R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(15
+ ,2.1
+ ,14.4
+ ,2.1
+ ,13.5
+ ,2.6
+ ,12.8
+ ,2.6
+ ,12.3
+ ,2.7
+ ,12.2
+ ,2.5
+ ,14.5
+ ,2.4
+ ,17.2
+ ,1.9
+ ,18
+ ,2.2
+ ,18.1
+ ,1.9
+ ,18
+ ,2
+ ,18.3
+ ,2.2
+ ,18.7
+ ,2.5
+ ,18.6
+ ,2.5
+ ,18.3
+ ,2.7
+ ,17.9
+ ,2.6
+ ,17.4
+ ,2.3
+ ,17.4
+ ,2
+ ,20.1
+ ,2.3
+ ,23.2
+ ,2.9
+ ,24.2
+ ,2.5
+ ,24.2
+ ,2.5
+ ,23.9
+ ,2.3
+ ,23.8
+ ,2.5
+ ,23.8
+ ,2.3
+ ,23.3
+ ,2.4
+ ,22.4
+ ,2.2
+ ,21.5
+ ,2.4
+ ,20.5
+ ,2.6
+ ,19.9
+ ,2.8
+ ,22
+ ,2.8
+ ,24.9
+ ,2.5
+ ,25.7
+ ,2.5
+ ,25.3
+ ,2.2
+ ,24.4
+ ,2.1
+ ,23.8
+ ,1.9
+ ,23.5
+ ,1.9
+ ,23
+ ,1.7
+ ,22.2
+ ,1.7
+ ,21.4
+ ,1.6
+ ,20.3
+ ,1.4
+ ,19.5
+ ,1.1
+ ,21.7
+ ,0.8
+ ,24.7
+ ,0.9
+ ,25.3
+ ,1
+ ,24.9
+ ,1
+ ,24.1
+ ,1.1
+ ,23.4
+ ,1.3
+ ,23.1
+ ,1.4
+ ,22.4
+ ,1.4
+ ,21.3
+ ,1.6
+ ,20.3
+ ,2
+ ,19.3
+ ,2.1
+ ,18.7
+ ,1.9
+ ,21
+ ,1.5
+ ,24
+ ,1.2
+ ,24.8
+ ,1.5
+ ,24.2
+ ,2.2
+ ,23.3
+ ,2.1
+ ,22.7
+ ,2.1
+ ,22.3
+ ,2.1
+ ,21.8
+ ,1.9
+ ,21.2
+ ,1.3
+ ,20.5
+ ,1.1
+ ,19.7
+ ,1.4
+ ,19.2
+ ,1.6
+ ,21.2
+ ,1.9
+ ,23.9
+ ,1.7
+ ,24.8
+ ,1.6
+ ,24.2
+ ,1.2
+ ,23
+ ,1.3
+ ,22.2
+ ,0.9
+ ,21.8
+ ,0.5
+ ,21.2
+ ,0.8
+ ,20.5
+ ,1
+ ,19.7
+ ,1.3
+ ,19
+ ,1.3
+ ,18.4
+ ,1.2
+ ,20.7
+ ,1.2
+ ,24.5
+ ,1
+ ,26
+ ,0.8
+ ,25.2
+ ,0.7
+ ,24.1
+ ,0.6
+ ,23.7
+ ,0.7
+ ,23.5
+ ,1
+ ,23.1
+ ,1
+ ,22.7
+ ,1.3
+ ,22.5
+ ,1.1
+ ,21.7
+ ,0.8
+ ,20.5
+ ,0.7
+ ,21.9
+ ,0.7
+ ,22.9
+ ,0.9
+ ,21.5
+ ,1.3
+ ,19
+ ,1.4
+ ,17
+ ,1.6
+ ,16.1
+ ,2.1
+ ,15.9
+ ,0.3
+ ,15.7
+ ,2.1
+ ,15.1
+ ,2.5
+ ,14.8
+ ,2.3
+ ,14.3
+ ,2.4
+ ,14.5
+ ,3
+ ,18.9
+ ,1.7
+ ,21.6
+ ,3.5
+ ,20.4
+ ,4
+ ,17.9
+ ,3.7
+ ,15.7
+ ,3.7
+ ,14.5
+ ,3
+ ,14
+ ,2.7
+ ,13.9
+ ,2.5
+ ,14.4
+ ,2.2
+ ,15.8
+ ,2.9
+ ,15.6
+ ,3.1
+ ,14.7
+ ,3
+ ,16.7
+ ,2.8
+ ,17.9
+ ,2.5
+ ,18.7
+ ,1.9
+ ,20.1
+ ,1.9
+ ,19.5
+ ,1.8
+ ,19.4
+ ,2
+ ,18.6
+ ,2.6
+ ,17.8
+ ,2.5
+ ,17.1
+ ,2.5
+ ,16.5
+ ,1.6
+ ,15.5
+ ,1.4
+ ,14.9
+ ,0.8
+ ,18.6
+ ,1.1
+ ,19.1
+ ,1.3
+ ,18.8
+ ,1.2
+ ,18.2
+ ,1.3
+ ,18
+ ,1.1
+ ,19
+ ,1.3
+ ,20.7
+ ,1.2
+ ,21.2
+ ,1.6
+ ,20.7
+ ,1.7
+ ,19.6
+ ,1.5
+ ,18.6
+ ,0.9
+ ,18.7
+ ,1.5
+ ,23.8
+ ,1.4
+ ,24.9
+ ,1.6
+ ,24.8
+ ,1.7
+ ,23.8
+ ,1.4
+ ,22.3
+ ,1.8
+ ,21.7
+ ,1.7
+ ,20.7
+ ,1.4
+ ,19.7
+ ,1.2
+ ,18.4
+ ,1
+ ,17.4
+ ,1.7
+ ,17
+ ,2.4
+ ,18
+ ,2
+ ,23.8
+ ,2.1
+ ,25.5
+ ,2
+ ,25.6
+ ,1.8
+ ,23.7
+ ,2.7
+ ,22
+ ,2.3
+ ,21.3
+ ,1.9
+ ,20.7
+ ,2
+ ,20.4
+ ,2.3
+ ,20.3
+ ,2.8
+ ,20.4
+ ,2.4
+ ,19.8
+ ,2.3
+ ,19.5
+ ,2.7
+ ,23.1
+ ,2.7
+ ,23.5
+ ,2.9
+ ,23.5
+ ,3
+ ,22.9
+ ,2.2
+ ,21.9
+ ,2.3
+ ,21.5
+ ,2.8
+ ,20.5
+ ,2.8
+ ,20.2
+ ,2.8
+ ,19.4
+ ,2.2
+ ,19.2
+ ,2.6
+ ,18.8
+ ,2.8
+ ,18.8
+ ,2.5
+ ,22.6
+ ,2.4
+ ,23.3
+ ,2.3
+ ,23
+ ,1.9
+ ,21.4
+ ,1.7
+ ,19.9
+ ,2
+ ,18.8
+ ,2.1
+ ,18.6
+ ,1.7
+ ,18.4
+ ,1.8
+ ,18.6
+ ,1.8
+ ,19.9
+ ,1.8
+ ,19.2
+ ,1.3
+ ,18.4
+ ,1.3
+ ,21.1
+ ,1.3
+ ,20.5
+ ,1.2
+ ,19.1
+ ,1.4
+ ,18.1
+ ,2.2
+ ,17
+ ,2.9
+ ,17.1
+ ,3.1
+ ,17.4
+ ,3.5
+ ,16.8
+ ,3.6
+ ,15.3
+ ,4.4
+ ,14.3
+ ,4.1
+ ,13.4
+ ,5.1
+ ,15.3
+ ,5.8
+ ,22.1
+ ,5.9
+ ,23.7
+ ,5.4
+ ,22.2
+ ,5.5
+ ,19.5
+ ,4.8
+ ,16.6
+ ,3.2
+ ,17.3
+ ,2.7
+ ,19.8
+ ,2.1
+ ,21.2
+ ,1.9
+ ,21.5
+ ,0.6
+ ,20.6
+ ,0.7
+ ,19.1
+ ,-0.2
+ ,19.6
+ ,-1
+ ,23.5
+ ,-1.7
+ ,24
+ ,-0.7
+ ,23.2
+ ,-1
+ ,21.2
+ ,-0.9)
+ ,dim=c(2
+ ,214)
+ ,dimnames=list(c('Y(Werkloosheid)'
+ ,'X(inflatie)')
+ ,1:214))
> y <- array(NA,dim=c(2,214),dimnames=list(c('Y(Werkloosheid)','X(inflatie)'),1:214))
> 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
Y(Werkloosheid) X(inflatie)
1 15.0 2.1
2 14.4 2.1
3 13.5 2.6
4 12.8 2.6
5 12.3 2.7
6 12.2 2.5
7 14.5 2.4
8 17.2 1.9
9 18.0 2.2
10 18.1 1.9
11 18.0 2.0
12 18.3 2.2
13 18.7 2.5
14 18.6 2.5
15 18.3 2.7
16 17.9 2.6
17 17.4 2.3
18 17.4 2.0
19 20.1 2.3
20 23.2 2.9
21 24.2 2.5
22 24.2 2.5
23 23.9 2.3
24 23.8 2.5
25 23.8 2.3
26 23.3 2.4
27 22.4 2.2
28 21.5 2.4
29 20.5 2.6
30 19.9 2.8
31 22.0 2.8
32 24.9 2.5
33 25.7 2.5
34 25.3 2.2
35 24.4 2.1
36 23.8 1.9
37 23.5 1.9
38 23.0 1.7
39 22.2 1.7
40 21.4 1.6
41 20.3 1.4
42 19.5 1.1
43 21.7 0.8
44 24.7 0.9
45 25.3 1.0
46 24.9 1.0
47 24.1 1.1
48 23.4 1.3
49 23.1 1.4
50 22.4 1.4
51 21.3 1.6
52 20.3 2.0
53 19.3 2.1
54 18.7 1.9
55 21.0 1.5
56 24.0 1.2
57 24.8 1.5
58 24.2 2.2
59 23.3 2.1
60 22.7 2.1
61 22.3 2.1
62 21.8 1.9
63 21.2 1.3
64 20.5 1.1
65 19.7 1.4
66 19.2 1.6
67 21.2 1.9
68 23.9 1.7
69 24.8 1.6
70 24.2 1.2
71 23.0 1.3
72 22.2 0.9
73 21.8 0.5
74 21.2 0.8
75 20.5 1.0
76 19.7 1.3
77 19.0 1.3
78 18.4 1.2
79 20.7 1.2
80 24.5 1.0
81 26.0 0.8
82 25.2 0.7
83 24.1 0.6
84 23.7 0.7
85 23.5 1.0
86 23.1 1.0
87 22.7 1.3
88 22.5 1.1
89 21.7 0.8
90 20.5 0.7
91 21.9 0.7
92 22.9 0.9
93 21.5 1.3
94 19.0 1.4
95 17.0 1.6
96 16.1 2.1
97 15.9 0.3
98 15.7 2.1
99 15.1 2.5
100 14.8 2.3
101 14.3 2.4
102 14.5 3.0
103 18.9 1.7
104 21.6 3.5
105 20.4 4.0
106 17.9 3.7
107 15.7 3.7
108 14.5 3.0
109 14.0 2.7
110 13.9 2.5
111 14.4 2.2
112 15.8 2.9
113 15.6 3.1
114 14.7 3.0
115 16.7 2.8
116 17.9 2.5
117 18.7 1.9
118 20.1 1.9
119 19.5 1.8
120 19.4 2.0
121 18.6 2.6
122 17.8 2.5
123 17.1 2.5
124 16.5 1.6
125 15.5 1.4
126 14.9 0.8
127 18.6 1.1
128 19.1 1.3
129 18.8 1.2
130 18.2 1.3
131 18.0 1.1
132 19.0 1.3
133 20.7 1.2
