R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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.
Natural language support but running in an English locale
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(14
+ ,12
+ ,41
+ ,18
+ ,11
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+ ,13
+ ,9
+ ,34
+ ,12
+ ,18
+ ,32
+ ,13
+ ,16
+ ,34)
+ ,dim=c(3
+ ,162)
+ ,dimnames=list(c('Happ'
+ ,'Depr'
+ ,'Conn')
+ ,1:162))
> y <- array(NA,dim=c(3,162),dimnames=list(c('Happ','Depr','Conn'),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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
Conn Happ Depr
1 41 14 12
2 39 18 11
3 30 11 14
4 31 12 12
5 34 16 21
6 35 18 12
7 39 14 22
8 34 14 11
9 36 15 10
10 37 15 13
11 38 17 10
12 36 19 8
13 38 10 15
14 39 16 14
15 33 18 10
16 32 14 14
17 36 14 14
18 38 17 11
19 39 14 10
20 32 16 13
21 32 18 7
22 31 11 14
23 39 14 12
24 37 12 14
25 39 17 11
26 41 9 9
27 36 16 11
28 33 14 15
29 33 15 14
30 34 11 13
31 31 16 9
32 27 13 15
33 37 17 10
34 34 15 11
35 34 14 13
36 32 16 8
37 29 9 20
38 36 15 12
39 29 17 10
40 35 13 10
41 37 15 9
42 34 16 14
43 38 16 8
44 35 12 14
45 38 12 11
46 37 11 13
47 38 15 9
48 33 15 11
49 36 17 15
50 38 13 11
51 32 16 10
52 32 14 14
53 32 11 18
54 34 12 14
55 32 12 11
56 37 15 12
57 39 16 13
58 29 15 9
59 37 12 10
60 35 12 15
61 30 8 20
62 38 13 12
63 34 11 12
64 31 14 14
65 34 15 13
66 35 10 11
67 36 11 17
68 30 12 12
69 39 15 13
70 35 15 14
71 38 14 13
72 31 16 15
73 34 15 13
74 38 15 10
75 34 13 11
76 39 12 19
77 37 17 13
78 34 13 17
79 28 15 13
80 37 13 9
81 33 15 11
82 37 16 10
83 35 15 9
84 37 16 12
85 32 15 12
86 33 14 13
87 38 15 13
88 33 14 12
89 29 13 15
90 33 7 22
91 31 17 13
92 36 13 15
93 35 15 13
94 32 14 15
95 29 13 10
96 39 16 11
97 37 12 16
98 35 14 11
99 37 17 11
100 32 15 10
101 38 17 10
102 37 12 16
103 36 16 12
104 32 11 11
105 33 15 16
106 40 9 19
107 38 16 11
108 41 15 16
109 36 10 15
110 43 10 24
111 30 15 14
112 31 11 15
113 32 13 11
114 32 14 15
115 37 18 12
116 37 16 10
117 33 14 14
118 34 14 13
119 33 14 9
120 38 14 15
121 33 12 15
122 31 14 14
123 38 15 11
124 37 15 8
125 33 15 11
126 31 13 11
127 39 17 8
128 44 17 10
129 33 19 11
130 35 15 13
131 32 13 11
132 28 9 20
133 40 15 10
134 27 15 15
135 37 15 12
136 32 16 14
137 28 11 23
138 34 14 14
139 30 11 16
140 35 15 11
141 31 13 12
142 32 15 10
143 30 16 14
144 30 14 12
145 31 15 12
146 40 16 11
147 32 16 12
148 36 11 13
149 32 12 11
150 35 9 19
151 38 16 12
152 42 13 17
153 34 16 9
154 35 12 12
155 35 9 19
156 33 13 18
157 36 13 15
158 32 14 14
159 33 19 11
160 34 13 9
161 32 12 18
162 34 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Happ Depr
33.23247 0.15847 -0.06451
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.6419 -2.5479 -0.1664 2.4552 9.7311
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.23247 2.82221 11.775 <2e-16 ***
Happ 0.15847 0.13491 1.175 0.242
Depr -0.06451 0.09961 -0.648 0.518
