R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2000
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+ ,11
+ ,13
+ ,9
+ ,84
+ ,51
+ ,2011
+ ,161
+ ,32
+ ,35
+ ,12
+ ,8
+ ,12
+ ,18
+ ,84
+ ,50
+ ,2011
+ ,162
+ ,34
+ ,36
+ ,13
+ ,8
+ ,13
+ ,16
+ ,69
+ ,46)
+ ,dim=c(10
+ ,162)
+ ,dimnames=list(c('jaar'
+ ,'volgnummer'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(10,162),dimnames=list(c('jaar','volgnummer','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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, 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
Separate jaar volgnummer Connected Learning Software Happiness Depression
1 38 2000 1 41 13 12 14 12
2 32 2000 2 39 16 11 18 11
3 35 2000 3 30 19 15 11 14
4 33 2000 4 31 15 6 12 12
5 37 2000 5 34 14 13 16 21
6 29 2000 6 35 13 10 18 12
7 31 2000 7 39 19 12 14 22
8 36 2000 8 34 15 14 14 11
9 35 2000 9 36 14 12 15 10
10 38 2000 10 37 15 6 15 13
11 31 2000 11 38 16 10 17 10
12 34 2000 12 36 16 12 19 8
13 35 2000 13 38 16 12 10 15
14 38 2001 14 39 16 11 16 14
15 37 2001 15 33 17 15 18 10
16 33 2001 16 32 15 12 14 14
17 32 2001 17 36 15 10 14 14
18 38 2001 18 38 20 12 17 11
19 38 2001 19 39 18 11 14 10
20 32 2001 20 32 16 12 16 13
21 33 2001 21 32 16 11 18 7
22 31 2001 22 31 16 12 11 14
23 38 2001 23 39 19 13 14 12
24 39 2001 24 37 16 11 12 14
25 32 2001 25 39 17 9 17 11
26 32 2001 26 41 17 13 9 9
27 35 2002 27 36 16 10 16 11
28 37 2002 28 33 15 14 14 15
29 33 2002 29 33 16 12 15 14
30 33 2002 30 34 14 10 11 13
31 28 2002 31 31 15 12 16 9
32 32 2002 32 27 12 8 13 15
33 31 2002 33 37 14 10 17 10
34 37 2002 34 34 16 12 15 11
35 30 2002 35 34 14 12 14 13
36 33 2002 36 32 7 7 16 8
37 31 2002 37 29 10 6 9 20
38 33 2002 38 36 14 12 15 12
39 31 2002 39 29 16 10 17 10
40 33 2003 40 35 16 10 13 10
41 32 2003 41 37 16 10 15 9
42 33 2003 42 34 14 12 16 14
43 32 2003 43 38 20 15 16 8
44 33 2003 44 35 14 10 12 14
45 28 2003 45 38 14 10 12 11
46 35 2003 46 37 11 12 11 13
47 39 2003 47 38 14 13 15 9
48 34 2003 48 33 15 11 15 11
49 38 2003 49 36 16 11 17 15
50 32 2003 50 38 14 12 13 11
51 38 2003 51 32 16 14 16 10
52 30 2003 52 32 14 10 14 14
53 33 2004 53 32 12 12 11 18
54 38 2004 54 34 16 13 12 14
55 32 2004 55 32 9 5 12 11
56 32 2004 56 37 14 6 15 12
57 34 2004 57 39 16 12 16 13
58 34 2004 58 29 16 12 15 9
59 36 2004 59 37 15 11 12 10
60 34 2004 60 35 16 10 12 15
61 28 2004 61 30 12 7 8 20
62 34 2004 62 38 16 12 13 12
63 35 2004 63 34 16 14 11 12
64 35 2004 64 31 14 11 14 14
65 31 2004 65 34 16 12 15 13
66 37 2004 66 35 17 13 10 11
67 35 2005 67 36 18 14 11 17
68 27 2005 68 30 18 11 12 12
69 40 2005 69 39 12 12 15 13
70 37 2005 70 35 16 12 15 14
71 36 2005 71 38 10 8 14 13
72 38 2005 72 31 14 11 16 15
73 39 2005 73 34 18 14 15 13
74 41 2005 74 38 18 14 15 10
75 27 2005 75 34 16 12 13 11
76 30 2005 76 39 17 9 12 19
77 37 2005 77 37 16 13 17 13
78 31 2005 78 34 16 11 13 17
79 31 2005 79 28 13 12 15 13
80 27 2005 80 37 16 12 13 9
81 36 2006 81 33 16 12 15 11
82 38 2006 82 37 20 12 16 10
83 37 2006 83 35 16 12 15 9
84 33 2006 84 37 15 12 16 12
85 34 2006 85 32 15 11 15 12
86 31 2006 86 33 16 10 14 13
87 39 2006 87 38 14 9 15 13
88 34 2006 88 33 16 12 14 12
89 32 2006 89 29 16 12 13 15
90 33 2006 90 33 15 12 7 22
91 36 2006 91 31 12 9 17 13
92 32 2006 92 36 17 15 13 15
93 41 2006 93 35 16 12 15 13
94 28 2006 94 32 15 12 14 15
95 30 2007 95 29 13 12 13 10
96 36 2007 96 39 16 10 16 11
97 35 2007 97 37 16 13 12 16
98 31 2007 98 35 16 9 14 11
99 34 2007 99 37 16 12 17 11
100 36 2007 100 32 14 10 15 10
101 36 2007 101 38 16 14 17 10
102 35 2007 102 37 16 11 12 16
103 37 2007 103 36 20 15 16 12
104 28 2007 104 32 15 11 11 11
105 39 2007 105 33 16 11 15 16
106 32 2007 106 40 13 12 9 19
107 35 2007 107 38 17 12 16 11
