R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(1
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+ ,0.462803
+ ,0.664357)
+ ,dim=c(5
+ ,195)
+ ,dimnames=list(c('status'
+ ,'HNR'
+ ,'NHR'
+ ,'RPDE'
+ ,'DFA')
+ ,1:195))
> y <- array(NA,dim=c(5,195),dimnames=list(c('status','HNR','NHR','RPDE','DFA'),1:195))
> 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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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
status HNR NHR RPDE DFA
1 1 21.033 0.02211 0.414783 0.815285
2 1 19.085 0.01929 0.458359 0.819521
3 1 20.651 0.01309 0.429895 0.825288
4 1 20.644 0.01353 0.434969 0.819235
5 1 19.649 0.01767 0.417356 0.823484
6 1 21.378 0.01222 0.415564 0.825069
7 1 24.886 0.00607 0.596040 0.764112
8 1 26.892 0.00344 0.637420 0.763262
9 1 21.812 0.01070 0.615551 0.773587
10 1 21.862 0.01022 0.547037 0.798463
11 1 21.118 0.01166 0.611137 0.776156
12 1 21.414 0.01141 0.583390 0.792520
13 1 25.703 0.00581 0.460600 0.646846
14 1 24.889 0.01041 0.430166 0.665833
15 1 24.922 0.00609 0.474791 0.654027
16 1 25.175 0.00839 0.565924 0.658245
17 1 22.333 0.01859 0.567380 0.644692
18 1 20.376 0.02919 0.631099 0.605417
19 1 17.280 0.03160 0.665318 0.719467
20 1 17.153 0.03365 0.649554 0.686080
21 1 17.536 0.03871 0.660125 0.704087
22 1 19.493 0.01849 0.629017 0.698951
23 1 22.468 0.01280 0.619060 0.679834
24 1 20.422 0.01840 0.537264 0.686894
25 1 23.831 0.01778 0.397937 0.732479
26 1 22.066 0.02887 0.522746 0.737948
27 1 25.908 0.01095 0.418622 0.720916
28 1 25.119 0.01328 0.358773 0.726652
29 1 25.970 0.00677 0.470478 0.676258
30 1 25.678 0.01170 0.427785 0.723797
31 0 26.775 0.00339 0.422229 0.741367
32 0 30.940 0.00167 0.432439 0.742055
33 0 30.775 0.00119 0.465946 0.738703
34 0 32.684 0.00072 0.368535 0.742133
35 0 33.047 0.00065 0.340068 0.741899
36 0 31.732 0.00135 0.344252 0.742737
37 1 23.216 0.00586 0.360148 0.778834
38 1 24.951 0.00340 0.341435 0.783626
39 1 26.738 0.00231 0.403884 0.766209
40 1 26.310 0.00265 0.396793 0.758324
41 1 26.822 0.00231 0.326480 0.765623
42 1 26.453 0.00257 0.306443 0.759203
43 0 22.736 0.00740 0.305062 0.654172
44 0 23.145 0.00675 0.457702 0.634267
45 0 25.368 0.00454 0.438296 0.635285
46 0 25.032 0.00476 0.431285 0.638928
47 0 24.602 0.00476 0.467489 0.631653
48 0 26.805 0.00432 0.610367 0.635204
49 0 23.162 0.00839 0.579597 0.733659
50 0 24.971 0.00462 0.538688 0.754073
51 0 25.135 0.00479 0.553134 0.775933
52 0 25.030 0.00474 0.507504 0.760361
53 0 24.692 0.00481 0.459766 0.766204
54 0 25.429 0.00484 0.420383 0.785714
55 1 21.028 0.01036 0.536009 0.819032
56 1 20.767 0.01180 0.558586 0.811843
57 1 21.422 0.00969 0.541781 0.821364
58 1 22.817 0.00681 0.530529 0.817756
59 1 22.603 0.00786 0.540049 0.813432
60 1 21.660 0.01143 0.547975 0.817396
61 0 25.554 0.00871 0.341788 0.678874
62 0 26.138 0.00301 0.447979 0.686264
63 0 25.856 0.00340 0.364867 0.694399
64 0 25.964 0.00351 0.256570 0.683296
65 0 26.415 0.00300 0.276850 0.673636
66 0 24.547 0.00420 0.305429 0.681811
67 1 19.560 0.02183 0.460139 0.720908
68 1 19.979 0.02659 0.498133 0.729067
69 1 20.338 0.04882 0.513237 0.731444
70 1 21.718 0.02431 0.487407 0.727313
71 1 20.264 0.02599 0.489345 0.730387
72 1 18.570 0.03361 0.543299 0.733232
73 1 25.742 0.00442 0.495954 0.762959
74 1 24.178 0.00623 0.509127 0.789532
75 1 25.438 0.00479 0.437031 0.815908
76 1 25.197 0.00472 0.463514 0.807217
77 1 23.370 0.00905 0.489538 0.789977
78 1 25.820 0.00420 0.429484 0.816340
79 1 21.875 0.01062 0.644954 0.779612
80 1 19.200 0.02220 0.594387 0.790117
81 1 19.055 0.01823 0.544805 0.770466
82 1 19.659 0.01825 0.576084 0.778747
83 1 20.536 0.01237 0.554610 0.787896
84 1 22.244 0.00882 0.576644 0.772416
85 1 13.893 0.05470 0.556494 0.729586
86 1 16.176 0.02782 0.583574 0.727747
87 1 15.924 0.03151 0.598714 0.712199
88 1 13.922 0.04824 0.602874 0.740837
89 1 14.739 0.04214 0.599371 0.743937
90 1 11.866 0.07223 0.590951 0.745526
91 1 11.744 0.08725 0.653410 0.733165
92 1 19.664 0.01658 0.501037 0.714360
93 1 18.780 0.01914 0.454444 0.734504
94 1 20.969 0.01211 0.447456 0.697790
95 1 22.219 0.00850 0.502380 0.712170
96 1 21.693 0.01018 0.447285 0.705658
97 1 22.663 0.00852 0.366329 0.693429
98 1 15.338 0.08151 0.629574 0.714485
99 1 15.433 0.10323 0.571010 0.690892
100 1 12.435 0.16744 0.638545 0.674953
101 1 8.867 0.31482 0.671299 0.656846
102 1 15.060 0.11843 0.639808 0.643327
103 1 10.489 0.25930 0.596362 0.641418
104 1 26.759 0.00495 0.296888 0.722356
105 1 28.409 0.00243 0.263654 0.691483
106 1 27.421 0.00578 0.365488 0.719974
107 1 29.746 0.00233 0.334171 0.677930
108 1 26.833 0.00659 0.393563 0.700246
109 1 29.928 0.00238 0.311369 0.676066
110 1 21.934 0.00947 0.497554 0.740539
111 1 23.239 0.00704 0.436084 0.727863
112 1 22.407 0.00830 0.338097 0.712466
113 1 21.305 0.01316 0.498877 0.722085
114 1 23.671 0.00620 0.441097 0.722254
115 1 21.864 0.01048 0.331508 0.715121
