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 = '1'
> 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, 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
UseLimit T40 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 1 0 0 0 1 1
7 0 0 0 0 0 0
8 0 1 0 0 0 0
9 0 0 0 0 0 1
10 1 0 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 0 1 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 0 1 1 1 1 1
21 1 0 0 0 1 0
22 1 0 1 0 1 1
23 0 0 0 0 1 1
24 1 0 0 0 1 1
25 0 1 1 0 0 1
26 0 0 1 0 1 0
27 1 0 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 1 0 0 0 0 0
33 1 0 0 0 1 0
34 0 1 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 0 1 0 0 1 0
41 0 0 1 1 1 1
42 0 0 1 0 0 1
43 1 0 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 0 1 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 0 1 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 0 1 1 1 1 0
68 1 0 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 1 0 1 0 0 0
75 0 0 0 0 0 1
76 0 1 0 0 1 1
77 0 0 0 0 0 1
78 0 0 1 0 1 1
79 0 1 1 1 0 1
80 0 1 0 0 1 0
81 0 0 0 0 0 0
82 1 0 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 1 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T40 Used CorrectAnalysis
0.23513 0.28917 -0.11038 -0.02083
Useful Outcome
0.09638 -0.06214
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6207 -0.2351 -0.1730 0.4757 0.9374
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.23513 0.07787 3.020 0.0034 **
T40 0.28917 0.11357 2.546 0.0128 *
Used -0.11038 0.11979 -0.921 0.3596
CorrectAnalysis -0.02083 0.18651 -0.112 0.9113
Useful 0.09638 0.10577 0.911 0.3649
Outcome -0.06214 0.09732 -0.639 0.5250
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4426 on 80 degrees of freedom
Multiple R-squared: 0.09442, Adjusted R-squared: 0.03782
F-statistic: 1.668 on 5 and 80 DF, p-value: 0.1519
> 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,] 0.3326225 0.6652449 0.6673775
[2,] 0.8224197 0.3551607 0.1775803
[3,] 0.8129486 0.3741028 0.1870514
[4,] 0.7253215 0.5493570 0.2746785
[5,] 0.6220099 0.7559802 0.3779901
[6,] 0.5734536 0.8530927 0.4265464
[7,] 0.4698225 0.9396451 0.5301775
[8,] 0.4767720 0.9535439 0.5232280
[9,] 0.3991963 0.7983925 0.6008037
[10,] 0.3671058 0.7342117 0.6328942
[11,] 0.2906989 0.5813977 0.7093011
[12,] 0.3569199 0.7138399 0.6430801
[13,] 0.3201079 0.6402159 0.6798921
[14,] 0.6668595 0.6662811 0.3331405
[15,] 0.7409562 0.5180876 0.2590438
[16,] 0.7561276 0.4877449 0.2438724
[17,] 0.7066672 0.5866655 0.2933328
[18,] 0.6529883 0.6940235 0.3470117
[19,] 0.8027215 0.3945569 0.1972785
[20,] 0.7726022 0.4547957 0.2273978
[21,] 0.7279472 0.5441056 0.2720528
[22,] 0.7524165 0.4951669 0.2475835
[23,] 0.7089248 0.5821504 0.2910752
[24,] 0.8092121 0.3815757 0.1907879
[25,] 0.8465532 0.3068935 0.1534468
[26,] 0.8612832 0.2774337 0.1387168
[27,] 0.8324615 0.3350770 0.1675385
[28,] 0.7990039 0.4019921 0.2009961
[29,] 0.8083007 0.3833987 0.1916993
[30,] 0.7706310 0.4587379 0.2293690
