R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0)
+ ,dim=c(3
+ ,154)
+ ,dimnames=list(c('Tvier'
+ ,'Ttwee'
+ ,'CorrectAnalysis
')
+ ,1:154))
> y <- array(NA,dim=c(3,154),dimnames=list(c('Tvier','Ttwee','CorrectAnalysis
'),1:154))
> 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 = '3'
> 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
CorrectAnalysis\r\r\r\r Tvier Ttwee
1 0 1 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 1 0
9 0 0 0
10 0 0 0
11 0 1 0
12 0 0 0
13 0 0 0
14 0 1 0
15 0 0 0
16 0 1 0
17 1 1 0
18 0 1 0
19 0 0 0
20 1 1 0
21 0 0 0
22 0 0 0
23 0 0 0
24 0 0 0
25 0 1 0
26 0 0 0
27 0 0 0
28 0 0 0
29 0 0 0
30 0 0 0
31 0 0 0
32 0 0 0
33 0 0 0
34 0 1 0
35 0 0 0
36 0 0 0
37 0 1 0
38 0 0 0
39 0 0 0
40 0 1 0
41 1 0 0
42 0 0 0
43 0 0 0
44 0 1 0
45 0 0 0
46 0 0 0
47 0 0 0
48 0 0 0
49 0 0 0
50 0 0 0
51 0 1 0
52 1 1 0
53 0 0 0
54 1 0 0
55 0 0 0
56 0 1 0
57 0 0 0
58 0 0 0
59 0 0 0
60 1 1 0
61 0 1 0
62 0 0 0
63 0 0 0
64 0 1 0
65 0 0 0
66 0 0 0
67 1 1 0
68 0 0 0
69 0 0 0
70 0 0 0
71 0 0 0
72 0 0 0
73 0 0 0
74 0 0 0
75 0 0 0
76 0 1 0
77 0 0 0
78 0 0 0
79 1 1 0
80 0 1 0
81 0 0 0
82 0 0 0
83 0 0 0
84 1 0 0
85 0 0 0
86 0 0 0
87 0 0 0
88 0 0 1
89 0 0 0
90 0 0 0
91 0 0 0
92 0 0 1
93 0 0 0
94 0 0 0
95 0 0 1
96 0 0 0
97 0 0 1
98 0 0 0
99 0 0 0
100 0 0 0
101 0 0 0
102 0 0 0
103 0 0 0
104 0 0 0
105 0 0 1
106 0 0 0
107 0 0 0
108 0 0 1
109 0 0 0
110 0 0 0
111 0 0 1
112 0 0 1
113 0 0 0
114 0 0 1
115 0 0 0
116 0 0 0
117 0 0 0
118 0 0 0
119 0 0 0
120 0 0 0
121 0 0 0
122 0 0 0
123 0 0 1
124 0 0 0
125 0 0 0
126 0 0 1
127 0 0 0
128 0 0 0
129 0 0 0
130 0 0 0
131 0 0 0
132 0 0 0
133 0 0 0
134 0 0 0
135 0 0 0
136 0 0 0
137 0 0 0
138 0 0 1
139 0 0 1
140 0 0 0
141 1 0 0
142 0 0 1
143 0 0 0
144 0 0 0
145 0 0 0
146 0 0 1
147 0 0 1
148 0 0 1
149 0 0 0
150 0 0 0
151 0 0 0
152 1 0 0
153 1 0 0
154 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Tvier Ttwee
0.05263 0.20824 -0.05263
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.26087 -0.05263 -0.05263 -0.05263 0.94737
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.05263 0.02425 2.171 0.031509 *
Tvier 0.20824 0.05917 3.519 0.000572 ***
Ttwee -0.05263 0.06730 -0.782 0.435438
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2589 on 151 degrees of freedom
Multiple R-squared: 0.08549, Adjusted R-squared: 0.07338
F-statistic: 7.058 on 2 and 151 DF, p-value: 0.001174
> 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,] 0.000000e+00 0.0000000000 1.0000000000
[2,] 0.000000e+00 0.0000000000 1.0000000000
[3,] 0.000000e+00 0.0000000000 1.0000000000
[4,] 0.000000e+00 0.0000000000 1.0000000000
[5,] 0.000000e+00 0.0000000000 1.0000000000
[6,] 0.000000e+00 0.0000000000 1.0000000000
[7,] 0.000000e+00 0.0000000000 1.0000000000
[8,] 0.000000e+00 0.0000000000 1.0000000000
[9,] 0.000000e+00 0.0000000000 1.0000000000
[10,] 0.000000e+00 0.0000000000 1.0000000000
[11,] 0.000000e+00 0.0000000000 1.0000000000
[12,] 4.062265e-01 0.8124529965 0.5937735017
[13,] 3.639672e-01 0.7279344688 0.6360327656
[14,] 2.925932e-01 0.5851864972 0.7074067514
[15,] 8.259593e-01 0.3480814065 0.1740407033
[16,] 7.771617e-01 0.4456766764 0.2228383382
[17,] 7.223594e-01 0.5552811231 0.2776405616
[18,] 6.626770e-01 0.6746460222 0.3373230111
[19,] 5.995593e-01 0.8008813709 0.4004406854
[20,] 5.887875e-01 0.8224249169 0.4112124585
[21,] 5.248229e-01 0.9503542056 0.4751771028
[22,] 4.610248e-01 0.9220495362 0.5389752319
[23,] 3.989631e-01 0.7979261868 0.6010369066
[24,] 3.400297e-01 0.6800593453 0.6599703273
[25,] 2.853574e-01 0.5707148044 0.7146425978
[26,] 2.357716e-01 0.4715431164 0.7642284418
