R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(41
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+ ,16)
+ ,dim=c(6
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:162))
> y <- array(NA,dim=c(6,162),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression'),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 Separate Learning Software Happiness Depression
1 41 38 13 12 14 12
2 39 32 16 11 18 11
3 30 35 19 15 11 14
4 31 33 15 6 12 12
5 34 37 14 13 16 21
6 35 29 13 10 18 12
7 39 31 19 12 14 22
8 34 36 15 14 14 11
9 36 35 14 12 15 10
10 37 38 15 6 15 13
11 38 31 16 10 17 10
12 36 34 16 12 19 8
13 38 35 16 12 10 15
14 39 38 16 11 16 14
15 33 37 17 15 18 10
16 32 33 15 12 14 14
17 36 32 15 10 14 14
18 38 38 20 12 17 11
19 39 38 18 11 14 10
20 32 32 16 12 16 13
21 32 33 16 11 18 7
22 31 31 16 12 11 14
23 39 38 19 13 14 12
24 37 39 16 11 12 14
25 39 32 17 9 17 11
26 41 32 17 13 9 9
27 36 35 16 10 16 11
28 33 37 15 14 14 15
29 33 33 16 12 15 14
30 34 33 14 10 11 13
31 31 28 15 12 16 9
32 27 32 12 8 13 15
33 37 31 14 10 17 10
34 34 37 16 12 15 11
35 34 30 14 12 14 13
36 32 33 7 7 16 8
37 29 31 10 6 9 20
38 36 33 14 12 15 12
39 29 31 16 10 17 10
40 35 33 16 10 13 10
41 37 32 16 10 15 9
42 34 33 14 12 16 14
43 38 32 20 15 16 8
44 35 33 14 10 12 14
45 38 28 14 10 12 11
46 37 35 11 12 11 13
47 38 39 14 13 15 9
48 33 34 15 11 15 11
49 36 38 16 11 17 15
50 38 32 14 12 13 11
51 32 38 16 14 16 10
52 32 30 14 10 14 14
53 32 33 12 12 11 18
54 34 38 16 13 12 14
55 32 32 9 5 12 11
56 37 32 14 6 15 12
57 39 34 16 12 16 13
58 29 34 16 12 15 9
59 37 36 15 11 12 10
60 35 34 16 10 12 15
61 30 28 12 7 8 20
62 38 34 16 12 13 12
63 34 35 16 14 11 12
64 31 35 14 11 14 14
65 34 31 16 12 15 13
66 35 37 17 13 10 11
67 36 35 18 14 11 17
68 30 27 18 11 12 12
69 39 40 12 12 15 13
70 35 37 16 12 15 14
71 38 36 10 8 14 13
72 31 38 14 11 16 15
73 34 39 18 14 15 13
74 38 41 18 14 15 10
75 34 27 16 12 13 11
76 39 30 17 9 12 19
77 37 37 16 13 17 13
78 34 31 16 11 13 17
79 28 31 13 12 15 13
80 37 27 16 12 13 9
81 33 36 16 12 15 11
82 37 38 20 12 16 10
83 35 37 16 12 15 9
84 37 33 15 12 16 12
85 32 34 15 11 15 12
86 33 31 16 10 14 13
87 38 39 14 9 15 13
88 33 34 16 12 14 12
89 29 32 16 12 13 15
90 33 33 15 12 7 22
91 31 36 12 9 17 13
92 36 32 17 15 13 15
93 35 41 16 12 15 13
94 32 28 15 12 14 15
95 29 30 13 12 13 10
96 39 36 16 10 16 11
97 37 35 16 13 12 16
98 35 31 16 9 14 11
99 37 34 16 12 17 11
100 32 36 14 10 15 10
101 38 36 16 14 17 10
102 37 35 16 11 12 16
103 36 37 20 15 16 12
104 32 28 15 11 11 11
105 33 39 16 11 15 16
106 40 32 13 12 9 19
107 38 35 17 12 16 11
108 41 39 16 12 15 16
109 36 35 16 11 10 15
110 43 42 12 7 10 24
111 30 34 16 12 15 14
112 31 33 16 14 11 15
113 32 41 17 11 13 11
114 32 33 13 11 14 15
115 37 34 12 10 18 12
116 37 32 18 13 16 10
117 33 40 14 13 14 14
118 34 40 14 8 14 13
119 33 35 13 11 14 9
120 38 36 16 12 14 15
121 33 37 13 11 12 15
122 31 27 16 13 14 14
123 38 39 13 12 15 11
124 37 38 16 14 15 8
125 33 31 15 13 15 11
126 31 33 16 15 13 11
127 39 32 15 10 17 8
128 44 39 17 11 17 10
129 33 36 15 9 19 11
130 35 33 12 11 15 13
131 32 33 16 10 13 11
132 28 32 10 11 9 20
133 40 37 16 8 15 10
134 27 30 12 11 15 15
135 37 38 14 12 15 12
136 32 29 15 12 16 14
137 28 22 13 9 11 23
138 34 35 15 11 14 14
139 30 35 11 10 11 16
140 35 34 12 8 15 11
141 31 35 8 9 13 12
142 32 34 16 8 15 10
143 30 34 15 9 16 14
144 30 35 17 15 14 12
145 31 23 16 11 15 12
146 40 31 10 8 16 11
147 32 27 18 13 16 12
148 36 36 13 12 11 13
149 32 31 16 12 12 11
150 35 32 13 9 9 19
