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(11974
+ ,10106
+ ,12069
+ ,11412
+ ,11180
+ ,10508
+ ,11288
+ ,10928
+ ,10199
+ ,11030
+ ,11234
+ ,13747
+ ,13912
+ ,12376
+ ,12264
+ ,11675
+ ,11271
+ ,10672
+ ,10933
+ ,10379
+ ,10187
+ ,10747
+ ,10970
+ ,12175
+ ,14200
+ ,11676
+ ,11258
+ ,10872
+ ,11148
+ ,10690
+ ,10684
+ ,11658
+ ,10178
+ ,10981
+ ,10773
+ ,11665
+ ,11359
+ ,10716
+ ,12928
+ ,12317
+ ,11641
+ ,10459
+ ,10953
+ ,10703
+ ,10703
+ ,11101
+ ,11334
+ ,13268
+ ,13145
+ ,12334
+ ,13153
+ ,11289
+ ,11374
+ ,10914
+ ,11299
+ ,11284
+ ,10694
+ ,11077
+ ,11104
+ ,12820
+ ,14915
+ ,11773
+ ,11608
+ ,11468
+ ,11511
+ ,11200
+ ,11164
+ ,10960
+ ,10667
+ ,11556
+ ,11372
+ ,12333
+ ,13102
+ ,11115
+ ,12572
+ ,11557
+ ,12059
+ ,11420
+ ,11185
+ ,11113
+ ,10706
+ ,11523
+ ,11391
+ ,12634
+ ,13469
+ ,11735
+ ,13281
+ ,11968
+ ,11623
+ ,11084
+ ,11509
+ ,11134
+ ,10438
+ ,11530
+ ,11491
+ ,13093
+ ,13106
+ ,11305
+ ,13113
+ ,12203
+ ,11309
+ ,11088
+ ,11234
+ ,11619
+ ,10942
+ ,11445
+ ,11291
+ ,13281
+ ,13726
+ ,11300
+ ,11983
+ ,11092
+ ,11093
+ ,10692
+ ,10786
+ ,11166
+ ,10553
+ ,11103
+ ,10969
+ ,12090
+ ,12544
+ ,12264
+ ,13783
+ ,11214
+ ,11453
+ ,10883
+ ,10381
+ ,10348
+ ,10024
+ ,10805
+ ,10796
+ ,11907
+ ,12261
+ ,11377
+ ,12689
+ ,11474
+ ,10992
+ ,10764
+ ,12164
+ ,10409
+ ,10398
+ ,10349
+ ,10865
+ ,11630
+ ,12221
+ ,10884
+ ,12019
+ ,11021
+ ,10799
+ ,10423
+ ,10484
+ ,10450
+ ,9906
+ ,11049
+ ,11281
+ ,12485
+ ,12849
+ ,11380
+ ,12079
+ ,11366
+ ,11328
+ ,10444
+ ,10854
+ ,10434
+ ,10137
+ ,10992
+ ,10906
+ ,12367
+ ,14371
+ ,11695
+ ,11546
+ ,10922
+ ,10670
+ ,10254
+ ,10573
+ ,10239
+ ,10253
+ ,11176
+ ,10719
+ ,11817)
+ ,dim=c(1
+ ,180)
+ ,dimnames=list(c('Aantal')
+ ,1:180))
> y <- array(NA,dim=c(1,180),dimnames=list(c('Aantal'),1:180))
> 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 = 'Include Monthly 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
Aantal M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 11974 1 0 0 0 0 0 0 0 0 0 0
2 10106 0 1 0 0 0 0 0 0 0 0 0
3 12069 0 0 1 0 0 0 0 0 0 0 0
4 11412 0 0 0 1 0 0 0 0 0 0 0
5 11180 0 0 0 0 1 0 0 0 0 0 0
6 10508 0 0 0 0 0 1 0 0 0 0 0
7 11288 0 0 0 0 0 0 1 0 0 0 0
8 10928 0 0 0 0 0 0 0 1 0 0 0
9 10199 0 0 0 0 0 0 0 0 1 0 0
10 11030 0 0 0 0 0 0 0 0 0 1 0
11 11234 0 0 0 0 0 0 0 0 0 0 1
12 13747 0 0 0 0 0 0 0 0 0 0 0
13 13912 1 0 0 0 0 0 0 0 0 0 0
14 12376 0 1 0 0 0 0 0 0 0 0 0
15 12264 0 0 1 0 0 0 0 0 0 0 0
16 11675 0 0 0 1 0 0 0 0 0 0 0
17 11271 0 0 0 0 1 0 0 0 0 0 0
18 10672 0 0 0 0 0 1 0 0 0 0 0
19 10933 0 0 0 0 0 0 1 0 0 0 0
20 10379 0 0 0 0 0 0 0 1 0 0 0
21 10187 0 0 0 0 0 0 0 0 1 0 0
22 10747 0 0 0 0 0 0 0 0 0 1 0
23 10970 0 0 0 0 0 0 0 0 0 0 1
24 12175 0 0 0 0 0 0 0 0 0 0 0
25 14200 1 0 0 0 0 0 0 0 0 0 0
26 11676 0 1 0 0 0 0 0 0 0 0 0
27 11258 0 0 1 0 0 0 0 0 0 0 0
28 10872 0 0 0 1 0 0 0 0 0 0 0
29 11148 0 0 0 0 1 0 0 0 0 0 0
30 10690 0 0 0 0 0 1 0 0 0 0 0
31 10684 0 0 0 0 0 0 1 0 0 0 0
32 11658 0 0 0 0 0 0 0 1 0 0 0
33 10178 0 0 0 0 0 0 0 0 1 0 0
34 10981 0 0 0 0 0 0 0 0 0 1 0
35 10773 0 0 0 0 0 0 0 0 0 0 1
36 11665 0 0 0 0 0 0 0 0 0 0 0
37 11359 1 0 0 0 0 0 0 0 0 0 0
38 10716 0 1 0 0 0 0 0 0 0 0 0
39 12928 0 0 1 0 0 0 0 0 0 0 0
40 12317 0 0 0 1 0 0 0 0 0 0 0
41 11641 0 0 0 0 1 0 0 0 0 0 0
42 10459 0 0 0 0 0 1 0 0 0 0 0
43 10953 0 0 0 0 0 0 1 0 0 0 0
44 10703 0 0 0 0 0 0 0 1 0 0 0
45 10703 0 0 0 0 0 0 0 0 1 0 0
46 11101 0 0 0 0 0 0 0 0 0 1 0
47 11334 0 0 0 0 0 0 0 0 0 0 1
48 13268 0 0 0 0 0 0 0 0 0 0 0
49 13145 1 0 0 0 0 0 0 0 0 0 0
50 12334 0 1 0 0 0 0 0 0 0 0 0
51 13153 0 0 1 0 0 0 0 0 0 0 0
52 11289 0 0 0 1 0 0 0 0 0 0 0
53 11374 0 0 0 0 1 0 0 0 0 0 0
54 10914 0 0 0 0 0 1 0 0 0 0 0
55 11299 0 0 0 0 0 0 1 0 0 0 0
56 11284 0 0 0 0 0 0 0 1 0 0 0
57 10694 0 0 0 0 0 0 0 0 1 0 0
58 11077 0 0 0 0 0 0 0 0 0 1 0
59 11104 0 0 0 0 0 0 0 0 0 0 1
60 12820 0 0 0 0 0 0 0 0 0 0 0
61 14915 1 0 0 0 0 0 0 0 0 0 0
62 11773 0 1 0 0 0 0 0 0 0 0 0
63 11608 0 0 1 0 0 0 0 0 0 0 0
64 11468 0 0 0 1 0 0 0 0 0 0 0
65 11511 0 0 0 0 1 0 0 0 0 0 0
66 11200 0 0 0 0 0 1 0 0 0 0 0
67 11164 0 0 0 0 0 0 1 0 0 0 0
68 10960 0 0 0 0 0 0 0 1 0 0 0
69 10667 0 0 0 0 0 0 0 0 1 0 0
70 11556 0 0 0 0 0 0 0 0 0 1 0
71 11372 0 0 0 0 0 0 0 0 0 0 1
72 12333 0 0 0 0 0 0 0 0 0 0 0
73 13102 1 0 0 0 0 0 0 0 0 0 0
74 11115 0 1 0 0 0 0 0 0 0 0 0
75 12572 0 0 1 0 0 0 0 0 0 0 0
