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.
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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(31.514,0,27.071,0,29.462,0,26.105,0,22.397,0,23.843,0,21.705,0,18.089,0,20.764,0,25.316,0,17.704,0,15.548,0,28.029,0,29.383,0,36.438,0,32.034,0,22.679,0,24.319,0,18.004,0,17.537,0,20.366,0,22.782,0,19.169,0,13.807,0,29.743,0,25.591,0,29.096,0,26.482,0,22.405,0,27.044,0,17.970,0,18.730,0,19.684,0,19.785,0,18.479,0,10.698,0,31.956,0,29.506,0,34.506,0,27.165,0,26.736,0,23.691,0,18.157,0,17.328,0,18.205,0,20.995,0,17.382,0,9.367,0,31.124,0,26.551,0,30.651,0,25.859,0,25.100,0,25.778,0,20.418,0,18.688,0,20.424,0,24.776,0,19.814,0,12.738,0,31.566,0,30.111,0,30.019,0,31.934,0,25.826,0,26.835,0,20.205,0,17.789,0,20.520,1,22.518,1,15.572,1,11.509,1,25.447,1,24.090,1,27.786,1,26.195,1,20.516,1,22.759,1,19.028,1,16.971,1,20.036,1,22.485,1,18.730,1,14.538,1),dim=c(2,84),dimnames=list(c('Y','X'),1:84))
> y <- array(NA,dim=c(2,84),dimnames=list(c('Y','X'),1:84))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X
1 31.514 0
2 27.071 0
3 29.462 0
4 26.105 0
5 22.397 0
6 23.843 0
7 21.705 0
8 18.089 0
9 20.764 0
10 25.316 0
11 17.704 0
12 15.548 0
13 28.029 0
14 29.383 0
15 36.438 0
16 32.034 0
17 22.679 0
18 24.319 0
19 18.004 0
20 17.537 0
21 20.366 0
22 22.782 0
23 19.169 0
24 13.807 0
25 29.743 0
26 25.591 0
27 29.096 0
28 26.482 0
29 22.405 0
30 27.044 0
31 17.970 0
32 18.730 0
33 19.684 0
34 19.785 0
35 18.479 0
36 10.698 0
37 31.956 0
38 29.506 0
39 34.506 0
40 27.165 0
41 26.736 0
42 23.691 0
43 18.157 0
44 17.328 0
45 18.205 0
46 20.995 0
47 17.382 0
48 9.367 0
49 31.124 0
50 26.551 0
51 30.651 0
52 25.859 0
53 25.100 0
54 25.778 0
55 20.418 0
56 18.688 0
57 20.424 0
58 24.776 0
59 19.814 0
60 12.738 0
61 31.566 0
62 30.111 0
63 30.019 0
64 31.934 0
65 25.826 0
66 26.835 0
67 20.205 0
68 17.789 0
69 20.520 1
70 22.518 1
71 15.572 1
72 11.509 1
73 25.447 1
74 24.090 1
75 27.786 1
76 26.195 1
77 20.516 1
78 22.759 1
79 19.028 1
80 16.971 1
81 20.036 1
82 22.485 1
83 18.730 1
84 14.538 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
23.54 -3.00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.17671 -4.48446 0.06177 3.56501 12.89429
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.5437 0.6811 34.565 <2e-16 ***
X -3.0000 1.5607 -1.922 0.058 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.617 on 82 degrees of freedom
Multiple R-squared: 0.04312, Adjusted R-squared: 0.03145
F-statistic: 3.695 on 1 and 82 DF, p-value: 0.05806
> 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,] 0.3164660 0.63293190 0.68353405
[2,] 0.2259803 0.45196066 0.77401967
[3,] 0.2140017 0.42800341 0.78599829
[4,] 0.3356308 0.67126159 0.66436920
[5,] 0.2851036 0.57020721 0.71489640
[6,] 0.1945501 0.38910019 0.80544990
[7,] 0.2435224 0.48704477 0.75647762
[8,] 0.3554358 0.71087158 0.64456421
[9,] 0.3263431 0.65268627 0.67365686
[10,] 0.3283823 0.65676461 0.67161770
[11,] 0.6382624 0.72347512 0.36173756
