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 'q()' to quit R.
> x <- array(list(2000
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+ ,2011
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+ ,2011
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+ ,13
+ ,8
+ ,13
+ ,16
+ ,69)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('Jaar'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging
')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('Jaar','Connected','Separate','Learning','Software','Happiness','Depression','Belonging
'),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\r
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
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Jaar Connected Separate Software
118.587702 -0.056289 0.107232 -0.017332 0.533475
Happiness Depression `Belonging\\r`
0.055998 -0.070137 0.004976
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1799 -1.1097 0.2409 1.1220 4.2727
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 118.587702 89.533399 1.325 0.1873
Jaar -0.056289 0.044646 -1.261 0.2093
Connected 0.107232 0.047160 2.274 0.0244 *
Separate -0.017332 0.044810 -0.387 0.6995
Software 0.533475 0.069310 7.697 1.56e-12 ***
Happiness 0.055998 0.076251 0.734 0.4638
Depression -0.070137 0.055997 -1.253 0.2123
`Belonging\\r` 0.004976 0.014654 0.340 0.7347
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.845 on 154 degrees of freedom
Multiple R-squared: 0.3605, Adjusted R-squared: 0.3315
F-statistic: 12.4 on 7 and 154 DF, p-value: 1.53e-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,] 0.44856451 0.89712903 0.55143549
[2,] 0.90467547 0.19064906 0.09532453
[3,] 0.84539413 0.30921173 0.15460587
[4,] 0.76221550 0.47556899 0.23778450
[5,] 0.68586932 0.62826135 0.31413068
[6,] 0.74730031 0.50539937 0.25269969
[7,] 0.68284052 0.63431897 0.31715948
[8,] 0.88608547 0.22782907 0.11391453
[9,] 0.86546522 0.26906956 0.13453478
[10,] 0.81536919 0.36926161 0.18463081
[11,] 0.75771876 0.48456248 0.24228124
[12,] 0.70886917 0.58226166 0.29113083
[13,] 0.69760189 0.60479622 0.30239811
[14,] 0.65296050 0.69407899 0.34703950
[15,] 0.60053180 0.79893640 0.39946820
[16,] 0.55054988 0.89890023 0.44945012
[17,] 0.51802406 0.96395187 0.48197594
[18,] 0.56259977 0.87480046 0.43740023
[19,] 0.50240473 0.99519055 0.49759527
[20,] 0.51020178 0.97959643 0.48979822
[21,] 0.45280734 0.90561468 0.54719266
[22,] 0.43917929 0.87835859 0.56082071
[23,] 0.41689799 0.83379597 0.58310201
[24,] 0.35789678 0.71579355 0.64210322
[25,] 0.33802689 0.67605377 0.66197311
[26,] 0.83753697 0.32492606 0.16246303
[27,] 0.82271086 0.35457828 0.17728914
[28,] 0.81059372 0.37881256 0.18940628
[29,] 0.83416239 0.33167522 0.16583761
[30,] 0.82567865 0.34864269 0.17432135
[31,] 0.80072132 0.39855736 0.19927868
[32,] 0.77966298 0.44067404 0.22033702
[33,] 0.79809885 0.40380229 0.20190115
[34,] 0.76061952 0.47876095 0.23938048
[35,] 0.72821286 0.54357427 0.27178714
[36,] 0.88709039 0.22581922 0.11290961
[37,] 0.89825416 0.20349168 0.10174584
[38,] 0.87663984 0.24672032 0.12336016
[39,] 0.85938481 0.28123037 0.14061519
[40,] 0.85483157 0.29033686 0.14516843
[41,] 0.82653272 0.34693456 0.17346728
[42,] 0.79418026 0.41163948 0.20581974
[43,] 0.80454897 0.39090207 0.19545103
