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 = '3'
> #'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
Learning Connected Separate Software Happiness Depression
1 13 41 38 12 14 12
2 16 39 32 11 18 11
3 19 30 35 15 11 14
4 15 31 33 6 12 12
5 14 34 37 13 16 21
6 13 35 29 10 18 12
7 19 39 31 12 14 22
8 15 34 36 14 14 11
9 14 36 35 12 15 10
10 15 37 38 6 15 13
11 16 38 31 10 17 10
12 16 36 34 12 19 8
13 16 38 35 12 10 15
14 16 39 38 11 16 14
15 17 33 37 15 18 10
16 15 32 33 12 14 14
17 15 36 32 10 14 14
18 20 38 38 12 17 11
19 18 39 38 11 14 10
20 16 32 32 12 16 13
21 16 32 33 11 18 7
22 16 31 31 12 11 14
23 19 39 38 13 14 12
24 16 37 39 11 12 14
25 17 39 32 9 17 11
26 17 41 32 13 9 9
27 16 36 35 10 16 11
28 15 33 37 14 14 15
29 16 33 33 12 15 14
30 14 34 33 10 11 13
31 15 31 28 12 16 9
32 12 27 32 8 13 15
33 14 37 31 10 17 10
34 16 34 37 12 15 11
35 14 34 30 12 14 13
36 7 32 33 7 16 8
37 10 29 31 6 9 20
38 14 36 33 12 15 12
39 16 29 31 10 17 10
40 16 35 33 10 13 10
41 16 37 32 10 15 9
42 14 34 33 12 16 14
43 20 38 32 15 16 8
44 14 35 33 10 12 14
45 14 38 28 10 12 11
46 11 37 35 12 11 13
47 14 38 39 13 15 9
48 15 33 34 11 15 11
49 16 36 38 11 17 15
50 14 38 32 12 13 11
51 16 32 38 14 16 10
52 14 32 30 10 14 14
53 12 32 33 12 11 18
54 16 34 38 13 12 14
55 9 32 32 5 12 11
56 14 37 32 6 15 12
57 16 39 34 12 16 13
58 16 29 34 12 15 9
59 15 37 36 11 12 10
60 16 35 34 10 12 15
61 12 30 28 7 8 20
62 16 38 34 12 13 12
63 16 34 35 14 11 12
64 14 31 35 11 14 14
65 16 34 31 12 15 13
66 17 35 37 13 10 11
67 18 36 35 14 11 17
68 18 30 27 11 12 12
69 12 39 40 12 15 13
70 16 35 37 12 15 14
71 10 38 36 8 14 13
72 14 31 38 11 16 15
73 18 34 39 14 15 13
74 18 38 41 14 15 10
75 16 34 27 12 13 11
76 17 39 30 9 12 19
77 16 37 37 13 17 13
78 16 34 31 11 13 17
79 13 28 31 12 15 13
80 16 37 27 12 13 9
81 16 33 36 12 15 11
82 20 37 38 12 16 10
83 16 35 37 12 15 9
84 15 37 33 12 16 12
85 15 32 34 11 15 12
86 16 33 31 10 14 13
87 14 38 39 9 15 13
88 16 33 34 12 14 12
89 16 29 32 12 13 15
90 15 33 33 12 7 22
91 12 31 36 9 17 13
92 17 36 32 15 13 15
93 16 35 41 12 15 13
94 15 32 28 12 14 15
95 13 29 30 12 13 10
96 16 39 36 10 16 11
97 16 37 35 13 12 16
98 16 35 31 9 14 11
99 16 37 34 12 17 11
100 14 32 36 10 15 10
101 16 38 36 14 17 10
102 16 37 35 11 12 16
103 20 36 37 15 16 12
104 15 32 28 11 11 11
105 16 33 39 11 15 16
106 13 40 32 12 9 19
107 17 38 35 12 16 11
108 16 41 39 12 15 16
109 16 36 35 11 10 15
110 12 43 42 7 10 24
111 16 30 34 12 15 14
112 16 31 33 14 11 15
113 17 32 41 11 13 11
114 13 32 33 11 14 15
115 12 37 34 10 18 12
116 18 37 32 13 16 10
117 14 33 40 13 14 14
118 14 34 40 8 14 13
119 13 33 35 11 14 9
120 16 38 36 12 14 15
121 13 33 37 11 12 15
122 16 31 27 13 14 14
123 13 38 39 12 15 11
124 16 37 38 14 15 8
125 15 33 31 13 15 11
126 16 31 33 15 13 11
127 15 39 32 10 17 8
128 17 44 39 11 17 10
129 15 33 36 9 19 11
130 12 35 33 11 15 13
131 16 32 33 10 13 11
132 10 28 32 11 9 20
133 16 40 37 8 15 10
134 12 27 30 11 15 15
135 14 37 38 12 15 12
136 15 32 29 12 16 14
137 13 28 22 9 11 23
138 15 34 35 11 14 14
139 11 30 35 10 11 16
140 12 35 34 8 15 11
141 8 31 35 9 13 12
142 16 32 34 8 15 10
143 15 30 34 9 16 14
144 17 30 35 15 14 12
145 16 31 23 11 15 12
146 10 40 31 8 16 11
147 18 32 27 13 16 12
148 13 36 36 12 11 13
149 16 32 31 12 12 11
150 13 35 32 9 9 19
151 10 38 39 7 16 12
152 15 42 37 13 13 17
153 16 34 38 9 16 9
154 16 35 39 6 12 12
155 14 35 34 8 9 19
156 10 33 31 8 13 18
157 17 36 32 15 13 15