151 38 39 10 7 16 12
152 42 37 15 13 13 17
153 34 38 16 9 16 9
154 35 39 16 6 12 12
155 35 34 14 8 9 19
156 33 31 10 8 13 18
157 36 32 17 15 13 15
158 32 37 13 6 14 14
159 33 36 15 9 19 11
160 34 32 16 11 13 9
161 32 35 12 8 12 18
162 34 36 13 8 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Learning Software Happiness Depression
19.67609 0.33095 0.32857 -0.13957 0.05389 -0.03571
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.2577 -2.2086 -0.3786 2.1208 7.4259
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.67609 3.77986 5.206 6.02e-07 ***
Separate 0.33095 0.06990 4.734 4.90e-06 ***
Learning 0.32857 0.13242 2.481 0.0142 *
Software -0.13957 0.13662 -1.022 0.3086
Happiness 0.05389 0.12634 0.427 0.6703
Depression -0.03571 0.09346 -0.382 0.7029
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.107 on 156 degrees of freedom
Multiple R-squared: 0.179, Adjusted R-squared: 0.1527
F-statistic: 6.803 on 5 and 156 DF, p-value: 9.109e-06
> 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,] 0.94658657 0.10682687 0.05341343
[2,] 0.90292681 0.19414637 0.09707319
[3,] 0.84249488 0.31501025 0.15750512
[4,] 0.82499380 0.35001239 0.17500620
[5,] 0.87190607 0.25618785 0.12809393
[6,] 0.82872480 0.34255039 0.17127520
[7,] 0.82988544 0.34022912 0.17011456
[8,] 0.82611442 0.34777116 0.17388558
[9,] 0.76711920 0.46576160 0.23288080
[10,] 0.70727141 0.58545717 0.29272859
[11,] 0.68394601 0.63210798 0.31605399
[12,] 0.68298426 0.63403147 0.31701574
[13,] 0.68999463 0.62001075 0.31000537
[14,] 0.64580077 0.70839847 0.35419923
[15,] 0.61821604 0.76356793 0.38178396
[16,] 0.54765089 0.90469822 0.45234911
[17,] 0.53997007 0.92005985 0.46002993
[18,] 0.80074403 0.39851195 0.19925597
[19,] 0.75298761 0.49402479 0.24701239
[20,] 0.72529629 0.54940742 0.27470371
[21,] 0.69235192 0.61529616 0.30764808
[22,] 0.63898096 0.72203807 0.36101904
[23,] 0.60961336 0.78077328 0.39038664
[24,] 0.76771994 0.46456012 0.23228006
[25,] 0.76349650 0.47300700 0.23650350
[26,] 0.73790383 0.52419234 0.26209617
[27,] 0.69527145 0.60945710 0.30472855
[28,] 0.64357309 0.71285381 0.35642691
[29,] 0.61818056 0.76363888 0.38181944
[30,] 0.58696935 0.82606129 0.41303065
[31,] 0.71061962 0.57876075 0.28938038
[32,] 0.66181607 0.67636785 0.33818393
[33,] 0.63188299 0.73623403 0.36811701
[34,] 0.57969143 0.84061714 0.42030857
[35,] 0.54625102 0.90749795 0.45374898
[36,] 0.50066940 0.99866119 0.49933060
[37,] 0.59518818 0.80962363 0.40481182
[38,] 0.61392267 0.77215466 0.38607733
[39,] 0.58045946 0.83908109 0.41954054
[40,] 0.55180830 0.89638341 0.44819170
[41,] 0.50085607 0.99828786 0.49914393
[42,] 0.53368476 0.93263049 0.46631524
[43,] 0.58112170 0.83775659 0.41887830
[44,] 0.54055329 0.91889342 0.45944671
[45,] 0.49375118 0.98750237 0.50624882
[46,] 0.46265233 0.92530467 0.53734767
[47,] 0.41603663 0.83207325 0.58396337
[48,] 0.40106072 0.80212144 0.59893928
[49,] 0.43014149 0.86028298 0.56985851
[50,] 0.58010495 0.83979011 0.41989505
[51,] 0.54554637 0.90890727 0.45445363
[52,] 0.49767554 0.99535109 0.50232446
[53,] 0.46320897 0.92641794 0.53679103
[54,] 0.46034067 0.92068133 0.53965933
[55,] 0.41887780 0.83775560 0.58112220
[56,] 0.43014764 0.86029529 0.56985236
[57,] 0.38478914 0.76957829 0.61521086
[58,] 0.34558563 0.69117127 0.65441437
[59,] 0.30527383 0.61054766 0.69472617
[60,] 0.32235676 0.64471351 0.67764324
[61,] 0.33303102 0.66606205 0.66696898
[62,] 0.29435511 0.58871021 0.70564489
[63,] 0.31777648 0.63555296 0.68222352
[64,] 0.37035734 0.74071468 0.62964266