108 39 2007 108 41 16 12 15 16
109 35 2008 109 36 16 11 10 15
110 42 2008 110 43 12 7 10 24
111 34 2008 111 30 16 12 15 14
112 33 2008 112 31 16 14 11 15
113 41 2008 113 32 17 11 13 11
114 33 2008 114 32 13 11 14 15
115 34 2008 115 37 12 10 18 12
116 32 2008 116 37 18 13 16 10
117 40 2008 117 33 14 13 14 14
118 40 2008 118 34 14 8 14 13
119 35 2008 119 33 13 11 14 9
120 36 2008 120 38 16 12 14 15
121 37 2008 121 33 13 11 12 15
122 27 2008 122 31 16 13 14 14
123 39 2009 123 38 13 12 15 11
124 38 2009 124 37 16 14 15 8
125 31 2009 125 33 15 13 15 11
126 33 2009 126 31 16 15 13 11
127 32 2009 127 39 15 10 17 8
128 39 2009 128 44 17 11 17 10
129 36 2009 129 33 15 9 19 11
130 33 2009 130 35 12 11 15 13
131 33 2009 131 32 16 10 13 11
132 32 2009 132 28 10 11 9 20
133 37 2009 133 40 16 8 15 10
134 30 2009 134 27 12 11 15 15
135 38 2009 135 37 14 12 15 12
136 29 2009 136 32 15 12 16 14
137 22 2010 137 28 13 9 11 23
138 35 2010 138 34 15 11 14 14
139 35 2010 139 30 11 10 11 16
140 34 2010 140 35 12 8 15 11
141 35 2010 141 31 8 9 13 12
142 34 2010 142 32 16 8 15 10
143 34 2010 143 30 15 9 16 14
144 35 2010 144 30 17 15 14 12
145 23 2010 145 31 16 11 15 12
146 31 2010 146 40 10 8 16 11
147 27 2010 147 32 18 13 16 12
148 36 2010 148 36 13 12 11 13
149 31 2010 149 32 16 12 12 11
150 32 2010 150 35 13 9 9 19
151 39 2011 151 38 10 7 16 12
152 37 2011 152 42 15 13 13 17
153 38 2011 153 34 16 9 16 9
154 39 2011 154 35 16 6 12 12
155 34 2011 155 35 14 8 9 19
156 31 2011 156 33 10 8 13 18
157 32 2011 157 36 17 15 13 15
158 37 2011 158 32 13 6 14 14
159 36 2011 159 33 15 9 19 11
160 32 2011 160 34 16 11 13 9
161 35 2011 161 32 12 8 12 18
162 36 2011 162 34 13 8 13 16
Belonging Belonging_Final
1 53 32
2 86 51
3 66 42
4 67 41
5 76 46
6 78 47
7 53 37
8 80 49
9 74 45
10 76 47
11 79 49
12 54 33
13 67 42
14 54 33
15 87 53
16 58 36
17 75 45
18 88 54
19 64 41
20 57 36
21 66 41
22 68 44
23 54 33
24 56 37
25 86 52
26 80 47
27 76 43
28 69 44
29 78 45
30 67 44
31 80 49
32 54 33
33 71 43
34 84 54
35 74 42
36 71 44
37 63 37
38 71 43
39 76 46
40 69 42
41 74 45
42 75 44
43 54 33
44 52 31
45 69 42
46 68 40
47 65 43
48 75 46
49 74 42
50 75 45
51 72 44
52 67 40
53 63 37
54 62 46
55 63 36
56 76 47
57 74 45
58 67 42
59 73 43
60 70 43
61 53 32
62 77 45
63 77 45
64 52 31
65 54 33
66 80 49
67 66 42
68 73 41
69 63 38
70 69 42
71 67 44
72 54 33
73 81 48
74 69 40
75 84 50
76 80 49
77 70 43
78 69 44
79 77 47
80 54 33
81 79 46
82 30 0
83 71 45
84 73 43
85 72 44
86 77 47
87 75 45
88 69 42
89 54 33
90 70 43
91 73 46
92 54 33
93 77 46
94 82 48
95 80 47
96 80 47
97 69 43
98 78 46
99 81 48
100 76 46
101 76 45
102 73 45
103 85 52
104 66 42
105 79 47
106 68 41
107 76 47
108 71 43
109 54 33
110 46 30
111 82 49
112 74 44
113 88 55
114 38 11
115 76 47
116 86 53
117 54 33
118 70 44
119 69 42
120 90 55
121 54 33
122 76 46
123 89 54
124 76 47
125 73 45
126 79 47
127 90 55
128 74 44
129 81 53
130 72 44
131 71 42
132 66 40
133 77 46
134 65 40
135 74 46
136 82 53
137 54 33
138 63 42
139 54 35
140 64 40
141 69 41
142 54 33
143 84 51
144 86 53
145 77 46
146 89 55
147 76 47
148 60 38
149 75 46
150 73 46
151 85 53
152 79 47
153 71 41
154 72 44
155 69 43
156 78 51
157 54 33
158 69 43
159 81 53
160 84 51
161 84 50
162 69 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) jaar volgnummer Connected
-3.302e+03 1.661e+00 -1.131e-01 3.847e-01
Learning Software Happiness Depression
-4.215e-02 1.272e-01 2.195e-01 -3.158e-03
Belonging Belonging_Final
-1.258e-01 1.456e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.451 -2.120 0.264 2.267 8.131
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.302e+03 1.823e+03 -1.811 0.0721 .
jaar 1.661e+00 9.117e-01 1.821 0.0705 .
volgnummer -1.131e-01 6.639e-02 -1.703 0.0906 .
