R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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+ ,1
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+ ,20
+ ,20
+ ,11
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+ ,1
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+ ,9
+ ,9
+ ,13
+ ,13
+ ,6
+ ,6
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+ ,19
+ ,1
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+ ,28
+ ,28
+ ,14
+ ,14
+ ,11
+ ,11
+ ,8
+ ,8
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+ ,24
+ ,1
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+ ,13
+ ,13
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+ ,10
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+ ,1
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+ ,31
+ ,16
+ ,16
+ ,19
+ ,19
+ ,16
+ ,16
+ ,17
+ ,17)
+ ,dim=c(12
+ ,159)
+ ,dimnames=list(c('B'
+ ,'O'
+ ,'CM'
+ ,'CM_B'
+ ,'D'
+ ,'D_B'
+ ,'PE'
+ ,'PE_B'
+ ,'PC'
+ ,'PC_B'
+ ,'PS'
+ ,'PS_B')
+ ,1:159))
> y <- array(NA,dim=c(12,159),dimnames=list(c('B','O','CM','CM_B','D','D_B','PE','PE_B','PC','PC_B','PS','PS_B'),1:159))
> 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 = '2'
> #'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.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
O B CM CM_B D D_B PE PE_B PC PC_B PS PS_B
1 26 1 24 24 14 14 11 11 12 12 24 24
2 23 1 25 25 11 11 7 7 8 8 25 25
3 25 0 17 0 6 0 17 0 8 0 30 0
4 23 1 18 18 12 12 10 10 8 8 19 19
5 20 1 18 18 8 8 12 12 9 9 22 22
6 29 0 16 10 0 12 0 7 0 22 1 25
7 20 20 10 10 11 11 4 4 25 25 1 21
8 16 16 11 11 11 11 11 11 23 23 1 22
9 18 18 16 16 12 12 7 7 17 17 1 25
10 17 17 11 11 13 13 7 7 21 21 1 24
11 23 23 13 13 14 14 12 12 19 19 1 18
12 30 30 12 12 16 16 10 10 19 19 1 22
13 23 23 8 8 11 11 10 10 15 15 1 15
14 18 18 12 12 10 10 8 8 16 16 1 22
15 15 15 11 11 11 11 8 8 23 23 1 28
16 12 12 4 4 15 15 4 4 27 27 1 20
17 21 21 9 9 9 9 9 9 22 22 1 12
18 15 15 8 8 11 11 8 8 14 14 1 24
19 20 20 8 8 17 17 7 7 22 22 1 20
20 31 31 14 14 17 17 11 11 23 23 1 21
21 27 27 15 15 11 11 9 9 23 23 1 20
22 34 34 16 16 18 18 11 11 21 21 1 21
23 21 21 9 9 14 14 13 13 19 19 1 23
24 31 31 14 14 10 10 8 8 18 18 1 28
25 19 19 11 11 11 11 8 8 20 20 1 24
26 16 16 8 8 15 15 9 9 23 23 1 24
27 20 20 9 9 15 15 6 6 25 25 1 24
28 21 21 9 9 13 13 9 9 19 19 1 23
29 22 22 9 9 16 16 9 9 24 24 1 23
30 17 17 9 9 13 13 6 6 22 22 1 29
31 24 24 10 10 9 9 6 6 25 25 1 24
32 25 25 16 16 18 18 16 16 26 26 1 18
33 26 26 11 11 18 18 5 5 29 29 1 25
34 25 25 8 8 12 12 7 7 32 32 1 21
35 17 17 9 9 17 17 9 9 25 25 1 26
36 32 32 16 16 9 9 6 6 29 29 1 22
37 33 33 11 11 9 9 6 6 28 28 1 22
38 13 13 16 16 12 12 5 5 17 17 0 22
39 0 32 12 0 18 0 12 0 28 0 1 23
40 25 25 12 12 12 12 7 7 29 29 1 30
41 29 29 14 14 18 18 10 10 26 26 1 23
42 22 22 9 9 14 14 9 9 25 25 1 17
43 18 18 10 10 15 15 8 8 14 14 1 23
44 17 17 9 9 16 16 5 5 25 25 1 23
45 20 20 10 10 10 10 8 8 26 26 1 25
46 15 15 12 12 11 11 8 8 20 20 1 24
47 20 20 14 14 14 14 10 10 18 18 1 24
48 33 33 14 14 9 9 6 6 32 32 1 23
49 29 29 10 10 12 12 8 8 25 25 1 21
50 23 23 14 14 17 17 7 7 25 25 1 24
51 26 26 16 16 5 5 4 4 23 23 1 24
52 18 18 9 9 12 12 8 8 21 21 1 28
53 20 20 10 10 12 12 8 8 20 20 1 16
54 11 11 6 6 6 6 4 4 15 15 1 20
55 28 28 8 8 24 24 20 20 30 30 1 29
56 26 26 13 13 12 12 8 8 24 24 1 27
57 22 22 10 10 12 12 8 8 26 26 1 22
58 17 17 8 8 14 14 6 6 24 24 1 28
59 12 12 7 7 7 7 4 4 22 22 1 16
60 14 14 15 15 13 13 8 8 14 14 1 25
61 17 17 9 9 12 12 9 9 24 24 1 24
62 21 21 10 10 13 13 6 6 24 24 0 28
63 0 19 12 0 14 0 7 0 24 0 1 24
64 18 18 13 13 8 8 9 9 24 24 1 23
65 10 10 10 10 11 11 5 5 19 19 1 30
66 29 29 11 11 9 9 5 5 31 31 1 24
67 31 31 8 8 11 11 8 8 22 22 1 21
68 19 19 9 9 13 13 8 8 27 27 1 25
69 9 9 13 13 10 10 6 6 19 19 0 25
70 0 20 11 0 11 0 8 0 25 0 1 22
71 28 28 8 8 12 12 7 7 20 20 1 23
72 19 19 9 9 9 9 7 7 21 21 1 26
73 30 30 9 9 15 15 9 9 27 27 1 23
74 29 29 15 15 18 18 11 11 23 23 1 25
75 26 26 9 9 15 15 6 6 25 25 1 21
76 23 23 10 10 12 12 8 8 20 20 1 25
77 13 13 14 14 13 13 6 6 21 21 1 24
78 21 21 12 12 14 14 9 9 22 22 1 29
79 19 19 12 12 10 10 8 8 23 23 1 22
