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(1
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+ ,dim=c(10
+ ,140)
+ ,dimnames=list(c('Y'
+ ,'X1'
+ ,'x2'
+ ,'x3'
+ ,'x4'
+ ,'x5'
+ ,'x6'
+ ,'x7'
+ ,'x8'
+ ,'x9')
+ ,1:140))
> y <- array(NA,dim=c(10,140),dimnames=list(c('Y','X1','x2','x3','x4','x5','x6','x7','x8','x9'),1:140))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Include Monthly 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
Y X1 x2 x3 x4 x5 x6 x7 x8 x9 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1 1901 61 17 56 84 4 21 51 9 1 0 0 0 0 0 0 0 0 0 0
2 2 2509 74 19 73 47 3 15 45 9 0 1 0 0 0 0 0 0 0 0 0
3 3 2114 57 18 62 63 3 17 44 9 0 0 1 0 0 0 0 0 0 0 0
4 4 1331 50 15 42 28 3 20 42 9 0 0 0 1 0 0 0 0 0 0 0
5 5 1399 48 15 59 22 2 12 38 9 0 0 0 0 1 0 0 0 0 0 0
6 6 7333 2 12 27 18 6 4 38 9 0 0 0 0 0 1 0 0 0 0 0
7 7 1170 31 20 78 27 5 11 35 9 0 0 0 0 0 0 1 0 0 0 0
8 8 1507 61 14 56 37 5 12 35 9 0 0 0 0 0 0 0 1 0 0 0
9 9 1107 36 15 59 20 5 9 34 9 0 0 0 0 0 0 0 0 1 0 0
10 10 2051 46 13 51 67 5 14 33 9 0 0 0 0 0 0 0 0 0 1 0
11 11 1290 30 17 47 28 4 11 32 9 0 0 0 0 0 0 0 0 0 0 1
12 12 820 49 10 35 45 3 14 31 9 0 0 0 0 0 0 0 0 0 0 0
13 13 1502 14 13 47 15 5 4 30 9 1 0 0 0 0 0 0 0 0 0 0
14 14 1451 12 12 47 23 6 7 30 9 0 1 0 0 0 0 0 0 0 0 0
15 15 1178 54 16 55 30 6 9 30 9 0 0 1 0 0 0 0 0 0 0 0
16 16 1514 44 15 54 27 2 14 29 9 0 0 0 1 0 0 0 0 0 0 0
17 17 883 40 15 60 43 5 13 29 9 0 0 0 0 1 0 0 0 0 0 0
18 18 1405 57 15 55 36 5 11 29 9 0 0 0 0 0 1 0 0 0 0 0
19 19 927 29 12 48 28 5 9 28 9 0 0 0 0 0 0 1 0 0 0 0
20 20 1352 32 13 47 28 9 8 27 9 0 0 0 0 0 0 0 1 0 0 0
21 21 1314 28 12 47 22 4 9 27 9 0 0 0 0 0 0 0 0 1 0 0
22 22 1307 40 15 52 27 4 11 27 9 0 0 0 0 0 0 0 0 0 1 0
23 23 1243 54 12 48 24 5 7 26 9 0 0 0 0 0 0 0 0 0 0 1
24 24 1232 56 12 48 52 3 15 26 9 0 0 0 0 0 0 0 0 0 0 0
25 25 1097 19 9 27 12 0 4 26 9 1 0 0 0 0 0 0 0 0 0 0
26 26 1100 67 12 12 24 5 10 26 9 0 1 0 0 0 0 0 0 0 0 0
27 27 1316 25 13 51 10 3 10 26 9 0 0 1 0 0 0 0 0 0 0 0
28 28 903 42 16 58 71 4 13 25 9 0 0 0 1 0 0 0 0 0 0 0
29 29 929 28 15 60 12 2 10 25 9 0 0 0 0 1 0 0 0 0 0 0
30 30 1049 57 13 46 24 5 10 25 9 0 0 0 0 0 1 0 0 0 0 0
31 31 1372 28 12 45 22 11 6 24 9 0 0 0 0 0 0 1 0 0 0 0
32 32 1470 35 13 42 21 5 8 24 9 0 0 0 0 0 0 0 1 0 0 0
33 33 821 10 12 41 13 3 7 24 9 0 0 0 0 0 0 0 0 1 0 0
34 34 1239 30 12 47 28 4 11 24 9 0 0 0 0 0 0 0 0 0 1 0
35 35 1384 23 8 32 19 5 10 24 9 0 0 0 0 0 0 0 0 0 0 1
36 36 820 32 15 56 29 5 11 24 9 0 0 0 0 0 0 0 0 0 0 0
37 37 1462 24 12 42 12 2 10 24 9 1 0 0 0 0 0 0 0 0 0 0
38 38 1202 42 12 41 32 6 8 23 9 0 1 0 0 0 0 0 0 0 0 0
39 39 1091 33 12 47 21 3 10 23 9 0 0 1 0 0 0 0 0 0 0 0
40 40 1228 19 14 47 19 4 5 23 9 0 0 0 1 0 0 0 0 0 0 0
41 41 707 17 15 49 15 8 5 23 9 0 0 0 0 1 0 0 0 0 0 0
42 42 868 49 15 52 14 14 5 23 9 0 0 0 0 0 1 0 0 0 0 0
43 43 1165 30 12 42 34 11 9 22 9 0 0 0 0 0 0 1 0 0 0 0
44 44 1106 3 13 55 8 8 2 22 9 0 0 0 0 0 0 0 1 0 0 0
