R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(8,5560,8.1,3922,7.7,3759,7.5,4138,7.6,4634,7.8,3996,7.8,4308,7.8,4143,7.5,4429,7.5,5219,7.1,4929,7.5,5755,7.5,5592,7.6,4163,7.7,4962,7.7,5208,7.9,4755,8.1,4491,8.2,5732,8.2,5731,8.2,5040,7.9,6102,7.3,4904,6.9,5369,6.7,5578,6.7,4619,6.9,4731,7,5011,7.1,5299,7.2,4146,7.1,4625,6.9,4736,7,4219,6.8,5116,6.4,4205,6.7,4121,6.6,5103,6.4,4300,6.3,4578,6.2,3809,6.5,5526,6.8,4247,6.8,3830,6.4,4394,6.1,4826,5.8,4409,6.1,4569,7.2,4106,7.3,4794,6.9,3914,6.1,3793,5.8,4405,6.2,4022,7.1,4100,7.7,4788,7.9,3163,7.7,3585,7.4,3903,7.5,4178,8,3863,8.1,4187),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.0 5560 1 0 0 0 0 0 0 0 0 0 0 1
2 8.1 3922 0 1 0 0 0 0 0 0 0 0 0 2
3 7.7 3759 0 0 1 0 0 0 0 0 0 0 0 3
4 7.5 4138 0 0 0 1 0 0 0 0 0 0 0 4
5 7.6 4634 0 0 0 0 1 0 0 0 0 0 0 5
6 7.8 3996 0 0 0 0 0 1 0 0 0 0 0 6
7 7.8 4308 0 0 0 0 0 0 1 0 0 0 0 7
8 7.8 4143 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 4429 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 5219 0 0 0 0 0 0 0 0 0 1 0 10
11 7.1 4929 0 0 0 0 0 0 0 0 0 0 1 11
12 7.5 5755 0 0 0 0 0 0 0 0 0 0 0 12
13 7.5 5592 1 0 0 0 0 0 0 0 0 0 0 13
14 7.6 4163 0 1 0 0 0 0 0 0 0 0 0 14
15 7.7 4962 0 0 1 0 0 0 0 0 0 0 0 15
16 7.7 5208 0 0 0 1 0 0 0 0 0 0 0 16
17 7.9 4755 0 0 0 0 1 0 0 0 0 0 0 17
18 8.1 4491 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 5732 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 5731 0 0 0 0 0 0 0 1 0 0 0 20
21 8.2 5040 0 0 0 0 0 0 0 0 1 0 0 21
22 7.9 6102 0 0 0 0 0 0 0 0 0 1 0 22
23 7.3 4904 0 0 0 0 0 0 0 0 0 0 1 23
24 6.9 5369 0 0 0 0 0 0 0 0 0 0 0 24
25 6.7 5578 1 0 0 0 0 0 0 0 0 0 0 25
26 6.7 4619 0 1 0 0 0 0 0 0 0 0 0 26
27 6.9 4731 0 0 1 0 0 0 0 0 0 0 0 27
28 7.0 5011 0 0 0 1 0 0 0 0 0 0 0 28
29 7.1 5299 0 0 0 0 1 0 0 0 0 0 0 29
30 7.2 4146 0 0 0 0 0 1 0 0 0 0 0 30
31 7.1 4625 0 0 0 0 0 0 1 0 0 0 0 31
32 6.9 4736 0 0 0 0 0 0 0 1 0 0 0 32
33 7.0 4219 0 0 0 0 0 0 0 0 1 0 0 33
34 6.8 5116 0 0 0 0 0 0 0 0 0 1 0 34
35 6.4 4205 0 0 0 0 0 0 0 0 0 0 1 35
36 6.7 4121 0 0 0 0 0 0 0 0 0 0 0 36
37 6.6 5103 1 0 0 0 0 0 0 0 0 0 0 37
38 6.4 4300 0 1 0 0 0 0 0 0 0 0 0 38
39 6.3 4578 0 0 1 0 0 0 0 0 0 0 0 39
40 6.2 3809 0 0 0 1 0 0 0 0 0 0 0 40
41 6.5 5526 0 0 0 0 1 0 0 0 0 0 0 41
42 6.8 4247 0 0 0 0 0 1 0 0 0 0 0 42
43 6.8 3830 0 0 0 0 0 0 1 0 0 0 0 43
44 6.4 4394 0 0 0 0 0 0 0 1 0 0 0 44
45 6.1 4826 0 0 0 0 0 0 0 0 1 0 0 45
46 5.8 4409 0 0 0 0 0 0 0 0 0 1 0 46
47 6.1 4569 0 0 0 0 0 0 0 0 0 0 1 47
48 7.2 4106 0 0 0 0 0 0 0 0 0 0 0 48
49 7.3 4794 1 0 0 0 0 0 0 0 0 0 0 49
50 6.9 3914 0 1 0 0 0 0 0 0 0 0 0 50
51 6.1 3793 0 0 1 0 0 0 0 0 0 0 0 51
52 5.8 4405 0 0 0 1 0 0 0 0 0 0 0 52
53 6.2 4022 0 0 0 0 1 0 0 0 0 0 0 53
54 7.1 4100 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 4788 0 0 0 0 0 0 1 0 0 0 0 55
56 7.9 3163 0 0 0 0 0 0 0 1 0 0 0 56
57 7.7 3585 0 0 0 0 0 0 0 0 1 0 0 57
58 7.4 3903 0 0 0 0 0 0 0 0 0 1 0 58
59 7.5 4178 0 0 0 0 0 0 0 0 0 0 1 59
60 8.0 3863 0 0 0 0 0 0 0 0 0 0 0 60
61 8.1 4187 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
8.5505669 -0.0001287 0.0738199 -0.3716268 -0.5290821 -0.5905780
M5 M6 M7 M8 M9 M10
-0.3084740 -0.0330163 0.1655072 0.0760396 -0.0464567 -0.1790029
M11 t
-0.4102944 -0.0192537
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.11855 -0.45146 -0.07897 0.46425 1.18887
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.5505669 0.8906400 9.600 1.17e-12 ***