80 28 28 11 11 13 13 6 6 25 25 1 27
81 23 23 14 14 13 13 10 10 25 25 0 26
82 0 18 6 0 11 0 8 0 17 0 1 22
83 21 21 12 12 13 13 8 8 19 19 1 24
84 20 20 8 8 16 16 10 10 25 25 0 27
85 0 23 14 0 8 0 5 0 19 0 1 24
86 21 21 11 11 16 16 7 7 20 20 1 24
87 21 21 10 10 11 11 5 5 26 26 1 29
88 15 15 14 14 9 9 8 8 23 23 1 22
89 28 28 12 12 16 16 14 14 27 27 0 21
90 0 19 10 0 12 0 7 0 17 0 1 24
91 26 26 14 14 14 14 8 8 17 17 1 24
92 10 10 5 5 8 8 6 6 19 19 0 23
93 0 16 11 0 9 0 5 0 17 0 1 20
94 22 22 10 10 15 15 6 6 22 22 1 27
95 19 19 9 9 11 11 10 10 21 21 1 26
96 31 31 10 10 21 21 12 12 32 32 1 25
97 31 31 16 16 14 14 9 9 21 21 1 21
98 29 29 13 13 18 18 12 12 21 21 1 21
99 19 19 9 9 12 12 7 7 18 18 1 19
100 22 22 10 10 13 13 8 8 18 18 1 21
101 23 23 10 10 15 15 10 10 23 23 1 21
102 15 15 7 7 12 12 6 6 19 19 1 16
103 20 20 9 9 19 19 10 10 20 20 1 22
104 18 18 8 8 15 15 10 10 21 21 1 29
105 23 23 14 14 11 11 10 10 20 20 0 15
106 0 25 14 0 11 0 5 0 17 0 1 17
107 21 21 8 8 10 10 7 7 18 18 1 15
108 24 24 9 9 13 13 10 10 19 19 1 21
109 25 25 14 14 15 15 11 11 22 22 0 21
110 0 17 14 0 12 0 6 0 15 0 1 19
111 13 13 8 8 12 12 7 7 14 14 1 24
112 28 28 8 8 16 16 12 12 18 18 1 20
113 21 21 8 8 9 9 11 11 24 24 0 17
114 0 25 7 0 18 0 11 0 35 0 1 23
115 9 9 6 6 8 8 11 11 29 29 1 24
116 16 16 8 8 13 13 5 5 21 21 1 14
117 19 19 6 6 17 17 8 8 25 25 1 19
118 17 17 11 11 9 9 6 6 20 20 1 24
119 25 25 14 14 15 15 9 9 22 22 1 13
120 20 20 11 11 8 8 4 4 13 13 1 22
121 29 29 11 11 7 7 4 4 26 26 1 16
122 14 14 11 11 12 12 7 7 17 17 0 19
123 0 22 14 0 14 0 11 0 25 0 1 25
124 15 15 8 8 6 6 6 6 20 20 1 25
125 19 19 20 20 8 8 7 7 19 19 1 23
126 20 20 11 11 17 17 8 8 21 21 0 24
127 0 15 8 0 10 0 4 0 22 0 1 26
128 20 20 11 11 11 11 8 8 24 24 1 26
129 18 18 10 10 14 14 9 9 21 21 1 25
130 33 33 14 14 11 11 8 8 26 26 1 18
131 22 22 11 11 13 13 11 11 24 24 1 21
132 16 16 9 9 12 12 8 8 16 16 1 26
133 17 17 9 9 11 11 5 5 23 23 1 23
134 16 16 8 8 9 9 4 4 18 18 1 23
135 21 21 10 10 12 12 8 8 16 16 1 22
136 26 26 13 13 20 20 10 10 26 26 1 20
137 18 18 13 13 12 12 6 6 19 19 1 13
138 18 18 12 12 13 13 9 9 21 21 1 24
139 17 17 8 8 12 12 9 9 21 21 1 15
140 22 22 13 13 12 12 13 13 22 22 1 14
141 30 30 14 14 9 9 9 9 23 23 0 22
142 0 30 12 0 15 0 10 0 29 0 1 10
143 24 24 14 14 24 24 20 20 21 21 1 24
144 21 21 15 15 7 7 5 5 21 21 1 22
145 21 21 13 13 17 17 11 11 23 23 1 24
146 29 29 16 16 11 11 6 6 27 27 1 19
147 31 31 9 9 17 17 9 9 25 25 0 20
148 0 20 9 0 11 0 7 0 21 0 1 13
149 16 16 9 9 12 12 9 9 10 10 1 20
150 22 22 8 8 14 14 10 10 20 20 1 22
151 20 20 7 7 11 11 9 9 26 26 1 24
152 28 28 16 16 16 16 8 8 24 24 1 29
153 38 38 11 11 21 21 7 7 29 29 1 12
154 22 22 9 9 14 14 6 6 19 19 1 20
155 20 20 11 11 20 20 13 13 24 24 1 21
156 17 17 9 9 13 13 6 6 19 19 1 24
157 28 28 14 14 11 11 8 8 24 24 1 22
158 22 22 13 13 15 15 10 10 22 22 1 20
159 31 31 16 16 19 19 16 16 17 17 1 26
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) B CM CM_B D D_B
-0.57826 0.92554 0.01322 0.01600 -1.31049 1.32525
PE PE_B PC PC_B PS PS_B
0.40633 -0.39459 -0.34199 0.36346 0.94211 0.01465
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.2377 -0.4046 -0.1673 0.2627 7.1572
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.57826 1.01873 -0.568 0.57115
B 0.92554 0.02646 34.981 < 2e-16 ***
CM 0.01322 0.08117 0.163 0.87086
CM_B 0.01600 0.07904 0.202 0.83983
D -1.31049 0.21818 -6.006 1.42e-08 ***
D_B 1.32525 0.22104 5.995 1.50e-08 ***
PE 0.40633 0.14355 2.831 0.00530 **
PE_B -0.39459 0.15185 -2.599 0.01031 *
PC -0.34199 0.10802 -3.166 0.00188 **
PC_B 0.36346 0.10928 3.326 0.00111 **
PS 0.94211 0.04818 19.552 < 2e-16 ***
PS_B 0.01465 0.03057 0.479 0.63246
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.433 on 147 degrees of freedom
Multiple R-squared: 0.9723, Adjusted R-squared: 0.9702
F-statistic: 469.1 on 11 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,] 0.931890325 1.362194e-01 6.810968e-02
[2,] 0.996805082 6.389836e-03 3.194918e-03
[3,] 0.992876311 1.424738e-02 7.123689e-03
[4,] 0.985384368 2.923126e-02 1.461563e-02
[5,] 0.983095290 3.380942e-02 1.690471e-02
