R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24
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+ ,20)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('Concern(Mistakes)'
+ ,'Doubts(actions)'
+ ,'Parental-Expectations'
+ ,'Parental-Criticism'
+ ,'Personal-Standards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('Concern(Mistakes)','Doubts(actions)','Parental-Expectations','Parental-Criticism','Personal-Standards','Organization'),1:159))
> 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 = '5'
> #'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
Personal-Standards Concern(Mistakes) Doubts(actions) Parental-Expectations
1 24 24 14 11
2 25 25 11 7
3 30 17 6 17
4 19 18 12 10
5 22 18 8 12
6 22 16 10 12
7 25 20 10 11
8 23 16 11 11
9 17 18 16 12
10 21 17 11 13
11 19 23 13 14
12 19 30 12 16
13 15 23 8 11
14 16 18 12 10
15 23 15 11 11
16 27 12 4 15
17 22 21 9 9
18 14 15 8 11
19 22 20 8 17
20 23 31 14 17
21 23 27 15 11
22 21 34 16 18
23 19 21 9 14
24 18 31 14 10
25 20 19 11 11
26 23 16 8 15
27 25 20 9 15
28 19 21 9 13
29 24 22 9 16
30 22 17 9 13
31 25 24 10 9
32 26 25 16 18
33 29 26 11 18
34 32 25 8 12
35 25 17 9 17
36 29 32 16 9
37 28 33 11 9
38 17 13 16 12
39 28 32 12 18
40 29 25 12 12
41 26 29 14 18
42 25 22 9 14
43 14 18 10 15
44 25 17 9 16
45 26 20 10 10
46 20 15 12 11
47 18 20 14 14
48 32 33 14 9
49 25 29 10 12
50 25 23 14 17
51 23 26 16 5
52 21 18 9 12
53 20 20 10 12
54 15 11 6 6
55 30 28 8 24
56 24 26 13 12
57 26 22 10 12
58 24 17 8 14
59 22 12 7 7
60 14 14 15 13
61 24 17 9 12
62 24 21 10 13
63 24 19 12 14
64 24 18 13 8
65 19 10 10 11
66 31 29 11 9
67 22 31 8 11
68 27 19 9 13
69 19 9 13 10
70 25 20 11 11
71 20 28 8 12
72 21 19 9 9
73 27 30 9 15
74 23 29 15 18
75 25 26 9 15
76 20 23 10 12
77 21 13 14 13
78 22 21 12 14
79 23 19 12 10
80 25 28 11 13
81 25 23 14 13
82 17 18 6 11
83 19 21 12 13
84 25 20 8 16
85 19 23 14 8
86 20 21 11 16
87 26 21 10 11
88 23 15 14 9
89 27 28 12 16
90 17 19 10 12
91 17 26 14 14
92 19 10 5 8
93 17 16 11 9
94 22 22 10 15
95 21 19 9 11
96 32 31 10 21
97 21 31 16 14
98 21 29 13 18
99 18 19 9 12
100 18 22 10 13
101 23 23 10 15
102 19 15 7 12
103 20 20 9 19
104 21 18 8 15
105 20 23 14 11
106 17 25 14 11
107 18 21 8 10
108 19 24 9 13
109 22 25 14 15
110 15 17 14 12
111 14 13 8 12
112 18 28 8 16
113 24 21 8 9
114 35 25 7 18
115 29 9 6 8
116 21 16 8 13
117 25 19 6 17
118 20 17 11 9
119 22 25 14 15
120 13 20 11 8
121 26 29 11 7
122 17 14 11 12
123 25 22 14 14
124 20 15 8 6
125 19 19 20 8
126 21 20 11 17
127 22 15 8 10
128 24 20 11 11
129 21 18 10 14
130 26 33 14 11
131 24 22 11 13
132 16 16 9 12
133 23 17 9 11
134 18 16 8 9
135 16 21 10 12
136 26 26 13 20
137 19 18 13 12
138 21 18 12 13
139 21 17 8 12
140 22 22 13 12
141 23 30 14 9
142 29 30 12 15
143 21 24 14 24
144 21 21 15 7
145 23 21 13 17
146 27 29 16 11
147 25 31 9 17
148 21 20 9 11
149 10 16 9 12
150 20 22 8 14
151 26 20 7 11
152 24 28 16 16
153 29 38 11 21
154 19 22 9 14
155 24 20 11 20
156 19 17 9 13
157 24 28 14 11
158 22 22 13 15
159 17 31 16 19
Parental-Criticism Organization M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 12 26 1 0 0 0 0 0 0 0 0 0 0
2 8 23 0 1 0 0 0 0 0 0 0 0 0
3 8 25 0 0 1 0 0 0 0 0 0 0 0
4 8 23 0 0 0 1 0 0 0 0 0 0 0
5 9 19 0 0 0 0 1 0 0 0 0 0 0
6 7 29 0 0 0 0 0 1 0 0 0 0 0
7 4 25 0 0 0 0 0 0 1 0 0 0 0
8 11 21 0 0 0 0 0 0 0 1 0 0 0
9 7 22 0 0 0 0 0 0 0 0 1 0 0
10 7 25 0 0 0 0 0 0 0 0 0 1 0
11 12 24 0 0 0 0 0 0 0 0 0 0 1
12 10 18 0 0 0 0 0 0 0 0 0 0 0
13 10 22 1 0 0 0 0 0 0 0 0 0 0
14 8 15 0 1 0 0 0 0 0 0 0 0 0
15 8 22 0 0 1 0 0 0 0 0 0 0 0
16 4 28 0 0 0 1 0 0 0 0 0 0 0
17 9 20 0 0 0 0 1 0 0 0 0 0 0
18 8 12 0 0 0 0 0 1 0 0 0 0 0
19 7 24 0 0 0 0 0 0 1 0 0 0 0
20 11 20 0 0 0 0 0 0 0 1 0 0 0
21 9 21 0 0 0 0 0 0 0 0 1 0 0
22 11 20 0 0 0 0 0 0 0 0 0 1 0
23 13 21 0 0 0 0 0 0 0 0 0 0 1
24 8 23 0 0 0 0 0 0 0 0 0 0 0
25 8 28 1 0 0 0 0 0 0 0 0 0 0
26 9 24 0 1 0 0 0 0 0 0 0 0 0
27 6 24 0 0 1 0 0 0 0 0 0 0 0
28 9 24 0 0 0 1 0 0 0 0 0 0 0