158 13 32 37 6 14 14
159 15 33 36 9 19 11
160 16 34 32 11 13 9
161 12 32 35 8 12 18
162 13 34 36 8 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Happiness Depression
5.84194 0.11556 -0.02408 0.54547 0.06415 -0.07758
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9692 -1.1392 0.1934 1.1185 4.0012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.84194 2.38305 2.451 0.0153 *
Connected 0.11556 0.04657 2.481 0.0142 *
Separate -0.02408 0.04429 -0.544 0.5874
Software 0.54547 0.06857 7.955 3.41e-13 ***
Happiness 0.06415 0.07479 0.858 0.3924
Depression -0.07758 0.05510 -1.408 0.1612
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.842 on 156 degrees of freedom
Multiple R-squared: 0.3539, Adjusted R-squared: 0.3332
F-statistic: 17.09 on 5 and 156 DF, p-value: 1.85e-13
> 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,] 0.60433516 0.79132968 0.39566484
[2,] 0.76670494 0.46659012 0.23329506
[3,] 0.70477648 0.59044704 0.29522352
[4,] 0.78424585 0.43150830 0.21575415
[5,] 0.71528033 0.56943934 0.28471967
[6,] 0.68379281 0.63241438 0.31620719
[7,] 0.66475828 0.67048344 0.33524172
[8,] 0.61328959 0.77342082 0.38671041
[9,] 0.53499161 0.93001678 0.46500839
[10,] 0.85842392 0.28315216 0.14157608
[11,] 0.86310549 0.27378902 0.13689451
[12,] 0.81768248 0.36463504 0.18231752
[13,] 0.76944628 0.46110744 0.23055372
[14,] 0.71162261 0.57675477 0.28837739
[15,] 0.73171177 0.53657646 0.26828823
[16,] 0.67832152 0.64335696 0.32167848
[17,] 0.66729965 0.66540069 0.33270035
[18,] 0.60517287 0.78965425 0.39482713
[19,] 0.54541875 0.90916250 0.45458125
[20,] 0.52023245 0.95953511 0.47976755
[21,] 0.45706478 0.91412955 0.54293522
[22,] 0.43626300 0.87252600 0.56373700
[23,] 0.37796133 0.75592266 0.62203867
[24,] 0.36415343 0.72830687 0.63584657
[25,] 0.34449994 0.68899989 0.65550006
[26,] 0.29070856 0.58141713 0.70929144
[27,] 0.27556514 0.55113028 0.72443486
[28,] 0.81231787 0.37536427 0.18768213
[29,] 0.80013946 0.39972107 0.19986054
[30,] 0.79933401 0.40133197 0.20066599
[31,] 0.82029084 0.35941833 0.17970916
[32,] 0.80017527 0.39964946 0.19982473
[33,] 0.76885093 0.46229814 0.23114907
[34,] 0.75437275 0.49125449 0.24562725
[35,] 0.76017630 0.47964741 0.23982370
[36,] 0.72254517 0.55490965 0.27745483
[37,] 0.69209803 0.61580394 0.30790197
[38,] 0.88540767 0.22918466 0.11459233
[39,] 0.91011246 0.17977508 0.08988754
[40,] 0.88776865 0.22446269 0.11223135
[41,] 0.86636694 0.26726613 0.13363306
[42,] 0.86791509 0.26416982 0.13208491
[43,] 0.84068671 0.31862658 0.15931329
[44,] 0.80859090 0.38281821 0.19140910
[45,] 0.83708731 0.32582538 0.16291269
[46,] 0.80613973 0.38772054 0.19386027
[47,] 0.82561274 0.34877451 0.17438726
[48,] 0.80562957 0.38874085 0.19437043
[49,] 0.77218725 0.45562551 0.22781275
[50,] 0.74853015 0.50293970 0.25146985
[51,] 0.70966517 0.58066966 0.29033483
[52,] 0.70610646 0.58778708 0.29389354
[53,] 0.66779271 0.66441458 0.33220729
[54,] 0.62334992 0.75330017 0.37665008
[55,] 0.57906319 0.84187363 0.42093681
[56,] 0.53418527 0.93162945 0.46581473
[57,] 0.49068334 0.98136668 0.50931666
[58,] 0.46203798 0.92407596 0.53796202
[59,] 0.45444002 0.90888004 0.54555998
[60,] 0.58440660 0.83118680 0.41559340
[61,] 0.72740536 0.54518929 0.27259464
[62,] 0.69106387 0.61787227 0.30893613
[63,] 0.78999354 0.42001292 0.21000646
[64,] 0.75569113 0.48861774 0.24430887
[65,] 0.74667354 0.50665293 0.25332646
[66,] 0.72001433 0.55997133 0.27998567
[67,] 0.68036980 0.63926039 0.31963020
[68,] 0.74752633 0.50494733 0.25247367