[27,] 1.917741e-01 0.3835482343 0.8082258828
[28,] 1.535578e-01 0.3071155914 0.8464422043
[29,] 1.477816e-01 0.2955631713 0.8522184144
[30,] 1.165199e-01 0.2330398808 0.8834800596
[31,] 9.047308e-02 0.1809461571 0.9095269214
[32,] 8.562806e-02 0.1712561146 0.9143719427
[33,] 6.544823e-02 0.1308964640 0.9345517680
[34,] 4.928263e-02 0.0985652633 0.9507173684
[35,] 4.629813e-02 0.0925962652 0.9537018674
[36,] 5.907129e-01 0.8185741374 0.4092870687
[37,] 5.396923e-01 0.9206153560 0.4603076780
[38,] 4.882895e-01 0.9765789323 0.5117105338
[39,] 4.813457e-01 0.9626913306 0.5186543347
[40,] 4.309010e-01 0.8618020836 0.5690989582
[41,] 3.818008e-01 0.7636015535 0.6181992232
[42,] 3.347566e-01 0.6695131192 0.6652434404
[43,] 2.903774e-01 0.5807547705 0.7096226147
[44,] 2.491491e-01 0.4982981311 0.7508509344
[45,] 2.114228e-01 0.4228455015 0.7885772493
[46,] 2.125978e-01 0.4251956203 0.7874021899
[47,] 5.745171e-01 0.8509657814 0.4254828907
[48,] 5.269271e-01 0.9461457837 0.4730728919
[49,] 9.389624e-01 0.1220751643 0.0610375822
[50,] 9.236220e-01 0.1527560672 0.0763780336
[51,] 9.288450e-01 0.1423100226 0.0711550113
[52,] 9.117731e-01 0.1764538733 0.0882269367
[53,] 8.918639e-01 0.2162721605 0.1081360802
[54,] 8.689582e-01 0.2620836736 0.1310418368
[55,] 9.696791e-01 0.0606418595 0.0303209298
[56,] 9.722668e-01 0.0554664813 0.0277332407
[57,] 9.641904e-01 0.0716192485 0.0358096243
[58,] 9.542977e-01 0.0914045599 0.0457022799
[59,] 9.629129e-01 0.0741741244 0.0370870622
[60,] 9.527920e-01 0.0944160751 0.0472080375
[61,] 9.405863e-01 0.1188274224 0.0594137112
[62,] 9.869761e-01 0.0260478909 0.0130239455
[63,] 9.827036e-01 0.0345928764 0.0172964382
[64,] 9.772935e-01 0.0454129079 0.0227064540
[65,] 9.705304e-01 0.0589392590 0.0294696295
[66,] 9.621829e-01 0.0756342849 0.0378171424
[67,] 9.520102e-01 0.0959796897 0.0479898448
[68,] 9.397695e-01 0.1204610750 0.0602305375
[69,] 9.252255e-01 0.1495490054 0.0747745027
[70,] 9.081614e-01 0.1836771592 0.0918385796
[71,] 9.224785e-01 0.1550430629 0.0775215314
[72,] 9.049884e-01 0.1900232522 0.0950116261
[73,] 8.847692e-01 0.2304616680 0.1152308340
[74,] 9.776056e-01 0.0447887558 0.0223943779
[75,] 9.735737e-01 0.0528525726 0.0264262863
[76,] 9.659746e-01 0.0680507893 0.0340253946
[77,] 9.566692e-01 0.0866615256 0.0433307628
[78,] 9.454162e-01 0.1091676212 0.0545838106
[79,] 9.991716e-01 0.0016567151 0.0008283575
[80,] 9.987830e-01 0.0024340352 0.0012170176
[81,] 9.982331e-01 0.0035338885 0.0017669442
[82,] 9.974648e-01 0.0050703088 0.0025351544
[83,] 9.963358e-01 0.0073284758 0.0036642379
[84,] 9.948662e-01 0.0102675220 0.0051337610
[85,] 9.928915e-01 0.0142170301 0.0071085150
[86,] 9.902719e-01 0.0194561108 0.0097280554
[87,] 9.866039e-01 0.0267921434 0.0133960717
[88,] 9.820936e-01 0.0358127038 0.0179063519
[89,] 9.763413e-01 0.0473174327 0.0236587163
[90,] 9.685781e-01 0.0628438971 0.0314219486
[91,] 9.594387e-01 0.0811225168 0.0405612584
[92,] 9.474168e-01 0.1051664271 0.0525832136
[93,] 9.336695e-01 0.1326610620 0.0663305310
[94,] 9.172695e-01 0.1654610487 0.0827305244
[95,] 8.979667e-01 0.2040665428 0.1020332714
[96,] 8.755530e-01 0.2488940720 0.1244470360
[97,] 8.498785e-01 0.3002429292 0.1501214646
[98,] 8.208686e-01 0.3582628062 0.1791314031
[99,] 7.885373e-01 0.4229253076 0.2114626538
[100,] 7.497376e-01 0.5005247060 0.2502623530
[101,] 7.107889e-01 0.5784221735 0.2892110868
[102,] 6.691751e-01 0.6616498574 0.3308249287
[103,] 6.210238e-01 0.7579523948 0.3789761974
[104,] 5.751123e-01 0.8497754363 0.4248877182
[105,] 5.281332e-01 0.9437336860 0.4718668430
[106,] 4.757182e-01 0.9514363318 0.5242818341
