R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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(41
+ ,14
+ ,39
+ ,18
+ ,30
+ ,11
+ ,31
+ ,12
+ ,34
+ ,16
+ ,35
+ ,18
+ ,39
+ ,14
+ ,34
+ ,14
+ ,36
+ ,15
+ ,37
+ ,15
+ ,38
+ ,17
+ ,36
+ ,19
+ ,38
+ ,10
+ ,39
+ ,16
+ ,33
+ ,18
+ ,32
+ ,14
+ ,36
+ ,14
+ ,38
+ ,17
+ ,39
+ ,14
+ ,32
+ ,16
+ ,32
+ ,18
+ ,31
+ ,11
+ ,39
+ ,14
+ ,37
+ ,12
+ ,39
+ ,17
+ ,41
+ ,9
+ ,36
+ ,16
+ ,33
+ ,14
+ ,33
+ ,15
+ ,34
+ ,11
+ ,31
+ ,16
+ ,27
+ ,13
+ ,37
+ ,17
+ ,34
+ ,15
+ ,34
+ ,14
+ ,32
+ ,16
+ ,29
+ ,9
+ ,36
+ ,15
+ ,29
+ ,17
+ ,35
+ ,13
+ ,37
+ ,15
+ ,34
+ ,16
+ ,38
+ ,16
+ ,35
+ ,12
+ ,38
+ ,12
+ ,37
+ ,11
+ ,38
+ ,15
+ ,33
+ ,15
+ ,36
+ ,17
+ ,38
+ ,13
+ ,32
+ ,16
+ ,32
+ ,14
+ ,32
+ ,11
+ ,34
+ ,12
+ ,32
+ ,12
+ ,37
+ ,15
+ ,39
+ ,16
+ ,29
+ ,15
+ ,37
+ ,12
+ ,35
+ ,12
+ ,30
+ ,8
+ ,38
+ ,13
+ ,34
+ ,11
+ ,31
+ ,14
+ ,34
+ ,15
+ ,35
+ ,10
+ ,36
+ ,11
+ ,30
+ ,12
+ ,39
+ ,15
+ ,35
+ ,15
+ ,38
+ ,14
+ ,31
+ ,16
+ ,34
+ ,15
+ ,38
+ ,15
+ ,34
+ ,13
+ ,39
+ ,12
+ ,37
+ ,17
+ ,34
+ ,13
+ ,28
+ ,15
+ ,37
+ ,13
+ ,33
+ ,15
+ ,37
+ ,16
+ ,35
+ ,15
+ ,37
+ ,16
+ ,32
+ ,15
+ ,33
+ ,14
+ ,38
+ ,15
+ ,33
+ ,14
+ ,29
+ ,13
+ ,33
+ ,7
+ ,31
+ ,17
+ ,36
+ ,13
+ ,35
+ ,15
+ ,32
+ ,14
+ ,29
+ ,13
+ ,39
+ ,16
+ ,37
+ ,12
+ ,35
+ ,14
+ ,37
+ ,17
+ ,32
+ ,15
+ ,38
+ ,17
+ ,37
+ ,12
+ ,36
+ ,16
+ ,32
+ ,11
+ ,33
+ ,15
+ ,40
+ ,9
+ ,38
+ ,16
+ ,41
+ ,15
+ ,36
+ ,10
+ ,43
+ ,10
+ ,30
+ ,15
+ ,31
+ ,11
+ ,32
+ ,13
+ ,32
+ ,14
+ ,37
+ ,18
+ ,37
+ ,16
+ ,33
+ ,14
+ ,34
+ ,14
+ ,33
+ ,14
+ ,38
+ ,14
+ ,33
+ ,12
+ ,31
+ ,14
+ ,38
+ ,15
+ ,37
+ ,15
+ ,33
+ ,15
+ ,31
+ ,13
+ ,39
+ ,17
+ ,44
+ ,17
+ ,33
+ ,19
+ ,35
+ ,15
+ ,32
+ ,13
+ ,28
+ ,9
+ ,40
+ ,15
+ ,27
+ ,15
+ ,37
+ ,15
+ ,32
+ ,16
+ ,28
+ ,11
+ ,34
+ ,14
+ ,30
+ ,11
+ ,35
+ ,15
+ ,31
+ ,13
+ ,32
+ ,15
+ ,30
+ ,16
+ ,30
+ ,14
+ ,31
+ ,15
+ ,40
+ ,16
+ ,32
+ ,16
+ ,36
+ ,11
+ ,32
+ ,12
+ ,35
+ ,9
+ ,38
+ ,16
+ ,42
+ ,13
+ ,34
+ ,16
+ ,35
+ ,12
+ ,35
+ ,9
+ ,33
+ ,13
+ ,36
+ ,13
+ ,32
+ ,14
+ ,33
+ ,19
+ ,34
+ ,13
+ ,32
+ ,12
+ ,34
+ ,13)
+ ,dim=c(2
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Happiness')
+ ,1:162))
> y <- array(NA,dim=c(2,162),dimnames=list(c('Connected','Happiness'),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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Connected Happiness
1 41 14
2 39 18
3 30 11
4 31 12
5 34 16
6 35 18
7 39 14
8 34 14
9 36 15
10 37 15
11 38 17
12 36 19
13 38 10
14 39 16
15 33 18
16 32 14
17 36 14
18 38 17
19 39 14
20 32 16
21 32 18
22 31 11
23 39 14
24 37 12
25 39 17
26 41 9
27 36 16
28 33 14
29 33 15
30 34 11
31 31 16
32 27 13
33 37 17
34 34 15
35 34 14
36 32 16
37 29 9
38 36 15
39 29 17
40 35 13
41 37 15
42 34 16
43 38 16
44 35 12
45 38 12
46 37 11
47 38 15
48 33 15
49 36 17
50 38 13
51 32 16
52 32 14
53 32 11
54 34 12
55 32 12
56 37 15
57 39 16
58 29 15
59 37 12
60 35 12
61 30 8
62 38 13
63 34 11
64 31 14
65 34 15
66 35 10
67 36 11
68 30 12
69 39 15
70 35 15
71 38 14
72 31 16
73 34 15
74 38 15
75 34 13
76 39 12
77 37 17
78 34 13
79 28 15
80 37 13
81 33 15
82 37 16
83 35 15
84 37 16
85 32 15
86 33 14
87 38 15
88 33 14
89 29 13
90 33 7
91 31 17
92 36 13
93 35 15
94 32 14
95 29 13
96 39 16
97 37 12
98 35 14
99 37 17
100 32 15
101 38 17
102 37 12
103 36 16
104 32 11
105 33 15
106 40 9
107 38 16
108 41 15
109 36 10
110 43 10
111 30 15
112 31 11
113 32 13
114 32 14
115 37 18
116 37 16
117 33 14
118 34 14
119 33 14
120 38 14
121 33 12
122 31 14
123 38 15
124 37 15
125 33 15
126 31 13
127 39 17
128 44 17
129 33 19
130 35 15
131 32 13
132 28 9
133 40 15
134 27 15
135 37 15
136 32 16
137 28 11
138 34 14
139 30 11
140 35 15
141 31 13
142 32 15
143 30 16
144 30 14
145 31 15
146 40 16
147 32 16
148 36 11
149 32 12
150 35 9