[69,] 0.71090874 0.57818252 0.28909126
[70,] 0.69577811 0.60844377 0.30422189
[71,] 0.69586185 0.60827631 0.30413815
[72,] 0.65506579 0.68986843 0.34493421
[73,] 0.61607019 0.76785962 0.38392981
[74,] 0.76762661 0.46474678 0.23237339
[75,] 0.73140675 0.53718650 0.26859325
[76,] 0.70228560 0.59542881 0.29771440
[77,] 0.66163408 0.67673183 0.33836592
[78,] 0.65702680 0.68594641 0.34297320
[79,] 0.61350341 0.77299317 0.38649659
[80,] 0.57414813 0.85170374 0.42585187
[81,] 0.55298024 0.89403953 0.44701976
[82,] 0.52400965 0.95198070 0.47599035
[83,] 0.50945179 0.98109643 0.49054821
[84,] 0.46852660 0.93705319 0.53147340
[85,] 0.42794252 0.85588503 0.57205748
[86,] 0.38501721 0.77003442 0.61498279
[87,] 0.39595929 0.79191858 0.60404071
[88,] 0.36265771 0.72531542 0.63734229
[89,] 0.32443182 0.64886363 0.67556818
[90,] 0.32709390 0.65418780 0.67290610
[91,] 0.28602555 0.57205109 0.71397445
[92,] 0.24903536 0.49807071 0.75096464
[93,] 0.22696001 0.45392002 0.77303999
[94,] 0.21297864 0.42595729 0.78702136
[95,] 0.26381573 0.52763147 0.73618427
[96,] 0.22683074 0.45366147 0.77316926
[97,] 0.22437014 0.44874028 0.77562986
[98,] 0.23601327 0.47202654 0.76398673
[99,] 0.21424123 0.42848247 0.78575877
[100,] 0.19212630 0.38425259 0.80787370
[101,] 0.18269238 0.36538477 0.81730762
[102,] 0.16818407 0.33636815 0.83181593
[103,] 0.15341136 0.30682271 0.84658864
[104,] 0.13000738 0.26001476 0.86999262
[105,] 0.15755923 0.31511846 0.84244077
[106,] 0.14030540 0.28061079 0.85969460
[107,] 0.17514535 0.35029070 0.82485465
[108,] 0.16642837 0.33285674 0.83357163
[109,] 0.14596960 0.29193920 0.85403040
[110,] 0.12777343 0.25554687 0.87222657
[111,] 0.13157142 0.26314285 0.86842858
[112,] 0.11958320 0.23916640 0.88041680
[113,] 0.10067855 0.20135710 0.89932145
[114,] 0.08126153 0.16252306 0.91873847
[115,] 0.09303037 0.18606074 0.90696963
[116,] 0.07619953 0.15239906 0.92380047
[117,] 0.06253042 0.12506084 0.93746958
[118,] 0.04860764 0.09721528 0.95139236
[119,] 0.03778137 0.07556274 0.96221863
[120,] 0.03209875 0.06419750 0.96790125
[121,] 0.02529830 0.05059659 0.97470170
[122,] 0.03374543 0.06749086 0.96625457
[123,] 0.02944556 0.05889113 0.97055444
[124,] 0.04390328 0.08780655 0.95609672
[125,] 0.04957517 0.09915033 0.95042483
[126,] 0.06244509 0.12489018 0.93755491
[127,] 0.05064528 0.10129055 0.94935472
[128,] 0.03642918 0.07285837 0.96357082
[129,] 0.02575957 0.05151913 0.97424043
[130,] 0.01776504 0.03553009 0.98223496
[131,] 0.02788027 0.05576054 0.97211973
[132,] 0.02203560 0.04407119 0.97796440
[133,] 0.48405077 0.96810153 0.51594923
[134,] 0.46020094 0.92040189 0.53979906
[135,] 0.39250665 0.78501330 0.60749335
[136,] 0.40519549 0.81039098 0.59480451
[137,] 0.35607767 0.71215535 0.64392233
[138,] 0.34409989 0.68819977 0.65590011
[139,] 0.39612777 0.79225554 0.60387223
[140,] 0.81477765 0.37044471 0.18522235
[141,] 0.78896449 0.42207101 0.21103551
[142,] 0.69057432 0.61885136 0.30942568
[143,] 0.88368300 0.23263400 0.11631700
[144,] 0.93362592 0.13274817 0.06637408
[145,] 0.90069498 0.19861003 0.09930502
> postscript(file="/var/www/rcomp/tmp/1fc891322158795.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2xz5t1322158795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3s0301322158795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ex711322158795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5wouz1322158795.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
