R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(13
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+ ,2)
+ ,dim=c(5
+ ,162)
+ ,dimnames=list(c('Perceived'
+ ,'Conected'
+ ,'Seperate'
+ ,'Age'
+ ,'Gender')
+ ,1:162))
> y <- array(NA,dim=c(5,162),dimnames=list(c('Perceived','Conected','Seperate','Age','Gender'),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'
> 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
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Perceived Conected Seperate Age Gender
1 13 41 38 7 2
2 16 39 32 5 2
3 19 30 35 5 2
4 15 31 33 5 1
5 14 34 37 8 2
6 13 35 29 6 2
7 19 39 31 5 2
8 15 34 36 6 2
9 14 36 35 5 2
10 15 37 38 4 2
11 16 38 31 6 1
12 16 36 34 5 2
13 16 38 35 5 1
14 16 39 38 6 2
15 17 33 37 7 2
16 15 32 33 6 1
17 15 36 32 7 1
18 20 38 38 6 2
19 18 39 38 8 1
20 16 32 32 7 2
21 16 32 33 5 1
22 16 31 31 5 2
23 19 39 38 7 2
24 16 37 39 7 2
25 17 39 32 5 1
26 17 41 32 4 2
27 16 36 35 10 1
28 15 33 37 6 2
29 16 33 33 5 2
30 14 34 33 5 1
31 15 31 28 5 2
32 12 27 32 5 1
33 14 37 31 6 2
34 16 34 37 5 2
35 14 34 30 5 1
36 7 32 33 5 1
37 10 29 31 5 1
38 14 36 33 5 1
39 16 29 31 5 2
40 16 35 33 5 1
41 16 37 32 5 1
42 14 34 33 7 2
43 20 38 32 5 1
44 14 35 33 6 1
45 14 38 28 7 2
46 11 37 35 7 2
47 14 38 39 5 2
48 15 33 34 5 2
49 16 36 38 4 2
50 14 38 32 5 1
51 16 32 38 4 2
52 14 32 30 5 1
53 12 32 33 5 1
54 16 34 38 7 2
55 9 32 32 5 1
56 14 37 32 5 2
57 16 39 34 6 2
58 16 29 34 4 2
59 15 37 36 6 1
60 16 35 34 6 2
61 12 30 28 5 1
62 16 38 34 7 1
63 16 34 35 6 2
64 14 31 35 8 2
65 16 34 31 7 2
66 17 35 37 5 1
67 18 36 35 6 2
68 18 30 27 6 1
69 12 39 40 5 2
70 16 35 37 5 1
71 10 38 36 5 1
72 14 31 38 5 2
73 18 34 39 4 2
74 18 38 41 6 1
75 16 34 27 6 1
76 17 39 30 6 2
77 16 37 37 6 2
78 16 34 31 7 2
79 13 28 31 5 1
80 16 37 27 7 1
81 16 33 36 6 1
82 20 37 38 5 1
83 16 35 37 5 2
84 15 37 33 4 1
85 15 32 34 8 2
86 16 33 31 8 2
87 14 38 39 5 1
88 16 33 34 5 2
89 16 29 32 6 2
90 15 33 33 4 2
91 12 31 36 5 2
92 17 36 32 5 2
93 16 35 41 5 2
94 15 32 28 5 2
95 13 29 30 6 2
96 16 39 36 6 2
97 16 37 35 5 2
98 16 35 31 6 2
99 16 37 34 5 1
100 14 32 36 7 1
101 16 38 36 5 2
102 16 37 35 6 1
103 20 36 37 6 2
104 15 32 28 6 1
105 16 33 39 4 2
106 13 40 32 5 1
107 17 38 35 5 2
108 16 41 39 7 1
109 16 36 35 6 1
110 12 43 42 9 2
111 16 30 34 6 2
112 16 31 33 6 2
113 17 32 41 5 2
114 13 32 33 6 1
115 12 37 34 5 2
116 18 37 32 8 1
117 14 33 40 7 2
118 14 34 40 5 2
119 13 33 35 7 2
120 16 38 36 6 2
121 13 33 37 6 2
122 16 31 27 9 2
123 13 38 39 7 2
124 16 37 38 6 2
125 15 33 31 5 2
126 16 31 33 5 2
127 15 39 32 6 1
128 17 44 39 6 2
129 15 33 36 7 2
130 12 35 33 5 2
131 16 32 33 5 1
132 10 28 32 5 1
133 16 40 37 6 2
134 12 27 30 4 1
135 14 37 38 5 1
136 15 32 29 7 2
137 13 28 22 5 1
138 15 34 35 7 1
139 11 30 35 7 2
140 12 35 34 6 2
141 8 31 35 5 1
142 16 32 34 8 2
143 15 30 34 5 1
144 17 30 35 5 2
145 16 31 23 5 1
146 10 40 31 6 2
147 18 32 27 4 2
148 13 36 36 5 1
149 16 32 31 5 1
150 13 35 32 7 1
151 10 38 39 6 2
152 15 42 37 7 2
153 16 34 38 10 1
154 16 35 39 6 2
155 14 35 34 8 2
156 10 33 31 4 2
157 17 36 32 5 2
158 13 32 37 6 2
159 15 33 36 7 2
160 16 34 32 7 2
161 12 32 35 6 2
162 13 34 36 6 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Conected Seperate Age Gender
9.59343 0.14352 -0.01734 0.02521 0.51320
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.2532 -1.1699 0.4343 1.1805 5.1159