[65,] 0.36425698 0.72851396 0.63574302
[66,] 0.32184558 0.64369116 0.67815442
[67,] 0.29136353 0.58272707 0.70863647
[68,] 0.36208210 0.72416419 0.63791790
[69,] 0.32456252 0.64912505 0.67543748
[70,] 0.28419179 0.56838357 0.71580821
[71,] 0.34404368 0.68808736 0.65595632
[72,] 0.39847174 0.79694349 0.60152826
[73,] 0.38409874 0.76819748 0.61590126
[74,] 0.34288767 0.68577533 0.65711233
[75,] 0.30509058 0.61018116 0.69490942
[76,] 0.29418927 0.58837853 0.70581073
[77,] 0.28309705 0.56619410 0.71690295
[78,] 0.25002482 0.50004964 0.74997518
[79,] 0.22608912 0.45217823 0.77391088
[80,] 0.20304187 0.40608374 0.79695813
[81,] 0.25501383 0.51002767 0.74498617
[82,] 0.22010377 0.44020753 0.77989623
[83,] 0.23630336 0.47260672 0.76369664
[84,] 0.21561924 0.43123849 0.78438076
[85,] 0.20035217 0.40070434 0.79964783
[86,] 0.16989314 0.33978628 0.83010686
[87,] 0.17359157 0.34718313 0.82640843
[88,] 0.17319839 0.34639678 0.82680161
[89,] 0.15851836 0.31703671 0.84148164
[90,] 0.13688881 0.27377762 0.86311119
[91,] 0.12156964 0.24313928 0.87843036
[92,] 0.12021994 0.24043988 0.87978006
[93,] 0.11095010 0.22190020 0.88904990
[94,] 0.09885483 0.19770966 0.90114517
[95,] 0.08074471 0.16148942 0.91925529
[96,] 0.06619589 0.13239178 0.93380411
[97,] 0.07359028 0.14718056 0.92640972
[98,] 0.18918012 0.37836024 0.81081988
[99,] 0.17584864 0.35169727 0.82415136
[100,] 0.20273796 0.40547592 0.79726204
[101,] 0.18260218 0.36520435 0.81739782
[102,] 0.34204469 0.68408937 0.65795531
[103,] 0.39100224 0.78200449 0.60899776
[104,] 0.36852703 0.73705407 0.63147297
[105,] 0.44836267 0.89672535 0.55163733
[106,] 0.40873660 0.81747320 0.59126340
[107,] 0.39078888 0.78157777 0.60921112
[108,] 0.36764370 0.73528740 0.63235630
[109,] 0.36958697 0.73917395 0.63041303
[110,] 0.35410621 0.70821242 0.64589379
[111,] 0.31664652 0.63329304 0.68335348
[112,] 0.30537730 0.61075460 0.69462270
[113,] 0.27260657 0.54521314 0.72739343
[114,] 0.23312044 0.46624088 0.76687956
[115,] 0.20664593 0.41329186 0.79335407
[116,] 0.17103954 0.34207908 0.82896046
[117,] 0.13836819 0.27673639 0.86163181
[118,] 0.13677160 0.27354320 0.86322840
[119,] 0.17306083 0.34612166 0.82693917
[120,] 0.35172237 0.70344475 0.64827763
[121,] 0.32408132 0.64816264 0.67591868
[122,] 0.28509122 0.57018244 0.71490878
[123,] 0.25553476 0.51106953 0.74446524
[124,] 0.29203743 0.58407487 0.70796257
[125,] 0.37149607 0.74299215 0.62850393
[126,] 0.52380103 0.95239795 0.47619897
[127,] 0.46863613 0.93727226 0.53136387
[128,] 0.40500693 0.81001385 0.59499307
[129,] 0.38282091 0.76564183 0.61717909
[130,] 0.31888977 0.63777954 0.68111023
[131,] 0.41358977 0.82717953 0.58641023
[132,] 0.35144977 0.70289954 0.64855023
[133,] 0.54332385 0.91335230 0.45667615
[134,] 0.47765481 0.95530963 0.52234519
[135,] 0.49262237 0.98524474 0.50737763
[136,] 0.75071067 0.49857865 0.24928933
[137,] 0.67645624 0.64708753 0.32354376
[138,] 0.95989584 0.08020832 0.04010416
[139,] 0.94316539 0.11366922 0.05683461
[140,] 0.96669005 0.06661991 0.03330995
[141,] 0.95781400 0.08437199 0.04218600
[142,] 0.92386328 0.15227344 0.07613672
[143,] 0.86888293 0.26223413 0.13111707
[144,] 0.98277852 0.03444297 0.01722148
[145,] 0.94852574 0.10294851 0.05147426
> postscript(file="/var/www/rcomp/tmp/1j0vd1322583127.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2nscu1322583127.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/38zbw1322583127.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/47d6x1322583127.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5rs4k1322583127.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