-3.17752076 0.12038092 2.73257916 3.25876690 -1.36826555 -1.86658631
7 8 9 10 11 12
3.66080667 -1.58529622 -1.89128782 2.57094939 0.74390007 -0.32713920
13 14 15 16 17 18
0.58625466 0.62591488 -0.32531600 -0.10275309 0.50187126 3.89911505
19 20 21 22 23 24
2.44390663 0.66728021 0.64305349 1.15709986 2.50812606 1.13772477
25 26 27 28 29 30
2.27547170 0.22054773 1.21307547 -1.13534201 0.71753602 -0.12805048
31 32 33 34 35 36
-0.62380437 -0.22543877 -1.14054209 0.46557049 -1.48369342 -5.96918635
37 38 39 40 41 42
-0.74519361 -1.78429468 1.78392066 1.39534982 0.93426744 -1.46217487
43 44 45 46 47 48
1.94963360 -0.23018278 -0.93000217 -4.51749771 -2.64912289 0.05435115
49 50 51 52 53 54
0.98601395 -1.98876488 -0.51190118 -0.08406140 -2.59997960 0.36937854
55 56 57 58 59 60
-2.41298268 1.34887868 -0.09346065 0.81595648 -0.24483563 1.87147782
61 62 63 64 65 66
0.58568666 0.13697774 -0.33934051 -0.39356234 0.47623555 1.12530901
67 68 69 70 71 72
1.81743674 3.50248827 -3.88481549 0.58274841 -3.61955719 -0.37204378
73 74 75 76 77 78
1.57795396 0.93115087 0.35305640 3.16870160 -0.39972083 1.46032487
79 80 81 82 83 84
-1.83041739 -0.14877431 0.55704632 4.00124735 0.19485548 -0.96400557
85 86 87 88 89 90
0.24748758 1.74688419 -0.15693305 0.65061391 1.36157006 0.85138909
91 92 93 94 95 96
-1.54858030 -0.08374146 0.60149790 -0.14558460 -2.07448692 0.89048396
97 98 99 100 101 102
0.10561613 1.90608020 -0.08165519 -0.31403668 -1.31756532 1.19655387
103 104 105 106 107 108
2.61147373 0.28202906 1.56265418 -2.34263968 0.89102204 0.09272257
109 110 111 112 113 114
1.36283922 -0.39740878 1.08829157 0.19190474 2.46678922 -1.47970563
115 116 117 118 119 120
-2.97729190 1.31128636 -1.59520567 0.93900226 -2.01257096 0.35372450
121 122 123 124 125 126
-1.37062930 0.32284377 -2.94849684 -1.18069452 -1.10883265 -0.79218457
127 128 129 130 131 132
-0.50273293 0.69774029 0.93684075 -3.04568938 1.81960194 -3.33289811
133 134 135 136 137 138
1.87652033 -2.03831552 -1.77944243 -0.32738726 0.62164911 0.25976413
139 140 141 142 143 144
-2.38491931 -1.54035792 -5.39362872 2.72873702 1.66054514 0.38496285
145 146 147 148 149 150
1.09814321 -4.25454624 1.92382266 -2.37785785 0.74465320 -0.12844386
151 152 153 154 155 156
-3.20772694 -1.41058351 1.90674891 3.94102764 1.46518905 -2.71013209
157 158 159 160 161 162
-0.08374146 1.26638822 0.93684075 0.86377819 -0.43409312 0.13956299
> postscript(file="/var/www/rcomp/tmp/6j9n51322158795.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 -3.17752076 NA
1 0.12038092 -3.17752076
2 2.73257916 0.12038092
3 3.25876690 2.73257916
4 -1.36826555 3.25876690
5 -1.86658631 -1.36826555
6 3.66080667 -1.86658631
7 -1.58529622 3.66080667
8 -1.89128782 -1.58529622
9 2.57094939 -1.89128782
10 0.74390007 2.57094939
11 -0.32713920 0.74390007
12 0.58625466 -0.32713920
13 0.62591488 0.58625466
14 -0.32531600 0.62591488
15 -0.10275309 -0.32531600
16 0.50187126 -0.10275309
17 3.89911505 0.50187126
18 2.44390663 3.89911505
19 0.66728021 2.44390663
20 0.64305349 0.66728021
21 1.15709986 0.64305349
22 2.50812606 1.15709986
23 1.13772477 2.50812606
24 2.27547170 1.13772477
25 0.22054773 2.27547170
26 1.21307547 0.22054773
27 -1.13534201 1.21307547
28 0.71753602 -1.13534201
29 -0.12805048 0.71753602
30 -0.62380437 -0.12805048
31 -0.22543877 -0.62380437
32 -1.14054209 -0.22543877
33 0.46557049 -1.14054209
34 -1.48369342 0.46557049
35 -5.96918635 -1.48369342
36 -0.74519361 -5.96918635
37 -1.78429468 -0.74519361
38 1.78392066 -1.78429468
39 1.39534982 1.78392066