116 1 23.693 0.06051 0.407701 0.662668
117 1 26.356 0.01554 0.450798 0.653823
118 1 25.690 0.01802 0.486738 0.676023
119 1 25.020 0.00856 0.470422 0.655239
120 1 24.581 0.00681 0.462516 0.582710
121 1 24.743 0.02350 0.487756 0.684130
122 1 27.166 0.01161 0.400088 0.656182
123 1 18.305 0.01968 0.538016 0.741480
124 1 18.784 0.01813 0.589956 0.732903
125 1 19.196 0.02020 0.618663 0.728421
126 1 18.857 0.01874 0.637518 0.735546
127 1 18.178 0.01794 0.623209 0.738245
128 1 18.330 0.01796 0.585169 0.736964
129 1 26.842 0.01724 0.457541 0.699787
130 1 26.369 0.00487 0.491345 0.718839
131 1 23.949 0.01610 0.467160 0.724045
132 1 26.017 0.01015 0.468621 0.735136
133 1 23.389 0.00903 0.470972 0.721308
134 1 25.619 0.00504 0.482296 0.723096
135 1 17.060 0.03031 0.637814 0.744064
136 1 17.707 0.02529 0.653427 0.706687
137 1 19.013 0.02278 0.647900 0.708144
138 1 16.747 0.03690 0.625362 0.708617
139 1 17.366 0.02629 0.640945 0.701404
140 1 18.801 0.01827 0.624811 0.696049
141 1 18.540 0.02485 0.677131 0.685057
142 1 15.648 0.04238 0.606344 0.665945
143 1 18.702 0.01728 0.606273 0.661735
144 1 18.687 0.02010 0.536102 0.632631
145 1 20.680 0.01049 0.497480 0.630409
146 1 20.366 0.01493 0.566849 0.574282
147 1 12.359 0.07530 0.561610 0.793509
148 1 14.367 0.06057 0.478024 0.768974
149 1 12.298 0.08069 0.552870 0.764036
150 1 14.989 0.07889 0.427627 0.775708
151 1 12.529 0.10952 0.507826 0.762726
152 1 8.441 0.21713 0.625866 0.768320
153 1 9.449 0.16265 0.584164 0.754449
154 1 21.520 0.04179 0.566867 0.670475
155 1 21.824 0.04611 0.651680 0.659333
156 1 22.431 0.02631 0.628300 0.652025
157 1 22.953 0.03191 0.611679 0.623731
158 1 19.075 0.10748 0.630547 0.646786
159 1 21.534 0.03828 0.635015 0.627337
160 1 19.651 0.02663 0.654945 0.675865
161 1 20.437 0.02073 0.653139 0.694571
162 1 19.388 0.02810 0.577802 0.684373
163 1 18.954 0.02707 0.685151 0.719576
164 1 21.219 0.01435 0.557045 0.673086
165 1 18.447 0.03882 0.671378 0.674562
166 0 24.078 0.00620 0.469928 0.628232
167 0 24.679 0.00533 0.384868 0.626710
168 0 21.083 0.00910 0.440988 0.628058
169 0 19.269 0.01337 0.372222 0.725216
170 0 21.020 0.00965 0.371837 0.646167
171 0 21.528 0.01049 0.522812 0.646818
172 0 26.436 0.00435 0.413295 0.756700
173 0 26.550 0.00430 0.369090 0.776158
174 0 26.547 0.00478 0.380253 0.766700
175 0 25.445 0.00590 0.387482 0.756482
176 0 26.005 0.00401 0.405991 0.761255
177 0 26.143 0.00415 0.361232 0.763242
178 1 24.151 0.00570 0.396610 0.745957
179 1 24.412 0.00488 0.402591 0.762508
180 1 23.683 0.00540 0.398499 0.778349
181 1 23.133 0.00611 0.352396 0.759320
182 1 22.866 0.00639 0.408598 0.768845
183 1 23.008 0.00595 0.329577 0.757180
184 0 23.079 0.00955 0.603515 0.669565
185 0 22.085 0.01179 0.663842 0.656516
186 0 24.199 0.00737 0.598515 0.654331
187 0 23.958 0.01397 0.566424 0.667654
188 0 25.023 0.00680 0.528485 0.663884
189 0 24.775 0.00703 0.555303 0.659132
190 0 19.368 0.04441 0.508479 0.683761
191 0 19.517 0.02764 0.448439 0.657899
192 0 19.147 0.01810 0.431674 0.683244
193 0 17.883 0.10715 0.407567 0.655683
194 0 19.020 0.07223 0.451221 0.643956
195 0 21.209 0.04398 0.462803 0.664357
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) HNR NHR RPDE DFA
-0.3627 -0.0279 -0.5409 0.7608 1.8958
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.82522 -0.20019 0.06908 0.29273 0.68046
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.36266 0.58423 -0.621 0.535512
HNR -0.02790 0.01075 -2.597 0.010152 *
NHR -0.54089 1.01519 -0.533 0.594797
RPDE 0.76076 0.34188 2.225 0.027244 *
DFA 1.89576 0.52199 3.632 0.000362 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3887 on 190 degrees of freedom
Multiple R-squared: 0.2066, Adjusted R-squared: 0.1899
F-statistic: 12.37 on 4 and 190 DF, p-value: 5.86e-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,] 4.398878e-48 8.797756e-48 1.000000e+00
[2,] 5.611401e-65 1.122280e-64 1.000000e+00
[3,] 8.319897e-81 1.663979e-80 1.000000e+00
[4,] 1.209017e-94 2.418035e-94 1.000000e+00
[5,] 2.835471e-107 5.670942e-107 1.000000e+00
[6,] 9.174407e-143 1.834881e-142 1.000000e+00
[7,] 1.887641e-140 3.775281e-140 1.000000e+00
[8,] 1.123187e-154 2.246373e-154 1.000000e+00
[9,] 0.000000e+00 0.000000e+00 1.000000e+00
[10,] 4.491955e-200 8.983909e-200 1.000000e+00
[11,] 2.640573e-198 5.281147e-198 1.000000e+00
[12,] 2.802517e-214 5.605035e-214 1.000000e+00
[13,] 9.749939e-244 1.949988e-243 1.000000e+00
[14,] 9.484011e-280 1.896802e-279 1.000000e+00
[15,] 1.769068e-257 3.538136e-257 1.000000e+00
[16,] 3.612535e-277 7.225071e-277 1.000000e+00
[17,] 5.119387e-288 1.023877e-287 1.000000e+00
[18,] 7.303000e-315 1.460600e-314 1.000000e+00
[19,] 0.000000e+00 0.000000e+00 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 9.514241e-06 1.902848e-05 9.999905e-01
[25,] 2.169859e-04 4.339718e-04 9.997830e-01
[26,] 5.043145e-04 1.008629e-03 9.994957e-01
[27,] 3.978789e-04 7.957579e-04 9.996021e-01
