R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(0.3
+ ,1.8
+ ,1.2
+ ,1.8
+ ,2.1
+ ,1.9
+ ,1.2
+ ,1.9
+ ,2.5
+ ,2.2
+ ,1.4
+ ,1.9
+ ,2.3
+ ,2.1
+ ,1.5
+ ,1.7
+ ,2.4
+ ,2.2
+ ,1.4
+ ,1.7
+ ,3
+ ,2.7
+ ,1.8
+ ,2.1
+ ,1.7
+ ,2.8
+ ,2
+ ,2
+ ,3.5
+ ,2.9
+ ,2.3
+ ,2
+ ,4
+ ,3.4
+ ,2.6
+ ,2.5
+ ,3.7
+ ,3
+ ,2.3
+ ,2.4
+ ,3.7
+ ,3.1
+ ,2.5
+ ,2.5
+ ,3
+ ,2.5
+ ,2.3
+ ,2.5
+ ,2.7
+ ,2.2
+ ,2.1
+ ,2
+ ,2.5
+ ,2.3
+ ,2.2
+ ,1.9
+ ,2.2
+ ,2.1
+ ,2.2
+ ,2.2
+ ,2.9
+ ,2.8
+ ,2.7
+ ,2.7
+ ,3.1
+ ,3.1
+ ,3.1
+ ,3.1
+ ,3
+ ,2.9
+ ,3.2
+ ,2.8
+ ,2.8
+ ,2.6
+ ,3.1
+ ,2.5
+ ,2.5
+ ,2.7
+ ,3.1
+ ,2.4
+ ,1.9
+ ,2.3
+ ,2.8
+ ,2.2
+ ,1.9
+ ,2.3
+ ,3
+ ,2.2
+ ,1.8
+ ,2.1
+ ,2.8
+ ,2
+ ,2
+ ,2.2
+ ,2.7
+ ,2.1
+ ,2.6
+ ,2.9
+ ,3.2
+ ,2.6
+ ,2.5
+ ,2.6
+ ,3.1
+ ,2.5
+ ,2.5
+ ,2.7
+ ,3
+ ,2.5
+ ,1.6
+ ,1.8
+ ,2
+ ,2.3
+ ,1.4
+ ,1.3
+ ,1.7
+ ,2
+ ,0.8
+ ,0.9
+ ,1.2
+ ,1.9
+ ,1.1
+ ,1.3
+ ,1.4
+ ,2
+ ,1.3
+ ,1.3
+ ,1.3
+ ,2.1
+ ,1.2
+ ,1.3
+ ,1.3
+ ,2.1
+ ,1.3
+ ,1.3
+ ,1.1
+ ,2.3
+ ,1.1
+ ,1.1
+ ,0.9
+ ,2.3
+ ,1.3
+ ,1.4
+ ,1.2
+ ,2.3
+ ,1.2
+ ,1.2
+ ,0.9
+ ,2.1
+ ,1.6
+ ,1.7
+ ,1.3
+ ,2.4
+ ,1.7
+ ,1.8
+ ,1.4
+ ,2.4
+ ,1.5
+ ,1.5
+ ,1.5
+ ,2.1
+ ,0.9
+ ,1
+ ,1.1
+ ,1.8
+ ,1.5
+ ,1.6
+ ,1.6
+ ,1.9
+ ,1.4
+ ,1.5
+ ,1.5
+ ,1.9
+ ,1.6
+ ,1.8
+ ,1.6
+ ,2.1
+ ,1.7
+ ,1.8
+ ,1.7
+ ,2.2
+ ,1.4
+ ,1.6
+ ,1.6
+ ,2
+ ,1.8
+ ,1.9
+ ,1.7
+ ,2.2
+ ,1.7
+ ,1.7
+ ,1.6
+ ,2
+ ,1.4
+ ,1.6
+ ,1.6
+ ,1.9
+ ,1.2
+ ,1.3
+ ,1.3
+ ,1.6
+ ,1
+ ,1.1
+ ,1.1
+ ,1.7
+ ,1.7
+ ,1.9
+ ,1.6
+ ,2
+ ,2.4
+ ,2.6
+ ,1.9
+ ,2.5
+ ,2
+ ,2.3
+ ,1.6
+ ,2.4
+ ,2.1
+ ,2.4
+ ,1.7
+ ,2.3
+ ,2
+ ,2.2
+ ,1.6
+ ,2.3
+ ,1.8
+ ,2
+ ,1.4
+ ,2.1
+ ,2.7
+ ,2.9
+ ,2.1
+ ,2.4
+ ,2.3
+ ,2.6
+ ,1.9
+ ,2.2
+ ,1.9
+ ,2.3
+ ,1.7
+ ,2.4
+ ,2
+ ,2.3
+ ,1.8
+ ,1.9
+ ,2.3
+ ,2.6
+ ,2
+ ,2.1
+ ,2.8
+ ,3.1
+ ,2.5
+ ,2.1
+ ,2.4
+ ,2.8
+ ,2.1
+ ,2.1
+ ,2.3
+ ,2.5
+ ,2.1
+ ,2
+ ,2.7
+ ,2.9
+ ,2.3
+ ,2.1
+ ,2.7
+ ,3.1
+ ,2.4
+ ,2.2
+ ,2.9
+ ,3.1
+ ,2.4
+ ,2.2
+ ,3
+ ,3.2
+ ,2.3
+ ,2.6
+ ,2.2
+ ,2.5
+ ,1.7
+ ,2.5
+ ,2.3
+ ,2.6
+ ,2
+ ,2.3
+ ,2.8
+ ,2.9
+ ,2.3
+ ,2.2
+ ,2.8
+ ,2.6
+ ,2
+ ,2.4
+ ,2.8
+ ,2.4
+ ,2
+ ,2.3
+ ,2.2
+ ,1.7
+ ,1.3
+ ,2.2
+ ,2.6
+ ,2
+ ,1.7
+ ,2.5
+ ,2.8
+ ,2.2
+ ,1.9
+ ,2.5
+ ,2.5
+ ,1.9
+ ,1.7
+ ,2.5
+ ,2.4
+ ,1.6
+ ,1.6
+ ,2.4
+ ,2.3
+ ,1.6
+ ,1.7
+ ,2.3
+ ,1.9
+ ,1.2
+ ,1.8
+ ,1.7
+ ,1.7
+ ,1.2
+ ,1.9
+ ,1.6
+ ,2
+ ,1.5
+ ,1.9
+ ,1.9
+ ,2.1
+ ,1.6
+ ,1.9
+ ,1.9
+ ,1.7
+ ,1.7
+ ,2
+ ,1.8
+ ,1.8
+ ,1.8
+ ,2.1
+ ,1.8
+ ,1.8
+ ,1.8
+ ,1.9
+ ,1.9
+ ,1.8
+ ,1.8
+ ,1.9
+ ,1.9
+ ,1.3
+ ,1.3
+ ,1.3
+ ,1.9
+ ,1.3
+ ,1.3
+ ,1.3
+ ,1.9
+ ,1.3
+ ,1.4
+ ,1.4
+ ,1.8
+ ,1.2
+ ,1.1
+ ,1.2
+ ,1.7
+ ,1.4
+ ,1.5
+ ,1.3
+ ,2.1
+ ,2.2
+ ,2.2
+ ,1.8
+ ,2.6
+ ,2.9
+ ,2.9
+ ,2.2
+ ,3.1
+ ,3.1
+ ,3.1
+ ,2.6
+ ,3.1
+ ,3.5
+ ,3.5
+ ,2.8
+ ,3.2
+ ,3.6
+ ,3.6
+ ,3.1
+ ,3.3
+ ,4.4
+ ,4.4
+ ,3.9
+ ,3.6
+ ,4.1
+ ,4.2
+ ,3.7
+ ,3.3
+ ,5.1
+ ,5.2
+ ,4.6
+ ,3.7
+ ,5.8
+ ,5.8
+ ,5.1
+ ,4
+ ,5.9
+ ,5.9
+ ,5.2
+ ,4
+ ,5.4
+ ,5.4
+ ,4.9
+ ,3.8
+ ,5.5
+ ,5.5
+ ,5.1
+ ,3.6
+ ,4.8
+ ,4.7
+ ,4.8
+ ,3.2
+ ,3.2
+ ,3.1
+ ,3.9
+ ,2.1
+ ,2.7
+ ,2.6
+ ,3.5
+ ,1.6
+ ,2.1
+ ,2.3
+ ,3.3
+ ,1.1
+ ,1.9
+ ,1.9
+ ,2.8
+ ,1.2
+ ,0.6
+ ,0.6
+ ,1.6
+ ,0.6
+ ,0.7
+ ,0.6
+ ,1.5
+ ,0.6
+ ,-0.2
+ ,-0.4
+ ,0.7
+ ,0
+ ,-1
+ ,-1.1
+ ,-0.1
+ ,-0.1
+ ,-1.7
+ ,-1.7
+ ,-0.7
+ ,-0.6
+ ,-0.7
+ ,-0.8
+ ,-0.2
+ ,-0.2
+ ,-1
+ ,-1.2
+ ,-0.6
+ ,-0.3
+ ,-0.9
+ ,-1
+ ,-0.6
+ ,-0.1
+ ,0
+ ,-0.1
+ ,-0.3
+ ,0.5
+ ,0.3
+ ,0.3
+ ,-0.3
+ ,0.9
+ ,0.8
+ ,0.6
+ ,-0.1
+ ,0.9
+ ,0.8
+ ,0.7
+ ,0.1
+ ,0.8
+ ,1.9
+ ,1.7
+ ,0.9
+ ,1.6
+ ,2.1
+ ,1.8
+ ,1.1
+ ,1.6
+ ,2.5
+ ,2.3
+ ,1.6
+ ,1.7
+ ,2.7
+ ,2.5
+ ,2
+ ,1.5
+ ,2.4
+ ,2.6
+ ,2.2
+ ,1.7
+ ,2.4
+ ,2.3
+ ,2.1
+ ,1.6
+ ,2.9
+ ,2.9
+ ,2.6
+ ,1.9
+ ,3.1
+ ,3
+ ,2.5
+ ,1.9
+ ,3
+ ,2.9
+ ,2.5
+ ,1.9
+ ,3.4
+ ,3.1
+ ,2.6
+ ,2.2
+ ,3.7
+ ,3.2
+ ,2.7
+ ,2.3
+ ,3.5
+ ,3.4
+ ,2.8
+ ,2.4
+ ,3.5
+ ,3.5
+ ,2.9
+ ,2.7
+ ,3.3
+ ,3.4
+ ,2.9
+ ,2.8
+ ,3.1
+ ,3.3
+ ,2.9
+ ,2.7
+ ,3.4
+ ,3.7
+ ,3.3
+ ,2.7
+ ,4
+ ,3.8
+ ,3.3
+ ,2.6
+ ,3.4
+ ,3.6
+ ,3.1
+ ,2.5
+ ,3.4
+ ,3.6
+ ,3
+ ,3
+ ,3.4
+ ,3.6
+ ,3.1
+ ,3
+ ,3.7
+ ,3.8
+ ,3.4
+ ,3
+ ,3.2
+ ,3.5
+ ,3.2
+ ,2.7
+ ,3.3
+ ,3.6
+ ,3.4
+ ,2.7
+ ,3.3
+ ,3.7
+ ,3.4
+ ,2.7
+ ,3.1
+ ,3.4
+ ,3.1
+ ,2.7
+ ,2.9
+ ,3.2
+ ,3
+ ,2.6
+ ,2.6
+ ,2.8
+ ,2.7
+ ,2.4
+ ,2.2
+ ,2.3
+ ,2.2
+ ,2.4
