R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
+ ,1
+ ,4
+ ,0
+ ,2
+ ,1
+ ,1
+ ,0
+ ,0
+ ,2
+ ,0
+ ,1
+ ,4
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+ ,FALSE
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+ ,1
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+ ,4
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+ ,1
+ ,0
+ ,0
+ ,0
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+ ,1
+ ,1
+ ,2
+ ,1
+ ,1
+ ,2
+ ,1
+ ,2
+ ,1
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,1
+ ,2
+ ,1
+ ,2
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
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+ ,4
+ ,1
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+ ,0
+ ,4
+ ,1
+ ,2
+ ,1
+ ,0
+ ,0
+ ,FALSE
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+ ,1
+ ,4
+ ,1
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+ ,FALSE
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+ ,1
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+ ,1
+ ,2
+ ,1
+ ,1
+ ,4
+ ,FALSE
+ ,FALSE
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+ ,1
+ ,0
+ ,1
+ ,2
+ ,1
+ ,1
+ ,0
+ ,1
+ ,2
+ ,1
+ ,1
+ ,0
+ ,1
+ ,2
+ ,1
+ ,1
+ ,4
+ ,1
+ ,2
+ ,1
+ ,1
+ ,4
+ ,1
+ ,2
+ ,0
+ ,0
+ ,0
+ ,FALSE
+ ,FALSE
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,2
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0)
+ ,dim=c(5
+ ,105)
+ ,dimnames=list(c('pre'
+ ,'post1'
+ ,'post2'
+ ,'post3'
+ ,'post4')
+ ,1:105))
> y <- array(NA,dim=c(5,105),dimnames=list(c('pre','post1','post2','post3','post4'),1:105))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> 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
post4 pre post1 post2 post3
1 2.0 1 1 4 0
2 2.0 1 1 0 0
3 1.5 0 1 4 1
4 0.0 0 0 0 0
5 1.0 1 1 0 1
6 2.0 1 1 0 1
7 2.0 1 1 0 1
8 1.0 0 1 0 1
9 2.0 0 1 4 1
10 2.0 1 1 1 0
11 2.0 0 0 4 0
12 0.0 0 1 0 1
13 0.0 0 1 2 1
14 2.0 0 1 0 0
15 0.0 0 0 0 0
16 2.0 1 1 0 1
17 2.0 1 1 1 0
18 0.5 1 1 0 1
19 2.0 0 1 0 1
20 0.0 0 0 2 1
21 2.0 1 1 2 1
22 0.0 1 1 1 0
23 0.0 0 0 2 0
24 0.0 1 0 0 0
25 2.0 1 1 3 1
26 0.0 1 0 0 1
27 0.0 1 1 0 0
28 0.0 0 0 0 0
29 2.0 0 0 1 0
30 1.0 1 1 0 1
31 0.5 1 0 0 0
32 2.0 1 1 4 0
33 0.5 0 0 0 1
34 0.0 0 0 1 0
35 0.5 0 0 0 1
36 0.0 1 1 0 0
37 2.0 1 1 4 0
38 0.0 0 1 1 1
39 1.0 0 1 0 1
40 2.0 1 1 4 1
41 1.0 1 1 0 1
42 2.0 1 1 4 1
43 0.0 1 1 0 0
44 0.5 1 1 0 1
45 0.0 0 0 0 1
46 2.0 0 1 4 1
47 0.0 0 1 0 0
48 1.0 1 1 0 0
49 2.0 1 1 4 1
50 0.5 0 0 4 0
51 2.0 0 1 0 1
52 2.0 1 1 1 1
53 2.0 0 1 0 1
54 0.0 0 0 4 0
55 0.0 0 1 0 0
56 0.0 0 1 2 1
57 0.5 0 1 0 1
58 0.0 0 1 4 0
59 2.0 0 0 4 0
60 0.0 0 0 0 0
61 0.0 0 1 0 1
62 2.0 1 1 4 1
63 1.0 1 1 0 1
64 0.0 1 0 0 1
65 2.0 0 0 2 1
66 1.0 0 1 0 0
67 2.0 0 1 0 1
68 0.0 0 0 0 0
69 1.0 1 1 4 1
70 2.0 1 1 4 1
71 0.0 0 1 2 0
72 0.0 0 1 0 0
73 0.0 0 1 0 0
74 0.0 0 1 4 0
75 2.0 1 1 0 1
76 2.0 1 0 0 1
77 2.0 0 0 1 1
78 2.0 1 1 2 1
79 2.0 1 0 0 1
80 2.0 1 1 2 1