[6,] 0.970856332 5.828734e-02 2.914367e-02
[7,] 0.963207522 7.358496e-02 3.679248e-02
[8,] 0.942149602 1.157008e-01 5.785040e-02
[9,] 0.912732285 1.745354e-01 8.726771e-02
[10,] 0.965010013 6.997997e-02 3.498999e-02
[11,] 0.947600649 1.047987e-01 5.239935e-02
[12,] 0.924726269 1.505475e-01 7.527373e-02
[13,] 0.895200220 2.095996e-01 1.047998e-01
[14,] 0.858080980 2.838380e-01 1.419190e-01
[15,] 0.813311839 3.733763e-01 1.866882e-01
[16,] 0.769778375 4.604433e-01 2.302216e-01
[17,] 0.762113147 4.757737e-01 2.378869e-01
[18,] 0.722634650 5.547307e-01 2.773654e-01
[19,] 0.663850487 6.722990e-01 3.361495e-01
[20,] 0.620709085 7.585818e-01 3.792909e-01
[21,] 0.560556488 8.788870e-01 4.394435e-01
[22,] 0.513812576 9.723748e-01 4.861874e-01
[23,] 0.470394143 9.407883e-01 5.296059e-01
[24,] 0.413657578 8.273152e-01 5.863424e-01
[25,] 0.598974370 8.020513e-01 4.010256e-01
[26,] 0.554492920 8.910142e-01 4.455071e-01
[27,] 0.495662277 9.913246e-01 5.043377e-01
[28,] 0.439305589 8.786112e-01 5.606944e-01
[29,] 0.385478527 7.709571e-01 6.145215e-01
[30,] 0.336057625 6.721153e-01 6.639424e-01
[31,] 0.287567284 5.751346e-01 7.124327e-01
[32,] 0.242217504 4.844350e-01 7.577825e-01
[33,] 0.201131977 4.022640e-01 7.988680e-01
[34,] 0.167170787 3.343416e-01 8.328292e-01
[35,] 0.136007452 2.720149e-01 8.639925e-01
[36,] 0.110051711 2.201034e-01 8.899483e-01
[37,] 0.086335833 1.726717e-01 9.136642e-01
[38,] 0.067658307 1.353166e-01 9.323417e-01
[39,] 0.052917541 1.058351e-01 9.470825e-01
[40,] 0.039803085 7.960617e-02 9.601969e-01
[41,] 0.030474924 6.094985e-02 9.695251e-01
[42,] 0.022482019 4.496404e-02 9.775180e-01
[43,] 0.016168704 3.233741e-02 9.838313e-01
[44,] 0.011709907 2.341981e-02 9.882901e-01
[45,] 0.008294450 1.658890e-02 9.917055e-01
[46,] 0.005993238 1.198648e-02 9.940068e-01
[47,] 0.004111996 8.223992e-03 9.958880e-01
[48,] 0.004409943 8.819886e-03 9.955901e-01
[49,] 0.011382821 2.276564e-02 9.886172e-01
[50,] 0.008065125 1.613025e-02 9.919349e-01
[51,] 0.005984439 1.196888e-02 9.940156e-01
[52,] 0.004203603 8.407205e-03 9.957964e-01
[53,] 0.003076827 6.153655e-03 9.969232e-01
[54,] 0.002100396 4.200792e-03 9.978996e-01
[55,] 0.001843086 3.686173e-03 9.981569e-01
[56,] 0.085734192 1.714684e-01 9.142658e-01
[57,] 0.070459362 1.409187e-01 9.295406e-01
[58,] 0.055587752 1.111755e-01 9.444122e-01
[59,] 0.043478561 8.695712e-02 9.565214e-01
[60,] 0.033217970 6.643594e-02 9.667820e-01
[61,] 0.025167636 5.033527e-02 9.748324e-01
[62,] 0.018841494 3.768299e-02 9.811585e-01
[63,] 0.015380349 3.076070e-02 9.846197e-01
[64,] 0.011556599 2.311320e-02 9.884434e-01
[65,] 0.008360040 1.672008e-02 9.916400e-01
[66,] 0.006000731 1.200146e-02 9.939993e-01
[67,] 0.005696939 1.139388e-02 9.943031e-01
[68,] 0.019309507 3.861901e-02 9.806905e-01
[69,] 0.014320149 2.864030e-02 9.856799e-01
[70,] 0.013347280 2.669456e-02 9.866527e-01
[71,] 0.969588943 6.082211e-02 3.041106e-02
[72,] 0.960508884 7.898223e-02 3.949112e-02
[73,] 0.951200003 9.759999e-02 4.880000e-02
[74,] 0.939805351 1.203893e-01 6.019465e-02
[75,] 0.927099248 1.458015e-01 7.290075e-02
[76,] 0.935662044 1.286759e-01 6.433796e-02
[77,] 0.918652260 1.626955e-01 8.134774e-02
[78,] 0.903973951 1.920521e-01 9.602605e-02
[79,] 0.925192040 1.496159e-01 7.480796e-02
[80,] 0.909344025 1.813119e-01 9.065597e-02
[81,] 0.888459865 2.230803e-01 1.115401e-01
[82,] 0.862500289 2.749994e-01 1.374997e-01
[83,] 0.833935875 3.321283e-01 1.660641e-01
[84,] 0.801463296 3.970734e-01 1.985367e-01
[85,] 0.764136061 4.717279e-01 2.358639e-01
[86,] 0.722561571 5.548769e-01 2.774384e-01
[87,] 0.677870535 6.442589e-01 3.221295e-01
[88,] 0.633106557 7.337869e-01 3.668934e-01
[89,] 0.584624928 8.307501e-01 4.153751e-01
[90,] 0.549708909 9.005822e-01 4.502911e-01
[91,] 0.509341852 9.813163e-01 4.906581e-01
[92,] 0.999533615 9.327693e-04 4.663847e-04
[93,] 0.999336541 1.326919e-03 6.634594e-04
[94,] 0.998921075 2.157850e-03 1.078925e-03
[95,] 0.998317423 3.365153e-03 1.682577e-03
[96,] 0.999676731 6.465379e-04 3.232690e-04
[97,] 0.999455626 1.088748e-03 5.443739e-04
[98,] 0.999142714 1.714571e-03 8.572857e-04
