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 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
Learning Jaar Volgnummer Connected Separate Software Happiness Depression
1 13 2000 1 41 38 12 14 12
2 16 2000 2 39 32 11 18 11
3 19 2000 3 30 35 15 11 14
4 15 2000 4 31 33 6 12 12
5 14 2000 5 34 37 13 16 21
6 13 2000 6 35 29 10 18 12
7 19 2000 7 39 31 12 14 22
8 15 2000 8 34 36 14 14 11
9 14 2000 9 36 35 12 15 10
10 15 2000 10 37 38 6 15 13
11 16 2000 11 38 31 10 17 10
12 16 2000 12 36 34 12 19 8
13 16 2000 13 38 35 12 10 15
14 16 2001 14 39 38 11 16 14
15 17 2001 15 33 37 15 18 10
16 15 2001 16 32 33 12 14 14
17 15 2001 17 36 32 10 14 14
18 20 2001 18 38 38 12 17 11
19 18 2001 19 39 38 11 14 10
20 16 2001 20 32 32 12 16 13
21 16 2001 21 32 33 11 18 7
22 16 2001 22 31 31 12 11 14
23 19 2001 23 39 38 13 14 12
24 16 2001 24 37 39 11 12 14
25 17 2001 25 39 32 9 17 11
26 17 2001 26 41 32 13 9 9
27 16 2002 27 36 35 10 16 11
28 15 2002 28 33 37 14 14 15
29 16 2002 29 33 33 12 15 14
30 14 2002 30 34 33 10 11 13
31 15 2002 31 31 28 12 16 9
32 12 2002 32 27 32 8 13 15
33 14 2002 33 37 31 10 17 10
34 16 2002 34 34 37 12 15 11
35 14 2002 35 34 30 12 14 13
36 7 2002 36 32 33 7 16 8
37 10 2002 37 29 31 6 9 20
38 14 2002 38 36 33 12 15 12
39 16 2002 39 29 31 10 17 10
40 16 2003 40 35 33 10 13 10
41 16 2003 41 37 32 10 15 9
42 14 2003 42 34 33 12 16 14
43 20 2003 43 38 32 15 16 8
44 14 2003 44 35 33 10 12 14
45 14 2003 45 38 28 10 12 11
46 11 2003 46 37 35 12 11 13
47 14 2003 47 38 39 13 15 9
48 15 2003 48 33 34 11 15 11
49 16 2003 49 36 38 11 17 15
50 14 2003 50 38 32 12 13 11
51 16 2003 51 32 38 14 16 10
52 14 2003 52 32 30 10 14 14
53 12 2004 53 32 33 12 11 18
54 16 2004 54 34 38 13 12 14
55 9 2004 55 32 32 5 12 11
56 14 2004 56 37 32 6 15 12
57 16 2004 57 39 34 12 16 13
58 16 2004 58 29 34 12 15 9
59 15 2004 59 37 36 11 12 10
60 16 2004 60 35 34 10 12 15
61 12 2004 61 30 28 7 8 20
62 16 2004 62 38 34 12 13 12
63 16 2004 63 34 35 14 11 12
64 14 2004 64 31 35 11 14 14
65 16 2004 65 34 31 12 15 13
66 17 2004 66 35 37 13 10 11
67 18 2005 67 36 35 14 11 17
68 18 2005 68 30 27 11 12 12
69 12 2005 69 39 40 12 15 13
70 16 2005 70 35 37 12 15 14
71 10 2005 71 38 36 8 14 13
72 14 2005 72 31 38 11 16 15
73 18 2005 73 34 39 14 15 13
74 18 2005 74 38 41 14 15 10
75 16 2005 75 34 27 12 13 11
76 17 2005 76 39 30 9 12 19
77 16 2005 77 37 37 13 17 13
78 16 2005 78 34 31 11 13 17
79 13 2005 79 28 31 12 15 13
80 16 2005 80 37 27 12 13 9
81 16 2006 81 33 36 12 15 11
82 20 2006 82 37 38 12 16 10
83 16 2006 83 35 37 12 15 9
84 15 2006 84 37 33 12 16 12
85 15 2006 85 32 34 11 15 12
86 16 2006 86 33 31 10 14 13
87 14 2006 87 38 39 9 15 13
88 16 2006 88 33 34 12 14 12
89 16 2006 89 29 32 12 13 15
90 15 2006 90 33 33 12 7 22
91 12 2006 91 31 36 9 17 13
92 17 2006 92 36 32 15 13 15
93 16 2006 93 35 41 12 15 13
94 15 2006 94 32 28 12 14 15
95 13 2007 95 29 30 12 13 10
96 16 2007 96 39 36 10 16 11
97 16 2007 97 37 35 13 12 16
98 16 2007 98 35 31 9 14 11
99 16 2007 99 37 34 12 17 11
100 14 2007 100 32 36 10 15 10
101 16 2007 101 38 36 14 17 10
102 16 2007 102 37 35 11 12 16
103 20 2007 103 36 37 15 16 12
104 15 2007 104 32 28 11 11 11
105 16 2007 105 33 39 11 15 16
106 13 2007 106 40 32 12 9 19
107 17 2007 107 38 35 12 16 11
108 16 2007 108 41 39 12 15 16
109 16 2008 109 36 35 11 10 15
110 12 2008 110 43 42 7 10 24
111 16 2008 111 30 34 12 15 14
112 16 2008 112 31 33 14 11 15
113 17 2008 113 32 41 11 13 11
114 13 2008 114 32 33 11 14 15
115 12 2008 115 37 34 10 18 12
116 18 2008 116 37 32 13 16 10
117 14 2008 117 33 40 13 14 14
118 14 2008 118 34 40 8 14 13
119 13 2008 119 33 35 11 14 9
120 16 2008 120 38 36 12 14 15
121 13 2008 121 33 37 11 12 15
122 16 2008 122 31 27 13 14 14
123 13 2009 123 38 39 12 15 11
124 16 2009 124 37 38 14 15 8
