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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+ ,4
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+ ,23
+ ,1
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+ ,16
+ ,16
+ ,9
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+ ,9
+ ,9
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+ ,0
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+ ,0
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+ ,0
+ ,1
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+ ,16
+ ,16
+ ,9
+ ,9
+ ,12
+ ,12
+ ,9
+ ,9
+ ,10
+ ,10
+ ,13
+ ,13)
+ ,dim=c(14
+ ,152)
+ ,dimnames=list(c('Gender'
+ ,'Anxiety'
+ ,'Concern'
+ ,'Concern_G'
+ ,'Doubts'
+ ,'Doubts_G'
+ ,'Expectations'
+ ,'Expectations_G'
+ ,'Criticism'
+ ,'Criticism_G'
+ ,'Perstandards'
+ ,'Perstandards_G'
+ ,'Organization'
+ ,'Organization_G')
+ ,1:152))
> y <- array(NA,dim=c(14,152),dimnames=list(c('Gender','Anxiety','Concern','Concern_G','Doubts','Doubts_G','Expectations','Expectations_G','Criticism','Criticism_G','Perstandards','Perstandards_G','Organization','Organization_G'),1:152))
> 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
Anxiety Gender Concern Concern_G Doubts Doubts_G Expectations
1 69 0 26 0 9 0 15
2 53 1 20 20 9 9 15
3 43 1 21 21 9 9 14
4 60 0 31 0 14 0 10
5 49 1 21 21 8 8 10
6 62 1 18 18 8 8 12
7 45 1 26 26 11 11 18
8 50 1 22 22 10 10 12
9 75 1 22 22 9 9 14
10 82 1 29 29 15 15 18
11 60 0 15 0 14 0 9
12 59 1 16 16 11 11 11
13 21 1 24 24 14 14 11
14 62 1 17 17 6 6 17
15 54 0 19 0 20 0 8
16 47 1 22 22 9 9 16
17 59 1 31 31 10 10 21
18 37 0 28 0 8 0 24
19 43 0 38 0 11 0 21
20 48 1 26 26 14 14 14
21 79 0 25 0 11 0 7
22 62 0 25 0 16 0 18
23 16 1 29 29 14 14 18
24 38 0 28 0 11 0 13
25 58 1 15 15 11 11 11
26 60 0 18 0 12 0 13
27 67 0 21 0 9 0 13
28 55 0 25 0 7 0 18
29 47 1 23 23 13 13 14
30 59 0 23 0 10 0 12
31 49 1 19 19 9 9 9
32 47 0 18 0 9 0 12
33 57 1 18 18 13 13 8
34 39 0 26 0 16 0 5
35 49 1 18 18 12 12 10
36 26 1 18 18 6 6 11
37 53 0 28 0 14 0 11
38 75 0 17 0 14 0 12
39 65 1 29 29 10 10 12
40 49 1 12 12 4 4 15
41 48 0 25 0 12 0 12
42 45 0 28 0 12 0 16
43 31 0 20 0 14 0 14
44 61 1 17 17 9 9 17
45 49 1 17 17 9 9 13
46 69 1 20 20 10 10 10
47 54 0 31 0 14 0 17
48 80 0 21 0 10 0 12
49 57 0 19 0 9 0 13
50 34 0 23 0 14 0 13
51 69 0 15 0 8 0 11
52 44 1 24 24 9 9 13
53 70 0 28 0 8 0 12
54 51 0 16 0 9 0 12
55 66 1 19 19 9 9 12
56 18 1 21 21 9 9 9
57 74 1 21 21 15 15 7
58 59 1 20 20 8 8 17
59 48 0 16 0 10 0 12
60 55 1 25 25 8 8 12
61 44 0 30 0 14 0 9
62 56 0 29 0 11 0 9
63 65 0 22 0 10 0 13
64 77 0 19 0 12 0 10
65 46 1 33 33 14 14 11
66 70 0 17 0 9 0 12
67 39 1 9 9 13 13 10
68 55 0 14 0 15 0 13
69 44 0 15 0 8 0 6
70 45 0 12 0 7 0 7
71 45 1 21 21 10 10 13
72 49 0 20 0 10 0 11
73 65 1 29 29 13 13 18
74 45 0 33 0 11 0 9
75 71 0 21 0 8 0 9
76 48 1 15 15 12 12 11
77 41 1 19 19 9 9 11
78 40 0 23 0 10 0 15
79 64 1 20 20 11 11 8
80 56 0 20 0 11 0 11
81 52 0 18 0 10 0 14
82 41 1 31 31 16 16 14
83 42 1 18 18 16 16 12
84 54 0 13 0 8 0 12
85 40 1 9 9 6 6 8
86 40 1 20 20 11 11 11
87 51 0 18 0 12 0 10
88 48 1 23 23 14 14 17
89 80 0 17 0 9 0 16