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.59343 2.18771 4.385 2.11e-05 ***
Conected 0.14352 0.05613 2.557 0.0115 *
Seperate -0.01734 0.05447 -0.318 0.7507
Age 0.02521 0.15280 0.165 0.8692
Gender 0.51320 0.37128 1.382 0.1689
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.218 on 157 degrees of freedom
Multiple R-squared: 0.0575, Adjusted R-squared: 0.03349
F-statistic: 2.395 on 4 and 157 DF, p-value: 0.05279
> 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,] 0.58500911 0.82998179 0.4149909
[2,] 0.70421599 0.59156802 0.2957840
[3,] 0.66321831 0.67356337 0.3367817
[4,] 0.61063434 0.77873132 0.3893657
[5,] 0.49410843 0.98821686 0.5058916
[6,] 0.39310889 0.78621778 0.6068911
[7,] 0.33089932 0.66179865 0.6691007
[8,] 0.33794182 0.67588365 0.6620582
[9,] 0.25988593 0.51977187 0.7401141
[10,] 0.19211587 0.38423174 0.8078841
[11,] 0.44920127 0.89840254 0.5507987
[12,] 0.49126752 0.98253504 0.5087325
[13,] 0.42464295 0.84928590 0.5753571
[14,] 0.35438101 0.70876202 0.6456190
[15,] 0.28946425 0.57892850 0.7105358
[16,] 0.33896945 0.67793890 0.6610306
[17,] 0.27903143 0.55806286 0.7209686
[18,] 0.23656549 0.47313097 0.7634345
[19,] 0.19030097 0.38060193 0.8096990
[20,] 0.15134542 0.30269085 0.8486546
[21,] 0.12372117 0.24744234 0.8762788
[22,] 0.09374034 0.18748067 0.9062597
[23,] 0.08793148 0.17586295 0.9120685
[24,] 0.06473499 0.12946998 0.9352650
[25,] 0.07712994 0.15425987 0.9228701
[26,] 0.07191475 0.14382950 0.9280853
[27,] 0.05357500 0.10714999 0.9464250
[28,] 0.04175525 0.08351051 0.9582447
[29,] 0.48938985 0.97877969 0.5106102
[30,] 0.56439543 0.87120913 0.4356046
[31,] 0.51422015 0.97155970 0.4857799
[32,] 0.49132364 0.98264727 0.5086764
[33,] 0.46060430 0.92120859 0.5393957
[34,] 0.41942352 0.83884704 0.5805765
[35,] 0.39000465 0.78000929 0.6099954
[36,] 0.57355883 0.85288235 0.4264412
[37,] 0.52850692 0.94298616 0.4714931
[38,] 0.50729790 0.98540419 0.4927021
[39,] 0.68605930 0.62788139 0.3139407
[40,] 0.69161729 0.61676541 0.3083827
[41,] 0.64546748 0.70906504 0.3545325
[42,] 0.60065815 0.79868371 0.3993419
[43,] 0.56833055 0.86333890 0.4316694
[44,] 0.52842158 0.94315684 0.4715784
[45,] 0.47880121 0.95760241 0.5211988
[46,] 0.47871665 0.95743329 0.5212834
[47,] 0.43526471 0.87052942 0.5647353
[48,] 0.63723830 0.72552340 0.3627617
[49,] 0.61419659 0.77160683 0.3858034
[50,] 0.56830154 0.86339692 0.4316985
[51,] 0.54847061 0.90305878 0.4515294
[52,] 0.50059810 0.99880379 0.4994019
[53,] 0.45790468 0.91580936 0.5420953
[54,] 0.43792511 0.87585022 0.5620749
[55,] 0.39815096 0.79630192 0.6018490
[56,] 0.35961375 0.71922750 0.6403863
[57,] 0.31925636 0.63851272 0.6807436
[58,] 0.28756510 0.57513020 0.7124349
[59,] 0.28486247 0.56972494 0.7151375
[60,] 0.29684300 0.59368601 0.7031570
[61,] 0.42839962 0.85679924 0.5716004
[62,] 0.53122963 0.93754074 0.4687704
[63,] 0.50021720 0.99956559 0.4997828
[64,] 0.68475237 0.63049525 0.3152476
[65,] 0.64629949 0.70740101 0.3537005
[66,] 0.67978436 0.64043128 0.3202156
[67,] 0.70611834 0.58776332 0.2938817
[68,] 0.68307077 0.63385846 0.3169292
[69,] 0.65292706 0.69414589 0.3470729
[70,] 0.61354376 0.77291248 0.3864562
[71,] 0.57558012 0.84883976 0.4244199
[72,] 0.53511792 0.92976416 0.4648821
[73,] 0.49717852 0.99435704 0.5028215
[74,] 0.47388529 0.94777058 0.5261147
[75,] 0.66618588 0.66762824 0.3338141
[76,] 0.63264180 0.73471641 0.3673582
[77,] 0.58986524 0.82026951 0.4101348
[78,] 0.54492490 0.91015020 0.4550751