5.82525994 4.43442518 -5.50151918 -3.90674320 -0.81917250 2.30913039
7 8 9 10 11 12
4.52760707 -0.92655649 1.36423249 0.31254837 3.64398778 0.75106855
13 14 15 16 17 18
3.15509075 2.66363053 -3.02634516 -2.10570356 1.94611545 0.32789788
19 20 21 22 23 24
1.97142561 -2.24681017 -3.03936886 -2.61070580 1.99340962 0.54823408
25 26 27 28 29 30
3.88061268 6.79856295 0.40978432 -2.11466387 -1.48816149 0.06968896
31 32 33 34 35 36
-1.73728128 -6.25770877 3.30112650 -1.91909683 1.18000711 -0.49702005
37 38 39 40 41 42
-3.15464262 2.09755557 -5.35601222 0.19764061 2.38510336 0.11508867
43 44 45 46 47 48
2.67905578 1.05151123 5.59913230 3.67262733 2.14428466 -1.73724118
49 50 51 52 53 54
-0.35454720 4.50057259 -4.06051524 -1.06341376 -0.81548645 -1.84168350
55 56 57 58 59 60
-0.77965300 2.59111117 4.09128840 -5.99766636 1.72681226 0.09913263
61 62 63 64 65 66
-1.62547479 3.21724327 -0.72679861 -3.57860147 0.13802911 -0.83865751
67 68 69 70 71 72
0.79461683 -3.20891775 3.47375014 -0.81196434 3.95031687 -4.64351991
73 74 75 76 77 78
-2.88758361 0.34338247 1.49818743 5.09764359 1.18411355 0.24908371
79 80 81 82 83 84
-4.87626281 4.42676576 -2.58814612 -0.65392439 -0.99051849 2.71509765
85 86 87 88 89 90
-2.70153035 -1.08721403 1.72886457 -1.83664530 -5.01372281 -0.44279695
91 92 93 94 95 96
-3.72892170 2.07640539 -2.17147802 -0.41523916 -3.54466746 3.07883361
97 98 99 100 101 102
2.22259030 0.70179845 1.96597818 -3.24584993 2.54749762 1.94345860
103 104 105 106 107 108
-0.83285446 -0.53598264 -3.54200995 7.33038286 2.36034667 4.59755590
109 110 111 112 113 114
1.01552490 6.77628143 -4.81911220 -2.95776469 -5.60325776 -1.55241986
115 116 117 118 119 120
2.98294618 2.12848446 -2.95422333 -2.68776343 -1.42858627 2.60858577
121 122 123 124 125 126
-1.76844557 -1.30900279 2.40470983 0.92195167 -0.46525734 -3.06881929
127 128 129 130 131 132
4.57018476 6.80737857 -2.89382858 1.65083928 -2.76664855 -3.78776407
133 134 135 136 137 138
3.48692893 -5.28488692 1.44280201 -0.88967784 -1.74374137 -0.90717084
139 140 141 142 143 144
-3.49937188 0.82976934 -1.90385011 -3.52021893 -4.96312896 -5.07746781
145 146 147 148 149 150
-0.38964187 7.42587164 -1.14534031 1.68453789 -1.77172686 1.91168531
151 152 153 154 155 156
2.67441092 6.87108050 -2.79405533 -2.22101684 0.78164867 0.83751316
157 158 159 160 161 162
2.07640539 -3.60976280 -2.89382858 -0.36755365 -2.08953985 -0.87437015
> postscript(file="/var/www/rcomp/tmp/69b2k1322583127.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 5.82525994 NA
1 4.43442518 5.82525994
2 -5.50151918 4.43442518
3 -3.90674320 -5.50151918
4 -0.81917250 -3.90674320
5 2.30913039 -0.81917250
6 4.52760707 2.30913039
7 -0.92655649 4.52760707
8 1.36423249 -0.92655649
9 0.31254837 1.36423249
10 3.64398778 0.31254837
11 0.75106855 3.64398778
12 3.15509075 0.75106855
13 2.66363053 3.15509075
14 -3.02634516 2.66363053
15 -2.10570356 -3.02634516
16 1.94611545 -2.10570356
17 0.32789788 1.94611545
18 1.97142561 0.32789788
19 -2.24681017 1.97142561
20 -3.03936886 -2.24681017
21 -2.61070580 -3.03936886
22 1.99340962 -2.61070580
23 0.54823408 1.99340962
24 3.88061268 0.54823408
25 6.79856295 3.88061268
26 0.40978432 6.79856295
27 -2.11466387 0.40978432
28 -1.48816149 -2.11466387
29 0.06968896 -1.48816149
30 -1.73728128 0.06968896
31 -6.25770877 -1.73728128
32 3.30112650 -6.25770877
33 -1.91909683 3.30112650
34 1.18000711 -1.91909683
35 -0.49702005 1.18000711
36 -3.15464262 -0.49702005
37 2.09755557 -3.15464262