[31,] 0.7599359 0.4801283 0.2400641
[32,] 0.8215334 0.3569332 0.1784666
[33,] 0.7783677 0.4432645 0.2216323
[34,] 0.7297254 0.5405493 0.2702746
[35,] 0.8333825 0.3332351 0.1666175
[36,] 0.8430457 0.3139086 0.1569543
[37,] 0.8252801 0.3494398 0.1747199
[38,] 0.7980389 0.4039222 0.2019611
[39,] 0.7578562 0.4842876 0.2421438
[40,] 0.7098855 0.5802289 0.2901145
[41,] 0.6679900 0.6640201 0.3320100
[42,] 0.6164345 0.7671309 0.3835655
[43,] 0.6356639 0.7286721 0.3643361
[44,] 0.6920831 0.6158338 0.3079169
[45,] 0.6359943 0.7280113 0.3640057
[46,] 0.5720410 0.8559179 0.4279590
[47,] 0.5168544 0.9662912 0.4831456
[48,] 0.6049125 0.7901751 0.3950875
[49,] 0.5366951 0.9266099 0.4633049
[50,] 0.4720443 0.9440886 0.5279557
[51,] 0.4087784 0.8175567 0.5912216
[52,] 0.6381652 0.7236695 0.3618348
[53,] 0.6325853 0.7348294 0.3674147
[54,] 0.5632170 0.8735661 0.4367830
[55,] 0.5090943 0.9818114 0.4909057
[56,] 0.5806493 0.8387015 0.4193507
[57,] 0.5288723 0.9422554 0.4711277
[58,] 0.4830757 0.9661514 0.5169243
[59,] 0.4352991 0.8705982 0.5647009
[60,] 0.6166225 0.7667549 0.3833775
[61,] 0.5295737 0.9408527 0.4704263
[62,] 0.6217656 0.7564688 0.3782344
[63,] 0.5714364 0.8571272 0.4285636
[64,] 0.4689847 0.9379693 0.5310153
[65,] 0.5912592 0.8174815 0.4087408
[66,] 0.5311682 0.9376636 0.4688318
[67,] 0.4266234 0.8532468 0.5733766
[68,] 0.3152496 0.6304993 0.6847504
[69,] 0.2435982 0.4871965 0.7564018
> postscript(file="/var/wessaorg/rcomp/tmp/1ufwa1356127055.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/25lyg1356127055.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qpcp1356127055.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hme91356127055.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5hsau1356127055.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
0.53783853 -0.23513434 -0.23513434 -0.23513434 -0.23513434 0.73062553
7 8 9 10 11 12
-0.23513434 -0.52430186 -0.17299395 0.76486566 0.47569814 -0.23513434
13 14 15 16 17 18
-0.22113220 0.47569814 -0.15899181 -0.44815934 0.51053270 0.47569814
19 20 21 22 23 24
-0.17299395 -0.42732691 0.66848514 0.84100819 -0.26937447 0.73062553
25 26 27 28 29 30
-0.35177882 -0.22113220 0.82700605 -0.12475168 -0.17299395 -0.33151486
31 32 33 34 35 36
-0.23513434 0.76486566 0.66848514 -0.46216147 -0.23513434 -0.23513434
37 38 39 40 41 42
0.48970027 -0.06261129 -0.26937447 -0.62068239 -0.13815938 -0.06261129
43 44 45 46 47 48
0.73062553 0.47569814 -0.33151486 -0.26937447 -0.23513434 -0.17299395
49 50 51 52 53 54
-0.26937447 -0.23513434 -0.41391921 0.51053270 -0.17299395 -0.10391925
55 56 57 58 59 60
-0.23513434 -0.35177882 -0.15899181 -0.17299395 -0.17299395 0.57267309
61 62 63 64 65 66
0.53783853 -0.22113220 -0.23513434 0.53783853 -0.23513434 -0.23513434
67 68 69 70 71 72
-0.48946730 0.76486566 -0.17299395 -0.12475168 -0.23513434 -0.17299395
73 74 75 76 77 78
-0.06261129 0.87524832 -0.17299395 -0.55854199 -0.17299395 -0.15899181
79 80 81 82 83 84
-0.33094639 -0.62068239 -0.23513434 0.93738871 -0.23513434 -0.10391925