[107,] 4.234427e-01 0.8468853348 0.5765573326
[108,] 3.769615e-01 0.7539230027 0.6230384987
[109,] 3.273612e-01 0.6547224942 0.6726387529
[110,] 2.848475e-01 0.5696950179 0.7151524910
[111,] 2.451422e-01 0.4902843017 0.7548578491
[112,] 2.086156e-01 0.4172312896 0.7913843552
[113,] 1.755191e-01 0.3510381298 0.8244809351
[114,] 1.459826e-01 0.2919652548 0.8540173726
[115,] 1.200213e-01 0.2400426792 0.8799786604
[116,] 9.754675e-02 0.1950934962 0.9024532519
[117,] 7.838333e-02 0.1567666691 0.9216166655
[118,] 5.948874e-02 0.1189774864 0.9405112568
[119,] 4.645512e-02 0.0929102436 0.9535448782
[120,] 3.587144e-02 0.0717428877 0.9641285561
[121,] 2.570919e-02 0.0514183737 0.9742908132
[122,] 1.926371e-02 0.0385274139 0.9807362931
[123,] 1.428466e-02 0.0285693237 0.9857153381
[124,] 1.049475e-02 0.0209894977 0.9895052512
[125,] 7.650980e-03 0.0153019607 0.9923490196
[126,] 5.546407e-03 0.0110928148 0.9944535926
[127,] 4.009406e-03 0.0080188123 0.9959905939
[128,] 2.901212e-03 0.0058024240 0.9970987880
[129,] 2.112371e-03 0.0042247420 0.9978876290
[130,] 1.558714e-03 0.0031174283 0.9984412859
[131,] 1.177389e-03 0.0023547776 0.9988226112
[132,] 9.234670e-04 0.0018469341 0.9990765330
[133,] 4.980701e-04 0.0009961403 0.9995019299
[134,] 2.570790e-04 0.0005141581 0.9997429210
[135,] 2.020577e-04 0.0004041153 0.9997979423
[136,] 6.547347e-03 0.0130946941 0.9934526529
[137,] 3.558694e-03 0.0071173886 0.9964413057
[138,] 2.486049e-03 0.0049720985 0.9975139508
[139,] 1.832355e-03 0.0036647105 0.9981676447
[140,] 1.504019e-03 0.0030080376 0.9984959812
[141,] 6.359523e-04 0.0012719046 0.9993640477
[142,] 2.373018e-04 0.0004746036 0.9997626982
[143,] 7.505823e-05 0.0001501165 0.9999249418
> postscript(file="/var/wessaorg/rcomp/tmp/1gj3f1356206641.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/wessaorg/rcomp/tmp/2whih1356206641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/35zr51356206641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4f2yf1356206641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5lw5x1356206641.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 154
Frequency = 1
1 2 3 4 5
-2.608696e-01 -5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02
6 7 8 9 10
-5.263158e-02 -5.263158e-02 -2.608696e-01 -5.263158e-02 -5.263158e-02
11 12 13 14 15
-2.608696e-01 -5.263158e-02 -5.263158e-02 -2.608696e-01 -5.263158e-02
16 17 18 19 20
-2.608696e-01 7.391304e-01 -2.608696e-01 -5.263158e-02 7.391304e-01
21 22 23 24 25
-5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02 -2.608696e-01
26 27 28 29 30
-5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02
31 32 33 34 35
-5.263158e-02 -5.263158e-02 -5.263158e-02 -2.608696e-01 -5.263158e-02
36 37 38 39 40
-5.263158e-02 -2.608696e-01 -5.263158e-02 -5.263158e-02 -2.608696e-01
41 42 43 44 45
9.473684e-01 -5.263158e-02 -5.263158e-02 -2.608696e-01 -5.263158e-02
46 47 48 49 50
-5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02
51 52 53 54 55
-2.608696e-01 7.391304e-01 -5.263158e-02 9.473684e-01 -5.263158e-02
56 57 58 59 60
-2.608696e-01 -5.263158e-02 -5.263158e-02 -5.263158e-02 7.391304e-01
61 62 63 64 65
-2.608696e-01 -5.263158e-02 -5.263158e-02 -2.608696e-01 -5.263158e-02
66 67 68 69 70
-5.263158e-02 7.391304e-01 -5.263158e-02 -5.263158e-02 -5.263158e-02
71 72 73 74 75
-5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02
76 77 78 79 80
-2.608696e-01 -5.263158e-02 -5.263158e-02 7.391304e-01 -2.608696e-01
81 82 83 84 85
-5.263158e-02 -5.263158e-02 -5.263158e-02 9.473684e-01 -5.263158e-02
86 87 88 89 90
-5.263158e-02 -5.263158e-02 -3.301734e-18 -5.263158e-02 -5.263158e-02
