R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(1
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+ ,4)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('G'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('G','I1','I2','I3','E1','E2','E3','A'),1:162))
> 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 = '8'
> 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
A G I1 I2 I3 E1 E2 E3
1 4 1 26 21 21 23 17 23
2 4 1 20 16 15 24 17 20
3 6 1 19 19 18 22 18 20
4 8 2 19 18 11 20 21 21
5 8 1 20 16 8 24 20 24
6 4 1 25 23 19 27 28 22
7 4 2 25 17 4 28 19 23
8 8 1 22 12 20 27 22 20
9 5 1 26 19 16 24 16 25
10 4 1 22 16 14 23 18 23
11 4 2 17 19 10 24 25 27
12 4 2 22 20 13 27 17 27
13 4 1 19 13 14 27 14 22
14 4 1 24 20 8 28 11 24
15 4 1 26 27 23 27 27 25
16 8 2 21 17 11 23 20 22
17 4 1 13 8 9 24 22 28
18 4 2 26 25 24 28 22 28
19 4 2 20 26 5 27 21 27
20 8 1 22 13 15 25 23 25
21 4 2 14 19 5 19 17 16
22 7 1 21 15 19 24 24 28
23 4 1 7 5 6 20 14 21
24 4 2 23 16 13 28 17 24
25 5 1 17 14 11 26 23 27
26 4 1 25 24 17 23 24 14
27 4 1 25 24 17 23 24 14
28 4 1 19 9 5 20 8 27
29 4 2 20 19 9 11 22 20
30 4 1 23 19 15 24 23 21
31 4 2 22 25 17 25 25 22
32 4 1 22 19 17 23 21 21
33 15 1 21 18 20 18 24 12
34 10 2 15 15 12 20 15 20
35 4 2 20 12 7 20 22 24
36 8 2 22 21 16 24 21 19
37 4 1 18 12 7 23 25 28
38 4 2 20 15 14 25 16 23
39 4 2 28 28 24 28 28 27
40 4 1 22 25 15 26 23 22
41 7 1 18 19 15 26 21 27
42 4 1 23 20 10 23 21 26
43 6 1 20 24 14 22 26 22
44 5 2 25 26 18 24 22 21
45 4 2 26 25 12 21 21 19
46 16 1 15 12 9 20 18 24
47 5 2 17 12 9 22 12 19
48 12 2 23 15 8 20 25 26
49 6 1 21 17 18 25 17 22
50 9 2 13 14 10 20 24 28
51 9 1 18 16 17 22 15 21
52 4 1 19 11 14 23 13 23
53 5 1 22 20 16 25 26 28
54 4 1 16 11 10 23 16 10
55 4 2 24 22 19 23 24 24
56 5 1 18 20 10 22 21 21
57 4 1 20 19 14 24 20 21
58 4 1 24 17 10 25 14 24
59 4 2 14 21 4 21 25 24
60 5 2 22 23 19 12 25 25
61 4 1 24 18 9 17 20 25
62 6 1 18 17 12 20 22 23
63 4 1 21 27 16 23 20 21
64 4 2 23 25 11 23 26 16
65 18 1 17 19 18 20 18 17
66 4 2 22 22 11 28 22 25
67 6 2 24 24 24 24 24 24
68 4 2 21 20 17 24 17 23
69 4 1 22 19 18 24 24 25
70 5 1 16 11 9 24 20 23
71 4 1 21 22 19 28 19 28
72 4 2 23 22 18 25 20 26
73 5 2 22 16 12 21 15 22
74 10 1 24 20 23 25 23 19
75 5 1 24 24 22 25 26 26
76 8 1 16 16 14 18 22 18
77 8 1 16 16 14 17 20 18
78 5 2 21 22 16 26 24 25
79 4 2 26 24 23 28 26 27
80 4 2 15 16 7 21 21 12
81 4 2 25 27 10 27 25 15
82 5 1 18 11 12 22 13 21
83 4 0 23 21 12 21 20 23
84 4 1 20 20 12 25 22 22
85 8 2 17 20 17 22 23 21
86 4 2 25 27 21 23 28 24
87 5 1 24 20 16 26 22 27
88 14 1 17 12 11 19 20 22
89 8 1 19 8 14 25 6 28
90 8 1 20 21 13 21 21 26
91 4 1 15 18 9 13 20 10
92 4 2 27 24 19 24 18 19
93 6 1 22 16 13 25 23 22
94 4 1 23 18 19 26 20 21
95 7 1 16 20 13 25 24 24
96 7 1 19 20 13 25 22 25
97 4 2 25 19 13 22 21 21
98 6 1 19 17 14 21 18 20
99 4 2 19 16 12 23 21 21
100 7 2 26 26 22 25 23 24
101 4 1 21 15 11 24 23 23
102 4 2 20 22 5 21 15 18
103 8 1 24 17 18 21 21 24
104 4 1 22 23 19 25 24 24
105 4 2 20 21 14 22 23 19
106 10 1 18 19 15 20 21 20
107 8 2 18 14 12 20 21 18
108 6 1 24 17 19 23 20 20
109 4 1 24 12 15 28 11 27
110 4 1 22 24 17 23 22 23
111 4 1 23 18 8 28 27 26
112 5 1 22 20 10 24 25 23
113 4 1 20 16 12 18 18 17
114 6 1 18 20 12 20 20 21
115 4 1 25 22 20 28 24 25
116 5 2 18 12 12 21 10 23
117 7 1 16 16 12 21 27 27
118 8 1 20 17 14 25 21 24
119 5 2 19 22 6 19 21 20
120 8 1 15 12 10 18 18 27
121 10 1 19 14 18 21 15 21
122 8 1 19 23 18 22 24 24
123 5 1 16 15 7 24 22 21
124 12 1 17 17 18 15 14 15
125 4 1 28 28 9 28 28 25
126 5 2 23 20 17 26 18 25
127 4 1 25 23 22 23 26 22
128 6 1 20 13 11 26 17 24
129 4 2 17 18 15 20 19 21
130 4 2 23 23 17 22 22 22
131 7 1 16 19 15 20 18 23
132 7 2 23 23 22 23 24 22
133 10 2 11 12 9 22 15 20
134 4 2 18 16 13 24 18 23
135 5 2 24 23 20 23 26 25