151 38 16
152 42 13
153 34 16
154 35 12
155 35 9
156 33 13
157 36 13
158 32 14
159 33 19
160 34 13
161 32 12
162 34 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Happiness
31.731 0.206
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.8219 -2.6158 -0.0857 2.7221 9.2083
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 31.731 1.608 19.739 <2e-16 ***
Happiness 0.206 0.113 1.824 0.0701 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.351 on 160 degrees of freedom
Multiple R-squared: 0.02036, Adjusted R-squared: 0.01424
F-statistic: 3.326 on 1 and 160 DF, p-value: 0.07007
> 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,] 0.80223481 0.395530386 0.197765193
[2,] 0.77170591 0.456588179 0.228294089
[3,] 0.80085939 0.398281221 0.199140611
[4,] 0.71114599 0.577708026 0.288854013
[5,] 0.60672389 0.786552222 0.393276111
[6,] 0.51409287 0.971814264 0.485907132
[7,] 0.41679363 0.833587262 0.583206369
[8,] 0.37875805 0.757516099 0.621241950
[9,] 0.45473841 0.909476818 0.545261591
[10,] 0.42239523 0.844790458 0.577604771
[11,] 0.45605941 0.912118829 0.543940585
[12,] 0.46475043 0.929500858 0.535249571
[13,] 0.38889854 0.777797071 0.611101465
[14,] 0.33646913 0.672938259 0.663530870
[15,] 0.34601990 0.692039804 0.653980098
[16,] 0.38448031 0.768960628 0.615519686
[17,] 0.42605746 0.852114920 0.573942540
[18,] 0.44037157 0.880743135 0.559628432
[19,] 0.46093468 0.921869369 0.539065316
[20,] 0.42117638 0.842352761 0.578823619
[21,] 0.41013476 0.820269516 0.589865242
[22,] 0.55759820 0.884803606 0.442401803
[23,] 0.49629830 0.992596601 0.503701699
[24,] 0.47628654 0.952573075 0.523713462
[25,] 0.45515532 0.910310649 0.544844675
[26,] 0.40732332 0.814646634 0.592676683
[27,] 0.45992738 0.919854765 0.540072617
[28,] 0.72069694 0.558606124 0.279303062
[29,] 0.68113870 0.637722602 0.318861301
[30,] 0.63651167 0.726976663 0.363488331
[31,] 0.58873255 0.822534893 0.411267447
[32,] 0.58490469 0.830190621 0.415095310
[33,] 0.64637871 0.707242579 0.353621290
[34,] 0.59935604 0.801287928 0.400643964
[35,] 0.71819738 0.563605230 0.281802615
[36,] 0.67289556 0.654208879 0.327104440
[37,] 0.64231808 0.715363844 0.357681922
[38,] 0.59807378 0.803852440 0.401926220
[39,] 0.58361168 0.832776646 0.416388323
[40,] 0.53452825 0.930943508 0.465471754
[41,] 0.53851411 0.922971774 0.461485887
[42,] 0.51798892 0.964022161 0.482011081
[43,] 0.50635562 0.987288764 0.493644382
[44,] 0.47517906 0.950358127 0.524820936
[45,] 0.42790978 0.855819569 0.572090216
[46,] 0.42483109 0.849662174 0.575168913
[47,] 0.42012625 0.840252510 0.579873745
[48,] 0.40787250 0.815744992 0.592127504
[49,] 0.38583675 0.771673492 0.614163254
[50,] 0.34172793 0.683455860 0.658272070
[51,] 0.32186243 0.643724851 0.678137574
[52,] 0.29558921 0.591178420 0.704410790
[53,] 0.30962933 0.619258665 0.690370668
[54,] 0.40500474 0.810009487 0.594995256
[55,] 0.38673739 0.773474771 0.613262614
[56,] 0.34405177 0.688103539 0.655948231
[57,] 0.35176598 0.703531960 0.648234020
[58,] 0.35485333 0.709706667 0.645146666
[59,] 0.31266350 0.625327002 0.687336499
[60,] 0.32230896 0.644617927 0.677691036
[61,] 0.28474425 0.569488494 0.715255753
[62,] 0.25081413 0.501628258 0.749185871
[63,] 0.22652138 0.453042766 0.773478617
[64,] 0.24972571 0.499451417 0.750274292
[65,] 0.26845277 0.536905542 0.731547229
[66,] 0.23214564 0.464291285 0.767854358
[67,] 0.23131829 0.462636586 0.768681707
[68,] 0.24880207 0.497604139 0.751197930
[69,] 0.21640509 0.432810180 0.783594910
[70,] 0.21209722 0.424194433 0.787902784
[71,] 0.18136448 0.362728968 0.818635516
[72,] 0.21253091 0.425061818 0.787469091
[73,] 0.18924719 0.378494375 0.810752812
[74,] 0.16089057 0.321781138 0.839109431
[75,] 0.26379605 0.527592097 0.736203951