Connected 3.847e-01 7.975e-02 4.823 3.4e-06 ***
Learning -4.215e-02 1.442e-01 -0.292 0.7705
Software 1.272e-01 1.453e-01 0.875 0.3829
Happiness 2.195e-01 1.354e-01 1.621 0.1071
Depression -3.158e-03 1.013e-01 -0.031 0.9752
Belonging -1.258e-01 7.968e-02 -1.579 0.1164
Belonging_Final 1.456e-01 1.146e-01 1.270 0.2059
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.294 on 152 degrees of freedom
Multiple R-squared: 0.1883, Adjusted R-squared: 0.1403
F-statistic: 3.919 on 9 and 152 DF, p-value: 0.0001678
> 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,] 0.84537877 0.30924246 0.154621232
[2,] 0.73537061 0.52925877 0.264629386
[3,] 0.64040873 0.71918254 0.359591271
[4,] 0.69360964 0.61278071 0.306390357
[5,] 0.74086066 0.51827868 0.259139338
[6,] 0.70271407 0.59457185 0.297285927
[7,] 0.64377919 0.71244162 0.356220812
[8,] 0.57358889 0.85282221 0.426411105
[9,] 0.48097939 0.96195877 0.519020613
[10,] 0.42732160 0.85464319 0.572678405
[11,] 0.34940221 0.69880442 0.650597789
[12,] 0.40994572 0.81989144 0.590054282
[13,] 0.39876581 0.79753161 0.601234193
[14,] 0.50381821 0.99236359 0.496181794
[15,] 0.42869275 0.85738550 0.571307251
[16,] 0.37118182 0.74236364 0.628818181
[17,] 0.31029338 0.62058676 0.689706620
[18,] 0.27318671 0.54637342 0.726813289
[19,] 0.34130870 0.68261740 0.658691299
[20,] 0.28612084 0.57224167 0.713879165
[21,] 0.26555947 0.53111895 0.734440527
[22,] 0.29624191 0.59248382 0.703758090
[23,] 0.26384389 0.52768778 0.736156108
[24,] 0.23418045 0.46836091 0.765819547
[25,] 0.19553084 0.39106168 0.804469158
[26,] 0.15594852 0.31189703 0.844051484
[27,] 0.12270578 0.24541156 0.877294220
[28,] 0.10010623 0.20021247 0.899893766
[29,] 0.08917275 0.17834550 0.910827248
[30,] 0.06841797 0.13683594 0.931582032
[31,] 0.07157612 0.14315224 0.928423880
[32,] 0.05419478 0.10838957 0.945805216
[33,] 0.09815820 0.19631640 0.901841799
[34,] 0.08518521 0.17037041 0.914814794
[35,] 0.09615809 0.19231619 0.903841906
[36,] 0.08012508 0.16025017 0.919874917
[37,] 0.12553196 0.25106393 0.874468036
[38,] 0.10935524 0.21871048 0.890644758
[39,] 0.13781627 0.27563253 0.862183734
[40,] 0.12447605 0.24895211 0.875523946
[41,] 0.09922702 0.19845405 0.900772976
[42,] 0.08328032 0.16656063 0.916719683
[43,] 0.06957208 0.13914416 0.930427918
[44,] 0.06065572 0.12131143 0.939344284
[45,] 0.05020662 0.10041323 0.949793383
[46,] 0.03936541 0.07873082 0.960634588
[47,] 0.03560879 0.07121759 0.964391207
[48,] 0.02682547 0.05365093 0.973174533
[49,] 0.02754128 0.05508256 0.972458719
[50,] 0.02077857 0.04155715 0.979221425
[51,] 0.01698818 0.03397636 0.983011820
[52,] 0.01436193 0.02872385 0.985638074
[53,] 0.01402095 0.02804190 0.985979052
[54,] 0.01568264 0.03136528 0.984317361
[55,] 0.01149567 0.02299134 0.988504328
[56,] 0.01575052 0.03150104 0.984249479
[57,] 0.01854044 0.03708089 0.981459556
[58,] 0.01611323 0.03222646 0.983886772
[59,] 0.01199208 0.02398416 0.988007922
[60,] 0.01438705 0.02877410 0.985612951
[61,] 0.02088142 0.04176285 0.979118576
[62,] 0.03479517 0.06959034 0.965204829
[63,] 0.07445441 0.14890883 0.925545587
[64,] 0.09311848 0.18623696 0.906881519
[65,] 0.07722725 0.15445450 0.922772751
[66,] 0.07448390 0.14896780 0.925516098
[67,] 0.06167738 0.12335475 0.938322623
[68,] 0.17658086 0.35316172 0.823419142
[69,] 0.15906708 0.31813417 0.840932917
[70,] 0.16540033 0.33080066 0.834599670
[71,] 0.14356570 0.28713141 0.856434296
[72,] 0.13622944 0.27245887 0.863770563
[73,] 0.11218849 0.22437698 0.887811508
[74,] 0.10656337 0.21312674 0.893436631
[75,] 0.11605070 0.23210141 0.883949297
[76,] 0.09449719 0.18899438 0.905502810
[77,] 0.07712314 0.15424627 0.922876863
[78,] 0.06154086 0.12308172 0.938459139
[79,] 0.05593177 0.11186353 0.944068234
[80,] 0.05622881 0.11245761 0.943771193
[81,] 0.11892532 0.23785064 0.881074680
[82,] 0.13522019 0.27044039 0.864779806
[83,] 0.12690242 0.25380484 0.873097581
[84,] 0.10681828 0.21363656 0.893181721
[85,] 0.08726652 0.17453305 0.912733475
[86,] 0.10157865 0.20315731 0.898421345
