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(14
+ ,10
+ ,-15
+ ,11
+ ,20
+ ,14
+ ,12
+ ,-14
+ ,11
+ ,19
+ ,15
+ ,13
+ ,-18
+ ,11
+ ,18
+ ,13
+ ,15
+ ,-13
+ ,11
+ ,13
+ ,8
+ ,3
+ ,-2
+ ,11
+ ,17
+ ,7
+ ,2
+ ,-1
+ ,9
+ ,17
+ ,3
+ ,-2
+ ,5
+ ,8
+ ,13
+ ,3
+ ,1
+ ,8
+ ,6
+ ,14
+ ,4
+ ,1
+ ,6
+ ,7
+ ,13
+ ,4
+ ,-1
+ ,7
+ ,8
+ ,17
+ ,0
+ ,-6
+ ,15
+ ,6
+ ,17
+ ,-4
+ ,-13
+ ,23
+ ,5
+ ,15
+ ,-14
+ ,-25
+ ,43
+ ,2
+ ,9
+ ,-18
+ ,-26
+ ,60
+ ,3
+ ,10
+ ,-8
+ ,-9
+ ,36
+ ,3
+ ,9
+ ,-1
+ ,1
+ ,28
+ ,7
+ ,14
+ ,1
+ ,3
+ ,23
+ ,8
+ ,18
+ ,2
+ ,6
+ ,23
+ ,7
+ ,18
+ ,0
+ ,2
+ ,22
+ ,7
+ ,12
+ ,1
+ ,5
+ ,22
+ ,6
+ ,16
+ ,0
+ ,5
+ ,24
+ ,6
+ ,12
+ ,-1
+ ,0
+ ,32
+ ,7
+ ,19
+ ,-3
+ ,-5
+ ,27
+ ,5
+ ,13
+ ,-3
+ ,-4
+ ,27
+ ,5
+ ,12
+ ,-3
+ ,-2
+ ,27
+ ,5
+ ,13
+ ,-4
+ ,-1
+ ,29
+ ,4
+ ,11
+ ,-8
+ ,-8
+ ,38
+ ,4
+ ,10
+ ,-9
+ ,-16
+ ,40
+ ,4
+ ,16
+ ,-13
+ ,-19
+ ,45
+ ,1
+ ,12
+ ,-18
+ ,-28
+ ,50
+ ,-1
+ ,6
+ ,-11
+ ,-11
+ ,43
+ ,3
+ ,8
+ ,-9
+ ,-4
+ ,44
+ ,4
+ ,6
+ ,-10
+ ,-9
+ ,44
+ ,3
+ ,8
+ ,-13
+ ,-12
+ ,49
+ ,2
+ ,8
+ ,-11
+ ,-10
+ ,42
+ ,1
+ ,9
+ ,-5
+ ,-2
+ ,36
+ ,4
+ ,13
+ ,-15
+ ,-13
+ ,57
+ ,3
+ ,8
+ ,-6
+ ,0
+ ,42
+ ,5
+ ,11
+ ,-6
+ ,0
+ ,39
+ ,6
+ ,8
+ ,-3
+ ,4
+ ,33
+ ,6
+ ,10
+ ,-1
+ ,7
+ ,32
+ ,6
+ ,15
+ ,-3
+ ,5
+ ,34
+ ,6
+ ,12
+ ,-4
+ ,2
+ ,37
+ ,6
+ ,13
+ ,-6
+ ,-2
+ ,38
+ ,5
+ ,12
+ ,0
+ ,6
+ ,28
+ ,6
+ ,15
+ ,-4
+ ,-3
+ ,31
+ ,5
+ ,13
+ ,-2
+ ,1
+ ,28
+ ,6
+ ,13
+ ,-2
+ ,0
+ ,30
+ ,5
+ ,16
+ ,-6
+ ,-7
+ ,39
+ ,7
+ ,14
+ ,-7
+ ,-6
+ ,38
+ ,4
+ ,12
+ ,-6
+ ,-4
+ ,39
+ ,5
+ ,15
+ ,-6
+ ,-4
+ ,38
+ ,6
+ ,14
+ ,-3
+ ,-2
+ ,37
+ ,6
+ ,19
+ ,-2
+ ,2
+ ,32
+ ,5
+ ,16
+ ,-5
+ ,-5
+ ,32
+ ,3
+ ,16
+ ,-11
+ ,-15
+ ,44
+ ,2
+ ,11
+ ,-11
+ ,-16
+ ,43
+ ,3
+ ,13
+ ,-11
+ ,-18
+ ,42
+ ,3
+ ,12
+ ,-10
+ ,-13
+ ,38
+ ,2
+ ,11
+ ,-14
+ ,-23
+ ,37
+ ,0
+ ,6
+ ,-8
+ ,-10
+ ,35
+ ,4
+ ,9
+ ,-9
+ ,-10
+ ,37
+ ,4
+ ,6
+ ,-5
+ ,-6
+ ,33
+ ,5
+ ,15
+ ,-1
+ ,-3
+ ,24
+ ,6
+ ,17
+ ,-2
+ ,-4
+ ,24
+ ,6
+ ,13
+ ,-5
+ ,-7
+ ,31
+ ,5
+ ,12
+ ,-4
+ ,-7
+ ,25
+ ,5
+ ,13
+ ,-6
+ ,-7
+ ,28
+ ,3
+ ,10
+ ,-2
+ ,-3
+ ,24
+ ,5
+ ,14
+ ,-2
+ ,0
+ ,25
+ ,5
+ ,13
+ ,-2
+ ,-5
+ ,16
+ ,5
+ ,10
+ ,-2
+ ,-3
+ ,17
+ ,3
+ ,11
+ ,2
+ ,3
+ ,11
+ ,6
+ ,12
+ ,1
+ ,2
+ ,12
+ ,6
+ ,7
+ ,-8
+ ,-7
+ ,39
+ ,4
+ ,11
+ ,-1
+ ,-1
+ ,19
+ ,6
+ ,9
+ ,1
+ ,0
+ ,14
+ ,5
+ ,13
+ ,-1
+ ,-3
+ ,15
+ ,4
+ ,12
+ ,2
+ ,4
+ ,7
+ ,5
+ ,5
+ ,2
+ ,2
+ ,12
+ ,5
+ ,13
+ ,1
+ ,3
+ ,12
+ ,4
+ ,11
+ ,-1
+ ,0
+ ,14
+ ,3
+ ,8
+ ,-2
+ ,-10
+ ,9
+ ,2
+ ,8
+ ,-2
+ ,-10
+ ,8
+ ,3
+ ,8
+ ,-1
+ ,-9
+ ,4
+ ,2
+ ,8
+ ,-8
+ ,-22
+ ,7
+ ,-1
+ ,0
+ ,-4
+ ,-16
+ ,3
+ ,0
+ ,3
+ ,-6
+ ,-18
+ ,5
+ ,-2
+ ,0
+ ,-3
+ ,-14
+ ,0
+ ,1
+ ,-1
+ ,-3
+ ,-12
+ ,-2
+ ,-2
+ ,-1
+ ,-7
+ ,-17
+ ,6
+ ,-2
+ ,-4
+ ,-9
+ ,-23
+ ,11
+ ,-2
+ ,1
+ ,-11
+ ,-28
+ ,9
+ ,-6
+ ,-1
+ ,-13
+ ,-31
+ ,17
+ ,-4
+ ,0
+ ,-11
+ ,-21
+ ,21
+ ,-2
+ ,-1
+ ,-9
+ ,-19
+ ,21
+ ,0
+ ,6
+ ,-17
+ ,-22
+ ,41
+ ,-5
+ ,0
+ ,-22
+ ,-22
+ ,57
+ ,-4
+ ,-3
+ ,-25
+ ,-25
+ ,65
+ ,-5
+ ,-3
+ ,-20
+ ,-16
+ ,68
+ ,-1
+ ,4
+ ,-24
+ ,-22
+ ,73
+ ,-2
+ ,1
+ ,-24
+ ,-21
+ ,71
+ ,-4
+ ,0
+ ,-22
+ ,-10
+ ,71
+ ,-1
+ ,-4
+ ,-19
+ ,-7
+ ,70
+ ,1
+ ,-2
+ ,-18
+ ,-5
+ ,69
+ ,1
+ ,3
+ ,-17
+ ,-4
+ ,65
+ ,-2
+ ,2
+ ,-11
+ ,7
+ ,57
+ ,1
+ ,5
+ ,-11
+ ,6
+ ,57
+ ,1
+ ,6
+ ,-12
+ ,3
+ ,57
+ ,3
+ ,6
+ ,-10
+ ,10
+ ,55
+ ,3
+ ,3
+ ,-15
+ ,0
+ ,65
+ ,1
+ ,4
+ ,-15
+ ,-2
+ ,65
+ ,1
+ ,7
+ ,-15
+ ,-1
+ ,64
+ ,0
+ ,5
+ ,-13
+ ,2
+ ,60
+ ,2
+ ,6
+ ,-8
+ ,8
+ ,43
+ ,2
+ ,1
+ ,-13
+ ,-6
+ ,47
+ ,-1
+ ,3
+ ,-9
+ ,-4
+ ,40
+ ,1
+ ,6
+ ,-7
+ ,4
+ ,31
+ ,0
+ ,0
+ ,-4
+ ,7
+ ,27
+ ,1
+ ,3
+ ,-4
+ ,3
+ ,24
+ ,1
+ ,4
+ ,-2
+ ,3
+ ,23
+ ,3
+ ,7
+ ,0
+ ,8
+ ,17
+ ,2
+ ,6
+ ,-2
+ ,3
+ ,16
+ ,0
+ ,6
+ ,-3
+ ,-3
+ ,15
+ ,0
+ ,6
+ ,1
+ ,4
+ ,8
+ ,3
+ ,6
+ ,-2
+ ,-5
+ ,5
+ ,-2
+ ,2
+ ,-1
+ ,-1
+ ,6
+ ,0
+ ,2
+ ,1
+ ,5
