R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(-15
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+ ,dim=c(13
+ ,60)
+ ,dimnames=list(c('X_1t'
+ ,'X_2t'
+ ,'X_3t'
+ ,'X_4t'
+ ,'X_5t'
+ ,'X_6t'
+ ,'X_7t'
+ ,'X_8t'
+ ,'X_9t'
+ ,'X_10t'
+ ,'X_11t'
+ ,'X_12t'
+ ,'Y_t')
+ ,1:60))
> y <- array(NA,dim=c(13,60),dimnames=list(c('X_1t','X_2t','X_3t','X_4t','X_5t','X_6t','X_7t','X_8t','X_9t','X_10t','X_11t','X_12t','Y_t'),1:60))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '13'
> 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
Y_t X_1t X_2t X_3t X_4t X_5t X_6t X_7t X_8t X_9t X_10t X_11t X_12t t
1 -8 -15 -7 55 23 39 24 -8 -2 19 4 -22 11 1
2 -1 -7 -1 54 20 19 23 -12 -3 18 6 -15 9 2
3 1 -6 0 52 20 14 19 -10 0 20 5 -16 13 3
4 -1 -6 -3 55 22 15 25 -11 -4 21 4 -22 12 4
5 2 2 4 56 25 7 21 -13 -3 18 5 -21 5 5
6 2 -4 2 54 22 12 19 -10 -3 19 5 -11 13 6
7 1 -4 3 53 26 12 20 -10 -3 19 4 -10 11 7
8 -1 -8 0 59 27 14 20 -11 -4 19 3 -6 8 8
9 -2 -10 -10 62 41 9 17 -11 -5 21 2 -8 8 9
10 -2 -16 -10 63 29 8 25 -11 -5 19 3 -15 8 10
11 -1 -14 -9 64 33 4 19 -10 -6 19 2 -16 8 11
12 -8 -30 -22 75 39 7 13 -13 -10 17 -1 -24 0 12
13 -4 -33 -16 77 27 3 15 -12 -11 16 0 -27 3 13
14 -6 -40 -18 79 27 5 15 -13 -13 16 -2 -33 0 14
15 -3 -38 -14 77 25 0 13 -15 -12 17 1 -29 -1 15
16 -3 -39 -12 82 19 -2 11 -16 -13 16 -2 -34 -1 16
17 -7 -46 -17 83 15 6 9 -18 -12 15 -2 -37 -4 17
18 -9 -50 -23 81 19 11 2 -17 -15 16 -2 -31 1 18
19 -11 -55 -28 78 23 9 -2 -18 -14 16 -6 -33 -1 19
20 -13 -66 -31 79 23 17 -4 -20 -16 16 -4 -25 0 20
21 -11 -63 -21 79 7 21 -2 -22 -16 18 -2 -27 -1 21
22 -9 -56 -19 73 1 21 1 -17 -12 19 0 -21 6 22
23 -17 -66 -22 72 7 41 -13 -19 -16 16 -5 -32 0 23
24 -22 -63 -22 67 4 57 -11 -18 -15 16 -4 -31 -3 24
25 -25 -69 -25 67 -8 65 -14 -26 -17 16 -5 -32 -3 25
26 -20 -69 -16 50 -14 68 -4 -19 -15 18 -1 -30 4 26
27 -24 -72 -22 45 -10 73 -9 -23 -14 16 -2 -34 1 27
28 -24 -69 -21 39 -11 71 -5 -21 -15 15 -4 -35 0 28
29 -22 -67 -10 39 -10 71 -4 -27 -14 15 -1 -37 -4 29
30 -19 -64 -7 37 -8 70 -8 -27 -16 16 1 -32 -2 30
31 -18 -61 -5 30 -8 69 -1 -21 -11 18 1 -28 3 31
32 -17 -58 -4 24 -7 65 -2 -22 -14 16 -2 -26 2 32
33 -11 -47 7 27 -8 57 -1 -24 -12 19 1 -24 5 33
34 -11 -44 6 19 -4 57 8 -21 -11 19 1 -27 6 34
35 -12 -42 3 19 3 57 8 -21 -13 18 3 -26 6 35
36 -10 -34 10 25 -5 55 6 -22 -12 17 3 -27 3 36
37 -15 -38 0 16 -4 65 7 -25 -12 19 1 -27 4 37
38 -15 -41 -2 20 5 65 2 -21 -10 22 1 -24 7 38
39 -15 -38 -1 25 3 64 3 -26 -12 19 0 -28 5 39
40 -13 -37 2 34 6 60 0 -27 -11 19 2 -23 6 40
41 -8 -22 8 39 10 43 5 -22 -10 16 2 -23 1 41
42 -13 -37 -6 40 16 47 -1 -22 -12 18 -1 -29 3 42
43 -9 -36 -4 38 11 40 3 -20 -12 20 1 -25 6 43
44 -7 -25 4 42 10 31 4 -21 -11 17 0 -24 0 44
45 -4 -15 7 46 21 27 8 -16 -12 17 1 -20 3 45
46 -4 -17 3 48 18 24 10 -17 -9 17 1 -22 4 46
47 -2 -19 3 51 20 23 14 -19 -6 20 3 -24 7 47
48 0 -12 8 55 18 17 15 -20 -7 21 2 -27 6 48