125 15 2009 125 33 31 13 15 11
126 16 2009 126 31 33 15 13 11
127 15 2009 127 39 32 10 17 8
128 17 2009 128 44 39 11 17 10
129 15 2009 129 33 36 9 19 11
130 12 2009 130 35 33 11 15 13
131 16 2009 131 32 33 10 13 11
132 10 2009 132 28 32 11 9 20
133 16 2009 133 40 37 8 15 10
134 12 2009 134 27 30 11 15 15
135 14 2009 135 37 38 12 15 12
136 15 2009 136 32 29 12 16 14
137 13 2010 137 28 22 9 11 23
138 15 2010 138 34 35 11 14 14
139 11 2010 139 30 35 10 11 16
140 12 2010 140 35 34 8 15 11
141 8 2010 141 31 35 9 13 12
142 16 2010 142 32 34 8 15 10
143 15 2010 143 30 34 9 16 14
144 17 2010 144 30 35 15 14 12
145 16 2010 145 31 23 11 15 12
146 10 2010 146 40 31 8 16 11
147 18 2010 147 32 27 13 16 12
148 13 2010 148 36 36 12 11 13
149 16 2010 149 32 31 12 12 11
150 13 2010 150 35 32 9 9 19
151 10 2011 151 38 39 7 16 12
152 15 2011 152 42 37 13 13 17
153 16 2011 153 34 38 9 16 9
154 16 2011 154 35 39 6 12 12
155 14 2011 155 35 34 8 9 19
156 10 2011 156 33 31 8 13 18
157 17 2011 157 36 32 15 13 15
158 13 2011 158 32 37 6 14 14
159 15 2011 159 33 36 9 19 11
160 16 2011 160 34 32 11 13 9
161 12 2011 161 32 35 8 12 18
162 13 2011 162 34 36 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
192.326462 -0.093294 0.002535 0.105539
Separate Software Happiness Depression
-0.013328 0.529685 0.052273 -0.063788
Belonging Belonging_Final
0.042922 -0.057536
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1294 -1.1943 0.2492 1.1071 4.1254
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.923e+02 1.036e+03 0.186 0.8530
Jaar -9.329e-02 5.182e-01 -0.180 0.8574
Volgnummer 2.535e-03 3.769e-02 0.067 0.9465
Connected 1.055e-01 4.738e-02 2.227 0.0274 *
Separate -1.333e-02 4.560e-02 -0.292 0.7705
Software 5.297e-01 6.974e-02 7.595 2.92e-12 ***
Happiness 5.227e-02 7.667e-02 0.682 0.4964
Depression -6.379e-02 5.671e-02 -1.125 0.2624
Belonging 4.292e-02 4.504e-02 0.953 0.3421
Belonging_Final -5.754e-02 6.461e-02 -0.891 0.3746
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.852 on 152 degrees of freedom
Multiple R-squared: 0.3639, Adjusted R-squared: 0.3262
F-statistic: 9.66 on 9 and 152 DF, p-value: 1.35e-11
> 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,] 0.73440255 0.53119489 0.2655974
[2,] 0.58720313 0.82559374 0.4127969
[3,] 0.46896271 0.93792543 0.5310373
[4,] 0.40164622 0.80329244 0.5983538
[5,] 0.30473028 0.60946056 0.6952697
[6,] 0.38652374 0.77304748 0.6134763
[7,] 0.32747019 0.65494038 0.6725298
[8,] 0.24667390 0.49334780 0.7533261
[9,] 0.18992410 0.37984820 0.8100759
[10,] 0.19137606 0.38275212 0.8086239
[11,] 0.38081528 0.76163056 0.6191847
[12,] 0.43842030 0.87684060 0.5615797
[13,] 0.39336054 0.78672108 0.6066395
[14,] 0.33025679 0.66051358 0.6697432
[15,] 0.28135162 0.56270324 0.7186484
[16,] 0.43514850 0.87029700 0.5648515
[17,] 0.37713479 0.75426958 0.6228652
[18,] 0.49489653 0.98979307 0.5051035
[19,] 0.44165551 0.88331102 0.5583445
[20,] 0.40782474 0.81564948 0.5921753
[21,] 0.39416684 0.78833367 0.6058332
[22,] 0.36258674 0.72517349 0.6374133
[23,] 0.31616422 0.63232843 0.6838358
[24,] 0.86071286 0.27857429 0.1392871
[25,] 0.83247181 0.33505638 0.1675282
[26,] 0.81245661 0.37508678 0.1875434
[27,] 0.84569654 0.30860692 0.1543035
[28,] 0.83171134 0.33657733 0.1682887
[29,] 0.80093591 0.39812818 0.1990641
[30,] 0.77451288 0.45097425 0.2254871
[31,] 0.79947010 0.40105979 0.2005299
[32,] 0.75893287 0.48213427 0.2410671
[33,] 0.73193494 0.53613011 0.2680651
[34,] 0.87154771 0.25690457 0.1284523
[35,] 0.89518331 0.20963338 0.1048167
[36,] 0.87344616 0.25310768 0.1265538
[37,] 0.87294079 0.25411843 0.1270592
[38,] 0.86594974 0.26810052 0.1340503
[39,] 0.83970329 0.32059341 0.1602967
[40,] 0.81089041 0.37821917 0.1891096
[41,] 0.82135267 0.35729467 0.1786473
[42,] 0.79241896 0.41516208 0.2075810