X -0.0001287 0.0001599 -0.805 0.424948
M1 0.0738199 0.3775357 0.196 0.845820
M2 -0.3716268 0.4022728 -0.924 0.360300
M3 -0.5290821 0.3952824 -1.338 0.187175
M4 -0.5905780 0.3912403 -1.510 0.137865
M5 -0.3084740 0.3890526 -0.793 0.431829
M6 -0.0330163 0.3978890 -0.083 0.934221
M7 0.1655072 0.3882546 0.426 0.671846
M8 0.0760396 0.3903050 0.195 0.846373
M9 -0.0464567 0.3900874 -0.119 0.905709
M10 -0.1790029 0.3900695 -0.459 0.648421
M11 -0.4102944 0.3878263 -1.058 0.295493
t -0.0192537 0.0051584 -3.732 0.000511 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6126 on 47 degrees of freedom
Multiple R-squared: 0.3283, Adjusted R-squared: 0.1425
F-statistic: 1.767 on 13 and 47 DF, p-value: 0.07764
> 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,] 0.079937228 0.159874456 0.9200628
[2,] 0.045056935 0.090113869 0.9549431
[3,] 0.017219816 0.034439631 0.9827802
[4,] 0.006380937 0.012761873 0.9936191
[5,] 0.012987456 0.025974911 0.9870125
[6,] 0.009585847 0.019171693 0.9904142
[7,] 0.006041431 0.012082862 0.9939586
[8,] 0.003809861 0.007619721 0.9961901
[9,] 0.008619420 0.017238840 0.9913806
[10,] 0.024414967 0.048829933 0.9755850
[11,] 0.021223935 0.042447871 0.9787761
[12,] 0.026986367 0.053972734 0.9730136
[13,] 0.036214546 0.072429093 0.9637855
[14,] 0.027337646 0.054675292 0.9726624
[15,] 0.017048883 0.034097766 0.9829511
[16,] 0.011887525 0.023775050 0.9881125
[17,] 0.010699193 0.021398386 0.9893008
[18,] 0.020910079 0.041820158 0.9790899
[19,] 0.016285522 0.032571043 0.9837145
[20,] 0.024833822 0.049667645 0.9751662
[21,] 0.014057370 0.028114739 0.9859426
[22,] 0.008202360 0.016404721 0.9917976
[23,] 0.013753419 0.027506837 0.9862466
[24,] 0.032943582 0.065887164 0.9670564
[25,] 0.424658504 0.849317007 0.5753415
[26,] 0.684294872 0.631410256 0.3157051
[27,] 0.588847312 0.822305377 0.4111527
[28,] 0.420971325 0.841942651 0.5790287
> postscript(file="/var/www/html/rcomp/tmp/12ht81258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2l81e1258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ku721258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/405xd1258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52kpt1258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
0.110324632 0.464248124 0.219982400 0.149501461 0.050476208 -0.087825224
7 8 9 10 11 12
-0.226946987 -0.139457844 -0.260905487 -0.007448836 -0.194220591 -0.078972078
13 14 15 16 17 18
-0.154512958 0.226304535 0.605828421 0.718233118 0.597091088 0.506915762
19 20 21 22 23 24
0.587337188 0.695929757 0.748762312 0.737219768 0.233607093 -0.497597668
25 26 27 28 29 30
-0.725269801 -0.383972977 0.007148143 0.223927940 0.098137366 -0.206433971
31 32 33 34 35 36
-0.424066270 -0.501061605 -0.325838829 -0.258613479 -0.525295159 -0.627144926
37 38 39 40 41 42
-0.655347860 -0.493977046 -0.381495141 -0.499700064 -0.241607735 -0.362392680
43 44 45 46 47 48
-0.595321746 -0.814025301 -0.916685747 -1.118545166 -0.547411178 0.101969552
49 50 51 52 53 54
0.235934865 0.187397364 -0.451463823 -0.591962456 -0.504096926 0.149736113
55 56 57 58 59 60
0.658997815 0.758614992 0.754667751 0.647387712 1.033319835 1.101745120
61
1.188871121
> postscript(file="/var/www/html/rcomp/tmp/6m5e31258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.110324632 NA
1 0.464248124 0.110324632
2 0.219982400 0.464248124