49 -2 -17 3 52 23 16 9 -20 -7 19 0 -25 6 49
50 -3 -21 -3 55 28 15 8 -20 -10 18 0 -26 6 50
51 1 -10 4 58 31 8 10 -19 -8 20 3 -24 6 51
52 -2 -19 -5 72 38 5 5 -20 -11 17 -2 -26 2 52
53 -1 -14 -1 70 27 6 4 -25 -12 15 0 -22 2 53
54 1 -8 5 70 21 5 8 -25 -11 17 1 -20 2 54
55 -3 -16 0 63 31 12 8 -22 -11 18 -1 -26 3 55
56 -4 -14 -6 66 31 8 10 -19 -9 20 -2 -22 -1 56
57 -9 -30 -13 65 29 17 8 -20 -9 19 -1 -29 -4 57
58 -9 -33 -15 55 24 22 10 -18 -12 20 -1 -30 4 58
59 -7 -37 -8 57 27 24 -8 -17 -10 22 1 -26 5 59
60 -14 -47 -20 60 36 36 -6 -17 -10 20 -2 -30 3 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X_1t X_2t X_3t X_4t X_5t
0.015860 -0.003797 0.261789 0.001831 -0.002958 -0.256112
X_6t X_7t X_8t X_9t X_10t X_11t
0.004941 -0.014213 0.004465 -0.009434 0.238885 -0.001451
X_12t t
0.240613 -0.002813
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.59798 -0.24408 0.04591 0.17513 0.56854
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.015860 1.301586 0.012 0.990
X_1t -0.003797 0.011363 -0.334 0.740
X_2t 0.261789 0.016405 15.958 < 2e-16 ***
X_3t 0.001831 0.008882 0.206 0.838
X_4t -0.002958 0.010088 -0.293 0.771
X_5t -0.256112 0.007685 -33.326 < 2e-16 ***
X_6t 0.004941 0.014220 0.347 0.730
X_7t -0.014213 0.021581 -0.659 0.513
X_8t 0.004465 0.034842 0.128 0.899
X_9t -0.009434 0.042162 -0.224 0.824
X_10t 0.238885 0.043385 5.506 1.58e-06 ***
X_11t -0.001451 0.012426 -0.117 0.908
X_12t 0.240613 0.023120 10.407 1.13e-13 ***
t -0.002813 0.005994 -0.469 0.641
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3277 on 46 degrees of freedom
Multiple R-squared: 0.9985, Adjusted R-squared: 0.998
F-statistic: 2299 on 13 and 46 DF, p-value: < 2.2e-16
> 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.6819014 0.63619727 0.318098633
[2,] 0.5221994 0.95560119 0.477800595
[3,] 0.5587070 0.88258599 0.441292994
[4,] 0.4596780 0.91935597 0.540322017
[5,] 0.3551301 0.71026025 0.644869877
[6,] 0.2903022 0.58060432 0.709697839
[7,] 0.2478364 0.49567277 0.752163616
[8,] 0.1861895 0.37237909 0.813810457
[9,] 0.2484854 0.49697080 0.751514601
[10,] 0.2476979 0.49539581 0.752302095
[11,] 0.1907303 0.38146056 0.809269719
[12,] 0.1426461 0.28529215 0.857353924
[13,] 0.4345075 0.86901494 0.565492530
[14,] 0.4830235 0.96604705 0.516976474
[15,] 0.7569868 0.48602637 0.243013183
[16,] 0.6771866 0.64562687 0.322813436
[17,] 0.6261812 0.74763769 0.373818845
[18,] 0.5909602 0.81807956 0.409039782
[19,] 0.5755090 0.84898193 0.424490967
[20,] 0.5075878 0.98482445 0.492412226
[21,] 0.6664611 0.66707779 0.333538894
[22,] 0.5847639 0.83047221 0.415236106
[23,] 0.8845025 0.23099502 0.115497512
[24,] 0.9729634 0.05407327 0.027036634
[25,] 0.9792098 0.04158048 0.020790239
[26,] 0.9594829 0.08103413 0.040517063
[27,] 0.9907112 0.01857750 0.009288751
