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(2.6
+ ,2.8
+ ,2.5
+ ,2.5
+ ,2.6
+ ,2.8
+ ,2.4
+ ,2.6
+ ,2.6
+ ,2.9
+ ,2.6
+ ,2.6
+ ,2.0
+ ,2.3
+ ,2.2
+ ,2.4
+ ,2.2
+ ,2.3
+ ,2.2
+ ,2.4
+ ,2.6
+ ,2.8
+ ,2.7
+ ,2.4
+ ,2.9
+ ,3.2
+ ,3.0
+ ,2.6
+ ,3.1
+ ,3.4
+ ,3.1
+ ,2.7
+ ,3.3
+ ,3.7
+ ,3.4
+ ,2.7
+ ,3.3
+ ,3.6
+ ,3.4
+ ,2.7
+ ,3.2
+ ,3.5
+ ,3.2
+ ,2.7
+ ,3.7
+ ,3.8
+ ,3.4
+ ,3.0
+ ,3.4
+ ,3.6
+ ,3.1
+ ,3.0
+ ,3.4
+ ,3.6
+ ,3.0
+ ,3.0
+ ,3.4
+ ,3.6
+ ,3.1
+ ,2.5
+ ,4.0
+ ,3.8
+ ,3.3
+ ,2.6
+ ,3.4
+ ,3.7
+ ,3.3
+ ,2.7
+ ,3.1
+ ,3.3
+ ,2.9
+ ,2.7
+ ,3.3
+ ,3.4
+ ,2.9
+ ,2.8
+ ,3.5
+ ,3.5
+ ,2.9
+ ,2.7
+ ,3.5
+ ,3.4
+ ,2.8
+ ,2.4
+ ,3.7
+ ,3.2
+ ,2.7
+ ,2.3
+ ,3.4
+ ,3.1
+ ,2.6
+ ,2.2
+ ,3.0
+ ,2.9
+ ,2.5
+ ,1.9
+ ,3.1
+ ,3.0
+ ,2.5
+ ,1.9
+ ,2.9
+ ,2.9
+ ,2.6
+ ,1.9
+ ,2.4
+ ,2.3
+ ,2.1
+ ,1.6
+ ,2.4
+ ,2.6
+ ,2.2
+ ,1.7
+ ,2.7
+ ,2.5
+ ,2.0
+ ,1.5
+ ,2.5
+ ,2.3
+ ,1.6
+ ,1.7
+ ,2.1
+ ,1.8
+ ,1.1
+ ,1.6
+ ,1.9
+ ,1.7
+ ,0.9
+ ,1.6
+ ,0.8
+ ,0.7
+ ,0.1
+ ,0.8
+ ,0.8
+ ,0.6
+ ,-0.1
+ ,0.9
+ ,0.3
+ ,0.3
+ ,-0.3
+ ,0.9
+ ,0.0
+ ,-0.1
+ ,-0.3
+ ,0.5
+ ,-0.9
+ ,-1.0
+ ,-0.6
+ ,-0.1
+ ,-1.0
+ ,-1.2
+ ,-0.6
+ ,-0.3
+ ,-0.7
+ ,-0.8
+ ,-0.2
+ ,-0.2
+ ,-1.7
+ ,-1.7
+ ,-0.7
+ ,-0.6
+ ,-1.0
+ ,-1.1
+ ,-0.1
+ ,-0.1
+ ,-0.2
+ ,-0.4
+ ,0.7
+ ,0.0
+ ,0.7
+ ,0.6
+ ,1.5
+ ,0.6
+ ,0.6
+ ,0.6
+ ,1.6
+ ,0.6
+ ,1.9
+ ,1.9
+ ,2.8
+ ,1.2
+ ,2.1
+ ,2.3
+ ,3.3
+ ,1.1
+ ,2.7
+ ,2.6
+ ,3.5
+ ,1.6
+ ,3.2
+ ,3.1
+ ,3.9
+ ,2.1
+ ,4.8
+ ,4.7
+ ,4.8
+ ,3.2
+ ,5.5
+ ,5.5
+ ,5.1
+ ,3.6
+ ,5.4
+ ,5.4
+ ,4.9
+ ,3.8
+ ,5.9
+ ,5.9
+ ,5.2
+ ,4.0
+ ,5.8
+ ,5.8
+ ,5.1
+ ,4.0
+ ,5.1
+ ,5.2
+ ,4.6
+ ,3.7
+ ,4.1
+ ,4.2
+ ,3.7
+ ,3.3
+ ,4.4
+ ,4.4
+ ,3.9
+ ,3.6
+ ,3.6
+ ,3.6
+ ,3.1
+ ,3.3
+ ,3.5
+ ,3.5
+ ,2.8
+ ,3.2
+ ,3.1
+ ,3.1
+ ,2.6
+ ,3.1
+ ,2.9
+ ,2.9
+ ,2.2
+ ,3.1
+ ,2.2
+ ,2.2
+ ,1.8
+ ,2.6
+ ,1.4
+ ,1.5
+ ,1.3
+ ,2.1
+ ,1.2
+ ,1.1
+ ,1.2
+ ,1.7
+ ,1.3
+ ,1.4
+ ,1.4
+ ,1.8
+ ,1.3
+ ,1.3
+ ,1.3
+ ,1.9
+ ,1.3
+ ,1.3
+ ,1.3
+ ,1.9
+ ,1.8
+ ,1.8
+ ,1.9
+ ,1.9
+ ,1.8
+ ,1.8
+ ,1.9
+ ,1.9
+ ,1.8
+ ,1.8
+ ,2.1
+ ,1.8
+ ,1.7
+ ,1.7
+ ,2.0
+ ,1.8
+ ,2.1
+ ,1.6
+ ,1.9
+ ,1.9
+ ,2.0
+ ,1.5
+ ,1.9
+ ,1.9
+ ,1.7
+ ,1.2
+ ,1.9
+ ,1.6
+ ,1.9
+ ,1.2
+ ,1.8
+ ,1.7
+ ,2.3
+ ,1.6
+ ,1.7
+ ,2.3
+ ,2.4
+ ,1.6
+ ,1.6
+ ,2.4
+ ,2.5
+ ,1.9
+ ,1.7
+ ,2.5
+ ,2.8
+ ,2.2
+ ,1.9
+ ,2.5
+ ,2.6
+ ,2.0
+ ,1.7
+ ,2.5
+ ,2.2
+ ,1.7
+ ,1.3
+ ,2.2
+ ,2.8
+ ,2.4
+ ,2.0
+ ,2.3
+ ,2.8
+ ,2.6
+ ,2.0
+ ,2.4
+ ,2.8
+ ,2.9
+ ,2.3
+ ,2.2
+ ,2.3
+ ,2.6
+ ,2.0
+ ,2.3
+ ,2.2
+ ,2.5
+ ,1.7
+ ,2.5
+ ,3.0
+ ,3.2
+ ,2.3
+ ,2.6
+ ,2.9
+ ,3.1
+ ,2.4
+ ,2.2
+ ,2.7
+ ,3.1
+ ,2.4
+ ,2.2
+ ,2.7
+ ,2.9
+ ,2.3
+ ,2.1
+ ,2.3
+ ,2.5
+ ,2.1
+ ,2.0
+ ,2.4
+ ,2.8
+ ,2.1
+ ,2.1
+ ,2.8
+ ,3.1
+ ,2.5
+ ,2.1
+ ,2.3
+ ,2.6
+ ,2.0
+ ,2.1
+ ,2.0
+ ,2.3
+ ,1.8
+ ,1.9
+ ,1.9
+ ,2.3
+ ,1.7
+ ,2.4
+ ,2.3
+ ,2.6
+ ,1.9
+ ,2.2
+ ,2.7
+ ,2.9
+ ,2.1
+ ,2.4
+ ,1.8
+ ,2.0
+ ,1.4
+ ,2.1
+ ,2.0
+ ,2.2
+ ,1.6
+ ,2.3
+ ,2.1
+ ,2.4
+ ,1.7
+ ,2.3
+ ,2.0
+ ,2.3
+ ,1.6
+ ,2.4
+ ,2.4
+ ,2.6
+ ,1.9
+ ,2.5
+ ,1.7
+ ,1.9
+ ,1.6
+ ,2.0
+ ,1.0
+ ,1.1
+ ,1.1
+ ,1.7
+ ,1.2
+ ,1.3
+ ,1.3
+ ,1.6
+ ,1.4
+ ,1.6
+ ,1.6
+ ,1.9
+ ,1.7
+ ,1.7
+ ,1.6
+ ,2.0
+ ,1.8
+ ,1.9
+ ,1.7
+ ,2.2
+ ,1.4
+ ,1.6
+ ,1.6
+ ,2.0
+ ,1.7
+ ,1.8
+ ,1.7
+ ,2.2
+ ,1.6
+ ,1.8
+ ,1.6
+ ,2.1
+ ,1.4
+ ,1.5
+ ,1.5
+ ,1.9
+ ,1.5
+ ,1.6
+ ,1.6
+ ,1.9
+ ,0.9
+ ,1.0
+ ,1.1
+ ,1.8
+ ,1.5
+ ,1.5
+ ,1.5
+ ,2.1
+ ,1.7
+ ,1.8
+ ,1.4
+ ,2.4
+ ,1.6
+ ,1.7
+ ,1.3
+ ,2.4
+ ,1.2
+ ,1.2
+ ,0.9
+ ,2.1
+ ,1.3
+ ,1.4
+ ,1.2
+ ,2.3
+ ,1.1
+ ,1.1
+ ,0.9
+ ,2.3
+ ,1.3
+ ,1.3
+ ,1.1
+ ,2.3
+ ,1.2
+ ,1.3
+ ,1.3
+ ,2.1
+ ,1.3
+ ,1.3
+ ,1.3
+ ,2.1
+ ,1.1
+ ,1.3
+ ,1.4
+ ,2.0
+ ,0.8
+ ,0.9
+ ,1.2
+ ,1.9
+ ,1.4
+ ,1.3
+ ,1.7
+ ,2.0
+ ,1.6
+ ,1.8
+ ,2.0
+ ,2.3
+ ,2.5
+ ,2.7
+ ,3.0
+ ,2.5
+ ,2.5
+ ,2.6
+ ,3.1
+ ,2.5
+ ,2.6
+ ,2.9
+ ,3.2
+ ,2.6
+ ,2.0
+ ,2.2
+ ,2.7
+ ,2.1
+ ,1.8
+ ,2.1
+ ,2.8
+ ,2.0
+ ,1.9
+ ,2.3
+ ,3.0
+ ,2.2
+ ,1.9
+ ,2.3
+ ,2.8
+ ,2.2
+ ,2.5
+ ,2.7
+ ,3.1
+ ,2.4
+ ,2.8
+ ,2.6
+ ,3.1
+ ,2.5
+ ,3.0
+ ,2.9
+ ,3.2
+ ,2.8
+ ,3.1
+ ,3.1
+ ,3.1
+ ,3.1
+ ,2.9
+ ,2.8
+ ,2.7
+ ,2.7
+ ,2.2
+ ,2.1
+ ,2.2
+ ,2.2
+ ,2.5
+ ,2.3
+ ,2.2
+ ,1.9
+ ,2.7
+ ,2.2
+ ,2.1
+ ,2.0
+ ,3.0
