R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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+ ,10
+ ,10
+ ,20
+ ,20
+ ,29
+ ,29
+ ,0
+ ,17
+ ,23
+ ,0
+ ,8
+ ,0
+ ,10
+ ,0
+ ,15
+ ,0
+ ,22
+ ,0
+ ,1
+ ,16
+ ,21
+ ,21
+ ,11
+ ,11
+ ,7
+ ,7
+ ,20
+ ,20
+ ,24
+ ,24
+ ,0
+ ,10
+ ,30
+ ,0
+ ,12
+ ,0
+ ,10
+ ,0
+ ,29
+ ,0
+ ,22
+ ,0
+ ,0
+ ,18
+ ,22
+ ,0
+ ,9
+ ,0
+ ,6
+ ,0
+ ,19
+ ,0
+ ,12
+ ,0
+ ,1
+ ,13
+ ,32
+ ,32
+ ,16
+ ,16
+ ,6
+ ,6
+ ,29
+ ,29
+ ,26
+ ,26
+ ,0
+ ,15
+ ,22
+ ,0
+ ,11
+ ,0
+ ,11
+ ,0
+ ,24
+ ,0
+ ,18
+ ,0
+ ,1
+ ,16
+ ,15
+ ,15
+ ,11
+ ,11
+ ,8
+ ,8
+ ,23
+ ,23
+ ,22
+ ,22
+ ,0
+ ,16
+ ,21
+ ,0
+ ,12
+ ,0
+ ,9
+ ,0
+ ,22
+ ,0
+ ,24
+ ,0
+ ,0
+ ,14
+ ,27
+ ,0
+ ,15
+ ,0
+ ,9
+ ,0
+ ,23
+ ,0
+ ,21
+ ,0
+ ,0
+ ,10
+ ,22
+ ,0
+ ,13
+ ,0
+ ,13
+ ,0
+ ,22
+ ,0
+ ,15
+ ,0
+ ,0
+ ,17
+ ,9
+ ,0
+ ,6
+ ,0
+ ,11
+ ,0
+ ,29
+ ,0
+ ,23
+ ,0
+ ,0
+ ,13
+ ,29
+ ,0
+ ,11
+ ,0
+ ,4
+ ,0
+ ,26
+ ,0
+ ,22
+ ,0
+ ,0
+ ,15
+ ,20
+ ,0
+ ,7
+ ,0
+ ,9
+ ,0
+ ,26
+ ,0
+ ,22
+ ,0
+ ,0
+ ,16
+ ,16
+ ,0
+ ,8
+ ,0
+ ,5
+ ,0
+ ,21
+ ,0
+ ,24
+ ,0
+ ,0
+ ,12
+ ,16
+ ,0
+ ,8
+ ,0
+ ,4
+ ,0
+ ,18
+ ,0
+ ,23
+ ,0
+ ,0
+ ,13
+ ,16
+ ,0
+ ,9
+ ,0
+ ,9
+ ,0
+ ,10
+ ,0
+ ,13
+ ,0)
+ ,dim=c(12
+ ,150)
+ ,dimnames=list(c('Gender'
+ ,'Selfconfidence'
+ ,'ConcernMistakes'
+ ,'ConcernMistakes_G'
+ ,'DoubtsActions'
+ ,'DoubtsActions_G'
+ ,'ParentalCriticism'
+ ,'ParentalCriticism_G'
+ ,'PersonalStandards'
+ ,'PersonalStandards_G'
+ ,'Organization'
+ ,'Organization_G')
+ ,1:150))
> y <- array(NA,dim=c(12,150),dimnames=list(c('Gender','Selfconfidence','ConcernMistakes','ConcernMistakes_G','DoubtsActions','DoubtsActions_G','ParentalCriticism','ParentalCriticism_G','PersonalStandards','PersonalStandards_G','Organization','Organization_G'),1:150))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'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
> 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
Selfconfidence Gender ConcernMistakes ConcernMistakes_G DoubtsActions
1 13 0 26 0 9
2 16 0 20 0 9
3 19 0 21 0 9
4 15 1 31 31 14
5 14 0 21 0 8
6 13 0 18 0 8
7 19 0 26 0 11
8 15 0 22 0 10
9 14 0 22 0 9
10 15 0 29 0 15
11 16 1 15 15 14
12 16 0 16 0 11
13 16 1 24 24 14
14 17 0 17 0 6
15 15 1 19 19 20
16 15 1 22 22 9
17 20 0 31 0 10
18 18 1 28 28 8
19 16 0 38 0 11
20 16 1 26 26 14
21 19 0 25 0 11
22 16 0 25 0 16
23 17 1 29 29 14
24 17 0 28 0 11
25 16 1 15 15 11
26 15 0 18 0 12
27 14 1 21 21 9
28 15 0 25 0 7
29 12 1 23 23 13
30 14 0 23 0 10
31 16 0 19 0 9
32 14 1 18 18 9
33 7 1 18 18 13
34 10 1 26 26 16
35 14 1 18 18 12
36 16 0 18 0 6
37 16 1 28 28 14
38 16 1 17 17 14
39 14 0 29 0 10
40 20 1 12 12 4
41 14 1 25 25 12
42 14 0 28 0 12
43 11 0 20 0 14
44 15 0 17 0 9
45 16 0 17 0 9
46 14 1 20 20 10
47 16 0 31 0 14
48 14 1 21 21 10
49 12 1 19 19 9
50 16 0 23 0 14
51 9 1 15 15 8
52 14 0 24 0 9
53 16 0 28 0 8
54 16 0 16 0 9
55 15 1 19 19 9
56 16 0 21 0 9
57 12 1 21 21 15
58 16 1 20 20 8
59 16 0 16 0 10
60 14 0 25 0 8
61 16 0 30 0 14
62 17 1 29 29 11
63 18 0 22 0 10
64 18 1 19 19 12
65 12 0 33 0 14
66 16 1 17 17 9
