R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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.
R is a collaborative project with many contributors.
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(1
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+ ,0)
+ ,dim=c(6
+ ,86)
+ ,dimnames=list(c('UseLimit'
+ ,'T40'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome')
+ ,1:86))
> y <- array(NA,dim=c(6,86),dimnames=list(c('UseLimit','T40','Used','CorrectAnalysis','Useful','Outcome'),1:86))
> 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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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, 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
T40 UseLimit Used CorrectAnalysis Useful Outcome
1 1 1 0 0 0 1
2 0 0 0 0 0 0
3 0 0 0 0 0 0
4 0 0 0 0 0 0
5 0 0 0 0 0 0
6 0 1 0 0 1 1
7 0 0 0 0 0 0
8 1 0 0 0 0 0
9 0 0 0 0 0 1
10 0 1 0 0 0 0
11 1 1 0 0 0 0
12 0 0 0 0 0 0
13 0 0 1 0 1 0
14 1 1 0 0 0 0
15 0 0 1 0 1 1
16 1 0 1 0 1 1
17 1 1 1 1 1 0
18 1 1 0 0 0 0
19 0 0 0 0 0 1
20 1 0 1 1 1 1
21 0 1 0 0 1 0
22 0 1 1 0 1 1
23 0 0 0 0 1 1
24 0 1 0 0 1 1
25 1 0 1 0 0 1
26 0 0 1 0 1 0
27 0 1 0 0 0 1
28 0 0 1 0 0 0
29 0 0 0 0 0 1
30 0 0 0 0 1 0
31 0 0 0 0 0 0
32 0 1 0 0 0 0
33 0 1 0 0 1 0
34 1 0 0 0 0 1
35 0 0 0 0 0 0
36 0 0 0 0 0 0
37 1 1 1 0 1 0
38 0 0 1 0 0 1
39 0 0 0 0 1 1
40 1 0 0 0 1 0
41 0 0 1 1 1 1
42 0 0 1 0 0 1
43 0 1 0 0 1 1
44 1 1 0 0 0 0
45 0 0 0 0 1 0
46 0 0 0 0 1 1
47 0 0 0 0 0 0
48 0 0 0 0 0 1
49 0 0 0 0 1 1
50 0 0 0 0 0 0
51 1 0 1 0 0 0
52 1 1 1 1 1 0
53 0 0 0 0 0 1
54 0 0 1 1 0 0
55 0 0 0 0 0 0
56 1 0 1 0 0 1
57 0 0 1 0 1 1
58 0 0 0 0 0 1
59 0 0 0 0 0 1
60 1 1 1 1 1 1
61 1 1 0 0 0 1
62 0 0 1 0 1 0
63 0 0 0 0 0 0
64 1 1 0 0 0 1
65 0 0 0 0 0 0
66 0 0 0 0 0 0
67 1 0 1 1 1 0
68 0 1 0 0 0 0
69 0 0 0 0 0 1
70 0 0 1 0 0 0
71 0 0 0 0 0 0
72 0 0 0 0 0 1
73 0 0 1 0 0 1
74 0 1 1 0 0 0
75 0 0 0 0 0 1
76 1 0 0 0 1 1
77 0 0 0 0 0 1
78 0 0 1 0 1 1
79 1 0 1 1 0 1
80 1 0 0 0 1 0
81 0 0 0 0 0 0
82 0 1 1 0 0 1
83 0 0 0 0 0 0
84 0 0 1 1 0 0
85 0 0 0 0 1 1
86 0 1 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit Used CorrectAnalysis
0.120068 0.259220 0.073801 0.374753
Useful Outcome
0.001126 0.024498
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.5942 -0.2125 -0.1329 0.1649 0.8799
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.120068 0.076648 1.566 0.1212
UseLimit 0.259220 0.101811 2.546 0.0128 *
Used 0.073801 0.113722 0.649 0.5182
CorrectAnalysis 0.374753 0.171564 2.184 0.0319 *
Useful 0.001126 0.100664 0.011 0.9911
Outcome 0.024498 0.092335 0.265 0.7914
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.419 on 80 degrees of freedom
Multiple R-squared: 0.1664, Adjusted R-squared: 0.1143
F-statistic: 3.193 on 5 and 80 DF, p-value: 0.01115
> 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,] 0.7933614 0.4132773 0.20663865
[2,] 0.8549192 0.2901615 0.14508076
[3,] 0.8564356 0.2871288 0.14356439
[4,] 0.7799945 0.4400110 0.22000548
[5,] 0.6870468 0.6259063 0.31295317
[6,] 0.6589689 0.6820622 0.34103108
[7,] 0.5647419 0.8705162 0.43525810
