R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(1
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+ ,dim=c(9
+ ,162)
+ ,dimnames=list(c('G'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A'
+ ,'T')
+ ,1:162))
> y <- array(NA,dim=c(9,162),dimnames=list(c('G','I1','I2','I3','E1','E2','E3','A','T'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '9'
> 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
T G I1 I2 I3 E1 E2 E3 A
1 127 1 26 21 21 23 17 23 4
2 108 1 20 16 15 24 17 20 4
3 110 1 19 19 18 22 18 20 6
4 102 2 19 18 11 20 21 21 8
5 104 1 20 16 8 24 20 24 8
6 140 1 25 23 19 27 28 22 4
7 112 2 25 17 4 28 19 23 4
8 115 1 22 12 20 27 22 20 8
9 121 1 26 19 16 24 16 25 5
10 112 1 22 16 14 23 18 23 4
11 118 2 17 19 10 24 25 27 4
12 122 2 22 20 13 27 17 27 4
13 105 1 19 13 14 27 14 22 4
14 111 1 24 20 8 28 11 24 4
15 151 1 26 27 23 27 27 25 4
16 106 2 21 17 11 23 20 22 8
17 100 1 13 8 9 24 22 28 4
18 149 2 26 25 24 28 22 28 4
19 122 2 20 26 5 27 21 27 4
20 115 1 22 13 15 25 23 25 8
21 86 2 14 19 5 19 17 16 4
22 124 1 21 15 19 24 24 28 7
23 69 1 7 5 6 20 14 21 4
24 117 2 23 16 13 28 17 24 4
25 113 1 17 14 11 26 23 27 5
26 123 1 25 24 17 23 24 14 4
27 123 1 25 24 17 23 24 14 4
28 84 1 19 9 5 20 8 27 4
29 97 2 20 19 9 11 22 20 4
30 121 1 23 19 15 24 23 21 4
31 132 2 22 25 17 25 25 22 4
32 119 1 22 19 17 23 21 21 4
33 98 1 21 18 20 18 24 12 15
34 87 2 15 15 12 20 15 20 10
35 101 2 20 12 7 20 22 24 4
36 115 2 22 21 16 24 21 19 8
37 109 1 18 12 7 23 25 28 4
38 109 2 20 15 14 25 16 23 4
39 159 2 28 28 24 28 28 27 4
40 129 1 22 25 15 26 23 22 4
41 119 1 18 19 15 26 21 27 7
42 119 1 23 20 10 23 21 26 4
43 122 1 20 24 14 22 26 22 6
44 131 2 25 26 18 24 22 21 5
45 120 2 26 25 12 21 21 19 4
46 82 1 15 12 9 20 18 24 16
47 86 2 17 12 9 22 12 19 5
48 105 2 23 15 8 20 25 26 12
49 114 1 21 17 18 25 17 22 6
50 100 2 13 14 10 20 24 28 9
51 100 1 18 16 17 22 15 21 9
52 99 1 19 11 14 23 13 23 4
53 132 1 22 20 16 25 26 28 5
54 82 1 16 11 10 23 16 10 4
55 132 2 24 22 19 23 24 24 4
56 107 1 18 20 10 22 21 21 5
57 114 1 20 19 14 24 20 21 4
58 110 1 24 17 10 25 14 24 4
59 105 2 14 21 4 21 25 24 4
60 121 2 22 23 19 12 25 25 5
61 109 1 24 18 9 17 20 25 4
62 106 1 18 17 12 20 22 23 6
63 124 1 21 27 16 23 20 21 4
64 120 2 23 25 11 23 26 16 4
65 91 1 17 19 18 20 18 17 18
66 126 2 22 22 11 28 22 25 4
67 138 2 24 24 24 24 24 24 6
68 118 2 21 20 17 24 17 23 4
69 128 1 22 19 18 24 24 25 4
70 98 1 16 11 9 24 20 23 5
71 133 1 21 22 19 28 19 28 4
72 130 2 23 22 18 25 20 26 4
73 103 2 22 16 12 21 15 22 5
74 124 1 24 20 23 25 23 19 10
75 142 1 24 24 22 25 26 26 5
76 96 1 16 16 14 18 22 18 8
77 93 1 16 16 14 17 20 18 8
78 129 2 21 22 16 26 24 25 5
79 150 2 26 24 23 28 26 27 4
80 88 2 15 16 7 21 21 12 4
81 125 2 25 27 10 27 25 15 4
82 92 1 18 11 12 22 13 21 5
83 0 0 23 21 12 21 20 23 4
84 117 1 20 20 12 25 22 22 4
85 112 2 17 20 17 22 23 21 8
86 144 2 25 27 21 23 28 24 4
87 130 1 24 20 16 26 22 27 5
88 87 1 17 12 11 19 20 22 14
89 92 1 19 8 14 25 6 28 8
90 114 1 20 21 13 21 21 26 8
91 81 1 15 18 9 13 20 10 4
92 127 2 27 24 19 24 18 19 4
93 115 1 22 16 13 25 23 22 6
94 123 1 23 18 19 26 20 21 4
95 115 1 16 20 13 25 24 24 7
96 117 1 19 20 13 25 22 25 7
97 117 2 25 19 13 22 21 21 4
98 103 1 19 17 14 21 18 20 6
99 108 2 19 16 12 23 21 21 4
100 139 2 26 26 22 25 23 24 7
101 113 1 21 15 11 24 23 23 4
102 97 2 20 22 5 21 15 18 4
103 117 1 24 17 18 21 21 24 8
104 133 1 22 23 19 25 24 24 4
105 115 2 20 21 14 22 23 19 4
106 103 1 18 19 15 20 21 20 10
107 95 2 18 14 12 20 21 18 8
108 117 1 24 17 19 23 20 20 6