67 10 1 9 9 13
68 14 0 14 0 15
69 18 0 15 0 8
70 18 1 12 12 7
71 16 1 21 21 10
72 16 0 20 0 10
73 16 0 29 0 13
74 13 1 33 33 11
75 16 1 21 21 8
76 16 1 15 15 12
77 20 1 19 19 9
78 16 0 23 0 10
79 15 1 20 20 11
80 15 0 20 0 11
81 16 0 18 0 10
82 14 1 31 31 16
83 15 0 18 0 16
84 12 0 13 0 8
85 17 0 9 0 6
86 16 0 20 0 11
87 15 0 18 0 12
88 13 0 23 0 14
89 16 0 17 0 9
90 16 0 17 0 11
91 16 0 16 0 8
92 16 1 31 31 8
93 14 1 15 15 7
94 16 0 28 0 16
95 16 1 26 26 13
96 20 0 20 0 8
97 15 1 19 19 11
98 16 0 25 0 14
99 13 1 18 18 10
100 17 0 20 0 10
101 16 1 33 33 14
102 12 0 24 0 14
103 16 0 22 0 10
104 16 0 32 0 12
105 17 0 31 0 9
106 13 1 13 13 16
107 12 0 18 0 8
108 18 1 17 17 9
109 14 0 29 0 16
110 14 0 22 0 13
111 13 0 18 0 13
112 16 0 22 0 8
113 13 0 25 0 14
114 16 0 20 0 11
115 13 0 20 0 9
116 16 0 17 0 8
117 15 0 21 0 13
118 16 0 26 0 13
119 15 1 10 10 10
120 17 0 15 0 8
121 15 0 20 0 7
122 12 0 14 0 11
123 16 1 16 16 11
124 10 1 23 23 14
125 16 0 11 0 6
126 14 1 19 19 10
127 15 0 30 0 9
128 13 1 21 21 12
129 15 1 20 20 11
130 11 0 22 0 14
131 12 0 30 0 12
132 8 1 25 25 14
133 16 0 28 0 8
134 15 1 23 23 14
135 17 0 23 0 8
136 16 1 21 21 11
137 10 0 30 0 12
138 18 0 22 0 9
139 13 1 32 32 16
140 15 0 22 0 11
141 16 1 15 15 11
142 16 0 21 0 12
143 14 0 27 0 15
144 10 0 22 0 13
145 17 0 9 0 6
146 13 0 29 0 11
147 15 0 20 0 7
148 16 0 16 0 8
149 12 0 16 0 8
150 13 0 16 0 9
DoubtsActions_G ParentalCriticism ParentalCriticism_G PersonalStandards
1 0 6 0 25
2 0 6 0 25
3 0 13 0 19
4 14 8 8 18
5 0 7 0 18
6 0 9 0 22
7 0 5 0 29
8 0 8 0 26
9 0 9 0 25
10 0 11 0 23
11 14 8 8 23
12 0 11 0 23
13 14 12 12 24
14 0 8 0 30
15 20 7 7 19
16 9 9 9 24
17 0 12 0 32
18 8 20 20 30
19 0 7 0 29
20 14 8 8 17
21 0 8 0 25
22 0 16 0 26
23 14 10 10 26
24 0 6 0 25
25 11 8 8 23
26 0 9 0 21
27 9 9 9 19
28 0 11 0 35
29 13 12 12 19
30 0 8 0 20
31 0 7 0 21
32 9 8 8 21
33 13 9 9 24
34 16 4 4 23
35 12 8 8 19
36 0 8 0 17
37 14 8 8 24
38 14 6 6 15
39 0 8 0 25
40 4 4 4 27
41 12 7 7 29
42 0 14 0 27
43 0 10 0 18
44 0 9 0 25
45 0 6 0 22
46 10 8 8 26
47 0 11 0 23
48 10 8 8 16
49 9 8 8 27
50 0 10 0 25
51 8 8 8 14
52 0 10 0 19
53 0 7 0 20
54 0 8 0 16
55 9 7 7 18
56 0 9 0 22
57 15 5 5 21
58 8 7 7 22
59 0 7 0 22
60 0 7 0 32
61 0 9 0 23
62 11 5 5 31
63 0 8 0 18
64 12 8 8 23
65 0 8 0 26
66 9 9 9 24
67 13 6 6 19
68 0 8 0 14
69 0 6 0 20
70 7 4 4 22
71 10 6 6 24
72 0 4 0 25
73 0 12 0 21
74 11 6 6 28
75 8 11 11 24
76 12 8 8 20
77 9 10 10 21
78 0 10 0 23
79 11 4 4 13
80 0 8 0 24
81 0 9 0 21
82 16 9 9 21
83 0 7 0 17
84 0 7 0 14
85 0 11 0 29
86 0 8 0 25
87 0 8 0 16
88 0 7 0 25
89 0 5 0 25
90 0 7 0 21
91 0 9 0 23
92 8 8 8 22
93 7 6 6 19
94 0 8 0 24
95 13 10 10 26
96 0 10 0 25
97 11 8 8 20
98 0 11 0 22
99 10 8 8 14
100 0 8 0 20
101 14 6 6 32
102 0 20 0 21
103 0 6 0 22
104 0 12 0 28
105 0 9 0 25
106 16 5 5 17
107 0 10 0 21
108 9 5 5 23
109 0 6 0 27
110 0 10 0 22
111 0 6 0 19
112 0 10 0 20
113 0 5 0 17
114 0 13 0 24
115 0 7 0 21
116 0 9 0 21
117 0 11 0 23
118 0 8 0 24
119 10 5 5 19
120 0 4 0 22
121 0 9 0 26
122 0 7 0 17
123 11 5 5 17
124 14 5 5 19
125 0 4 0 15