40 0.93426744 1.39534982
41 -1.46217487 0.93426744
42 1.94963360 -1.46217487
43 -0.23018278 1.94963360
44 -0.93000217 -0.23018278
45 -4.51749771 -0.93000217
46 -2.64912289 -4.51749771
47 0.05435115 -2.64912289
48 0.98601395 0.05435115
49 -1.98876488 0.98601395
50 -0.51190118 -1.98876488
51 -0.08406140 -0.51190118
52 -2.59997960 -0.08406140
53 0.36937854 -2.59997960
54 -2.41298268 0.36937854
55 1.34887868 -2.41298268
56 -0.09346065 1.34887868
57 0.81595648 -0.09346065
58 -0.24483563 0.81595648
59 1.87147782 -0.24483563
60 0.58568666 1.87147782
61 0.13697774 0.58568666
62 -0.33934051 0.13697774
63 -0.39356234 -0.33934051
64 0.47623555 -0.39356234
65 1.12530901 0.47623555
66 1.81743674 1.12530901
67 3.50248827 1.81743674
68 -3.88481549 3.50248827
69 0.58274841 -3.88481549
70 -3.61955719 0.58274841
71 -0.37204378 -3.61955719
72 1.57795396 -0.37204378
73 0.93115087 1.57795396
74 0.35305640 0.93115087
75 3.16870160 0.35305640
76 -0.39972083 3.16870160
77 1.46032487 -0.39972083
78 -1.83041739 1.46032487
79 -0.14877431 -1.83041739
80 0.55704632 -0.14877431
81 4.00124735 0.55704632
82 0.19485548 4.00124735
83 -0.96400557 0.19485548
84 0.24748758 -0.96400557
85 1.74688419 0.24748758
86 -0.15693305 1.74688419
87 0.65061391 -0.15693305
88 1.36157006 0.65061391
89 0.85138909 1.36157006
90 -1.54858030 0.85138909
91 -0.08374146 -1.54858030
92 0.60149790 -0.08374146
93 -0.14558460 0.60149790
94 -2.07448692 -0.14558460
95 0.89048396 -2.07448692
96 0.10561613 0.89048396
97 1.90608020 0.10561613
98 -0.08165519 1.90608020
99 -0.31403668 -0.08165519
100 -1.31756532 -0.31403668
101 1.19655387 -1.31756532
102 2.61147373 1.19655387
103 0.28202906 2.61147373
104 1.56265418 0.28202906
105 -2.34263968 1.56265418
106 0.89102204 -2.34263968
107 0.09272257 0.89102204
108 1.36283922 0.09272257
109 -0.39740878 1.36283922
110 1.08829157 -0.39740878
111 0.19190474 1.08829157
112 2.46678922 0.19190474
113 -1.47970563 2.46678922
114 -2.97729190 -1.47970563
115 1.31128636 -2.97729190
116 -1.59520567 1.31128636
117 0.93900226 -1.59520567
118 -2.01257096 0.93900226
119 0.35372450 -2.01257096
120 -1.37062930 0.35372450
121 0.32284377 -1.37062930
122 -2.94849684 0.32284377
123 -1.18069452 -2.94849684
124 -1.10883265 -1.18069452
125 -0.79218457 -1.10883265
126 -0.50273293 -0.79218457
127 0.69774029 -0.50273293
128 0.93684075 0.69774029
129 -3.04568938 0.93684075
130 1.81960194 -3.04568938
131 -3.33289811 1.81960194
132 1.87652033 -3.33289811
133 -2.03831552 1.87652033
134 -1.77944243 -2.03831552
135 -0.32738726 -1.77944243
136 0.62164911 -0.32738726
137 0.25976413 0.62164911
138 -2.38491931 0.25976413
139 -1.54035792 -2.38491931
140 -5.39362872 -1.54035792
141 2.72873702 -5.39362872
142 1.66054514 2.72873702
143 0.38496285 1.66054514
144 1.09814321 0.38496285
145 -4.25454624 1.09814321
146 1.92382266 -4.25454624
147 -2.37785785 1.92382266
148 0.74465320 -2.37785785
149 -0.12844386 0.74465320
150 -3.20772694 -0.12844386
151 -1.41058351 -3.20772694
152 1.90674891 -1.41058351
153 3.94102764 1.90674891
154 1.46518905 3.94102764
155 -2.71013209 1.46518905
156 -0.08374146 -2.71013209
157 1.26638822 -0.08374146
158 0.93684075 1.26638822
159 0.86377819 0.93684075
160 -0.43409312 0.86377819
161 0.13956299 -0.43409312
162 NA 0.13956299
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.12038092 -3.17752076
[2,] 2.73257916 0.12038092
[3,] 3.25876690 2.73257916
[4,] -1.36826555 3.25876690
[5,] -1.86658631 -1.36826555
[6,] 3.66080667 -1.86658631
