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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> x <- array(list(4
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+ ,dim=c(7
+ ,154)
+ ,dimnames=list(c('Weeks'
+ ,'UseLimit'
+ ,'Group'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome')
+ ,1:154))
> y <- array(NA,dim=c(7,154),dimnames=list(c('Weeks','UseLimit','Group','Used','CorrectAnalysis','Useful','Outcome'),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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> 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
Weeks UseLimit Group Used CorrectAnalysis Useful Outcome
1 4 1 1 0 0 0 1
2 4 0 0 0 0 0 0
3 4 0 0 0 0 0 0
4 4 0 0 0 0 0 0
5 4 0 0 0 0 0 0
6 4 1 0 0 0 1 1
7 4 0 0 0 0 0 0
8 4 0 1 0 0 0 0
9 4 0 0 0 0 0 1
10 4 1 0 0 0 0 0
11 4 1 1 0 0 0 0
12 4 0 0 0 0 0 0
13 4 0 0 1 0 1 0
14 4 1 1 0 0 0 0
15 4 0 0 1 0 1 1
16 4 0 1 1 0 1 1
17 4 1 1 1 1 1 0
18 4 1 1 0 0 0 0
19 4 0 0 0 0 0 1
20 4 0 1 1 1 1 1
21 4 1 0 0 0 1 0
22 4 1 0 1 0 1 1
23 4 0 0 0 0 1 1
24 4 1 0 0 0 1 1
25 4 0 1 1 0 0 1
26 4 0 0 1 0 1 0
27 4 1 0 0 0 0 1
28 4 0 0 1 0 0 0
29 4 0 0 0 0 0 1
30 4 0 0 0 0 1 0
31 4 0 0 0 0 0 0
32 4 1 0 0 0 0 0
33 4 1 0 0 0 1 0
34 4 0 1 0 0 0 1
35 4 0 0 0 0 0 0
36 4 0 0 0 0 0 0
37 4 1 1 1 0 1 0
38 4 0 0 1 0 0 1
39 4 0 0 0 0 1 1
40 4 0 1 0 0 1 0
41 4 0 0 1 1 1 1
42 4 0 0 1 0 0 1
43 4 1 0 0 0 1 1
44 4 1 1 0 0 0 0
45 4 0 0 0 0 1 0
46 4 0 0 0 0 1 1
47 4 0 0 0 0 0 0
48 4 0 0 0 0 0 1
49 4 0 0 0 0 1 1
50 4 0 0 0 0 0 0
51 4 0 1 1 0 0 0
52 4 1 1 1 1 1 0
53 4 0 0 0 0 0 1
54 4 0 0 1 1 0 0
55 4 0 0 0 0 0 0
56 4 0 1 1 0 0 1
57 4 0 0 1 0 1 1
58 4 0 0 0 0 0 1
59 4 0 0 0 0 0 1
60 4 1 1 1 1 1 1
61 4 1 1 0 0 0 1
62 4 0 0 1 0 1 0
63 4 0 0 0 0 0 0
64 4 1 1 0 0 0 1
65 4 0 0 0 0 0 0
66 4 0 0 0 0 0 0
67 4 0 1 1 1 1 0
68 4 1 0 0 0 0 0
69 4 0 0 0 0 0 1
70 4 0 0 1 0 0 0
71 4 0 0 0 0 0 0
72 4 0 0 0 0 0 1
73 4 0 0 1 0 0 1
74 4 1 0 1 0 0 0
75 4 0 0 0 0 0 1
76 4 0 1 0 0 1 1
77 4 0 0 0 0 0 1
78 4 0 0 1 0 1 1
79 4 0 1 1 1 0 1
80 4 0 1 0 0 1 0
81 4 0 0 0 0 0 0
82 4 1 0 1 0 0 1
83 4 0 0 0 0 0 0
84 4 0 0 1 1 0 0
85 4 0 0 0 0 1 1
86 4 1 0 0 0 0 0
87 2 1 4 0 0 0 1
88 2 1 3 1 0 0 1
89 2 0 4 0 0 0 0
90 2 0 4 0 0 0 1
91 2 0 4 0 0 1 0
92 2 1 3 0 0 0 0
93 2 1 4 0 0 1 0
94 2 0 4 0 0 0 0
95 2 0 3 0 0 0 0
96 2 0 4 0 0 0 1
97 2 1 3 0 0 0 0
98 2 0 4 0 0 0 0
99 2 1 4 0 0 0 0
100 2 0 4 0 0 0 1
101 2 1 4 0 0 0 1
102 2 0 4 0 0 0 0
103 2 0 4 0 0 0 0
104 2 0 4 0 0 0 0
105 2 0 3 1 0 0 0
106 2 0 4 0 0 0 0
107 2 0 4 0 0 0 0
108 2 1 3 1 0 0 0
109 2 0 4 0 0 0 0
110 2 1 4 0 0 0 0
111 2 1 3 1 0 1 0
112 2 0 3 0 0 0 0
113 2 0 4 1 0 0 0
114 2 1 3 1 0 0 0
115 2 1 4 0 0 0 0
116 2 0 4 0 0 0 0
117 2 1 4 0 0 0 1
118 2 1 4 0 0 0 0
119 2 0 4 0 0 0 0
120 2 0 4 0 0 0 1
121 2 1 4 0 0 0 0
122 2 0 4 0 0 0 0
123 2 1 3 1 0 0 0
124 2 0 4 1 0 1 1
125 2 0 4 0 0 0 1
126 2 0 3 0 0 0 0
127 2 0 4 0 0 1 0
128 2 0 4 0 0 0 1
129 2 0 4 0 0 0 0
130 2 0 4 0 0 0 1
131 2 1 4 0 0 0 0
132 2 1 4 0 0 0 1
133 2 1 4 1 0 0 0
134 2 0 4 0 0 0 0
135 2 0 4 0 0 0 0
136 2 0 4 0 0 0 0
137 2 1 4 1 0 1 1
138 2 1 3 1 0 1 1
139 2 0 3 0 0 0 0
140 2 0 4 0 0 0 0
141 2 0 4 1 1 0 1
142 2 0 3 1 0 0 1
143 2 1 4 0 0 0 0
144 2 0 4 0 0 1 1
145 2 0 4 0 0 1 0
146 2 0 3 0 0 0 1
147 2 0 3 1 0 0 0
148 2 0 3 0 0 0 0
149 2 1 4 0 0 0 0
150 2 0 4 0 0 1 1
151 2 0 4 0 0 0 1
152 2 1 4 1 1 0 0
153 2 1 4 1 1 1 0
154 2 1 4 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit Group Used
