R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,5
+ ,5
+ ,5
+ ,1
+ ,5
+ ,5
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,5
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,5
+ ,3
+ ,5
+ ,1
+ ,5
+ ,5
+ ,2
+ ,1
+ ,3
+ ,5
+ ,3
+ ,2
+ ,5
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,5
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,5
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,5
+ ,4
+ ,5
+ ,1
+ ,5
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,5
+ ,4
+ ,2
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,1
+ ,5
+ ,5
+ ,4
+ ,3
+ ,3
+ ,3
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,2
+ ,4
+ ,3
+ ,4
+ ,3
+ ,4
+ ,2
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,3
+ ,4
+ ,5
+ ,5
+ ,1
+ ,4
+ ,4
+ ,5
+ ,5
+ ,5
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,5
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,5
+ ,1
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,2
+ ,5
+ ,5
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,3
+ ,4
+ ,4
+ ,3
+ ,4
+ ,1
+ ,5
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,5
+ ,2
+ ,4
+ ,1
+ ,5
+ ,3
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,1
+ ,3
+ ,4
+ ,4
+ ,3
+ ,4
+ ,2
+ ,4
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,5
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,3
+ ,5
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,5
+ ,5
+ ,4
+ ,5
+ ,4
+ ,3
+ ,4
+ ,2
+ ,3
+ ,3
+ ,5
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,3
+ ,5
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,3
+ ,3
+ ,2
+ ,2
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,5
+ ,4
+ ,1
+ ,4
+ ,4
+ ,1
+ ,3
+ ,2
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,5
+ ,4
+ ,5
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,3
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,3
+ ,4
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,3
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,5
+ ,3
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,3
+ ,3
+ ,1
+ ,4
+ ,3
+ ,5
+ ,5
+ ,4
+ ,1
+ ,5
+ ,5
+ ,4
+ ,4
+ ,5
+ ,2
+ ,5
+ ,5
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,5
+ ,4
+ ,4
+ ,1
+ ,5
+ ,4
+ ,5
+ ,4
+ ,4
+ ,2
+ ,4
+ ,3
+ ,4
+ ,4
+ ,3
+ ,1
+ ,4
+ ,5
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,1
+ ,5
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,2
+ ,5
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,5
+ ,5
+ ,4
+ ,2
+ ,4
+ ,4
+ ,5
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,5
+ ,5
+ ,4
+ ,1
+ ,4
+ ,5
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,3
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,5
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,5
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,5
+ ,4
+ ,1
+ ,4
+ ,4
+ ,5
+ ,5
+ ,5
+ ,1
+ ,5
+ ,4
+ ,4
+ ,3
+ ,4
+ ,3
+ ,4
+ ,4
+ ,4
+ ,5
+ ,4
+ ,1
+ ,5
+ ,4
+ ,3
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,5
+ ,4
+ ,4
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,2
+ ,4
+ ,4
+ ,4
+ ,2
+ ,3
+ ,5
+ ,5
+ ,1
+ ,5
+ ,5
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,5
+ ,5
+ ,5
+ ,2
+ ,5
+ ,5
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,5
+ ,5
+ ,4
+ ,2
+ ,4
+ ,4
+ ,5
+ ,5
+ ,5
+ ,1
+ ,5
+ ,5
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,5
+ ,4
+ ,4
+ ,3
+ ,4
+ ,3
+ ,5
+ ,5
+ ,5
+ ,1
+ ,5
+ ,5
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,3
+ ,4
+ ,1
+ ,4
+ ,3
+ ,5
+ ,5
+ ,5
+ ,1
+ ,5
+ ,4
+ ,4
+ ,3
+ ,4
+ ,1
+ ,5
+ ,4
+ ,4
+ ,5
+ ,4
+ ,3
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,5
+ ,4
+ ,5
+ ,5
+ ,5
+ ,1
+ ,5
+ ,5
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,3
+ ,2
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,5
+ ,5
+ ,5
+ ,5
+ ,1
+ ,4
+ ,5
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,3
+ ,4
+ ,3
+ ,3
+ ,4
+ ,3
+ ,3
+ ,3
+ ,4
+ ,3
+ ,3
+ ,4
+ ,2
+ ,4
+ ,2
+ ,5
+ ,3
+ ,3
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,5
+ ,5
+ ,5
+ ,1
+ ,5
+ ,5
+ ,5
+ ,4
+ ,5
+ ,1
+ ,5
+ ,5
+ ,5
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,1
+ ,5
+ ,5
+ ,5
+ ,4
+ ,4
+ ,3
+ ,5
+ ,3
+ ,4
+ ,3
+ ,3
+ ,2
+ ,3
+ ,3
+ ,4
+ ,5
+ ,5
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,5
+ ,5
+ ,4
+ ,5
+ ,1
+ ,5
+ ,5
+ ,5
+ ,5