109 113 1 24 12 15 28 11 27 4
110 127 1 22 24 17 23 22 23 4
111 126 1 23 18 8 28 27 26 4
112 119 1 22 20 10 24 25 23 5
113 97 1 20 16 12 18 18 17 4
114 105 1 18 20 12 20 20 21 6
115 140 1 25 22 20 28 24 25 4
116 91 2 18 12 12 21 10 23 5
117 112 1 16 16 12 21 27 27 7
118 113 1 20 17 14 25 21 24 8
119 102 2 19 22 6 19 21 20 5
120 92 1 15 12 10 18 18 27 8
121 98 1 19 14 18 21 15 21 10
122 122 1 19 23 18 22 24 24 8
123 100 1 16 15 7 24 22 21 5
124 84 1 17 17 18 15 14 15 12
125 142 1 28 28 9 28 28 25 4
126 124 2 23 20 17 26 18 25 5
127 137 1 25 23 22 23 26 22 4
128 105 1 20 13 11 26 17 24 6
129 106 2 17 18 15 20 19 21 4
130 125 2 23 23 17 22 22 22 4
131 104 1 16 19 15 20 18 23 7
132 130 2 23 23 22 23 24 22 7
133 79 2 11 12 9 22 15 20 10
134 108 2 18 16 13 24 18 23 4
135 136 2 24 23 20 23 26 25 5
136 98 1 23 13 14 22 11 23 8
137 120 1 21 22 14 26 26 22 11
138 108 2 16 18 12 23 21 25 7
139 139 2 24 23 20 27 23 26 4
140 123 1 23 20 20 23 23 22 8
141 90 1 18 10 8 21 15 24 6
142 119 1 20 17 17 26 22 24 7
143 105 1 9 18 9 23 26 25 5
144 110 2 24 15 18 21 16 20 4
145 135 1 25 23 22 27 20 26 8
146 101 1 20 17 10 19 18 21 4
147 114 2 21 17 13 23 22 26 8
148 118 2 25 22 15 25 16 21 6
149 120 2 22 20 18 23 19 22 4
150 108 2 21 20 18 22 20 16 9
151 114 1 21 19 12 22 19 26 5
152 122 1 22 18 12 25 23 28 6
153 132 1 27 22 20 25 24 18 4
154 130 2 24 20 12 28 25 25 4
155 130 2 24 22 16 28 21 23 4
156 112 2 21 18 16 20 21 21 5
157 114 1 18 16 18 25 23 20 6
158 103 1 16 16 16 19 27 25 16
159 115 1 22 16 13 25 23 22 6
160 108 1 20 16 17 22 18 21 6
161 94 2 18 17 13 18 16 16 4
162 105 1 20 18 17 20 16 18 4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G I1 I2 I3 E1
-10.1011 5.0770 0.7629 0.6472 1.2343 1.4072
E2 E3 A
1.1557 0.8546 -0.8022
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-104.961 -1.624 0.935 2.579 7.763
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -10.1011 7.6833 -1.315 0.190582
G 5.0770 1.4858 3.417 0.000812 ***
I1 0.7629 0.2866 2.662 0.008606 **
I2 0.6472 0.2551 2.537 0.012191 *
I3 1.2343 0.1946 6.343 2.41e-09 ***
E1 1.4072 0.2780 5.062 1.18e-06 ***
E2 1.1557 0.2133 5.417 2.30e-07 ***
E3 0.8546 0.2186 3.910 0.000138 ***
A -0.8022 0.3069 -2.614 0.009836 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.921 on 153 degrees of freedom
Multiple R-squared: 0.7767, Adjusted R-squared: 0.765
F-statistic: 66.53 on 8 and 153 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,] 1.085352e-45 2.170704e-45 1.000000e+00
[2,] 7.674363e-61 1.534873e-60 1.000000e+00
[3,] 1.129869e-75 2.259737e-75 1.000000e+00
[4,] 1.585058e-90 3.170117e-90 1.000000e+00
[5,] 6.447502e-106 1.289500e-105 1.000000e+00
[6,] 2.553335e-125 5.106670e-125 1.000000e+00
[7,] 4.070723e-140 8.141447e-140 1.000000e+00
[8,] 1.163149e-148 2.326298e-148 1.000000e+00
[9,] 3.508659e-164 7.017319e-164 1.000000e+00
[10,] 1.153945e-183 2.307890e-183 1.000000e+00
[11,] 9.154949e-201 1.830990e-200 1.000000e+00
[12,] 1.010952e-207 2.021903e-207 1.000000e+00
[13,] 1.975139e-224 3.950278e-224 1.000000e+00
[14,] 1.905030e-245 3.810060e-245 1.000000e+00
[15,] 6.734512e-261 1.346902e-260 1.000000e+00
[16,] 1.110185e-273 2.220370e-273 1.000000e+00
[17,] 2.792805e-295 5.585610e-295 1.000000e+00
[18,] 1.426251e-294 2.852501e-294 1.000000e+00
[19,] 0.000000e+00 0.000000e+00 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,] 1.000000e+00 0.000000e+00 0.000000e+00
[73,] 1.000000e+00 0.000000e+00 0.000000e+00
[74,] 1.000000e+00 0.000000e+00 0.000000e+00
[75,] 1.000000e+00 0.000000e+00 0.000000e+00
[76,] 1.000000e+00 0.000000e+00 0.000000e+00