81 2.0 0 0 0 1
82 2.0 0 0 4 1
83 2.0 0 0 4 1
84 2.0 1 0 0 1
85 0.0 0 0 0 0
86 2.0 0 0 4 1
87 0.0 1 0 0 0
88 2.0 1 1 4 1
89 2.0 0 0 2 1
90 0.0 0 0 2 0
91 0.0 1 1 0 0
92 2.0 1 1 0 1
93 0.0 1 1 4 0
94 2.0 0 1 0 1
95 2.0 1 1 0 1
96 2.0 1 1 0 1
97 2.0 1 1 4 1
98 2.0 1 1 4 1
99 0.0 0 0 0 0
100 0.0 0 0 0 0
101 0.0 1 1 2 0
102 2.0 0 0 1 1
103 0.0 0 0 0 0
104 2.0 0 0 2 1
105 0.0 0 1 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pre post1 post2 post3
0.17622 0.37104 0.01815 0.15747 0.85594
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.40319 -0.53216 -0.05121 0.57866 1.80563
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.17622 0.15169 1.162 0.248122
pre 0.37104 0.15947 2.327 0.022000 *
post1 0.01815 0.16661 0.109 0.913459
post2 0.15747 0.04439 3.547 0.000595 ***
post3 0.85594 0.15084 5.674 1.36e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7493 on 100 degrees of freedom
Multiple R-squared: 0.3728, Adjusted R-squared: 0.3478
F-statistic: 14.86 on 4 and 100 DF, p-value: 1.452e-09
> 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,] 0.2666390 5.332781e-01 7.333610e-01
[2,] 0.2029257 4.058514e-01 7.970743e-01
[3,] 0.1155181 2.310362e-01 8.844819e-01
[4,] 0.2427806 4.855612e-01 7.572194e-01
[5,] 0.2707448 5.414896e-01 7.292552e-01
[6,] 0.4086908 8.173817e-01 5.913092e-01
[7,] 0.6244632 7.510736e-01 3.755368e-01
[8,] 0.5688681 8.622639e-01 4.311319e-01
[9,] 0.5295369 9.409261e-01 4.704631e-01
[10,] 0.4962587 9.925173e-01 5.037413e-01
[11,] 0.5630078 8.739844e-01 4.369922e-01
[12,] 0.7009834 5.980332e-01 2.990166e-01
[13,] 0.6918721 6.162557e-01 3.081279e-01
[14,] 0.6323239 7.353523e-01 3.676761e-01
[15,] 0.8900888 2.198224e-01 1.099112e-01
[16,] 0.8731103 2.537794e-01 1.268897e-01
[17,] 0.8480686 3.038628e-01 1.519314e-01
[18,] 0.8049757 3.900486e-01 1.950243e-01
[19,] 0.8149617 3.700767e-01 1.850383e-01
[20,] 0.8865311 2.269378e-01 1.134689e-01
[21,] 0.8514565 2.970870e-01 1.485435e-01
[22,] 0.9603772 7.924552e-02 3.962276e-02
[23,] 0.9474443 1.051113e-01 5.255567e-02
[24,] 0.9282006 1.435988e-01 7.179941e-02
[25,] 0.9296365 1.407271e-01 7.036355e-02
[26,] 0.9227418 1.545165e-01 7.725824e-02
[27,] 0.9053959 1.892082e-01 9.460408e-02
[28,] 0.8991455 2.017089e-01 1.008545e-01
[29,] 0.9190023 1.619955e-01 8.099774e-02
[30,] 0.9335795 1.328410e-01 6.642050e-02
[31,] 0.9691065 6.178708e-02 3.089354e-02
[32,] 0.9590930 8.181409e-02 4.090704e-02
[33,] 0.9443422 1.113155e-01 5.565777e-02
[34,] 0.9315363 1.369273e-01 6.846365e-02
[35,] 0.9099192 1.801617e-01 9.008083e-02
[36,] 0.9170038 1.659924e-01 8.299620e-02
[37,] 0.9294613 1.410773e-01 7.053867e-02
[38,] 0.9633915 7.321697e-02 3.660849e-02
[39,] 0.9517567 9.648666e-02 4.824333e-02
[40,] 0.9455476 1.089049e-01 5.445243e-02
[41,] 0.9475675 1.048650e-01 5.243248e-02
[42,] 0.9304848 1.390304e-01 6.951520e-02
[43,] 0.9173840 1.652320e-01 8.261600e-02