29 9 23 0 0 0 0 1 0 0 0 0 0 0
30 6 23 0 0 0 0 0 1 0 0 0 0 0
31 6 29 0 0 0 0 0 0 1 0 0 0 0
32 16 24 0 0 0 0 0 0 0 1 0 0 0
33 5 18 0 0 0 0 0 0 0 0 1 0 0
34 7 25 0 0 0 0 0 0 0 0 0 1 0
35 9 21 0 0 0 0 0 0 0 0 0 0 1
36 6 26 0 0 0 0 0 0 0 0 0 0 0
37 6 22 1 0 0 0 0 0 0 0 0 0 0
38 5 22 0 1 0 0 0 0 0 0 0 0 0
39 12 22 0 0 1 0 0 0 0 0 0 0 0
40 7 23 0 0 0 1 0 0 0 0 0 0 0
41 10 30 0 0 0 0 1 0 0 0 0 0 0
42 9 23 0 0 0 0 0 1 0 0 0 0 0
43 8 17 0 0 0 0 0 0 1 0 0 0 0
44 5 23 0 0 0 0 0 0 0 1 0 0 0
45 8 23 0 0 0 0 0 0 0 0 1 0 0
46 8 25 0 0 0 0 0 0 0 0 0 1 0
47 10 24 0 0 0 0 0 0 0 0 0 0 1
48 6 24 0 0 0 0 0 0 0 0 0 0 0
49 8 23 1 0 0 0 0 0 0 0 0 0 0
50 7 21 0 1 0 0 0 0 0 0 0 0 0
51 4 24 0 0 1 0 0 0 0 0 0 0 0
52 8 24 0 0 0 1 0 0 0 0 0 0 0
53 8 28 0 0 0 0 1 0 0 0 0 0 0
54 4 16 0 0 0 0 0 1 0 0 0 0 0
55 20 20 0 0 0 0 0 0 1 0 0 0 0
56 8 29 0 0 0 0 0 0 0 1 0 0 0
57 8 27 0 0 0 0 0 0 0 0 1 0 0
58 6 22 0 0 0 0 0 0 0 0 0 1 0
59 4 28 0 0 0 0 0 0 0 0 0 0 1
60 8 16 0 0 0 0 0 0 0 0 0 0 0
61 9 25 1 0 0 0 0 0 0 0 0 0 0
62 6 24 0 1 0 0 0 0 0 0 0 0 0
63 7 28 0 0 1 0 0 0 0 0 0 0 0
64 9 24 0 0 0 1 0 0 0 0 0 0 0
65 5 23 0 0 0 0 1 0 0 0 0 0 0
66 5 30 0 0 0 0 0 1 0 0 0 0 0
67 8 24 0 0 0 0 0 0 1 0 0 0 0
68 8 21 0 0 0 0 0 0 0 1 0 0 0
69 6 25 0 0 0 0 0 0 0 0 1 0 0
70 8 25 0 0 0 0 0 0 0 0 0 1 0
71 7 22 0 0 0 0 0 0 0 0 0 0 1
72 7 23 0 0 0 0 0 0 0 0 0 0 0
73 9 26 1 0 0 0 0 0 0 0 0 0 0
74 11 23 0 1 0 0 0 0 0 0 0 0 0
75 6 25 0 0 1 0 0 0 0 0 0 0 0
76 8 21 0 0 0 1 0 0 0 0 0 0 0
77 6 25 0 0 0 0 1 0 0 0 0 0 0
78 9 24 0 0 0 0 0 1 0 0 0 0 0
79 8 29 0 0 0 0 0 0 1 0 0 0 0
80 6 22 0 0 0 0 0 0 0 1 0 0 0
81 10 27 0 0 0 0 0 0 0 0 1 0 0
82 8 26 0 0 0 0 0 0 0 0 0 1 0
83 8 22 0 0 0 0 0 0 0 0 0 0 1
84 10 24 0 0 0 0 0 0 0 0 0 0 0
85 5 27 1 0 0 0 0 0 0 0 0 0 0
86 7 24 0 1 0 0 0 0 0 0 0 0 0
87 5 24 0 0 1 0 0 0 0 0 0 0 0
88 8 29 0 0 0 1 0 0 0 0 0 0 0
89 14 22 0 0 0 0 1 0 0 0 0 0 0
90 7 21 0 0 0 0 0 1 0 0 0 0 0
91 8 24 0 0 0 0 0 0 1 0 0 0 0
92 6 24 0 0 0 0 0 0 0 1 0 0 0
93 5 23 0 0 0 0 0 0 0 0 1 0 0
94 6 20 0 0 0 0 0 0 0 0 0 1 0
95 10 27 0 0 0 0 0 0 0 0 0 0 1
96 12 26 0 0 0 0 0 0 0 0 0 0 0
97 9 25 1 0 0 0 0 0 0 0 0 0 0
98 12 21 0 1 0 0 0 0 0 0 0 0 0
99 7 21 0 0 1 0 0 0 0 0 0 0 0
100 8 19 0 0 0 1 0 0 0 0 0 0 0
101 10 21 0 0 0 0 1 0 0 0 0 0 0
102 6 21 0 0 0 0 0 1 0 0 0 0 0
103 10 16 0 0 0 0 0 0 1 0 0 0 0
104 10 22 0 0 0 0 0 0 0 1 0 0 0
105 10 29 0 0 0 0 0 0 0 0 1 0 0
106 5 15 0 0 0 0 0 0 0 0 0 1 0
107 7 17 0 0 0 0 0 0 0 0 0 0 1
108 10 15 0 0 0 0 0 0 0 0 0 0 0
109 11 21 1 0 0 0 0 0 0 0 0 0 0
110 6 21 0 1 0 0 0 0 0 0 0 0 0
111 7 19 0 0 1 0 0 0 0 0 0 0 0
112 12 24 0 0 0 1 0 0 0 0 0 0 0
113 11 20 0 0 0 0 1 0 0 0 0 0 0
114 11 17 0 0 0 0 0 1 0 0 0 0 0
115 11 23 0 0 0 0 0 0 1 0 0 0 0
116 5 24 0 0 0 0 0 0 0 1 0 0 0
117 8 14 0 0 0 0 0 0 0 0 1 0 0
118 6 19 0 0 0 0 0 0 0 0 0 1 0
119 9 24 0 0 0 0 0 0 0 0 0 0 1
120 4 13 0 0 0 0 0 0 0 0 0 0 0
121 4 22 1 0 0 0 0 0 0 0 0 0 0
122 7 16 0 1 0 0 0 0 0 0 0 0 0
123 11 19 0 0 1 0 0 0 0 0 0 0 0
124 6 25 0 0 0 1 0 0 0 0 0 0 0
125 7 25 0 0 0 0 1 0 0 0 0 0 0
126 8 23 0 0 0 0 0 1 0 0 0 0 0
127 4 24 0 0 0 0 0 0 1 0 0 0 0
128 8 26 0 0 0 0 0 0 0 1 0 0 0
129 9 26 0 0 0 0 0 0 0 0 1 0 0
130 8 25 0 0 0 0 0 0 0 0 0 1 0
131 11 18 0 0 0 0 0 0 0 0 0 0 1
132 8 21 0 0 0 0 0 0 0 0 0 0 0
133 5 26 1 0 0 0 0 0 0 0 0 0 0
134 4 23 0 1 0 0 0 0 0 0 0 0 0
135 8 23 0 0 1 0 0 0 0 0 0 0 0
136 10 22 0 0 0 1 0 0 0 0 0 0 0
137 6 20 0 0 0 0 1 0 0 0 0 0 0
138 9 13 0 0 0 0 0 1 0 0 0 0 0
139 9 24 0 0 0 0 0 0 1 0 0 0 0
140 13 15 0 0 0 0 0 0 0 1 0 0 0
141 9 14 0 0 0 0 0 0 0 0 1 0 0
142 10 22 0 0 0 0 0 0 0 0 0 1 0
143 20 10 0 0 0 0 0 0 0 0 0 0 1
144 5 24 0 0 0 0 0 0 0 0 0 0 0
145 11 22 1 0 0 0 0 0 0 0 0 0 0
146 6 24 0 1 0 0 0 0 0 0 0 0 0
147 9 19 0 0 1 0 0 0 0 0 0 0 0
148 7 20 0 0 0 1 0 0 0 0 0 0 0
149 9 13 0 0 0 0 1 0 0 0 0 0 0
150 10 20 0 0 0 0 0 1 0 0 0 0 0
151 9 22 0 0 0 0 0 0 1 0 0 0 0
152 8 24 0 0 0 0 0 0 0 1 0 0 0
153 7 29 0 0 0 0 0 0 0 0 1 0 0
154 6 12 0 0 0 0 0 0 0 0 0 1 0
155 13 20 0 0 0 0 0 0 0 0 0 0 1
156 6 21 0 0 0 0 0 0 0 0 0 0 0
157 8 24 1 0 0 0 0 0 0 0 0 0 0