+ ,2.5
+ ,2.3
+ ,2.5
+ ,3.7
+ ,3.1
+ ,2.5
+ ,2.5
+ ,3.7
+ ,3.0
+ ,2.3
+ ,2.4
+ ,4.0
+ ,3.4
+ ,2.6
+ ,2.5
+ ,3.5
+ ,2.9
+ ,2.3
+ ,2.0
+ ,1.7
+ ,2.8
+ ,2.0
+ ,2.0
+ ,3.0
+ ,2.7
+ ,1.8
+ ,2.1
+ ,2.4
+ ,2.2
+ ,1.4
+ ,1.7
+ ,2.3
+ ,2.1
+ ,1.5
+ ,1.7
+ ,2.5
+ ,2.2
+ ,1.4
+ ,1.9
+ ,2.1
+ ,1.9
+ ,1.2
+ ,1.9
+ ,0.3
+ ,1.8
+ ,1.2
+ ,1.8)
+ ,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 2.5 2.6 2.8
2 2.6 2.6 2.8
3 2.6 2.6 2.9
4 2.4 2.0 2.3
5 2.4 2.2 2.3
6 2.4 2.6 2.8
7 2.6 2.9 3.2
8 2.7 3.1 3.4
9 2.7 3.3 3.7
10 2.7 3.3 3.6
11 2.7 3.2 3.5
12 3.0 3.7 3.8
13 3.0 3.4 3.6
14 3.0 3.4 3.6
15 2.5 3.4 3.6
16 2.6 4.0 3.8
17 2.7 3.4 3.7
18 2.7 3.1 3.3
19 2.8 3.3 3.4
20 2.7 3.5 3.5
21 2.4 3.5 3.4
22 2.3 3.7 3.2
23 2.2 3.4 3.1
24 1.9 3.0 2.9
25 1.9 3.1 3.0
26 1.9 2.9 2.9
27 1.6 2.4 2.3
28 1.7 2.4 2.6
29 1.5 2.7 2.5
30 1.7 2.5 2.3
31 1.6 2.1 1.8
32 1.6 1.9 1.7
33 0.8 0.8 0.7
34 0.9 0.8 0.6
35 0.9 0.3 0.3
36 0.5 0.0 -0.1
37 -0.1 -0.9 -1.0
38 -0.3 -1.0 -1.2
39 -0.2 -0.7 -0.8
40 -0.6 -1.7 -1.7
41 -0.1 -1.0 -1.1
42 0.0 -0.2 -0.4
43 0.6 0.7 0.6
44 0.6 0.6 0.6
45 1.2 1.9 1.9
46 1.1 2.1 2.3
47 1.6 2.7 2.6
48 2.1 3.2 3.1
49 3.2 4.8 4.7
50 3.6 5.5 5.5
51 3.8 5.4 5.4
52 4.0 5.9 5.9
53 4.0 5.8 5.8
54 3.7 5.1 5.2
55 3.3 4.1 4.2
56 3.6 4.4 4.4
57 3.3 3.6 3.6
58 3.2 3.5 3.5
59 3.1 3.1 3.1
60 3.1 2.9 2.9
61 2.6 2.2 2.2
62 2.1 1.4 1.5
63 1.7 1.2 1.1
64 1.8 1.3 1.4
65 1.9 1.3 1.3
66 1.9 1.3 1.3
67 1.9 1.8 1.8
68 1.9 1.8 1.8
69 1.8 1.8 1.8
70 1.8 1.7 1.7
71 1.9 2.1 1.6
72 1.9 2.0 1.5
73 1.6 1.7 1.2
74 1.7 1.9 1.2
75 2.3 2.3 1.6
76 2.4 2.4 1.6
77 2.5 2.5 1.9
78 2.5 2.8 2.2
79 2.5 2.6 2.0
80 2.2 2.2 1.7
81 2.3 2.8 2.4
82 2.4 2.8 2.6
83 2.2 2.8 2.9
84 2.3 2.3 2.6
85 2.5 2.2 2.5
86 2.6 3.0 3.2
87 2.2 2.9 3.1
88 2.2 2.7 3.1
89 2.1 2.7 2.9
90 2.0 2.3 2.5
91 2.1 2.4 2.8
92 2.1 2.8 3.1
93 2.1 2.3 2.6
94 1.9 2.0 2.3
95 2.4 1.9 2.3
96 2.2 2.3 2.6
97 2.4 2.7 2.9
98 2.1 1.8 2.0
99 2.3 2.0 2.2
100 2.3 2.1 2.4
101 2.4 2.0 2.3
102 2.5 2.4 2.6
103 2.0 1.7 1.9
104 1.7 1.0 1.1
105 1.6 1.2 1.3
106 1.9 1.4 1.6
107 2.0 1.7 1.7
108 2.2 1.8 1.9
109 2.0 1.4 1.6
110 2.2 1.7 1.8
111 2.1 1.6 1.8
112 1.9 1.4 1.5
113 1.9 1.5 1.6
114 1.8 0.9 1.0
115 2.1 1.5 1.5
116 2.4 1.7 1.8
117 2.4 1.6 1.7
118 2.1 1.2 1.2
119 2.3 1.3 1.4
120 2.3 1.1 1.1
121 2.3 1.3 1.3
122 2.1 1.2 1.3
123 2.1 1.3 1.3
124 2.0 1.1 1.3
125 1.9 0.8 0.9
126 2.0 1.4 1.3
127 2.3 1.6 1.8
128 2.5 2.5 2.7
129 2.5 2.5 2.6
130 2.6 2.6 2.9
131 2.1 2.0 2.2
132 2.0 1.8 2.1
133 2.2 1.9 2.3
134 2.2 1.9 2.3
135 2.4 2.5 2.7
136 2.5 2.8 2.6
137 2.8 3.0 2.9
138 3.1 3.1 3.1
139 2.7 2.9 2.8
140 2.2 2.2 2.1
141 1.9 2.5 2.3
142 2.0 2.7 2.2
143 2.5 3.0 2.5
144 2.5 3.7 3.1
145 2.4 3.7 3.0
146 2.5 4.0 3.4
147 2.0 3.5 2.9
148 2.0 1.7 2.8
149 2.1 3.0 2.7
150 1.7 2.4 2.2
151 1.7 2.3 2.1
152 1.9 2.5 2.2
153 1.9 2.1 1.9
154 1.8 0.3 1.8
Gezondheidsindex_Belgie
1 2.5
2 2.4
3 2.6
4 2.2
5 2.2
6 2.7
7 3.0
8 3.1
9 3.4
10 3.4
11 3.2
12 3.4
13 3.1
14 3.0
15 3.1
16 3.3
17 3.3
18 2.9
19 2.9
20 2.9
21 2.8
22 2.7
23 2.6
24 2.5
25 2.5
26 2.6
27 2.1
28 2.2
29 2.0
30 1.6
31 1.1
32 0.9
33 0.1
34 -0.1
35 -0.3
36 -0.3
37 -0.6
38 -0.6
39 -0.2
40 -0.7
41 -0.1
42 0.7
43 1.5
44 1.6
45 2.8
46 3.3
47 3.5
48 3.9
49 4.8
50 5.1
51 4.9
52 5.2
53 5.1
54 4.6
55 3.7
56 3.9
57 3.1
58 2.8
59 2.6
60 2.2
61 1.8
62 1.3
63 1.2
64 1.4
65 1.3
66 1.3
67 1.9
68 1.9
69 2.1
70 2.0
71 1.9
72 1.9
73 1.9
74 1.8
75 1.7
76 1.6
77 1.7
78 1.9
79 1.7
80 1.3
81 2.0
82 2.0
83 2.3
84 2.0
85 1.7
86 2.3
87 2.4
88 2.4
89 2.3
90 2.1
91 2.1
92 2.5
93 2.0
94 1.8
95 1.7
96 1.9
97 2.1
98 1.4
99 1.6
100 1.7
101 1.6
102 1.9
103 1.6
104 1.1
105 1.3
106 1.6
107 1.6
108 1.7
109 1.6
110 1.7
111 1.6
112 1.5
113 1.6
114 1.1
115 1.5
116 1.4
117 1.3
118 0.9
119 1.2
120 0.9
121 1.1
122 1.3
123 1.3
124 1.4
125 1.2
126 1.7
127 2.0
128 3.0
129 3.1
130 3.2
131 2.7
132 2.8
133 3.0
134 2.8
135 3.1
136 3.1
137 3.2
138 3.1
139 2.7
140 2.2
141 2.2
142 2.1
143 2.3
144 2.5
145 2.3
146 2.6
147 2.3
148 2.0
149 1.8
150 1.4
151 1.5
152 1.4
153 1.2
154 1.2
> 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,] 3.110526e-03 6.221051e-03 9.968895e-01
[2,] 5.270236e-04 1.054047e-03 9.994730e-01