38 -5.35601222 2.09755557
39 0.19764061 -5.35601222
40 2.38510336 0.19764061
41 0.11508867 2.38510336
42 2.67905578 0.11508867
43 1.05151123 2.67905578
44 5.59913230 1.05151123
45 3.67262733 5.59913230
46 2.14428466 3.67262733
47 -1.73724118 2.14428466
48 -0.35454720 -1.73724118
49 4.50057259 -0.35454720
50 -4.06051524 4.50057259
51 -1.06341376 -4.06051524
52 -0.81548645 -1.06341376
53 -1.84168350 -0.81548645
54 -0.77965300 -1.84168350
55 2.59111117 -0.77965300
56 4.09128840 2.59111117
57 -5.99766636 4.09128840
58 1.72681226 -5.99766636
59 0.09913263 1.72681226
60 -1.62547479 0.09913263
61 3.21724327 -1.62547479
62 -0.72679861 3.21724327
63 -3.57860147 -0.72679861
64 0.13802911 -3.57860147
65 -0.83865751 0.13802911
66 0.79461683 -0.83865751
67 -3.20891775 0.79461683
68 3.47375014 -3.20891775
69 -0.81196434 3.47375014
70 3.95031687 -0.81196434
71 -4.64351991 3.95031687
72 -2.88758361 -4.64351991
73 0.34338247 -2.88758361
74 1.49818743 0.34338247
75 5.09764359 1.49818743
76 1.18411355 5.09764359
77 0.24908371 1.18411355
78 -4.87626281 0.24908371
79 4.42676576 -4.87626281
80 -2.58814612 4.42676576
81 -0.65392439 -2.58814612
82 -0.99051849 -0.65392439
83 2.71509765 -0.99051849
84 -2.70153035 2.71509765
85 -1.08721403 -2.70153035
86 1.72886457 -1.08721403
87 -1.83664530 1.72886457
88 -5.01372281 -1.83664530
89 -0.44279695 -5.01372281
90 -3.72892170 -0.44279695
91 2.07640539 -3.72892170
92 -2.17147802 2.07640539
93 -0.41523916 -2.17147802
94 -3.54466746 -0.41523916
95 3.07883361 -3.54466746
96 2.22259030 3.07883361
97 0.70179845 2.22259030
98 1.96597818 0.70179845
99 -3.24584993 1.96597818
100 2.54749762 -3.24584993
101 1.94345860 2.54749762
102 -0.83285446 1.94345860
103 -0.53598264 -0.83285446
104 -3.54200995 -0.53598264
105 7.33038286 -3.54200995
106 2.36034667 7.33038286
107 4.59755590 2.36034667
108 1.01552490 4.59755590
109 6.77628143 1.01552490
110 -4.81911220 6.77628143
111 -2.95776469 -4.81911220
112 -5.60325776 -2.95776469
113 -1.55241986 -5.60325776
114 2.98294618 -1.55241986
115 2.12848446 2.98294618
116 -2.95422333 2.12848446
117 -2.68776343 -2.95422333
118 -1.42858627 -2.68776343
119 2.60858577 -1.42858627
120 -1.76844557 2.60858577
121 -1.30900279 -1.76844557
122 2.40470983 -1.30900279
123 0.92195167 2.40470983
124 -0.46525734 0.92195167
125 -3.06881929 -0.46525734
126 4.57018476 -3.06881929
127 6.80737857 4.57018476
128 -2.89382858 6.80737857
129 1.65083928 -2.89382858
130 -2.76664855 1.65083928
131 -3.78776407 -2.76664855
132 3.48692893 -3.78776407
133 -5.28488692 3.48692893
134 1.44280201 -5.28488692
135 -0.88967784 1.44280201
136 -1.74374137 -0.88967784
137 -0.90717084 -1.74374137
138 -3.49937188 -0.90717084
139 0.82976934 -3.49937188
140 -1.90385011 0.82976934
141 -3.52021893 -1.90385011
142 -4.96312896 -3.52021893
143 -5.07746781 -4.96312896
144 -0.38964187 -5.07746781
145 7.42587164 -0.38964187
146 -1.14534031 7.42587164
147 1.68453789 -1.14534031
148 -1.77172686 1.68453789
149 1.91168531 -1.77172686
150 2.67441092 1.91168531
151 6.87108050 2.67441092
152 -2.79405533 6.87108050
153 -2.22101684 -2.79405533
154 0.78164867 -2.22101684
155 0.83751316 0.78164867
156 2.07640539 0.83751316
157 -3.60976280 2.07640539
158 -2.89382858 -3.60976280
159 -0.36755365 -2.89382858
160 -2.08953985 -0.36755365
161 -0.87437015 -2.08953985
162 NA -0.87437015
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.43442518 5.82525994
[2,] -5.50151918 4.43442518
[3,] -3.90674320 -5.50151918
[4,] -0.81917250 -3.90674320
[5,] 2.30913039 -0.81917250
[6,] 4.52760707 2.30913039