91 92 93 94 95
-5.263158e-02 -3.301734e-18 -5.263158e-02 -5.263158e-02 -3.301734e-18
96 97 98 99 100
-5.263158e-02 -3.301734e-18 -5.263158e-02 -5.263158e-02 -5.263158e-02
101 102 103 104 105
-5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02 -3.301734e-18
106 107 108 109 110
-5.263158e-02 -5.263158e-02 -3.301734e-18 -5.263158e-02 -5.263158e-02
111 112 113 114 115
-3.301734e-18 -3.301734e-18 -5.263158e-02 -3.301734e-18 -5.263158e-02
116 117 118 119 120
-5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02
121 122 123 124 125
-5.263158e-02 -5.263158e-02 -3.301734e-18 -5.263158e-02 -5.263158e-02
126 127 128 129 130
-3.301734e-18 -5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02
131 132 133 134 135
-5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02 -5.263158e-02
136 137 138 139 140
-5.263158e-02 -5.263158e-02 -3.301734e-18 -3.301734e-18 -5.263158e-02
141 142 143 144 145
9.473684e-01 -3.301734e-18 -5.263158e-02 -5.263158e-02 -5.263158e-02
146 147 148 149 150
-3.301734e-18 -3.301734e-18 -3.301734e-18 -5.263158e-02 -5.263158e-02
151 152 153 154
-5.263158e-02 9.473684e-01 9.473684e-01 -5.263158e-02
> postscript(file="/var/wessaorg/rcomp/tmp/6nmz21356206641.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.608696e-01 NA
1 -5.263158e-02 -2.608696e-01
2 -5.263158e-02 -5.263158e-02
3 -5.263158e-02 -5.263158e-02
4 -5.263158e-02 -5.263158e-02
5 -5.263158e-02 -5.263158e-02
6 -5.263158e-02 -5.263158e-02
7 -2.608696e-01 -5.263158e-02
8 -5.263158e-02 -2.608696e-01
9 -5.263158e-02 -5.263158e-02
10 -2.608696e-01 -5.263158e-02
11 -5.263158e-02 -2.608696e-01
12 -5.263158e-02 -5.263158e-02
13 -2.608696e-01 -5.263158e-02
14 -5.263158e-02 -2.608696e-01
15 -2.608696e-01 -5.263158e-02
16 7.391304e-01 -2.608696e-01
17 -2.608696e-01 7.391304e-01
18 -5.263158e-02 -2.608696e-01
19 7.391304e-01 -5.263158e-02
20 -5.263158e-02 7.391304e-01
21 -5.263158e-02 -5.263158e-02
22 -5.263158e-02 -5.263158e-02
23 -5.263158e-02 -5.263158e-02
24 -2.608696e-01 -5.263158e-02
25 -5.263158e-02 -2.608696e-01
26 -5.263158e-02 -5.263158e-02
27 -5.263158e-02 -5.263158e-02
28 -5.263158e-02 -5.263158e-02
29 -5.263158e-02 -5.263158e-02
30 -5.263158e-02 -5.263158e-02
31 -5.263158e-02 -5.263158e-02
32 -5.263158e-02 -5.263158e-02
33 -2.608696e-01 -5.263158e-02
34 -5.263158e-02 -2.608696e-01
35 -5.263158e-02 -5.263158e-02
36 -2.608696e-01 -5.263158e-02
37 -5.263158e-02 -2.608696e-01
38 -5.263158e-02 -5.263158e-02
39 -2.608696e-01 -5.263158e-02
40 9.473684e-01 -2.608696e-01
41 -5.263158e-02 9.473684e-01
42 -5.263158e-02 -5.263158e-02
43 -2.608696e-01 -5.263158e-02
44 -5.263158e-02 -2.608696e-01
45 -5.263158e-02 -5.263158e-02
46 -5.263158e-02 -5.263158e-02
47 -5.263158e-02 -5.263158e-02
48 -5.263158e-02 -5.263158e-02
49 -5.263158e-02 -5.263158e-02
50 -2.608696e-01 -5.263158e-02
51 7.391304e-01 -2.608696e-01
52 -5.263158e-02 7.391304e-01
53 9.473684e-01 -5.263158e-02
54 -5.263158e-02 9.473684e-01
55 -2.608696e-01 -5.263158e-02
56 -5.263158e-02 -2.608696e-01
57 -5.263158e-02 -5.263158e-02
58 -5.263158e-02 -5.263158e-02
59 7.391304e-01 -5.263158e-02
60 -2.608696e-01 7.391304e-01
61 -5.263158e-02 -2.608696e-01
62 -5.263158e-02 -5.263158e-02
63 -2.608696e-01 -5.263158e-02
64 -5.263158e-02 -2.608696e-01
65 -5.263158e-02 -5.263158e-02
66 7.391304e-01 -5.263158e-02
67 -5.263158e-02 7.391304e-01
68 -5.263158e-02 -5.263158e-02
69 -5.263158e-02 -5.263158e-02
70 -5.263158e-02 -5.263158e-02
71 -5.263158e-02 -5.263158e-02
72 -5.263158e-02 -5.263158e-02
73 -5.263158e-02 -5.263158e-02
74 -5.263158e-02 -5.263158e-02
75 -2.608696e-01 -5.263158e-02