[43,] 0.80943437 0.38113126 0.1905656
[44,] 0.78977219 0.42045563 0.2102278
[45,] 0.75293829 0.49412343 0.2470617
[46,] 0.73833873 0.52332255 0.2616613
[47,] 0.70800411 0.58399178 0.2919959
[48,] 0.71340698 0.57318604 0.2865930
[49,] 0.67887992 0.64224016 0.3211201
[50,] 0.64273273 0.71453454 0.3572673
[51,] 0.60984310 0.78031380 0.3901569
[52,] 0.56583198 0.86833603 0.4341680
[53,] 0.52647061 0.94705877 0.4735294
[54,] 0.49571214 0.99142427 0.5042879
[55,] 0.49725741 0.99451482 0.5027426
[56,] 0.64447881 0.71104239 0.3555212
[57,] 0.74865060 0.50269881 0.2513494
[58,] 0.71631249 0.56737502 0.2836875
[59,] 0.81740553 0.36518893 0.1825945
[60,] 0.78702112 0.42595776 0.2129789
[61,] 0.78057042 0.43885917 0.2194296
[62,] 0.76344620 0.47310760 0.2365538
[63,] 0.72578449 0.54843101 0.2742155
[64,] 0.76310502 0.47378995 0.2368950
[65,] 0.72613119 0.54773763 0.2738688
[66,] 0.70460846 0.59078308 0.2953915
[67,] 0.71580212 0.56839576 0.2841979
[68,] 0.67498468 0.65003063 0.3250153
[69,] 0.63847598 0.72304805 0.3615240
[70,] 0.76267713 0.47464574 0.2373229
[71,] 0.72750681 0.54498638 0.2724932
[72,] 0.69720123 0.60559755 0.3027988
[73,] 0.65589173 0.68821655 0.3441083
[74,] 0.64317143 0.71365713 0.3568286
[75,] 0.59889681 0.80220637 0.4011032
[76,] 0.55652073 0.88695854 0.4434793
[77,] 0.53306617 0.93386765 0.4669338
[78,] 0.49378006 0.98756011 0.5062199
[79,] 0.48276424 0.96552847 0.5172358
[80,] 0.43943067 0.87886134 0.5605693
[81,] 0.39539200 0.79078400 0.6046080
[82,] 0.35186216 0.70372432 0.6481378
[83,] 0.37347186 0.74694372 0.6265281
[84,] 0.33650949 0.67301898 0.6634905
[85,] 0.29554335 0.59108670 0.7044567
[86,] 0.28890917 0.57781834 0.7110908
[87,] 0.24867043 0.49734086 0.7513296
[88,] 0.21582185 0.43164370 0.7841782
[89,] 0.19382297 0.38764594 0.8061770
[90,] 0.17551111 0.35102222 0.8244889
[91,] 0.21405818 0.42811636 0.7859418
[92,] 0.18060512 0.36121025 0.8193949
[93,] 0.17193121 0.34386242 0.8280688
[94,] 0.18302597 0.36605194 0.8169740
[95,] 0.16274124 0.32548249 0.8372588
[96,] 0.14353868 0.28707737 0.8564613
[97,] 0.13895144 0.27790289 0.8610486
[98,] 0.13602698 0.27205395 0.8639730
[99,] 0.12377144 0.24754288 0.8762286
[100,] 0.10639104 0.21278209 0.8936090
[101,] 0.14811855 0.29623711 0.8518814
[102,] 0.13839880 0.27679760 0.8616012
[103,] 0.15577697 0.31155394 0.8442230
[104,] 0.16490147 0.32980295 0.8350985
[105,] 0.14081180 0.28162359 0.8591882
[106,] 0.13801062 0.27602124 0.8619894
[107,] 0.12800927 0.25601855 0.8719907
[108,] 0.13594376 0.27188752 0.8640562
[109,] 0.11060500 0.22121000 0.8893950
[110,] 0.09420462 0.18840924 0.9057954
[111,] 0.09001536 0.18003072 0.9099846
[112,] 0.06963228 0.13926456 0.9303677
[113,] 0.05286615 0.10573231 0.9471338
[114,] 0.03964554 0.07929107 0.9603545
[115,] 0.02870401 0.05740801 0.9712960
[116,] 0.02689071 0.05378141 0.9731093
[117,] 0.02971876 0.05943752 0.9702812
[118,] 0.02932031 0.05864061 0.9706797
[119,] 0.03160282 0.06320564 0.9683972
[120,] 0.03397922 0.06795844 0.9660208
[121,] 0.06788489 0.13576978 0.9321151
[122,] 0.07000583 0.14001166 0.9299942
[123,] 0.05247340 0.10494680 0.9475266
[124,] 0.04312782 0.08625565 0.9568722
[125,] 0.03012378 0.06024757 0.9698762
[126,] 0.03543152 0.07086303 0.9645685
[127,] 0.02911965 0.05823931 0.9708803
[128,] 0.01887282 0.03774565 0.9811272
[129,] 0.49901067 0.99802134 0.5009893
[130,] 0.43455590 0.86911181 0.5654441
[131,] 0.36363582 0.72727165 0.6363642
[132,] 0.27676394 0.55352788 0.7232361
[133,] 0.19856014 0.39712029 0.8014399
[134,] 0.21544937 0.43089874 0.7845506
[135,] 0.22238474 0.44476948 0.7776153
[136,] 0.47024728 0.94049456 0.5297527
[137,] 0.61451413 0.77097173 0.3854859