[3,] 7.000125e-05 1.400025e-04 9.999300e-01
[4,] 1.429937e-05 2.859875e-05 9.999857e-01
[5,] 1.601165e-06 3.202329e-06 9.999984e-01
[6,] 4.862081e-06 9.724162e-06 9.999951e-01
[7,] 2.243125e-06 4.486249e-06 9.999978e-01
[8,] 3.607100e-07 7.214200e-07 9.999996e-01
[9,] 2.248218e-04 4.496436e-04 9.997752e-01
[10,] 2.767920e-04 5.535840e-04 9.997232e-01
[11,] 1.229839e-04 2.459678e-04 9.998770e-01
[12,] 4.196991e-05 8.393982e-05 9.999580e-01
[13,] 1.492870e-05 2.985741e-05 9.999851e-01
[14,] 5.866450e-06 1.173290e-05 9.999941e-01
[15,] 1.380655e-05 2.761310e-05 9.999862e-01
[16,] 5.342869e-06 1.068574e-05 9.999947e-01
[17,] 4.521551e-06 9.043102e-06 9.999955e-01
[18,] 9.274350e-05 1.854870e-04 9.999073e-01
[19,] 6.481785e-04 1.296357e-03 9.993518e-01
[20,] 1.778780e-03 3.557561e-03 9.982212e-01
[21,] 2.717367e-03 5.434734e-03 9.972826e-01
[22,] 1.288263e-02 2.576526e-02 9.871174e-01
[23,] 1.899837e-02 3.799674e-02 9.810016e-01
[24,] 1.443055e-02 2.886110e-02 9.855694e-01
[25,] 1.296301e-02 2.592602e-02 9.870370e-01
[26,] 9.793537e-03 1.958707e-02 9.902065e-01
[27,] 9.060685e-03 1.812137e-02 9.909393e-01
[28,] 6.937973e-03 1.387595e-02 9.930620e-01
[29,] 4.851986e-03 9.703972e-03 9.951480e-01
[30,] 3.593123e-03 7.186245e-03 9.964069e-01
[31,] 2.812610e-03 5.625220e-03 9.971874e-01
[32,] 2.376590e-03 4.753180e-03 9.976234e-01
[33,] 2.804311e-03 5.608622e-03 9.971957e-01
[34,] 2.732619e-03 5.465237e-03 9.972674e-01
[35,] 2.709779e-03 5.419559e-03 9.972902e-01
[36,] 3.289496e-03 6.578992e-03 9.967105e-01
[37,] 3.757184e-03 7.514367e-03 9.962428e-01
[38,] 4.831172e-03 9.662343e-03 9.951688e-01
[39,] 7.537250e-03 1.507450e-02 9.924628e-01
[40,] 6.519114e-02 1.303823e-01 9.348089e-01
[41,] 9.323950e-02 1.864790e-01 9.067605e-01
[42,] 1.183137e-01 2.366273e-01 8.816863e-01
[43,] 1.150822e-01 2.301645e-01 8.849178e-01
[44,] 9.306322e-02 1.861264e-01 9.069368e-01
[45,] 8.433494e-02 1.686699e-01 9.156651e-01
[46,] 6.866693e-02 1.373339e-01 9.313331e-01
[47,] 5.889993e-02 1.177999e-01 9.411001e-01
[48,] 4.976111e-02 9.952221e-02 9.502389e-01
[49,] 5.160288e-02 1.032058e-01 9.483971e-01
[50,] 1.134285e-01 2.268570e-01 8.865715e-01
[51,] 2.820776e-01 5.641552e-01 7.179224e-01
[52,] 4.411701e-01 8.823403e-01 5.588299e-01
[53,] 7.043886e-01 5.912227e-01 2.956114e-01
[54,] 9.112390e-01 1.775219e-01 8.876096e-02
[55,] 9.639516e-01 7.209672e-02 3.604836e-02
[56,] 9.737915e-01 5.241706e-02 2.620853e-02
[57,] 9.833389e-01 3.332210e-02 1.666105e-02
[58,] 9.829616e-01 3.407688e-02 1.703844e-02
[59,] 9.869661e-01 2.606774e-02 1.303387e-02
[60,] 9.895754e-01 2.084922e-02 1.042461e-02
[61,] 9.889655e-01 2.206903e-02 1.103452e-02
[62,] 9.882846e-01 2.343078e-02 1.171539e-02
[63,] 9.890223e-01 2.195547e-02 1.097774e-02
[64,] 9.899055e-01 2.018909e-02 1.009454e-02
[65,] 9.955160e-01 8.968001e-03 4.484001e-03
[66,] 9.978543e-01 4.291434e-03 2.145717e-03
[67,] 9.992907e-01 1.418521e-03 7.092606e-04
[68,] 9.997751e-01 4.497210e-04 2.248605e-04
[69,] 9.998814e-01 2.372569e-04 1.186285e-04
[70,] 9.999344e-01 1.312646e-04 6.563232e-05
[71,] 9.999563e-01 8.735834e-05 4.367917e-05
[72,] 9.999533e-01 9.339874e-05 4.669937e-05
[73,] 9.999623e-01 7.544524e-05 3.772262e-05
[74,] 9.999484e-01 1.031598e-04 5.157992e-05
[75,] 9.999167e-01 1.666545e-04 8.332726e-05
[76,] 9.998773e-01 2.453599e-04 1.226799e-04
[77,] 9.998326e-01 3.347955e-04 1.673978e-04
[78,] 9.997452e-01 5.095854e-04 2.547927e-04
[79,] 9.997880e-01 4.240339e-04 2.120169e-04
[80,] 9.997659e-01 4.682044e-04 2.341022e-04
[81,] 9.997116e-01 5.768132e-04 2.884066e-04
[82,] 9.996135e-01 7.729064e-04 3.864532e-04
[83,] 9.995523e-01 8.954808e-04 4.477404e-04
[84,] 9.994623e-01 1.075412e-03 5.377060e-04
[85,] 9.992862e-01 1.427608e-03 7.138042e-04
[86,] 9.993408e-01 1.318377e-03 6.591886e-04
[87,] 9.990886e-01 1.822801e-03 9.114005e-04
[88,] 9.990289e-01 1.942113e-03 9.710565e-04
[89,] 9.990688e-01 1.862454e-03 9.312268e-04
[90,] 9.985899e-01 2.820280e-03 1.410140e-03
[91,] 9.979332e-01 4.133669e-03 2.066835e-03
[92,] 9.970840e-01 5.832065e-03 2.916033e-03