4.04968 0.03593 -0.53670 -0.11233
CorrectAnalysis Useful Outcome
0.29846 0.06121 0.04456
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.48415 -0.09424 0.00071 0.09710 0.59935
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.04968 0.03749 108.031 < 2e-16 ***
UseLimit 0.03593 0.04121 0.872 0.384674
Group -0.53670 0.01097 -48.942 < 2e-16 ***
Used -0.11233 0.04784 -2.348 0.020200 *
CorrectAnalysis 0.29846 0.07968 3.746 0.000258 ***
Useful 0.06121 0.04571 1.339 0.182629
Outcome 0.04456 0.03974 1.121 0.264052
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2347 on 147 degrees of freedom
Multiple R-squared: 0.9467, Adjusted R-squared: 0.9445
F-statistic: 435 on 6 and 147 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 6.466953e-46 1.293391e-45 1.000000e+00
[2,] 1.392322e-63 2.784645e-63 1.000000e+00
[3,] 5.546166e-74 1.109233e-73 1.000000e+00
[4,] 3.904877e-101 7.809754e-101 1.000000e+00
[5,] 1.990581e-102 3.981161e-102 1.000000e+00
[6,] 7.961106e-117 1.592221e-116 1.000000e+00
[7,] 0.000000e+00 0.000000e+00 1.000000e+00
[8,] 2.102188e-157 4.204377e-157 1.000000e+00
[9,] 9.543417e-162 1.908683e-161 1.000000e+00
[10,] 5.894451e-175 1.178890e-174 1.000000e+00
[11,] 4.243545e-199 8.487089e-199 1.000000e+00
[12,] 3.681759e-232 7.363519e-232 1.000000e+00
[13,] 1.348304e-221 2.696608e-221 1.000000e+00
[14,] 2.445316e-232 4.890632e-232 1.000000e+00
[15,] 4.928363e-250 9.856725e-250 1.000000e+00
[16,] 9.658100e-268 1.931620e-267 1.000000e+00
[17,] 2.124634e-309 4.249267e-309 1.000000e+00
[18,] 1.138539e-295 2.277079e-295 1.000000e+00
[19,] 3.058361e-305 6.116721e-305 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,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 0.000000e+00 0.000000e+00 1.000000e+00
[55,] 0.000000e+00 0.000000e+00 1.000000e+00
[56,] 0.000000e+00 0.000000e+00 1.000000e+00
[57,] 0.000000e+00 0.000000e+00 1.000000e+00
[58,] 0.000000e+00 0.000000e+00 1.000000e+00
[59,] 0.000000e+00 0.000000e+00 1.000000e+00
[60,] 0.000000e+00 0.000000e+00 1.000000e+00
[61,] 0.000000e+00 0.000000e+00 1.000000e+00
[62,] 0.000000e+00 0.000000e+00 1.000000e+00
[63,] 0.000000e+00 0.000000e+00 1.000000e+00
[64,] 0.000000e+00 0.000000e+00 1.000000e+00
[65,] 0.000000e+00 0.000000e+00 1.000000e+00
[66,] 0.000000e+00 0.000000e+00 1.000000e+00
[67,] 0.000000e+00 0.000000e+00 1.000000e+00
[68,] 0.000000e+00 0.000000e+00 1.000000e+00
[69,] 0.000000e+00 0.000000e+00 1.000000e+00
[70,] 0.000000e+00 0.000000e+00 1.000000e+00
[71,] 0.000000e+00 0.000000e+00 1.000000e+00
[72,] 0.000000e+00 0.000000e+00 1.000000e+00
[73,] 0.000000e+00 0.000000e+00 1.000000e+00
[74,] 0.000000e+00 0.000000e+00 1.000000e+00
[75,] 0.000000e+00 0.000000e+00 1.000000e+00
[76,] 0.000000e+00 0.000000e+00 1.000000e+00
[77,] 1.000000e+00 8.293611e-19 4.146805e-19
[78,] 1.000000e+00 0.000000e+00 0.000000e+00
[79,] 1.000000e+00 0.000000e+00 0.000000e+00
[80,] 1.000000e+00 0.000000e+00 0.000000e+00
[81,] 1.000000e+00 0.000000e+00 0.000000e+00
[82,] 1.000000e+00 0.000000e+00 0.000000e+00
[83,] 1.000000e+00 0.000000e+00 0.000000e+00
[84,] 1.000000e+00 0.000000e+00 0.000000e+00
[85,] 1.000000e+00 0.000000e+00 0.000000e+00
[86,] 1.000000e+00 0.000000e+00 0.000000e+00
[87,] 1.000000e+00 0.000000e+00 0.000000e+00
[88,] 1.000000e+00 0.000000e+00 0.000000e+00
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 0.000000e+00 0.000000e+00
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 0.000000e+00 0.000000e+00