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.357 on 159 degrees of freedom
Multiple R-squared: 0.02294, Adjusted R-squared: 0.01065
F-statistic: 1.867 on 2 and 159 DF, p-value: 0.1580
> 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,] 0.82444859 0.35110283 0.17555141
[2,] 0.87515296 0.24969408 0.12484704
[3,] 0.79597186 0.40805628 0.20402814
[4,] 0.70173252 0.59653497 0.29826748
[5,] 0.61101754 0.77796493 0.38898246
[6,] 0.51174928 0.97650144 0.48825072
[7,] 0.45441591 0.90883182 0.54558409
[8,] 0.53093559 0.93812882 0.46906441
[9,] 0.49124288 0.98248575 0.50875712
[10,] 0.51058527 0.97882946 0.48941473
[11,] 0.51955047 0.96089906 0.48044953
[12,] 0.44016430 0.88032861 0.55983570
[13,] 0.38694470 0.77388939 0.61305530
[14,] 0.40759155 0.81518311 0.59240845
[15,] 0.44622934 0.89245868 0.55377066
[16,] 0.47104527 0.94209055 0.52895473
[17,] 0.47944207 0.95888415 0.52055793
[18,] 0.50478375 0.99043249 0.49521625
[19,] 0.46386908 0.92773815 0.53613092
[20,] 0.45351137 0.90702273 0.54648863
[21,] 0.60165989 0.79668022 0.39834011
[22,] 0.54026714 0.91946572 0.45973286
[23,] 0.51559542 0.96880916 0.48440458
[24,] 0.49006960 0.98013919 0.50993040
[25,] 0.44174461 0.88348922 0.55825539
[26,] 0.50054267 0.99891466 0.49945733
[27,] 0.74574885 0.50850230 0.25425115
[28,] 0.70601865 0.58796270 0.29398135
[29,] 0.66325974 0.67348053 0.33674026
[30,] 0.61572946 0.76854108 0.38427054
[31,] 0.61751862 0.76496275 0.38248138
[32,] 0.66354596 0.67290809 0.33645404
[33,] 0.61648233 0.76703534 0.38351767
[34,] 0.73646055 0.52707890 0.26353945
[35,] 0.69142569 0.61714861 0.30857431
[36,] 0.65699168 0.68601665 0.34300832
[37,] 0.61092687 0.77814625 0.38907313
[38,] 0.58816741 0.82366519 0.41183259
[39,] 0.53874021 0.92251957 0.46125979
[40,] 0.53397292 0.93205417 0.46602708
[41,] 0.51049886 0.97900227 0.48950114
[42,] 0.49060833 0.98121666 0.50939167
[43,] 0.46136780 0.92273559 0.53863220
[44,] 0.41757146 0.83514293 0.58242854
[45,] 0.40910397 0.81820794 0.59089603
[46,] 0.40909394 0.81818788 0.59090606
[47,] 0.39236873 0.78473746 0.60763127
[48,] 0.36109868 0.72219736 0.63890132
[49,] 0.31741792 0.63483584 0.68258208
[50,] 0.30462583 0.60925167 0.69537417
[51,] 0.27875907 0.55751814 0.72124093
[52,] 0.29687197 0.59374394 0.70312803
[53,] 0.40477384 0.80954768 0.59522616
[54,] 0.38115914 0.76231829 0.61884086
[55,] 0.33879011 0.67758021 0.66120989
[56,] 0.33552889 0.67105777 0.66447111
[57,] 0.33597641 0.67195281 0.66402359
[58,] 0.29531462 0.59062924 0.70468538
[59,] 0.30054954 0.60109907 0.69945046
[60,] 0.26326633 0.52653266 0.73673367
[61,] 0.22973667 0.45947333 0.77026333
[62,] 0.20984817 0.41969634 0.79015183
[63,] 0.23549917 0.47099833 0.76450083
[64,] 0.25576972 0.51153945 0.74423028
[65,] 0.22024447 0.44048893 0.77975553
[66,] 0.22000371 0.44000743 0.77999629
[67,] 0.23033649 0.46067299 0.76966351
[68,] 0.19874040 0.39748081 0.80125960
[69,] 0.19224731 0.38449462 0.80775269
[70,] 0.16404405 0.32808810 0.83595595