76 -5.263158e-02 -2.608696e-01
77 -5.263158e-02 -5.263158e-02
78 7.391304e-01 -5.263158e-02
79 -2.608696e-01 7.391304e-01
80 -5.263158e-02 -2.608696e-01
81 -5.263158e-02 -5.263158e-02
82 -5.263158e-02 -5.263158e-02
83 9.473684e-01 -5.263158e-02
84 -5.263158e-02 9.473684e-01
85 -5.263158e-02 -5.263158e-02
86 -5.263158e-02 -5.263158e-02
87 -3.301734e-18 -5.263158e-02
88 -5.263158e-02 -3.301734e-18
89 -5.263158e-02 -5.263158e-02
90 -5.263158e-02 -5.263158e-02
91 -3.301734e-18 -5.263158e-02
92 -5.263158e-02 -3.301734e-18
93 -5.263158e-02 -5.263158e-02
94 -3.301734e-18 -5.263158e-02
95 -5.263158e-02 -3.301734e-18
96 -3.301734e-18 -5.263158e-02
97 -5.263158e-02 -3.301734e-18
98 -5.263158e-02 -5.263158e-02
99 -5.263158e-02 -5.263158e-02
100 -5.263158e-02 -5.263158e-02
101 -5.263158e-02 -5.263158e-02
102 -5.263158e-02 -5.263158e-02
103 -5.263158e-02 -5.263158e-02
104 -3.301734e-18 -5.263158e-02
105 -5.263158e-02 -3.301734e-18
106 -5.263158e-02 -5.263158e-02
107 -3.301734e-18 -5.263158e-02
108 -5.263158e-02 -3.301734e-18
109 -5.263158e-02 -5.263158e-02
110 -3.301734e-18 -5.263158e-02
111 -3.301734e-18 -3.301734e-18
112 -5.263158e-02 -3.301734e-18
113 -3.301734e-18 -5.263158e-02
114 -5.263158e-02 -3.301734e-18
115 -5.263158e-02 -5.263158e-02
116 -5.263158e-02 -5.263158e-02
117 -5.263158e-02 -5.263158e-02
118 -5.263158e-02 -5.263158e-02
119 -5.263158e-02 -5.263158e-02
120 -5.263158e-02 -5.263158e-02
121 -5.263158e-02 -5.263158e-02
122 -3.301734e-18 -5.263158e-02
123 -5.263158e-02 -3.301734e-18
124 -5.263158e-02 -5.263158e-02
125 -3.301734e-18 -5.263158e-02
126 -5.263158e-02 -3.301734e-18
127 -5.263158e-02 -5.263158e-02
128 -5.263158e-02 -5.263158e-02
129 -5.263158e-02 -5.263158e-02
130 -5.263158e-02 -5.263158e-02
131 -5.263158e-02 -5.263158e-02
132 -5.263158e-02 -5.263158e-02
133 -5.263158e-02 -5.263158e-02
134 -5.263158e-02 -5.263158e-02
135 -5.263158e-02 -5.263158e-02
136 -5.263158e-02 -5.263158e-02
137 -3.301734e-18 -5.263158e-02
138 -3.301734e-18 -3.301734e-18
139 -5.263158e-02 -3.301734e-18
140 9.473684e-01 -5.263158e-02
141 -3.301734e-18 9.473684e-01
142 -5.263158e-02 -3.301734e-18
143 -5.263158e-02 -5.263158e-02
144 -5.263158e-02 -5.263158e-02
145 -3.301734e-18 -5.263158e-02
146 -3.301734e-18 -3.301734e-18
147 -3.301734e-18 -3.301734e-18
148 -5.263158e-02 -3.301734e-18
149 -5.263158e-02 -5.263158e-02
150 -5.263158e-02 -5.263158e-02
151 9.473684e-01 -5.263158e-02
152 9.473684e-01 9.473684e-01
153 -5.263158e-02 9.473684e-01
154 NA -5.263158e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.263158e-02 -2.608696e-01
[2,] -5.263158e-02 -5.263158e-02
[3,] -5.263158e-02 -5.263158e-02
[4,] -5.263158e-02 -5.263158e-02
[5,] -5.263158e-02 -5.263158e-02
[6,] -5.263158e-02 -5.263158e-02
[7,] -2.608696e-01 -5.263158e-02
[8,] -5.263158e-02 -2.608696e-01
[9,] -5.263158e-02 -5.263158e-02
[10,] -2.608696e-01 -5.263158e-02
[11,] -5.263158e-02 -2.608696e-01
[12,] -5.263158e-02 -5.263158e-02
[13,] -2.608696e-01 -5.263158e-02
[14,] -5.263158e-02 -2.608696e-01
[15,] -2.608696e-01 -5.263158e-02
[16,] 7.391304e-01 -2.608696e-01
[17,] -2.608696e-01 7.391304e-01
[18,] -5.263158e-02 -2.608696e-01
[19,] 7.391304e-01 -5.263158e-02
[20,] -5.263158e-02 7.391304e-01
[21,] -5.263158e-02 -5.263158e-02
[22,] -5.263158e-02 -5.263158e-02
[23,] -5.263158e-02 -5.263158e-02
[24,] -2.608696e-01 -5.263158e-02
[25,] -5.263158e-02 -2.608696e-01
[26,] -5.263158e-02 -5.263158e-02
[27,] -5.263158e-02 -5.263158e-02
[28,] -5.263158e-02 -5.263158e-02
[29,] -5.263158e-02 -5.263158e-02
[30,] -5.263158e-02 -5.263158e-02
[31,] -5.263158e-02 -5.263158e-02
[32,] -5.263158e-02 -5.263158e-02