+ ,5
+ ,1
+ ,2
+ ,-3
+ ,0
+ ,12
+ ,-1
+ ,3
+ ,-4
+ ,-6
+ ,8
+ ,-2
+ ,-1
+ ,-9
+ ,-13
+ ,17
+ ,-1
+ ,-4
+ ,-9
+ ,-15
+ ,22
+ ,-1
+ ,4
+ ,-7
+ ,-8
+ ,24
+ ,1
+ ,5
+ ,-14
+ ,-20
+ ,36
+ ,-2
+ ,3
+ ,-12
+ ,-10
+ ,31
+ ,-5
+ ,-1
+ ,-16
+ ,-22
+ ,34
+ ,-5
+ ,-4
+ ,-20
+ ,-25
+ ,47
+ ,-6
+ ,0
+ ,-12
+ ,-10
+ ,33
+ ,-4
+ ,-1
+ ,-12
+ ,-8
+ ,35
+ ,-3
+ ,-1
+ ,-10
+ ,-9
+ ,31
+ ,-3
+ ,3
+ ,-10
+ ,-5
+ ,35
+ ,-1
+ ,2
+ ,-13
+ ,-7
+ ,39
+ ,-2
+ ,-4
+ ,-16
+ ,-11
+ ,46
+ ,-3
+ ,-3
+ ,-14
+ ,-11
+ ,40
+ ,-3
+ ,-1
+ ,-17
+ ,-16
+ ,50
+ ,-3
+ ,3)
+ ,dim=c(5
+ ,145)
+ ,dimnames=list(c('consumentenvertrouwen'
+ ,'situatie'
+ ,'werkloosheid'
+ ,'financiële'
+ ,'spaarvermogen')
+ ,1:145))
> y <- array(NA,dim=c(5,145),dimnames=list(c('consumentenvertrouwen','situatie','werkloosheid','financiële','spaarvermogen'),1:145))
> 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'
> 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
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
consumentenvertrouwen situatie werkloosheid financi\303\253le spaarvermogen
1 14 10 -15 11 20
2 14 12 -14 11 19
3 15 13 -18 11 18
4 13 15 -13 11 13
5 8 3 -2 11 17
6 7 2 -1 9 17
7 3 -2 5 8 13
8 3 1 8 6 14
9 4 1 6 7 13
10 4 -1 7 8 17
11 0 -6 15 6 17
12 -4 -13 23 5 15
13 -14 -25 43 2 9
14 -18 -26 60 3 10
15 -8 -9 36 3 9
16 -1 1 28 7 14
17 1 3 23 8 18
18 2 6 23 7 18
19 0 2 22 7 12
20 1 5 22 6 16
21 0 5 24 6 12
22 -1 0 32 7 19
23 -3 -5 27 5 13
24 -3 -4 27 5 12
25 -3 -2 27 5 13
26 -4 -1 29 4 11
27 -8 -8 38 4 10
28 -9 -16 40 4 16
29 -13 -19 45 1 12
30 -18 -28 50 -1 6
31 -11 -11 43 3 8
32 -9 -4 44 4 6
33 -10 -9 44 3 8
34 -13 -12 49 2 8
35 -11 -10 42 1 9
36 -5 -2 36 4 13
37 -15 -13 57 3 8
38 -6 0 42 5 11
39 -6 0 39 6 8
40 -3 4 33 6 10
41 -1 7 32 6 15
42 -3 5 34 6 12
43 -4 2 37 6 13
44 -6 -2 38 5 12
45 0 6 28 6 15
46 -4 -3 31 5 13
47 -2 1 28 6 13
48 -2 0 30 5 16
49 -6 -7 39 7 14
50 -7 -6 38 4 12
51 -6 -4 39 5 15
52 -6 -4 38 6 14
53 -3 -2 37 6 19
54 -2 2 32 5 16
55 -5 -5 32 3 16
56 -11 -15 44 2 11
57 -11 -16 43 3 13
58 -11 -18 42 3 12
59 -10 -13 38 2 11
60 -14 -23 37 0 6
61 -8 -10 35 4 9
62 -9 -10 37 4 6
63 -5 -6 33 5 15
64 -1 -3 24 6 17
65 -2 -4 24 6 13
66 -5 -7 31 5 12
67 -4 -7 25 5 13
68 -6 -7 28 3 10
69 -2 -3 24 5 14
70 -2 0 25 5 13
71 -2 -5 16 5 10
72 -2 -3 17 3 11
73 2 3 11 6 12
74 1 2 12 6 7
75 -8 -7 39 4 11
76 -1 -1 19 6 9
77 1 0 14 5 13
78 -1 -3 15 4 12
79 2 4 7 5 5
80 2 2 12 5 13
81 1 3 12 4 11
82 -1 0 14 3 8
83 -2 -10 9 2 8
84 -2 -10 8 3 8
85 -1 -9 4 2 8
86 -8 -22 7 -1 0
87 -4 -16 3 0 3
88 -6 -18 5 -2 0
89 -3 -14 0 1 -1
90 -3 -12 -2 -2 -1
91 -7 -17 6 -2 -4
92 -9 -23 11 -2 1
93 -11 -28 9 -6 -1
94 -13 -31 17 -4 0
95 -11 -21 21 -2 -1
96 -9 -19 21 0 6
97 -17 -22 41 -5 0
98 -22 -22 57 -4 -3
99 -25 -25 65 -5 -3
100 -20 -16 68 -1 4
101 -24 -22 73 -2 1
102 -24 -21 71 -4 0
103 -22 -10 71 -1 -4
104 -19 -7 70 1 -2
105 -18 -5 69 1 3
106 -17 -4 65 -2 2
107 -11 7 57 1 5
108 -11 6 57 1 6
109 -12 3 57 3 6
110 -10 10 55 3 3
111 -15 0 65 1 4
112 -15 -2 65 1 7
113 -15 -1 64 0 5
114 -13 2 60 2 6
115 -8 8 43 2 1
116 -13 -6 47 -1 3
117 -9 -4 40 1 6
118 -7 4 31 0 0
119 -4 7 27 1 3
120 -4 3 24 1 4
121 -2 3 23 3 7
122 0 8 17 2 6
123 -2 3 16 0 6
124 -3 -3 15 0 6
125 1 4 8 3 6
126 -2 -5 5 -2 2
127 -1 -1 6 0 2
128 1 5 5 1 2
129 -3 0 12 -1 3
130 -4 -6 8 -2 -1
131 -9 -13 17 -1 -4
132 -9 -15 22 -1 4
133 -7 -8 24 1 5
134 -14 -20 36 -2 3
135 -12 -10 31 -5 -1
136 -16 -22 34 -5 -4
137 -20 -25 47 -6 0
138 -12 -10 33 -4 -1
139 -12 -8 35 -3 -1
140 -10 -9 31 -3 3
141 -10 -5 35 -1 2
142 -13 -7 39 -2 -4
143 -16 -11 46 -3 -3
144 -14 -11 40 -3 -1
145 -17 -16 50 -3 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) situatie werkloosheid
-0.03134 0.24763 -0.24994
`financi\\303\\253le` spaarvermogen
0.28792 0.23303