[76,] 0.24855635 0.497112698 0.751443651
[77,] 0.22420854 0.448417076 0.775791462
[78,] 0.20256285 0.405125697 0.797437151
[79,] 0.17234932 0.344698643 0.827650678
[80,] 0.15412684 0.308253671 0.845873164
[81,] 0.14642412 0.292848248 0.853575876
[82,] 0.12750294 0.255005878 0.872497061
[83,] 0.12502737 0.250054749 0.874972626
[84,] 0.10805569 0.216111383 0.891944309
[85,] 0.14459075 0.289181493 0.855409254
[86,] 0.12054438 0.241088758 0.879455621
[87,] 0.13364131 0.267282622 0.866358689
[88,] 0.11563344 0.231266886 0.884366557
[89,] 0.09503135 0.190062704 0.904968648
[90,] 0.08731679 0.174633574 0.912683213
[91,] 0.11799729 0.235994575 0.882002713
[92,] 0.12640503 0.252810057 0.873594971
[93,] 0.11921636 0.238432730 0.880783635
[94,] 0.09822121 0.196442428 0.901778786
[95,] 0.08460219 0.169204389 0.915397805
[96,] 0.07874950 0.157499009 0.921250496
[97,] 0.07340335 0.146806701 0.926596650
[98,] 0.06864552 0.137291037 0.931354481
[99,] 0.05583169 0.111663377 0.944168312
[100,] 0.04748687 0.094973749 0.952513125
[101,] 0.03954350 0.079087000 0.960456500
[102,] 0.07440065 0.148801306 0.925599347
[103,] 0.07083629 0.141672590 0.929163705
[104,] 0.11796881 0.235937617 0.882031191
[105,] 0.10854533 0.217090653 0.891454674
[106,] 0.37130828 0.742616566 0.628691717
[107,] 0.41306427 0.826128545 0.586935727
[108,] 0.39016515 0.780330301 0.609834850
[109,] 0.36016049 0.720320972 0.639839514
[110,] 0.33604218 0.672084355 0.663957822
[111,] 0.29892789 0.597855778 0.701072111
[112,] 0.27229964 0.544599279 0.727700360
[113,] 0.23811837 0.476236748 0.761881626
[114,] 0.20159044 0.403180881 0.798409560
[115,] 0.17263680 0.345273593 0.827363203
[116,] 0.17967153 0.359343059 0.820328471
[117,] 0.14993504 0.299870084 0.850064958
[118,] 0.14642667 0.292853333 0.853573333
[119,] 0.14670044 0.293400880 0.853299560
[120,] 0.13333366 0.266667314 0.866666343
[121,] 0.11145968 0.222919355 0.888540322
[122,] 0.10370570 0.207411407 0.896294296
[123,] 0.11093329 0.221866581 0.889066709
[124,] 0.36595271 0.731905426 0.634047287
[125,] 0.32981831 0.659636616 0.670181692
[126,] 0.28510152 0.570203035 0.714898483
[127,] 0.24982777 0.499655549 0.750172226
[128,] 0.31064230 0.621284609 0.689357696
[129,] 0.43868127 0.877362533 0.561318733
[130,] 0.63323179 0.733536418 0.366768209
[131,] 0.62853766 0.742924679 0.371462340
[132,] 0.58944404 0.821111914 0.410555957
[133,] 0.71415038 0.571699236 0.285849618
[134,] 0.65555053 0.688898940 0.344449470
[135,] 0.69281117 0.614377654 0.307188827
[136,] 0.63747051 0.725058986 0.362529493
[137,] 0.63268654 0.734626916 0.367313458
[138,] 0.59041035 0.819179293 0.409589647
[139,] 0.63281092 0.734378165 0.367189082
[140,] 0.69708363 0.605832732 0.302916366
[141,] 0.72163691 0.556726175 0.278363088
[142,] 0.83987103 0.320257941 0.160128971
[143,] 0.81913926 0.361721478 0.180860739
[144,] 0.76590350 0.468193001 0.234096500
[145,] 0.74959029 0.500819416 0.250409708
[146,] 0.66768027 0.664639454 0.332319727
[147,] 0.69403739 0.611925216 0.305962608
[148,] 0.99663836 0.006723285 0.003361643
[149,] 0.99179680 0.016406406 0.008203203
[150,] 0.98307127 0.033857454 0.016928727
[151,] 0.96255205 0.074895893 0.037447947
[152,] 0.91330727 0.173385468 0.086692734
[153,] 0.95044465 0.099110709 0.049555355
> postscript(file="/var/wessaorg/rcomp/tmp/1p7571324680951.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/27i751324680951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35wgw1324680951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4kath1324680951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rczn1324680951.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
6.384173894 3.560060060 -3.997740731 -3.203769190 -1.027883023 -0.439939940