[87,] 0.08927252 0.17854503 0.910727484
[88,] 0.07781231 0.15562462 0.922187690
[89,] 0.06169980 0.12339959 0.938300203
[90,] 0.04958105 0.09916210 0.950418948
[91,] 0.04279622 0.08559243 0.957203783
[92,] 0.08485427 0.16970855 0.915145727
[93,] 0.13209242 0.26418484 0.867907582
[94,] 0.14890508 0.29781016 0.851094922
[95,] 0.12503804 0.25007608 0.874961962
[96,] 0.11841383 0.23682765 0.881586174
[97,] 0.11426972 0.22853944 0.885730278
[98,] 0.11064970 0.22129941 0.889350295
[99,] 0.09249320 0.18498640 0.907506801
[100,] 0.07299049 0.14598097 0.927009514
[101,] 0.15409127 0.30818254 0.845908729
[102,] 0.12791879 0.25583758 0.872081209
[103,] 0.11082410 0.22164820 0.889175900
[104,] 0.10785934 0.21571868 0.892140659
[105,] 0.18066745 0.36133490 0.819332549
[106,] 0.24055747 0.48111494 0.759442532
[107,] 0.20093337 0.40186674 0.799066629
[108,] 0.18978121 0.37956243 0.810218786
[109,] 0.23062491 0.46124982 0.769375090
[110,] 0.23882364 0.47764727 0.761176364
[111,] 0.22857315 0.45714629 0.771426854
[112,] 0.20042836 0.40085672 0.799571640
[113,] 0.18821663 0.37643326 0.811783369
[114,] 0.15851810 0.31703621 0.841481897
[115,] 0.19309377 0.38618753 0.806906233
[116,] 0.16359812 0.32719623 0.836401884
[117,] 0.14609820 0.29219640 0.853901801
[118,] 0.11586221 0.23172442 0.884137788
[119,] 0.08772467 0.17544935 0.912275327
[120,] 0.08505841 0.17011682 0.914941590
[121,] 0.06765513 0.13531027 0.932344865
[122,] 0.06108880 0.12217761 0.938911197
[123,] 0.13954213 0.27908425 0.860457874
[124,] 0.13238111 0.26476222 0.867618890
[125,] 0.35862823 0.71725646 0.641371771
[126,] 0.28891806 0.57783612 0.711081942
[127,] 0.22624736 0.45249471 0.773752644
[128,] 0.17442469 0.34884938 0.825575309
[129,] 0.12844280 0.25688559 0.871557205
[130,] 0.08837410 0.17674819 0.911625904
[131,] 0.09095770 0.18191539 0.909042303
[132,] 0.43469240 0.86938481 0.565307596
[133,] 0.62256689 0.75486623 0.377433113
[134,] 0.96711328 0.06577344 0.032886721
[135,] 0.96557809 0.06884381 0.034421905
[136,] 0.97297875 0.05404249 0.027021246
[137,] 0.99298496 0.01403007 0.007015036
> postscript(file="/var/fisher/rcomp/tmp/1dw1x1355674453.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2stiy1355674453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3mrc31355674453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/40yzm1355674453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/53qr61355674453.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 = 162
Frequency = 1
1 2 3 4 5 6
0.72431887 -3.63464312 2.89783419 1.64766790 3.22943482 -5.06416134
7 8 9 10 11 12
-5.27139576 2.95754673 1.11818668 4.62166638 -3.47856610 -1.11209115
13 14 15 16 17 18
1.55459305 1.10392093 2.84741927 -0.64111451 -1.98362111 2.97410704
19 20 21 22 23 24
2.27345172 -1.71468076 -0.52787263 -0.78390119 2.42622440 4.55109856
25 26 27 28 29 30
-2.32461852 -1.76701384 0.49695372 2.63848676 -0.18781924 -0.65306728
31 32 33 34 35 36
-4.80046398 0.96871447 -4.14541289 3.42807724 -2.82867182 0.27108484
37 38 39 40 41 42
1.37849986 -1.00450267 -0.11308788 -1.38918288 -3.29520404 -1.29908975
43 44 45 46 47 48
-4.91309252 -1.32675882 -6.83954808 0.66852061 2.69145713 0.85209299
49 50 51 52 53 54
3.88345363 -2.42987612 4.92764224 -2.13015893 -0.41187583 2.30519663
55 56 57 58 59 60
-0.51810051 -2.86571836 -2.37746934 1.34505606 1.73677670 0.42680191
61 62 63 64 65 66
-2.96783906 -0.39476397 2.44159096 2.24622765 -3.09971255 4.57690174
67 68 69 70 71 72
-0.38338267 -4.78982313 3.00442070 2.00051657 -0.11114484 4.01468667
73 74 75 76 77 78
5.18756421 5.40725784 -5.89733839 -4.39691677 1.43354683 -2.42590871
79 80 81 82 83 84
-0.14026011 -7.79217973 2.01922139 3.07086255 1.60871132 -2.75693295
85 86 87 88 89 90
0.35469024 -2.33258383 3.72004083 0.35736481 -0.33909373 1.08954202
91 92 93 94 95 96
2.94441943 -3.03189428 7.36125696 -3.85010046 -2.23040339 -0.23849058
97 98 99 100 101 102
-0.64558409 -3.01365836 -1.62355688 2.68069536 -0.23196108 0.38615470
103 104 105 106 107 108
2.14408244 -4.74688676 6.06929160 -2.94809685 -0.32564929 2.77973958
