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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Type 'q()' to quit R.
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,2
+ ,51
+ ,0
+ ,3
+ ,0
+ ,17
+ ,0
+ ,39
+ ,0
+ ,24
+ ,0
+ ,16
+ ,2
+ ,58
+ ,0
+ ,4
+ ,0
+ ,35
+ ,0
+ ,38
+ ,0
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+ ,0
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+ ,2
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+ ,0
+ ,5
+ ,0
+ ,34
+ ,0
+ ,41
+ ,0
+ ,37
+ ,0
+ ,30)
+ ,dim=c(12
+ ,195)
+ ,dimnames=list(c('geslacht'
+ ,'leeftijd'
+ ,'leeftijd_man'
+ ,'opleiding'
+ ,'opleiding_man'
+ ,'Neuroticisme'
+ ,'Neuroticisme_man'
+ ,'Extraversie'
+ ,'Extraversie_man'
+ ,'Openheid'
+ ,'Openheid_man'
+ ,'Intrinsieke_waarden')
+ ,1:195))
> y <- array(NA,dim=c(12,195),dimnames=list(c('geslacht','leeftijd','leeftijd_man','opleiding','opleiding_man','Neuroticisme','Neuroticisme_man','Extraversie','Extraversie_man','Openheid','Openheid_man','Intrinsieke_waarden'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '12'
> ylab = ''
> xlab = ''
> main = ''
> #'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
Intrinsieke_waarden geslacht leeftijd leeftijd_man opleiding opleiding_man
1 40 1 27 27 5 5
2 45 1 36 36 4 4
3 38 1 25 25 4 4
4 28 1 27 27 3 3
5 39 1 50 50 4 4
6 37 1 41 41 4 4
7 30 1 48 48 5 5
8 29 1 44 44 2 2
9 39 1 28 28 3 3
10 35 1 56 56 3 3
11 34 1 50 50 5 5
12 38 1 47 47 4 4
13 21 1 52 52 2 2
14 35 1 45 45 4 4
15 52 1 3 3 30 30
16 46 52 4 4 21 21
17 58 2 2 29 29 51
18 54 3 3 28 28 46
19 29 5 5 19 19 47
20 43 3 3 26 26 46
21 45 2 2 34 34 50
22 46 3 3 24 24 51
23 25 5 5 20 20 47
24 47 4 4 21 21 46
25 41 2 2 33 33 43
26 29 3 3 22 22 55
27 45 4 4 18 18 52
28 54 5 5 20 20 56
29 28 2 2 26 26 46
30 37 4 4 23 23 51
31 56 3 3 34 34 39
32 43 2 2 25 25 45
33 34 3 3 33 33 31
34 42 3 3 46 46 29
35 46 3 3 24 24 48
36 25 3 3 42 42 32
37 25 4 4 30 30 45
38 25 5 5 19 19 33
39 48 3 3 37 37 40
40 27 4 4 23 23 44
41 28 5 5 34 34 46
42 25 3 3 38 38 39
43 26 1 1 26 26 39
44 51 3 3 36 36 41
45 29 3 3 20 20 52
46 29 3 3 27 27 42
47 43 4 4 27 27 44
48 44 2 2 41 41 33
49 25 3 3 33 33 46
50 51 3 3 37 37 45
51 42 2 2 30 30 40
52 25 5 5 20 20 48
53 51 4 4 20 20 53
54 46 3 3 31 31 45
55 29 5 5 32 32 46
56 3 22 22 37 37 30
57 27 25 25 46 46 30
58 20 27 20 20 15 15
59 40 20 55 55 31 31
60 33 40 40 33 33 38
61 24 40 40 27 27 32
62 41 45 45 31 31 24
63 28 41 41 22 22 20
64 37 39 39 41 41 28
65 46 45 45 25 25 30
66 39 28 28 30 1 52
67 25 45 45 43 1 4
68 1 24 1 3 3 21
69 1 38 38 5 5 28
70 1 32 32 5 5 29
71 47 44 44 4 4 36
72 52 47 5 5 27 27
73 27 52 1 1 29 29
74 27 27 2 2 42 42
75 25 27 4 4 47 47
76 28 25 4 4 17 17
77 25 28 4 4 34 34
78 52 25 3 3 32 32
79 44 52 4 4 25 25
80 42 44 3 3 27 27
81 45 42 3 3 32 32
82 45 45 5 5 26 26
83 50 45 2 2 29 29
84 49 50 5 5 28 28
85 52 49 3 3 19 19
86 25 52 2 2 46 46
87 0 0 3 0 35 0
88 0 3 0 15 0 53
89 0 4 0 30 0 45
90 0 2 0 27 0 47
91 0 3 0 33 0 42
92 0 5 0 30 0 40
93 0 3 0 25 0 45
94 0 3 0 23 0 40
95 0 4 0 35 0 47
96 0 4 0 39 0 31
97 0 4 0 23 0 46
98 0 3 0 32 0 34
99 0 5 0 35 0 51
100 0 3 0 23 0 44
101 0 4 0 28 0 47
102 0 3 0 33 0 38
103 0 3 0 33 0 48
104 0 2 0 40 0 36
105 0 1 0 35 0 35
106 0 3 0 35 0 49
107 3 4 0 32 0 38
108 3 0 35 0 36 0
109 2 0 35 0 47 0
110 2 0 40 0 53 0
111 4 0 35 0 39 0
112 5 0 20 0 55 0
113 3 0 28 0 41 0
114 5 0 46 0 33 0
115 3 0 22 0 42 0
116 4 0 25 0 46 0
117 3 0 31 0 33 0
118 3 0 21 0 51 0
119 3 0 23 0 46 0
120 4 0 34 0 50 0
121 4 0 31 0 46 0
122 4 0 31 0 48 0
123 3 0 26 0 44 0
124 3 0 36 0 38 0
125 3 0 28 0 42 0
126 5 0 32 0 38 0
127 5 0 33 0 55 0
128 4 0 17 0 51 0
129 4 0 36 0 53 0
130 4 0 40 0 47 0
131 5 0 33 0 49 0
132 3 0 35 0 46 0
133 3 0 23 0 42 0
134 2 0 15 0 56 0
135 5 0 38 0 35 0
136 2 0 41 0 46 0
137 3 0 45 0 35 0
138 4 0 27 0 48 0
139 4 0 46 0 42 0
140 4 0 44 0 39 0
141 3 0 44 0 45 0
142 5 0 30 0 42 0
143 2 0 44 0 32 0
144 3 0 33 0 39 0
145 3 0 23 0 36 0
146 4 0 33 0 38 0
147 4 0 33 0 49 0
148 4 0 25 0 43 0
149 2 0 16 0 48 0
150 3 0 36 0 45 0
151 3 0 35 0 32 0
152 3 0 32 0 42 0
153 3 0 36 0 45 0
154 3 0 51 0 29 0
155 3 0 30 0 51 0
156 4 0 29 0 50 0
157 4 0 26 0 44 0
158 4 0 20 0 41 0
159 4 0 29 0 47 0
160 3 0 32 0 42 0
161 3 0 32 0 51 0
162 5 0 34 0 43 0
163 3 0 25 0 41 0
164 3 0 39 0 37 0
165 3 0 21 0 46 0
166 4 0 38 0 38 0
167 4 0 25 0 21 0
168 3 0 38 0 31 0
169 3 0 31 0 49 0
170 4 0 27 0 40 0
171 4 0 26 0 46 0
172 4 0 37 0 45 0
173 3 0 33 0 43 0
174 3 0 41 0 45 0
175 3 0 19 0 48 0
176 3 0 37 0 43 0
177 5 0 33 0 34 0
178 3 0 27 0 55 0
179 4 0 37 0 43 0
180 4 0 34 0 44 0
181 2 0 27 0 44 0
182 3 0 37 0 41 0
183 3 0 31 0 46 0
184 4 0 42 0 49 0
185 4 0 33 0 55 0
186 3 0 39 0 51 0
187 3 0 27 0 46 0
188 4 0 35 0 37 0
189 4 0 23 0 43 0
190 3 0 32 0 41 0
191 3 0 22 0 45 0
192 4 0 17 0 39 0
193 5 0 35 0 38 0
194 5 0 34 0 41 0
195 4 5 26 26 49 49
Neuroticisme Neuroticisme_man Extraversie Extraversie_man Openheid
1 26 26 49 49 35
2 25 25 45 45 34
3 17 17 54 54 13
4 37 37 36 36 35