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.72661 -0.22154 0.02836 0.26368 0.62901
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.031345 0.071042 -0.441 0.66
situatie 0.247632 0.003853 64.266 <2e-16 ***
werkloosheid -0.249945 0.001461 -171.098 <2e-16 ***
`financi\\303\\253le` 0.287919 0.018729 15.373 <2e-16 ***
spaarvermogen 0.233027 0.009475 24.593 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3155 on 140 degrees of freedom
Multiple R-squared: 0.9983, Adjusted R-squared: 0.9982
F-statistic: 2.045e+04 on 4 and 140 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.001529998 0.003059997 0.99847000
[2,] 0.315078798 0.630157597 0.68492120
[3,] 0.195186184 0.390372367 0.80481382
[4,] 0.152174589 0.304349178 0.84782541
[5,] 0.272164622 0.544329245 0.72783538
[6,] 0.372384745 0.744769490 0.62761525
[7,] 0.319635452 0.639270903 0.68036455
[8,] 0.238554371 0.477108742 0.76144563
[9,] 0.198033790 0.396067580 0.80196621
[10,] 0.413329700 0.826659400 0.58667030
[11,] 0.332800519 0.665601039 0.66719948
[12,] 0.262143036 0.524286072 0.73785696
[13,] 0.237866430 0.475732861 0.76213357
[14,] 0.183756922 0.367513844 0.81624308
[15,] 0.329586705 0.659173411 0.67041329
[16,] 0.399954303 0.799908605 0.60004570
[17,] 0.427928488 0.855856977 0.57207151
[18,] 0.465277062 0.930554123 0.53472294
[19,] 0.503890260 0.992219479 0.49610974
[20,] 0.463314233 0.926628466 0.53668577
[21,] 0.400445916 0.800891831 0.59955408
[22,] 0.359480139 0.718960277 0.64051986
[23,] 0.342399530 0.684799059 0.65760047
[24,] 0.406429961 0.812859922 0.59357004
[25,] 0.382816526 0.765633053 0.61718347
[26,] 0.395077483 0.790154966 0.60492252
[27,] 0.449664384 0.899328768 0.55033562
[28,] 0.514729726 0.970540548 0.48527027
[29,] 0.498362763 0.996725525 0.50163724
[30,] 0.582596227 0.834807546 0.41740377
[31,] 0.604339520 0.791320961 0.39566048
[32,] 0.560691412 0.878617176 0.43930859
[33,] 0.515227513 0.969544974 0.48477249
[34,] 0.465351036 0.930702072 0.53464896
[35,] 0.490047733 0.980095467 0.50995227
[36,] 0.444470036 0.888940072 0.55552996
[37,] 0.443232555 0.886465110 0.55676744
[38,] 0.431455593 0.862911187 0.56854441
[39,] 0.380852418 0.761704835 0.61914758
[40,] 0.332694055 0.665388110 0.66730595
[41,] 0.340559096 0.681118192 0.65944090
[42,] 0.312954402 0.625908805 0.68704560
[43,] 0.271914300 0.543828599 0.72808570
[44,] 0.257371454 0.514742909 0.74262855
[45,] 0.344433792 0.688867585 0.65556621
[46,] 0.480899615 0.961799229 0.51910039
[47,] 0.494468718 0.988937435 0.50553128
[48,] 0.502120745 0.995758510 0.49787926
[49,] 0.650707663 0.698584674 0.34929234
[50,] 0.623144207 0.753711586 0.37685579
[51,] 0.656198826 0.687602347 0.34380117
[52,] 0.675102994 0.649794013 0.32489701
[53,] 0.678586816 0.642826367 0.32141318
[54,] 0.637773852 0.724452296 0.36222615
[55,] 0.620428748 0.759142505 0.37957125
[56,] 0.589747963 0.820504074 0.41025204
[57,] 0.562895623 0.874208754 0.43710438
[58,] 0.582568244 0.834863512 0.41743176
[59,] 0.616977694 0.766044612 0.38302231
[60,] 0.633574896 0.732850209 0.36642510
[61,] 0.639873207 0.720253586 0.36012679
[62,] 0.617933173 0.764133654 0.38206683
[63,] 0.579996777 0.840006446 0.42000322
[64,] 0.599007871 0.801984257 0.40099213
[65,] 0.582341562 0.835316876 0.41765844
[66,] 0.606830358 0.786339284 0.39316964
[67,] 0.597028222 0.805943555 0.40297178
[68,] 0.570597127 0.858805745 0.42940287
[69,] 0.567345182 0.865309636 0.43265482
[70,] 0.543241317 0.913517366 0.45675868
[71,] 0.531563650 0.936872701 0.46843635
[72,] 0.527793364 0.944413272 0.47220664
[73,] 0.501358042 0.997283917 0.49864196
[74,] 0.506298310 0.987403380 0.49370169
[75,] 0.467556890 0.935113780 0.53244311
[76,] 0.554202302 0.891595395 0.44579770