158 10 22 0 1 0 0 0 0 0 0 0 0 0
159 16 20 0 0 1 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) `Concern(Mistakes)` `Doubts(actions)`
6.85249 0.33416 -0.37009
`Parental-Expectations` `Parental-Criticism` Organization
0.15932 0.06496 0.40340
M1 M2 M3
-0.11299 0.30166 0.66375
M4 M5 M6
0.15196 0.37282 1.02662
M7 M8 M9
0.41740 1.66381 1.41002
M10 M11
0.92426 -0.53567
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.4103 -2.3668 0.1038 2.1821 10.9175
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.85249 2.47877 2.764 0.00646 **
`Concern(Mistakes)` 0.33416 0.05969 5.598 1.08e-07 ***
`Doubts(actions)` -0.37009 0.11629 -3.183 0.00179 **
`Parental-Expectations` 0.15932 0.10663 1.494 0.13736
`Parental-Criticism` 0.06496 0.13943 0.466 0.64202
Organization 0.40340 0.07614 5.298 4.36e-07 ***
M1 -0.11299 1.36789 -0.083 0.93429
M2 0.30166 1.36988 0.220 0.82603
M3 0.66375 1.36239 0.487 0.62687
M4 0.15196 1.40456 0.108 0.91400
M5 0.37282 1.39956 0.266 0.79033
M6 1.02662 1.40188 0.732 0.46518
M7 0.41740 1.42349 0.293 0.76978
M8 1.66381 1.39745 1.191 0.23580
M9 1.41002 1.38214 1.020 0.30938
M10 0.92426 1.37174 0.674 0.50154
M11 -0.53567 1.42223 -0.377 0.70700
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.481 on 142 degrees of freedom
Multiple R-squared: 0.3877, Adjusted R-squared: 0.3187
F-statistic: 5.62 on 16 and 142 DF, p-value: 3.061e-09
> 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,] 0.75743009 0.48513982 0.24256991
[2,] 0.70130538 0.59738923 0.29869462
[3,] 0.57949027 0.84101946 0.42050973
[4,] 0.45310083 0.90620166 0.54689917
[5,] 0.39829576 0.79659153 0.60170424
[6,] 0.29599784 0.59199568 0.70400216
[7,] 0.22646894 0.45293788 0.77353106
[8,] 0.16875530 0.33751059 0.83124470
[9,] 0.21509101 0.43018201 0.78490899
[10,] 0.15324607 0.30649215 0.84675393
[11,] 0.12076620 0.24153239 0.87923380
[12,] 0.08903053 0.17806105 0.91096947
[13,] 0.05983780 0.11967560 0.94016220
[14,] 0.16270137 0.32540273 0.83729863
[15,] 0.25816592 0.51633184 0.74183408
[16,] 0.33364571 0.66729141 0.66635429
[17,] 0.47362316 0.94724631 0.52637684
[18,] 0.45265404 0.90530808 0.54734596
[19,] 0.39362756 0.78725512 0.60637244
[20,] 0.34132469 0.68264937 0.65867531
[21,] 0.47878671 0.95757342 0.52121329
[22,] 0.44137545 0.88275090 0.55862455
[23,] 0.41086848 0.82173697 0.58913152
[24,] 0.38244564 0.76489127 0.61755436
[25,] 0.38455977 0.76911955 0.61544023
[26,] 0.34632031 0.69264062 0.65367969
[27,] 0.29223290 0.58446580 0.70776710
[28,] 0.26430576 0.52861152 0.73569424
[29,] 0.40605859 0.81211718 0.59394141
[30,] 0.35093744 0.70187488 0.64906256
[31,] 0.35263613 0.70527226 0.64736387
[32,] 0.37396227 0.74792454 0.62603773
[33,] 0.32526793 0.65053586 0.67473207
[34,] 0.37496711 0.74993421 0.62503289
[35,] 0.34418936 0.68837873 0.65581064
[36,] 0.48338772 0.96677543 0.51661228
[37,] 0.51535303 0.96929394 0.48464697
[38,] 0.47953806 0.95907613 0.52046194
[39,] 0.43702777 0.87405553 0.56297223
[40,] 0.39090554 0.78181109 0.60909446
[41,] 0.35316065 0.70632130 0.64683935
[42,] 0.34089130 0.68178259 0.65910870
[43,] 0.30981742 0.61963485 0.69018258
[44,] 0.28244598 0.56489196 0.71755402
[45,] 0.32148815 0.64297630 0.67851185
[46,] 0.28536868 0.57073735 0.71463132
[47,] 0.28706046 0.57412093 0.71293954
[48,] 0.33794271 0.67588542 0.66205729
[49,] 0.35119199 0.70238398 0.64880801
[50,] 0.30781004 0.61562009 0.69218996
[51,] 0.28218872 0.56437745 0.71781128
[52,] 0.30782657 0.61565315 0.69217343
[53,] 0.26745793 0.53491585 0.73254207
[54,] 0.22726005 0.45452009 0.77273995
[55,] 0.19716972 0.39433944 0.80283028
[56,] 0.19621520 0.39243040 0.80378480
[57,] 0.17607818 0.35215636 0.82392182
[58,] 0.16730411 0.33460822 0.83269589
[59,] 0.13837659 0.27675319 0.86162341
[60,] 0.11318995 0.22637991 0.88681005
[61,] 0.09397621 0.18795241 0.90602379
[62,] 0.07689873 0.15379745 0.92310127
[63,] 0.17330496 0.34660992 0.82669504
[64,] 0.14497248 0.28994496 0.85502752
[65,] 0.12275054 0.24550107 0.87724946
