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)
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> x <- array(list(1.2999,0,1.3074,1,1.3242,1,1.3516,1,1.3511,0,1.3419,0,1.3716,1,1.3622,0,1.3896,1,1.4227,1,1.4684,1,1.457,0,1.4718,1,1.4748,1,1.5527,1,1.5751,1,1.5557,0,1.5553,0,1.577,1,1.4975,0,1.437,0,1.3322,0,1.2732,0,1.3449,1,1.3239,0,1.2785,0,1.305,1,1.319,1,1.365,1,1.4016,1,1.4088,1,1.4268,1,1.4562,1,1.4816,1,1.4914,1,1.4614,0,1.4272,0,1.3686,0,1.3569,0,1.3406,0,1.2565,0,1.2209,0,1.277,1,1.2894,1,1.3067,1,1.3898,1,1.3661,0,1.322,0,1.336,0,1.3649,1,1.3999,1,1.4442,1,1.4349,0,1.4388,1,1.4264,0,1.4343,1,1.377,0,1.3706,0,1.3556,0,1.3179,0,1.2905,0,1.3224,1,1.3201,0,1.3162,0,1.2789,0,1.2526,0,1.2288,0,1.24,1,1.2856,1),dim=c(2,69),dimnames=list(c('Exchange_rate','Dummies'),1:69))
> y <- array(NA,dim=c(2,69),dimnames=list(c('Exchange_rate','Dummies'),1:69))
> 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'
> 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
Exchange_rate Dummies M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.2999 0 1 0 0 0 0 0 0 0 0 0 0 1
2 1.3074 1 0 1 0 0 0 0 0 0 0 0 0 2
3 1.3242 1 0 0 1 0 0 0 0 0 0 0 0 3
4 1.3516 1 0 0 0 1 0 0 0 0 0 0 0 4
5 1.3511 0 0 0 0 0 1 0 0 0 0 0 0 5
6 1.3419 0 0 0 0 0 0 1 0 0 0 0 0 6
7 1.3716 1 0 0 0 0 0 0 1 0 0 0 0 7
8 1.3622 0 0 0 0 0 0 0 0 1 0 0 0 8
9 1.3896 1 0 0 0 0 0 0 0 0 1 0 0 9
10 1.4227 1 0 0 0 0 0 0 0 0 0 1 0 10
11 1.4684 1 0 0 0 0 0 0 0 0 0 0 1 11
12 1.4570 0 0 0 0 0 0 0 0 0 0 0 0 12
13 1.4718 1 1 0 0 0 0 0 0 0 0 0 0 13
14 1.4748 1 0 1 0 0 0 0 0 0 0 0 0 14
15 1.5527 1 0 0 1 0 0 0 0 0 0 0 0 15
16 1.5751 1 0 0 0 1 0 0 0 0 0 0 0 16
17 1.5557 0 0 0 0 0 1 0 0 0 0 0 0 17
18 1.5553 0 0 0 0 0 0 1 0 0 0 0 0 18
19 1.5770 1 0 0 0 0 0 0 1 0 0 0 0 19
20 1.4975 0 0 0 0 0 0 0 0 1 0 0 0 20
21 1.4370 0 0 0 0 0 0 0 0 0 1 0 0 21
22 1.3322 0 0 0 0 0 0 0 0 0 0 1 0 22
23 1.2732 0 0 0 0 0 0 0 0 0 0 0 1 23
24 1.3449 1 0 0 0 0 0 0 0 0 0 0 0 24
25 1.3239 0 1 0 0 0 0 0 0 0 0 0 0 25
26 1.2785 0 0 1 0 0 0 0 0 0 0 0 0 26
27 1.3050 1 0 0 1 0 0 0 0 0 0 0 0 27
28 1.3190 1 0 0 0 1 0 0 0 0 0 0 0 28
29 1.3650 1 0 0 0 0 1 0 0 0 0 0 0 29
30 1.4016 1 0 0 0 0 0 1 0 0 0 0 0 30
31 1.4088 1 0 0 0 0 0 0 1 0 0 0 0 31
32 1.4268 1 0 0 0 0 0 0 0 1 0 0 0 32
33 1.4562 1 0 0 0 0 0 0 0 0 1 0 0 33
34 1.4816 1 0 0 0 0 0 0 0 0 0 1 0 34
35 1.4914 1 0 0 0 0 0 0 0 0 0 0 1 35
36 1.4614 0 0 0 0 0 0 0 0 0 0 0 0 36
37 1.4272 0 1 0 0 0 0 0 0 0 0 0 0 37
38 1.3686 0 0 1 0 0 0 0 0 0 0 0 0 38
39 1.3569 0 0 0 1 0 0 0 0 0 0 0 0 39
40 1.3406 0 0 0 0 1 0 0 0 0 0 0 0 40
41 1.2565 0 0 0 0 0 1 0 0 0 0 0 0 41
42 1.2209 0 0 0 0 0 0 1 0 0 0 0 0 42
43 1.2770 1 0 0 0 0 0 0 1 0 0 0 0 43
44 1.2894 1 0 0 0 0 0 0 0 1 0 0 0 44
45 1.3067 1 0 0 0 0 0 0 0 0 1 0 0 45
46 1.3898 1 0 0 0 0 0 0 0 0 0 1 0 46
47 1.3661 0 0 0 0 0 0 0 0 0 0 0 1 47
48 1.3220 0 0 0 0 0 0 0 0 0 0 0 0 48
49 1.3360 0 1 0 0 0 0 0 0 0 0 0 0 49
50 1.3649 1 0 1 0 0 0 0 0 0 0 0 0 50
51 1.3999 1 0 0 1 0 0 0 0 0 0 0 0 51
52 1.4442 1 0 0 0 1 0 0 0 0 0 0 0 52
53 1.4349 0 0 0 0 0 1 0 0 0 0 0 0 53
54 1.4388 1 0 0 0 0 0 1 0 0 0 0 0 54
55 1.4264 0 0 0 0 0 0 0 1 0 0 0 0 55
56 1.4343 1 0 0 0 0 0 0 0 1 0 0 0 56