134 21.2 1.6
135 20.7 1.7
136 19.6 1.5
137 18.6 0.9
138 18.7 1.5
139 23.8 1.4
140 24.9 1.6
141 24.8 1.7
142 23.8 1.4
143 22.3 1.8
144 21.7 1.7
145 20.7 1.4
146 19.7 1.2
147 18.4 1.0
148 17.4 1.7
149 17.0 2.4
150 18.0 2.0
151 23.8 2.1
152 25.5 2.0
153 25.6 1.8
154 23.7 2.7
155 22.0 2.3
156 21.3 1.9
157 20.7 2.0
158 20.4 2.3
159 20.3 2.8
160 20.4 2.4
161 19.8 2.3
162 19.5 2.7
163 23.1 2.7
164 23.5 2.9
165 23.5 3.0
166 22.9 2.2
167 21.9 2.3
168 21.5 2.8
169 20.5 2.8
170 20.2 2.8
171 19.4 2.2
172 19.2 2.6
173 18.8 2.8
174 18.8 2.5
175 22.6 2.4
176 23.3 2.3
177 23.0 1.9
178 21.4 1.7
179 19.9 2.0
180 18.8 2.1
181 18.6 1.7
182 18.4 1.8
183 18.6 1.8
184 19.9 1.8
185 19.2 1.3
186 18.4 1.3
187 21.1 1.3
188 20.5 1.2
189 19.1 1.4
190 18.1 2.2
191 17.0 2.9
192 17.1 3.1
193 17.4 3.5
194 16.8 3.6
195 15.3 4.4
196 14.3 4.1
197 13.4 5.1
198 15.3 5.8
199 22.1 5.9
200 23.7 5.4
201 22.2 5.5
202 19.5 4.8
203 16.6 3.2
204 17.3 2.7
205 19.8 2.1
206 21.2 1.9
207 21.5 0.6
208 20.6 0.7
209 19.1 -0.2
210 19.6 -1.0
211 23.5 -1.7
212 24.0 -0.7
213 23.2 -1.0
214 21.2 -0.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `X(inflatie)`
21.915 -0.898
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.46986 -1.92906 -0.07906 2.43518 6.63436
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 21.9149 0.4351 50.370 < 2e-16 ***
`X(inflatie)` -0.8980 0.1954 -4.596 7.38e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.012 on 212 degrees of freedom
Multiple R-squared: 0.09062, Adjusted R-squared: 0.08633
F-statistic: 21.13 on 1 and 212 DF, p-value: 7.383e-06
> 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,] 0.004947771 0.0098955425 0.9950522288
[2,] 0.004708292 0.0094165849 0.9952917075
[3,] 0.002616036 0.0052320714 0.9973839643
[4,] 0.002071143 0.0041422861 0.9979288570
[5,] 0.020801009 0.0416020185 0.9791989908
[6,] 0.010873236 0.0217464711 0.9891267644
[7,] 0.006532166 0.0130643319 0.9934678340
[8,] 0.012476210 0.0249524202 0.9875237899
[9,] 0.088760726 0.1775214519 0.9112392741
[10,] 0.165263106 0.3305262128 0.8347368936
[11,] 0.248628455 0.4972569101 0.7513715450
[12,] 0.249105894 0.4982117888 0.7508941056
[13,] 0.201452947 0.4029058949 0.7985470526
[14,] 0.153216169 0.3064323379 0.8467838310
[15,] 0.197603124 0.3952062479 0.8023968760
[16,] 0.579495800 0.8410083993 0.4205041997
[17,] 0.828507623 0.3429847539 0.1714923769
[18,] 0.931352539 0.1372949220 0.0686474610
[19,] 0.969700606 0.0605987884 0.0302993942
[20,] 0.984285622 0.0314287560 0.0157143780
[21,] 0.991807199 0.0163856030 0.0081928015
[22,] 0.994386526 0.0112269488 0.0056134744
[23,] 0.994940963 0.0101180731 0.0050590366
[24,] 0.994100856 0.0117982881 0.0058991440
[25,] 0.991941317 0.0161173665 0.0080586832
[26,] 0.988547958 0.0229040832 0.0114520416
[27,] 0.986808643 0.0263827141 0.0131913570
[28,] 0.993237233 0.0135255330 0.0067627665
[29,] 0.997422594 0.0051548122 0.0025774061
[30,] 0.999006200 0.0019875994 0.0009937997
[31,] 0.999420311 0.0011593787 0.0005796894
[32,] 0.999546101 0.0009077975 0.0004538988
[33,] 0.999568661 0.0008626779 0.0004313390
[34,] 0.999489571 0.0010208578 0.0005104289
[35,] 0.999294211 0.0014115775 0.0007057887
[36,] 0.998955424 0.0020891517 0.0010445759
[37,] 0.998489552 0.0030208955 0.0015104477
[38,] 0.997999223 0.0040015537 0.0020007768
[39,] 0.997133977 0.0057320454 0.0028660227
[40,] 0.997124248 0.0057515040 0.0028757520
[41,] 0.997369805 0.0052603904 0.0026301952
[42,] 0.997257154 0.0054856915 0.0027428458
[43,] 0.996755982 0.0064880359 0.0032440180
[44,] 0.995958656 0.0080826889 0.0040413444
[45,] 0.994909221 0.0101815584 0.0050907792
[46,] 0.993288934 0.0134221313 0.0067110657
[47,] 0.991000181 0.0179996382 0.0089998191
[48,] 0.987980388 0.0240392233 0.0120196116
[49,] 0.984531650 0.0309367007 0.0154683503
[50,] 0.981733222 0.0365335565 0.0182667783
[51,] 0.976548846 0.0469023083 0.0234511541
[52,] 0.973807001 0.0523859972 0.0261929986
[53,] 0.975715761 0.0485684772 0.0242842386
[54,] 0.980007246 0.0399855083 0.0199927542
[55,] 0.979763141 0.0404737185 0.0202368593
[56,] 0.977656444 0.0446871113 0.0223435557
[57,] 0.974230426 0.0515391480 0.0257695740
[58,] 0.968485422 0.0630291568 0.0315145784
[59,] 0.961492819 0.0770143618 0.0385071809
[60,] 0.955639474 0.0887210524 0.0443605262
[61,] 0.949419024 0.1011619517 0.0505809758
[62,] 0.942863745 0.1142725094 0.0571362547
[63,] 0.930886943 0.1382261141 0.0691130571
[64,] 0.931224588 0.1375508242 0.0687754121
[65,] 0.938865571 0.1222688589 0.0611344294
[66,] 0.936360896 0.1272782087 0.0636391043
[67,] 0.927573541 0.1448529188 0.0724264594
[68,] 0.915507004 0.1689859916 0.0844929958
[69,] 0.904828329 0.1903433427 0.0951716714
[70,] 0.891808663 0.2163826739 0.1081913370
[71,] 0.878426953 0.2431460947 0.1215730473
[72,] 0.865347544 0.2693049114 0.1346524557
[73,] 0.857775737 0.2844485253 0.1422242626
[74,] 0.858902425 0.2821951508 0.1410975754
[75,] 0.837965928 0.3240681442 0.1620340721
[76,] 0.839412528 0.3211749444 0.1605874722
[77,] 0.864451211 0.2710975786 0.1355487893
[78,] 0.871865735 0.2562685305 0.1281342652
[79,] 0.864810803 0.2703783930 0.1351891965
