R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(7
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+ ,5)
+ ,dim=c(6
+ ,162)
+ ,dimnames=list(c('X_1t'
+ ,'X_2t'
+ ,'X_3t'
+ ,'X_4t'
+ ,'X_5t'
+ ,'Y_t')
+ ,1:162))
> y <- array(NA,dim=c(6,162),dimnames=list(c('X_1t','X_2t','X_3t','X_4t','X_5t','Y_t'),1:162))
> 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 = '6'
> par3 <- 'No Linear Trend'
> par2 <- 'Include Monthly Dummies'
> par1 <- '6'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y_t X_1t X_2t X_3t X_4t X_5t M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3 7 41 38 14 12 1 0 0 0 0 0 0 0 0 0 0
2 5 5 39 32 18 11 0 1 0 0 0 0 0 0 0 0 0
3 4 5 30 35 11 14 0 0 1 0 0 0 0 0 0 0 0
4 4 5 31 33 12 12 0 0 0 1 0 0 0 0 0 0 0
5 5 8 34 37 16 21 0 0 0 0 1 0 0 0 0 0 0
6 5 6 35 29 18 12 0 0 0 0 0 1 0 0 0 0 0
7 2 5 39 31 14 22 0 0 0 0 0 0 1 0 0 0 0
8 5 6 34 36 14 11 0 0 0 0 0 0 0 1 0 0 0
9 4 5 36 35 15 10 0 0 0 0 0 0 0 0 1 0 0
10 4 4 37 38 15 13 0 0 0 0 0 0 0 0 0 1 0
11 5 6 38 31 17 10 0 0 0 0 0 0 0 0 0 0 1
12 3 5 36 34 19 8 0 0 0 0 0 0 0 0 0 0 0
13 5 5 38 35 10 15 1 0 0 0 0 0 0 0 0 0 0
14 3 6 39 38 16 14 0 1 0 0 0 0 0 0 0 0 0
15 5 7 33 37 18 10 0 0 1 0 0 0 0 0 0 0 0
16 3 6 32 33 14 14 0 0 0 1 0 0 0 0 0 0 0
17 4 7 36 32 14 14 0 0 0 0 1 0 0 0 0 0 0
18 5 6 38 38 17 11 0 0 0 0 0 1 0 0 0 0 0
19 4 8 39 38 14 10 0 0 0 0 0 0 1 0 0 0 0
20 3 7 32 32 16 13 0 0 0 0 0 0 0 1 0 0 0
21 4 5 32 33 18 7 0 0 0 0 0 0 0 0 1 0 0
22 4 5 31 31 11 14 0 0 0 0 0 0 0 0 0 1 0
23 3 7 39 38 14 12 0 0 0 0 0 0 0 0 0 0 1
24 3 7 37 39 12 14 0 0 0 0 0 0 0 0 0 0 0
25 4 5 39 32 17 11 1 0 0 0 0 0 0 0 0 0 0
26 5 4 41 32 9 9 0 1 0 0 0 0 0 0 0 0 0
27 4 10 36 35 16 11 0 0 1 0 0 0 0 0 0 0 0
28 4 6 33 37 14 15 0 0 0 1 0 0 0 0 0 0 0
29 4 5 33 33 15 14 0 0 0 0 1 0 0 0 0 0 0
30 4 5 34 33 11 13 0 0 0 0 0 1 0 0 0 0 0
31 4 5 31 28 16 9 0 0 0 0 0 0 1 0 0 0 0
32 3 5 27 32 13 15 0 0 0 0 0 0 0 1 0 0 0
33 4 6 37 31 17 10 0 0 0 0 0 0 0 0 1 0 0
34 5 5 34 37 15 11 0 0 0 0 0 0 0 0 0 1 0
35 4 5 34 30 14 13 0 0 0 0 0 0 0 0 0 0 1
36 4 5 32 33 16 8 0 0 0 0 0 0 0 0 0 0 0
37 3 5 29 31 9 20 1 0 0 0 0 0 0 0 0 0 0
38 4 5 36 33 15 12 0 1 0 0 0 0 0 0 0 0 0
39 4 5 29 31 17 10 0 0 1 0 0 0 0 0 0 0 0
40 4 5 35 33 13 10 0 0 0 1 0 0 0 0 0 0 0
41 5 5 37 32 15 9 0 0 0 0 1 0 0 0 0 0 0
42 4 7 34 33 16 14 0 0 0 0 0 1 0 0 0 0 0
43 3 5 38 32 16 8 0 0 0 0 0 0 1 0 0 0 0
44 3 6 35 33 12 14 0 0 0 0 0 0 0 1 0 0 0
45 4 7 38 28 12 11 0 0 0 0 0 0 0 0 1 0 0
46 4 7 37 35 11 13 0 0 0 0 0 0 0 0 0 1 0
47 4 5 38 39 15 9 0 0 0 0 0 0 0 0 0 0 1
48 5 5 33 34 15 11 0 0 0 0 0 0 0 0 0 0 0
49 4 4 36 38 17 15 1 0 0 0 0 0 0 0 0 0 0
50 5 5 38 32 13 11 0 1 0 0 0 0 0 0 0 0 0
51 4 4 32 38 16 10 0 0 1 0 0 0 0 0 0 0 0
52 4 5 32 30 14 14 0 0 0 1 0 0 0 0 0 0 0
53 4 5 32 33 11 18 0 0 0 0 1 0 0 0 0 0 0
54 4 7 34 38 12 14 0 0 0 0 0 1 0 0 0 0 0
55 4 5 32 32 12 11 0 0 0 0 0 0 1 0 0 0 0
56 5 5 37 32 15 12 0 0 0 0 0 0 0 1 0 0 0
57 4 6 39 34 16 13 0 0 0 0 0 0 0 0 1 0 0
58 4 4 29 34 15 9 0 0 0 0 0 0 0 0 0 1 0
59 4 6 37 36 12 10 0 0 0 0 0 0 0 0 0 0 1
60 4 6 35 34 12 15 0 0 0 0 0 0 0 0 0 0 0
61 3 5 30 28 8 20 1 0 0 0 0 0 0 0 0 0 0
62 4 7 38 34 13 12 0 1 0 0 0 0 0 0 0 0 0
63 5 6 34 35 11 12 0 0 1 0 0 0 0 0 0 0 0
64 1 8 31 35 14 14 0 0 0 1 0 0 0 0 0 0 0
65 3 7 34 31 15 13 0 0 0 0 1 0 0 0 0 0 0
66 5 5 35 37 10 11 0 0 0 0 0 1 0 0 0 0 0
67 4 6 36 35 11 17 0 0 0 0 0 0 1 0 0 0 0
68 4 6 30 27 12 12 0 0 0 0 0 0 0 1 0 0 0
69 3 5 39 40 15 13 0 0 0 0 0 0 0 0 1 0 0
70 4 5 35 37 15 14 0 0 0 0 0 0 0 0 0 1 0
71 4 5 38 36 14 13 0 0 0 0 0 0 0 0 0 0 1
72 3 5 31 38 16 15 0 0 0 0 0 0 0 0 0 0 0
73 5 4 34 39 15 13 1 0 0 0 0 0 0 0 0 0 0
74 4 6 38 41 15 10 0 1 0 0 0 0 0 0 0 0 0
75 5 6 34 27 13 11 0 0 1 0 0 0 0 0 0 0 0
76 4 6 39 30 12 19 0 0 0 1 0 0 0 0 0 0 0
77 4 6 37 37 17 13 0 0 0 0 1 0 0 0 0 0 0
78 4 7 34 31 13 17 0 0 0 0 0 1 0 0 0 0 0
79 4 5 28 31 15 13 0 0 0 0 0 0 1 0 0 0 0
80 3 7 37 27 13 9 0 0 0 0 0 0 0 1 0 0 0
81 5 6 33 36 15 11 0 0 0 0 0 0 0 0 1 0 0
82 NA 5 37 38 16 10 0 0 0 0 0 0 0 0 0 1 0
83 5 5 35 37 15 9 0 0 0 0 0 0 0 0 0 0 1
84 4 4 37 33 16 12 0 0 0 0 0 0 0 0 0 0 0
85 4 8 32 34 15 12 1 0 0 0 0 0 0 0 0 0 0