[112,] 1.000000e+00 0.000000e+00 0.000000e+00
[113,] 1.000000e+00 0.000000e+00 0.000000e+00
[114,] 1.000000e+00 0.000000e+00 0.000000e+00
[115,] 1.000000e+00 0.000000e+00 0.000000e+00
[116,] 1.000000e+00 0.000000e+00 0.000000e+00
[117,] 1.000000e+00 1.192760e-311 5.963802e-312
[118,] 1.000000e+00 3.134427e-301 1.567213e-301
[119,] 1.000000e+00 2.546455e-314 1.273227e-314
[120,] 1.000000e+00 1.497102e-272 7.485510e-273
[121,] 1.000000e+00 3.946645e-254 1.973323e-254
[122,] 1.000000e+00 3.687038e-237 1.843519e-237
[123,] 1.000000e+00 1.493755e-225 7.468777e-226
[124,] 1.000000e+00 3.890227e-235 1.945113e-235
[125,] 1.000000e+00 8.575202e-202 4.287601e-202
[126,] 1.000000e+00 1.668463e-177 8.342315e-178
[127,] 1.000000e+00 1.886051e-164 9.430253e-165
[128,] 1.000000e+00 5.077209e-161 2.538605e-161
[129,] 1.000000e+00 0.000000e+00 0.000000e+00
[130,] 1.000000e+00 1.080744e-118 5.403718e-119
[131,] 1.000000e+00 1.223186e-104 6.115929e-105
[132,] 1.000000e+00 3.060129e-102 1.530065e-102
[133,] 1.000000e+00 1.298145e-74 6.490725e-75
[134,] 1.000000e+00 6.144759e-64 3.072379e-64
[135,] 1.000000e+00 2.691776e-46 1.345888e-46
> postscript(file="/var/wessaorg/rcomp/tmp/1t4z11355679607.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2fa4o1355679607.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3adz11355679607.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4apqi1355679607.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5g6ci1355679607.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
4.065242e-01 -4.968117e-02 -4.968117e-02 -4.968117e-02 -4.968117e-02
6 7 8 9 10
-1.913788e-01 -4.968117e-02 4.870149e-01 -9.424111e-02 -8.561185e-02
11 12 13 14 15
4.510842e-01 -4.968117e-02 1.446221e-03 4.510842e-01 -4.311372e-02
16 17 18 19 20
4.935823e-01 2.037562e-01 4.510842e-01 -9.424111e-02 1.951269e-01
21 22 23 24 25
-1.468188e-01 -7.904441e-02 -1.554481e-01 -1.913788e-01 5.547893e-01
26 27 28 29 30
1.446221e-03 -1.301718e-01 6.265319e-02 -9.424111e-02 -1.108881e-01
31 32 33 34 35
-4.968117e-02 -8.561185e-02 -1.468188e-01 4.424549e-01 -4.968117e-02
36 37 38 39 40
-4.968117e-02 5.022116e-01 1.809325e-02 -1.554481e-01 4.258079e-01
41 42 43 44 45
-3.415691e-01 1.809325e-02 -1.913788e-01 4.510842e-01 -1.108881e-01
46 47 48 49 50
-1.554481e-01 -4.968117e-02 -9.424111e-02 -1.554481e-01 -4.968117e-02
51 52 53 54 55
5.993492e-01 2.037562e-01 -9.424111e-02 -2.358022e-01 -4.968117e-02
56 57 58 59 60
5.547893e-01 -4.311372e-02 -9.424111e-02 -9.424111e-02 1.591962e-01
61 62 63 64 65
4.065242e-01 1.446221e-03 -4.968117e-02 4.065242e-01 -4.968117e-02
66 67 68 69 70
-4.968117e-02 2.396868e-01 -8.561185e-02 -9.424111e-02 6.265319e-02
71 72 73 74 75
-4.968117e-02 -9.424111e-02 1.809325e-02 2.672251e-02 -9.424111e-02
76 77 78 79 80
3.812479e-01 -9.424111e-02 -4.311372e-02 2.563339e-01 4.258079e-01
81 82 83 84 85
-4.968117e-02 -1.783744e-02 -4.968117e-02 -2.358022e-01 -1.554481e-01
86 87 88 89 90
-8.561185e-02 1.661231e-02 -4.077494e-01 9.710293e-02 5.254299e-02
91 92 93 94 95
3.589596e-02 -4.755238e-01 -3.471892e-05 9.710293e-02 -4.395931e-01
96 97 98 99 100
5.254299e-02 -4.755238e-01 9.710293e-02 6.117225e-02 5.254299e-02
101 102 103 104 105
1.661231e-02 9.710293e-02 9.710293e-02 9.710293e-02 -3.272587e-01
106 107 108 109 110
9.710293e-02 9.710293e-02 -3.631894e-01 9.710293e-02 6.117225e-02
111 112 113 114 115