[80,] 0.855131268 0.2897374648 0.1448687324
[81,] 0.846216979 0.3075660421 0.1537830210
[82,] 0.833426164 0.3331476718 0.1665738359
[83,] 0.818941117 0.3621177654 0.1810588827
[84,] 0.801010939 0.3979781212 0.1989890606
[85,] 0.779706429 0.4405871429 0.2202935715
[86,] 0.763882162 0.4722356762 0.2361178381
[87,] 0.740620857 0.5187582867 0.2593791433
[88,] 0.722884032 0.5542319360 0.2771159680
[89,] 0.695231485 0.6095370304 0.3047685152
[90,] 0.679072348 0.6418553046 0.3209276523
[91,] 0.700539326 0.5989213488 0.2994606744
[92,] 0.727243694 0.5455126115 0.2727563058
[93,] 0.829815661 0.3403686784 0.1701843392
[94,] 0.855867047 0.2882659056 0.1441329528
[95,] 0.881249256 0.2375014889 0.1187507444
[96,] 0.911634129 0.1767317412 0.0883658706
[97,] 0.940714299 0.1185714014 0.0592857007
[98,] 0.952933957 0.0941320863 0.0470660431
[99,] 0.945275861 0.1094482776 0.0547241388
[100,] 0.948785034 0.1024299326 0.0512149663
[101,] 0.946826746 0.1063465076 0.0531732538
[102,] 0.935801426 0.1283971488 0.0641985744
[103,] 0.932904563 0.1341908737 0.0670954368
[104,] 0.947842981 0.1043140373 0.0521570186
[105,] 0.967224276 0.0655514482 0.0327757241
[106,] 0.982158862 0.0356822752 0.0178411376
[107,] 0.990358400 0.0192832004 0.0096416002
[108,] 0.991114244 0.0177715112 0.0088857556
[109,] 0.991926534 0.0161469320 0.0080734660
[110,] 0.994401236 0.0111975275 0.0055987638
[111,] 0.994124444 0.0117511119 0.0058755560
[112,] 0.992951192 0.0140976166 0.0070488083
[113,] 0.991358080 0.0172838393 0.0086419196
[114,] 0.988726416 0.0225471676 0.0112735838
[115,] 0.985670975 0.0286580499 0.0143290250
[116,] 0.981839308 0.0363213846 0.0181606923
[117,] 0.977537920 0.0449241594 0.0224620797
[118,] 0.974168471 0.0516630570 0.0258315285
[119,] 0.972844865 0.0543102692 0.0271551346
[120,] 0.978360473 0.0432790541 0.0216395271
[121,] 0.988049410 0.0239011791 0.0119505896
[122,] 0.996288826 0.0074223487 0.0037111744
[123,] 0.995978515 0.0080429696 0.0040214848
[124,] 0.995151652 0.0096966957 0.0048483478
[125,] 0.994495534 0.0110089325 0.0055044663
[126,] 0.994298674 0.0114026518 0.0057013259
[127,] 0.994575774 0.0108484527 0.0054242264
[128,] 0.993615283 0.0127694336 0.0063847168
[129,] 0.991523029 0.0169539418 0.0084769709
[130,] 0.988944882 0.0221102357 0.0110551179
[131,] 0.985557798 0.0288844041 0.0144422020
[132,] 0.981989661 0.0360206772 0.0180103386
[133,] 0.981453979 0.0370920414 0.0185460207
[134,] 0.979057965 0.0418840705 0.0209420353
[135,] 0.979608532 0.0407829364 0.0203914682
[136,] 0.985524518 0.0289509633 0.0144754816
[137,] 0.990067059 0.0198658813 0.0099329406
[138,] 0.990701630 0.0185967395 0.0092983698
[139,] 0.989233486 0.0215330284 0.0107665142
[140,] 0.986562610 0.0268747800 0.0134373900
[141,] 0.982369397 0.0352612069 0.0176306034
[142,] 0.978030562 0.0439388764 0.0219694382
[143,] 0.977267473 0.0454650542 0.0227325271
[144,] 0.978413137 0.0431737262 0.0215868631
[145,] 0.979154683 0.0416906347 0.0208453173
[146,] 0.977352568 0.0452948649 0.0226474325
[147,] 0.981062167 0.0378756665 0.0189378333
[148,] 0.991087247 0.0178255063 0.0089127531
[149,] 0.996293268 0.0074134634 0.0037067317
[150,] 0.997641327 0.0047173461 0.0023586730
[151,] 0.997290188 0.0054196233 0.0027098116
[152,] 0.996372550 0.0072549010 0.0036274505
[153,] 0.994969866 0.0100602686 0.0050301343
[154,] 0.993071793 0.0138564148 0.0069282074
[155,] 0.990733781 0.0185324381 0.0092662190
[156,] 0.987534574 0.0249308510 0.0124654255
[157,] 0.983132144 0.0337357116 0.0168678558
[158,] 0.977496847 0.0450063068 0.0225031534
[159,] 0.981490539 0.0370189230 0.0185094615
[160,] 0.987577406 0.0248451882 0.0124225941
[161,] 0.992389143 0.0152217142 0.0076108571
[162,] 0.993352871 0.0132942578 0.0066471289
[163,] 0.992676518 0.0146469645 0.0073234823
[164,] 0.991898551 0.0162028980 0.0081014490
[165,] 0.989426584 0.0211468321 0.0105734160
[166,] 0.985878678 0.0282426442 0.0141213221
[167,] 0.980480748 0.0390385034 0.0195192517
[168,] 0.973329219 0.0533415617 0.0266707809
[169,] 0.964221480 0.0715570401 0.0357785201
[170,] 0.952970845 0.0940583099 0.0470291549
[171,] 0.957072894 0.0858542117 0.0429271059
[172,] 0.968357000 0.0632859995 0.0316429998
[173,] 0.973776447 0.0524471067 0.0262235534
[174,] 0.968478500 0.0630430003 0.0315215001
[175,] 0.957435088 0.0851298246 0.0425649123
[176,] 0.943502496 0.1129950079 0.0564975040
[177,] 0.928196456 0.1436070882 0.0718035441
[178,] 0.910703192 0.1785936158 0.0892968079
[179,] 0.888469644 0.2230607116 0.1115303558
[180,] 0.857822105 0.2843557896 0.1421778948
[181,] 0.824512169 0.3509756612 0.1754878306
[182,] 0.796940495 0.4061190095 0.2030595048
[183,] 0.757723385 0.4845532304 0.2422766152
[184,] 0.707926471 0.5841470582 0.2920735291
[185,] 0.656057269 0.6878854626 0.3439427313
[186,] 0.607471532 0.7850569364 0.3925284682
[187,] 0.573395648 0.8532087033 0.4266043516
[188,] 0.533363346 0.9332733088 0.4666366544
[189,] 0.480157789 0.9603155787 0.5198422106
[190,] 0.442422720 0.8848454409 0.5575772796
[191,] 0.453576292 0.9071525840 0.5464237080
[192,] 0.566187761 0.8676244772 0.4338122386