[44,] 0.9318287 1.363426e-01 6.817128e-02
[45,] 0.9237137 1.525726e-01 7.628630e-02
[46,] 0.9337310 1.325379e-01 6.626897e-02
[47,] 0.9357847 1.284307e-01 6.421533e-02
[48,] 0.9269285 1.461431e-01 7.307154e-02
[49,] 0.9822505 3.549893e-02 1.774946e-02
[50,] 0.9880024 2.399522e-02 1.199761e-02
[51,] 0.9904946 1.901079e-02 9.505397e-03
[52,] 0.9991037 1.792663e-03 8.963314e-04
[53,] 0.9985100 2.979915e-03 1.489957e-03
[54,] 0.9999922 1.552335e-05 7.761675e-06
[55,] 0.9999850 3.004593e-05 1.502297e-05
[56,] 0.9999942 1.150564e-05 5.752818e-06
[57,] 1.0000000 5.143923e-12 2.571962e-12
[58,] 1.0000000 8.575941e-12 4.287971e-12
[59,] 1.0000000 9.433417e-16 4.716708e-16
[60,] 1.0000000 2.543789e-15 1.271894e-15
[61,] 1.0000000 1.168370e-14 5.841848e-15
[62,] 1.0000000 0.000000e+00 0.000000e+00
[63,] 1.0000000 0.000000e+00 0.000000e+00
[64,] 1.0000000 0.000000e+00 0.000000e+00
[65,] 1.0000000 0.000000e+00 0.000000e+00
[66,] 1.0000000 0.000000e+00 0.000000e+00
[67,] 1.0000000 0.000000e+00 0.000000e+00
[68,] 1.0000000 0.000000e+00 0.000000e+00
[69,] 1.0000000 0.000000e+00 0.000000e+00
[70,] 1.0000000 0.000000e+00 0.000000e+00
[71,] 1.0000000 0.000000e+00 0.000000e+00
[72,] 1.0000000 0.000000e+00 0.000000e+00
[73,] 1.0000000 1.242982e-314 6.214912e-315
[74,] 1.0000000 1.071022e-307 5.355108e-308
[75,] 1.0000000 6.735638e-285 3.367819e-285
[76,] 1.0000000 3.838516e-266 1.919258e-266
[77,] 1.0000000 2.999315e-256 1.499658e-256
[78,] 1.0000000 9.820656e-239 4.910328e-239
[79,] 1.0000000 7.997380e-218 3.998690e-218
[80,] 1.0000000 5.291973e-211 2.645987e-211
[81,] 1.0000000 1.072494e-183 5.362468e-184
[82,] 1.0000000 3.366023e-169 1.683011e-169
[83,] 1.0000000 4.730113e-157 2.365057e-157
[84,] 1.0000000 2.771314e-143 1.385657e-143
[85,] 1.0000000 1.740679e-128 8.703397e-129
[86,] 1.0000000 1.587628e-108 7.938141e-109
[87,] 1.0000000 2.507618e-94 1.253809e-94
[88,] 1.0000000 3.267601e-81 1.633800e-81
[89,] 1.0000000 3.026927e-64 1.513463e-64
[90,] 1.0000000 3.232931e-51 1.616466e-51
> postscript(file="/var/fisher/rcomp/tmp/1cvgw1354876438.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/24nkl1354876438.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3u2or1354876438.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4m5f31354876438.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5us531354876438.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 105
Frequency = 1
1 2 3 4 5 6
0.80472677 1.43459623 -0.18017888 -0.17621604 -0.42134499 0.57865501
7 8 9 10 11 12
0.57865501 -0.05030942 0.31982112 1.27712886 1.19391451 -1.05030942
13 14 15 16 17 18
-1.36524415 1.80563180 -0.17621604 0.57865501 1.27712886 -0.92134499
19 20 21 22 23 24
0.94969058 -1.34709199 0.26372028 -0.72287114 -0.49115077 -0.54725161
25 26 27 28 29 30
0.10625292 -1.40319283 -0.56540377 -0.17621604 1.66631660 -0.42134499
31 32 33 34 35 36