7 8 9 10 11 12
4.384173894 -0.615826106 1.178145435 2.178145435 2.766088518 0.354031602
13 14 15 16 17 18
4.208287727 3.972116977 -2.439939940 -2.615826106 1.384173894 2.766088518
19 20 21 22 23 24
4.384173894 -3.027883023 -3.439939940 -2.997740731 4.384173894 2.796230810
25 26 27 28 29 30
3.766088518 7.414316185 0.972116977 -1.615826106 -1.821854565 0.002259269
31 32 33 34 35 36
-4.027883023 -7.409797648 1.766088518 -0.821854565 -0.615826106 -3.027883023
37 38 39 40 41 42
-4.585683815 1.178145435 -6.233911482 0.590202352 2.178145435 -1.027883023
43 44 45 46 47 48
2.972116977 0.796230810 3.796230810 3.002259269 3.178145435 -1.821854565
49 50 51 52 53 54
0.766088518 3.590202352 -3.027883023 -2.615826106 -1.997740731 -0.203769190
55 56 57 58 59 60
-2.203769190 2.178145435 3.972116977 -5.821854565 2.796230810 0.796230810
61 62 63 64 65 66
-3.379655356 3.590202352 0.002259269 -3.615826106 -0.821854565 1.208287727
67 68 69 70 71 72
2.002259269 -4.203769190 4.178145435 0.178145435 3.384173894 -4.027883023
73 74 75 76 77 78
-0.821854565 3.178145435 -0.409797648 4.796230810 1.766088518 -0.409797648
79 80 81 82 83 84
-6.821854565 2.590202352 -1.821854565 1.972116977 0.178145435 1.972116977
85 86 87 88 89 90
-2.821854565 -1.615826106 3.178145435 -1.615826106 -5.409797648 -0.173626898
91 92 93 94 95 96
-4.233911482 1.590202352 0.178145435 -2.615826106 -5.409797648 3.972116977
97 98 99 100 101 102
2.796230810 0.384173894 1.766088518 -2.821854565 2.766088518 2.796230810
103 104 105 106 107 108
0.972116977 -1.997740731 -1.821854565 6.414316185 2.972116977 6.178145435
109 110 111 112 113 114
2.208287727 9.208287727 -4.821854565 -2.997740731 -2.409797648 -2.615826106
115 116 117 118 119 120
1.560060060 1.972116977 -1.615826106 -0.615826106 -1.615826106 3.384173894
121 122 123 124 125 126
-1.203769190 -3.615826106 3.178145435 2.178145435 -1.821854565 -3.409797648
127 128 129 130 131 132
3.766088518 8.766088518 -2.645968398 0.178145435 -2.409797648 -5.585683815
133 134 135 136 137 138
5.178145435 -7.821854565 2.178145435 -3.027883023 -5.997740731 -0.615826106
139 140 141 142 143 144
-3.997740731 0.178145435 -3.409797648 -2.821854565 -5.027883023 -4.615826106
145 146 147 148 149 150
-3.821854565 4.972116977 -3.027883023 2.002259269 -2.203769190 1.414316185
151 152 153 154 155 156
2.972116977 7.590202352 -1.027883023 0.796230810 1.414316185 -1.409797648
157 158 159 160 161 162
1.590202352 -2.615826106 -2.645968398 -0.409797648 -2.203769190 -0.409797648
> postscript(file="/var/wessaorg/rcomp/tmp/6yxie1324680951.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 6.384173894 NA
1 3.560060060 6.384173894
2 -3.997740731 3.560060060
3 -3.203769190 -3.997740731
4 -1.027883023 -3.203769190
5 -0.439939940 -1.027883023
6 4.384173894 -0.439939940
7 -0.615826106 4.384173894
8 1.178145435 -0.615826106
9 2.178145435 1.178145435
10 2.766088518 2.178145435
11 0.354031602 2.766088518
12 4.208287727 0.354031602
13 3.972116977 4.208287727
14 -2.439939940 3.972116977
15 -2.615826106 -2.439939940
16 1.384173894 -2.615826106
17 2.766088518 1.384173894
18 4.384173894 2.766088518
19 -3.027883023 4.384173894
20 -3.439939940 -3.027883023
21 -2.997740731 -3.439939940
22 4.384173894 -2.997740731
23 2.796230810 4.384173894
24 3.766088518 2.796230810
25 7.414316185 3.766088518
26 0.972116977 7.414316185
27 -1.615826106 0.972116977
28 -1.821854565 -1.615826106
29 0.002259269 -1.821854565
30 -4.027883023 0.002259269
31 -7.409797648 -4.027883023
32 1.766088518 -7.409797648
33 -0.821854565 1.766088518
34 -0.615826106 -0.821854565
35 -3.027883023 -0.615826106
36 -4.585683815 -3.027883023
37 1.178145435 -4.585683815
38 -6.233911482 1.178145435
39 0.590202352 -6.233911482