> postscript(file="/var/fisher/rcomp/tmp/1a6s71352129122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2z65g1352129122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/34u4y1352129122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4llsu1352129122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5tqc71352129122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 60
Frequency = 1
1 2 3 4 5
0.0398893570 0.3297916221 0.1263619444 -0.3746949289 0.1895407225
6 7 8 9 10
0.1200611558 -0.4086308530 -0.1746642893 0.4679446521 -0.1528994129
11 12 13 14 15
-0.1329696422 -0.4057555505 -0.0143865910 0.1796105575 0.3762324287
16 17 18 19 20
0.0126443943 0.0180988826 -0.2502158656 -0.0173879617 0.0597010744
21 22 23 24 25
0.1736337256 -0.4328545160 0.1116256256 -0.2918299459 -0.3655496010
26 27 28 29 30
0.4859726416 0.2491153726 0.2179617107 -0.5001461498 0.5685429137
31 32 33 34 35
-0.3341513631 0.3531866531 0.0111483487 0.0623623307 -0.5979824829
36 37 38 39 40
-0.2420379120 0.1524816547 0.0702237896 0.1703810063 -0.3554167184
41 42 43 44 45
-0.0009561143 -0.0655121999 0.4473342683 -0.2856005296 0.0714241792
46 47 48 49 50
0.0519216957 0.5556563889 0.1960796848 -0.2555238137 0.0634724285
51 52 53 54 55
-0.2133627859 0.4876753380 0.1370554548 0.0767098898 -0.5261966605
56 57 58 59 60
0.2748385938 -0.1906184566 -0.2765144517 -0.2034669058 0.1606452160
> postscript(file="/var/fisher/rcomp/tmp/61ntr1352129122.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.0398893570 NA
1 0.3297916221 0.0398893570
2 0.1263619444 0.3297916221
3 -0.3746949289 0.1263619444
4 0.1895407225 -0.3746949289
5 0.1200611558 0.1895407225
6 -0.4086308530 0.1200611558
7 -0.1746642893 -0.4086308530
8 0.4679446521 -0.1746642893
9 -0.1528994129 0.4679446521
10 -0.1329696422 -0.1528994129
11 -0.4057555505 -0.1329696422
12 -0.0143865910 -0.4057555505
13 0.1796105575 -0.0143865910
14 0.3762324287 0.1796105575
15 0.0126443943 0.3762324287
16 0.0180988826 0.0126443943
17 -0.2502158656 0.0180988826
18 -0.0173879617 -0.2502158656
19 0.0597010744 -0.0173879617
20 0.1736337256 0.0597010744
21 -0.4328545160 0.1736337256
22 0.1116256256 -0.4328545160
23 -0.2918299459 0.1116256256
24 -0.3655496010 -0.2918299459
25 0.4859726416 -0.3655496010
26 0.2491153726 0.4859726416
27 0.2179617107 0.2491153726
28 -0.5001461498 0.2179617107
29 0.5685429137 -0.5001461498
30 -0.3341513631 0.5685429137
31 0.3531866531 -0.3341513631
32 0.0111483487 0.3531866531
33 0.0623623307 0.0111483487
34 -0.5979824829 0.0623623307
35 -0.2420379120 -0.5979824829
36 0.1524816547 -0.2420379120
37 0.0702237896 0.1524816547
38 0.1703810063 0.0702237896
39 -0.3554167184 0.1703810063
40 -0.0009561143 -0.3554167184
41 -0.0655121999 -0.0009561143
42 0.4473342683 -0.0655121999
43 -0.2856005296 0.4473342683
44 0.0714241792 -0.2856005296
45 0.0519216957 0.0714241792
46 0.5556563889 0.0519216957
47 0.1960796848 0.5556563889
48 -0.2555238137 0.1960796848
49 0.0634724285 -0.2555238137