> postscript(file="/var/wessaorg/rcomp/tmp/1rhud1355677670.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2y7tw1355677670.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/34ovb1355677670.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/48bio1355677670.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5x24a1355677670.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
-3.317054793 -0.254937820 2.511521748 2.863649861 -1.843605241 -2.176198646
7 8 9 10 11 12
3.711075670 -1.926644491 -2.182884673 2.147727694 0.518009862 -0.372467390
13 14 15 16 17 18
0.304052896 0.521686607 -0.605110215 -0.235475284 0.174016929 3.592648210
19 20 21 22 23 24
2.389463782 0.615643344 0.570221934 1.016075434 2.416467525 1.074128343
25 26 27 28 29 30
1.949222452 -0.122667502 0.828037421 -1.232274417 0.326420982 -0.162368602
31 32 33 34 35 36
-0.761129497 -0.434496612 -1.247475014 0.330439337 -1.846746778 -6.129444113
37 38 39 40 41 42
-1.240333916 -1.955204506 1.539617512 1.303206583 0.865926462 -1.699824272
43 44 45 46 47 48
2.158850179 -0.302720012 -0.976665305 -4.732034945 -2.479352956 -0.090505206
49 50 51 52 53 54
0.607042782 -2.132600365 -0.630681055 -0.276931865 -2.794503886 0.782155552
55 56 57 58 59 60
-2.661433867 1.260522654 -0.122255798 0.855565292 -0.414329848 1.744950044
61 62 63 64 65 66
0.404017200 -0.065130416 -0.587007149 -0.445552672 0.565463685 0.946049436
67 68 69 70 71 72
1.903549535 3.287470106 -3.857747406 0.558284445 -3.466053899 -0.344087907
73 74 75 76 77 78
1.389859897 0.855244339 0.247495995 3.023040274 -0.353946552 1.504239324
79 80 81 82 83 84
-1.925220407 0.100192113 0.430993066 3.373454854 0.386439842 -0.942309255
85 86 87 88 89 90
0.278593118 1.734276286 -0.241142406 0.701732104 1.464348413 0.701740831
91 92 93 94 95 96
-1.513662617 0.128916111 0.469553281 -0.309321873 -2.113650105 0.874735563
97 98 99 100 101 102
0.250931519 1.887719526 -0.045479162 -0.293996358 -1.210585985 1.241006735
103 104 105 106 107 108
2.675361385 0.531514134 1.409585794 -2.322766796 1.051376700 0.141221535
109 110 111 112 113 114
1.587947664 -0.196454451 1.066686246 0.214495024 2.474380266 -1.817348451
115 116 117 118 119 120
-2.764788415 1.509924047 -1.381329151 1.041367939 -1.838626661 0.344110727
121 122 123 124 125 126
-1.203750238 0.447477468 -2.852256255 -0.858074688 -0.797005727 -0.659094840
127 128 129 130 131 132
-0.283160333 0.931664939 1.286047852 -2.821770101 1.926816138 -3.313849973
133 134 135 136 137 138
1.994392290 -1.829695284 -1.543130826 -0.003251159 0.891995427 0.770727934
139 140 141 142 143 144
-2.012019927 -1.165785895 -5.251265485 3.108445520 1.738152842 0.577030700
145 146 147 148 149 150
1.359039223 -4.010973688 2.290556946 -1.990404843 0.999173962 0.035376334
151 152 153 154 155 156
-2.962562310 -1.203938227 2.100944136 4.125394993 1.671417893 -2.358920816
157 158 159 160 161 162
0.430590640 1.499475801 1.396576266 1.118042429 -0.475543607 0.558015323
> postscript(file="/var/wessaorg/rcomp/tmp/6my5q1355677670.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 -3.317054793 NA
1 -0.254937820 -3.317054793
2 2.511521748 -0.254937820
3 2.863649861 2.511521748
4 -1.843605241 2.863649861
5 -2.176198646 -1.843605241
6 3.711075670 -2.176198646
7 -1.926644491 3.711075670
8 -2.182884673 -1.926644491
9 2.147727694 -2.182884673
10 0.518009862 2.147727694
11 -0.372467390 0.518009862
12 0.304052896 -0.372467390
13 0.521686607 0.304052896
14 -0.605110215 0.521686607
15 -0.235475284 -0.605110215
16 0.174016929 -0.235475284
17 3.592648210 0.174016929
18 2.389463782 3.592648210
19 0.615643344 2.389463782
20 0.570221934 0.615643344
21 1.016075434 0.570221934
22 2.416467525 1.016075434
23 1.074128343 2.416467525
24 1.949222452 1.074128343
25 -0.122667502 1.949222452
26 0.828037421 -0.122667502
27 -1.232274417 0.828037421
28 0.326420982 -1.232274417
29 -0.162368602 0.326420982
30 -0.761129497 -0.162368602
31 -0.434496612 -0.761129497