136 8 1 23 13 14 22 11 23
137 11 1 21 22 14 26 26 22
138 7 2 16 18 12 23 21 25
139 4 2 24 23 20 27 23 26
140 8 1 23 20 20 23 23 22
141 6 1 18 10 8 21 15 24
142 7 1 20 17 17 26 22 24
143 5 1 9 18 9 23 26 25
144 4 2 24 15 18 21 16 20
145 8 1 25 23 22 27 20 26
146 4 1 20 17 10 19 18 21
147 8 2 21 17 13 23 22 26
148 6 2 25 22 15 25 16 21
149 4 2 22 20 18 23 19 22
150 9 2 21 20 18 22 20 16
151 5 1 21 19 12 22 19 26
152 6 1 22 18 12 25 23 28
153 4 1 27 22 20 25 24 18
154 4 2 24 20 12 28 25 25
155 4 2 24 22 16 28 21 23
156 5 2 21 18 16 20 21 21
157 6 1 18 16 18 25 23 20
158 16 1 16 16 16 19 27 25
159 6 1 22 16 13 25 23 22
160 6 1 20 16 17 22 18 21
161 4 2 18 17 13 18 16 16
162 4 1 20 18 17 20 16 18
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G I1 I2 I3 E1
12.4312412 -0.3929378 -0.1864745 -0.1408935 0.1972449 -0.1808680
E2 E3
0.0841583 0.0002303
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.7777 -1.4346 -0.4772 0.9477 10.3569
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.4312412 1.7513544 7.098 4.35e-11 ***
G -0.3929378 0.3888912 -1.010 0.3139
I1 -0.1864745 0.0737533 -2.528 0.0125 *
I2 -0.1408935 0.0660314 -2.134 0.0344 *
I3 0.1972449 0.0485598 4.062 7.73e-05 ***
E1 -0.1808680 0.0715312 -2.529 0.0125 *
E2 0.0841583 0.0556065 1.513 0.1322
E3 0.0002303 0.0573916 0.004 0.9968
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.343 on 154 degrees of freedom
Multiple R-squared: 0.2389, Adjusted R-squared: 0.2043
F-statistic: 6.904 on 7 and 154 DF, p-value: 3.815e-07
> 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,] 0.497949325 0.995898651 0.502050675
[2,] 0.454541633 0.909083267 0.545458367
[3,] 0.326200697 0.652401394 0.673799303
[4,] 0.273879198 0.547758396 0.726120802
[5,] 0.193376693 0.386753386 0.806623307
[6,] 0.175356402 0.350712805 0.824643598
[7,] 0.142409221 0.284818441 0.857590779
[8,] 0.098060312 0.196120624 0.901939688
[9,] 0.065016661 0.130033322 0.934983339
[10,] 0.074759689 0.149519378 0.925240311
[11,] 0.074638434 0.149276867 0.925361566
[12,] 0.052477697 0.104955394 0.947522303
[13,] 0.043325554 0.086651108 0.956674446
[14,] 0.030292341 0.060584683 0.969707659
[15,] 0.019002114 0.038004227 0.980997886
[16,] 0.016704834 0.033409669 0.983295166
[17,] 0.011639399 0.023278799 0.988360601
[18,] 0.011412169 0.022824338 0.988587831
[19,] 0.016198992 0.032397984 0.983801008
[20,] 0.012308144 0.024616288 0.987691856
[21,] 0.007901864 0.015803729 0.992098136
[22,] 0.005557103 0.011114207 0.994442897
[23,] 0.334155751 0.668311502 0.665844249
[24,] 0.438760786 0.877521572 0.561239214
[25,] 0.447353099 0.894706198 0.552646901
[26,] 0.428917281 0.857834562 0.571082719
[27,] 0.389599489 0.779198979 0.610400511
[28,] 0.377839579 0.755679157 0.622160421
[29,] 0.327399510 0.654799020 0.672600490
[30,] 0.277343148 0.554686297 0.722656852
[31,] 0.296033913 0.592067827 0.703966087
[32,] 0.251278626 0.502557251 0.748721374
[33,] 0.217504072 0.435008143 0.782495928
[34,] 0.180558067 0.361116134 0.819441933
[35,] 0.151610353 0.303220705 0.848389647
[36,] 0.852291425 0.295417151 0.147708575
[37,] 0.826552046 0.346895908 0.173447954
[38,] 0.953634300 0.092731399 0.046365700
[39,] 0.940490137 0.119019726 0.059509863
[40,] 0.931191291 0.137617419 0.068808709
[41,] 0.929110207 0.141779586 0.070889793
[42,] 0.930510933 0.138978134 0.069489067
[43,] 0.914135942 0.171728116 0.085864058
[44,] 0.919990552 0.160018896 0.080009448
[45,] 0.913223801 0.173552398 0.086776199
[46,] 0.893095269 0.213809462 0.106904731
[47,] 0.880558482 0.238883036 0.119441518
[48,] 0.854506500 0.290987000 0.145493500
[49,] 0.831528193 0.336943615 0.168471807
[50,] 0.844075295 0.311849410 0.155924705
[51,] 0.822030825 0.355938351 0.177969175
[52,] 0.791815718 0.416368563 0.208184282
[53,] 0.761499184 0.477001632 0.238500816
[54,] 0.726932975 0.546134049 0.273067025