76 11557 0 0 0 1 0 0 0 0 0 0 0
77 12059 0 0 0 0 1 0 0 0 0 0 0
78 11420 0 0 0 0 0 1 0 0 0 0 0
79 11185 0 0 0 0 0 0 1 0 0 0 0
80 11113 0 0 0 0 0 0 0 1 0 0 0
81 10706 0 0 0 0 0 0 0 0 1 0 0
82 11523 0 0 0 0 0 0 0 0 0 1 0
83 11391 0 0 0 0 0 0 0 0 0 0 1
84 12634 0 0 0 0 0 0 0 0 0 0 0
85 13469 1 0 0 0 0 0 0 0 0 0 0
86 11735 0 1 0 0 0 0 0 0 0 0 0
87 13281 0 0 1 0 0 0 0 0 0 0 0
88 11968 0 0 0 1 0 0 0 0 0 0 0
89 11623 0 0 0 0 1 0 0 0 0 0 0
90 11084 0 0 0 0 0 1 0 0 0 0 0
91 11509 0 0 0 0 0 0 1 0 0 0 0
92 11134 0 0 0 0 0 0 0 1 0 0 0
93 10438 0 0 0 0 0 0 0 0 1 0 0
94 11530 0 0 0 0 0 0 0 0 0 1 0
95 11491 0 0 0 0 0 0 0 0 0 0 1
96 13093 0 0 0 0 0 0 0 0 0 0 0
97 13106 1 0 0 0 0 0 0 0 0 0 0
98 11305 0 1 0 0 0 0 0 0 0 0 0
99 13113 0 0 1 0 0 0 0 0 0 0 0
100 12203 0 0 0 1 0 0 0 0 0 0 0
101 11309 0 0 0 0 1 0 0 0 0 0 0
102 11088 0 0 0 0 0 1 0 0 0 0 0
103 11234 0 0 0 0 0 0 1 0 0 0 0
104 11619 0 0 0 0 0 0 0 1 0 0 0
105 10942 0 0 0 0 0 0 0 0 1 0 0
106 11445 0 0 0 0 0 0 0 0 0 1 0
107 11291 0 0 0 0 0 0 0 0 0 0 1
108 13281 0 0 0 0 0 0 0 0 0 0 0
109 13726 1 0 0 0 0 0 0 0 0 0 0
110 11300 0 1 0 0 0 0 0 0 0 0 0
111 11983 0 0 1 0 0 0 0 0 0 0 0
112 11092 0 0 0 1 0 0 0 0 0 0 0
113 11093 0 0 0 0 1 0 0 0 0 0 0
114 10692 0 0 0 0 0 1 0 0 0 0 0
115 10786 0 0 0 0 0 0 1 0 0 0 0
116 11166 0 0 0 0 0 0 0 1 0 0 0
117 10553 0 0 0 0 0 0 0 0 1 0 0
118 11103 0 0 0 0 0 0 0 0 0 1 0
119 10969 0 0 0 0 0 0 0 0 0 0 1
120 12090 0 0 0 0 0 0 0 0 0 0 0
121 12544 1 0 0 0 0 0 0 0 0 0 0
122 12264 0 1 0 0 0 0 0 0 0 0 0
123 13783 0 0 1 0 0 0 0 0 0 0 0
124 11214 0 0 0 1 0 0 0 0 0 0 0
125 11453 0 0 0 0 1 0 0 0 0 0 0
126 10883 0 0 0 0 0 1 0 0 0 0 0
127 10381 0 0 0 0 0 0 1 0 0 0 0
128 10348 0 0 0 0 0 0 0 1 0 0 0
129 10024 0 0 0 0 0 0 0 0 1 0 0
130 10805 0 0 0 0 0 0 0 0 0 1 0
131 10796 0 0 0 0 0 0 0 0 0 0 1
132 11907 0 0 0 0 0 0 0 0 0 0 0
133 12261 1 0 0 0 0 0 0 0 0 0 0
134 11377 0 1 0 0 0 0 0 0 0 0 0
135 12689 0 0 1 0 0 0 0 0 0 0 0
136 11474 0 0 0 1 0 0 0 0 0 0 0
137 10992 0 0 0 0 1 0 0 0 0 0 0
138 10764 0 0 0 0 0 1 0 0 0 0 0
139 12164 0 0 0 0 0 0 1 0 0 0 0
140 10409 0 0 0 0 0 0 0 1 0 0 0
141 10398 0 0 0 0 0 0 0 0 1 0 0
142 10349 0 0 0 0 0 0 0 0 0 1 0
143 10865 0 0 0 0 0 0 0 0 0 0 1
144 11630 0 0 0 0 0 0 0 0 0 0 0
145 12221 1 0 0 0 0 0 0 0 0 0 0
146 10884 0 1 0 0 0 0 0 0 0 0 0
147 12019 0 0 1 0 0 0 0 0 0 0 0
148 11021 0 0 0 1 0 0 0 0 0 0 0
149 10799 0 0 0 0 1 0 0 0 0 0 0
150 10423 0 0 0 0 0 1 0 0 0 0 0
151 10484 0 0 0 0 0 0 1 0 0 0 0
152 10450 0 0 0 0 0 0 0 1 0 0 0
153 9906 0 0 0 0 0 0 0 0 1 0 0
154 11049 0 0 0 0 0 0 0 0 0 1 0
155 11281 0 0 0 0 0 0 0 0 0 0 1
156 12485 0 0 0 0 0 0 0 0 0 0 0
157 12849 1 0 0 0 0 0 0 0 0 0 0
158 11380 0 1 0 0 0 0 0 0 0 0 0
159 12079 0 0 1 0 0 0 0 0 0 0 0
160 11366 0 0 0 1 0 0 0 0 0 0 0
161 11328 0 0 0 0 1 0 0 0 0 0 0
162 10444 0 0 0 0 0 1 0 0 0 0 0
163 10854 0 0 0 0 0 0 1 0 0 0 0
164 10434 0 0 0 0 0 0 0 1 0 0 0
165 10137 0 0 0 0 0 0 0 0 1 0 0
166 10992 0 0 0 0 0 0 0 0 0 1 0
167 10906 0 0 0 0 0 0 0 0 0 0 1
168 12367 0 0 0 0 0 0 0 0 0 0 0
169 14371 1 0 0 0 0 0 0 0 0 0 0
170 11695 0 1 0 0 0 0 0 0 0 0 0
171 11546 0 0 1 0 0 0 0 0 0 0 0
172 10922 0 0 0 1 0 0 0 0 0 0 0
173 10670 0 0 0 0 1 0 0 0 0 0 0
174 10254 0 0 0 0 0 1 0 0 0 0 0
175 10573 0 0 0 0 0 0 1 0 0 0 0
176 10239 0 0 0 0 0 0 0 1 0 0 0
177 10253 0 0 0 0 0 0 0 0 1 0 0
178 11176 0 0 0 0 0 0 0 0 0 1 0
179 10719 0 0 0 0 0 0 0 0 0 0 1
180 11817 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
12487.47 656.13 -1018.40 -64.47 -1030.80 -1190.73
M6 M7 M8 M9 M10 M11
-1721.13 -1454.73 -1632.53 -2088.47 -1389.87 -1387.73
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1784.60 -323.43 -31.67 292.20 1771.40
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12487.47 137.44 90.860 < 2e-16 ***
M1 656.13 194.36 3.376 0.000914 ***
M2 -1018.40 194.36 -5.240 4.77e-07 ***
M3 -64.47 194.36 -0.332 0.740545
M4 -1030.80 194.36 -5.303 3.54e-07 ***
M5 -1190.73 194.36 -6.126 6.18e-09 ***
M6 -1721.13 194.36 -8.855 1.14e-15 ***
M7 -1454.73 194.36 -7.485 3.84e-12 ***
M8 -1632.53 194.36 -8.399 1.81e-14 ***
M9 -2088.47 194.36 -10.745 < 2e-16 ***
M10 -1389.87 194.36 -7.151 2.53e-11 ***
M11 -1387.73 194.36 -7.140 2.69e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 532.3 on 168 degrees of freedom
Multiple R-squared: 0.6959, Adjusted R-squared: 0.676
F-statistic: 34.95 on 11 and 168 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,] 0.9988431 0.002313821 0.001156911