32 -1.247475014 -0.434496612
33 0.330439337 -1.247475014
34 -1.846746778 0.330439337
35 -6.129444113 -1.846746778
36 -1.240333916 -6.129444113
37 -1.955204506 -1.240333916
38 1.539617512 -1.955204506
39 1.303206583 1.539617512
40 0.865926462 1.303206583
41 -1.699824272 0.865926462
42 2.158850179 -1.699824272
43 -0.302720012 2.158850179
44 -0.976665305 -0.302720012
45 -4.732034945 -0.976665305
46 -2.479352956 -4.732034945
47 -0.090505206 -2.479352956
48 0.607042782 -0.090505206
49 -2.132600365 0.607042782
50 -0.630681055 -2.132600365
51 -0.276931865 -0.630681055
52 -2.794503886 -0.276931865
53 0.782155552 -2.794503886
54 -2.661433867 0.782155552
55 1.260522654 -2.661433867
56 -0.122255798 1.260522654
57 0.855565292 -0.122255798
58 -0.414329848 0.855565292
59 1.744950044 -0.414329848
60 0.404017200 1.744950044
61 -0.065130416 0.404017200
62 -0.587007149 -0.065130416
63 -0.445552672 -0.587007149
64 0.565463685 -0.445552672
65 0.946049436 0.565463685
66 1.903549535 0.946049436
67 3.287470106 1.903549535
68 -3.857747406 3.287470106
69 0.558284445 -3.857747406
70 -3.466053899 0.558284445
71 -0.344087907 -3.466053899
72 1.389859897 -0.344087907
73 0.855244339 1.389859897
74 0.247495995 0.855244339
75 3.023040274 0.247495995
76 -0.353946552 3.023040274
77 1.504239324 -0.353946552
78 -1.925220407 1.504239324
79 0.100192113 -1.925220407
80 0.430993066 0.100192113
81 3.373454854 0.430993066
82 0.386439842 3.373454854
83 -0.942309255 0.386439842
84 0.278593118 -0.942309255
85 1.734276286 0.278593118
86 -0.241142406 1.734276286
87 0.701732104 -0.241142406
88 1.464348413 0.701732104
89 0.701740831 1.464348413
90 -1.513662617 0.701740831
91 0.128916111 -1.513662617
92 0.469553281 0.128916111
93 -0.309321873 0.469553281
94 -2.113650105 -0.309321873
95 0.874735563 -2.113650105
96 0.250931519 0.874735563
97 1.887719526 0.250931519
98 -0.045479162 1.887719526
99 -0.293996358 -0.045479162
100 -1.210585985 -0.293996358
101 1.241006735 -1.210585985
102 2.675361385 1.241006735
103 0.531514134 2.675361385
104 1.409585794 0.531514134
105 -2.322766796 1.409585794
106 1.051376700 -2.322766796
107 0.141221535 1.051376700
108 1.587947664 0.141221535
109 -0.196454451 1.587947664
110 1.066686246 -0.196454451
111 0.214495024 1.066686246
112 2.474380266 0.214495024
113 -1.817348451 2.474380266
114 -2.764788415 -1.817348451
115 1.509924047 -2.764788415
116 -1.381329151 1.509924047
117 1.041367939 -1.381329151
118 -1.838626661 1.041367939
119 0.344110727 -1.838626661
120 -1.203750238 0.344110727
121 0.447477468 -1.203750238
122 -2.852256255 0.447477468
123 -0.858074688 -2.852256255
124 -0.797005727 -0.858074688
125 -0.659094840 -0.797005727
126 -0.283160333 -0.659094840
127 0.931664939 -0.283160333
128 1.286047852 0.931664939
129 -2.821770101 1.286047852
130 1.926816138 -2.821770101
131 -3.313849973 1.926816138
132 1.994392290 -3.313849973
133 -1.829695284 1.994392290
134 -1.543130826 -1.829695284
135 -0.003251159 -1.543130826
136 0.891995427 -0.003251159
137 0.770727934 0.891995427
138 -2.012019927 0.770727934
139 -1.165785895 -2.012019927
140 -5.251265485 -1.165785895
141 3.108445520 -5.251265485
142 1.738152842 3.108445520
143 0.577030700 1.738152842
144 1.359039223 0.577030700
145 -4.010973688 1.359039223
146 2.290556946 -4.010973688
147 -1.990404843 2.290556946
148 0.999173962 -1.990404843
149 0.035376334 0.999173962
150 -2.962562310 0.035376334
151 -1.203938227 -2.962562310
152 2.100944136 -1.203938227
153 4.125394993 2.100944136
154 1.671417893 4.125394993
155 -2.358920816 1.671417893
156 0.430590640 -2.358920816
157 1.499475801 0.430590640
158 1.396576266 1.499475801
159 1.118042429 1.396576266
160 -0.475543607 1.118042429
161 0.558015323 -0.475543607
162 NA 0.558015323
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.254937820 -3.317054793