[28,] 2.645542e-04 5.291084e-04 9.997354e-01
[29,] 2.203698e-04 4.407395e-04 9.997796e-01
[30,] 1.231933e-04 2.463867e-04 9.998768e-01
[31,] 7.940360e-05 1.588072e-04 9.999206e-01
[32,] 8.370991e-05 1.674198e-04 9.999163e-01
[33,] 6.772794e-05 1.354559e-04 9.999323e-01
[34,] 5.769463e-05 1.153893e-04 9.999423e-01
[35,] 4.058593e-05 8.117186e-05 9.999594e-01
[36,] 8.861400e-03 1.772280e-02 9.911386e-01
[37,] 4.314182e-02 8.628364e-02 9.568582e-01
[38,] 6.896358e-02 1.379272e-01 9.310364e-01
[39,] 9.382804e-02 1.876561e-01 9.061720e-01
[40,] 1.181899e-01 2.363798e-01 8.818101e-01
[41,] 1.373005e-01 2.746010e-01 8.626995e-01
[42,] 2.632954e-01 5.265908e-01 7.367046e-01
[43,] 3.738332e-01 7.476663e-01 6.261668e-01
[44,] 4.998885e-01 9.997769e-01 5.001115e-01
[45,] 6.015406e-01 7.969187e-01 3.984594e-01
[46,] 6.975827e-01 6.048346e-01 3.024173e-01
[47,] 7.812080e-01 4.375840e-01 2.187920e-01
[48,] 7.474820e-01 5.050360e-01 2.525180e-01
[49,] 7.107377e-01 5.785245e-01 2.892623e-01
[50,] 6.731651e-01 6.536697e-01 3.268349e-01
[51,] 6.398711e-01 7.202577e-01 3.601289e-01
[52,] 6.033855e-01 7.932290e-01 3.966145e-01
[53,] 5.607325e-01 8.785350e-01 4.392675e-01
[54,] 5.945058e-01 8.109883e-01 4.054942e-01
[55,] 6.200465e-01 7.599069e-01 3.799535e-01
[56,] 6.407061e-01 7.185878e-01 3.592939e-01
[57,] 6.393134e-01 7.213732e-01 3.606866e-01
[58,] 6.312722e-01 7.374555e-01 3.687278e-01
[59,] 6.401901e-01 7.196198e-01 3.598099e-01
[60,] 6.033960e-01 7.932081e-01 3.966040e-01
[61,] 5.642036e-01 8.715929e-01 4.357964e-01
[62,] 5.512144e-01 8.975711e-01 4.487856e-01
[63,] 5.130064e-01 9.739872e-01 4.869936e-01
[64,] 4.727549e-01 9.455097e-01 5.272451e-01
[65,] 4.375887e-01 8.751775e-01 5.624113e-01
[66,] 4.303793e-01 8.607585e-01 5.696207e-01
[67,] 4.010252e-01 8.020503e-01 5.989748e-01
[68,] 3.750122e-01 7.500244e-01 6.249878e-01
[69,] 3.484272e-01 6.968544e-01 6.515728e-01
[70,] 3.154263e-01 6.308525e-01 6.845737e-01
[71,] 2.915568e-01 5.831135e-01 7.084432e-01
[72,] 2.564239e-01 5.128478e-01 7.435761e-01
[73,] 2.283459e-01 4.566918e-01 7.716541e-01
[74,] 1.980431e-01 3.960861e-01 8.019569e-01
[75,] 1.704135e-01 3.408270e-01 8.295865e-01
[76,] 1.447951e-01 2.895901e-01 8.552049e-01
[77,] 1.238384e-01 2.476768e-01 8.761616e-01
[78,] 1.263767e-01 2.527534e-01 8.736233e-01
[79,] 1.077424e-01 2.154848e-01 8.922576e-01
[80,] 9.134101e-02 1.826820e-01 9.086590e-01
[81,] 8.593215e-02 1.718643e-01 9.140679e-01
[82,] 7.598435e-02 1.519687e-01 9.240156e-01
[83,] 7.631365e-02 1.526273e-01 9.236864e-01
[84,] 7.110412e-02 1.422082e-01 9.288959e-01
[85,] 6.117218e-02 1.223444e-01 9.388278e-01
[86,] 5.162411e-02 1.032482e-01 9.483759e-01
[87,] 4.782720e-02 9.565441e-02 9.521728e-01
[88,] 4.260440e-02 8.520880e-02 9.573956e-01
[89,] 3.915672e-02 7.831345e-02 9.608433e-01
[90,] 4.071812e-02 8.143625e-02 9.592819e-01
[91,] 3.246978e-02 6.493957e-02 9.675302e-01
[92,] 2.620955e-02 5.241910e-02 9.737905e-01
[93,] 2.053496e-02 4.106992e-02 9.794650e-01
[94,] 1.597663e-02 3.195326e-02 9.840234e-01
[95,] 1.269561e-02 2.539122e-02 9.873044e-01
[96,] 9.833436e-03 1.966687e-02 9.901666e-01
[97,] 1.231684e-02 2.463369e-02 9.876832e-01
[98,] 1.969249e-02 3.938497e-02 9.803075e-01
[99,] 2.253762e-02 4.507523e-02 9.774624e-01
[100,] 3.475730e-02 6.951460e-02 9.652427e-01
[101,] 3.913258e-02 7.826515e-02 9.608674e-01
[102,] 6.051597e-02 1.210319e-01 9.394840e-01
[103,] 5.160930e-02 1.032186e-01 9.483907e-01
[104,] 4.748224e-02 9.496449e-02 9.525178e-01
[105,] 4.930875e-02 9.861751e-02 9.506912e-01
[106,] 4.261368e-02 8.522736e-02 9.573863e-01
[107,] 4.028090e-02 8.056181e-02 9.597191e-01
[108,] 4.374646e-02 8.749292e-02 9.562535e-01
[109,] 5.235613e-02 1.047123e-01 9.476439e-01
[110,] 6.559901e-02 1.311980e-01 9.344010e-01
[111,] 7.177531e-02 1.435506e-01 9.282247e-01
[112,] 8.325829e-02 1.665166e-01 9.167417e-01
[113,] 1.262945e-01 2.525891e-01 8.737055e-01
[114,] 1.358796e-01 2.717591e-01 8.641204e-01
[115,] 2.111681e-01 4.223362e-01 7.888319e-01
[116,] 1.824563e-01 3.649126e-01 8.175437e-01
[117,] 1.553581e-01 3.107162e-01 8.446419e-01
[118,] 1.311028e-01 2.622055e-01 8.688972e-01
[119,] 1.102620e-01 2.205239e-01 8.897380e-01
[120,] 9.189604e-02 1.837921e-01 9.081040e-01
[121,] 7.521296e-02 1.504259e-01 9.247870e-01
[122,] 9.690071e-02 1.938014e-01 9.030993e-01
[123,] 1.057054e-01 2.114108e-01 8.942946e-01
[124,] 1.115279e-01 2.230558e-01 8.884721e-01
[125,] 1.267845e-01 2.535690e-01 8.732155e-01
[126,] 1.332056e-01 2.664112e-01 8.667944e-01
[127,] 1.559538e-01 3.119076e-01 8.440462e-01
[128,] 1.341232e-01 2.682465e-01 8.658768e-01
[129,] 1.119860e-01 2.239721e-01 8.880140e-01
[130,] 9.200637e-02 1.840127e-01 9.079936e-01
[131,] 7.488924e-02 1.497785e-01 9.251108e-01
[132,] 6.037106e-02 1.207421e-01 9.396289e-01
[133,] 4.797420e-02 9.594841e-02 9.520258e-01
[134,] 3.768537e-02 7.537074e-02 9.623146e-01
[135,] 2.928890e-02 5.857780e-02 9.707111e-01