90 38 0 17 0 11 0 13
91 57 0 16 0 8 0 15
92 28 1 31 31 8 8 11
93 51 1 15 15 7 7 12
94 46 1 28 28 16 16 16
95 58 1 26 26 13 13 20
96 67 1 20 20 8 8 16
97 72 1 19 19 11 11 11
98 26 1 25 25 14 14 15
99 54 1 18 18 10 10 15
100 53 0 20 0 10 0 12
101 64 1 33 33 14 14 9
102 47 1 24 24 14 14 24
103 43 1 22 22 10 10 15
104 66 1 32 32 12 12 18
105 54 1 31 31 9 9 17
106 62 1 13 13 16 16 12
107 52 1 18 18 8 8 15
108 64 1 17 17 9 9 11
109 55 1 29 29 16 16 11
110 57 0 22 0 13 0 15
111 74 1 18 18 13 13 12
112 32 1 22 22 8 8 14
113 38 1 25 25 14 14 11
114 66 1 20 20 11 11 20
115 37 0 20 0 9 0 11
116 26 1 17 17 8 8 12
117 64 1 21 21 13 13 17
118 28 1 26 26 13 13 12
119 66 0 10 0 10 0 11
120 65 1 15 15 8 8 10
121 48 1 20 20 7 7 11
122 44 1 14 14 11 11 12
123 64 0 16 0 11 0 9
124 39 1 23 23 14 14 8
125 50 1 11 11 6 6 6
126 66 1 19 19 10 10 12
127 48 0 30 0 9 0 15
128 70 0 21 0 12 0 13
129 66 0 20 0 11 0 17
130 61 1 22 22 14 14 14
131 31 1 30 30 12 12 16
132 61 0 25 0 14 0 15
133 54 1 28 28 8 8 16
134 34 1 23 23 14 14 11
135 62 0 23 0 8 0 11
136 47 1 21 21 11 11 16
137 52 1 30 30 12 12 15
138 37 0 22 0 9 0 14
139 46 1 32 32 16 16 9
140 38 0 22 0 11 0 13
141 63 1 15 15 11 11 11
142 34 0 21 0 12 0 14
143 46 1 27 27 15 15 11
144 40 1 22 22 13 13 12
145 30 1 9 9 6 6 8
146 35 1 29 29 11 11 7
147 51 1 20 20 7 7 11
148 56 1 16 16 8 8 13
149 68 1 16 16 8 8 9
150 39 1 16 16 9 9 12
151 44 0 18 0 12 0 10
152 58 1 16 16 9 9 12
Expectations_G Criticism Criticism_G Perstandards Perstandards_G
1 0 6 0 25 0
2 15 6 6 25 25
3 14 13 13 19 19
4 0 8 0 18 0
5 10 7 7 18 18
6 12 9 9 22 22
7 18 5 5 29 29
8 12 8 8 26 26
9 14 9 9 25 25
10 18 11 11 23 23
11 0 8 0 23 0
12 11 11 11 23 23
13 11 12 12 24 24
14 17 8 8 30 30
15 0 7 0 19 0
16 16 9 9 24 24
17 21 12 12 32 32
18 0 20 0 30 0
19 0 7 0 29 0
20 14 8 8 17 17
21 0 8 0 25 0
22 0 16 0 26 0
23 18 10 10 26 26
24 0 6 0 25 0
25 11 8 8 23 23
26 0 9 0 21 0
27 0 9 0 19 0
28 0 11 0 35 0
29 14 12 12 19 19
30 0 8 0 20 0
31 9 7 7 21 21
32 0 8 0 21 0
33 8 9 9 24 24
34 0 4 0 23 0
35 10 8 8 19 19
36 11 8 8 17 17
37 0 8 0 24 0
38 0 6 0 15 0
39 12 8 8 25 25
40 15 4 4 27 27
41 0 7 0 29 0
42 0 14 0 27 0
43 0 10 0 18 0
44 17 9 9 25 25
45 13 6 6 22 22
46 10 8 8 26 26
47 0 11 0 23 0
48 0 8 0 16 0
49 0 8 0 27 0
50 0 10 0 25 0
51 0 8 0 14 0
52 13 10 10 19 19
53 0 7 0 20 0
54 0 8 0 16 0
55 12 7 7 18 18
56 9 9 9 22 22
57 7 5 5 21 21
58 17 7 7 22 22
59 0 7 0 22 0
60 12 7 7 32 32
61 0 9 0 23 0
62 0 5 0 31 0
63 0 8 0 18 0
64 0 8 0 23 0
65 11 8 8 26 26
66 0 9 0 24 0
67 10 6 6 19 19
68 0 8 0 14 0
69 0 6 0 20 0
70 0 4 0 22 0
71 13 6 6 24 24
72 0 4 0 25 0
73 18 12 12 21 21
74 0 6 0 28 0
75 0 11 0 24 0
76 11 8 8 20 20
77 11 10 10 21 21
78 0 10 0 23 0
79 8 4 4 13 13
80 0 8 0 24 0
81 0 9 0 21 0
82 14 9 9 21 21
83 12 7 7 17 17
84 0 7 0 14 0
85 8 11 11 29 29
86 11 8 8 25 25
87 0 8 0 16 0
88 17 7 7 25 25
89 0 5 0 25 0
90 0 7 0 21 0
91 0 9 0 23 0
92 11 8 8 22 22
93 12 6 6 19 19
94 16 8 8 24 24
95 20 10 10 26 26
96 16 10 10 25 25
97 11 8 8 20 20
98 15 11 11 22 22
99 15 8 8 14 14
100 0 8 0 20 0
101 9 6 6 32 32
102 24 20 20 21 21
103 15 6 6 22 22
104 18 12 12 28 28
105 17 9 9 25 25
106 12 5 5 17 17
