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.
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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(20604.6,2.05,18714.9,2.03,18492.6,2.04,18183.6,2.03,19435.1,2.01,22686.8,2.01,20396.7,2.01,19233.6,2.01,22751,2.01,19864,2.01,17165.4,2.02,22309.7,2.02,21786.3,2.03,21927.6,2.05,20957.9,2.08,19726,2.07,21315.7,2.06,24771.5,2.05,22592.4,2.05,21942.1,2.05,23973.7,2.05,20815.7,2.05,19931.4,2.06,24436.8,2.06,22838.7,2.07,24465.3,2.07,23007.3,2.3,22720.8,2.31,23045.7,2.31,27198.5,2.53,22401.9,2.58,25122.7,2.59,26100.5,2.73,22904.9,2.82,22040.4,3,25981.5,3.04,26157.1,3.23,25975.4,3.32,22589.8,3.49,25370.4,3.57,25091.1,3.56,28760.9,3.72,24325.9,3.82,25821.7,3.82,27645.7,3.98,26296.9,4.06,24141.5,4.08,27268.1,4.19,29060.3,4.16,28226.4,4.17,23268.5,4.21,26938.2,4.21,27217.5,4.17,27540.5,4.19,29167.6,4.25,26671.5,4.25,30184,4.2,28422.3,4.33,23774.3,4.41,29601,4.56,28523.6,5.18,23622,3.42,21320.3,2.71,20423.6,2.29,21174.9,2,23050.2,1.64,21202.9,1.3,20476.4,1.08,23173.3,1,22468,1,19842.7,1),dim=c(2,71),dimnames=list(c('Y','X'),1:71))
> y <- array(NA,dim=c(2,71),dimnames=list(c('Y','X'),1:71))
> 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 20604.6 2.05 1 0 0 0 0 0 0 0 0 0 0
2 18714.9 2.03 0 1 0 0 0 0 0 0 0 0 0
3 18492.6 2.04 0 0 1 0 0 0 0 0 0 0 0
4 18183.6 2.03 0 0 0 1 0 0 0 0 0 0 0
5 19435.1 2.01 0 0 0 0 1 0 0 0 0 0 0
6 22686.8 2.01 0 0 0 0 0 1 0 0 0 0 0
7 20396.7 2.01 0 0 0 0 0 0 1 0 0 0 0
8 19233.6 2.01 0 0 0 0 0 0 0 1 0 0 0
9 22751.0 2.01 0 0 0 0 0 0 0 0 1 0 0
10 19864.0 2.01 0 0 0 0 0 0 0 0 0 1 0
11 17165.4 2.02 0 0 0 0 0 0 0 0 0 0 1
12 22309.7 2.02 0 0 0 0 0 0 0 0 0 0 0
13 21786.3 2.03 1 0 0 0 0 0 0 0 0 0 0
14 21927.6 2.05 0 1 0 0 0 0 0 0 0 0 0
15 20957.9 2.08 0 0 1 0 0 0 0 0 0 0 0
16 19726.0 2.07 0 0 0 1 0 0 0 0 0 0 0
17 21315.7 2.06 0 0 0 0 1 0 0 0 0 0 0
18 24771.5 2.05 0 0 0 0 0 1 0 0 0 0 0
19 22592.4 2.05 0 0 0 0 0 0 1 0 0 0 0
20 21942.1 2.05 0 0 0 0 0 0 0 1 0 0 0
21 23973.7 2.05 0 0 0 0 0 0 0 0 1 0 0
22 20815.7 2.05 0 0 0 0 0 0 0 0 0 1 0
23 19931.4 2.06 0 0 0 0 0 0 0 0 0 0 1
24 24436.8 2.06 0 0 0 0 0 0 0 0 0 0 0
25 22838.7 2.07 1 0 0 0 0 0 0 0 0 0 0
26 24465.3 2.07 0 1 0 0 0 0 0 0 0 0 0
27 23007.3 2.30 0 0 1 0 0 0 0 0 0 0 0
28 22720.8 2.31 0 0 0 1 0 0 0 0 0 0 0
29 23045.7 2.31 0 0 0 0 1 0 0 0 0 0 0
30 27198.5 2.53 0 0 0 0 0 1 0 0 0 0 0
31 22401.9 2.58 0 0 0 0 0 0 1 0 0 0 0
32 25122.7 2.59 0 0 0 0 0 0 0 1 0 0 0
33 26100.5 2.73 0 0 0 0 0 0 0 0 1 0 0
34 22904.9 2.82 0 0 0 0 0 0 0 0 0 1 0
35 22040.4 3.00 0 0 0 0 0 0 0 0 0 0 1
36 25981.5 3.04 0 0 0 0 0 0 0 0 0 0 0
37 26157.1 3.23 1 0 0 0 0 0 0 0 0 0 0
38 25975.4 3.32 0 1 0 0 0 0 0 0 0 0 0
39 22589.8 3.49 0 0 1 0 0 0 0 0 0 0 0
40 25370.4 3.57 0 0 0 1 0 0 0 0 0 0 0
41 25091.1 3.56 0 0 0 0 1 0 0 0 0 0 0
42 28760.9 3.72 0 0 0 0 0 1 0 0 0 0 0
43 24325.9 3.82 0 0 0 0 0 0 1 0 0 0 0
44 25821.7 3.82 0 0 0 0 0 0 0 1 0 0 0
45 27645.7 3.98 0 0 0 0 0 0 0 0 1 0 0
46 26296.9 4.06 0 0 0 0 0 0 0 0 0 1 0
47 24141.5 4.08 0 0 0 0 0 0 0 0 0 0 1