[12,] 0.6835590 0.63288207 0.31644103
[13,] 0.6193482 0.76130369 0.38065185
[14,] 0.5424524 0.91509529 0.45754764
[15,] 0.5677391 0.86452179 0.43226089
[16,] 0.5954080 0.80918408 0.40459204
[17,] 0.5516867 0.89662652 0.44831326
[18,] 0.4806331 0.96126622 0.51936689
[19,] 0.4569238 0.91384768 0.54307616
[20,] 0.5951792 0.80964153 0.40482076
[21,] 0.6041465 0.79170703 0.39585352
[22,] 0.5431607 0.91367864 0.45683932
[23,] 0.5345489 0.93090216 0.46545108
[24,] 0.4815844 0.96316889 0.51841556
[25,] 0.4184710 0.83694201 0.58152900
[26,] 0.3752035 0.75040697 0.62479651
[27,] 0.3779945 0.75598899 0.62200551
[28,] 0.3626589 0.72531771 0.63734115
[29,] 0.3307108 0.66142167 0.66928916
[30,] 0.2981872 0.59637440 0.70181280
[31,] 0.2865001 0.57300022 0.71349989
[32,] 0.5369331 0.92613379 0.46306690
[33,] 0.6105914 0.77881724 0.38940862
[34,] 0.6153333 0.76933333 0.38466666
[35,] 0.7637189 0.47256229 0.23628114
[36,] 0.7339623 0.53207532 0.26603766
[37,] 0.6979836 0.60403272 0.30201636
[38,] 0.6406267 0.71874665 0.35937333
[39,] 0.6318639 0.73627215 0.36813608
[40,] 0.6412154 0.71756929 0.35878465
[41,] 0.6334652 0.73306958 0.36653479
[42,] 0.5846755 0.83064906 0.41532453
[43,] 0.5984406 0.80311878 0.40155939
[44,] 0.8810603 0.23787939 0.11893969
[45,] 0.8998282 0.20034351 0.10017175
[46,] 0.8751069 0.24978620 0.12489310
[47,] 0.8909501 0.21809978 0.10904989
[48,] 0.8616587 0.27668257 0.13834128
[49,] 0.8237183 0.35256346 0.17628173
[50,] 0.7836253 0.43274933 0.21637467
[51,] 0.7492707 0.50145864 0.25072932
[52,] 0.7424810 0.51503792 0.25751896
[53,] 0.7113903 0.57721948 0.28860974
[54,] 0.6497811 0.70043787 0.35021893
[55,] 0.6308059 0.73838820 0.36919410
[56,] 0.8704158 0.25916849 0.12958424
[57,] 0.8791418 0.24171640 0.12085820
[58,] 0.8727046 0.25459080 0.12729540
[59,] 0.8725780 0.25484403 0.12742201
[60,] 0.9250553 0.14988936 0.07494468
[61,] 0.9078381 0.18432385 0.09216193
[62,] 0.9213659 0.15726821 0.07863411
[63,] 0.8906498 0.21870038 0.10935019
[64,] 0.8507503 0.29849936 0.14924968
[65,] 0.7917764 0.41644714 0.20822357
[66,] 0.7295551 0.54088971 0.27044486
[67,] 0.7175470 0.56490607 0.28245304
[68,] 0.8778466 0.24430686 0.12215343
[69,] 0.8639239 0.27215218 0.13607609
[70,] 0.8219467 0.35610669 0.17805335
[71,] 0.9035982 0.19280356 0.09640178
[72,] 0.9544848 0.09103049 0.04551525
[73,] 0.9085051 0.18298974 0.09149487
[74,] 0.8928015 0.21439704 0.10719852
[75,] 0.7751515 0.44969694 0.22484847
> postscript(file="/var/www/html/rcomp/tmp/15w491291109984.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2f6lc1291109984.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3f6lc1291109984.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4f6lc1291109984.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58fke1291109984.ps",horizontal=F,onefile=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 = 84
Frequency = 1
1 2 3 4 5 6
7.9702941 3.5272941 5.9182941 2.5612941 -1.1467059 0.2992941
7 8 9 10 11 12
-1.8387059 -5.4547059 -2.7797059 1.7722941 -5.8397059 -7.9957059
13 14 15 16 17 18