45 45 1429 56 12 48 27 3 9 22 9 0 0 0 0 0 0 0 0 1 0 0
46 46 1671 37 13 48 31 3 13 22 9 0 0 0 0 0 0 0 0 0 1 0
47 47 1579 26 12 38 21 11 7 22 9 0 0 0 0 0 0 0 0 0 0 1
48 48 774 19 12 48 10 3 5 21 10 0 0 0 0 0 0 0 0 0 0 0
49 49 934 22 13 50 21 4 7 21 10 1 0 0 0 0 0 0 0 0 0 0
50 50 825 53 12 39 19 3 8 21 10 0 1 0 0 0 0 0 0 0 0 0
51 51 1375 35 12 48 27 5 8 21 10 0 0 1 0 0 0 0 0 0 0 0
52 52 968 12 9 36 17 6 5 21 10 0 0 0 1 0 0 0 0 0 0 0
53 53 1156 34 13 49 30 8 5 21 10 0 0 0 0 1 0 0 0 0 0 0
54 54 1374 28 13 39 19 3 10 21 10 0 0 0 0 0 1 0 0 0 0 0
55 55 1224 38 12 41 17 3 5 21 10 0 0 0 0 0 0 1 0 0 0 0
56 56 804 38 15 45 24 5 10 21 10 0 0 0 0 0 0 0 1 0 0 0
57 57 998 45 15 60 36 5 10 21 10 0 0 0 0 0 0 0 0 1 0 0
58 58 1112 15 13 45 16 3 7 21 10 0 0 0 0 0 0 0 0 0 1 0
59 59 1153 35 14 41 16 3 10 20 10 0 0 0 0 0 0 0 0 0 0 1
60 60 613 27 14 52 30 3 9 20 10 0 0 0 0 0 0 0 0 0 0 0
61 61 729 23 12 46 18 5 10 20 10 1 0 0 0 0 0 0 0 0 0 0
62 62 813 33 12 39 26 3 10 20 10 0 1 0 0 0 0 0 0 0 0 0
63 63 912 23 9 32 17 3 5 20 10 0 0 1 0 0 0 0 0 0 0 0
64 64 1178 26 14 52 28 6 8 20 10 0 0 0 1 0 0 0 0 0 0 0
65 65 1201 32 16 54 20 4 6 19 10 0 0 0 0 1 0 0 0 0 0 0
66 66 1165 35 15 51 27 3 7 19 10 0 0 0 0 0 1 0 0 0 0 0
67 67 705 18 13 52 13 13 6 18 10 0 0 0 0 0 0 1 0 0 0 0
68 68 814 18 16 57 10 5 3 17 10 0 0 0 0 0 0 0 1 0 0 0
69 69 1082 41 12 47 29 6 9 17 10 0 0 0 0 0 0 0 0 1 0 0
70 70 885 39 12 45 34 5 11 17 10 0 0 0 0 0 0 0 0 0 1 0
71 71 837 56 12 41 30 3 9 17 10 0 0 0 0 0 0 0 0 0 0 1
72 72 586 35 12 43 16 4 10 16 10 0 0 0 0 0 0 0 0 0 0 0
73 73 913 37 10 31 22 4 9 16 10 1 0 0 0 0 0 0 0 0 0 0
74 74 547 26 15 32 22 7 7 15 10 0 1 0 0 0 0 0 0 0 0 0
75 75 758 33 12 41 31 4 6 15 10 0 0 1 0 0 0 0 0 0 0 0
76 76 848 7 9 27 10 5 6 15 10 0 0 0 1 0 0 0 0 0 0 0
77 77 634 16 10 40 7 7 5 15 10 0 0 0 0 1 0 0 0 0 0 0
78 78 501 13 13 46 10 3 5 15 10 0 0 0 0 0 1 0 0 0 0 0
79 79 849 54 12 32 55 6 8 15 10 0 0 0 0 0 0 1 0 0 0 0
80 80 733 30 13 9 25 8 7 15 10 0 0 0 0 0 0 0 1 0 0 0
81 81 634 9 16 64 9 5 5 15 10 0 0 0 0 0 0 0 0 1 0 0
82 82 1010 35 15 30 31 5 10 15 10 0 0 0 0 0 0 0 0 0 1 0
83 83 778 0 12 46 0 0 0 15 10 0 0 0 0 0 0 0 0 0 0 1
84 84 480 40 12 37 24 3 10 15 10 0 0 0 0 0 0 0 0 0 0 0
85 85 848 22 12 22 14 5 6 15 10 1 0 0 0 0 0 0 0 0 0 0
86 86 714 29 12 20 11 3 6 14 10 0 1 0 0 0 0 0 0 0 0 0
87 87 871 25 12 21 8 8 4 14 10 0 0 1 0 0 0 0 0 0 0 0
88 88 776 17 14 44 9 9 3 14 10 0 0 0 1 0 0 0 0 0 0 0
89 89 815 32 12 24 18 9 7 14 10 0 0 0 0 1 0 0 0 0 0 0
90 90 811 40 12 33 14 4 5 14 10 0 0 0 0 0 1 0 0 0 0 0
91 91 529 24 12 45 27 2 8 13 10 0 0 0 0 0 0 1 0 0 0 0
92 92 642 18 13 35 10 0 0 13 10 0 0 0 0 0 0 0 1 0 0 0
93 93 562 15 8 31 16 3 5 13 10 0 0 0 0 0 0 0 0 1 0 0
94 94 626 17 16 20 13 7 5 13 10 0 0 0 0 0 0 0 0 0 1 0
95 95 636 28 12 13 10 5 5 13 11 0 0 0 0 0 0 0 0 0 0 1
96 96 935 18 11 33 16 3 5 13 11 0 0 0 0 0 0 0 0 0 0 0
97 97 473 16 15 58 11 3 6 12 11 1 0 0 0 0 0 0 0 0 0 0
98 98 836 28 13 26 8 3 5 12 11 0 1 0 0 0 0 0 0 0 0 0