[99,] 0.998775300 2.449400e-03 1.224700e-03
[100,] 1.000000000 0.000000e+00 0.000000e+00
[101,] 1.000000000 0.000000e+00 0.000000e+00
[102,] 1.000000000 0.000000e+00 0.000000e+00
[103,] 1.000000000 0.000000e+00 0.000000e+00
[104,] 1.000000000 0.000000e+00 0.000000e+00
[105,] 1.000000000 0.000000e+00 0.000000e+00
[106,] 1.000000000 0.000000e+00 0.000000e+00
[107,] 1.000000000 0.000000e+00 0.000000e+00
[108,] 1.000000000 0.000000e+00 0.000000e+00
[109,] 1.000000000 0.000000e+00 0.000000e+00
[110,] 1.000000000 2.290277e-314 1.145139e-314
[111,] 1.000000000 2.191008e-298 1.095504e-298
[112,] 1.000000000 2.864604e-294 1.432302e-294
[113,] 1.000000000 1.570538e-271 7.852692e-272
[114,] 1.000000000 5.088876e-253 2.544438e-253
[115,] 1.000000000 1.923954e-247 9.619768e-248
[116,] 1.000000000 3.049168e-231 1.524584e-231
[117,] 1.000000000 5.818687e-221 2.909343e-221
[118,] 1.000000000 5.498094e-203 2.749047e-203
[119,] 1.000000000 2.462167e-192 1.231084e-192
[120,] 1.000000000 9.463719e-175 4.731860e-175
[121,] 1.000000000 1.012793e-163 5.063964e-164
[122,] 1.000000000 2.029766e-151 1.014883e-151
[123,] 1.000000000 7.769017e-137 3.884509e-137
[124,] 1.000000000 1.171069e-124 5.855343e-125
[125,] 1.000000000 3.269597e-111 1.634798e-111
[126,] 1.000000000 3.389362e-95 1.694681e-95
[127,] 1.000000000 4.738031e-82 2.369015e-82
[128,] 1.000000000 3.335816e-72 1.667908e-72
[129,] 1.000000000 5.279790e-57 2.639895e-57
[130,] 1.000000000 6.402959e-43 3.201479e-43
> postscript(file="/var/www/html/rcomp/tmp/1thqz1291398544.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2thqz1291398544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/348721291398544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/448721291398544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/548721291398544.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 = 159
Frequency = 1
1 2 3 4 5 6
1.395800449 -2.413032788 0.781681660 3.482063340 -2.374116531 6.761298370
7 8 9 10 11 12
-0.220585670 -0.601559460 -0.481638719 -0.496000138 -0.050280333 0.435558252
13 14 15 16 17 18
0.293437781 -0.281549585 -0.728696109 -0.728310616 0.050180467 -0.389167335
19 20 21 22 23 24
-0.206857914 0.353830947 0.153445761 0.546968292 -0.167311138 0.497201350
25 26 27 28 29 30
-0.307818030 -0.578750858 -0.317837141 -0.105584042 -0.182769976 -0.520536110
31 32 33 34 35 36
0.039361490 -0.245342281 -0.062577910 0.009878480 -0.635275720 0.403150219
37 38 39 40 41 42
0.645198248 0.155575440 -2.187949684 -0.174433175 0.108162397 -0.086826020
43 44 45 46 47 48
-0.268609338 -0.529600068 -0.332863995 -0.634898291 -0.345835867 0.456987455
49 50 51 52 53 54
0.387868522 -0.281815348 0.138431795 -0.418671119 -0.101695749 -0.470689116
55 56 57 58 59 60
-0.170742395 0.010385800 -0.169506995 -0.534372816 -0.531925928 -0.712355551
61 62 63 64 65 66
-0.510700971 0.661907253 5.251085666 -0.479433049 -0.979975583 0.265360888
67 68 69 70 71 72
0.674423540 -0.443861652 -0.123734455 0.372268939 0.461659829 -0.258884715
73 74 75 76 77 78
0.363285434 0.102321421 0.172898940 -0.010147652 -0.869782539 -0.360328907
79 80 81 82 83 84
-0.357402194 0.205007913 0.654807203 -0.446493780 -0.196157611 0.547814294
85 86 87 88 89 90
-7.237715680 -0.220948998 -0.296532798 -0.698944002 1.024625958 0.262612920
91 92 93 94 95 96
0.145908909 0.243325191 -0.034208564 -0.177656777 -0.323630495 0.148072407
97 98 99 100 101 102
0.406099356 0.250569405 -0.136197527 0.002173240 -0.083735695 -0.341395702
103 104 105 106 107 108
-0.287176871 -0.471863188 0.952842063 -5.738759161 0.130072364 0.135367075
109 110 111 112 113 114
0.900143213 1.856403753 -0.541115192 0.430806279 0.881828825 7.157152280
115 116 117 118 119 120
-1.090563268 -0.312820441 -0.284392499 -0.403743108 0.098717550 0.037510619
121 122 123 124 125 126
0.531188875 0.396614773 1.150074462 -0.435374972 -0.478669049 0.598715656
127 128 129 130 131 132
4.269853030 -0.348548020 -0.445206888 0.606074262 -0.191117032 -0.430931831
133 134 135 136 137 138
-0.412850471 -0.309461307 -0.029232731 -0.071581630 -0.249384851 -0.474240827
