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.
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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(1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,1,0,1,1,1,1,0,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,1,1,0,0,0,0,1,1,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0),dim=c(3,86),dimnames=list(c('T40','used','correctanalysis'),1:86))
> y <- array(NA,dim=c(3,86),dimnames=list(c('T40','used','correctanalysis'),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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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
correctanalysis T40 used
1 0 1 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 1 0
9 0 0 0
10 0 0 0
11 0 1 0
12 0 0 0
13 0 0 1
14 0 1 0
15 0 0 1
16 0 1 1
17 1 1 1
18 0 1 0
19 0 0 0
20 1 1 1
21 0 0 0
22 0 0 1
23 0 0 0
24 0 0 0
25 0 1 1
26 0 0 1
27 0 0 0
28 0 0 1
29 0 0 0
30 0 0 0
31 0 0 0
32 0 0 0
33 0 0 0
34 0 1 0
35 0 0 0
36 0 0 0
37 0 1 1
38 0 0 1
39 0 0 0
40 0 1 0
41 1 0 1
42 0 0 1
43 0 0 0
44 0 1 0
45 0 0 0
46 0 0 0
47 0 0 0
48 0 0 0
49 0 0 0
50 0 0 0
51 0 1 1
52 1 1 1
53 0 0 0
54 1 0 1
55 0 0 0
56 0 1 1
57 0 0 1
58 0 0 0
59 0 0 0
60 1 1 1
61 0 1 0
62 0 0 1
63 0 0 0
64 0 1 0
65 0 0 0
66 0 0 0
67 1 1 1
68 0 0 0
69 0 0 0
70 0 0 1
71 0 0 0
72 0 0 0
73 0 0 1
74 0 0 1
75 0 0 0
76 0 1 0
77 0 0 0
78 0 0 1
79 1 1 1
80 0 1 0
81 0 0 0
82 0 0 1
83 0 0 0
84 1 0 1
85 0 0 0
86 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T40 used
-0.03148 0.15216 0.29313
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.41381 -0.12068 0.03148 0.03148 0.73835
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.03148 0.03706 -0.850 0.3980
T40 0.15216 0.06530 2.330 0.0222 *
used 0.29313 0.06168 4.752 8.34e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2628 on 83 degrees of freedom
Multiple R-squared: 0.2886, Adjusted R-squared: 0.2715
F-statistic: 16.84 on 2 and 83 DF, p-value: 7.27e-07
> 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,] 0.000000000 0.00000000 1.0000000
[2,] 0.000000000 0.00000000 1.0000000
[3,] 0.000000000 0.00000000 1.0000000
[4,] 0.000000000 0.00000000 1.0000000
[5,] 0.000000000 0.00000000 1.0000000
[6,] 0.000000000 0.00000000 1.0000000
[7,] 0.000000000 0.00000000 1.0000000
[8,] 0.000000000 0.00000000 1.0000000
[9,] 0.000000000 0.00000000 1.0000000
[10,] 0.000000000 0.00000000 1.0000000
[11,] 0.000000000 0.00000000 1.0000000
[12,] 0.181671911 0.36334382 0.8183281
[13,] 0.135926785 0.27185357 0.8640732
[14,] 0.095217918 0.19043584 0.9047821
[15,] 0.361835225 0.72367045 0.6381648
[16,] 0.293158885 0.58631777 0.7068411
[17,] 0.293859049 0.58771810 0.7061410
[18,] 0.234256015 0.46851203 0.7657440
[19,] 0.182369697 0.36473939 0.8176303
[20,] 0.256645916 0.51329183 0.7433541
[21,] 0.235986164 0.47197233 0.7640138
[22,] 0.185669172 0.37133834 0.8143308
[23,] 0.167322395 0.33464479 0.8326776
[24,] 0.128010294 0.25602059 0.8719897