[2,] 0.9970344 0.005931137 0.002965568
[3,] 0.9931604 0.013679252 0.006839626
[4,] 0.9862381 0.027523796 0.013761898
[5,] 0.9762911 0.047417879 0.023708940
[6,] 0.9665802 0.066839695 0.033419848
[7,] 0.9457320 0.108536091 0.054268046
[8,] 0.9209127 0.158174689 0.079087344
[9,] 0.8878119 0.224376156 0.112188078
[10,] 0.9473112 0.105377682 0.052688841
[11,] 0.9751515 0.049697000 0.024848500
[12,] 0.9663257 0.067348563 0.033674281
[13,] 0.9769012 0.046197587 0.023098793
[14,] 0.9752655 0.049468997 0.024734498
[15,] 0.9637185 0.072563076 0.036281538
[16,] 0.9483667 0.103266501 0.051633251
[17,] 0.9358013 0.128397336 0.064198668
[18,] 0.9527362 0.094527630 0.047263815
[19,] 0.9355057 0.128988576 0.064494288
[20,] 0.9137646 0.172470862 0.086235431
[21,] 0.8936540 0.212691929 0.106345964
[22,] 0.9402218 0.119556450 0.059778225
[23,] 0.9966121 0.006775881 0.003387940
[24,] 0.9969112 0.006177675 0.003088838
[25,] 0.9978153 0.004369368 0.002184684
[26,] 0.9986618 0.002676349 0.001338174
[27,] 0.9982387 0.003522692 0.001761346
[28,] 0.9974772 0.005045676 0.002522838
[29,] 0.9962160 0.007567986 0.003783993
[30,] 0.9946905 0.010618907 0.005309453
[31,] 0.9934863 0.013027337 0.006513668
[32,] 0.9908128 0.018374335 0.009187168
[33,] 0.9879660 0.024068085 0.012034043
[34,] 0.9899134 0.020173177 0.010086588
[35,] 0.9865269 0.026946200 0.013473100
[36,] 0.9923061 0.015387758 0.007693879
[37,] 0.9947176 0.010564865 0.005282432
[38,] 0.9928279 0.014344122 0.007172061
[39,] 0.9899801 0.020039774 0.010019887
[40,] 0.9868896 0.026220745 0.013110373
[41,] 0.9833937 0.033212696 0.016606348
[42,] 0.9805208 0.038958387 0.019479193
[43,] 0.9761469 0.047706113 0.023853056
[44,] 0.9686414 0.062717200 0.031358600
[45,] 0.9590613 0.081877425 0.040938712
[46,] 0.9504823 0.099035388 0.049517694
[47,] 0.9969109 0.006178205 0.003089103
[48,] 0.9960232 0.007953534 0.003976767
[49,] 0.9971828 0.005634350 0.002817175
[50,] 0.9959901 0.008019809 0.004009905
[51,] 0.9946137 0.010772637 0.005386319
[52,] 0.9939752 0.012049635 0.006024817
[53,] 0.9918067 0.016386677 0.008193338
[54,] 0.9889858 0.022028489 0.011014245
[55,] 0.9861001 0.027799792 0.013899896
[56,] 0.9849764 0.030047152 0.015023576
[57,] 0.9813029 0.037394193 0.018697097
[58,] 0.9767988 0.046402327 0.023201163
[59,] 0.9699548 0.060090493 0.030045246
[60,] 0.9650394 0.069921153 0.034960577
[61,] 0.9571227 0.085754507 0.042877254
[62,] 0.9460327 0.107934632 0.053967316
[63,] 0.9564349 0.087130263 0.043565131
[64,] 0.9606881 0.078623832 0.039311916
[65,] 0.9508595 0.098280931 0.049140465
[66,] 0.9415958 0.116808301 0.058404151
[67,] 0.9319460 0.136108005 0.068054003
[68,] 0.9258852 0.148229631 0.074114815
[69,] 0.9138121 0.172375883 0.086187941
[70,] 0.8974672 0.205065620 0.102532810
[71,] 0.8849679 0.230064271 0.115032135
[72,] 0.8672927 0.265414680 0.132707340
[73,] 0.9002972 0.199405567 0.099702784
[74,] 0.8993402 0.201319698 0.100659849
[75,] 0.8891448 0.221710434 0.110855217
[76,] 0.8756452 0.248709549 0.124354774
[77,] 0.8716685 0.256663088 0.128331544
[78,] 0.8567654 0.286469125 0.143234562
[79,] 0.8301861 0.339627748 0.169813874
[80,] 0.8214824 0.357035264 0.178517632
[81,] 0.8093870 0.381226035 0.190613018
[82,] 0.8272795 0.345441064 0.172720532
[83,] 0.7980817 0.403836638 0.201918319
[84,] 0.7676061 0.464787726 0.232393863
[85,] 0.7918548 0.416290319 0.208145159
[86,] 0.8364528 0.327094352 0.163547176
[87,] 0.8112539 0.377492142 0.188746071
[88,] 0.7972390 0.405521991 0.202760996
[89,] 0.7723444 0.455311214 0.227655607
[90,] 0.8331591 0.333681831 0.166840916
[91,] 0.8435875 0.312824963 0.156412482
[92,] 0.8341827 0.331634631 0.165817316
[93,] 0.8127504 0.374499114 0.187249557
[94,] 0.8865054 0.226989136 0.113494568
[95,] 0.9078812 0.184237646 0.092118823
[96,] 0.8897431 0.220513757 0.110256879
[97,] 0.8830337 0.233932639 0.116966319
[98,] 0.8669310 0.266138084 0.133069042
[99,] 0.8434849 0.313030284 0.156515142
[100,] 0.8154891 0.369021814 0.184510907
[101,] 0.7868910 0.426218043 0.213109022
[102,] 0.8024248 0.395150370 0.197575185
[103,] 0.7811416 0.437716811 0.218858406
[104,] 0.7485001 0.502999832 0.251499916
[105,] 0.7104440 0.579112083 0.289556042
[106,] 0.6877309 0.624538230 0.312269115
[107,] 0.6816983 0.636603320 0.318301660