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,2
+ ,4
+ ,4
+ ,4
+ ,5
+ ,4
+ ,2
+ ,4
+ ,4
+ ,5
+ ,5
+ ,5
+ ,2
+ ,5
+ ,4
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,3
+ ,4
+ ,4
+ ,4
+ ,1
+ ,4
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,5
+ ,4
+ ,4
+ ,1
+ ,5
+ ,5
+ ,5
+ ,5
+ ,5
+ ,1
+ ,5
+ ,5
+ ,5
+ ,5
+ ,4
+ ,3
+ ,4
+ ,5)
+ ,dim=c(6
+ ,161)
+ ,dimnames=list(c('Part_of_team'
+ ,'Respect_of_coach'
+ ,'Respect_of_team'
+ ,'Be_on_different_team'
+ ,'Be_liked'
+ ,'Proudness')
+ ,1:161))
> y <- array(NA,dim=c(6,161),dimnames=list(c('Part_of_team','Respect_of_coach','Respect_of_team','Be_on_different_team','Be_liked','Proudness'),1:161))
> 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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
Part_of_team Respect_of_coach Respect_of_team Be_on_different_team Be_liked
1 3 3 3 3 3
2 5 5 5 1 5
3 4 4 4 3 3
4 4 4 4 3 4
5 5 4 4 1 4
6 5 3 5 1 5
7 2 1 3 5 3
8 5 4 4 2 4
9 4 4 4 2 4
10 4 4 4 2 5
11 5 4 5 4 5
12 3 3 3 3 3
13 5 4 4 2 4
14 3 3 3 3 3
15 5 4 5 1 5
16 3 3 3 3 3
17 4 5 4 2 4
18 5 4 5 1 5
19 4 3 3 3 4
20 3 3 3 3 3
21 4 4 3 2 4
22 4 3 4 2 4
23 3 3 3 3 3
24 3 3 3 3 4
25 4 5 5 1 4
26 5 5 5 1 4
27 4 4 4 1 4
28 4 4 4 1 4
29 4 5 4 1 4
30 4 4 4 3 4
31 4 4 5 1 4
32 3 3 3 3 3
33 4 4 4 1 4
34 5 4 5 2 5
35 4 4 4 1 4
36 4 4 4 2 4
37 3 4 4 2 4
38 4 4 4 2 3
39 4 3 4 1 5
40 4 4 4 3 4
41 5 2 4 1 5
42 4 4 4 1 4
43 3 3 3 3 3
44 3 3 3 3 3
45 4 4 4 1 3
46 4 3 4 2 4
47 4 4 4 4 4
48 5 4 4 2 4
49 4 4 4 1 4
50 5 4 4 1 4
51 4 4 4 2 4
52 4 4 4 2 4
53 4 3 3 3 4
54 4 4 5 5 4
55 4 3 4 2 3
56 5 4 4 1 4
57 4 4 4 1 4
58 4 3 5 1 4
59 4 4 4 1 4
60 4 4 4 2 4
61 3 3 2 2 3
62 4 4 4 2 4
63 5 4 1 4 4
64 3 2 3 3 3
65 3 3 3 3 3
66 4 5 1 4 4
67 4 3 2 4 4
68 4 4 1 4 4
69 4 3 3 3 4
70 3 4 2 4 4
71 4 4 2 4 4
72 3 3 3 3 3
73 3 4 1 4 4
74 3 3 1 4 3
75 5 4 1 5 5
76 4 5 2 5 5
77 4 4 2 4 4
78 2 4 1 4 4
79 4 4 1 4 4
80 3 3 3 3 3
81 4 4 1 5 4
82 4 4 2 4 3
83 4 3 1 4 5
84 4 4 2 4 4
85 4 5 1 5 4
86 4 4 1 4 4
87 4 4 2 4 4
88 3 3 3 3 3
89 4 4 2 4 4
90 3 4 2 5 4
91 3 3 3 3 3
92 5 4 2 4 4
93 4 4 1 4 4
94 5 4 1 4 5
95 4 4 1 4 4
96 3 4 2 4 4
97 3 4 1 4 4
98 4 4 1 4 5
99 4 4 1 4 5
100 4 4 2 4 4
101 5 4 1 4 4
102 5 5 1 5 4
103 3 4 3 4 4
104 5 4 1 5 4
105 4 4 2 4 4
106 4 4 3 4 4
107 4 4 2 4 3
108 3 3 3 3 3
109 4 4 4 2 3
110 5 1 5 5 4
111 4 1 4 4 5
112 5 2 5 5 1
113 1 1 1 1 5
114 4 2 4 4 5
115 5 1 5 5 3
116 3 3 3 3 4
117 4 4 4 5 4
118 3 4 3 5 5
119 1 5 5 3 3
120 3 3 3 4 3
121 1 4 3 5 5
122 1 5 4 4 3
123 1 5 4 4 5
124 3 4 4 4 4
125 2 5 4 5 5
126 1 5 5 4 4
127 2 4 4 5 4
128 4 5 4 4 4
129 2 4 4 4 4
130 1 4 4 4 4
131 2 4 4 4 4
132 1 4 4 3 2
133 2 4 4 4 4
134 2 4 5 5 5
135 1 4 5 3 3
136 3 3 3 4 3
137 3 3 4 3 3
138 4 3 3 4 2
139 2 5 3 3 4
140 1 4 4 3 3
141 3 3 3 5 5
142 1 5 5 5 4
143 1 5 5 5 4
144 1 4 4 5 4
145 1 5 5 5 4
146 3 5 3 4 3
147 2 3 3 4 5
148 2 4 4 4 4
149 1 4 5 5 4
150 1 5 5 5 5
151 2 4 4 4 4
152 1 4 4 4 4
153 2 4 4 4 5
154 2 4 4 5 5
155 2 5 4 3 3
156 3 3 3 4 4
157 1 4 4 5 4
158 4 5 4 5 4
159 1 5 5 5 5
160 1 5 5 5 5
161 3 4 5 3 3
Proudness
1 3
2 5
3 4
4 4
5 4
6 5
7 2
8 4
9 4
10 4
11 4
12 3
13 4
14 3
15 4
16 3
17 4
18 5
19 3
20 3
21 3
22 3
23 3
24 3
25 4
26 4
27 4
28 4
29 4
30 4
31 4
32 3
33 4
34 5
35 4
36 4
37 4
38 4
39 4
40 4
41 3
42 4
43 3
44 3
45 4
46 3
47 4
48 4
49 3
50 4
51 4
52 4
53 4
54 5
55 3
56 4
57 4
58 4
59 4
60 4
61 3
62 4
63 1
64 3
65 5
66 4
67 4
68 3
69 4
70 4
71 3
72 5
73 4
74 5
75 4
76 4
77 4
78 4
79 3
80 5
81 5
82 4
83 4
84 5
85 4
86 4
87 3
88 4
89 4
90 3
91 5
92 5
93 5
94 4
95 4
96 4
97 4
98 4
99 4
100 4
101 5
102 4
103 4
104 3
105 5
106 4
107 3
108 2
109 5
110 4
111 5
112 1
113 5
114 5
115 3
116 4
117 4
118 5
119 3
120 4
121 5
122 4
123 4
124 4
125 5
126 4
127 5
128 4
129 4
130 4
131 4
132 4
133 4
134 5
135 3
136 4
137 3
138 4
139 4
140 3
141 5
142 5
143 4
144 5
145 4
146 3
147 5
148 4
149 5
150 4
151 4
152 4
153 4
154 5
155 3
156 4
157 5
158 4
159 5
160 4
161 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Respect_of_coach Respect_of_team
5.8105 -0.1566 -0.2958
Be_on_different_team Be_liked Proudness
-0.4237 0.2918 -0.1564
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.611657 -0.588529 -0.008263 0.716900 2.964783
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.81049 0.64826 8.963 9.52e-16 ***
Respect_of_coach -0.15661 0.10182 -1.538 0.1261
