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(1567
+ ,0
+ ,2237
+ ,0
+ ,2598
+ ,0
+ ,3729
+ ,0
+ ,5715
+ ,0
+ ,5776
+ ,0
+ ,5852
+ ,0
+ ,6878
+ ,0
+ ,5488
+ ,0
+ ,3583
+ ,0
+ ,2054
+ ,0
+ ,2282
+ ,0
+ ,1552
+ ,0
+ ,2261
+ ,0
+ ,2446
+ ,0
+ ,3519
+ ,0
+ ,5161
+ ,0
+ ,5085
+ ,0
+ ,5711
+ ,0
+ ,6057
+ ,0
+ ,5224
+ ,0
+ ,3363
+ ,0
+ ,1899
+ ,0
+ ,2115
+ ,0
+ ,1491
+ ,0
+ ,2061
+ ,0
+ ,2419
+ ,0
+ ,3430
+ ,0
+ ,4778
+ ,0
+ ,4862
+ ,0
+ ,6176
+ ,0
+ ,5664
+ ,0
+ ,5529
+ ,0
+ ,3418
+ ,0
+ ,1941
+ ,0
+ ,2402
+ ,0
+ ,1579
+ ,0
+ ,2146
+ ,0
+ ,2462
+ ,0
+ ,3695
+ ,0
+ ,4831
+ ,0
+ ,5134
+ ,0
+ ,6250
+ ,0
+ ,5760
+ ,0
+ ,6249
+ ,0
+ ,2917
+ ,0
+ ,1741
+ ,0
+ ,2359
+ ,0
+ ,1511
+ ,0
+ ,2059
+ ,0
+ ,2635
+ ,0
+ ,2867
+ ,0
+ ,4403
+ ,0
+ ,5720
+ ,0
+ ,4502
+ ,0
+ ,5749
+ ,0
+ ,5627
+ ,0
+ ,2846
+ ,0
+ ,1762
+ ,0
+ ,2429
+ ,0
+ ,1169
+ ,0
+ ,2154
+ ,0
+ ,2249
+ ,0
+ ,2687
+ ,0
+ ,4359
+ ,0
+ ,5382
+ ,0
+ ,4459
+ ,0
+ ,6398
+ ,0
+ ,4596
+ ,0
+ ,3024
+ ,0
+ ,1887
+ ,0
+ ,2070
+ ,0
+ ,1351
+ ,0
+ ,2218
+ ,0
+ ,2461
+ ,0
+ ,3028
+ ,0
+ ,4784
+ ,0
+ ,4975
+ ,1
+ ,4607
+ ,1
+ ,6249
+ ,1
+ ,4809
+ ,1
+ ,3157
+ ,1
+ ,1910
+ ,1
+ ,2228
+ ,1
+ ,1673
+ ,1
+ ,2589
+ ,1
+ ,2332
+ ,1
+ ,3785
+ ,1
+ ,4916
+ ,1
+ ,5207
+ ,1
+ ,6055
+ ,1
+ ,5751
+ ,1
+ ,5247
+ ,1
+ ,3387
+ ,1
+ ,2091
+ ,1
+ ,2401
+ ,1
+ ,1664
+ ,1
+ ,2205
+ ,1
+ ,2295
+ ,1
+ ,3762
+ ,1
+ ,4890
+ ,1
+ ,5117
+ ,1
+ ,6099
+ ,1
+ ,5865
+ ,1
+ ,5594
+ ,1
+ ,3229
+ ,1
+ ,2106
+ ,1
+ ,2410
+ ,1
+ ,1583
+ ,1
+ ,2092
+ ,1
+ ,2612
+ ,1
+ ,3665
+ ,1
+ ,4880
+ ,1
+ ,5875
+ ,1
+ ,5892
+ ,1
+ ,6078
+ ,1
+ ,6515
+ ,1
+ ,3164
+ ,1
+ ,2028
+ ,1
+ ,2677
+ ,1
+ ,1580
+ ,1
+ ,2196
+ ,1
+ ,2838
+ ,1
+ ,3087
+ ,1
+ ,4726
+ ,1
+ ,6521
+ ,1
+ ,6739
+ ,1
+ ,5943
+ ,1
+ ,6265
+ ,1
+ ,3323
+ ,1
+ ,2098
+ ,1
+ ,2544
+ ,1
+ ,1442
+ ,1
+ ,2307
+ ,1
+ ,2811
+ ,1
+ ,3461
+ ,1
+ ,5451
+ ,1
+ ,5481
+ ,1
+ ,5114
+ ,1
+ ,8381
+ ,1
+ ,5215
+ ,1
+ ,3700
+ ,1
+ ,2122
+ ,1
+ ,2311
+ ,1
+ ,1515
+ ,1
+ ,2351
+ ,1
+ ,2289
+ ,1
+ ,3380
+ ,1
+ ,5398
+ ,1
+ ,5242
+ ,1
+ ,5162
+ ,1
+ ,6391
+ ,1
+ ,5958
+ ,1
+ ,3727
+ ,1
+ ,1883
+ ,1
+ ,2191
+ ,1)
+ ,dim=c(2
+ ,156)
+ ,dimnames=list(c('Aantalhuwelijken'
+ ,'Dummy')
+ ,1:156))
> y <- array(NA,dim=c(2,156),dimnames=list(c('Aantalhuwelijken','Dummy'),1:156))
> 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
Aantalhuwelijken Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1567 0 1 0 0 0 0 0 0 0 0 0 0
2 2237 0 0 1 0 0 0 0 0 0 0 0 0
3 2598 0 0 0 1 0 0 0 0 0 0 0 0
4 3729 0 0 0 0 1 0 0 0 0 0 0 0
5 5715 0 0 0 0 0 1 0 0 0 0 0 0
6 5776 0 0 0 0 0 0 1 0 0 0 0 0
7 5852 0 0 0 0 0 0 0 1 0 0 0 0
8 6878 0 0 0 0 0 0 0 0 1 0 0 0
9 5488 0 0 0 0 0 0 0 0 0 1 0 0
10 3583 0 0 0 0 0 0 0 0 0 0 1 0
11 2054 0 0 0 0 0 0 0 0 0 0 0 1
12 2282 0 0 0 0 0 0 0 0 0 0 0 0
13 1552 0 1 0 0 0 0 0 0 0 0 0 0
14 2261 0 0 1 0 0 0 0 0 0 0 0 0
15 2446 0 0 0 1 0 0 0 0 0 0 0 0
16 3519 0 0 0 0 1 0 0 0 0 0 0 0
17 5161 0 0 0 0 0 1 0 0 0 0 0 0
18 5085 0 0 0 0 0 0 1 0 0 0 0 0
19 5711 0 0 0 0 0 0 0 1 0 0 0 0
20 6057 0 0 0 0 0 0 0 0 1 0 0 0
21 5224 0 0 0 0 0 0 0 0 0 1 0 0
22 3363 0 0 0 0 0 0 0 0 0 0 1 0
23 1899 0 0 0 0 0 0 0 0 0 0 0 1
24 2115 0 0 0 0 0 0 0 0 0 0 0 0
25 1491 0 1 0 0 0 0 0 0 0 0 0 0
26 2061 0 0 1 0 0 0 0 0 0 0 0 0
27 2419 0 0 0 1 0 0 0 0 0 0 0 0
28 3430 0 0 0 0 1 0 0 0 0 0 0 0
29 4778 0 0 0 0 0 1 0 0 0 0 0 0
30 4862 0 0 0 0 0 0 1 0 0 0 0 0
31 6176 0 0 0 0 0 0 0 1 0 0 0 0
32 5664 0 0 0 0 0 0 0 0 1 0 0 0
33 5529 0 0 0 0 0 0 0 0 0 1 0 0
34 3418 0 0 0 0 0 0 0 0 0 0 1 0
35 1941 0 0 0 0 0 0 0 0 0 0 0 1
36 2402 0 0 0 0 0 0 0 0 0 0 0 0
37 1579 0 1 0 0 0 0 0 0 0 0 0 0
38 2146 0 0 1 0 0 0 0 0 0 0 0 0
39 2462 0 0 0 1 0 0 0 0 0 0 0 0
40 3695 0 0 0 0 1 0 0 0 0 0 0 0
41 4831 0 0 0 0 0 1 0 0 0 0 0 0
42 5134 0 0 0 0 0 0 1 0 0 0 0 0
43 6250 0 0 0 0 0 0 0 1 0 0 0 0
44 5760 0 0 0 0 0 0 0 0 1 0 0 0
45 6249 0 0 0 0 0 0 0 0 0 1 0 0
46 2917 0 0 0 0 0 0 0 0 0 0 1 0