40 2.178145435 0.590202352
41 -1.027883023 2.178145435
42 2.972116977 -1.027883023
43 0.796230810 2.972116977
44 3.796230810 0.796230810
45 3.002259269 3.796230810
46 3.178145435 3.002259269
47 -1.821854565 3.178145435
48 0.766088518 -1.821854565
49 3.590202352 0.766088518
50 -3.027883023 3.590202352
51 -2.615826106 -3.027883023
52 -1.997740731 -2.615826106
53 -0.203769190 -1.997740731
54 -2.203769190 -0.203769190
55 2.178145435 -2.203769190
56 3.972116977 2.178145435
57 -5.821854565 3.972116977
58 2.796230810 -5.821854565
59 0.796230810 2.796230810
60 -3.379655356 0.796230810
61 3.590202352 -3.379655356
62 0.002259269 3.590202352
63 -3.615826106 0.002259269
64 -0.821854565 -3.615826106
65 1.208287727 -0.821854565
66 2.002259269 1.208287727
67 -4.203769190 2.002259269
68 4.178145435 -4.203769190
69 0.178145435 4.178145435
70 3.384173894 0.178145435
71 -4.027883023 3.384173894
72 -0.821854565 -4.027883023
73 3.178145435 -0.821854565
74 -0.409797648 3.178145435
75 4.796230810 -0.409797648
76 1.766088518 4.796230810
77 -0.409797648 1.766088518
78 -6.821854565 -0.409797648
79 2.590202352 -6.821854565
80 -1.821854565 2.590202352
81 1.972116977 -1.821854565
82 0.178145435 1.972116977
83 1.972116977 0.178145435
84 -2.821854565 1.972116977
85 -1.615826106 -2.821854565
86 3.178145435 -1.615826106
87 -1.615826106 3.178145435
88 -5.409797648 -1.615826106
89 -0.173626898 -5.409797648
90 -4.233911482 -0.173626898
91 1.590202352 -4.233911482
92 0.178145435 1.590202352
93 -2.615826106 0.178145435
94 -5.409797648 -2.615826106
95 3.972116977 -5.409797648
96 2.796230810 3.972116977
97 0.384173894 2.796230810
98 1.766088518 0.384173894
99 -2.821854565 1.766088518
100 2.766088518 -2.821854565
101 2.796230810 2.766088518
102 0.972116977 2.796230810
103 -1.997740731 0.972116977
104 -1.821854565 -1.997740731
105 6.414316185 -1.821854565
106 2.972116977 6.414316185
107 6.178145435 2.972116977
108 2.208287727 6.178145435
109 9.208287727 2.208287727
110 -4.821854565 9.208287727
111 -2.997740731 -4.821854565
112 -2.409797648 -2.997740731
113 -2.615826106 -2.409797648
114 1.560060060 -2.615826106
115 1.972116977 1.560060060
116 -1.615826106 1.972116977
117 -0.615826106 -1.615826106
118 -1.615826106 -0.615826106
119 3.384173894 -1.615826106
120 -1.203769190 3.384173894
121 -3.615826106 -1.203769190
122 3.178145435 -3.615826106
123 2.178145435 3.178145435
124 -1.821854565 2.178145435
125 -3.409797648 -1.821854565
126 3.766088518 -3.409797648
127 8.766088518 3.766088518
128 -2.645968398 8.766088518
129 0.178145435 -2.645968398
130 -2.409797648 0.178145435
131 -5.585683815 -2.409797648
132 5.178145435 -5.585683815
133 -7.821854565 5.178145435
134 2.178145435 -7.821854565
135 -3.027883023 2.178145435
136 -5.997740731 -3.027883023
137 -0.615826106 -5.997740731
138 -3.997740731 -0.615826106
139 0.178145435 -3.997740731
140 -3.409797648 0.178145435
141 -2.821854565 -3.409797648
142 -5.027883023 -2.821854565
143 -4.615826106 -5.027883023
144 -3.821854565 -4.615826106
145 4.972116977 -3.821854565
146 -3.027883023 4.972116977
147 2.002259269 -3.027883023
148 -2.203769190 2.002259269
149 1.414316185 -2.203769190
150 2.972116977 1.414316185
151 7.590202352 2.972116977
152 -1.027883023 7.590202352
153 0.796230810 -1.027883023
154 1.414316185 0.796230810
155 -1.409797648 1.414316185
156 1.590202352 -1.409797648
157 -2.615826106 1.590202352
158 -2.645968398 -2.615826106
159 -0.409797648 -2.645968398
160 -2.203769190 -0.409797648
161 -0.409797648 -2.203769190
162 NA -0.409797648
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.560060060 6.384173894
[2,] -3.997740731 3.560060060
[3,] -3.203769190 -3.997740731
[4,] -1.027883023 -3.203769190
[5,] -0.439939940 -1.027883023