[44,] 0.78020578 0.43958845 0.21979422
[45,] 0.79661547 0.40676906 0.20338453
[46,] 0.78971672 0.42056656 0.21028328
[47,] 0.75428832 0.49142337 0.24571168
[48,] 0.74258506 0.51482988 0.25741494
[49,] 0.70759214 0.58481572 0.29240786
[50,] 0.71335367 0.57329266 0.28664633
[51,] 0.67796788 0.64406423 0.32203212
[52,] 0.63652597 0.72694805 0.36347403
[53,] 0.59662588 0.80674825 0.40337412
[54,] 0.55336260 0.89327480 0.44663740
[55,] 0.51392560 0.97214880 0.48607440
[56,] 0.48673383 0.97346766 0.51326617
[57,] 0.48561105 0.97122209 0.51438895
[58,] 0.60919102 0.78161797 0.39080898
[59,] 0.73377031 0.53245938 0.26622969
[60,] 0.70032583 0.59934834 0.29967417
[61,] 0.80305157 0.39389686 0.19694843
[62,] 0.77326177 0.45347646 0.22673823
[63,] 0.76466434 0.47067131 0.23533566
[64,] 0.74186242 0.51627516 0.25813758
[65,] 0.70305082 0.59389836 0.29694918
[66,] 0.75229817 0.49540365 0.24770183
[67,] 0.71556601 0.56886799 0.28443399
[68,] 0.69344766 0.61310469 0.30655234
[69,] 0.70157484 0.59685032 0.29842516
[70,] 0.66332095 0.67335810 0.33667905
[71,] 0.62482583 0.75034833 0.37517417
[72,] 0.78910146 0.42179708 0.21089854
[73,] 0.75456311 0.49087378 0.24543689
[74,] 0.72712258 0.54575483 0.27287742
[75,] 0.68752498 0.62495004 0.31247502
[76,] 0.67360958 0.65278084 0.32639042
[77,] 0.63139984 0.73720032 0.36860016
[78,] 0.58975746 0.82048508 0.41024254
[79,] 0.56610541 0.86778917 0.43389459
[80,] 0.52781892 0.94436215 0.47218108
[81,] 0.52006324 0.95987353 0.47993676
[82,] 0.47911181 0.95822363 0.52088819
[83,] 0.43562497 0.87124993 0.56437503
[84,] 0.39063538 0.78127075 0.60936462
[85,] 0.41423077 0.82846154 0.58576923
[86,] 0.37695668 0.75391336 0.62304332
[87,] 0.33514619 0.67029237 0.66485381
[88,] 0.32806453 0.65612907 0.67193547
[89,] 0.28581408 0.57162815 0.71418592
[90,] 0.25134590 0.50269180 0.74865410
[91,] 0.22848911 0.45697822 0.77151089
[92,] 0.20810734 0.41621468 0.79189266
[93,] 0.25034453 0.50068907 0.74965547
[94,] 0.21337430 0.42674860 0.78662570
[95,] 0.20592115 0.41184230 0.79407885
[96,] 0.21758668 0.43517336 0.78241332
[97,] 0.19534382 0.39068764 0.80465618
[98,] 0.17405977 0.34811954 0.82594023
[99,] 0.16907583 0.33815166 0.83092417
[100,] 0.16443265 0.32886531 0.83556735
[101,] 0.14914779 0.29829557 0.85085221
[102,] 0.12693336 0.25386671 0.87306664
[103,] 0.15998773 0.31997546 0.84001227
[104,] 0.13743665 0.27487330 0.86256335
[105,] 0.15917855 0.31835709 0.84082145
[106,] 0.15920552 0.31841105 0.84079448
[107,] 0.13617757 0.27235515 0.86382243
[108,] 0.13122485 0.26244970 0.86877515
[109,] 0.12258262 0.24516525 0.87741738
[110,] 0.13602503 0.27205006 0.86397497
[111,] 0.11220773 0.22441546 0.88779227
[112,] 0.10172289 0.20344577 0.89827711
[113,] 0.10146992 0.20293984 0.89853008
[114,] 0.08077705 0.16155410 0.91922295
[115,] 0.06391353 0.12782706 0.93608647
[116,] 0.04834892 0.09669785 0.95165108
[117,] 0.03563036 0.07126072 0.96436964
[118,] 0.03523792 0.07047584 0.96476208
[119,] 0.03304171 0.06608341 0.96695829
[120,] 0.03374159 0.06748319 0.96625841
[121,] 0.03657729 0.07315458 0.96342271
[122,] 0.03886152 0.07772303 0.96113848
[123,] 0.09051908 0.18103816 0.90948092
[124,] 0.08373383 0.16746765 0.91626617
[125,] 0.06963233 0.13926466 0.93036767
[126,] 0.06146945 0.12293889 0.93853055