+ ,2
+ ,2.3
+ ,2.2
+ ,2.4
+ ,2.6
+ ,2.9
+ ,2.6
+ ,2.6
+ ,2.6
+ ,2.8
+ ,2.4
+ ,2.6
+ ,2.6
+ ,2.8
+ ,2.5
+ ,2.5)
+ ,dim=c(4
+ ,154)
+ ,dimnames=list(c('HICP_Belgie'
+ ,'Consumptieprijsindex_Belgie'
+ ,'Gezondheidsindex_Belgie'
+ ,'HICP_Eurogebied')
+ ,1:154))
> y <- array(NA,dim=c(4,154),dimnames=list(c('HICP_Belgie','Consumptieprijsindex_Belgie','Gezondheidsindex_Belgie','HICP_Eurogebied'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> 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
HICP_Eurogebied HICP_Belgie Consumptieprijsindex_Belgie
1 1.8 0.3 1.8
2 1.9 2.1 1.9
3 1.9 2.5 2.2
4 1.7 2.3 2.1
5 1.7 2.4 2.2
6 2.1 3.0 2.7
7 2.0 1.7 2.8
8 2.0 3.5 2.9
9 2.5 4.0 3.4
10 2.4 3.7 3.0
11 2.5 3.7 3.1
12 2.5 3.0 2.5
13 2.0 2.7 2.2
14 1.9 2.5 2.3
15 2.2 2.2 2.1
16 2.7 2.9 2.8
17 3.1 3.1 3.1
18 2.8 3.0 2.9
19 2.5 2.8 2.6
20 2.4 2.5 2.7
21 2.2 1.9 2.3
22 2.2 1.9 2.3
23 2.0 1.8 2.1
24 2.1 2.0 2.2
25 2.6 2.6 2.9
26 2.5 2.5 2.6
27 2.5 2.5 2.7
28 2.3 1.6 1.8
29 2.0 1.4 1.3
30 1.9 0.8 0.9
31 2.0 1.1 1.3
32 2.1 1.3 1.3
33 2.1 1.2 1.3
34 2.3 1.3 1.3
35 2.3 1.1 1.1
36 2.3 1.3 1.4
37 2.1 1.2 1.2
38 2.4 1.6 1.7
39 2.4 1.7 1.8
40 2.1 1.5 1.5
41 1.8 0.9 1.0
42 1.9 1.5 1.6
43 1.9 1.4 1.5
44 2.1 1.6 1.8
45 2.2 1.7 1.8
46 2.0 1.4 1.6
47 2.2 1.8 1.9
48 2.0 1.7 1.7
49 1.9 1.4 1.6
50 1.6 1.2 1.3
51 1.7 1.0 1.1
52 2.0 1.7 1.9
53 2.5 2.4 2.6
54 2.4 2.0 2.3
55 2.3 2.1 2.4
56 2.3 2.0 2.2
57 2.1 1.8 2.0
58 2.4 2.7 2.9
59 2.2 2.3 2.6
60 2.4 1.9 2.3
61 1.9 2.0 2.3
62 2.1 2.3 2.6
63 2.1 2.8 3.1
64 2.1 2.4 2.8
65 2.0 2.3 2.5
66 2.1 2.7 2.9
67 2.2 2.7 3.1
68 2.2 2.9 3.1
69 2.6 3.0 3.2
70 2.5 2.2 2.5
71 2.3 2.3 2.6
72 2.2 2.8 2.9
73 2.4 2.8 2.6
74 2.3 2.8 2.4
75 2.2 2.2 1.7
76 2.5 2.6 2.0
77 2.5 2.8 2.2
78 2.5 2.5 1.9
79 2.4 2.4 1.6
80 2.3 2.3 1.6
81 1.7 1.9 1.2
82 1.6 1.7 1.2
83 1.9 2.0 1.5
84 1.9 2.1 1.6
85 1.8 1.7 1.7
86 1.8 1.8 1.8
87 1.9 1.8 1.8
88 1.9 1.8 1.8
89 1.9 1.3 1.3
90 1.9 1.3 1.3
91 1.8 1.3 1.4
92 1.7 1.2 1.1
93 2.1 1.4 1.5
94 2.6 2.2 2.2
95 3.1 2.9 2.9
96 3.1 3.1 3.1
97 3.2 3.5 3.5
98 3.3 3.6 3.6
99 3.6 4.4 4.4
100 3.3 4.1 4.2
101 3.7 5.1 5.2
102 4.0 5.8 5.8
103 4.0 5.9 5.9
104 3.8 5.4 5.4
105 3.6 5.5 5.5
106 3.2 4.8 4.7
107 2.1 3.2 3.1
108 1.6 2.7 2.6
109 1.1 2.1 2.3
110 1.2 1.9 1.9
111 0.6 0.6 0.6
112 0.6 0.7 0.6
113 0.0 -0.2 -0.4
114 -0.1 -1.0 -1.1
115 -0.6 -1.7 -1.7
116 -0.2 -0.7 -0.8
117 -0.3 -1.0 -1.2
118 -0.1 -0.9 -1.0
119 0.5 0.0 -0.1
120 0.9 0.3 0.3
121 0.9 0.8 0.6
122 0.8 0.8 0.7
123 1.6 1.9 1.7
124 1.6 2.1 1.8
125 1.7 2.5 2.3
126 1.5 2.7 2.5
127 1.7 2.4 2.6
128 1.6 2.4 2.3
129 1.9 2.9 2.9
130 1.9 3.1 3.0
131 1.9 3.0 2.9
132 2.2 3.4 3.1
133 2.3 3.7 3.2
134 2.4 3.5 3.4
135 2.7 3.5 3.5
136 2.8 3.3 3.4
137 2.7 3.1 3.3
138 2.7 3.4 3.7
139 2.6 4.0 3.8
140 2.5 3.4 3.6
141 3.0 3.4 3.6
142 3.0 3.4 3.6
143 3.0 3.7 3.8
144 2.7 3.2 3.5
145 2.7 3.3 3.6
146 2.7 3.3 3.7
147 2.7 3.1 3.4
148 2.6 2.9 3.2
149 2.4 2.6 2.8
150 2.4 2.2 2.3
151 2.4 2.0 2.3
152 2.6 2.6 2.9
153 2.6 2.6 2.8
154 2.5 2.6 2.8
Gezondheidsindex_Belgie
1 1.2
2 1.2
3 1.4
4 1.5
5 1.4
6 1.8
7 2.0
8 2.3
9 2.6
10 2.3
11 2.5
12 2.3
13 2.1
14 2.2
15 2.2
16 2.7
17 3.1
18 3.2
19 3.1
20 3.1
21 2.8
22 3.0
23 2.8
24 2.7
25 3.2
26 3.1
27 3.0
28 2.0
29 1.7
30 1.2
31 1.4
32 1.3
33 1.3
34 1.1
35 0.9
36 1.2
37 0.9
38 1.3
39 1.4
40 1.5
41 1.1
42 1.6
43 1.5
44 1.6
45 1.7
46 1.6
47 1.7
48 1.6
49 1.6
50 1.3
51 1.1
52 1.6
53 1.9
54 1.6
55 1.7
56 1.6
57 1.4
58 2.1
59 1.9
60 1.7
61 1.8
62 2.0
63 2.5
64 2.1
65 2.1
66 2.3
67 2.4
68 2.4
69 2.3
70 1.7
71 2.0
72 2.3
73 2.0
74 2.0
75 1.3
76 1.7
77 1.9
78 1.7
79 1.6
80 1.7
81 1.8
82 1.9
83 1.9
84 1.9
85 2.0
86 2.1
87 1.9
88 1.9
89 1.3
90 1.3
91 1.4
92 1.2
93 1.3
94 1.8
95 2.2
96 2.6
97 2.8
98 3.1
99 3.9
100 3.7
101 4.6
102 5.1
103 5.2
104 4.9
105 5.1
106 4.8
107 3.9
108 3.5
109 3.3
110 2.8
111 1.6
112 1.5
113 0.7
114 -0.1
115 -0.7
116 -0.2
117 -0.6
118 -0.6
119 -0.3
120 -0.3
121 -0.1
122 0.1
123 0.9
124 1.1
125 1.6
126 2.0
127 2.2
128 2.1
129 2.6
130 2.5
131 2.5
132 2.6
133 2.7
134 2.8
135 2.9
136 2.9
137 2.9
138 3.3
139 3.3
140 3.1
141 3.0
142 3.1
143 3.4
144 3.2
145 3.4
146 3.4
147 3.1
148 3.0
149 2.7
150 2.2
151 2.2
152 2.6
153 2.4
154 2.5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) HICP_Belgie
0.92112 0.03742