[6,] 4.384173894 -0.439939940
[7,] -0.615826106 4.384173894
[8,] 1.178145435 -0.615826106
[9,] 2.178145435 1.178145435
[10,] 2.766088518 2.178145435
[11,] 0.354031602 2.766088518
[12,] 4.208287727 0.354031602
[13,] 3.972116977 4.208287727
[14,] -2.439939940 3.972116977
[15,] -2.615826106 -2.439939940
[16,] 1.384173894 -2.615826106
[17,] 2.766088518 1.384173894
[18,] 4.384173894 2.766088518
[19,] -3.027883023 4.384173894
[20,] -3.439939940 -3.027883023
[21,] -2.997740731 -3.439939940
[22,] 4.384173894 -2.997740731
[23,] 2.796230810 4.384173894
[24,] 3.766088518 2.796230810
[25,] 7.414316185 3.766088518
[26,] 0.972116977 7.414316185
[27,] -1.615826106 0.972116977
[28,] -1.821854565 -1.615826106
[29,] 0.002259269 -1.821854565
[30,] -4.027883023 0.002259269
[31,] -7.409797648 -4.027883023
[32,] 1.766088518 -7.409797648
[33,] -0.821854565 1.766088518
[34,] -0.615826106 -0.821854565
[35,] -3.027883023 -0.615826106
[36,] -4.585683815 -3.027883023
[37,] 1.178145435 -4.585683815
[38,] -6.233911482 1.178145435
[39,] 0.590202352 -6.233911482
[40,] 2.178145435 0.590202352
[41,] -1.027883023 2.178145435
[42,] 2.972116977 -1.027883023
[43,] 0.796230810 2.972116977
[44,] 3.796230810 0.796230810
[45,] 3.002259269 3.796230810
[46,] 3.178145435 3.002259269
[47,] -1.821854565 3.178145435
[48,] 0.766088518 -1.821854565
[49,] 3.590202352 0.766088518
[50,] -3.027883023 3.590202352
[51,] -2.615826106 -3.027883023
[52,] -1.997740731 -2.615826106
[53,] -0.203769190 -1.997740731
[54,] -2.203769190 -0.203769190
[55,] 2.178145435 -2.203769190
[56,] 3.972116977 2.178145435
[57,] -5.821854565 3.972116977
[58,] 2.796230810 -5.821854565
[59,] 0.796230810 2.796230810
[60,] -3.379655356 0.796230810
[61,] 3.590202352 -3.379655356
[62,] 0.002259269 3.590202352
[63,] -3.615826106 0.002259269
[64,] -0.821854565 -3.615826106
[65,] 1.208287727 -0.821854565
[66,] 2.002259269 1.208287727
[67,] -4.203769190 2.002259269
[68,] 4.178145435 -4.203769190
[69,] 0.178145435 4.178145435
[70,] 3.384173894 0.178145435
[71,] -4.027883023 3.384173894
[72,] -0.821854565 -4.027883023
[73,] 3.178145435 -0.821854565
[74,] -0.409797648 3.178145435
[75,] 4.796230810 -0.409797648
[76,] 1.766088518 4.796230810
[77,] -0.409797648 1.766088518
[78,] -6.821854565 -0.409797648
[79,] 2.590202352 -6.821854565
[80,] -1.821854565 2.590202352
[81,] 1.972116977 -1.821854565
[82,] 0.178145435 1.972116977
[83,] 1.972116977 0.178145435
[84,] -2.821854565 1.972116977
[85,] -1.615826106 -2.821854565
[86,] 3.178145435 -1.615826106
[87,] -1.615826106 3.178145435
[88,] -5.409797648 -1.615826106
[89,] -0.173626898 -5.409797648
[90,] -4.233911482 -0.173626898
[91,] 1.590202352 -4.233911482
[92,] 0.178145435 1.590202352
[93,] -2.615826106 0.178145435
[94,] -5.409797648 -2.615826106
[95,] 3.972116977 -5.409797648
[96,] 2.796230810 3.972116977
[97,] 0.384173894 2.796230810
[98,] 1.766088518 0.384173894
[99,] -2.821854565 1.766088518
[100,] 2.766088518 -2.821854565
[101,] 2.796230810 2.766088518
[102,] 0.972116977 2.796230810
[103,] -1.997740731 0.972116977
[104,] -1.821854565 -1.997740731
[105,] 6.414316185 -1.821854565
[106,] 2.972116977 6.414316185
[107,] 6.178145435 2.972116977
[108,] 2.208287727 6.178145435
[109,] 9.208287727 2.208287727
[110,] -4.821854565 9.208287727
[111,] -2.997740731 -4.821854565
[112,] -2.409797648 -2.997740731
[113,] -2.615826106 -2.409797648
[114,] 1.560060060 -2.615826106
[115,] 1.972116977 1.560060060
[116,] -1.615826106 1.972116977
[117,] -0.615826106 -1.615826106
[118,] -1.615826106 -0.615826106
[119,] 3.384173894 -1.615826106
[120,] -1.203769190 3.384173894
[121,] -3.615826106 -1.203769190
[122,] 3.178145435 -3.615826106
[123,] 2.178145435 3.178145435
[124,] -1.821854565 2.178145435