139 140 141 142 143 144
-0.285211738 -0.112789455 1.348388305 -2.923251842 -0.377419654 -0.173684838
145 146 147 148 149 150
-0.405541573 0.237132051 1.437230792 -0.431084022 -0.225933092 -0.035223958
151 152 153 154 155 156
-0.257050791 -0.016691257 0.953676418 0.033293784 -0.466852130 -0.382866862
157 158 159
0.218101021 -0.209742613 0.262753698
> postscript(file="/var/www/html/rcomp/tmp/6f0p51291398544.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.395800449 NA
1 -2.413032788 1.395800449
2 0.781681660 -2.413032788
3 3.482063340 0.781681660
4 -2.374116531 3.482063340
5 6.761298370 -2.374116531
6 -0.220585670 6.761298370
7 -0.601559460 -0.220585670
8 -0.481638719 -0.601559460
9 -0.496000138 -0.481638719
10 -0.050280333 -0.496000138
11 0.435558252 -0.050280333
12 0.293437781 0.435558252
13 -0.281549585 0.293437781
14 -0.728696109 -0.281549585
15 -0.728310616 -0.728696109
16 0.050180467 -0.728310616
17 -0.389167335 0.050180467
18 -0.206857914 -0.389167335
19 0.353830947 -0.206857914
20 0.153445761 0.353830947
21 0.546968292 0.153445761
22 -0.167311138 0.546968292
23 0.497201350 -0.167311138
24 -0.307818030 0.497201350
25 -0.578750858 -0.307818030
26 -0.317837141 -0.578750858
27 -0.105584042 -0.317837141
28 -0.182769976 -0.105584042
29 -0.520536110 -0.182769976
30 0.039361490 -0.520536110
31 -0.245342281 0.039361490
32 -0.062577910 -0.245342281
33 0.009878480 -0.062577910
34 -0.635275720 0.009878480
35 0.403150219 -0.635275720
36 0.645198248 0.403150219
37 0.155575440 0.645198248
38 -2.187949684 0.155575440
39 -0.174433175 -2.187949684
40 0.108162397 -0.174433175
41 -0.086826020 0.108162397
42 -0.268609338 -0.086826020
43 -0.529600068 -0.268609338
44 -0.332863995 -0.529600068
45 -0.634898291 -0.332863995
46 -0.345835867 -0.634898291
47 0.456987455 -0.345835867
48 0.387868522 0.456987455
49 -0.281815348 0.387868522
50 0.138431795 -0.281815348
51 -0.418671119 0.138431795
52 -0.101695749 -0.418671119
53 -0.470689116 -0.101695749
54 -0.170742395 -0.470689116
55 0.010385800 -0.170742395
56 -0.169506995 0.010385800
57 -0.534372816 -0.169506995
58 -0.531925928 -0.534372816
59 -0.712355551 -0.531925928
60 -0.510700971 -0.712355551
61 0.661907253 -0.510700971
62 5.251085666 0.661907253
63 -0.479433049 5.251085666
64 -0.979975583 -0.479433049
65 0.265360888 -0.979975583
66 0.674423540 0.265360888
67 -0.443861652 0.674423540
68 -0.123734455 -0.443861652
69 0.372268939 -0.123734455
70 0.461659829 0.372268939
71 -0.258884715 0.461659829
72 0.363285434 -0.258884715
73 0.102321421 0.363285434
74 0.172898940 0.102321421
75 -0.010147652 0.172898940
76 -0.869782539 -0.010147652
77 -0.360328907 -0.869782539
78 -0.357402194 -0.360328907
79 0.205007913 -0.357402194
80 0.654807203 0.205007913
81 -0.446493780 0.654807203
82 -0.196157611 -0.446493780
83 0.547814294 -0.196157611
84 -7.237715680 0.547814294
85 -0.220948998 -7.237715680
86 -0.296532798 -0.220948998
87 -0.698944002 -0.296532798
88 1.024625958 -0.698944002
89 0.262612920 1.024625958
90 0.145908909 0.262612920
91 0.243325191 0.145908909
92 -0.034208564 0.243325191
93 -0.177656777 -0.034208564
94 -0.323630495 -0.177656777
95 0.148072407 -0.323630495
96 0.406099356 0.148072407
97 0.250569405 0.406099356
98 -0.136197527 0.250569405
99 0.002173240 -0.136197527
100 -0.083735695 0.002173240
101 -0.341395702 -0.083735695
102 -0.287176871 -0.341395702
103 -0.471863188 -0.287176871
104 0.952842063 -0.471863188
105 -5.738759161 0.952842063
106 0.130072364 -5.738759161
107 0.135367075 0.130072364
108 0.900143213 0.135367075
109 1.856403753 0.900143213
110 -0.541115192 1.856403753
111 0.430806279 -0.541115192
112 0.881828825 0.430806279
113 7.157152280 0.881828825
114 -1.090563268 7.157152280
115 -0.312820441 -1.090563268
116 -0.284392499 -0.312820441
117 -0.403743108 -0.284392499
118 0.098717550 -0.403743108
119 0.037510619 0.098717550
120 0.531188875 0.037510619
121 0.396614773 0.531188875
122 1.150074462 0.396614773
123 -0.435374972 1.150074462
124 -0.478669049 -0.435374972
125 0.598715656 -0.478669049
126 4.269853030 0.598715656
127 -0.348548020 4.269853030
128 -0.445206888 -0.348548020
