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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> x <- array(list(2000
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+ ,dim=c(9
+ ,162)
+ ,dimnames=list(c('jaar'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(9,162),dimnames=list(c('jaar','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
Learning jaar Connected Separate Software Happiness Depression Belonging
1 13 2000 41 38 12 14 12 53
2 16 2000 39 32 11 18 11 86
3 19 2000 30 35 15 11 14 66
4 15 2000 31 33 6 12 12 67
5 14 2000 34 37 13 16 21 76
6 13 2000 35 29 10 18 12 78
7 19 2000 39 31 12 14 22 53
8 15 2000 34 36 14 14 11 80
9 14 2000 36 35 12 15 10 74
10 15 2000 37 38 6 15 13 76
11 16 2000 38 31 10 17 10 79
12 16 2000 36 34 12 19 8 54
13 16 2000 38 35 12 10 15 67
14 16 2001 39 38 11 16 14 54
15 17 2001 33 37 15 18 10 87
16 15 2001 32 33 12 14 14 58
17 15 2001 36 32 10 14 14 75
18 20 2001 38 38 12 17 11 88
19 18 2001 39 38 11 14 10 64
20 16 2001 32 32 12 16 13 57
21 16 2001 32 33 11 18 7 66
22 16 2001 31 31 12 11 14 68
23 19 2001 39 38 13 14 12 54
24 16 2001 37 39 11 12 14 56
25 17 2001 39 32 9 17 11 86
26 17 2001 41 32 13 9 9 80
27 16 2002 36 35 10 16 11 76
28 15 2002 33 37 14 14 15 69
29 16 2002 33 33 12 15 14 78
30 14 2002 34 33 10 11 13 67
31 15 2002 31 28 12 16 9 80
32 12 2002 27 32 8 13 15 54
33 14 2002 37 31 10 17 10 71
34 16 2002 34 37 12 15 11 84
35 14 2002 34 30 12 14 13 74
36 7 2002 32 33 7 16 8 71
37 10 2002 29 31 6 9 20 63
38 14 2002 36 33 12 15 12 71
39 16 2002 29 31 10 17 10 76
40 16 2003 35 33 10 13 10 69
41 16 2003 37 32 10 15 9 74
42 14 2003 34 33 12 16 14 75
43 20 2003 38 32 15 16 8 54
44 14 2003 35 33 10 12 14 52
45 14 2003 38 28 10 12 11 69
46 11 2003 37 35 12 11 13 68
47 14 2003 38 39 13 15 9 65
48 15 2003 33 34 11 15 11 75
49 16 2003 36 38 11 17 15 74
50 14 2003 38 32 12 13 11 75
51 16 2003 32 38 14 16 10 72
52 14 2003 32 30 10 14 14 67
53 12 2004 32 33 12 11 18 63
54 16 2004 34 38 13 12 14 62
55 9 2004 32 32 5 12 11 63
56 14 2004 37 32 6 15 12 76
57 16 2004 39 34 12 16 13 74
58 16 2004 29 34 12 15 9 67
59 15 2004 37 36 11 12 10 73
60 16 2004 35 34 10 12 15 70
61 12 2004 30 28 7 8 20 53
62 16 2004 38 34 12 13 12 77
63 16 2004 34 35 14 11 12 77
64 14 2004 31 35 11 14 14 52
65 16 2004 34 31 12 15 13 54
66 17 2004 35 37 13 10 11 80
67 18 2005 36 35 14 11 17 66
68 18 2005 30 27 11 12 12 73
69 12 2005 39 40 12 15 13 63
70 16 2005 35 37 12 15 14 69
71 10 2005 38 36 8 14 13 67
72 14 2005 31 38 11 16 15 54
73 18 2005 34 39 14 15 13 81
74 18 2005 38 41 14 15 10 69
75 16 2005 34 27 12 13 11 84
76 17 2005 39 30 9 12 19 80
77 16 2005 37 37 13 17 13 70
78 16 2005 34 31 11 13 17 69
79 13 2005 28 31 12 15 13 77
80 16 2005 37 27 12 13 9 54
81 16 2006 33 36 12 15 11 79
82 20 2006 37 38 12 16 10 30
83 16 2006 35 37 12 15 9 71
84 15 2006 37 33 12 16 12 73
85 15 2006 32 34 11 15 12 72
86 16 2006 33 31 10 14 13 77
87 14 2006 38 39 9 15 13 75
88 16 2006 33 34 12 14 12 69
89 16 2006 29 32 12 13 15 54
90 15 2006 33 33 12 7 22 70
91 12 2006 31 36 9 17 13 73
92 17 2006 36 32 15 13 15 54
93 16 2006 35 41 12 15 13 77
94 15 2006 32 28 12 14 15 82
95 13 2007 29 30 12 13 10 80
96 16 2007 39 36 10 16 11 80
97 16 2007 37 35 13 12 16 69
98 16 2007 35 31 9 14 11 78
99 16 2007 37 34 12 17 11 81
100 14 2007 32 36 10 15 10 76
101 16 2007 38 36 14 17 10 76
102 16 2007 37 35 11 12 16 73
103 20 2007 36 37 15 16 12 85
104 15 2007 32 28 11 11 11 66
105 16 2007 33 39 11 15 16 79
106 13 2007 40 32 12 9 19 68
107 17 2007 38 35 12 16 11 76
108 16 2007 41 39 12 15 16 71
109 16 2008 36 35 11 10 15 54
110 12 2008 43 42 7 10 24 46
111 16 2008 30 34 12 15 14 82
112 16 2008 31 33 14 11 15 74
113 17 2008 32 41 11 13 11 88
114 13 2008 32 33 11 14 15 38
115 12 2008 37 34 10 18 12 76
116 18 2008 37 32 13 16 10 86
117 14 2008 33 40 13 14 14 54
118 14 2008 34 40 8 14 13 70
119 13 2008 33 35 11 14 9 69
120 16 2008 38 36 12 14 15 90
121 13 2008 33 37 11 12 15 54