[66,] 0.11314323 0.22628646 0.88685677
[67,] 0.10802053 0.21604106 0.89197947
[68,] 0.13445026 0.26890051 0.86554974
[69,] 0.13388430 0.26776860 0.86611570
[70,] 0.12214275 0.24428551 0.87785725
[71,] 0.13702292 0.27404585 0.86297708
[72,] 0.22234526 0.44469052 0.77765474
[73,] 0.20362740 0.40725480 0.79637260
[74,] 0.20146583 0.40293167 0.79853417
[75,] 0.16708166 0.33416332 0.83291834
[76,] 0.14471362 0.28942723 0.85528638
[77,] 0.17418045 0.34836089 0.82581955
[78,] 0.17938110 0.35876219 0.82061890
[79,] 0.17524320 0.35048639 0.82475680
[80,] 0.16968907 0.33937814 0.83031093
[81,] 0.15357787 0.30715575 0.84642213
[82,] 0.12826549 0.25653099 0.87173451
[83,] 0.11602039 0.23204077 0.88397961
[84,] 0.11088878 0.22177756 0.88911122
[85,] 0.09538621 0.19077242 0.90461379
[86,] 0.11946316 0.23892632 0.88053684
[87,] 0.11066435 0.22132870 0.88933565
[88,] 0.10113846 0.20227692 0.89886154
[89,] 0.07979277 0.15958554 0.92020723
[90,] 0.06600141 0.13200282 0.93399859
[91,] 0.06661987 0.13323974 0.93338013
[92,] 0.06640267 0.13280533 0.93359733
[93,] 0.18966179 0.37932357 0.81033821
[94,] 0.18415611 0.36831222 0.81584389
[95,] 0.70930046 0.58139908 0.29069954
[96,] 0.92071612 0.15856777 0.07928388
[97,] 0.89678183 0.20643634 0.10321817
[98,] 0.93245222 0.13509556 0.06754778
[99,] 0.90953294 0.18093412 0.09046706
[100,] 0.92501659 0.14996683 0.07498341
[101,] 0.94092822 0.11814357 0.05907178
[102,] 0.92164500 0.15671000 0.07835500
[103,] 0.89382944 0.21234112 0.10617056
[104,] 0.97318229 0.05363543 0.02681771
[105,] 0.95894952 0.08210095 0.04105048
[106,] 0.94202940 0.11594120 0.05797060
[107,] 0.91929096 0.16141809 0.08070904
[108,] 0.89115224 0.21769551 0.10884776
[109,] 0.85656179 0.28687641 0.14343821
[110,] 0.83310065 0.33379870 0.16689935
[111,] 0.79565976 0.40868049 0.20434024
[112,] 0.73686301 0.52627399 0.26313699
[113,] 0.66629952 0.66740095 0.33370048
[114,] 0.59864203 0.80271595 0.40135797
[115,] 0.51920049 0.96159901 0.48079951
[116,] 0.45077479 0.90154957 0.54922521
[117,] 0.38468875 0.76937751 0.61531125
[118,] 0.46953662 0.93907324 0.53046338
[119,] 0.65790962 0.68418076 0.34209038
[120,] 0.52986784 0.94026433 0.47013216
> postscript(file="/var/www/html/rcomp/tmp/1t9ps1291052534.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2t9ps1291052534.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3t9ps1291052534.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44iod1291052534.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54iod1291052534.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 = 159
Frequency = 1
1 2 3 4 5 6
1.40160015 2.64982620 5.71055940 -0.96923913 1.55954898 -1.58986120
7 8 9 10 11 12
2.65053256 2.26974445 -2.59720089 -0.99726866 -2.88279783 -3.89596116
13 14 15 16 17 18
-7.74127317 -0.89172035 3.39543886 3.52120178 1.00171960 -3.04369842
19 20 21 22 23 24
-1.83702437 -2.18482144 0.45810057 -3.86687200 -2.54960717 -5.42112685
25 26 27 28 29 30
-2.58486518 0.80406460 1.67031907 -4.02829533 0.34214205 0.03194363
31 32 33 34 35 36
-0.11098478 1.46258546 5.66671164 6.37851933 4.56890276 5.06392899
37 38 39 40 41 42
3.60590651 0.31185998 1.70982326 6.43799146 -1.35389586 1.00696983
43 44 45 46 47 48
-5.35103860 1.98176752 3.36420449 -0.70518891 -2.38031936 7.79639168
49 50 51 52 53 54
0.56117156 3.70701382 1.97910891 -0.80155120 -3.93423816 -2.00444931
55 56 57 58 59 60
3.14365735 -2.52329168 0.76365093 2.00830195 1.59364321 -1.02451122
61 62 63 64 65 66
2.32919381 1.38697553 0.59550293 4.25112584 0.77852744 3.64066213
67 68 69 70 71 72
-4.62180450 4.40333274 0.47331516 2.25393663 -3.95381293 -0.03743698
73 74 75 76 77 78
0.10380908 -1.15378632 -0.73802135 -1.89203503 2.06603252 -0.95199852
79 80 81 82 83 84
0.01074876 -0.13737529 0.62063078 -7.33161447 -1.35862335 1.54482170
85 86 87 88 89 90
-2.73498703 -2.78583991 3.40847936 2.51231969 2.52609060 -4.36511486
91 92 93 94 95 96
-6.20841748 -2.35333577 -3.57488573 -0.27480790 -1.62888135 3.87598286