Respect_of_team -0.29580 0.07044 -4.199 4.50e-05 ***
Be_on_different_team -0.42370 0.06709 -6.315 2.71e-09 ***
Be_liked 0.29184 0.13300 2.194 0.0297 *
Proudness -0.15638 0.12587 -1.242 0.2160
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.054 on 155 degrees of freedom
Multiple R-squared: 0.2687, Adjusted R-squared: 0.2451
F-statistic: 11.39 on 5 and 155 DF, p-value: 2.269e-09
> 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,] 1.384579e-01 2.769158e-01 0.86154208
[2,] 5.558553e-02 1.111711e-01 0.94441447
[3,] 4.361000e-02 8.722000e-02 0.95639000
[4,] 1.733123e-02 3.466245e-02 0.98266877
[5,] 1.668793e-02 3.337586e-02 0.98331207
[6,] 6.870776e-03 1.374155e-02 0.99312922
[7,] 5.290472e-03 1.058094e-02 0.99470953
[8,] 2.179515e-03 4.359030e-03 0.99782048
[9,] 1.626525e-03 3.253050e-03 0.99837348
[10,] 8.104270e-04 1.620854e-03 0.99918957
[11,] 1.197331e-03 2.394663e-03 0.99880267
[12,] 5.338068e-04 1.067614e-03 0.99946619
[13,] 2.360068e-04 4.720137e-04 0.99976399
[14,] 9.315530e-05 1.863106e-04 0.99990684
[15,] 3.854790e-05 7.709581e-05 0.99996145
[16,] 2.121135e-05 4.242271e-05 0.99997879
[17,] 4.361794e-05 8.723587e-05 0.99995638
[18,] 2.691093e-05 5.382185e-05 0.99997309
[19,] 1.398156e-05 2.796312e-05 0.99998602
[20,] 6.828068e-06 1.365614e-05 0.99999317
[21,] 4.082286e-06 8.164571e-06 0.99999592
[22,] 1.758587e-06 3.517175e-06 0.99999824
[23,] 1.134350e-06 2.268700e-06 0.99999887
[24,] 4.619126e-07 9.238252e-07 0.99999954
[25,] 1.969252e-07 3.938505e-07 0.99999980
[26,] 1.153853e-07 2.307705e-07 0.99999988
[27,] 4.829540e-08 9.659080e-08 0.99999995
[28,] 1.998992e-08 3.997985e-08 0.99999998
[29,] 1.631121e-07 3.262243e-07 0.99999984
[30,] 8.401771e-08 1.680354e-07 0.99999992
[31,] 4.219974e-08 8.439948e-08 0.99999996
[32,] 1.898218e-08 3.796436e-08 0.99999998
[33,] 4.417878e-08 8.835756e-08 0.99999996
[34,] 1.992719e-08 3.985438e-08 0.99999998
[35,] 8.518950e-09 1.703790e-08 0.99999999
[36,] 3.616554e-09 7.233108e-09 1.00000000
[37,] 1.727594e-09 3.455188e-09 1.00000000
[38,] 6.877044e-10 1.375409e-09 1.00000000
[39,] 3.321879e-10 6.643757e-10 1.00000000
[40,] 1.334158e-09 2.668317e-09 1.00000000
[41,] 5.572798e-10 1.114560e-09 1.00000000
[42,] 1.443333e-09 2.886667e-09 1.00000000
[43,] 7.634914e-10 1.526983e-09 1.00000000
[44,] 4.147000e-10 8.293999e-10 1.00000000
[45,] 2.173676e-10 4.347351e-10 1.00000000
[46,] 2.240049e-10 4.480099e-10 1.00000000
[47,] 1.733903e-10 3.467806e-10 1.00000000
[48,] 6.246428e-10 1.249286e-09 1.00000000
[49,] 5.503238e-10 1.100648e-09 1.00000000
[50,] 8.480595e-10 1.696119e-09 1.00000000
[51,] 1.350891e-09 2.701782e-09 1.00000000
[52,] 2.171017e-09 4.342034e-09 1.00000000
[53,] 1.029303e-09 2.058605e-09 1.00000000
[54,] 2.550615e-09 5.101231e-09 1.00000000
[55,] 6.528195e-08 1.305639e-07 0.99999993
[56,] 3.880243e-08 7.760485e-08 0.99999996
[57,] 1.902451e-08 3.804902e-08 0.99999998
[58,] 9.702137e-09 1.940427e-08 0.99999999
[59,] 6.239883e-09 1.247977e-08 0.99999999
[60,] 3.027541e-09 6.055082e-09 1.00000000
[61,] 2.310828e-09 4.621656e-09 1.00000000
[62,] 3.962874e-09 7.925748e-09 1.00000000
[63,] 1.947742e-09 3.895483e-09 1.00000000
[64,] 9.205401e-10 1.841080e-09 1.00000000
[65,] 1.201305e-09 2.402611e-09 1.00000000
[66,] 1.472958e-09 2.945916e-09 1.00000000
[67,] 1.849283e-09 3.698567e-09 1.00000000
[68,] 2.092734e-09 4.185468e-09 1.00000000
[69,] 1.180596e-09 2.361192e-09 1.00000000
[70,] 9.282141e-08 1.856428e-07 0.99999991
[71,] 5.662704e-08 1.132541e-07 0.99999994
[72,] 2.912714e-08 5.825428e-08 0.99999997
[73,] 2.378886e-08 4.757772e-08 0.99999998
[74,] 2.948520e-08 5.897039e-08 0.99999997
[75,] 1.540316e-08 3.080632e-08 0.99999998
[76,] 1.027777e-08 2.055555e-08 0.99999999
[77,] 5.291897e-09 1.058379e-08 0.99999999
[78,] 2.930508e-09 5.861015e-09 1.00000000
[79,] 1.500705e-09 3.001409e-09 1.00000000
[80,] 7.425253e-10 1.485051e-09 1.00000000
[81,] 4.100331e-10 8.200662e-10 1.00000000
[82,] 8.559956e-10 1.711991e-09 1.00000000
[83,] 4.167111e-10 8.334222e-10 1.00000000
[84,] 4.762319e-09 9.524638e-09 1.00000000
[85,] 2.589313e-09 5.178626e-09 1.00000000
[86,] 3.729981e-09 7.459962e-09 1.00000000
[87,] 1.912865e-09 3.825729e-09 1.00000000
[88,] 2.349888e-09 4.699776e-09 1.00000000
[89,] 3.707275e-09 7.414549e-09 1.00000000