[193,] 0.780505299 0.4389894013 0.2194947007
[194,] 0.880582541 0.2388349173 0.1194174587
[195,] 0.870380215 0.2592395690 0.1296197845
[196,] 0.938843412 0.1223131766 0.0611565883
[197,] 0.980650994 0.0386980127 0.0193490063
[198,] 0.984133322 0.0317333565 0.0158666783
[199,] 0.976715683 0.0465686347 0.0232843174
[200,] 0.972662298 0.0546754037 0.0273377019
[201,] 0.945947524 0.1081049522 0.0540524761
[202,] 0.912115948 0.1757681037 0.0878840519
[203,] 0.858689201 0.2826215977 0.1413107988
[204,] 0.786123246 0.4277535087 0.2138767543
[205,] 0.695841316 0.6083173681 0.3041586841
> postscript(file="/var/www/html/rcomp/tmp/1mwzv1262132558.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2m54a1262132558.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3v20s1262132558.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kdeu1262132558.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tv2j1262132558.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 = 214
Frequency = 1
1 2 3 4 5 6
-5.029060906 -5.629060906 -6.080058099 -6.780058099 -7.190257538 -7.469858660
7 8 9 10 11 12
-5.259659222 -3.008662028 -1.939260344 -2.108662028 -2.118861467 -1.639260344
13 14 15 16 17 18
-0.969858660 -1.069858660 -1.190257538 -1.680058099 -2.449459783 -2.718861467
19 20 21 22 23 24
0.250540217 3.889343585 4.530141340 4.530141340 4.050540217 4.130141340
25 26 27 28 29 30
3.950540217 3.540340778 2.460739656 1.740340778 0.919941901 0.499543023
31 32 33 34 35 36
2.599543023 5.230141340 6.030141340 5.360739656 4.370939094 3.591337972
37 38 39 40 41 42
3.291337972 2.611736849 1.811736849 0.921936288 -0.357664835 -1.427066519
43 44 45 46 47 48
0.503531797 3.593332358 4.283132920 3.883132920 3.172933481 2.652534604
49 50 51 52 53 54
2.442335165 1.742335165 0.821936288 0.181138533 -0.729060906 -1.508662028
55 56 57 58 59 60
0.432135726 3.162734042 4.232135726 4.260739656 3.270939094 2.670939094
61 62 63 64 65 66
2.270939094 1.591337972 0.452534604 -0.427066519 -0.957664835 -1.278063712
67 68 69 70 71 72
0.991337972 3.511736849 4.321936288 3.362734042 2.252534604 1.093332358
73 74 75 76 77 78
0.334130113 0.003531797 -0.516867080 -1.047465396 -1.747465396 -2.437265958
79 80 81 82 83 84
-0.137265958 3.483132920 4.803531797 3.913731236 2.723930674 2.413731236
85 86 87 88 89 90
2.483132920 2.083132920 1.952534604 1.572933481 0.503531797 -0.786268764
91 92 93 94 95 96
0.613731236 1.793332358 0.752534604 -1.657664835 -3.478063712 -3.929060906
97 98 99 100 101 102
-5.745471010 -4.329060906 -4.569858660 -5.049459783 -5.459659222 -4.720855854
103 104 105 106 107 108
-1.488263151 2.828146953 2.077149759 -0.692251925 -2.892251925 -4.720855854
109 110 111 112 113 114
-5.490257538 -5.769858660 -5.539260344 -3.510656415 -3.531055293 -4.520855854
115 116 117 118 119 120
-2.700456977 -1.769858660 -1.508662028 -0.108662028 -0.798462590 -0.718861467
121 122 123 124 125 126
-0.980058099 -1.869858660 -2.569858660 -3.978063712 -5.157664835 -6.296468203
127 128 129 130 131 132
-2.327066519 -1.647465396 -2.037265958 -2.547465396 -2.927066519 -1.747465396
133 134 135 136 137 138
-0.137265958 0.721936288 0.311736849 -0.967864274 -2.506667642 -1.867864274
139 140 141 142 143 144
3.142335165 4.421936288 4.411736849 3.142335165 2.001537410 1.311736849
145 146 147 148 149 150
0.042335165 -1.137265958 -2.616867080 -2.988263151 -2.759659222 -2.118861467
151 152 153 154 155 156
3.770939094 5.381138533 5.301537410 4.209742462 2.150540217 1.091337972
157 158 159 160 161 162
0.581138533 0.550540217 0.899543023 0.640340778 -0.049459783 0.009742462
163 164 165 166 167 168
3.609742462 4.189343585 4.279144146 2.960739656 2.050540217 2.099543023
169 170 171 172 173 174
1.099543023 0.799543023 -0.539260344 -0.380058099 -0.600456977 -0.869858660
175 176 177 178 179 180
2.840340778 3.450540217 2.791337972 1.011736849 -0.218861467 -1.229060906
181 182 183 184 185 186
-1.788263151 -1.898462590 -1.698462590 -0.398462590 -1.547465396 -2.347465396
187 188 189 190 191 192
0.352534604 -0.337265958 -1.557664835 -1.839260344 -2.310656415 -2.031055293
193 194 195 196 197 198
-1.371853047 -1.882052486 -2.663647995 -3.933049679 -3.935044066 -1.406440137
199 200 201 202 203 204
5.483360425 6.634357618 5.224158179 1.895554250 -2.441254731 -2.190257538
205 206 207 208 209 210
-0.229060906 0.991337972 0.123930674 -0.686268764 -2.994473816 -3.212878307
211 212 213 214
0.058517764 1.456523377 0.387121693 -1.523077746
> postscript(file="/var/www/html/rcomp/tmp/6u1011262132558.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 = 214
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.029060906 NA
1 -5.629060906 -5.029060906
2 -6.080058099 -5.629060906
3 -6.780058099 -6.080058099
4 -7.190257538 -6.780058099
5 -7.469858660 -7.190257538
6 -5.259659222 -7.469858660
7 -3.008662028 -5.259659222
8 -1.939260344 -3.008662028
9 -2.108662028 -1.939260344
10 -2.118861467 -2.108662028
11 -1.639260344 -2.118861467
12 -0.969858660 -1.639260344
13 -1.069858660 -0.969858660
14 -1.190257538 -1.069858660
15 -1.680058099 -1.190257538