[108,] 0.7490969 0.501806136 0.250903068
[109,] 0.9526017 0.094796502 0.047398251
[110,] 0.9399739 0.120052221 0.060026111
[111,] 0.9349767 0.130046642 0.065023321
[112,] 0.9254236 0.149152772 0.074576386
[113,] 0.9345494 0.130901128 0.065450564
[114,] 0.9237994 0.152401202 0.076200601
[115,] 0.9079312 0.184137506 0.092068753
[116,] 0.8868447 0.226310660 0.113155330
[117,] 0.8632793 0.273441415 0.136720707
[118,] 0.8477771 0.304445709 0.152222854
[119,] 0.8935070 0.212985969 0.106492984
[120,] 0.8656528 0.268694477 0.134347238
[121,] 0.8857376 0.228524750 0.114262375
[122,] 0.8667609 0.266478204 0.133239102
[123,] 0.8367834 0.326433109 0.163216555
[124,] 0.8137364 0.372527227 0.186263613
[125,] 0.9670092 0.065981621 0.032990810
[126,] 0.9563492 0.087301642 0.043650821
[127,] 0.9445301 0.110939756 0.055469878
[128,] 0.9590389 0.081922103 0.040961051
[129,] 0.9436048 0.112790462 0.056395231
[130,] 0.9523015 0.095396981 0.047698491
[131,] 0.9951013 0.009797492 0.004898746
[132,] 0.9965158 0.006968378 0.003484189
[133,] 0.9946897 0.010620511 0.005310255
[134,] 0.9915400 0.016919996 0.008459998
[135,] 0.9874016 0.025196839 0.012598420
[136,] 0.9799811 0.040037832 0.020018916
[137,] 0.9716936 0.056612810 0.028306405
[138,] 0.9573693 0.085261309 0.042630655
[139,] 0.9427766 0.114446723 0.057223361
[140,] 0.9133772 0.173245523 0.086622761
[141,] 0.9002374 0.199525244 0.099762622
[142,] 0.8760800 0.247839917 0.123919958
[143,] 0.9937873 0.012425499 0.006212750
[144,] 0.9898211 0.020357772 0.010178886
[145,] 0.9903345 0.019330901 0.009665451
[146,] 0.9882783 0.023443371 0.011721685
[147,] 0.9954154 0.009169194 0.004584597
[148,] 0.9891896 0.021620723 0.010810362
[149,] 0.9796007 0.040798690 0.020399345
[150,] 0.9526787 0.094642562 0.047321281
[151,] 0.8811956 0.237608781 0.118804390
> postscript(file="/var/www/html/rcomp/tmp/1da4j1292938524.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/251l41292938524.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/351l41292938524.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/451l41292938524.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/551l41292938524.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 = 180
Frequency = 1
1 2 3 4 5 6
-1169.600000 -1363.066667 -354.000000 -44.666667 -116.733333 -258.333333
7 8 9 10 11 12
255.266667 73.066667 -200.000000 -67.600000 134.266667 1259.533333
13 14 15 16 17 18
768.400000 906.933333 -159.000000 218.333333 -25.733333 -94.333333
19 20 21 22 23 24
-99.733333 -475.933333 -212.000000 -350.600000 -129.733333 -312.466667
25 26 27 28 29 30
1056.400000 206.933333 -1165.000000 -584.666667 -148.733333 -76.333333
31 32 33 34 35 36
-348.733333 803.066667 -221.000000 -116.600000 -326.733333 -822.466667
37 38 39 40 41 42
-1784.600000 -753.066667 505.000000 860.333333 344.266667 -307.333333
43 44 45 46 47 48
-79.733333 -151.933333 304.000000 3.400000 234.266667 780.533333
49 50 51 52 53 54
1.400000 864.933333 730.000000 -167.666667 77.266667 147.666667
55 56 57 58 59 60
266.266667 429.066667 295.000000 -20.600000 4.266667 332.533333
61 62 63 64 65 66
1771.400000 303.933333 -815.000000 11.333333 214.266667 433.666667
67 68 69 70 71 72
131.266667 105.066667 268.000000 458.400000 272.266667 -154.466667
73 74 75 76 77 78
-41.600000 -354.066667 149.000000 100.333333 762.266667 653.666667
79 80 81 82 83 84
152.266667 258.066667 307.000000 425.400000 291.266667 146.533333
85 86 87 88 89 90
325.400000 265.933333 858.000000 511.333333 326.266667 317.666667
91 92 93 94 95 96
476.266667 279.066667 39.000000 432.400000 391.266667 605.533333
97 98 99 100 101 102
-37.600000 -164.066667 690.000000 746.333333 12.266667 321.666667
103 104 105 106 107 108
201.266667 764.066667 543.000000 347.400000 191.266667 793.533333
109 110 111 112 113 114
582.400000 -169.066667 -440.000000 -364.666667 -203.733333 -74.333333
115 116 117 118 119 120
-246.733333 311.066667 154.000000 5.400000 -130.733333 -397.466667
121 122 123 124 125 126
-599.600000 794.933333 1360.000000 -242.666667 156.266667 116.666667
127 128 129 130 131 132
-651.733333 -506.933333 -375.000000 -292.600000 -303.733333 -580.466667
133 134 135 136 137 138
-882.600000 -92.066667 266.000000 17.333333 -304.733333 -2.333333
139 140 141 142 143 144
1131.266667 -445.933333 -1.000000 -748.600000 -234.733333 -857.466667