122 16 2008 31 27 13 14 14 76
123 13 2009 38 39 12 15 11 89
124 16 2009 37 38 14 15 8 76
125 15 2009 33 31 13 15 11 73
126 16 2009 31 33 15 13 11 79
127 15 2009 39 32 10 17 8 90
128 17 2009 44 39 11 17 10 74
129 15 2009 33 36 9 19 11 81
130 12 2009 35 33 11 15 13 72
131 16 2009 32 33 10 13 11 71
132 10 2009 28 32 11 9 20 66
133 16 2009 40 37 8 15 10 77
134 12 2009 27 30 11 15 15 65
135 14 2009 37 38 12 15 12 74
136 15 2009 32 29 12 16 14 82
137 13 2010 28 22 9 11 23 54
138 15 2010 34 35 11 14 14 63
139 11 2010 30 35 10 11 16 54
140 12 2010 35 34 8 15 11 64
141 8 2010 31 35 9 13 12 69
142 16 2010 32 34 8 15 10 54
143 15 2010 30 34 9 16 14 84
144 17 2010 30 35 15 14 12 86
145 16 2010 31 23 11 15 12 77
146 10 2010 40 31 8 16 11 89
147 18 2010 32 27 13 16 12 76
148 13 2010 36 36 12 11 13 60
149 16 2010 32 31 12 12 11 75
150 13 2010 35 32 9 9 19 73
151 10 2011 38 39 7 16 12 85
152 15 2011 42 37 13 13 17 79
153 16 2011 34 38 9 16 9 71
154 16 2011 35 39 6 12 12 72
155 14 2011 35 34 8 9 19 69
156 10 2011 33 31 8 13 18 78
157 17 2011 36 32 15 13 15 54
158 13 2011 32 37 6 14 14 69
159 15 2011 33 36 9 19 11 81
160 16 2011 34 32 11 13 9 84
161 12 2011 32 35 8 12 18 84
162 13 2011 34 36 8 13 16 69
Belonging_Final
1 32
2 51
3 42
4 41
5 46
6 47
7 37
8 49
9 45
10 47
11 49
12 33
13 42
14 33
15 53
16 36
17 45
18 54
19 41
20 36
21 41
22 44
23 33
24 37
25 52
26 47
27 43
28 44
29 45
30 44
31 49
32 33
33 43
34 54
35 42
36 44
37 37
38 43
39 46
40 42
41 45
42 44
43 33
44 31
45 42
46 40
47 43
48 46
49 42
50 45
51 44
52 40
53 37
54 46
55 36
56 47
57 45
58 42
59 43
60 43
61 32
62 45
63 45
64 31
65 33
66 49
67 42
68 41
69 38
70 42
71 44
72 33
73 48
74 40
75 50
76 49
77 43
78 44
79 47
80 33
81 46
82 0
83 45
84 43
85 44
86 47
87 45
88 42
89 33
90 43
91 46
92 33
93 46
94 48
95 47
96 47
97 43
98 46
99 48
100 46
101 45
102 45
103 52
104 42
105 47
106 41
107 47
108 43
109 33
110 30
111 49
112 44
113 55
114 11
115 47
116 53
117 33
118 44
119 42
120 55
121 33
122 46
123 54
124 47
125 45
126 47
127 55
128 44
129 53
130 44
131 42
132 40
133 46
134 40
135 46
136 53
137 33
138 42
139 35
140 40
141 41
142 33
143 51
144 53
145 46
146 55
147 47
148 38
149 46
150 46
151 53
152 47
153 41
154 44
155 43
156 51
157 33
158 43
159 53
160 51
161 50
162 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) jaar Connected Separate
122.88585 -0.05856 0.10557 -0.01375
Software Happiness Depression Belonging
0.52984 0.05223 -0.06368 0.04267
Belonging_Final
-0.05700
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1217 -1.1834 0.2422 1.1161 4.1220
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 122.88585 89.72244 1.370 0.1728
jaar -0.05856 0.04475 -1.309 0.1926
Connected 0.10557 0.04723 2.235 0.0268 *
Separate -0.01375 0.04502 -0.305 0.7605
Software 0.52984 0.06948 7.626 2.39e-12 ***
Happiness 0.05223 0.07642 0.683 0.4954
Depression -0.06368 0.05650 -1.127 0.2615
Belonging 0.04267 0.04474 0.954 0.3417
Belonging_Final -0.05700 0.06391 -0.892 0.3739
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.846 on 153 degrees of freedom
Multiple R-squared: 0.3638, Adjusted R-squared: 0.3306
F-statistic: 10.94 on 8 and 153 DF, p-value: 3.909e-12
> 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,] 0.76238322 0.47523356 0.2376168
[2,] 0.62581866 0.74836268 0.3741813
[3,] 0.48318572 0.96637144 0.5168143
[4,] 0.37127681 0.74255362 0.6287232
[5,] 0.34338270 0.68676541 0.6566173
[6,] 0.24994042 0.49988085 0.7500596
[7,] 0.33343455 0.66686909 0.6665655
[8,] 0.27933820 0.55867640 0.7206618
[9,] 0.21652657 0.43305314 0.7834734
[10,] 0.17073466 0.34146933 0.8292653
[11,] 0.19144522 0.38289044 0.8085548
[12,] 0.31621326 0.63242652 0.6837867
[13,] 0.38424162 0.76848324 0.6157584
[14,] 0.33530485 0.67060969 0.6646952
[15,] 0.27590963 0.55181926 0.7240904
[16,] 0.23378593 0.46757187 0.7662141
[17,] 0.37999349 0.75998697 0.6200065
[18,] 0.32658319 0.65316639 0.6734168
[19,] 0.46319824 0.92639648 0.5368018