57 1.3770 0 0 0 0 0 0 0 0 0 1 0 0 57
58 1.3706 0 0 0 0 0 0 0 0 0 0 1 0 58
59 1.3556 0 0 0 0 0 0 0 0 0 0 0 1 59
60 1.3179 0 0 0 0 0 0 0 0 0 0 0 0 60
61 1.2905 0 1 0 0 0 0 0 0 0 0 0 0 61
62 1.3224 1 0 1 0 0 0 0 0 0 0 0 0 62
63 1.3201 0 0 0 1 0 0 0 0 0 0 0 0 63
64 1.3162 0 0 0 0 1 0 0 0 0 0 0 0 64
65 1.2789 0 0 0 0 0 1 0 0 0 0 0 0 65
66 1.2526 0 0 0 0 0 0 1 0 0 0 0 0 66
67 1.2288 0 0 0 0 0 0 0 1 0 0 0 0 67
68 1.2400 1 0 0 0 0 0 0 0 1 0 0 0 68
69 1.2856 1 0 0 0 0 0 0 0 0 1 0 0 69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummies M1 M2 M3 M4
1.427591 0.031414 -0.028770 -0.048448 -0.023269 -0.007141
M5 M6 M7 M8 M9 M10
-0.007388 -0.016312 -0.012221 -0.017309 -0.015514 0.003217
M11 t
0.002538 -0.001479
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.128273 -0.062196 0.003268 0.060788 0.170638
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4275906 0.0438135 32.583 < 2e-16 ***
Dummies 0.0314144 0.0234095 1.342 0.18513
M1 -0.0287697 0.0522715 -0.550 0.58428
M2 -0.0484482 0.0532687 -0.910 0.36705
M3 -0.0232695 0.0532733 -0.437 0.66397
M4 -0.0071408 0.0532832 -0.134 0.89388
M5 -0.0073882 0.0521985 -0.142 0.88796
M6 -0.0163119 0.0522814 -0.312 0.75622
M7 -0.0122213 0.0533454 -0.229 0.81964
M8 -0.0173093 0.0533770 -0.324 0.74695
M9 -0.0155139 0.0534139 -0.290 0.77257
M10 0.0032168 0.0552788 0.058 0.95381
M11 0.0025384 0.0547023 0.046 0.96316
t -0.0014787 0.0005369 -2.754 0.00796 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08619 on 55 degrees of freedom
Multiple R-squared: 0.1908, Adjusted R-squared: -0.0004567
F-statistic: 0.9976 on 13 and 55 DF, p-value: 0.4662
> 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,] 0.017721227 0.035442455 0.982278773
[2,] 0.004239829 0.008479659 0.995760171
[3,] 0.001015577 0.002031153 0.998984423
[4,] 0.004543644 0.009087288 0.995456356
[5,] 0.030039662 0.060079324 0.969960338
[6,] 0.162997183 0.325994366 0.837002817
[7,] 0.468580802 0.937161603 0.531419198
[8,] 0.945900865 0.108198269 0.054099135
[9,] 0.956582055 0.086835890 0.043417945
[10,] 0.958404019 0.083191962 0.041595981
[11,] 0.984438720 0.031122560 0.015561280
[12,] 0.992978302 0.014043395 0.007021698
[13,] 0.993979890 0.012040220 0.006020110
[14,] 0.990401312 0.019197376 0.009598688
[15,] 0.983880364 0.032239272 0.016119636
[16,] 0.974219866 0.051560267 0.025780134
[17,] 0.962721333 0.074557334 0.037278667
[18,] 0.948441548 0.103116904 0.051558452
[19,] 0.933843482 0.132313036 0.066156518
[20,] 0.929379270 0.141241461 0.070620730
[21,] 0.916115499 0.167769002 0.083884501
[22,] 0.881251949 0.237496102 0.118748051
[23,] 0.829546382 0.340907236 0.170453618
[24,] 0.773438929 0.453122143 0.226561071
[25,] 0.813931368 0.372137263 0.186068632
[26,] 0.901703495 0.196593011 0.098296505
[27,] 0.928372061 0.143255878 0.071627939
[28,] 0.956087039 0.087825922 0.043912961
[29,] 0.982135714 0.035728572 0.017864286
[30,] 0.978396278 0.043207444 0.021603722
[31,] 0.974332841 0.051334317 0.025667159
[32,] 0.978871271 0.042257458 0.021128729
[33,] 0.973162839 0.053674321 0.026837161
[34,] 0.978065154 0.043869692 0.021934846
[35,] 0.980934515 0.038130969 0.019065485