[77,] 1.000000e+00 0.000000e+00 0.000000e+00
[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 0.000000e+00 0.000000e+00
[118,] 1.000000e+00 0.000000e+00 0.000000e+00
[119,] 1.000000e+00 0.000000e+00 0.000000e+00
[120,] 1.000000e+00 0.000000e+00 0.000000e+00
[121,] 1.000000e+00 1.564406e-316 7.822029e-317
[122,] 1.000000e+00 9.209446e-295 4.604723e-295
[123,] 1.000000e+00 1.758877e-296 8.794386e-297
[124,] 1.000000e+00 1.090096e-281 5.450479e-282
[125,] 1.000000e+00 1.579992e-255 7.899961e-256
[126,] 1.000000e+00 1.093949e-238 5.469745e-239
[127,] 1.000000e+00 1.120716e-229 5.603580e-230
[128,] 1.000000e+00 1.178186e-212 5.890929e-213
[129,] 1.000000e+00 5.344549e-194 2.672274e-194
[130,] 1.000000e+00 1.831011e-178 9.155054e-179
[131,] 1.000000e+00 1.356047e-170 6.780233e-171
[132,] 1.000000e+00 4.590751e-156 2.295375e-156
[133,] 1.000000e+00 6.100171e-139 3.050086e-139
[134,] 1.000000e+00 2.045749e-126 1.022875e-126
[135,] 1.000000e+00 1.472684e-106 7.363418e-107
[136,] 1.000000e+00 6.311635e-91 3.155817e-91
[137,] 1.000000e+00 4.546791e-79 2.273396e-79
[138,] 1.000000e+00 1.552897e-61 7.764487e-62
[139,] 1.000000e+00 7.049150e-47 3.524575e-47
> postscript(file="/var/fisher/rcomp/tmp/1327h1354973958.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/2os7l1354973958.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3v60v1354973958.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4qio91354973958.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/54p2l1354973959.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 162
Frequency = 1
1 2 3 4 5
4.217495e+00 1.593311e+00 1.974956e+00 -1.717746e+00 2.556589e+00
6 7 8 9 10
1.667685e+00 -8.727200e-01 -3.305942e+00 4.524751e+00 2.989470e+00
11 12 13 14 15
-2.193026e+00 -1.333815e+00 6.839933e-02 5.479282e+00 2.970663e+00
16 17 18 19 20
-2.516904e+00 -1.098019e+00 -2.238708e+00 1.559881e+00 -3.961582e-01
21 22 23 24 25
-5.087879e-02 2.075614e-02 -1.019525e+00 -3.351179e+00 1.850783e-01
26 27 28 29 30
3.577341e+00 3.577341e+00 5.277258e+00 3.495537e+00 2.574155e+00
31 32 33 34 35
-1.665055e+00 2.587138e+00 -6.204535e-01 -3.565461e+00 -1.588607e+00
36 37 38 39 40
-3.039158e+00 1.907120e+00 -3.417515e+00 -1.785749e+00 3.784586e+00
41 42 43 44 45
1.164763e+00 5.543764e+00 3.958206e+00 -3.042064e-01 2.269944e+00
46 47 48 49 50
8.397832e-02 -2.950764e+00 -1.811704e+00 9.684406e-01 -3.964038e+00
51 52 53 54 55
9.329963e-01 1.292829e+00 2.401350e+00 -8.386463e-01 -1.456757e+00
56 57 58 59 60
3.840793e+00 2.564198e+00 4.707106e+00 -7.785187e-03 2.693166e+00
61 62 63 64 65
7.763166e+00 3.065795e+00 5.562054e+00 -2.360896e-01 -4.941712e-01
66 67 68 69 70
-1.636208e+00 -2.725263e+00 -1.867869e+00 2.060226e+00 5.810371e-02
71 72 73 74 75
2.232898e+00 -1.360497e+00 -6.806987e-01 -5.946529e-01 2.590222e+00
76 77 78 79 80
1.462260e+00 2.180837e+00 -2.739261e+00 -3.125334e+00 -3.359333e+00
81 82 83 84 85
-4.407348e-01 1.442905e+00 -1.049611e+02 2.812288e+00 -4.017721e+00
86 87 88 89 90
-5.471136e-01 2.945624e+00 2.902249e-01 4.458222e-01 4.505874e+00
91 92 93 94 95
5.077272e+00 -2.401202e-01 1.089958e+00 9.369867e-01 1.015837e+00
96 97 98 99 100
2.183851e+00 -4.343596e-01 2.613676e+00 -3.088102e+00 -1.526370e+00
101 102 103 104 105
1.916773e+00 2.217630e+00 2.581105e+00 2.684387e+00 -1.750682e+00
106 107 108 109 110
1.996964e+00 -4.036317e+00 1.502030e+00 1.453634e+00 4.486048e+00
111 112 113 114 115
2.336593e+00 3.642775e+00 4.147493e+00 4.144630e+00 1.732378e+00
116 117 118 119 120
-2.116266e+00 1.437083e+00 9.409211e-01 1.719675e+00 2.682484e+00