[20,] 0.41292237 0.82584474 0.5870776
[21,] 0.37148244 0.74296489 0.6285176
[22,] 0.35742047 0.71484094 0.6425795
[23,] 0.32518906 0.65037812 0.6748109
[24,] 0.27998507 0.55997015 0.7200149
[25,] 0.84269711 0.31460578 0.1573029
[26,] 0.81591984 0.36816032 0.1840802
[27,] 0.80216021 0.39567958 0.1978398
[28,] 0.82678084 0.34643832 0.1732192
[29,] 0.81659766 0.36680469 0.1834023
[30,] 0.78750117 0.42499765 0.2124988
[31,] 0.76063681 0.47872638 0.2393632
[32,] 0.78813143 0.42373713 0.2118686
[33,] 0.74684861 0.50630278 0.2531514
[34,] 0.71795695 0.56408610 0.2820431
[35,] 0.86865130 0.26269739 0.1313487
[36,] 0.89815981 0.20368038 0.1018402
[37,] 0.87510576 0.24978848 0.1248942
[38,] 0.86699418 0.26601163 0.1330058
[39,] 0.86247920 0.27504160 0.1375208
[40,] 0.83434864 0.33130273 0.1656514
[41,] 0.80298529 0.39402943 0.1970147
[42,] 0.81251656 0.37496688 0.1874834
[43,] 0.78255443 0.43489114 0.2174456
[44,] 0.79967009 0.40065981 0.2003299
[45,] 0.78082615 0.43834770 0.2191739
[46,] 0.74348907 0.51302186 0.2565109
[47,] 0.72888404 0.54223191 0.2711160
[48,] 0.69822800 0.60354401 0.3017720
[49,] 0.70299406 0.59401187 0.2970059
[50,] 0.66786768 0.66426465 0.3321323
[51,] 0.63135262 0.73729475 0.3686474
[52,] 0.59845194 0.80309611 0.4015481
[53,] 0.55454226 0.89091547 0.4454577
[54,] 0.51644192 0.96711616 0.4835581
[55,] 0.48873848 0.97747696 0.5112615
[56,] 0.48712564 0.97425129 0.5128744
[57,] 0.63231633 0.73536734 0.3676837
[58,] 0.74217937 0.51564125 0.2578206
[59,] 0.70944163 0.58111673 0.2905584
[60,] 0.81453934 0.37092131 0.1854607
[61,] 0.78417693 0.43164614 0.2158231
[62,] 0.77794361 0.44411279 0.2220564
[63,] 0.76121817 0.47756365 0.2387818
[64,] 0.72370606 0.55258788 0.2762939
[65,] 0.76302205 0.47395589 0.2369779
[66,] 0.72597903 0.54804195 0.2740210
[67,] 0.70576263 0.58847475 0.2942374
[68,] 0.71529220 0.56941561 0.2847078
[69,] 0.67457938 0.65084124 0.3254206
[70,] 0.63816936 0.72366128 0.3618306
[71,] 0.76382926 0.47234148 0.2361707
[72,] 0.72919829 0.54160343 0.2708017
[73,] 0.69978466 0.60043067 0.3002153
[74,] 0.65899207 0.68201586 0.3410079
[75,] 0.64620745 0.70758510 0.3537925
[76,] 0.60246365 0.79507271 0.3975364
[77,] 0.56041886 0.87916229 0.4395811
[78,] 0.53736095 0.92527809 0.4626390
[79,] 0.49845673 0.99691347 0.5015433
[80,] 0.48749469 0.97498938 0.5125053
[81,] 0.44503588 0.89007176 0.5549641
[82,] 0.40136063 0.80272126 0.5986394
[83,] 0.35793811 0.71587622 0.6420619
[84,] 0.38156949 0.76313899 0.6184305
[85,] 0.34485194 0.68970387 0.6551481
[86,] 0.30380092 0.60760184 0.6961991
[87,] 0.29695278 0.59390556 0.7030472
[88,] 0.25640350 0.51280700 0.7435965
[89,] 0.22362585 0.44725170 0.7763741
[90,] 0.20168444 0.40336888 0.7983156
[91,] 0.18303548 0.36607096 0.8169645
[92,] 0.22329109 0.44658218 0.7767089
[93,] 0.18923889 0.37847777 0.8107611
[94,] 0.18149116 0.36298232 0.8185088
[95,] 0.19287708 0.38575416 0.8071229
[96,] 0.17265552 0.34531104 0.8273445
[97,] 0.15267163 0.30534326 0.8473284
[98,] 0.14833185 0.29666371 0.8516681
[99,] 0.14378533 0.28757066 0.8562147
[100,] 0.12877460 0.25754920 0.8712254
[101,] 0.10848120 0.21696241 0.8915188
[102,] 0.13927775 0.27855550 0.8607222
[103,] 0.12778143 0.25556285 0.8722186
[104,] 0.14737793 0.29475587 0.8526221
[105,] 0.15033990 0.30067980 0.8496601
[106,] 0.12759422 0.25518845 0.8724058
[107,] 0.12516939 0.25033878 0.8748306
[108,] 0.11486485 0.22972970 0.8851352
[109,] 0.12693878 0.25387756 0.8730612
[110,] 0.10348504 0.20697009 0.8965150
[111,] 0.09242718 0.18485435 0.9075728
[112,] 0.09287396 0.18574791 0.9071260
[113,] 0.07256884 0.14513769 0.9274312
[114,] 0.05638172 0.11276345 0.9436183
[115,] 0.04223022 0.08446045 0.9577698
[116,] 0.03069063 0.06138125 0.9693094
[117,] 0.02851459 0.05702919 0.9714854
[118,] 0.02872434 0.05744867 0.9712757
[119,] 0.02958691 0.05917381 0.9704131
[120,] 0.03041451 0.06082902 0.9695855
[121,] 0.03351093 0.06702187 0.9664891