48 27268.1 4.19 0 0 0 0 0 0 0 0 0 0 0
49 29060.3 4.16 1 0 0 0 0 0 0 0 0 0 0
50 28226.4 4.17 0 1 0 0 0 0 0 0 0 0 0
51 23268.5 4.21 0 0 1 0 0 0 0 0 0 0 0
52 26938.2 4.21 0 0 0 1 0 0 0 0 0 0 0
53 27217.5 4.17 0 0 0 0 1 0 0 0 0 0 0
54 27540.5 4.19 0 0 0 0 0 1 0 0 0 0 0
55 29167.6 4.25 0 0 0 0 0 0 1 0 0 0 0
56 26671.5 4.25 0 0 0 0 0 0 0 1 0 0 0
57 30184.0 4.20 0 0 0 0 0 0 0 0 1 0 0
58 28422.3 4.33 0 0 0 0 0 0 0 0 0 1 0
59 23774.3 4.41 0 0 0 0 0 0 0 0 0 0 1
60 29601.0 4.56 0 0 0 0 0 0 0 0 0 0 0
61 28523.6 5.18 1 0 0 0 0 0 0 0 0 0 0
62 23622.0 3.42 0 1 0 0 0 0 0 0 0 0 0
63 21320.3 2.71 0 0 1 0 0 0 0 0 0 0 0
64 20423.6 2.29 0 0 0 1 0 0 0 0 0 0 0
65 21174.9 2.00 0 0 0 0 1 0 0 0 0 0 0
66 23050.2 1.64 0 0 0 0 0 1 0 0 0 0 0
67 21202.9 1.30 0 0 0 0 0 0 1 0 0 0 0
68 20476.4 1.08 0 0 0 0 0 0 0 1 0 0 0
69 23173.3 1.00 0 0 0 0 0 0 0 0 1 0 0
70 22468.0 1.00 0 0 0 0 0 0 0 0 0 1 0
71 19842.7 1.00 0 0 0 0 0 0 0 0 0 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
18648.2 2290.9 -967.3 -1340.0 -3468.0 -2713.4
M5 M6 M7 M8 M9 M10
-1919.2 857.4 -1413.1 -1469.5 892.3 -1398.3
M11
-3825.5
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3243.80 -1018.22 -68.14 1023.32 2927.21
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18648.2 877.4 21.253 < 2e-16 ***
X 2290.9 175.5 13.052 < 2e-16 ***
M1 -967.3 918.0 -1.054 0.296378
M2 -1340.0 919.7 -1.457 0.150536
M3 -3468.0 920.2 -3.769 0.000386 ***
M4 -2713.4 921.0 -2.946 0.004626 **
M5 -1919.2 921.9 -2.082 0.041789 *
M6 857.4 921.8 0.930 0.356162
M7 -1413.1 922.2 -1.532 0.130876
M8 -1469.5 922.8 -1.592 0.116725
M9 892.3 922.3 0.967 0.337330
M10 -1398.3 921.5 -1.517 0.134586
M11 -3825.5 920.8 -4.155 0.000108 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1516 on 58 degrees of freedom
Multiple R-squared: 0.8061, Adjusted R-squared: 0.766
F-statistic: 20.09 on 12 and 58 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,] 0.65550451 0.68899097 0.3444955
[2,] 0.50725449 0.98549101 0.4927455
[3,] 0.35837493 0.71674985 0.6416251
[4,] 0.23828346 0.47656692 0.7617165
[5,] 0.16706656 0.33413311 0.8329334
[6,] 0.11716844 0.23433687 0.8828316
[7,] 0.10303350 0.20606700 0.8969665
[8,] 0.07397116 0.14794232 0.9260288
[9,] 0.04262342 0.08524684 0.9573766
[10,] 0.02547754 0.05095509 0.9745225
[11,] 0.10860647 0.21721295 0.8913935
[12,] 0.64414298 0.71171403 0.3558570
[13,] 0.60542611 0.78914777 0.3945739
[14,] 0.60016960 0.79966080 0.3998304
[15,] 0.72312555 0.55374891 0.2768745
[16,] 0.86157923 0.27684155 0.1384208
[17,] 0.84082947 0.31834105 0.1591705
[18,] 0.82462199 0.35075602 0.1753780
[19,] 0.86241240 0.27517519 0.1375876
[20,] 0.82809424 0.34381151 0.1719058
[21,] 0.80297753 0.39404494 0.1970225
[22,] 0.74200018 0.51599963 0.2579998
[23,] 0.69113793 0.61772415 0.3088621
[24,] 0.72080922 0.55838156 0.2791908
[25,] 0.66185363 0.67629274 0.3381464
[26,] 0.58863430 0.82273141 0.4113657
[27,] 0.58014987 0.83970026 0.4198501
[28,] 0.69883569 0.60232861 0.3011643
[29,] 0.62121882 0.75756236 0.3787812