[55,] 0.997165532 0.005668937 0.002834468
[56,] 0.996027297 0.007945405 0.003972703
[57,] 0.994415273 0.011169455 0.005584727
[58,] 0.993072153 0.013855694 0.006927847
[59,] 0.993292359 0.013415281 0.006707641
[60,] 0.992179124 0.015641752 0.007820876
[61,] 0.990458846 0.019082309 0.009541154
[62,] 0.987771746 0.024456509 0.012228254
[63,] 0.983593495 0.032813009 0.016406505
[64,] 0.988256555 0.023486890 0.011743445
[65,] 0.985467461 0.029065077 0.014532539
[66,] 0.980551001 0.038897998 0.019448999
[67,] 0.974288560 0.051422880 0.025711440
[68,] 0.966546728 0.066906545 0.033453272
[69,] 0.959990702 0.080018597 0.040009298
[70,] 0.956904367 0.086191266 0.043095633
[71,] 0.956678363 0.086643274 0.043321637
[72,] 0.951034402 0.097931196 0.048965598
[73,] 0.943509353 0.112981294 0.056490647
[74,] 0.932432739 0.135134521 0.067567261
[75,] 0.919310544 0.161378912 0.080689456
[76,] 0.909440760 0.181118481 0.090559240
[77,] 0.891023654 0.217952692 0.108976346
[78,] 0.977882739 0.044234522 0.022117261
[79,] 0.973779971 0.052440058 0.026220029
[80,] 0.970903032 0.058193936 0.029096968
[81,] 0.978416505 0.043166990 0.021583495
[82,] 0.971605355 0.056789290 0.028394645
[83,] 0.963107160 0.073785679 0.036892840
[84,] 0.960178695 0.079642610 0.039821305
[85,] 0.949821466 0.100357069 0.050178534
[86,] 0.940141740 0.119716520 0.059858260
[87,] 0.925148829 0.149702342 0.074851171
[88,] 0.907744868 0.184510264 0.092255132
[89,] 0.897868790 0.204262420 0.102131210
[90,] 0.892731189 0.214537622 0.107268811
[91,] 0.885220691 0.229558618 0.114779309
[92,] 0.865503108 0.268993785 0.134496892
[93,] 0.843935139 0.312129722 0.156064861
[94,] 0.841021711 0.317956578 0.158978289
[95,] 0.823608876 0.352782247 0.176391124
[96,] 0.836275496 0.327449007 0.163724504
[97,] 0.815491105 0.369017790 0.184508895
[98,] 0.780807963 0.438384073 0.219192037
[99,] 0.751318592 0.497362816 0.248681408
[100,] 0.737809220 0.524381559 0.262190780
[101,] 0.696146107 0.607707786 0.303853893
[102,] 0.649629219 0.700741561 0.350370781
[103,] 0.657999857 0.684000286 0.342000143
[104,] 0.612093975 0.775812051 0.387906025
[105,] 0.592544608 0.814910784 0.407455392
[106,] 0.547741444 0.904517112 0.452258556
[107,] 0.509008895 0.981982210 0.490991105
[108,] 0.479265364 0.958530727 0.520734636
[109,] 0.433414568 0.866829137 0.566585432
[110,] 0.382617708 0.765235415 0.617382292
[111,] 0.370738395 0.741476789 0.629261605
[112,] 0.323745807 0.647491615 0.676254193
[113,] 0.288155498 0.576310996 0.711844502
[114,] 0.411013542 0.822027084 0.588986458
[115,] 0.376555964 0.753111928 0.623444036
[116,] 0.322047336 0.644094672 0.677952664
[117,] 0.340628025 0.681256051 0.659371975
[118,] 0.288972706 0.577945412 0.711027294
[119,] 0.289451026 0.578902053 0.710548974
[120,] 0.249386096 0.498772192 0.750613904
[121,] 0.202552134 0.405104267 0.797447866
[122,] 0.162087380 0.324174760 0.837912620
[123,] 0.247381706 0.494763411 0.752618294
[124,] 0.207659444 0.415318888 0.792340556
[125,] 0.214025694 0.428051388 0.785974306
[126,] 0.287843472 0.575686945 0.712156528
[127,] 0.532555306 0.934889389 0.467444694
[128,] 0.469396449 0.938792897 0.530603551
[129,] 0.507879055 0.984241889 0.492120945
[130,] 0.431846911 0.863693822 0.568153089
[131,] 0.435878970 0.871757940 0.564121030
[132,] 0.362584885 0.725169769 0.637415115
[133,] 0.492074749 0.984149497 0.507925251
[134,] 0.426836720 0.853673441 0.573163280
[135,] 0.358572670 0.717145339 0.641427330
[136,] 0.284062101 0.568124202 0.715937899
[137,] 0.239985911 0.479971822 0.760014089
[138,] 0.571227704 0.857544591 0.428772296
[139,] 0.453963325 0.907926651 0.546036675
[140,] 0.688468453 0.623063094 0.311531547
[141,] 0.571338959 0.857322081 0.428661041