121 122 123 124 125
4.397603e-01 2.637973e+00 1.335484e+00 2.354913e+00 6.514260e+00
126 127 128 129 130
-2.270780e+00 2.905147e+00 8.436909e-01 -2.026509e+00 -4.447960e-01
131 132 133 134 135
3.019334e+00 -2.927945e+00 -5.683742e+00 -3.208816e+00 -1.701980e+00
136 137 138 139 140
2.874197e+00 8.720789e-01 -3.105537e+00 -2.520638e+00 1.517192e+00
141 142 143 144 145
2.361455e+00 -1.269742e-01 6.317521e-01 -2.213531e+00 2.000901e+00
146 147 148 149 150
5.143144e+00 -2.715174e+00 -6.833734e-01 -1.914584e+00 -3.761362e+00
151 152 153 154 155
4.769139e+00 2.902128e+00 2.410396e+00 -2.568852e+00 -2.468433e+00
156 157 158 159 160
-1.821553e+00 -1.320454e+00 -7.561970e-01 1.089958e+00 1.533421e+00
161 162
-1.119198e+00 3.324063e+00
> postscript(file="/var/fisher/rcomp/tmp/6h2yt1354973959.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 4.217495e+00 NA
1 1.593311e+00 4.217495e+00
2 1.974956e+00 1.593311e+00
3 -1.717746e+00 1.974956e+00
4 2.556589e+00 -1.717746e+00
5 1.667685e+00 2.556589e+00
6 -8.727200e-01 1.667685e+00
7 -3.305942e+00 -8.727200e-01
8 4.524751e+00 -3.305942e+00
9 2.989470e+00 4.524751e+00
10 -2.193026e+00 2.989470e+00
11 -1.333815e+00 -2.193026e+00
12 6.839933e-02 -1.333815e+00
13 5.479282e+00 6.839933e-02
14 2.970663e+00 5.479282e+00
15 -2.516904e+00 2.970663e+00
16 -1.098019e+00 -2.516904e+00
17 -2.238708e+00 -1.098019e+00
18 1.559881e+00 -2.238708e+00
19 -3.961582e-01 1.559881e+00
20 -5.087879e-02 -3.961582e-01
21 2.075614e-02 -5.087879e-02
22 -1.019525e+00 2.075614e-02
23 -3.351179e+00 -1.019525e+00
24 1.850783e-01 -3.351179e+00
25 3.577341e+00 1.850783e-01
26 3.577341e+00 3.577341e+00
27 5.277258e+00 3.577341e+00
28 3.495537e+00 5.277258e+00
29 2.574155e+00 3.495537e+00
30 -1.665055e+00 2.574155e+00
31 2.587138e+00 -1.665055e+00
32 -6.204535e-01 2.587138e+00
33 -3.565461e+00 -6.204535e-01
34 -1.588607e+00 -3.565461e+00
35 -3.039158e+00 -1.588607e+00
36 1.907120e+00 -3.039158e+00
37 -3.417515e+00 1.907120e+00
38 -1.785749e+00 -3.417515e+00
39 3.784586e+00 -1.785749e+00
40 1.164763e+00 3.784586e+00
41 5.543764e+00 1.164763e+00
42 3.958206e+00 5.543764e+00
43 -3.042064e-01 3.958206e+00
44 2.269944e+00 -3.042064e-01
45 8.397832e-02 2.269944e+00
46 -2.950764e+00 8.397832e-02
47 -1.811704e+00 -2.950764e+00
48 9.684406e-01 -1.811704e+00
49 -3.964038e+00 9.684406e-01
50 9.329963e-01 -3.964038e+00
51 1.292829e+00 9.329963e-01
52 2.401350e+00 1.292829e+00
53 -8.386463e-01 2.401350e+00
54 -1.456757e+00 -8.386463e-01
55 3.840793e+00 -1.456757e+00
56 2.564198e+00 3.840793e+00
57 4.707106e+00 2.564198e+00
58 -7.785187e-03 4.707106e+00
59 2.693166e+00 -7.785187e-03
60 7.763166e+00 2.693166e+00
61 3.065795e+00 7.763166e+00
62 5.562054e+00 3.065795e+00
63 -2.360896e-01 5.562054e+00
64 -4.941712e-01 -2.360896e-01
65 -1.636208e+00 -4.941712e-01
66 -2.725263e+00 -1.636208e+00
67 -1.867869e+00 -2.725263e+00
68 2.060226e+00 -1.867869e+00
69 5.810371e-02 2.060226e+00
70 2.232898e+00 5.810371e-02
71 -1.360497e+00 2.232898e+00
72 -6.806987e-01 -1.360497e+00
73 -5.946529e-01 -6.806987e-01
74 2.590222e+00 -5.946529e-01
75 1.462260e+00 2.590222e+00
76 2.180837e+00 1.462260e+00
77 -2.739261e+00 2.180837e+00
78 -3.125334e+00 -2.739261e+00
79 -3.359333e+00 -3.125334e+00
80 -4.407348e-01 -3.359333e+00
81 1.442905e+00 -4.407348e-01
82 -1.049611e+02 1.442905e+00
83 2.812288e+00 -1.049611e+02
84 -4.017721e+00 2.812288e+00
85 -5.471136e-01 -4.017721e+00
86 2.945624e+00 -5.471136e-01
87 2.902249e-01 2.945624e+00
88 4.458222e-01 2.902249e-01
89 4.505874e+00 4.458222e-01
90 5.077272e+00 4.505874e+00