[7,] -0.92655649 4.52760707
[8,] 1.36423249 -0.92655649
[9,] 0.31254837 1.36423249
[10,] 3.64398778 0.31254837
[11,] 0.75106855 3.64398778
[12,] 3.15509075 0.75106855
[13,] 2.66363053 3.15509075
[14,] -3.02634516 2.66363053
[15,] -2.10570356 -3.02634516
[16,] 1.94611545 -2.10570356
[17,] 0.32789788 1.94611545
[18,] 1.97142561 0.32789788
[19,] -2.24681017 1.97142561
[20,] -3.03936886 -2.24681017
[21,] -2.61070580 -3.03936886
[22,] 1.99340962 -2.61070580
[23,] 0.54823408 1.99340962
[24,] 3.88061268 0.54823408
[25,] 6.79856295 3.88061268
[26,] 0.40978432 6.79856295
[27,] -2.11466387 0.40978432
[28,] -1.48816149 -2.11466387
[29,] 0.06968896 -1.48816149
[30,] -1.73728128 0.06968896
[31,] -6.25770877 -1.73728128
[32,] 3.30112650 -6.25770877
[33,] -1.91909683 3.30112650
[34,] 1.18000711 -1.91909683
[35,] -0.49702005 1.18000711
[36,] -3.15464262 -0.49702005
[37,] 2.09755557 -3.15464262
[38,] -5.35601222 2.09755557
[39,] 0.19764061 -5.35601222
[40,] 2.38510336 0.19764061
[41,] 0.11508867 2.38510336
[42,] 2.67905578 0.11508867
[43,] 1.05151123 2.67905578
[44,] 5.59913230 1.05151123
[45,] 3.67262733 5.59913230
[46,] 2.14428466 3.67262733
[47,] -1.73724118 2.14428466
[48,] -0.35454720 -1.73724118
[49,] 4.50057259 -0.35454720
[50,] -4.06051524 4.50057259
[51,] -1.06341376 -4.06051524
[52,] -0.81548645 -1.06341376
[53,] -1.84168350 -0.81548645
[54,] -0.77965300 -1.84168350
[55,] 2.59111117 -0.77965300
[56,] 4.09128840 2.59111117
[57,] -5.99766636 4.09128840
[58,] 1.72681226 -5.99766636
[59,] 0.09913263 1.72681226
[60,] -1.62547479 0.09913263
[61,] 3.21724327 -1.62547479
[62,] -0.72679861 3.21724327
[63,] -3.57860147 -0.72679861
[64,] 0.13802911 -3.57860147
[65,] -0.83865751 0.13802911
[66,] 0.79461683 -0.83865751
[67,] -3.20891775 0.79461683
[68,] 3.47375014 -3.20891775
[69,] -0.81196434 3.47375014
[70,] 3.95031687 -0.81196434
[71,] -4.64351991 3.95031687
[72,] -2.88758361 -4.64351991
[73,] 0.34338247 -2.88758361
[74,] 1.49818743 0.34338247
[75,] 5.09764359 1.49818743
[76,] 1.18411355 5.09764359
[77,] 0.24908371 1.18411355
[78,] -4.87626281 0.24908371
[79,] 4.42676576 -4.87626281
[80,] -2.58814612 4.42676576
[81,] -0.65392439 -2.58814612
[82,] -0.99051849 -0.65392439
[83,] 2.71509765 -0.99051849
[84,] -2.70153035 2.71509765
[85,] -1.08721403 -2.70153035
[86,] 1.72886457 -1.08721403
[87,] -1.83664530 1.72886457
[88,] -5.01372281 -1.83664530
[89,] -0.44279695 -5.01372281
[90,] -3.72892170 -0.44279695
[91,] 2.07640539 -3.72892170
[92,] -2.17147802 2.07640539
[93,] -0.41523916 -2.17147802
[94,] -3.54466746 -0.41523916
[95,] 3.07883361 -3.54466746
[96,] 2.22259030 3.07883361
[97,] 0.70179845 2.22259030
[98,] 1.96597818 0.70179845
[99,] -3.24584993 1.96597818
[100,] 2.54749762 -3.24584993
[101,] 1.94345860 2.54749762
[102,] -0.83285446 1.94345860
[103,] -0.53598264 -0.83285446
[104,] -3.54200995 -0.53598264
[105,] 7.33038286 -3.54200995
[106,] 2.36034667 7.33038286
[107,] 4.59755590 2.36034667
[108,] 1.01552490 4.59755590
[109,] 6.77628143 1.01552490
[110,] -4.81911220 6.77628143
[111,] -2.95776469 -4.81911220
[112,] -5.60325776 -2.95776469
[113,] -1.55241986 -5.60325776
[114,] 2.98294618 -1.55241986
[115,] 2.12848446 2.98294618
[116,] -2.95422333 2.12848446
[117,] -2.68776343 -2.95422333
[118,] -1.42858627 -2.68776343
[119,] 2.60858577 -1.42858627
[120,] -1.76844557 2.60858577
[121,] -1.30900279 -1.76844557
[122,] 2.40470983 -1.30900279
[123,] 0.92195167 2.40470983
[124,] -0.46525734 0.92195167
[125,] -3.06881929 -0.46525734
[126,] 4.57018476 -3.06881929
[127,] 6.80737857 4.57018476