> postscript(file="/var/fisher/rcomp/tmp/1nyoz1355073902.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/2n3bd1355073902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/30a8t1355073902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4v1571355073902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5yvnp1355073902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5 6
-1.649370783 -2.107657060 -0.909079880 2.109237301 3.019660852 -1.361607778
7 8 9 10 11 12
2.082704430 0.837305957 0.319632216 -1.803180421 -1.540116462 0.157285181
13 14 15 16 17 18
-1.724948931 1.810029362 -1.317071368 2.967824661 -3.779291095 -0.802196814
19 20 21 22 23 24
1.871022858 1.517378025 -1.341855638 -0.442006084 -4.777677133 -0.038255455
25 26 27 28 29 30
-1.304714038 -1.211220792 -1.211220792 -1.275596549 -2.880644703 -1.630733307
31 32 33 34 35 36
-0.961077701 -2.224248854 6.701906535 3.248594211 -1.845518944 2.829049354
37 38 39 40 41 42
-2.322197915 -1.394032522 -0.511286936 -0.610341188 0.965565072 -0.517318348
43 44 45 46 47 48
0.097114445 0.613831614 0.384896779 8.771314399 -0.594961579 6.686404573
49 50 51 52 53 54
-0.191616313 1.369974320 1.931254653 -2.646279770 -0.946778206 -2.666204777
55 56 57 58 59 60
-1.683337178 -0.629407188 -1.740436942 0.197780335 -1.176196722 -2.989328860
61 62 63 64 65 66
-1.416205182 -0.892932231 -1.002172209 -0.035646495 10.356926139 0.594098915
67 68 69 70 71 72
-0.206906537 -1.359851440 -2.494021874 -1.627719077 -1.311488225 -0.974658225
73 74 75 76 77 78
-0.124783608 3.300004328 -1.193263857 -0.161848831 -0.174400180 -0.108652418
79 80 81 82 83 84
-1.082248380 -1.946527474 1.624194773 -1.618671887 -1.440739096 -1.192732675
85 86 87 88 89 90
1.028025480 -1.523518194 -0.056097758 6.401049640 1.880732341 2.110681205
91 92 93 94 95 96
-3.814492821 -0.155157454 0.335239129 -1.946395393 0.695347349 1.422857090
97 98 99 100 101 102
-0.663776153 -0.582755383 -1.827190652 2.107345398 -1.778737380 0.729263638
103 104 105 106 107 108
1.307241417 -1.946594497 -1.679462418 2.881969376 1.162634888 -0.443188009
109 110 111 112 113 114
-0.698523521 -1.604400293 -0.005225635 0.141132808 -2.684597676 -0.301474534
115 116 117 118 119 120
-1.182935833 -1.013694238 -0.647619448 2.073794829 0.478397921 0.211642708
121 122 123 124 125 126
2.457829313 1.148623046 -0.837511396 3.507893158 2.054908802 0.290214532
127 128 129 130 131 132
-2.508497609 0.619456641 -2.884374480 -1.346519184 -0.239195545 0.679807853
133 134 135 136 137 138
3.033486349 -1.778027697 -0.908235463 2.368853766 5.725273793 0.894251714
139 140 141 142 143 144
-0.932518976 1.342837611 -0.320461412 0.578769899 -2.633064357 -2.159895036
145 146 147 148 149 150
2.719002905 -1.969267690 2.404097122 2.327810042 -1.719576111 2.830304938
151 152 153 154 155 156
-0.438201863 0.812889181 -1.350978684 0.235541089 0.065442460 -1.504038061
157 158 159 160 161 162
-1.396422481 7.202125771 0.335239129 -0.948271323 -2.552413282 -2.859212751
> postscript(file="/var/fisher/rcomp/tmp/6clxh1355073902.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.649370783 NA
1 -2.107657060 -1.649370783
2 -0.909079880 -2.107657060
3 2.109237301 -0.909079880
4 3.019660852 2.109237301
5 -1.361607778 3.019660852
6 2.082704430 -1.361607778
7 0.837305957 2.082704430
8 0.319632216 0.837305957
9 -1.803180421 0.319632216
10 -1.540116462 -1.803180421
11 0.157285181 -1.540116462
12 -1.724948931 0.157285181
13 1.810029362 -1.724948931
14 -1.317071368 1.810029362
15 2.967824661 -1.317071368
16 -3.779291095 2.967824661
17 -0.802196814 -3.779291095
18 1.871022858 -0.802196814
19 1.517378025 1.871022858
20 -1.341855638 1.517378025
21 -0.442006084 -1.341855638
22 -4.777677133 -0.442006084
23 -0.038255455 -4.777677133
24 -1.304714038 -0.038255455
25 -1.211220792 -1.304714038
26 -1.211220792 -1.211220792
27 -1.275596549 -1.211220792
28 -2.880644703 -1.275596549
29 -1.630733307 -2.880644703