[8,] 0.7428852 0.5142295 0.25711475
[9,] 0.6644998 0.6710003 0.33550017
[10,] 0.6484682 0.7030635 0.35153177
[11,] 0.5854259 0.8291481 0.41457405
[12,] 0.5656438 0.8687124 0.43435621
[13,] 0.4956447 0.9912895 0.50435526
[14,] 0.6481470 0.7037059 0.35185297
[15,] 0.6054654 0.7890691 0.39453455
[16,] 0.5546222 0.8907555 0.44537777
[17,] 0.5675461 0.8649078 0.43245390
[18,] 0.5015485 0.9969030 0.49845149
[19,] 0.5753482 0.8493037 0.42465184
[20,] 0.6068479 0.7863041 0.39315206
[21,] 0.5538167 0.8923666 0.44618330
[22,] 0.5030920 0.9938160 0.49690799
[23,] 0.4418279 0.8836557 0.55817213
[24,] 0.4435637 0.8871274 0.55643629
[25,] 0.4163661 0.8327323 0.58363385
[26,] 0.6042877 0.7914245 0.39571226
[27,] 0.5461154 0.9077692 0.45388461
[28,] 0.4863008 0.9726016 0.51369922
[29,] 0.5314655 0.9370690 0.46853448
[30,] 0.5347996 0.9304009 0.46520043
[31,] 0.4795002 0.9590004 0.52049981
[32,] 0.7223435 0.5553130 0.27765649
[33,] 0.7934693 0.4130614 0.20653071
[34,] 0.7618988 0.4762024 0.23810122
[35,] 0.7708826 0.4582348 0.22911741
[36,] 0.8214546 0.3570908 0.17854538
[37,] 0.7812705 0.4374591 0.21872953
[38,] 0.7508264 0.4983473 0.24917365
[39,] 0.7006454 0.5987092 0.29935458
[40,] 0.6470787 0.7058426 0.35292132
[41,] 0.6208456 0.7583087 0.37915437
[42,] 0.5607050 0.8785900 0.43929499
[43,] 0.8157667 0.3684666 0.18423328
[44,] 0.7672447 0.4655107 0.23275533
[45,] 0.7223732 0.5552536 0.27762679
[46,] 0.7550370 0.4899259 0.24496297
[47,] 0.6992194 0.6015612 0.30078061
[48,] 0.9323013 0.1353974 0.06769871
[49,] 0.9155517 0.1688967 0.08444834
[50,] 0.8882809 0.2234381 0.11171907
[51,] 0.8561702 0.2876596 0.14382980
[52,] 0.8485869 0.3028261 0.15141305
[53,] 0.8860321 0.2279357 0.11396785
[54,] 0.8536869 0.2926262 0.14631312
[55,] 0.8033062 0.3933876 0.19669382
[56,] 0.9238279 0.1523443 0.07617213
[57,] 0.8881778 0.2236444 0.11182220
[58,] 0.8408365 0.3183271 0.15916353
[59,] 0.7967341 0.4065318 0.20326591
[60,] 0.7454287 0.5091426 0.25457128
[61,] 0.6652626 0.6694749 0.33473743
[62,] 0.6022474 0.7955052 0.39775258
[63,] 0.5017112 0.9965776 0.49828880
[64,] 0.4007649 0.8015299 0.59923505
[65,] 0.3365246 0.6730492 0.66347541
[66,] 0.2809732 0.5619463 0.71902684
[67,] 0.1887160 0.3774321 0.81128396
[68,] 0.2002963 0.4005926 0.79970371
[69,] 0.1113102 0.2226204 0.88868978
> postscript(file="/var/fisher/rcomp/tmp/16emt1356113649.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2ycn51356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3z5mg1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4cjhk1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5vqz41356113649.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 = 86
Frequency = 1
1 2 3 4 5 6 7
0.5962144 -0.1200677 -0.1200677 -0.1200677 -0.1200677 -0.4049111 -0.1200677
8 9 10 11 12 13 14
0.8799323 -0.1445660 -0.3792873 0.6207127 -0.1200677 -0.1949945 0.6207127
15 16 17 18 19 20 21
-0.2194928 0.7805072 0.1710332 0.6207127 -0.1445660 0.4057545 -0.3804128
22 23 24 25 26 27 28
-0.4787124 -0.1456916 -0.4049111 0.7816327 -0.1949945 -0.4037856 -0.1938690
29 30 31 32 33 34 35