91 -2.401202e-01 5.077272e+00
92 1.089958e+00 -2.401202e-01
93 9.369867e-01 1.089958e+00
94 1.015837e+00 9.369867e-01
95 2.183851e+00 1.015837e+00
96 -4.343596e-01 2.183851e+00
97 2.613676e+00 -4.343596e-01
98 -3.088102e+00 2.613676e+00
99 -1.526370e+00 -3.088102e+00
100 1.916773e+00 -1.526370e+00
101 2.217630e+00 1.916773e+00
102 2.581105e+00 2.217630e+00
103 2.684387e+00 2.581105e+00
104 -1.750682e+00 2.684387e+00
105 1.996964e+00 -1.750682e+00
106 -4.036317e+00 1.996964e+00
107 1.502030e+00 -4.036317e+00
108 1.453634e+00 1.502030e+00
109 4.486048e+00 1.453634e+00
110 2.336593e+00 4.486048e+00
111 3.642775e+00 2.336593e+00
112 4.147493e+00 3.642775e+00
113 4.144630e+00 4.147493e+00
114 1.732378e+00 4.144630e+00
115 -2.116266e+00 1.732378e+00
116 1.437083e+00 -2.116266e+00
117 9.409211e-01 1.437083e+00
118 1.719675e+00 9.409211e-01
119 2.682484e+00 1.719675e+00
120 4.397603e-01 2.682484e+00
121 2.637973e+00 4.397603e-01
122 1.335484e+00 2.637973e+00
123 2.354913e+00 1.335484e+00
124 6.514260e+00 2.354913e+00
125 -2.270780e+00 6.514260e+00
126 2.905147e+00 -2.270780e+00
127 8.436909e-01 2.905147e+00
128 -2.026509e+00 8.436909e-01
129 -4.447960e-01 -2.026509e+00
130 3.019334e+00 -4.447960e-01
131 -2.927945e+00 3.019334e+00
132 -5.683742e+00 -2.927945e+00
133 -3.208816e+00 -5.683742e+00
134 -1.701980e+00 -3.208816e+00
135 2.874197e+00 -1.701980e+00
136 8.720789e-01 2.874197e+00
137 -3.105537e+00 8.720789e-01
138 -2.520638e+00 -3.105537e+00
139 1.517192e+00 -2.520638e+00
140 2.361455e+00 1.517192e+00
141 -1.269742e-01 2.361455e+00
142 6.317521e-01 -1.269742e-01
143 -2.213531e+00 6.317521e-01
144 2.000901e+00 -2.213531e+00
145 5.143144e+00 2.000901e+00
146 -2.715174e+00 5.143144e+00
147 -6.833734e-01 -2.715174e+00
148 -1.914584e+00 -6.833734e-01
149 -3.761362e+00 -1.914584e+00
150 4.769139e+00 -3.761362e+00
151 2.902128e+00 4.769139e+00
152 2.410396e+00 2.902128e+00
153 -2.568852e+00 2.410396e+00
154 -2.468433e+00 -2.568852e+00
155 -1.821553e+00 -2.468433e+00
156 -1.320454e+00 -1.821553e+00
157 -7.561970e-01 -1.320454e+00
158 1.089958e+00 -7.561970e-01
159 1.533421e+00 1.089958e+00
160 -1.119198e+00 1.533421e+00
161 3.324063e+00 -1.119198e+00
162 NA 3.324063e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.593311e+00 4.217495e+00
[2,] 1.974956e+00 1.593311e+00
[3,] -1.717746e+00 1.974956e+00
[4,] 2.556589e+00 -1.717746e+00
[5,] 1.667685e+00 2.556589e+00
[6,] -8.727200e-01 1.667685e+00
[7,] -3.305942e+00 -8.727200e-01
[8,] 4.524751e+00 -3.305942e+00
[9,] 2.989470e+00 4.524751e+00
[10,] -2.193026e+00 2.989470e+00
[11,] -1.333815e+00 -2.193026e+00
[12,] 6.839933e-02 -1.333815e+00
[13,] 5.479282e+00 6.839933e-02
[14,] 2.970663e+00 5.479282e+00
[15,] -2.516904e+00 2.970663e+00
[16,] -1.098019e+00 -2.516904e+00
[17,] -2.238708e+00 -1.098019e+00
[18,] 1.559881e+00 -2.238708e+00
[19,] -3.961582e-01 1.559881e+00
[20,] -5.087879e-02 -3.961582e-01
[21,] 2.075614e-02 -5.087879e-02
[22,] -1.019525e+00 2.075614e-02
[23,] -3.351179e+00 -1.019525e+00
[24,] 1.850783e-01 -3.351179e+00
[25,] 3.577341e+00 1.850783e-01
[26,] 3.577341e+00 3.577341e+00
[27,] 5.277258e+00 3.577341e+00
[28,] 3.495537e+00 5.277258e+00
[29,] 2.574155e+00 3.495537e+00
[30,] -1.665055e+00 2.574155e+00
[31,] 2.587138e+00 -1.665055e+00
[32,] -6.204535e-01 2.587138e+00
[33,] -3.565461e+00 -6.204535e-01
[34,] -1.588607e+00 -3.565461e+00
[35,] -3.039158e+00 -1.588607e+00
[36,] 1.907120e+00 -3.039158e+00
[37,] -3.417515e+00 1.907120e+00
[38,] -1.785749e+00 -3.417515e+00
[39,] 3.784586e+00 -1.785749e+00
[40,] 1.164763e+00 3.784586e+00