3 0.149501461 0.219982400
4 0.050476208 0.149501461
5 -0.087825224 0.050476208
6 -0.226946987 -0.087825224
7 -0.139457844 -0.226946987
8 -0.260905487 -0.139457844
9 -0.007448836 -0.260905487
10 -0.194220591 -0.007448836
11 -0.078972078 -0.194220591
12 -0.154512958 -0.078972078
13 0.226304535 -0.154512958
14 0.605828421 0.226304535
15 0.718233118 0.605828421
16 0.597091088 0.718233118
17 0.506915762 0.597091088
18 0.587337188 0.506915762
19 0.695929757 0.587337188
20 0.748762312 0.695929757
21 0.737219768 0.748762312
22 0.233607093 0.737219768
23 -0.497597668 0.233607093
24 -0.725269801 -0.497597668
25 -0.383972977 -0.725269801
26 0.007148143 -0.383972977
27 0.223927940 0.007148143
28 0.098137366 0.223927940
29 -0.206433971 0.098137366
30 -0.424066270 -0.206433971
31 -0.501061605 -0.424066270
32 -0.325838829 -0.501061605
33 -0.258613479 -0.325838829
34 -0.525295159 -0.258613479
35 -0.627144926 -0.525295159
36 -0.655347860 -0.627144926
37 -0.493977046 -0.655347860
38 -0.381495141 -0.493977046
39 -0.499700064 -0.381495141
40 -0.241607735 -0.499700064
41 -0.362392680 -0.241607735
42 -0.595321746 -0.362392680
43 -0.814025301 -0.595321746
44 -0.916685747 -0.814025301
45 -1.118545166 -0.916685747
46 -0.547411178 -1.118545166
47 0.101969552 -0.547411178
48 0.235934865 0.101969552
49 0.187397364 0.235934865
50 -0.451463823 0.187397364
51 -0.591962456 -0.451463823
52 -0.504096926 -0.591962456
53 0.149736113 -0.504096926
54 0.658997815 0.149736113
55 0.758614992 0.658997815
56 0.754667751 0.758614992
57 0.647387712 0.754667751
58 1.033319835 0.647387712
59 1.101745120 1.033319835
60 1.188871121 1.101745120
61 NA 1.188871121
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.464248124 0.110324632
[2,] 0.219982400 0.464248124
[3,] 0.149501461 0.219982400
[4,] 0.050476208 0.149501461
[5,] -0.087825224 0.050476208
[6,] -0.226946987 -0.087825224
[7,] -0.139457844 -0.226946987
[8,] -0.260905487 -0.139457844
[9,] -0.007448836 -0.260905487
[10,] -0.194220591 -0.007448836
[11,] -0.078972078 -0.194220591
[12,] -0.154512958 -0.078972078
[13,] 0.226304535 -0.154512958
[14,] 0.605828421 0.226304535
[15,] 0.718233118 0.605828421
[16,] 0.597091088 0.718233118
[17,] 0.506915762 0.597091088
[18,] 0.587337188 0.506915762
[19,] 0.695929757 0.587337188
[20,] 0.748762312 0.695929757
[21,] 0.737219768 0.748762312
[22,] 0.233607093 0.737219768
[23,] -0.497597668 0.233607093
[24,] -0.725269801 -0.497597668
[25,] -0.383972977 -0.725269801
[26,] 0.007148143 -0.383972977
[27,] 0.223927940 0.007148143
[28,] 0.098137366 0.223927940
[29,] -0.206433971 0.098137366
[30,] -0.424066270 -0.206433971
[31,] -0.501061605 -0.424066270
[32,] -0.325838829 -0.501061605
[33,] -0.258613479 -0.325838829
[34,] -0.525295159 -0.258613479
[35,] -0.627144926 -0.525295159
[36,] -0.655347860 -0.627144926
[37,] -0.493977046 -0.655347860
[38,] -0.381495141 -0.493977046
[39,] -0.499700064 -0.381495141
[40,] -0.241607735 -0.499700064
[41,] -0.362392680 -0.241607735
[42,] -0.595321746 -0.362392680
[43,] -0.814025301 -0.595321746
[44,] -0.916685747 -0.814025301
[45,] -1.118545166 -0.916685747
[46,] -0.547411178 -1.118545166
[47,] 0.101969552 -0.547411178
[48,] 0.235934865 0.101969552
[49,] 0.187397364 0.235934865
[50,] -0.451463823 0.187397364
[51,] -0.591962456 -0.451463823
[52,] -0.504096926 -0.591962456
[53,] 0.149736113 -0.504096926