[77,] 0.509805052 0.980389896 0.49019495
[78,] 0.471996834 0.943993669 0.52800317
[79,] 0.509496651 0.981006698 0.49050335
[80,] 0.493891483 0.987782965 0.50610852
[81,] 0.533737518 0.932524963 0.46626248
[82,] 0.599194480 0.801611040 0.40080552
[83,] 0.597415714 0.805168572 0.40258429
[84,] 0.575872046 0.848255908 0.42412795
[85,] 0.535146679 0.929706642 0.46485332
[86,] 0.500848437 0.998303126 0.49915156
[87,] 0.463669561 0.927339122 0.53633044
[88,] 0.479222803 0.958445605 0.52077720
[89,] 0.482715534 0.965431068 0.51728447
[90,] 0.445529262 0.891058524 0.55447074
[91,] 0.498697299 0.997394597 0.50130270
[92,] 0.527342643 0.945314713 0.47265736
[93,] 0.583292739 0.833414522 0.41670726
[94,] 0.580973706 0.838052588 0.41902629
[95,] 0.577157264 0.845685471 0.42284274
[96,] 0.642115157 0.715769685 0.35788484
[97,] 0.819920661 0.360158678 0.18007934
[98,] 0.818248866 0.363502268 0.18175113
[99,] 0.855720930 0.288558141 0.14427907
[100,] 0.826599116 0.346801768 0.17340088
[101,] 0.799206900 0.401586199 0.20079310
[102,] 0.889592272 0.220815456 0.11040773
[103,] 0.873065723 0.253868555 0.12693428
[104,] 0.857168157 0.285663686 0.14283184
[105,] 0.823284653 0.353430694 0.17671535
[106,] 0.824997464 0.350005073 0.17500254
[107,] 0.811257213 0.377485573 0.18874279
[108,] 0.765045091 0.469909819 0.23495491
[109,] 0.713343718 0.573312563 0.28665628
[110,] 0.800871909 0.398256183 0.19912809
[111,] 0.784482262 0.431035476 0.21551774
[112,] 0.732000036 0.535999929 0.26799996
[113,] 0.672768006 0.654463988 0.32723199
[114,] 0.909416477 0.181167047 0.09058352
[115,] 0.945186640 0.109626719 0.05481336
[116,] 0.923409551 0.153180898 0.07659045
[117,] 0.907020567 0.185958867 0.09297943
[118,] 0.869419781 0.261160437 0.13058022
[119,] 0.913976877 0.172046247 0.08602312
[120,] 0.914201294 0.171597411 0.08579871
[121,] 0.936924786 0.126150427 0.06307521
[122,] 0.960219638 0.079560724 0.03978036
[123,] 0.950755758 0.098488485 0.04924424
[124,] 0.956823711 0.086352578 0.04317629
[125,] 0.959797261 0.080405478 0.04020274
[126,] 0.986827645 0.026344710 0.01317236
[127,] 0.988767527 0.022464946 0.01123247
[128,] 0.977249764 0.045500471 0.02275024
[129,] 0.958306756 0.083386487 0.04169324
[130,] 0.931429138 0.137141724 0.06857086
> postscript(file="/var/wessaorg/rcomp/tmp/18vb01352119348.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2m2ks1352119348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/31it61352119348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/48kyv1352119348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5t2sw1352119348.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 = 145
Frequency = 1
1 2 3 4 5 6
-0.02180319 -0.03409514 -0.04847948 -0.12888307 -0.34001393 -0.26659880
7 8 9 10 11 12
-0.55637347 -0.20662387 0.23859446 -0.23622456 -0.42266717 0.06428958
13 14 15 16 17 18
0.29669208 0.27244080 0.29704645 0.50435469 -0.46066171 0.08436134
19 20 21 22 23 24
0.22310779 -0.16397800 0.26802060 0.58663021 0.54906736 0.53446255
25 26 27 28 29 30
-0.19382870 -0.18759742 0.02835788 0.11114056 -0.10037279 0.35204129
31 32 33 34 35 36
-0.22504811 0.46960793 0.52963273 -0.18982768 -0.37981400 0.34359437
37 38 39 40 41 42
-0.23055570 0.52613493 0.18746279 0.23121099 0.07323401 -0.23253056
43 44 45 46 47 48
0.02717294 -0.21140777 0.32108649 0.05358286 0.02530101 0.36166029
49 50 51 52 53 54
0.23480458 0.06703942 -0.16528047 -0.47011726 0.61953778 0.36628601
55 56 57 58 59 60
-0.32445162 0.60426234 -0.15202405 0.32632232 -0.39067100 -0.42332141
61 62 63 64 65 66
0.00681448 0.20578587 -0.16968573 0.08394073 0.26368158 0.27713815
67 68 69 70 71 72
-0.45555836 -0.43080386 0.07094146 -0.18898251 -0.50124473 -0.40375288
73 74 75 76 77 78