107 15 10 10 21 21
108 11 5 5 23 23
109 11 6 6 27 27
110 0 10 0 22 0
111 12 6 6 19 19
112 14 10 10 20 20
113 11 5 5 17 17
114 20 13 13 24 24
115 0 7 0 21 0
116 12 9 9 21 21
117 17 11 11 23 23
118 12 8 8 24 24
119 0 5 0 19 0
120 10 4 4 22 22
121 11 9 9 26 26
122 12 7 7 17 17
123 0 5 0 17 0
124 8 5 5 19 19
125 6 4 4 15 15
126 12 7 7 17 17
127 0 9 0 27 0
128 0 8 0 19 0
129 0 8 0 21 0
130 14 11 11 25 25
131 16 10 10 19 19
132 0 9 0 22 0
133 16 12 12 18 18
134 11 10 10 20 20
135 0 10 0 15 0
136 16 7 7 20 20
137 15 10 10 29 29
138 0 6 0 19 0
139 9 6 6 29 29
140 0 11 0 24 0
141 11 8 8 23 23
142 0 9 0 22 0
143 11 9 9 23 23
144 12 13 13 22 22
145 8 11 11 29 29
146 7 4 4 26 26
147 11 9 9 26 26
148 13 5 5 21 21
149 9 4 4 18 18
150 12 9 9 10 10
151 0 8 0 19 0
152 12 9 9 10 10
Organization Organization_G
1 25 0
2 24 24
3 21 21
4 23 0
5 17 17
6 19 19
7 18 18
8 27 27
9 23 23
10 23 23
11 29 0
12 21 21
13 26 26
14 25 25
15 25 0
16 23 23
17 26 26
18 20 0
19 29 0
20 24 24
21 23 0
22 24 0
23 30 30
24 22 0
25 22 22
26 13 0
27 24 0
28 17 0
29 24 24
30 21 0
31 23 23
32 24 0
33 24 24
34 24 0
35 23 23
36 26 26
37 24 0
38 21 0
39 23 23
40 28 28
41 23 0
42 22 0
43 24 0
44 21 21
45 23 23
46 23 23
47 20 0
48 23 0
49 21 0
50 27 0
51 12 0
52 15 15
53 22 0
54 21 0
55 21 21
56 20 20
57 24 24
58 24 24
59 29 0
60 25 25
61 14 0
62 30 0
63 19 0
64 29 0
65 25 25
66 25 0
67 25 25
68 16 0
69 25 0
70 28 0
71 24 24
72 25 0
73 21 21
74 22 0
75 20 0
76 25 25
77 27 27
78 21 0
79 13 13
80 26 0
81 26 0
82 25 25
83 22 22
84 19 0
85 23 23
86 25 25
87 15 0
88 21 21
89 23 0
90 25 0
91 24 0
92 24 24
93 21 21
94 24 24
95 22 22
96 24 24
97 28 28
98 21 21
99 17 17
100 28 0
101 24 24
102 10 10
103 20 20
104 22 22
105 19 19
106 22 22
107 22 22
108 26 26
109 24 24
110 22 0
111 20 20
112 20 20
113 15 15
114 20 20
115 20 0
116 24 24
117 22 22
118 29 29
119 23 0
120 24 24
121 22 22
122 16 16
123 23 0
124 27 27
125 16 16
126 21 21
127 26 0
128 22 0
129 23 0
130 19 19
131 18 18
132 24 0
133 24 24
134 29 29
135 22 0
136 24 24
137 22 22
138 12 0
139 26 26
140 18 0
141 22 22
142 24 0
143 21 21
144 15 15
145 23 23
146 22 22
147 22 22
148 24 24
149 23 23
150 13 13
151 23 0
152 13 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Concern Concern_G Doubts
72.67601 -18.28435 -0.08862 -0.39749 -0.60321
Doubts_G Expectations Expectations_G Criticism Criticism_G
1.07713 -0.03912 1.21867 -0.13902 -1.39669
Perstandards Perstandards_G Organization Organization_G
-0.61531 1.19280 0.24899 -0.87790
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-32.9513 -8.9276 0.7461 9.1139 31.4405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 72.67601 14.77301 4.920 2.43e-06 ***
Gender -18.28435 18.84001 -0.971 0.3335
Concern -0.08862 0.39118 -0.227 0.8211
Concern_G -0.39749 0.50060 -0.794 0.4285
Doubts -0.60321 0.72433 -0.833 0.4064
Doubts_G 1.07713 0.91912 1.172 0.2433
Expectations -0.03912 0.68080 -0.057 0.9543
Expectations_G 1.21867 0.84632 1.440 0.1521
Criticism -0.13902 0.84518 -0.164 0.8696
Criticism_G -1.39669 1.05450 -1.325 0.1875
Perstandards -0.61531 0.52102 -1.181 0.2396
Perstandards_G 1.19280 0.65111 1.832 0.0691 .