4.4852941 5.8392941 12.8942941 8.4902941 -0.8647059 0.7752941
19 20 21 22 23 24
-5.5397059 -6.0067059 -3.1777059 -0.7617059 -4.3747059 -9.7367059
25 26 27 28 29 30
6.1992941 2.0472941 5.5522941 2.9382941 -1.1387059 3.5002941
31 32 33 34 35 36
-5.5737059 -4.8137059 -3.8597059 -3.7587059 -5.0647059 -12.8457059
37 38 39 40 41 42
8.4122941 5.9622941 10.9622941 3.6212941 3.1922941 0.1472941
43 44 45 46 47 48
-5.3867059 -6.2157059 -5.3387059 -2.5487059 -6.1617059 -14.1767059
49 50 51 52 53 54
7.5802941 3.0072941 7.1072941 2.3152941 1.5562941 2.2342941
55 56 57 58 59 60
-3.1257059 -4.8557059 -3.1197059 1.2322941 -3.7297059 -10.8057059
61 62 63 64 65 66
8.0222941 6.5672941 6.4752941 8.3902941 2.2822941 3.2912941
67 68 69 70 71 72
-3.3387059 -5.7547059 -0.0237500 1.9742500 -4.9717500 -9.0347500
73 74 75 76 77 78
4.9032500 3.5462500 7.2422500 5.6512500 -0.0277500 2.2152500
79 80 81 82 83 84
-1.5157500 -3.5727500 -0.5077500 1.9412500 -1.8137500 -6.0057500
> postscript(file="/var/www/html/rcomp/tmp/68fke1291109984.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 7.9702941 NA
1 3.5272941 7.9702941
2 5.9182941 3.5272941
3 2.5612941 5.9182941
4 -1.1467059 2.5612941
5 0.2992941 -1.1467059
6 -1.8387059 0.2992941
7 -5.4547059 -1.8387059
8 -2.7797059 -5.4547059
9 1.7722941 -2.7797059
10 -5.8397059 1.7722941
11 -7.9957059 -5.8397059
12 4.4852941 -7.9957059
13 5.8392941 4.4852941
14 12.8942941 5.8392941
15 8.4902941 12.8942941
16 -0.8647059 8.4902941
17 0.7752941 -0.8647059
18 -5.5397059 0.7752941
19 -6.0067059 -5.5397059
20 -3.1777059 -6.0067059
21 -0.7617059 -3.1777059
22 -4.3747059 -0.7617059
23 -9.7367059 -4.3747059
24 6.1992941 -9.7367059
25 2.0472941 6.1992941
26 5.5522941 2.0472941
27 2.9382941 5.5522941
28 -1.1387059 2.9382941
29 3.5002941 -1.1387059
30 -5.5737059 3.5002941
31 -4.8137059 -5.5737059
32 -3.8597059 -4.8137059
33 -3.7587059 -3.8597059
34 -5.0647059 -3.7587059
35 -12.8457059 -5.0647059
36 8.4122941 -12.8457059
37 5.9622941 8.4122941
38 10.9622941 5.9622941
39 3.6212941 10.9622941
40 3.1922941 3.6212941
41 0.1472941 3.1922941
42 -5.3867059 0.1472941
43 -6.2157059 -5.3867059
44 -5.3387059 -6.2157059
45 -2.5487059 -5.3387059
46 -6.1617059 -2.5487059
47 -14.1767059 -6.1617059
48 7.5802941 -14.1767059
49 3.0072941 7.5802941
50 7.1072941 3.0072941
51 2.3152941 7.1072941
52 1.5562941 2.3152941
53 2.2342941 1.5562941
54 -3.1257059 2.2342941
55 -4.8557059 -3.1257059
56 -3.1197059 -4.8557059
57 1.2322941 -3.1197059
58 -3.7297059 1.2322941
59 -10.8057059 -3.7297059
60 8.0222941 -10.8057059
61 6.5672941 8.0222941
62 6.4752941 6.5672941
63 8.3902941 6.4752941
64 2.2822941 8.3902941
65 3.2912941 2.2822941
66 -3.3387059 3.2912941
67 -5.7547059 -3.3387059
68 -0.0237500 -5.7547059
69 1.9742500 -0.0237500
70 -4.9717500 1.9742500
71 -9.0347500 -4.9717500
72 4.9032500 -9.0347500
73 3.5462500 4.9032500
74 7.2422500 3.5462500
75 5.6512500 7.2422500
76 -0.0277500 5.6512500
77 2.2152500 -0.0277500
78 -1.5157500 2.2152500
79 -3.5727500 -1.5157500
80 -0.5077500 -3.5727500