5 27 27 46 46 35
6 36 36 42 42 36
7 25 25 41 41 27
8 29 29 45 45 29
9 26 26 42 42 15
10 24 24 45 45 33
11 29 29 43 43 32
12 26 26 45 45 21
13 21 21 42 42 25
14 21 21 47 47 22
15 41 41 26 26 36
16 44 44 34 34 1
17 51 34 34 37 1
18 46 36 36 37 1
19 47 36 36 37 1
20 46 26 26 32 1
21 50 34 34 31 1
22 51 33 33 42 1
23 47 37 37 31 1
24 46 29 29 44 1
25 43 35 35 35 1
26 55 28 28 32 1
27 52 25 25 38 1
28 56 32 32 40 1
29 46 27 27 45 1
30 51 27 27 42 1
31 39 31 31 34 1
32 45 16 16 11 1
33 31 25 25 35 1
34 29 27 27 39 1
35 48 32 32 32 1
36 32 25 25 18 1
37 45 27 27 34 1
38 33 29 29 34 1
39 40 28 28 28 1
40 44 32 32 30 1
41 46 29 29 36 1
42 39 32 32 40 1
43 39 16 16 22 1
44 41 26 26 28 1
45 52 32 32 34 1
46 42 24 24 23 1
47 44 26 26 29 1
48 33 19 19 35 1
49 46 25 25 36 1
50 45 24 24 32 1
51 40 23 23 35 1
52 48 28 28 45 1
53 53 28 28 41 1
54 45 26 26 37 1
55 46 34 34 33 1
56 30 40 1 38 38
57 30 1 41 41 1
58 13 1 44 44 4
59 1 41 41 5 5
60 1 46 46 4 4
61 1 41 41 4 4
62 1 25 25 3 3
63 1 55 55 5 5
64 1 21 21 3 3
65 1 3 3 26 26
66 52 3 3 30 30
67 4 31 31 35 35
68 21 45 45 29 29
69 28 34 34 25 25
70 29 41 41 27 27
71 36 45 45 24 24
72 45 45 35 35 30
73 40 40 32 32 17
74 40 40 24 24 26
75 44 44 38 38 27
76 44 44 36 36 33
77 48 48 24 24 47
78 51 51 18 18 37
79 49 49 34 34 34
80 33 33 23 23 24
81 45 45 34 34 39
82 44 44 32 32 33
83 44 44 24 24 35
84 40 40 34 34 26
85 48 48 33 33 32
86 49 49 33 33 22
87 36 0 28 0 2
88 0 32 0 38 2
89 0 29 0 30 2
90 0 27 0 30 2
91 0 28 0 26 2
92 0 28 0 31 2
93 0 30 0 27 2
94 0 25 0 25 2
95 0 31 0 27 2
96 0 37 0 40 2
97 0 37 0 34 2
98 0 34 0 32 2
99 0 32 0 33 2
100 0 28 0 27 2
101 0 25 0 33 2
102 0 26 0 25 2
103 0 33 0 33 2
104 0 31 0 18 2
105 0 22 0 26 2
106 0 29 0 26 2
107 0 24 0 32 2
108 32 0 29 2 50
109 23 0 35 2 47
110 20 0 30 2 45
111 26 0 24 2 41
112 36 0 34 2 45
113 26 0 27 2 40
114 33 0 31 2 34
115 29 0 41 2 52
116 35 0 25 2 41
117 24 0 19 2 48
118 31 0 33 2 45
119 29 0 27 2 25
120 29 0 27 2 26
121 29 0 30 2 50
122 34 0 21 2 48
123 32 0 32 2 51
124 31 0 31 2 53
125 31 0 36 2 32
126 28 0 28 2 31
127 25 0 45 2 30
128 36 0 35 2 47
129 36 0 35 2 33
130 36 0 36 2 21
131 32 0 38 2 36
132 29 0 28 2 50
133 31 0 23 2 48
134 34 0 37 2 48
135 27 0 29 2 49
136 33 0 24 2 43
137 35 0 37 2 48
138 33 0 27 2 48
139 27 0 25 2 49
140 32 0 21 2 25
141 38 0 32 2 46
142 37 0 31 2 53
143 38 0 29 2 49
144 28 0 36 2 20
145 21 0 25 2 44
146 35 0 36 2 38
147 31 0 34 2 42
148 30 0 32 2 46
149 24 0 27 2 49
150 27 0 24 2 51
151 26 0 26 2 47
152 28 0 22 2 44
153 34 0 29 2 47
154 29 0 30 2 46
155 26 0 24 2 28
156 28 0 26 2 47
157 33 0 37 2 28
158 32 0 36 2 45
159 31 0 34 2 46
160 37 0 35 2 22
161 19 0 44 2 33
162 27 0 40 2 47
163 38 0 36 2 42
164 35 0 28 2 47
165 35 0 18 2 50
166 30 0 23 2 49
167 21 0 20 2 46
168 33 0 37 2 45
169 29 0 33 2 52
170 31 0 43 2 40
171 31 0 22 2 49
172 31 0 28 2 46
173 26 0 23 2 32
174 26 0 38 2 41
175 23 0 21 2 43
176 27 0 25 2 28
177 24 0 25 2 45
178 24 0 39 2 43
179 35 0 25 2 47
180 22 0 20 2 52
181 34 0 34 2 40
182 28 0 22 2 48
183 29 0 39 2 51
184 38 0 35 2 49
185 24 0 21 2 31
186 25 0 27 2 43
187 37 0 31 2 31
188 33 0 20 2 28
189 30 0 28 2 43
190 22 0 26 2 31
191 28 0 36 2 51
192 24 0 16 2 58
193 33 0 34 2 25
194 37 0 30 1 27
195 35 35 40 1 36
Openheid_man
1 35
2 34
3 13
4 35
5 35
6 36
7 27
8 29
9 15
10 33
11 32
12 21
13 25
14 22
15 1
16 46
17 58
18 54
19 29
20 43
21 45
22 46
23 25
24 47
25 41
26 29
27 45
28 54
29 28
30 37
31 56
32 43
33 34
34 42
35 46
36 25
37 25
38 25
39 48
40 27
41 28
42 25
43 26
44 51
45 29
46 29
47 43
48 44
49 25
50 51
51 42
52 25
53 51
54 46
55 29
56 3
57 1
58 4
59 40
60 33
61 24
62 41
63 28
64 37
65 46
66 39
67 25
68 27
69 18
70 38
71 1
72 1
73 1
74 1
75 1
76 1
77 1
78 1
79 1
80 1
81 1
82 1
83 1
84 1
85 1
86 2
87 44
88 43
89 47
90 41
91 47
92 40
93 46
94 49
95 25
96 41
97 26
98 37
99 41
100 26
101 50
102 30
103 47
104 48
105 48
106 26
107 0
108 0
109 0
110 0
111 0
112 0
113 0
114 0
115 0
116 0
117 0
118 0
119 0
120 0
121 0
122 0
123 0
124 0
125 0
126 0
127 0
128 0
129 0
130 0
131 0
132 0
133 0
134 0
135 0
136 0
137 0
138 0
139 0
140 0
141 0
142 0
143 0
144 0
145 0
146 0
147 0
148 0
149 0
150 0
151 0
152 0
153 0
154 0
155 0
156 0
157 0
158 0
159 0
160 0
161 0
162 0
163 0
164 0
165 0
166 0
167 0
168 0
169 0
170 0
171 0
172 0
173 0
174 0
175 0
176 0
177 0
178 0
179 0
180 0
181 0
182 0
183 0
184 0
185 0
186 0
187 0
188 0
189 0
190 0
191 0
192 0
193 0
194 27
195 36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) geslacht leeftijd leeftijd_man
-24.89665 0.45681 0.03183 0.31888
opleiding opleiding_man Neuroticisme Neuroticisme_man
0.18312 -0.13535 0.56714 0.09664
Extraversie Extraversie_man Openheid Openheid_man
0.11451 0.26773 -0.03268 0.30518
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-31.3717 -4.1782 -0.1494 4.0761 37.6730
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -24.89665 4.42499 -5.626 6.81e-08 ***
geslacht 0.45681 0.06634 6.886 8.91e-11 ***
leeftijd 0.03183 0.07968 0.399 0.690004