[41,] 5.543764e+00 1.164763e+00
[42,] 3.958206e+00 5.543764e+00
[43,] -3.042064e-01 3.958206e+00
[44,] 2.269944e+00 -3.042064e-01
[45,] 8.397832e-02 2.269944e+00
[46,] -2.950764e+00 8.397832e-02
[47,] -1.811704e+00 -2.950764e+00
[48,] 9.684406e-01 -1.811704e+00
[49,] -3.964038e+00 9.684406e-01
[50,] 9.329963e-01 -3.964038e+00
[51,] 1.292829e+00 9.329963e-01
[52,] 2.401350e+00 1.292829e+00
[53,] -8.386463e-01 2.401350e+00
[54,] -1.456757e+00 -8.386463e-01
[55,] 3.840793e+00 -1.456757e+00
[56,] 2.564198e+00 3.840793e+00
[57,] 4.707106e+00 2.564198e+00
[58,] -7.785187e-03 4.707106e+00
[59,] 2.693166e+00 -7.785187e-03
[60,] 7.763166e+00 2.693166e+00
[61,] 3.065795e+00 7.763166e+00
[62,] 5.562054e+00 3.065795e+00
[63,] -2.360896e-01 5.562054e+00
[64,] -4.941712e-01 -2.360896e-01
[65,] -1.636208e+00 -4.941712e-01
[66,] -2.725263e+00 -1.636208e+00
[67,] -1.867869e+00 -2.725263e+00
[68,] 2.060226e+00 -1.867869e+00
[69,] 5.810371e-02 2.060226e+00
[70,] 2.232898e+00 5.810371e-02
[71,] -1.360497e+00 2.232898e+00
[72,] -6.806987e-01 -1.360497e+00
[73,] -5.946529e-01 -6.806987e-01
[74,] 2.590222e+00 -5.946529e-01
[75,] 1.462260e+00 2.590222e+00
[76,] 2.180837e+00 1.462260e+00
[77,] -2.739261e+00 2.180837e+00
[78,] -3.125334e+00 -2.739261e+00
[79,] -3.359333e+00 -3.125334e+00
[80,] -4.407348e-01 -3.359333e+00
[81,] 1.442905e+00 -4.407348e-01
[82,] -1.049611e+02 1.442905e+00
[83,] 2.812288e+00 -1.049611e+02
[84,] -4.017721e+00 2.812288e+00
[85,] -5.471136e-01 -4.017721e+00
[86,] 2.945624e+00 -5.471136e-01
[87,] 2.902249e-01 2.945624e+00
[88,] 4.458222e-01 2.902249e-01
[89,] 4.505874e+00 4.458222e-01
[90,] 5.077272e+00 4.505874e+00
[91,] -2.401202e-01 5.077272e+00
[92,] 1.089958e+00 -2.401202e-01
[93,] 9.369867e-01 1.089958e+00
[94,] 1.015837e+00 9.369867e-01
[95,] 2.183851e+00 1.015837e+00
[96,] -4.343596e-01 2.183851e+00
[97,] 2.613676e+00 -4.343596e-01
[98,] -3.088102e+00 2.613676e+00
[99,] -1.526370e+00 -3.088102e+00
[100,] 1.916773e+00 -1.526370e+00
[101,] 2.217630e+00 1.916773e+00
[102,] 2.581105e+00 2.217630e+00
[103,] 2.684387e+00 2.581105e+00
[104,] -1.750682e+00 2.684387e+00
[105,] 1.996964e+00 -1.750682e+00
[106,] -4.036317e+00 1.996964e+00
[107,] 1.502030e+00 -4.036317e+00
[108,] 1.453634e+00 1.502030e+00
[109,] 4.486048e+00 1.453634e+00
[110,] 2.336593e+00 4.486048e+00
[111,] 3.642775e+00 2.336593e+00
[112,] 4.147493e+00 3.642775e+00
[113,] 4.144630e+00 4.147493e+00
[114,] 1.732378e+00 4.144630e+00
[115,] -2.116266e+00 1.732378e+00
[116,] 1.437083e+00 -2.116266e+00
[117,] 9.409211e-01 1.437083e+00
[118,] 1.719675e+00 9.409211e-01
[119,] 2.682484e+00 1.719675e+00
[120,] 4.397603e-01 2.682484e+00
[121,] 2.637973e+00 4.397603e-01
[122,] 1.335484e+00 2.637973e+00
[123,] 2.354913e+00 1.335484e+00
[124,] 6.514260e+00 2.354913e+00
[125,] -2.270780e+00 6.514260e+00
[126,] 2.905147e+00 -2.270780e+00
[127,] 8.436909e-01 2.905147e+00
[128,] -2.026509e+00 8.436909e-01
[129,] -4.447960e-01 -2.026509e+00
[130,] 3.019334e+00 -4.447960e-01
[131,] -2.927945e+00 3.019334e+00
[132,] -5.683742e+00 -2.927945e+00
[133,] -3.208816e+00 -5.683742e+00
[134,] -1.701980e+00 -3.208816e+00
[135,] 2.874197e+00 -1.701980e+00
[136,] 8.720789e-01 2.874197e+00
[137,] -3.105537e+00 8.720789e-01
[138,] -2.520638e+00 -3.105537e+00
[139,] 1.517192e+00 -2.520638e+00
[140,] 2.361455e+00 1.517192e+00
[141,] -1.269742e-01 2.361455e+00
[142,] 6.317521e-01 -1.269742e-01
[143,] -2.213531e+00 6.317521e-01
[144,] 2.000901e+00 -2.213531e+00
[145,] 5.143144e+00 2.000901e+00
[146,] -2.715174e+00 5.143144e+00
[147,] -6.833734e-01 -2.715174e+00