-4.243964e-01 -4.395931e-01 2.094373e-01 -3.631894e-01 6.117225e-02
116 117 118 119 120
9.710293e-02 1.661231e-02 6.117225e-02 9.710293e-02 5.254299e-02
121 122 123 124 125
6.117225e-02 9.710293e-02 -3.631894e-01 1.036704e-01 5.254299e-02
126 127 128 129 130
-4.395931e-01 3.589596e-02 5.254299e-02 9.710293e-02 5.254299e-02
131 132 133 134 135
6.117225e-02 1.661231e-02 1.735066e-01 9.710293e-02 9.710293e-02
136 137 138 139 140
9.710293e-02 6.773969e-02 -4.689563e-01 -4.395931e-01 9.710293e-02
141 142 143 144 145
-1.335781e-01 -3.718187e-01 6.117225e-02 -8.663981e-03 3.589596e-02
146 147 148 149 150
-4.841530e-01 -3.272587e-01 -4.395931e-01 6.117225e-02 -8.663981e-03
151 152 153 154
5.254299e-02 -1.249488e-01 -1.861558e-01 1.735066e-01
> postscript(file="/var/wessaorg/rcomp/tmp/6a9wq1355679607.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 4.065242e-01 NA
1 -4.968117e-02 4.065242e-01
2 -4.968117e-02 -4.968117e-02
3 -4.968117e-02 -4.968117e-02
4 -4.968117e-02 -4.968117e-02
5 -1.913788e-01 -4.968117e-02
6 -4.968117e-02 -1.913788e-01
7 4.870149e-01 -4.968117e-02
8 -9.424111e-02 4.870149e-01
9 -8.561185e-02 -9.424111e-02
10 4.510842e-01 -8.561185e-02
11 -4.968117e-02 4.510842e-01
12 1.446221e-03 -4.968117e-02
13 4.510842e-01 1.446221e-03
14 -4.311372e-02 4.510842e-01
15 4.935823e-01 -4.311372e-02
16 2.037562e-01 4.935823e-01
17 4.510842e-01 2.037562e-01
18 -9.424111e-02 4.510842e-01
19 1.951269e-01 -9.424111e-02
20 -1.468188e-01 1.951269e-01
21 -7.904441e-02 -1.468188e-01
22 -1.554481e-01 -7.904441e-02
23 -1.913788e-01 -1.554481e-01
24 5.547893e-01 -1.913788e-01
25 1.446221e-03 5.547893e-01
26 -1.301718e-01 1.446221e-03
27 6.265319e-02 -1.301718e-01
28 -9.424111e-02 6.265319e-02
29 -1.108881e-01 -9.424111e-02
30 -4.968117e-02 -1.108881e-01
31 -8.561185e-02 -4.968117e-02
32 -1.468188e-01 -8.561185e-02
33 4.424549e-01 -1.468188e-01
34 -4.968117e-02 4.424549e-01
35 -4.968117e-02 -4.968117e-02
36 5.022116e-01 -4.968117e-02
37 1.809325e-02 5.022116e-01
38 -1.554481e-01 1.809325e-02
39 4.258079e-01 -1.554481e-01
40 -3.415691e-01 4.258079e-01
41 1.809325e-02 -3.415691e-01
42 -1.913788e-01 1.809325e-02
43 4.510842e-01 -1.913788e-01
44 -1.108881e-01 4.510842e-01
45 -1.554481e-01 -1.108881e-01
46 -4.968117e-02 -1.554481e-01
47 -9.424111e-02 -4.968117e-02
48 -1.554481e-01 -9.424111e-02
49 -4.968117e-02 -1.554481e-01
50 5.993492e-01 -4.968117e-02
51 2.037562e-01 5.993492e-01
52 -9.424111e-02 2.037562e-01
53 -2.358022e-01 -9.424111e-02
54 -4.968117e-02 -2.358022e-01
55 5.547893e-01 -4.968117e-02
56 -4.311372e-02 5.547893e-01
57 -9.424111e-02 -4.311372e-02
58 -9.424111e-02 -9.424111e-02
59 1.591962e-01 -9.424111e-02
60 4.065242e-01 1.591962e-01
61 1.446221e-03 4.065242e-01
62 -4.968117e-02 1.446221e-03
63 4.065242e-01 -4.968117e-02
64 -4.968117e-02 4.065242e-01
65 -4.968117e-02 -4.968117e-02
66 2.396868e-01 -4.968117e-02
67 -8.561185e-02 2.396868e-01
68 -9.424111e-02 -8.561185e-02
69 6.265319e-02 -9.424111e-02
70 -4.968117e-02 6.265319e-02
71 -9.424111e-02 -4.968117e-02
72 1.809325e-02 -9.424111e-02
73 2.672251e-02 1.809325e-02
74 -9.424111e-02 2.672251e-02
75 3.812479e-01 -9.424111e-02
76 -9.424111e-02 3.812479e-01
77 -4.311372e-02 -9.424111e-02
78 2.563339e-01 -4.311372e-02
79 4.258079e-01 2.563339e-01
80 -4.968117e-02 4.258079e-01
81 -1.783744e-02 -4.968117e-02
82 -4.968117e-02 -1.783744e-02
83 -2.358022e-01 -4.968117e-02
84 -1.554481e-01 -2.358022e-01