leeftijd_man 0.31888 0.09233 3.454 0.000687 ***
opleiding 0.18312 0.07893 2.320 0.021441 *
opleiding_man -0.13535 0.08830 -1.533 0.127030
Neuroticisme 0.56714 0.07265 7.807 4.38e-13 ***
Neuroticisme_man 0.09664 0.09363 1.032 0.303387
Extraversie 0.11451 0.08756 1.308 0.192594
Extraversie_man 0.26773 0.10094 2.652 0.008693 **
Openheid -0.03268 0.07339 -0.445 0.656613
Openheid_man 0.30518 0.06808 4.483 1.30e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.62 on 183 degrees of freedom
Multiple R-squared: 0.7931, Adjusted R-squared: 0.7807
F-statistic: 63.79 on 11 and 183 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,] 7.260228e-01 5.479543e-01 2.739772e-01
[2,] 5.865022e-01 8.269956e-01 4.134978e-01
[3,] 4.483586e-01 8.967172e-01 5.516414e-01
[4,] 3.279287e-01 6.558573e-01 6.720713e-01
[5,] 2.227837e-01 4.455675e-01 7.772163e-01
[6,] 1.453570e-01 2.907141e-01 8.546430e-01
[7,] 8.992492e-02 1.798498e-01 9.100751e-01
[8,] 5.394652e-02 1.078930e-01 9.460535e-01
[9,] 3.071976e-02 6.143951e-02 9.692802e-01
[10,] 1.785722e-02 3.571444e-02 9.821428e-01
[11,] 9.616598e-03 1.923320e-02 9.903834e-01
[12,] 5.037551e-03 1.007510e-02 9.949624e-01
[13,] 2.623013e-03 5.246027e-03 9.973770e-01
[14,] 1.481286e-03 2.962572e-03 9.985187e-01
[15,] 7.019111e-04 1.403822e-03 9.992981e-01
[16,] 3.454334e-04 6.908669e-04 9.996546e-01
[17,] 2.178939e-04 4.357879e-04 9.997821e-01
[18,] 1.111968e-04 2.223935e-04 9.998888e-01
[19,] 5.387815e-05 1.077563e-04 9.999461e-01
[20,] 3.358811e-05 6.717623e-05 9.999664e-01
[21,] 1.777081e-05 3.554162e-05 9.999822e-01
[22,] 8.920056e-06 1.784011e-05 9.999911e-01
[23,] 4.300028e-06 8.600056e-06 9.999957e-01
[24,] 1.876433e-06 3.752866e-06 9.999981e-01
[25,] 9.752078e-07 1.950416e-06 9.999990e-01
[26,] 3.902262e-07 7.804525e-07 9.999996e-01
[27,] 2.512386e-07 5.024771e-07 9.999997e-01
[28,] 1.040023e-07 2.080046e-07 9.999999e-01
[29,] 5.969512e-08 1.193902e-07 9.999999e-01
[30,] 3.589091e-08 7.178183e-08 1.000000e+00
[31,] 1.478678e-08 2.957357e-08 1.000000e+00
[32,] 5.608677e-09 1.121735e-08 1.000000e+00
[33,] 3.125845e-09 6.251690e-09 1.000000e+00
[34,] 1.779952e-09 3.559903e-09 1.000000e+00
[35,] 6.914505e-10 1.382901e-09 1.000000e+00
[36,] 5.306937e-10 1.061387e-09 1.000000e+00
[37,] 4.958435e-10 9.916871e-10 1.000000e+00
[38,] 2.108041e-10 4.216082e-10 1.000000e+00
[39,] 7.878320e-10 1.575664e-09 1.000000e+00
[40,] 6.485391e-09 1.297078e-08 1.000000e+00
[41,] 9.692307e-09 1.938461e-08 1.000000e+00
[42,] 5.114382e-05 1.022876e-04 9.999489e-01
[43,] 5.321482e-04 1.064296e-03 9.994679e-01
[44,] 3.640229e-04 7.280458e-04 9.996360e-01
[45,] 4.962578e-03 9.925157e-03 9.950374e-01
[46,] 3.733243e-03 7.466486e-03 9.962668e-01
[47,] 4.332316e-03 8.664633e-03 9.956677e-01
[48,] 1.208153e-02 2.416306e-02 9.879185e-01
[49,] 1.866454e-02 3.732908e-02 9.813355e-01
[50,] 1.518885e-02 3.037770e-02 9.848112e-01
[51,] 3.475957e-02 6.951915e-02 9.652404e-01
[52,] 6.446274e-01 7.107451e-01 3.553726e-01
[53,] 8.279600e-01 3.440800e-01 1.720400e-01
[54,] 9.954988e-01 9.002387e-03 4.501194e-03
[55,] 9.999995e-01 9.840610e-07 4.920305e-07
[56,] 1.000000e+00 1.752664e-15 8.763319e-16
[57,] 1.000000e+00 2.415012e-19 1.207506e-19
[58,] 1.000000e+00 1.311561e-22 6.557806e-23
[59,] 1.000000e+00 4.260472e-26 2.130236e-26
[60,] 1.000000e+00 1.165218e-25 5.826090e-26
[61,] 1.000000e+00 8.983312e-26 4.491656e-26
[62,] 1.000000e+00 1.194751e-25 5.973755e-26
[63,] 1.000000e+00 2.200365e-27 1.100182e-27
[64,] 1.000000e+00 5.211490e-35 2.605745e-35
[65,] 1.000000e+00 5.017291e-36 2.508645e-36
[66,] 1.000000e+00 3.327733e-36 1.663866e-36
[67,] 1.000000e+00 2.602273e-37 1.301137e-37
[68,] 1.000000e+00 2.613952e-37 1.306976e-37
[69,] 1.000000e+00 3.631268e-41 1.815634e-41
[70,] 1.000000e+00 2.846019e-45 1.423009e-45
[71,] 1.000000e+00 2.490463e-78 1.245232e-78
[72,] 1.000000e+00 2.195694e-78 1.097847e-78
[73,] 1.000000e+00 1.729767e-83 8.648834e-84
[74,] 1.000000e+00 1.414346e-82 7.071730e-83
[75,] 1.000000e+00 1.517557e-81 7.587784e-82
[76,] 1.000000e+00 1.699861e-80 8.499304e-81
[77,] 1.000000e+00 2.159058e-79 1.079529e-79
[78,] 1.000000e+00 1.254686e-78 6.273428e-79
[79,] 1.000000e+00 1.734541e-77 8.672704e-78
[80,] 1.000000e+00 2.104068e-76 1.052034e-76
[81,] 1.000000e+00 1.616495e-75 8.082473e-76
[82,] 1.000000e+00 1.976467e-74 9.882336e-75
[83,] 1.000000e+00 2.365424e-73 1.182712e-73
[84,] 1.000000e+00 3.182235e-72 1.591117e-72
[85,] 1.000000e+00 1.790879e-71 8.954396e-72
[86,] 1.000000e+00 1.941256e-70 9.706279e-71
[87,] 1.000000e+00 3.838781e-70 1.919391e-70
[88,] 1.000000e+00 5.713419e-70 2.856709e-70
[89,] 1.000000e+00 6.867513e-69 3.433757e-69
[90,] 1.000000e+00 8.675275e-68 4.337637e-68
[91,] 1.000000e+00 1.106278e-66 5.531389e-67
[92,] 1.000000e+00 1.376163e-65 6.880814e-66
[93,] 1.000000e+00 8.931881e-65 4.465940e-65
[94,] 1.000000e+00 4.991814e-64 2.495907e-64
[95,] 1.000000e+00 1.480754e-63 7.403769e-64
[96,] 1.000000e+00 4.503300e-63 2.251650e-63