[122,] 0.07213271 0.14426543 0.9278673
[123,] 0.06707387 0.13414774 0.9329261
[124,] 0.05384767 0.10769534 0.9461523
[125,] 0.04856029 0.09712058 0.9514397
[126,] 0.03404964 0.06809929 0.9659504
[127,] 0.03043184 0.06086367 0.9695682
[128,] 0.03378698 0.06757397 0.9662130
[129,] 0.02330112 0.04660224 0.9766989
[130,] 0.56754906 0.86490188 0.4324509
[131,] 0.50744412 0.98511176 0.4925559
[132,] 0.44164054 0.88328108 0.5583595
[133,] 0.34975218 0.69950436 0.6502478
[134,] 0.26485587 0.52971174 0.7351441
[135,] 0.26735203 0.53470405 0.7326480
[136,] 0.29715106 0.59430213 0.7028489
[137,] 0.60962572 0.78074856 0.3903743
[138,] 0.53819105 0.92361791 0.4618090
[139,] 0.37209625 0.74419250 0.6279037
> postscript(file="/var/fisher/rcomp/tmp/1hqtg1355673318.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2hevh1355673318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34yct1355673318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4jlxj1355673318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5pq5k1355673318.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
-3.326182299 -0.265476050 2.503640720 2.859906790 -1.845577370 -2.177524392
7 8 9 10 11 12
3.710541464 -1.921201311 -2.174278651 2.160159413 0.529457031 -0.354848287
13 14 15 16 17 18
0.321831724 0.510602605 -0.616463911 -0.244210603 0.167003369 3.589195178
19 20 21 22 23 24
2.389620751 0.616579113 0.574580501 1.019831235 2.428010288 1.087073597
25 26 27 28 29 30
1.962009464 -0.107015144 0.814633767 -1.245628084 0.316090070 -0.172140010
31 32 33 34 35 36
-0.769470576 -0.436559342 -1.248465890 0.331455314 -1.842490169 -6.121703091
37 38 39 40 41 42
-1.230507684 -1.943269722 1.553747452 1.285998645 0.850606417 -1.712125394
43 44 45 46 47 48
2.149355175 -0.308621637 -0.983550638 -4.733173856 -2.478195496 -0.087764632
49 50 51 52 53 54
0.615435688 -2.125512907 -0.618624468 -0.264696368 -2.813483108 0.763028957
55 56 57 58 59 60
-2.673310907 1.248246666 -0.131674618 0.849267088 -0.416648032 1.743268244
61 62 63 64 65 66
0.403917416 -0.061121679 -0.580317580 -0.434574010 0.576621352 0.959990195
67 68 69 70 71 72
1.883914175 3.270538510 -3.867994890 0.548694155 -3.474486555 -0.346884027
73 74 75 76 77 78
1.388317938 0.858554885 0.246107345 3.024389434 -0.346102484 1.511124975
79 80 81 82 83 84
-1.914851418 0.113213237 0.414880952 3.373078560 0.374519938 -0.952162751
85 86 87 88 89 90
0.271197241 1.727770212 -0.241145120 0.702026097 1.467175140 0.705005086
91 92 93 94 95 96
-1.505489271 0.138636970 0.485187856 -0.296602263 -2.131650698 0.861802560
97 98 99 100 101 102
0.238386133 1.877996630 -0.052138723 -0.296949111 -1.211220293 1.241383452
103 104 105 106 107 108
2.678368144 0.534546849 1.419938144 -2.313337687 1.064636262 0.158900935
109 110 111 112 113 114
1.573074035 -0.206806717 1.055255413 0.205219598 2.469584026 -1.812331313
115 116 117 118 119 120
-2.766063378 1.509276989 -1.373751123 1.050463227 -1.828292364 0.354707462
121 122 123 124 125 126
-1.187170032 0.460866908 -2.866760470 -0.869918965 -0.808967280 -0.667600462
127 128 129 130 131 132
-0.290049255 0.931600591 1.284869769 -2.819894488 1.932430688 -3.307474071
133 134 135 136 137 138
2.006365724 -1.818494074 -1.527163965 0.009720201 0.872951511 0.757712641
139 140 141 142 143 144
-2.021061731 -1.172024233 -5.254065893 3.108734428 1.738347023 0.578782261
145 146 147 148 149 150
1.360428311 -4.005173058 2.297610266 -1.976512666 1.013352907 0.051393363
151 152 153 154 155 156
-2.975261839 -1.215002329 2.095952427 4.121952127 1.666987087 -2.363751351
157 158 159 160 161 162
0.431458237 1.505095517 1.401998276 1.125722693 -0.464010081 0.571113135
> postscript(file="/var/fisher/rcomp/tmp/6xqfy1355673318.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 -3.326182299 NA
1 -0.265476050 -3.326182299
2 2.503640720 -0.265476050
3 2.859906790 2.503640720
4 -1.845577370 2.859906790
5 -2.177524392 -1.845577370
6 3.710541464 -2.177524392