[2,] 2.511521748 -0.254937820
[3,] 2.863649861 2.511521748
[4,] -1.843605241 2.863649861
[5,] -2.176198646 -1.843605241
[6,] 3.711075670 -2.176198646
[7,] -1.926644491 3.711075670
[8,] -2.182884673 -1.926644491
[9,] 2.147727694 -2.182884673
[10,] 0.518009862 2.147727694
[11,] -0.372467390 0.518009862
[12,] 0.304052896 -0.372467390
[13,] 0.521686607 0.304052896
[14,] -0.605110215 0.521686607
[15,] -0.235475284 -0.605110215
[16,] 0.174016929 -0.235475284
[17,] 3.592648210 0.174016929
[18,] 2.389463782 3.592648210
[19,] 0.615643344 2.389463782
[20,] 0.570221934 0.615643344
[21,] 1.016075434 0.570221934
[22,] 2.416467525 1.016075434
[23,] 1.074128343 2.416467525
[24,] 1.949222452 1.074128343
[25,] -0.122667502 1.949222452
[26,] 0.828037421 -0.122667502
[27,] -1.232274417 0.828037421
[28,] 0.326420982 -1.232274417
[29,] -0.162368602 0.326420982
[30,] -0.761129497 -0.162368602
[31,] -0.434496612 -0.761129497
[32,] -1.247475014 -0.434496612
[33,] 0.330439337 -1.247475014
[34,] -1.846746778 0.330439337
[35,] -6.129444113 -1.846746778
[36,] -1.240333916 -6.129444113
[37,] -1.955204506 -1.240333916
[38,] 1.539617512 -1.955204506
[39,] 1.303206583 1.539617512
[40,] 0.865926462 1.303206583
[41,] -1.699824272 0.865926462
[42,] 2.158850179 -1.699824272
[43,] -0.302720012 2.158850179
[44,] -0.976665305 -0.302720012
[45,] -4.732034945 -0.976665305
[46,] -2.479352956 -4.732034945
[47,] -0.090505206 -2.479352956
[48,] 0.607042782 -0.090505206
[49,] -2.132600365 0.607042782
[50,] -0.630681055 -2.132600365
[51,] -0.276931865 -0.630681055
[52,] -2.794503886 -0.276931865
[53,] 0.782155552 -2.794503886
[54,] -2.661433867 0.782155552
[55,] 1.260522654 -2.661433867
[56,] -0.122255798 1.260522654
[57,] 0.855565292 -0.122255798
[58,] -0.414329848 0.855565292
[59,] 1.744950044 -0.414329848
[60,] 0.404017200 1.744950044
[61,] -0.065130416 0.404017200
[62,] -0.587007149 -0.065130416
[63,] -0.445552672 -0.587007149
[64,] 0.565463685 -0.445552672
[65,] 0.946049436 0.565463685
[66,] 1.903549535 0.946049436
[67,] 3.287470106 1.903549535
[68,] -3.857747406 3.287470106
[69,] 0.558284445 -3.857747406
[70,] -3.466053899 0.558284445
[71,] -0.344087907 -3.466053899
[72,] 1.389859897 -0.344087907
[73,] 0.855244339 1.389859897
[74,] 0.247495995 0.855244339
[75,] 3.023040274 0.247495995
[76,] -0.353946552 3.023040274
[77,] 1.504239324 -0.353946552
[78,] -1.925220407 1.504239324
[79,] 0.100192113 -1.925220407
[80,] 0.430993066 0.100192113
[81,] 3.373454854 0.430993066
[82,] 0.386439842 3.373454854
[83,] -0.942309255 0.386439842
[84,] 0.278593118 -0.942309255
[85,] 1.734276286 0.278593118
[86,] -0.241142406 1.734276286
[87,] 0.701732104 -0.241142406
[88,] 1.464348413 0.701732104
[89,] 0.701740831 1.464348413
[90,] -1.513662617 0.701740831
[91,] 0.128916111 -1.513662617
[92,] 0.469553281 0.128916111
[93,] -0.309321873 0.469553281
[94,] -2.113650105 -0.309321873
[95,] 0.874735563 -2.113650105
[96,] 0.250931519 0.874735563
[97,] 1.887719526 0.250931519
[98,] -0.045479162 1.887719526
[99,] -0.293996358 -0.045479162
[100,] -1.210585985 -0.293996358
[101,] 1.241006735 -1.210585985
[102,] 2.675361385 1.241006735
[103,] 0.531514134 2.675361385
[104,] 1.409585794 0.531514134
[105,] -2.322766796 1.409585794
[106,] 1.051376700 -2.322766796
[107,] 0.141221535 1.051376700
[108,] 1.587947664 0.141221535
[109,] -0.196454451 1.587947664
[110,] 1.066686246 -0.196454451
[111,] 0.214495024 1.066686246
[112,] 2.474380266 0.214495024
[113,] -1.817348451 2.474380266
[114,] -2.764788415 -1.817348451
[115,] 1.509924047 -2.764788415
[116,] -1.381329151 1.509924047
[117,] 1.041367939 -1.381329151
[118,] -1.838626661 1.041367939
[119,] 0.344110727 -1.838626661
[120,] -1.203750238 0.344110727
[121,] 0.447477468 -1.203750238
[122,] -2.852256255 0.447477468
[123,] -0.858074688 -2.852256255