30 -0.961077701 -1.630733307
31 -2.224248854 -0.961077701
32 6.701906535 -2.224248854
33 3.248594211 6.701906535
34 -1.845518944 3.248594211
35 2.829049354 -1.845518944
36 -2.322197915 2.829049354
37 -1.394032522 -2.322197915
38 -0.511286936 -1.394032522
39 -0.610341188 -0.511286936
40 0.965565072 -0.610341188
41 -0.517318348 0.965565072
42 0.097114445 -0.517318348
43 0.613831614 0.097114445
44 0.384896779 0.613831614
45 8.771314399 0.384896779
46 -0.594961579 8.771314399
47 6.686404573 -0.594961579
48 -0.191616313 6.686404573
49 1.369974320 -0.191616313
50 1.931254653 1.369974320
51 -2.646279770 1.931254653
52 -0.946778206 -2.646279770
53 -2.666204777 -0.946778206
54 -1.683337178 -2.666204777
55 -0.629407188 -1.683337178
56 -1.740436942 -0.629407188
57 0.197780335 -1.740436942
58 -1.176196722 0.197780335
59 -2.989328860 -1.176196722
60 -1.416205182 -2.989328860
61 -0.892932231 -1.416205182
62 -1.002172209 -0.892932231
63 -0.035646495 -1.002172209
64 10.356926139 -0.035646495
65 0.594098915 10.356926139
66 -0.206906537 0.594098915
67 -1.359851440 -0.206906537
68 -2.494021874 -1.359851440
69 -1.627719077 -2.494021874
70 -1.311488225 -1.627719077
71 -0.974658225 -1.311488225
72 -0.124783608 -0.974658225
73 3.300004328 -0.124783608
74 -1.193263857 3.300004328
75 -0.161848831 -1.193263857
76 -0.174400180 -0.161848831
77 -0.108652418 -0.174400180
78 -1.082248380 -0.108652418
79 -1.946527474 -1.082248380
80 1.624194773 -1.946527474
81 -1.618671887 1.624194773
82 -1.440739096 -1.618671887
83 -1.192732675 -1.440739096
84 1.028025480 -1.192732675
85 -1.523518194 1.028025480
86 -0.056097758 -1.523518194
87 6.401049640 -0.056097758
88 1.880732341 6.401049640
89 2.110681205 1.880732341
90 -3.814492821 2.110681205
91 -0.155157454 -3.814492821
92 0.335239129 -0.155157454
93 -1.946395393 0.335239129
94 0.695347349 -1.946395393
95 1.422857090 0.695347349
96 -0.663776153 1.422857090
97 -0.582755383 -0.663776153
98 -1.827190652 -0.582755383
99 2.107345398 -1.827190652
100 -1.778737380 2.107345398
101 0.729263638 -1.778737380
102 1.307241417 0.729263638
103 -1.946594497 1.307241417
104 -1.679462418 -1.946594497
105 2.881969376 -1.679462418
106 1.162634888 2.881969376
107 -0.443188009 1.162634888
108 -0.698523521 -0.443188009
109 -1.604400293 -0.698523521
110 -0.005225635 -1.604400293
111 0.141132808 -0.005225635
112 -2.684597676 0.141132808
113 -0.301474534 -2.684597676
114 -1.182935833 -0.301474534
115 -1.013694238 -1.182935833
116 -0.647619448 -1.013694238
117 2.073794829 -0.647619448
118 0.478397921 2.073794829
119 0.211642708 0.478397921
120 2.457829313 0.211642708
121 1.148623046 2.457829313
122 -0.837511396 1.148623046
123 3.507893158 -0.837511396
124 2.054908802 3.507893158
125 0.290214532 2.054908802
126 -2.508497609 0.290214532
127 0.619456641 -2.508497609
128 -2.884374480 0.619456641
129 -1.346519184 -2.884374480
130 -0.239195545 -1.346519184
131 0.679807853 -0.239195545
132 3.033486349 0.679807853
133 -1.778027697 3.033486349
134 -0.908235463 -1.778027697
135 2.368853766 -0.908235463
136 5.725273793 2.368853766
137 0.894251714 5.725273793
138 -0.932518976 0.894251714
139 1.342837611 -0.932518976
140 -0.320461412 1.342837611
141 0.578769899 -0.320461412
142 -2.633064357 0.578769899
143 -2.159895036 -2.633064357
144 2.719002905 -2.159895036
145 -1.969267690 2.719002905
146 2.404097122 -1.969267690
147 2.327810042 2.404097122
148 -1.719576111 2.327810042
149 2.830304938 -1.719576111
150 -0.438201863 2.830304938
151 0.812889181 -0.438201863
152 -1.350978684 0.812889181
153 0.235541089 -1.350978684
154 0.065442460 0.235541089
155 -1.504038061 0.065442460
156 -1.396422481 -1.504038061
157 7.202125771 -1.396422481
158 0.335239129 7.202125771
159 -0.948271323 0.335239129
160 -2.552413282 -0.948271323
161 -2.859212751 -2.552413282
162 NA -2.859212751