[36,] 0.979676385 0.040647229 0.020323615
> postscript(file="/var/fisher/rcomp/tmp/1buy61356033783.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/2qrva1356033783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3tryk1356033783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/42r3g1356033783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5v5go1356033783.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 69
Frequency = 1
1 2 3 4 5 6
-0.097442167 -0.100199347 -0.107099347 -0.094349347 -0.061708833 -0.060506440
7 8 9 10 11 12
-0.064832681 -0.036251653 -0.040582681 -0.024734726 0.023122401 0.047153891
13 14 15 16 17 18
0.060787962 0.084945143 0.139145143 0.146895143 0.160635657 0.170638051
19 20 21 22 23 24
0.158311810 0.116792838 0.055976172 -0.066075874 -0.122918746 -0.078615980
25 26 27 28 29 30
-0.037953185 -0.062196004 -0.090810366 -0.091460366 -0.043734213 0.003268180
31 32 33 34 35 36
0.007856301 0.032422967 0.061506301 0.069654255 0.081611383 0.087042872
37 38 39 40 41 42
0.083091306 0.045648486 0.010248486 -0.020701514 -0.103075361 -0.128272967
43 44 45 46 47 48
-0.106199208 -0.087232542 -0.070249208 -0.004401254 0.005470235 -0.034612637
49 50 51 52 53 54
0.009635796 0.028278616 0.039578616 0.069228616 0.093069130 0.075957162
55 56 57 58 59 60
0.092359644 0.075411949 0.049209644 0.025557599 0.012714726 -0.020968146
61 62 63 64 65 66
-0.018119713 0.003523106 0.008937468 -0.009612532 -0.045186379 -0.061083986
67 68 69
-0.087495865 -0.101143560 -0.055860227
> postscript(file="/var/fisher/rcomp/tmp/6sf3g1356033783.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 = 69
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.097442167 NA
1 -0.100199347 -0.097442167
2 -0.107099347 -0.100199347
3 -0.094349347 -0.107099347
4 -0.061708833 -0.094349347
5 -0.060506440 -0.061708833
6 -0.064832681 -0.060506440
7 -0.036251653 -0.064832681
8 -0.040582681 -0.036251653
9 -0.024734726 -0.040582681
10 0.023122401 -0.024734726
11 0.047153891 0.023122401
12 0.060787962 0.047153891
13 0.084945143 0.060787962
14 0.139145143 0.084945143
15 0.146895143 0.139145143
16 0.160635657 0.146895143
17 0.170638051 0.160635657
18 0.158311810 0.170638051
19 0.116792838 0.158311810
20 0.055976172 0.116792838
21 -0.066075874 0.055976172
22 -0.122918746 -0.066075874
23 -0.078615980 -0.122918746
24 -0.037953185 -0.078615980
25 -0.062196004 -0.037953185
26 -0.090810366 -0.062196004
27 -0.091460366 -0.090810366
28 -0.043734213 -0.091460366
29 0.003268180 -0.043734213
30 0.007856301 0.003268180
31 0.032422967 0.007856301
32 0.061506301 0.032422967
33 0.069654255 0.061506301
34 0.081611383 0.069654255
35 0.087042872 0.081611383
36 0.083091306 0.087042872
37 0.045648486 0.083091306
38 0.010248486 0.045648486
39 -0.020701514 0.010248486
40 -0.103075361 -0.020701514
41 -0.128272967 -0.103075361
42 -0.106199208 -0.128272967
43 -0.087232542 -0.106199208
44 -0.070249208 -0.087232542
45 -0.004401254 -0.070249208
46 0.005470235 -0.004401254
47 -0.034612637 0.005470235
48 0.009635796 -0.034612637
49 0.028278616 0.009635796
50 0.039578616 0.028278616