[124,] -0.797005727 -0.858074688
[125,] -0.659094840 -0.797005727
[126,] -0.283160333 -0.659094840
[127,] 0.931664939 -0.283160333
[128,] 1.286047852 0.931664939
[129,] -2.821770101 1.286047852
[130,] 1.926816138 -2.821770101
[131,] -3.313849973 1.926816138
[132,] 1.994392290 -3.313849973
[133,] -1.829695284 1.994392290
[134,] -1.543130826 -1.829695284
[135,] -0.003251159 -1.543130826
[136,] 0.891995427 -0.003251159
[137,] 0.770727934 0.891995427
[138,] -2.012019927 0.770727934
[139,] -1.165785895 -2.012019927
[140,] -5.251265485 -1.165785895
[141,] 3.108445520 -5.251265485
[142,] 1.738152842 3.108445520
[143,] 0.577030700 1.738152842
[144,] 1.359039223 0.577030700
[145,] -4.010973688 1.359039223
[146,] 2.290556946 -4.010973688
[147,] -1.990404843 2.290556946
[148,] 0.999173962 -1.990404843
[149,] 0.035376334 0.999173962
[150,] -2.962562310 0.035376334
[151,] -1.203938227 -2.962562310
[152,] 2.100944136 -1.203938227
[153,] 4.125394993 2.100944136
[154,] 1.671417893 4.125394993
[155,] -2.358920816 1.671417893
[156,] 0.430590640 -2.358920816
[157,] 1.499475801 0.430590640
[158,] 1.396576266 1.499475801
[159,] 1.118042429 1.396576266
[160,] -0.475543607 1.118042429
[161,] 0.558015323 -0.475543607
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.254937820 -3.317054793
2 2.511521748 -0.254937820
3 2.863649861 2.511521748
4 -1.843605241 2.863649861
5 -2.176198646 -1.843605241
6 3.711075670 -2.176198646
7 -1.926644491 3.711075670
8 -2.182884673 -1.926644491
9 2.147727694 -2.182884673
10 0.518009862 2.147727694
11 -0.372467390 0.518009862
12 0.304052896 -0.372467390
13 0.521686607 0.304052896
14 -0.605110215 0.521686607
15 -0.235475284 -0.605110215
16 0.174016929 -0.235475284
17 3.592648210 0.174016929
18 2.389463782 3.592648210
19 0.615643344 2.389463782
20 0.570221934 0.615643344
21 1.016075434 0.570221934
22 2.416467525 1.016075434
23 1.074128343 2.416467525
24 1.949222452 1.074128343
25 -0.122667502 1.949222452
26 0.828037421 -0.122667502
27 -1.232274417 0.828037421
28 0.326420982 -1.232274417
29 -0.162368602 0.326420982
30 -0.761129497 -0.162368602
31 -0.434496612 -0.761129497
32 -1.247475014 -0.434496612
33 0.330439337 -1.247475014
34 -1.846746778 0.330439337
35 -6.129444113 -1.846746778
36 -1.240333916 -6.129444113
37 -1.955204506 -1.240333916
38 1.539617512 -1.955204506
39 1.303206583 1.539617512
40 0.865926462 1.303206583
41 -1.699824272 0.865926462
42 2.158850179 -1.699824272
43 -0.302720012 2.158850179
44 -0.976665305 -0.302720012
45 -4.732034945 -0.976665305
46 -2.479352956 -4.732034945
47 -0.090505206 -2.479352956
48 0.607042782 -0.090505206
49 -2.132600365 0.607042782
50 -0.630681055 -2.132600365
51 -0.276931865 -0.630681055
52 -2.794503886 -0.276931865
53 0.782155552 -2.794503886
54 -2.661433867 0.782155552
55 1.260522654 -2.661433867
56 -0.122255798 1.260522654
57 0.855565292 -0.122255798
58 -0.414329848 0.855565292
59 1.744950044 -0.414329848
60 0.404017200 1.744950044
61 -0.065130416 0.404017200
62 -0.587007149 -0.065130416
63 -0.445552672 -0.587007149
64 0.565463685 -0.445552672
65 0.946049436 0.565463685
66 1.903549535 0.946049436
67 3.287470106 1.903549535
68 -3.857747406 3.287470106
69 0.558284445 -3.857747406
70 -3.466053899 0.558284445
71 -0.344087907 -3.466053899
72 1.389859897 -0.344087907
73 0.855244339 1.389859897
74 0.247495995 0.855244339
75 3.023040274 0.247495995
76 -0.353946552 3.023040274
77 1.504239324 -0.353946552
78 -1.925220407 1.504239324
79 0.100192113 -1.925220407
80 0.430993066 0.100192113
81 3.373454854 0.430993066
82 0.386439842 3.373454854
83 -0.942309255 0.386439842
84 0.278593118 -0.942309255
85 1.734276286 0.278593118
86 -0.241142406 1.734276286
87 0.701732104 -0.241142406
88 1.464348413 0.701732104
89 0.701740831 1.464348413
90 -1.513662617 0.701740831