7 -1.921201311 3.710541464
8 -2.174278651 -1.921201311
9 2.160159413 -2.174278651
10 0.529457031 2.160159413
11 -0.354848287 0.529457031
12 0.321831724 -0.354848287
13 0.510602605 0.321831724
14 -0.616463911 0.510602605
15 -0.244210603 -0.616463911
16 0.167003369 -0.244210603
17 3.589195178 0.167003369
18 2.389620751 3.589195178
19 0.616579113 2.389620751
20 0.574580501 0.616579113
21 1.019831235 0.574580501
22 2.428010288 1.019831235
23 1.087073597 2.428010288
24 1.962009464 1.087073597
25 -0.107015144 1.962009464
26 0.814633767 -0.107015144
27 -1.245628084 0.814633767
28 0.316090070 -1.245628084
29 -0.172140010 0.316090070
30 -0.769470576 -0.172140010
31 -0.436559342 -0.769470576
32 -1.248465890 -0.436559342
33 0.331455314 -1.248465890
34 -1.842490169 0.331455314
35 -6.121703091 -1.842490169
36 -1.230507684 -6.121703091
37 -1.943269722 -1.230507684
38 1.553747452 -1.943269722
39 1.285998645 1.553747452
40 0.850606417 1.285998645
41 -1.712125394 0.850606417
42 2.149355175 -1.712125394
43 -0.308621637 2.149355175
44 -0.983550638 -0.308621637
45 -4.733173856 -0.983550638
46 -2.478195496 -4.733173856
47 -0.087764632 -2.478195496
48 0.615435688 -0.087764632
49 -2.125512907 0.615435688
50 -0.618624468 -2.125512907
51 -0.264696368 -0.618624468
52 -2.813483108 -0.264696368
53 0.763028957 -2.813483108
54 -2.673310907 0.763028957
55 1.248246666 -2.673310907
56 -0.131674618 1.248246666
57 0.849267088 -0.131674618
58 -0.416648032 0.849267088
59 1.743268244 -0.416648032
60 0.403917416 1.743268244
61 -0.061121679 0.403917416
62 -0.580317580 -0.061121679
63 -0.434574010 -0.580317580
64 0.576621352 -0.434574010
65 0.959990195 0.576621352
66 1.883914175 0.959990195
67 3.270538510 1.883914175
68 -3.867994890 3.270538510
69 0.548694155 -3.867994890
70 -3.474486555 0.548694155
71 -0.346884027 -3.474486555
72 1.388317938 -0.346884027
73 0.858554885 1.388317938
74 0.246107345 0.858554885
75 3.024389434 0.246107345
76 -0.346102484 3.024389434
77 1.511124975 -0.346102484
78 -1.914851418 1.511124975
79 0.113213237 -1.914851418
80 0.414880952 0.113213237
81 3.373078560 0.414880952
82 0.374519938 3.373078560
83 -0.952162751 0.374519938
84 0.271197241 -0.952162751
85 1.727770212 0.271197241
86 -0.241145120 1.727770212
87 0.702026097 -0.241145120
88 1.467175140 0.702026097
89 0.705005086 1.467175140
90 -1.505489271 0.705005086
91 0.138636970 -1.505489271
92 0.485187856 0.138636970
93 -0.296602263 0.485187856
94 -2.131650698 -0.296602263
95 0.861802560 -2.131650698
96 0.238386133 0.861802560
97 1.877996630 0.238386133
98 -0.052138723 1.877996630
99 -0.296949111 -0.052138723
100 -1.211220293 -0.296949111
101 1.241383452 -1.211220293
102 2.678368144 1.241383452
103 0.534546849 2.678368144
104 1.419938144 0.534546849
105 -2.313337687 1.419938144
106 1.064636262 -2.313337687
107 0.158900935 1.064636262
108 1.573074035 0.158900935
109 -0.206806717 1.573074035
110 1.055255413 -0.206806717
111 0.205219598 1.055255413
112 2.469584026 0.205219598
113 -1.812331313 2.469584026
114 -2.766063378 -1.812331313
115 1.509276989 -2.766063378
116 -1.373751123 1.509276989
117 1.050463227 -1.373751123
118 -1.828292364 1.050463227
119 0.354707462 -1.828292364
120 -1.187170032 0.354707462
121 0.460866908 -1.187170032
122 -2.866760470 0.460866908
123 -0.869918965 -2.866760470
124 -0.808967280 -0.869918965
125 -0.667600462 -0.808967280
126 -0.290049255 -0.667600462
127 0.931600591 -0.290049255
128 1.284869769 0.931600591
129 -2.819894488 1.284869769
130 1.932430688 -2.819894488
131 -3.307474071 1.932430688
132 2.006365724 -3.307474071
133 -1.818494074 2.006365724
134 -1.527163965 -1.818494074
135 0.009720201 -1.527163965
136 0.872951511 0.009720201
137 0.757712641 0.872951511
138 -2.021061731 0.757712641
139 -1.172024233 -2.021061731
140 -5.254065893 -1.172024233
141 3.108734428 -5.254065893
142 1.738347023 3.108734428
143 0.578782261 1.738347023
144 1.360428311 0.578782261
145 -4.005173058 1.360428311