[33,] -2.608696e-01 -5.263158e-02
[34,] -5.263158e-02 -2.608696e-01
[35,] -5.263158e-02 -5.263158e-02
[36,] -2.608696e-01 -5.263158e-02
[37,] -5.263158e-02 -2.608696e-01
[38,] -5.263158e-02 -5.263158e-02
[39,] -2.608696e-01 -5.263158e-02
[40,] 9.473684e-01 -2.608696e-01
[41,] -5.263158e-02 9.473684e-01
[42,] -5.263158e-02 -5.263158e-02
[43,] -2.608696e-01 -5.263158e-02
[44,] -5.263158e-02 -2.608696e-01
[45,] -5.263158e-02 -5.263158e-02
[46,] -5.263158e-02 -5.263158e-02
[47,] -5.263158e-02 -5.263158e-02
[48,] -5.263158e-02 -5.263158e-02
[49,] -5.263158e-02 -5.263158e-02
[50,] -2.608696e-01 -5.263158e-02
[51,] 7.391304e-01 -2.608696e-01
[52,] -5.263158e-02 7.391304e-01
[53,] 9.473684e-01 -5.263158e-02
[54,] -5.263158e-02 9.473684e-01
[55,] -2.608696e-01 -5.263158e-02
[56,] -5.263158e-02 -2.608696e-01
[57,] -5.263158e-02 -5.263158e-02
[58,] -5.263158e-02 -5.263158e-02
[59,] 7.391304e-01 -5.263158e-02
[60,] -2.608696e-01 7.391304e-01
[61,] -5.263158e-02 -2.608696e-01
[62,] -5.263158e-02 -5.263158e-02
[63,] -2.608696e-01 -5.263158e-02
[64,] -5.263158e-02 -2.608696e-01
[65,] -5.263158e-02 -5.263158e-02
[66,] 7.391304e-01 -5.263158e-02
[67,] -5.263158e-02 7.391304e-01
[68,] -5.263158e-02 -5.263158e-02
[69,] -5.263158e-02 -5.263158e-02
[70,] -5.263158e-02 -5.263158e-02
[71,] -5.263158e-02 -5.263158e-02
[72,] -5.263158e-02 -5.263158e-02
[73,] -5.263158e-02 -5.263158e-02
[74,] -5.263158e-02 -5.263158e-02
[75,] -2.608696e-01 -5.263158e-02
[76,] -5.263158e-02 -2.608696e-01
[77,] -5.263158e-02 -5.263158e-02
[78,] 7.391304e-01 -5.263158e-02
[79,] -2.608696e-01 7.391304e-01
[80,] -5.263158e-02 -2.608696e-01
[81,] -5.263158e-02 -5.263158e-02
[82,] -5.263158e-02 -5.263158e-02
[83,] 9.473684e-01 -5.263158e-02
[84,] -5.263158e-02 9.473684e-01
[85,] -5.263158e-02 -5.263158e-02
[86,] -5.263158e-02 -5.263158e-02
[87,] -3.301734e-18 -5.263158e-02
[88,] -5.263158e-02 -3.301734e-18
[89,] -5.263158e-02 -5.263158e-02
[90,] -5.263158e-02 -5.263158e-02
[91,] -3.301734e-18 -5.263158e-02
[92,] -5.263158e-02 -3.301734e-18
[93,] -5.263158e-02 -5.263158e-02
[94,] -3.301734e-18 -5.263158e-02
[95,] -5.263158e-02 -3.301734e-18
[96,] -3.301734e-18 -5.263158e-02
[97,] -5.263158e-02 -3.301734e-18
[98,] -5.263158e-02 -5.263158e-02
[99,] -5.263158e-02 -5.263158e-02
[100,] -5.263158e-02 -5.263158e-02
[101,] -5.263158e-02 -5.263158e-02
[102,] -5.263158e-02 -5.263158e-02
[103,] -5.263158e-02 -5.263158e-02
[104,] -3.301734e-18 -5.263158e-02
[105,] -5.263158e-02 -3.301734e-18
[106,] -5.263158e-02 -5.263158e-02
[107,] -3.301734e-18 -5.263158e-02
[108,] -5.263158e-02 -3.301734e-18
[109,] -5.263158e-02 -5.263158e-02
[110,] -3.301734e-18 -5.263158e-02
[111,] -3.301734e-18 -3.301734e-18
[112,] -5.263158e-02 -3.301734e-18
[113,] -3.301734e-18 -5.263158e-02
[114,] -5.263158e-02 -3.301734e-18
[115,] -5.263158e-02 -5.263158e-02
[116,] -5.263158e-02 -5.263158e-02
[117,] -5.263158e-02 -5.263158e-02
[118,] -5.263158e-02 -5.263158e-02
[119,] -5.263158e-02 -5.263158e-02
[120,] -5.263158e-02 -5.263158e-02
[121,] -5.263158e-02 -5.263158e-02
[122,] -3.301734e-18 -5.263158e-02
[123,] -5.263158e-02 -3.301734e-18
[124,] -5.263158e-02 -5.263158e-02
[125,] -3.301734e-18 -5.263158e-02
[126,] -5.263158e-02 -3.301734e-18
[127,] -5.263158e-02 -5.263158e-02
[128,] -5.263158e-02 -5.263158e-02
[129,] -5.263158e-02 -5.263158e-02
[130,] -5.263158e-02 -5.263158e-02
[131,] -5.263158e-02 -5.263158e-02
[132,] -5.263158e-02 -5.263158e-02
[133,] -5.263158e-02 -5.263158e-02
[134,] -5.263158e-02 -5.263158e-02
[135,] -5.263158e-02 -5.263158e-02
[136,] -5.263158e-02 -5.263158e-02
[137,] -3.301734e-18 -5.263158e-02