47 1741 0 0 0 0 0 0 0 0 0 0 0 1
48 2359 0 0 0 0 0 0 0 0 0 0 0 0
49 1511 0 1 0 0 0 0 0 0 0 0 0 0
50 2059 0 0 1 0 0 0 0 0 0 0 0 0
51 2635 0 0 0 1 0 0 0 0 0 0 0 0
52 2867 0 0 0 0 1 0 0 0 0 0 0 0
53 4403 0 0 0 0 0 1 0 0 0 0 0 0
54 5720 0 0 0 0 0 0 1 0 0 0 0 0
55 4502 0 0 0 0 0 0 0 1 0 0 0 0
56 5749 0 0 0 0 0 0 0 0 1 0 0 0
57 5627 0 0 0 0 0 0 0 0 0 1 0 0
58 2846 0 0 0 0 0 0 0 0 0 0 1 0
59 1762 0 0 0 0 0 0 0 0 0 0 0 1
60 2429 0 0 0 0 0 0 0 0 0 0 0 0
61 1169 0 1 0 0 0 0 0 0 0 0 0 0
62 2154 0 0 1 0 0 0 0 0 0 0 0 0
63 2249 0 0 0 1 0 0 0 0 0 0 0 0
64 2687 0 0 0 0 1 0 0 0 0 0 0 0
65 4359 0 0 0 0 0 1 0 0 0 0 0 0
66 5382 0 0 0 0 0 0 1 0 0 0 0 0
67 4459 0 0 0 0 0 0 0 1 0 0 0 0
68 6398 0 0 0 0 0 0 0 0 1 0 0 0
69 4596 0 0 0 0 0 0 0 0 0 1 0 0
70 3024 0 0 0 0 0 0 0 0 0 0 1 0
71 1887 0 0 0 0 0 0 0 0 0 0 0 1
72 2070 0 0 0 0 0 0 0 0 0 0 0 0
73 1351 0 1 0 0 0 0 0 0 0 0 0 0
74 2218 0 0 1 0 0 0 0 0 0 0 0 0
75 2461 0 0 0 1 0 0 0 0 0 0 0 0
76 3028 0 0 0 0 1 0 0 0 0 0 0 0
77 4784 0 0 0 0 0 1 0 0 0 0 0 0
78 4975 1 0 0 0 0 0 1 0 0 0 0 0
79 4607 1 0 0 0 0 0 0 1 0 0 0 0
80 6249 1 0 0 0 0 0 0 0 1 0 0 0
81 4809 1 0 0 0 0 0 0 0 0 1 0 0
82 3157 1 0 0 0 0 0 0 0 0 0 1 0
83 1910 1 0 0 0 0 0 0 0 0 0 0 1
84 2228 1 0 0 0 0 0 0 0 0 0 0 0
85 1673 1 1 0 0 0 0 0 0 0 0 0 0
86 2589 1 0 1 0 0 0 0 0 0 0 0 0
87 2332 1 0 0 1 0 0 0 0 0 0 0 0
88 3785 1 0 0 0 1 0 0 0 0 0 0 0
89 4916 1 0 0 0 0 1 0 0 0 0 0 0
90 5207 1 0 0 0 0 0 1 0 0 0 0 0
91 6055 1 0 0 0 0 0 0 1 0 0 0 0
92 5751 1 0 0 0 0 0 0 0 1 0 0 0
93 5247 1 0 0 0 0 0 0 0 0 1 0 0
94 3387 1 0 0 0 0 0 0 0 0 0 1 0
95 2091 1 0 0 0 0 0 0 0 0 0 0 1
96 2401 1 0 0 0 0 0 0 0 0 0 0 0
97 1664 1 1 0 0 0 0 0 0 0 0 0 0
98 2205 1 0 1 0 0 0 0 0 0 0 0 0
99 2295 1 0 0 1 0 0 0 0 0 0 0 0
100 3762 1 0 0 0 1 0 0 0 0 0 0 0
101 4890 1 0 0 0 0 1 0 0 0 0 0 0
102 5117 1 0 0 0 0 0 1 0 0 0 0 0
103 6099 1 0 0 0 0 0 0 1 0 0 0 0
104 5865 1 0 0 0 0 0 0 0 1 0 0 0
105 5594 1 0 0 0 0 0 0 0 0 1 0 0
106 3229 1 0 0 0 0 0 0 0 0 0 1 0
107 2106 1 0 0 0 0 0 0 0 0 0 0 1
108 2410 1 0 0 0 0 0 0 0 0 0 0 0
109 1583 1 1 0 0 0 0 0 0 0 0 0 0
110 2092 1 0 1 0 0 0 0 0 0 0 0 0
111 2612 1 0 0 1 0 0 0 0 0 0 0 0
112 3665 1 0 0 0 1 0 0 0 0 0 0 0
113 4880 1 0 0 0 0 1 0 0 0 0 0 0
114 5875 1 0 0 0 0 0 1 0 0 0 0 0
115 5892 1 0 0 0 0 0 0 1 0 0 0 0
116 6078 1 0 0 0 0 0 0 0 1 0 0 0
117 6515 1 0 0 0 0 0 0 0 0 1 0 0
118 3164 1 0 0 0 0 0 0 0 0 0 1 0
119 2028 1 0 0 0 0 0 0 0 0 0 0 1
120 2677 1 0 0 0 0 0 0 0 0 0 0 0
121 1580 1 1 0 0 0 0 0 0 0 0 0 0
122 2196 1 0 1 0 0 0 0 0 0 0 0 0
123 2838 1 0 0 1 0 0 0 0 0 0 0 0
124 3087 1 0 0 0 1 0 0 0 0 0 0 0
125 4726 1 0 0 0 0 1 0 0 0 0 0 0
126 6521 1 0 0 0 0 0 1 0 0 0 0 0
127 6739 1 0 0 0 0 0 0 1 0 0 0 0
128 5943 1 0 0 0 0 0 0 0 1 0 0 0
129 6265 1 0 0 0 0 0 0 0 0 1 0 0
130 3323 1 0 0 0 0 0 0 0 0 0 1 0
131 2098 1 0 0 0 0 0 0 0 0 0 0 1
132 2544 1 0 0 0 0 0 0 0 0 0 0 0
133 1442 1 1 0 0 0 0 0 0 0 0 0 0
134 2307 1 0 1 0 0 0 0 0 0 0 0 0
135 2811 1 0 0 1 0 0 0 0 0 0 0 0
136 3461 1 0 0 0 1 0 0 0 0 0 0 0
137 5451 1 0 0 0 0 1 0 0 0 0 0 0
138 5481 1 0 0 0 0 0 1 0 0 0 0 0
139 5114 1 0 0 0 0 0 0 1 0 0 0 0
140 8381 1 0 0 0 0 0 0 0 1 0 0 0
141 5215 1 0 0 0 0 0 0 0 0 1 0 0
142 3700 1 0 0 0 0 0 0 0 0 0 1 0
143 2122 1 0 0 0 0 0 0 0 0 0 0 1
144 2311 1 0 0 0 0 0 0 0 0 0 0 0
145 1515 1 1 0 0 0 0 0 0 0 0 0 0
146 2351 1 0 1 0 0 0 0 0 0 0 0 0
147 2289 1 0 0 1 0 0 0 0 0 0 0 0
148 3380 1 0 0 0 1 0 0 0 0 0 0 0
149 5398 1 0 0 0 0 1 0 0 0 0 0 0
150 5242 1 0 0 0 0 0 1 0 0 0 0 0
151 5162 1 0 0 0 0 0 0 1 0 0 0 0
152 6391 1 0 0 0 0 0 0 0 1 0 0 0
153 5958 1 0 0 0 0 0 0 0 0 1 0 0
154 3727 1 0 0 0 0 0 0 0 0 0 1 0
155 1883 1 0 0 0 0 0 0 0 0 0 0 1
156 2191 1 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) Dummy M1 M2 M3 M4
2248.7 169.5 -813.3 -105.7 169.0 1065.0
M5 M6 M7 M8 M9 M10
2618.7 3073.7 3246.1 3903.5 3222.8 955.3
M11
-376.7
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1057.221 -211.201 -2.095 180.706 2059.395