86 5 8 33 31 14 13 0 1 0 0 0 0 0 0 0 0 0
87 4 5 38 39 15 13 0 0 1 0 0 0 0 0 0 0 0
88 4 5 33 34 14 12 0 0 0 1 0 0 0 0 0 0 0
89 3 6 29 32 13 15 0 0 0 0 1 0 0 0 0 0 0
90 4 4 33 33 7 22 0 0 0 0 0 1 0 0 0 0 0
91 4 5 31 36 17 13 0 0 0 0 0 0 1 0 0 0 0
92 3 5 36 32 13 15 0 0 0 0 0 0 0 1 0 0 0
93 5 5 35 41 15 13 0 0 0 0 0 0 0 0 1 0 0
94 5 5 32 28 14 15 0 0 0 0 0 0 0 0 0 1 0
95 5 6 29 30 13 10 0 0 0 0 0 0 0 0 0 0 1
96 4 6 39 36 16 11 0 0 0 0 0 0 0 0 0 0 0
97 4 5 37 35 12 16 1 0 0 0 0 0 0 0 0 0 0
98 4 6 35 31 14 11 0 1 0 0 0 0 0 0 0 0 0
99 4 5 37 34 17 11 0 0 1 0 0 0 0 0 0 0 0
100 4 7 32 36 15 10 0 0 0 1 0 0 0 0 0 0 0
101 4 5 38 36 17 10 0 0 0 0 1 0 0 0 0 0 0
102 4 6 37 35 12 16 0 0 0 0 0 1 0 0 0 0 0
103 5 6 36 37 16 12 0 0 0 0 0 0 1 0 0 0 0
104 4 6 32 28 11 11 0 0 0 0 0 0 0 1 0 0 0
105 4 4 33 39 15 16 0 0 0 0 0 0 0 0 1 0 0
106 3 5 40 32 9 19 0 0 0 0 0 0 0 0 0 1 0
107 5 5 38 35 16 11 0 0 0 0 0 0 0 0 0 0 1
108 4 7 41 39 15 16 0 0 0 0 0 0 0 0 0 0 0
109 3 6 36 35 10 15 1 0 0 0 0 0 0 0 0 0 0
110 2 9 43 42 10 24 0 1 0 0 0 0 0 0 0 0 0
111 5 6 30 34 15 14 0 0 1 0 0 0 0 0 0 0 0
112 4 6 31 33 11 15 0 0 0 1 0 0 0 0 0 0 0
113 5 5 32 41 13 11 0 0 0 0 1 0 0 0 0 0 0
114 1 6 32 33 14 15 0 0 0 0 0 1 0 0 0 0 0
115 5 5 37 34 18 12 0 0 0 0 0 0 1 0 0 0 0
116 5 8 37 32 16 10 0 0 0 0 0 0 0 1 0 0 0
117 3 7 33 40 14 14 0 0 0 0 0 0 0 0 1 0 0
118 4 5 34 40 14 13 0 0 0 0 0 0 0 0 0 1 0
119 5 7 33 35 14 9 0 0 0 0 0 0 0 0 0 0 1
120 5 6 38 36 14 15 0 0 0 0 0 0 0 0 0 0 0
121 3 6 33 37 12 15 1 0 0 0 0 0 0 0 0 0 0
122 4 9 31 27 14 14 0 1 0 0 0 0 0 0 0 0 0
123 5 7 38 39 15 11 0 0 1 0 0 0 0 0 0 0 0
124 4 6 37 38 15 8 0 0 0 1 0 0 0 0 0 0 0
125 4 5 33 31 15 11 0 0 0 0 1 0 0 0 0 0 0
126 4 5 31 33 13 11 0 0 0 0 0 1 0 0 0 0 0
127 5 6 39 32 17 8 0 0 0 0 0 0 1 0 0 0 0
128 4 6 44 39 17 10 0 0 0 0 0 0 0 1 0 0 0
129 5 7 33 36 19 11 0 0 0 0 0 0 0 0 1 0 0
130 4 5 35 33 15 13 0 0 0 0 0 0 0 0 0 1 0
131 4 5 32 33 13 11 0 0 0 0 0 0 0 0 0 0 1
132 4 5 28 32 9 20 0 0 0 0 0 0 0 0 0 0 0
133 4 6 40 37 15 10 1 0 0 0 0 0 0 0 0 0 0
134 3 4 27 30 15 15 0 1 0 0 0 0 0 0 0 0 0
135 4 5 37 38 15 12 0 0 1 0 0 0 0 0 0 0 0
136 5 7 32 29 16 14 0 0 0 1 0 0 0 0 0 0 0
137 3 5 28 22 11 23 0 0 0 0 1 0 0 0 0 0 0
138 4 7 34 35 14 14 0 0 0 0 0 1 0 0 0 0 0
139 3 7 30 35 11 16 0 0 0 0 0 0 1 0 0 0 0
140 4 6 35 34 15 11 0 0 0 0 0 0 0 1 0 0 0
141 3 5 31 35 13 12 0 0 0 0 0 0 0 0 1 0 0
142 3 8 32 34 15 10 0 0 0 0 0 0 0 0 0 1 0
143 5 5 30 34 16 14 0 0 0 0 0 0 0 0 0 0 1
144 5 5 30 35 14 12 0 0 0 0 0 0 0 0 0 0 0
145 5 5 31 23 15 12 1 0 0 0 0 0 0 0 0 0 0
146 5 6 40 31 16 11 0 1 0 0 0 0 0 0 0 0 0
147 5 4 32 27 16 12 0 0 1 0 0 0 0 0 0 0 0
148 4 5 36 36 11 13 0 0 0 1 0 0 0 0 0 0 0
149 4 5 32 31 12 11 0 0 0 0 1 0 0 0 0 0 0
150 4 7 35 32 9 19 0 0 0 0 0 1 0 0 0 0 0
151 5 6 38 39 16 12 0 0 0 0 0 0 1 0 0 0 0
152 5 7 42 37 13 17 0 0 0 0 0 0 0 1 0 0 0
153 4 10 34 38 16 9 0 0 0 0 0 0 0 0 1 0 0
154 4 6 35 39 12 12 0 0 0 0 0 0 0 0 0 1 0
155 4 8 35 34 9 19 0 0 0 0 0 0 0 0 0 0 1
156 5 4 33 31 13 18 0 0 0 0 0 0 0 0 0 0 0
157 3 5 36 32 13 15 1 0 0 0 0 0 0 0 0 0 0
158 4 6 32 37 14 14 0 1 0 0 0 0 0 0 0 0 0
159 5 7 33 36 19 11 0 0 1 0 0 0 0 0 0 0 0
160 5 7 34 32 13 9 0 0 0 1 0 0 0 0 0 0 0
161 5 6 32 35 12 18 0 0 0 0 1 0 0 0 0 0 0
162 5 6 34 36 13 16 0 0 0 0 0 1 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X_1t X_2t X_3t X_4t X_5t
4.76674 -0.07026 0.01836 -0.01258 0.02433 -0.06372
M1 M2 M3 M4 M5 M6
-0.22045 -0.02620 0.32563 -0.19245 0.08390 0.14636
M7 M8 M9 M10 M11
-0.14454 -0.20343 -0.17318 -0.08080 0.20727
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.04884 -0.40663 0.02278 0.52828 1.50136
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.76674 1.01976 4.674 6.71e-06 ***
X_1t -0.07026 0.05484 -1.281 0.20214
X_2t 0.01836 0.02047 0.897 0.37133
X_3t -0.01258 0.01934 -0.650 0.51661
X_4t 0.02433 0.03270 0.744 0.45795
X_5t -0.06372 0.02401 -2.654 0.00885 **
M1 -0.22045 0.30009 -0.735 0.46377
M2 -0.02620 0.30434 -0.086 0.93151
M3 0.32563 0.30126 1.081 0.28156
M4 -0.19245 0.30310 -0.635 0.52647
M5 0.08390 0.29970 0.280 0.77992
M6 0.14636 0.30198 0.485 0.62864
M7 -0.14454 0.30398 -0.475 0.63515
M8 -0.20343 0.31260 -0.651 0.51623