97 98 99 100 101 102
-3.07698215 -3.15212563 -3.37232768 -2.91039119 0.27923998 -2.07380797
103 104 105 106 107 108
-0.75322460 -2.48455497 -4.86754017 -2.07767352 -1.27907536 -1.31314461
109 110 111 112 113 114
0.51214601 -3.42650693 -4.93067770 -8.41030729 2.50171037 10.91754476
115 116 117 118 119 120
9.67592789 -1.97962100 3.73339355 1.12536733 -0.14545947 -3.24319740
121 122 123 124 125 126
3.39108194 0.41773644 4.70400232 -0.48676064 1.01327234 -1.99752355
127 128 129 130 131 132
1.14384826 0.11098432 -2.87991284 0.02018044 4.35586037 -3.77107168
133 134 135 136 137 138
1.34494201 -2.51184253 -6.54231043 2.40792236 0.20148427 3.64720861
139 140 141 142 143 144
-1.16788962 2.13616913 2.22798551 3.72548801 2.68793334 1.55995240
145 146 147 148 149 150
1.75664438 4.25291153 -0.50189994 0.36801866 -6.98163411 -3.21787514
151 152 153 154 155 156
3.42566912 -0.70158347 -3.38845300 -0.25836824 2.97223716 -1.13462772
157 158 159
1.13161208 0.39143357 -7.08799701
> postscript(file="/var/www/html/rcomp/tmp/64iod1291052534.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.40160015 NA
1 2.64982620 1.40160015
2 5.71055940 2.64982620
3 -0.96923913 5.71055940
4 1.55954898 -0.96923913
5 -1.58986120 1.55954898
6 2.65053256 -1.58986120
7 2.26974445 2.65053256
8 -2.59720089 2.26974445
9 -0.99726866 -2.59720089
10 -2.88279783 -0.99726866
11 -3.89596116 -2.88279783
12 -7.74127317 -3.89596116
13 -0.89172035 -7.74127317
14 3.39543886 -0.89172035
15 3.52120178 3.39543886
16 1.00171960 3.52120178
17 -3.04369842 1.00171960
18 -1.83702437 -3.04369842
19 -2.18482144 -1.83702437
20 0.45810057 -2.18482144
21 -3.86687200 0.45810057
22 -2.54960717 -3.86687200
23 -5.42112685 -2.54960717
24 -2.58486518 -5.42112685
25 0.80406460 -2.58486518
26 1.67031907 0.80406460
27 -4.02829533 1.67031907
28 0.34214205 -4.02829533
29 0.03194363 0.34214205
30 -0.11098478 0.03194363
31 1.46258546 -0.11098478
32 5.66671164 1.46258546
33 6.37851933 5.66671164
34 4.56890276 6.37851933
35 5.06392899 4.56890276
36 3.60590651 5.06392899
37 0.31185998 3.60590651
38 1.70982326 0.31185998
39 6.43799146 1.70982326
40 -1.35389586 6.43799146
41 1.00696983 -1.35389586
42 -5.35103860 1.00696983
43 1.98176752 -5.35103860
44 3.36420449 1.98176752
45 -0.70518891 3.36420449
46 -2.38031936 -0.70518891
47 7.79639168 -2.38031936
48 0.56117156 7.79639168
49 3.70701382 0.56117156
50 1.97910891 3.70701382
51 -0.80155120 1.97910891
52 -3.93423816 -0.80155120
53 -2.00444931 -3.93423816
54 3.14365735 -2.00444931
55 -2.52329168 3.14365735
56 0.76365093 -2.52329168
57 2.00830195 0.76365093
58 1.59364321 2.00830195
59 -1.02451122 1.59364321
60 2.32919381 -1.02451122
61 1.38697553 2.32919381
62 0.59550293 1.38697553
63 4.25112584 0.59550293
64 0.77852744 4.25112584
65 3.64066213 0.77852744
66 -4.62180450 3.64066213
67 4.40333274 -4.62180450
68 0.47331516 4.40333274
69 2.25393663 0.47331516
70 -3.95381293 2.25393663
71 -0.03743698 -3.95381293
72 0.10380908 -0.03743698
73 -1.15378632 0.10380908
74 -0.73802135 -1.15378632
75 -1.89203503 -0.73802135
76 2.06603252 -1.89203503
77 -0.95199852 2.06603252
78 0.01074876 -0.95199852
79 -0.13737529 0.01074876
80 0.62063078 -0.13737529
81 -7.33161447 0.62063078
82 -1.35862335 -7.33161447
83 1.54482170 -1.35862335
84 -2.73498703 1.54482170
85 -2.78583991 -2.73498703
86 3.40847936 -2.78583991
87 2.51231969 3.40847936
88 2.52609060 2.51231969
89 -4.36511486 2.52609060
90 -6.20841748 -4.36511486
91 -2.35333577 -6.20841748
92 -3.57488573 -2.35333577
93 -0.27480790 -3.57488573
94 -1.62888135 -0.27480790
95 3.87598286 -1.62888135
96 -3.07698215 3.87598286
97 -3.15212563 -3.07698215
98 -3.37232768 -3.15212563
99 -2.91039119 -3.37232768
100 0.27923998 -2.91039119
101 -2.07380797 0.27923998
102 -0.75322460 -2.07380797
103 -2.48455497 -0.75322460
104 -4.86754017 -2.48455497
105 -2.07767352 -4.86754017
106 -1.27907536 -2.07767352
107 -1.31314461 -1.27907536
108 0.51214601 -1.31314461