-0.04725161 0.80472677 -0.53215726 -0.33368340 -0.53215726 -0.56540377
37 38 39 40 41 42
0.80472677 -1.20777679 -0.05030942 -0.05121445 -0.42134499 -0.05121445
43 44 45 46 47 48
-0.56540377 -0.92134499 -1.03215726 0.31982112 -0.19436820 0.43459623
49 50 51 52 53 54
-0.05121445 -0.30608549 0.94969058 0.42118764 0.94969058 -0.80608549
55 56 57 58 59 60
-0.19436820 -1.36524415 -0.55030942 -0.82423766 1.19391451 -0.17621604
61 62 63 64 65 66
-1.05030942 -0.05121445 -0.42134499 -1.40319283 0.65290801 0.80563180
67 68 69 70 71 72
0.94969058 -0.17621604 -1.05121445 -0.05121445 -0.50930293 -0.19436820
73 74 75 76 77 78
-0.19436820 -0.82423766 0.57865501 0.59680717 0.81037538 0.26372028
79 80 81 82 83 84
0.59680717 0.26372028 0.96784274 0.33797329 0.33797329 0.59680717
85 86 87 88 89 90
-0.17621604 0.33797329 -0.54725161 -0.05121445 0.65290801 -0.49115077
91 92 93 94 95 96
-0.56540377 0.57865501 -1.19527323 0.94969058 0.57865501 0.57865501
97 98 99 100 101 102
-0.05121445 -0.05121445 -0.17621604 -0.17621604 -0.88033850 0.81037538
103 104 105
-0.17621604 0.65290801 -0.35183557
> postscript(file="/var/fisher/rcomp/tmp/602qe1354876438.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 = 105
Frequency = 1
lag(myerror, k = 1) myerror
0 0.80472677 NA
1 1.43459623 0.80472677
2 -0.18017888 1.43459623
3 -0.17621604 -0.18017888
4 -0.42134499 -0.17621604
5 0.57865501 -0.42134499
6 0.57865501 0.57865501
7 -0.05030942 0.57865501
8 0.31982112 -0.05030942
9 1.27712886 0.31982112
10 1.19391451 1.27712886
11 -1.05030942 1.19391451
12 -1.36524415 -1.05030942
13 1.80563180 -1.36524415
14 -0.17621604 1.80563180
15 0.57865501 -0.17621604
16 1.27712886 0.57865501
17 -0.92134499 1.27712886
18 0.94969058 -0.92134499
19 -1.34709199 0.94969058
20 0.26372028 -1.34709199
21 -0.72287114 0.26372028
22 -0.49115077 -0.72287114
23 -0.54725161 -0.49115077
24 0.10625292 -0.54725161
25 -1.40319283 0.10625292
26 -0.56540377 -1.40319283
27 -0.17621604 -0.56540377
28 1.66631660 -0.17621604
29 -0.42134499 1.66631660
30 -0.04725161 -0.42134499
31 0.80472677 -0.04725161
32 -0.53215726 0.80472677
33 -0.33368340 -0.53215726
34 -0.53215726 -0.33368340
35 -0.56540377 -0.53215726
36 0.80472677 -0.56540377
37 -1.20777679 0.80472677
38 -0.05030942 -1.20777679
39 -0.05121445 -0.05030942
40 -0.42134499 -0.05121445
41 -0.05121445 -0.42134499
42 -0.56540377 -0.05121445
43 -0.92134499 -0.56540377
44 -1.03215726 -0.92134499
45 0.31982112 -1.03215726
46 -0.19436820 0.31982112
47 0.43459623 -0.19436820
48 -0.05121445 0.43459623
49 -0.30608549 -0.05121445
50 0.94969058 -0.30608549
51 0.42118764 0.94969058
52 0.94969058 0.42118764
53 -0.80608549 0.94969058
54 -0.19436820 -0.80608549
55 -1.36524415 -0.19436820
56 -0.55030942 -1.36524415
57 -0.82423766 -0.55030942
58 1.19391451 -0.82423766
59 -0.17621604 1.19391451
60 -1.05030942 -0.17621604
61 -0.05121445 -1.05030942
62 -0.42134499 -0.05121445