Organization 0.24899 0.46437 0.536 0.5927
Organization_G -0.87790 0.63550 -1.381 0.1694
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.17 on 138 degrees of freedom
Multiple R-squared: 0.1205, Adjusted R-squared: 0.03766
F-statistic: 1.455 on 13 and 138 DF, p-value: 0.1424
> 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,] 0.9930112 0.01397762 0.00698881
[2,] 0.9840925 0.03181500 0.01590750
[3,] 0.9676119 0.06477614 0.03238807
[4,] 0.9465042 0.10699151 0.05349575
[5,] 0.9248632 0.15027359 0.07513679
[6,] 0.9474767 0.10504669 0.05252335
[7,] 0.9867853 0.02642950 0.01321475
[8,] 0.9889676 0.02206486 0.01103243
[9,] 0.9820041 0.03599188 0.01799594
[10,] 0.9780169 0.04396629 0.02198315
[11,] 0.9667085 0.06658296 0.03329148
[12,] 0.9512274 0.09754523 0.04877262
[13,] 0.9301488 0.13970234 0.06985117
[14,] 0.9034151 0.19316976 0.09658488
[15,] 0.8755026 0.24899479 0.12449739
[16,] 0.8740627 0.25187461 0.12593731
[17,] 0.8646896 0.27062090 0.13531045
[18,] 0.8996695 0.20066099 0.10033049
[19,] 0.8675456 0.26490884 0.13245442
[20,] 0.8582051 0.28358976 0.14179488
[21,] 0.8194157 0.36116853 0.18058426
[22,] 0.8295876 0.34082475 0.17041238
[23,] 0.8578277 0.28434461 0.14217231
[24,] 0.8373346 0.32533083 0.16266542
[25,] 0.7985302 0.40293951 0.20146976
[26,] 0.7603362 0.47932750 0.23966375
[27,] 0.8588422 0.28231568 0.14115784
[28,] 0.8234368 0.35312643 0.17656322
[29,] 0.7874867 0.42502661 0.21251331
[30,] 0.8011540 0.39769201 0.19884600
[31,] 0.7646318 0.47073635 0.23536817
[32,] 0.7926980 0.41460401 0.20730201
[33,] 0.7520619 0.49587616 0.24793808
[34,] 0.7724853 0.45502946 0.22751473
[35,] 0.7588442 0.48231158 0.24115579
[36,] 0.7615609 0.47687822 0.23843911
[37,] 0.7480024 0.50399510 0.25199755
[38,] 0.7423374 0.51532512 0.25766256
[39,] 0.7690873 0.46182535 0.23091268
[40,] 0.9002877 0.19942455 0.09971228
[41,] 0.9404168 0.11916635 0.05958317
[42,] 0.9271340 0.14573204 0.07286602
[43,] 0.9174078 0.16518441 0.08259220
[44,] 0.8962015 0.20759707 0.10379854
[45,] 0.8869029 0.22619415 0.11309707
[46,] 0.8637104 0.27257914 0.13628957
[47,] 0.8501496 0.29970086 0.14985043
[48,] 0.8830755 0.23384891 0.11692446
[49,] 0.8570329 0.28593427 0.14296713
[50,] 0.8527283 0.29454330 0.14727165
[51,] 0.8661588 0.26768237 0.13384119
[52,] 0.8364994 0.32700116 0.16350058
[53,] 0.8609061 0.27818775 0.13909387
[54,] 0.8710285 0.25794305 0.12897152
[55,] 0.8572980 0.28540406 0.14270203
[56,] 0.8340137 0.33197263 0.16598631
[57,] 0.8451429 0.30971412 0.15485706
[58,] 0.8181215 0.36375708 0.18187854
[59,] 0.8879868 0.22402632 0.11201316
[60,] 0.8618306 0.27633887 0.13816943
[61,] 0.8318775 0.33624496 0.16812248
[62,] 0.8233792 0.35324168 0.17662084
[63,] 0.8230339 0.35393221 0.17696611
[64,] 0.7983419 0.40331621 0.20165811
[65,] 0.7665436 0.46691289 0.23345644
[66,] 0.7321809 0.53563820 0.26781910
[67,] 0.7174159 0.56516814 0.28258407
[68,] 0.6957500 0.60849994 0.30424997
[69,] 0.6711732 0.65765364 0.32882682
[70,] 0.6500603 0.69987947 0.34993974
[71,] 0.6193860 0.76122790 0.38061395
[72,] 0.6353104 0.72937924 0.36468962
[73,] 0.7442963 0.51140741 0.25570371
[74,] 0.7905779 0.41884422 0.20942211
[75,] 0.7522547 0.49549063 0.24774531
[76,] 0.7445472 0.51090563 0.25545282
[77,] 0.7047995 0.59040096 0.29520048
[78,] 0.6827415 0.63451704 0.31725852
[79,] 0.6403398 0.71932037 0.35966018
[80,] 0.6369822 0.72603559 0.36301780
[81,] 0.7936920 0.41261600 0.20630800
[82,] 0.8667569 0.26648619 0.13324310
[83,] 0.8342999 0.33140030 0.16570015
[84,] 0.8149952 0.37000963 0.18500481
[85,] 0.8353575 0.32928493 0.16464246
[86,] 0.8098771 0.38024582 0.19012291
[87,] 0.8487400 0.30252001 0.15126000
[88,] 0.8618876 0.27622479 0.13811239
[89,] 0.8268520 0.34629608 0.17314804
[90,] 0.7948670 0.41026599 0.20513299
[91,] 0.7502467 0.49950656 0.24975328
[92,] 0.7222894 0.55542124 0.27771062
[93,] 0.6761551 0.64768989 0.32384494
[94,] 0.6257933 0.74841345 0.37420673
[95,] 0.6670744 0.66585113 0.33292556
[96,] 0.6897631 0.62047372 0.31023686
[97,] 0.7086219 0.58275618 0.29137809
[98,] 0.6679385 0.66412292 0.33206146
[99,] 0.6468869 0.70622623 0.35311312
[100,] 0.7472527 0.50549467 0.25274733
[101,] 0.7221393 0.55572134 0.27786067
[102,] 0.7641640 0.47167200 0.23583600
[103,] 0.7308267 0.53834658 0.26917329