[90,] 2.165505e-09 4.331010e-09 1.00000000
[91,] 1.235610e-09 2.471220e-09 1.00000000
[92,] 7.249632e-10 1.449926e-09 1.00000000
[93,] 4.499607e-09 8.999214e-09 1.00000000
[94,] 1.392178e-08 2.784355e-08 0.99999999
[95,] 1.661472e-08 3.322944e-08 0.99999998
[96,] 4.488858e-08 8.977717e-08 0.99999996
[97,] 6.231926e-08 1.246385e-07 0.99999994
[98,] 1.086087e-07 2.172175e-07 0.99999989
[99,] 1.209703e-07 2.419405e-07 0.99999988
[100,] 6.919013e-08 1.383803e-07 0.99999993
[101,] 1.663453e-05 3.326907e-05 0.99998337
[102,] 6.930133e-05 1.386027e-04 0.99993070
[103,] 8.277676e-05 1.655535e-04 0.99991722
[104,] 1.410675e-03 2.821351e-03 0.99858932
[105,] 5.662658e-03 1.132532e-02 0.99433734
[106,] 1.313781e-02 2.627562e-02 0.98686219
[107,] 1.701158e-02 3.402315e-02 0.98298842
[108,] 1.634806e-02 3.269612e-02 0.98365194
[109,] 2.717673e-02 5.435346e-02 0.97282327
[110,] 3.878321e-02 7.756642e-02 0.96121679
[111,] 1.655497e-01 3.310994e-01 0.83445031
[112,] 1.367791e-01 2.735583e-01 0.86322086
[113,] 4.167095e-01 8.334191e-01 0.58329047
[114,] 5.673656e-01 8.652687e-01 0.43263436
[115,] 7.360197e-01 5.279606e-01 0.26398031
[116,] 7.506454e-01 4.987091e-01 0.24935457
[117,] 7.576692e-01 4.846617e-01 0.24233083
[118,] 7.886989e-01 4.226021e-01 0.21130106
[119,] 7.608833e-01 4.782335e-01 0.23911674
[120,] 9.409151e-01 1.181698e-01 0.05908488
[121,] 9.294661e-01 1.410679e-01 0.07053394
[122,] 9.526242e-01 9.475152e-02 0.04737576
[123,] 9.402540e-01 1.194920e-01 0.05974600
[124,] 9.385974e-01 1.228052e-01 0.06140262
[125,] 9.214819e-01 1.570362e-01 0.07851811
[126,] 9.371767e-01 1.256465e-01 0.06282327
[127,] 9.369510e-01 1.260980e-01 0.06304899
[128,] 9.123557e-01 1.752886e-01 0.08764432
[129,] 8.831033e-01 2.337934e-01 0.11689672
[130,] 8.828426e-01 2.343148e-01 0.11715741
[131,] 8.502991e-01 2.994017e-01 0.14970086
[132,] 9.173186e-01 1.653629e-01 0.08268144
[133,] 9.009133e-01 1.981734e-01 0.09908668
[134,] 8.716060e-01 2.567881e-01 0.12839403
[135,] 8.433979e-01 3.132042e-01 0.15660210
[136,] 8.144469e-01 3.711062e-01 0.18555308
[137,] 7.888013e-01 4.223974e-01 0.21119872
[138,] 7.406728e-01 5.186545e-01 0.25932723
[139,] 6.792346e-01 6.415308e-01 0.32076539
[140,] 5.764006e-01 8.471987e-01 0.42359937
[141,] 4.671625e-01 9.343250e-01 0.53283751
[142,] 4.248337e-01 8.496675e-01 0.57516626
[143,] 2.964779e-01 5.929558e-01 0.70352210
[144,] 3.012924e-01 6.025847e-01 0.69870765
> postscript(file="/var/www/rcomp/tmp/1w23q1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2pt3t1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3pt3t1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4pt3t1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5h22w1290522067.ps",horizontal=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 = 161
Frequency = 1
1 2 3 4 5 6
-0.588528534 1.197979585 1.020263273 0.728425293 0.881025758 0.884768555
7 8 9 10 11 12
-1.210722664 1.304725525 0.304725525 0.012887545 2.156090737 -0.588528534
13 14 15 16 17 18
1.304725525 -0.588528534 0.884991434 -0.588528534 0.461331040 1.041374070
19 20 21 22 23 24
0.119633486 -0.588528534 -0.147460767 -0.008262625 -0.588528534 -0.880366514
25 26 27 28 29 30
0.333434929 1.333434929 -0.118974242 -0.118974242 0.037631272 0.728425293
31 32 33 34 35 36
0.176829414 -0.588528534 -0.118974242 1.465073838 -0.118974242 0.304725525
37 38 39 40 41 42
-0.695274475 0.596563506 -0.567417737 0.728425293 0.119594112 -0.118974242
43 44 45 46 47 48
-0.588528534 -0.588528534 0.172863738 -0.008262625 1.152125061 1.304725525
49 50 51 52 53 54
-0.275356878 0.881025758 0.304725525 0.304725525 0.276016122 2.028011121
55 56 57 58 59 60
0.283575355 0.881025758 -0.118974242 0.020223899 -0.118974242 0.304725525
61 62 63 64 65 66
-1.308031957 0.304725525 0.795566184 -0.745134048 -0.275763261 0.421319607
67 68 69 70 71 72
0.403912234 0.108331456 0.276016122 -0.439482252 0.404135112 -0.275763261
73 74 75 76 77 78
-0.735285908 -0.443670806 1.396575879 0.848985050 0.560517748 -1.735285908
79 80 81 82 83 84
0.108331456 -0.275763261 0.844796496 0.852355729 -0.183729403 0.716900384
85 86 87 88 89 90
0.845019374 0.264714092 0.404135112 -0.432145897 0.560517748 -0.172165120
91 92 93 94 95 96
-0.275763261 1.716900384 0.421096728 0.972876112 0.264714092 -0.439482252
97 98 99 100 101 102