M9 -0.17318 0.30681 -0.564 0.57333
M10 -0.08080 0.31047 -0.260 0.79504
M11 0.20727 0.30780 0.673 0.50177
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7695 on 144 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1589, Adjusted R-squared: 0.06543
F-statistic: 1.7 on 16 and 144 DF, p-value: 0.05266
> 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,] 0.58295822 0.83408356 0.41704178
[2,] 0.96590477 0.06819046 0.03409523
[3,] 0.94635875 0.10728249 0.05364125
[4,] 0.94853684 0.10292633 0.05146316
[5,] 0.96962371 0.06075257 0.03037629
[6,] 0.95076543 0.09846914 0.04923457
[7,] 0.92554669 0.14890662 0.07445331
[8,] 0.89608981 0.20782038 0.10391019
[9,] 0.87923017 0.24153967 0.12076983
[10,] 0.88521648 0.22956704 0.11478352
[11,] 0.88139156 0.23721688 0.11860844
[12,] 0.84080278 0.31839444 0.15919722
[13,] 0.81795014 0.36409973 0.18204986
[14,] 0.76736020 0.46527960 0.23263980
[15,] 0.74612813 0.50774373 0.25387187
[16,] 0.69255772 0.61488456 0.30744228
[17,] 0.65863069 0.68273862 0.34136931
[18,] 0.59414431 0.81171138 0.40585569
[19,] 0.53086784 0.93826431 0.46913216
[20,] 0.49640279 0.99280558 0.50359721
[21,] 0.43212097 0.86424195 0.56787903
[22,] 0.37859641 0.75719281 0.62140359
[23,] 0.32711684 0.65423369 0.67288316
[24,] 0.39034051 0.78068102 0.60965949
[25,] 0.36308165 0.72616330 0.63691835
[26,] 0.31235469 0.62470938 0.68764531
[27,] 0.26510569 0.53021138 0.73489431
[28,] 0.23319914 0.46639827 0.76680086
[29,] 0.35319235 0.70638470 0.64680765
[30,] 0.30358021 0.60716042 0.69641979
[31,] 0.28834378 0.57668755 0.71165622
[32,] 0.25980880 0.51961759 0.74019120
[33,] 0.22105801 0.44211601 0.77894199
[34,] 0.18529481 0.37058961 0.81470519
[35,] 0.15344869 0.30689738 0.84655131
[36,] 0.13526283 0.27052566 0.86473717
[37,] 0.17619608 0.35239217 0.82380392
[38,] 0.14730089 0.29460177 0.85269911
[39,] 0.12821773 0.25643546 0.87178227
[40,] 0.10713540 0.21427081 0.89286460
[41,] 0.09448411 0.18896822 0.90551589
[42,] 0.07635291 0.15270582 0.92364709
[43,] 0.05957487 0.11914973 0.94042513
[44,] 0.05673414 0.11346828 0.94326586
[45,] 0.34208840 0.68417680 0.65791160
[46,] 0.42418535 0.84837070 0.57581465
[47,] 0.41434249 0.82868498 0.58565751
[48,] 0.40926754 0.81853508 0.59073246
[49,] 0.36243953 0.72487905 0.63756047
[50,] 0.39167411 0.78334821 0.60832589
[51,] 0.34357231 0.68714461 0.65642769
[52,] 0.31341940 0.62683880 0.68658060
[53,] 0.33145433 0.66290866 0.66854567
[54,] 0.39282068 0.78564137 0.60717932
[55,] 0.34630241 0.69260482 0.65369759
[56,] 0.31161171 0.62322342 0.68838829
[57,] 0.28281194 0.56562389 0.71718806
[58,] 0.24888792 0.49777584 0.75111208
[59,] 0.21104605 0.42209210 0.78895395
[60,] 0.20260885 0.40521770 0.79739115
[61,] 0.26433119 0.52866238 0.73566881
[62,] 0.31845472 0.63690944 0.68154528
[63,] 0.29950553 0.59901107 0.70049447
[64,] 0.28428699 0.56857398 0.71571301
[65,] 0.25449024 0.50898047 0.74550976
[66,] 0.31684152 0.63368303 0.68315848
[67,] 0.28774268 0.57548535 0.71225732
[68,] 0.25458897 0.50917793 0.74541103
[69,] 0.28192882 0.56385764 0.71807118
[70,] 0.27450453 0.54900907 0.72549547
[71,] 0.25359754 0.50719509 0.74640246
[72,] 0.27456005 0.54912010 0.72543995
[73,] 0.33297150 0.66594300 0.66702850
[74,] 0.37524260 0.75048521 0.62475740
[75,] 0.35341395 0.70682791 0.64658605
[76,] 0.36316831 0.72633663 0.63683169
[77,] 0.33347229 0.66694457 0.66652771
[78,] 0.29289680 0.58579359 0.70710320
[79,] 0.29863888 0.59727776 0.70136112
[80,] 0.27172483 0.54344966 0.72827517
[81,] 0.26995602 0.53991204 0.73004398
[82,] 0.23733598 0.47467195 0.76266402
[83,] 0.25410858 0.50821715 0.74589142
[84,] 0.21462714 0.42925427 0.78537286
[85,] 0.18977118 0.37954236 0.81022882
[86,] 0.16813819 0.33627638 0.83186181
[87,] 0.14536467 0.29072934 0.85463533
[88,] 0.16768671 0.33537342 0.83231329
[89,] 0.14723960 0.29447919 0.85276040
[90,] 0.20122956 0.40245912 0.79877044
[91,] 0.19751160 0.39502319 0.80248840
[92,] 0.16771174 0.33542347 0.83228826
[93,] 0.18576329 0.37152658 0.81423671
[94,] 0.94949730 0.10100539 0.05050270
[95,] 0.93866407 0.12267187 0.06133593
