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(1.4761,1.4721,1.487,1.5167,1.5812,1.554,1.5508,1.5764,1.5611,1.4735,1.4303,1.2757,1.2727,1.3917,1.2816,1.2644,1.3308,1.3275,1.4098,1.4134,1.4138,1.4272,1.4643,1.48,1.5023,1.4406,1.3966,1.357,1.3479,1.3315,1.2307,1.2271,1.3028,1.268,1.3648,1.3857,1.2998,1.3362,1.3692,1.3834,1.4207,1.486,1.4385,1.4453,1.426,1.445,1.3503,1.4001,1.3418,1.2939,1.3176,1.3443,1.3356,1.3214,1.2403,1.259,1.2284,1.2611,1.293,1.2993,1.2986),dim=c(1,61),dimnames=list(c(''),1:61))
> y <- array(NA,dim=c(1,61),dimnames=list(c(''),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'
> 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, 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
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.4761 1 0 0 0 0 0 0 0 0 0 0 1
2 1.4721 0 1 0 0 0 0 0 0 0 0 0 2
3 1.4870 0 0 1 0 0 0 0 0 0 0 0 3
4 1.5167 0 0 0 1 0 0 0 0 0 0 0 4
5 1.5812 0 0 0 0 1 0 0 0 0 0 0 5
6 1.5540 0 0 0 0 0 1 0 0 0 0 0 6
7 1.5508 0 0 0 0 0 0 1 0 0 0 0 7
8 1.5764 0 0 0 0 0 0 0 1 0 0 0 8
9 1.5611 0 0 0 0 0 0 0 0 1 0 0 9
10 1.4735 0 0 0 0 0 0 0 0 0 1 0 10
11 1.4303 0 0 0 0 0 0 0 0 0 0 1 11
12 1.2757 0 0 0 0 0 0 0 0 0 0 0 12
13 1.2727 1 0 0 0 0 0 0 0 0 0 0 13
14 1.3917 0 1 0 0 0 0 0 0 0 0 0 14
15 1.2816 0 0 1 0 0 0 0 0 0 0 0 15
16 1.2644 0 0 0 1 0 0 0 0 0 0 0 16
17 1.3308 0 0 0 0 1 0 0 0 0 0 0 17
18 1.3275 0 0 0 0 0 1 0 0 0 0 0 18
19 1.4098 0 0 0 0 0 0 1 0 0 0 0 19
20 1.4134 0 0 0 0 0 0 0 1 0 0 0 20
21 1.4138 0 0 0 0 0 0 0 0 1 0 0 21
22 1.4272 0 0 0 0 0 0 0 0 0 1 0 22
23 1.4643 0 0 0 0 0 0 0 0 0 0 1 23
24 1.4800 0 0 0 0 0 0 0 0 0 0 0 24
25 1.5023 1 0 0 0 0 0 0 0 0 0 0 25
26 1.4406 0 1 0 0 0 0 0 0 0 0 0 26
27 1.3966 0 0 1 0 0 0 0 0 0 0 0 27
28 1.3570 0 0 0 1 0 0 0 0 0 0 0 28
29 1.3479 0 0 0 0 1 0 0 0 0 0 0 29
30 1.3315 0 0 0 0 0 1 0 0 0 0 0 30
31 1.2307 0 0 0 0 0 0 1 0 0 0 0 31
32 1.2271 0 0 0 0 0 0 0 1 0 0 0 32
33 1.3028 0 0 0 0 0 0 0 0 1 0 0 33
34 1.2680 0 0 0 0 0 0 0 0 0 1 0 34
35 1.3648 0 0 0 0 0 0 0 0 0 0 1 35
36 1.3857 0 0 0 0 0 0 0 0 0 0 0 36
37 1.2998 1 0 0 0 0 0 0 0 0 0 0 37
38 1.3362 0 1 0 0 0 0 0 0 0 0 0 38
39 1.3692 0 0 1 0 0 0 0 0 0 0 0 39
40 1.3834 0 0 0 1 0 0 0 0 0 0 0 40
41 1.4207 0 0 0 0 1 0 0 0 0 0 0 41
42 1.4860 0 0 0 0 0 1 0 0 0 0 0 42
43 1.4385 0 0 0 0 0 0 1 0 0 0 0 43
44 1.4453 0 0 0 0 0 0 0 1 0 0 0 44
45 1.4260 0 0 0 0 0 0 0 0 1 0 0 45
46 1.4450 0 0 0 0 0 0 0 0 0 1 0 46
47 1.3503 0 0 0 0 0 0 0 0 0 0 1 47
48 1.4001 0 0 0 0 0 0 0 0 0 0 0 48
49 1.3418 1 0 0 0 0 0 0 0 0 0 0 49
50 1.2939 0 1 0 0 0 0 0 0 0 0 0 50
51 1.3176 0 0 1 0 0 0 0 0 0 0 0 51
52 1.3443 0 0 0 1 0 0 0 0 0 0 0 52
53 1.3356 0 0 0 0 1 0 0 0 0 0 0 53
54 1.3214 0 0 0 0 0 1 0 0 0 0 0 54
55 1.2403 0 0 0 0 0 0 1 0 0 0 0 55
56 1.2590 0 0 0 0 0 0 0 1 0 0 0 56
57 1.2284 0 0 0 0 0 0 0 0 1 0 0 57
58 1.2611 0 0 0 0 0 0 0 0 0 1 0 58
59 1.2930 0 0 0 0 0 0 0 0 0 0 1 59
60 1.2993 0 0 0 0 0 0 0 0 0 0 0 60
61 1.2986 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) M1 M2 M3 M4 M5
1.4791000 -0.0183517 -0.0120767 -0.0254950 -0.0196533 0.0135083
M6 M7 M8 M9 M10 M11
0.0174300 -0.0095483 0.0037533 0.0090150 0.0006367 0.0092983
t
-0.0030817
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.16642 -0.04693 0.00674 0.05370 0.11890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4791000 0.0447678 33.039 < 2e-16 ***