[148,] -1.914584e+00 -6.833734e-01
[149,] -3.761362e+00 -1.914584e+00
[150,] 4.769139e+00 -3.761362e+00
[151,] 2.902128e+00 4.769139e+00
[152,] 2.410396e+00 2.902128e+00
[153,] -2.568852e+00 2.410396e+00
[154,] -2.468433e+00 -2.568852e+00
[155,] -1.821553e+00 -2.468433e+00
[156,] -1.320454e+00 -1.821553e+00
[157,] -7.561970e-01 -1.320454e+00
[158,] 1.089958e+00 -7.561970e-01
[159,] 1.533421e+00 1.089958e+00
[160,] -1.119198e+00 1.533421e+00
[161,] 3.324063e+00 -1.119198e+00
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.593311e+00 4.217495e+00
2 1.974956e+00 1.593311e+00
3 -1.717746e+00 1.974956e+00
4 2.556589e+00 -1.717746e+00
5 1.667685e+00 2.556589e+00
6 -8.727200e-01 1.667685e+00
7 -3.305942e+00 -8.727200e-01
8 4.524751e+00 -3.305942e+00
9 2.989470e+00 4.524751e+00
10 -2.193026e+00 2.989470e+00
11 -1.333815e+00 -2.193026e+00
12 6.839933e-02 -1.333815e+00
13 5.479282e+00 6.839933e-02
14 2.970663e+00 5.479282e+00
15 -2.516904e+00 2.970663e+00
16 -1.098019e+00 -2.516904e+00
17 -2.238708e+00 -1.098019e+00
18 1.559881e+00 -2.238708e+00
19 -3.961582e-01 1.559881e+00
20 -5.087879e-02 -3.961582e-01
21 2.075614e-02 -5.087879e-02
22 -1.019525e+00 2.075614e-02
23 -3.351179e+00 -1.019525e+00
24 1.850783e-01 -3.351179e+00
25 3.577341e+00 1.850783e-01
26 3.577341e+00 3.577341e+00
27 5.277258e+00 3.577341e+00
28 3.495537e+00 5.277258e+00
29 2.574155e+00 3.495537e+00
30 -1.665055e+00 2.574155e+00
31 2.587138e+00 -1.665055e+00
32 -6.204535e-01 2.587138e+00
33 -3.565461e+00 -6.204535e-01
34 -1.588607e+00 -3.565461e+00
35 -3.039158e+00 -1.588607e+00
36 1.907120e+00 -3.039158e+00
37 -3.417515e+00 1.907120e+00
38 -1.785749e+00 -3.417515e+00
39 3.784586e+00 -1.785749e+00
40 1.164763e+00 3.784586e+00
41 5.543764e+00 1.164763e+00
42 3.958206e+00 5.543764e+00
43 -3.042064e-01 3.958206e+00
44 2.269944e+00 -3.042064e-01
45 8.397832e-02 2.269944e+00
46 -2.950764e+00 8.397832e-02
47 -1.811704e+00 -2.950764e+00
48 9.684406e-01 -1.811704e+00
49 -3.964038e+00 9.684406e-01
50 9.329963e-01 -3.964038e+00
51 1.292829e+00 9.329963e-01
52 2.401350e+00 1.292829e+00
53 -8.386463e-01 2.401350e+00
54 -1.456757e+00 -8.386463e-01
55 3.840793e+00 -1.456757e+00
56 2.564198e+00 3.840793e+00
57 4.707106e+00 2.564198e+00
58 -7.785187e-03 4.707106e+00
59 2.693166e+00 -7.785187e-03
60 7.763166e+00 2.693166e+00
61 3.065795e+00 7.763166e+00
62 5.562054e+00 3.065795e+00
63 -2.360896e-01 5.562054e+00
64 -4.941712e-01 -2.360896e-01
65 -1.636208e+00 -4.941712e-01
66 -2.725263e+00 -1.636208e+00
67 -1.867869e+00 -2.725263e+00
68 2.060226e+00 -1.867869e+00
69 5.810371e-02 2.060226e+00
70 2.232898e+00 5.810371e-02
71 -1.360497e+00 2.232898e+00
72 -6.806987e-01 -1.360497e+00
73 -5.946529e-01 -6.806987e-01
74 2.590222e+00 -5.946529e-01
75 1.462260e+00 2.590222e+00
76 2.180837e+00 1.462260e+00
77 -2.739261e+00 2.180837e+00
78 -3.125334e+00 -2.739261e+00
79 -3.359333e+00 -3.125334e+00
80 -4.407348e-01 -3.359333e+00
81 1.442905e+00 -4.407348e-01
82 -1.049611e+02 1.442905e+00
83 2.812288e+00 -1.049611e+02
84 -4.017721e+00 2.812288e+00
85 -5.471136e-01 -4.017721e+00
86 2.945624e+00 -5.471136e-01
87 2.902249e-01 2.945624e+00
88 4.458222e-01 2.902249e-01
89 4.505874e+00 4.458222e-01
90 5.077272e+00 4.505874e+00
91 -2.401202e-01 5.077272e+00
92 1.089958e+00 -2.401202e-01
93 9.369867e-01 1.089958e+00
94 1.015837e+00 9.369867e-01
95 2.183851e+00 1.015837e+00
96 -4.343596e-01 2.183851e+00
97 2.613676e+00 -4.343596e-01
98 -3.088102e+00 2.613676e+00
99 -1.526370e+00 -3.088102e+00
100 1.916773e+00 -1.526370e+00
101 2.217630e+00 1.916773e+00