-0.48599884 0.17671410 -0.20235646 0.20316993 0.06162377 -0.42458896
79 80 81 82 83 84
0.18569917 0.06646996 -0.42718853 -0.19740196 0.31711294 -0.22075106
85 86 87 88 89 90
-0.18024350 -0.48321758 0.04421002 0.31428368 0.44330105 0.31190458
91 92 93 94 95 96
0.24870537 -0.18091412 0.17508707 0.10867677 0.28932509 -0.41296764
97 98 99 100 101 102
0.16658491 -0.42313444 -0.39276020 0.34551936 0.06803664 0.12938029
103 104 105 106 107 108
-0.52622044 0.43904597 -0.47129899 0.37807399 0.09172374 0.10632855
109 110 111 112 113 114
-0.72661361 -0.26084590 0.05773416 -0.14608342 0.11031320 -0.44122783
115 116 117 118 119 120
-0.01094693 -0.14661619 0.33358573 -0.21089202 0.05943165 0.06709787
121 122 123 124 125 126
0.54223314 0.32535005 -0.11059651 0.12525073 -0.22154493 0.62901299
127 128 129 130 131 132
0.31259157 0.28893545 -0.38047924 0.32556129 -0.28034810 -0.39957731
133 134 135 136 137 138
-0.44197711 -0.14124251 -0.07142097 0.34907956 -0.30293062 0.14054969
139 140 141 142 143 144
-0.14274370 0.17299996 -0.16055961 0.02056633 -0.18439950 -0.15012322
145
-0.34462311
> postscript(file="/var/wessaorg/rcomp/tmp/6v5ta1352119348.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 = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.02180319 NA
1 -0.03409514 -0.02180319
2 -0.04847948 -0.03409514
3 -0.12888307 -0.04847948
4 -0.34001393 -0.12888307
5 -0.26659880 -0.34001393
6 -0.55637347 -0.26659880
7 -0.20662387 -0.55637347
8 0.23859446 -0.20662387
9 -0.23622456 0.23859446
10 -0.42266717 -0.23622456
11 0.06428958 -0.42266717
12 0.29669208 0.06428958
13 0.27244080 0.29669208
14 0.29704645 0.27244080
15 0.50435469 0.29704645
16 -0.46066171 0.50435469
17 0.08436134 -0.46066171
18 0.22310779 0.08436134
19 -0.16397800 0.22310779
20 0.26802060 -0.16397800
21 0.58663021 0.26802060
22 0.54906736 0.58663021
23 0.53446255 0.54906736
24 -0.19382870 0.53446255
25 -0.18759742 -0.19382870
26 0.02835788 -0.18759742
27 0.11114056 0.02835788
28 -0.10037279 0.11114056
29 0.35204129 -0.10037279
30 -0.22504811 0.35204129
31 0.46960793 -0.22504811
32 0.52963273 0.46960793
33 -0.18982768 0.52963273
34 -0.37981400 -0.18982768
35 0.34359437 -0.37981400
36 -0.23055570 0.34359437
37 0.52613493 -0.23055570
38 0.18746279 0.52613493
39 0.23121099 0.18746279
40 0.07323401 0.23121099
41 -0.23253056 0.07323401
42 0.02717294 -0.23253056
43 -0.21140777 0.02717294
44 0.32108649 -0.21140777
45 0.05358286 0.32108649
46 0.02530101 0.05358286
47 0.36166029 0.02530101
48 0.23480458 0.36166029
49 0.06703942 0.23480458
50 -0.16528047 0.06703942
51 -0.47011726 -0.16528047
52 0.61953778 -0.47011726
53 0.36628601 0.61953778
54 -0.32445162 0.36628601
55 0.60426234 -0.32445162
56 -0.15202405 0.60426234
57 0.32632232 -0.15202405
58 -0.39067100 0.32632232
59 -0.42332141 -0.39067100
60 0.00681448 -0.42332141
61 0.20578587 0.00681448
62 -0.16968573 0.20578587
63 0.08394073 -0.16968573
64 0.26368158 0.08394073
65 0.27713815 0.26368158
66 -0.45555836 0.27713815
67 -0.43080386 -0.45555836
68 0.07094146 -0.43080386
69 -0.18898251 0.07094146
70 -0.50124473 -0.18898251
71 -0.40375288 -0.50124473
72 -0.48599884 -0.40375288
73 0.17671410 -0.48599884
74 -0.20235646 0.17671410
75 0.20316993 -0.20235646
76 0.06162377 0.20316993
77 -0.42458896 0.06162377
78 0.18569917 -0.42458896
79 0.06646996 0.18569917
80 -0.42718853 0.06646996
81 -0.19740196 -0.42718853
82 0.31711294 -0.19740196
83 -0.22075106 0.31711294
84 -0.18024350 -0.22075106
85 -0.48321758 -0.18024350
86 0.04421002 -0.48321758
87 0.31428368 0.04421002
88 0.44330105 0.31428368
89 0.31190458 0.44330105
90 0.24870537 0.31190458
91 -0.18091412 0.24870537
92 0.17508707 -0.18091412
93 0.10867677 0.17508707
94 0.28932509 0.10867677
95 -0.41296764 0.28932509
96 0.16658491 -0.41296764