126 10 7 7 17
127 0 9 0 27
128 12 8 8 19
129 11 8 8 21
130 0 11 0 25
131 0 10 0 19
132 14 9 9 22
133 0 12 0 18
134 14 10 10 20
135 0 10 0 15
136 11 7 7 20
137 0 10 0 29
138 0 6 0 19
139 16 6 6 29
140 0 11 0 24
141 11 8 8 23
142 0 9 0 22
143 0 9 0 23
144 0 13 0 22
145 0 11 0 29
146 0 4 0 26
147 0 9 0 26
148 0 5 0 21
149 0 4 0 18
150 0 9 0 10
PersonalStandards_G Organization Organization_G
1 0 25 0
2 0 24 0
3 0 21 0
4 18 23 23
5 0 17 0
6 0 19 0
7 0 18 0
8 0 27 0
9 0 23 0
10 0 23 0
11 23 29 29
12 0 21 0
13 24 26 26
14 0 25 0
15 19 25 25
16 24 23 23
17 0 26 0
18 30 20 20
19 0 29 0
20 17 24 24
21 0 23 0
22 0 24 0
23 26 30 30
24 0 22 0
25 23 22 22
26 0 13 0
27 19 24 24
28 0 17 0
29 19 24 24
30 0 21 0
31 0 23 0
32 21 24 24
33 24 24 24
34 23 24 24
35 19 23 23
36 0 26 0
37 24 24 24
38 15 21 21
39 0 23 0
40 27 28 28
41 29 23 23
42 0 22 0
43 0 24 0
44 0 21 0
45 0 23 0
46 26 23 23
47 0 20 0
48 16 23 23
49 27 21 21
50 0 27 0
51 14 12 12
52 0 15 0
53 0 22 0
54 0 21 0
55 18 21 21
56 0 20 0
57 21 24 24
58 22 24 24
59 0 29 0
60 0 25 0
61 0 14 0
62 31 30 30
63 0 19 0
64 23 29 29
65 0 25 0
66 24 25 25
67 19 25 25
68 0 16 0
69 0 25 0
70 22 28 28
71 24 24 24
72 0 25 0
73 0 21 0
74 28 22 22
75 24 20 20
76 20 25 25
77 21 27 27
78 0 21 0
79 13 13 13
80 0 26 0
81 0 26 0
82 21 25 25
83 0 22 0
84 0 19 0
85 0 23 0
86 0 25 0
87 0 15 0
88 0 21 0
89 0 23 0
90 0 25 0
91 0 24 0
92 22 24 24
93 19 21 21
94 0 24 0
95 26 22 22
96 0 24 0
97 20 28 28
98 0 21 0
99 14 17 17
100 0 28 0
101 32 24 24
102 0 10 0
103 0 20 0
104 0 22 0
105 0 19 0
106 17 22 22
107 0 22 0
108 23 26 26
109 0 24 0
110 0 22 0
111 0 20 0
112 0 20 0
113 0 15 0
114 0 20 0
115 0 20 0
116 0 24 0
117 0 22 0
118 0 29 0
119 19 23 23
120 0 24 0
121 0 22 0
122 0 16 0
123 17 23 23
124 19 27 27
125 0 16 0
126 17 21 21
127 0 26 0
128 19 22 22
129 21 23 23
130 0 19 0
131 0 18 0
132 22 24 24
133 0 24 0
134 20 29 29
135 0 22 0
136 20 24 24
137 0 22 0
138 0 12 0
139 29 26 26
140 0 18 0
141 23 22 22
142 0 24 0
143 0 21 0
144 0 15 0
145 0 23 0
146 0 22 0
147 0 22 0
148 0 24 0
149 0 23 0
150 0 13 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender ConcernMistakes
13.690460 -3.238577 0.008773
ConcernMistakes_G DoubtsActions DoubtsActions_G
0.023914 -0.212820 -0.186727
ParentalCriticism ParentalCriticism_G PersonalStandards
0.027288 0.056977 0.032324
PersonalStandards_G Organization Organization_G
-0.038112 0.117975 0.201262
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.127 -1.065 0.294 1.196 4.225
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.690460 1.818071 7.530 5.98e-12 ***
Gender -3.238577 2.978129 -1.087 0.2787
ConcernMistakes 0.008773 0.047808 0.184 0.8547
ConcernMistakes_G 0.023914 0.077534 0.308 0.7582
DoubtsActions -0.212820 0.095570 -2.227 0.0276 *
DoubtsActions_G -0.186727 0.146837 -1.272 0.2056
ParentalCriticism 0.027288 0.087395 0.312 0.7553
ParentalCriticism_G 0.056977 0.144895 0.393 0.6948
PersonalStandards 0.032324 0.061268 0.528 0.5986
PersonalStandards_G -0.038112 0.104694 -0.364 0.7164
Organization 0.117975 0.063028 1.872 0.0634 .