[97,] 1.000000e+00 5.125252e-62 2.562626e-62
[98,] 1.000000e+00 7.445986e-62 3.722993e-62
[99,] 1.000000e+00 8.177207e-61 4.088604e-61
[100,] 1.000000e+00 3.358267e-60 1.679133e-60
[101,] 1.000000e+00 3.563288e-59 1.781644e-59
[102,] 1.000000e+00 3.431265e-58 1.715633e-58
[103,] 1.000000e+00 3.603938e-57 1.801969e-57
[104,] 1.000000e+00 3.831056e-56 1.915528e-56
[105,] 1.000000e+00 3.800013e-55 1.900006e-55
[106,] 1.000000e+00 4.349573e-54 2.174787e-54
[107,] 1.000000e+00 4.372876e-53 2.186438e-53
[108,] 1.000000e+00 3.847289e-52 1.923645e-52
[109,] 1.000000e+00 4.113871e-51 2.056936e-51
[110,] 1.000000e+00 4.260330e-50 2.130165e-50
[111,] 1.000000e+00 3.760949e-49 1.880475e-49
[112,] 1.000000e+00 1.259290e-48 6.296449e-49
[113,] 1.000000e+00 3.621348e-48 1.810674e-48
[114,] 1.000000e+00 3.041485e-47 1.520742e-47
[115,] 1.000000e+00 2.531499e-46 1.265749e-46
[116,] 1.000000e+00 2.350815e-45 1.175407e-45
[117,] 1.000000e+00 3.692153e-45 1.846077e-45
[118,] 1.000000e+00 4.119222e-44 2.059611e-44
[119,] 1.000000e+00 4.363785e-43 2.181892e-43
[120,] 1.000000e+00 1.502775e-42 7.513875e-43
[121,] 1.000000e+00 3.478621e-42 1.739311e-42
[122,] 1.000000e+00 8.642126e-42 4.321063e-42
[123,] 1.000000e+00 8.676727e-41 4.338364e-41
[124,] 1.000000e+00 8.853151e-40 4.426576e-40
[125,] 1.000000e+00 8.597790e-39 4.298895e-39
[126,] 1.000000e+00 8.215887e-38 4.107944e-38
[127,] 1.000000e+00 8.151686e-37 4.075843e-37
[128,] 1.000000e+00 1.674289e-36 8.371447e-37
[129,] 1.000000e+00 2.091385e-36 1.045692e-36
[130,] 1.000000e+00 2.234607e-35 1.117304e-35
[131,] 1.000000e+00 2.121904e-34 1.060952e-34
[132,] 1.000000e+00 2.364831e-33 1.182415e-33
[133,] 1.000000e+00 2.101856e-32 1.050928e-32
[134,] 1.000000e+00 2.037196e-31 1.018598e-31
[135,] 1.000000e+00 4.509191e-31 2.254595e-31
[136,] 1.000000e+00 4.840962e-30 2.420481e-30
[137,] 1.000000e+00 3.800802e-29 1.900401e-29
[138,] 1.000000e+00 3.633533e-28 1.816767e-28
[139,] 1.000000e+00 3.788647e-27 1.894323e-27
[140,] 1.000000e+00 2.267067e-26 1.133533e-26
[141,] 1.000000e+00 2.627244e-25 1.313622e-25
[142,] 1.000000e+00 2.401162e-24 1.200581e-24
[143,] 1.000000e+00 1.964521e-23 9.822605e-24
[144,] 1.000000e+00 1.726284e-22 8.631418e-23
[145,] 1.000000e+00 1.425531e-21 7.127656e-22
[146,] 1.000000e+00 1.566967e-20 7.834834e-21
[147,] 1.000000e+00 1.737864e-19 8.689318e-20
[148,] 1.000000e+00 2.590623e-19 1.295311e-19
[149,] 1.000000e+00 2.907349e-18 1.453675e-18
[150,] 1.000000e+00 2.042321e-17 1.021161e-17
[151,] 1.000000e+00 1.753003e-16 8.765013e-17
[152,] 1.000000e+00 2.073325e-15 1.036662e-15
[153,] 1.000000e+00 2.130265e-14 1.065132e-14
[154,] 1.000000e+00 1.088563e-13 5.442816e-14
[155,] 1.000000e+00 1.178586e-12 5.892930e-13
[156,] 1.000000e+00 9.668475e-12 4.834237e-12
[157,] 1.000000e+00 9.442833e-11 4.721417e-11
[158,] 1.000000e+00 9.216741e-10 4.608371e-10
[159,] 1.000000e+00 7.363844e-09 3.681922e-09
[160,] 1.000000e+00 6.547244e-08 3.273622e-08
[161,] 9.999997e-01 6.142516e-07 3.071258e-07
[162,] 9.999980e-01 3.999656e-06 1.999828e-06
[163,] 9.999882e-01 2.353417e-05 1.176708e-05
[164,] 9.998983e-01 2.033306e-04 1.016653e-04
[165,] 9.991721e-01 1.655704e-03 8.278522e-04
[166,] 9.941237e-01 1.175253e-02 5.876264e-03
> postscript(file="/var/www/html/rcomp/tmp/15bq41293484432.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25bq41293484432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yk771293484432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4yk771293484432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yk771293484432.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 = 195
Frequency = 1
1 2 3 4 5 6
9.20636321 13.56294425 18.01327569 -5.03051464 0.67066416 -2.89043557
7 8 9 10 11 12
-2.25694080 -6.43985243 16.07680921 -2.46728685 -3.74044068 5.58378132
13 14 15 16 17 18
-9.69863837 5.56711165 37.67297877 -11.47107933 10.58762730 9.55831594
19 20 21 22 23 24
-4.70327421 6.36943198 1.08311624 3.14362150 -6.58931652 7.32385011
25 26 27 28 29 30
0.54622641 -5.65840378 7.30050843 8.32062694 -7.25592598 -1.82951659
31 32 33 34 35 36
12.81736930 15.52572467 2.48671152 0.88960638 7.32744113 -4.16502473
37 38 39 40 41 42
-8.94883300 0.84362851 7.56085190 -3.59821476 -10.75055565 -12.54765138
43 44 45 46 47 48
3.34602853 10.13783408 -4.73911616 0.69894544 7.04555418 6.31088721
49 50 51 52 53 54
-10.51114729 7.26007289 6.57618596 -8.86901161 8.59700982 5.03701115
55 56 57 58 59 60
-9.30427940 -28.20850715 -12.40583040 -0.54586613 12.38390833 5.63745015
61 62 63 64 65 66
2.63968454 12.53106365 2.62495674 9.04960498 19.06840159 -1.57438957
67 68 69 70 71 72
-10.11609050 -20.22996216 -25.82005841 -31.37172355 17.39630080 9.81040483
73 74 75 76 77 78
-12.12555477 1.67498775 -9.23906483 -2.93190828 -5.72502456 23.06694285
79 80 81 82 83 84
-2.16952077 14.23837027 6.23340530 5.68032562 14.71245788 8.96261855
85 86 87 88 89 90
8.81886114 -21.78628841 -18.59363846 -0.40688072 -5.51867240 -1.35335679