[128,] -2.89382858 6.80737857
[129,] 1.65083928 -2.89382858
[130,] -2.76664855 1.65083928
[131,] -3.78776407 -2.76664855
[132,] 3.48692893 -3.78776407
[133,] -5.28488692 3.48692893
[134,] 1.44280201 -5.28488692
[135,] -0.88967784 1.44280201
[136,] -1.74374137 -0.88967784
[137,] -0.90717084 -1.74374137
[138,] -3.49937188 -0.90717084
[139,] 0.82976934 -3.49937188
[140,] -1.90385011 0.82976934
[141,] -3.52021893 -1.90385011
[142,] -4.96312896 -3.52021893
[143,] -5.07746781 -4.96312896
[144,] -0.38964187 -5.07746781
[145,] 7.42587164 -0.38964187
[146,] -1.14534031 7.42587164
[147,] 1.68453789 -1.14534031
[148,] -1.77172686 1.68453789
[149,] 1.91168531 -1.77172686
[150,] 2.67441092 1.91168531
[151,] 6.87108050 2.67441092
[152,] -2.79405533 6.87108050
[153,] -2.22101684 -2.79405533
[154,] 0.78164867 -2.22101684
[155,] 0.83751316 0.78164867
[156,] 2.07640539 0.83751316
[157,] -3.60976280 2.07640539
[158,] -2.89382858 -3.60976280
[159,] -0.36755365 -2.89382858
[160,] -2.08953985 -0.36755365
[161,] -0.87437015 -2.08953985
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.43442518 5.82525994
2 -5.50151918 4.43442518
3 -3.90674320 -5.50151918
4 -0.81917250 -3.90674320
5 2.30913039 -0.81917250
6 4.52760707 2.30913039
7 -0.92655649 4.52760707
8 1.36423249 -0.92655649
9 0.31254837 1.36423249
10 3.64398778 0.31254837
11 0.75106855 3.64398778
12 3.15509075 0.75106855
13 2.66363053 3.15509075
14 -3.02634516 2.66363053
15 -2.10570356 -3.02634516
16 1.94611545 -2.10570356
17 0.32789788 1.94611545
18 1.97142561 0.32789788
19 -2.24681017 1.97142561
20 -3.03936886 -2.24681017
21 -2.61070580 -3.03936886
22 1.99340962 -2.61070580
23 0.54823408 1.99340962
24 3.88061268 0.54823408
25 6.79856295 3.88061268
26 0.40978432 6.79856295
27 -2.11466387 0.40978432
28 -1.48816149 -2.11466387
29 0.06968896 -1.48816149
30 -1.73728128 0.06968896
31 -6.25770877 -1.73728128
32 3.30112650 -6.25770877
33 -1.91909683 3.30112650
34 1.18000711 -1.91909683
35 -0.49702005 1.18000711
36 -3.15464262 -0.49702005
37 2.09755557 -3.15464262
38 -5.35601222 2.09755557
39 0.19764061 -5.35601222
40 2.38510336 0.19764061
41 0.11508867 2.38510336
42 2.67905578 0.11508867
43 1.05151123 2.67905578
44 5.59913230 1.05151123
45 3.67262733 5.59913230
46 2.14428466 3.67262733
47 -1.73724118 2.14428466
48 -0.35454720 -1.73724118
49 4.50057259 -0.35454720
50 -4.06051524 4.50057259
51 -1.06341376 -4.06051524
52 -0.81548645 -1.06341376
53 -1.84168350 -0.81548645
54 -0.77965300 -1.84168350
55 2.59111117 -0.77965300
56 4.09128840 2.59111117
57 -5.99766636 4.09128840
58 1.72681226 -5.99766636
59 0.09913263 1.72681226
60 -1.62547479 0.09913263
61 3.21724327 -1.62547479
62 -0.72679861 3.21724327
63 -3.57860147 -0.72679861
64 0.13802911 -3.57860147
65 -0.83865751 0.13802911
66 0.79461683 -0.83865751
67 -3.20891775 0.79461683
68 3.47375014 -3.20891775
69 -0.81196434 3.47375014
70 3.95031687 -0.81196434
71 -4.64351991 3.95031687
72 -2.88758361 -4.64351991
73 0.34338247 -2.88758361
74 1.49818743 0.34338247
75 5.09764359 1.49818743
76 1.18411355 5.09764359
77 0.24908371 1.18411355
78 -4.87626281 0.24908371
79 4.42676576 -4.87626281
80 -2.58814612 4.42676576
81 -0.65392439 -2.58814612
82 -0.99051849 -0.65392439
83 2.71509765 -0.99051849
84 -2.70153035 2.71509765
85 -1.08721403 -2.70153035
86 1.72886457 -1.08721403
87 -1.83664530 1.72886457
88 -5.01372281 -1.83664530
89 -0.44279695 -5.01372281
90 -3.72892170 -0.44279695
91 2.07640539 -3.72892170
92 -2.17147802 2.07640539
93 -0.41523916 -2.17147802
94 -3.54466746 -0.41523916