146 2.297610266 -4.005173058
147 -1.976512666 2.297610266
148 1.013352907 -1.976512666
149 0.051393363 1.013352907
150 -2.975261839 0.051393363
151 -1.215002329 -2.975261839
152 2.095952427 -1.215002329
153 4.121952127 2.095952427
154 1.666987087 4.121952127
155 -2.363751351 1.666987087
156 0.431458237 -2.363751351
157 1.505095517 0.431458237
158 1.401998276 1.505095517
159 1.125722693 1.401998276
160 -0.464010081 1.125722693
161 0.571113135 -0.464010081
162 NA 0.571113135
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.265476050 -3.326182299
[2,] 2.503640720 -0.265476050
[3,] 2.859906790 2.503640720
[4,] -1.845577370 2.859906790
[5,] -2.177524392 -1.845577370
[6,] 3.710541464 -2.177524392
[7,] -1.921201311 3.710541464
[8,] -2.174278651 -1.921201311
[9,] 2.160159413 -2.174278651
[10,] 0.529457031 2.160159413
[11,] -0.354848287 0.529457031
[12,] 0.321831724 -0.354848287
[13,] 0.510602605 0.321831724
[14,] -0.616463911 0.510602605
[15,] -0.244210603 -0.616463911
[16,] 0.167003369 -0.244210603
[17,] 3.589195178 0.167003369
[18,] 2.389620751 3.589195178
[19,] 0.616579113 2.389620751
[20,] 0.574580501 0.616579113
[21,] 1.019831235 0.574580501
[22,] 2.428010288 1.019831235
[23,] 1.087073597 2.428010288
[24,] 1.962009464 1.087073597
[25,] -0.107015144 1.962009464
[26,] 0.814633767 -0.107015144
[27,] -1.245628084 0.814633767
[28,] 0.316090070 -1.245628084
[29,] -0.172140010 0.316090070
[30,] -0.769470576 -0.172140010
[31,] -0.436559342 -0.769470576
[32,] -1.248465890 -0.436559342
[33,] 0.331455314 -1.248465890
[34,] -1.842490169 0.331455314
[35,] -6.121703091 -1.842490169
[36,] -1.230507684 -6.121703091
[37,] -1.943269722 -1.230507684
[38,] 1.553747452 -1.943269722
[39,] 1.285998645 1.553747452
[40,] 0.850606417 1.285998645
[41,] -1.712125394 0.850606417
[42,] 2.149355175 -1.712125394
[43,] -0.308621637 2.149355175
[44,] -0.983550638 -0.308621637
[45,] -4.733173856 -0.983550638
[46,] -2.478195496 -4.733173856
[47,] -0.087764632 -2.478195496
[48,] 0.615435688 -0.087764632
[49,] -2.125512907 0.615435688
[50,] -0.618624468 -2.125512907
[51,] -0.264696368 -0.618624468
[52,] -2.813483108 -0.264696368
[53,] 0.763028957 -2.813483108
[54,] -2.673310907 0.763028957
[55,] 1.248246666 -2.673310907
[56,] -0.131674618 1.248246666
[57,] 0.849267088 -0.131674618
[58,] -0.416648032 0.849267088
[59,] 1.743268244 -0.416648032
[60,] 0.403917416 1.743268244
[61,] -0.061121679 0.403917416
[62,] -0.580317580 -0.061121679
[63,] -0.434574010 -0.580317580
[64,] 0.576621352 -0.434574010
[65,] 0.959990195 0.576621352
[66,] 1.883914175 0.959990195
[67,] 3.270538510 1.883914175
[68,] -3.867994890 3.270538510
[69,] 0.548694155 -3.867994890
[70,] -3.474486555 0.548694155
[71,] -0.346884027 -3.474486555
[72,] 1.388317938 -0.346884027
[73,] 0.858554885 1.388317938
[74,] 0.246107345 0.858554885
[75,] 3.024389434 0.246107345
[76,] -0.346102484 3.024389434
[77,] 1.511124975 -0.346102484
[78,] -1.914851418 1.511124975
[79,] 0.113213237 -1.914851418
[80,] 0.414880952 0.113213237
[81,] 3.373078560 0.414880952
[82,] 0.374519938 3.373078560
[83,] -0.952162751 0.374519938
[84,] 0.271197241 -0.952162751
[85,] 1.727770212 0.271197241
[86,] -0.241145120 1.727770212
[87,] 0.702026097 -0.241145120
[88,] 1.467175140 0.702026097
[89,] 0.705005086 1.467175140
[90,] -1.505489271 0.705005086
[91,] 0.138636970 -1.505489271
[92,] 0.485187856 0.138636970
[93,] -0.296602263 0.485187856
[94,] -2.131650698 -0.296602263
[95,] 0.861802560 -2.131650698
[96,] 0.238386133 0.861802560
[97,] 1.877996630 0.238386133
[98,] -0.052138723 1.877996630
[99,] -0.296949111 -0.052138723
[100,] -1.211220293 -0.296949111
[101,] 1.241383452 -1.211220293
[102,] 2.678368144 1.241383452
[103,] 0.534546849 2.678368144
[104,] 1.419938144 0.534546849
[105,] -2.313337687 1.419938144
[106,] 1.064636262 -2.313337687
[107,] 0.158900935 1.064636262
[108,] 1.573074035 0.158900935
[109,] -0.206806717 1.573074035
[110,] 1.055255413 -0.206806717