129 0.606074262 -0.445206888
130 -0.191117032 0.606074262
131 -0.430931831 -0.191117032
132 -0.412850471 -0.430931831
133 -0.309461307 -0.412850471
134 -0.029232731 -0.309461307
135 -0.071581630 -0.029232731
136 -0.249384851 -0.071581630
137 -0.474240827 -0.249384851
138 -0.285211738 -0.474240827
139 -0.112789455 -0.285211738
140 1.348388305 -0.112789455
141 -2.923251842 1.348388305
142 -0.377419654 -2.923251842
143 -0.173684838 -0.377419654
144 -0.405541573 -0.173684838
145 0.237132051 -0.405541573
146 1.437230792 0.237132051
147 -0.431084022 1.437230792
148 -0.225933092 -0.431084022
149 -0.035223958 -0.225933092
150 -0.257050791 -0.035223958
151 -0.016691257 -0.257050791
152 0.953676418 -0.016691257
153 0.033293784 0.953676418
154 -0.466852130 0.033293784
155 -0.382866862 -0.466852130
156 0.218101021 -0.382866862
157 -0.209742613 0.218101021
158 0.262753698 -0.209742613
159 NA 0.262753698
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.413032788 1.395800449
[2,] 0.781681660 -2.413032788
[3,] 3.482063340 0.781681660
[4,] -2.374116531 3.482063340
[5,] 6.761298370 -2.374116531
[6,] -0.220585670 6.761298370
[7,] -0.601559460 -0.220585670
[8,] -0.481638719 -0.601559460
[9,] -0.496000138 -0.481638719
[10,] -0.050280333 -0.496000138
[11,] 0.435558252 -0.050280333
[12,] 0.293437781 0.435558252
[13,] -0.281549585 0.293437781
[14,] -0.728696109 -0.281549585
[15,] -0.728310616 -0.728696109
[16,] 0.050180467 -0.728310616
[17,] -0.389167335 0.050180467
[18,] -0.206857914 -0.389167335
[19,] 0.353830947 -0.206857914
[20,] 0.153445761 0.353830947
[21,] 0.546968292 0.153445761
[22,] -0.167311138 0.546968292
[23,] 0.497201350 -0.167311138
[24,] -0.307818030 0.497201350
[25,] -0.578750858 -0.307818030
[26,] -0.317837141 -0.578750858
[27,] -0.105584042 -0.317837141
[28,] -0.182769976 -0.105584042
[29,] -0.520536110 -0.182769976
[30,] 0.039361490 -0.520536110
[31,] -0.245342281 0.039361490
[32,] -0.062577910 -0.245342281
[33,] 0.009878480 -0.062577910
[34,] -0.635275720 0.009878480
[35,] 0.403150219 -0.635275720
[36,] 0.645198248 0.403150219
[37,] 0.155575440 0.645198248
[38,] -2.187949684 0.155575440
[39,] -0.174433175 -2.187949684
[40,] 0.108162397 -0.174433175
[41,] -0.086826020 0.108162397
[42,] -0.268609338 -0.086826020
[43,] -0.529600068 -0.268609338
[44,] -0.332863995 -0.529600068
[45,] -0.634898291 -0.332863995
[46,] -0.345835867 -0.634898291
[47,] 0.456987455 -0.345835867
[48,] 0.387868522 0.456987455
[49,] -0.281815348 0.387868522
[50,] 0.138431795 -0.281815348
[51,] -0.418671119 0.138431795
[52,] -0.101695749 -0.418671119
[53,] -0.470689116 -0.101695749
[54,] -0.170742395 -0.470689116
[55,] 0.010385800 -0.170742395
[56,] -0.169506995 0.010385800
[57,] -0.534372816 -0.169506995
[58,] -0.531925928 -0.534372816
[59,] -0.712355551 -0.531925928
[60,] -0.510700971 -0.712355551
[61,] 0.661907253 -0.510700971
[62,] 5.251085666 0.661907253
[63,] -0.479433049 5.251085666
[64,] -0.979975583 -0.479433049
[65,] 0.265360888 -0.979975583
[66,] 0.674423540 0.265360888
[67,] -0.443861652 0.674423540
[68,] -0.123734455 -0.443861652
[69,] 0.372268939 -0.123734455
[70,] 0.461659829 0.372268939
[71,] -0.258884715 0.461659829
[72,] 0.363285434 -0.258884715
[73,] 0.102321421 0.363285434
[74,] 0.172898940 0.102321421
[75,] -0.010147652 0.172898940
[76,] -0.869782539 -0.010147652
[77,] -0.360328907 -0.869782539
[78,] -0.357402194 -0.360328907
[79,] 0.205007913 -0.357402194
[80,] 0.654807203 0.205007913
[81,] -0.446493780 0.654807203
[82,] -0.196157611 -0.446493780
[83,] 0.547814294 -0.196157611
[84,] -7.237715680 0.547814294
[85,] -0.220948998 -7.237715680
[86,] -0.296532798 -0.220948998
[87,] -0.698944002 -0.296532798
[88,] 1.024625958 -0.698944002
[89,] 0.262612920 1.024625958
[90,] 0.145908909 0.262612920
[91,] 0.243325191 0.145908909
[92,] -0.034208564 0.243325191
[93,] -0.177656777 -0.034208564
[94,] -0.323630495 -0.177656777
[95,] 0.148072407 -0.323630495
[96,] 0.406099356 0.148072407
[97,] 0.250569405 0.406099356
[98,] -0.136197527 0.250569405
[99,] 0.002173240 -0.136197527
[100,] -0.083735695 0.002173240