51 0.069228616 0.039578616
52 0.093069130 0.069228616
53 0.075957162 0.093069130
54 0.092359644 0.075957162
55 0.075411949 0.092359644
56 0.049209644 0.075411949
57 0.025557599 0.049209644
58 0.012714726 0.025557599
59 -0.020968146 0.012714726
60 -0.018119713 -0.020968146
61 0.003523106 -0.018119713
62 0.008937468 0.003523106
63 -0.009612532 0.008937468
64 -0.045186379 -0.009612532
65 -0.061083986 -0.045186379
66 -0.087495865 -0.061083986
67 -0.101143560 -0.087495865
68 -0.055860227 -0.101143560
69 NA -0.055860227
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.100199347 -0.097442167
[2,] -0.107099347 -0.100199347
[3,] -0.094349347 -0.107099347
[4,] -0.061708833 -0.094349347
[5,] -0.060506440 -0.061708833
[6,] -0.064832681 -0.060506440
[7,] -0.036251653 -0.064832681
[8,] -0.040582681 -0.036251653
[9,] -0.024734726 -0.040582681
[10,] 0.023122401 -0.024734726
[11,] 0.047153891 0.023122401
[12,] 0.060787962 0.047153891
[13,] 0.084945143 0.060787962
[14,] 0.139145143 0.084945143
[15,] 0.146895143 0.139145143
[16,] 0.160635657 0.146895143
[17,] 0.170638051 0.160635657
[18,] 0.158311810 0.170638051
[19,] 0.116792838 0.158311810
[20,] 0.055976172 0.116792838
[21,] -0.066075874 0.055976172
[22,] -0.122918746 -0.066075874
[23,] -0.078615980 -0.122918746
[24,] -0.037953185 -0.078615980
[25,] -0.062196004 -0.037953185
[26,] -0.090810366 -0.062196004
[27,] -0.091460366 -0.090810366
[28,] -0.043734213 -0.091460366
[29,] 0.003268180 -0.043734213
[30,] 0.007856301 0.003268180
[31,] 0.032422967 0.007856301
[32,] 0.061506301 0.032422967
[33,] 0.069654255 0.061506301
[34,] 0.081611383 0.069654255
[35,] 0.087042872 0.081611383
[36,] 0.083091306 0.087042872
[37,] 0.045648486 0.083091306
[38,] 0.010248486 0.045648486
[39,] -0.020701514 0.010248486
[40,] -0.103075361 -0.020701514
[41,] -0.128272967 -0.103075361
[42,] -0.106199208 -0.128272967
[43,] -0.087232542 -0.106199208
[44,] -0.070249208 -0.087232542
[45,] -0.004401254 -0.070249208
[46,] 0.005470235 -0.004401254
[47,] -0.034612637 0.005470235
[48,] 0.009635796 -0.034612637
[49,] 0.028278616 0.009635796
[50,] 0.039578616 0.028278616
[51,] 0.069228616 0.039578616
[52,] 0.093069130 0.069228616
[53,] 0.075957162 0.093069130
[54,] 0.092359644 0.075957162
[55,] 0.075411949 0.092359644
[56,] 0.049209644 0.075411949
[57,] 0.025557599 0.049209644
[58,] 0.012714726 0.025557599
[59,] -0.020968146 0.012714726
[60,] -0.018119713 -0.020968146
[61,] 0.003523106 -0.018119713
[62,] 0.008937468 0.003523106
[63,] -0.009612532 0.008937468
[64,] -0.045186379 -0.009612532
[65,] -0.061083986 -0.045186379
[66,] -0.087495865 -0.061083986
[67,] -0.101143560 -0.087495865
[68,] -0.055860227 -0.101143560
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.100199347 -0.097442167
2 -0.107099347 -0.100199347
3 -0.094349347 -0.107099347
4 -0.061708833 -0.094349347
5 -0.060506440 -0.061708833
6 -0.064832681 -0.060506440
7 -0.036251653 -0.064832681
8 -0.040582681 -0.036251653
9 -0.024734726 -0.040582681
10 0.023122401 -0.024734726
11 0.047153891 0.023122401
12 0.060787962 0.047153891
13 0.084945143 0.060787962
14 0.139145143 0.084945143
15 0.146895143 0.139145143