Organization_G 0.201262 0.114274 1.761 0.0804 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.074 on 138 degrees of freedom
Multiple R-squared: 0.2286, Adjusted R-squared: 0.1671
F-statistic: 3.717 on 11 and 138 DF, p-value: 0.0001176
> 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,] 0.92525409 0.14949182 0.07474591
[2,] 0.85818948 0.28362105 0.14181052
[3,] 0.78434515 0.43130969 0.21565485
[4,] 0.69213704 0.61572593 0.30786296
[5,] 0.58421995 0.83156010 0.41578005
[6,] 0.48463488 0.96926977 0.51536512
[7,] 0.64242008 0.71515984 0.35757992
[8,] 0.65135341 0.69729317 0.34864659
[9,] 0.58443195 0.83113611 0.41556805
[10,] 0.53068064 0.93863873 0.46931936
[11,] 0.47595493 0.95190986 0.52404507
[12,] 0.43449445 0.86898890 0.56550555
[13,] 0.38626292 0.77252584 0.61373708
[14,] 0.57982589 0.84034822 0.42017411
[15,] 0.60054364 0.79891271 0.39945636
[16,] 0.55089432 0.89821137 0.44910568
[17,] 0.49328159 0.98656319 0.50671841
[18,] 0.43317963 0.86635926 0.56682037
[19,] 0.96751357 0.06497285 0.03248643
[20,] 0.97374030 0.05251940 0.02625970
[21,] 0.96303274 0.07393453 0.03696726
[22,] 0.95232277 0.09535445 0.04767723
[23,] 0.95131773 0.09736455 0.04868227
[24,] 0.96697079 0.06605842 0.03302921
[25,] 0.96322671 0.07354659 0.03677329
[26,] 0.97728841 0.04542319 0.02271159
[27,] 0.96829872 0.06340256 0.03170128
[28,] 0.96600460 0.06799080 0.03399540
[29,] 0.98151849 0.03696303 0.01848151
[30,] 0.97449082 0.05101836 0.02550918
[31,] 0.96626036 0.06747928 0.03373964
[32,] 0.95661651 0.08676697 0.04338349
[33,] 0.94765863 0.10468273 0.05234137
[34,] 0.93565010 0.12869980 0.06434990
[35,] 0.94315368 0.11369265 0.05684632
[36,] 0.92799429 0.14401142 0.07200571
[37,] 0.94169066 0.11661868 0.05830934
[38,] 0.92606345 0.14787311 0.07393655
[39,] 0.90764349 0.18471303 0.09235651
[40,] 0.89256378 0.21487243 0.10743622
[41,] 0.87075542 0.25848916 0.12924458
[42,] 0.84473689 0.31052623 0.15526311
[43,] 0.81765687 0.36468626 0.18234313
[44,] 0.78378939 0.43242122 0.21621061
[45,] 0.74499773 0.51000454 0.25500227
[46,] 0.75521727 0.48956547 0.24478273
[47,] 0.75786514 0.48426971 0.24213486
[48,] 0.71617770 0.56764460 0.28382230
[49,] 0.76490913 0.47018173 0.23509087
[50,] 0.76076345 0.47847311 0.23923655
[51,] 0.81013854 0.37972291 0.18986146
[52,] 0.77595061 0.44809878 0.22404939
[53,] 0.86095717 0.27808565 0.13904283
[54,] 0.83634074 0.32731853 0.16365926
[55,] 0.84021698 0.31956605 0.15978302
[56,] 0.81319955 0.37360090 0.18680045
[57,] 0.78389009 0.43221982 0.21610991
[58,] 0.74661075 0.50677851 0.25338925
[59,] 0.72155681 0.55688638 0.27844319
[60,] 0.70599669 0.58800661 0.29400331
[61,] 0.68626816 0.62746368 0.31373184
[62,] 0.65580225 0.68839551 0.34419775
[63,] 0.72077920 0.55844159 0.27922080
[64,] 0.68370996 0.63258008 0.31629004
[65,] 0.78450445 0.43099110 0.21549555
[66,] 0.74877205 0.50245590 0.25122795
[67,] 0.70719857 0.58560287 0.29280143
[68,] 0.70815536 0.58368929 0.29184464
[69,] 0.68029541 0.63940919 0.31970459
[70,] 0.73532812 0.52934376 0.26467188
[71,] 0.69561991 0.60876019 0.30438009
[72,] 0.65287349 0.69425302 0.34712651
[73,] 0.62642981 0.74714039 0.37357019
[74,] 0.60067696 0.79864607 0.39932304
[75,] 0.55333182 0.89333636 0.44666818
[76,] 0.50802639 0.98394723 0.49197361
[77,] 0.45598202 0.91196404 0.54401798
[78,] 0.42503407 0.85006813 0.57496593
[79,] 0.44013752 0.88027505 0.55986248
[80,] 0.44110580 0.88221160 0.55889420
[81,] 0.43741572 0.87483143 0.56258428
[82,] 0.59075137 0.81849727 0.40924863
[83,] 0.54976908 0.90046184 0.45023092
[84,] 0.55661535 0.88676929 0.44338465
[85,] 0.50664000 0.98672001 0.49336000
[86,] 0.47775761 0.95551521 0.52224239
[87,] 0.47348494 0.94696988 0.52651506
[88,] 0.44759725 0.89519449 0.55240275
[89,] 0.41363618 0.82727236 0.58636382
[90,] 0.39229630 0.78459259 0.60770370
[91,] 0.40668051 0.81336102 0.59331949
[92,] 0.43085676 0.86171353 0.56914324
[93,] 0.54758168 0.90483664 0.45241832
[94,] 0.59818742 0.80362516 0.40181258
[95,] 0.57547938 0.84904125 0.42452062
[96,] 0.52062733 0.95874535 0.47937267
[97,] 0.46986388 0.93972775 0.53013612
[98,] 0.41676201 0.83352402 0.58323799
[99,] 0.37193571 0.74387143 0.62806429
[100,] 0.36192475 0.72384951 0.63807525
[101,] 0.34940506 0.69881012 0.65059494
[102,] 0.29083705 0.58167410 0.70916295
[103,] 0.26786733 0.53573465 0.73213267
[104,] 0.26352857 0.52705714 0.73647143