[136,] 2.335115e-02 4.670231e-02 9.766488e-01
[137,] 2.305102e-02 4.610203e-02 9.769490e-01
[138,] 3.131933e-02 6.263867e-02 9.686807e-01
[139,] 5.408036e-02 1.081607e-01 9.459196e-01
[140,] 4.898237e-02 9.796473e-02 9.510176e-01
[141,] 3.846053e-02 7.692105e-02 9.615395e-01
[142,] 3.150021e-02 6.300042e-02 9.684998e-01
[143,] 2.501309e-02 5.002617e-02 9.749869e-01
[144,] 1.890665e-02 3.781330e-02 9.810933e-01
[145,] 1.758718e-02 3.517435e-02 9.824128e-01
[146,] 2.076379e-02 4.152759e-02 9.792362e-01
[147,] 2.188893e-02 4.377786e-02 9.781111e-01
[148,] 2.013047e-02 4.026095e-02 9.798695e-01
[149,] 2.391978e-02 4.783955e-02 9.760802e-01
[150,] 5.682661e-02 1.136532e-01 9.431734e-01
[151,] 7.682881e-02 1.536576e-01 9.231712e-01
[152,] 1.845183e-01 3.690366e-01 8.154817e-01
[153,] 1.726772e-01 3.453545e-01 8.273228e-01
[154,] 1.571155e-01 3.142309e-01 8.428845e-01
[155,] 1.640911e-01 3.281823e-01 8.359089e-01
[156,] 1.324048e-01 2.648096e-01 8.675952e-01
[157,] 2.144661e-01 4.289321e-01 7.855339e-01
[158,] 3.109425e-01 6.218850e-01 6.890575e-01
[159,] 3.093638e-01 6.187276e-01 6.906362e-01
[160,] 3.276863e-01 6.553726e-01 6.723137e-01
[161,] 3.165316e-01 6.330631e-01 6.834684e-01
[162,] 5.583784e-01 8.832432e-01 4.416216e-01
[163,] 5.204769e-01 9.590461e-01 4.795231e-01
[164,] 4.884708e-01 9.769417e-01 5.115292e-01
[165,] 4.811610e-01 9.623221e-01 5.188390e-01
[166,] 5.331302e-01 9.337396e-01 4.668698e-01
[167,] 5.926206e-01 8.147587e-01 4.073794e-01
[168,] 7.128590e-01 5.742820e-01 2.871410e-01
[169,] 8.973058e-01 2.053884e-01 1.026942e-01
[170,] 9.999905e-01 1.901827e-05 9.509136e-06
[171,] 9.999888e-01 2.236100e-05 1.118050e-05
[172,] 9.999614e-01 7.728618e-05 3.864309e-05
[173,] 9.999060e-01 1.879845e-04 9.399223e-05
[174,] 9.997153e-01 5.694356e-04 2.847178e-04
[175,] 9.990711e-01 1.857828e-03 9.289141e-04
[176,] 1.000000e+00 0.000000e+00 0.000000e+00
[177,] 1.000000e+00 0.000000e+00 0.000000e+00
[178,] 1.000000e+00 0.000000e+00 0.000000e+00
[179,] 1.000000e+00 0.000000e+00 0.000000e+00
[180,] 1.000000e+00 0.000000e+00 0.000000e+00
> postscript(file="/var/fisher/rcomp/tmp/1fe7d1386318622.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/20vnh1386318622.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3k40t1386318622.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ouiy1386318622.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/55kn81386318622.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 = 195
Frequency = 1
1 2 3 4 5 6
0.100348104 0.003288604 0.054350956 0.062008567 0.041829466 0.085482695
7 8 9 10 11 12
0.158297954 0.182978181 0.042226126 0.048325178 0.021869165 0.020079474
13 14 15 16 17 18
0.506297582 0.473231556 0.460248269 0.391225315 0.342030385 0.319140796
19 20 21 22 23 24
-0.008183437 0.064667953 0.035912507 0.112982149 0.236728954 0.231513128
25 26 27 28 29 30
0.345871458 0.197305880 0.406314053 0.420216029 0.450993978 0.387869787
31 32 33 34 35 36
-0.615098156 -0.508888222 -0.532887734 -0.412273534 -0.380082942 -0.421167100
37 38 39 40 41 42
0.263134787 0.315365736 0.350147015 0.358731404 0.412487195 0.429745987
43 44 45 46 47 48
-0.471189608 -0.538515939 -0.464851806 -0.475680466 -0.501429092 -0.555625980
49 50 51 52 53 54
-0.818309711 -0.777452342 -0.825215541 -0.763938334 -0.748091313 -0.734536669
55 56 57 58 59 60
-0.005473550 -0.015524082 -0.003654535 0.049110907 0.044662666 0.006737526
61 62 63 64 65 66
-0.466621637 -0.548204919 -0.508056417 -0.401547421 -0.386354591 -0.475065894
67 68 69 70 71 72
0.203507876 0.173401842 0.179445944 0.232175219 0.185212500 0.095628858
73 74 75 76 77 78
0.259616361 0.156559300 0.195781919 0.185348600 0.149598607 0.211043814
79 80 81 82 83 84
0.010150269 -0.039669910 0.029110226 0.006479443 0.026761274 0.085081556
85 86 87 88 89 90
-0.026587734 0.005458516 0.018380455 -0.085885683 -0.069601077 -0.130095014
91 92 93 94 95 96
-0.149457502 0.184870060 0.158847137 0.291039098 0.254919318 0.295410523
97 98 99 100 101 102
0.406348486 0.001263676 0.104941877 0.034860920 0.024431300 0.140589083
103 104 105 106 107 108
0.125914678 0.516693223 0.645179084 0.487941166 0.654476969 0.488014162
109 110 111 112 113 114
0.680462607 0.197382633 0.303274610 0.384474727 0.215805936 0.321693555
115 116 117 118 119 120
0.370482461 0.490049678 0.524010671 0.437342028 0.465344743 0.595660969
121 122 123 124 125 126
0.397739559 0.578591417 0.069083138 0.058356087 0.057629125 0.019529353
127 128 129 130 131 132
0.005920185 0.041539699 0.446224290 0.364501358 0.311582242 0.343928021
133 134 135 136 137 138
0.294421724 0.342480650 -0.040725703 0.033591608 0.070116577 0.030777181
139 140 141 142 143 144
0.044128934 0.102256184 0.079568230 0.098440653 0.178112231 0.287776021
145 146 147 148 149 150
0.371781041 0.419051576 -0.183321502 -0.025160713 -0.119585543 0.027677168
151 152 153 154 155 156
-0.060795481 -0.217058325 -0.160379646 0.283406667 0.250826083 0.288693654
