3.03403292 -4.26034979
[49,] 2.41763374 -3.03403292
[50,] 2.44096708 2.41763374
[51,] 2.40930041 2.44096708
[52,] 2.41096708 2.40930041
[53,] 2.40596708 2.41096708
[54,] 3.20596708 2.40596708
[55,] 4.49465021 3.20596708
[56,] 4.70131687 4.49465021
[57,] 4.77965021 4.70131687
[58,] 4.89131687 4.77965021
[59,] 4.93965021 4.89131687
[60,] 6.75596708 4.93965021
[61,] 6.13763374 6.75596708
[62,] 6.56096708 6.13763374
[63,] 6.68930041 6.56096708
[64,] 6.69096708 6.68930041
[65,] 6.77596708 6.69096708
[66,] 6.58596708 6.77596708
[67,] 7.48465021 6.58596708
[68,] 7.74131687 7.48465021
[69,] 7.73965021 7.74131687
[70,] 7.77131687 7.73965021
[71,] 7.82965021 7.77131687
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.18026749 1.06806584
2 -0.29693416 -0.18026749
3 -0.31860082 -0.29693416
4 -0.31693416 -0.31860082
5 -0.32193416 -0.31693416
6 -0.60193416 -0.32193416
7 0.14674897 -0.60193416
8 0.03341564 0.14674897
9 -0.01825103 0.03341564
10 -0.07658436 -0.01825103
11 -0.11825103 -0.07658436
12 1.17806584 -0.11825103
13 0.05973251 1.17806584
14 -0.11693416 0.05973251
15 -0.12860082 -0.11693416
16 -0.03693416 -0.12860082
17 -0.05193416 -0.03693416
18 0.09806584 -0.05193416
19 -4.27534979 0.09806584
20 -4.28868313 -4.27534979
21 -4.23034979 -4.28868313
22 -4.20868313 -4.23034979
23 -4.22034979 -4.20868313
24 -3.00403292 -4.22034979
25 -4.24236626 -3.00403292
26 -4.26903292 -4.24236626
27 -4.30069959 -4.26903292
28 -4.33903292 -4.30069959
29 -4.40403292 -4.33903292
30 -4.61403292 -4.40403292
31 -3.86534979 -4.61403292
32 -4.06868313 -3.86534979
33 -4.07034979 -4.06868313
34 -4.12868313 -4.07034979
35 -4.17034979 -4.12868313
36 -2.96403292 -4.17034979
37 -4.19236626 -2.96403292
38 -4.31903292 -4.19236626
39 -4.35069959 -4.31903292
40 -4.40903292 -4.35069959
41 -4.40403292 -4.40903292
42 -4.67403292 -4.40403292
43 -3.98534979 -4.67403292
44 -4.11868313 -3.98534979
45 -4.20034979 -4.11868313
46 -4.24868313 -4.20034979
47 -4.26034979 -4.24868313
48 -3.03403292 -4.26034979
49 2.41763374 -3.03403292
50 2.44096708 2.41763374
51 2.40930041 2.44096708
52 2.41096708 2.40930041
53 2.40596708 2.41096708
54 3.20596708 2.40596708
55 4.49465021 3.20596708
56 4.70131687 4.49465021
57 4.77965021 4.70131687
58 4.89131687 4.77965021
59 4.93965021 4.89131687
60 6.75596708 4.93965021
61 6.13763374 6.75596708
62 6.56096708 6.13763374
63 6.68930041 6.56096708
64 6.69096708 6.68930041
65 6.77596708 6.69096708
66 6.58596708 6.77596708
67 7.48465021 6.58596708
68 7.74131687 7.48465021
69 7.73965021 7.74131687
70 7.77131687 7.73965021
71 7.82965021 7.77131687
> 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/7aey21259057119.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8uvup1259057119.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91orr1259057119.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104jra1259057119.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vgbx1259057119.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/12b71w1259057119.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/135uqm1259057119.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/14n8fz1259057119.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/15hayo1259057119.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/16z44y1259057119.tab")
+ }
>
> system("convert tmp/15opq1259057119.ps tmp/15opq1259057119.png")
> system("convert tmp/2n75z1259057119.ps tmp/2n75z1259057119.png")
> system("convert tmp/3aocl1259057119.ps tmp/3aocl1259057119.png")
> system("convert tmp/4pr1d1259057119.ps tmp/4pr1d1259057119.png")
> system("convert tmp/5itc31259057119.ps tmp/5itc31259057119.png")
> system("convert tmp/6fqg01259057119.ps tmp/6fqg01259057119.png")
> system("convert tmp/7aey21259057119.ps tmp/7aey21259057119.png")
> system("convert tmp/8uvup1259057119.ps tmp/8uvup1259057119.png")
> system("convert tmp/91orr1259057119.ps tmp/91orr1259057119.png")
> system("convert tmp/104jra1259057119.ps tmp/104jra1259057119.png")
>
>
> proc.time()
user system elapsed
2.562 1.587 3.622