-0.1445660 -0.1211933 -0.1200677 -0.3792873 -0.3804128 0.8554340 -0.1200677
36 37 38 39 40 41 42
-0.1200677 0.5457859 -0.2183673 -0.1456916 0.8788067 -0.5942455 -0.2183673
43 44 45 46 47 48 49
-0.4049111 0.6207127 -0.1211933 -0.1456916 -0.1200677 -0.1445660 -0.1456916
50 51 52 53 54 55 56
-0.1200677 0.8061310 0.1710332 -0.1445660 -0.5686216 -0.1200677 0.7816327
57 58 59 60 61 62 63
-0.2194928 -0.1445660 -0.1445660 0.1465349 0.5962144 -0.1949945 -0.1200677
64 65 66 67 68 69 70
0.5962144 -0.1200677 -0.1200677 0.4302528 -0.3792873 -0.1445660 -0.1938690
71 72 73 74 75 76 77
-0.1200677 -0.1445660 -0.2183673 -0.4530885 -0.1445660 0.8543084 -0.1445660
78 79 80 81 82 83 84
-0.2194928 0.4068801 0.8788067 -0.1200677 -0.4775868 -0.1200677 -0.5686216
85 86
-0.1456916 -0.3792873
> postscript(file="/var/fisher/rcomp/tmp/6u8jp1356113649.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 = 86
Frequency = 1
lag(myerror, k = 1) myerror
0 0.5962144 NA
1 -0.1200677 0.5962144
2 -0.1200677 -0.1200677
3 -0.1200677 -0.1200677
4 -0.1200677 -0.1200677
5 -0.4049111 -0.1200677
6 -0.1200677 -0.4049111
7 0.8799323 -0.1200677
8 -0.1445660 0.8799323
9 -0.3792873 -0.1445660
10 0.6207127 -0.3792873
11 -0.1200677 0.6207127
12 -0.1949945 -0.1200677
13 0.6207127 -0.1949945
14 -0.2194928 0.6207127
15 0.7805072 -0.2194928
16 0.1710332 0.7805072
17 0.6207127 0.1710332
18 -0.1445660 0.6207127
19 0.4057545 -0.1445660
20 -0.3804128 0.4057545
21 -0.4787124 -0.3804128
22 -0.1456916 -0.4787124
23 -0.4049111 -0.1456916
24 0.7816327 -0.4049111
25 -0.1949945 0.7816327
26 -0.4037856 -0.1949945
27 -0.1938690 -0.4037856
28 -0.1445660 -0.1938690
29 -0.1211933 -0.1445660
30 -0.1200677 -0.1211933
31 -0.3792873 -0.1200677
32 -0.3804128 -0.3792873
33 0.8554340 -0.3804128
34 -0.1200677 0.8554340
35 -0.1200677 -0.1200677
36 0.5457859 -0.1200677
37 -0.2183673 0.5457859
38 -0.1456916 -0.2183673
39 0.8788067 -0.1456916
40 -0.5942455 0.8788067
41 -0.2183673 -0.5942455
42 -0.4049111 -0.2183673
43 0.6207127 -0.4049111
44 -0.1211933 0.6207127
45 -0.1456916 -0.1211933
46 -0.1200677 -0.1456916
47 -0.1445660 -0.1200677
48 -0.1456916 -0.1445660
49 -0.1200677 -0.1456916
50 0.8061310 -0.1200677
51 0.1710332 0.8061310
52 -0.1445660 0.1710332
53 -0.5686216 -0.1445660
54 -0.1200677 -0.5686216
55 0.7816327 -0.1200677
56 -0.2194928 0.7816327
57 -0.1445660 -0.2194928
58 -0.1445660 -0.1445660
59 0.1465349 -0.1445660
60 0.5962144 0.1465349
61 -0.1949945 0.5962144
62 -0.1200677 -0.1949945
63 0.5962144 -0.1200677
64 -0.1200677 0.5962144
65 -0.1200677 -0.1200677
66 0.4302528 -0.1200677
67 -0.3792873 0.4302528
68 -0.1445660 -0.3792873
69 -0.1938690 -0.1445660
70 -0.1200677 -0.1938690
71 -0.1445660 -0.1200677
72 -0.2183673 -0.1445660
73 -0.4530885 -0.2183673
74 -0.1445660 -0.4530885
75 0.8543084 -0.1445660
76 -0.1445660 0.8543084
77 -0.2194928 -0.1445660
78 0.4068801 -0.2194928
79 0.8788067 0.4068801
80 -0.1200677 0.8788067
81 -0.4775868 -0.1200677
82 -0.1200677 -0.4775868
83 -0.5686216 -0.1200677
84 -0.1456916 -0.5686216
85 -0.3792873 -0.1456916
86 NA -0.3792873
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1200677 0.5962144