157 158 159 160 161 162
0.372570484 0.247180848 0.311833918 0.145834036 0.130485700 0.181848771
163 164 165 166 167 168
0.020779601 0.262688518 0.108802125 -0.510640967 -0.426747254 -0.570292779
169 170 171 172 173 174
-0.750471114 -0.553476362 -0.654936958 -0.646308710 -0.646413345 -0.636799683
175 176 177 178 179 180
-0.653070571 -0.661596985 -0.627387006 0.323724706 0.294636880 0.247659932
181 182 183 184 185 186
0.303845285 0.235733863 0.321687582 -0.716687357 -0.764366733 -0.653932536
187 188 189 190 191 192
-0.657930808 -0.596084049 -0.614272648 -0.755989348 -0.666198875 -0.716976542
193 194 195
-0.633490165 -0.631631882 -0.633320848
> postscript(file="/var/fisher/rcomp/tmp/6y75u1386318622.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 = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 0.100348104 NA
1 0.003288604 0.100348104
2 0.054350956 0.003288604
3 0.062008567 0.054350956
4 0.041829466 0.062008567
5 0.085482695 0.041829466
6 0.158297954 0.085482695
7 0.182978181 0.158297954
8 0.042226126 0.182978181
9 0.048325178 0.042226126
10 0.021869165 0.048325178
11 0.020079474 0.021869165
12 0.506297582 0.020079474
13 0.473231556 0.506297582
14 0.460248269 0.473231556
15 0.391225315 0.460248269
16 0.342030385 0.391225315
17 0.319140796 0.342030385
18 -0.008183437 0.319140796
19 0.064667953 -0.008183437
20 0.035912507 0.064667953
21 0.112982149 0.035912507
22 0.236728954 0.112982149
23 0.231513128 0.236728954
24 0.345871458 0.231513128
25 0.197305880 0.345871458
26 0.406314053 0.197305880
27 0.420216029 0.406314053
28 0.450993978 0.420216029
29 0.387869787 0.450993978
30 -0.615098156 0.387869787
31 -0.508888222 -0.615098156
32 -0.532887734 -0.508888222
33 -0.412273534 -0.532887734
34 -0.380082942 -0.412273534
35 -0.421167100 -0.380082942
36 0.263134787 -0.421167100
37 0.315365736 0.263134787
38 0.350147015 0.315365736
39 0.358731404 0.350147015
40 0.412487195 0.358731404
41 0.429745987 0.412487195
42 -0.471189608 0.429745987
43 -0.538515939 -0.471189608
44 -0.464851806 -0.538515939
45 -0.475680466 -0.464851806
46 -0.501429092 -0.475680466
47 -0.555625980 -0.501429092
48 -0.818309711 -0.555625980
49 -0.777452342 -0.818309711
50 -0.825215541 -0.777452342
51 -0.763938334 -0.825215541
52 -0.748091313 -0.763938334
53 -0.734536669 -0.748091313
54 -0.005473550 -0.734536669
55 -0.015524082 -0.005473550
56 -0.003654535 -0.015524082
57 0.049110907 -0.003654535
58 0.044662666 0.049110907
59 0.006737526 0.044662666
60 -0.466621637 0.006737526
61 -0.548204919 -0.466621637
62 -0.508056417 -0.548204919
63 -0.401547421 -0.508056417
64 -0.386354591 -0.401547421
65 -0.475065894 -0.386354591
66 0.203507876 -0.475065894
67 0.173401842 0.203507876
68 0.179445944 0.173401842
69 0.232175219 0.179445944
70 0.185212500 0.232175219
71 0.095628858 0.185212500
72 0.259616361 0.095628858
73 0.156559300 0.259616361
74 0.195781919 0.156559300
75 0.185348600 0.195781919
76 0.149598607 0.185348600
77 0.211043814 0.149598607
78 0.010150269 0.211043814
79 -0.039669910 0.010150269
80 0.029110226 -0.039669910
81 0.006479443 0.029110226
82 0.026761274 0.006479443
83 0.085081556 0.026761274
84 -0.026587734 0.085081556
85 0.005458516 -0.026587734
86 0.018380455 0.005458516
87 -0.085885683 0.018380455
88 -0.069601077 -0.085885683
89 -0.130095014 -0.069601077
90 -0.149457502 -0.130095014
91 0.184870060 -0.149457502
92 0.158847137 0.184870060
93 0.291039098 0.158847137
94 0.254919318 0.291039098
95 0.295410523 0.254919318
96 0.406348486 0.295410523
97 0.001263676 0.406348486
98 0.104941877 0.001263676
99 0.034860920 0.104941877
100 0.024431300 0.034860920
101 0.140589083 0.024431300
102 0.125914678 0.140589083
103 0.516693223 0.125914678
104 0.645179084 0.516693223
105 0.487941166 0.645179084
106 0.654476969 0.487941166
107 0.488014162 0.654476969
108 0.680462607 0.488014162
109 0.197382633 0.680462607
110 0.303274610 0.197382633
111 0.384474727 0.303274610
112 0.215805936 0.384474727
113 0.321693555 0.215805936
114 0.370482461 0.321693555
115 0.490049678 0.370482461
116 0.524010671 0.490049678
117 0.437342028 0.524010671
118 0.465344743 0.437342028
119 0.595660969 0.465344743
120 0.397739559 0.595660969
121 0.578591417 0.397739559
122 0.069083138 0.578591417
123 0.058356087 0.069083138
124 0.057629125 0.058356087
125 0.019529353 0.057629125
126 0.005920185 0.019529353
127 0.041539699 0.005920185
128 0.446224290 0.041539699
129 0.364501358 0.446224290
130 0.311582242 0.364501358
131 0.343928021 0.311582242
132 0.294421724 0.343928021
133 0.342480650 0.294421724
134 -0.040725703 0.342480650
135 0.033591608 -0.040725703
136 0.070116577 0.033591608
137 0.030777181 0.070116577
138 0.044128934 0.030777181
139 0.102256184 0.044128934
140 0.079568230 0.102256184
141 0.098440653 0.079568230
142 0.178112231 0.098440653
143 0.287776021 0.178112231
144 0.371781041 0.287776021
145 0.419051576 0.371781041