[104,] 0.6931717 0.61365662 0.30682831
[105,] 0.6241982 0.75160362 0.37580181
[106,] 0.6141925 0.77161508 0.38580754
[107,] 0.5944303 0.81113950 0.40556975
[108,] 0.5374787 0.92504256 0.46252128
[109,] 0.4558318 0.91166362 0.54416819
[110,] 0.4558806 0.91176120 0.54411940
[111,] 0.6314883 0.73702332 0.36851166
[112,] 0.5458983 0.90820349 0.45410175
[113,] 0.4509367 0.90187337 0.54906331
[114,] 0.4513796 0.90275917 0.54862042
[115,] 0.5370936 0.92581280 0.46290640
[116,] 0.4196056 0.83921118 0.58039441
[117,] 0.3871099 0.77421986 0.61289007
[118,] 0.2685994 0.53719885 0.73140057
[119,] 0.1571519 0.31430387 0.84284806
> postscript(file="/var/www/rcomp/tmp/1zflz1290513336.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2ap221290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ap221290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ap221290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ap221290513336.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 = 152
Frequency = 1
1 2 3 4 5 6
14.6361815 -3.7568885 0.2368686 5.3685842 0.2765607 11.4784427
7 8 9 10 11 12
-20.9458584 1.6607135 24.3730718 31.4405121 5.4941358 11.0157647
13 14 15 16 17 18
-20.4143305 3.6603524 1.8244719 -5.4085313 6.4687733 -11.1699938
19 20 21 22 23 24
-7.2548639 0.6610049 26.2170153 14.1418726 -32.9513194 -14.3114319
25 26 27 28 29 30
5.5514576 7.6022190 9.0889885 8.2985634 3.6644469 3.0535780
31 32 33 34 35 36
2.0510051 -10.1243963 10.8166267 -12.7922041 1.6542683 -16.6400335
37 38 39 40 41 42
1.5847169 17.5801088 18.1252950 -10.9868725 -1.6619347 -4.2481068
43 44 45 46 47 48
-24.4207387 3.1460636 -5.7525816 19.5319220 3.8829919 20.9171176
49 50 51 52 53 54
4.4421696 -17.6337692 9.6480432 -4.5058719 12.9022653 -8.6312422
55 56 57 58 59 60
15.9869980 -27.3696024 25.0962952 3.6261063 -9.4670588 2.8084251
61 62 63 64 65 66
-5.3027241 5.1816744 8.2714247 21.6813221 1.0339375 14.5229439
67 68 69 70 71 72
-15.0082060 -1.1357795 -15.3705363 -14.9948629 -8.8081248 -5.7268610
73 74 75 76 77 78
16.8211984 -5.1788692 16.6798147 -1.3032561 -1.1853057 -13.7050933
79 80 81 82 83 84
11.4924333 1.5681192 -4.8018939 -5.0016179 -10.6101510 -7.3719930
85 86 87 88 89 90
-7.6858442 -9.2862248 -5.2286863 -12.3782975 25.2366490 -18.3554706
91 92 93 94 95 96
0.5821455 -13.4137279 -2.1227861 -7.7161123 0.6738586 15.6803068
97 98 99 100 101 102
27.0018460 -23.1717129 0.8183212 -4.9551873 14.2277835 -5.7929689
103 104 105 106 107 108
-14.0417970 16.3399562 1.6937499 3.8878970 1.9397477 11.3800645
109 110 111 112 113 114
2.8638509 4.1516294 18.8631008 -15.6166195 -16.5538280 11.2093039
115 116 117 118 119 120
-19.1293466 -21.2855996 11.0501140 -17.4038094 7.3320271 10.8452621
121 122 123 124 125 126
-0.3190726 -11.9584786 5.1581152 -6.5953917 -1.4220760 16.0905639
127 128 129 130 131 132
-4.6106422 14.2575851 10.7038709 8.5592156 -17.6628685 8.3837271
133 134 135 136 137 138
13.6829881 -6.7751556 1.7605197 -6.9750292 1.2574773 -18.2124921
139 140 141 142 143 144
-2.2158750 -13.7674637 10.5514576 -20.2163151 -1.6041338 -7.3195699
145 146 147 148 149 150
-17.6858442 -13.8001238 2.6809274 0.9059277 17.1919444 -9.8113552
151 152
-12.3746343 9.1886448
> postscript(file="/var/www/rcomp/tmp/6lg151290513336.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 = 152
Frequency = 1
lag(myerror, k = 1) myerror
0 14.6361815 NA
1 -3.7568885 14.6361815
2 0.2368686 -3.7568885
3 5.3685842 0.2368686
4 0.2765607 5.3685842
5 11.4784427 0.2765607
6 -20.9458584 11.4784427
7 1.6607135 -20.9458584
8 24.3730718 1.6607135
9 31.4405121 24.3730718
10 5.4941358 31.4405121
11 11.0157647 5.4941358
12 -20.4143305 11.0157647
13 3.6603524 -20.4143305
14 1.8244719 3.6603524
15 -5.4085313 1.8244719
16 6.4687733 -5.4085313
17 -11.1699938 6.4687733
18 -7.2548639 -11.1699938
19 0.6610049 -7.2548639
20 26.2170153 0.6610049
21 14.1418726 26.2170153
22 -32.9513194 14.1418726
23 -14.3114319 -32.9513194
24 5.5514576 -14.3114319
25 7.6022190 5.5514576
26 9.0889885 7.6022190
27 8.2985634 9.0889885
28 3.6644469 8.2985634
29 3.0535780 3.6644469
30 2.0510051 3.0535780
31 -10.1243963 2.0510051
32 10.8166267 -10.1243963
33 -12.7922041 10.8166267
34 1.6542683 -12.7922041
35 -16.6400335 1.6542683
36 1.5847169 -16.6400335