[7,] -1.58529622 3.66080667
[8,] -1.89128782 -1.58529622
[9,] 2.57094939 -1.89128782
[10,] 0.74390007 2.57094939
[11,] -0.32713920 0.74390007
[12,] 0.58625466 -0.32713920
[13,] 0.62591488 0.58625466
[14,] -0.32531600 0.62591488
[15,] -0.10275309 -0.32531600
[16,] 0.50187126 -0.10275309
[17,] 3.89911505 0.50187126
[18,] 2.44390663 3.89911505
[19,] 0.66728021 2.44390663
[20,] 0.64305349 0.66728021
[21,] 1.15709986 0.64305349
[22,] 2.50812606 1.15709986
[23,] 1.13772477 2.50812606
[24,] 2.27547170 1.13772477
[25,] 0.22054773 2.27547170
[26,] 1.21307547 0.22054773
[27,] -1.13534201 1.21307547
[28,] 0.71753602 -1.13534201
[29,] -0.12805048 0.71753602
[30,] -0.62380437 -0.12805048
[31,] -0.22543877 -0.62380437
[32,] -1.14054209 -0.22543877
[33,] 0.46557049 -1.14054209
[34,] -1.48369342 0.46557049
[35,] -5.96918635 -1.48369342
[36,] -0.74519361 -5.96918635
[37,] -1.78429468 -0.74519361
[38,] 1.78392066 -1.78429468
[39,] 1.39534982 1.78392066
[40,] 0.93426744 1.39534982
[41,] -1.46217487 0.93426744
[42,] 1.94963360 -1.46217487
[43,] -0.23018278 1.94963360
[44,] -0.93000217 -0.23018278
[45,] -4.51749771 -0.93000217
[46,] -2.64912289 -4.51749771
[47,] 0.05435115 -2.64912289
[48,] 0.98601395 0.05435115
[49,] -1.98876488 0.98601395
[50,] -0.51190118 -1.98876488
[51,] -0.08406140 -0.51190118
[52,] -2.59997960 -0.08406140
[53,] 0.36937854 -2.59997960
[54,] -2.41298268 0.36937854
[55,] 1.34887868 -2.41298268
[56,] -0.09346065 1.34887868
[57,] 0.81595648 -0.09346065
[58,] -0.24483563 0.81595648
[59,] 1.87147782 -0.24483563
[60,] 0.58568666 1.87147782
[61,] 0.13697774 0.58568666
[62,] -0.33934051 0.13697774
[63,] -0.39356234 -0.33934051
[64,] 0.47623555 -0.39356234
[65,] 1.12530901 0.47623555
[66,] 1.81743674 1.12530901
[67,] 3.50248827 1.81743674
[68,] -3.88481549 3.50248827
[69,] 0.58274841 -3.88481549
[70,] -3.61955719 0.58274841
[71,] -0.37204378 -3.61955719
[72,] 1.57795396 -0.37204378
[73,] 0.93115087 1.57795396
[74,] 0.35305640 0.93115087
[75,] 3.16870160 0.35305640
[76,] -0.39972083 3.16870160
[77,] 1.46032487 -0.39972083
[78,] -1.83041739 1.46032487
[79,] -0.14877431 -1.83041739
[80,] 0.55704632 -0.14877431
[81,] 4.00124735 0.55704632
[82,] 0.19485548 4.00124735
[83,] -0.96400557 0.19485548
[84,] 0.24748758 -0.96400557
[85,] 1.74688419 0.24748758
[86,] -0.15693305 1.74688419
[87,] 0.65061391 -0.15693305
[88,] 1.36157006 0.65061391
[89,] 0.85138909 1.36157006
[90,] -1.54858030 0.85138909
[91,] -0.08374146 -1.54858030
[92,] 0.60149790 -0.08374146
[93,] -0.14558460 0.60149790
[94,] -2.07448692 -0.14558460
[95,] 0.89048396 -2.07448692
[96,] 0.10561613 0.89048396
[97,] 1.90608020 0.10561613
[98,] -0.08165519 1.90608020
[99,] -0.31403668 -0.08165519
[100,] -1.31756532 -0.31403668
[101,] 1.19655387 -1.31756532
[102,] 2.61147373 1.19655387
[103,] 0.28202906 2.61147373
[104,] 1.56265418 0.28202906
[105,] -2.34263968 1.56265418
[106,] 0.89102204 -2.34263968
[107,] 0.09272257 0.89102204
[108,] 1.36283922 0.09272257
[109,] -0.39740878 1.36283922
[110,] 1.08829157 -0.39740878
[111,] 0.19190474 1.08829157
[112,] 2.46678922 0.19190474
[113,] -1.47970563 2.46678922
[114,] -2.97729190 -1.47970563
[115,] 1.31128636 -2.97729190
[116,] -1.59520567 1.31128636
[117,] 0.93900226 -1.59520567
[118,] -2.01257096 0.93900226
[119,] 0.35372450 -2.01257096
[120,] -1.37062930 0.35372450
[121,] 0.32284377 -1.37062930
[122,] -2.94849684 0.32284377
[123,] -1.18069452 -2.94849684
[124,] -1.10883265 -1.18069452
[125,] -0.79218457 -1.10883265
[126,] -0.50273293 -0.79218457