[79,] 0.50727155 0.98545689 0.4927284
[80,] 0.47321403 0.94642806 0.5267860
[81,] 0.44061383 0.88122765 0.5593862
[82,] 0.41930343 0.83860687 0.5806966
[83,] 0.37603655 0.75207311 0.6239634
[84,] 0.38995374 0.77990747 0.6100463
[85,] 0.37483150 0.74966300 0.6251685
[86,] 0.34517155 0.69034310 0.6548284
[87,] 0.30398047 0.60796093 0.6960195
[88,] 0.27922666 0.55845332 0.7207733
[89,] 0.24591438 0.49182875 0.7540856
[90,] 0.21689114 0.43378228 0.7831089
[91,] 0.18967954 0.37935909 0.8103205
[92,] 0.17004488 0.34008975 0.8299551
[93,] 0.14248104 0.28496208 0.8575190
[94,] 0.12286514 0.24573027 0.8771349
[95,] 0.10820767 0.21641534 0.8917923
[96,] 0.22655315 0.45310630 0.7734469
[97,] 0.19584604 0.39169207 0.8041540
[98,] 0.18778535 0.37557069 0.8122147
[99,] 0.18204810 0.36409620 0.8179519
[100,] 0.18174301 0.36348601 0.8182570
[101,] 0.16200580 0.32401160 0.8379942
[102,] 0.15027795 0.30055590 0.8497221
[103,] 0.22314761 0.44629522 0.7768524
[104,] 0.20869984 0.41739969 0.7913002
[105,] 0.19320363 0.38640726 0.8067964
[106,] 0.24900869 0.49801737 0.7509913
[107,] 0.21946334 0.43892668 0.7805367
[108,] 0.24430997 0.48861995 0.7556900
[109,] 0.26947860 0.53895720 0.7305214
[110,] 0.23463194 0.46926388 0.7653681
[111,] 0.20574145 0.41148291 0.7942585
[112,] 0.18859575 0.37719150 0.8114042
[113,] 0.16659051 0.33318102 0.8334095
[114,] 0.14611774 0.29223548 0.8538823
[115,] 0.11990891 0.23981783 0.8800911
[116,] 0.11296542 0.22593084 0.8870346
[117,] 0.10104068 0.20208136 0.8989593
[118,] 0.08149226 0.16298452 0.9185077
[119,] 0.08066192 0.16132384 0.9193381
[120,] 0.06215968 0.12431936 0.9378403
[121,] 0.06350238 0.12700475 0.9364976
[122,] 0.04967455 0.09934909 0.9503255
[123,] 0.04903608 0.09807215 0.9509639
[124,] 0.05262068 0.10524135 0.9473793
[125,] 0.07075612 0.14151225 0.9292439
[126,] 0.06878056 0.13756113 0.9312194
[127,] 0.05839244 0.11678489 0.9416076
[128,] 0.04871605 0.09743211 0.9512839
[129,] 0.03489556 0.06979112 0.9651044
[130,] 0.03686471 0.07372943 0.9631353
[131,] 0.02774535 0.05549070 0.9722546
[132,] 0.05390925 0.10781850 0.9460908
[133,] 0.05286838 0.10573677 0.9471316
[134,] 0.24418755 0.48837510 0.7558125
[135,] 0.19192318 0.38384636 0.8080768
[136,] 0.14587744 0.29175488 0.8541226
[137,] 0.14878376 0.29756752 0.8512162
[138,] 0.11020170 0.22040341 0.8897983
[139,] 0.26029161 0.52058322 0.7397084
[140,] 0.33047856 0.66095712 0.6695214
[141,] 0.25360209 0.50720419 0.7463979
[142,] 0.30570875 0.61141749 0.6942913
[143,] 0.23495915 0.46991829 0.7650409
[144,] 0.43376892 0.86753785 0.5662311
[145,] 0.65566202 0.68867597 0.3443380
[146,] 0.51088102 0.97823797 0.4891190
[147,] 0.34859177 0.69718353 0.6514082
> postscript(file="/var/wessaorg/rcomp/tmp/1b5sd1352126050.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2sphs1352126050.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38v511352126050.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hkn11352126050.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5htz11352126050.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
-3.02177485 0.21166341 4.55534901 0.89035481 -1.05969121 -2.29148580
7 8 9 10 11 12
3.19432575 -0.02660298 -1.30576606 -0.37205931 0.82583228 0.67689627
13 14 15 16 17 18
0.92039588 0.29047645 2.10904092 0.72162268 0.10499536 4.43399563
19 20 21 22 23 24
2.75325086 1.16587178 1.74683563 1.34247918 3.26526351 0.56963953