37 17.5801088 1.5847169
38 18.1252950 17.5801088
39 -10.9868725 18.1252950
40 -1.6619347 -10.9868725
41 -4.2481068 -1.6619347
42 -24.4207387 -4.2481068
43 3.1460636 -24.4207387
44 -5.7525816 3.1460636
45 19.5319220 -5.7525816
46 3.8829919 19.5319220
47 20.9171176 3.8829919
48 4.4421696 20.9171176
49 -17.6337692 4.4421696
50 9.6480432 -17.6337692
51 -4.5058719 9.6480432
52 12.9022653 -4.5058719
53 -8.6312422 12.9022653
54 15.9869980 -8.6312422
55 -27.3696024 15.9869980
56 25.0962952 -27.3696024
57 3.6261063 25.0962952
58 -9.4670588 3.6261063
59 2.8084251 -9.4670588
60 -5.3027241 2.8084251
61 5.1816744 -5.3027241
62 8.2714247 5.1816744
63 21.6813221 8.2714247
64 1.0339375 21.6813221
65 14.5229439 1.0339375
66 -15.0082060 14.5229439
67 -1.1357795 -15.0082060
68 -15.3705363 -1.1357795
69 -14.9948629 -15.3705363
70 -8.8081248 -14.9948629
71 -5.7268610 -8.8081248
72 16.8211984 -5.7268610
73 -5.1788692 16.8211984
74 16.6798147 -5.1788692
75 -1.3032561 16.6798147
76 -1.1853057 -1.3032561
77 -13.7050933 -1.1853057
78 11.4924333 -13.7050933
79 1.5681192 11.4924333
80 -4.8018939 1.5681192
81 -5.0016179 -4.8018939
82 -10.6101510 -5.0016179
83 -7.3719930 -10.6101510
84 -7.6858442 -7.3719930
85 -9.2862248 -7.6858442
86 -5.2286863 -9.2862248
87 -12.3782975 -5.2286863
88 25.2366490 -12.3782975
89 -18.3554706 25.2366490
90 0.5821455 -18.3554706
91 -13.4137279 0.5821455
92 -2.1227861 -13.4137279
93 -7.7161123 -2.1227861
94 0.6738586 -7.7161123
95 15.6803068 0.6738586
96 27.0018460 15.6803068
97 -23.1717129 27.0018460
98 0.8183212 -23.1717129
99 -4.9551873 0.8183212
100 14.2277835 -4.9551873
101 -5.7929689 14.2277835
102 -14.0417970 -5.7929689
103 16.3399562 -14.0417970
104 1.6937499 16.3399562
105 3.8878970 1.6937499
106 1.9397477 3.8878970
107 11.3800645 1.9397477
108 2.8638509 11.3800645
109 4.1516294 2.8638509
110 18.8631008 4.1516294
111 -15.6166195 18.8631008
112 -16.5538280 -15.6166195
113 11.2093039 -16.5538280
114 -19.1293466 11.2093039
115 -21.2855996 -19.1293466
116 11.0501140 -21.2855996
117 -17.4038094 11.0501140
118 7.3320271 -17.4038094
119 10.8452621 7.3320271
120 -0.3190726 10.8452621
121 -11.9584786 -0.3190726
122 5.1581152 -11.9584786
123 -6.5953917 5.1581152
124 -1.4220760 -6.5953917
125 16.0905639 -1.4220760
126 -4.6106422 16.0905639
127 14.2575851 -4.6106422
128 10.7038709 14.2575851
129 8.5592156 10.7038709
130 -17.6628685 8.5592156
131 8.3837271 -17.6628685
132 13.6829881 8.3837271
133 -6.7751556 13.6829881
134 1.7605197 -6.7751556
135 -6.9750292 1.7605197
136 1.2574773 -6.9750292
137 -18.2124921 1.2574773
138 -2.2158750 -18.2124921
139 -13.7674637 -2.2158750
140 10.5514576 -13.7674637
141 -20.2163151 10.5514576
142 -1.6041338 -20.2163151
143 -7.3195699 -1.6041338
144 -17.6858442 -7.3195699
145 -13.8001238 -17.6858442
146 2.6809274 -13.8001238
147 0.9059277 2.6809274
148 17.1919444 0.9059277
149 -9.8113552 17.1919444
150 -12.3746343 -9.8113552
151 9.1886448 -12.3746343
152 NA 9.1886448
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.7568885 14.6361815
[2,] 0.2368686 -3.7568885
[3,] 5.3685842 0.2368686
[4,] 0.2765607 5.3685842
[5,] 11.4784427 0.2765607
[6,] -20.9458584 11.4784427
[7,] 1.6607135 -20.9458584
[8,] 24.3730718 1.6607135
[9,] 31.4405121 24.3730718
[10,] 5.4941358 31.4405121
[11,] 11.0157647 5.4941358
[12,] -20.4143305 11.0157647
[13,] 3.6603524 -20.4143305
[14,] 1.8244719 3.6603524
[15,] -5.4085313 1.8244719
[16,] 6.4687733 -5.4085313
[17,] -11.1699938 6.4687733
[18,] -7.2548639 -11.1699938
[19,] 0.6610049 -7.2548639
[20,] 26.2170153 0.6610049
[21,] 14.1418726 26.2170153
[22,] -32.9513194 14.1418726
[23,] -14.3114319 -32.9513194
[24,] 5.5514576 -14.3114319
[25,] 7.6022190 5.5514576
[26,] 9.0889885 7.6022190
[27,] 8.2985634 9.0889885
[28,] 3.6644469 8.2985634
[29,] 3.0535780 3.6644469
[30,] 2.0510051 3.0535780
[31,] -10.1243963 2.0510051
[32,] 10.8166267 -10.1243963
[33,] -12.7922041 10.8166267
[34,] 1.6542683 -12.7922041
[35,] -16.6400335 1.6542683
[36,] 1.5847169 -16.6400335
[37,] 17.5801088 1.5847169
[38,] 18.1252950 17.5801088
[39,] -10.9868725 18.1252950
[40,] -1.6619347 -10.9868725
[41,] -4.2481068 -1.6619347
[42,] -24.4207387 -4.2481068
[43,] 3.1460636 -24.4207387
[44,] -5.7525816 3.1460636
[45,] 19.5319220 -5.7525816