50 -0.2133627859 0.0634724285
51 0.4876753380 -0.2133627859
52 0.1370554548 0.4876753380
53 0.0767098898 0.1370554548
54 -0.5261966605 0.0767098898
55 0.2748385938 -0.5261966605
56 -0.1906184566 0.2748385938
57 -0.2765144517 -0.1906184566
58 -0.2034669058 -0.2765144517
59 0.1606452160 -0.2034669058
60 NA 0.1606452160
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.3297916221 0.0398893570
[2,] 0.1263619444 0.3297916221
[3,] -0.3746949289 0.1263619444
[4,] 0.1895407225 -0.3746949289
[5,] 0.1200611558 0.1895407225
[6,] -0.4086308530 0.1200611558
[7,] -0.1746642893 -0.4086308530
[8,] 0.4679446521 -0.1746642893
[9,] -0.1528994129 0.4679446521
[10,] -0.1329696422 -0.1528994129
[11,] -0.4057555505 -0.1329696422
[12,] -0.0143865910 -0.4057555505
[13,] 0.1796105575 -0.0143865910
[14,] 0.3762324287 0.1796105575
[15,] 0.0126443943 0.3762324287
[16,] 0.0180988826 0.0126443943
[17,] -0.2502158656 0.0180988826
[18,] -0.0173879617 -0.2502158656
[19,] 0.0597010744 -0.0173879617
[20,] 0.1736337256 0.0597010744
[21,] -0.4328545160 0.1736337256
[22,] 0.1116256256 -0.4328545160
[23,] -0.2918299459 0.1116256256
[24,] -0.3655496010 -0.2918299459
[25,] 0.4859726416 -0.3655496010
[26,] 0.2491153726 0.4859726416
[27,] 0.2179617107 0.2491153726
[28,] -0.5001461498 0.2179617107
[29,] 0.5685429137 -0.5001461498
[30,] -0.3341513631 0.5685429137
[31,] 0.3531866531 -0.3341513631
[32,] 0.0111483487 0.3531866531
[33,] 0.0623623307 0.0111483487
[34,] -0.5979824829 0.0623623307
[35,] -0.2420379120 -0.5979824829
[36,] 0.1524816547 -0.2420379120
[37,] 0.0702237896 0.1524816547
[38,] 0.1703810063 0.0702237896
[39,] -0.3554167184 0.1703810063
[40,] -0.0009561143 -0.3554167184
[41,] -0.0655121999 -0.0009561143
[42,] 0.4473342683 -0.0655121999
[43,] -0.2856005296 0.4473342683
[44,] 0.0714241792 -0.2856005296
[45,] 0.0519216957 0.0714241792
[46,] 0.5556563889 0.0519216957
[47,] 0.1960796848 0.5556563889
[48,] -0.2555238137 0.1960796848
[49,] 0.0634724285 -0.2555238137
[50,] -0.2133627859 0.0634724285
[51,] 0.4876753380 -0.2133627859
[52,] 0.1370554548 0.4876753380
[53,] 0.0767098898 0.1370554548
[54,] -0.5261966605 0.0767098898
[55,] 0.2748385938 -0.5261966605
[56,] -0.1906184566 0.2748385938
[57,] -0.2765144517 -0.1906184566
[58,] -0.2034669058 -0.2765144517
[59,] 0.1606452160 -0.2034669058
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.3297916221 0.0398893570
2 0.1263619444 0.3297916221
3 -0.3746949289 0.1263619444
4 0.1895407225 -0.3746949289
5 0.1200611558 0.1895407225
6 -0.4086308530 0.1200611558
7 -0.1746642893 -0.4086308530
8 0.4679446521 -0.1746642893
9 -0.1528994129 0.4679446521
10 -0.1329696422 -0.1528994129
11 -0.4057555505 -0.1329696422
12 -0.0143865910 -0.4057555505
13 0.1796105575 -0.0143865910
14 0.3762324287 0.1796105575
15 0.0126443943 0.3762324287
16 0.0180988826 0.0126443943
17 -0.2502158656 0.0180988826
18 -0.0173879617 -0.2502158656