102 2.581105e+00 2.217630e+00
103 2.684387e+00 2.581105e+00
104 -1.750682e+00 2.684387e+00
105 1.996964e+00 -1.750682e+00
106 -4.036317e+00 1.996964e+00
107 1.502030e+00 -4.036317e+00
108 1.453634e+00 1.502030e+00
109 4.486048e+00 1.453634e+00
110 2.336593e+00 4.486048e+00
111 3.642775e+00 2.336593e+00
112 4.147493e+00 3.642775e+00
113 4.144630e+00 4.147493e+00
114 1.732378e+00 4.144630e+00
115 -2.116266e+00 1.732378e+00
116 1.437083e+00 -2.116266e+00
117 9.409211e-01 1.437083e+00
118 1.719675e+00 9.409211e-01
119 2.682484e+00 1.719675e+00
120 4.397603e-01 2.682484e+00
121 2.637973e+00 4.397603e-01
122 1.335484e+00 2.637973e+00
123 2.354913e+00 1.335484e+00
124 6.514260e+00 2.354913e+00
125 -2.270780e+00 6.514260e+00
126 2.905147e+00 -2.270780e+00
127 8.436909e-01 2.905147e+00
128 -2.026509e+00 8.436909e-01
129 -4.447960e-01 -2.026509e+00
130 3.019334e+00 -4.447960e-01
131 -2.927945e+00 3.019334e+00
132 -5.683742e+00 -2.927945e+00
133 -3.208816e+00 -5.683742e+00
134 -1.701980e+00 -3.208816e+00
135 2.874197e+00 -1.701980e+00
136 8.720789e-01 2.874197e+00
137 -3.105537e+00 8.720789e-01
138 -2.520638e+00 -3.105537e+00
139 1.517192e+00 -2.520638e+00
140 2.361455e+00 1.517192e+00
141 -1.269742e-01 2.361455e+00
142 6.317521e-01 -1.269742e-01
143 -2.213531e+00 6.317521e-01
144 2.000901e+00 -2.213531e+00
145 5.143144e+00 2.000901e+00
146 -2.715174e+00 5.143144e+00
147 -6.833734e-01 -2.715174e+00
148 -1.914584e+00 -6.833734e-01
149 -3.761362e+00 -1.914584e+00
150 4.769139e+00 -3.761362e+00
151 2.902128e+00 4.769139e+00
152 2.410396e+00 2.902128e+00
153 -2.568852e+00 2.410396e+00
154 -2.468433e+00 -2.568852e+00
155 -1.821553e+00 -2.468433e+00
156 -1.320454e+00 -1.821553e+00
157 -7.561970e-01 -1.320454e+00
158 1.089958e+00 -7.561970e-01
159 1.533421e+00 1.089958e+00
160 -1.119198e+00 1.533421e+00
161 3.324063e+00 -1.119198e+00
> 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/7m15s1354973959.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/89gd71354973959.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/9dkjw1354973959.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/10y4cw1354973959.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11o9ip1354973959.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12rwwh1354973959.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13t5ny1354973959.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14q7k61354973959.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/15ya611354973959.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/16whza1354973959.tab")
+ }
>
> try(system("convert tmp/1327h1354973958.ps tmp/1327h1354973958.png",intern=TRUE))
character(0)
> try(system("convert tmp/2os7l1354973958.ps tmp/2os7l1354973958.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v60v1354973958.ps tmp/3v60v1354973958.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qio91354973958.ps tmp/4qio91354973958.png",intern=TRUE))
character(0)
> try(system("convert tmp/54p2l1354973959.ps tmp/54p2l1354973959.png",intern=TRUE))
character(0)
> try(system("convert tmp/6h2yt1354973959.ps tmp/6h2yt1354973959.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m15s1354973959.ps tmp/7m15s1354973959.png",intern=TRUE))
character(0)
> try(system("convert tmp/89gd71354973959.ps tmp/89gd71354973959.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dkjw1354973959.ps tmp/9dkjw1354973959.png",intern=TRUE))
character(0)
> try(system("convert tmp/10y4cw1354973959.ps tmp/10y4cw1354973959.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.011 1.506 9.567