Consumptieprijsindex_Belgie Gezondheidsindex_Belgie
0.57796 -0.09230
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.92442 -0.29484 0.05612 0.24592 0.78503
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.92112 0.06539 14.087 < 2e-16 ***
HICP_Belgie 0.03742 0.09678 0.387 0.700
Consumptieprijsindex_Belgie 0.57796 0.11010 5.249 5.13e-07 ***
Gezondheidsindex_Belgie -0.09230 0.07070 -1.305 0.194
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3637 on 150 degrees of freedom
Multiple R-squared: 0.7723, Adjusted R-squared: 0.7678
F-statistic: 169.6 on 3 and 150 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 6.895605e-02 1.379121e-01 9.310439e-01
[2,] 2.698237e-02 5.396473e-02 9.730176e-01
[3,] 3.609993e-02 7.219986e-02 9.639001e-01
[4,] 2.971644e-02 5.943288e-02 9.702836e-01
[5,] 2.187653e-02 4.375306e-02 9.781235e-01
[6,] 1.538744e-02 3.077488e-02 9.846126e-01
[7,] 1.335303e-02 2.670606e-02 9.866470e-01
[8,] 1.270016e-02 2.540032e-02 9.872998e-01
[9,] 6.997225e-03 1.399445e-02 9.930028e-01
[10,] 7.494377e-03 1.498875e-02 9.925056e-01
[11,] 1.165441e-02 2.330882e-02 9.883456e-01
[12,] 6.561573e-03 1.312315e-02 9.934384e-01
[13,] 5.444415e-03 1.088883e-02 9.945556e-01
[14,] 5.598662e-03 1.119732e-02 9.944013e-01
[15,] 4.416832e-03 8.833665e-03 9.955832e-01
[16,] 3.760539e-03 7.521078e-03 9.962395e-01
[17,] 3.326288e-03 6.652575e-03 9.966737e-01
[18,] 2.025476e-03 4.050952e-03 9.979745e-01
[19,] 1.113101e-03 2.226203e-03 9.988869e-01
[20,] 6.179021e-04 1.235804e-03 9.993821e-01
[21,] 3.287891e-04 6.575782e-04 9.996712e-01
[22,] 8.419554e-04 1.683911e-03 9.991580e-01
[23,] 8.629928e-04 1.725986e-03 9.991370e-01
[24,] 1.173139e-03 2.346279e-03 9.988269e-01
[25,] 1.068391e-03 2.136782e-03 9.989316e-01
[26,] 1.320072e-03 2.640143e-03 9.986799e-01
[27,] 1.469166e-03 2.938331e-03 9.985308e-01
[28,] 4.308971e-03 8.617943e-03 9.956910e-01
[29,] 1.168275e-02 2.336550e-02 9.883172e-01
[30,] 1.696959e-02 3.393918e-02 9.830304e-01
[31,] 1.606724e-02 3.213449e-02 9.839328e-01
[32,] 2.373406e-02 4.746813e-02 9.762659e-01
[33,] 2.986632e-02 5.973263e-02 9.701337e-01
[34,] 2.468709e-02 4.937418e-02 9.753129e-01
[35,] 2.337573e-02 4.675146e-02 9.766243e-01
[36,] 1.993824e-02 3.987648e-02 9.800618e-01
[37,] 1.671686e-02 3.343372e-02 9.832831e-01
[38,] 1.285980e-02 2.571961e-02 9.871402e-01
[39,] 1.077163e-02 2.154326e-02 9.892284e-01
[40,] 8.846576e-03 1.769315e-02 9.911534e-01
[41,] 7.078199e-03 1.415640e-02 9.929218e-01
[42,] 5.412707e-03 1.082541e-02 9.945873e-01
[43,] 4.637311e-03 9.274621e-03 9.953627e-01
[44,] 6.023914e-03 1.204783e-02 9.939761e-01
[45,] 5.771514e-03 1.154303e-02 9.942285e-01
[46,] 4.195202e-03 8.390403e-03 9.958048e-01
[47,] 4.419121e-03 8.838242e-03 9.955809e-01
[48,] 4.613552e-03 9.227105e-03 9.953864e-01
[49,] 3.516667e-03 7.033334e-03 9.964833e-01
[50,] 2.916033e-03 5.832065e-03 9.970840e-01
[51,] 2.066835e-03 4.133669e-03 9.979332e-01
[52,] 1.410140e-03 2.820280e-03 9.985899e-01
[53,] 9.312268e-04 1.862454e-03 9.990688e-01
[54,] 9.710565e-04 1.942113e-03 9.990289e-01
[55,] 9.114005e-04 1.822801e-03 9.990886e-01
[56,] 6.591886e-04 1.318377e-03 9.993408e-01
[57,] 7.138042e-04 1.427608e-03 9.992862e-01
[58,] 5.377060e-04 1.075412e-03 9.994623e-01
[59,] 4.477404e-04 8.954808e-04 9.995523e-01
[60,] 3.864532e-04 7.729064e-04 9.996135e-01
[61,] 2.884066e-04 5.768132e-04 9.997116e-01
[62,] 2.341022e-04 4.682044e-04 9.997659e-01
[63,] 2.120169e-04 4.240339e-04 9.997880e-01
[64,] 2.547927e-04 5.095854e-04 9.997452e-01
[65,] 1.673978e-04 3.347955e-04 9.998326e-01
[66,] 1.226799e-04 2.453599e-04 9.998773e-01
[67,] 8.332726e-05 1.666545e-04 9.999167e-01
[68,] 5.157992e-05 1.031598e-04 9.999484e-01
[69,] 3.772262e-05 7.544524e-05 9.999623e-01
[70,] 4.669937e-05 9.339874e-05 9.999533e-01
[71,] 4.367917e-05 8.735834e-05 9.999563e-01
[72,] 6.563232e-05 1.312646e-04 9.999344e-01
[73,] 1.186285e-04 2.372569e-04 9.998814e-01
[74,] 2.248605e-04 4.497210e-04 9.997751e-01
[75,] 7.092606e-04 1.418521e-03 9.992907e-01
[76,] 2.145717e-03 4.291434e-03 9.978543e-01
[77,] 4.484001e-03 8.968001e-03 9.955160e-01
[78,] 1.009454e-02 2.018909e-02 9.899055e-01
[79,] 1.097774e-02 2.195547e-02 9.890223e-01