85 86
-0.26937447 0.76486566
> postscript(file="/var/wessaorg/rcomp/tmp/6xkmq1356127055.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 0.53783853 NA
1 -0.23513434 0.53783853
2 -0.23513434 -0.23513434
3 -0.23513434 -0.23513434
4 -0.23513434 -0.23513434
5 0.73062553 -0.23513434
6 -0.23513434 0.73062553
7 -0.52430186 -0.23513434
8 -0.17299395 -0.52430186
9 0.76486566 -0.17299395
10 0.47569814 0.76486566
11 -0.23513434 0.47569814
12 -0.22113220 -0.23513434
13 0.47569814 -0.22113220
14 -0.15899181 0.47569814
15 -0.44815934 -0.15899181
16 0.51053270 -0.44815934
17 0.47569814 0.51053270
18 -0.17299395 0.47569814
19 -0.42732691 -0.17299395
20 0.66848514 -0.42732691
21 0.84100819 0.66848514
22 -0.26937447 0.84100819
23 0.73062553 -0.26937447
24 -0.35177882 0.73062553
25 -0.22113220 -0.35177882
26 0.82700605 -0.22113220
27 -0.12475168 0.82700605
28 -0.17299395 -0.12475168
29 -0.33151486 -0.17299395
30 -0.23513434 -0.33151486
31 0.76486566 -0.23513434
32 0.66848514 0.76486566
33 -0.46216147 0.66848514
34 -0.23513434 -0.46216147
35 -0.23513434 -0.23513434
36 0.48970027 -0.23513434
37 -0.06261129 0.48970027
38 -0.26937447 -0.06261129
39 -0.62068239 -0.26937447
40 -0.13815938 -0.62068239
41 -0.06261129 -0.13815938
42 0.73062553 -0.06261129
43 0.47569814 0.73062553
44 -0.33151486 0.47569814
45 -0.26937447 -0.33151486
46 -0.23513434 -0.26937447
47 -0.17299395 -0.23513434
48 -0.26937447 -0.17299395
49 -0.23513434 -0.26937447
50 -0.41391921 -0.23513434
51 0.51053270 -0.41391921
52 -0.17299395 0.51053270
53 -0.10391925 -0.17299395
54 -0.23513434 -0.10391925
55 -0.35177882 -0.23513434
56 -0.15899181 -0.35177882
57 -0.17299395 -0.15899181
58 -0.17299395 -0.17299395
59 0.57267309 -0.17299395
60 0.53783853 0.57267309
61 -0.22113220 0.53783853
62 -0.23513434 -0.22113220
63 0.53783853 -0.23513434
64 -0.23513434 0.53783853
65 -0.23513434 -0.23513434
66 -0.48946730 -0.23513434
67 0.76486566 -0.48946730
68 -0.17299395 0.76486566
69 -0.12475168 -0.17299395
70 -0.23513434 -0.12475168
71 -0.17299395 -0.23513434
72 -0.06261129 -0.17299395
73 0.87524832 -0.06261129
74 -0.17299395 0.87524832
75 -0.55854199 -0.17299395
76 -0.17299395 -0.55854199
77 -0.15899181 -0.17299395
78 -0.33094639 -0.15899181
79 -0.62068239 -0.33094639
80 -0.23513434 -0.62068239
81 0.93738871 -0.23513434
82 -0.23513434 0.93738871
83 -0.10391925 -0.23513434
84 -0.26937447 -0.10391925
85 0.76486566 -0.26937447
86 NA 0.76486566
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.23513434 0.53783853
[2,] -0.23513434 -0.23513434
[3,] -0.23513434 -0.23513434
[4,] -0.23513434 -0.23513434
[5,] 0.73062553 -0.23513434
[6,] -0.23513434 0.73062553
[7,] -0.52430186 -0.23513434
[8,] -0.17299395 -0.52430186
[9,] 0.76486566 -0.17299395
[10,] 0.47569814 0.76486566
[11,] -0.23513434 0.47569814
[12,] -0.22113220 -0.23513434
[13,] 0.47569814 -0.22113220
[14,] -0.15899181 0.47569814
[15,] -0.44815934 -0.15899181
[16,] 0.51053270 -0.44815934
[17,] 0.47569814 0.51053270