99 99 938 17 12 36 29 7 6 12 11 0 0 1 0 0 0 0 0 0 0 0
100 100 656 25 13 32 12 4 4 12 11 0 0 0 1 0 0 0 0 0 0 0
101 101 566 2 13 34 1 0 0 12 11 0 0 0 0 1 0 0 0 0 0 0
102 102 765 10 12 15 26 5 8 12 11 0 0 0 0 0 1 0 0 0 0 0
103 103 705 9 12 40 5 5 2 11 11 0 0 0 0 0 0 1 0 0 0 0
104 104 558 7 12 37 5 5 2 11 11 0 0 0 0 0 0 0 1 0 0 0
105 105 582 27 14 26 24 6 8 11 11 0 0 0 0 0 0 0 0 1 0 0
106 106 608 25 12 31 19 6 3 11 11 0 0 0 0 0 0 0 0 0 1 0
107 107 567 16 16 47 10 5 3 11 11 0 0 0 0 0 0 0 0 0 0 1
108 108 434 28 8 21 6 6 3 11 11 0 0 0 0 0 0 0 0 0 0 0
109 109 479 7 8 21 61 0 3 11 11 1 0 0 0 0 0 0 0 0 0 0
110 110 488 0 5 9 25 25 1 10 11 0 1 0 0 0 0 0 0 0 0 0
111 111 507 16 9 28 7 2 2 10 11 0 0 1 0 0 0 0 0 0 0 0
112 112 394 10 11 24 10 5 2 10 11 0 0 0 1 0 0 0 0 0 0 0
113 113 504 0 4 15 3 3 1 9 11 0 0 0 0 1 0 0 0 0 0 0
114 114 368 2 8 19 1 1 2 9 11 0 0 0 0 0 1 0 0 0 0 0
115 115 386 5 13 35 38 5 7 9 11 0 0 0 0 0 0 1 0 0 0 0
116 116 451 36 13 45 13 4 4 9 11 0 0 0 0 0 0 0 1 0 0 0
117 117 580 10 12 20 2 0 1 9 11 0 0 0 0 0 0 0 0 1 0 0
118 118 565 43 13 1 8 4 6 9 11 0 0 0 0 0 0 0 0 0 1 0
119 119 510 14 12 29 30 10 3 9 11 0 0 0 0 0 0 0 0 0 0 1
120 120 495 12 12 33 11 6 2 8 11 0 0 0 0 0 0 0 0 0 0 0
121 121 596 15 10 32 69 23 3 8 11 1 0 0 0 0 0 0 0 0 0 0
122 122 412 8 12 11 2 0 2 8 11 0 1 0 0 0 0 0 0 0 0 0
123 123 338 39 5 10 23 6 5 7 11 0 0 1 0 0 0 0 0 0 0 0
124 124 446 10 13 18 8 4 4 7 11 0 0 0 1 0 0 0 0 0 0 0
125 125 418 0 12 41 0 0 0 7 11 0 0 0 0 1 0 0 0 0 0 0
126 126 335 7 6 0 2 0 0 6 11 0 0 0 0 0 1 0 0 0 0 0
127 127 349 10 9 10 4 2 3 6 11 0 0 0 0 0 0 1 0 0 0 0
128 128 308 3 12 24 4 4 2 5 11 0 0 0 0 0 0 0 1 0 0 0
129 129 466 8 15 28 0 0 0 5 11 0 0 0 0 0 0 0 0 1 0 0
130 130 228 0 11 38 9 9 1 5 11 0 0 0 0 0 0 0 0 0 1 0
131 131 428 8 3 4 5 5 3 5 11 0 0 0 0 0 0 0 0 0 0 1
132 132 242 1 8 25 0 0 0 5 11 0 0 0 0 0 0 0 0 0 0 0
133 133 352 0 12 40 0 0 0 5 11 1 0 0 0 0 0 0 0 0 0 0
134 134 244 8 0 0 13 4 4 5 11 0 1 0 0 0 0 0 0 0 0 0
135 135 269 3 9 23 1 0 1 5 11 0 0 1 0 0 0 0 0 0 0 0
136 136 242 0 4 13 0 0 0 4 11 0 0 0 1 0 0 0 0 0 0 0
137 137 291 0 14 6 39 0 2 4 11 0 0 0 0 1 0 0 0 0 0 0
138 138 213 0 9 31 10 0 0 4 11 0 0 0 0 0 1 0 0 0 0 0
139 139 135 0 0 0 1 0 1 3 11 0 0 0 0 0 0 1 0 0 0 0
140 140 210 3 1 3 3 3 3 3 11 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1 x2 x3 x4 x5
-8.406e+01 -3.035e-04 -7.549e-02 2.218e-01 -1.923e-01 1.043e-01
x6 x7 x8 x9 M1 M2
-2.440e-01 -1.060e-02 -2.395e+00 2.016e+01 2.198e+00 1.132e+00
M3 M4 M5 M6 M7 M8
2.751e+00 2.132e+00 2.558e+00 2.917e+00 2.326e+00 2.981e+00
M9 M10 M11
3.004e+00 2.526e+00 1.531e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.956 -4.619 -0.858 4.540 28.141
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.406e+01 1.876e+01 -4.481 1.72e-05 ***
X1 -3.035e-04 1.353e-03 -0.224 0.82290
x2 -7.549e-02 6.277e-02 -1.203 0.23147
x3 2.218e-01 2.908e-01 0.763 0.44720
x4 -1.923e-01 6.933e-02 -2.774 0.00643 **
x5 1.043e-01 6.141e-02 1.699 0.09198 .