16 -2.449459783 -1.680058099
17 -2.718861467 -2.449459783
18 0.250540217 -2.718861467
19 3.889343585 0.250540217
20 4.530141340 3.889343585
21 4.530141340 4.530141340
22 4.050540217 4.530141340
23 4.130141340 4.050540217
24 3.950540217 4.130141340
25 3.540340778 3.950540217
26 2.460739656 3.540340778
27 1.740340778 2.460739656
28 0.919941901 1.740340778
29 0.499543023 0.919941901
30 2.599543023 0.499543023
31 5.230141340 2.599543023
32 6.030141340 5.230141340
33 5.360739656 6.030141340
34 4.370939094 5.360739656
35 3.591337972 4.370939094
36 3.291337972 3.591337972
37 2.611736849 3.291337972
38 1.811736849 2.611736849
39 0.921936288 1.811736849
40 -0.357664835 0.921936288
41 -1.427066519 -0.357664835
42 0.503531797 -1.427066519
43 3.593332358 0.503531797
44 4.283132920 3.593332358
45 3.883132920 4.283132920
46 3.172933481 3.883132920
47 2.652534604 3.172933481
48 2.442335165 2.652534604
49 1.742335165 2.442335165
50 0.821936288 1.742335165
51 0.181138533 0.821936288
52 -0.729060906 0.181138533
53 -1.508662028 -0.729060906
54 0.432135726 -1.508662028
55 3.162734042 0.432135726
56 4.232135726 3.162734042
57 4.260739656 4.232135726
58 3.270939094 4.260739656
59 2.670939094 3.270939094
60 2.270939094 2.670939094
61 1.591337972 2.270939094
62 0.452534604 1.591337972
63 -0.427066519 0.452534604
64 -0.957664835 -0.427066519
65 -1.278063712 -0.957664835
66 0.991337972 -1.278063712
67 3.511736849 0.991337972
68 4.321936288 3.511736849
69 3.362734042 4.321936288
70 2.252534604 3.362734042
71 1.093332358 2.252534604
72 0.334130113 1.093332358
73 0.003531797 0.334130113
74 -0.516867080 0.003531797
75 -1.047465396 -0.516867080
76 -1.747465396 -1.047465396
77 -2.437265958 -1.747465396
78 -0.137265958 -2.437265958
79 3.483132920 -0.137265958
80 4.803531797 3.483132920
81 3.913731236 4.803531797
82 2.723930674 3.913731236
83 2.413731236 2.723930674
84 2.483132920 2.413731236
85 2.083132920 2.483132920
86 1.952534604 2.083132920
87 1.572933481 1.952534604
88 0.503531797 1.572933481
89 -0.786268764 0.503531797
90 0.613731236 -0.786268764
91 1.793332358 0.613731236
92 0.752534604 1.793332358
93 -1.657664835 0.752534604
94 -3.478063712 -1.657664835
95 -3.929060906 -3.478063712
96 -5.745471010 -3.929060906
97 -4.329060906 -5.745471010
98 -4.569858660 -4.329060906
99 -5.049459783 -4.569858660
100 -5.459659222 -5.049459783
101 -4.720855854 -5.459659222
102 -1.488263151 -4.720855854
103 2.828146953 -1.488263151
104 2.077149759 2.828146953
105 -0.692251925 2.077149759
106 -2.892251925 -0.692251925
107 -4.720855854 -2.892251925
108 -5.490257538 -4.720855854
109 -5.769858660 -5.490257538
110 -5.539260344 -5.769858660
111 -3.510656415 -5.539260344
112 -3.531055293 -3.510656415
113 -4.520855854 -3.531055293
114 -2.700456977 -4.520855854
115 -1.769858660 -2.700456977
116 -1.508662028 -1.769858660
117 -0.108662028 -1.508662028
118 -0.798462590 -0.108662028
119 -0.718861467 -0.798462590
120 -0.980058099 -0.718861467
121 -1.869858660 -0.980058099
122 -2.569858660 -1.869858660
123 -3.978063712 -2.569858660
124 -5.157664835 -3.978063712
125 -6.296468203 -5.157664835
126 -2.327066519 -6.296468203
127 -1.647465396 -2.327066519
128 -2.037265958 -1.647465396
129 -2.547465396 -2.037265958
130 -2.927066519 -2.547465396
131 -1.747465396 -2.927066519
132 -0.137265958 -1.747465396
133 0.721936288 -0.137265958
134 0.311736849 0.721936288
135 -0.967864274 0.311736849
136 -2.506667642 -0.967864274
137 -1.867864274 -2.506667642
138 3.142335165 -1.867864274
139 4.421936288 3.142335165
140 4.411736849 4.421936288
141 3.142335165 4.411736849
142 2.001537410 3.142335165
143 1.311736849 2.001537410
144 0.042335165 1.311736849
145 -1.137265958 0.042335165
146 -2.616867080 -1.137265958
147 -2.988263151 -2.616867080
148 -2.759659222 -2.988263151
149 -2.118861467 -2.759659222
150 3.770939094 -2.118861467
151 5.381138533 3.770939094
152 5.301537410 5.381138533
153 4.209742462 5.301537410
154 2.150540217 4.209742462
155 1.091337972 2.150540217
156 0.581138533 1.091337972
157 0.550540217 0.581138533
158 0.899543023 0.550540217
159 0.640340778 0.899543023
160 -0.049459783 0.640340778
161 0.009742462 -0.049459783
162 3.609742462 0.009742462
163 4.189343585 3.609742462
164 4.279144146 4.189343585
165 2.960739656 4.279144146
166 2.050540217 2.960739656
167 2.099543023 2.050540217
168 1.099543023 2.099543023
169 0.799543023 1.099543023
170 -0.539260344 0.799543023
171 -0.380058099 -0.539260344
172 -0.600456977 -0.380058099
173 -0.869858660 -0.600456977
174 2.840340778 -0.869858660
175 3.450540217 2.840340778
176 2.791337972 3.450540217
177 1.011736849 2.791337972
178 -0.218861467 1.011736849
179 -1.229060906 -0.218861467
180 -1.788263151 -1.229060906
181 -1.898462590 -1.788263151
182 -1.698462590 -1.898462590
183 -0.398462590 -1.698462590
184 -1.547465396 -0.398462590
185 -2.347465396 -1.547465396
186 0.352534604 -2.347465396
187 -0.337265958 0.352534604
188 -1.557664835 -0.337265958
189 -1.839260344 -1.557664835
190 -2.310656415 -1.839260344
191 -2.031055293 -2.310656415
192 -1.371853047 -2.031055293
193 -1.882052486 -1.371853047