109 -3.42650693 0.51214601
110 -4.93067770 -3.42650693
111 -8.41030729 -4.93067770
112 2.50171037 -8.41030729
113 10.91754476 2.50171037
114 9.67592789 10.91754476
115 -1.97962100 9.67592789
116 3.73339355 -1.97962100
117 1.12536733 3.73339355
118 -0.14545947 1.12536733
119 -3.24319740 -0.14545947
120 3.39108194 -3.24319740
121 0.41773644 3.39108194
122 4.70400232 0.41773644
123 -0.48676064 4.70400232
124 1.01327234 -0.48676064
125 -1.99752355 1.01327234
126 1.14384826 -1.99752355
127 0.11098432 1.14384826
128 -2.87991284 0.11098432
129 0.02018044 -2.87991284
130 4.35586037 0.02018044
131 -3.77107168 4.35586037
132 1.34494201 -3.77107168
133 -2.51184253 1.34494201
134 -6.54231043 -2.51184253
135 2.40792236 -6.54231043
136 0.20148427 2.40792236
137 3.64720861 0.20148427
138 -1.16788962 3.64720861
139 2.13616913 -1.16788962
140 2.22798551 2.13616913
141 3.72548801 2.22798551
142 2.68793334 3.72548801
143 1.55995240 2.68793334
144 1.75664438 1.55995240
145 4.25291153 1.75664438
146 -0.50189994 4.25291153
147 0.36801866 -0.50189994
148 -6.98163411 0.36801866
149 -3.21787514 -6.98163411
150 3.42566912 -3.21787514
151 -0.70158347 3.42566912
152 -3.38845300 -0.70158347
153 -0.25836824 -3.38845300
154 2.97223716 -0.25836824
155 -1.13462772 2.97223716
156 1.13161208 -1.13462772
157 0.39143357 1.13161208
158 -7.08799701 0.39143357
159 NA -7.08799701
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.64982620 1.40160015
[2,] 5.71055940 2.64982620
[3,] -0.96923913 5.71055940
[4,] 1.55954898 -0.96923913
[5,] -1.58986120 1.55954898
[6,] 2.65053256 -1.58986120
[7,] 2.26974445 2.65053256
[8,] -2.59720089 2.26974445
[9,] -0.99726866 -2.59720089
[10,] -2.88279783 -0.99726866
[11,] -3.89596116 -2.88279783
[12,] -7.74127317 -3.89596116
[13,] -0.89172035 -7.74127317
[14,] 3.39543886 -0.89172035
[15,] 3.52120178 3.39543886
[16,] 1.00171960 3.52120178
[17,] -3.04369842 1.00171960
[18,] -1.83702437 -3.04369842
[19,] -2.18482144 -1.83702437
[20,] 0.45810057 -2.18482144
[21,] -3.86687200 0.45810057
[22,] -2.54960717 -3.86687200
[23,] -5.42112685 -2.54960717
[24,] -2.58486518 -5.42112685
[25,] 0.80406460 -2.58486518
[26,] 1.67031907 0.80406460
[27,] -4.02829533 1.67031907
[28,] 0.34214205 -4.02829533
[29,] 0.03194363 0.34214205
[30,] -0.11098478 0.03194363
[31,] 1.46258546 -0.11098478
[32,] 5.66671164 1.46258546
[33,] 6.37851933 5.66671164
[34,] 4.56890276 6.37851933
[35,] 5.06392899 4.56890276
[36,] 3.60590651 5.06392899
[37,] 0.31185998 3.60590651
[38,] 1.70982326 0.31185998
[39,] 6.43799146 1.70982326
[40,] -1.35389586 6.43799146
[41,] 1.00696983 -1.35389586
[42,] -5.35103860 1.00696983
[43,] 1.98176752 -5.35103860
[44,] 3.36420449 1.98176752
[45,] -0.70518891 3.36420449
[46,] -2.38031936 -0.70518891
[47,] 7.79639168 -2.38031936
[48,] 0.56117156 7.79639168
[49,] 3.70701382 0.56117156
[50,] 1.97910891 3.70701382
[51,] -0.80155120 1.97910891
[52,] -3.93423816 -0.80155120
[53,] -2.00444931 -3.93423816
[54,] 3.14365735 -2.00444931
[55,] -2.52329168 3.14365735
[56,] 0.76365093 -2.52329168
[57,] 2.00830195 0.76365093
[58,] 1.59364321 2.00830195
[59,] -1.02451122 1.59364321
[60,] 2.32919381 -1.02451122
[61,] 1.38697553 2.32919381
[62,] 0.59550293 1.38697553
[63,] 4.25112584 0.59550293
[64,] 0.77852744 4.25112584
[65,] 3.64066213 0.77852744
[66,] -4.62180450 3.64066213
[67,] 4.40333274 -4.62180450
[68,] 0.47331516 4.40333274
[69,] 2.25393663 0.47331516
[70,] -3.95381293 2.25393663
[71,] -0.03743698 -3.95381293
[72,] 0.10380908 -0.03743698
[73,] -1.15378632 0.10380908
[74,] -0.73802135 -1.15378632
[75,] -1.89203503 -0.73802135
[76,] 2.06603252 -1.89203503
[77,] -0.95199852 2.06603252
[78,] 0.01074876 -0.95199852
[79,] -0.13737529 0.01074876
[80,] 0.62063078 -0.13737529
[81,] -7.33161447 0.62063078
[82,] -1.35862335 -7.33161447
[83,] 1.54482170 -1.35862335
[84,] -2.73498703 1.54482170
[85,] -2.78583991 -2.73498703
[86,] 3.40847936 -2.78583991
[87,] 2.51231969 3.40847936
[88,] 2.52609060 2.51231969