[127,] 0.04417369 0.08834739 0.95582631
[128,] 0.03600232 0.07200463 0.96399768
[129,] 0.04361490 0.08722981 0.95638510
[130,] 0.03126021 0.06252043 0.96873979
[131,] 0.49244757 0.98489515 0.50755243
[132,] 0.44400403 0.88800806 0.55599597
[133,] 0.42654113 0.85308225 0.57345887
[134,] 0.34122335 0.68244671 0.65877665
[135,] 0.27603125 0.55206250 0.72396875
[136,] 0.24631868 0.49263735 0.75368132
[137,] 0.38151753 0.76303507 0.61848247
[138,] 0.69400748 0.61198505 0.30599252
[139,] 0.61406156 0.77187688 0.38593844
[140,] 0.46668283 0.93336567 0.53331717
[141,] 0.70594139 0.58811723 0.29405861
> postscript(file="/var/wessaorg/rcomp/tmp/1zvvi1355680853.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2qb6w1355680853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3s4a61355680853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ln6y1355680853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/508n21355680853.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
-3.356142995 -0.170517999 2.414571606 2.872702713 -1.751525104 -2.150169015
7 8 9 10 11 12
3.438374289 -1.911613038 -2.172739899 2.173334080 0.473546592 -0.454826450
13 14 15 16 17 18
0.278303230 0.471387289 -0.593194630 -0.306030519 0.230076219 3.609563447
19 20 21 22 23 24
2.253077294 0.499480105 0.472685393 0.884775930 2.376158331 0.917222741
25 26 27 28 29 30
2.008718413 -0.002082411 1.010975516 -1.339191169 0.487516672 -0.344179277
31 32 33 34 35 36
-0.801314515 -0.450977794 -1.266839867 0.209345262 -1.665946833 -6.179870832
37 38 39 40 41 42
-1.085923770 -1.939624076 1.566135215 1.272518105 0.833711923 -1.604497546
43 44 45 46 47 48
2.032481994 -0.306348905 -1.009699157 -4.646847904 -2.707843237 -0.100873958
49 50 51 52 53 54
0.820287088 -2.093174264 -0.635949549 -0.223278480 -2.713498850 0.293648428
55 56 57 58 59 60
-2.543466314 1.224361562 -0.132197444 0.750395951 -0.331041214 1.747848111
61 62 63 64 65 66
0.439706019 0.057963773 -0.450732107 -0.431941505 0.507475782 0.981108008
67 68 69 70 71 72
1.796512215 3.460159699 -3.861190012 0.556025765 -3.653289316 -0.375467808
73 74 75 76 77 78
1.501126067 0.956157276 0.316891296 3.070152942 -0.379021422 1.415151339
79 80 81 82 83 84
-1.907284441 0.004189141 0.549277649 4.272682766 0.251675686 -0.887650472
85 86 87 88 89 90
0.260287718 1.735793823 -0.174283645 0.690505880 1.425813579 0.761559369
91 92 93 94 95 96
-1.577701654 0.074767370 0.571698390 -0.160522068 -2.032613812 0.968152787
97 98 99 100 101 102
0.194270480 1.965843503 0.020029506 -0.275462987 -1.164748405 1.241317983
103 104 105 106 107 108
2.685064114 0.396294780 1.541723357 -2.262562228 1.011005348 0.190200114
109 110 111 112 113 114
1.541232840 -0.283125672 1.144372312 0.286793286 2.456434805 -1.208886125
115 116 117 118 119 120
-2.817714534 1.469162535 -1.411493623 0.998902463 -1.856522932 0.407513127
121 122 123 124 125 126
-1.214405047 0.468196295 -2.815775940 -0.938555553 -0.872135787 -0.607816907
127 128 129 130 131 132
-0.304762643 0.966809678 1.084625234 -2.839735463 1.992130861 -3.249641875
133 134 135 136 137 138
2.058567001 -1.858772987 -1.581104512 -0.156458130 0.858404618 0.529363657
139 140 141 142 143 144
-2.155185719 -1.266160772 -5.196122175 3.035152528 1.791425163 0.569676853