[2,] -0.1200677 -0.1200677
[3,] -0.1200677 -0.1200677
[4,] -0.1200677 -0.1200677
[5,] -0.4049111 -0.1200677
[6,] -0.1200677 -0.4049111
[7,] 0.8799323 -0.1200677
[8,] -0.1445660 0.8799323
[9,] -0.3792873 -0.1445660
[10,] 0.6207127 -0.3792873
[11,] -0.1200677 0.6207127
[12,] -0.1949945 -0.1200677
[13,] 0.6207127 -0.1949945
[14,] -0.2194928 0.6207127
[15,] 0.7805072 -0.2194928
[16,] 0.1710332 0.7805072
[17,] 0.6207127 0.1710332
[18,] -0.1445660 0.6207127
[19,] 0.4057545 -0.1445660
[20,] -0.3804128 0.4057545
[21,] -0.4787124 -0.3804128
[22,] -0.1456916 -0.4787124
[23,] -0.4049111 -0.1456916
[24,] 0.7816327 -0.4049111
[25,] -0.1949945 0.7816327
[26,] -0.4037856 -0.1949945
[27,] -0.1938690 -0.4037856
[28,] -0.1445660 -0.1938690
[29,] -0.1211933 -0.1445660
[30,] -0.1200677 -0.1211933
[31,] -0.3792873 -0.1200677
[32,] -0.3804128 -0.3792873
[33,] 0.8554340 -0.3804128
[34,] -0.1200677 0.8554340
[35,] -0.1200677 -0.1200677
[36,] 0.5457859 -0.1200677
[37,] -0.2183673 0.5457859
[38,] -0.1456916 -0.2183673
[39,] 0.8788067 -0.1456916
[40,] -0.5942455 0.8788067
[41,] -0.2183673 -0.5942455
[42,] -0.4049111 -0.2183673
[43,] 0.6207127 -0.4049111
[44,] -0.1211933 0.6207127
[45,] -0.1456916 -0.1211933
[46,] -0.1200677 -0.1456916
[47,] -0.1445660 -0.1200677
[48,] -0.1456916 -0.1445660
[49,] -0.1200677 -0.1456916
[50,] 0.8061310 -0.1200677
[51,] 0.1710332 0.8061310
[52,] -0.1445660 0.1710332
[53,] -0.5686216 -0.1445660
[54,] -0.1200677 -0.5686216
[55,] 0.7816327 -0.1200677
[56,] -0.2194928 0.7816327
[57,] -0.1445660 -0.2194928
[58,] -0.1445660 -0.1445660
[59,] 0.1465349 -0.1445660
[60,] 0.5962144 0.1465349
[61,] -0.1949945 0.5962144
[62,] -0.1200677 -0.1949945
[63,] 0.5962144 -0.1200677
[64,] -0.1200677 0.5962144
[65,] -0.1200677 -0.1200677
[66,] 0.4302528 -0.1200677
[67,] -0.3792873 0.4302528
[68,] -0.1445660 -0.3792873
[69,] -0.1938690 -0.1445660
[70,] -0.1200677 -0.1938690
[71,] -0.1445660 -0.1200677
[72,] -0.2183673 -0.1445660
[73,] -0.4530885 -0.2183673
[74,] -0.1445660 -0.4530885
[75,] 0.8543084 -0.1445660
[76,] -0.1445660 0.8543084
[77,] -0.2194928 -0.1445660
[78,] 0.4068801 -0.2194928
[79,] 0.8788067 0.4068801
[80,] -0.1200677 0.8788067
[81,] -0.4775868 -0.1200677
[82,] -0.1200677 -0.4775868
[83,] -0.5686216 -0.1200677
[84,] -0.1456916 -0.5686216
[85,] -0.3792873 -0.1456916
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1200677 0.5962144
2 -0.1200677 -0.1200677
3 -0.1200677 -0.1200677
4 -0.1200677 -0.1200677
5 -0.4049111 -0.1200677
6 -0.1200677 -0.4049111
7 0.8799323 -0.1200677
8 -0.1445660 0.8799323
9 -0.3792873 -0.1445660
10 0.6207127 -0.3792873
11 -0.1200677 0.6207127
12 -0.1949945 -0.1200677
13 0.6207127 -0.1949945
14 -0.2194928 0.6207127
15 0.7805072 -0.2194928
16 0.1710332 0.7805072
17 0.6207127 0.1710332
18 -0.1445660 0.6207127
19 0.4057545 -0.1445660
20 -0.3804128 0.4057545
21 -0.4787124 -0.3804128
22 -0.1456916 -0.4787124
23 -0.4049111 -0.1456916
24 0.7816327 -0.4049111
25 -0.1949945 0.7816327
26 -0.4037856 -0.1949945
27 -0.1938690 -0.4037856
28 -0.1445660 -0.1938690
29 -0.1211933 -0.1445660