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2248.67 119.13 18.875 < 2e-16 ***
Dummy 169.48 65.86 2.573 0.0111 *
M1 -813.27 160.92 -5.054 1.30e-06 ***
M2 -105.66 160.92 -0.657 0.5125
M3 169.04 160.92 1.050 0.2953
M4 1065.04 160.92 6.618 6.79e-10 ***
M5 2618.65 160.92 16.273 < 2e-16 ***
M6 3073.69 160.84 19.110 < 2e-16 ***
M7 3246.08 160.84 20.181 < 2e-16 ***
M8 3903.46 160.84 24.269 < 2e-16 ***
M9 3222.85 160.84 20.037 < 2e-16 ***
M10 955.31 160.84 5.939 2.08e-08 ***
M11 -376.69 160.84 -2.342 0.0206 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 410.1 on 143 degrees of freedom
Multiple R-squared: 0.9447, Adjusted R-squared: 0.94
F-statistic: 203.5 on 12 and 143 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,] 0.0224569505 0.0449139010 0.9775430
[2,] 0.1065022789 0.2130045578 0.8934977
[3,] 0.2222030305 0.4444060609 0.7777970
[4,] 0.1371154858 0.2742309716 0.8628845
[5,] 0.2752898656 0.5505797312 0.7247101
[6,] 0.2062752667 0.4125505333 0.7937247
[7,] 0.1474280286 0.2948560571 0.8525720
[8,] 0.0981135055 0.1962270110 0.9018865
[9,] 0.0638768715 0.1277537430 0.9361231
[10,] 0.0386495282 0.0772990564 0.9613505
[11,] 0.0249574326 0.0499148652 0.9750426
[12,] 0.0145529323 0.0291058647 0.9854471
[13,] 0.0092722255 0.0185444510 0.9907278
[14,] 0.0216401029 0.0432802058 0.9783599
[15,] 0.0313961966 0.0627923931 0.9686038
[16,] 0.0336300899 0.0672601798 0.9663699
[17,] 0.0784795783 0.1569591566 0.9215204
[18,] 0.0572864467 0.1145728933 0.9427136
[19,] 0.0403288842 0.0806577683 0.9596711
[20,] 0.0270739132 0.0541478265 0.9729261
[21,] 0.0194985657 0.0389971315 0.9805014
[22,] 0.0128113215 0.0256226431 0.9871887
[23,] 0.0080917289 0.0161834578 0.9919083
[24,] 0.0050154641 0.0100309283 0.9949845
[25,] 0.0036214175 0.0072428350 0.9963786
[26,] 0.0034147910 0.0068295820 0.9965852
[27,] 0.0021294519 0.0042589039 0.9978705
[28,] 0.0029306025 0.0058612049 0.9970694
[29,] 0.0032118541 0.0064237081 0.9967881
[30,] 0.0157331340 0.0314662679 0.9842669
[31,] 0.0186825385 0.0373650770 0.9813175
[32,] 0.0140470475 0.0280940950 0.9859530
[33,] 0.0099688875 0.0199377750 0.9900311
[34,] 0.0069084799 0.0138169599 0.9930915
[35,] 0.0046865200 0.0093730401 0.9953135
[36,] 0.0035218134 0.0070436267 0.9964782
[37,] 0.0075273912 0.0150547824 0.9924726
[38,] 0.0137316777 0.0274633554 0.9862683
[39,] 0.0171306558 0.0342613116 0.9828693
[40,] 0.1772051004 0.3544102007 0.8227949
[41,] 0.1620343537 0.3240687074 0.8379656
[42,] 0.1389969782 0.2779939563 0.8610030
[43,] 0.1342464957 0.2684929913 0.8657535
[44,] 0.1095886885 0.2191773770 0.8904113
[45,] 0.0938261172 0.1876522344 0.9061739
[46,] 0.0833960498 0.1667920997 0.9166040
[47,] 0.0662238125 0.1324476250 0.9337762
[48,] 0.0546876815 0.1093753631 0.9453123
[49,] 0.0772552404 0.1545104808 0.9227448
[50,] 0.0868224411 0.1736448821 0.9131776
[51,] 0.0704234310 0.1408468620 0.9295766
[52,] 0.2087344327 0.4174688653 0.7912656
[53,] 0.1952173486 0.3904346971 0.8047827
[54,] 0.3204126356 0.6408252712 0.6795874
[55,] 0.2825581579 0.5651163159 0.7174418
[56,] 0.2421041458 0.4842082917 0.7578959
[57,] 0.2111273808 0.4222547617 0.7888726
[58,] 0.1780995279 0.3561990558 0.8219005
[59,] 0.1492955318 0.2985910636 0.8507045
[60,] 0.1251313391 0.2502626782 0.8748687
[61,] 0.1078054458 0.2156108916 0.8921946
[62,] 0.0868013450 0.1736026901 0.9131987
[63,] 0.0809904816 0.1619809631 0.9190095
[64,] 0.1408376321 0.2816752642 0.8591624
[65,] 0.1455549743 0.2911099486 0.8544450
[66,] 0.1890917433 0.3781834867 0.8109083
[67,] 0.1725435049 0.3450870098 0.8274565
[68,] 0.1530551141 0.3061102282 0.8469449
[69,] 0.1310918457 0.2621836915 0.8689082
[70,] 0.1190280719 0.2380561437 0.8809719
[71,] 0.1235825554 0.2471651107 0.8764174
[72,] 0.1027349461 0.2054698922 0.8972651
[73,] 0.1057893195 0.2115786389 0.8942107
[74,] 0.0863050845 0.1726101689 0.9136949