145 146 147 148 149 150
1.377148549 -4.034701341 2.221270949 -2.088459839 0.982902044 0.018008543
151 152 153 154 155 156
-3.001782178 -1.117686536 2.202108689 4.142062138 1.662337561 -2.514103500
157 158 159 160 161 162
0.356210329 1.472299760 1.197202417 1.134480609 -0.311401205 0.369828017
> postscript(file="/var/wessaorg/rcomp/tmp/6ijwf1355680853.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 -3.356142995 NA
1 -0.170517999 -3.356142995
2 2.414571606 -0.170517999
3 2.872702713 2.414571606
4 -1.751525104 2.872702713
5 -2.150169015 -1.751525104
6 3.438374289 -2.150169015
7 -1.911613038 3.438374289
8 -2.172739899 -1.911613038
9 2.173334080 -2.172739899
10 0.473546592 2.173334080
11 -0.454826450 0.473546592
12 0.278303230 -0.454826450
13 0.471387289 0.278303230
14 -0.593194630 0.471387289
15 -0.306030519 -0.593194630
16 0.230076219 -0.306030519
17 3.609563447 0.230076219
18 2.253077294 3.609563447
19 0.499480105 2.253077294
20 0.472685393 0.499480105
21 0.884775930 0.472685393
22 2.376158331 0.884775930
23 0.917222741 2.376158331
24 2.008718413 0.917222741
25 -0.002082411 2.008718413
26 1.010975516 -0.002082411
27 -1.339191169 1.010975516
28 0.487516672 -1.339191169
29 -0.344179277 0.487516672
30 -0.801314515 -0.344179277
31 -0.450977794 -0.801314515
32 -1.266839867 -0.450977794
33 0.209345262 -1.266839867
34 -1.665946833 0.209345262
35 -6.179870832 -1.665946833
36 -1.085923770 -6.179870832
37 -1.939624076 -1.085923770
38 1.566135215 -1.939624076
39 1.272518105 1.566135215
40 0.833711923 1.272518105
41 -1.604497546 0.833711923
42 2.032481994 -1.604497546
43 -0.306348905 2.032481994
44 -1.009699157 -0.306348905
45 -4.646847904 -1.009699157
46 -2.707843237 -4.646847904
47 -0.100873958 -2.707843237
48 0.820287088 -0.100873958
49 -2.093174264 0.820287088
50 -0.635949549 -2.093174264
51 -0.223278480 -0.635949549
52 -2.713498850 -0.223278480
53 0.293648428 -2.713498850
54 -2.543466314 0.293648428
55 1.224361562 -2.543466314
56 -0.132197444 1.224361562
57 0.750395951 -0.132197444
58 -0.331041214 0.750395951
59 1.747848111 -0.331041214
60 0.439706019 1.747848111
61 0.057963773 0.439706019
62 -0.450732107 0.057963773
63 -0.431941505 -0.450732107
64 0.507475782 -0.431941505
65 0.981108008 0.507475782
66 1.796512215 0.981108008
67 3.460159699 1.796512215
68 -3.861190012 3.460159699
69 0.556025765 -3.861190012
70 -3.653289316 0.556025765
71 -0.375467808 -3.653289316
72 1.501126067 -0.375467808
73 0.956157276 1.501126067
74 0.316891296 0.956157276
75 3.070152942 0.316891296
76 -0.379021422 3.070152942
77 1.415151339 -0.379021422
78 -1.907284441 1.415151339
79 0.004189141 -1.907284441
80 0.549277649 0.004189141
81 4.272682766 0.549277649
82 0.251675686 4.272682766
83 -0.887650472 0.251675686
84 0.260287718 -0.887650472
85 1.735793823 0.260287718
86 -0.174283645 1.735793823
87 0.690505880 -0.174283645
88 1.425813579 0.690505880
89 0.761559369 1.425813579
90 -1.577701654 0.761559369
91 0.074767370 -1.577701654
92 0.571698390 0.074767370
93 -0.160522068 0.571698390
94 -2.032613812 -0.160522068
95 0.968152787 -2.032613812
96 0.194270480 0.968152787
97 1.965843503 0.194270480
98 0.020029506 1.965843503
99 -0.275462987 0.020029506
100 -1.164748405 -0.275462987
101 1.241317983 -1.164748405
102 2.685064114 1.241317983