[54,] 0.658997815 0.149736113
[55,] 0.758614992 0.658997815
[56,] 0.754667751 0.758614992
[57,] 0.647387712 0.754667751
[58,] 1.033319835 0.647387712
[59,] 1.101745120 1.033319835
[60,] 1.188871121 1.101745120
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.464248124 0.110324632
2 0.219982400 0.464248124
3 0.149501461 0.219982400
4 0.050476208 0.149501461
5 -0.087825224 0.050476208
6 -0.226946987 -0.087825224
7 -0.139457844 -0.226946987
8 -0.260905487 -0.139457844
9 -0.007448836 -0.260905487
10 -0.194220591 -0.007448836
11 -0.078972078 -0.194220591
12 -0.154512958 -0.078972078
13 0.226304535 -0.154512958
14 0.605828421 0.226304535
15 0.718233118 0.605828421
16 0.597091088 0.718233118
17 0.506915762 0.597091088
18 0.587337188 0.506915762
19 0.695929757 0.587337188
20 0.748762312 0.695929757
21 0.737219768 0.748762312
22 0.233607093 0.737219768
23 -0.497597668 0.233607093
24 -0.725269801 -0.497597668
25 -0.383972977 -0.725269801
26 0.007148143 -0.383972977
27 0.223927940 0.007148143
28 0.098137366 0.223927940
29 -0.206433971 0.098137366
30 -0.424066270 -0.206433971
31 -0.501061605 -0.424066270
32 -0.325838829 -0.501061605
33 -0.258613479 -0.325838829
34 -0.525295159 -0.258613479
35 -0.627144926 -0.525295159
36 -0.655347860 -0.627144926
37 -0.493977046 -0.655347860
38 -0.381495141 -0.493977046
39 -0.499700064 -0.381495141
40 -0.241607735 -0.499700064
41 -0.362392680 -0.241607735
42 -0.595321746 -0.362392680
43 -0.814025301 -0.595321746
44 -0.916685747 -0.814025301
45 -1.118545166 -0.916685747
46 -0.547411178 -1.118545166
47 0.101969552 -0.547411178
48 0.235934865 0.101969552
49 0.187397364 0.235934865
50 -0.451463823 0.187397364
51 -0.591962456 -0.451463823
52 -0.504096926 -0.591962456
53 0.149736113 -0.504096926
54 0.658997815 0.149736113
55 0.758614992 0.658997815
56 0.754667751 0.758614992
57 0.647387712 0.754667751
58 1.033319835 0.647387712
59 1.101745120 1.033319835
60 1.188871121 1.101745120
> 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/7xtjw1258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8t0yw1258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90ca61258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hzb21258735798.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11fgri1258735798.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/12s0uc1258735798.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/13lgim1258735798.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/14xila1258735798.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/156zn21258735798.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/160aud1258735798.tab")
+ }
>
> system("convert tmp/12ht81258735798.ps tmp/12ht81258735798.png")
> system("convert tmp/2l81e1258735798.ps tmp/2l81e1258735798.png")
> system("convert tmp/3ku721258735798.ps tmp/3ku721258735798.png")
> system("convert tmp/405xd1258735798.ps tmp/405xd1258735798.png")
> system("convert tmp/52kpt1258735798.ps tmp/52kpt1258735798.png")
> system("convert tmp/6m5e31258735798.ps tmp/6m5e31258735798.png")
> system("convert tmp/7xtjw1258735798.ps tmp/7xtjw1258735798.png")
> system("convert tmp/8t0yw1258735798.ps tmp/8t0yw1258735798.png")
> system("convert tmp/90ca61258735798.ps tmp/90ca61258735798.png")
> system("convert tmp/10hzb21258735798.ps tmp/10hzb21258735798.png")
>
>
> proc.time()
user system elapsed
2.402 1.540 2.933