[111,] 0.205219598 1.055255413
[112,] 2.469584026 0.205219598
[113,] -1.812331313 2.469584026
[114,] -2.766063378 -1.812331313
[115,] 1.509276989 -2.766063378
[116,] -1.373751123 1.509276989
[117,] 1.050463227 -1.373751123
[118,] -1.828292364 1.050463227
[119,] 0.354707462 -1.828292364
[120,] -1.187170032 0.354707462
[121,] 0.460866908 -1.187170032
[122,] -2.866760470 0.460866908
[123,] -0.869918965 -2.866760470
[124,] -0.808967280 -0.869918965
[125,] -0.667600462 -0.808967280
[126,] -0.290049255 -0.667600462
[127,] 0.931600591 -0.290049255
[128,] 1.284869769 0.931600591
[129,] -2.819894488 1.284869769
[130,] 1.932430688 -2.819894488
[131,] -3.307474071 1.932430688
[132,] 2.006365724 -3.307474071
[133,] -1.818494074 2.006365724
[134,] -1.527163965 -1.818494074
[135,] 0.009720201 -1.527163965
[136,] 0.872951511 0.009720201
[137,] 0.757712641 0.872951511
[138,] -2.021061731 0.757712641
[139,] -1.172024233 -2.021061731
[140,] -5.254065893 -1.172024233
[141,] 3.108734428 -5.254065893
[142,] 1.738347023 3.108734428
[143,] 0.578782261 1.738347023
[144,] 1.360428311 0.578782261
[145,] -4.005173058 1.360428311
[146,] 2.297610266 -4.005173058
[147,] -1.976512666 2.297610266
[148,] 1.013352907 -1.976512666
[149,] 0.051393363 1.013352907
[150,] -2.975261839 0.051393363
[151,] -1.215002329 -2.975261839
[152,] 2.095952427 -1.215002329
[153,] 4.121952127 2.095952427
[154,] 1.666987087 4.121952127
[155,] -2.363751351 1.666987087
[156,] 0.431458237 -2.363751351
[157,] 1.505095517 0.431458237
[158,] 1.401998276 1.505095517
[159,] 1.125722693 1.401998276
[160,] -0.464010081 1.125722693
[161,] 0.571113135 -0.464010081
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.265476050 -3.326182299
2 2.503640720 -0.265476050
3 2.859906790 2.503640720
4 -1.845577370 2.859906790
5 -2.177524392 -1.845577370
6 3.710541464 -2.177524392
7 -1.921201311 3.710541464
8 -2.174278651 -1.921201311
9 2.160159413 -2.174278651
10 0.529457031 2.160159413
11 -0.354848287 0.529457031
12 0.321831724 -0.354848287
13 0.510602605 0.321831724
14 -0.616463911 0.510602605
15 -0.244210603 -0.616463911
16 0.167003369 -0.244210603
17 3.589195178 0.167003369
18 2.389620751 3.589195178
19 0.616579113 2.389620751
20 0.574580501 0.616579113
21 1.019831235 0.574580501
22 2.428010288 1.019831235
23 1.087073597 2.428010288
24 1.962009464 1.087073597
25 -0.107015144 1.962009464
26 0.814633767 -0.107015144
27 -1.245628084 0.814633767
28 0.316090070 -1.245628084
29 -0.172140010 0.316090070
30 -0.769470576 -0.172140010
31 -0.436559342 -0.769470576
32 -1.248465890 -0.436559342
33 0.331455314 -1.248465890
34 -1.842490169 0.331455314
35 -6.121703091 -1.842490169
36 -1.230507684 -6.121703091
37 -1.943269722 -1.230507684
38 1.553747452 -1.943269722
39 1.285998645 1.553747452
40 0.850606417 1.285998645
41 -1.712125394 0.850606417
42 2.149355175 -1.712125394
43 -0.308621637 2.149355175
44 -0.983550638 -0.308621637
45 -4.733173856 -0.983550638
46 -2.478195496 -4.733173856
47 -0.087764632 -2.478195496
48 0.615435688 -0.087764632
49 -2.125512907 0.615435688
50 -0.618624468 -2.125512907
51 -0.264696368 -0.618624468
52 -2.813483108 -0.264696368
53 0.763028957 -2.813483108
54 -2.673310907 0.763028957
55 1.248246666 -2.673310907
56 -0.131674618 1.248246666
57 0.849267088 -0.131674618
58 -0.416648032 0.849267088
59 1.743268244 -0.416648032
60 0.403917416 1.743268244
61 -0.061121679 0.403917416
62 -0.580317580 -0.061121679
63 -0.434574010 -0.580317580
64 0.576621352 -0.434574010
65 0.959990195 0.576621352
66 1.883914175 0.959990195
67 3.270538510 1.883914175
68 -3.867994890 3.270538510
69 0.548694155 -3.867994890
70 -3.474486555 0.548694155
71 -0.346884027 -3.474486555
72 1.388317938 -0.346884027
73 0.858554885 1.388317938
74 0.246107345 0.858554885
75 3.024389434 0.246107345
76 -0.346102484 3.024389434
77 1.511124975 -0.346102484
78 -1.914851418 1.511124975
79 0.113213237 -1.914851418
80 0.414880952 0.113213237