194 -2.663647995 -1.882052486
195 -3.933049679 -2.663647995
196 -3.935044066 -3.933049679
197 -1.406440137 -3.935044066
198 5.483360425 -1.406440137
199 6.634357618 5.483360425
200 5.224158179 6.634357618
201 1.895554250 5.224158179
202 -2.441254731 1.895554250
203 -2.190257538 -2.441254731
204 -0.229060906 -2.190257538
205 0.991337972 -0.229060906
206 0.123930674 0.991337972
207 -0.686268764 0.123930674
208 -2.994473816 -0.686268764
209 -3.212878307 -2.994473816
210 0.058517764 -3.212878307
211 1.456523377 0.058517764
212 0.387121693 1.456523377
213 -1.523077746 0.387121693
214 NA -1.523077746
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.629060906 -5.029060906
[2,] -6.080058099 -5.629060906
[3,] -6.780058099 -6.080058099
[4,] -7.190257538 -6.780058099
[5,] -7.469858660 -7.190257538
[6,] -5.259659222 -7.469858660
[7,] -3.008662028 -5.259659222
[8,] -1.939260344 -3.008662028
[9,] -2.108662028 -1.939260344
[10,] -2.118861467 -2.108662028
[11,] -1.639260344 -2.118861467
[12,] -0.969858660 -1.639260344
[13,] -1.069858660 -0.969858660
[14,] -1.190257538 -1.069858660
[15,] -1.680058099 -1.190257538
[16,] -2.449459783 -1.680058099
[17,] -2.718861467 -2.449459783
[18,] 0.250540217 -2.718861467
[19,] 3.889343585 0.250540217
[20,] 4.530141340 3.889343585
[21,] 4.530141340 4.530141340
[22,] 4.050540217 4.530141340
[23,] 4.130141340 4.050540217
[24,] 3.950540217 4.130141340
[25,] 3.540340778 3.950540217
[26,] 2.460739656 3.540340778
[27,] 1.740340778 2.460739656
[28,] 0.919941901 1.740340778
[29,] 0.499543023 0.919941901
[30,] 2.599543023 0.499543023
[31,] 5.230141340 2.599543023
[32,] 6.030141340 5.230141340
[33,] 5.360739656 6.030141340
[34,] 4.370939094 5.360739656
[35,] 3.591337972 4.370939094
[36,] 3.291337972 3.591337972
[37,] 2.611736849 3.291337972
[38,] 1.811736849 2.611736849
[39,] 0.921936288 1.811736849
[40,] -0.357664835 0.921936288
[41,] -1.427066519 -0.357664835
[42,] 0.503531797 -1.427066519
[43,] 3.593332358 0.503531797
[44,] 4.283132920 3.593332358
[45,] 3.883132920 4.283132920
[46,] 3.172933481 3.883132920
[47,] 2.652534604 3.172933481
[48,] 2.442335165 2.652534604
[49,] 1.742335165 2.442335165
[50,] 0.821936288 1.742335165
[51,] 0.181138533 0.821936288
[52,] -0.729060906 0.181138533
[53,] -1.508662028 -0.729060906
[54,] 0.432135726 -1.508662028
[55,] 3.162734042 0.432135726
[56,] 4.232135726 3.162734042
[57,] 4.260739656 4.232135726
[58,] 3.270939094 4.260739656
[59,] 2.670939094 3.270939094
[60,] 2.270939094 2.670939094
[61,] 1.591337972 2.270939094
[62,] 0.452534604 1.591337972
[63,] -0.427066519 0.452534604
[64,] -0.957664835 -0.427066519
[65,] -1.278063712 -0.957664835
[66,] 0.991337972 -1.278063712
[67,] 3.511736849 0.991337972
[68,] 4.321936288 3.511736849
[69,] 3.362734042 4.321936288
[70,] 2.252534604 3.362734042
[71,] 1.093332358 2.252534604
[72,] 0.334130113 1.093332358
[73,] 0.003531797 0.334130113
[74,] -0.516867080 0.003531797
[75,] -1.047465396 -0.516867080
[76,] -1.747465396 -1.047465396
[77,] -2.437265958 -1.747465396
[78,] -0.137265958 -2.437265958
[79,] 3.483132920 -0.137265958
[80,] 4.803531797 3.483132920
[81,] 3.913731236 4.803531797
[82,] 2.723930674 3.913731236
[83,] 2.413731236 2.723930674
[84,] 2.483132920 2.413731236
[85,] 2.083132920 2.483132920
[86,] 1.952534604 2.083132920
[87,] 1.572933481 1.952534604
[88,] 0.503531797 1.572933481
[89,] -0.786268764 0.503531797
[90,] 0.613731236 -0.786268764
[91,] 1.793332358 0.613731236
[92,] 0.752534604 1.793332358
[93,] -1.657664835 0.752534604
[94,] -3.478063712 -1.657664835
[95,] -3.929060906 -3.478063712
[96,] -5.745471010 -3.929060906
[97,] -4.329060906 -5.745471010
[98,] -4.569858660 -4.329060906
[99,] -5.049459783 -4.569858660
[100,] -5.459659222 -5.049459783
[101,] -4.720855854 -5.459659222
[102,] -1.488263151 -4.720855854
[103,] 2.828146953 -1.488263151
[104,] 2.077149759 2.828146953
[105,] -0.692251925 2.077149759
[106,] -2.892251925 -0.692251925
[107,] -4.720855854 -2.892251925
[108,] -5.490257538 -4.720855854
[109,] -5.769858660 -5.490257538
[110,] -5.539260344 -5.769858660
[111,] -3.510656415 -5.539260344
[112,] -3.531055293 -3.510656415
[113,] -4.520855854 -3.531055293
[114,] -2.700456977 -4.520855854
[115,] -1.769858660 -2.700456977
[116,] -1.508662028 -1.769858660
[117,] -0.108662028 -1.508662028
[118,] -0.798462590 -0.108662028
[119,] -0.718861467 -0.798462590
[120,] -0.980058099 -0.718861467
[121,] -1.869858660 -0.980058099
[122,] -2.569858660 -1.869858660
[123,] -3.978063712 -2.569858660
[124,] -5.157664835 -3.978063712
[125,] -6.296468203 -5.157664835
[126,] -2.327066519 -6.296468203
[127,] -1.647465396 -2.327066519
[128,] -2.037265958 -1.647465396
[129,] -2.547465396 -2.037265958
[130,] -2.927066519 -2.547465396
[131,] -1.747465396 -2.927066519
[132,] -0.137265958 -1.747465396
[133,] 0.721936288 -0.137265958
[134,] 0.311736849 0.721936288
[135,] -0.967864274 0.311736849
[136,] -2.506667642 -0.967864274
[137,] -1.867864274 -2.506667642
[138,] 3.142335165 -1.867864274
[139,] 4.421936288 3.142335165
[140,] 4.411736849 4.421936288
[141,] 3.142335165 4.411736849
[142,] 2.001537410 3.142335165