[138,] -3.301734e-18 -3.301734e-18
[139,] -5.263158e-02 -3.301734e-18
[140,] 9.473684e-01 -5.263158e-02
[141,] -3.301734e-18 9.473684e-01
[142,] -5.263158e-02 -3.301734e-18
[143,] -5.263158e-02 -5.263158e-02
[144,] -5.263158e-02 -5.263158e-02
[145,] -3.301734e-18 -5.263158e-02
[146,] -3.301734e-18 -3.301734e-18
[147,] -3.301734e-18 -3.301734e-18
[148,] -5.263158e-02 -3.301734e-18
[149,] -5.263158e-02 -5.263158e-02
[150,] -5.263158e-02 -5.263158e-02
[151,] 9.473684e-01 -5.263158e-02
[152,] 9.473684e-01 9.473684e-01
[153,] -5.263158e-02 9.473684e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.263158e-02 -2.608696e-01
2 -5.263158e-02 -5.263158e-02
3 -5.263158e-02 -5.263158e-02
4 -5.263158e-02 -5.263158e-02
5 -5.263158e-02 -5.263158e-02
6 -5.263158e-02 -5.263158e-02
7 -2.608696e-01 -5.263158e-02
8 -5.263158e-02 -2.608696e-01
9 -5.263158e-02 -5.263158e-02
10 -2.608696e-01 -5.263158e-02
11 -5.263158e-02 -2.608696e-01
12 -5.263158e-02 -5.263158e-02
13 -2.608696e-01 -5.263158e-02
14 -5.263158e-02 -2.608696e-01
15 -2.608696e-01 -5.263158e-02
16 7.391304e-01 -2.608696e-01
17 -2.608696e-01 7.391304e-01
18 -5.263158e-02 -2.608696e-01
19 7.391304e-01 -5.263158e-02
20 -5.263158e-02 7.391304e-01
21 -5.263158e-02 -5.263158e-02
22 -5.263158e-02 -5.263158e-02
23 -5.263158e-02 -5.263158e-02
24 -2.608696e-01 -5.263158e-02
25 -5.263158e-02 -2.608696e-01
26 -5.263158e-02 -5.263158e-02
27 -5.263158e-02 -5.263158e-02
28 -5.263158e-02 -5.263158e-02
29 -5.263158e-02 -5.263158e-02
30 -5.263158e-02 -5.263158e-02
31 -5.263158e-02 -5.263158e-02
32 -5.263158e-02 -5.263158e-02
33 -2.608696e-01 -5.263158e-02
34 -5.263158e-02 -2.608696e-01
35 -5.263158e-02 -5.263158e-02
36 -2.608696e-01 -5.263158e-02
37 -5.263158e-02 -2.608696e-01
38 -5.263158e-02 -5.263158e-02
39 -2.608696e-01 -5.263158e-02
40 9.473684e-01 -2.608696e-01
41 -5.263158e-02 9.473684e-01
42 -5.263158e-02 -5.263158e-02
43 -2.608696e-01 -5.263158e-02
44 -5.263158e-02 -2.608696e-01
45 -5.263158e-02 -5.263158e-02
46 -5.263158e-02 -5.263158e-02
47 -5.263158e-02 -5.263158e-02
48 -5.263158e-02 -5.263158e-02
49 -5.263158e-02 -5.263158e-02
50 -2.608696e-01 -5.263158e-02
51 7.391304e-01 -2.608696e-01
52 -5.263158e-02 7.391304e-01
53 9.473684e-01 -5.263158e-02
54 -5.263158e-02 9.473684e-01
55 -2.608696e-01 -5.263158e-02
56 -5.263158e-02 -2.608696e-01
57 -5.263158e-02 -5.263158e-02
58 -5.263158e-02 -5.263158e-02
59 7.391304e-01 -5.263158e-02
60 -2.608696e-01 7.391304e-01
61 -5.263158e-02 -2.608696e-01
62 -5.263158e-02 -5.263158e-02
63 -2.608696e-01 -5.263158e-02
64 -5.263158e-02 -2.608696e-01
65 -5.263158e-02 -5.263158e-02
66 7.391304e-01 -5.263158e-02
67 -5.263158e-02 7.391304e-01
68 -5.263158e-02 -5.263158e-02
69 -5.263158e-02 -5.263158e-02
70 -5.263158e-02 -5.263158e-02
71 -5.263158e-02 -5.263158e-02
72 -5.263158e-02 -5.263158e-02
73 -5.263158e-02 -5.263158e-02
74 -5.263158e-02 -5.263158e-02
75 -2.608696e-01 -5.263158e-02
76 -5.263158e-02 -2.608696e-01
77 -5.263158e-02 -5.263158e-02
78 7.391304e-01 -5.263158e-02
79 -2.608696e-01 7.391304e-01
80 -5.263158e-02 -2.608696e-01
81 -5.263158e-02 -5.263158e-02
82 -5.263158e-02 -5.263158e-02
83 9.473684e-01 -5.263158e-02
84 -5.263158e-02 9.473684e-01
85 -5.263158e-02 -5.263158e-02
86 -5.263158e-02 -5.263158e-02
87 -3.301734e-18 -5.263158e-02
88 -5.263158e-02 -3.301734e-18
89 -5.263158e-02 -5.263158e-02
90 -5.263158e-02 -5.263158e-02
91 -3.301734e-18 -5.263158e-02
92 -5.263158e-02 -3.301734e-18
93 -5.263158e-02 -5.263158e-02
94 -3.301734e-18 -5.263158e-02
95 -5.263158e-02 -3.301734e-18
96 -3.301734e-18 -5.263158e-02
97 -5.263158e-02 -3.301734e-18