[75,] 0.0760798249 0.1521596498 0.9239202
[76,] 0.0834322857 0.1668645713 0.9165677
[77,] 0.0989452588 0.1978905175 0.9010547
[78,] 0.1016657067 0.2033314134 0.8983343
[79,] 0.0832028498 0.1664056996 0.9167972
[80,] 0.0672631584 0.1345263168 0.9327368
[81,] 0.0527752000 0.1055504001 0.9472248
[82,] 0.0416493575 0.0832987149 0.9583506
[83,] 0.0314420113 0.0628840227 0.9685580
[84,] 0.0258712307 0.0517424614 0.9741288
[85,] 0.0236637712 0.0473275424 0.9763362
[86,] 0.0179790738 0.0359581475 0.9820209
[87,] 0.0188000305 0.0376000609 0.9812000
[88,] 0.0200459751 0.0400919501 0.9799540
[89,] 0.0263997661 0.0527995322 0.9736002
[90,] 0.0226950635 0.0453901270 0.9773049
[91,] 0.0173178299 0.0346356599 0.9826822
[92,] 0.0126399173 0.0252798346 0.9873601
[93,] 0.0089096082 0.0178192163 0.9910904
[94,] 0.0061621136 0.0123242272 0.9938379
[95,] 0.0043901874 0.0087803748 0.9956098
[96,] 0.0029681827 0.0059363654 0.9970318
[97,] 0.0023128749 0.0046257498 0.9976871
[98,] 0.0016485502 0.0032971005 0.9983514
[99,] 0.0014292738 0.0028585476 0.9985707
[100,] 0.0010330318 0.0020660635 0.9989670
[101,] 0.0013925321 0.0027850642 0.9986075
[102,] 0.0037368572 0.0074737143 0.9962631
[103,] 0.0029305103 0.0058610206 0.9970695
[104,] 0.0018445649 0.0036891298 0.9981554
[105,] 0.0013853021 0.0027706043 0.9986147
[106,] 0.0008437173 0.0016874345 0.9991563
[107,] 0.0005070346 0.0010140693 0.9994930
[108,] 0.0003462382 0.0006924764 0.9996538
[109,] 0.0002521449 0.0005042899 0.9997479
[110,] 0.0002620008 0.0005240016 0.9997380
[111,] 0.0018980026 0.0037960053 0.9981020
[112,] 0.0366053906 0.0732107812 0.9633946
[113,] 0.1974105329 0.3948210658 0.8025895
[114,] 0.2483639820 0.4967279641 0.7516360
[115,] 0.2183279801 0.4366559602 0.7816720
[116,] 0.1641947212 0.3283894423 0.8358053
[117,] 0.1293143989 0.2586287979 0.8706856
[118,] 0.0898303695 0.1796607390 0.9101696
[119,] 0.0586223927 0.1172447854 0.9413776
[120,] 0.0486693741 0.0973387482 0.9513306
[121,] 0.0287124703 0.0574249406 0.9712875
[122,] 0.0167220245 0.0334440491 0.9832780
[123,] 0.0088592227 0.0177184453 0.9911408
[124,] 0.0044266697 0.0088533395 0.9955733
[125,] 0.6290763302 0.7418473396 0.3709237
> postscript(file="/var/www/html/rcomp/tmp/1seln1293625695.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23nk81293625695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33nk81293625695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43nk81293625695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ee1s1293625695.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 = 156
Frequency = 1
1 2 3 4 5 6
131.605311 93.989927 180.297619 415.297619 847.682234 453.642094
7 8 9 10 11 12
357.257479 725.872863 16.488248 379.026709 182.026709 33.334402
13 14 15 16 17 18
116.605311 117.989927 28.297619 205.297619 293.682234 -237.357906
19 20 21 22 23 24
216.257479 -95.127137 -247.511752 159.026709 27.026709 -133.665598
25 26 27 28 29 30
55.605311 -82.010073 1.297619 116.297619 -89.317766 -460.357906
31 32 33 34 35 36
681.257479 -488.127137 57.488248 214.026709 69.026709 153.334402
37 38 39 40 41 42
143.605311 2.989927 44.297619 381.297619 -36.317766 -188.357906
43 44 45 46 47 48
755.257479 -392.127137 777.488248 -286.973291 -130.973291 110.334402
49 50 51 52 53 54
75.605311 -84.010073 217.297619 -446.702381 -464.317766 397.642094
55 56 57 58 59 60
-992.742521 -403.127137 155.488248 -357.973291 -109.973291 180.334402
61 62 63 64 65 66
-266.394689 10.989927 -168.702381 -626.702381 -508.317766 59.642094
67 68 69 70 71 72
-1035.742521 245.872863 -875.511752 -179.973291 15.026709 -178.665598
73 74 75 76 77 78
-84.394689 74.989927 43.297619 -285.702381 -83.317766 -516.836081
79 80 81 82 83 84
-1057.220696 -72.605311 -831.989927 -216.451465 -131.451465 -190.143773
85 86 87 88 89 90
68.127137 276.511752 -255.180556 301.819444 -120.795940 -284.836081