-0.735285908 -0.027123888 -0.027123888 0.560517748 1.421096728 1.845019374
103 104 105 106 107 108
-0.143678595 1.532031224 0.716900384 0.856321405 0.695973092 -0.744911170
109 110 111 112 113 114
0.752946142 2.401811941 0.546853173 2.964783488 -4.611657099 0.703458687
115 116 117 118 119 120
2.537267285 -0.723983878 1.575824828 0.144565828 -1.683710192 -0.008446130
121 122 123 124 125 126
-1.855434172 -1.399431444 -1.983107405 0.152125061 -0.403025001 -1.395465768
127 128 129 130 131 132
-0.267792535 1.308730575 -0.847874939 -1.847874939 -0.847874939 -1.687898746
133 134 135 136 137 138
-0.847874939 -0.263826860 -1.840315707 -0.008446130 -0.292724877 1.283391851
139 140 141 142 143 144
-1.410772848 -2.136119363 -0.012039687 -0.815383365 -0.971766001 -1.267792535
145 146 147 148 149 150
-0.971766001 0.148382263 -1.435739454 -0.847874939 -0.971988879 -1.263603981
151 152 153 154 155 156
-0.847874939 -1.847874939 -1.139712920 -0.559630516 -0.979513848 -0.300284110
157 158 159 160 161
-1.267792535 1.732430343 -1.107221345 -1.263603981 0.159684293
> postscript(file="/var/www/rcomp/tmp/6h22w1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 161
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.588528534 NA
1 1.197979585 -0.588528534
2 1.020263273 1.197979585
3 0.728425293 1.020263273
4 0.881025758 0.728425293
5 0.884768555 0.881025758
6 -1.210722664 0.884768555
7 1.304725525 -1.210722664
8 0.304725525 1.304725525
9 0.012887545 0.304725525
10 2.156090737 0.012887545
11 -0.588528534 2.156090737
12 1.304725525 -0.588528534
13 -0.588528534 1.304725525
14 0.884991434 -0.588528534
15 -0.588528534 0.884991434
16 0.461331040 -0.588528534
17 1.041374070 0.461331040
18 0.119633486 1.041374070
19 -0.588528534 0.119633486
20 -0.147460767 -0.588528534
21 -0.008262625 -0.147460767
22 -0.588528534 -0.008262625
23 -0.880366514 -0.588528534
24 0.333434929 -0.880366514
25 1.333434929 0.333434929
26 -0.118974242 1.333434929
27 -0.118974242 -0.118974242
28 0.037631272 -0.118974242
29 0.728425293 0.037631272
30 0.176829414 0.728425293
31 -0.588528534 0.176829414
32 -0.118974242 -0.588528534
33 1.465073838 -0.118974242
34 -0.118974242 1.465073838
35 0.304725525 -0.118974242
36 -0.695274475 0.304725525
37 0.596563506 -0.695274475
38 -0.567417737 0.596563506
39 0.728425293 -0.567417737
40 0.119594112 0.728425293
41 -0.118974242 0.119594112
42 -0.588528534 -0.118974242
43 -0.588528534 -0.588528534
44 0.172863738 -0.588528534
45 -0.008262625 0.172863738
46 1.152125061 -0.008262625
47 1.304725525 1.152125061
48 -0.275356878 1.304725525
49 0.881025758 -0.275356878
50 0.304725525 0.881025758
51 0.304725525 0.304725525
52 0.276016122 0.304725525
53 2.028011121 0.276016122
54 0.283575355 2.028011121
55 0.881025758 0.283575355
56 -0.118974242 0.881025758
57 0.020223899 -0.118974242
58 -0.118974242 0.020223899
59 0.304725525 -0.118974242
60 -1.308031957 0.304725525
61 0.304725525 -1.308031957
62 0.795566184 0.304725525
63 -0.745134048 0.795566184
64 -0.275763261 -0.745134048
65 0.421319607 -0.275763261
66 0.403912234 0.421319607
67 0.108331456 0.403912234
68 0.276016122 0.108331456
69 -0.439482252 0.276016122
70 0.404135112 -0.439482252
71 -0.275763261 0.404135112
72 -0.735285908 -0.275763261
73 -0.443670806 -0.735285908
74 1.396575879 -0.443670806
75 0.848985050 1.396575879
76 0.560517748 0.848985050
77 -1.735285908 0.560517748
78 0.108331456 -1.735285908
79 -0.275763261 0.108331456
80 0.844796496 -0.275763261
81 0.852355729 0.844796496
82 -0.183729403 0.852355729
83 0.716900384 -0.183729403
84 0.845019374 0.716900384
85 0.264714092 0.845019374
86 0.404135112 0.264714092
87 -0.432145897 0.404135112
88 0.560517748 -0.432145897
89 -0.172165120 0.560517748
90 -0.275763261 -0.172165120
91 1.716900384 -0.275763261
92 0.421096728 1.716900384
93 0.972876112 0.421096728
94 0.264714092 0.972876112
95 -0.439482252 0.264714092
96 -0.735285908 -0.439482252
97 -0.027123888 -0.735285908
98 -0.027123888 -0.027123888
99 0.560517748 -0.027123888
100 1.421096728 0.560517748
101 1.845019374 1.421096728
102 -0.143678595 1.845019374
103 1.532031224 -0.143678595
104 0.716900384 1.532031224
105 0.856321405 0.716900384
106 0.695973092 0.856321405
107 -0.744911170 0.695973092
108 0.752946142 -0.744911170
109 2.401811941 0.752946142