[105,] 0.25493588 0.50987177 0.74506412
[106,] 0.23128006 0.46256012 0.76871994
[107,] 0.18750623 0.37501247 0.81249377
[108,] 0.16477027 0.32954053 0.83522973
[109,] 0.17396434 0.34792867 0.82603566
[110,] 0.25201507 0.50403015 0.74798493
[111,] 0.19625943 0.39251886 0.80374057
[112,] 0.14841599 0.29683197 0.85158401
[113,] 0.10748624 0.21497247 0.89251376
[114,] 0.09617154 0.19234307 0.90382846
[115,] 0.06306166 0.12612331 0.93693834
[116,] 0.04901951 0.09803901 0.95098049
[117,] 0.03517917 0.07035833 0.96482083
[118,] 0.02891854 0.05783707 0.97108146
[119,] 0.01535978 0.03071956 0.98464022
[120,] 0.00702238 0.01404476 0.99297762
[121,] 0.01248050 0.02496100 0.98751950
> postscript(file="/var/www/rcomp/tmp/1lpoy1290479068.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2ezn11290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3ezn11290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ezn11290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ezn11290479068.ps",horizontal=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 = 150
Frequency = 1
1 2 3 4 5 6
-2.92440338 0.24621162 3.59429162 1.21607205 -0.95057305 -2.34407800
7 8 9 10 11 12
4.22505476 -0.99934149 -1.73522493 0.49035615 0.85258210 0.98907801
13 14 15 16 17 18
1.18483652 0.29989643 3.45718254 -0.53701914 3.73657412 1.93283285
19 20 21 22 23 24
-0.23252910 2.05448342 3.69138430 1.38687929 0.92455535 1.83761644
25 26 27 28 29 30
1.88860188 1.24737921 -1.85250751 -0.85715490 -2.57248694 -1.10631703
31 32 33 34 35 36
0.47496933 -1.65860534 -7.12731726 -2.77463568 -0.15230051 -0.40663495
37 38 39 40 41 42
2.02962229 3.46333629 -1.55652924 1.63461661 -0.23897023 -1.23251917
43 44 45 46 47 48
-3.57256940 -0.45540800 0.48747992 -0.97625673 1.61391490 -1.06682040
49 50 51 52 53 54
-2.69885436 0.82091425 -3.16975461 -0.64231170 0.33348895 0.87157326
55 56 57 58 59 60
0.33332190 0.72444724 -1.10658700 -0.03347485 -0.02606620 -2.38200961
61 62 63 64 65 66
2.38511636 0.17617590 3.20305552 1.92273782 -3.00861584 -0.01205762
67 68 69 70 71 72
-3.92851226 0.82056694 2.12090449 0.80432105 0.82877321 0.39563354
73 74 75 76 77 78
1.33802629 -1.50230131 0.88530309 1.31307405 3.18246490 0.74213299
79 80 81 82 83 84
3.87748638 -0.58635052 0.28806050 0.30979007 1.22075795 -2.98703956
85 86 87 88 89 90
0.55649245 0.49930031 1.20033913 -1.38936918 0.41779513 0.68220618
91 92 93 94 95 96
0.05126804 -0.47730013 -1.24497135 1.64351537 2.17696960 3.92423768
97 98 99 100 101 102
-1.17493514 1.58090383 -0.06490886 1.09417625 2.08101688 -1.32586652
103 104 105 106 107 108
1.01035950 0.75464005 1.65771634 1.16978335 -3.69296769 1.99997725
109 110 111 112 113 114
-0.40765432 -0.69628348 -1.21911306 0.54021399 -0.38589243 0.98505888
115 116 117 118 119 120
-2.17987827 0.10714354 0.25287703 0.43272474 0.56289779 1.22880772
121 122 123 124 125 126
-1.05766822 -2.10039928 1.75474626 -4.54079688 1.00833279 -0.27291819
127 128 129 130 131 132
-1.22398577 -0.93112492 0.39435263 -3.25379901 -2.41041608 -5.96815609
133 134 135 136 137 138
0.02574530 -0.59480920 1.45711221 1.12090520 -5.20556097 3.83831417
139 140 141 142 143 144
-0.74303862 0.25803954 1.88860188 0.89100740 -0.20157000 -3.95232206
145 146 147 148 149 150
0.55649245 -2.14890446 -1.05766822 0.22507042 -3.53269276 -1.01796692
> postscript(file="/var/www/rcomp/tmp/6785m1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 150
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.92440338 NA
1 0.24621162 -2.92440338
2 3.59429162 0.24621162
3 1.21607205 3.59429162
4 -0.95057305 1.21607205
5 -2.34407800 -0.95057305
6 4.22505476 -2.34407800
7 -0.99934149 4.22505476
8 -1.73522493 -0.99934149
9 0.49035615 -1.73522493
10 0.85258210 0.49035615
11 0.98907801 0.85258210
12 1.18483652 0.98907801
13 0.29989643 1.18483652
14 3.45718254 0.29989643
15 -0.53701914 3.45718254
16 3.73657412 -0.53701914
17 1.93283285 3.73657412
18 -0.23252910 1.93283285
19 2.05448342 -0.23252910
20 3.69138430 2.05448342
21 1.38687929 3.69138430
22 0.92455535 1.38687929
23 1.83761644 0.92455535
24 1.88860188 1.83761644
25 1.24737921 1.88860188
26 -1.85250751 1.24737921
27 -0.85715490 -1.85250751
28 -2.57248694 -0.85715490
29 -1.10631703 -2.57248694
30 0.47496933 -1.10631703
31 -1.65860534 0.47496933
32 -7.12731726 -1.65860534
33 -2.77463568 -7.12731726
34 -0.15230051 -2.77463568
35 -0.40663495 -0.15230051