[125,] -3.409797648 -1.821854565
[126,] 3.766088518 -3.409797648
[127,] 8.766088518 3.766088518
[128,] -2.645968398 8.766088518
[129,] 0.178145435 -2.645968398
[130,] -2.409797648 0.178145435
[131,] -5.585683815 -2.409797648
[132,] 5.178145435 -5.585683815
[133,] -7.821854565 5.178145435
[134,] 2.178145435 -7.821854565
[135,] -3.027883023 2.178145435
[136,] -5.997740731 -3.027883023
[137,] -0.615826106 -5.997740731
[138,] -3.997740731 -0.615826106
[139,] 0.178145435 -3.997740731
[140,] -3.409797648 0.178145435
[141,] -2.821854565 -3.409797648
[142,] -5.027883023 -2.821854565
[143,] -4.615826106 -5.027883023
[144,] -3.821854565 -4.615826106
[145,] 4.972116977 -3.821854565
[146,] -3.027883023 4.972116977
[147,] 2.002259269 -3.027883023
[148,] -2.203769190 2.002259269
[149,] 1.414316185 -2.203769190
[150,] 2.972116977 1.414316185
[151,] 7.590202352 2.972116977
[152,] -1.027883023 7.590202352
[153,] 0.796230810 -1.027883023
[154,] 1.414316185 0.796230810
[155,] -1.409797648 1.414316185
[156,] 1.590202352 -1.409797648
[157,] -2.615826106 1.590202352
[158,] -2.645968398 -2.615826106
[159,] -0.409797648 -2.645968398
[160,] -2.203769190 -0.409797648
[161,] -0.409797648 -2.203769190
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.560060060 6.384173894
2 -3.997740731 3.560060060
3 -3.203769190 -3.997740731
4 -1.027883023 -3.203769190
5 -0.439939940 -1.027883023
6 4.384173894 -0.439939940
7 -0.615826106 4.384173894
8 1.178145435 -0.615826106
9 2.178145435 1.178145435
10 2.766088518 2.178145435
11 0.354031602 2.766088518
12 4.208287727 0.354031602
13 3.972116977 4.208287727
14 -2.439939940 3.972116977
15 -2.615826106 -2.439939940
16 1.384173894 -2.615826106
17 2.766088518 1.384173894
18 4.384173894 2.766088518
19 -3.027883023 4.384173894
20 -3.439939940 -3.027883023
21 -2.997740731 -3.439939940
22 4.384173894 -2.997740731
23 2.796230810 4.384173894
24 3.766088518 2.796230810
25 7.414316185 3.766088518
26 0.972116977 7.414316185
27 -1.615826106 0.972116977
28 -1.821854565 -1.615826106
29 0.002259269 -1.821854565
30 -4.027883023 0.002259269
31 -7.409797648 -4.027883023
32 1.766088518 -7.409797648
33 -0.821854565 1.766088518
34 -0.615826106 -0.821854565
35 -3.027883023 -0.615826106
36 -4.585683815 -3.027883023
37 1.178145435 -4.585683815
38 -6.233911482 1.178145435
39 0.590202352 -6.233911482
40 2.178145435 0.590202352
41 -1.027883023 2.178145435
42 2.972116977 -1.027883023
43 0.796230810 2.972116977
44 3.796230810 0.796230810
45 3.002259269 3.796230810
46 3.178145435 3.002259269
47 -1.821854565 3.178145435
48 0.766088518 -1.821854565
49 3.590202352 0.766088518
50 -3.027883023 3.590202352
51 -2.615826106 -3.027883023
52 -1.997740731 -2.615826106
53 -0.203769190 -1.997740731
54 -2.203769190 -0.203769190
55 2.178145435 -2.203769190
56 3.972116977 2.178145435
57 -5.821854565 3.972116977
58 2.796230810 -5.821854565
59 0.796230810 2.796230810
60 -3.379655356 0.796230810
61 3.590202352 -3.379655356
62 0.002259269 3.590202352
63 -3.615826106 0.002259269
64 -0.821854565 -3.615826106
65 1.208287727 -0.821854565
66 2.002259269 1.208287727
67 -4.203769190 2.002259269
68 4.178145435 -4.203769190
69 0.178145435 4.178145435
70 3.384173894 0.178145435
71 -4.027883023 3.384173894
72 -0.821854565 -4.027883023
73 3.178145435 -0.821854565
74 -0.409797648 3.178145435
75 4.796230810 -0.409797648
76 1.766088518 4.796230810
77 -0.409797648 1.766088518
78 -6.821854565 -0.409797648
79 2.590202352 -6.821854565
80 -1.821854565 2.590202352
81 1.972116977 -1.821854565
82 0.178145435 1.972116977
83 1.972116977 0.178145435
84 -2.821854565 1.972116977
85 -1.615826106 -2.821854565
86 3.178145435 -1.615826106
87 -1.615826106 3.178145435
88 -5.409797648 -1.615826106
89 -0.173626898 -5.409797648