145 146 147 148 149 150
-922.600000 -585.066667 -404.000000 -435.666667 -497.733333 -343.333333
151 152 153 154 155 156
-548.733333 -404.933333 -493.000000 -48.600000 181.266667 -2.466667
157 158 159 160 161 162
-294.600000 -89.066667 -344.000000 -90.666667 31.266667 -322.333333
163 164 165 166 167 168
-178.733333 -420.933333 -262.000000 -105.600000 -193.733333 -120.466667
169 170 171 172 173 174
1227.400000 225.933333 -877.000000 -534.666667 -626.733333 -512.333333
175 176 177 178 179 180
-459.733333 -615.933333 -146.000000 78.400000 -380.733333 -670.466667
> postscript(file="/var/www/html/rcomp/tmp/6ya3p1292938524.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 = 180
Frequency = 1
lag(myerror, k = 1) myerror
0 -1169.600000 NA
1 -1363.066667 -1169.600000
2 -354.000000 -1363.066667
3 -44.666667 -354.000000
4 -116.733333 -44.666667
5 -258.333333 -116.733333
6 255.266667 -258.333333
7 73.066667 255.266667
8 -200.000000 73.066667
9 -67.600000 -200.000000
10 134.266667 -67.600000
11 1259.533333 134.266667
12 768.400000 1259.533333
13 906.933333 768.400000
14 -159.000000 906.933333
15 218.333333 -159.000000
16 -25.733333 218.333333
17 -94.333333 -25.733333
18 -99.733333 -94.333333
19 -475.933333 -99.733333
20 -212.000000 -475.933333
21 -350.600000 -212.000000
22 -129.733333 -350.600000
23 -312.466667 -129.733333
24 1056.400000 -312.466667
25 206.933333 1056.400000
26 -1165.000000 206.933333
27 -584.666667 -1165.000000
28 -148.733333 -584.666667
29 -76.333333 -148.733333
30 -348.733333 -76.333333
31 803.066667 -348.733333
32 -221.000000 803.066667
33 -116.600000 -221.000000
34 -326.733333 -116.600000
35 -822.466667 -326.733333
36 -1784.600000 -822.466667
37 -753.066667 -1784.600000
38 505.000000 -753.066667
39 860.333333 505.000000
40 344.266667 860.333333
41 -307.333333 344.266667
42 -79.733333 -307.333333
43 -151.933333 -79.733333
44 304.000000 -151.933333
45 3.400000 304.000000
46 234.266667 3.400000
47 780.533333 234.266667
48 1.400000 780.533333
49 864.933333 1.400000
50 730.000000 864.933333
51 -167.666667 730.000000
52 77.266667 -167.666667
53 147.666667 77.266667
54 266.266667 147.666667
55 429.066667 266.266667
56 295.000000 429.066667
57 -20.600000 295.000000
58 4.266667 -20.600000
59 332.533333 4.266667
60 1771.400000 332.533333
61 303.933333 1771.400000
62 -815.000000 303.933333
63 11.333333 -815.000000
64 214.266667 11.333333
65 433.666667 214.266667
66 131.266667 433.666667
67 105.066667 131.266667
68 268.000000 105.066667
69 458.400000 268.000000
70 272.266667 458.400000
71 -154.466667 272.266667
72 -41.600000 -154.466667
73 -354.066667 -41.600000
74 149.000000 -354.066667
75 100.333333 149.000000
76 762.266667 100.333333
77 653.666667 762.266667
78 152.266667 653.666667
79 258.066667 152.266667
80 307.000000 258.066667
81 425.400000 307.000000
82 291.266667 425.400000
83 146.533333 291.266667
84 325.400000 146.533333
85 265.933333 325.400000
86 858.000000 265.933333
87 511.333333 858.000000
88 326.266667 511.333333
89 317.666667 326.266667
90 476.266667 317.666667
91 279.066667 476.266667
92 39.000000 279.066667
93 432.400000 39.000000
94 391.266667 432.400000
95 605.533333 391.266667
96 -37.600000 605.533333
97 -164.066667 -37.600000
98 690.000000 -164.066667
99 746.333333 690.000000
100 12.266667 746.333333
101 321.666667 12.266667
102 201.266667 321.666667
103 764.066667 201.266667
104 543.000000 764.066667
105 347.400000 543.000000
106 191.266667 347.400000
107 793.533333 191.266667
108 582.400000 793.533333
109 -169.066667 582.400000
110 -440.000000 -169.066667
111 -364.666667 -440.000000
112 -203.733333 -364.666667
113 -74.333333 -203.733333
114 -246.733333 -74.333333
115 311.066667 -246.733333
116 154.000000 311.066667
117 5.400000 154.000000
118 -130.733333 5.400000
119 -397.466667 -130.733333
120 -599.600000 -397.466667
121 794.933333 -599.600000
122 1360.000000 794.933333
123 -242.666667 1360.000000
124 156.266667 -242.666667
125 116.666667 156.266667
126 -651.733333 116.666667
127 -506.933333 -651.733333
128 -375.000000 -506.933333
129 -292.600000 -375.000000
130 -303.733333 -292.600000
131 -580.466667 -303.733333
132 -882.600000 -580.466667
133 -92.066667 -882.600000
134 266.000000 -92.066667
135 17.333333 266.000000
136 -304.733333 17.333333
137 -2.333333 -304.733333
138 1131.266667 -2.333333