146 -0.183321502 0.419051576
147 -0.025160713 -0.183321502
148 -0.119585543 -0.025160713
149 0.027677168 -0.119585543
150 -0.060795481 0.027677168
151 -0.217058325 -0.060795481
152 -0.160379646 -0.217058325
153 0.283406667 -0.160379646
154 0.250826083 0.283406667
155 0.288693654 0.250826083
156 0.372570484 0.288693654
157 0.247180848 0.372570484
158 0.311833918 0.247180848
159 0.145834036 0.311833918
160 0.130485700 0.145834036
161 0.181848771 0.130485700
162 0.020779601 0.181848771
163 0.262688518 0.020779601
164 0.108802125 0.262688518
165 -0.510640967 0.108802125
166 -0.426747254 -0.510640967
167 -0.570292779 -0.426747254
168 -0.750471114 -0.570292779
169 -0.553476362 -0.750471114
170 -0.654936958 -0.553476362
171 -0.646308710 -0.654936958
172 -0.646413345 -0.646308710
173 -0.636799683 -0.646413345
174 -0.653070571 -0.636799683
175 -0.661596985 -0.653070571
176 -0.627387006 -0.661596985
177 0.323724706 -0.627387006
178 0.294636880 0.323724706
179 0.247659932 0.294636880
180 0.303845285 0.247659932
181 0.235733863 0.303845285
182 0.321687582 0.235733863
183 -0.716687357 0.321687582
184 -0.764366733 -0.716687357
185 -0.653932536 -0.764366733
186 -0.657930808 -0.653932536
187 -0.596084049 -0.657930808
188 -0.614272648 -0.596084049
189 -0.755989348 -0.614272648
190 -0.666198875 -0.755989348
191 -0.716976542 -0.666198875
192 -0.633490165 -0.716976542
193 -0.631631882 -0.633490165
194 -0.633320848 -0.631631882
195 NA -0.633320848
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.003288604 0.100348104
[2,] 0.054350956 0.003288604
[3,] 0.062008567 0.054350956
[4,] 0.041829466 0.062008567
[5,] 0.085482695 0.041829466
[6,] 0.158297954 0.085482695
[7,] 0.182978181 0.158297954
[8,] 0.042226126 0.182978181
[9,] 0.048325178 0.042226126
[10,] 0.021869165 0.048325178
[11,] 0.020079474 0.021869165
[12,] 0.506297582 0.020079474
[13,] 0.473231556 0.506297582
[14,] 0.460248269 0.473231556
[15,] 0.391225315 0.460248269
[16,] 0.342030385 0.391225315
[17,] 0.319140796 0.342030385
[18,] -0.008183437 0.319140796
[19,] 0.064667953 -0.008183437
[20,] 0.035912507 0.064667953
[21,] 0.112982149 0.035912507
[22,] 0.236728954 0.112982149
[23,] 0.231513128 0.236728954
[24,] 0.345871458 0.231513128
[25,] 0.197305880 0.345871458
[26,] 0.406314053 0.197305880
[27,] 0.420216029 0.406314053
[28,] 0.450993978 0.420216029
[29,] 0.387869787 0.450993978
[30,] -0.615098156 0.387869787
[31,] -0.508888222 -0.615098156
[32,] -0.532887734 -0.508888222
[33,] -0.412273534 -0.532887734
[34,] -0.380082942 -0.412273534
[35,] -0.421167100 -0.380082942
[36,] 0.263134787 -0.421167100
[37,] 0.315365736 0.263134787
[38,] 0.350147015 0.315365736
[39,] 0.358731404 0.350147015
[40,] 0.412487195 0.358731404
[41,] 0.429745987 0.412487195
[42,] -0.471189608 0.429745987
[43,] -0.538515939 -0.471189608
[44,] -0.464851806 -0.538515939
[45,] -0.475680466 -0.464851806
[46,] -0.501429092 -0.475680466
[47,] -0.555625980 -0.501429092
[48,] -0.818309711 -0.555625980
[49,] -0.777452342 -0.818309711
[50,] -0.825215541 -0.777452342
[51,] -0.763938334 -0.825215541
[52,] -0.748091313 -0.763938334
[53,] -0.734536669 -0.748091313
[54,] -0.005473550 -0.734536669
[55,] -0.015524082 -0.005473550
[56,] -0.003654535 -0.015524082
[57,] 0.049110907 -0.003654535
[58,] 0.044662666 0.049110907
[59,] 0.006737526 0.044662666
[60,] -0.466621637 0.006737526
[61,] -0.548204919 -0.466621637
[62,] -0.508056417 -0.548204919
[63,] -0.401547421 -0.508056417
[64,] -0.386354591 -0.401547421
[65,] -0.475065894 -0.386354591
[66,] 0.203507876 -0.475065894
[67,] 0.173401842 0.203507876
[68,] 0.179445944 0.173401842
[69,] 0.232175219 0.179445944
[70,] 0.185212500 0.232175219
[71,] 0.095628858 0.185212500
[72,] 0.259616361 0.095628858
[73,] 0.156559300 0.259616361
[74,] 0.195781919 0.156559300
[75,] 0.185348600 0.195781919
[76,] 0.149598607 0.185348600
[77,] 0.211043814 0.149598607
[78,] 0.010150269 0.211043814
[79,] -0.039669910 0.010150269
[80,] 0.029110226 -0.039669910
[81,] 0.006479443 0.029110226
[82,] 0.026761274 0.006479443
[83,] 0.085081556 0.026761274
[84,] -0.026587734 0.085081556
[85,] 0.005458516 -0.026587734
[86,] 0.018380455 0.005458516
[87,] -0.085885683 0.018380455
[88,] -0.069601077 -0.085885683
[89,] -0.130095014 -0.069601077
[90,] -0.149457502 -0.130095014
[91,] 0.184870060 -0.149457502
[92,] 0.158847137 0.184870060
[93,] 0.291039098 0.158847137
[94,] 0.254919318 0.291039098
[95,] 0.295410523 0.254919318
[96,] 0.406348486 0.295410523
[97,] 0.001263676 0.406348486
[98,] 0.104941877 0.001263676
[99,] 0.034860920 0.104941877
[100,] 0.024431300 0.034860920
[101,] 0.140589083 0.024431300
[102,] 0.125914678 0.140589083
[103,] 0.516693223 0.125914678
[104,] 0.645179084 0.516693223
[105,] 0.487941166 0.645179084
[106,] 0.654476969 0.487941166
[107,] 0.488014162 0.654476969
[108,] 0.680462607 0.488014162
[109,] 0.197382633 0.680462607
[110,] 0.303274610 0.197382633
[111,] 0.384474727 0.303274610