85 -8.561185e-02 -1.554481e-01
86 1.661231e-02 -8.561185e-02
87 -4.077494e-01 1.661231e-02
88 9.710293e-02 -4.077494e-01
89 5.254299e-02 9.710293e-02
90 3.589596e-02 5.254299e-02
91 -4.755238e-01 3.589596e-02
92 -3.471892e-05 -4.755238e-01
93 9.710293e-02 -3.471892e-05
94 -4.395931e-01 9.710293e-02
95 5.254299e-02 -4.395931e-01
96 -4.755238e-01 5.254299e-02
97 9.710293e-02 -4.755238e-01
98 6.117225e-02 9.710293e-02
99 5.254299e-02 6.117225e-02
100 1.661231e-02 5.254299e-02
101 9.710293e-02 1.661231e-02
102 9.710293e-02 9.710293e-02
103 9.710293e-02 9.710293e-02
104 -3.272587e-01 9.710293e-02
105 9.710293e-02 -3.272587e-01
106 9.710293e-02 9.710293e-02
107 -3.631894e-01 9.710293e-02
108 9.710293e-02 -3.631894e-01
109 6.117225e-02 9.710293e-02
110 -4.243964e-01 6.117225e-02
111 -4.395931e-01 -4.243964e-01
112 2.094373e-01 -4.395931e-01
113 -3.631894e-01 2.094373e-01
114 6.117225e-02 -3.631894e-01
115 9.710293e-02 6.117225e-02
116 1.661231e-02 9.710293e-02
117 6.117225e-02 1.661231e-02
118 9.710293e-02 6.117225e-02
119 5.254299e-02 9.710293e-02
120 6.117225e-02 5.254299e-02
121 9.710293e-02 6.117225e-02
122 -3.631894e-01 9.710293e-02
123 1.036704e-01 -3.631894e-01
124 5.254299e-02 1.036704e-01
125 -4.395931e-01 5.254299e-02
126 3.589596e-02 -4.395931e-01
127 5.254299e-02 3.589596e-02
128 9.710293e-02 5.254299e-02
129 5.254299e-02 9.710293e-02
130 6.117225e-02 5.254299e-02
131 1.661231e-02 6.117225e-02
132 1.735066e-01 1.661231e-02
133 9.710293e-02 1.735066e-01
134 9.710293e-02 9.710293e-02
135 9.710293e-02 9.710293e-02
136 6.773969e-02 9.710293e-02
137 -4.689563e-01 6.773969e-02
138 -4.395931e-01 -4.689563e-01
139 9.710293e-02 -4.395931e-01
140 -1.335781e-01 9.710293e-02
141 -3.718187e-01 -1.335781e-01
142 6.117225e-02 -3.718187e-01
143 -8.663981e-03 6.117225e-02
144 3.589596e-02 -8.663981e-03
145 -4.841530e-01 3.589596e-02
146 -3.272587e-01 -4.841530e-01
147 -4.395931e-01 -3.272587e-01
148 6.117225e-02 -4.395931e-01
149 -8.663981e-03 6.117225e-02
150 5.254299e-02 -8.663981e-03
151 -1.249488e-01 5.254299e-02
152 -1.861558e-01 -1.249488e-01
153 1.735066e-01 -1.861558e-01
154 NA 1.735066e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.968117e-02 4.065242e-01
[2,] -4.968117e-02 -4.968117e-02
[3,] -4.968117e-02 -4.968117e-02
[4,] -4.968117e-02 -4.968117e-02
[5,] -1.913788e-01 -4.968117e-02
[6,] -4.968117e-02 -1.913788e-01
[7,] 4.870149e-01 -4.968117e-02
[8,] -9.424111e-02 4.870149e-01
[9,] -8.561185e-02 -9.424111e-02
[10,] 4.510842e-01 -8.561185e-02
[11,] -4.968117e-02 4.510842e-01
[12,] 1.446221e-03 -4.968117e-02
[13,] 4.510842e-01 1.446221e-03
[14,] -4.311372e-02 4.510842e-01
[15,] 4.935823e-01 -4.311372e-02
[16,] 2.037562e-01 4.935823e-01
[17,] 4.510842e-01 2.037562e-01
[18,] -9.424111e-02 4.510842e-01
[19,] 1.951269e-01 -9.424111e-02
[20,] -1.468188e-01 1.951269e-01
[21,] -7.904441e-02 -1.468188e-01
[22,] -1.554481e-01 -7.904441e-02
[23,] -1.913788e-01 -1.554481e-01
[24,] 5.547893e-01 -1.913788e-01
[25,] 1.446221e-03 5.547893e-01
[26,] -1.301718e-01 1.446221e-03
[27,] 6.265319e-02 -1.301718e-01
[28,] -9.424111e-02 6.265319e-02
[29,] -1.108881e-01 -9.424111e-02
[30,] -4.968117e-02 -1.108881e-01
[31,] -8.561185e-02 -4.968117e-02
[32,] -1.468188e-01 -8.561185e-02
[33,] 4.424549e-01 -1.468188e-01
[34,] -4.968117e-02 4.424549e-01
[35,] -4.968117e-02 -4.968117e-02
[36,] 5.022116e-01 -4.968117e-02
[37,] 1.809325e-02 5.022116e-01