[18,] -0.17299395 0.47569814
[19,] -0.42732691 -0.17299395
[20,] 0.66848514 -0.42732691
[21,] 0.84100819 0.66848514
[22,] -0.26937447 0.84100819
[23,] 0.73062553 -0.26937447
[24,] -0.35177882 0.73062553
[25,] -0.22113220 -0.35177882
[26,] 0.82700605 -0.22113220
[27,] -0.12475168 0.82700605
[28,] -0.17299395 -0.12475168
[29,] -0.33151486 -0.17299395
[30,] -0.23513434 -0.33151486
[31,] 0.76486566 -0.23513434
[32,] 0.66848514 0.76486566
[33,] -0.46216147 0.66848514
[34,] -0.23513434 -0.46216147
[35,] -0.23513434 -0.23513434
[36,] 0.48970027 -0.23513434
[37,] -0.06261129 0.48970027
[38,] -0.26937447 -0.06261129
[39,] -0.62068239 -0.26937447
[40,] -0.13815938 -0.62068239
[41,] -0.06261129 -0.13815938
[42,] 0.73062553 -0.06261129
[43,] 0.47569814 0.73062553
[44,] -0.33151486 0.47569814
[45,] -0.26937447 -0.33151486
[46,] -0.23513434 -0.26937447
[47,] -0.17299395 -0.23513434
[48,] -0.26937447 -0.17299395
[49,] -0.23513434 -0.26937447
[50,] -0.41391921 -0.23513434
[51,] 0.51053270 -0.41391921
[52,] -0.17299395 0.51053270
[53,] -0.10391925 -0.17299395
[54,] -0.23513434 -0.10391925
[55,] -0.35177882 -0.23513434
[56,] -0.15899181 -0.35177882
[57,] -0.17299395 -0.15899181
[58,] -0.17299395 -0.17299395
[59,] 0.57267309 -0.17299395
[60,] 0.53783853 0.57267309
[61,] -0.22113220 0.53783853
[62,] -0.23513434 -0.22113220
[63,] 0.53783853 -0.23513434
[64,] -0.23513434 0.53783853
[65,] -0.23513434 -0.23513434
[66,] -0.48946730 -0.23513434
[67,] 0.76486566 -0.48946730
[68,] -0.17299395 0.76486566
[69,] -0.12475168 -0.17299395
[70,] -0.23513434 -0.12475168
[71,] -0.17299395 -0.23513434
[72,] -0.06261129 -0.17299395
[73,] 0.87524832 -0.06261129
[74,] -0.17299395 0.87524832
[75,] -0.55854199 -0.17299395
[76,] -0.17299395 -0.55854199
[77,] -0.15899181 -0.17299395
[78,] -0.33094639 -0.15899181
[79,] -0.62068239 -0.33094639
[80,] -0.23513434 -0.62068239
[81,] 0.93738871 -0.23513434
[82,] -0.23513434 0.93738871
[83,] -0.10391925 -0.23513434
[84,] -0.26937447 -0.10391925
[85,] 0.76486566 -0.26937447
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.23513434 0.53783853
2 -0.23513434 -0.23513434
3 -0.23513434 -0.23513434
4 -0.23513434 -0.23513434
5 0.73062553 -0.23513434
6 -0.23513434 0.73062553
7 -0.52430186 -0.23513434
8 -0.17299395 -0.52430186
9 0.76486566 -0.17299395
10 0.47569814 0.76486566
11 -0.23513434 0.47569814
12 -0.22113220 -0.23513434
13 0.47569814 -0.22113220
14 -0.15899181 0.47569814
15 -0.44815934 -0.15899181
16 0.51053270 -0.44815934
17 0.47569814 0.51053270
18 -0.17299395 0.47569814
19 -0.42732691 -0.17299395
20 0.66848514 -0.42732691
21 0.84100819 0.66848514
22 -0.26937447 0.84100819
23 0.73062553 -0.26937447
24 -0.35177882 0.73062553
25 -0.22113220 -0.35177882
26 0.82700605 -0.22113220
27 -0.12475168 0.82700605
28 -0.17299395 -0.12475168
29 -0.33151486 -0.17299395
30 -0.23513434 -0.33151486
31 0.76486566 -0.23513434
32 0.66848514 0.76486566
33 -0.46216147 0.66848514
34 -0.23513434 -0.46216147
35 -0.23513434 -0.23513434