91 92 93 94 95 96
390.779304 -570.605311 -393.989927 13.548535 49.548535 -17.143773
97 98 99 100 101 102
59.127137 -107.488248 -292.180556 278.819444 -146.795940 -374.836081
103 104 105 106 107 108
434.779304 -456.605311 -46.989927 -144.451465 64.548535 -8.143773
109 110 111 112 113 114
-21.872863 -220.488248 24.819444 181.819444 -156.795940 383.163919
115 116 117 118 119 120
227.779304 -243.605311 874.010073 -209.451465 -13.451465 258.856227
121 122 123 124 125 126
-24.872863 -116.488248 250.819444 -396.180556 -310.795940 1029.163919
127 128 129 130 131 132
1074.779304 -378.605311 624.010073 -50.451465 56.548535 125.856227
133 134 135 136 137 138
-162.872863 -5.488248 223.819444 -22.180556 414.204060 -10.836081
139 140 141 142 143 144
-550.220696 2059.394689 -425.989927 326.548535 80.548535 -107.143773
145 146 147 148 149 150
-89.872863 38.511752 -298.180556 -103.180556 361.204060 -249.836081
151 152 153 154 155 156
-502.220696 69.394689 317.010073 353.548535 -158.451465 -227.143773
> postscript(file="/var/www/html/rcomp/tmp/6ee1s1293625695.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 131.605311 NA
1 93.989927 131.605311
2 180.297619 93.989927
3 415.297619 180.297619
4 847.682234 415.297619
5 453.642094 847.682234
6 357.257479 453.642094
7 725.872863 357.257479
8 16.488248 725.872863
9 379.026709 16.488248
10 182.026709 379.026709
11 33.334402 182.026709
12 116.605311 33.334402
13 117.989927 116.605311
14 28.297619 117.989927
15 205.297619 28.297619
16 293.682234 205.297619
17 -237.357906 293.682234
18 216.257479 -237.357906
19 -95.127137 216.257479
20 -247.511752 -95.127137
21 159.026709 -247.511752
22 27.026709 159.026709
23 -133.665598 27.026709
24 55.605311 -133.665598
25 -82.010073 55.605311
26 1.297619 -82.010073
27 116.297619 1.297619
28 -89.317766 116.297619
29 -460.357906 -89.317766
30 681.257479 -460.357906
31 -488.127137 681.257479
32 57.488248 -488.127137
33 214.026709 57.488248
34 69.026709 214.026709
35 153.334402 69.026709
36 143.605311 153.334402
37 2.989927 143.605311
38 44.297619 2.989927
39 381.297619 44.297619
40 -36.317766 381.297619
41 -188.357906 -36.317766
42 755.257479 -188.357906
43 -392.127137 755.257479
44 777.488248 -392.127137
45 -286.973291 777.488248
46 -130.973291 -286.973291
47 110.334402 -130.973291
48 75.605311 110.334402
49 -84.010073 75.605311
50 217.297619 -84.010073
51 -446.702381 217.297619
52 -464.317766 -446.702381
53 397.642094 -464.317766
54 -992.742521 397.642094
55 -403.127137 -992.742521
56 155.488248 -403.127137
57 -357.973291 155.488248
58 -109.973291 -357.973291
59 180.334402 -109.973291
60 -266.394689 180.334402
61 10.989927 -266.394689
62 -168.702381 10.989927
63 -626.702381 -168.702381
64 -508.317766 -626.702381
65 59.642094 -508.317766
66 -1035.742521 59.642094
67 245.872863 -1035.742521
68 -875.511752 245.872863
69 -179.973291 -875.511752
70 15.026709 -179.973291
71 -178.665598 15.026709
72 -84.394689 -178.665598
73 74.989927 -84.394689
74 43.297619 74.989927
75 -285.702381 43.297619
76 -83.317766 -285.702381
77 -516.836081 -83.317766
78 -1057.220696 -516.836081
79 -72.605311 -1057.220696
80 -831.989927 -72.605311
81 -216.451465 -831.989927
82 -131.451465 -216.451465
83 -190.143773 -131.451465
84 68.127137 -190.143773
85 276.511752 68.127137
86 -255.180556 276.511752
87 301.819444 -255.180556
88 -120.795940 301.819444
89 -284.836081 -120.795940
90 390.779304 -284.836081
91 -570.605311 390.779304
92 -393.989927 -570.605311
93 13.548535 -393.989927
94 49.548535 13.548535
95 -17.143773 49.548535
96 59.127137 -17.143773
97 -107.488248 59.127137
98 -292.180556 -107.488248
99 278.819444 -292.180556
100 -146.795940 278.819444
101 -374.836081 -146.795940
102 434.779304 -374.836081
103 -456.605311 434.779304
104 -46.989927 -456.605311
105 -144.451465 -46.989927
106 64.548535 -144.451465