109 110 111 112 113 114
-0.30632528 3.91271251 1.19378021 0.27026402 8.13085093 -0.01725873
115 116 117 118 119 120
-2.08945700 -3.28756506 5.53239419 6.30523414 1.53201601 1.48974474
121 122 123 124 125 126
3.63892110 -5.17312533 2.82942253 1.57316983 -3.76695009 -0.19399882
127 128 129 130 131 132
-4.23258081 0.50882614 1.15794569 -1.81705424 0.34374771 1.18378735
133 134 135 136 137 138
1.47737540 -1.57964802 2.89338190 -4.25372915 -10.45113362 -0.68073870
139 140 141 142 143 144
1.48093608 -1.39624816 1.88534010 -0.08986777 1.57059474 2.39813199
145 146 147 148 149 150
-9.73981959 -4.98313528 -6.55926590 1.32917737 -1.39575763 -0.74958574
151 152 153 154 155 156
2.60898969 -0.57603932 3.34829400 5.03468160 0.25778340 -2.94176129
157 158 159 160 161 162
-3.98655905 3.84990126 1.22830833 -1.27626428 3.21247086 2.06755677
> postscript(file="/var/fisher/rcomp/tmp/6ozkc1355674453.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 0.72431887 NA
1 -3.63464312 0.72431887
2 2.89783419 -3.63464312
3 1.64766790 2.89783419
4 3.22943482 1.64766790
5 -5.06416134 3.22943482
6 -5.27139576 -5.06416134
7 2.95754673 -5.27139576
8 1.11818668 2.95754673
9 4.62166638 1.11818668
10 -3.47856610 4.62166638
11 -1.11209115 -3.47856610
12 1.55459305 -1.11209115
13 1.10392093 1.55459305
14 2.84741927 1.10392093
15 -0.64111451 2.84741927
16 -1.98362111 -0.64111451
17 2.97410704 -1.98362111
18 2.27345172 2.97410704
19 -1.71468076 2.27345172
20 -0.52787263 -1.71468076
21 -0.78390119 -0.52787263
22 2.42622440 -0.78390119
23 4.55109856 2.42622440
24 -2.32461852 4.55109856
25 -1.76701384 -2.32461852
26 0.49695372 -1.76701384
27 2.63848676 0.49695372
28 -0.18781924 2.63848676
29 -0.65306728 -0.18781924
30 -4.80046398 -0.65306728
31 0.96871447 -4.80046398
32 -4.14541289 0.96871447
33 3.42807724 -4.14541289
34 -2.82867182 3.42807724
35 0.27108484 -2.82867182
36 1.37849986 0.27108484
37 -1.00450267 1.37849986
38 -0.11308788 -1.00450267
39 -1.38918288 -0.11308788
40 -3.29520404 -1.38918288
41 -1.29908975 -3.29520404
42 -4.91309252 -1.29908975
43 -1.32675882 -4.91309252
44 -6.83954808 -1.32675882
45 0.66852061 -6.83954808
46 2.69145713 0.66852061
47 0.85209299 2.69145713
48 3.88345363 0.85209299
49 -2.42987612 3.88345363
50 4.92764224 -2.42987612
51 -2.13015893 4.92764224
52 -0.41187583 -2.13015893
53 2.30519663 -0.41187583
54 -0.51810051 2.30519663
55 -2.86571836 -0.51810051
56 -2.37746934 -2.86571836
57 1.34505606 -2.37746934
58 1.73677670 1.34505606
59 0.42680191 1.73677670
60 -2.96783906 0.42680191
61 -0.39476397 -2.96783906
62 2.44159096 -0.39476397
63 2.24622765 2.44159096
64 -3.09971255 2.24622765
65 4.57690174 -3.09971255
66 -0.38338267 4.57690174
67 -4.78982313 -0.38338267
68 3.00442070 -4.78982313
69 2.00051657 3.00442070
70 -0.11114484 2.00051657
71 4.01468667 -0.11114484
72 5.18756421 4.01468667
73 5.40725784 5.18756421
74 -5.89733839 5.40725784
75 -4.39691677 -5.89733839
76 1.43354683 -4.39691677
77 -2.42590871 1.43354683
78 -0.14026011 -2.42590871
79 -7.79217973 -0.14026011
80 2.01922139 -7.79217973
81 3.07086255 2.01922139
82 1.60871132 3.07086255
83 -2.75693295 1.60871132
84 0.35469024 -2.75693295
85 -2.33258383 0.35469024
86 3.72004083 -2.33258383
87 0.35736481 3.72004083
88 -0.33909373 0.35736481
89 1.08954202 -0.33909373
90 2.94441943 1.08954202
91 -3.03189428 2.94441943
92 7.36125696 -3.03189428
93 -3.85010046 7.36125696
94 -2.23040339 -3.85010046
95 -0.23849058 -2.23040339
96 -0.64558409 -0.23849058
97 -3.01365836 -0.64558409
98 -1.62355688 -3.01365836
99 2.68069536 -1.62355688
100 -0.23196108 2.68069536
101 0.38615470 -0.23196108
102 2.14408244 0.38615470
103 -4.74688676 2.14408244
104 6.06929160 -4.74688676
105 -2.94809685 6.06929160
106 -0.32564929 -2.94809685
107 2.77973958 -0.32564929
108 -0.30632528 2.77973958
109 3.91271251 -0.30632528
110 1.19378021 3.91271251
111 0.27026402 1.19378021
112 8.13085093 0.27026402
113 -0.01725873 8.13085093
114 -2.08945700 -0.01725873
115 -3.28756506 -2.08945700
116 5.53239419 -3.28756506