110 0.546853173 2.401811941
111 2.964783488 0.546853173
112 -4.611657099 2.964783488
113 0.703458687 -4.611657099
114 2.537267285 0.703458687
115 -0.723983878 2.537267285
116 1.575824828 -0.723983878
117 0.144565828 1.575824828
118 -1.683710192 0.144565828
119 -0.008446130 -1.683710192
120 -1.855434172 -0.008446130
121 -1.399431444 -1.855434172
122 -1.983107405 -1.399431444
123 0.152125061 -1.983107405
124 -0.403025001 0.152125061
125 -1.395465768 -0.403025001
126 -0.267792535 -1.395465768
127 1.308730575 -0.267792535
128 -0.847874939 1.308730575
129 -1.847874939 -0.847874939
130 -0.847874939 -1.847874939
131 -1.687898746 -0.847874939
132 -0.847874939 -1.687898746
133 -0.263826860 -0.847874939
134 -1.840315707 -0.263826860
135 -0.008446130 -1.840315707
136 -0.292724877 -0.008446130
137 1.283391851 -0.292724877
138 -1.410772848 1.283391851
139 -2.136119363 -1.410772848
140 -0.012039687 -2.136119363
141 -0.815383365 -0.012039687
142 -0.971766001 -0.815383365
143 -1.267792535 -0.971766001
144 -0.971766001 -1.267792535
145 0.148382263 -0.971766001
146 -1.435739454 0.148382263
147 -0.847874939 -1.435739454
148 -0.971988879 -0.847874939
149 -1.263603981 -0.971988879
150 -0.847874939 -1.263603981
151 -1.847874939 -0.847874939
152 -1.139712920 -1.847874939
153 -0.559630516 -1.139712920
154 -0.979513848 -0.559630516
155 -0.300284110 -0.979513848
156 -1.267792535 -0.300284110
157 1.732430343 -1.267792535
158 -1.107221345 1.732430343
159 -1.263603981 -1.107221345
160 0.159684293 -1.263603981
161 NA 0.159684293
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.197979585 -0.588528534
[2,] 1.020263273 1.197979585
[3,] 0.728425293 1.020263273
[4,] 0.881025758 0.728425293
[5,] 0.884768555 0.881025758
[6,] -1.210722664 0.884768555
[7,] 1.304725525 -1.210722664
[8,] 0.304725525 1.304725525
[9,] 0.012887545 0.304725525
[10,] 2.156090737 0.012887545
[11,] -0.588528534 2.156090737
[12,] 1.304725525 -0.588528534
[13,] -0.588528534 1.304725525
[14,] 0.884991434 -0.588528534
[15,] -0.588528534 0.884991434
[16,] 0.461331040 -0.588528534
[17,] 1.041374070 0.461331040
[18,] 0.119633486 1.041374070
[19,] -0.588528534 0.119633486
[20,] -0.147460767 -0.588528534
[21,] -0.008262625 -0.147460767
[22,] -0.588528534 -0.008262625
[23,] -0.880366514 -0.588528534
[24,] 0.333434929 -0.880366514
[25,] 1.333434929 0.333434929
[26,] -0.118974242 1.333434929
[27,] -0.118974242 -0.118974242
[28,] 0.037631272 -0.118974242
[29,] 0.728425293 0.037631272
[30,] 0.176829414 0.728425293
[31,] -0.588528534 0.176829414
[32,] -0.118974242 -0.588528534
[33,] 1.465073838 -0.118974242
[34,] -0.118974242 1.465073838
[35,] 0.304725525 -0.118974242
[36,] -0.695274475 0.304725525
[37,] 0.596563506 -0.695274475
[38,] -0.567417737 0.596563506
[39,] 0.728425293 -0.567417737
[40,] 0.119594112 0.728425293
[41,] -0.118974242 0.119594112
[42,] -0.588528534 -0.118974242
[43,] -0.588528534 -0.588528534
[44,] 0.172863738 -0.588528534
[45,] -0.008262625 0.172863738
[46,] 1.152125061 -0.008262625
[47,] 1.304725525 1.152125061
[48,] -0.275356878 1.304725525
[49,] 0.881025758 -0.275356878
[50,] 0.304725525 0.881025758
[51,] 0.304725525 0.304725525
[52,] 0.276016122 0.304725525
[53,] 2.028011121 0.276016122
[54,] 0.283575355 2.028011121
[55,] 0.881025758 0.283575355
[56,] -0.118974242 0.881025758
[57,] 0.020223899 -0.118974242
[58,] -0.118974242 0.020223899
[59,] 0.304725525 -0.118974242
[60,] -1.308031957 0.304725525
[61,] 0.304725525 -1.308031957
[62,] 0.795566184 0.304725525
[63,] -0.745134048 0.795566184
[64,] -0.275763261 -0.745134048
[65,] 0.421319607 -0.275763261
[66,] 0.403912234 0.421319607
[67,] 0.108331456 0.403912234
[68,] 0.276016122 0.108331456
[69,] -0.439482252 0.276016122
[70,] 0.404135112 -0.439482252
[71,] -0.275763261 0.404135112
[72,] -0.735285908 -0.275763261
[73,] -0.443670806 -0.735285908
[74,] 1.396575879 -0.443670806
[75,] 0.848985050 1.396575879
[76,] 0.560517748 0.848985050
[77,] -1.735285908 0.560517748
[78,] 0.108331456 -1.735285908
[79,] -0.275763261 0.108331456
[80,] 0.844796496 -0.275763261
[81,] 0.852355729 0.844796496
[82,] -0.183729403 0.852355729
[83,] 0.716900384 -0.183729403
[84,] 0.845019374 0.716900384
[85,] 0.264714092 0.845019374
[86,] 0.404135112 0.264714092
[87,] -0.432145897 0.404135112