[46,] 3.8829919 19.5319220
[47,] 20.9171176 3.8829919
[48,] 4.4421696 20.9171176
[49,] -17.6337692 4.4421696
[50,] 9.6480432 -17.6337692
[51,] -4.5058719 9.6480432
[52,] 12.9022653 -4.5058719
[53,] -8.6312422 12.9022653
[54,] 15.9869980 -8.6312422
[55,] -27.3696024 15.9869980
[56,] 25.0962952 -27.3696024
[57,] 3.6261063 25.0962952
[58,] -9.4670588 3.6261063
[59,] 2.8084251 -9.4670588
[60,] -5.3027241 2.8084251
[61,] 5.1816744 -5.3027241
[62,] 8.2714247 5.1816744
[63,] 21.6813221 8.2714247
[64,] 1.0339375 21.6813221
[65,] 14.5229439 1.0339375
[66,] -15.0082060 14.5229439
[67,] -1.1357795 -15.0082060
[68,] -15.3705363 -1.1357795
[69,] -14.9948629 -15.3705363
[70,] -8.8081248 -14.9948629
[71,] -5.7268610 -8.8081248
[72,] 16.8211984 -5.7268610
[73,] -5.1788692 16.8211984
[74,] 16.6798147 -5.1788692
[75,] -1.3032561 16.6798147
[76,] -1.1853057 -1.3032561
[77,] -13.7050933 -1.1853057
[78,] 11.4924333 -13.7050933
[79,] 1.5681192 11.4924333
[80,] -4.8018939 1.5681192
[81,] -5.0016179 -4.8018939
[82,] -10.6101510 -5.0016179
[83,] -7.3719930 -10.6101510
[84,] -7.6858442 -7.3719930
[85,] -9.2862248 -7.6858442
[86,] -5.2286863 -9.2862248
[87,] -12.3782975 -5.2286863
[88,] 25.2366490 -12.3782975
[89,] -18.3554706 25.2366490
[90,] 0.5821455 -18.3554706
[91,] -13.4137279 0.5821455
[92,] -2.1227861 -13.4137279
[93,] -7.7161123 -2.1227861
[94,] 0.6738586 -7.7161123
[95,] 15.6803068 0.6738586
[96,] 27.0018460 15.6803068
[97,] -23.1717129 27.0018460
[98,] 0.8183212 -23.1717129
[99,] -4.9551873 0.8183212
[100,] 14.2277835 -4.9551873
[101,] -5.7929689 14.2277835
[102,] -14.0417970 -5.7929689
[103,] 16.3399562 -14.0417970
[104,] 1.6937499 16.3399562
[105,] 3.8878970 1.6937499
[106,] 1.9397477 3.8878970
[107,] 11.3800645 1.9397477
[108,] 2.8638509 11.3800645
[109,] 4.1516294 2.8638509
[110,] 18.8631008 4.1516294
[111,] -15.6166195 18.8631008
[112,] -16.5538280 -15.6166195
[113,] 11.2093039 -16.5538280
[114,] -19.1293466 11.2093039
[115,] -21.2855996 -19.1293466
[116,] 11.0501140 -21.2855996
[117,] -17.4038094 11.0501140
[118,] 7.3320271 -17.4038094
[119,] 10.8452621 7.3320271
[120,] -0.3190726 10.8452621
[121,] -11.9584786 -0.3190726
[122,] 5.1581152 -11.9584786
[123,] -6.5953917 5.1581152
[124,] -1.4220760 -6.5953917
[125,] 16.0905639 -1.4220760
[126,] -4.6106422 16.0905639
[127,] 14.2575851 -4.6106422
[128,] 10.7038709 14.2575851
[129,] 8.5592156 10.7038709
[130,] -17.6628685 8.5592156
[131,] 8.3837271 -17.6628685
[132,] 13.6829881 8.3837271
[133,] -6.7751556 13.6829881
[134,] 1.7605197 -6.7751556
[135,] -6.9750292 1.7605197
[136,] 1.2574773 -6.9750292
[137,] -18.2124921 1.2574773
[138,] -2.2158750 -18.2124921
[139,] -13.7674637 -2.2158750
[140,] 10.5514576 -13.7674637
[141,] -20.2163151 10.5514576
[142,] -1.6041338 -20.2163151
[143,] -7.3195699 -1.6041338
[144,] -17.6858442 -7.3195699
[145,] -13.8001238 -17.6858442
[146,] 2.6809274 -13.8001238
[147,] 0.9059277 2.6809274
[148,] 17.1919444 0.9059277
[149,] -9.8113552 17.1919444
[150,] -12.3746343 -9.8113552
[151,] 9.1886448 -12.3746343
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.7568885 14.6361815
2 0.2368686 -3.7568885
3 5.3685842 0.2368686
4 0.2765607 5.3685842
5 11.4784427 0.2765607
6 -20.9458584 11.4784427
7 1.6607135 -20.9458584
8 24.3730718 1.6607135
9 31.4405121 24.3730718
10 5.4941358 31.4405121
11 11.0157647 5.4941358
12 -20.4143305 11.0157647
13 3.6603524 -20.4143305
14 1.8244719 3.6603524
15 -5.4085313 1.8244719
16 6.4687733 -5.4085313
17 -11.1699938 6.4687733
18 -7.2548639 -11.1699938
19 0.6610049 -7.2548639
20 26.2170153 0.6610049
21 14.1418726 26.2170153
22 -32.9513194 14.1418726
23 -14.3114319 -32.9513194
24 5.5514576 -14.3114319
25 7.6022190 5.5514576
26 9.0889885 7.6022190
27 8.2985634 9.0889885
28 3.6644469 8.2985634
29 3.0535780 3.6644469
30 2.0510051 3.0535780
31 -10.1243963 2.0510051
32 10.8166267 -10.1243963
33 -12.7922041 10.8166267
34 1.6542683 -12.7922041
35 -16.6400335 1.6542683
36 1.5847169 -16.6400335
37 17.5801088 1.5847169
38 18.1252950 17.5801088
39 -10.9868725 18.1252950
40 -1.6619347 -10.9868725
41 -4.2481068 -1.6619347
42 -24.4207387 -4.2481068
43 3.1460636 -24.4207387
44 -5.7525816 3.1460636
45 19.5319220 -5.7525816
46 3.8829919 19.5319220
47 20.9171176 3.8829919
48 4.4421696 20.9171176