19 0.0597010744 -0.0173879617
20 0.1736337256 0.0597010744
21 -0.4328545160 0.1736337256
22 0.1116256256 -0.4328545160
23 -0.2918299459 0.1116256256
24 -0.3655496010 -0.2918299459
25 0.4859726416 -0.3655496010
26 0.2491153726 0.4859726416
27 0.2179617107 0.2491153726
28 -0.5001461498 0.2179617107
29 0.5685429137 -0.5001461498
30 -0.3341513631 0.5685429137
31 0.3531866531 -0.3341513631
32 0.0111483487 0.3531866531
33 0.0623623307 0.0111483487
34 -0.5979824829 0.0623623307
35 -0.2420379120 -0.5979824829
36 0.1524816547 -0.2420379120
37 0.0702237896 0.1524816547
38 0.1703810063 0.0702237896
39 -0.3554167184 0.1703810063
40 -0.0009561143 -0.3554167184
41 -0.0655121999 -0.0009561143
42 0.4473342683 -0.0655121999
43 -0.2856005296 0.4473342683
44 0.0714241792 -0.2856005296
45 0.0519216957 0.0714241792
46 0.5556563889 0.0519216957
47 0.1960796848 0.5556563889
48 -0.2555238137 0.1960796848
49 0.0634724285 -0.2555238137
50 -0.2133627859 0.0634724285
51 0.4876753380 -0.2133627859
52 0.1370554548 0.4876753380
53 0.0767098898 0.1370554548
54 -0.5261966605 0.0767098898
55 0.2748385938 -0.5261966605
56 -0.1906184566 0.2748385938
57 -0.2765144517 -0.1906184566
58 -0.2034669058 -0.2765144517
59 0.1606452160 -0.2034669058
> 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/7fwpg1352129122.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/8trh71352129122.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/9fu6e1352129122.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/10bz6w1352129122.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11e63l1352129122.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12vxw51352129122.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13nlh61352129122.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/146aez1352129122.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/15qoru1352129122.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/1623mu1352129122.tab")
+ }
>
> try(system("convert tmp/1a6s71352129122.ps tmp/1a6s71352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z65g1352129122.ps tmp/2z65g1352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/34u4y1352129122.ps tmp/34u4y1352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/4llsu1352129122.ps tmp/4llsu1352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tqc71352129122.ps tmp/5tqc71352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/61ntr1352129122.ps tmp/61ntr1352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fwpg1352129122.ps tmp/7fwpg1352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/8trh71352129122.ps tmp/8trh71352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fu6e1352129122.ps tmp/9fu6e1352129122.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bz6w1352129122.ps tmp/10bz6w1352129122.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.199 1.085 7.281