[25,] 0.095778372 0.19155674 0.9042216
[26,] 0.070074206 0.14014841 0.9299258
[27,] 0.050126814 0.10025363 0.9498732
[28,] 0.035056539 0.07011308 0.9649435
[29,] 0.026503016 0.05300603 0.9734970
[30,] 0.017850374 0.03570075 0.9821496
[31,] 0.011752839 0.02350568 0.9882472
[32,] 0.018293728 0.03658746 0.9817063
[33,] 0.016163339 0.03232668 0.9838367
[34,] 0.010664597 0.02132919 0.9893354
[35,] 0.007644919 0.01528984 0.9923551
[36,] 0.150924195 0.30184839 0.8490758
[37,] 0.147107321 0.29421464 0.8528927
[38,] 0.113677420 0.22735484 0.8863226
[39,] 0.092071153 0.18414231 0.9079288
[40,] 0.068603902 0.13720780 0.9313961
[41,] 0.050050422 0.10010084 0.9499496
[42,] 0.035739105 0.07147821 0.9642609
[43,] 0.024969365 0.04993873 0.9750306
[44,] 0.017063061 0.03412612 0.9829369
[45,] 0.011401351 0.02280270 0.9885986
[46,] 0.022932766 0.04586553 0.9770672
[47,] 0.088538775 0.17707755 0.9114612
[48,] 0.065664605 0.13132921 0.9343354
[49,] 0.330860521 0.66172104 0.6691395
[50,] 0.274802840 0.54960568 0.7251972
[51,] 0.430336155 0.86067231 0.5696638
[52,] 0.435978250 0.87195650 0.5640217
[53,] 0.373372357 0.74674471 0.6266276
[54,] 0.313930375 0.62786075 0.6860696
[55,] 0.475523603 0.95104721 0.5244764
[56,] 0.449137353 0.89827471 0.5508626
[57,] 0.452158834 0.90431767 0.5478412
[58,] 0.383728826 0.76745765 0.6162712
[59,] 0.376996764 0.75399353 0.6230032
[60,] 0.310318688 0.62063738 0.6896813
[61,] 0.248896115 0.49779223 0.7511039
[62,] 0.372894689 0.74578938 0.6271053
[63,] 0.301626606 0.60325321 0.6983734
[64,] 0.236446852 0.47289370 0.7635531
[65,] 0.229219777 0.45843955 0.7707802
[66,] 0.171371844 0.34274369 0.8286282
[67,] 0.123190112 0.24638022 0.8768099
[68,] 0.127178636 0.25435727 0.8728214
[69,] 0.161275608 0.32255122 0.8387244
[70,] 0.109516193 0.21903239 0.8904838
[71,] 0.084310526 0.16862105 0.9156895
[72,] 0.050390672 0.10078134 0.9496093
[73,] 0.107619932 0.21523986 0.8923801
[74,] 0.124644892 0.24928978 0.8753551
[75,] 0.064495261 0.12899052 0.9355047
> postscript(file="/var/wessaorg/rcomp/tmp/1w9fs1356054425.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2xudd1356054425.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3cj3l1356054425.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/42wv81356054425.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5lvdz1356054425.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
-0.1206752 0.0314805 0.0314805 0.0314805 0.0314805 0.0314805 0.0314805
8 9 10 11 12 13 14
-0.1206752 0.0314805 0.0314805 -0.1206752 0.0314805 -0.2616531 -0.1206752
15 16 17 18 19 20 21
-0.2616531 -0.4138088 0.5861912 -0.1206752 0.0314805 0.5861912 0.0314805
22 23 24 25 26 27 28
-0.2616531 0.0314805 0.0314805 -0.4138088 -0.2616531 0.0314805 -0.2616531
29 30 31 32 33 34 35
0.0314805 0.0314805 0.0314805 0.0314805 0.0314805 -0.1206752 0.0314805
36 37 38 39 40 41 42
0.0314805 -0.4138088 -0.2616531 0.0314805 -0.1206752 0.7383469 -0.2616531
43 44 45 46 47 48 49