[89,] -4.36511486 2.52609060
[90,] -6.20841748 -4.36511486
[91,] -2.35333577 -6.20841748
[92,] -3.57488573 -2.35333577
[93,] -0.27480790 -3.57488573
[94,] -1.62888135 -0.27480790
[95,] 3.87598286 -1.62888135
[96,] -3.07698215 3.87598286
[97,] -3.15212563 -3.07698215
[98,] -3.37232768 -3.15212563
[99,] -2.91039119 -3.37232768
[100,] 0.27923998 -2.91039119
[101,] -2.07380797 0.27923998
[102,] -0.75322460 -2.07380797
[103,] -2.48455497 -0.75322460
[104,] -4.86754017 -2.48455497
[105,] -2.07767352 -4.86754017
[106,] -1.27907536 -2.07767352
[107,] -1.31314461 -1.27907536
[108,] 0.51214601 -1.31314461
[109,] -3.42650693 0.51214601
[110,] -4.93067770 -3.42650693
[111,] -8.41030729 -4.93067770
[112,] 2.50171037 -8.41030729
[113,] 10.91754476 2.50171037
[114,] 9.67592789 10.91754476
[115,] -1.97962100 9.67592789
[116,] 3.73339355 -1.97962100
[117,] 1.12536733 3.73339355
[118,] -0.14545947 1.12536733
[119,] -3.24319740 -0.14545947
[120,] 3.39108194 -3.24319740
[121,] 0.41773644 3.39108194
[122,] 4.70400232 0.41773644
[123,] -0.48676064 4.70400232
[124,] 1.01327234 -0.48676064
[125,] -1.99752355 1.01327234
[126,] 1.14384826 -1.99752355
[127,] 0.11098432 1.14384826
[128,] -2.87991284 0.11098432
[129,] 0.02018044 -2.87991284
[130,] 4.35586037 0.02018044
[131,] -3.77107168 4.35586037
[132,] 1.34494201 -3.77107168
[133,] -2.51184253 1.34494201
[134,] -6.54231043 -2.51184253
[135,] 2.40792236 -6.54231043
[136,] 0.20148427 2.40792236
[137,] 3.64720861 0.20148427
[138,] -1.16788962 3.64720861
[139,] 2.13616913 -1.16788962
[140,] 2.22798551 2.13616913
[141,] 3.72548801 2.22798551
[142,] 2.68793334 3.72548801
[143,] 1.55995240 2.68793334
[144,] 1.75664438 1.55995240
[145,] 4.25291153 1.75664438
[146,] -0.50189994 4.25291153
[147,] 0.36801866 -0.50189994
[148,] -6.98163411 0.36801866
[149,] -3.21787514 -6.98163411
[150,] 3.42566912 -3.21787514
[151,] -0.70158347 3.42566912
[152,] -3.38845300 -0.70158347
[153,] -0.25836824 -3.38845300
[154,] 2.97223716 -0.25836824
[155,] -1.13462772 2.97223716
[156,] 1.13161208 -1.13462772
[157,] 0.39143357 1.13161208
[158,] -7.08799701 0.39143357
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.64982620 1.40160015
2 5.71055940 2.64982620
3 -0.96923913 5.71055940
4 1.55954898 -0.96923913
5 -1.58986120 1.55954898
6 2.65053256 -1.58986120
7 2.26974445 2.65053256
8 -2.59720089 2.26974445
9 -0.99726866 -2.59720089
10 -2.88279783 -0.99726866
11 -3.89596116 -2.88279783
12 -7.74127317 -3.89596116
13 -0.89172035 -7.74127317
14 3.39543886 -0.89172035
15 3.52120178 3.39543886
16 1.00171960 3.52120178
17 -3.04369842 1.00171960
18 -1.83702437 -3.04369842
19 -2.18482144 -1.83702437
20 0.45810057 -2.18482144
21 -3.86687200 0.45810057
22 -2.54960717 -3.86687200
23 -5.42112685 -2.54960717
24 -2.58486518 -5.42112685
25 0.80406460 -2.58486518
26 1.67031907 0.80406460
27 -4.02829533 1.67031907
28 0.34214205 -4.02829533
29 0.03194363 0.34214205
30 -0.11098478 0.03194363
31 1.46258546 -0.11098478
32 5.66671164 1.46258546
33 6.37851933 5.66671164
34 4.56890276 6.37851933
35 5.06392899 4.56890276
36 3.60590651 5.06392899
37 0.31185998 3.60590651
38 1.70982326 0.31185998
39 6.43799146 1.70982326
40 -1.35389586 6.43799146
41 1.00696983 -1.35389586
42 -5.35103860 1.00696983
43 1.98176752 -5.35103860
44 3.36420449 1.98176752
45 -0.70518891 3.36420449
46 -2.38031936 -0.70518891
47 7.79639168 -2.38031936
48 0.56117156 7.79639168
49 3.70701382 0.56117156
50 1.97910891 3.70701382
51 -0.80155120 1.97910891
52 -3.93423816 -0.80155120
53 -2.00444931 -3.93423816
54 3.14365735 -2.00444931
55 -2.52329168 3.14365735
56 0.76365093 -2.52329168
57 2.00830195 0.76365093
58 1.59364321 2.00830195
59 -1.02451122 1.59364321
60 2.32919381 -1.02451122
61 1.38697553 2.32919381
62 0.59550293 1.38697553
63 4.25112584 0.59550293
64 0.77852744 4.25112584
65 3.64066213 0.77852744
66 -4.62180450 3.64066213
67 4.40333274 -4.62180450
68 0.47331516 4.40333274
69 2.25393663 0.47331516
70 -3.95381293 2.25393663