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.107657060 -1.649370783
[2,] -0.909079880 -2.107657060
[3,] 2.109237301 -0.909079880
[4,] 3.019660852 2.109237301
[5,] -1.361607778 3.019660852
[6,] 2.082704430 -1.361607778
[7,] 0.837305957 2.082704430
[8,] 0.319632216 0.837305957
[9,] -1.803180421 0.319632216
[10,] -1.540116462 -1.803180421
[11,] 0.157285181 -1.540116462
[12,] -1.724948931 0.157285181
[13,] 1.810029362 -1.724948931
[14,] -1.317071368 1.810029362
[15,] 2.967824661 -1.317071368
[16,] -3.779291095 2.967824661
[17,] -0.802196814 -3.779291095
[18,] 1.871022858 -0.802196814
[19,] 1.517378025 1.871022858
[20,] -1.341855638 1.517378025
[21,] -0.442006084 -1.341855638
[22,] -4.777677133 -0.442006084
[23,] -0.038255455 -4.777677133
[24,] -1.304714038 -0.038255455
[25,] -1.211220792 -1.304714038
[26,] -1.211220792 -1.211220792
[27,] -1.275596549 -1.211220792
[28,] -2.880644703 -1.275596549
[29,] -1.630733307 -2.880644703
[30,] -0.961077701 -1.630733307
[31,] -2.224248854 -0.961077701
[32,] 6.701906535 -2.224248854
[33,] 3.248594211 6.701906535
[34,] -1.845518944 3.248594211
[35,] 2.829049354 -1.845518944
[36,] -2.322197915 2.829049354
[37,] -1.394032522 -2.322197915
[38,] -0.511286936 -1.394032522
[39,] -0.610341188 -0.511286936
[40,] 0.965565072 -0.610341188
[41,] -0.517318348 0.965565072
[42,] 0.097114445 -0.517318348
[43,] 0.613831614 0.097114445
[44,] 0.384896779 0.613831614
[45,] 8.771314399 0.384896779
[46,] -0.594961579 8.771314399
[47,] 6.686404573 -0.594961579
[48,] -0.191616313 6.686404573
[49,] 1.369974320 -0.191616313
[50,] 1.931254653 1.369974320
[51,] -2.646279770 1.931254653
[52,] -0.946778206 -2.646279770
[53,] -2.666204777 -0.946778206
[54,] -1.683337178 -2.666204777
[55,] -0.629407188 -1.683337178
[56,] -1.740436942 -0.629407188
[57,] 0.197780335 -1.740436942
[58,] -1.176196722 0.197780335
[59,] -2.989328860 -1.176196722
[60,] -1.416205182 -2.989328860
[61,] -0.892932231 -1.416205182
[62,] -1.002172209 -0.892932231
[63,] -0.035646495 -1.002172209
[64,] 10.356926139 -0.035646495
[65,] 0.594098915 10.356926139
[66,] -0.206906537 0.594098915
[67,] -1.359851440 -0.206906537
[68,] -2.494021874 -1.359851440
[69,] -1.627719077 -2.494021874
[70,] -1.311488225 -1.627719077
[71,] -0.974658225 -1.311488225
[72,] -0.124783608 -0.974658225
[73,] 3.300004328 -0.124783608
[74,] -1.193263857 3.300004328
[75,] -0.161848831 -1.193263857
[76,] -0.174400180 -0.161848831
[77,] -0.108652418 -0.174400180
[78,] -1.082248380 -0.108652418
[79,] -1.946527474 -1.082248380
[80,] 1.624194773 -1.946527474
[81,] -1.618671887 1.624194773
[82,] -1.440739096 -1.618671887
[83,] -1.192732675 -1.440739096
[84,] 1.028025480 -1.192732675
[85,] -1.523518194 1.028025480
[86,] -0.056097758 -1.523518194
[87,] 6.401049640 -0.056097758
[88,] 1.880732341 6.401049640
[89,] 2.110681205 1.880732341
[90,] -3.814492821 2.110681205
[91,] -0.155157454 -3.814492821
[92,] 0.335239129 -0.155157454
[93,] -1.946395393 0.335239129
[94,] 0.695347349 -1.946395393
[95,] 1.422857090 0.695347349
[96,] -0.663776153 1.422857090
[97,] -0.582755383 -0.663776153
[98,] -1.827190652 -0.582755383
[99,] 2.107345398 -1.827190652
[100,] -1.778737380 2.107345398
[101,] 0.729263638 -1.778737380
[102,] 1.307241417 0.729263638
[103,] -1.946594497 1.307241417
[104,] -1.679462418 -1.946594497
[105,] 2.881969376 -1.679462418
[106,] 1.162634888 2.881969376
[107,] -0.443188009 1.162634888
[108,] -0.698523521 -0.443188009
[109,] -1.604400293 -0.698523521
[110,] -0.005225635 -1.604400293
[111,] 0.141132808 -0.005225635
[112,] -2.684597676 0.141132808
[113,] -0.301474534 -2.684597676
[114,] -1.182935833 -0.301474534
[115,] -1.013694238 -1.182935833
[116,] -0.647619448 -1.013694238
[117,] 2.073794829 -0.647619448
[118,] 0.478397921 2.073794829
[119,] 0.211642708 0.478397921
[120,] 2.457829313 0.211642708