107 -8.143773 64.548535
108 -21.872863 -8.143773
109 -220.488248 -21.872863
110 24.819444 -220.488248
111 181.819444 24.819444
112 -156.795940 181.819444
113 383.163919 -156.795940
114 227.779304 383.163919
115 -243.605311 227.779304
116 874.010073 -243.605311
117 -209.451465 874.010073
118 -13.451465 -209.451465
119 258.856227 -13.451465
120 -24.872863 258.856227
121 -116.488248 -24.872863
122 250.819444 -116.488248
123 -396.180556 250.819444
124 -310.795940 -396.180556
125 1029.163919 -310.795940
126 1074.779304 1029.163919
127 -378.605311 1074.779304
128 624.010073 -378.605311
129 -50.451465 624.010073
130 56.548535 -50.451465
131 125.856227 56.548535
132 -162.872863 125.856227
133 -5.488248 -162.872863
134 223.819444 -5.488248
135 -22.180556 223.819444
136 414.204060 -22.180556
137 -10.836081 414.204060
138 -550.220696 -10.836081
139 2059.394689 -550.220696
140 -425.989927 2059.394689
141 326.548535 -425.989927
142 80.548535 326.548535
143 -107.143773 80.548535
144 -89.872863 -107.143773
145 38.511752 -89.872863
146 -298.180556 38.511752
147 -103.180556 -298.180556
148 361.204060 -103.180556
149 -249.836081 361.204060
150 -502.220696 -249.836081
151 69.394689 -502.220696
152 317.010073 69.394689
153 353.548535 317.010073
154 -158.451465 353.548535
155 -227.143773 -158.451465
156 NA -227.143773
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 93.989927 131.605311
[2,] 180.297619 93.989927
[3,] 415.297619 180.297619
[4,] 847.682234 415.297619
[5,] 453.642094 847.682234
[6,] 357.257479 453.642094
[7,] 725.872863 357.257479
[8,] 16.488248 725.872863
[9,] 379.026709 16.488248
[10,] 182.026709 379.026709
[11,] 33.334402 182.026709
[12,] 116.605311 33.334402
[13,] 117.989927 116.605311
[14,] 28.297619 117.989927
[15,] 205.297619 28.297619
[16,] 293.682234 205.297619
[17,] -237.357906 293.682234
[18,] 216.257479 -237.357906
[19,] -95.127137 216.257479
[20,] -247.511752 -95.127137
[21,] 159.026709 -247.511752
[22,] 27.026709 159.026709
[23,] -133.665598 27.026709
[24,] 55.605311 -133.665598
[25,] -82.010073 55.605311
[26,] 1.297619 -82.010073
[27,] 116.297619 1.297619
[28,] -89.317766 116.297619
[29,] -460.357906 -89.317766
[30,] 681.257479 -460.357906
[31,] -488.127137 681.257479
[32,] 57.488248 -488.127137
[33,] 214.026709 57.488248
[34,] 69.026709 214.026709
[35,] 153.334402 69.026709
[36,] 143.605311 153.334402
[37,] 2.989927 143.605311
[38,] 44.297619 2.989927
[39,] 381.297619 44.297619
[40,] -36.317766 381.297619
[41,] -188.357906 -36.317766
[42,] 755.257479 -188.357906
[43,] -392.127137 755.257479
[44,] 777.488248 -392.127137
[45,] -286.973291 777.488248
[46,] -130.973291 -286.973291
[47,] 110.334402 -130.973291
[48,] 75.605311 110.334402
[49,] -84.010073 75.605311
[50,] 217.297619 -84.010073
[51,] -446.702381 217.297619
[52,] -464.317766 -446.702381
[53,] 397.642094 -464.317766
[54,] -992.742521 397.642094
[55,] -403.127137 -992.742521
[56,] 155.488248 -403.127137
[57,] -357.973291 155.488248
[58,] -109.973291 -357.973291
[59,] 180.334402 -109.973291
[60,] -266.394689 180.334402
[61,] 10.989927 -266.394689
[62,] -168.702381 10.989927
[63,] -626.702381 -168.702381
[64,] -508.317766 -626.702381
[65,] 59.642094 -508.317766
[66,] -1035.742521 59.642094
[67,] 245.872863 -1035.742521
[68,] -875.511752 245.872863
[69,] -179.973291 -875.511752
[70,] 15.026709 -179.973291
[71,] -178.665598 15.026709
[72,] -84.394689 -178.665598
[73,] 74.989927 -84.394689
[74,] 43.297619 74.989927
[75,] -285.702381 43.297619
[76,] -83.317766 -285.702381
[77,] -516.836081 -83.317766
[78,] -1057.220696 -516.836081
[79,] -72.605311 -1057.220696
[80,] -831.989927 -72.605311
[81,] -216.451465 -831.989927
[82,] -131.451465 -216.451465
[83,] -190.143773 -131.451465
[84,] 68.127137 -190.143773
[85,] 276.511752 68.127137
[86,] -255.180556 276.511752
[87,] 301.819444 -255.180556