81 1.9412500 -0.5077500
82 -1.8137500 1.9412500
83 -6.0057500 -1.8137500
84 NA -6.0057500
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.5272941 7.9702941
[2,] 5.9182941 3.5272941
[3,] 2.5612941 5.9182941
[4,] -1.1467059 2.5612941
[5,] 0.2992941 -1.1467059
[6,] -1.8387059 0.2992941
[7,] -5.4547059 -1.8387059
[8,] -2.7797059 -5.4547059
[9,] 1.7722941 -2.7797059
[10,] -5.8397059 1.7722941
[11,] -7.9957059 -5.8397059
[12,] 4.4852941 -7.9957059
[13,] 5.8392941 4.4852941
[14,] 12.8942941 5.8392941
[15,] 8.4902941 12.8942941
[16,] -0.8647059 8.4902941
[17,] 0.7752941 -0.8647059
[18,] -5.5397059 0.7752941
[19,] -6.0067059 -5.5397059
[20,] -3.1777059 -6.0067059
[21,] -0.7617059 -3.1777059
[22,] -4.3747059 -0.7617059
[23,] -9.7367059 -4.3747059
[24,] 6.1992941 -9.7367059
[25,] 2.0472941 6.1992941
[26,] 5.5522941 2.0472941
[27,] 2.9382941 5.5522941
[28,] -1.1387059 2.9382941
[29,] 3.5002941 -1.1387059
[30,] -5.5737059 3.5002941
[31,] -4.8137059 -5.5737059
[32,] -3.8597059 -4.8137059
[33,] -3.7587059 -3.8597059
[34,] -5.0647059 -3.7587059
[35,] -12.8457059 -5.0647059
[36,] 8.4122941 -12.8457059
[37,] 5.9622941 8.4122941
[38,] 10.9622941 5.9622941
[39,] 3.6212941 10.9622941
[40,] 3.1922941 3.6212941
[41,] 0.1472941 3.1922941
[42,] -5.3867059 0.1472941
[43,] -6.2157059 -5.3867059
[44,] -5.3387059 -6.2157059
[45,] -2.5487059 -5.3387059
[46,] -6.1617059 -2.5487059
[47,] -14.1767059 -6.1617059
[48,] 7.5802941 -14.1767059
[49,] 3.0072941 7.5802941
[50,] 7.1072941 3.0072941
[51,] 2.3152941 7.1072941
[52,] 1.5562941 2.3152941
[53,] 2.2342941 1.5562941
[54,] -3.1257059 2.2342941
[55,] -4.8557059 -3.1257059
[56,] -3.1197059 -4.8557059
[57,] 1.2322941 -3.1197059
[58,] -3.7297059 1.2322941
[59,] -10.8057059 -3.7297059
[60,] 8.0222941 -10.8057059
[61,] 6.5672941 8.0222941
[62,] 6.4752941 6.5672941
[63,] 8.3902941 6.4752941
[64,] 2.2822941 8.3902941
[65,] 3.2912941 2.2822941
[66,] -3.3387059 3.2912941
[67,] -5.7547059 -3.3387059
[68,] -0.0237500 -5.7547059
[69,] 1.9742500 -0.0237500
[70,] -4.9717500 1.9742500
[71,] -9.0347500 -4.9717500
[72,] 4.9032500 -9.0347500
[73,] 3.5462500 4.9032500
[74,] 7.2422500 3.5462500
[75,] 5.6512500 7.2422500
[76,] -0.0277500 5.6512500
[77,] 2.2152500 -0.0277500
[78,] -1.5157500 2.2152500
[79,] -3.5727500 -1.5157500
[80,] -0.5077500 -3.5727500
[81,] 1.9412500 -0.5077500
[82,] -1.8137500 1.9412500
[83,] -6.0057500 -1.8137500
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.5272941 7.9702941
2 5.9182941 3.5272941
3 2.5612941 5.9182941
4 -1.1467059 2.5612941
5 0.2992941 -1.1467059
6 -1.8387059 0.2992941
7 -5.4547059 -1.8387059
8 -2.7797059 -5.4547059
9 1.7722941 -2.7797059
10 -5.8397059 1.7722941
11 -7.9957059 -5.8397059
12 4.4852941 -7.9957059
13 5.8392941 4.4852941
14 12.8942941 5.8392941
15 8.4902941 12.8942941
16 -0.8647059 8.4902941
17 0.7752941 -0.8647059
18 -5.5397059 0.7752941
19 -6.0067059 -5.5397059
20 -3.1777059 -6.0067059
21 -0.7617059 -3.1777059
22 -4.3747059 -0.7617059
23 -9.7367059 -4.3747059
24 6.1992941 -9.7367059
25 2.0472941 6.1992941