139 -445.933333 1131.266667
140 -1.000000 -445.933333
141 -748.600000 -1.000000
142 -234.733333 -748.600000
143 -857.466667 -234.733333
144 -922.600000 -857.466667
145 -585.066667 -922.600000
146 -404.000000 -585.066667
147 -435.666667 -404.000000
148 -497.733333 -435.666667
149 -343.333333 -497.733333
150 -548.733333 -343.333333
151 -404.933333 -548.733333
152 -493.000000 -404.933333
153 -48.600000 -493.000000
154 181.266667 -48.600000
155 -2.466667 181.266667
156 -294.600000 -2.466667
157 -89.066667 -294.600000
158 -344.000000 -89.066667
159 -90.666667 -344.000000
160 31.266667 -90.666667
161 -322.333333 31.266667
162 -178.733333 -322.333333
163 -420.933333 -178.733333
164 -262.000000 -420.933333
165 -105.600000 -262.000000
166 -193.733333 -105.600000
167 -120.466667 -193.733333
168 1227.400000 -120.466667
169 225.933333 1227.400000
170 -877.000000 225.933333
171 -534.666667 -877.000000
172 -626.733333 -534.666667
173 -512.333333 -626.733333
174 -459.733333 -512.333333
175 -615.933333 -459.733333
176 -146.000000 -615.933333
177 78.400000 -146.000000
178 -380.733333 78.400000
179 -670.466667 -380.733333
180 NA -670.466667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1363.066667 -1169.600000
[2,] -354.000000 -1363.066667
[3,] -44.666667 -354.000000
[4,] -116.733333 -44.666667
[5,] -258.333333 -116.733333
[6,] 255.266667 -258.333333
[7,] 73.066667 255.266667
[8,] -200.000000 73.066667
[9,] -67.600000 -200.000000
[10,] 134.266667 -67.600000
[11,] 1259.533333 134.266667
[12,] 768.400000 1259.533333
[13,] 906.933333 768.400000
[14,] -159.000000 906.933333
[15,] 218.333333 -159.000000
[16,] -25.733333 218.333333
[17,] -94.333333 -25.733333
[18,] -99.733333 -94.333333
[19,] -475.933333 -99.733333
[20,] -212.000000 -475.933333
[21,] -350.600000 -212.000000
[22,] -129.733333 -350.600000
[23,] -312.466667 -129.733333
[24,] 1056.400000 -312.466667
[25,] 206.933333 1056.400000
[26,] -1165.000000 206.933333
[27,] -584.666667 -1165.000000
[28,] -148.733333 -584.666667
[29,] -76.333333 -148.733333
[30,] -348.733333 -76.333333
[31,] 803.066667 -348.733333
[32,] -221.000000 803.066667
[33,] -116.600000 -221.000000
[34,] -326.733333 -116.600000
[35,] -822.466667 -326.733333
[36,] -1784.600000 -822.466667
[37,] -753.066667 -1784.600000
[38,] 505.000000 -753.066667
[39,] 860.333333 505.000000
[40,] 344.266667 860.333333
[41,] -307.333333 344.266667
[42,] -79.733333 -307.333333
[43,] -151.933333 -79.733333
[44,] 304.000000 -151.933333
[45,] 3.400000 304.000000
[46,] 234.266667 3.400000
[47,] 780.533333 234.266667
[48,] 1.400000 780.533333
[49,] 864.933333 1.400000
[50,] 730.000000 864.933333
[51,] -167.666667 730.000000
[52,] 77.266667 -167.666667
[53,] 147.666667 77.266667
[54,] 266.266667 147.666667
[55,] 429.066667 266.266667
[56,] 295.000000 429.066667
[57,] -20.600000 295.000000
[58,] 4.266667 -20.600000
[59,] 332.533333 4.266667
[60,] 1771.400000 332.533333
[61,] 303.933333 1771.400000
[62,] -815.000000 303.933333
[63,] 11.333333 -815.000000
[64,] 214.266667 11.333333
[65,] 433.666667 214.266667
[66,] 131.266667 433.666667
[67,] 105.066667 131.266667
[68,] 268.000000 105.066667
[69,] 458.400000 268.000000
[70,] 272.266667 458.400000
[71,] -154.466667 272.266667
[72,] -41.600000 -154.466667
[73,] -354.066667 -41.600000
[74,] 149.000000 -354.066667
[75,] 100.333333 149.000000
[76,] 762.266667 100.333333
[77,] 653.666667 762.266667
[78,] 152.266667 653.666667
[79,] 258.066667 152.266667
[80,] 307.000000 258.066667
[81,] 425.400000 307.000000
[82,] 291.266667 425.400000
[83,] 146.533333 291.266667
[84,] 325.400000 146.533333
[85,] 265.933333 325.400000
[86,] 858.000000 265.933333
[87,] 511.333333 858.000000
[88,] 326.266667 511.333333
[89,] 317.666667 326.266667
[90,] 476.266667 317.666667
[91,] 279.066667 476.266667
[92,] 39.000000 279.066667
[93,] 432.400000 39.000000
[94,] 391.266667 432.400000
[95,] 605.533333 391.266667
[96,] -37.600000 605.533333
[97,] -164.066667 -37.600000
[98,] 690.000000 -164.066667
[99,] 746.333333 690.000000
[100,] 12.266667 746.333333
[101,] 321.666667 12.266667
[102,] 201.266667 321.666667
[103,] 764.066667 201.266667
[104,] 543.000000 764.066667
[105,] 347.400000 543.000000
[106,] 191.266667 347.400000
[107,] 793.533333 191.266667
[108,] 582.400000 793.533333
[109,] -169.066667 582.400000