36 2.02962229 -0.40663495
37 3.46333629 2.02962229
38 -1.55652924 3.46333629
39 1.63461661 -1.55652924
40 -0.23897023 1.63461661
41 -1.23251917 -0.23897023
42 -3.57256940 -1.23251917
43 -0.45540800 -3.57256940
44 0.48747992 -0.45540800
45 -0.97625673 0.48747992
46 1.61391490 -0.97625673
47 -1.06682040 1.61391490
48 -2.69885436 -1.06682040
49 0.82091425 -2.69885436
50 -3.16975461 0.82091425
51 -0.64231170 -3.16975461
52 0.33348895 -0.64231170
53 0.87157326 0.33348895
54 0.33332190 0.87157326
55 0.72444724 0.33332190
56 -1.10658700 0.72444724
57 -0.03347485 -1.10658700
58 -0.02606620 -0.03347485
59 -2.38200961 -0.02606620
60 2.38511636 -2.38200961
61 0.17617590 2.38511636
62 3.20305552 0.17617590
63 1.92273782 3.20305552
64 -3.00861584 1.92273782
65 -0.01205762 -3.00861584
66 -3.92851226 -0.01205762
67 0.82056694 -3.92851226
68 2.12090449 0.82056694
69 0.80432105 2.12090449
70 0.82877321 0.80432105
71 0.39563354 0.82877321
72 1.33802629 0.39563354
73 -1.50230131 1.33802629
74 0.88530309 -1.50230131
75 1.31307405 0.88530309
76 3.18246490 1.31307405
77 0.74213299 3.18246490
78 3.87748638 0.74213299
79 -0.58635052 3.87748638
80 0.28806050 -0.58635052
81 0.30979007 0.28806050
82 1.22075795 0.30979007
83 -2.98703956 1.22075795
84 0.55649245 -2.98703956
85 0.49930031 0.55649245
86 1.20033913 0.49930031
87 -1.38936918 1.20033913
88 0.41779513 -1.38936918
89 0.68220618 0.41779513
90 0.05126804 0.68220618
91 -0.47730013 0.05126804
92 -1.24497135 -0.47730013
93 1.64351537 -1.24497135
94 2.17696960 1.64351537
95 3.92423768 2.17696960
96 -1.17493514 3.92423768
97 1.58090383 -1.17493514
98 -0.06490886 1.58090383
99 1.09417625 -0.06490886
100 2.08101688 1.09417625
101 -1.32586652 2.08101688
102 1.01035950 -1.32586652
103 0.75464005 1.01035950
104 1.65771634 0.75464005
105 1.16978335 1.65771634
106 -3.69296769 1.16978335
107 1.99997725 -3.69296769
108 -0.40765432 1.99997725
109 -0.69628348 -0.40765432
110 -1.21911306 -0.69628348
111 0.54021399 -1.21911306
112 -0.38589243 0.54021399
113 0.98505888 -0.38589243
114 -2.17987827 0.98505888
115 0.10714354 -2.17987827
116 0.25287703 0.10714354
117 0.43272474 0.25287703
118 0.56289779 0.43272474
119 1.22880772 0.56289779
120 -1.05766822 1.22880772
121 -2.10039928 -1.05766822
122 1.75474626 -2.10039928
123 -4.54079688 1.75474626
124 1.00833279 -4.54079688
125 -0.27291819 1.00833279
126 -1.22398577 -0.27291819
127 -0.93112492 -1.22398577
128 0.39435263 -0.93112492
129 -3.25379901 0.39435263
130 -2.41041608 -3.25379901
131 -5.96815609 -2.41041608
132 0.02574530 -5.96815609
133 -0.59480920 0.02574530
134 1.45711221 -0.59480920
135 1.12090520 1.45711221
136 -5.20556097 1.12090520
137 3.83831417 -5.20556097
138 -0.74303862 3.83831417
139 0.25803954 -0.74303862
140 1.88860188 0.25803954
141 0.89100740 1.88860188
142 -0.20157000 0.89100740
143 -3.95232206 -0.20157000
144 0.55649245 -3.95232206
145 -2.14890446 0.55649245
146 -1.05766822 -2.14890446
147 0.22507042 -1.05766822
148 -3.53269276 0.22507042
149 -1.01796692 -3.53269276
150 NA -1.01796692
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.24621162 -2.92440338
[2,] 3.59429162 0.24621162
[3,] 1.21607205 3.59429162
[4,] -0.95057305 1.21607205
[5,] -2.34407800 -0.95057305
[6,] 4.22505476 -2.34407800
[7,] -0.99934149 4.22505476
[8,] -1.73522493 -0.99934149
[9,] 0.49035615 -1.73522493
[10,] 0.85258210 0.49035615
[11,] 0.98907801 0.85258210
[12,] 1.18483652 0.98907801
[13,] 0.29989643 1.18483652
[14,] 3.45718254 0.29989643
[15,] -0.53701914 3.45718254
[16,] 3.73657412 -0.53701914
[17,] 1.93283285 3.73657412
[18,] -0.23252910 1.93283285
[19,] 2.05448342 -0.23252910
[20,] 3.69138430 2.05448342
[21,] 1.38687929 3.69138430
[22,] 0.92455535 1.38687929
[23,] 1.83761644 0.92455535
[24,] 1.88860188 1.83761644
[25,] 1.24737921 1.88860188
[26,] -1.85250751 1.24737921
[27,] -0.85715490 -1.85250751
[28,] -2.57248694 -0.85715490
[29,] -1.10631703 -2.57248694
[30,] 0.47496933 -1.10631703
[31,] -1.65860534 0.47496933
[32,] -7.12731726 -1.65860534
[33,] -2.77463568 -7.12731726
[34,] -0.15230051 -2.77463568
[35,] -0.40663495 -0.15230051
[36,] 2.02962229 -0.40663495
[37,] 3.46333629 2.02962229
[38,] -1.55652924 3.46333629
[39,] 1.63461661 -1.55652924
[40,] -0.23897023 1.63461661
[41,] -1.23251917 -0.23897023
[42,] -3.57256940 -1.23251917
[43,] -0.45540800 -3.57256940
[44,] 0.48747992 -0.45540800