36 0.48970027 -0.23513434
37 -0.06261129 0.48970027
38 -0.26937447 -0.06261129
39 -0.62068239 -0.26937447
40 -0.13815938 -0.62068239
41 -0.06261129 -0.13815938
42 0.73062553 -0.06261129
43 0.47569814 0.73062553
44 -0.33151486 0.47569814
45 -0.26937447 -0.33151486
46 -0.23513434 -0.26937447
47 -0.17299395 -0.23513434
48 -0.26937447 -0.17299395
49 -0.23513434 -0.26937447
50 -0.41391921 -0.23513434
51 0.51053270 -0.41391921
52 -0.17299395 0.51053270
53 -0.10391925 -0.17299395
54 -0.23513434 -0.10391925
55 -0.35177882 -0.23513434
56 -0.15899181 -0.35177882
57 -0.17299395 -0.15899181
58 -0.17299395 -0.17299395
59 0.57267309 -0.17299395
60 0.53783853 0.57267309
61 -0.22113220 0.53783853
62 -0.23513434 -0.22113220
63 0.53783853 -0.23513434
64 -0.23513434 0.53783853
65 -0.23513434 -0.23513434
66 -0.48946730 -0.23513434
67 0.76486566 -0.48946730
68 -0.17299395 0.76486566
69 -0.12475168 -0.17299395
70 -0.23513434 -0.12475168
71 -0.17299395 -0.23513434
72 -0.06261129 -0.17299395
73 0.87524832 -0.06261129
74 -0.17299395 0.87524832
75 -0.55854199 -0.17299395
76 -0.17299395 -0.55854199
77 -0.15899181 -0.17299395
78 -0.33094639 -0.15899181
79 -0.62068239 -0.33094639
80 -0.23513434 -0.62068239
81 0.93738871 -0.23513434
82 -0.23513434 0.93738871
83 -0.10391925 -0.23513434
84 -0.26937447 -0.10391925
85 0.76486566 -0.26937447
> 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/73dhk1356127055.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/84kva1356127055.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9jfes1356127055.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10spxt1356127055.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110k3u1356127055.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/12zmhz1356127055.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/139vkr1356127055.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/147zgy1356127055.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/15ju041356127055.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/1666vg1356127055.tab")
+ }
>
> try(system("convert tmp/1ufwa1356127055.ps tmp/1ufwa1356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/25lyg1356127055.ps tmp/25lyg1356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qpcp1356127055.ps tmp/3qpcp1356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hme91356127055.ps tmp/4hme91356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hsau1356127055.ps tmp/5hsau1356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xkmq1356127055.ps tmp/6xkmq1356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/73dhk1356127055.ps tmp/73dhk1356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/84kva1356127055.ps tmp/84kva1356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jfes1356127055.ps tmp/9jfes1356127055.png",intern=TRUE))
character(0)
> try(system("convert tmp/10spxt1356127055.ps tmp/10spxt1356127055.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.024 0.856 6.901