[80,] 1.171539e-02 2.343078e-02 9.882846e-01
[81,] 1.103452e-02 2.206903e-02 9.889655e-01
[82,] 1.042461e-02 2.084922e-02 9.895754e-01
[83,] 1.303387e-02 2.606774e-02 9.869661e-01
[84,] 1.703844e-02 3.407688e-02 9.829616e-01
[85,] 1.666105e-02 3.332210e-02 9.833389e-01
[86,] 2.620853e-02 5.241706e-02 9.737915e-01
[87,] 3.604836e-02 7.209672e-02 9.639516e-01
[88,] 8.876096e-02 1.775219e-01 9.112390e-01
[89,] 2.956114e-01 5.912227e-01 7.043886e-01
[90,] 5.588299e-01 8.823403e-01 4.411701e-01
[91,] 7.179224e-01 5.641552e-01 2.820776e-01
[92,] 8.865715e-01 2.268570e-01 1.134285e-01
[93,] 9.483971e-01 1.032058e-01 5.160288e-02
[94,] 9.502389e-01 9.952221e-02 4.976111e-02
[95,] 9.411001e-01 1.177999e-01 5.889993e-02
[96,] 9.313331e-01 1.373339e-01 6.866693e-02
[97,] 9.156651e-01 1.686699e-01 8.433494e-02
[98,] 9.069368e-01 1.861264e-01 9.306322e-02
[99,] 8.849178e-01 2.301645e-01 1.150822e-01
[100,] 8.816863e-01 2.366273e-01 1.183137e-01
[101,] 9.067605e-01 1.864790e-01 9.323950e-02
[102,] 9.348089e-01 1.303823e-01 6.519114e-02
[103,] 9.924628e-01 1.507450e-02 7.537250e-03
[104,] 9.951688e-01 9.662343e-03 4.831172e-03
[105,] 9.962428e-01 7.514367e-03 3.757184e-03
[106,] 9.967105e-01 6.578992e-03 3.289496e-03
[107,] 9.972902e-01 5.419559e-03 2.709779e-03
[108,] 9.972674e-01 5.465237e-03 2.732619e-03
[109,] 9.971957e-01 5.608622e-03 2.804311e-03
[110,] 9.976234e-01 4.753180e-03 2.376590e-03
[111,] 9.971874e-01 5.625220e-03 2.812610e-03
[112,] 9.964069e-01 7.186245e-03 3.593123e-03
[113,] 9.951480e-01 9.703972e-03 4.851986e-03
[114,] 9.930620e-01 1.387595e-02 6.937973e-03
[115,] 9.909393e-01 1.812137e-02 9.060685e-03
[116,] 9.902065e-01 1.958707e-02 9.793537e-03
[117,] 9.870370e-01 2.592602e-02 1.296301e-02
[118,] 9.855694e-01 2.886110e-02 1.443055e-02
[119,] 9.810016e-01 3.799674e-02 1.899837e-02
[120,] 9.871174e-01 2.576526e-02 1.288263e-02
[121,] 9.972826e-01 5.434734e-03 2.717367e-03
[122,] 9.982212e-01 3.557561e-03 1.778780e-03
[123,] 9.993518e-01 1.296357e-03 6.481785e-04
[124,] 9.999073e-01 1.854870e-04 9.274350e-05
[125,] 9.999955e-01 9.043102e-06 4.521551e-06
[126,] 9.999947e-01 1.068574e-05 5.342869e-06
[127,] 9.999862e-01 2.761310e-05 1.380655e-05
[128,] 9.999941e-01 1.173290e-05 5.866450e-06
[129,] 9.999851e-01 2.985741e-05 1.492870e-05
[130,] 9.999580e-01 8.393982e-05 4.196991e-05
[131,] 9.998770e-01 2.459678e-04 1.229839e-04
[132,] 9.997232e-01 5.535840e-04 2.767920e-04
[133,] 9.997752e-01 4.496436e-04 2.248218e-04
[134,] 9.999996e-01 7.214200e-07 3.607100e-07
[135,] 9.999978e-01 4.486249e-06 2.243125e-06
[136,] 9.999951e-01 9.724162e-06 4.862081e-06
[137,] 9.999984e-01 3.202329e-06 1.601165e-06
[138,] 9.999857e-01 2.859875e-05 1.429937e-05
[139,] 9.999300e-01 1.400025e-04 7.000125e-05
[140,] 9.994730e-01 1.054047e-03 5.270236e-04
[141,] 9.968895e-01 6.221051e-03 3.110526e-03
> postscript(file="/var/fisher/rcomp/tmp/1cll61353065292.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/2r5ps1353065292.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/31upw1353065292.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4afpe1353065292.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5etei1353065292.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 154
Frequency = 1
1 2 3 4 5 6
-0.061916466 -0.087070086 -0.256966206 -0.382456069 -0.453224112 -0.327736237
7 8 9 10 11 12
-0.418424903 -0.515888461 -0.295888513 -0.181168578 -0.120504465 0.234005723
13 14 15 16 17 18
-0.099840250 -0.240921970 0.185896169 0.301280113 0.557328222 0.385892194
19 20 21 22 23 24
0.257534147 0.110964500 0.136910717 0.155370758 0.056244668 0.081734531
25 26 27 28 29 30
0.200860569 0.268760429 0.201734480 0.463276477 0.432050247 0.539536423
31 32 33 34 35 36
0.415586467 0.498872259 0.502614353 0.680412218 0.785028222 0.631846309
37 38 39 40 41 42
0.523490199 0.556462262 0.504154260 0.394256255 0.368768379 0.145690346
43 44 45 46 47 48
0.197998349 0.226356395 0.331844322 0.249432440 0.270306299 0.180410230
49 50 51 52 53 54
0.149432440 0.002614353 0.207230357 0.064818372 0.161742276 0.222408376
55 56 57 58 59 60
0.070100374 0.180204305 0.084820309 -0.104411751 -0.134515630 0.235380490
61 62 63 64 65 66