[96,] 0.93623370 0.12753259 0.06376630
[97,] 0.92830756 0.14338487 0.07169244
[98,] 0.91346172 0.17307657 0.08653828
[99,] 0.89786569 0.20426863 0.10213431
[100,] 0.89800930 0.20398140 0.10199070
[101,] 0.87260156 0.25479688 0.12739844
[102,] 0.83517720 0.32964559 0.16482280
[103,] 0.80248259 0.39503482 0.19751741
[104,] 0.80731672 0.38536657 0.19268328
[105,] 0.78484094 0.43031812 0.21515906
[106,] 0.73048408 0.53903184 0.26951592
[107,] 0.67258174 0.65483653 0.32741826
[108,] 0.76480277 0.47039446 0.23519723
[109,] 0.73540989 0.52918022 0.26459011
[110,] 0.66611718 0.66776565 0.33388282
[111,] 0.62124578 0.75750844 0.37875422
[112,] 0.55208217 0.89583566 0.44791783
[113,] 0.51680108 0.96639784 0.48319892
[114,] 0.47175529 0.94351058 0.52824471
[115,] 0.46239705 0.92479410 0.53760295
[116,] 0.39429271 0.78858543 0.60570729
[117,] 0.45146542 0.90293084 0.54853458
[118,] 0.44475833 0.88951666 0.55524167
[119,] 0.36117922 0.72235844 0.63882078
[120,] 0.32188178 0.64376357 0.67811822
[121,] 0.24386068 0.48772136 0.75613932
[122,] 0.74566052 0.50867895 0.25433948
[123,] 0.73160627 0.53678746 0.26839373
> postscript(file="/var/wessaorg/rcomp/tmp/1pvkr1383236108.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$resid)
Warning message:
In x[, 1] - mysum$resid :
longer object length is not a multiple of shorter object length
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2csih1383236108.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3b51d1383236108.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4euw31383236108.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/53ept1383236108.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 161
Frequency = 1
1 2 3 4 5 6
-0.905236006 0.560193863 -0.227214887 0.095586868 1.501361769 0.557301701
7 8 9 10 11 12
-1.535832313 1.047109221 -0.190747589 -0.142864594 0.363383158 -1.601268992
13 14 15 16 17 18
1.260063981 -1.054271465 0.458190557 -0.773744464 -0.065834656 0.576039114
19 20 21 22 23 24
-0.001603420 -0.817459697 -0.406626569 0.110550326 -1.296243320 -0.863586945
25 26 27 28 29 30
-0.221222984 0.544796880 -0.298861402 0.321920578 -0.163047697 -0.210243679
31 32 33 34 35 36
-0.303687610 -0.665769102 -0.237815571 0.842456493 -0.381881271 -0.467419355
37 38 39 40 41 42
-0.282125540 -0.235444024 -0.660032263 -0.129602220 0.432373827 -0.127676104
43 44 45 46 47 48
-1.445589177 -0.769159842 -0.038252282 0.127527300 -0.621315338 0.742282535
49 50 51 52 53 54
0.093903949 0.700220589 -0.672993001 0.118265342 0.207507894 0.032542010
55 56 57 58 59 60
-0.046970106 0.910856786 -0.021316964 -0.301185981 -0.433708704 0.103697753
61 62 63 64 65 66
-0.313876060 -0.070388394 0.642191547 -2.589713359 -1.129749178 0.718608258
67 68 69 70 71 72
0.394225432 0.119730183 -0.991786440 0.015247926 -0.379846969 -0.940171774
73 74 75 76 77 78
1.064428758 -0.228715635 0.429197176 0.427282905 -0.228288725 0.111321138
79 80 81 83 84 85
0.068305997 -1.153980863 1.010873344 0.408599641 -0.374596901 0.255588667
86 87 88 89 90 91
1.093304930 -0.484809004 0.022783104 -0.919555130 0.408629367 0.027451514
92 93 94 95 96 97
-0.830971734 1.094213234 1.045178864 0.613346210 -0.296772817 0.293467238
98 99 100 101 102 103
-0.211360979 -0.705434439 0.055048513 -0.520629482 -0.003084207 0.979128318
104 105 106 107 108 109
0.056213184 0.226658300 -0.674828872 0.431476877 0.117418657 -0.632962977
110 111 112 113 114 115
-1.083443033 0.733133475 0.381329671 0.813439480 -3.048841643 0.804113926
116 117 118 119 120 121
0.969875129 -0.653078724 0.031951037 0.585015640 1.025114423 -0.601411219
122 123 124 125 126 127
0.213688603 0.528282168 -0.209271029 -0.379347543 -0.331276006 0.581982146
128 129 130 131 132 133
-0.235438698 0.983797988 -0.098772964 -0.410538073 0.528356105 -0.121483483
134 135 136 137 138 139
-0.987084305 -0.542745198 1.197543401 -0.538829020 -0.053855188 -0.489093894
140 141 142 143 144 145
-0.020733367 -0.922868477 -1.011492379 0.756894659 0.897976297 0.924821370
146 147 148 149 150 151
0.648191430 0.316099413 0.129586411 -0.287989168 0.330309795 0.967569187