M1 -0.0183517 0.0522097 -0.351 0.727
M2 -0.0120767 0.0547996 -0.220 0.827
M3 -0.0254950 0.0547297 -0.466 0.643
M4 -0.0196533 0.0546670 -0.360 0.721
M5 0.0135083 0.0546116 0.247 0.806
M6 0.0174300 0.0545636 0.319 0.751
M7 -0.0095483 0.0545229 -0.175 0.862
M8 0.0037533 0.0544896 0.069 0.945
M9 0.0090150 0.0544637 0.166 0.869
M10 0.0006367 0.0544451 0.012 0.991
M11 0.0092983 0.0544340 0.171 0.865
t -0.0030817 0.0006351 -4.852 1.33e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08606 on 48 degrees of freedom
Multiple R-squared: 0.3404, Adjusted R-squared: 0.1755
F-statistic: 2.064 on 12 and 48 DF, p-value: 0.03823
> 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,] 0.30588746 0.61177493 0.694112535
[2,] 0.22625750 0.45251500 0.773742501
[3,] 0.14422843 0.28845687 0.855771566
[4,] 0.10274750 0.20549499 0.897252505
[5,] 0.05720630 0.11441260 0.942793699
[6,] 0.03336051 0.06672102 0.966639489
[7,] 0.07829424 0.15658847 0.921705765
[8,] 0.25977864 0.51955729 0.740221356
[9,] 0.78394673 0.43210653 0.216053267
[10,] 0.94505485 0.10989031 0.054945154
[11,] 0.94268984 0.11462031 0.057310157
[12,] 0.92387212 0.15225575 0.076127877
[13,] 0.88724696 0.22550608 0.112753039
[14,] 0.84264892 0.31470216 0.157351079
[15,] 0.81205479 0.37589043 0.187945214
[16,] 0.86634607 0.26730785 0.133653927
[17,] 0.93706972 0.12586056 0.062930279
[18,] 0.92959763 0.14080474 0.070402370
[19,] 0.96882410 0.06235180 0.031175898
[20,] 0.95646794 0.08706412 0.043532060
[21,] 0.95612104 0.08775791 0.043878957
[22,] 0.99010028 0.01979945 0.009899723
[23,] 0.98629271 0.02741458 0.013707290
[24,] 0.98295564 0.03408871 0.017044356
[25,] 0.98460400 0.03079200 0.015395999
[26,] 0.97549562 0.04900875 0.024504376
[27,] 0.95787443 0.08425115 0.042125573
[28,] 0.93823526 0.12352949 0.061764744
[29,] 0.90399529 0.19200941 0.096004706
[30,] 0.89136114 0.21727771 0.108638857
> postscript(file="/var/fisher/rcomp/tmp/1v6aa1356088911.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/28epn1356088911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lab31356088911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ytlx1356088911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/54yxs1356088911.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 = 61
Frequency = 1
1 2 3 4 5 6
0.01843333 0.01124000 0.04264000 0.06958000 0.10400000 0.07596000
7 8 9 10 11 12
0.10282000 0.11820000 0.10072000 0.02458000 -0.02420000 -0.16642000
13 14 15 16 17 18
-0.14798667 -0.03218000 -0.12578000 -0.14574000 -0.10942000 -0.11356000
19 20 21 22 23 24
-0.00120000 -0.00782000 -0.00960000 0.01526000 0.04678000 0.07486000
25 26 27 28 29 30
0.11859333 0.05370000 0.02620000 -0.01616000 -0.05534000 -0.07258000
31 32 33 34 35 36
-0.14332000 -0.15714000 -0.08362000 -0.10696000 -0.01574000 0.01754000
37 38 39 40 41 42
-0.04692667 -0.01372000 0.03578000 0.04722000 0.05444000 0.11890000
43 44 45 46 47 48
0.10146000 0.09804000 0.07656000 0.10702000 0.00674000 0.06892000
49 50 51 52 53 54
0.03205333 -0.01904000 0.02116000 0.04510000 0.00632000 -0.00872000
55 56 57 58 59 60
-0.05976000 -0.05128000 -0.08406000 -0.03990000 -0.01358000 0.00510000
61
0.02583333
> postscript(file="/var/fisher/rcomp/tmp/6m3ns1356088911.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.01843333 NA