0.0314805 -0.1206752 0.0314805 0.0314805 0.0314805 0.0314805 0.0314805
50 51 52 53 54 55 56
0.0314805 -0.4138088 0.5861912 0.0314805 0.7383469 0.0314805 -0.4138088
57 58 59 60 61 62 63
-0.2616531 0.0314805 0.0314805 0.5861912 -0.1206752 -0.2616531 0.0314805
64 65 66 67 68 69 70
-0.1206752 0.0314805 0.0314805 0.5861912 0.0314805 0.0314805 -0.2616531
71 72 73 74 75 76 77
0.0314805 0.0314805 -0.2616531 -0.2616531 0.0314805 -0.1206752 0.0314805
78 79 80 81 82 83 84
-0.2616531 0.5861912 -0.1206752 0.0314805 -0.2616531 0.0314805 0.7383469
85 86
0.0314805 0.0314805
> postscript(file="/var/wessaorg/rcomp/tmp/6zz481356054425.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 -0.1206752 NA
1 0.0314805 -0.1206752
2 0.0314805 0.0314805
3 0.0314805 0.0314805
4 0.0314805 0.0314805
5 0.0314805 0.0314805
6 0.0314805 0.0314805
7 -0.1206752 0.0314805
8 0.0314805 -0.1206752
9 0.0314805 0.0314805
10 -0.1206752 0.0314805
11 0.0314805 -0.1206752
12 -0.2616531 0.0314805
13 -0.1206752 -0.2616531
14 -0.2616531 -0.1206752
15 -0.4138088 -0.2616531
16 0.5861912 -0.4138088
17 -0.1206752 0.5861912
18 0.0314805 -0.1206752
19 0.5861912 0.0314805
20 0.0314805 0.5861912
21 -0.2616531 0.0314805
22 0.0314805 -0.2616531
23 0.0314805 0.0314805
24 -0.4138088 0.0314805
25 -0.2616531 -0.4138088
26 0.0314805 -0.2616531
27 -0.2616531 0.0314805
28 0.0314805 -0.2616531
29 0.0314805 0.0314805
30 0.0314805 0.0314805
31 0.0314805 0.0314805
32 0.0314805 0.0314805
33 -0.1206752 0.0314805
34 0.0314805 -0.1206752
35 0.0314805 0.0314805
36 -0.4138088 0.0314805
37 -0.2616531 -0.4138088
38 0.0314805 -0.2616531
39 -0.1206752 0.0314805
40 0.7383469 -0.1206752
41 -0.2616531 0.7383469
42 0.0314805 -0.2616531
43 -0.1206752 0.0314805
44 0.0314805 -0.1206752
45 0.0314805 0.0314805
46 0.0314805 0.0314805
47 0.0314805 0.0314805
48 0.0314805 0.0314805
49 0.0314805 0.0314805
50 -0.4138088 0.0314805
51 0.5861912 -0.4138088
52 0.0314805 0.5861912
53 0.7383469 0.0314805
54 0.0314805 0.7383469
55 -0.4138088 0.0314805
56 -0.2616531 -0.4138088
57 0.0314805 -0.2616531
58 0.0314805 0.0314805
59 0.5861912 0.0314805
60 -0.1206752 0.5861912
61 -0.2616531 -0.1206752
62 0.0314805 -0.2616531
63 -0.1206752 0.0314805
64 0.0314805 -0.1206752
65 0.0314805 0.0314805
66 0.5861912 0.0314805
67 0.0314805 0.5861912
68 0.0314805 0.0314805
69 -0.2616531 0.0314805
70 0.0314805 -0.2616531
71 0.0314805 0.0314805
72 -0.2616531 0.0314805
73 -0.2616531 -0.2616531
74 0.0314805 -0.2616531
75 -0.1206752 0.0314805
76 0.0314805 -0.1206752
77 -0.2616531 0.0314805
78 0.5861912 -0.2616531
79 -0.1206752 0.5861912
80 0.0314805 -0.1206752
81 -0.2616531 0.0314805
82 0.0314805 -0.2616531
83 0.7383469 0.0314805
84 0.0314805 0.7383469
85 0.0314805 0.0314805
86 NA 0.0314805
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0314805 -0.1206752
[2,] 0.0314805 0.0314805
[3,] 0.0314805 0.0314805
[4,] 0.0314805 0.0314805
[5,] 0.0314805 0.0314805
[6,] 0.0314805 0.0314805
[7,] -0.1206752 0.0314805