[110,] -440.000000 -169.066667
[111,] -364.666667 -440.000000
[112,] -203.733333 -364.666667
[113,] -74.333333 -203.733333
[114,] -246.733333 -74.333333
[115,] 311.066667 -246.733333
[116,] 154.000000 311.066667
[117,] 5.400000 154.000000
[118,] -130.733333 5.400000
[119,] -397.466667 -130.733333
[120,] -599.600000 -397.466667
[121,] 794.933333 -599.600000
[122,] 1360.000000 794.933333
[123,] -242.666667 1360.000000
[124,] 156.266667 -242.666667
[125,] 116.666667 156.266667
[126,] -651.733333 116.666667
[127,] -506.933333 -651.733333
[128,] -375.000000 -506.933333
[129,] -292.600000 -375.000000
[130,] -303.733333 -292.600000
[131,] -580.466667 -303.733333
[132,] -882.600000 -580.466667
[133,] -92.066667 -882.600000
[134,] 266.000000 -92.066667
[135,] 17.333333 266.000000
[136,] -304.733333 17.333333
[137,] -2.333333 -304.733333
[138,] 1131.266667 -2.333333
[139,] -445.933333 1131.266667
[140,] -1.000000 -445.933333
[141,] -748.600000 -1.000000
[142,] -234.733333 -748.600000
[143,] -857.466667 -234.733333
[144,] -922.600000 -857.466667
[145,] -585.066667 -922.600000
[146,] -404.000000 -585.066667
[147,] -435.666667 -404.000000
[148,] -497.733333 -435.666667
[149,] -343.333333 -497.733333
[150,] -548.733333 -343.333333
[151,] -404.933333 -548.733333
[152,] -493.000000 -404.933333
[153,] -48.600000 -493.000000
[154,] 181.266667 -48.600000
[155,] -2.466667 181.266667
[156,] -294.600000 -2.466667
[157,] -89.066667 -294.600000
[158,] -344.000000 -89.066667
[159,] -90.666667 -344.000000
[160,] 31.266667 -90.666667
[161,] -322.333333 31.266667
[162,] -178.733333 -322.333333
[163,] -420.933333 -178.733333
[164,] -262.000000 -420.933333
[165,] -105.600000 -262.000000
[166,] -193.733333 -105.600000
[167,] -120.466667 -193.733333
[168,] 1227.400000 -120.466667
[169,] 225.933333 1227.400000
[170,] -877.000000 225.933333
[171,] -534.666667 -877.000000
[172,] -626.733333 -534.666667
[173,] -512.333333 -626.733333
[174,] -459.733333 -512.333333
[175,] -615.933333 -459.733333
[176,] -146.000000 -615.933333
[177,] 78.400000 -146.000000
[178,] -380.733333 78.400000
[179,] -670.466667 -380.733333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1363.066667 -1169.600000
2 -354.000000 -1363.066667
3 -44.666667 -354.000000
4 -116.733333 -44.666667
5 -258.333333 -116.733333
6 255.266667 -258.333333
7 73.066667 255.266667
8 -200.000000 73.066667
9 -67.600000 -200.000000
10 134.266667 -67.600000
11 1259.533333 134.266667
12 768.400000 1259.533333
13 906.933333 768.400000
14 -159.000000 906.933333
15 218.333333 -159.000000
16 -25.733333 218.333333
17 -94.333333 -25.733333
18 -99.733333 -94.333333
19 -475.933333 -99.733333
20 -212.000000 -475.933333
21 -350.600000 -212.000000
22 -129.733333 -350.600000
23 -312.466667 -129.733333
24 1056.400000 -312.466667
25 206.933333 1056.400000
26 -1165.000000 206.933333
27 -584.666667 -1165.000000
28 -148.733333 -584.666667
29 -76.333333 -148.733333
30 -348.733333 -76.333333
31 803.066667 -348.733333
32 -221.000000 803.066667
33 -116.600000 -221.000000
34 -326.733333 -116.600000
35 -822.466667 -326.733333
36 -1784.600000 -822.466667
37 -753.066667 -1784.600000
38 505.000000 -753.066667
39 860.333333 505.000000
40 344.266667 860.333333
41 -307.333333 344.266667
42 -79.733333 -307.333333
43 -151.933333 -79.733333
44 304.000000 -151.933333
45 3.400000 304.000000
46 234.266667 3.400000
47 780.533333 234.266667
48 1.400000 780.533333
49 864.933333 1.400000
50 730.000000 864.933333
51 -167.666667 730.000000
52 77.266667 -167.666667
53 147.666667 77.266667
54 266.266667 147.666667
55 429.066667 266.266667
56 295.000000 429.066667
57 -20.600000 295.000000
58 4.266667 -20.600000
59 332.533333 4.266667
60 1771.400000 332.533333
61 303.933333 1771.400000
62 -815.000000 303.933333
63 11.333333 -815.000000
64 214.266667 11.333333
65 433.666667 214.266667
66 131.266667 433.666667
67 105.066667 131.266667
68 268.000000 105.066667
69 458.400000 268.000000
70 272.266667 458.400000
71 -154.466667 272.266667
72 -41.600000 -154.466667
73 -354.066667 -41.600000
74 149.000000 -354.066667
75 100.333333 149.000000
76 762.266667 100.333333
77 653.666667 762.266667
78 152.266667 653.666667
79 258.066667 152.266667
80 307.000000 258.066667
81 425.400000 307.000000
82 291.266667 425.400000
83 146.533333 291.266667
84 325.400000 146.533333