[93,] 9.964833e-01 7.033334e-03 3.516667e-03
[94,] 9.953864e-01 9.227105e-03 4.613552e-03
[95,] 9.955809e-01 8.838242e-03 4.419121e-03
[96,] 9.958048e-01 8.390403e-03 4.195202e-03
[97,] 9.942285e-01 1.154303e-02 5.771514e-03
[98,] 9.939761e-01 1.204783e-02 6.023914e-03
[99,] 9.953627e-01 9.274621e-03 4.637311e-03
[100,] 9.945873e-01 1.082541e-02 5.412707e-03
[101,] 9.929218e-01 1.415640e-02 7.078199e-03
[102,] 9.911534e-01 1.769315e-02 8.846576e-03
[103,] 9.892284e-01 2.154326e-02 1.077163e-02
[104,] 9.871402e-01 2.571961e-02 1.285980e-02
[105,] 9.832831e-01 3.343372e-02 1.671686e-02
[106,] 9.800618e-01 3.987648e-02 1.993824e-02
[107,] 9.766243e-01 4.675146e-02 2.337573e-02
[108,] 9.753129e-01 4.937418e-02 2.468709e-02
[109,] 9.701337e-01 5.973263e-02 2.986632e-02
[110,] 9.762659e-01 4.746813e-02 2.373406e-02
[111,] 9.839328e-01 3.213449e-02 1.606724e-02
[112,] 9.830304e-01 3.393918e-02 1.696959e-02
[113,] 9.883172e-01 2.336550e-02 1.168275e-02
[114,] 9.956910e-01 8.617943e-03 4.308971e-03
[115,] 9.985308e-01 2.938331e-03 1.469166e-03
[116,] 9.986799e-01 2.640143e-03 1.320072e-03
[117,] 9.989316e-01 2.136782e-03 1.068391e-03
[118,] 9.988269e-01 2.346279e-03 1.173139e-03
[119,] 9.991370e-01 1.725986e-03 8.629928e-04
[120,] 9.991580e-01 1.683911e-03 8.419554e-04
[121,] 9.996712e-01 6.575782e-04 3.287891e-04
[122,] 9.993821e-01 1.235804e-03 6.179021e-04
[123,] 9.988869e-01 2.226203e-03 1.113101e-03
[124,] 9.979745e-01 4.050952e-03 2.025476e-03
[125,] 9.966737e-01 6.652575e-03 3.326288e-03
[126,] 9.962395e-01 7.521078e-03 3.760539e-03
[127,] 9.955832e-01 8.833665e-03 4.416832e-03
[128,] 9.944013e-01 1.119732e-02 5.598662e-03
[129,] 9.945556e-01 1.088883e-02 5.444415e-03
[130,] 9.934384e-01 1.312315e-02 6.561573e-03
[131,] 9.883456e-01 2.330882e-02 1.165441e-02
[132,] 9.925056e-01 1.498875e-02 7.494377e-03
[133,] 9.930028e-01 1.399445e-02 6.997225e-03
[134,] 9.872998e-01 2.540032e-02 1.270016e-02
[135,] 9.866470e-01 2.670606e-02 1.335303e-02
[136,] 9.846126e-01 3.077488e-02 1.538744e-02
[137,] 9.781235e-01 4.375306e-02 2.187653e-02
[138,] 9.702836e-01 5.943288e-02 2.971644e-02
[139,] 9.639001e-01 7.219986e-02 3.609993e-02
[140,] 9.730176e-01 5.396473e-02 2.698237e-02
[141,] 9.310439e-01 1.379121e-01 6.895605e-02
> postscript(file="/var/fisher/rcomp/tmp/18ezr1353062132.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/2nnvd1353062132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3h4mg1353062132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4oon31353062132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5fdfq1353062132.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.094046354 0.184816333 0.145480446 0.277788499 0.270304312 0.012506395
7 8 9 10 11 12
-0.002213540 -0.016059564 -0.169241477 -0.111445548 -0.068367566 0.057994219
13 14 15 16 17 18
0.157122296 0.147892276 -0.342877704 -0.362462083 -0.182213591 0.023276323
19 20 21 22 23 24
0.057996207 -0.107283910 -0.358718002 -0.359840353 -0.400048163 -0.578717950
25 26 27 28 29 30
-0.640255973 -0.565745836 -0.546409897 -0.610567662 -0.782458057 -0.496302094
31 32 33 34 35 36
-0.338504177 -0.291684102 -0.546401946 -0.407066059 -0.233427844 -0.391017848
37 38 39 40 41 42
-0.464865706 -0.545531754 -0.651021669 -0.539587474 -0.357177580 -0.617845668
43 44 45 46 47 48
-0.555643636 -0.542671521 -0.631905568 -0.924423368 -0.601803677 -0.372573708
49 50 51 52 53 54
-0.174111884 -0.234983910 0.008094072 -0.071905980 -0.019597978 0.007222149
55 56 57 58 59 60
0.139532190 0.331174092 0.449638109 0.383486069 0.511178119 0.597334082
61 62 63 64 65 66
0.491180158 0.379538307 0.208976189 0.150306351 0.298872259 0.298872259
67 68 69 70 71 72
0.046562269 0.046562269 -0.034977690 0.017330312 0.150927845 0.212465867
73 74 75 76 77 78
0.097079935 0.180365727 0.524983616 0.612011501 0.544111642 0.377957615
79 80 81 82 83 84
0.482573619 0.334009698 0.071595778 0.056003921 -0.289693804 -0.025285610
85 86 87 88 89 90
0.208562351 -0.070565778 -0.399797734 -0.392313547 -0.385951710 -0.258259660
91 92 93 94 95 96
-0.335389540 -0.486825620 -0.225285610 -0.259131583 0.235380490 -0.134515630