97 -0.42313444 0.16658491
98 -0.39276020 -0.42313444
99 0.34551936 -0.39276020
100 0.06803664 0.34551936
101 0.12938029 0.06803664
102 -0.52622044 0.12938029
103 0.43904597 -0.52622044
104 -0.47129899 0.43904597
105 0.37807399 -0.47129899
106 0.09172374 0.37807399
107 0.10632855 0.09172374
108 -0.72661361 0.10632855
109 -0.26084590 -0.72661361
110 0.05773416 -0.26084590
111 -0.14608342 0.05773416
112 0.11031320 -0.14608342
113 -0.44122783 0.11031320
114 -0.01094693 -0.44122783
115 -0.14661619 -0.01094693
116 0.33358573 -0.14661619
117 -0.21089202 0.33358573
118 0.05943165 -0.21089202
119 0.06709787 0.05943165
120 0.54223314 0.06709787
121 0.32535005 0.54223314
122 -0.11059651 0.32535005
123 0.12525073 -0.11059651
124 -0.22154493 0.12525073
125 0.62901299 -0.22154493
126 0.31259157 0.62901299
127 0.28893545 0.31259157
128 -0.38047924 0.28893545
129 0.32556129 -0.38047924
130 -0.28034810 0.32556129
131 -0.39957731 -0.28034810
132 -0.44197711 -0.39957731
133 -0.14124251 -0.44197711
134 -0.07142097 -0.14124251
135 0.34907956 -0.07142097
136 -0.30293062 0.34907956
137 0.14054969 -0.30293062
138 -0.14274370 0.14054969
139 0.17299996 -0.14274370
140 -0.16055961 0.17299996
141 0.02056633 -0.16055961
142 -0.18439950 0.02056633
143 -0.15012322 -0.18439950
144 -0.34462311 -0.15012322
145 NA -0.34462311
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.03409514 -0.02180319
[2,] -0.04847948 -0.03409514
[3,] -0.12888307 -0.04847948
[4,] -0.34001393 -0.12888307
[5,] -0.26659880 -0.34001393
[6,] -0.55637347 -0.26659880
[7,] -0.20662387 -0.55637347
[8,] 0.23859446 -0.20662387
[9,] -0.23622456 0.23859446
[10,] -0.42266717 -0.23622456
[11,] 0.06428958 -0.42266717
[12,] 0.29669208 0.06428958
[13,] 0.27244080 0.29669208
[14,] 0.29704645 0.27244080
[15,] 0.50435469 0.29704645
[16,] -0.46066171 0.50435469
[17,] 0.08436134 -0.46066171
[18,] 0.22310779 0.08436134
[19,] -0.16397800 0.22310779
[20,] 0.26802060 -0.16397800
[21,] 0.58663021 0.26802060
[22,] 0.54906736 0.58663021
[23,] 0.53446255 0.54906736
[24,] -0.19382870 0.53446255
[25,] -0.18759742 -0.19382870
[26,] 0.02835788 -0.18759742
[27,] 0.11114056 0.02835788
[28,] -0.10037279 0.11114056
[29,] 0.35204129 -0.10037279
[30,] -0.22504811 0.35204129
[31,] 0.46960793 -0.22504811
[32,] 0.52963273 0.46960793
[33,] -0.18982768 0.52963273
[34,] -0.37981400 -0.18982768
[35,] 0.34359437 -0.37981400
[36,] -0.23055570 0.34359437
[37,] 0.52613493 -0.23055570
[38,] 0.18746279 0.52613493
[39,] 0.23121099 0.18746279
[40,] 0.07323401 0.23121099
[41,] -0.23253056 0.07323401
[42,] 0.02717294 -0.23253056
[43,] -0.21140777 0.02717294
[44,] 0.32108649 -0.21140777
[45,] 0.05358286 0.32108649
[46,] 0.02530101 0.05358286
[47,] 0.36166029 0.02530101
[48,] 0.23480458 0.36166029
[49,] 0.06703942 0.23480458
[50,] -0.16528047 0.06703942
[51,] -0.47011726 -0.16528047
[52,] 0.61953778 -0.47011726
[53,] 0.36628601 0.61953778
[54,] -0.32445162 0.36628601
[55,] 0.60426234 -0.32445162
[56,] -0.15202405 0.60426234
[57,] 0.32632232 -0.15202405
[58,] -0.39067100 0.32632232
[59,] -0.42332141 -0.39067100
[60,] 0.00681448 -0.42332141
[61,] 0.20578587 0.00681448
[62,] -0.16968573 0.20578587
[63,] 0.08394073 -0.16968573
[64,] 0.26368158 0.08394073
[65,] 0.27713815 0.26368158
[66,] -0.45555836 0.27713815
[67,] -0.43080386 -0.45555836
[68,] 0.07094146 -0.43080386
[69,] -0.18898251 0.07094146
[70,] -0.50124473 -0.18898251
[71,] -0.40375288 -0.50124473
[72,] -0.48599884 -0.40375288
[73,] 0.17671410 -0.48599884
[74,] -0.20235646 0.17671410
[75,] 0.20316993 -0.20235646
[76,] 0.06162377 0.20316993
[77,] -0.42458896 0.06162377
[78,] 0.18569917 -0.42458896
[79,] 0.06646996 0.18569917
[80,] -0.42718853 0.06646996
[81,] -0.19740196 -0.42718853