[112,] 0.215805936 0.384474727
[113,] 0.321693555 0.215805936
[114,] 0.370482461 0.321693555
[115,] 0.490049678 0.370482461
[116,] 0.524010671 0.490049678
[117,] 0.437342028 0.524010671
[118,] 0.465344743 0.437342028
[119,] 0.595660969 0.465344743
[120,] 0.397739559 0.595660969
[121,] 0.578591417 0.397739559
[122,] 0.069083138 0.578591417
[123,] 0.058356087 0.069083138
[124,] 0.057629125 0.058356087
[125,] 0.019529353 0.057629125
[126,] 0.005920185 0.019529353
[127,] 0.041539699 0.005920185
[128,] 0.446224290 0.041539699
[129,] 0.364501358 0.446224290
[130,] 0.311582242 0.364501358
[131,] 0.343928021 0.311582242
[132,] 0.294421724 0.343928021
[133,] 0.342480650 0.294421724
[134,] -0.040725703 0.342480650
[135,] 0.033591608 -0.040725703
[136,] 0.070116577 0.033591608
[137,] 0.030777181 0.070116577
[138,] 0.044128934 0.030777181
[139,] 0.102256184 0.044128934
[140,] 0.079568230 0.102256184
[141,] 0.098440653 0.079568230
[142,] 0.178112231 0.098440653
[143,] 0.287776021 0.178112231
[144,] 0.371781041 0.287776021
[145,] 0.419051576 0.371781041
[146,] -0.183321502 0.419051576
[147,] -0.025160713 -0.183321502
[148,] -0.119585543 -0.025160713
[149,] 0.027677168 -0.119585543
[150,] -0.060795481 0.027677168
[151,] -0.217058325 -0.060795481
[152,] -0.160379646 -0.217058325
[153,] 0.283406667 -0.160379646
[154,] 0.250826083 0.283406667
[155,] 0.288693654 0.250826083
[156,] 0.372570484 0.288693654
[157,] 0.247180848 0.372570484
[158,] 0.311833918 0.247180848
[159,] 0.145834036 0.311833918
[160,] 0.130485700 0.145834036
[161,] 0.181848771 0.130485700
[162,] 0.020779601 0.181848771
[163,] 0.262688518 0.020779601
[164,] 0.108802125 0.262688518
[165,] -0.510640967 0.108802125
[166,] -0.426747254 -0.510640967
[167,] -0.570292779 -0.426747254
[168,] -0.750471114 -0.570292779
[169,] -0.553476362 -0.750471114
[170,] -0.654936958 -0.553476362
[171,] -0.646308710 -0.654936958
[172,] -0.646413345 -0.646308710
[173,] -0.636799683 -0.646413345
[174,] -0.653070571 -0.636799683
[175,] -0.661596985 -0.653070571
[176,] -0.627387006 -0.661596985
[177,] 0.323724706 -0.627387006
[178,] 0.294636880 0.323724706
[179,] 0.247659932 0.294636880
[180,] 0.303845285 0.247659932
[181,] 0.235733863 0.303845285
[182,] 0.321687582 0.235733863
[183,] -0.716687357 0.321687582
[184,] -0.764366733 -0.716687357
[185,] -0.653932536 -0.764366733
[186,] -0.657930808 -0.653932536
[187,] -0.596084049 -0.657930808
[188,] -0.614272648 -0.596084049
[189,] -0.755989348 -0.614272648
[190,] -0.666198875 -0.755989348
[191,] -0.716976542 -0.666198875
[192,] -0.633490165 -0.716976542
[193,] -0.631631882 -0.633490165
[194,] -0.633320848 -0.631631882
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.003288604 0.100348104
2 0.054350956 0.003288604
3 0.062008567 0.054350956
4 0.041829466 0.062008567
5 0.085482695 0.041829466
6 0.158297954 0.085482695
7 0.182978181 0.158297954
8 0.042226126 0.182978181
9 0.048325178 0.042226126
10 0.021869165 0.048325178
11 0.020079474 0.021869165
12 0.506297582 0.020079474
13 0.473231556 0.506297582
14 0.460248269 0.473231556
15 0.391225315 0.460248269
16 0.342030385 0.391225315
17 0.319140796 0.342030385
18 -0.008183437 0.319140796
19 0.064667953 -0.008183437
20 0.035912507 0.064667953
21 0.112982149 0.035912507
22 0.236728954 0.112982149
23 0.231513128 0.236728954
24 0.345871458 0.231513128
25 0.197305880 0.345871458
26 0.406314053 0.197305880
27 0.420216029 0.406314053
28 0.450993978 0.420216029
29 0.387869787 0.450993978
30 -0.615098156 0.387869787
31 -0.508888222 -0.615098156
32 -0.532887734 -0.508888222
33 -0.412273534 -0.532887734
34 -0.380082942 -0.412273534
35 -0.421167100 -0.380082942
36 0.263134787 -0.421167100
37 0.315365736 0.263134787
38 0.350147015 0.315365736
39 0.358731404 0.350147015
40 0.412487195 0.358731404
41 0.429745987 0.412487195
42 -0.471189608 0.429745987
43 -0.538515939 -0.471189608
44 -0.464851806 -0.538515939
45 -0.475680466 -0.464851806
46 -0.501429092 -0.475680466
47 -0.555625980 -0.501429092
48 -0.818309711 -0.555625980
49 -0.777452342 -0.818309711
50 -0.825215541 -0.777452342
51 -0.763938334 -0.825215541
52 -0.748091313 -0.763938334
53 -0.734536669 -0.748091313
54 -0.005473550 -0.734536669
55 -0.015524082 -0.005473550
56 -0.003654535 -0.015524082
57 0.049110907 -0.003654535
58 0.044662666 0.049110907
59 0.006737526 0.044662666
60 -0.466621637 0.006737526
61 -0.548204919 -0.466621637
62 -0.508056417 -0.548204919
63 -0.401547421 -0.508056417
64 -0.386354591 -0.401547421
65 -0.475065894 -0.386354591
66 0.203507876 -0.475065894
67 0.173401842 0.203507876
68 0.179445944 0.173401842
69 0.232175219 0.179445944
70 0.185212500 0.232175219
71 0.095628858 0.185212500
72 0.259616361 0.095628858
73 0.156559300 0.259616361
74 0.195781919 0.156559300
75 0.185348600 0.195781919
76 0.149598607 0.185348600
77 0.211043814 0.149598607
78 0.010150269 0.211043814
79 -0.039669910 0.010150269