152 153 154 155 156 157
1.389728803 0.146949750 0.056232843 0.364817488 1.128971014 -0.813955697
158 159 160 161 162
0.110311540 0.484995859 0.952984278 1.278587629 1.040225444
> postscript(file="/var/wessaorg/rcomp/tmp/6s7yt1383236108.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 = 161
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.905236006 NA
1 0.560193863 -0.905236006
2 -0.227214887 0.560193863
3 0.095586868 -0.227214887
4 1.501361769 0.095586868
5 0.557301701 1.501361769
6 -1.535832313 0.557301701
7 1.047109221 -1.535832313
8 -0.190747589 1.047109221
9 -0.142864594 -0.190747589
10 0.363383158 -0.142864594
11 -1.601268992 0.363383158
12 1.260063981 -1.601268992
13 -1.054271465 1.260063981
14 0.458190557 -1.054271465
15 -0.773744464 0.458190557
16 -0.065834656 -0.773744464
17 0.576039114 -0.065834656
18 -0.001603420 0.576039114
19 -0.817459697 -0.001603420
20 -0.406626569 -0.817459697
21 0.110550326 -0.406626569
22 -1.296243320 0.110550326
23 -0.863586945 -1.296243320
24 -0.221222984 -0.863586945
25 0.544796880 -0.221222984
26 -0.298861402 0.544796880
27 0.321920578 -0.298861402
28 -0.163047697 0.321920578
29 -0.210243679 -0.163047697
30 -0.303687610 -0.210243679
31 -0.665769102 -0.303687610
32 -0.237815571 -0.665769102
33 0.842456493 -0.237815571
34 -0.381881271 0.842456493
35 -0.467419355 -0.381881271
36 -0.282125540 -0.467419355
37 -0.235444024 -0.282125540
38 -0.660032263 -0.235444024
39 -0.129602220 -0.660032263
40 0.432373827 -0.129602220
41 -0.127676104 0.432373827
42 -1.445589177 -0.127676104
43 -0.769159842 -1.445589177
44 -0.038252282 -0.769159842
45 0.127527300 -0.038252282
46 -0.621315338 0.127527300
47 0.742282535 -0.621315338
48 0.093903949 0.742282535
49 0.700220589 0.093903949
50 -0.672993001 0.700220589
51 0.118265342 -0.672993001
52 0.207507894 0.118265342
53 0.032542010 0.207507894
54 -0.046970106 0.032542010
55 0.910856786 -0.046970106
56 -0.021316964 0.910856786
57 -0.301185981 -0.021316964
58 -0.433708704 -0.301185981
59 0.103697753 -0.433708704
60 -0.313876060 0.103697753
61 -0.070388394 -0.313876060
62 0.642191547 -0.070388394
63 -2.589713359 0.642191547
64 -1.129749178 -2.589713359
65 0.718608258 -1.129749178
66 0.394225432 0.718608258
67 0.119730183 0.394225432
68 -0.991786440 0.119730183
69 0.015247926 -0.991786440
70 -0.379846969 0.015247926
71 -0.940171774 -0.379846969
72 1.064428758 -0.940171774
73 -0.228715635 1.064428758
74 0.429197176 -0.228715635
75 0.427282905 0.429197176
76 -0.228288725 0.427282905
77 0.111321138 -0.228288725
78 0.068305997 0.111321138
79 -1.153980863 0.068305997
80 1.010873344 -1.153980863
81 0.408599641 1.010873344
82 -0.374596901 0.408599641
83 0.255588667 -0.374596901
84 1.093304930 0.255588667
85 -0.484809004 1.093304930
86 0.022783104 -0.484809004
87 -0.919555130 0.022783104
88 0.408629367 -0.919555130
89 0.027451514 0.408629367
90 -0.830971734 0.027451514
91 1.094213234 -0.830971734
92 1.045178864 1.094213234
93 0.613346210 1.045178864
94 -0.296772817 0.613346210
95 0.293467238 -0.296772817
96 -0.211360979 0.293467238
97 -0.705434439 -0.211360979
98 0.055048513 -0.705434439
99 -0.520629482 0.055048513
100 -0.003084207 -0.520629482
101 0.979128318 -0.003084207
102 0.056213184 0.979128318
103 0.226658300 0.056213184
104 -0.674828872 0.226658300
105 0.431476877 -0.674828872
106 0.117418657 0.431476877
107 -0.632962977 0.117418657
108 -1.083443033 -0.632962977
109 0.733133475 -1.083443033
110 0.381329671 0.733133475
111 0.813439480 0.381329671
112 -3.048841643 0.813439480
113 0.804113926 -3.048841643
114 0.969875129 0.804113926
115 -0.653078724 0.969875129
116 0.031951037 -0.653078724
117 0.585015640 0.031951037
118 1.025114423 0.585015640
119 -0.601411219 1.025114423
120 0.213688603 -0.601411219
121 0.528282168 0.213688603
122 -0.209271029 0.528282168
123 -0.379347543 -0.209271029
124 -0.331276006 -0.379347543
125 0.581982146 -0.331276006
126 -0.235438698 0.581982146
127 0.983797988 -0.235438698
128 -0.098772964 0.983797988
129 -0.410538073 -0.098772964
130 0.528356105 -0.410538073
131 -0.121483483 0.528356105