x6 -2.440e-01 1.882e-01 -1.297 0.19726
x7 -1.060e-02 3.489e-01 -0.030 0.97582
x8 -2.395e+00 2.061e-01 -11.620 < 2e-16 ***
x9 2.016e+01 1.635e+00 12.330 < 2e-16 ***
M1 2.198e+00 3.106e+00 0.708 0.48051
M2 1.132e+00 3.152e+00 0.359 0.72023
M3 2.751e+00 3.037e+00 0.906 0.36689
M4 2.132e+00 3.060e+00 0.697 0.48742
M5 2.558e+00 3.096e+00 0.826 0.41023
M6 2.917e+00 3.157e+00 0.924 0.35731
M7 2.326e+00 3.053e+00 0.762 0.44766
M8 2.981e+00 3.104e+00 0.960 0.33886
M9 3.004e+00 3.117e+00 0.964 0.33723
M10 2.526e+00 3.177e+00 0.795 0.42811
M11 1.531e+00 3.136e+00 0.488 0.62622
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.183 on 119 degrees of freedom
Multiple R-squared: 0.9731, Adjusted R-squared: 0.9686
F-statistic: 215.6 on 20 and 119 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,] 0.6803337 6.393327e-01 3.196663e-01
[2,] 0.7011033 5.977933e-01 2.988967e-01
[3,] 0.5847148 8.305705e-01 4.152852e-01
[4,] 0.5585130 8.829740e-01 4.414870e-01
[5,] 0.9131600 1.736800e-01 8.683998e-02
[6,] 0.9398691 1.202618e-01 6.013088e-02
[7,] 0.9468580 1.062839e-01 5.314196e-02
[8,] 0.9811629 3.767421e-02 1.883710e-02
[9,] 0.9915259 1.694816e-02 8.474081e-03
[10,] 0.9977144 4.571159e-03 2.285579e-03
[11,] 0.9990794 1.841257e-03 9.206283e-04
[12,] 0.9985582 2.883623e-03 1.441811e-03
[13,] 0.9999454 1.091527e-04 5.457636e-05
[14,] 0.9999321 1.357638e-04 6.788190e-05
[15,] 0.9999405 1.190653e-04 5.953265e-05
[16,] 0.9999296 1.408733e-04 7.043663e-05
[17,] 0.9999997 5.192980e-07 2.596490e-07
[18,] 1.0000000 8.663879e-08 4.331940e-08
[19,] 1.0000000 8.566827e-08 4.283413e-08
[20,] 0.9999999 1.221263e-07 6.106313e-08
[21,] 1.0000000 2.179282e-08 1.089641e-08
[22,] 1.0000000 5.738956e-09 2.869478e-09
[23,] 1.0000000 3.548502e-09 1.774251e-09
[24,] 1.0000000 4.813725e-09 2.406862e-09
[25,] 1.0000000 2.868154e-09 1.434077e-09
[26,] 1.0000000 6.577910e-10 3.288955e-10
[27,] 1.0000000 7.811871e-10 3.905936e-10
[28,] 1.0000000 1.386460e-09 6.932300e-10
[29,] 1.0000000 1.507636e-09 7.538178e-10
[30,] 1.0000000 2.747067e-09 1.373533e-09
[31,] 1.0000000 5.354339e-09 2.677169e-09
[32,] 1.0000000 3.576931e-09 1.788466e-09
[33,] 1.0000000 6.956151e-09 3.478076e-09
[34,] 1.0000000 1.313243e-08 6.566216e-09
[35,] 1.0000000 1.526705e-08 7.633527e-09
[36,] 1.0000000 3.448536e-08 1.724268e-08
[37,] 1.0000000 4.724418e-08 2.362209e-08
[38,] 1.0000000 7.690304e-08 3.845152e-08
[39,] 1.0000000 6.133813e-08 3.066906e-08
[40,] 1.0000000 2.623304e-08 1.311652e-08
[41,] 1.0000000 9.604658e-09 4.802329e-09
[42,] 1.0000000 1.301861e-08 6.509303e-09
[43,] 1.0000000 1.037156e-08 5.185781e-09
[44,] 1.0000000 2.174243e-08 1.087122e-08
[45,] 1.0000000 3.119928e-08 1.559964e-08
[46,] 1.0000000 6.217475e-08 3.108737e-08
[47,] 0.9999999 1.259759e-07 6.298794e-08
[48,] 0.9999999 1.757440e-07 8.787202e-08
[49,] 0.9999999 1.554836e-07 7.774180e-08
[50,] 0.9999999 1.829861e-07 9.149307e-08
[51,] 1.0000000 2.665030e-08 1.332515e-08
[52,] 1.0000000 2.507906e-09 1.253953e-09
[53,] 1.0000000 9.080347e-10 4.540173e-10
[54,] 1.0000000 3.282032e-10 1.641016e-10
[55,] 1.0000000 5.180119e-11 2.590060e-11
[56,] 1.0000000 1.578402e-11 7.892012e-12
[57,] 1.0000000 1.876302e-11 9.381508e-12
[58,] 1.0000000 3.147249e-11 1.573625e-11
[59,] 1.0000000 7.844866e-11 3.922433e-11
[60,] 1.0000000 4.685260e-11 2.342630e-11
[61,] 1.0000000 4.691063e-11 2.345531e-11
[62,] 1.0000000 7.400390e-11 3.700195e-11
[63,] 1.0000000 7.690661e-11 3.845330e-11
[64,] 1.0000000 1.923213e-10 9.616065e-11
[65,] 1.0000000 3.853432e-10 1.926716e-10
[66,] 1.0000000 9.148793e-10 4.574396e-10
[67,] 1.0000000 1.050886e-09 5.254431e-10
[68,] 1.0000000 1.580322e-09 7.901611e-10
[69,] 1.0000000 2.431183e-09 1.215591e-09
[70,] 1.0000000 4.235875e-09 2.117938e-09
[71,] 1.0000000 1.378347e-08 6.891734e-09
[72,] 1.0000000 1.120354e-08 5.601770e-09
[73,] 1.0000000 3.838930e-08 1.919465e-08
[74,] 1.0000000 1.323340e-08 6.616702e-09
[75,] 1.0000000 8.644904e-09 4.322452e-09
[76,] 1.0000000 2.356272e-08 1.178136e-08
[77,] 1.0000000 3.571755e-08 1.785878e-08
[78,] 0.9999999 1.278820e-07 6.394098e-08
[79,] 0.9999998 4.253314e-07 2.126657e-07
[80,] 0.9999995 1.074143e-06 5.370714e-07
[81,] 0.9999982 3.627418e-06 1.813709e-06
[82,] 0.9999950 1.008993e-05 5.044963e-06
[83,] 0.9999862 2.757279e-05 1.378639e-05
[84,] 0.9999674 6.515120e-05 3.257560e-05