[88,] 0.560517748 -0.432145897
[89,] -0.172165120 0.560517748
[90,] -0.275763261 -0.172165120
[91,] 1.716900384 -0.275763261
[92,] 0.421096728 1.716900384
[93,] 0.972876112 0.421096728
[94,] 0.264714092 0.972876112
[95,] -0.439482252 0.264714092
[96,] -0.735285908 -0.439482252
[97,] -0.027123888 -0.735285908
[98,] -0.027123888 -0.027123888
[99,] 0.560517748 -0.027123888
[100,] 1.421096728 0.560517748
[101,] 1.845019374 1.421096728
[102,] -0.143678595 1.845019374
[103,] 1.532031224 -0.143678595
[104,] 0.716900384 1.532031224
[105,] 0.856321405 0.716900384
[106,] 0.695973092 0.856321405
[107,] -0.744911170 0.695973092
[108,] 0.752946142 -0.744911170
[109,] 2.401811941 0.752946142
[110,] 0.546853173 2.401811941
[111,] 2.964783488 0.546853173
[112,] -4.611657099 2.964783488
[113,] 0.703458687 -4.611657099
[114,] 2.537267285 0.703458687
[115,] -0.723983878 2.537267285
[116,] 1.575824828 -0.723983878
[117,] 0.144565828 1.575824828
[118,] -1.683710192 0.144565828
[119,] -0.008446130 -1.683710192
[120,] -1.855434172 -0.008446130
[121,] -1.399431444 -1.855434172
[122,] -1.983107405 -1.399431444
[123,] 0.152125061 -1.983107405
[124,] -0.403025001 0.152125061
[125,] -1.395465768 -0.403025001
[126,] -0.267792535 -1.395465768
[127,] 1.308730575 -0.267792535
[128,] -0.847874939 1.308730575
[129,] -1.847874939 -0.847874939
[130,] -0.847874939 -1.847874939
[131,] -1.687898746 -0.847874939
[132,] -0.847874939 -1.687898746
[133,] -0.263826860 -0.847874939
[134,] -1.840315707 -0.263826860
[135,] -0.008446130 -1.840315707
[136,] -0.292724877 -0.008446130
[137,] 1.283391851 -0.292724877
[138,] -1.410772848 1.283391851
[139,] -2.136119363 -1.410772848
[140,] -0.012039687 -2.136119363
[141,] -0.815383365 -0.012039687
[142,] -0.971766001 -0.815383365
[143,] -1.267792535 -0.971766001
[144,] -0.971766001 -1.267792535
[145,] 0.148382263 -0.971766001
[146,] -1.435739454 0.148382263
[147,] -0.847874939 -1.435739454
[148,] -0.971988879 -0.847874939
[149,] -1.263603981 -0.971988879
[150,] -0.847874939 -1.263603981
[151,] -1.847874939 -0.847874939
[152,] -1.139712920 -1.847874939
[153,] -0.559630516 -1.139712920
[154,] -0.979513848 -0.559630516
[155,] -0.300284110 -0.979513848
[156,] -1.267792535 -0.300284110
[157,] 1.732430343 -1.267792535
[158,] -1.107221345 1.732430343
[159,] -1.263603981 -1.107221345
[160,] 0.159684293 -1.263603981
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.197979585 -0.588528534
2 1.020263273 1.197979585
3 0.728425293 1.020263273
4 0.881025758 0.728425293
5 0.884768555 0.881025758
6 -1.210722664 0.884768555
7 1.304725525 -1.210722664
8 0.304725525 1.304725525
9 0.012887545 0.304725525
10 2.156090737 0.012887545
11 -0.588528534 2.156090737
12 1.304725525 -0.588528534
13 -0.588528534 1.304725525
14 0.884991434 -0.588528534
15 -0.588528534 0.884991434
16 0.461331040 -0.588528534
17 1.041374070 0.461331040
18 0.119633486 1.041374070
19 -0.588528534 0.119633486
20 -0.147460767 -0.588528534
21 -0.008262625 -0.147460767
22 -0.588528534 -0.008262625
23 -0.880366514 -0.588528534
24 0.333434929 -0.880366514
25 1.333434929 0.333434929
26 -0.118974242 1.333434929
27 -0.118974242 -0.118974242
28 0.037631272 -0.118974242
29 0.728425293 0.037631272
30 0.176829414 0.728425293
31 -0.588528534 0.176829414
32 -0.118974242 -0.588528534
33 1.465073838 -0.118974242
34 -0.118974242 1.465073838
35 0.304725525 -0.118974242
36 -0.695274475 0.304725525
37 0.596563506 -0.695274475
38 -0.567417737 0.596563506
39 0.728425293 -0.567417737
40 0.119594112 0.728425293
41 -0.118974242 0.119594112
42 -0.588528534 -0.118974242
43 -0.588528534 -0.588528534
44 0.172863738 -0.588528534
45 -0.008262625 0.172863738
46 1.152125061 -0.008262625
47 1.304725525 1.152125061
48 -0.275356878 1.304725525
49 0.881025758 -0.275356878
50 0.304725525 0.881025758
51 0.304725525 0.304725525
52 0.276016122 0.304725525
53 2.028011121 0.276016122
54 0.283575355 2.028011121
55 0.881025758 0.283575355
56 -0.118974242 0.881025758
57 0.020223899 -0.118974242
58 -0.118974242 0.020223899
59 0.304725525 -0.118974242
60 -1.308031957 0.304725525
61 0.304725525 -1.308031957
62 0.795566184 0.304725525
63 -0.745134048 0.795566184
64 -0.275763261 -0.745134048
65 0.421319607 -0.275763261
66 0.403912234 0.421319607
67 0.108331456 0.403912234
68 0.276016122 0.108331456