97 98 99 100 101 102
-0.104411751 0.084820309 0.180204305 0.070100374 0.222408376 0.161742276
103 104 105 106 107 108
0.064818372 0.207230357 0.002614353 0.149432440 0.180410230 0.270306299
109 110 111 112 113 114
0.249432440 0.331844322 0.226356395 0.197998349 0.145690346 0.368768379
115 116 117 118 119 120
0.394256255 0.504154260 0.556462262 0.523490199 0.631846309 0.785028222
121 122 123 124 125 126
0.680412218 0.502614353 0.498872259 0.415586467 0.539536423 0.432050247
127 128 129 130 131 132
0.463276477 0.201734480 0.268760429 0.200860569 0.081734531 0.056244668
133 134 135 136 137 138
0.155370758 0.136910717 0.110964500 0.257534147 0.385892194 0.557328222
139 140 141 142 143 144
0.301280113 0.185896169 -0.240921970 -0.099840250 0.234005723 -0.120504465
145 146 147 148 149 150
-0.181168578 -0.295888513 -0.515888461 -0.418424903 -0.327736237 -0.453224112
151 152 153 154
-0.382456069 -0.256966206 -0.087070086 -0.061916466
> postscript(file="/var/fisher/rcomp/tmp/63faz1353062132.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 0.094046354 NA
1 0.184816333 0.094046354
2 0.145480446 0.184816333
3 0.277788499 0.145480446
4 0.270304312 0.277788499
5 0.012506395 0.270304312
6 -0.002213540 0.012506395
7 -0.016059564 -0.002213540
8 -0.169241477 -0.016059564
9 -0.111445548 -0.169241477
10 -0.068367566 -0.111445548
11 0.057994219 -0.068367566
12 0.157122296 0.057994219
13 0.147892276 0.157122296
14 -0.342877704 0.147892276
15 -0.362462083 -0.342877704
16 -0.182213591 -0.362462083
17 0.023276323 -0.182213591
18 0.057996207 0.023276323
19 -0.107283910 0.057996207
20 -0.358718002 -0.107283910
21 -0.359840353 -0.358718002
22 -0.400048163 -0.359840353
23 -0.578717950 -0.400048163
24 -0.640255973 -0.578717950
25 -0.565745836 -0.640255973
26 -0.546409897 -0.565745836
27 -0.610567662 -0.546409897
28 -0.782458057 -0.610567662
29 -0.496302094 -0.782458057
30 -0.338504177 -0.496302094
31 -0.291684102 -0.338504177
32 -0.546401946 -0.291684102
33 -0.407066059 -0.546401946
34 -0.233427844 -0.407066059
35 -0.391017848 -0.233427844
36 -0.464865706 -0.391017848
37 -0.545531754 -0.464865706
38 -0.651021669 -0.545531754
39 -0.539587474 -0.651021669
40 -0.357177580 -0.539587474
41 -0.617845668 -0.357177580
42 -0.555643636 -0.617845668
43 -0.542671521 -0.555643636
44 -0.631905568 -0.542671521
45 -0.924423368 -0.631905568
46 -0.601803677 -0.924423368
47 -0.372573708 -0.601803677
48 -0.174111884 -0.372573708
49 -0.234983910 -0.174111884
50 0.008094072 -0.234983910
51 -0.071905980 0.008094072
52 -0.019597978 -0.071905980
53 0.007222149 -0.019597978
54 0.139532190 0.007222149
55 0.331174092 0.139532190
56 0.449638109 0.331174092
57 0.383486069 0.449638109
58 0.511178119 0.383486069
59 0.597334082 0.511178119
60 0.491180158 0.597334082
61 0.379538307 0.491180158
62 0.208976189 0.379538307
63 0.150306351 0.208976189
64 0.298872259 0.150306351
65 0.298872259 0.298872259
66 0.046562269 0.298872259
67 0.046562269 0.046562269
68 -0.034977690 0.046562269
69 0.017330312 -0.034977690
70 0.150927845 0.017330312
71 0.212465867 0.150927845
72 0.097079935 0.212465867
73 0.180365727 0.097079935
74 0.524983616 0.180365727
75 0.612011501 0.524983616
76 0.544111642 0.612011501
77 0.377957615 0.544111642
78 0.482573619 0.377957615
79 0.334009698 0.482573619
80 0.071595778 0.334009698
81 0.056003921 0.071595778
82 -0.289693804 0.056003921
83 -0.025285610 -0.289693804
84 0.208562351 -0.025285610
85 -0.070565778 0.208562351
86 -0.399797734 -0.070565778
87 -0.392313547 -0.399797734
88 -0.385951710 -0.392313547
89 -0.258259660 -0.385951710
90 -0.335389540 -0.258259660
91 -0.486825620 -0.335389540
92 -0.225285610 -0.486825620
93 -0.259131583 -0.225285610
94 0.235380490 -0.259131583
95 -0.134515630 0.235380490
96 -0.104411751 -0.134515630
97 0.084820309 -0.104411751
98 0.180204305 0.084820309
99 0.070100374 0.180204305
100 0.222408376 0.070100374
101 0.161742276 0.222408376
102 0.064818372 0.161742276
103 0.207230357 0.064818372
104 0.002614353 0.207230357
105 0.149432440 0.002614353
106 0.180410230 0.149432440