91 92 93 94 95 96
-5.25700182 -4.68709092 -2.45572331 -2.39159585 0.48146608 -11.90293828
97 98 99 100 101 102
1.41354232 -5.15540528 -6.01981334 4.34353352 -5.94237160 -0.14937186
103 104 105 106 107 108
-6.80220950 -6.29772974 -5.65396348 1.36481505 10.18720697 -0.18026630
109 110 111 112 113 114
1.12454658 2.07525647 3.95160097 -4.18655048 2.43197896 0.69988901
115 116 117 118 119 120
-0.47249957 -2.23064736 6.11322836 -2.53571665 -0.51608486 -0.56603330
121 122 123 124 125 126
0.70276474 -1.53394021 -1.66957944 -0.14217475 -1.87884508 3.31109896
127 128 129 130 131 132
-0.11168251 -4.40772233 -5.83630538 -5.37159867 -1.98526959 -0.19554621
133 134 135 136 137 138
0.29182339 -6.32172031 4.71035530 -3.42581596 -2.99830328 -1.52652709
139 140 141 142 143 144
2.63189638 0.08292442 -5.99189491 -2.08647628 -4.16977155 -0.17941319
145 146 147 148 149 150
6.70215729 -2.37798244 -1.76401285 0.51623763 1.96052779 1.58072791
151 152 153 154 155 156
4.20050846 1.69052691 -3.09248969 2.04843384 0.48846998 0.96107613
157 158 159 160 161 162
-2.56092524 -0.58334999 -1.13972107 -5.62129145 2.26799465 2.04777152
163 164 165 166 167 168
-5.24336822 -2.17565631 -2.00762980 2.14663960 11.02319152 -1.00677341
169 170 171 172 173 174
-1.12475679 -1.02088314 0.61103889 -0.34108700 2.10316403 0.05877413
175 176 177 178 179 180
3.92313095 1.04895877 7.08135080 -0.24164881 -1.86718465 6.15390528
181 182 183 184 185 186
-4.42419319 1.84521163 -1.29512974 -5.90619092 2.23633635 0.91581131
187 188 189 190 191 192
-5.44244547 0.38106729 0.93989151 4.39357193 0.08513836 7.13049547
193 194 195
-0.50321367 -10.73796955 -22.73659702
> postscript(file="/var/www/html/rcomp/tmp/6deey1293484432.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 = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 9.20636321 NA
1 13.56294425 9.20636321
2 18.01327569 13.56294425
3 -5.03051464 18.01327569
4 0.67066416 -5.03051464
5 -2.89043557 0.67066416
6 -2.25694080 -2.89043557
7 -6.43985243 -2.25694080
8 16.07680921 -6.43985243
9 -2.46728685 16.07680921
10 -3.74044068 -2.46728685
11 5.58378132 -3.74044068
12 -9.69863837 5.58378132
13 5.56711165 -9.69863837
14 37.67297877 5.56711165
15 -11.47107933 37.67297877
16 10.58762730 -11.47107933
17 9.55831594 10.58762730
18 -4.70327421 9.55831594
19 6.36943198 -4.70327421
20 1.08311624 6.36943198
21 3.14362150 1.08311624
22 -6.58931652 3.14362150
23 7.32385011 -6.58931652
24 0.54622641 7.32385011
25 -5.65840378 0.54622641
26 7.30050843 -5.65840378
27 8.32062694 7.30050843
28 -7.25592598 8.32062694
29 -1.82951659 -7.25592598
30 12.81736930 -1.82951659
31 15.52572467 12.81736930
32 2.48671152 15.52572467
33 0.88960638 2.48671152
34 7.32744113 0.88960638
35 -4.16502473 7.32744113
36 -8.94883300 -4.16502473
37 0.84362851 -8.94883300
38 7.56085190 0.84362851
39 -3.59821476 7.56085190
40 -10.75055565 -3.59821476
41 -12.54765138 -10.75055565
42 3.34602853 -12.54765138
43 10.13783408 3.34602853
44 -4.73911616 10.13783408
45 0.69894544 -4.73911616
46 7.04555418 0.69894544
47 6.31088721 7.04555418
48 -10.51114729 6.31088721
49 7.26007289 -10.51114729
50 6.57618596 7.26007289
51 -8.86901161 6.57618596
52 8.59700982 -8.86901161
53 5.03701115 8.59700982
54 -9.30427940 5.03701115
55 -28.20850715 -9.30427940
56 -12.40583040 -28.20850715
57 -0.54586613 -12.40583040
58 12.38390833 -0.54586613
59 5.63745015 12.38390833
60 2.63968454 5.63745015
61 12.53106365 2.63968454
62 2.62495674 12.53106365
63 9.04960498 2.62495674
64 19.06840159 9.04960498
65 -1.57438957 19.06840159
66 -10.11609050 -1.57438957
67 -20.22996216 -10.11609050
68 -25.82005841 -20.22996216
69 -31.37172355 -25.82005841
70 17.39630080 -31.37172355
71 9.81040483 17.39630080
72 -12.12555477 9.81040483
73 1.67498775 -12.12555477
74 -9.23906483 1.67498775
75 -2.93190828 -9.23906483
76 -5.72502456 -2.93190828
77 23.06694285 -5.72502456
78 -2.16952077 23.06694285
79 14.23837027 -2.16952077
80 6.23340530 14.23837027
81 5.68032562 6.23340530
82 14.71245788 5.68032562
83 8.96261855 14.71245788
84 8.81886114 8.96261855
85 -21.78628841 8.81886114
86 -18.59363846 -21.78628841
87 -0.40688072 -18.59363846
88 -5.51867240 -0.40688072
89 -1.35335679 -5.51867240
90 -5.25700182 -1.35335679
91 -4.68709092 -5.25700182
92 -2.45572331 -4.68709092
93 -2.39159585 -2.45572331
94 0.48146608 -2.39159585
95 -11.90293828 0.48146608
96 1.41354232 -11.90293828
97 -5.15540528 1.41354232
98 -6.01981334 -5.15540528
99 4.34353352 -6.01981334
100 -5.94237160 4.34353352
101 -0.14937186 -5.94237160
102 -6.80220950 -0.14937186
103 -6.29772974 -6.80220950
104 -5.65396348 -6.29772974
105 1.36481505 -5.65396348
106 10.18720697 1.36481505
107 -0.18026630 10.18720697
108 1.12454658 -0.18026630
109 2.07525647 1.12454658
110 3.95160097 2.07525647
111 -4.18655048 3.95160097
112 2.43197896 -4.18655048
113 0.69988901 2.43197896
114 -0.47249957 0.69988901
115 -2.23064736 -0.47249957
116 6.11322836 -2.23064736
117 -2.53571665 6.11322836