[88,] -120.795940 301.819444
[89,] -284.836081 -120.795940
[90,] 390.779304 -284.836081
[91,] -570.605311 390.779304
[92,] -393.989927 -570.605311
[93,] 13.548535 -393.989927
[94,] 49.548535 13.548535
[95,] -17.143773 49.548535
[96,] 59.127137 -17.143773
[97,] -107.488248 59.127137
[98,] -292.180556 -107.488248
[99,] 278.819444 -292.180556
[100,] -146.795940 278.819444
[101,] -374.836081 -146.795940
[102,] 434.779304 -374.836081
[103,] -456.605311 434.779304
[104,] -46.989927 -456.605311
[105,] -144.451465 -46.989927
[106,] 64.548535 -144.451465
[107,] -8.143773 64.548535
[108,] -21.872863 -8.143773
[109,] -220.488248 -21.872863
[110,] 24.819444 -220.488248
[111,] 181.819444 24.819444
[112,] -156.795940 181.819444
[113,] 383.163919 -156.795940
[114,] 227.779304 383.163919
[115,] -243.605311 227.779304
[116,] 874.010073 -243.605311
[117,] -209.451465 874.010073
[118,] -13.451465 -209.451465
[119,] 258.856227 -13.451465
[120,] -24.872863 258.856227
[121,] -116.488248 -24.872863
[122,] 250.819444 -116.488248
[123,] -396.180556 250.819444
[124,] -310.795940 -396.180556
[125,] 1029.163919 -310.795940
[126,] 1074.779304 1029.163919
[127,] -378.605311 1074.779304
[128,] 624.010073 -378.605311
[129,] -50.451465 624.010073
[130,] 56.548535 -50.451465
[131,] 125.856227 56.548535
[132,] -162.872863 125.856227
[133,] -5.488248 -162.872863
[134,] 223.819444 -5.488248
[135,] -22.180556 223.819444
[136,] 414.204060 -22.180556
[137,] -10.836081 414.204060
[138,] -550.220696 -10.836081
[139,] 2059.394689 -550.220696
[140,] -425.989927 2059.394689
[141,] 326.548535 -425.989927
[142,] 80.548535 326.548535
[143,] -107.143773 80.548535
[144,] -89.872863 -107.143773
[145,] 38.511752 -89.872863
[146,] -298.180556 38.511752
[147,] -103.180556 -298.180556
[148,] 361.204060 -103.180556
[149,] -249.836081 361.204060
[150,] -502.220696 -249.836081
[151,] 69.394689 -502.220696
[152,] 317.010073 69.394689
[153,] 353.548535 317.010073
[154,] -158.451465 353.548535
[155,] -227.143773 -158.451465
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 93.989927 131.605311
2 180.297619 93.989927
3 415.297619 180.297619
4 847.682234 415.297619
5 453.642094 847.682234
6 357.257479 453.642094
7 725.872863 357.257479
8 16.488248 725.872863
9 379.026709 16.488248
10 182.026709 379.026709
11 33.334402 182.026709
12 116.605311 33.334402
13 117.989927 116.605311
14 28.297619 117.989927
15 205.297619 28.297619
16 293.682234 205.297619
17 -237.357906 293.682234
18 216.257479 -237.357906
19 -95.127137 216.257479
20 -247.511752 -95.127137
21 159.026709 -247.511752
22 27.026709 159.026709
23 -133.665598 27.026709
24 55.605311 -133.665598
25 -82.010073 55.605311
26 1.297619 -82.010073
27 116.297619 1.297619
28 -89.317766 116.297619
29 -460.357906 -89.317766
30 681.257479 -460.357906
31 -488.127137 681.257479
32 57.488248 -488.127137
33 214.026709 57.488248
34 69.026709 214.026709
35 153.334402 69.026709
36 143.605311 153.334402
37 2.989927 143.605311
38 44.297619 2.989927
39 381.297619 44.297619
40 -36.317766 381.297619
41 -188.357906 -36.317766
42 755.257479 -188.357906
43 -392.127137 755.257479
44 777.488248 -392.127137
45 -286.973291 777.488248
46 -130.973291 -286.973291
47 110.334402 -130.973291
48 75.605311 110.334402
49 -84.010073 75.605311
50 217.297619 -84.010073
51 -446.702381 217.297619
52 -464.317766 -446.702381
53 397.642094 -464.317766
54 -992.742521 397.642094
55 -403.127137 -992.742521
56 155.488248 -403.127137
57 -357.973291 155.488248
58 -109.973291 -357.973291
59 180.334402 -109.973291
60 -266.394689 180.334402
61 10.989927 -266.394689
62 -168.702381 10.989927
63 -626.702381 -168.702381
64 -508.317766 -626.702381
65 59.642094 -508.317766
66 -1035.742521 59.642094
67 245.872863 -1035.742521
68 -875.511752 245.872863
69 -179.973291 -875.511752
70 15.026709 -179.973291
71 -178.665598 15.026709