49 -17.6337692 4.4421696
50 9.6480432 -17.6337692
51 -4.5058719 9.6480432
52 12.9022653 -4.5058719
53 -8.6312422 12.9022653
54 15.9869980 -8.6312422
55 -27.3696024 15.9869980
56 25.0962952 -27.3696024
57 3.6261063 25.0962952
58 -9.4670588 3.6261063
59 2.8084251 -9.4670588
60 -5.3027241 2.8084251
61 5.1816744 -5.3027241
62 8.2714247 5.1816744
63 21.6813221 8.2714247
64 1.0339375 21.6813221
65 14.5229439 1.0339375
66 -15.0082060 14.5229439
67 -1.1357795 -15.0082060
68 -15.3705363 -1.1357795
69 -14.9948629 -15.3705363
70 -8.8081248 -14.9948629
71 -5.7268610 -8.8081248
72 16.8211984 -5.7268610
73 -5.1788692 16.8211984
74 16.6798147 -5.1788692
75 -1.3032561 16.6798147
76 -1.1853057 -1.3032561
77 -13.7050933 -1.1853057
78 11.4924333 -13.7050933
79 1.5681192 11.4924333
80 -4.8018939 1.5681192
81 -5.0016179 -4.8018939
82 -10.6101510 -5.0016179
83 -7.3719930 -10.6101510
84 -7.6858442 -7.3719930
85 -9.2862248 -7.6858442
86 -5.2286863 -9.2862248
87 -12.3782975 -5.2286863
88 25.2366490 -12.3782975
89 -18.3554706 25.2366490
90 0.5821455 -18.3554706
91 -13.4137279 0.5821455
92 -2.1227861 -13.4137279
93 -7.7161123 -2.1227861
94 0.6738586 -7.7161123
95 15.6803068 0.6738586
96 27.0018460 15.6803068
97 -23.1717129 27.0018460
98 0.8183212 -23.1717129
99 -4.9551873 0.8183212
100 14.2277835 -4.9551873
101 -5.7929689 14.2277835
102 -14.0417970 -5.7929689
103 16.3399562 -14.0417970
104 1.6937499 16.3399562
105 3.8878970 1.6937499
106 1.9397477 3.8878970
107 11.3800645 1.9397477
108 2.8638509 11.3800645
109 4.1516294 2.8638509
110 18.8631008 4.1516294
111 -15.6166195 18.8631008
112 -16.5538280 -15.6166195
113 11.2093039 -16.5538280
114 -19.1293466 11.2093039
115 -21.2855996 -19.1293466
116 11.0501140 -21.2855996
117 -17.4038094 11.0501140
118 7.3320271 -17.4038094
119 10.8452621 7.3320271
120 -0.3190726 10.8452621
121 -11.9584786 -0.3190726
122 5.1581152 -11.9584786
123 -6.5953917 5.1581152
124 -1.4220760 -6.5953917
125 16.0905639 -1.4220760
126 -4.6106422 16.0905639
127 14.2575851 -4.6106422
128 10.7038709 14.2575851
129 8.5592156 10.7038709
130 -17.6628685 8.5592156
131 8.3837271 -17.6628685
132 13.6829881 8.3837271
133 -6.7751556 13.6829881
134 1.7605197 -6.7751556
135 -6.9750292 1.7605197
136 1.2574773 -6.9750292
137 -18.2124921 1.2574773
138 -2.2158750 -18.2124921
139 -13.7674637 -2.2158750
140 10.5514576 -13.7674637
141 -20.2163151 10.5514576
142 -1.6041338 -20.2163151
143 -7.3195699 -1.6041338
144 -17.6858442 -7.3195699
145 -13.8001238 -17.6858442
146 2.6809274 -13.8001238
147 0.9059277 2.6809274
148 17.1919444 0.9059277
149 -9.8113552 17.1919444
150 -12.3746343 -9.8113552
151 9.1886448 -12.3746343
> 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/7vpiq1290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8vpiq1290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9vpiq1290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/106g0t1290513336.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119hyz1290513336.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/12dzf51290513336.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/1310cy1290513336.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/14cat11290513336.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/15gs971290513336.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/16jtqd1290513336.tab")
+ }
>
> try(system("convert tmp/1zflz1290513336.ps tmp/1zflz1290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ap221290513336.ps tmp/2ap221290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ap221290513336.ps tmp/3ap221290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ap221290513336.ps tmp/4ap221290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ap221290513336.ps tmp/5ap221290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lg151290513336.ps tmp/6lg151290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vpiq1290513336.ps tmp/7vpiq1290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vpiq1290513336.ps tmp/8vpiq1290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vpiq1290513336.ps tmp/9vpiq1290513336.png",intern=TRUE))
character(0)
> try(system("convert tmp/106g0t1290513336.ps tmp/106g0t1290513336.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.410 2.250 8.738