63 -1.40319283 -0.42134499
64 0.65290801 -1.40319283
65 0.80563180 0.65290801
66 0.94969058 0.80563180
67 -0.17621604 0.94969058
68 -1.05121445 -0.17621604
69 -0.05121445 -1.05121445
70 -0.50930293 -0.05121445
71 -0.19436820 -0.50930293
72 -0.19436820 -0.19436820
73 -0.82423766 -0.19436820
74 0.57865501 -0.82423766
75 0.59680717 0.57865501
76 0.81037538 0.59680717
77 0.26372028 0.81037538
78 0.59680717 0.26372028
79 0.26372028 0.59680717
80 0.96784274 0.26372028
81 0.33797329 0.96784274
82 0.33797329 0.33797329
83 0.59680717 0.33797329
84 -0.17621604 0.59680717
85 0.33797329 -0.17621604
86 -0.54725161 0.33797329
87 -0.05121445 -0.54725161
88 0.65290801 -0.05121445
89 -0.49115077 0.65290801
90 -0.56540377 -0.49115077
91 0.57865501 -0.56540377
92 -1.19527323 0.57865501
93 0.94969058 -1.19527323
94 0.57865501 0.94969058
95 0.57865501 0.57865501
96 -0.05121445 0.57865501
97 -0.05121445 -0.05121445
98 -0.17621604 -0.05121445
99 -0.17621604 -0.17621604
100 -0.88033850 -0.17621604
101 0.81037538 -0.88033850
102 -0.17621604 0.81037538
103 0.65290801 -0.17621604
104 -0.35183557 0.65290801
105 NA -0.35183557
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.43459623 0.80472677
[2,] -0.18017888 1.43459623
[3,] -0.17621604 -0.18017888
[4,] -0.42134499 -0.17621604
[5,] 0.57865501 -0.42134499
[6,] 0.57865501 0.57865501
[7,] -0.05030942 0.57865501
[8,] 0.31982112 -0.05030942
[9,] 1.27712886 0.31982112
[10,] 1.19391451 1.27712886
[11,] -1.05030942 1.19391451
[12,] -1.36524415 -1.05030942
[13,] 1.80563180 -1.36524415
[14,] -0.17621604 1.80563180
[15,] 0.57865501 -0.17621604
[16,] 1.27712886 0.57865501
[17,] -0.92134499 1.27712886
[18,] 0.94969058 -0.92134499
[19,] -1.34709199 0.94969058
[20,] 0.26372028 -1.34709199
[21,] -0.72287114 0.26372028
[22,] -0.49115077 -0.72287114
[23,] -0.54725161 -0.49115077
[24,] 0.10625292 -0.54725161
[25,] -1.40319283 0.10625292
[26,] -0.56540377 -1.40319283
[27,] -0.17621604 -0.56540377
[28,] 1.66631660 -0.17621604
[29,] -0.42134499 1.66631660
[30,] -0.04725161 -0.42134499
[31,] 0.80472677 -0.04725161
[32,] -0.53215726 0.80472677
[33,] -0.33368340 -0.53215726
[34,] -0.53215726 -0.33368340
[35,] -0.56540377 -0.53215726
[36,] 0.80472677 -0.56540377
[37,] -1.20777679 0.80472677
[38,] -0.05030942 -1.20777679
[39,] -0.05121445 -0.05030942
[40,] -0.42134499 -0.05121445
[41,] -0.05121445 -0.42134499
[42,] -0.56540377 -0.05121445
[43,] -0.92134499 -0.56540377
[44,] -1.03215726 -0.92134499
[45,] 0.31982112 -1.03215726
[46,] -0.19436820 0.31982112
[47,] 0.43459623 -0.19436820
[48,] -0.05121445 0.43459623
[49,] -0.30608549 -0.05121445
[50,] 0.94969058 -0.30608549
[51,] 0.42118764 0.94969058
[52,] 0.94969058 0.42118764
[53,] -0.80608549 0.94969058
[54,] -0.19436820 -0.80608549
[55,] -1.36524415 -0.19436820
[56,] -0.55030942 -1.36524415
[57,] -0.82423766 -0.55030942
[58,] 1.19391451 -0.82423766