117 6.30523414 5.53239419
118 1.53201601 6.30523414
119 1.48974474 1.53201601
120 3.63892110 1.48974474
121 -5.17312533 3.63892110
122 2.82942253 -5.17312533
123 1.57316983 2.82942253
124 -3.76695009 1.57316983
125 -0.19399882 -3.76695009
126 -4.23258081 -0.19399882
127 0.50882614 -4.23258081
128 1.15794569 0.50882614
129 -1.81705424 1.15794569
130 0.34374771 -1.81705424
131 1.18378735 0.34374771
132 1.47737540 1.18378735
133 -1.57964802 1.47737540
134 2.89338190 -1.57964802
135 -4.25372915 2.89338190
136 -10.45113362 -4.25372915
137 -0.68073870 -10.45113362
138 1.48093608 -0.68073870
139 -1.39624816 1.48093608
140 1.88534010 -1.39624816
141 -0.08986777 1.88534010
142 1.57059474 -0.08986777
143 2.39813199 1.57059474
144 -9.73981959 2.39813199
145 -4.98313528 -9.73981959
146 -6.55926590 -4.98313528
147 1.32917737 -6.55926590
148 -1.39575763 1.32917737
149 -0.74958574 -1.39575763
150 2.60898969 -0.74958574
151 -0.57603932 2.60898969
152 3.34829400 -0.57603932
153 5.03468160 3.34829400
154 0.25778340 5.03468160
155 -2.94176129 0.25778340
156 -3.98655905 -2.94176129
157 3.84990126 -3.98655905
158 1.22830833 3.84990126
159 -1.27626428 1.22830833
160 3.21247086 -1.27626428
161 2.06755677 3.21247086
162 NA 2.06755677
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.63464312 0.72431887
[2,] 2.89783419 -3.63464312
[3,] 1.64766790 2.89783419
[4,] 3.22943482 1.64766790
[5,] -5.06416134 3.22943482
[6,] -5.27139576 -5.06416134
[7,] 2.95754673 -5.27139576
[8,] 1.11818668 2.95754673
[9,] 4.62166638 1.11818668
[10,] -3.47856610 4.62166638
[11,] -1.11209115 -3.47856610
[12,] 1.55459305 -1.11209115
[13,] 1.10392093 1.55459305
[14,] 2.84741927 1.10392093
[15,] -0.64111451 2.84741927
[16,] -1.98362111 -0.64111451
[17,] 2.97410704 -1.98362111
[18,] 2.27345172 2.97410704
[19,] -1.71468076 2.27345172
[20,] -0.52787263 -1.71468076
[21,] -0.78390119 -0.52787263
[22,] 2.42622440 -0.78390119
[23,] 4.55109856 2.42622440
[24,] -2.32461852 4.55109856
[25,] -1.76701384 -2.32461852
[26,] 0.49695372 -1.76701384
[27,] 2.63848676 0.49695372
[28,] -0.18781924 2.63848676
[29,] -0.65306728 -0.18781924
[30,] -4.80046398 -0.65306728
[31,] 0.96871447 -4.80046398
[32,] -4.14541289 0.96871447
[33,] 3.42807724 -4.14541289
[34,] -2.82867182 3.42807724
[35,] 0.27108484 -2.82867182
[36,] 1.37849986 0.27108484
[37,] -1.00450267 1.37849986
[38,] -0.11308788 -1.00450267
[39,] -1.38918288 -0.11308788
[40,] -3.29520404 -1.38918288
[41,] -1.29908975 -3.29520404
[42,] -4.91309252 -1.29908975
[43,] -1.32675882 -4.91309252
[44,] -6.83954808 -1.32675882
[45,] 0.66852061 -6.83954808
[46,] 2.69145713 0.66852061
[47,] 0.85209299 2.69145713
[48,] 3.88345363 0.85209299
[49,] -2.42987612 3.88345363
[50,] 4.92764224 -2.42987612
[51,] -2.13015893 4.92764224
[52,] -0.41187583 -2.13015893
[53,] 2.30519663 -0.41187583
[54,] -0.51810051 2.30519663
[55,] -2.86571836 -0.51810051
[56,] -2.37746934 -2.86571836
[57,] 1.34505606 -2.37746934
[58,] 1.73677670 1.34505606
[59,] 0.42680191 1.73677670
[60,] -2.96783906 0.42680191
[61,] -0.39476397 -2.96783906
[62,] 2.44159096 -0.39476397
[63,] 2.24622765 2.44159096
[64,] -3.09971255 2.24622765
[65,] 4.57690174 -3.09971255
[66,] -0.38338267 4.57690174
[67,] -4.78982313 -0.38338267
[68,] 3.00442070 -4.78982313
[69,] 2.00051657 3.00442070
[70,] -0.11114484 2.00051657
[71,] 4.01468667 -0.11114484
[72,] 5.18756421 4.01468667
[73,] 5.40725784 5.18756421
[74,] -5.89733839 5.40725784
[75,] -4.39691677 -5.89733839
[76,] 1.43354683 -4.39691677
[77,] -2.42590871 1.43354683
[78,] -0.14026011 -2.42590871
[79,] -7.79217973 -0.14026011
[80,] 2.01922139 -7.79217973
[81,] 3.07086255 2.01922139
[82,] 1.60871132 3.07086255
[83,] -2.75693295 1.60871132
[84,] 0.35469024 -2.75693295
[85,] -2.33258383 0.35469024
[86,] 3.72004083 -2.33258383
[87,] 0.35736481 3.72004083
[88,] -0.33909373 0.35736481
[89,] 1.08954202 -0.33909373
[90,] 2.94441943 1.08954202
[91,] -3.03189428 2.94441943
[92,] 7.36125696 -3.03189428
[93,] -3.85010046 7.36125696
[94,] -2.23040339 -3.85010046