118 -0.51608486 -2.53571665
119 -0.56603330 -0.51608486
120 0.70276474 -0.56603330
121 -1.53394021 0.70276474
122 -1.66957944 -1.53394021
123 -0.14217475 -1.66957944
124 -1.87884508 -0.14217475
125 3.31109896 -1.87884508
126 -0.11168251 3.31109896
127 -4.40772233 -0.11168251
128 -5.83630538 -4.40772233
129 -5.37159867 -5.83630538
130 -1.98526959 -5.37159867
131 -0.19554621 -1.98526959
132 0.29182339 -0.19554621
133 -6.32172031 0.29182339
134 4.71035530 -6.32172031
135 -3.42581596 4.71035530
136 -2.99830328 -3.42581596
137 -1.52652709 -2.99830328
138 2.63189638 -1.52652709
139 0.08292442 2.63189638
140 -5.99189491 0.08292442
141 -2.08647628 -5.99189491
142 -4.16977155 -2.08647628
143 -0.17941319 -4.16977155
144 6.70215729 -0.17941319
145 -2.37798244 6.70215729
146 -1.76401285 -2.37798244
147 0.51623763 -1.76401285
148 1.96052779 0.51623763
149 1.58072791 1.96052779
150 4.20050846 1.58072791
151 1.69052691 4.20050846
152 -3.09248969 1.69052691
153 2.04843384 -3.09248969
154 0.48846998 2.04843384
155 0.96107613 0.48846998
156 -2.56092524 0.96107613
157 -0.58334999 -2.56092524
158 -1.13972107 -0.58334999
159 -5.62129145 -1.13972107
160 2.26799465 -5.62129145
161 2.04777152 2.26799465
162 -5.24336822 2.04777152
163 -2.17565631 -5.24336822
164 -2.00762980 -2.17565631
165 2.14663960 -2.00762980
166 11.02319152 2.14663960
167 -1.00677341 11.02319152
168 -1.12475679 -1.00677341
169 -1.02088314 -1.12475679
170 0.61103889 -1.02088314
171 -0.34108700 0.61103889
172 2.10316403 -0.34108700
173 0.05877413 2.10316403
174 3.92313095 0.05877413
175 1.04895877 3.92313095
176 7.08135080 1.04895877
177 -0.24164881 7.08135080
178 -1.86718465 -0.24164881
179 6.15390528 -1.86718465
180 -4.42419319 6.15390528
181 1.84521163 -4.42419319
182 -1.29512974 1.84521163
183 -5.90619092 -1.29512974
184 2.23633635 -5.90619092
185 0.91581131 2.23633635
186 -5.44244547 0.91581131
187 0.38106729 -5.44244547
188 0.93989151 0.38106729
189 4.39357193 0.93989151
190 0.08513836 4.39357193
191 7.13049547 0.08513836
192 -0.50321367 7.13049547
193 -10.73796955 -0.50321367
194 -22.73659702 -10.73796955
195 NA -22.73659702
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 13.56294425 9.20636321
[2,] 18.01327569 13.56294425
[3,] -5.03051464 18.01327569
[4,] 0.67066416 -5.03051464
[5,] -2.89043557 0.67066416
[6,] -2.25694080 -2.89043557
[7,] -6.43985243 -2.25694080
[8,] 16.07680921 -6.43985243
[9,] -2.46728685 16.07680921
[10,] -3.74044068 -2.46728685
[11,] 5.58378132 -3.74044068
[12,] -9.69863837 5.58378132
[13,] 5.56711165 -9.69863837
[14,] 37.67297877 5.56711165
[15,] -11.47107933 37.67297877
[16,] 10.58762730 -11.47107933
[17,] 9.55831594 10.58762730
[18,] -4.70327421 9.55831594
[19,] 6.36943198 -4.70327421
[20,] 1.08311624 6.36943198
[21,] 3.14362150 1.08311624
[22,] -6.58931652 3.14362150
[23,] 7.32385011 -6.58931652
[24,] 0.54622641 7.32385011
[25,] -5.65840378 0.54622641
[26,] 7.30050843 -5.65840378
[27,] 8.32062694 7.30050843
[28,] -7.25592598 8.32062694
[29,] -1.82951659 -7.25592598
[30,] 12.81736930 -1.82951659
[31,] 15.52572467 12.81736930
[32,] 2.48671152 15.52572467
[33,] 0.88960638 2.48671152
[34,] 7.32744113 0.88960638
[35,] -4.16502473 7.32744113
[36,] -8.94883300 -4.16502473
[37,] 0.84362851 -8.94883300
[38,] 7.56085190 0.84362851
[39,] -3.59821476 7.56085190
[40,] -10.75055565 -3.59821476
[41,] -12.54765138 -10.75055565
[42,] 3.34602853 -12.54765138
[43,] 10.13783408 3.34602853
[44,] -4.73911616 10.13783408
[45,] 0.69894544 -4.73911616
[46,] 7.04555418 0.69894544
[47,] 6.31088721 7.04555418
[48,] -10.51114729 6.31088721
[49,] 7.26007289 -10.51114729
[50,] 6.57618596 7.26007289
[51,] -8.86901161 6.57618596
[52,] 8.59700982 -8.86901161
[53,] 5.03701115 8.59700982
[54,] -9.30427940 5.03701115
[55,] -28.20850715 -9.30427940
[56,] -12.40583040 -28.20850715
[57,] -0.54586613 -12.40583040
[58,] 12.38390833 -0.54586613
[59,] 5.63745015 12.38390833
[60,] 2.63968454 5.63745015
[61,] 12.53106365 2.63968454
[62,] 2.62495674 12.53106365
[63,] 9.04960498 2.62495674
[64,] 19.06840159 9.04960498
[65,] -1.57438957 19.06840159
[66,] -10.11609050 -1.57438957
[67,] -20.22996216 -10.11609050
[68,] -25.82005841 -20.22996216
[69,] -31.37172355 -25.82005841
[70,] 17.39630080 -31.37172355
[71,] 9.81040483 17.39630080
[72,] -12.12555477 9.81040483
[73,] 1.67498775 -12.12555477
[74,] -9.23906483 1.67498775
[75,] -2.93190828 -9.23906483
[76,] -5.72502456 -2.93190828
[77,] 23.06694285 -5.72502456
[78,] -2.16952077 23.06694285
[79,] 14.23837027 -2.16952077
[80,] 6.23340530 14.23837027
[81,] 5.68032562 6.23340530
[82,] 14.71245788 5.68032562
[83,] 8.96261855 14.71245788
[84,] 8.81886114 8.96261855
[85,] -21.78628841 8.81886114
[86,] -18.59363846 -21.78628841
[87,] -0.40688072 -18.59363846
[88,] -5.51867240 -0.40688072
[89,] -1.35335679 -5.51867240
[90,] -5.25700182 -1.35335679
[91,] -4.68709092 -5.25700182
[92,] -2.45572331 -4.68709092
[93,] -2.39159585 -2.45572331
[94,] 0.48146608 -2.39159585