[30,] 0.61011701 0.77976597 0.3898830
[31,] 0.59940179 0.80119642 0.4005982
[32,] 0.50612494 0.98775012 0.4938751
[33,] 0.47870119 0.95740237 0.5212988
[34,] 0.52184191 0.95631618 0.4781581
[35,] 0.63408500 0.73183001 0.3659150
[36,] 0.59453820 0.81092360 0.4054618
[37,] 0.67982139 0.64035722 0.3201786
[38,] 0.66660361 0.66679277 0.3333964
[39,] 0.55166287 0.89667426 0.4483371
[40,] 0.68409078 0.63181845 0.3159092
> postscript(file="/var/www/html/rcomp/tmp/1d3ke1258476296.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2x3vf1258476296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nhoo1258476296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a0hs1258476296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5o9ju1258476296.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 = 71
Frequency = 1
1 2 3 4 5 6
-1772.61849 -3243.80460 -1360.96260 -2401.72059 -1898.57288 -1423.48527
7 8 9 10 11 12
-1443.05380 -2549.76705 -1394.15950 -1990.55008 -2284.83255 -966.07334
13 14 15 16 17 18
-545.10139 -76.92170 1012.70320 -950.95479 -132.51563 569.58053
19 20 21 22 23 24
661.01200 67.09875 -263.09370 -1130.48428 389.53325 1069.39246
25 26 27 28 29 30
415.66441 2414.96120 2558.11511 1494.04001 1024.77062 1896.97013
31 32 33 34 35 36
-743.64114 2010.63705 305.92491 -805.24262 345.12956 369.05457
37 38 39 40 41 42
1076.67262 1061.49245 -585.50234 1257.16272 206.60188 733.25269
43 44 45 46 47 48
-1660.30134 -108.11459 -1012.44383 -253.90282 -27.89383 -978.82867
49 50 51 52 53 54
1849.37747 1365.26571 -1556.21793 1358.81552 935.58033 -1563.84916
55 56 57 58 59 60
2196.33102 -243.38224 1021.86807 1252.96633 -1151.07598 506.45498
61 62 63 64 65 66
-1023.99462 -1520.99305 -68.13544 -757.34289 -135.86433 -212.46892
67 68 69 70 71
989.65325 823.52809 1341.90405 2927.21347 2729.13955
> postscript(file="/var/www/html/rcomp/tmp/6exmm1258476296.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 = 71
Frequency = 1
lag(myerror, k = 1) myerror
0 -1772.61849 NA
1 -3243.80460 -1772.61849
2 -1360.96260 -3243.80460
3 -2401.72059 -1360.96260
4 -1898.57288 -2401.72059
5 -1423.48527 -1898.57288
6 -1443.05380 -1423.48527
7 -2549.76705 -1443.05380
8 -1394.15950 -2549.76705
9 -1990.55008 -1394.15950
10 -2284.83255 -1990.55008
11 -966.07334 -2284.83255
12 -545.10139 -966.07334
13 -76.92170 -545.10139
14 1012.70320 -76.92170
15 -950.95479 1012.70320
16 -132.51563 -950.95479
17 569.58053 -132.51563
18 661.01200 569.58053
19 67.09875 661.01200
20 -263.09370 67.09875
21 -1130.48428 -263.09370
22 389.53325 -1130.48428
23 1069.39246 389.53325
24 415.66441 1069.39246
25 2414.96120 415.66441
26 2558.11511 2414.96120
27 1494.04001 2558.11511
28 1024.77062 1494.04001
29 1896.97013 1024.77062
30 -743.64114 1896.97013
31 2010.63705 -743.64114
32 305.92491 2010.63705
33 -805.24262 305.92491
34 345.12956 -805.24262
35 369.05457 345.12956
36 1076.67262 369.05457
37 1061.49245 1076.67262
38 -585.50234 1061.49245
39 1257.16272 -585.50234
40 206.60188 1257.16272
41 733.25269 206.60188