[85,] 0.9999045 1.910652e-04 9.553260e-05
[86,] 0.9997686 4.627655e-04 2.313828e-04
[87,] 0.9993318 1.336493e-03 6.682467e-04
[88,] 0.9989271 2.145741e-03 1.072871e-03
[89,] 0.9969633 6.073486e-03 3.036743e-03
[90,] 0.9944949 1.101015e-02 5.505073e-03
[91,] 0.9835078 3.298447e-02 1.649223e-02
[92,] 0.9925124 1.497528e-02 7.487639e-03
[93,] 0.9774815 4.503703e-02 2.251852e-02
> postscript(file="/var/fisher/rcomp/tmp/1g0nf1354793893.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/22jny1354793893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39tas1354793893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4x3md1354793893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5uiiu1354793893.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 = 140
Frequency = 1
1 2 3 4 5
28.141237672 23.381480195 15.421806540 11.986286984 6.416180288
6 7 8 9 10
1.204327840 2.855939419 1.634908009 0.304807143 -5.515754095
11 12 13 14 15
-5.219100976 -6.532302168 -7.406366599 -5.843552438 -3.432593057
16 17 18 19 20
-5.441626985 -5.155307184 -3.325083832 -6.255586988 -7.398695077
21 22 23 24 25
-7.096922501 -4.919281377 -3.871525444 -4.516602898 -8.598702448
26 27 28 29 30
-6.426718147 -1.899275623 -6.922938319 -1.157485767 -1.059654342
31 32 33 34 35
-2.295200914 -3.528987554 -4.271006630 -1.280611214 -0.596123963
36 37 38 39 40
4.474563221 1.870883198 1.498182066 1.756269348 2.316463450
41 42 43 44 45
4.136787508 9.387872191 3.205911310 5.679596954 6.503464198
46 47 48 49 50
6.023734151 8.389867445 -11.313786947 -12.955942079 -10.501309869
51 52 53 54 55
-10.928196232 -11.556056452 -8.519517108 -10.208145730 -7.145425585
56 57 58 59 60
-7.012867938 -3.815109653 -5.442635105 -5.279960079 -2.871772114
61 62 63 64 65
-3.296575754 -3.118845378 -4.258508967 0.022516053 -1.072784144
66 67 68 69 70
-1.534896481 0.763802261 -2.627289486 -2.543276096 -2.405246826
71 72 73 74 75
-0.002958789 0.571273845 -2.877391082 -4.353632942 -3.665505452
76 77 78 79 80
-3.574467783 0.682416498 0.256533001 -1.352749316 -3.893640429
81 82 83 84 85
6.297455719 1.292379167 6.225151052 8.288973716 4.447393386
86 87 88 89 90
4.046612894 4.876760667 9.972968142 7.390803491 9.541942347
91 92 93 94 95
7.940556211 6.922397399 8.147251225 7.194181566 -10.776696164
96 97 98 99 100
-5.954792019 -5.385222573 -7.712127195 -8.189869285 -6.023041403
101 102 103 104 105
-6.699572065 -10.129960323 -4.091859448 -4.519257395 -6.258917802
106 107 108 109 110
-3.050267374 1.137813125 1.969114083 -8.001508195 -0.665207880
111 112 113 114 115
-1.027096366 -0.688813204 -3.178972357 -2.814662168 -1.853545730
116 117 118 119 120
5.107236336 -0.286724283 0.204532731 4.738080537 6.483459956
121 122 123 124 125
3.902254373 2.267874310 2.236556059 2.530032133 6.801910732
126 127 128 129 130
-1.212812066 2.178339164 2.092506317 3.018978680 7.898968377
131 132 133 134 135
5.255453254 9.401871325 10.159940100 7.427244385 9.109652371
136 137 138 139 140
7.378677385 0.355540109 9.894539563 6.049819616 7.544092866
> postscript(file="/var/fisher/rcomp/tmp/6yp2f1354793893.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 = 140
Frequency = 1
lag(myerror, k = 1) myerror
0 28.141237672 NA
1 23.381480195 28.141237672
2 15.421806540 23.381480195
3 11.986286984 15.421806540
4 6.416180288 11.986286984
5 1.204327840 6.416180288
6 2.855939419 1.204327840
7 1.634908009 2.855939419
8 0.304807143 1.634908009
9 -5.515754095 0.304807143
10 -5.219100976 -5.515754095
11 -6.532302168 -5.219100976
12 -7.406366599 -6.532302168
13 -5.843552438 -7.406366599
14 -3.432593057 -5.843552438
15 -5.441626985 -3.432593057
16 -5.155307184 -5.441626985
17 -3.325083832 -5.155307184
18 -6.255586988 -3.325083832
19 -7.398695077 -6.255586988
20 -7.096922501 -7.398695077
21 -4.919281377 -7.096922501
22 -3.871525444 -4.919281377
23 -4.516602898 -3.871525444
24 -8.598702448 -4.516602898
25 -6.426718147 -8.598702448
26 -1.899275623 -6.426718147
27 -6.922938319 -1.899275623
28 -1.157485767 -6.922938319
29 -1.059654342 -1.157485767
30 -2.295200914 -1.059654342
31 -3.528987554 -2.295200914
32 -4.271006630 -3.528987554
33 -1.280611214 -4.271006630
34 -0.596123963 -1.280611214
35 4.474563221 -0.596123963
36 1.870883198 4.474563221
37 1.498182066 1.870883198
38 1.756269348 1.498182066
39 2.316463450 1.756269348
40 4.136787508 2.316463450