[82,] 0.31711294 -0.19740196
[83,] -0.22075106 0.31711294
[84,] -0.18024350 -0.22075106
[85,] -0.48321758 -0.18024350
[86,] 0.04421002 -0.48321758
[87,] 0.31428368 0.04421002
[88,] 0.44330105 0.31428368
[89,] 0.31190458 0.44330105
[90,] 0.24870537 0.31190458
[91,] -0.18091412 0.24870537
[92,] 0.17508707 -0.18091412
[93,] 0.10867677 0.17508707
[94,] 0.28932509 0.10867677
[95,] -0.41296764 0.28932509
[96,] 0.16658491 -0.41296764
[97,] -0.42313444 0.16658491
[98,] -0.39276020 -0.42313444
[99,] 0.34551936 -0.39276020
[100,] 0.06803664 0.34551936
[101,] 0.12938029 0.06803664
[102,] -0.52622044 0.12938029
[103,] 0.43904597 -0.52622044
[104,] -0.47129899 0.43904597
[105,] 0.37807399 -0.47129899
[106,] 0.09172374 0.37807399
[107,] 0.10632855 0.09172374
[108,] -0.72661361 0.10632855
[109,] -0.26084590 -0.72661361
[110,] 0.05773416 -0.26084590
[111,] -0.14608342 0.05773416
[112,] 0.11031320 -0.14608342
[113,] -0.44122783 0.11031320
[114,] -0.01094693 -0.44122783
[115,] -0.14661619 -0.01094693
[116,] 0.33358573 -0.14661619
[117,] -0.21089202 0.33358573
[118,] 0.05943165 -0.21089202
[119,] 0.06709787 0.05943165
[120,] 0.54223314 0.06709787
[121,] 0.32535005 0.54223314
[122,] -0.11059651 0.32535005
[123,] 0.12525073 -0.11059651
[124,] -0.22154493 0.12525073
[125,] 0.62901299 -0.22154493
[126,] 0.31259157 0.62901299
[127,] 0.28893545 0.31259157
[128,] -0.38047924 0.28893545
[129,] 0.32556129 -0.38047924
[130,] -0.28034810 0.32556129
[131,] -0.39957731 -0.28034810
[132,] -0.44197711 -0.39957731
[133,] -0.14124251 -0.44197711
[134,] -0.07142097 -0.14124251
[135,] 0.34907956 -0.07142097
[136,] -0.30293062 0.34907956
[137,] 0.14054969 -0.30293062
[138,] -0.14274370 0.14054969
[139,] 0.17299996 -0.14274370
[140,] -0.16055961 0.17299996
[141,] 0.02056633 -0.16055961
[142,] -0.18439950 0.02056633
[143,] -0.15012322 -0.18439950
[144,] -0.34462311 -0.15012322
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.03409514 -0.02180319
2 -0.04847948 -0.03409514
3 -0.12888307 -0.04847948
4 -0.34001393 -0.12888307
5 -0.26659880 -0.34001393
6 -0.55637347 -0.26659880
7 -0.20662387 -0.55637347
8 0.23859446 -0.20662387
9 -0.23622456 0.23859446
10 -0.42266717 -0.23622456
11 0.06428958 -0.42266717
12 0.29669208 0.06428958
13 0.27244080 0.29669208
14 0.29704645 0.27244080
15 0.50435469 0.29704645
16 -0.46066171 0.50435469
17 0.08436134 -0.46066171
18 0.22310779 0.08436134
19 -0.16397800 0.22310779
20 0.26802060 -0.16397800
21 0.58663021 0.26802060
22 0.54906736 0.58663021
23 0.53446255 0.54906736
24 -0.19382870 0.53446255
25 -0.18759742 -0.19382870
26 0.02835788 -0.18759742
27 0.11114056 0.02835788
28 -0.10037279 0.11114056
29 0.35204129 -0.10037279
30 -0.22504811 0.35204129
31 0.46960793 -0.22504811
32 0.52963273 0.46960793
33 -0.18982768 0.52963273
34 -0.37981400 -0.18982768
35 0.34359437 -0.37981400
36 -0.23055570 0.34359437
37 0.52613493 -0.23055570
38 0.18746279 0.52613493
39 0.23121099 0.18746279
40 0.07323401 0.23121099
41 -0.23253056 0.07323401
42 0.02717294 -0.23253056
43 -0.21140777 0.02717294
44 0.32108649 -0.21140777
45 0.05358286 0.32108649
46 0.02530101 0.05358286
47 0.36166029 0.02530101
48 0.23480458 0.36166029
49 0.06703942 0.23480458
50 -0.16528047 0.06703942
51 -0.47011726 -0.16528047
52 0.61953778 -0.47011726
53 0.36628601 0.61953778
54 -0.32445162 0.36628601
55 0.60426234 -0.32445162
56 -0.15202405 0.60426234
57 0.32632232 -0.15202405
58 -0.39067100 0.32632232
59 -0.42332141 -0.39067100
60 0.00681448 -0.42332141
61 0.20578587 0.00681448
62 -0.16968573 0.20578587
63 0.08394073 -0.16968573
64 0.26368158 0.08394073
65 0.27713815 0.26368158
66 -0.45555836 0.27713815
67 -0.43080386 -0.45555836
68 0.07094146 -0.43080386
69 -0.18898251 0.07094146
70 -0.50124473 -0.18898251