25 26 27 28 29 30
1.72486371 0.94983800 1.08136953 0.13425386 1.09011615 -0.54020273
31 32 33 34 35 36
0.29046619 -1.55290614 -1.54384884 1.01594762 -0.59221572 -7.25316437
37 38 39 40 41 42
-3.85728216 -0.82724109 1.62951754 1.31627809 1.01190207 -1.10382891
43 44 45 46 47 48
4.86838289 -0.70893485 -1.76459395 -4.49971113 -1.52345377 0.10745381
49 50 51 52 53 54
0.77145987 -1.13161711 1.34553659 -0.30517736 -2.25316437 0.98285940
55 56 57 58 59 60
-5.27050204 -1.50129823 0.22112580 1.70674347 0.05603978 0.79520251
61 62 63 64 65 66
-2.05281433 0.85263233 0.95605935 -0.66380899 0.86149576 2.38562874
67 68 69 70 71 72
2.66902100 3.90463506 -3.64963528 1.38562874 -5.06226646 -0.53615718
73 74 75 76 77 78
3.07583589 2.99920891 1.33055835 1.15177514 0.56017714 0.86149576
79 80 81 82 83 84
-0.71376298 0.87478787 1.63011649 5.11592805 0.87242844 0.05445267
85 86 87 88 89 90
0.17533416 0.97980200 -1.01025347 1.10745381 1.62164226 0.11532909
91 92 93 94 95 96
-2.57083250 1.64222095 0.94177909 0.14694701 -1.41303307 0.25580112
97 98 99 100 101 102
0.55071476 0.74318952 1.04657739 -0.25157727 0.42453324 1.03870212
103 104 105 106 107 108
4.70369632 0.63493437 1.21935507 -2.41865547 1.40719558 0.50876311
109 110 111 112 113 114
1.18222129 -4.28988844 1.51279841 1.35194156 2.37233663 -1.27837732
115 116 117 118 119 120
-3.46662290 2.93626324 -0.83894609 -0.93203939 -1.92563441 0.39932030
121 122 123 124 125 126
-1.86574614 1.17227676 -2.57387965 0.57751481 0.05544082 1.37715451
127 128 129 130 131 132
-0.30034923 0.59021822 0.09170325 -3.19692221 1.74683563 -3.69642532
133 134 135 136 137 138
0.12961961 -1.56236853 -0.88407195 0.11385879 -0.86980195 0.44404671
139 140 141 142 143 144
-3.49507687 -3.20479749 -6.07496987 1.17533416 1.05121165 2.55534901
145 146 147 148 149 150
1.71697818 -5.97440637 3.15482229 -1.77522810 1.71216030 -1.75148546
151 152 153 154 155 156
-5.54866671 -1.18263169 1.42042087 0.88189083 -1.25522337 -4.91934623
157 158 159 160 161 162
1.64222095 -1.72222696 0.09170325 0.87883342 -2.75690229 -2.02660298
> postscript(file="/var/wessaorg/rcomp/tmp/68wmr1352126050.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 -3.02177485 NA
1 0.21166341 -3.02177485
2 4.55534901 0.21166341
3 0.89035481 4.55534901
4 -1.05969121 0.89035481
5 -2.29148580 -1.05969121
6 3.19432575 -2.29148580
7 -0.02660298 3.19432575
8 -1.30576606 -0.02660298
9 -0.37205931 -1.30576606
10 0.82583228 -0.37205931
11 0.67689627 0.82583228
12 0.92039588 0.67689627
13 0.29047645 0.92039588
14 2.10904092 0.29047645
15 0.72162268 2.10904092
16 0.10499536 0.72162268
17 4.43399563 0.10499536
18 2.75325086 4.43399563
19 1.16587178 2.75325086
20 1.74683563 1.16587178
21 1.34247918 1.74683563
22 3.26526351 1.34247918
23 0.56963953 3.26526351
24 1.72486371 0.56963953
25 0.94983800 1.72486371
26 1.08136953 0.94983800
27 0.13425386 1.08136953
28 1.09011615 0.13425386
29 -0.54020273 1.09011615
30 0.29046619 -0.54020273
31 -1.55290614 0.29046619
32 -1.54384884 -1.55290614
33 1.01594762 -1.54384884
34 -0.59221572 1.01594762
35 -7.25316437 -0.59221572
36 -3.85728216 -7.25316437
37 -0.82724109 -3.85728216
38 1.62951754 -0.82724109
39 1.31627809 1.62951754
40 1.01190207 1.31627809
41 -1.10382891 1.01190207
42 4.86838289 -1.10382891
43 -0.70893485 4.86838289
44 -1.76459395 -0.70893485
45 -4.49971113 -1.76459395
46 -1.52345377 -4.49971113
47 0.10745381 -1.52345377
48 0.77145987 0.10745381