41 9.387872191 4.136787508
42 3.205911310 9.387872191
43 5.679596954 3.205911310
44 6.503464198 5.679596954
45 6.023734151 6.503464198
46 8.389867445 6.023734151
47 -11.313786947 8.389867445
48 -12.955942079 -11.313786947
49 -10.501309869 -12.955942079
50 -10.928196232 -10.501309869
51 -11.556056452 -10.928196232
52 -8.519517108 -11.556056452
53 -10.208145730 -8.519517108
54 -7.145425585 -10.208145730
55 -7.012867938 -7.145425585
56 -3.815109653 -7.012867938
57 -5.442635105 -3.815109653
58 -5.279960079 -5.442635105
59 -2.871772114 -5.279960079
60 -3.296575754 -2.871772114
61 -3.118845378 -3.296575754
62 -4.258508967 -3.118845378
63 0.022516053 -4.258508967
64 -1.072784144 0.022516053
65 -1.534896481 -1.072784144
66 0.763802261 -1.534896481
67 -2.627289486 0.763802261
68 -2.543276096 -2.627289486
69 -2.405246826 -2.543276096
70 -0.002958789 -2.405246826
71 0.571273845 -0.002958789
72 -2.877391082 0.571273845
73 -4.353632942 -2.877391082
74 -3.665505452 -4.353632942
75 -3.574467783 -3.665505452
76 0.682416498 -3.574467783
77 0.256533001 0.682416498
78 -1.352749316 0.256533001
79 -3.893640429 -1.352749316
80 6.297455719 -3.893640429
81 1.292379167 6.297455719
82 6.225151052 1.292379167
83 8.288973716 6.225151052
84 4.447393386 8.288973716
85 4.046612894 4.447393386
86 4.876760667 4.046612894
87 9.972968142 4.876760667
88 7.390803491 9.972968142
89 9.541942347 7.390803491
90 7.940556211 9.541942347
91 6.922397399 7.940556211
92 8.147251225 6.922397399
93 7.194181566 8.147251225
94 -10.776696164 7.194181566
95 -5.954792019 -10.776696164
96 -5.385222573 -5.954792019
97 -7.712127195 -5.385222573
98 -8.189869285 -7.712127195
99 -6.023041403 -8.189869285
100 -6.699572065 -6.023041403
101 -10.129960323 -6.699572065
102 -4.091859448 -10.129960323
103 -4.519257395 -4.091859448
104 -6.258917802 -4.519257395
105 -3.050267374 -6.258917802
106 1.137813125 -3.050267374
107 1.969114083 1.137813125
108 -8.001508195 1.969114083
109 -0.665207880 -8.001508195
110 -1.027096366 -0.665207880
111 -0.688813204 -1.027096366
112 -3.178972357 -0.688813204
113 -2.814662168 -3.178972357
114 -1.853545730 -2.814662168
115 5.107236336 -1.853545730
116 -0.286724283 5.107236336
117 0.204532731 -0.286724283
118 4.738080537 0.204532731
119 6.483459956 4.738080537
120 3.902254373 6.483459956
121 2.267874310 3.902254373
122 2.236556059 2.267874310
123 2.530032133 2.236556059
124 6.801910732 2.530032133
125 -1.212812066 6.801910732
126 2.178339164 -1.212812066
127 2.092506317 2.178339164
128 3.018978680 2.092506317
129 7.898968377 3.018978680
130 5.255453254 7.898968377
131 9.401871325 5.255453254
132 10.159940100 9.401871325
133 7.427244385 10.159940100
134 9.109652371 7.427244385
135 7.378677385 9.109652371
136 0.355540109 7.378677385
137 9.894539563 0.355540109
138 6.049819616 9.894539563
139 7.544092866 6.049819616
140 NA 7.544092866
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 23.381480195 28.141237672
[2,] 15.421806540 23.381480195
[3,] 11.986286984 15.421806540
[4,] 6.416180288 11.986286984
[5,] 1.204327840 6.416180288
[6,] 2.855939419 1.204327840
[7,] 1.634908009 2.855939419
[8,] 0.304807143 1.634908009
[9,] -5.515754095 0.304807143
[10,] -5.219100976 -5.515754095
[11,] -6.532302168 -5.219100976
[12,] -7.406366599 -6.532302168
[13,] -5.843552438 -7.406366599
[14,] -3.432593057 -5.843552438
[15,] -5.441626985 -3.432593057
[16,] -5.155307184 -5.441626985
[17,] -3.325083832 -5.155307184
[18,] -6.255586988 -3.325083832
[19,] -7.398695077 -6.255586988
[20,] -7.096922501 -7.398695077
[21,] -4.919281377 -7.096922501
[22,] -3.871525444 -4.919281377
[23,] -4.516602898 -3.871525444
[24,] -8.598702448 -4.516602898
[25,] -6.426718147 -8.598702448
[26,] -1.899275623 -6.426718147
[27,] -6.922938319 -1.899275623
[28,] -1.157485767 -6.922938319
[29,] -1.059654342 -1.157485767
[30,] -2.295200914 -1.059654342
[31,] -3.528987554 -2.295200914
[32,] -4.271006630 -3.528987554
[33,] -1.280611214 -4.271006630
[34,] -0.596123963 -1.280611214
[35,] 4.474563221 -0.596123963
[36,] 1.870883198 4.474563221
[37,] 1.498182066 1.870883198
[38,] 1.756269348 1.498182066
[39,] 2.316463450 1.756269348
[40,] 4.136787508 2.316463450
[41,] 9.387872191 4.136787508
[42,] 3.205911310 9.387872191
[43,] 5.679596954 3.205911310
[44,] 6.503464198 5.679596954
[45,] 6.023734151 6.503464198