[38,] -1.554481e-01 1.809325e-02
[39,] 4.258079e-01 -1.554481e-01
[40,] -3.415691e-01 4.258079e-01
[41,] 1.809325e-02 -3.415691e-01
[42,] -1.913788e-01 1.809325e-02
[43,] 4.510842e-01 -1.913788e-01
[44,] -1.108881e-01 4.510842e-01
[45,] -1.554481e-01 -1.108881e-01
[46,] -4.968117e-02 -1.554481e-01
[47,] -9.424111e-02 -4.968117e-02
[48,] -1.554481e-01 -9.424111e-02
[49,] -4.968117e-02 -1.554481e-01
[50,] 5.993492e-01 -4.968117e-02
[51,] 2.037562e-01 5.993492e-01
[52,] -9.424111e-02 2.037562e-01
[53,] -2.358022e-01 -9.424111e-02
[54,] -4.968117e-02 -2.358022e-01
[55,] 5.547893e-01 -4.968117e-02
[56,] -4.311372e-02 5.547893e-01
[57,] -9.424111e-02 -4.311372e-02
[58,] -9.424111e-02 -9.424111e-02
[59,] 1.591962e-01 -9.424111e-02
[60,] 4.065242e-01 1.591962e-01
[61,] 1.446221e-03 4.065242e-01
[62,] -4.968117e-02 1.446221e-03
[63,] 4.065242e-01 -4.968117e-02
[64,] -4.968117e-02 4.065242e-01
[65,] -4.968117e-02 -4.968117e-02
[66,] 2.396868e-01 -4.968117e-02
[67,] -8.561185e-02 2.396868e-01
[68,] -9.424111e-02 -8.561185e-02
[69,] 6.265319e-02 -9.424111e-02
[70,] -4.968117e-02 6.265319e-02
[71,] -9.424111e-02 -4.968117e-02
[72,] 1.809325e-02 -9.424111e-02
[73,] 2.672251e-02 1.809325e-02
[74,] -9.424111e-02 2.672251e-02
[75,] 3.812479e-01 -9.424111e-02
[76,] -9.424111e-02 3.812479e-01
[77,] -4.311372e-02 -9.424111e-02
[78,] 2.563339e-01 -4.311372e-02
[79,] 4.258079e-01 2.563339e-01
[80,] -4.968117e-02 4.258079e-01
[81,] -1.783744e-02 -4.968117e-02
[82,] -4.968117e-02 -1.783744e-02
[83,] -2.358022e-01 -4.968117e-02
[84,] -1.554481e-01 -2.358022e-01
[85,] -8.561185e-02 -1.554481e-01
[86,] 1.661231e-02 -8.561185e-02
[87,] -4.077494e-01 1.661231e-02
[88,] 9.710293e-02 -4.077494e-01
[89,] 5.254299e-02 9.710293e-02
[90,] 3.589596e-02 5.254299e-02
[91,] -4.755238e-01 3.589596e-02
[92,] -3.471892e-05 -4.755238e-01
[93,] 9.710293e-02 -3.471892e-05
[94,] -4.395931e-01 9.710293e-02
[95,] 5.254299e-02 -4.395931e-01
[96,] -4.755238e-01 5.254299e-02
[97,] 9.710293e-02 -4.755238e-01
[98,] 6.117225e-02 9.710293e-02
[99,] 5.254299e-02 6.117225e-02
[100,] 1.661231e-02 5.254299e-02
[101,] 9.710293e-02 1.661231e-02
[102,] 9.710293e-02 9.710293e-02
[103,] 9.710293e-02 9.710293e-02
[104,] -3.272587e-01 9.710293e-02
[105,] 9.710293e-02 -3.272587e-01
[106,] 9.710293e-02 9.710293e-02
[107,] -3.631894e-01 9.710293e-02
[108,] 9.710293e-02 -3.631894e-01
[109,] 6.117225e-02 9.710293e-02
[110,] -4.243964e-01 6.117225e-02
[111,] -4.395931e-01 -4.243964e-01
[112,] 2.094373e-01 -4.395931e-01
[113,] -3.631894e-01 2.094373e-01
[114,] 6.117225e-02 -3.631894e-01
[115,] 9.710293e-02 6.117225e-02
[116,] 1.661231e-02 9.710293e-02
[117,] 6.117225e-02 1.661231e-02
[118,] 9.710293e-02 6.117225e-02
[119,] 5.254299e-02 9.710293e-02
[120,] 6.117225e-02 5.254299e-02
[121,] 9.710293e-02 6.117225e-02
[122,] -3.631894e-01 9.710293e-02
[123,] 1.036704e-01 -3.631894e-01
[124,] 5.254299e-02 1.036704e-01
[125,] -4.395931e-01 5.254299e-02
[126,] 3.589596e-02 -4.395931e-01
[127,] 5.254299e-02 3.589596e-02
[128,] 9.710293e-02 5.254299e-02
[129,] 5.254299e-02 9.710293e-02
[130,] 6.117225e-02 5.254299e-02
[131,] 1.661231e-02 6.117225e-02
[132,] 1.735066e-01 1.661231e-02
[133,] 9.710293e-02 1.735066e-01
[134,] 9.710293e-02 9.710293e-02
[135,] 9.710293e-02 9.710293e-02
[136,] 6.773969e-02 9.710293e-02
[137,] -4.689563e-01 6.773969e-02
[138,] -4.395931e-01 -4.689563e-01
[139,] 9.710293e-02 -4.395931e-01