85 265.933333 325.400000
86 858.000000 265.933333
87 511.333333 858.000000
88 326.266667 511.333333
89 317.666667 326.266667
90 476.266667 317.666667
91 279.066667 476.266667
92 39.000000 279.066667
93 432.400000 39.000000
94 391.266667 432.400000
95 605.533333 391.266667
96 -37.600000 605.533333
97 -164.066667 -37.600000
98 690.000000 -164.066667
99 746.333333 690.000000
100 12.266667 746.333333
101 321.666667 12.266667
102 201.266667 321.666667
103 764.066667 201.266667
104 543.000000 764.066667
105 347.400000 543.000000
106 191.266667 347.400000
107 793.533333 191.266667
108 582.400000 793.533333
109 -169.066667 582.400000
110 -440.000000 -169.066667
111 -364.666667 -440.000000
112 -203.733333 -364.666667
113 -74.333333 -203.733333
114 -246.733333 -74.333333
115 311.066667 -246.733333
116 154.000000 311.066667
117 5.400000 154.000000
118 -130.733333 5.400000
119 -397.466667 -130.733333
120 -599.600000 -397.466667
121 794.933333 -599.600000
122 1360.000000 794.933333
123 -242.666667 1360.000000
124 156.266667 -242.666667
125 116.666667 156.266667
126 -651.733333 116.666667
127 -506.933333 -651.733333
128 -375.000000 -506.933333
129 -292.600000 -375.000000
130 -303.733333 -292.600000
131 -580.466667 -303.733333
132 -882.600000 -580.466667
133 -92.066667 -882.600000
134 266.000000 -92.066667
135 17.333333 266.000000
136 -304.733333 17.333333
137 -2.333333 -304.733333
138 1131.266667 -2.333333
139 -445.933333 1131.266667
140 -1.000000 -445.933333
141 -748.600000 -1.000000
142 -234.733333 -748.600000
143 -857.466667 -234.733333
144 -922.600000 -857.466667
145 -585.066667 -922.600000
146 -404.000000 -585.066667
147 -435.666667 -404.000000
148 -497.733333 -435.666667
149 -343.333333 -497.733333
150 -548.733333 -343.333333
151 -404.933333 -548.733333
152 -493.000000 -404.933333
153 -48.600000 -493.000000
154 181.266667 -48.600000
155 -2.466667 181.266667
156 -294.600000 -2.466667
157 -89.066667 -294.600000
158 -344.000000 -89.066667
159 -90.666667 -344.000000
160 31.266667 -90.666667
161 -322.333333 31.266667
162 -178.733333 -322.333333
163 -420.933333 -178.733333
164 -262.000000 -420.933333
165 -105.600000 -262.000000
166 -193.733333 -105.600000
167 -120.466667 -193.733333
168 1227.400000 -120.466667
169 225.933333 1227.400000
170 -877.000000 225.933333
171 -534.666667 -877.000000
172 -626.733333 -534.666667
173 -512.333333 -626.733333
174 -459.733333 -512.333333
175 -615.933333 -459.733333
176 -146.000000 -615.933333
177 78.400000 -146.000000
178 -380.733333 78.400000
179 -670.466667 -380.733333
> 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/7r12s1292938524.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8r12s1292938524.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r12s1292938524.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
hat values (leverages) are all = 0.06666667
and there are no factor predictors; no plot no. 5
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jt1d1292938524.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nbi11292938524.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/12qcyp1292938524.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/13m4ef1292938524.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/1484vl1292938524.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/15t5tr1292938524.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/16e5sx1292938524.tab")
+ }
>
> try(system("convert tmp/1da4j1292938524.ps tmp/1da4j1292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/251l41292938524.ps tmp/251l41292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/351l41292938524.ps tmp/351l41292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/451l41292938524.ps tmp/451l41292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/551l41292938524.ps tmp/551l41292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ya3p1292938524.ps tmp/6ya3p1292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/7r12s1292938524.ps tmp/7r12s1292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/8r12s1292938524.ps tmp/8r12s1292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/9r12s1292938524.ps tmp/9r12s1292938524.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jt1d1292938524.ps tmp/10jt1d1292938524.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.399 1.774 9.621