26 5.5522941 2.0472941
27 2.9382941 5.5522941
28 -1.1387059 2.9382941
29 3.5002941 -1.1387059
30 -5.5737059 3.5002941
31 -4.8137059 -5.5737059
32 -3.8597059 -4.8137059
33 -3.7587059 -3.8597059
34 -5.0647059 -3.7587059
35 -12.8457059 -5.0647059
36 8.4122941 -12.8457059
37 5.9622941 8.4122941
38 10.9622941 5.9622941
39 3.6212941 10.9622941
40 3.1922941 3.6212941
41 0.1472941 3.1922941
42 -5.3867059 0.1472941
43 -6.2157059 -5.3867059
44 -5.3387059 -6.2157059
45 -2.5487059 -5.3387059
46 -6.1617059 -2.5487059
47 -14.1767059 -6.1617059
48 7.5802941 -14.1767059
49 3.0072941 7.5802941
50 7.1072941 3.0072941
51 2.3152941 7.1072941
52 1.5562941 2.3152941
53 2.2342941 1.5562941
54 -3.1257059 2.2342941
55 -4.8557059 -3.1257059
56 -3.1197059 -4.8557059
57 1.2322941 -3.1197059
58 -3.7297059 1.2322941
59 -10.8057059 -3.7297059
60 8.0222941 -10.8057059
61 6.5672941 8.0222941
62 6.4752941 6.5672941
63 8.3902941 6.4752941
64 2.2822941 8.3902941
65 3.2912941 2.2822941
66 -3.3387059 3.2912941
67 -5.7547059 -3.3387059
68 -0.0237500 -5.7547059
69 1.9742500 -0.0237500
70 -4.9717500 1.9742500
71 -9.0347500 -4.9717500
72 4.9032500 -9.0347500
73 3.5462500 4.9032500
74 7.2422500 3.5462500
75 5.6512500 7.2422500
76 -0.0277500 5.6512500
77 2.2152500 -0.0277500
78 -1.5157500 2.2152500
79 -3.5727500 -1.5157500
80 -0.5077500 -3.5727500
81 1.9412500 -0.5077500
82 -1.8137500 1.9412500
83 -6.0057500 -1.8137500
> 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/7j6kh1291109984.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8j6kh1291109984.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cg131291109984.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cg131291109984.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fghq1291109984.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/120gye1291109984.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/137hdq1291109984.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/1409ub1291109984.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/153rbz1291109984.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/16hj981291109984.tab")
+ }
>
> try(system("convert tmp/15w491291109984.ps tmp/15w491291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/2f6lc1291109984.ps tmp/2f6lc1291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/3f6lc1291109984.ps tmp/3f6lc1291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/4f6lc1291109984.ps tmp/4f6lc1291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/58fke1291109984.ps tmp/58fke1291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/68fke1291109984.ps tmp/68fke1291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/7j6kh1291109984.ps tmp/7j6kh1291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/8j6kh1291109984.ps tmp/8j6kh1291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cg131291109984.ps tmp/9cg131291109984.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cg131291109984.ps tmp/10cg131291109984.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.755 1.658 7.451