[143,] 1.311736849 2.001537410
[144,] 0.042335165 1.311736849
[145,] -1.137265958 0.042335165
[146,] -2.616867080 -1.137265958
[147,] -2.988263151 -2.616867080
[148,] -2.759659222 -2.988263151
[149,] -2.118861467 -2.759659222
[150,] 3.770939094 -2.118861467
[151,] 5.381138533 3.770939094
[152,] 5.301537410 5.381138533
[153,] 4.209742462 5.301537410
[154,] 2.150540217 4.209742462
[155,] 1.091337972 2.150540217
[156,] 0.581138533 1.091337972
[157,] 0.550540217 0.581138533
[158,] 0.899543023 0.550540217
[159,] 0.640340778 0.899543023
[160,] -0.049459783 0.640340778
[161,] 0.009742462 -0.049459783
[162,] 3.609742462 0.009742462
[163,] 4.189343585 3.609742462
[164,] 4.279144146 4.189343585
[165,] 2.960739656 4.279144146
[166,] 2.050540217 2.960739656
[167,] 2.099543023 2.050540217
[168,] 1.099543023 2.099543023
[169,] 0.799543023 1.099543023
[170,] -0.539260344 0.799543023
[171,] -0.380058099 -0.539260344
[172,] -0.600456977 -0.380058099
[173,] -0.869858660 -0.600456977
[174,] 2.840340778 -0.869858660
[175,] 3.450540217 2.840340778
[176,] 2.791337972 3.450540217
[177,] 1.011736849 2.791337972
[178,] -0.218861467 1.011736849
[179,] -1.229060906 -0.218861467
[180,] -1.788263151 -1.229060906
[181,] -1.898462590 -1.788263151
[182,] -1.698462590 -1.898462590
[183,] -0.398462590 -1.698462590
[184,] -1.547465396 -0.398462590
[185,] -2.347465396 -1.547465396
[186,] 0.352534604 -2.347465396
[187,] -0.337265958 0.352534604
[188,] -1.557664835 -0.337265958
[189,] -1.839260344 -1.557664835
[190,] -2.310656415 -1.839260344
[191,] -2.031055293 -2.310656415
[192,] -1.371853047 -2.031055293
[193,] -1.882052486 -1.371853047
[194,] -2.663647995 -1.882052486
[195,] -3.933049679 -2.663647995
[196,] -3.935044066 -3.933049679
[197,] -1.406440137 -3.935044066
[198,] 5.483360425 -1.406440137
[199,] 6.634357618 5.483360425
[200,] 5.224158179 6.634357618
[201,] 1.895554250 5.224158179
[202,] -2.441254731 1.895554250
[203,] -2.190257538 -2.441254731
[204,] -0.229060906 -2.190257538
[205,] 0.991337972 -0.229060906
[206,] 0.123930674 0.991337972
[207,] -0.686268764 0.123930674
[208,] -2.994473816 -0.686268764
[209,] -3.212878307 -2.994473816
[210,] 0.058517764 -3.212878307
[211,] 1.456523377 0.058517764
[212,] 0.387121693 1.456523377
[213,] -1.523077746 0.387121693
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.629060906 -5.029060906
2 -6.080058099 -5.629060906
3 -6.780058099 -6.080058099
4 -7.190257538 -6.780058099
5 -7.469858660 -7.190257538
6 -5.259659222 -7.469858660
7 -3.008662028 -5.259659222
8 -1.939260344 -3.008662028
9 -2.108662028 -1.939260344
10 -2.118861467 -2.108662028
11 -1.639260344 -2.118861467
12 -0.969858660 -1.639260344
13 -1.069858660 -0.969858660
14 -1.190257538 -1.069858660
15 -1.680058099 -1.190257538
16 -2.449459783 -1.680058099
17 -2.718861467 -2.449459783
18 0.250540217 -2.718861467
19 3.889343585 0.250540217
20 4.530141340 3.889343585
21 4.530141340 4.530141340
22 4.050540217 4.530141340
23 4.130141340 4.050540217
24 3.950540217 4.130141340
25 3.540340778 3.950540217
26 2.460739656 3.540340778
27 1.740340778 2.460739656
28 0.919941901 1.740340778
29 0.499543023 0.919941901
30 2.599543023 0.499543023
31 5.230141340 2.599543023
32 6.030141340 5.230141340
33 5.360739656 6.030141340
34 4.370939094 5.360739656
35 3.591337972 4.370939094
36 3.291337972 3.591337972
37 2.611736849 3.291337972
38 1.811736849 2.611736849
39 0.921936288 1.811736849
40 -0.357664835 0.921936288
41 -1.427066519 -0.357664835
42 0.503531797 -1.427066519
43 3.593332358 0.503531797
44 4.283132920 3.593332358
45 3.883132920 4.283132920
46 3.172933481 3.883132920
47 2.652534604 3.172933481
48 2.442335165 2.652534604
49 1.742335165 2.442335165
50 0.821936288 1.742335165
51 0.181138533 0.821936288
52 -0.729060906 0.181138533
53 -1.508662028 -0.729060906
54 0.432135726 -1.508662028
55 3.162734042 0.432135726
56 4.232135726 3.162734042
57 4.260739656 4.232135726
58 3.270939094 4.260739656
59 2.670939094 3.270939094
60 2.270939094 2.670939094
61 1.591337972 2.270939094
62 0.452534604 1.591337972
63 -0.427066519 0.452534604
64 -0.957664835 -0.427066519
65 -1.278063712 -0.957664835
66 0.991337972 -1.278063712
67 3.511736849 0.991337972
68 4.321936288 3.511736849
69 3.362734042 4.321936288
70 2.252534604 3.362734042
71 1.093332358 2.252534604
72 0.334130113 1.093332358
73 0.003531797 0.334130113
74 -0.516867080 0.003531797
75 -1.047465396 -0.516867080
76 -1.747465396 -1.047465396
77 -2.437265958 -1.747465396
78 -0.137265958 -2.437265958
79 3.483132920 -0.137265958
80 4.803531797 3.483132920
81 3.913731236 4.803531797
82 2.723930674 3.913731236
83 2.413731236 2.723930674
84 2.483132920 2.413731236
85 2.083132920 2.483132920
86 1.952534604 2.083132920
87 1.572933481 1.952534604
88 0.503531797 1.572933481
89 -0.786268764 0.503531797
90 0.613731236 -0.786268764
91 1.793332358 0.613731236
92 0.752534604 1.793332358
93 -1.657664835 0.752534604
94 -3.478063712 -1.657664835
95 -3.929060906 -3.478063712
96 -5.745471010 -3.929060906
97 -4.329060906 -5.745471010
98 -4.569858660 -4.329060906
99 -5.049459783 -4.569858660