[95,] -11.90293828 0.48146608
[96,] 1.41354232 -11.90293828
[97,] -5.15540528 1.41354232
[98,] -6.01981334 -5.15540528
[99,] 4.34353352 -6.01981334
[100,] -5.94237160 4.34353352
[101,] -0.14937186 -5.94237160
[102,] -6.80220950 -0.14937186
[103,] -6.29772974 -6.80220950
[104,] -5.65396348 -6.29772974
[105,] 1.36481505 -5.65396348
[106,] 10.18720697 1.36481505
[107,] -0.18026630 10.18720697
[108,] 1.12454658 -0.18026630
[109,] 2.07525647 1.12454658
[110,] 3.95160097 2.07525647
[111,] -4.18655048 3.95160097
[112,] 2.43197896 -4.18655048
[113,] 0.69988901 2.43197896
[114,] -0.47249957 0.69988901
[115,] -2.23064736 -0.47249957
[116,] 6.11322836 -2.23064736
[117,] -2.53571665 6.11322836
[118,] -0.51608486 -2.53571665
[119,] -0.56603330 -0.51608486
[120,] 0.70276474 -0.56603330
[121,] -1.53394021 0.70276474
[122,] -1.66957944 -1.53394021
[123,] -0.14217475 -1.66957944
[124,] -1.87884508 -0.14217475
[125,] 3.31109896 -1.87884508
[126,] -0.11168251 3.31109896
[127,] -4.40772233 -0.11168251
[128,] -5.83630538 -4.40772233
[129,] -5.37159867 -5.83630538
[130,] -1.98526959 -5.37159867
[131,] -0.19554621 -1.98526959
[132,] 0.29182339 -0.19554621
[133,] -6.32172031 0.29182339
[134,] 4.71035530 -6.32172031
[135,] -3.42581596 4.71035530
[136,] -2.99830328 -3.42581596
[137,] -1.52652709 -2.99830328
[138,] 2.63189638 -1.52652709
[139,] 0.08292442 2.63189638
[140,] -5.99189491 0.08292442
[141,] -2.08647628 -5.99189491
[142,] -4.16977155 -2.08647628
[143,] -0.17941319 -4.16977155
[144,] 6.70215729 -0.17941319
[145,] -2.37798244 6.70215729
[146,] -1.76401285 -2.37798244
[147,] 0.51623763 -1.76401285
[148,] 1.96052779 0.51623763
[149,] 1.58072791 1.96052779
[150,] 4.20050846 1.58072791
[151,] 1.69052691 4.20050846
[152,] -3.09248969 1.69052691
[153,] 2.04843384 -3.09248969
[154,] 0.48846998 2.04843384
[155,] 0.96107613 0.48846998
[156,] -2.56092524 0.96107613
[157,] -0.58334999 -2.56092524
[158,] -1.13972107 -0.58334999
[159,] -5.62129145 -1.13972107
[160,] 2.26799465 -5.62129145
[161,] 2.04777152 2.26799465
[162,] -5.24336822 2.04777152
[163,] -2.17565631 -5.24336822
[164,] -2.00762980 -2.17565631
[165,] 2.14663960 -2.00762980
[166,] 11.02319152 2.14663960
[167,] -1.00677341 11.02319152
[168,] -1.12475679 -1.00677341
[169,] -1.02088314 -1.12475679
[170,] 0.61103889 -1.02088314
[171,] -0.34108700 0.61103889
[172,] 2.10316403 -0.34108700
[173,] 0.05877413 2.10316403
[174,] 3.92313095 0.05877413
[175,] 1.04895877 3.92313095
[176,] 7.08135080 1.04895877
[177,] -0.24164881 7.08135080
[178,] -1.86718465 -0.24164881
[179,] 6.15390528 -1.86718465
[180,] -4.42419319 6.15390528
[181,] 1.84521163 -4.42419319
[182,] -1.29512974 1.84521163
[183,] -5.90619092 -1.29512974
[184,] 2.23633635 -5.90619092
[185,] 0.91581131 2.23633635
[186,] -5.44244547 0.91581131
[187,] 0.38106729 -5.44244547
[188,] 0.93989151 0.38106729
[189,] 4.39357193 0.93989151
[190,] 0.08513836 4.39357193
[191,] 7.13049547 0.08513836
[192,] -0.50321367 7.13049547
[193,] -10.73796955 -0.50321367
[194,] -22.73659702 -10.73796955
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 13.56294425 9.20636321
2 18.01327569 13.56294425
3 -5.03051464 18.01327569
4 0.67066416 -5.03051464
5 -2.89043557 0.67066416
6 -2.25694080 -2.89043557
7 -6.43985243 -2.25694080
8 16.07680921 -6.43985243
9 -2.46728685 16.07680921
10 -3.74044068 -2.46728685
11 5.58378132 -3.74044068
12 -9.69863837 5.58378132
13 5.56711165 -9.69863837
14 37.67297877 5.56711165
15 -11.47107933 37.67297877
16 10.58762730 -11.47107933
17 9.55831594 10.58762730
18 -4.70327421 9.55831594
19 6.36943198 -4.70327421
20 1.08311624 6.36943198
21 3.14362150 1.08311624
22 -6.58931652 3.14362150
23 7.32385011 -6.58931652
24 0.54622641 7.32385011
25 -5.65840378 0.54622641
26 7.30050843 -5.65840378
27 8.32062694 7.30050843
28 -7.25592598 8.32062694
29 -1.82951659 -7.25592598
30 12.81736930 -1.82951659
31 15.52572467 12.81736930
32 2.48671152 15.52572467
33 0.88960638 2.48671152
34 7.32744113 0.88960638
35 -4.16502473 7.32744113
36 -8.94883300 -4.16502473
37 0.84362851 -8.94883300
38 7.56085190 0.84362851
39 -3.59821476 7.56085190
40 -10.75055565 -3.59821476
41 -12.54765138 -10.75055565
42 3.34602853 -12.54765138
43 10.13783408 3.34602853
44 -4.73911616 10.13783408
45 0.69894544 -4.73911616
46 7.04555418 0.69894544
47 6.31088721 7.04555418
48 -10.51114729 6.31088721
49 7.26007289 -10.51114729
50 6.57618596 7.26007289
51 -8.86901161 6.57618596
52 8.59700982 -8.86901161
53 5.03701115 8.59700982
54 -9.30427940 5.03701115
55 -28.20850715 -9.30427940
56 -12.40583040 -28.20850715
57 -0.54586613 -12.40583040
58 12.38390833 -0.54586613
59 5.63745015 12.38390833
60 2.63968454 5.63745015
61 12.53106365 2.63968454
62 2.62495674 12.53106365
63 9.04960498 2.62495674
64 19.06840159 9.04960498
65 -1.57438957 19.06840159
66 -10.11609050 -1.57438957
67 -20.22996216 -10.11609050
68 -25.82005841 -20.22996216
69 -31.37172355 -25.82005841
70 17.39630080 -31.37172355
71 9.81040483 17.39630080
72 -12.12555477 9.81040483