42 -1660.30134 733.25269
43 -108.11459 -1660.30134
44 -1012.44383 -108.11459
45 -253.90282 -1012.44383
46 -27.89383 -253.90282
47 -978.82867 -27.89383
48 1849.37747 -978.82867
49 1365.26571 1849.37747
50 -1556.21793 1365.26571
51 1358.81552 -1556.21793
52 935.58033 1358.81552
53 -1563.84916 935.58033
54 2196.33102 -1563.84916
55 -243.38224 2196.33102
56 1021.86807 -243.38224
57 1252.96633 1021.86807
58 -1151.07598 1252.96633
59 506.45498 -1151.07598
60 -1023.99462 506.45498
61 -1520.99305 -1023.99462
62 -68.13544 -1520.99305
63 -757.34289 -68.13544
64 -135.86433 -757.34289
65 -212.46892 -135.86433
66 989.65325 -212.46892
67 823.52809 989.65325
68 1341.90405 823.52809
69 2927.21347 1341.90405
70 2729.13955 2927.21347
71 NA 2729.13955
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3243.80460 -1772.61849
[2,] -1360.96260 -3243.80460
[3,] -2401.72059 -1360.96260
[4,] -1898.57288 -2401.72059
[5,] -1423.48527 -1898.57288
[6,] -1443.05380 -1423.48527
[7,] -2549.76705 -1443.05380
[8,] -1394.15950 -2549.76705
[9,] -1990.55008 -1394.15950
[10,] -2284.83255 -1990.55008
[11,] -966.07334 -2284.83255
[12,] -545.10139 -966.07334
[13,] -76.92170 -545.10139
[14,] 1012.70320 -76.92170
[15,] -950.95479 1012.70320
[16,] -132.51563 -950.95479
[17,] 569.58053 -132.51563
[18,] 661.01200 569.58053
[19,] 67.09875 661.01200
[20,] -263.09370 67.09875
[21,] -1130.48428 -263.09370
[22,] 389.53325 -1130.48428
[23,] 1069.39246 389.53325
[24,] 415.66441 1069.39246
[25,] 2414.96120 415.66441
[26,] 2558.11511 2414.96120
[27,] 1494.04001 2558.11511
[28,] 1024.77062 1494.04001
[29,] 1896.97013 1024.77062
[30,] -743.64114 1896.97013
[31,] 2010.63705 -743.64114
[32,] 305.92491 2010.63705
[33,] -805.24262 305.92491
[34,] 345.12956 -805.24262
[35,] 369.05457 345.12956
[36,] 1076.67262 369.05457
[37,] 1061.49245 1076.67262
[38,] -585.50234 1061.49245
[39,] 1257.16272 -585.50234
[40,] 206.60188 1257.16272
[41,] 733.25269 206.60188
[42,] -1660.30134 733.25269
[43,] -108.11459 -1660.30134
[44,] -1012.44383 -108.11459
[45,] -253.90282 -1012.44383
[46,] -27.89383 -253.90282
[47,] -978.82867 -27.89383
[48,] 1849.37747 -978.82867
[49,] 1365.26571 1849.37747
[50,] -1556.21793 1365.26571
[51,] 1358.81552 -1556.21793
[52,] 935.58033 1358.81552
[53,] -1563.84916 935.58033
[54,] 2196.33102 -1563.84916
[55,] -243.38224 2196.33102
[56,] 1021.86807 -243.38224
[57,] 1252.96633 1021.86807
[58,] -1151.07598 1252.96633
[59,] 506.45498 -1151.07598
[60,] -1023.99462 506.45498
[61,] -1520.99305 -1023.99462
[62,] -68.13544 -1520.99305
[63,] -757.34289 -68.13544
[64,] -135.86433 -757.34289
[65,] -212.46892 -135.86433
[66,] 989.65325 -212.46892
[67,] 823.52809 989.65325
[68,] 1341.90405 823.52809
[69,] 2927.21347 1341.90405
[70,] 2729.13955 2927.21347
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3243.80460 -1772.61849
2 -1360.96260 -3243.80460
3 -2401.72059 -1360.96260
4 -1898.57288 -2401.72059
5 -1423.48527 -1898.57288
6 -1443.05380 -1423.48527
7 -2549.76705 -1443.05380