107 0.270306299 0.180410230
108 0.249432440 0.270306299
109 0.331844322 0.249432440
110 0.226356395 0.331844322
111 0.197998349 0.226356395
112 0.145690346 0.197998349
113 0.368768379 0.145690346
114 0.394256255 0.368768379
115 0.504154260 0.394256255
116 0.556462262 0.504154260
117 0.523490199 0.556462262
118 0.631846309 0.523490199
119 0.785028222 0.631846309
120 0.680412218 0.785028222
121 0.502614353 0.680412218
122 0.498872259 0.502614353
123 0.415586467 0.498872259
124 0.539536423 0.415586467
125 0.432050247 0.539536423
126 0.463276477 0.432050247
127 0.201734480 0.463276477
128 0.268760429 0.201734480
129 0.200860569 0.268760429
130 0.081734531 0.200860569
131 0.056244668 0.081734531
132 0.155370758 0.056244668
133 0.136910717 0.155370758
134 0.110964500 0.136910717
135 0.257534147 0.110964500
136 0.385892194 0.257534147
137 0.557328222 0.385892194
138 0.301280113 0.557328222
139 0.185896169 0.301280113
140 -0.240921970 0.185896169
141 -0.099840250 -0.240921970
142 0.234005723 -0.099840250
143 -0.120504465 0.234005723
144 -0.181168578 -0.120504465
145 -0.295888513 -0.181168578
146 -0.515888461 -0.295888513
147 -0.418424903 -0.515888461
148 -0.327736237 -0.418424903
149 -0.453224112 -0.327736237
150 -0.382456069 -0.453224112
151 -0.256966206 -0.382456069
152 -0.087070086 -0.256966206
153 -0.061916466 -0.087070086
154 NA -0.061916466
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.184816333 0.094046354
[2,] 0.145480446 0.184816333
[3,] 0.277788499 0.145480446
[4,] 0.270304312 0.277788499
[5,] 0.012506395 0.270304312
[6,] -0.002213540 0.012506395
[7,] -0.016059564 -0.002213540
[8,] -0.169241477 -0.016059564
[9,] -0.111445548 -0.169241477
[10,] -0.068367566 -0.111445548
[11,] 0.057994219 -0.068367566
[12,] 0.157122296 0.057994219
[13,] 0.147892276 0.157122296
[14,] -0.342877704 0.147892276
[15,] -0.362462083 -0.342877704
[16,] -0.182213591 -0.362462083
[17,] 0.023276323 -0.182213591
[18,] 0.057996207 0.023276323
[19,] -0.107283910 0.057996207
[20,] -0.358718002 -0.107283910
[21,] -0.359840353 -0.358718002
[22,] -0.400048163 -0.359840353
[23,] -0.578717950 -0.400048163
[24,] -0.640255973 -0.578717950
[25,] -0.565745836 -0.640255973
[26,] -0.546409897 -0.565745836
[27,] -0.610567662 -0.546409897
[28,] -0.782458057 -0.610567662
[29,] -0.496302094 -0.782458057
[30,] -0.338504177 -0.496302094
[31,] -0.291684102 -0.338504177
[32,] -0.546401946 -0.291684102
[33,] -0.407066059 -0.546401946
[34,] -0.233427844 -0.407066059
[35,] -0.391017848 -0.233427844
[36,] -0.464865706 -0.391017848
[37,] -0.545531754 -0.464865706
[38,] -0.651021669 -0.545531754
[39,] -0.539587474 -0.651021669
[40,] -0.357177580 -0.539587474
[41,] -0.617845668 -0.357177580
[42,] -0.555643636 -0.617845668
[43,] -0.542671521 -0.555643636
[44,] -0.631905568 -0.542671521
[45,] -0.924423368 -0.631905568
[46,] -0.601803677 -0.924423368
[47,] -0.372573708 -0.601803677
[48,] -0.174111884 -0.372573708
[49,] -0.234983910 -0.174111884
[50,] 0.008094072 -0.234983910
[51,] -0.071905980 0.008094072
[52,] -0.019597978 -0.071905980
[53,] 0.007222149 -0.019597978
[54,] 0.139532190 0.007222149
[55,] 0.331174092 0.139532190
[56,] 0.449638109 0.331174092
[57,] 0.383486069 0.449638109
[58,] 0.511178119 0.383486069
[59,] 0.597334082 0.511178119
[60,] 0.491180158 0.597334082
[61,] 0.379538307 0.491180158
[62,] 0.208976189 0.379538307
[63,] 0.150306351 0.208976189
[64,] 0.298872259 0.150306351
[65,] 0.298872259 0.298872259
[66,] 0.046562269 0.298872259
[67,] 0.046562269 0.046562269
[68,] -0.034977690 0.046562269
[69,] 0.017330312 -0.034977690
[70,] 0.150927845 0.017330312
[71,] 0.212465867 0.150927845
[72,] 0.097079935 0.212465867
[73,] 0.180365727 0.097079935
[74,] 0.524983616 0.180365727
[75,] 0.612011501 0.524983616
[76,] 0.544111642 0.612011501
[77,] 0.377957615 0.544111642
[78,] 0.482573619 0.377957615
[79,] 0.334009698 0.482573619
[80,] 0.071595778 0.334009698
[81,] 0.056003921 0.071595778
[82,] -0.289693804 0.056003921
[83,] -0.025285610 -0.289693804
[84,] 0.208562351 -0.025285610
[85,] -0.070565778 0.208562351