103 0.396294780 2.685064114
104 1.541723357 0.396294780
105 -2.262562228 1.541723357
106 1.011005348 -2.262562228
107 0.190200114 1.011005348
108 1.541232840 0.190200114
109 -0.283125672 1.541232840
110 1.144372312 -0.283125672
111 0.286793286 1.144372312
112 2.456434805 0.286793286
113 -1.208886125 2.456434805
114 -2.817714534 -1.208886125
115 1.469162535 -2.817714534
116 -1.411493623 1.469162535
117 0.998902463 -1.411493623
118 -1.856522932 0.998902463
119 0.407513127 -1.856522932
120 -1.214405047 0.407513127
121 0.468196295 -1.214405047
122 -2.815775940 0.468196295
123 -0.938555553 -2.815775940
124 -0.872135787 -0.938555553
125 -0.607816907 -0.872135787
126 -0.304762643 -0.607816907
127 0.966809678 -0.304762643
128 1.084625234 0.966809678
129 -2.839735463 1.084625234
130 1.992130861 -2.839735463
131 -3.249641875 1.992130861
132 2.058567001 -3.249641875
133 -1.858772987 2.058567001
134 -1.581104512 -1.858772987
135 -0.156458130 -1.581104512
136 0.858404618 -0.156458130
137 0.529363657 0.858404618
138 -2.155185719 0.529363657
139 -1.266160772 -2.155185719
140 -5.196122175 -1.266160772
141 3.035152528 -5.196122175
142 1.791425163 3.035152528
143 0.569676853 1.791425163
144 1.377148549 0.569676853
145 -4.034701341 1.377148549
146 2.221270949 -4.034701341
147 -2.088459839 2.221270949
148 0.982902044 -2.088459839
149 0.018008543 0.982902044
150 -3.001782178 0.018008543
151 -1.117686536 -3.001782178
152 2.202108689 -1.117686536
153 4.142062138 2.202108689
154 1.662337561 4.142062138
155 -2.514103500 1.662337561
156 0.356210329 -2.514103500
157 1.472299760 0.356210329
158 1.197202417 1.472299760
159 1.134480609 1.197202417
160 -0.311401205 1.134480609
161 0.369828017 -0.311401205
162 NA 0.369828017
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.170517999 -3.356142995
[2,] 2.414571606 -0.170517999
[3,] 2.872702713 2.414571606
[4,] -1.751525104 2.872702713
[5,] -2.150169015 -1.751525104
[6,] 3.438374289 -2.150169015
[7,] -1.911613038 3.438374289
[8,] -2.172739899 -1.911613038
[9,] 2.173334080 -2.172739899
[10,] 0.473546592 2.173334080
[11,] -0.454826450 0.473546592
[12,] 0.278303230 -0.454826450
[13,] 0.471387289 0.278303230
[14,] -0.593194630 0.471387289
[15,] -0.306030519 -0.593194630
[16,] 0.230076219 -0.306030519
[17,] 3.609563447 0.230076219
[18,] 2.253077294 3.609563447
[19,] 0.499480105 2.253077294
[20,] 0.472685393 0.499480105
[21,] 0.884775930 0.472685393
[22,] 2.376158331 0.884775930
[23,] 0.917222741 2.376158331
[24,] 2.008718413 0.917222741
[25,] -0.002082411 2.008718413
[26,] 1.010975516 -0.002082411
[27,] -1.339191169 1.010975516
[28,] 0.487516672 -1.339191169
[29,] -0.344179277 0.487516672
[30,] -0.801314515 -0.344179277
[31,] -0.450977794 -0.801314515
[32,] -1.266839867 -0.450977794
[33,] 0.209345262 -1.266839867
[34,] -1.665946833 0.209345262
[35,] -6.179870832 -1.665946833
[36,] -1.085923770 -6.179870832
[37,] -1.939624076 -1.085923770
[38,] 1.566135215 -1.939624076
[39,] 1.272518105 1.566135215
[40,] 0.833711923 1.272518105
[41,] -1.604497546 0.833711923
[42,] 2.032481994 -1.604497546
[43,] -0.306348905 2.032481994
[44,] -1.009699157 -0.306348905
[45,] -4.646847904 -1.009699157
[46,] -2.707843237 -4.646847904
[47,] -0.100873958 -2.707843237
[48,] 0.820287088 -0.100873958