[95,] -0.23849058 -2.23040339
[96,] -0.64558409 -0.23849058
[97,] -3.01365836 -0.64558409
[98,] -1.62355688 -3.01365836
[99,] 2.68069536 -1.62355688
[100,] -0.23196108 2.68069536
[101,] 0.38615470 -0.23196108
[102,] 2.14408244 0.38615470
[103,] -4.74688676 2.14408244
[104,] 6.06929160 -4.74688676
[105,] -2.94809685 6.06929160
[106,] -0.32564929 -2.94809685
[107,] 2.77973958 -0.32564929
[108,] -0.30632528 2.77973958
[109,] 3.91271251 -0.30632528
[110,] 1.19378021 3.91271251
[111,] 0.27026402 1.19378021
[112,] 8.13085093 0.27026402
[113,] -0.01725873 8.13085093
[114,] -2.08945700 -0.01725873
[115,] -3.28756506 -2.08945700
[116,] 5.53239419 -3.28756506
[117,] 6.30523414 5.53239419
[118,] 1.53201601 6.30523414
[119,] 1.48974474 1.53201601
[120,] 3.63892110 1.48974474
[121,] -5.17312533 3.63892110
[122,] 2.82942253 -5.17312533
[123,] 1.57316983 2.82942253
[124,] -3.76695009 1.57316983
[125,] -0.19399882 -3.76695009
[126,] -4.23258081 -0.19399882
[127,] 0.50882614 -4.23258081
[128,] 1.15794569 0.50882614
[129,] -1.81705424 1.15794569
[130,] 0.34374771 -1.81705424
[131,] 1.18378735 0.34374771
[132,] 1.47737540 1.18378735
[133,] -1.57964802 1.47737540
[134,] 2.89338190 -1.57964802
[135,] -4.25372915 2.89338190
[136,] -10.45113362 -4.25372915
[137,] -0.68073870 -10.45113362
[138,] 1.48093608 -0.68073870
[139,] -1.39624816 1.48093608
[140,] 1.88534010 -1.39624816
[141,] -0.08986777 1.88534010
[142,] 1.57059474 -0.08986777
[143,] 2.39813199 1.57059474
[144,] -9.73981959 2.39813199
[145,] -4.98313528 -9.73981959
[146,] -6.55926590 -4.98313528
[147,] 1.32917737 -6.55926590
[148,] -1.39575763 1.32917737
[149,] -0.74958574 -1.39575763
[150,] 2.60898969 -0.74958574
[151,] -0.57603932 2.60898969
[152,] 3.34829400 -0.57603932
[153,] 5.03468160 3.34829400
[154,] 0.25778340 5.03468160
[155,] -2.94176129 0.25778340
[156,] -3.98655905 -2.94176129
[157,] 3.84990126 -3.98655905
[158,] 1.22830833 3.84990126
[159,] -1.27626428 1.22830833
[160,] 3.21247086 -1.27626428
[161,] 2.06755677 3.21247086
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.63464312 0.72431887
2 2.89783419 -3.63464312
3 1.64766790 2.89783419
4 3.22943482 1.64766790
5 -5.06416134 3.22943482
6 -5.27139576 -5.06416134
7 2.95754673 -5.27139576
8 1.11818668 2.95754673
9 4.62166638 1.11818668
10 -3.47856610 4.62166638
11 -1.11209115 -3.47856610
12 1.55459305 -1.11209115
13 1.10392093 1.55459305
14 2.84741927 1.10392093
15 -0.64111451 2.84741927
16 -1.98362111 -0.64111451
17 2.97410704 -1.98362111
18 2.27345172 2.97410704
19 -1.71468076 2.27345172
20 -0.52787263 -1.71468076
21 -0.78390119 -0.52787263
22 2.42622440 -0.78390119
23 4.55109856 2.42622440
24 -2.32461852 4.55109856
25 -1.76701384 -2.32461852
26 0.49695372 -1.76701384
27 2.63848676 0.49695372
28 -0.18781924 2.63848676
29 -0.65306728 -0.18781924
30 -4.80046398 -0.65306728
31 0.96871447 -4.80046398
32 -4.14541289 0.96871447
33 3.42807724 -4.14541289
34 -2.82867182 3.42807724
35 0.27108484 -2.82867182
36 1.37849986 0.27108484
37 -1.00450267 1.37849986
38 -0.11308788 -1.00450267
39 -1.38918288 -0.11308788
40 -3.29520404 -1.38918288
41 -1.29908975 -3.29520404
42 -4.91309252 -1.29908975
43 -1.32675882 -4.91309252
44 -6.83954808 -1.32675882
45 0.66852061 -6.83954808
46 2.69145713 0.66852061
47 0.85209299 2.69145713
48 3.88345363 0.85209299
49 -2.42987612 3.88345363
50 4.92764224 -2.42987612
51 -2.13015893 4.92764224
52 -0.41187583 -2.13015893
53 2.30519663 -0.41187583
54 -0.51810051 2.30519663
55 -2.86571836 -0.51810051
56 -2.37746934 -2.86571836
57 1.34505606 -2.37746934
58 1.73677670 1.34505606
59 0.42680191 1.73677670
60 -2.96783906 0.42680191
61 -0.39476397 -2.96783906
62 2.44159096 -0.39476397
63 2.24622765 2.44159096
64 -3.09971255 2.24622765
65 4.57690174 -3.09971255
66 -0.38338267 4.57690174
67 -4.78982313 -0.38338267
68 3.00442070 -4.78982313
69 2.00051657 3.00442070
70 -0.11114484 2.00051657
71 4.01468667 -0.11114484
72 5.18756421 4.01468667
73 5.40725784 5.18756421
74 -5.89733839 5.40725784
75 -4.39691677 -5.89733839
76 1.43354683 -4.39691677