[86,] -0.399797734 -0.070565778
[87,] -0.392313547 -0.399797734
[88,] -0.385951710 -0.392313547
[89,] -0.258259660 -0.385951710
[90,] -0.335389540 -0.258259660
[91,] -0.486825620 -0.335389540
[92,] -0.225285610 -0.486825620
[93,] -0.259131583 -0.225285610
[94,] 0.235380490 -0.259131583
[95,] -0.134515630 0.235380490
[96,] -0.104411751 -0.134515630
[97,] 0.084820309 -0.104411751
[98,] 0.180204305 0.084820309
[99,] 0.070100374 0.180204305
[100,] 0.222408376 0.070100374
[101,] 0.161742276 0.222408376
[102,] 0.064818372 0.161742276
[103,] 0.207230357 0.064818372
[104,] 0.002614353 0.207230357
[105,] 0.149432440 0.002614353
[106,] 0.180410230 0.149432440
[107,] 0.270306299 0.180410230
[108,] 0.249432440 0.270306299
[109,] 0.331844322 0.249432440
[110,] 0.226356395 0.331844322
[111,] 0.197998349 0.226356395
[112,] 0.145690346 0.197998349
[113,] 0.368768379 0.145690346
[114,] 0.394256255 0.368768379
[115,] 0.504154260 0.394256255
[116,] 0.556462262 0.504154260
[117,] 0.523490199 0.556462262
[118,] 0.631846309 0.523490199
[119,] 0.785028222 0.631846309
[120,] 0.680412218 0.785028222
[121,] 0.502614353 0.680412218
[122,] 0.498872259 0.502614353
[123,] 0.415586467 0.498872259
[124,] 0.539536423 0.415586467
[125,] 0.432050247 0.539536423
[126,] 0.463276477 0.432050247
[127,] 0.201734480 0.463276477
[128,] 0.268760429 0.201734480
[129,] 0.200860569 0.268760429
[130,] 0.081734531 0.200860569
[131,] 0.056244668 0.081734531
[132,] 0.155370758 0.056244668
[133,] 0.136910717 0.155370758
[134,] 0.110964500 0.136910717
[135,] 0.257534147 0.110964500
[136,] 0.385892194 0.257534147
[137,] 0.557328222 0.385892194
[138,] 0.301280113 0.557328222
[139,] 0.185896169 0.301280113
[140,] -0.240921970 0.185896169
[141,] -0.099840250 -0.240921970
[142,] 0.234005723 -0.099840250
[143,] -0.120504465 0.234005723
[144,] -0.181168578 -0.120504465
[145,] -0.295888513 -0.181168578
[146,] -0.515888461 -0.295888513
[147,] -0.418424903 -0.515888461
[148,] -0.327736237 -0.418424903
[149,] -0.453224112 -0.327736237
[150,] -0.382456069 -0.453224112
[151,] -0.256966206 -0.382456069
[152,] -0.087070086 -0.256966206
[153,] -0.061916466 -0.087070086
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.184816333 0.094046354
2 0.145480446 0.184816333
3 0.277788499 0.145480446
4 0.270304312 0.277788499
5 0.012506395 0.270304312
6 -0.002213540 0.012506395
7 -0.016059564 -0.002213540
8 -0.169241477 -0.016059564
9 -0.111445548 -0.169241477
10 -0.068367566 -0.111445548
11 0.057994219 -0.068367566
12 0.157122296 0.057994219
13 0.147892276 0.157122296
14 -0.342877704 0.147892276
15 -0.362462083 -0.342877704
16 -0.182213591 -0.362462083
17 0.023276323 -0.182213591
18 0.057996207 0.023276323
19 -0.107283910 0.057996207
20 -0.358718002 -0.107283910
21 -0.359840353 -0.358718002
22 -0.400048163 -0.359840353
23 -0.578717950 -0.400048163
24 -0.640255973 -0.578717950
25 -0.565745836 -0.640255973
26 -0.546409897 -0.565745836
27 -0.610567662 -0.546409897
28 -0.782458057 -0.610567662
29 -0.496302094 -0.782458057
30 -0.338504177 -0.496302094
31 -0.291684102 -0.338504177
32 -0.546401946 -0.291684102
33 -0.407066059 -0.546401946
34 -0.233427844 -0.407066059
35 -0.391017848 -0.233427844
36 -0.464865706 -0.391017848
37 -0.545531754 -0.464865706
38 -0.651021669 -0.545531754
39 -0.539587474 -0.651021669
40 -0.357177580 -0.539587474
41 -0.617845668 -0.357177580
42 -0.555643636 -0.617845668
43 -0.542671521 -0.555643636
44 -0.631905568 -0.542671521
45 -0.924423368 -0.631905568
46 -0.601803677 -0.924423368
47 -0.372573708 -0.601803677
48 -0.174111884 -0.372573708
49 -0.234983910 -0.174111884
50 0.008094072 -0.234983910
51 -0.071905980 0.008094072
52 -0.019597978 -0.071905980
53 0.007222149 -0.019597978
54 0.139532190 0.007222149
55 0.331174092 0.139532190
56 0.449638109 0.331174092
57 0.383486069 0.449638109
58 0.511178119 0.383486069
59 0.597334082 0.511178119
60 0.491180158 0.597334082
61 0.379538307 0.491180158
62 0.208976189 0.379538307
63 0.150306351 0.208976189
64 0.298872259 0.150306351
65 0.298872259 0.298872259
66 0.046562269 0.298872259
67 0.046562269 0.046562269