[121,] 1.148623046 2.457829313
[122,] -0.837511396 1.148623046
[123,] 3.507893158 -0.837511396
[124,] 2.054908802 3.507893158
[125,] 0.290214532 2.054908802
[126,] -2.508497609 0.290214532
[127,] 0.619456641 -2.508497609
[128,] -2.884374480 0.619456641
[129,] -1.346519184 -2.884374480
[130,] -0.239195545 -1.346519184
[131,] 0.679807853 -0.239195545
[132,] 3.033486349 0.679807853
[133,] -1.778027697 3.033486349
[134,] -0.908235463 -1.778027697
[135,] 2.368853766 -0.908235463
[136,] 5.725273793 2.368853766
[137,] 0.894251714 5.725273793
[138,] -0.932518976 0.894251714
[139,] 1.342837611 -0.932518976
[140,] -0.320461412 1.342837611
[141,] 0.578769899 -0.320461412
[142,] -2.633064357 0.578769899
[143,] -2.159895036 -2.633064357
[144,] 2.719002905 -2.159895036
[145,] -1.969267690 2.719002905
[146,] 2.404097122 -1.969267690
[147,] 2.327810042 2.404097122
[148,] -1.719576111 2.327810042
[149,] 2.830304938 -1.719576111
[150,] -0.438201863 2.830304938
[151,] 0.812889181 -0.438201863
[152,] -1.350978684 0.812889181
[153,] 0.235541089 -1.350978684
[154,] 0.065442460 0.235541089
[155,] -1.504038061 0.065442460
[156,] -1.396422481 -1.504038061
[157,] 7.202125771 -1.396422481
[158,] 0.335239129 7.202125771
[159,] -0.948271323 0.335239129
[160,] -2.552413282 -0.948271323
[161,] -2.859212751 -2.552413282
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.107657060 -1.649370783
2 -0.909079880 -2.107657060
3 2.109237301 -0.909079880
4 3.019660852 2.109237301
5 -1.361607778 3.019660852
6 2.082704430 -1.361607778
7 0.837305957 2.082704430
8 0.319632216 0.837305957
9 -1.803180421 0.319632216
10 -1.540116462 -1.803180421
11 0.157285181 -1.540116462
12 -1.724948931 0.157285181
13 1.810029362 -1.724948931
14 -1.317071368 1.810029362
15 2.967824661 -1.317071368
16 -3.779291095 2.967824661
17 -0.802196814 -3.779291095
18 1.871022858 -0.802196814
19 1.517378025 1.871022858
20 -1.341855638 1.517378025
21 -0.442006084 -1.341855638
22 -4.777677133 -0.442006084
23 -0.038255455 -4.777677133
24 -1.304714038 -0.038255455
25 -1.211220792 -1.304714038
26 -1.211220792 -1.211220792
27 -1.275596549 -1.211220792
28 -2.880644703 -1.275596549
29 -1.630733307 -2.880644703
30 -0.961077701 -1.630733307
31 -2.224248854 -0.961077701
32 6.701906535 -2.224248854
33 3.248594211 6.701906535
34 -1.845518944 3.248594211
35 2.829049354 -1.845518944
36 -2.322197915 2.829049354
37 -1.394032522 -2.322197915
38 -0.511286936 -1.394032522
39 -0.610341188 -0.511286936
40 0.965565072 -0.610341188
41 -0.517318348 0.965565072
42 0.097114445 -0.517318348
43 0.613831614 0.097114445
44 0.384896779 0.613831614
45 8.771314399 0.384896779
46 -0.594961579 8.771314399
47 6.686404573 -0.594961579
48 -0.191616313 6.686404573
49 1.369974320 -0.191616313
50 1.931254653 1.369974320
51 -2.646279770 1.931254653
52 -0.946778206 -2.646279770
53 -2.666204777 -0.946778206
54 -1.683337178 -2.666204777
55 -0.629407188 -1.683337178
56 -1.740436942 -0.629407188
57 0.197780335 -1.740436942
58 -1.176196722 0.197780335
59 -2.989328860 -1.176196722
60 -1.416205182 -2.989328860
61 -0.892932231 -1.416205182
62 -1.002172209 -0.892932231
63 -0.035646495 -1.002172209
64 10.356926139 -0.035646495
65 0.594098915 10.356926139
66 -0.206906537 0.594098915
67 -1.359851440 -0.206906537
68 -2.494021874 -1.359851440
69 -1.627719077 -2.494021874
70 -1.311488225 -1.627719077
71 -0.974658225 -1.311488225
72 -0.124783608 -0.974658225
73 3.300004328 -0.124783608
74 -1.193263857 3.300004328
75 -0.161848831 -1.193263857
76 -0.174400180 -0.161848831
77 -0.108652418 -0.174400180
78 -1.082248380 -0.108652418
79 -1.946527474 -1.082248380
80 1.624194773 -1.946527474
81 -1.618671887 1.624194773
82 -1.440739096 -1.618671887
83 -1.192732675 -1.440739096
84 1.028025480 -1.192732675
85 -1.523518194 1.028025480
86 -0.056097758 -1.523518194