[101,] -0.341395702 -0.083735695
[102,] -0.287176871 -0.341395702
[103,] -0.471863188 -0.287176871
[104,] 0.952842063 -0.471863188
[105,] -5.738759161 0.952842063
[106,] 0.130072364 -5.738759161
[107,] 0.135367075 0.130072364
[108,] 0.900143213 0.135367075
[109,] 1.856403753 0.900143213
[110,] -0.541115192 1.856403753
[111,] 0.430806279 -0.541115192
[112,] 0.881828825 0.430806279
[113,] 7.157152280 0.881828825
[114,] -1.090563268 7.157152280
[115,] -0.312820441 -1.090563268
[116,] -0.284392499 -0.312820441
[117,] -0.403743108 -0.284392499
[118,] 0.098717550 -0.403743108
[119,] 0.037510619 0.098717550
[120,] 0.531188875 0.037510619
[121,] 0.396614773 0.531188875
[122,] 1.150074462 0.396614773
[123,] -0.435374972 1.150074462
[124,] -0.478669049 -0.435374972
[125,] 0.598715656 -0.478669049
[126,] 4.269853030 0.598715656
[127,] -0.348548020 4.269853030
[128,] -0.445206888 -0.348548020
[129,] 0.606074262 -0.445206888
[130,] -0.191117032 0.606074262
[131,] -0.430931831 -0.191117032
[132,] -0.412850471 -0.430931831
[133,] -0.309461307 -0.412850471
[134,] -0.029232731 -0.309461307
[135,] -0.071581630 -0.029232731
[136,] -0.249384851 -0.071581630
[137,] -0.474240827 -0.249384851
[138,] -0.285211738 -0.474240827
[139,] -0.112789455 -0.285211738
[140,] 1.348388305 -0.112789455
[141,] -2.923251842 1.348388305
[142,] -0.377419654 -2.923251842
[143,] -0.173684838 -0.377419654
[144,] -0.405541573 -0.173684838
[145,] 0.237132051 -0.405541573
[146,] 1.437230792 0.237132051
[147,] -0.431084022 1.437230792
[148,] -0.225933092 -0.431084022
[149,] -0.035223958 -0.225933092
[150,] -0.257050791 -0.035223958
[151,] -0.016691257 -0.257050791
[152,] 0.953676418 -0.016691257
[153,] 0.033293784 0.953676418
[154,] -0.466852130 0.033293784
[155,] -0.382866862 -0.466852130
[156,] 0.218101021 -0.382866862
[157,] -0.209742613 0.218101021
[158,] 0.262753698 -0.209742613
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.413032788 1.395800449
2 0.781681660 -2.413032788
3 3.482063340 0.781681660
4 -2.374116531 3.482063340
5 6.761298370 -2.374116531
6 -0.220585670 6.761298370
7 -0.601559460 -0.220585670
8 -0.481638719 -0.601559460
9 -0.496000138 -0.481638719
10 -0.050280333 -0.496000138
11 0.435558252 -0.050280333
12 0.293437781 0.435558252
13 -0.281549585 0.293437781
14 -0.728696109 -0.281549585
15 -0.728310616 -0.728696109
16 0.050180467 -0.728310616
17 -0.389167335 0.050180467
18 -0.206857914 -0.389167335
19 0.353830947 -0.206857914
20 0.153445761 0.353830947
21 0.546968292 0.153445761
22 -0.167311138 0.546968292
23 0.497201350 -0.167311138
24 -0.307818030 0.497201350
25 -0.578750858 -0.307818030
26 -0.317837141 -0.578750858
27 -0.105584042 -0.317837141
28 -0.182769976 -0.105584042
29 -0.520536110 -0.182769976
30 0.039361490 -0.520536110
31 -0.245342281 0.039361490
32 -0.062577910 -0.245342281
33 0.009878480 -0.062577910
34 -0.635275720 0.009878480
35 0.403150219 -0.635275720
36 0.645198248 0.403150219
37 0.155575440 0.645198248
38 -2.187949684 0.155575440
39 -0.174433175 -2.187949684
40 0.108162397 -0.174433175
41 -0.086826020 0.108162397
42 -0.268609338 -0.086826020
43 -0.529600068 -0.268609338
44 -0.332863995 -0.529600068
45 -0.634898291 -0.332863995
46 -0.345835867 -0.634898291
47 0.456987455 -0.345835867
48 0.387868522 0.456987455
49 -0.281815348 0.387868522
50 0.138431795 -0.281815348
51 -0.418671119 0.138431795
52 -0.101695749 -0.418671119
53 -0.470689116 -0.101695749
54 -0.170742395 -0.470689116
55 0.010385800 -0.170742395
56 -0.169506995 0.010385800
57 -0.534372816 -0.169506995
58 -0.531925928 -0.534372816
59 -0.712355551 -0.531925928
60 -0.510700971 -0.712355551
61 0.661907253 -0.510700971
62 5.251085666 0.661907253
63 -0.479433049 5.251085666
64 -0.979975583 -0.479433049
65 0.265360888 -0.979975583
66 0.674423540 0.265360888
67 -0.443861652 0.674423540
68 -0.123734455 -0.443861652
69 0.372268939 -0.123734455
70 0.461659829 0.372268939
71 -0.258884715 0.461659829
72 0.363285434 -0.258884715
73 0.102321421 0.363285434
74 0.172898940 0.102321421
75 -0.010147652 0.172898940
76 -0.869782539 -0.010147652
77 -0.360328907 -0.869782539