-0.259131583 -0.225285610 -0.486825620 -0.335389540 -0.258259660 -0.385951710
67 68 69 70 71 72
-0.392313547 -0.399797734 -0.070565778 0.208562351 -0.025285610 -0.289693804
73 74 75 76 77 78
0.056003921 0.071595778 0.334009698 0.482573619 0.377957615 0.544111642
79 80 81 82 83 84
0.612011501 0.524983616 0.180365727 0.097079935 0.212465867 0.150927845
85 86 87 88 89 90
0.017330312 -0.034977690 0.046562269 0.046562269 0.298872259 0.298872259
91 92 93 94 95 96
0.150306351 0.208976189 0.379538307 0.491180158 0.597334082 0.511178119
97 98 99 100 101 102
0.383486069 0.449638109 0.331174092 0.139532190 0.007222149 -0.019597978
103 104 105 106 107 108
-0.071905980 0.008094072 -0.234983910 -0.174111884 -0.372573708 -0.601803677
109 110 111 112 113 114
-0.924423368 -0.631905568 -0.542671521 -0.555643636 -0.617845668 -0.357177580
115 116 117 118 119 120
-0.539587474 -0.651021669 -0.545531754 -0.464865706 -0.391017848 -0.233427844
121 122 123 124 125 126
-0.407066059 -0.546401946 -0.291684102 -0.338504177 -0.496302094 -0.782458057
127 128 129 130 131 132
-0.610567662 -0.546409897 -0.565745836 -0.640255973 -0.578717950 -0.400048163
133 134 135 136 137 138
-0.359840353 -0.358718002 -0.107283910 0.057996207 0.023276323 -0.182213591
139 140 141 142 143 144
-0.362462083 -0.342877704 0.147892276 0.157122296 0.057994219 -0.068367566
145 146 147 148 149 150
-0.111445548 -0.169241477 -0.016059564 -0.002213540 0.012506395 0.270304312
151 152 153 154
0.277788499 0.145480446 0.184816333 0.094046354
> postscript(file="/var/fisher/rcomp/tmp/67m0e1353065292.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.061916466 NA
1 -0.087070086 -0.061916466
2 -0.256966206 -0.087070086
3 -0.382456069 -0.256966206
4 -0.453224112 -0.382456069
5 -0.327736237 -0.453224112
6 -0.418424903 -0.327736237
7 -0.515888461 -0.418424903
8 -0.295888513 -0.515888461
9 -0.181168578 -0.295888513
10 -0.120504465 -0.181168578
11 0.234005723 -0.120504465
12 -0.099840250 0.234005723
13 -0.240921970 -0.099840250
14 0.185896169 -0.240921970
15 0.301280113 0.185896169
16 0.557328222 0.301280113
17 0.385892194 0.557328222
18 0.257534147 0.385892194
19 0.110964500 0.257534147
20 0.136910717 0.110964500
21 0.155370758 0.136910717
22 0.056244668 0.155370758
23 0.081734531 0.056244668
24 0.200860569 0.081734531
25 0.268760429 0.200860569
26 0.201734480 0.268760429
27 0.463276477 0.201734480
28 0.432050247 0.463276477
29 0.539536423 0.432050247
30 0.415586467 0.539536423
31 0.498872259 0.415586467
32 0.502614353 0.498872259
33 0.680412218 0.502614353
34 0.785028222 0.680412218
35 0.631846309 0.785028222
36 0.523490199 0.631846309
37 0.556462262 0.523490199
38 0.504154260 0.556462262
39 0.394256255 0.504154260
40 0.368768379 0.394256255
41 0.145690346 0.368768379
42 0.197998349 0.145690346
43 0.226356395 0.197998349
44 0.331844322 0.226356395
45 0.249432440 0.331844322
46 0.270306299 0.249432440
47 0.180410230 0.270306299
48 0.149432440 0.180410230
49 0.002614353 0.149432440
50 0.207230357 0.002614353
51 0.064818372 0.207230357
52 0.161742276 0.064818372
53 0.222408376 0.161742276
54 0.070100374 0.222408376
55 0.180204305 0.070100374
56 0.084820309 0.180204305
57 -0.104411751 0.084820309
58 -0.134515630 -0.104411751
59 0.235380490 -0.134515630
60 -0.259131583 0.235380490
61 -0.225285610 -0.259131583
62 -0.486825620 -0.225285610
63 -0.335389540 -0.486825620
64 -0.258259660 -0.335389540
65 -0.385951710 -0.258259660
66 -0.392313547 -0.385951710
67 -0.399797734 -0.392313547
68 -0.070565778 -0.399797734
69 0.208562351 -0.070565778
70 -0.025285610 0.208562351
71 -0.289693804 -0.025285610
72 0.056003921 -0.289693804
73 0.071595778 0.056003921
74 0.334009698 0.071595778
75 0.482573619 0.334009698
76 0.377957615 0.482573619
77 0.544111642 0.377957615
78 0.612011501 0.544111642
79 0.524983616 0.612011501
80 0.180365727 0.524983616
81 0.097079935 0.180365727
82 0.212465867 0.097079935
83 0.150927845 0.212465867
84 0.017330312 0.150927845
85 -0.034977690 0.017330312
86 0.046562269 -0.034977690
87 0.046562269 0.046562269
88 0.298872259 0.046562269
89 0.298872259 0.298872259
90 0.150306351 0.298872259
91 0.208976189 0.150306351
92 0.379538307 0.208976189
93 0.491180158 0.379538307
94 0.597334082 0.491180158