100 -5.459659222 -5.049459783
101 -4.720855854 -5.459659222
102 -1.488263151 -4.720855854
103 2.828146953 -1.488263151
104 2.077149759 2.828146953
105 -0.692251925 2.077149759
106 -2.892251925 -0.692251925
107 -4.720855854 -2.892251925
108 -5.490257538 -4.720855854
109 -5.769858660 -5.490257538
110 -5.539260344 -5.769858660
111 -3.510656415 -5.539260344
112 -3.531055293 -3.510656415
113 -4.520855854 -3.531055293
114 -2.700456977 -4.520855854
115 -1.769858660 -2.700456977
116 -1.508662028 -1.769858660
117 -0.108662028 -1.508662028
118 -0.798462590 -0.108662028
119 -0.718861467 -0.798462590
120 -0.980058099 -0.718861467
121 -1.869858660 -0.980058099
122 -2.569858660 -1.869858660
123 -3.978063712 -2.569858660
124 -5.157664835 -3.978063712
125 -6.296468203 -5.157664835
126 -2.327066519 -6.296468203
127 -1.647465396 -2.327066519
128 -2.037265958 -1.647465396
129 -2.547465396 -2.037265958
130 -2.927066519 -2.547465396
131 -1.747465396 -2.927066519
132 -0.137265958 -1.747465396
133 0.721936288 -0.137265958
134 0.311736849 0.721936288
135 -0.967864274 0.311736849
136 -2.506667642 -0.967864274
137 -1.867864274 -2.506667642
138 3.142335165 -1.867864274
139 4.421936288 3.142335165
140 4.411736849 4.421936288
141 3.142335165 4.411736849
142 2.001537410 3.142335165
143 1.311736849 2.001537410
144 0.042335165 1.311736849
145 -1.137265958 0.042335165
146 -2.616867080 -1.137265958
147 -2.988263151 -2.616867080
148 -2.759659222 -2.988263151
149 -2.118861467 -2.759659222
150 3.770939094 -2.118861467
151 5.381138533 3.770939094
152 5.301537410 5.381138533
153 4.209742462 5.301537410
154 2.150540217 4.209742462
155 1.091337972 2.150540217
156 0.581138533 1.091337972
157 0.550540217 0.581138533
158 0.899543023 0.550540217
159 0.640340778 0.899543023
160 -0.049459783 0.640340778
161 0.009742462 -0.049459783
162 3.609742462 0.009742462
163 4.189343585 3.609742462
164 4.279144146 4.189343585
165 2.960739656 4.279144146
166 2.050540217 2.960739656
167 2.099543023 2.050540217
168 1.099543023 2.099543023
169 0.799543023 1.099543023
170 -0.539260344 0.799543023
171 -0.380058099 -0.539260344
172 -0.600456977 -0.380058099
173 -0.869858660 -0.600456977
174 2.840340778 -0.869858660
175 3.450540217 2.840340778
176 2.791337972 3.450540217
177 1.011736849 2.791337972
178 -0.218861467 1.011736849
179 -1.229060906 -0.218861467
180 -1.788263151 -1.229060906
181 -1.898462590 -1.788263151
182 -1.698462590 -1.898462590
183 -0.398462590 -1.698462590
184 -1.547465396 -0.398462590
185 -2.347465396 -1.547465396
186 0.352534604 -2.347465396
187 -0.337265958 0.352534604
188 -1.557664835 -0.337265958
189 -1.839260344 -1.557664835
190 -2.310656415 -1.839260344
191 -2.031055293 -2.310656415
192 -1.371853047 -2.031055293
193 -1.882052486 -1.371853047
194 -2.663647995 -1.882052486
195 -3.933049679 -2.663647995
196 -3.935044066 -3.933049679
197 -1.406440137 -3.935044066
198 5.483360425 -1.406440137
199 6.634357618 5.483360425
200 5.224158179 6.634357618
201 1.895554250 5.224158179
202 -2.441254731 1.895554250
203 -2.190257538 -2.441254731
204 -0.229060906 -2.190257538
205 0.991337972 -0.229060906
206 0.123930674 0.991337972
207 -0.686268764 0.123930674
208 -2.994473816 -0.686268764
209 -3.212878307 -2.994473816
210 0.058517764 -3.212878307
211 1.456523377 0.058517764
212 0.387121693 1.456523377
213 -1.523077746 0.387121693
> 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/html/rcomp/tmp/7rgoq1262132558.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v13k1262132558.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97d891262132558.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/105cb51262132558.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11ypjb1262132558.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/html/rcomp/tmp/12l64d1262132558.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/html/rcomp/tmp/13jznc1262132559.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/html/rcomp/tmp/14z5cf1262132559.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/html/rcomp/tmp/15y22e1262132559.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/html/rcomp/tmp/16zuwa1262132559.tab")
+ }
> try(system("convert tmp/1mwzv1262132558.ps tmp/1mwzv1262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m54a1262132558.ps tmp/2m54a1262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v20s1262132558.ps tmp/3v20s1262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kdeu1262132558.ps tmp/4kdeu1262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tv2j1262132558.ps tmp/5tv2j1262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/6u1011262132558.ps tmp/6u1011262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rgoq1262132558.ps tmp/7rgoq1262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v13k1262132558.ps tmp/8v13k1262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/97d891262132558.ps tmp/97d891262132558.png",intern=TRUE))
character(0)
> try(system("convert tmp/105cb51262132558.ps tmp/105cb51262132558.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.032 1.839 5.992