80 0.029110226 -0.039669910
81 0.006479443 0.029110226
82 0.026761274 0.006479443
83 0.085081556 0.026761274
84 -0.026587734 0.085081556
85 0.005458516 -0.026587734
86 0.018380455 0.005458516
87 -0.085885683 0.018380455
88 -0.069601077 -0.085885683
89 -0.130095014 -0.069601077
90 -0.149457502 -0.130095014
91 0.184870060 -0.149457502
92 0.158847137 0.184870060
93 0.291039098 0.158847137
94 0.254919318 0.291039098
95 0.295410523 0.254919318
96 0.406348486 0.295410523
97 0.001263676 0.406348486
98 0.104941877 0.001263676
99 0.034860920 0.104941877
100 0.024431300 0.034860920
101 0.140589083 0.024431300
102 0.125914678 0.140589083
103 0.516693223 0.125914678
104 0.645179084 0.516693223
105 0.487941166 0.645179084
106 0.654476969 0.487941166
107 0.488014162 0.654476969
108 0.680462607 0.488014162
109 0.197382633 0.680462607
110 0.303274610 0.197382633
111 0.384474727 0.303274610
112 0.215805936 0.384474727
113 0.321693555 0.215805936
114 0.370482461 0.321693555
115 0.490049678 0.370482461
116 0.524010671 0.490049678
117 0.437342028 0.524010671
118 0.465344743 0.437342028
119 0.595660969 0.465344743
120 0.397739559 0.595660969
121 0.578591417 0.397739559
122 0.069083138 0.578591417
123 0.058356087 0.069083138
124 0.057629125 0.058356087
125 0.019529353 0.057629125
126 0.005920185 0.019529353
127 0.041539699 0.005920185
128 0.446224290 0.041539699
129 0.364501358 0.446224290
130 0.311582242 0.364501358
131 0.343928021 0.311582242
132 0.294421724 0.343928021
133 0.342480650 0.294421724
134 -0.040725703 0.342480650
135 0.033591608 -0.040725703
136 0.070116577 0.033591608
137 0.030777181 0.070116577
138 0.044128934 0.030777181
139 0.102256184 0.044128934
140 0.079568230 0.102256184
141 0.098440653 0.079568230
142 0.178112231 0.098440653
143 0.287776021 0.178112231
144 0.371781041 0.287776021
145 0.419051576 0.371781041
146 -0.183321502 0.419051576
147 -0.025160713 -0.183321502
148 -0.119585543 -0.025160713
149 0.027677168 -0.119585543
150 -0.060795481 0.027677168
151 -0.217058325 -0.060795481
152 -0.160379646 -0.217058325
153 0.283406667 -0.160379646
154 0.250826083 0.283406667
155 0.288693654 0.250826083
156 0.372570484 0.288693654
157 0.247180848 0.372570484
158 0.311833918 0.247180848
159 0.145834036 0.311833918
160 0.130485700 0.145834036
161 0.181848771 0.130485700
162 0.020779601 0.181848771
163 0.262688518 0.020779601
164 0.108802125 0.262688518
165 -0.510640967 0.108802125
166 -0.426747254 -0.510640967
167 -0.570292779 -0.426747254
168 -0.750471114 -0.570292779
169 -0.553476362 -0.750471114
170 -0.654936958 -0.553476362
171 -0.646308710 -0.654936958
172 -0.646413345 -0.646308710
173 -0.636799683 -0.646413345
174 -0.653070571 -0.636799683
175 -0.661596985 -0.653070571
176 -0.627387006 -0.661596985
177 0.323724706 -0.627387006
178 0.294636880 0.323724706
179 0.247659932 0.294636880
180 0.303845285 0.247659932
181 0.235733863 0.303845285
182 0.321687582 0.235733863
183 -0.716687357 0.321687582
184 -0.764366733 -0.716687357
185 -0.653932536 -0.764366733
186 -0.657930808 -0.653932536
187 -0.596084049 -0.657930808
188 -0.614272648 -0.596084049
189 -0.755989348 -0.614272648
190 -0.666198875 -0.755989348
191 -0.716976542 -0.666198875
192 -0.633490165 -0.716976542
193 -0.631631882 -0.633490165
194 -0.633320848 -0.631631882
> 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/7fae91386318622.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8qmrr1386318622.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9swgq1386318622.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10dcsn1386318623.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/11e6mn1386318623.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12m2nr1386318623.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13fbur1386318623.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14u43c1386318623.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15c5k21386318623.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/16c65y1386318623.tab")
+ }
>
> try(system("convert tmp/1fe7d1386318622.ps tmp/1fe7d1386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/20vnh1386318622.ps tmp/20vnh1386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/3k40t1386318622.ps tmp/3k40t1386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ouiy1386318622.ps tmp/4ouiy1386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/55kn81386318622.ps tmp/55kn81386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y75u1386318622.ps tmp/6y75u1386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fae91386318622.ps tmp/7fae91386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qmrr1386318622.ps tmp/8qmrr1386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/9swgq1386318622.ps tmp/9swgq1386318622.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dcsn1386318623.ps tmp/10dcsn1386318623.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
18.294 3.240 21.592