98 -5.263158e-02 -5.263158e-02
99 -5.263158e-02 -5.263158e-02
100 -5.263158e-02 -5.263158e-02
101 -5.263158e-02 -5.263158e-02
102 -5.263158e-02 -5.263158e-02
103 -5.263158e-02 -5.263158e-02
104 -3.301734e-18 -5.263158e-02
105 -5.263158e-02 -3.301734e-18
106 -5.263158e-02 -5.263158e-02
107 -3.301734e-18 -5.263158e-02
108 -5.263158e-02 -3.301734e-18
109 -5.263158e-02 -5.263158e-02
110 -3.301734e-18 -5.263158e-02
111 -3.301734e-18 -3.301734e-18
112 -5.263158e-02 -3.301734e-18
113 -3.301734e-18 -5.263158e-02
114 -5.263158e-02 -3.301734e-18
115 -5.263158e-02 -5.263158e-02
116 -5.263158e-02 -5.263158e-02
117 -5.263158e-02 -5.263158e-02
118 -5.263158e-02 -5.263158e-02
119 -5.263158e-02 -5.263158e-02
120 -5.263158e-02 -5.263158e-02
121 -5.263158e-02 -5.263158e-02
122 -3.301734e-18 -5.263158e-02
123 -5.263158e-02 -3.301734e-18
124 -5.263158e-02 -5.263158e-02
125 -3.301734e-18 -5.263158e-02
126 -5.263158e-02 -3.301734e-18
127 -5.263158e-02 -5.263158e-02
128 -5.263158e-02 -5.263158e-02
129 -5.263158e-02 -5.263158e-02
130 -5.263158e-02 -5.263158e-02
131 -5.263158e-02 -5.263158e-02
132 -5.263158e-02 -5.263158e-02
133 -5.263158e-02 -5.263158e-02
134 -5.263158e-02 -5.263158e-02
135 -5.263158e-02 -5.263158e-02
136 -5.263158e-02 -5.263158e-02
137 -3.301734e-18 -5.263158e-02
138 -3.301734e-18 -3.301734e-18
139 -5.263158e-02 -3.301734e-18
140 9.473684e-01 -5.263158e-02
141 -3.301734e-18 9.473684e-01
142 -5.263158e-02 -3.301734e-18
143 -5.263158e-02 -5.263158e-02
144 -5.263158e-02 -5.263158e-02
145 -3.301734e-18 -5.263158e-02
146 -3.301734e-18 -3.301734e-18
147 -3.301734e-18 -3.301734e-18
148 -5.263158e-02 -3.301734e-18
149 -5.263158e-02 -5.263158e-02
150 -5.263158e-02 -5.263158e-02
151 9.473684e-01 -5.263158e-02
152 9.473684e-01 9.473684e-01
153 -5.263158e-02 9.473684e-01
> 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/wessaorg/rcomp/tmp/7rehz1356206642.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/wessaorg/rcomp/tmp/85zvd1356206642.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/wessaorg/rcomp/tmp/9wi7n1356206642.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/wessaorg/rcomp/tmp/10qr1y1356206642.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11fpe91356206642.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12efkf1356206642.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13i3uc1356206642.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14f6ls1356206642.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/wessaorg/rcomp/tmp/15rge21356206642.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/wessaorg/rcomp/tmp/164c0z1356206642.tab")
+ }
>
> try(system("convert tmp/1gj3f1356206641.ps tmp/1gj3f1356206641.png",intern=TRUE))
character(0)
> try(system("convert tmp/2whih1356206641.ps tmp/2whih1356206641.png",intern=TRUE))
character(0)
> try(system("convert tmp/35zr51356206641.ps tmp/35zr51356206641.png",intern=TRUE))
character(0)
> try(system("convert tmp/4f2yf1356206641.ps tmp/4f2yf1356206641.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lw5x1356206641.ps tmp/5lw5x1356206641.png",intern=TRUE))
character(0)
> try(system("convert tmp/6nmz21356206641.ps tmp/6nmz21356206641.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rehz1356206642.ps tmp/7rehz1356206642.png",intern=TRUE))
character(0)
> try(system("convert tmp/85zvd1356206642.ps tmp/85zvd1356206642.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wi7n1356206642.ps tmp/9wi7n1356206642.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qr1y1356206642.ps tmp/10qr1y1356206642.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.035 1.146 11.218