49 -1.13161711 0.77145987
50 1.34553659 -1.13161711
51 -0.30517736 1.34553659
52 -2.25316437 -0.30517736
53 0.98285940 -2.25316437
54 -5.27050204 0.98285940
55 -1.50129823 -5.27050204
56 0.22112580 -1.50129823
57 1.70674347 0.22112580
58 0.05603978 1.70674347
59 0.79520251 0.05603978
60 -2.05281433 0.79520251
61 0.85263233 -2.05281433
62 0.95605935 0.85263233
63 -0.66380899 0.95605935
64 0.86149576 -0.66380899
65 2.38562874 0.86149576
66 2.66902100 2.38562874
67 3.90463506 2.66902100
68 -3.64963528 3.90463506
69 1.38562874 -3.64963528
70 -5.06226646 1.38562874
71 -0.53615718 -5.06226646
72 3.07583589 -0.53615718
73 2.99920891 3.07583589
74 1.33055835 2.99920891
75 1.15177514 1.33055835
76 0.56017714 1.15177514
77 0.86149576 0.56017714
78 -0.71376298 0.86149576
79 0.87478787 -0.71376298
80 1.63011649 0.87478787
81 5.11592805 1.63011649
82 0.87242844 5.11592805
83 0.05445267 0.87242844
84 0.17533416 0.05445267
85 0.97980200 0.17533416
86 -1.01025347 0.97980200
87 1.10745381 -1.01025347
88 1.62164226 1.10745381
89 0.11532909 1.62164226
90 -2.57083250 0.11532909
91 1.64222095 -2.57083250
92 0.94177909 1.64222095
93 0.14694701 0.94177909
94 -1.41303307 0.14694701
95 0.25580112 -1.41303307
96 0.55071476 0.25580112
97 0.74318952 0.55071476
98 1.04657739 0.74318952
99 -0.25157727 1.04657739
100 0.42453324 -0.25157727
101 1.03870212 0.42453324
102 4.70369632 1.03870212
103 0.63493437 4.70369632
104 1.21935507 0.63493437
105 -2.41865547 1.21935507
106 1.40719558 -2.41865547
107 0.50876311 1.40719558
108 1.18222129 0.50876311
109 -4.28988844 1.18222129
110 1.51279841 -4.28988844
111 1.35194156 1.51279841
112 2.37233663 1.35194156
113 -1.27837732 2.37233663
114 -3.46662290 -1.27837732
115 2.93626324 -3.46662290
116 -0.83894609 2.93626324
117 -0.93203939 -0.83894609
118 -1.92563441 -0.93203939
119 0.39932030 -1.92563441
120 -1.86574614 0.39932030
121 1.17227676 -1.86574614
122 -2.57387965 1.17227676
123 0.57751481 -2.57387965
124 0.05544082 0.57751481
125 1.37715451 0.05544082
126 -0.30034923 1.37715451
127 0.59021822 -0.30034923
128 0.09170325 0.59021822
129 -3.19692221 0.09170325
130 1.74683563 -3.19692221
131 -3.69642532 1.74683563
132 0.12961961 -3.69642532
133 -1.56236853 0.12961961
134 -0.88407195 -1.56236853
135 0.11385879 -0.88407195
136 -0.86980195 0.11385879
137 0.44404671 -0.86980195
138 -3.49507687 0.44404671
139 -3.20479749 -3.49507687
140 -6.07496987 -3.20479749
141 1.17533416 -6.07496987
142 1.05121165 1.17533416
143 2.55534901 1.05121165
144 1.71697818 2.55534901
145 -5.97440637 1.71697818
146 3.15482229 -5.97440637
147 -1.77522810 3.15482229
148 1.71216030 -1.77522810
149 -1.75148546 1.71216030
150 -5.54866671 -1.75148546
151 -1.18263169 -5.54866671
152 1.42042087 -1.18263169
153 0.88189083 1.42042087
154 -1.25522337 0.88189083
155 -4.91934623 -1.25522337
156 1.64222095 -4.91934623
157 -1.72222696 1.64222095
158 0.09170325 -1.72222696
159 0.87883342 0.09170325
160 -2.75690229 0.87883342
161 -2.02660298 -2.75690229
162 NA -2.02660298
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.21166341 -3.02177485
[2,] 4.55534901 0.21166341
[3,] 0.89035481 4.55534901
[4,] -1.05969121 0.89035481
[5,] -2.29148580 -1.05969121
[6,] 3.19432575 -2.29148580
[7,] -0.02660298 3.19432575
[8,] -1.30576606 -0.02660298
[9,] -0.37205931 -1.30576606
[10,] 0.82583228 -0.37205931
[11,] 0.67689627 0.82583228
[12,] 0.92039588 0.67689627
[13,] 0.29047645 0.92039588