[59,] -0.17621604 1.19391451
[60,] -1.05030942 -0.17621604
[61,] -0.05121445 -1.05030942
[62,] -0.42134499 -0.05121445
[63,] -1.40319283 -0.42134499
[64,] 0.65290801 -1.40319283
[65,] 0.80563180 0.65290801
[66,] 0.94969058 0.80563180
[67,] -0.17621604 0.94969058
[68,] -1.05121445 -0.17621604
[69,] -0.05121445 -1.05121445
[70,] -0.50930293 -0.05121445
[71,] -0.19436820 -0.50930293
[72,] -0.19436820 -0.19436820
[73,] -0.82423766 -0.19436820
[74,] 0.57865501 -0.82423766
[75,] 0.59680717 0.57865501
[76,] 0.81037538 0.59680717
[77,] 0.26372028 0.81037538
[78,] 0.59680717 0.26372028
[79,] 0.26372028 0.59680717
[80,] 0.96784274 0.26372028
[81,] 0.33797329 0.96784274
[82,] 0.33797329 0.33797329
[83,] 0.59680717 0.33797329
[84,] -0.17621604 0.59680717
[85,] 0.33797329 -0.17621604
[86,] -0.54725161 0.33797329
[87,] -0.05121445 -0.54725161
[88,] 0.65290801 -0.05121445
[89,] -0.49115077 0.65290801
[90,] -0.56540377 -0.49115077
[91,] 0.57865501 -0.56540377
[92,] -1.19527323 0.57865501
[93,] 0.94969058 -1.19527323
[94,] 0.57865501 0.94969058
[95,] 0.57865501 0.57865501
[96,] -0.05121445 0.57865501
[97,] -0.05121445 -0.05121445
[98,] -0.17621604 -0.05121445
[99,] -0.17621604 -0.17621604
[100,] -0.88033850 -0.17621604
[101,] 0.81037538 -0.88033850
[102,] -0.17621604 0.81037538
[103,] 0.65290801 -0.17621604
[104,] -0.35183557 0.65290801
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.43459623 0.80472677
2 -0.18017888 1.43459623
3 -0.17621604 -0.18017888
4 -0.42134499 -0.17621604
5 0.57865501 -0.42134499
6 0.57865501 0.57865501
7 -0.05030942 0.57865501
8 0.31982112 -0.05030942
9 1.27712886 0.31982112
10 1.19391451 1.27712886
11 -1.05030942 1.19391451
12 -1.36524415 -1.05030942
13 1.80563180 -1.36524415
14 -0.17621604 1.80563180
15 0.57865501 -0.17621604
16 1.27712886 0.57865501
17 -0.92134499 1.27712886
18 0.94969058 -0.92134499
19 -1.34709199 0.94969058
20 0.26372028 -1.34709199
21 -0.72287114 0.26372028
22 -0.49115077 -0.72287114
23 -0.54725161 -0.49115077
24 0.10625292 -0.54725161
25 -1.40319283 0.10625292
26 -0.56540377 -1.40319283
27 -0.17621604 -0.56540377
28 1.66631660 -0.17621604
29 -0.42134499 1.66631660
30 -0.04725161 -0.42134499
31 0.80472677 -0.04725161
32 -0.53215726 0.80472677
33 -0.33368340 -0.53215726
34 -0.53215726 -0.33368340
35 -0.56540377 -0.53215726
36 0.80472677 -0.56540377
37 -1.20777679 0.80472677
38 -0.05030942 -1.20777679
39 -0.05121445 -0.05030942
40 -0.42134499 -0.05121445
41 -0.05121445 -0.42134499
42 -0.56540377 -0.05121445
43 -0.92134499 -0.56540377
44 -1.03215726 -0.92134499
45 0.31982112 -1.03215726
46 -0.19436820 0.31982112
47 0.43459623 -0.19436820
48 -0.05121445 0.43459623
49 -0.30608549 -0.05121445
50 0.94969058 -0.30608549
51 0.42118764 0.94969058
52 0.94969058 0.42118764
53 -0.80608549 0.94969058
54 -0.19436820 -0.80608549
55 -1.36524415 -0.19436820
56 -0.55030942 -1.36524415
57 -0.82423766 -0.55030942