73 1.67498775 -12.12555477
74 -9.23906483 1.67498775
75 -2.93190828 -9.23906483
76 -5.72502456 -2.93190828
77 23.06694285 -5.72502456
78 -2.16952077 23.06694285
79 14.23837027 -2.16952077
80 6.23340530 14.23837027
81 5.68032562 6.23340530
82 14.71245788 5.68032562
83 8.96261855 14.71245788
84 8.81886114 8.96261855
85 -21.78628841 8.81886114
86 -18.59363846 -21.78628841
87 -0.40688072 -18.59363846
88 -5.51867240 -0.40688072
89 -1.35335679 -5.51867240
90 -5.25700182 -1.35335679
91 -4.68709092 -5.25700182
92 -2.45572331 -4.68709092
93 -2.39159585 -2.45572331
94 0.48146608 -2.39159585
95 -11.90293828 0.48146608
96 1.41354232 -11.90293828
97 -5.15540528 1.41354232
98 -6.01981334 -5.15540528
99 4.34353352 -6.01981334
100 -5.94237160 4.34353352
101 -0.14937186 -5.94237160
102 -6.80220950 -0.14937186
103 -6.29772974 -6.80220950
104 -5.65396348 -6.29772974
105 1.36481505 -5.65396348
106 10.18720697 1.36481505
107 -0.18026630 10.18720697
108 1.12454658 -0.18026630
109 2.07525647 1.12454658
110 3.95160097 2.07525647
111 -4.18655048 3.95160097
112 2.43197896 -4.18655048
113 0.69988901 2.43197896
114 -0.47249957 0.69988901
115 -2.23064736 -0.47249957
116 6.11322836 -2.23064736
117 -2.53571665 6.11322836
118 -0.51608486 -2.53571665
119 -0.56603330 -0.51608486
120 0.70276474 -0.56603330
121 -1.53394021 0.70276474
122 -1.66957944 -1.53394021
123 -0.14217475 -1.66957944
124 -1.87884508 -0.14217475
125 3.31109896 -1.87884508
126 -0.11168251 3.31109896
127 -4.40772233 -0.11168251
128 -5.83630538 -4.40772233
129 -5.37159867 -5.83630538
130 -1.98526959 -5.37159867
131 -0.19554621 -1.98526959
132 0.29182339 -0.19554621
133 -6.32172031 0.29182339
134 4.71035530 -6.32172031
135 -3.42581596 4.71035530
136 -2.99830328 -3.42581596
137 -1.52652709 -2.99830328
138 2.63189638 -1.52652709
139 0.08292442 2.63189638
140 -5.99189491 0.08292442
141 -2.08647628 -5.99189491
142 -4.16977155 -2.08647628
143 -0.17941319 -4.16977155
144 6.70215729 -0.17941319
145 -2.37798244 6.70215729
146 -1.76401285 -2.37798244
147 0.51623763 -1.76401285
148 1.96052779 0.51623763
149 1.58072791 1.96052779
150 4.20050846 1.58072791
151 1.69052691 4.20050846
152 -3.09248969 1.69052691
153 2.04843384 -3.09248969
154 0.48846998 2.04843384
155 0.96107613 0.48846998
156 -2.56092524 0.96107613
157 -0.58334999 -2.56092524
158 -1.13972107 -0.58334999
159 -5.62129145 -1.13972107
160 2.26799465 -5.62129145
161 2.04777152 2.26799465
162 -5.24336822 2.04777152
163 -2.17565631 -5.24336822
164 -2.00762980 -2.17565631
165 2.14663960 -2.00762980
166 11.02319152 2.14663960
167 -1.00677341 11.02319152
168 -1.12475679 -1.00677341
169 -1.02088314 -1.12475679
170 0.61103889 -1.02088314
171 -0.34108700 0.61103889
172 2.10316403 -0.34108700
173 0.05877413 2.10316403
174 3.92313095 0.05877413
175 1.04895877 3.92313095
176 7.08135080 1.04895877
177 -0.24164881 7.08135080
178 -1.86718465 -0.24164881
179 6.15390528 -1.86718465
180 -4.42419319 6.15390528
181 1.84521163 -4.42419319
182 -1.29512974 1.84521163
183 -5.90619092 -1.29512974
184 2.23633635 -5.90619092
185 0.91581131 2.23633635
186 -5.44244547 0.91581131
187 0.38106729 -5.44244547
188 0.93989151 0.38106729
189 4.39357193 0.93989151
190 0.08513836 4.39357193
191 7.13049547 0.08513836
192 -0.50321367 7.13049547
193 -10.73796955 -0.50321367
194 -22.73659702 -10.73796955
> 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/7j3ov1293484432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8j3ov1293484432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9j3ov1293484432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ccnf1293484432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xcml1293484432.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/120d291293484432.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/13w4001293484432.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/14ingo1293484432.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/15lofu1293484432.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/16pod01293484432.tab")
+ }
>
> try(system("convert tmp/15bq41293484432.ps tmp/15bq41293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/25bq41293484432.ps tmp/25bq41293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/3yk771293484432.ps tmp/3yk771293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/4yk771293484432.ps tmp/4yk771293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yk771293484432.ps tmp/5yk771293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/6deey1293484432.ps tmp/6deey1293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/7j3ov1293484432.ps tmp/7j3ov1293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/8j3ov1293484432.ps tmp/8j3ov1293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/9j3ov1293484432.ps tmp/9j3ov1293484432.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ccnf1293484432.ps tmp/10ccnf1293484432.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.763 1.903 16.065