[49,] -2.093174264 0.820287088
[50,] -0.635949549 -2.093174264
[51,] -0.223278480 -0.635949549
[52,] -2.713498850 -0.223278480
[53,] 0.293648428 -2.713498850
[54,] -2.543466314 0.293648428
[55,] 1.224361562 -2.543466314
[56,] -0.132197444 1.224361562
[57,] 0.750395951 -0.132197444
[58,] -0.331041214 0.750395951
[59,] 1.747848111 -0.331041214
[60,] 0.439706019 1.747848111
[61,] 0.057963773 0.439706019
[62,] -0.450732107 0.057963773
[63,] -0.431941505 -0.450732107
[64,] 0.507475782 -0.431941505
[65,] 0.981108008 0.507475782
[66,] 1.796512215 0.981108008
[67,] 3.460159699 1.796512215
[68,] -3.861190012 3.460159699
[69,] 0.556025765 -3.861190012
[70,] -3.653289316 0.556025765
[71,] -0.375467808 -3.653289316
[72,] 1.501126067 -0.375467808
[73,] 0.956157276 1.501126067
[74,] 0.316891296 0.956157276
[75,] 3.070152942 0.316891296
[76,] -0.379021422 3.070152942
[77,] 1.415151339 -0.379021422
[78,] -1.907284441 1.415151339
[79,] 0.004189141 -1.907284441
[80,] 0.549277649 0.004189141
[81,] 4.272682766 0.549277649
[82,] 0.251675686 4.272682766
[83,] -0.887650472 0.251675686
[84,] 0.260287718 -0.887650472
[85,] 1.735793823 0.260287718
[86,] -0.174283645 1.735793823
[87,] 0.690505880 -0.174283645
[88,] 1.425813579 0.690505880
[89,] 0.761559369 1.425813579
[90,] -1.577701654 0.761559369
[91,] 0.074767370 -1.577701654
[92,] 0.571698390 0.074767370
[93,] -0.160522068 0.571698390
[94,] -2.032613812 -0.160522068
[95,] 0.968152787 -2.032613812
[96,] 0.194270480 0.968152787
[97,] 1.965843503 0.194270480
[98,] 0.020029506 1.965843503
[99,] -0.275462987 0.020029506
[100,] -1.164748405 -0.275462987
[101,] 1.241317983 -1.164748405
[102,] 2.685064114 1.241317983
[103,] 0.396294780 2.685064114
[104,] 1.541723357 0.396294780
[105,] -2.262562228 1.541723357
[106,] 1.011005348 -2.262562228
[107,] 0.190200114 1.011005348
[108,] 1.541232840 0.190200114
[109,] -0.283125672 1.541232840
[110,] 1.144372312 -0.283125672
[111,] 0.286793286 1.144372312
[112,] 2.456434805 0.286793286
[113,] -1.208886125 2.456434805
[114,] -2.817714534 -1.208886125
[115,] 1.469162535 -2.817714534
[116,] -1.411493623 1.469162535
[117,] 0.998902463 -1.411493623
[118,] -1.856522932 0.998902463
[119,] 0.407513127 -1.856522932
[120,] -1.214405047 0.407513127
[121,] 0.468196295 -1.214405047
[122,] -2.815775940 0.468196295
[123,] -0.938555553 -2.815775940
[124,] -0.872135787 -0.938555553
[125,] -0.607816907 -0.872135787
[126,] -0.304762643 -0.607816907
[127,] 0.966809678 -0.304762643
[128,] 1.084625234 0.966809678
[129,] -2.839735463 1.084625234
[130,] 1.992130861 -2.839735463
[131,] -3.249641875 1.992130861
[132,] 2.058567001 -3.249641875
[133,] -1.858772987 2.058567001
[134,] -1.581104512 -1.858772987
[135,] -0.156458130 -1.581104512
[136,] 0.858404618 -0.156458130
[137,] 0.529363657 0.858404618
[138,] -2.155185719 0.529363657
[139,] -1.266160772 -2.155185719
[140,] -5.196122175 -1.266160772
[141,] 3.035152528 -5.196122175
[142,] 1.791425163 3.035152528
[143,] 0.569676853 1.791425163
[144,] 1.377148549 0.569676853
[145,] -4.034701341 1.377148549
[146,] 2.221270949 -4.034701341
[147,] -2.088459839 2.221270949
[148,] 0.982902044 -2.088459839
[149,] 0.018008543 0.982902044
[150,] -3.001782178 0.018008543
[151,] -1.117686536 -3.001782178
[152,] 2.202108689 -1.117686536