[46,] 8.389867445 6.023734151
[47,] -11.313786947 8.389867445
[48,] -12.955942079 -11.313786947
[49,] -10.501309869 -12.955942079
[50,] -10.928196232 -10.501309869
[51,] -11.556056452 -10.928196232
[52,] -8.519517108 -11.556056452
[53,] -10.208145730 -8.519517108
[54,] -7.145425585 -10.208145730
[55,] -7.012867938 -7.145425585
[56,] -3.815109653 -7.012867938
[57,] -5.442635105 -3.815109653
[58,] -5.279960079 -5.442635105
[59,] -2.871772114 -5.279960079
[60,] -3.296575754 -2.871772114
[61,] -3.118845378 -3.296575754
[62,] -4.258508967 -3.118845378
[63,] 0.022516053 -4.258508967
[64,] -1.072784144 0.022516053
[65,] -1.534896481 -1.072784144
[66,] 0.763802261 -1.534896481
[67,] -2.627289486 0.763802261
[68,] -2.543276096 -2.627289486
[69,] -2.405246826 -2.543276096
[70,] -0.002958789 -2.405246826
[71,] 0.571273845 -0.002958789
[72,] -2.877391082 0.571273845
[73,] -4.353632942 -2.877391082
[74,] -3.665505452 -4.353632942
[75,] -3.574467783 -3.665505452
[76,] 0.682416498 -3.574467783
[77,] 0.256533001 0.682416498
[78,] -1.352749316 0.256533001
[79,] -3.893640429 -1.352749316
[80,] 6.297455719 -3.893640429
[81,] 1.292379167 6.297455719
[82,] 6.225151052 1.292379167
[83,] 8.288973716 6.225151052
[84,] 4.447393386 8.288973716
[85,] 4.046612894 4.447393386
[86,] 4.876760667 4.046612894
[87,] 9.972968142 4.876760667
[88,] 7.390803491 9.972968142
[89,] 9.541942347 7.390803491
[90,] 7.940556211 9.541942347
[91,] 6.922397399 7.940556211
[92,] 8.147251225 6.922397399
[93,] 7.194181566 8.147251225
[94,] -10.776696164 7.194181566
[95,] -5.954792019 -10.776696164
[96,] -5.385222573 -5.954792019
[97,] -7.712127195 -5.385222573
[98,] -8.189869285 -7.712127195
[99,] -6.023041403 -8.189869285
[100,] -6.699572065 -6.023041403
[101,] -10.129960323 -6.699572065
[102,] -4.091859448 -10.129960323
[103,] -4.519257395 -4.091859448
[104,] -6.258917802 -4.519257395
[105,] -3.050267374 -6.258917802
[106,] 1.137813125 -3.050267374
[107,] 1.969114083 1.137813125
[108,] -8.001508195 1.969114083
[109,] -0.665207880 -8.001508195
[110,] -1.027096366 -0.665207880
[111,] -0.688813204 -1.027096366
[112,] -3.178972357 -0.688813204
[113,] -2.814662168 -3.178972357
[114,] -1.853545730 -2.814662168
[115,] 5.107236336 -1.853545730
[116,] -0.286724283 5.107236336
[117,] 0.204532731 -0.286724283
[118,] 4.738080537 0.204532731
[119,] 6.483459956 4.738080537
[120,] 3.902254373 6.483459956
[121,] 2.267874310 3.902254373
[122,] 2.236556059 2.267874310
[123,] 2.530032133 2.236556059
[124,] 6.801910732 2.530032133
[125,] -1.212812066 6.801910732
[126,] 2.178339164 -1.212812066
[127,] 2.092506317 2.178339164
[128,] 3.018978680 2.092506317
[129,] 7.898968377 3.018978680
[130,] 5.255453254 7.898968377
[131,] 9.401871325 5.255453254
[132,] 10.159940100 9.401871325
[133,] 7.427244385 10.159940100
[134,] 9.109652371 7.427244385
[135,] 7.378677385 9.109652371
[136,] 0.355540109 7.378677385
[137,] 9.894539563 0.355540109
[138,] 6.049819616 9.894539563
[139,] 7.544092866 6.049819616
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 23.381480195 28.141237672
2 15.421806540 23.381480195
3 11.986286984 15.421806540
4 6.416180288 11.986286984
5 1.204327840 6.416180288
6 2.855939419 1.204327840
7 1.634908009 2.855939419
8 0.304807143 1.634908009
9 -5.515754095 0.304807143
10 -5.219100976 -5.515754095
11 -6.532302168 -5.219100976
12 -7.406366599 -6.532302168
13 -5.843552438 -7.406366599
14 -3.432593057 -5.843552438
15 -5.441626985 -3.432593057
16 -5.155307184 -5.441626985
17 -3.325083832 -5.155307184
18 -6.255586988 -3.325083832
19 -7.398695077 -6.255586988
20 -7.096922501 -7.398695077
21 -4.919281377 -7.096922501
22 -3.871525444 -4.919281377
23 -4.516602898 -3.871525444
24 -8.598702448 -4.516602898
25 -6.426718147 -8.598702448
26 -1.899275623 -6.426718147
27 -6.922938319 -1.899275623
28 -1.157485767 -6.922938319
29 -1.059654342 -1.157485767
30 -2.295200914 -1.059654342
31 -3.528987554 -2.295200914
32 -4.271006630 -3.528987554
33 -1.280611214 -4.271006630
34 -0.596123963 -1.280611214
35 4.474563221 -0.596123963
36 1.870883198 4.474563221
37 1.498182066 1.870883198
38 1.756269348 1.498182066
39 2.316463450 1.756269348
40 4.136787508 2.316463450
41 9.387872191 4.136787508
42 3.205911310 9.387872191
43 5.679596954 3.205911310
44 6.503464198 5.679596954
45 6.023734151 6.503464198