71 -0.40375288 -0.50124473
72 -0.48599884 -0.40375288
73 0.17671410 -0.48599884
74 -0.20235646 0.17671410
75 0.20316993 -0.20235646
76 0.06162377 0.20316993
77 -0.42458896 0.06162377
78 0.18569917 -0.42458896
79 0.06646996 0.18569917
80 -0.42718853 0.06646996
81 -0.19740196 -0.42718853
82 0.31711294 -0.19740196
83 -0.22075106 0.31711294
84 -0.18024350 -0.22075106
85 -0.48321758 -0.18024350
86 0.04421002 -0.48321758
87 0.31428368 0.04421002
88 0.44330105 0.31428368
89 0.31190458 0.44330105
90 0.24870537 0.31190458
91 -0.18091412 0.24870537
92 0.17508707 -0.18091412
93 0.10867677 0.17508707
94 0.28932509 0.10867677
95 -0.41296764 0.28932509
96 0.16658491 -0.41296764
97 -0.42313444 0.16658491
98 -0.39276020 -0.42313444
99 0.34551936 -0.39276020
100 0.06803664 0.34551936
101 0.12938029 0.06803664
102 -0.52622044 0.12938029
103 0.43904597 -0.52622044
104 -0.47129899 0.43904597
105 0.37807399 -0.47129899
106 0.09172374 0.37807399
107 0.10632855 0.09172374
108 -0.72661361 0.10632855
109 -0.26084590 -0.72661361
110 0.05773416 -0.26084590
111 -0.14608342 0.05773416
112 0.11031320 -0.14608342
113 -0.44122783 0.11031320
114 -0.01094693 -0.44122783
115 -0.14661619 -0.01094693
116 0.33358573 -0.14661619
117 -0.21089202 0.33358573
118 0.05943165 -0.21089202
119 0.06709787 0.05943165
120 0.54223314 0.06709787
121 0.32535005 0.54223314
122 -0.11059651 0.32535005
123 0.12525073 -0.11059651
124 -0.22154493 0.12525073
125 0.62901299 -0.22154493
126 0.31259157 0.62901299
127 0.28893545 0.31259157
128 -0.38047924 0.28893545
129 0.32556129 -0.38047924
130 -0.28034810 0.32556129
131 -0.39957731 -0.28034810
132 -0.44197711 -0.39957731
133 -0.14124251 -0.44197711
134 -0.07142097 -0.14124251
135 0.34907956 -0.07142097
136 -0.30293062 0.34907956
137 0.14054969 -0.30293062
138 -0.14274370 0.14054969
139 0.17299996 -0.14274370
140 -0.16055961 0.17299996
141 0.02056633 -0.16055961
142 -0.18439950 0.02056633
143 -0.15012322 -0.18439950
144 -0.34462311 -0.15012322
> 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/wessaorg/rcomp/tmp/79wnm1352119348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8d5za1352119348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9p1k11352119348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10k8gm1352119348.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11r54f1352119348.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/wessaorg/rcomp/tmp/126j5d1352119349.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/wessaorg/rcomp/tmp/13f1241352119349.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/wessaorg/rcomp/tmp/14is8j1352119349.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/wessaorg/rcomp/tmp/15uos21352119349.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/wessaorg/rcomp/tmp/16jfas1352119349.tab")
+ }
>
> try(system("convert tmp/18vb01352119348.ps tmp/18vb01352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m2ks1352119348.ps tmp/2m2ks1352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/31it61352119348.ps tmp/31it61352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/48kyv1352119348.ps tmp/48kyv1352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/5t2sw1352119348.ps tmp/5t2sw1352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v5ta1352119348.ps tmp/6v5ta1352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/79wnm1352119348.ps tmp/79wnm1352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d5za1352119348.ps tmp/8d5za1352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/9p1k11352119348.ps tmp/9p1k11352119348.png",intern=TRUE))
character(0)
> try(system("convert tmp/10k8gm1352119348.ps tmp/10k8gm1352119348.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.227 0.828 8.054