81 3.373078560 0.414880952
82 0.374519938 3.373078560
83 -0.952162751 0.374519938
84 0.271197241 -0.952162751
85 1.727770212 0.271197241
86 -0.241145120 1.727770212
87 0.702026097 -0.241145120
88 1.467175140 0.702026097
89 0.705005086 1.467175140
90 -1.505489271 0.705005086
91 0.138636970 -1.505489271
92 0.485187856 0.138636970
93 -0.296602263 0.485187856
94 -2.131650698 -0.296602263
95 0.861802560 -2.131650698
96 0.238386133 0.861802560
97 1.877996630 0.238386133
98 -0.052138723 1.877996630
99 -0.296949111 -0.052138723
100 -1.211220293 -0.296949111
101 1.241383452 -1.211220293
102 2.678368144 1.241383452
103 0.534546849 2.678368144
104 1.419938144 0.534546849
105 -2.313337687 1.419938144
106 1.064636262 -2.313337687
107 0.158900935 1.064636262
108 1.573074035 0.158900935
109 -0.206806717 1.573074035
110 1.055255413 -0.206806717
111 0.205219598 1.055255413
112 2.469584026 0.205219598
113 -1.812331313 2.469584026
114 -2.766063378 -1.812331313
115 1.509276989 -2.766063378
116 -1.373751123 1.509276989
117 1.050463227 -1.373751123
118 -1.828292364 1.050463227
119 0.354707462 -1.828292364
120 -1.187170032 0.354707462
121 0.460866908 -1.187170032
122 -2.866760470 0.460866908
123 -0.869918965 -2.866760470
124 -0.808967280 -0.869918965
125 -0.667600462 -0.808967280
126 -0.290049255 -0.667600462
127 0.931600591 -0.290049255
128 1.284869769 0.931600591
129 -2.819894488 1.284869769
130 1.932430688 -2.819894488
131 -3.307474071 1.932430688
132 2.006365724 -3.307474071
133 -1.818494074 2.006365724
134 -1.527163965 -1.818494074
135 0.009720201 -1.527163965
136 0.872951511 0.009720201
137 0.757712641 0.872951511
138 -2.021061731 0.757712641
139 -1.172024233 -2.021061731
140 -5.254065893 -1.172024233
141 3.108734428 -5.254065893
142 1.738347023 3.108734428
143 0.578782261 1.738347023
144 1.360428311 0.578782261
145 -4.005173058 1.360428311
146 2.297610266 -4.005173058
147 -1.976512666 2.297610266
148 1.013352907 -1.976512666
149 0.051393363 1.013352907
150 -2.975261839 0.051393363
151 -1.215002329 -2.975261839
152 2.095952427 -1.215002329
153 4.121952127 2.095952427
154 1.666987087 4.121952127
155 -2.363751351 1.666987087
156 0.431458237 -2.363751351
157 1.505095517 0.431458237
158 1.401998276 1.505095517
159 1.125722693 1.401998276
160 -0.464010081 1.125722693
161 0.571113135 -0.464010081
> 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/7qrcn1355673318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8s7cs1355673318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9gq571355673318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10bbmp1355673318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11booi1355673318.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/125iwy1355673319.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/136xxn1355673319.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/14eoi01355673319.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/15ovl91355673319.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/16lcz11355673319.tab")
+ }
>
> try(system("convert tmp/1hqtg1355673318.ps tmp/1hqtg1355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hevh1355673318.ps tmp/2hevh1355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/34yct1355673318.ps tmp/34yct1355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jlxj1355673318.ps tmp/4jlxj1355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pq5k1355673318.ps tmp/5pq5k1355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xqfy1355673318.ps tmp/6xqfy1355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qrcn1355673318.ps tmp/7qrcn1355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/8s7cs1355673318.ps tmp/8s7cs1355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gq571355673318.ps tmp/9gq571355673318.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bbmp1355673318.ps tmp/10bbmp1355673318.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.210 1.781 11.002