95 0.511178119 0.597334082
96 0.383486069 0.511178119
97 0.449638109 0.383486069
98 0.331174092 0.449638109
99 0.139532190 0.331174092
100 0.007222149 0.139532190
101 -0.019597978 0.007222149
102 -0.071905980 -0.019597978
103 0.008094072 -0.071905980
104 -0.234983910 0.008094072
105 -0.174111884 -0.234983910
106 -0.372573708 -0.174111884
107 -0.601803677 -0.372573708
108 -0.924423368 -0.601803677
109 -0.631905568 -0.924423368
110 -0.542671521 -0.631905568
111 -0.555643636 -0.542671521
112 -0.617845668 -0.555643636
113 -0.357177580 -0.617845668
114 -0.539587474 -0.357177580
115 -0.651021669 -0.539587474
116 -0.545531754 -0.651021669
117 -0.464865706 -0.545531754
118 -0.391017848 -0.464865706
119 -0.233427844 -0.391017848
120 -0.407066059 -0.233427844
121 -0.546401946 -0.407066059
122 -0.291684102 -0.546401946
123 -0.338504177 -0.291684102
124 -0.496302094 -0.338504177
125 -0.782458057 -0.496302094
126 -0.610567662 -0.782458057
127 -0.546409897 -0.610567662
128 -0.565745836 -0.546409897
129 -0.640255973 -0.565745836
130 -0.578717950 -0.640255973
131 -0.400048163 -0.578717950
132 -0.359840353 -0.400048163
133 -0.358718002 -0.359840353
134 -0.107283910 -0.358718002
135 0.057996207 -0.107283910
136 0.023276323 0.057996207
137 -0.182213591 0.023276323
138 -0.362462083 -0.182213591
139 -0.342877704 -0.362462083
140 0.147892276 -0.342877704
141 0.157122296 0.147892276
142 0.057994219 0.157122296
143 -0.068367566 0.057994219
144 -0.111445548 -0.068367566
145 -0.169241477 -0.111445548
146 -0.016059564 -0.169241477
147 -0.002213540 -0.016059564
148 0.012506395 -0.002213540
149 0.270304312 0.012506395
150 0.277788499 0.270304312
151 0.145480446 0.277788499
152 0.184816333 0.145480446
153 0.094046354 0.184816333
154 NA 0.094046354
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.087070086 -0.061916466
[2,] -0.256966206 -0.087070086
[3,] -0.382456069 -0.256966206
[4,] -0.453224112 -0.382456069
[5,] -0.327736237 -0.453224112
[6,] -0.418424903 -0.327736237
[7,] -0.515888461 -0.418424903
[8,] -0.295888513 -0.515888461
[9,] -0.181168578 -0.295888513
[10,] -0.120504465 -0.181168578
[11,] 0.234005723 -0.120504465
[12,] -0.099840250 0.234005723
[13,] -0.240921970 -0.099840250
[14,] 0.185896169 -0.240921970
[15,] 0.301280113 0.185896169
[16,] 0.557328222 0.301280113
[17,] 0.385892194 0.557328222
[18,] 0.257534147 0.385892194
[19,] 0.110964500 0.257534147
[20,] 0.136910717 0.110964500
[21,] 0.155370758 0.136910717
[22,] 0.056244668 0.155370758
[23,] 0.081734531 0.056244668
[24,] 0.200860569 0.081734531
[25,] 0.268760429 0.200860569
[26,] 0.201734480 0.268760429
[27,] 0.463276477 0.201734480
[28,] 0.432050247 0.463276477
[29,] 0.539536423 0.432050247
[30,] 0.415586467 0.539536423
[31,] 0.498872259 0.415586467
[32,] 0.502614353 0.498872259
[33,] 0.680412218 0.502614353
[34,] 0.785028222 0.680412218
[35,] 0.631846309 0.785028222
[36,] 0.523490199 0.631846309
[37,] 0.556462262 0.523490199
[38,] 0.504154260 0.556462262
[39,] 0.394256255 0.504154260
[40,] 0.368768379 0.394256255
[41,] 0.145690346 0.368768379
[42,] 0.197998349 0.145690346
[43,] 0.226356395 0.197998349
[44,] 0.331844322 0.226356395
[45,] 0.249432440 0.331844322
[46,] 0.270306299 0.249432440
[47,] 0.180410230 0.270306299
[48,] 0.149432440 0.180410230
[49,] 0.002614353 0.149432440
[50,] 0.207230357 0.002614353
[51,] 0.064818372 0.207230357
[52,] 0.161742276 0.064818372
[53,] 0.222408376 0.161742276
[54,] 0.070100374 0.222408376
[55,] 0.180204305 0.070100374
[56,] 0.084820309 0.180204305
[57,] -0.104411751 0.084820309
[58,] -0.134515630 -0.104411751
[59,] 0.235380490 -0.134515630
[60,] -0.259131583 0.235380490
[61,] -0.225285610 -0.259131583
[62,] -0.486825620 -0.225285610
[63,] -0.335389540 -0.486825620
[64,] -0.258259660 -0.335389540
[65,] -0.385951710 -0.258259660
[66,] -0.392313547 -0.385951710
[67,] -0.399797734 -0.392313547
[68,] -0.070565778 -0.399797734
[69,] 0.208562351 -0.070565778
[70,] -0.025285610 0.208562351
[71,] -0.289693804 -0.025285610
[72,] 0.056003921 -0.289693804
[73,] 0.071595778 0.056003921
[74,] 0.334009698 0.071595778
[75,] 0.482573619 0.334009698
[76,] 0.377957615 0.482573619
[77,] 0.544111642 0.377957615
[78,] 0.612011501 0.544111642