1 0.01124000 0.01843333
2 0.04264000 0.01124000
3 0.06958000 0.04264000
4 0.10400000 0.06958000
5 0.07596000 0.10400000
6 0.10282000 0.07596000
7 0.11820000 0.10282000
8 0.10072000 0.11820000
9 0.02458000 0.10072000
10 -0.02420000 0.02458000
11 -0.16642000 -0.02420000
12 -0.14798667 -0.16642000
13 -0.03218000 -0.14798667
14 -0.12578000 -0.03218000
15 -0.14574000 -0.12578000
16 -0.10942000 -0.14574000
17 -0.11356000 -0.10942000
18 -0.00120000 -0.11356000
19 -0.00782000 -0.00120000
20 -0.00960000 -0.00782000
21 0.01526000 -0.00960000
22 0.04678000 0.01526000
23 0.07486000 0.04678000
24 0.11859333 0.07486000
25 0.05370000 0.11859333
26 0.02620000 0.05370000
27 -0.01616000 0.02620000
28 -0.05534000 -0.01616000
29 -0.07258000 -0.05534000
30 -0.14332000 -0.07258000
31 -0.15714000 -0.14332000
32 -0.08362000 -0.15714000
33 -0.10696000 -0.08362000
34 -0.01574000 -0.10696000
35 0.01754000 -0.01574000
36 -0.04692667 0.01754000
37 -0.01372000 -0.04692667
38 0.03578000 -0.01372000
39 0.04722000 0.03578000
40 0.05444000 0.04722000
41 0.11890000 0.05444000
42 0.10146000 0.11890000
43 0.09804000 0.10146000
44 0.07656000 0.09804000
45 0.10702000 0.07656000
46 0.00674000 0.10702000
47 0.06892000 0.00674000
48 0.03205333 0.06892000
49 -0.01904000 0.03205333
50 0.02116000 -0.01904000
51 0.04510000 0.02116000
52 0.00632000 0.04510000
53 -0.00872000 0.00632000
54 -0.05976000 -0.00872000
55 -0.05128000 -0.05976000
56 -0.08406000 -0.05128000
57 -0.03990000 -0.08406000
58 -0.01358000 -0.03990000
59 0.00510000 -0.01358000
60 0.02583333 0.00510000
61 NA 0.02583333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.01124000 0.01843333
[2,] 0.04264000 0.01124000
[3,] 0.06958000 0.04264000
[4,] 0.10400000 0.06958000
[5,] 0.07596000 0.10400000
[6,] 0.10282000 0.07596000
[7,] 0.11820000 0.10282000
[8,] 0.10072000 0.11820000
[9,] 0.02458000 0.10072000
[10,] -0.02420000 0.02458000
[11,] -0.16642000 -0.02420000
[12,] -0.14798667 -0.16642000
[13,] -0.03218000 -0.14798667
[14,] -0.12578000 -0.03218000
[15,] -0.14574000 -0.12578000
[16,] -0.10942000 -0.14574000
[17,] -0.11356000 -0.10942000
[18,] -0.00120000 -0.11356000
[19,] -0.00782000 -0.00120000
[20,] -0.00960000 -0.00782000
[21,] 0.01526000 -0.00960000
[22,] 0.04678000 0.01526000
[23,] 0.07486000 0.04678000
[24,] 0.11859333 0.07486000
[25,] 0.05370000 0.11859333
[26,] 0.02620000 0.05370000
[27,] -0.01616000 0.02620000
[28,] -0.05534000 -0.01616000
[29,] -0.07258000 -0.05534000
[30,] -0.14332000 -0.07258000
[31,] -0.15714000 -0.14332000
[32,] -0.08362000 -0.15714000
[33,] -0.10696000 -0.08362000
[34,] -0.01574000 -0.10696000
[35,] 0.01754000 -0.01574000
[36,] -0.04692667 0.01754000
[37,] -0.01372000 -0.04692667
[38,] 0.03578000 -0.01372000
[39,] 0.04722000 0.03578000
[40,] 0.05444000 0.04722000
[41,] 0.11890000 0.05444000
[42,] 0.10146000 0.11890000
[43,] 0.09804000 0.10146000
[44,] 0.07656000 0.09804000
[45,] 0.10702000 0.07656000
[46,] 0.00674000 0.10702000
[47,] 0.06892000 0.00674000
[48,] 0.03205333 0.06892000
[49,] -0.01904000 0.03205333
[50,] 0.02116000 -0.01904000
[51,] 0.04510000 0.02116000
[52,] 0.00632000 0.04510000
[53,] -0.00872000 0.00632000
[54,] -0.05976000 -0.00872000
[55,] -0.05128000 -0.05976000
[56,] -0.08406000 -0.05128000
[57,] -0.03990000 -0.08406000
[58,] -0.01358000 -0.03990000