[14,] 2.10904092 0.29047645
[15,] 0.72162268 2.10904092
[16,] 0.10499536 0.72162268
[17,] 4.43399563 0.10499536
[18,] 2.75325086 4.43399563
[19,] 1.16587178 2.75325086
[20,] 1.74683563 1.16587178
[21,] 1.34247918 1.74683563
[22,] 3.26526351 1.34247918
[23,] 0.56963953 3.26526351
[24,] 1.72486371 0.56963953
[25,] 0.94983800 1.72486371
[26,] 1.08136953 0.94983800
[27,] 0.13425386 1.08136953
[28,] 1.09011615 0.13425386
[29,] -0.54020273 1.09011615
[30,] 0.29046619 -0.54020273
[31,] -1.55290614 0.29046619
[32,] -1.54384884 -1.55290614
[33,] 1.01594762 -1.54384884
[34,] -0.59221572 1.01594762
[35,] -7.25316437 -0.59221572
[36,] -3.85728216 -7.25316437
[37,] -0.82724109 -3.85728216
[38,] 1.62951754 -0.82724109
[39,] 1.31627809 1.62951754
[40,] 1.01190207 1.31627809
[41,] -1.10382891 1.01190207
[42,] 4.86838289 -1.10382891
[43,] -0.70893485 4.86838289
[44,] -1.76459395 -0.70893485
[45,] -4.49971113 -1.76459395
[46,] -1.52345377 -4.49971113
[47,] 0.10745381 -1.52345377
[48,] 0.77145987 0.10745381
[49,] -1.13161711 0.77145987
[50,] 1.34553659 -1.13161711
[51,] -0.30517736 1.34553659
[52,] -2.25316437 -0.30517736
[53,] 0.98285940 -2.25316437
[54,] -5.27050204 0.98285940
[55,] -1.50129823 -5.27050204
[56,] 0.22112580 -1.50129823
[57,] 1.70674347 0.22112580
[58,] 0.05603978 1.70674347
[59,] 0.79520251 0.05603978
[60,] -2.05281433 0.79520251
[61,] 0.85263233 -2.05281433
[62,] 0.95605935 0.85263233
[63,] -0.66380899 0.95605935
[64,] 0.86149576 -0.66380899
[65,] 2.38562874 0.86149576
[66,] 2.66902100 2.38562874
[67,] 3.90463506 2.66902100
[68,] -3.64963528 3.90463506
[69,] 1.38562874 -3.64963528
[70,] -5.06226646 1.38562874
[71,] -0.53615718 -5.06226646
[72,] 3.07583589 -0.53615718
[73,] 2.99920891 3.07583589
[74,] 1.33055835 2.99920891
[75,] 1.15177514 1.33055835
[76,] 0.56017714 1.15177514
[77,] 0.86149576 0.56017714
[78,] -0.71376298 0.86149576
[79,] 0.87478787 -0.71376298
[80,] 1.63011649 0.87478787
[81,] 5.11592805 1.63011649
[82,] 0.87242844 5.11592805
[83,] 0.05445267 0.87242844
[84,] 0.17533416 0.05445267
[85,] 0.97980200 0.17533416
[86,] -1.01025347 0.97980200
[87,] 1.10745381 -1.01025347
[88,] 1.62164226 1.10745381
[89,] 0.11532909 1.62164226
[90,] -2.57083250 0.11532909
[91,] 1.64222095 -2.57083250
[92,] 0.94177909 1.64222095
[93,] 0.14694701 0.94177909
[94,] -1.41303307 0.14694701
[95,] 0.25580112 -1.41303307
[96,] 0.55071476 0.25580112
[97,] 0.74318952 0.55071476
[98,] 1.04657739 0.74318952
[99,] -0.25157727 1.04657739
[100,] 0.42453324 -0.25157727
[101,] 1.03870212 0.42453324
[102,] 4.70369632 1.03870212
[103,] 0.63493437 4.70369632
[104,] 1.21935507 0.63493437
[105,] -2.41865547 1.21935507
[106,] 1.40719558 -2.41865547
[107,] 0.50876311 1.40719558
[108,] 1.18222129 0.50876311
[109,] -4.28988844 1.18222129
[110,] 1.51279841 -4.28988844
[111,] 1.35194156 1.51279841
[112,] 2.37233663 1.35194156
[113,] -1.27837732 2.37233663
[114,] -3.46662290 -1.27837732
[115,] 2.93626324 -3.46662290
[116,] -0.83894609 2.93626324
[117,] -0.93203939 -0.83894609
[118,] -1.92563441 -0.93203939
[119,] 0.39932030 -1.92563441
[120,] -1.86574614 0.39932030
[121,] 1.17227676 -1.86574614
[122,] -2.57387965 1.17227676
[123,] 0.57751481 -2.57387965
[124,] 0.05544082 0.57751481
[125,] 1.37715451 0.05544082
[126,] -0.30034923 1.37715451
[127,] 0.59021822 -0.30034923
[128,] 0.09170325 0.59021822
[129,] -3.19692221 0.09170325
[130,] 1.74683563 -3.19692221