[45,] -0.97625673 0.48747992
[46,] 1.61391490 -0.97625673
[47,] -1.06682040 1.61391490
[48,] -2.69885436 -1.06682040
[49,] 0.82091425 -2.69885436
[50,] -3.16975461 0.82091425
[51,] -0.64231170 -3.16975461
[52,] 0.33348895 -0.64231170
[53,] 0.87157326 0.33348895
[54,] 0.33332190 0.87157326
[55,] 0.72444724 0.33332190
[56,] -1.10658700 0.72444724
[57,] -0.03347485 -1.10658700
[58,] -0.02606620 -0.03347485
[59,] -2.38200961 -0.02606620
[60,] 2.38511636 -2.38200961
[61,] 0.17617590 2.38511636
[62,] 3.20305552 0.17617590
[63,] 1.92273782 3.20305552
[64,] -3.00861584 1.92273782
[65,] -0.01205762 -3.00861584
[66,] -3.92851226 -0.01205762
[67,] 0.82056694 -3.92851226
[68,] 2.12090449 0.82056694
[69,] 0.80432105 2.12090449
[70,] 0.82877321 0.80432105
[71,] 0.39563354 0.82877321
[72,] 1.33802629 0.39563354
[73,] -1.50230131 1.33802629
[74,] 0.88530309 -1.50230131
[75,] 1.31307405 0.88530309
[76,] 3.18246490 1.31307405
[77,] 0.74213299 3.18246490
[78,] 3.87748638 0.74213299
[79,] -0.58635052 3.87748638
[80,] 0.28806050 -0.58635052
[81,] 0.30979007 0.28806050
[82,] 1.22075795 0.30979007
[83,] -2.98703956 1.22075795
[84,] 0.55649245 -2.98703956
[85,] 0.49930031 0.55649245
[86,] 1.20033913 0.49930031
[87,] -1.38936918 1.20033913
[88,] 0.41779513 -1.38936918
[89,] 0.68220618 0.41779513
[90,] 0.05126804 0.68220618
[91,] -0.47730013 0.05126804
[92,] -1.24497135 -0.47730013
[93,] 1.64351537 -1.24497135
[94,] 2.17696960 1.64351537
[95,] 3.92423768 2.17696960
[96,] -1.17493514 3.92423768
[97,] 1.58090383 -1.17493514
[98,] -0.06490886 1.58090383
[99,] 1.09417625 -0.06490886
[100,] 2.08101688 1.09417625
[101,] -1.32586652 2.08101688
[102,] 1.01035950 -1.32586652
[103,] 0.75464005 1.01035950
[104,] 1.65771634 0.75464005
[105,] 1.16978335 1.65771634
[106,] -3.69296769 1.16978335
[107,] 1.99997725 -3.69296769
[108,] -0.40765432 1.99997725
[109,] -0.69628348 -0.40765432
[110,] -1.21911306 -0.69628348
[111,] 0.54021399 -1.21911306
[112,] -0.38589243 0.54021399
[113,] 0.98505888 -0.38589243
[114,] -2.17987827 0.98505888
[115,] 0.10714354 -2.17987827
[116,] 0.25287703 0.10714354
[117,] 0.43272474 0.25287703
[118,] 0.56289779 0.43272474
[119,] 1.22880772 0.56289779
[120,] -1.05766822 1.22880772
[121,] -2.10039928 -1.05766822
[122,] 1.75474626 -2.10039928
[123,] -4.54079688 1.75474626
[124,] 1.00833279 -4.54079688
[125,] -0.27291819 1.00833279
[126,] -1.22398577 -0.27291819
[127,] -0.93112492 -1.22398577
[128,] 0.39435263 -0.93112492
[129,] -3.25379901 0.39435263
[130,] -2.41041608 -3.25379901
[131,] -5.96815609 -2.41041608
[132,] 0.02574530 -5.96815609
[133,] -0.59480920 0.02574530
[134,] 1.45711221 -0.59480920
[135,] 1.12090520 1.45711221
[136,] -5.20556097 1.12090520
[137,] 3.83831417 -5.20556097
[138,] -0.74303862 3.83831417
[139,] 0.25803954 -0.74303862
[140,] 1.88860188 0.25803954
[141,] 0.89100740 1.88860188
[142,] -0.20157000 0.89100740
[143,] -3.95232206 -0.20157000
[144,] 0.55649245 -3.95232206
[145,] -2.14890446 0.55649245
[146,] -1.05766822 -2.14890446
[147,] 0.22507042 -1.05766822
[148,] -3.53269276 0.22507042
[149,] -1.01796692 -3.53269276
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.24621162 -2.92440338
2 3.59429162 0.24621162
3 1.21607205 3.59429162
4 -0.95057305 1.21607205
5 -2.34407800 -0.95057305
6 4.22505476 -2.34407800
7 -0.99934149 4.22505476
8 -1.73522493 -0.99934149
9 0.49035615 -1.73522493
10 0.85258210 0.49035615
11 0.98907801 0.85258210
12 1.18483652 0.98907801
13 0.29989643 1.18483652
14 3.45718254 0.29989643
15 -0.53701914 3.45718254
16 3.73657412 -0.53701914
17 1.93283285 3.73657412
18 -0.23252910 1.93283285
19 2.05448342 -0.23252910
20 3.69138430 2.05448342
21 1.38687929 3.69138430
22 0.92455535 1.38687929
23 1.83761644 0.92455535
24 1.88860188 1.83761644
25 1.24737921 1.88860188
26 -1.85250751 1.24737921
27 -0.85715490 -1.85250751
28 -2.57248694 -0.85715490
29 -1.10631703 -2.57248694
30 0.47496933 -1.10631703
31 -1.65860534 0.47496933
32 -7.12731726 -1.65860534
33 -2.77463568 -7.12731726
34 -0.15230051 -2.77463568
35 -0.40663495 -0.15230051
36 2.02962229 -0.40663495
37 3.46333629 2.02962229
38 -1.55652924 3.46333629
39 1.63461661 -1.55652924
40 -0.23897023 1.63461661
41 -1.23251917 -0.23897023
42 -3.57256940 -1.23251917
43 -0.45540800 -3.57256940
44 0.48747992 -0.45540800
45 -0.97625673 0.48747992
46 1.61391490 -0.97625673
47 -1.06682040 1.61391490