8 -1394.15950 -2549.76705
9 -1990.55008 -1394.15950
10 -2284.83255 -1990.55008
11 -966.07334 -2284.83255
12 -545.10139 -966.07334
13 -76.92170 -545.10139
14 1012.70320 -76.92170
15 -950.95479 1012.70320
16 -132.51563 -950.95479
17 569.58053 -132.51563
18 661.01200 569.58053
19 67.09875 661.01200
20 -263.09370 67.09875
21 -1130.48428 -263.09370
22 389.53325 -1130.48428
23 1069.39246 389.53325
24 415.66441 1069.39246
25 2414.96120 415.66441
26 2558.11511 2414.96120
27 1494.04001 2558.11511
28 1024.77062 1494.04001
29 1896.97013 1024.77062
30 -743.64114 1896.97013
31 2010.63705 -743.64114
32 305.92491 2010.63705
33 -805.24262 305.92491
34 345.12956 -805.24262
35 369.05457 345.12956
36 1076.67262 369.05457
37 1061.49245 1076.67262
38 -585.50234 1061.49245
39 1257.16272 -585.50234
40 206.60188 1257.16272
41 733.25269 206.60188
42 -1660.30134 733.25269
43 -108.11459 -1660.30134
44 -1012.44383 -108.11459
45 -253.90282 -1012.44383
46 -27.89383 -253.90282
47 -978.82867 -27.89383
48 1849.37747 -978.82867
49 1365.26571 1849.37747
50 -1556.21793 1365.26571
51 1358.81552 -1556.21793
52 935.58033 1358.81552
53 -1563.84916 935.58033
54 2196.33102 -1563.84916
55 -243.38224 2196.33102
56 1021.86807 -243.38224
57 1252.96633 1021.86807
58 -1151.07598 1252.96633
59 506.45498 -1151.07598
60 -1023.99462 506.45498
61 -1520.99305 -1023.99462
62 -68.13544 -1520.99305
63 -757.34289 -68.13544
64 -135.86433 -757.34289
65 -212.46892 -135.86433
66 989.65325 -212.46892
67 823.52809 989.65325
68 1341.90405 823.52809
69 2927.21347 1341.90405
70 2729.13955 2927.21347
> 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/7oxvl1258476296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8f9mw1258476296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kz9t1258476296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103wbg1258476296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11o6rt1258476296.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/12vx441258476296.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/13kxa51258476296.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/14zcah1258476296.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/15vkp01258476296.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/16ypfk1258476296.tab")
+ }
>
> system("convert tmp/1d3ke1258476296.ps tmp/1d3ke1258476296.png")
> system("convert tmp/2x3vf1258476296.ps tmp/2x3vf1258476296.png")
> system("convert tmp/3nhoo1258476296.ps tmp/3nhoo1258476296.png")
> system("convert tmp/4a0hs1258476296.ps tmp/4a0hs1258476296.png")
> system("convert tmp/5o9ju1258476296.ps tmp/5o9ju1258476296.png")
> system("convert tmp/6exmm1258476296.ps tmp/6exmm1258476296.png")
> system("convert tmp/7oxvl1258476296.ps tmp/7oxvl1258476296.png")
> system("convert tmp/8f9mw1258476296.ps tmp/8f9mw1258476296.png")
> system("convert tmp/9kz9t1258476296.ps tmp/9kz9t1258476296.png")
> system("convert tmp/103wbg1258476296.ps tmp/103wbg1258476296.png")
>
>
> proc.time()
user system elapsed
2.576 1.602 3.405