[131,] -3.69642532 1.74683563
[132,] 0.12961961 -3.69642532
[133,] -1.56236853 0.12961961
[134,] -0.88407195 -1.56236853
[135,] 0.11385879 -0.88407195
[136,] -0.86980195 0.11385879
[137,] 0.44404671 -0.86980195
[138,] -3.49507687 0.44404671
[139,] -3.20479749 -3.49507687
[140,] -6.07496987 -3.20479749
[141,] 1.17533416 -6.07496987
[142,] 1.05121165 1.17533416
[143,] 2.55534901 1.05121165
[144,] 1.71697818 2.55534901
[145,] -5.97440637 1.71697818
[146,] 3.15482229 -5.97440637
[147,] -1.77522810 3.15482229
[148,] 1.71216030 -1.77522810
[149,] -1.75148546 1.71216030
[150,] -5.54866671 -1.75148546
[151,] -1.18263169 -5.54866671
[152,] 1.42042087 -1.18263169
[153,] 0.88189083 1.42042087
[154,] -1.25522337 0.88189083
[155,] -4.91934623 -1.25522337
[156,] 1.64222095 -4.91934623
[157,] -1.72222696 1.64222095
[158,] 0.09170325 -1.72222696
[159,] 0.87883342 0.09170325
[160,] -2.75690229 0.87883342
[161,] -2.02660298 -2.75690229
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.21166341 -3.02177485
2 4.55534901 0.21166341
3 0.89035481 4.55534901
4 -1.05969121 0.89035481
5 -2.29148580 -1.05969121
6 3.19432575 -2.29148580
7 -0.02660298 3.19432575
8 -1.30576606 -0.02660298
9 -0.37205931 -1.30576606
10 0.82583228 -0.37205931
11 0.67689627 0.82583228
12 0.92039588 0.67689627
13 0.29047645 0.92039588
14 2.10904092 0.29047645
15 0.72162268 2.10904092
16 0.10499536 0.72162268
17 4.43399563 0.10499536
18 2.75325086 4.43399563
19 1.16587178 2.75325086
20 1.74683563 1.16587178
21 1.34247918 1.74683563
22 3.26526351 1.34247918
23 0.56963953 3.26526351
24 1.72486371 0.56963953
25 0.94983800 1.72486371
26 1.08136953 0.94983800
27 0.13425386 1.08136953
28 1.09011615 0.13425386
29 -0.54020273 1.09011615
30 0.29046619 -0.54020273
31 -1.55290614 0.29046619
32 -1.54384884 -1.55290614
33 1.01594762 -1.54384884
34 -0.59221572 1.01594762
35 -7.25316437 -0.59221572
36 -3.85728216 -7.25316437
37 -0.82724109 -3.85728216
38 1.62951754 -0.82724109
39 1.31627809 1.62951754
40 1.01190207 1.31627809
41 -1.10382891 1.01190207
42 4.86838289 -1.10382891
43 -0.70893485 4.86838289
44 -1.76459395 -0.70893485
45 -4.49971113 -1.76459395
46 -1.52345377 -4.49971113
47 0.10745381 -1.52345377
48 0.77145987 0.10745381
49 -1.13161711 0.77145987
50 1.34553659 -1.13161711
51 -0.30517736 1.34553659
52 -2.25316437 -0.30517736
53 0.98285940 -2.25316437
54 -5.27050204 0.98285940
55 -1.50129823 -5.27050204
56 0.22112580 -1.50129823
57 1.70674347 0.22112580
58 0.05603978 1.70674347
59 0.79520251 0.05603978
60 -2.05281433 0.79520251
61 0.85263233 -2.05281433
62 0.95605935 0.85263233
63 -0.66380899 0.95605935
64 0.86149576 -0.66380899
65 2.38562874 0.86149576
66 2.66902100 2.38562874
67 3.90463506 2.66902100
68 -3.64963528 3.90463506
69 1.38562874 -3.64963528
70 -5.06226646 1.38562874
71 -0.53615718 -5.06226646
72 3.07583589 -0.53615718
73 2.99920891 3.07583589
74 1.33055835 2.99920891
75 1.15177514 1.33055835
76 0.56017714 1.15177514
77 0.86149576 0.56017714
78 -0.71376298 0.86149576
79 0.87478787 -0.71376298
80 1.63011649 0.87478787
81 5.11592805 1.63011649
82 0.87242844 5.11592805
83 0.05445267 0.87242844
84 0.17533416 0.05445267
85 0.97980200 0.17533416
86 -1.01025347 0.97980200
87 1.10745381 -1.01025347
88 1.62164226 1.10745381
89 0.11532909 1.62164226
90 -2.57083250 0.11532909
91 1.64222095 -2.57083250
92 0.94177909 1.64222095
93 0.14694701 0.94177909
94 -1.41303307 0.14694701
95 0.25580112 -1.41303307