72 -84.394689 -178.665598
73 74.989927 -84.394689
74 43.297619 74.989927
75 -285.702381 43.297619
76 -83.317766 -285.702381
77 -516.836081 -83.317766
78 -1057.220696 -516.836081
79 -72.605311 -1057.220696
80 -831.989927 -72.605311
81 -216.451465 -831.989927
82 -131.451465 -216.451465
83 -190.143773 -131.451465
84 68.127137 -190.143773
85 276.511752 68.127137
86 -255.180556 276.511752
87 301.819444 -255.180556
88 -120.795940 301.819444
89 -284.836081 -120.795940
90 390.779304 -284.836081
91 -570.605311 390.779304
92 -393.989927 -570.605311
93 13.548535 -393.989927
94 49.548535 13.548535
95 -17.143773 49.548535
96 59.127137 -17.143773
97 -107.488248 59.127137
98 -292.180556 -107.488248
99 278.819444 -292.180556
100 -146.795940 278.819444
101 -374.836081 -146.795940
102 434.779304 -374.836081
103 -456.605311 434.779304
104 -46.989927 -456.605311
105 -144.451465 -46.989927
106 64.548535 -144.451465
107 -8.143773 64.548535
108 -21.872863 -8.143773
109 -220.488248 -21.872863
110 24.819444 -220.488248
111 181.819444 24.819444
112 -156.795940 181.819444
113 383.163919 -156.795940
114 227.779304 383.163919
115 -243.605311 227.779304
116 874.010073 -243.605311
117 -209.451465 874.010073
118 -13.451465 -209.451465
119 258.856227 -13.451465
120 -24.872863 258.856227
121 -116.488248 -24.872863
122 250.819444 -116.488248
123 -396.180556 250.819444
124 -310.795940 -396.180556
125 1029.163919 -310.795940
126 1074.779304 1029.163919
127 -378.605311 1074.779304
128 624.010073 -378.605311
129 -50.451465 624.010073
130 56.548535 -50.451465
131 125.856227 56.548535
132 -162.872863 125.856227
133 -5.488248 -162.872863
134 223.819444 -5.488248
135 -22.180556 223.819444
136 414.204060 -22.180556
137 -10.836081 414.204060
138 -550.220696 -10.836081
139 2059.394689 -550.220696
140 -425.989927 2059.394689
141 326.548535 -425.989927
142 80.548535 326.548535
143 -107.143773 80.548535
144 -89.872863 -107.143773
145 38.511752 -89.872863
146 -298.180556 38.511752
147 -103.180556 -298.180556
148 361.204060 -103.180556
149 -249.836081 361.204060
150 -502.220696 -249.836081
151 69.394689 -502.220696
152 317.010073 69.394689
153 353.548535 317.010073
154 -158.451465 353.548535
155 -227.143773 -158.451465
> 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/776je1293625695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/876je1293625695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hfiy1293625695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hfiy1293625695.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lyz41293625695.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/126yfa1293625695.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/13k8d11293625695.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/14n8b71293625695.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/159rsv1293625695.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/16ca811293625695.tab")
+ }
>
> try(system("convert tmp/1seln1293625695.ps tmp/1seln1293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/23nk81293625695.ps tmp/23nk81293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/33nk81293625695.ps tmp/33nk81293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/43nk81293625695.ps tmp/43nk81293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ee1s1293625695.ps tmp/5ee1s1293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ee1s1293625695.ps tmp/6ee1s1293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/776je1293625695.ps tmp/776je1293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/876je1293625695.ps tmp/876je1293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hfiy1293625695.ps tmp/9hfiy1293625695.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hfiy1293625695.ps tmp/10hfiy1293625695.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.018 1.771 8.737