68 -0.034977690 0.046562269
69 0.017330312 -0.034977690
70 0.150927845 0.017330312
71 0.212465867 0.150927845
72 0.097079935 0.212465867
73 0.180365727 0.097079935
74 0.524983616 0.180365727
75 0.612011501 0.524983616
76 0.544111642 0.612011501
77 0.377957615 0.544111642
78 0.482573619 0.377957615
79 0.334009698 0.482573619
80 0.071595778 0.334009698
81 0.056003921 0.071595778
82 -0.289693804 0.056003921
83 -0.025285610 -0.289693804
84 0.208562351 -0.025285610
85 -0.070565778 0.208562351
86 -0.399797734 -0.070565778
87 -0.392313547 -0.399797734
88 -0.385951710 -0.392313547
89 -0.258259660 -0.385951710
90 -0.335389540 -0.258259660
91 -0.486825620 -0.335389540
92 -0.225285610 -0.486825620
93 -0.259131583 -0.225285610
94 0.235380490 -0.259131583
95 -0.134515630 0.235380490
96 -0.104411751 -0.134515630
97 0.084820309 -0.104411751
98 0.180204305 0.084820309
99 0.070100374 0.180204305
100 0.222408376 0.070100374
101 0.161742276 0.222408376
102 0.064818372 0.161742276
103 0.207230357 0.064818372
104 0.002614353 0.207230357
105 0.149432440 0.002614353
106 0.180410230 0.149432440
107 0.270306299 0.180410230
108 0.249432440 0.270306299
109 0.331844322 0.249432440
110 0.226356395 0.331844322
111 0.197998349 0.226356395
112 0.145690346 0.197998349
113 0.368768379 0.145690346
114 0.394256255 0.368768379
115 0.504154260 0.394256255
116 0.556462262 0.504154260
117 0.523490199 0.556462262
118 0.631846309 0.523490199
119 0.785028222 0.631846309
120 0.680412218 0.785028222
121 0.502614353 0.680412218
122 0.498872259 0.502614353
123 0.415586467 0.498872259
124 0.539536423 0.415586467
125 0.432050247 0.539536423
126 0.463276477 0.432050247
127 0.201734480 0.463276477
128 0.268760429 0.201734480
129 0.200860569 0.268760429
130 0.081734531 0.200860569
131 0.056244668 0.081734531
132 0.155370758 0.056244668
133 0.136910717 0.155370758
134 0.110964500 0.136910717
135 0.257534147 0.110964500
136 0.385892194 0.257534147
137 0.557328222 0.385892194
138 0.301280113 0.557328222
139 0.185896169 0.301280113
140 -0.240921970 0.185896169
141 -0.099840250 -0.240921970
142 0.234005723 -0.099840250
143 -0.120504465 0.234005723
144 -0.181168578 -0.120504465
145 -0.295888513 -0.181168578
146 -0.515888461 -0.295888513
147 -0.418424903 -0.515888461
148 -0.327736237 -0.418424903
149 -0.453224112 -0.327736237
150 -0.382456069 -0.453224112
151 -0.256966206 -0.382456069
152 -0.087070086 -0.256966206
153 -0.061916466 -0.087070086
> 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/79b8z1353062132.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/8jv4g1353062132.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/9hass1353062132.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/10ced11353062132.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11crb21353062132.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12pep11353062132.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13a3ew1353062132.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14s8ty1353062132.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/15u6461353062132.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/16rov11353062132.tab")
+ }
>
> try(system("convert tmp/18ezr1353062132.ps tmp/18ezr1353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nnvd1353062132.ps tmp/2nnvd1353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/3h4mg1353062132.ps tmp/3h4mg1353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/4oon31353062132.ps tmp/4oon31353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fdfq1353062132.ps tmp/5fdfq1353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/63faz1353062132.ps tmp/63faz1353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/79b8z1353062132.ps tmp/79b8z1353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jv4g1353062132.ps tmp/8jv4g1353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hass1353062132.ps tmp/9hass1353062132.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ced11353062132.ps tmp/10ced11353062132.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.508 1.304 8.821