[79,] 0.524983616 0.612011501
[80,] 0.180365727 0.524983616
[81,] 0.097079935 0.180365727
[82,] 0.212465867 0.097079935
[83,] 0.150927845 0.212465867
[84,] 0.017330312 0.150927845
[85,] -0.034977690 0.017330312
[86,] 0.046562269 -0.034977690
[87,] 0.046562269 0.046562269
[88,] 0.298872259 0.046562269
[89,] 0.298872259 0.298872259
[90,] 0.150306351 0.298872259
[91,] 0.208976189 0.150306351
[92,] 0.379538307 0.208976189
[93,] 0.491180158 0.379538307
[94,] 0.597334082 0.491180158
[95,] 0.511178119 0.597334082
[96,] 0.383486069 0.511178119
[97,] 0.449638109 0.383486069
[98,] 0.331174092 0.449638109
[99,] 0.139532190 0.331174092
[100,] 0.007222149 0.139532190
[101,] -0.019597978 0.007222149
[102,] -0.071905980 -0.019597978
[103,] 0.008094072 -0.071905980
[104,] -0.234983910 0.008094072
[105,] -0.174111884 -0.234983910
[106,] -0.372573708 -0.174111884
[107,] -0.601803677 -0.372573708
[108,] -0.924423368 -0.601803677
[109,] -0.631905568 -0.924423368
[110,] -0.542671521 -0.631905568
[111,] -0.555643636 -0.542671521
[112,] -0.617845668 -0.555643636
[113,] -0.357177580 -0.617845668
[114,] -0.539587474 -0.357177580
[115,] -0.651021669 -0.539587474
[116,] -0.545531754 -0.651021669
[117,] -0.464865706 -0.545531754
[118,] -0.391017848 -0.464865706
[119,] -0.233427844 -0.391017848
[120,] -0.407066059 -0.233427844
[121,] -0.546401946 -0.407066059
[122,] -0.291684102 -0.546401946
[123,] -0.338504177 -0.291684102
[124,] -0.496302094 -0.338504177
[125,] -0.782458057 -0.496302094
[126,] -0.610567662 -0.782458057
[127,] -0.546409897 -0.610567662
[128,] -0.565745836 -0.546409897
[129,] -0.640255973 -0.565745836
[130,] -0.578717950 -0.640255973
[131,] -0.400048163 -0.578717950
[132,] -0.359840353 -0.400048163
[133,] -0.358718002 -0.359840353
[134,] -0.107283910 -0.358718002
[135,] 0.057996207 -0.107283910
[136,] 0.023276323 0.057996207
[137,] -0.182213591 0.023276323
[138,] -0.362462083 -0.182213591
[139,] -0.342877704 -0.362462083
[140,] 0.147892276 -0.342877704
[141,] 0.157122296 0.147892276
[142,] 0.057994219 0.157122296
[143,] -0.068367566 0.057994219
[144,] -0.111445548 -0.068367566
[145,] -0.169241477 -0.111445548
[146,] -0.016059564 -0.169241477
[147,] -0.002213540 -0.016059564
[148,] 0.012506395 -0.002213540
[149,] 0.270304312 0.012506395
[150,] 0.277788499 0.270304312
[151,] 0.145480446 0.277788499
[152,] 0.184816333 0.145480446
[153,] 0.094046354 0.184816333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.087070086 -0.061916466
2 -0.256966206 -0.087070086
3 -0.382456069 -0.256966206
4 -0.453224112 -0.382456069
5 -0.327736237 -0.453224112
6 -0.418424903 -0.327736237
7 -0.515888461 -0.418424903
8 -0.295888513 -0.515888461
9 -0.181168578 -0.295888513
10 -0.120504465 -0.181168578
11 0.234005723 -0.120504465
12 -0.099840250 0.234005723
13 -0.240921970 -0.099840250
14 0.185896169 -0.240921970
15 0.301280113 0.185896169
16 0.557328222 0.301280113
17 0.385892194 0.557328222
18 0.257534147 0.385892194
19 0.110964500 0.257534147
20 0.136910717 0.110964500
21 0.155370758 0.136910717
22 0.056244668 0.155370758
23 0.081734531 0.056244668
24 0.200860569 0.081734531
25 0.268760429 0.200860569
26 0.201734480 0.268760429
27 0.463276477 0.201734480
28 0.432050247 0.463276477
29 0.539536423 0.432050247
30 0.415586467 0.539536423
31 0.498872259 0.415586467
32 0.502614353 0.498872259
33 0.680412218 0.502614353
34 0.785028222 0.680412218
35 0.631846309 0.785028222
36 0.523490199 0.631846309
37 0.556462262 0.523490199
38 0.504154260 0.556462262
39 0.394256255 0.504154260
40 0.368768379 0.394256255
41 0.145690346 0.368768379
42 0.197998349 0.145690346
43 0.226356395 0.197998349
44 0.331844322 0.226356395
45 0.249432440 0.331844322
46 0.270306299 0.249432440
47 0.180410230 0.270306299
48 0.149432440 0.180410230
49 0.002614353 0.149432440
50 0.207230357 0.002614353
51 0.064818372 0.207230357
52 0.161742276 0.064818372
53 0.222408376 0.161742276
54 0.070100374 0.222408376
55 0.180204305 0.070100374
56 0.084820309 0.180204305
57 -0.104411751 0.084820309
58 -0.134515630 -0.104411751
59 0.235380490 -0.134515630
60 -0.259131583 0.235380490
61 -0.225285610 -0.259131583
62 -0.486825620 -0.225285610
63 -0.335389540 -0.486825620
64 -0.258259660 -0.335389540