[140,] -1.335781e-01 9.710293e-02
[141,] -3.718187e-01 -1.335781e-01
[142,] 6.117225e-02 -3.718187e-01
[143,] -8.663981e-03 6.117225e-02
[144,] 3.589596e-02 -8.663981e-03
[145,] -4.841530e-01 3.589596e-02
[146,] -3.272587e-01 -4.841530e-01
[147,] -4.395931e-01 -3.272587e-01
[148,] 6.117225e-02 -4.395931e-01
[149,] -8.663981e-03 6.117225e-02
[150,] 5.254299e-02 -8.663981e-03
[151,] -1.249488e-01 5.254299e-02
[152,] -1.861558e-01 -1.249488e-01
[153,] 1.735066e-01 -1.861558e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.968117e-02 4.065242e-01
2 -4.968117e-02 -4.968117e-02
3 -4.968117e-02 -4.968117e-02
4 -4.968117e-02 -4.968117e-02
5 -1.913788e-01 -4.968117e-02
6 -4.968117e-02 -1.913788e-01
7 4.870149e-01 -4.968117e-02
8 -9.424111e-02 4.870149e-01
9 -8.561185e-02 -9.424111e-02
10 4.510842e-01 -8.561185e-02
11 -4.968117e-02 4.510842e-01
12 1.446221e-03 -4.968117e-02
13 4.510842e-01 1.446221e-03
14 -4.311372e-02 4.510842e-01
15 4.935823e-01 -4.311372e-02
16 2.037562e-01 4.935823e-01
17 4.510842e-01 2.037562e-01
18 -9.424111e-02 4.510842e-01
19 1.951269e-01 -9.424111e-02
20 -1.468188e-01 1.951269e-01
21 -7.904441e-02 -1.468188e-01
22 -1.554481e-01 -7.904441e-02
23 -1.913788e-01 -1.554481e-01
24 5.547893e-01 -1.913788e-01
25 1.446221e-03 5.547893e-01
26 -1.301718e-01 1.446221e-03
27 6.265319e-02 -1.301718e-01
28 -9.424111e-02 6.265319e-02
29 -1.108881e-01 -9.424111e-02
30 -4.968117e-02 -1.108881e-01
31 -8.561185e-02 -4.968117e-02
32 -1.468188e-01 -8.561185e-02
33 4.424549e-01 -1.468188e-01
34 -4.968117e-02 4.424549e-01
35 -4.968117e-02 -4.968117e-02
36 5.022116e-01 -4.968117e-02
37 1.809325e-02 5.022116e-01
38 -1.554481e-01 1.809325e-02
39 4.258079e-01 -1.554481e-01
40 -3.415691e-01 4.258079e-01
41 1.809325e-02 -3.415691e-01
42 -1.913788e-01 1.809325e-02
43 4.510842e-01 -1.913788e-01
44 -1.108881e-01 4.510842e-01
45 -1.554481e-01 -1.108881e-01
46 -4.968117e-02 -1.554481e-01
47 -9.424111e-02 -4.968117e-02
48 -1.554481e-01 -9.424111e-02
49 -4.968117e-02 -1.554481e-01
50 5.993492e-01 -4.968117e-02
51 2.037562e-01 5.993492e-01
52 -9.424111e-02 2.037562e-01
53 -2.358022e-01 -9.424111e-02
54 -4.968117e-02 -2.358022e-01
55 5.547893e-01 -4.968117e-02
56 -4.311372e-02 5.547893e-01
57 -9.424111e-02 -4.311372e-02
58 -9.424111e-02 -9.424111e-02
59 1.591962e-01 -9.424111e-02
60 4.065242e-01 1.591962e-01
61 1.446221e-03 4.065242e-01
62 -4.968117e-02 1.446221e-03
63 4.065242e-01 -4.968117e-02
64 -4.968117e-02 4.065242e-01
65 -4.968117e-02 -4.968117e-02
66 2.396868e-01 -4.968117e-02
67 -8.561185e-02 2.396868e-01
68 -9.424111e-02 -8.561185e-02
69 6.265319e-02 -9.424111e-02
70 -4.968117e-02 6.265319e-02
71 -9.424111e-02 -4.968117e-02
72 1.809325e-02 -9.424111e-02
73 2.672251e-02 1.809325e-02
74 -9.424111e-02 2.672251e-02
75 3.812479e-01 -9.424111e-02
76 -9.424111e-02 3.812479e-01
77 -4.311372e-02 -9.424111e-02
78 2.563339e-01 -4.311372e-02
79 4.258079e-01 2.563339e-01
80 -4.968117e-02 4.258079e-01
81 -1.783744e-02 -4.968117e-02
82 -4.968117e-02 -1.783744e-02
83 -2.358022e-01 -4.968117e-02
84 -1.554481e-01 -2.358022e-01
85 -8.561185e-02 -1.554481e-01
86 1.661231e-02 -8.561185e-02
87 -4.077494e-01 1.661231e-02
88 9.710293e-02 -4.077494e-01
89 5.254299e-02 9.710293e-02
90 3.589596e-02 5.254299e-02
91 -4.755238e-01 3.589596e-02
92 -3.471892e-05 -4.755238e-01
93 9.710293e-02 -3.471892e-05
94 -4.395931e-01 9.710293e-02
95 5.254299e-02 -4.395931e-01