[8,] 0.0314805 -0.1206752
[9,] 0.0314805 0.0314805
[10,] -0.1206752 0.0314805
[11,] 0.0314805 -0.1206752
[12,] -0.2616531 0.0314805
[13,] -0.1206752 -0.2616531
[14,] -0.2616531 -0.1206752
[15,] -0.4138088 -0.2616531
[16,] 0.5861912 -0.4138088
[17,] -0.1206752 0.5861912
[18,] 0.0314805 -0.1206752
[19,] 0.5861912 0.0314805
[20,] 0.0314805 0.5861912
[21,] -0.2616531 0.0314805
[22,] 0.0314805 -0.2616531
[23,] 0.0314805 0.0314805
[24,] -0.4138088 0.0314805
[25,] -0.2616531 -0.4138088
[26,] 0.0314805 -0.2616531
[27,] -0.2616531 0.0314805
[28,] 0.0314805 -0.2616531
[29,] 0.0314805 0.0314805
[30,] 0.0314805 0.0314805
[31,] 0.0314805 0.0314805
[32,] 0.0314805 0.0314805
[33,] -0.1206752 0.0314805
[34,] 0.0314805 -0.1206752
[35,] 0.0314805 0.0314805
[36,] -0.4138088 0.0314805
[37,] -0.2616531 -0.4138088
[38,] 0.0314805 -0.2616531
[39,] -0.1206752 0.0314805
[40,] 0.7383469 -0.1206752
[41,] -0.2616531 0.7383469
[42,] 0.0314805 -0.2616531
[43,] -0.1206752 0.0314805
[44,] 0.0314805 -0.1206752
[45,] 0.0314805 0.0314805
[46,] 0.0314805 0.0314805
[47,] 0.0314805 0.0314805
[48,] 0.0314805 0.0314805
[49,] 0.0314805 0.0314805
[50,] -0.4138088 0.0314805
[51,] 0.5861912 -0.4138088
[52,] 0.0314805 0.5861912
[53,] 0.7383469 0.0314805
[54,] 0.0314805 0.7383469
[55,] -0.4138088 0.0314805
[56,] -0.2616531 -0.4138088
[57,] 0.0314805 -0.2616531
[58,] 0.0314805 0.0314805
[59,] 0.5861912 0.0314805
[60,] -0.1206752 0.5861912
[61,] -0.2616531 -0.1206752
[62,] 0.0314805 -0.2616531
[63,] -0.1206752 0.0314805
[64,] 0.0314805 -0.1206752
[65,] 0.0314805 0.0314805
[66,] 0.5861912 0.0314805
[67,] 0.0314805 0.5861912
[68,] 0.0314805 0.0314805
[69,] -0.2616531 0.0314805
[70,] 0.0314805 -0.2616531
[71,] 0.0314805 0.0314805
[72,] -0.2616531 0.0314805
[73,] -0.2616531 -0.2616531
[74,] 0.0314805 -0.2616531
[75,] -0.1206752 0.0314805
[76,] 0.0314805 -0.1206752
[77,] -0.2616531 0.0314805
[78,] 0.5861912 -0.2616531
[79,] -0.1206752 0.5861912
[80,] 0.0314805 -0.1206752
[81,] -0.2616531 0.0314805
[82,] 0.0314805 -0.2616531
[83,] 0.7383469 0.0314805
[84,] 0.0314805 0.7383469
[85,] 0.0314805 0.0314805
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0314805 -0.1206752
2 0.0314805 0.0314805
3 0.0314805 0.0314805
4 0.0314805 0.0314805
5 0.0314805 0.0314805
6 0.0314805 0.0314805
7 -0.1206752 0.0314805
8 0.0314805 -0.1206752
9 0.0314805 0.0314805
10 -0.1206752 0.0314805
11 0.0314805 -0.1206752
12 -0.2616531 0.0314805
13 -0.1206752 -0.2616531
14 -0.2616531 -0.1206752
15 -0.4138088 -0.2616531
16 0.5861912 -0.4138088
17 -0.1206752 0.5861912
18 0.0314805 -0.1206752
19 0.5861912 0.0314805
20 0.0314805 0.5861912
21 -0.2616531 0.0314805
22 0.0314805 -0.2616531
23 0.0314805 0.0314805
24 -0.4138088 0.0314805
25 -0.2616531 -0.4138088
26 0.0314805 -0.2616531
27 -0.2616531 0.0314805
28 0.0314805 -0.2616531
29 0.0314805 0.0314805
30 0.0314805 0.0314805
31 0.0314805 0.0314805
32 0.0314805 0.0314805
33 -0.1206752 0.0314805
34 0.0314805 -0.1206752