95 3.07883361 -3.54466746
96 2.22259030 3.07883361
97 0.70179845 2.22259030
98 1.96597818 0.70179845
99 -3.24584993 1.96597818
100 2.54749762 -3.24584993
101 1.94345860 2.54749762
102 -0.83285446 1.94345860
103 -0.53598264 -0.83285446
104 -3.54200995 -0.53598264
105 7.33038286 -3.54200995
106 2.36034667 7.33038286
107 4.59755590 2.36034667
108 1.01552490 4.59755590
109 6.77628143 1.01552490
110 -4.81911220 6.77628143
111 -2.95776469 -4.81911220
112 -5.60325776 -2.95776469
113 -1.55241986 -5.60325776
114 2.98294618 -1.55241986
115 2.12848446 2.98294618
116 -2.95422333 2.12848446
117 -2.68776343 -2.95422333
118 -1.42858627 -2.68776343
119 2.60858577 -1.42858627
120 -1.76844557 2.60858577
121 -1.30900279 -1.76844557
122 2.40470983 -1.30900279
123 0.92195167 2.40470983
124 -0.46525734 0.92195167
125 -3.06881929 -0.46525734
126 4.57018476 -3.06881929
127 6.80737857 4.57018476
128 -2.89382858 6.80737857
129 1.65083928 -2.89382858
130 -2.76664855 1.65083928
131 -3.78776407 -2.76664855
132 3.48692893 -3.78776407
133 -5.28488692 3.48692893
134 1.44280201 -5.28488692
135 -0.88967784 1.44280201
136 -1.74374137 -0.88967784
137 -0.90717084 -1.74374137
138 -3.49937188 -0.90717084
139 0.82976934 -3.49937188
140 -1.90385011 0.82976934
141 -3.52021893 -1.90385011
142 -4.96312896 -3.52021893
143 -5.07746781 -4.96312896
144 -0.38964187 -5.07746781
145 7.42587164 -0.38964187
146 -1.14534031 7.42587164
147 1.68453789 -1.14534031
148 -1.77172686 1.68453789
149 1.91168531 -1.77172686
150 2.67441092 1.91168531
151 6.87108050 2.67441092
152 -2.79405533 6.87108050
153 -2.22101684 -2.79405533
154 0.78164867 -2.22101684
155 0.83751316 0.78164867
156 2.07640539 0.83751316
157 -3.60976280 2.07640539
158 -2.89382858 -3.60976280
159 -0.36755365 -2.89382858
160 -2.08953985 -0.36755365
161 -0.87437015 -2.08953985
> 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/www/rcomp/tmp/7dfj31322583127.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8bqwu1322583127.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9qs8d1322583127.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10kk8s1322583127.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/11jkor1322583127.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/www/rcomp/tmp/12xvoo1322583127.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/www/rcomp/tmp/13nt9x1322583127.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/www/rcomp/tmp/145cii1322583127.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/www/rcomp/tmp/15s2l31322583127.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/www/rcomp/tmp/16siyi1322583127.tab")
+ }
>
> try(system("convert tmp/1j0vd1322583127.ps tmp/1j0vd1322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nscu1322583127.ps tmp/2nscu1322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/38zbw1322583127.ps tmp/38zbw1322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/47d6x1322583127.ps tmp/47d6x1322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rs4k1322583127.ps tmp/5rs4k1322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/69b2k1322583127.ps tmp/69b2k1322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dfj31322583127.ps tmp/7dfj31322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bqwu1322583127.ps tmp/8bqwu1322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qs8d1322583127.ps tmp/9qs8d1322583127.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kk8s1322583127.ps tmp/10kk8s1322583127.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.352 0.716 7.221