90 -4.233911482 -0.173626898
91 1.590202352 -4.233911482
92 0.178145435 1.590202352
93 -2.615826106 0.178145435
94 -5.409797648 -2.615826106
95 3.972116977 -5.409797648
96 2.796230810 3.972116977
97 0.384173894 2.796230810
98 1.766088518 0.384173894
99 -2.821854565 1.766088518
100 2.766088518 -2.821854565
101 2.796230810 2.766088518
102 0.972116977 2.796230810
103 -1.997740731 0.972116977
104 -1.821854565 -1.997740731
105 6.414316185 -1.821854565
106 2.972116977 6.414316185
107 6.178145435 2.972116977
108 2.208287727 6.178145435
109 9.208287727 2.208287727
110 -4.821854565 9.208287727
111 -2.997740731 -4.821854565
112 -2.409797648 -2.997740731
113 -2.615826106 -2.409797648
114 1.560060060 -2.615826106
115 1.972116977 1.560060060
116 -1.615826106 1.972116977
117 -0.615826106 -1.615826106
118 -1.615826106 -0.615826106
119 3.384173894 -1.615826106
120 -1.203769190 3.384173894
121 -3.615826106 -1.203769190
122 3.178145435 -3.615826106
123 2.178145435 3.178145435
124 -1.821854565 2.178145435
125 -3.409797648 -1.821854565
126 3.766088518 -3.409797648
127 8.766088518 3.766088518
128 -2.645968398 8.766088518
129 0.178145435 -2.645968398
130 -2.409797648 0.178145435
131 -5.585683815 -2.409797648
132 5.178145435 -5.585683815
133 -7.821854565 5.178145435
134 2.178145435 -7.821854565
135 -3.027883023 2.178145435
136 -5.997740731 -3.027883023
137 -0.615826106 -5.997740731
138 -3.997740731 -0.615826106
139 0.178145435 -3.997740731
140 -3.409797648 0.178145435
141 -2.821854565 -3.409797648
142 -5.027883023 -2.821854565
143 -4.615826106 -5.027883023
144 -3.821854565 -4.615826106
145 4.972116977 -3.821854565
146 -3.027883023 4.972116977
147 2.002259269 -3.027883023
148 -2.203769190 2.002259269
149 1.414316185 -2.203769190
150 2.972116977 1.414316185
151 7.590202352 2.972116977
152 -1.027883023 7.590202352
153 0.796230810 -1.027883023
154 1.414316185 0.796230810
155 -1.409797648 1.414316185
156 1.590202352 -1.409797648
157 -2.615826106 1.590202352
158 -2.645968398 -2.615826106
159 -0.409797648 -2.645968398
160 -2.203769190 -0.409797648
161 -0.409797648 -2.203769190
> 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/7aoj61324680951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8vrek1324680951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9zjs61324680951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1010c61324680951.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xs3h1324680951.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/122xzt1324680951.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/139zh81324680951.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/14x0n71324680951.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/15p6dk1324680951.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/16yr5x1324680951.tab")
+ }
>
> try(system("convert tmp/1p7571324680951.ps tmp/1p7571324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/27i751324680951.ps tmp/27i751324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/35wgw1324680951.ps tmp/35wgw1324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kath1324680951.ps tmp/4kath1324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rczn1324680951.ps tmp/5rczn1324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yxie1324680951.ps tmp/6yxie1324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aoj61324680951.ps tmp/7aoj61324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vrek1324680951.ps tmp/8vrek1324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zjs61324680951.ps tmp/9zjs61324680951.png",intern=TRUE))
character(0)
> try(system("convert tmp/1010c61324680951.ps tmp/1010c61324680951.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.948 0.816 5.799