[127,] 0.69774029 -0.50273293
[128,] 0.93684075 0.69774029
[129,] -3.04568938 0.93684075
[130,] 1.81960194 -3.04568938
[131,] -3.33289811 1.81960194
[132,] 1.87652033 -3.33289811
[133,] -2.03831552 1.87652033
[134,] -1.77944243 -2.03831552
[135,] -0.32738726 -1.77944243
[136,] 0.62164911 -0.32738726
[137,] 0.25976413 0.62164911
[138,] -2.38491931 0.25976413
[139,] -1.54035792 -2.38491931
[140,] -5.39362872 -1.54035792
[141,] 2.72873702 -5.39362872
[142,] 1.66054514 2.72873702
[143,] 0.38496285 1.66054514
[144,] 1.09814321 0.38496285
[145,] -4.25454624 1.09814321
[146,] 1.92382266 -4.25454624
[147,] -2.37785785 1.92382266
[148,] 0.74465320 -2.37785785
[149,] -0.12844386 0.74465320
[150,] -3.20772694 -0.12844386
[151,] -1.41058351 -3.20772694
[152,] 1.90674891 -1.41058351
[153,] 3.94102764 1.90674891
[154,] 1.46518905 3.94102764
[155,] -2.71013209 1.46518905
[156,] -0.08374146 -2.71013209
[157,] 1.26638822 -0.08374146
[158,] 0.93684075 1.26638822
[159,] 0.86377819 0.93684075
[160,] -0.43409312 0.86377819
[161,] 0.13956299 -0.43409312
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.12038092 -3.17752076
2 2.73257916 0.12038092
3 3.25876690 2.73257916
4 -1.36826555 3.25876690
5 -1.86658631 -1.36826555
6 3.66080667 -1.86658631
7 -1.58529622 3.66080667
8 -1.89128782 -1.58529622
9 2.57094939 -1.89128782
10 0.74390007 2.57094939
11 -0.32713920 0.74390007
12 0.58625466 -0.32713920
13 0.62591488 0.58625466
14 -0.32531600 0.62591488
15 -0.10275309 -0.32531600
16 0.50187126 -0.10275309
17 3.89911505 0.50187126
18 2.44390663 3.89911505
19 0.66728021 2.44390663
20 0.64305349 0.66728021
21 1.15709986 0.64305349
22 2.50812606 1.15709986
23 1.13772477 2.50812606
24 2.27547170 1.13772477
25 0.22054773 2.27547170
26 1.21307547 0.22054773
27 -1.13534201 1.21307547
28 0.71753602 -1.13534201
29 -0.12805048 0.71753602
30 -0.62380437 -0.12805048
31 -0.22543877 -0.62380437
32 -1.14054209 -0.22543877
33 0.46557049 -1.14054209
34 -1.48369342 0.46557049
35 -5.96918635 -1.48369342
36 -0.74519361 -5.96918635
37 -1.78429468 -0.74519361
38 1.78392066 -1.78429468
39 1.39534982 1.78392066
40 0.93426744 1.39534982
41 -1.46217487 0.93426744
42 1.94963360 -1.46217487
43 -0.23018278 1.94963360
44 -0.93000217 -0.23018278
45 -4.51749771 -0.93000217
46 -2.64912289 -4.51749771
47 0.05435115 -2.64912289
48 0.98601395 0.05435115
49 -1.98876488 0.98601395
50 -0.51190118 -1.98876488
51 -0.08406140 -0.51190118
52 -2.59997960 -0.08406140
53 0.36937854 -2.59997960
54 -2.41298268 0.36937854
55 1.34887868 -2.41298268
56 -0.09346065 1.34887868
57 0.81595648 -0.09346065
58 -0.24483563 0.81595648
59 1.87147782 -0.24483563
60 0.58568666 1.87147782
61 0.13697774 0.58568666
62 -0.33934051 0.13697774
63 -0.39356234 -0.33934051
64 0.47623555 -0.39356234
65 1.12530901 0.47623555
66 1.81743674 1.12530901
67 3.50248827 1.81743674
68 -3.88481549 3.50248827
69 0.58274841 -3.88481549
70 -3.61955719 0.58274841
71 -0.37204378 -3.61955719
72 1.57795396 -0.37204378
73 0.93115087 1.57795396
74 0.35305640 0.93115087
75 3.16870160 0.35305640
76 -0.39972083 3.16870160
77 1.46032487 -0.39972083
78 -1.83041739 1.46032487
79 -0.14877431 -1.83041739
80 0.55704632 -0.14877431
81 4.00124735 0.55704632
82 0.19485548 4.00124735
83 -0.96400557 0.19485548
84 0.24748758 -0.96400557
85 1.74688419 0.24748758
86 -0.15693305 1.74688419
87 0.65061391 -0.15693305
88 1.36157006 0.65061391
89 0.85138909 1.36157006
90 -1.54858030 0.85138909
91 -0.08374146 -1.54858030
92 0.60149790 -0.08374146