30 -0.1200677 -0.1211933
31 -0.3792873 -0.1200677
32 -0.3804128 -0.3792873
33 0.8554340 -0.3804128
34 -0.1200677 0.8554340
35 -0.1200677 -0.1200677
36 0.5457859 -0.1200677
37 -0.2183673 0.5457859
38 -0.1456916 -0.2183673
39 0.8788067 -0.1456916
40 -0.5942455 0.8788067
41 -0.2183673 -0.5942455
42 -0.4049111 -0.2183673
43 0.6207127 -0.4049111
44 -0.1211933 0.6207127
45 -0.1456916 -0.1211933
46 -0.1200677 -0.1456916
47 -0.1445660 -0.1200677
48 -0.1456916 -0.1445660
49 -0.1200677 -0.1456916
50 0.8061310 -0.1200677
51 0.1710332 0.8061310
52 -0.1445660 0.1710332
53 -0.5686216 -0.1445660
54 -0.1200677 -0.5686216
55 0.7816327 -0.1200677
56 -0.2194928 0.7816327
57 -0.1445660 -0.2194928
58 -0.1445660 -0.1445660
59 0.1465349 -0.1445660
60 0.5962144 0.1465349
61 -0.1949945 0.5962144
62 -0.1200677 -0.1949945
63 0.5962144 -0.1200677
64 -0.1200677 0.5962144
65 -0.1200677 -0.1200677
66 0.4302528 -0.1200677
67 -0.3792873 0.4302528
68 -0.1445660 -0.3792873
69 -0.1938690 -0.1445660
70 -0.1200677 -0.1938690
71 -0.1445660 -0.1200677
72 -0.2183673 -0.1445660
73 -0.4530885 -0.2183673
74 -0.1445660 -0.4530885
75 0.8543084 -0.1445660
76 -0.1445660 0.8543084
77 -0.2194928 -0.1445660
78 0.4068801 -0.2194928
79 0.8788067 0.4068801
80 -0.1200677 0.8788067
81 -0.4775868 -0.1200677
82 -0.1200677 -0.4775868
83 -0.5686216 -0.1200677
84 -0.1456916 -0.5686216
85 -0.3792873 -0.1456916
> 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/fisher/rcomp/tmp/7pqdm1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8orbh1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9h08m1356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10rlt81356113649.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/114sj31356113649.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/fisher/rcomp/tmp/120zv91356113649.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/fisher/rcomp/tmp/139sq61356113649.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/fisher/rcomp/tmp/14jksz1356113649.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/fisher/rcomp/tmp/15vmrs1356113649.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/fisher/rcomp/tmp/16tbrn1356113649.tab")
+ }
>
> try(system("convert tmp/16emt1356113649.ps tmp/16emt1356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ycn51356113649.ps tmp/2ycn51356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z5mg1356113649.ps tmp/3z5mg1356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cjhk1356113649.ps tmp/4cjhk1356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vqz41356113649.ps tmp/5vqz41356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/6u8jp1356113649.ps tmp/6u8jp1356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/7pqdm1356113649.ps tmp/7pqdm1356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/8orbh1356113649.ps tmp/8orbh1356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/9h08m1356113649.ps tmp/9h08m1356113649.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rlt81356113649.ps tmp/10rlt81356113649.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.467 1.812 8.295