[153,] 4.142062138 2.202108689
[154,] 1.662337561 4.142062138
[155,] -2.514103500 1.662337561
[156,] 0.356210329 -2.514103500
[157,] 1.472299760 0.356210329
[158,] 1.197202417 1.472299760
[159,] 1.134480609 1.197202417
[160,] -0.311401205 1.134480609
[161,] 0.369828017 -0.311401205
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.170517999 -3.356142995
2 2.414571606 -0.170517999
3 2.872702713 2.414571606
4 -1.751525104 2.872702713
5 -2.150169015 -1.751525104
6 3.438374289 -2.150169015
7 -1.911613038 3.438374289
8 -2.172739899 -1.911613038
9 2.173334080 -2.172739899
10 0.473546592 2.173334080
11 -0.454826450 0.473546592
12 0.278303230 -0.454826450
13 0.471387289 0.278303230
14 -0.593194630 0.471387289
15 -0.306030519 -0.593194630
16 0.230076219 -0.306030519
17 3.609563447 0.230076219
18 2.253077294 3.609563447
19 0.499480105 2.253077294
20 0.472685393 0.499480105
21 0.884775930 0.472685393
22 2.376158331 0.884775930
23 0.917222741 2.376158331
24 2.008718413 0.917222741
25 -0.002082411 2.008718413
26 1.010975516 -0.002082411
27 -1.339191169 1.010975516
28 0.487516672 -1.339191169
29 -0.344179277 0.487516672
30 -0.801314515 -0.344179277
31 -0.450977794 -0.801314515
32 -1.266839867 -0.450977794
33 0.209345262 -1.266839867
34 -1.665946833 0.209345262
35 -6.179870832 -1.665946833
36 -1.085923770 -6.179870832
37 -1.939624076 -1.085923770
38 1.566135215 -1.939624076
39 1.272518105 1.566135215
40 0.833711923 1.272518105
41 -1.604497546 0.833711923
42 2.032481994 -1.604497546
43 -0.306348905 2.032481994
44 -1.009699157 -0.306348905
45 -4.646847904 -1.009699157
46 -2.707843237 -4.646847904
47 -0.100873958 -2.707843237
48 0.820287088 -0.100873958
49 -2.093174264 0.820287088
50 -0.635949549 -2.093174264
51 -0.223278480 -0.635949549
52 -2.713498850 -0.223278480
53 0.293648428 -2.713498850
54 -2.543466314 0.293648428
55 1.224361562 -2.543466314
56 -0.132197444 1.224361562
57 0.750395951 -0.132197444
58 -0.331041214 0.750395951
59 1.747848111 -0.331041214
60 0.439706019 1.747848111
61 0.057963773 0.439706019
62 -0.450732107 0.057963773
63 -0.431941505 -0.450732107
64 0.507475782 -0.431941505
65 0.981108008 0.507475782
66 1.796512215 0.981108008
67 3.460159699 1.796512215
68 -3.861190012 3.460159699
69 0.556025765 -3.861190012
70 -3.653289316 0.556025765
71 -0.375467808 -3.653289316
72 1.501126067 -0.375467808
73 0.956157276 1.501126067
74 0.316891296 0.956157276
75 3.070152942 0.316891296
76 -0.379021422 3.070152942
77 1.415151339 -0.379021422
78 -1.907284441 1.415151339
79 0.004189141 -1.907284441
80 0.549277649 0.004189141
81 4.272682766 0.549277649
82 0.251675686 4.272682766
83 -0.887650472 0.251675686
84 0.260287718 -0.887650472
85 1.735793823 0.260287718
86 -0.174283645 1.735793823
87 0.690505880 -0.174283645
88 1.425813579 0.690505880
89 0.761559369 1.425813579
90 -1.577701654 0.761559369
91 0.074767370 -1.577701654
92 0.571698390 0.074767370
93 -0.160522068 0.571698390
94 -2.032613812 -0.160522068
95 0.968152787 -2.032613812
96 0.194270480 0.968152787
97 1.965843503 0.194270480
98 0.020029506 1.965843503
99 -0.275462987 0.020029506
100 -1.164748405 -0.275462987
101 1.241317983 -1.164748405
102 2.685064114 1.241317983
103 0.396294780 2.685064114
104 1.541723357 0.396294780
105 -2.262562228 1.541723357