87 6.401049640 -0.056097758
88 1.880732341 6.401049640
89 2.110681205 1.880732341
90 -3.814492821 2.110681205
91 -0.155157454 -3.814492821
92 0.335239129 -0.155157454
93 -1.946395393 0.335239129
94 0.695347349 -1.946395393
95 1.422857090 0.695347349
96 -0.663776153 1.422857090
97 -0.582755383 -0.663776153
98 -1.827190652 -0.582755383
99 2.107345398 -1.827190652
100 -1.778737380 2.107345398
101 0.729263638 -1.778737380
102 1.307241417 0.729263638
103 -1.946594497 1.307241417
104 -1.679462418 -1.946594497
105 2.881969376 -1.679462418
106 1.162634888 2.881969376
107 -0.443188009 1.162634888
108 -0.698523521 -0.443188009
109 -1.604400293 -0.698523521
110 -0.005225635 -1.604400293
111 0.141132808 -0.005225635
112 -2.684597676 0.141132808
113 -0.301474534 -2.684597676
114 -1.182935833 -0.301474534
115 -1.013694238 -1.182935833
116 -0.647619448 -1.013694238
117 2.073794829 -0.647619448
118 0.478397921 2.073794829
119 0.211642708 0.478397921
120 2.457829313 0.211642708
121 1.148623046 2.457829313
122 -0.837511396 1.148623046
123 3.507893158 -0.837511396
124 2.054908802 3.507893158
125 0.290214532 2.054908802
126 -2.508497609 0.290214532
127 0.619456641 -2.508497609
128 -2.884374480 0.619456641
129 -1.346519184 -2.884374480
130 -0.239195545 -1.346519184
131 0.679807853 -0.239195545
132 3.033486349 0.679807853
133 -1.778027697 3.033486349
134 -0.908235463 -1.778027697
135 2.368853766 -0.908235463
136 5.725273793 2.368853766
137 0.894251714 5.725273793
138 -0.932518976 0.894251714
139 1.342837611 -0.932518976
140 -0.320461412 1.342837611
141 0.578769899 -0.320461412
142 -2.633064357 0.578769899
143 -2.159895036 -2.633064357
144 2.719002905 -2.159895036
145 -1.969267690 2.719002905
146 2.404097122 -1.969267690
147 2.327810042 2.404097122
148 -1.719576111 2.327810042
149 2.830304938 -1.719576111
150 -0.438201863 2.830304938
151 0.812889181 -0.438201863
152 -1.350978684 0.812889181
153 0.235541089 -1.350978684
154 0.065442460 0.235541089
155 -1.504038061 0.065442460
156 -1.396422481 -1.504038061
157 7.202125771 -1.396422481
158 0.335239129 7.202125771
159 -0.948271323 0.335239129
160 -2.552413282 -0.948271323
161 -2.859212751 -2.552413282
> 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/7fdh01355073902.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/8emc01355073902.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/986f61355073902.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/10z8br1355073902.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/110mlt1355073902.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12omad1355073902.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13p8x31355073902.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14jpaa1355073902.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/15sc4m1355073902.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/161n641355073902.tab")
+ }
>
> try(system("convert tmp/1nyoz1355073902.ps tmp/1nyoz1355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n3bd1355073902.ps tmp/2n3bd1355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/30a8t1355073902.ps tmp/30a8t1355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v1571355073902.ps tmp/4v1571355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yvnp1355073902.ps tmp/5yvnp1355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/6clxh1355073902.ps tmp/6clxh1355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fdh01355073902.ps tmp/7fdh01355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/8emc01355073902.ps tmp/8emc01355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/986f61355073902.ps tmp/986f61355073902.png",intern=TRUE))
character(0)
> try(system("convert tmp/10z8br1355073902.ps tmp/10z8br1355073902.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.045 1.712 10.769