46 8.389867445 6.023734151
47 -11.313786947 8.389867445
48 -12.955942079 -11.313786947
49 -10.501309869 -12.955942079
50 -10.928196232 -10.501309869
51 -11.556056452 -10.928196232
52 -8.519517108 -11.556056452
53 -10.208145730 -8.519517108
54 -7.145425585 -10.208145730
55 -7.012867938 -7.145425585
56 -3.815109653 -7.012867938
57 -5.442635105 -3.815109653
58 -5.279960079 -5.442635105
59 -2.871772114 -5.279960079
60 -3.296575754 -2.871772114
61 -3.118845378 -3.296575754
62 -4.258508967 -3.118845378
63 0.022516053 -4.258508967
64 -1.072784144 0.022516053
65 -1.534896481 -1.072784144
66 0.763802261 -1.534896481
67 -2.627289486 0.763802261
68 -2.543276096 -2.627289486
69 -2.405246826 -2.543276096
70 -0.002958789 -2.405246826
71 0.571273845 -0.002958789
72 -2.877391082 0.571273845
73 -4.353632942 -2.877391082
74 -3.665505452 -4.353632942
75 -3.574467783 -3.665505452
76 0.682416498 -3.574467783
77 0.256533001 0.682416498
78 -1.352749316 0.256533001
79 -3.893640429 -1.352749316
80 6.297455719 -3.893640429
81 1.292379167 6.297455719
82 6.225151052 1.292379167
83 8.288973716 6.225151052
84 4.447393386 8.288973716
85 4.046612894 4.447393386
86 4.876760667 4.046612894
87 9.972968142 4.876760667
88 7.390803491 9.972968142
89 9.541942347 7.390803491
90 7.940556211 9.541942347
91 6.922397399 7.940556211
92 8.147251225 6.922397399
93 7.194181566 8.147251225
94 -10.776696164 7.194181566
95 -5.954792019 -10.776696164
96 -5.385222573 -5.954792019
97 -7.712127195 -5.385222573
98 -8.189869285 -7.712127195
99 -6.023041403 -8.189869285
100 -6.699572065 -6.023041403
101 -10.129960323 -6.699572065
102 -4.091859448 -10.129960323
103 -4.519257395 -4.091859448
104 -6.258917802 -4.519257395
105 -3.050267374 -6.258917802
106 1.137813125 -3.050267374
107 1.969114083 1.137813125
108 -8.001508195 1.969114083
109 -0.665207880 -8.001508195
110 -1.027096366 -0.665207880
111 -0.688813204 -1.027096366
112 -3.178972357 -0.688813204
113 -2.814662168 -3.178972357
114 -1.853545730 -2.814662168
115 5.107236336 -1.853545730
116 -0.286724283 5.107236336
117 0.204532731 -0.286724283
118 4.738080537 0.204532731
119 6.483459956 4.738080537
120 3.902254373 6.483459956
121 2.267874310 3.902254373
122 2.236556059 2.267874310
123 2.530032133 2.236556059
124 6.801910732 2.530032133
125 -1.212812066 6.801910732
126 2.178339164 -1.212812066
127 2.092506317 2.178339164
128 3.018978680 2.092506317
129 7.898968377 3.018978680
130 5.255453254 7.898968377
131 9.401871325 5.255453254
132 10.159940100 9.401871325
133 7.427244385 10.159940100
134 9.109652371 7.427244385
135 7.378677385 9.109652371
136 0.355540109 7.378677385
137 9.894539563 0.355540109
138 6.049819616 9.894539563
139 7.544092866 6.049819616
> 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/7eplc1354793893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8het21354793893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9rr281354793893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10zi571354793893.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11c9ye1354793893.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/1218ms1354793893.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/13h82a1354793893.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/14569e1354793893.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/15pmmx1354793893.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/160v6o1354793893.tab")
+ }
>
> try(system("convert tmp/1g0nf1354793893.ps tmp/1g0nf1354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/22jny1354793893.ps tmp/22jny1354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/39tas1354793893.ps tmp/39tas1354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/4x3md1354793893.ps tmp/4x3md1354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uiiu1354793893.ps tmp/5uiiu1354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yp2f1354793893.ps tmp/6yp2f1354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/7eplc1354793893.ps tmp/7eplc1354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/8het21354793893.ps tmp/8het21354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rr281354793893.ps tmp/9rr281354793893.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zi571354793893.ps tmp/10zi571354793893.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.756 1.499 9.274