132 -0.987084305 -0.121483483
133 -0.542745198 -0.987084305
134 1.197543401 -0.542745198
135 -0.538829020 1.197543401
136 -0.053855188 -0.538829020
137 -0.489093894 -0.053855188
138 -0.020733367 -0.489093894
139 -0.922868477 -0.020733367
140 -1.011492379 -0.922868477
141 0.756894659 -1.011492379
142 0.897976297 0.756894659
143 0.924821370 0.897976297
144 0.648191430 0.924821370
145 0.316099413 0.648191430
146 0.129586411 0.316099413
147 -0.287989168 0.129586411
148 0.330309795 -0.287989168
149 0.967569187 0.330309795
150 1.389728803 0.967569187
151 0.146949750 1.389728803
152 0.056232843 0.146949750
153 0.364817488 0.056232843
154 1.128971014 0.364817488
155 -0.813955697 1.128971014
156 0.110311540 -0.813955697
157 0.484995859 0.110311540
158 0.952984278 0.484995859
159 1.278587629 0.952984278
160 1.040225444 1.278587629
161 NA 1.040225444
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.560193863 -0.905236006
[2,] -0.227214887 0.560193863
[3,] 0.095586868 -0.227214887
[4,] 1.501361769 0.095586868
[5,] 0.557301701 1.501361769
[6,] -1.535832313 0.557301701
[7,] 1.047109221 -1.535832313
[8,] -0.190747589 1.047109221
[9,] -0.142864594 -0.190747589
[10,] 0.363383158 -0.142864594
[11,] -1.601268992 0.363383158
[12,] 1.260063981 -1.601268992
[13,] -1.054271465 1.260063981
[14,] 0.458190557 -1.054271465
[15,] -0.773744464 0.458190557
[16,] -0.065834656 -0.773744464
[17,] 0.576039114 -0.065834656
[18,] -0.001603420 0.576039114
[19,] -0.817459697 -0.001603420
[20,] -0.406626569 -0.817459697
[21,] 0.110550326 -0.406626569
[22,] -1.296243320 0.110550326
[23,] -0.863586945 -1.296243320
[24,] -0.221222984 -0.863586945
[25,] 0.544796880 -0.221222984
[26,] -0.298861402 0.544796880
[27,] 0.321920578 -0.298861402
[28,] -0.163047697 0.321920578
[29,] -0.210243679 -0.163047697
[30,] -0.303687610 -0.210243679
[31,] -0.665769102 -0.303687610
[32,] -0.237815571 -0.665769102
[33,] 0.842456493 -0.237815571
[34,] -0.381881271 0.842456493
[35,] -0.467419355 -0.381881271
[36,] -0.282125540 -0.467419355
[37,] -0.235444024 -0.282125540
[38,] -0.660032263 -0.235444024
[39,] -0.129602220 -0.660032263
[40,] 0.432373827 -0.129602220
[41,] -0.127676104 0.432373827
[42,] -1.445589177 -0.127676104
[43,] -0.769159842 -1.445589177
[44,] -0.038252282 -0.769159842
[45,] 0.127527300 -0.038252282
[46,] -0.621315338 0.127527300
[47,] 0.742282535 -0.621315338
[48,] 0.093903949 0.742282535
[49,] 0.700220589 0.093903949
[50,] -0.672993001 0.700220589
[51,] 0.118265342 -0.672993001
[52,] 0.207507894 0.118265342
[53,] 0.032542010 0.207507894
[54,] -0.046970106 0.032542010
[55,] 0.910856786 -0.046970106
[56,] -0.021316964 0.910856786
[57,] -0.301185981 -0.021316964
[58,] -0.433708704 -0.301185981
[59,] 0.103697753 -0.433708704
[60,] -0.313876060 0.103697753
[61,] -0.070388394 -0.313876060
[62,] 0.642191547 -0.070388394
[63,] -2.589713359 0.642191547
[64,] -1.129749178 -2.589713359
[65,] 0.718608258 -1.129749178
[66,] 0.394225432 0.718608258
[67,] 0.119730183 0.394225432
[68,] -0.991786440 0.119730183
[69,] 0.015247926 -0.991786440
[70,] -0.379846969 0.015247926
[71,] -0.940171774 -0.379846969
[72,] 1.064428758 -0.940171774
[73,] -0.228715635 1.064428758
[74,] 0.429197176 -0.228715635
[75,] 0.427282905 0.429197176
[76,] -0.228288725 0.427282905
[77,] 0.111321138 -0.228288725
[78,] 0.068305997 0.111321138
[79,] -1.153980863 0.068305997
[80,] 1.010873344 -1.153980863
[81,] 0.408599641 1.010873344
[82,] -0.374596901 0.408599641
[83,] 0.255588667 -0.374596901
[84,] 1.093304930 0.255588667
[85,] -0.484809004 1.093304930
[86,] 0.022783104 -0.484809004
[87,] -0.919555130 0.022783104
[88,] 0.408629367 -0.919555130
[89,] 0.027451514 0.408629367
[90,] -0.830971734 0.027451514
[91,] 1.094213234 -0.830971734
[92,] 1.045178864 1.094213234
[93,] 0.613346210 1.045178864
[94,] -0.296772817 0.613346210
[95,] 0.293467238 -0.296772817
[96,] -0.211360979 0.293467238
[97,] -0.705434439 -0.211360979
[98,] 0.055048513 -0.705434439
[99,] -0.520629482 0.055048513
[100,] -0.003084207 -0.520629482
[101,] 0.979128318 -0.003084207
[102,] 0.056213184 0.979128318