96 0.55071476 0.25580112
97 0.74318952 0.55071476
98 1.04657739 0.74318952
99 -0.25157727 1.04657739
100 0.42453324 -0.25157727
101 1.03870212 0.42453324
102 4.70369632 1.03870212
103 0.63493437 4.70369632
104 1.21935507 0.63493437
105 -2.41865547 1.21935507
106 1.40719558 -2.41865547
107 0.50876311 1.40719558
108 1.18222129 0.50876311
109 -4.28988844 1.18222129
110 1.51279841 -4.28988844
111 1.35194156 1.51279841
112 2.37233663 1.35194156
113 -1.27837732 2.37233663
114 -3.46662290 -1.27837732
115 2.93626324 -3.46662290
116 -0.83894609 2.93626324
117 -0.93203939 -0.83894609
118 -1.92563441 -0.93203939
119 0.39932030 -1.92563441
120 -1.86574614 0.39932030
121 1.17227676 -1.86574614
122 -2.57387965 1.17227676
123 0.57751481 -2.57387965
124 0.05544082 0.57751481
125 1.37715451 0.05544082
126 -0.30034923 1.37715451
127 0.59021822 -0.30034923
128 0.09170325 0.59021822
129 -3.19692221 0.09170325
130 1.74683563 -3.19692221
131 -3.69642532 1.74683563
132 0.12961961 -3.69642532
133 -1.56236853 0.12961961
134 -0.88407195 -1.56236853
135 0.11385879 -0.88407195
136 -0.86980195 0.11385879
137 0.44404671 -0.86980195
138 -3.49507687 0.44404671
139 -3.20479749 -3.49507687
140 -6.07496987 -3.20479749
141 1.17533416 -6.07496987
142 1.05121165 1.17533416
143 2.55534901 1.05121165
144 1.71697818 2.55534901
145 -5.97440637 1.71697818
146 3.15482229 -5.97440637
147 -1.77522810 3.15482229
148 1.71216030 -1.77522810
149 -1.75148546 1.71216030
150 -5.54866671 -1.75148546
151 -1.18263169 -5.54866671
152 1.42042087 -1.18263169
153 0.88189083 1.42042087
154 -1.25522337 0.88189083
155 -4.91934623 -1.25522337
156 1.64222095 -4.91934623
157 -1.72222696 1.64222095
158 0.09170325 -1.72222696
159 0.87883342 0.09170325
160 -2.75690229 0.87883342
161 -2.02660298 -2.75690229
> 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/7h5ux1352126050.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/880151352126050.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ubfd1352126050.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10csbt1352126050.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h0oi1352126050.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/12l4ea1352126050.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/13sye01352126050.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/142s861352126050.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/15rrlu1352126050.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/16q2at1352126050.tab")
+ }
>
> try(system("convert tmp/1b5sd1352126050.ps tmp/1b5sd1352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/2sphs1352126050.ps tmp/2sphs1352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/38v511352126050.ps tmp/38v511352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hkn11352126050.ps tmp/4hkn11352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/5htz11352126050.ps tmp/5htz11352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/68wmr1352126050.ps tmp/68wmr1352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/7h5ux1352126050.ps tmp/7h5ux1352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/880151352126050.ps tmp/880151352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ubfd1352126050.ps tmp/9ubfd1352126050.png",intern=TRUE))
character(0)
> try(system("convert tmp/10csbt1352126050.ps tmp/10csbt1352126050.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.350 1.227 9.565