35 0.0314805 0.0314805
36 -0.4138088 0.0314805
37 -0.2616531 -0.4138088
38 0.0314805 -0.2616531
39 -0.1206752 0.0314805
40 0.7383469 -0.1206752
41 -0.2616531 0.7383469
42 0.0314805 -0.2616531
43 -0.1206752 0.0314805
44 0.0314805 -0.1206752
45 0.0314805 0.0314805
46 0.0314805 0.0314805
47 0.0314805 0.0314805
48 0.0314805 0.0314805
49 0.0314805 0.0314805
50 -0.4138088 0.0314805
51 0.5861912 -0.4138088
52 0.0314805 0.5861912
53 0.7383469 0.0314805
54 0.0314805 0.7383469
55 -0.4138088 0.0314805
56 -0.2616531 -0.4138088
57 0.0314805 -0.2616531
58 0.0314805 0.0314805
59 0.5861912 0.0314805
60 -0.1206752 0.5861912
61 -0.2616531 -0.1206752
62 0.0314805 -0.2616531
63 -0.1206752 0.0314805
64 0.0314805 -0.1206752
65 0.0314805 0.0314805
66 0.5861912 0.0314805
67 0.0314805 0.5861912
68 0.0314805 0.0314805
69 -0.2616531 0.0314805
70 0.0314805 -0.2616531
71 0.0314805 0.0314805
72 -0.2616531 0.0314805
73 -0.2616531 -0.2616531
74 0.0314805 -0.2616531
75 -0.1206752 0.0314805
76 0.0314805 -0.1206752
77 -0.2616531 0.0314805
78 0.5861912 -0.2616531
79 -0.1206752 0.5861912
80 0.0314805 -0.1206752
81 -0.2616531 0.0314805
82 0.0314805 -0.2616531
83 0.7383469 0.0314805
84 0.0314805 0.7383469
85 0.0314805 0.0314805
> 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/wessaorg/rcomp/tmp/7yorg1356054425.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8mrti1356054425.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9e38s1356054425.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10gl961356054425.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11lwpm1356054425.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/wessaorg/rcomp/tmp/128xtu1356054425.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/wessaorg/rcomp/tmp/13tj141356054425.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/wessaorg/rcomp/tmp/146yif1356054425.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/wessaorg/rcomp/tmp/152jbu1356054425.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/wessaorg/rcomp/tmp/16hgi91356054425.tab")
+ }
>
> try(system("convert tmp/1w9fs1356054425.ps tmp/1w9fs1356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xudd1356054425.ps tmp/2xudd1356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cj3l1356054425.ps tmp/3cj3l1356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/42wv81356054425.ps tmp/42wv81356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lvdz1356054425.ps tmp/5lvdz1356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zz481356054425.ps tmp/6zz481356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yorg1356054425.ps tmp/7yorg1356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mrti1356054425.ps tmp/8mrti1356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/9e38s1356054425.ps tmp/9e38s1356054425.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gl961356054425.ps tmp/10gl961356054425.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.706 1.106 7.821