78 -0.357402194 -0.360328907
79 0.205007913 -0.357402194
80 0.654807203 0.205007913
81 -0.446493780 0.654807203
82 -0.196157611 -0.446493780
83 0.547814294 -0.196157611
84 -7.237715680 0.547814294
85 -0.220948998 -7.237715680
86 -0.296532798 -0.220948998
87 -0.698944002 -0.296532798
88 1.024625958 -0.698944002
89 0.262612920 1.024625958
90 0.145908909 0.262612920
91 0.243325191 0.145908909
92 -0.034208564 0.243325191
93 -0.177656777 -0.034208564
94 -0.323630495 -0.177656777
95 0.148072407 -0.323630495
96 0.406099356 0.148072407
97 0.250569405 0.406099356
98 -0.136197527 0.250569405
99 0.002173240 -0.136197527
100 -0.083735695 0.002173240
101 -0.341395702 -0.083735695
102 -0.287176871 -0.341395702
103 -0.471863188 -0.287176871
104 0.952842063 -0.471863188
105 -5.738759161 0.952842063
106 0.130072364 -5.738759161
107 0.135367075 0.130072364
108 0.900143213 0.135367075
109 1.856403753 0.900143213
110 -0.541115192 1.856403753
111 0.430806279 -0.541115192
112 0.881828825 0.430806279
113 7.157152280 0.881828825
114 -1.090563268 7.157152280
115 -0.312820441 -1.090563268
116 -0.284392499 -0.312820441
117 -0.403743108 -0.284392499
118 0.098717550 -0.403743108
119 0.037510619 0.098717550
120 0.531188875 0.037510619
121 0.396614773 0.531188875
122 1.150074462 0.396614773
123 -0.435374972 1.150074462
124 -0.478669049 -0.435374972
125 0.598715656 -0.478669049
126 4.269853030 0.598715656
127 -0.348548020 4.269853030
128 -0.445206888 -0.348548020
129 0.606074262 -0.445206888
130 -0.191117032 0.606074262
131 -0.430931831 -0.191117032
132 -0.412850471 -0.430931831
133 -0.309461307 -0.412850471
134 -0.029232731 -0.309461307
135 -0.071581630 -0.029232731
136 -0.249384851 -0.071581630
137 -0.474240827 -0.249384851
138 -0.285211738 -0.474240827
139 -0.112789455 -0.285211738
140 1.348388305 -0.112789455
141 -2.923251842 1.348388305
142 -0.377419654 -2.923251842
143 -0.173684838 -0.377419654
144 -0.405541573 -0.173684838
145 0.237132051 -0.405541573
146 1.437230792 0.237132051
147 -0.431084022 1.437230792
148 -0.225933092 -0.431084022
149 -0.035223958 -0.225933092
150 -0.257050791 -0.035223958
151 -0.016691257 -0.257050791
152 0.953676418 -0.016691257
153 0.033293784 0.953676418
154 -0.466852130 0.033293784
155 -0.382866862 -0.466852130
156 0.218101021 -0.382866862
157 -0.209742613 0.218101021
158 0.262753698 -0.209742613
> 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/www/html/rcomp/tmp/7796q1291398544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8796q1291398544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9796q1291398544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10i0nt1291398544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/11eb6c1291398545.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/www/html/rcomp/tmp/12hb5i1291398545.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/www/html/rcomp/tmp/13vllq1291398545.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/www/html/rcomp/tmp/14hmjw1291398545.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/www/html/rcomp/tmp/1524zk1291398545.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/www/html/rcomp/tmp/1654g81291398545.tab")
+ }
> try(system("convert tmp/1thqz1291398544.ps tmp/1thqz1291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/2thqz1291398544.ps tmp/2thqz1291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/348721291398544.ps tmp/348721291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/448721291398544.ps tmp/448721291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/548721291398544.ps tmp/548721291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/6f0p51291398544.ps tmp/6f0p51291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/7796q1291398544.ps tmp/7796q1291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/8796q1291398544.ps tmp/8796q1291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/9796q1291398544.ps tmp/9796q1291398544.png",intern=TRUE))
character(0)
> try(system("convert tmp/10i0nt1291398544.ps tmp/10i0nt1291398544.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.653 1.769 9.873