106 1.011005348 -2.262562228
107 0.190200114 1.011005348
108 1.541232840 0.190200114
109 -0.283125672 1.541232840
110 1.144372312 -0.283125672
111 0.286793286 1.144372312
112 2.456434805 0.286793286
113 -1.208886125 2.456434805
114 -2.817714534 -1.208886125
115 1.469162535 -2.817714534
116 -1.411493623 1.469162535
117 0.998902463 -1.411493623
118 -1.856522932 0.998902463
119 0.407513127 -1.856522932
120 -1.214405047 0.407513127
121 0.468196295 -1.214405047
122 -2.815775940 0.468196295
123 -0.938555553 -2.815775940
124 -0.872135787 -0.938555553
125 -0.607816907 -0.872135787
126 -0.304762643 -0.607816907
127 0.966809678 -0.304762643
128 1.084625234 0.966809678
129 -2.839735463 1.084625234
130 1.992130861 -2.839735463
131 -3.249641875 1.992130861
132 2.058567001 -3.249641875
133 -1.858772987 2.058567001
134 -1.581104512 -1.858772987
135 -0.156458130 -1.581104512
136 0.858404618 -0.156458130
137 0.529363657 0.858404618
138 -2.155185719 0.529363657
139 -1.266160772 -2.155185719
140 -5.196122175 -1.266160772
141 3.035152528 -5.196122175
142 1.791425163 3.035152528
143 0.569676853 1.791425163
144 1.377148549 0.569676853
145 -4.034701341 1.377148549
146 2.221270949 -4.034701341
147 -2.088459839 2.221270949
148 0.982902044 -2.088459839
149 0.018008543 0.982902044
150 -3.001782178 0.018008543
151 -1.117686536 -3.001782178
152 2.202108689 -1.117686536
153 4.142062138 2.202108689
154 1.662337561 4.142062138
155 -2.514103500 1.662337561
156 0.356210329 -2.514103500
157 1.472299760 0.356210329
158 1.197202417 1.472299760
159 1.134480609 1.197202417
160 -0.311401205 1.134480609
161 0.369828017 -0.311401205
> 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/7b9cz1355680853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8kd3q1355680853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/94wqa1355680853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10pt5k1355680853.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1140711355680853.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/129zdw1355680853.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/13zwbj1355680853.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/14g4jy1355680853.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/15gk4a1355680853.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/160c3m1355680853.tab")
+ }
>
> try(system("convert tmp/1zvvi1355680853.ps tmp/1zvvi1355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qb6w1355680853.ps tmp/2qb6w1355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/3s4a61355680853.ps tmp/3s4a61355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ln6y1355680853.ps tmp/4ln6y1355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/508n21355680853.ps tmp/508n21355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ijwf1355680853.ps tmp/6ijwf1355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/7b9cz1355680853.ps tmp/7b9cz1355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kd3q1355680853.ps tmp/8kd3q1355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/94wqa1355680853.ps tmp/94wqa1355680853.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pt5k1355680853.ps tmp/10pt5k1355680853.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.350 0.887 9.382