58 1.19391451 -0.82423766
59 -0.17621604 1.19391451
60 -1.05030942 -0.17621604
61 -0.05121445 -1.05030942
62 -0.42134499 -0.05121445
63 -1.40319283 -0.42134499
64 0.65290801 -1.40319283
65 0.80563180 0.65290801
66 0.94969058 0.80563180
67 -0.17621604 0.94969058
68 -1.05121445 -0.17621604
69 -0.05121445 -1.05121445
70 -0.50930293 -0.05121445
71 -0.19436820 -0.50930293
72 -0.19436820 -0.19436820
73 -0.82423766 -0.19436820
74 0.57865501 -0.82423766
75 0.59680717 0.57865501
76 0.81037538 0.59680717
77 0.26372028 0.81037538
78 0.59680717 0.26372028
79 0.26372028 0.59680717
80 0.96784274 0.26372028
81 0.33797329 0.96784274
82 0.33797329 0.33797329
83 0.59680717 0.33797329
84 -0.17621604 0.59680717
85 0.33797329 -0.17621604
86 -0.54725161 0.33797329
87 -0.05121445 -0.54725161
88 0.65290801 -0.05121445
89 -0.49115077 0.65290801
90 -0.56540377 -0.49115077
91 0.57865501 -0.56540377
92 -1.19527323 0.57865501
93 0.94969058 -1.19527323
94 0.57865501 0.94969058
95 0.57865501 0.57865501
96 -0.05121445 0.57865501
97 -0.05121445 -0.05121445
98 -0.17621604 -0.05121445
99 -0.17621604 -0.17621604
100 -0.88033850 -0.17621604
101 0.81037538 -0.88033850
102 -0.17621604 0.81037538
103 0.65290801 -0.17621604
104 -0.35183557 0.65290801
> 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/7i1qk1354876438.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/8kai41354876438.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/9w3x91354876438.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/10zrch1354876438.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11yqfi1354876438.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12m1da1354876438.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1360241354876438.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14b8jg1354876438.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/15zqt51354876438.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/16a1ya1354876438.tab")
+ }
>
> try(system("convert tmp/1cvgw1354876438.ps tmp/1cvgw1354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/24nkl1354876438.ps tmp/24nkl1354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u2or1354876438.ps tmp/3u2or1354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/4m5f31354876438.ps tmp/4m5f31354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/5us531354876438.ps tmp/5us531354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/602qe1354876438.ps tmp/602qe1354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/7i1qk1354876438.ps tmp/7i1qk1354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kai41354876438.ps tmp/8kai41354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w3x91354876438.ps tmp/9w3x91354876438.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zrch1354876438.ps tmp/10zrch1354876438.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.863 1.563 8.433