65 -0.385951710 -0.258259660
66 -0.392313547 -0.385951710
67 -0.399797734 -0.392313547
68 -0.070565778 -0.399797734
69 0.208562351 -0.070565778
70 -0.025285610 0.208562351
71 -0.289693804 -0.025285610
72 0.056003921 -0.289693804
73 0.071595778 0.056003921
74 0.334009698 0.071595778
75 0.482573619 0.334009698
76 0.377957615 0.482573619
77 0.544111642 0.377957615
78 0.612011501 0.544111642
79 0.524983616 0.612011501
80 0.180365727 0.524983616
81 0.097079935 0.180365727
82 0.212465867 0.097079935
83 0.150927845 0.212465867
84 0.017330312 0.150927845
85 -0.034977690 0.017330312
86 0.046562269 -0.034977690
87 0.046562269 0.046562269
88 0.298872259 0.046562269
89 0.298872259 0.298872259
90 0.150306351 0.298872259
91 0.208976189 0.150306351
92 0.379538307 0.208976189
93 0.491180158 0.379538307
94 0.597334082 0.491180158
95 0.511178119 0.597334082
96 0.383486069 0.511178119
97 0.449638109 0.383486069
98 0.331174092 0.449638109
99 0.139532190 0.331174092
100 0.007222149 0.139532190
101 -0.019597978 0.007222149
102 -0.071905980 -0.019597978
103 0.008094072 -0.071905980
104 -0.234983910 0.008094072
105 -0.174111884 -0.234983910
106 -0.372573708 -0.174111884
107 -0.601803677 -0.372573708
108 -0.924423368 -0.601803677
109 -0.631905568 -0.924423368
110 -0.542671521 -0.631905568
111 -0.555643636 -0.542671521
112 -0.617845668 -0.555643636
113 -0.357177580 -0.617845668
114 -0.539587474 -0.357177580
115 -0.651021669 -0.539587474
116 -0.545531754 -0.651021669
117 -0.464865706 -0.545531754
118 -0.391017848 -0.464865706
119 -0.233427844 -0.391017848
120 -0.407066059 -0.233427844
121 -0.546401946 -0.407066059
122 -0.291684102 -0.546401946
123 -0.338504177 -0.291684102
124 -0.496302094 -0.338504177
125 -0.782458057 -0.496302094
126 -0.610567662 -0.782458057
127 -0.546409897 -0.610567662
128 -0.565745836 -0.546409897
129 -0.640255973 -0.565745836
130 -0.578717950 -0.640255973
131 -0.400048163 -0.578717950
132 -0.359840353 -0.400048163
133 -0.358718002 -0.359840353
134 -0.107283910 -0.358718002
135 0.057996207 -0.107283910
136 0.023276323 0.057996207
137 -0.182213591 0.023276323
138 -0.362462083 -0.182213591
139 -0.342877704 -0.362462083
140 0.147892276 -0.342877704
141 0.157122296 0.147892276
142 0.057994219 0.157122296
143 -0.068367566 0.057994219
144 -0.111445548 -0.068367566
145 -0.169241477 -0.111445548
146 -0.016059564 -0.169241477
147 -0.002213540 -0.016059564
148 0.012506395 -0.002213540
149 0.270304312 0.012506395
150 0.277788499 0.270304312
151 0.145480446 0.277788499
152 0.184816333 0.145480446
153 0.094046354 0.184816333
> 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/7ye501353065292.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/8o4461353065292.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/9zip01353065292.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/10mwd11353065292.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11b2fw1353065292.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/128ytr1353065292.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13nzee1353065292.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14qbrn1353065292.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/1546np1353065292.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/166y6k1353065292.tab")
+ }
>
> try(system("convert tmp/1cll61353065292.ps tmp/1cll61353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r5ps1353065292.ps tmp/2r5ps1353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/31upw1353065292.ps tmp/31upw1353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/4afpe1353065292.ps tmp/4afpe1353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/5etei1353065292.ps tmp/5etei1353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/67m0e1353065292.ps tmp/67m0e1353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ye501353065292.ps tmp/7ye501353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/8o4461353065292.ps tmp/8o4461353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zip01353065292.ps tmp/9zip01353065292.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mwd11353065292.ps tmp/10mwd11353065292.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.572 1.287 8.879