[103,] 0.226658300 0.056213184
[104,] -0.674828872 0.226658300
[105,] 0.431476877 -0.674828872
[106,] 0.117418657 0.431476877
[107,] -0.632962977 0.117418657
[108,] -1.083443033 -0.632962977
[109,] 0.733133475 -1.083443033
[110,] 0.381329671 0.733133475
[111,] 0.813439480 0.381329671
[112,] -3.048841643 0.813439480
[113,] 0.804113926 -3.048841643
[114,] 0.969875129 0.804113926
[115,] -0.653078724 0.969875129
[116,] 0.031951037 -0.653078724
[117,] 0.585015640 0.031951037
[118,] 1.025114423 0.585015640
[119,] -0.601411219 1.025114423
[120,] 0.213688603 -0.601411219
[121,] 0.528282168 0.213688603
[122,] -0.209271029 0.528282168
[123,] -0.379347543 -0.209271029
[124,] -0.331276006 -0.379347543
[125,] 0.581982146 -0.331276006
[126,] -0.235438698 0.581982146
[127,] 0.983797988 -0.235438698
[128,] -0.098772964 0.983797988
[129,] -0.410538073 -0.098772964
[130,] 0.528356105 -0.410538073
[131,] -0.121483483 0.528356105
[132,] -0.987084305 -0.121483483
[133,] -0.542745198 -0.987084305
[134,] 1.197543401 -0.542745198
[135,] -0.538829020 1.197543401
[136,] -0.053855188 -0.538829020
[137,] -0.489093894 -0.053855188
[138,] -0.020733367 -0.489093894
[139,] -0.922868477 -0.020733367
[140,] -1.011492379 -0.922868477
[141,] 0.756894659 -1.011492379
[142,] 0.897976297 0.756894659
[143,] 0.924821370 0.897976297
[144,] 0.648191430 0.924821370
[145,] 0.316099413 0.648191430
[146,] 0.129586411 0.316099413
[147,] -0.287989168 0.129586411
[148,] 0.330309795 -0.287989168
[149,] 0.967569187 0.330309795
[150,] 1.389728803 0.967569187
[151,] 0.146949750 1.389728803
[152,] 0.056232843 0.146949750
[153,] 0.364817488 0.056232843
[154,] 1.128971014 0.364817488
[155,] -0.813955697 1.128971014
[156,] 0.110311540 -0.813955697
[157,] 0.484995859 0.110311540
[158,] 0.952984278 0.484995859
[159,] 1.278587629 0.952984278
[160,] 1.040225444 1.278587629
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.560193863 -0.905236006
2 -0.227214887 0.560193863
3 0.095586868 -0.227214887
4 1.501361769 0.095586868
5 0.557301701 1.501361769
6 -1.535832313 0.557301701
7 1.047109221 -1.535832313
8 -0.190747589 1.047109221
9 -0.142864594 -0.190747589
10 0.363383158 -0.142864594
11 -1.601268992 0.363383158
12 1.260063981 -1.601268992
13 -1.054271465 1.260063981
14 0.458190557 -1.054271465
15 -0.773744464 0.458190557
16 -0.065834656 -0.773744464
17 0.576039114 -0.065834656
18 -0.001603420 0.576039114
19 -0.817459697 -0.001603420
20 -0.406626569 -0.817459697
21 0.110550326 -0.406626569
22 -1.296243320 0.110550326
23 -0.863586945 -1.296243320
24 -0.221222984 -0.863586945
25 0.544796880 -0.221222984
26 -0.298861402 0.544796880
27 0.321920578 -0.298861402
28 -0.163047697 0.321920578
29 -0.210243679 -0.163047697
30 -0.303687610 -0.210243679
31 -0.665769102 -0.303687610
32 -0.237815571 -0.665769102
33 0.842456493 -0.237815571
34 -0.381881271 0.842456493
35 -0.467419355 -0.381881271
36 -0.282125540 -0.467419355
37 -0.235444024 -0.282125540
38 -0.660032263 -0.235444024
39 -0.129602220 -0.660032263
40 0.432373827 -0.129602220
41 -0.127676104 0.432373827
42 -1.445589177 -0.127676104
43 -0.769159842 -1.445589177
44 -0.038252282 -0.769159842
45 0.127527300 -0.038252282
46 -0.621315338 0.127527300
47 0.742282535 -0.621315338
48 0.093903949 0.742282535
49 0.700220589 0.093903949
50 -0.672993001 0.700220589
51 0.118265342 -0.672993001
52 0.207507894 0.118265342
53 0.032542010 0.207507894
54 -0.046970106 0.032542010
55 0.910856786 -0.046970106
56 -0.021316964 0.910856786
57 -0.301185981 -0.021316964
58 -0.433708704 -0.301185981
59 0.103697753 -0.433708704
60 -0.313876060 0.103697753
61 -0.070388394 -0.313876060
62 0.642191547 -0.070388394
63 -2.589713359 0.642191547
64 -1.129749178 -2.589713359
65 0.718608258 -1.129749178
66 0.394225432 0.718608258
67 0.119730183 0.394225432
68 -0.991786440 0.119730183
69 0.015247926 -0.991786440
70 -0.379846969 0.015247926
71 -0.940171774 -0.379846969
72 1.064428758 -0.940171774
73 -0.228715635 1.064428758
74 0.429197176 -0.228715635
75 0.427282905 0.429197176
76 -0.228288725 0.427282905
77 0.111321138 -0.228288725
78 0.068305997 0.111321138