71 -0.03743698 -3.95381293
72 0.10380908 -0.03743698
73 -1.15378632 0.10380908
74 -0.73802135 -1.15378632
75 -1.89203503 -0.73802135
76 2.06603252 -1.89203503
77 -0.95199852 2.06603252
78 0.01074876 -0.95199852
79 -0.13737529 0.01074876
80 0.62063078 -0.13737529
81 -7.33161447 0.62063078
82 -1.35862335 -7.33161447
83 1.54482170 -1.35862335
84 -2.73498703 1.54482170
85 -2.78583991 -2.73498703
86 3.40847936 -2.78583991
87 2.51231969 3.40847936
88 2.52609060 2.51231969
89 -4.36511486 2.52609060
90 -6.20841748 -4.36511486
91 -2.35333577 -6.20841748
92 -3.57488573 -2.35333577
93 -0.27480790 -3.57488573
94 -1.62888135 -0.27480790
95 3.87598286 -1.62888135
96 -3.07698215 3.87598286
97 -3.15212563 -3.07698215
98 -3.37232768 -3.15212563
99 -2.91039119 -3.37232768
100 0.27923998 -2.91039119
101 -2.07380797 0.27923998
102 -0.75322460 -2.07380797
103 -2.48455497 -0.75322460
104 -4.86754017 -2.48455497
105 -2.07767352 -4.86754017
106 -1.27907536 -2.07767352
107 -1.31314461 -1.27907536
108 0.51214601 -1.31314461
109 -3.42650693 0.51214601
110 -4.93067770 -3.42650693
111 -8.41030729 -4.93067770
112 2.50171037 -8.41030729
113 10.91754476 2.50171037
114 9.67592789 10.91754476
115 -1.97962100 9.67592789
116 3.73339355 -1.97962100
117 1.12536733 3.73339355
118 -0.14545947 1.12536733
119 -3.24319740 -0.14545947
120 3.39108194 -3.24319740
121 0.41773644 3.39108194
122 4.70400232 0.41773644
123 -0.48676064 4.70400232
124 1.01327234 -0.48676064
125 -1.99752355 1.01327234
126 1.14384826 -1.99752355
127 0.11098432 1.14384826
128 -2.87991284 0.11098432
129 0.02018044 -2.87991284
130 4.35586037 0.02018044
131 -3.77107168 4.35586037
132 1.34494201 -3.77107168
133 -2.51184253 1.34494201
134 -6.54231043 -2.51184253
135 2.40792236 -6.54231043
136 0.20148427 2.40792236
137 3.64720861 0.20148427
138 -1.16788962 3.64720861
139 2.13616913 -1.16788962
140 2.22798551 2.13616913
141 3.72548801 2.22798551
142 2.68793334 3.72548801
143 1.55995240 2.68793334
144 1.75664438 1.55995240
145 4.25291153 1.75664438
146 -0.50189994 4.25291153
147 0.36801866 -0.50189994
148 -6.98163411 0.36801866
149 -3.21787514 -6.98163411
150 3.42566912 -3.21787514
151 -0.70158347 3.42566912
152 -3.38845300 -0.70158347
153 -0.25836824 -3.38845300
154 2.97223716 -0.25836824
155 -1.13462772 2.97223716
156 1.13161208 -1.13462772
157 0.39143357 1.13161208
158 -7.08799701 0.39143357
> 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/7f95y1291052534.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qj5i1291052534.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qj5i1291052534.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qj5i1291052534.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11la291291052534.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/127bjf1291052534.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/13llz61291052534.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/1463xc1291052534.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/15zcwx1291052534.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/16dmco1291052534.tab")
+ }
>
> try(system("convert tmp/1t9ps1291052534.ps tmp/1t9ps1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t9ps1291052534.ps tmp/2t9ps1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t9ps1291052534.ps tmp/3t9ps1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/44iod1291052534.ps tmp/44iod1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/54iod1291052534.ps tmp/54iod1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/64iod1291052534.ps tmp/64iod1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/7f95y1291052534.ps tmp/7f95y1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qj5i1291052534.ps tmp/8qj5i1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qj5i1291052534.ps tmp/9qj5i1291052534.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qj5i1291052534.ps tmp/10qj5i1291052534.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.290 1.816 10.137