93 -0.14558460 0.60149790
94 -2.07448692 -0.14558460
95 0.89048396 -2.07448692
96 0.10561613 0.89048396
97 1.90608020 0.10561613
98 -0.08165519 1.90608020
99 -0.31403668 -0.08165519
100 -1.31756532 -0.31403668
101 1.19655387 -1.31756532
102 2.61147373 1.19655387
103 0.28202906 2.61147373
104 1.56265418 0.28202906
105 -2.34263968 1.56265418
106 0.89102204 -2.34263968
107 0.09272257 0.89102204
108 1.36283922 0.09272257
109 -0.39740878 1.36283922
110 1.08829157 -0.39740878
111 0.19190474 1.08829157
112 2.46678922 0.19190474
113 -1.47970563 2.46678922
114 -2.97729190 -1.47970563
115 1.31128636 -2.97729190
116 -1.59520567 1.31128636
117 0.93900226 -1.59520567
118 -2.01257096 0.93900226
119 0.35372450 -2.01257096
120 -1.37062930 0.35372450
121 0.32284377 -1.37062930
122 -2.94849684 0.32284377
123 -1.18069452 -2.94849684
124 -1.10883265 -1.18069452
125 -0.79218457 -1.10883265
126 -0.50273293 -0.79218457
127 0.69774029 -0.50273293
128 0.93684075 0.69774029
129 -3.04568938 0.93684075
130 1.81960194 -3.04568938
131 -3.33289811 1.81960194
132 1.87652033 -3.33289811
133 -2.03831552 1.87652033
134 -1.77944243 -2.03831552
135 -0.32738726 -1.77944243
136 0.62164911 -0.32738726
137 0.25976413 0.62164911
138 -2.38491931 0.25976413
139 -1.54035792 -2.38491931
140 -5.39362872 -1.54035792
141 2.72873702 -5.39362872
142 1.66054514 2.72873702
143 0.38496285 1.66054514
144 1.09814321 0.38496285
145 -4.25454624 1.09814321
146 1.92382266 -4.25454624
147 -2.37785785 1.92382266
148 0.74465320 -2.37785785
149 -0.12844386 0.74465320
150 -3.20772694 -0.12844386
151 -1.41058351 -3.20772694
152 1.90674891 -1.41058351
153 3.94102764 1.90674891
154 1.46518905 3.94102764
155 -2.71013209 1.46518905
156 -0.08374146 -2.71013209
157 1.26638822 -0.08374146
158 0.93684075 1.26638822
159 0.86377819 0.93684075
160 -0.43409312 0.86377819
161 0.13956299 -0.43409312
> 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/7ticy1322158795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8lyi11322158795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9bqb81322158795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10pg1i1322158795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h5jw1322158795.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/122qxy1322158795.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/13eryy1322158795.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/141m3u1322158795.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/155fsr1322158795.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/16ebsq1322158795.tab")
+ }
>
> try(system("convert tmp/1fc891322158795.ps tmp/1fc891322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xz5t1322158795.ps tmp/2xz5t1322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/3s0301322158795.ps tmp/3s0301322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ex711322158795.ps tmp/4ex711322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wouz1322158795.ps tmp/5wouz1322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j9n51322158795.ps tmp/6j9n51322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ticy1322158795.ps tmp/7ticy1322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lyi11322158795.ps tmp/8lyi11322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bqb81322158795.ps tmp/9bqb81322158795.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pg1i1322158795.ps tmp/10pg1i1322158795.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.204 0.672 6.989