[59,] 0.00510000 -0.01358000
[60,] 0.02583333 0.00510000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.01124000 0.01843333
2 0.04264000 0.01124000
3 0.06958000 0.04264000
4 0.10400000 0.06958000
5 0.07596000 0.10400000
6 0.10282000 0.07596000
7 0.11820000 0.10282000
8 0.10072000 0.11820000
9 0.02458000 0.10072000
10 -0.02420000 0.02458000
11 -0.16642000 -0.02420000
12 -0.14798667 -0.16642000
13 -0.03218000 -0.14798667
14 -0.12578000 -0.03218000
15 -0.14574000 -0.12578000
16 -0.10942000 -0.14574000
17 -0.11356000 -0.10942000
18 -0.00120000 -0.11356000
19 -0.00782000 -0.00120000
20 -0.00960000 -0.00782000
21 0.01526000 -0.00960000
22 0.04678000 0.01526000
23 0.07486000 0.04678000
24 0.11859333 0.07486000
25 0.05370000 0.11859333
26 0.02620000 0.05370000
27 -0.01616000 0.02620000
28 -0.05534000 -0.01616000
29 -0.07258000 -0.05534000
30 -0.14332000 -0.07258000
31 -0.15714000 -0.14332000
32 -0.08362000 -0.15714000
33 -0.10696000 -0.08362000
34 -0.01574000 -0.10696000
35 0.01754000 -0.01574000
36 -0.04692667 0.01754000
37 -0.01372000 -0.04692667
38 0.03578000 -0.01372000
39 0.04722000 0.03578000
40 0.05444000 0.04722000
41 0.11890000 0.05444000
42 0.10146000 0.11890000
43 0.09804000 0.10146000
44 0.07656000 0.09804000
45 0.10702000 0.07656000
46 0.00674000 0.10702000
47 0.06892000 0.00674000
48 0.03205333 0.06892000
49 -0.01904000 0.03205333
50 0.02116000 -0.01904000
51 0.04510000 0.02116000
52 0.00632000 0.04510000
53 -0.00872000 0.00632000
54 -0.05976000 -0.00872000
55 -0.05128000 -0.05976000
56 -0.08406000 -0.05128000
57 -0.03990000 -0.08406000
58 -0.01358000 -0.03990000
59 0.00510000 -0.01358000
60 0.02583333 0.00510000
> 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/7ank51356088911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8ufwl1356088911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9nhjf1356088911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10nha91356088911.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ol8a1356088911.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/121gjg1356088911.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/13nktp1356088911.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/144flk1356088911.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/1591kp1356088911.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/168hac1356088911.tab")
+ }
>
> try(system("convert tmp/1v6aa1356088911.ps tmp/1v6aa1356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/28epn1356088911.ps tmp/28epn1356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lab31356088911.ps tmp/3lab31356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ytlx1356088911.ps tmp/4ytlx1356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/54yxs1356088911.ps tmp/54yxs1356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/6m3ns1356088911.ps tmp/6m3ns1356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ank51356088911.ps tmp/7ank51356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ufwl1356088911.ps tmp/8ufwl1356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nhjf1356088911.ps tmp/9nhjf1356088911.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nha91356088911.ps tmp/10nha91356088911.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.884 1.766 7.646