79 -1.153980863 0.068305997
80 1.010873344 -1.153980863
81 0.408599641 1.010873344
82 -0.374596901 0.408599641
83 0.255588667 -0.374596901
84 1.093304930 0.255588667
85 -0.484809004 1.093304930
86 0.022783104 -0.484809004
87 -0.919555130 0.022783104
88 0.408629367 -0.919555130
89 0.027451514 0.408629367
90 -0.830971734 0.027451514
91 1.094213234 -0.830971734
92 1.045178864 1.094213234
93 0.613346210 1.045178864
94 -0.296772817 0.613346210
95 0.293467238 -0.296772817
96 -0.211360979 0.293467238
97 -0.705434439 -0.211360979
98 0.055048513 -0.705434439
99 -0.520629482 0.055048513
100 -0.003084207 -0.520629482
101 0.979128318 -0.003084207
102 0.056213184 0.979128318
103 0.226658300 0.056213184
104 -0.674828872 0.226658300
105 0.431476877 -0.674828872
106 0.117418657 0.431476877
107 -0.632962977 0.117418657
108 -1.083443033 -0.632962977
109 0.733133475 -1.083443033
110 0.381329671 0.733133475
111 0.813439480 0.381329671
112 -3.048841643 0.813439480
113 0.804113926 -3.048841643
114 0.969875129 0.804113926
115 -0.653078724 0.969875129
116 0.031951037 -0.653078724
117 0.585015640 0.031951037
118 1.025114423 0.585015640
119 -0.601411219 1.025114423
120 0.213688603 -0.601411219
121 0.528282168 0.213688603
122 -0.209271029 0.528282168
123 -0.379347543 -0.209271029
124 -0.331276006 -0.379347543
125 0.581982146 -0.331276006
126 -0.235438698 0.581982146
127 0.983797988 -0.235438698
128 -0.098772964 0.983797988
129 -0.410538073 -0.098772964
130 0.528356105 -0.410538073
131 -0.121483483 0.528356105
132 -0.987084305 -0.121483483
133 -0.542745198 -0.987084305
134 1.197543401 -0.542745198
135 -0.538829020 1.197543401
136 -0.053855188 -0.538829020
137 -0.489093894 -0.053855188
138 -0.020733367 -0.489093894
139 -0.922868477 -0.020733367
140 -1.011492379 -0.922868477
141 0.756894659 -1.011492379
142 0.897976297 0.756894659
143 0.924821370 0.897976297
144 0.648191430 0.924821370
145 0.316099413 0.648191430
146 0.129586411 0.316099413
147 -0.287989168 0.129586411
148 0.330309795 -0.287989168
149 0.967569187 0.330309795
150 1.389728803 0.967569187
151 0.146949750 1.389728803
152 0.056232843 0.146949750
153 0.364817488 0.056232843
154 1.128971014 0.364817488
155 -0.813955697 1.128971014
156 0.110311540 -0.813955697
157 0.484995859 0.110311540
158 0.952984278 0.484995859
159 1.278587629 0.952984278
160 1.040225444 1.278587629
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/769d31383236108.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8v2m51383236108.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9mqco1383236108.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wqj11383236108.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/110d7a1383236108.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12a9nr1383236109.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> 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, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1379cl1383236109.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14p8vn1383236109.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/152ftq1383236109.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/163fk41383236109.tab")
+ }
>
> try(system("convert tmp/1pvkr1383236108.ps tmp/1pvkr1383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/2csih1383236108.ps tmp/2csih1383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b51d1383236108.ps tmp/3b51d1383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/4euw31383236108.ps tmp/4euw31383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/53ept1383236108.ps tmp/53ept1383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/6s7yt1383236108.ps tmp/6s7yt1383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/769d31383236108.ps tmp/769d31383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v2m51383236108.ps tmp/8v2m51383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mqco1383236108.ps tmp/9mqco1383236108.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wqj11383236108.ps tmp/10wqj11383236108.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.003 2.276 14.259