69 -0.439482252 0.276016122
70 0.404135112 -0.439482252
71 -0.275763261 0.404135112
72 -0.735285908 -0.275763261
73 -0.443670806 -0.735285908
74 1.396575879 -0.443670806
75 0.848985050 1.396575879
76 0.560517748 0.848985050
77 -1.735285908 0.560517748
78 0.108331456 -1.735285908
79 -0.275763261 0.108331456
80 0.844796496 -0.275763261
81 0.852355729 0.844796496
82 -0.183729403 0.852355729
83 0.716900384 -0.183729403
84 0.845019374 0.716900384
85 0.264714092 0.845019374
86 0.404135112 0.264714092
87 -0.432145897 0.404135112
88 0.560517748 -0.432145897
89 -0.172165120 0.560517748
90 -0.275763261 -0.172165120
91 1.716900384 -0.275763261
92 0.421096728 1.716900384
93 0.972876112 0.421096728
94 0.264714092 0.972876112
95 -0.439482252 0.264714092
96 -0.735285908 -0.439482252
97 -0.027123888 -0.735285908
98 -0.027123888 -0.027123888
99 0.560517748 -0.027123888
100 1.421096728 0.560517748
101 1.845019374 1.421096728
102 -0.143678595 1.845019374
103 1.532031224 -0.143678595
104 0.716900384 1.532031224
105 0.856321405 0.716900384
106 0.695973092 0.856321405
107 -0.744911170 0.695973092
108 0.752946142 -0.744911170
109 2.401811941 0.752946142
110 0.546853173 2.401811941
111 2.964783488 0.546853173
112 -4.611657099 2.964783488
113 0.703458687 -4.611657099
114 2.537267285 0.703458687
115 -0.723983878 2.537267285
116 1.575824828 -0.723983878
117 0.144565828 1.575824828
118 -1.683710192 0.144565828
119 -0.008446130 -1.683710192
120 -1.855434172 -0.008446130
121 -1.399431444 -1.855434172
122 -1.983107405 -1.399431444
123 0.152125061 -1.983107405
124 -0.403025001 0.152125061
125 -1.395465768 -0.403025001
126 -0.267792535 -1.395465768
127 1.308730575 -0.267792535
128 -0.847874939 1.308730575
129 -1.847874939 -0.847874939
130 -0.847874939 -1.847874939
131 -1.687898746 -0.847874939
132 -0.847874939 -1.687898746
133 -0.263826860 -0.847874939
134 -1.840315707 -0.263826860
135 -0.008446130 -1.840315707
136 -0.292724877 -0.008446130
137 1.283391851 -0.292724877
138 -1.410772848 1.283391851
139 -2.136119363 -1.410772848
140 -0.012039687 -2.136119363
141 -0.815383365 -0.012039687
142 -0.971766001 -0.815383365
143 -1.267792535 -0.971766001
144 -0.971766001 -1.267792535
145 0.148382263 -0.971766001
146 -1.435739454 0.148382263
147 -0.847874939 -1.435739454
148 -0.971988879 -0.847874939
149 -1.263603981 -0.971988879
150 -0.847874939 -1.263603981
151 -1.847874939 -0.847874939
152 -1.139712920 -1.847874939
153 -0.559630516 -1.139712920
154 -0.979513848 -0.559630516
155 -0.300284110 -0.979513848
156 -1.267792535 -0.300284110
157 1.732430343 -1.267792535
158 -1.107221345 1.732430343
159 -1.263603981 -1.107221345
160 0.159684293 -1.263603981
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7aujz1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8aujz1290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/93l021290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/103l021290522067.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11b76e1290522067.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12amxw1290522067.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13yndp1290522067.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1426bd1290522067.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15norj1290522067.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16qpqp1290522067.tab")
+ }
>
> try(system("convert tmp/1w23q1290522067.ps tmp/1w23q1290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/2pt3t1290522067.ps tmp/2pt3t1290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pt3t1290522067.ps tmp/3pt3t1290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pt3t1290522067.ps tmp/4pt3t1290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h22w1290522067.ps tmp/5h22w1290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/6h22w1290522067.ps tmp/6h22w1290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aujz1290522067.ps tmp/7aujz1290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/8aujz1290522067.ps tmp/8aujz1290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/93l021290522067.ps tmp/93l021290522067.png",intern=TRUE))
character(0)
> try(system("convert tmp/103l021290522067.ps tmp/103l021290522067.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.480 2.280 7.689