77 -2.42590871 1.43354683
78 -0.14026011 -2.42590871
79 -7.79217973 -0.14026011
80 2.01922139 -7.79217973
81 3.07086255 2.01922139
82 1.60871132 3.07086255
83 -2.75693295 1.60871132
84 0.35469024 -2.75693295
85 -2.33258383 0.35469024
86 3.72004083 -2.33258383
87 0.35736481 3.72004083
88 -0.33909373 0.35736481
89 1.08954202 -0.33909373
90 2.94441943 1.08954202
91 -3.03189428 2.94441943
92 7.36125696 -3.03189428
93 -3.85010046 7.36125696
94 -2.23040339 -3.85010046
95 -0.23849058 -2.23040339
96 -0.64558409 -0.23849058
97 -3.01365836 -0.64558409
98 -1.62355688 -3.01365836
99 2.68069536 -1.62355688
100 -0.23196108 2.68069536
101 0.38615470 -0.23196108
102 2.14408244 0.38615470
103 -4.74688676 2.14408244
104 6.06929160 -4.74688676
105 -2.94809685 6.06929160
106 -0.32564929 -2.94809685
107 2.77973958 -0.32564929
108 -0.30632528 2.77973958
109 3.91271251 -0.30632528
110 1.19378021 3.91271251
111 0.27026402 1.19378021
112 8.13085093 0.27026402
113 -0.01725873 8.13085093
114 -2.08945700 -0.01725873
115 -3.28756506 -2.08945700
116 5.53239419 -3.28756506
117 6.30523414 5.53239419
118 1.53201601 6.30523414
119 1.48974474 1.53201601
120 3.63892110 1.48974474
121 -5.17312533 3.63892110
122 2.82942253 -5.17312533
123 1.57316983 2.82942253
124 -3.76695009 1.57316983
125 -0.19399882 -3.76695009
126 -4.23258081 -0.19399882
127 0.50882614 -4.23258081
128 1.15794569 0.50882614
129 -1.81705424 1.15794569
130 0.34374771 -1.81705424
131 1.18378735 0.34374771
132 1.47737540 1.18378735
133 -1.57964802 1.47737540
134 2.89338190 -1.57964802
135 -4.25372915 2.89338190
136 -10.45113362 -4.25372915
137 -0.68073870 -10.45113362
138 1.48093608 -0.68073870
139 -1.39624816 1.48093608
140 1.88534010 -1.39624816
141 -0.08986777 1.88534010
142 1.57059474 -0.08986777
143 2.39813199 1.57059474
144 -9.73981959 2.39813199
145 -4.98313528 -9.73981959
146 -6.55926590 -4.98313528
147 1.32917737 -6.55926590
148 -1.39575763 1.32917737
149 -0.74958574 -1.39575763
150 2.60898969 -0.74958574
151 -0.57603932 2.60898969
152 3.34829400 -0.57603932
153 5.03468160 3.34829400
154 0.25778340 5.03468160
155 -2.94176129 0.25778340
156 -3.98655905 -2.94176129
157 3.84990126 -3.98655905
158 1.22830833 3.84990126
159 -1.27626428 1.22830833
160 3.21247086 -1.27626428
161 2.06755677 3.21247086
> 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/fisher/rcomp/tmp/7i9zn1355674453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8xyqx1355674453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9kuyw1355674453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/1042xj1355674453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/1178fx1355674453.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/fisher/rcomp/tmp/12yir21355674453.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/fisher/rcomp/tmp/13c4so1355674453.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/fisher/rcomp/tmp/14si0x1355674454.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/fisher/rcomp/tmp/15q5c61355674454.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/fisher/rcomp/tmp/16dss01355674454.tab")
+ }
>
> try(system("convert tmp/1dw1x1355674453.ps tmp/1dw1x1355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/2stiy1355674453.ps tmp/2stiy1355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mrc31355674453.ps tmp/3mrc31355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/40yzm1355674453.ps tmp/40yzm1355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/53qr61355674453.ps tmp/53qr61355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ozkc1355674453.ps tmp/6ozkc1355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/7i9zn1355674453.ps tmp/7i9zn1355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xyqx1355674453.ps tmp/8xyqx1355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kuyw1355674453.ps tmp/9kuyw1355674453.png",intern=TRUE))
character(0)
> try(system("convert tmp/1042xj1355674453.ps tmp/1042xj1355674453.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.387 1.709 10.103