16 0.160635657 0.146895143
17 0.170638051 0.160635657
18 0.158311810 0.170638051
19 0.116792838 0.158311810
20 0.055976172 0.116792838
21 -0.066075874 0.055976172
22 -0.122918746 -0.066075874
23 -0.078615980 -0.122918746
24 -0.037953185 -0.078615980
25 -0.062196004 -0.037953185
26 -0.090810366 -0.062196004
27 -0.091460366 -0.090810366
28 -0.043734213 -0.091460366
29 0.003268180 -0.043734213
30 0.007856301 0.003268180
31 0.032422967 0.007856301
32 0.061506301 0.032422967
33 0.069654255 0.061506301
34 0.081611383 0.069654255
35 0.087042872 0.081611383
36 0.083091306 0.087042872
37 0.045648486 0.083091306
38 0.010248486 0.045648486
39 -0.020701514 0.010248486
40 -0.103075361 -0.020701514
41 -0.128272967 -0.103075361
42 -0.106199208 -0.128272967
43 -0.087232542 -0.106199208
44 -0.070249208 -0.087232542
45 -0.004401254 -0.070249208
46 0.005470235 -0.004401254
47 -0.034612637 0.005470235
48 0.009635796 -0.034612637
49 0.028278616 0.009635796
50 0.039578616 0.028278616
51 0.069228616 0.039578616
52 0.093069130 0.069228616
53 0.075957162 0.093069130
54 0.092359644 0.075957162
55 0.075411949 0.092359644
56 0.049209644 0.075411949
57 0.025557599 0.049209644
58 0.012714726 0.025557599
59 -0.020968146 0.012714726
60 -0.018119713 -0.020968146
61 0.003523106 -0.018119713
62 0.008937468 0.003523106
63 -0.009612532 0.008937468
64 -0.045186379 -0.009612532
65 -0.061083986 -0.045186379
66 -0.087495865 -0.061083986
67 -0.101143560 -0.087495865
68 -0.055860227 -0.101143560
> 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/7buu21356033783.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/8rqn71356033783.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/9jiwe1356033783.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/10lv711356033783.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11ybmz1356033783.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12wiu21356033783.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/139exl1356033784.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14ls4g1356033784.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/15dzws1356033784.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/16t5x31356033784.tab")
+ }
>
> try(system("convert tmp/1buy61356033783.ps tmp/1buy61356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qrva1356033783.ps tmp/2qrva1356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tryk1356033783.ps tmp/3tryk1356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/42r3g1356033783.ps tmp/42r3g1356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v5go1356033783.ps tmp/5v5go1356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sf3g1356033783.ps tmp/6sf3g1356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/7buu21356033783.ps tmp/7buu21356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rqn71356033783.ps tmp/8rqn71356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jiwe1356033783.ps tmp/9jiwe1356033783.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lv711356033783.ps tmp/10lv711356033783.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.789 1.841 8.651