48 -2.69885436 -1.06682040
49 0.82091425 -2.69885436
50 -3.16975461 0.82091425
51 -0.64231170 -3.16975461
52 0.33348895 -0.64231170
53 0.87157326 0.33348895
54 0.33332190 0.87157326
55 0.72444724 0.33332190
56 -1.10658700 0.72444724
57 -0.03347485 -1.10658700
58 -0.02606620 -0.03347485
59 -2.38200961 -0.02606620
60 2.38511636 -2.38200961
61 0.17617590 2.38511636
62 3.20305552 0.17617590
63 1.92273782 3.20305552
64 -3.00861584 1.92273782
65 -0.01205762 -3.00861584
66 -3.92851226 -0.01205762
67 0.82056694 -3.92851226
68 2.12090449 0.82056694
69 0.80432105 2.12090449
70 0.82877321 0.80432105
71 0.39563354 0.82877321
72 1.33802629 0.39563354
73 -1.50230131 1.33802629
74 0.88530309 -1.50230131
75 1.31307405 0.88530309
76 3.18246490 1.31307405
77 0.74213299 3.18246490
78 3.87748638 0.74213299
79 -0.58635052 3.87748638
80 0.28806050 -0.58635052
81 0.30979007 0.28806050
82 1.22075795 0.30979007
83 -2.98703956 1.22075795
84 0.55649245 -2.98703956
85 0.49930031 0.55649245
86 1.20033913 0.49930031
87 -1.38936918 1.20033913
88 0.41779513 -1.38936918
89 0.68220618 0.41779513
90 0.05126804 0.68220618
91 -0.47730013 0.05126804
92 -1.24497135 -0.47730013
93 1.64351537 -1.24497135
94 2.17696960 1.64351537
95 3.92423768 2.17696960
96 -1.17493514 3.92423768
97 1.58090383 -1.17493514
98 -0.06490886 1.58090383
99 1.09417625 -0.06490886
100 2.08101688 1.09417625
101 -1.32586652 2.08101688
102 1.01035950 -1.32586652
103 0.75464005 1.01035950
104 1.65771634 0.75464005
105 1.16978335 1.65771634
106 -3.69296769 1.16978335
107 1.99997725 -3.69296769
108 -0.40765432 1.99997725
109 -0.69628348 -0.40765432
110 -1.21911306 -0.69628348
111 0.54021399 -1.21911306
112 -0.38589243 0.54021399
113 0.98505888 -0.38589243
114 -2.17987827 0.98505888
115 0.10714354 -2.17987827
116 0.25287703 0.10714354
117 0.43272474 0.25287703
118 0.56289779 0.43272474
119 1.22880772 0.56289779
120 -1.05766822 1.22880772
121 -2.10039928 -1.05766822
122 1.75474626 -2.10039928
123 -4.54079688 1.75474626
124 1.00833279 -4.54079688
125 -0.27291819 1.00833279
126 -1.22398577 -0.27291819
127 -0.93112492 -1.22398577
128 0.39435263 -0.93112492
129 -3.25379901 0.39435263
130 -2.41041608 -3.25379901
131 -5.96815609 -2.41041608
132 0.02574530 -5.96815609
133 -0.59480920 0.02574530
134 1.45711221 -0.59480920
135 1.12090520 1.45711221
136 -5.20556097 1.12090520
137 3.83831417 -5.20556097
138 -0.74303862 3.83831417
139 0.25803954 -0.74303862
140 1.88860188 0.25803954
141 0.89100740 1.88860188
142 -0.20157000 0.89100740
143 -3.95232206 -0.20157000
144 0.55649245 -3.95232206
145 -2.14890446 0.55649245
146 -1.05766822 -2.14890446
147 0.22507042 -1.05766822
148 -3.53269276 0.22507042
149 -1.01796692 -3.53269276
> 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/rcomp/tmp/70hmp1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/80hmp1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/90hmp1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10s8ls1290479068.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11hamv1290479069.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/rcomp/tmp/1291lg1290479069.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/rcomp/tmp/13ykia1290479069.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/rcomp/tmp/141lgy1290479069.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/rcomp/tmp/15ucxj1290479069.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/rcomp/tmp/16q4da1290479069.tab")
+ }
>
> try(system("convert tmp/1lpoy1290479068.ps tmp/1lpoy1290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ezn11290479068.ps tmp/2ezn11290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ezn11290479068.ps tmp/3ezn11290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ezn11290479068.ps tmp/4ezn11290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ezn11290479068.ps tmp/5ezn11290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/6785m1290479068.ps tmp/6785m1290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/70hmp1290479068.ps tmp/70hmp1290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/80hmp1290479068.ps tmp/80hmp1290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/90hmp1290479068.ps tmp/90hmp1290479068.png",intern=TRUE))
character(0)
> try(system("convert tmp/10s8ls1290479068.ps tmp/10s8ls1290479068.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.00 1.81 7.82