96 -4.755238e-01 5.254299e-02
97 9.710293e-02 -4.755238e-01
98 6.117225e-02 9.710293e-02
99 5.254299e-02 6.117225e-02
100 1.661231e-02 5.254299e-02
101 9.710293e-02 1.661231e-02
102 9.710293e-02 9.710293e-02
103 9.710293e-02 9.710293e-02
104 -3.272587e-01 9.710293e-02
105 9.710293e-02 -3.272587e-01
106 9.710293e-02 9.710293e-02
107 -3.631894e-01 9.710293e-02
108 9.710293e-02 -3.631894e-01
109 6.117225e-02 9.710293e-02
110 -4.243964e-01 6.117225e-02
111 -4.395931e-01 -4.243964e-01
112 2.094373e-01 -4.395931e-01
113 -3.631894e-01 2.094373e-01
114 6.117225e-02 -3.631894e-01
115 9.710293e-02 6.117225e-02
116 1.661231e-02 9.710293e-02
117 6.117225e-02 1.661231e-02
118 9.710293e-02 6.117225e-02
119 5.254299e-02 9.710293e-02
120 6.117225e-02 5.254299e-02
121 9.710293e-02 6.117225e-02
122 -3.631894e-01 9.710293e-02
123 1.036704e-01 -3.631894e-01
124 5.254299e-02 1.036704e-01
125 -4.395931e-01 5.254299e-02
126 3.589596e-02 -4.395931e-01
127 5.254299e-02 3.589596e-02
128 9.710293e-02 5.254299e-02
129 5.254299e-02 9.710293e-02
130 6.117225e-02 5.254299e-02
131 1.661231e-02 6.117225e-02
132 1.735066e-01 1.661231e-02
133 9.710293e-02 1.735066e-01
134 9.710293e-02 9.710293e-02
135 9.710293e-02 9.710293e-02
136 6.773969e-02 9.710293e-02
137 -4.689563e-01 6.773969e-02
138 -4.395931e-01 -4.689563e-01
139 9.710293e-02 -4.395931e-01
140 -1.335781e-01 9.710293e-02
141 -3.718187e-01 -1.335781e-01
142 6.117225e-02 -3.718187e-01
143 -8.663981e-03 6.117225e-02
144 3.589596e-02 -8.663981e-03
145 -4.841530e-01 3.589596e-02
146 -3.272587e-01 -4.841530e-01
147 -4.395931e-01 -3.272587e-01
148 6.117225e-02 -4.395931e-01
149 -8.663981e-03 6.117225e-02
150 5.254299e-02 -8.663981e-03
151 -1.249488e-01 5.254299e-02
152 -1.861558e-01 -1.249488e-01
153 1.735066e-01 -1.861558e-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/77z8e1355679607.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8vqok1355679607.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9a8yc1355679607.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10pzo31355679607.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11do3i1355679607.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/129ho01355679607.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/131okc1355679607.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/144fh51355679607.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/15m2yt1355679607.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/16g8731355679607.tab")
+ }
>
> try(system("convert tmp/1t4z11355679607.ps tmp/1t4z11355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fa4o1355679607.ps tmp/2fa4o1355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/3adz11355679607.ps tmp/3adz11355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/4apqi1355679607.ps tmp/4apqi1355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/5g6ci1355679607.ps tmp/5g6ci1355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/6a9wq1355679607.ps tmp/6a9wq1355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/77z8e1355679607.ps tmp/77z8e1355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vqok1355679607.ps tmp/8vqok1355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/9a8yc1355679607.ps tmp/9a8yc1355679607.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pzo31355679607.ps tmp/10pzo31355679607.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.662 1.143 11.806