91 0.128916111 -1.513662617
92 0.469553281 0.128916111
93 -0.309321873 0.469553281
94 -2.113650105 -0.309321873
95 0.874735563 -2.113650105
96 0.250931519 0.874735563
97 1.887719526 0.250931519
98 -0.045479162 1.887719526
99 -0.293996358 -0.045479162
100 -1.210585985 -0.293996358
101 1.241006735 -1.210585985
102 2.675361385 1.241006735
103 0.531514134 2.675361385
104 1.409585794 0.531514134
105 -2.322766796 1.409585794
106 1.051376700 -2.322766796
107 0.141221535 1.051376700
108 1.587947664 0.141221535
109 -0.196454451 1.587947664
110 1.066686246 -0.196454451
111 0.214495024 1.066686246
112 2.474380266 0.214495024
113 -1.817348451 2.474380266
114 -2.764788415 -1.817348451
115 1.509924047 -2.764788415
116 -1.381329151 1.509924047
117 1.041367939 -1.381329151
118 -1.838626661 1.041367939
119 0.344110727 -1.838626661
120 -1.203750238 0.344110727
121 0.447477468 -1.203750238
122 -2.852256255 0.447477468
123 -0.858074688 -2.852256255
124 -0.797005727 -0.858074688
125 -0.659094840 -0.797005727
126 -0.283160333 -0.659094840
127 0.931664939 -0.283160333
128 1.286047852 0.931664939
129 -2.821770101 1.286047852
130 1.926816138 -2.821770101
131 -3.313849973 1.926816138
132 1.994392290 -3.313849973
133 -1.829695284 1.994392290
134 -1.543130826 -1.829695284
135 -0.003251159 -1.543130826
136 0.891995427 -0.003251159
137 0.770727934 0.891995427
138 -2.012019927 0.770727934
139 -1.165785895 -2.012019927
140 -5.251265485 -1.165785895
141 3.108445520 -5.251265485
142 1.738152842 3.108445520
143 0.577030700 1.738152842
144 1.359039223 0.577030700
145 -4.010973688 1.359039223
146 2.290556946 -4.010973688
147 -1.990404843 2.290556946
148 0.999173962 -1.990404843
149 0.035376334 0.999173962
150 -2.962562310 0.035376334
151 -1.203938227 -2.962562310
152 2.100944136 -1.203938227
153 4.125394993 2.100944136
154 1.671417893 4.125394993
155 -2.358920816 1.671417893
156 0.430590640 -2.358920816
157 1.499475801 0.430590640
158 1.396576266 1.499475801
159 1.118042429 1.396576266
160 -0.475543607 1.118042429
161 0.558015323 -0.475543607
> 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/wessaorg/rcomp/tmp/7i6n31355677670.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8u85s1355677670.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9elfc1355677670.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/104zje1355677670.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/114bf31355677670.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/wessaorg/rcomp/tmp/12si1g1355677670.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/wessaorg/rcomp/tmp/13n49i1355677670.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/wessaorg/rcomp/tmp/14z55m1355677670.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/wessaorg/rcomp/tmp/15osoa1355677670.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/wessaorg/rcomp/tmp/16u3dg1355677670.tab")
+ }
>
> try(system("convert tmp/1rhud1355677670.ps tmp/1rhud1355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/2y7tw1355677670.ps tmp/2y7tw1355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/34ovb1355677670.ps tmp/34ovb1355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/48bio1355677670.ps tmp/48bio1355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x24a1355677670.ps tmp/5x24a1355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/6my5q1355677670.ps tmp/6my5q1355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/7i6n31355677670.ps tmp/7i6n31355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u85s1355677670.ps tmp/8u85s1355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/9elfc1355677670.ps tmp/9elfc1355677670.png",intern=TRUE))
character(0)
> try(system("convert tmp/104zje1355677670.ps tmp/104zje1355677670.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.053 0.991 9.072