[71,] 0.20369016 0.40738032 0.79630984
[72,] 0.18169328 0.36338655 0.81830672
[73,] 0.15328038 0.30656077 0.84671962
[74,] 0.25105641 0.50211282 0.74894359
[75,] 0.23403558 0.46807116 0.76596442
[76,] 0.21101164 0.42202328 0.78898836
[77,] 0.18928592 0.37857184 0.81071408
[78,] 0.16067608 0.32135216 0.83932392
[79,] 0.14323900 0.28647799 0.85676100
[80,] 0.13572689 0.27145379 0.86427311
[81,] 0.11740366 0.23480731 0.88259634
[82,] 0.11546866 0.23093731 0.88453134
[83,] 0.09944869 0.19889738 0.90055131
[84,] 0.13129550 0.26259101 0.86870450
[85,] 0.10861578 0.21723156 0.89138422
[86,] 0.11966406 0.23932811 0.88033594
[87,] 0.10341309 0.20682618 0.89658691
[88,] 0.08436290 0.16872580 0.91563710
[89,] 0.07655539 0.15311078 0.92344461
[90,] 0.10665713 0.21331427 0.89334287
[91,] 0.11399269 0.22798538 0.88600731
[92,] 0.10818844 0.21637687 0.89181156
[93,] 0.08857807 0.17715615 0.91142193
[94,] 0.07583573 0.15167146 0.92416427
[95,] 0.07094623 0.14189247 0.92905377
[96,] 0.06565441 0.13130881 0.93434559
[97,] 0.06175740 0.12351480 0.93824260
[98,] 0.04992604 0.09985208 0.95007396
[99,] 0.04276386 0.08552772 0.95723614
[100,] 0.03500166 0.07000331 0.96499834
[101,] 0.06838490 0.13676979 0.93161510
[102,] 0.06477096 0.12954191 0.93522904
[103,] 0.11219124 0.22438248 0.88780876
[104,] 0.10206459 0.20412918 0.89793541
[105,] 0.42620890 0.85241780 0.57379110
[106,] 0.45882322 0.91764645 0.54117678
[107,] 0.43725064 0.87450128 0.56274936
[108,] 0.41544931 0.83089861 0.58455069
[109,] 0.38503327 0.77006654 0.61496673
[110,] 0.35246572 0.70493145 0.64753428
[111,] 0.32009855 0.64019709 0.67990145
[112,] 0.28151631 0.56303261 0.71848369
[113,] 0.24087937 0.48175875 0.75912063
[114,] 0.21745654 0.43491308 0.78254346
[115,] 0.23951891 0.47903782 0.76048109
[116,] 0.20341782 0.40683564 0.79658218
[117,] 0.19581009 0.39162017 0.80418991
[118,] 0.19077758 0.38155515 0.80922242
[119,] 0.16355271 0.32710543 0.83644729
[120,] 0.14016008 0.28032016 0.85983992
[121,] 0.14368526 0.28737051 0.85631474
[122,] 0.14064991 0.28129983 0.85935009
[123,] 0.41476847 0.82953694 0.58523153
[124,] 0.37055965 0.74111930 0.62944035
[125,] 0.32450777 0.64901554 0.67549223
[126,] 0.30053488 0.60106977 0.69946512
[127,] 0.34802352 0.69604703 0.65197648
[128,] 0.44612666 0.89225331 0.55387334
[129,] 0.61305220 0.77389561 0.38694780
[130,] 0.60477079 0.79045842 0.39522921
[131,] 0.55826980 0.88346039 0.44173020
[132,] 0.67572558 0.64854884 0.32427442
[133,] 0.61298329 0.77403342 0.38701671
[134,] 0.65252256 0.69495488 0.34747744
[135,] 0.59636386 0.80727227 0.40363614
[136,] 0.58160938 0.83678124 0.41839062
[137,] 0.53234702 0.93530596 0.46765298
[138,] 0.58551805 0.82896389 0.41448195
[139,] 0.64173182 0.71653636 0.35826818
[140,] 0.66314726 0.67370548 0.33685274
[141,] 0.79802485 0.40395029 0.20197515
[142,] 0.77275050 0.45449899 0.22724950
[143,] 0.71997514 0.56004971 0.28002486
[144,] 0.67631981 0.64736038 0.32368019
[145,] 0.58302805 0.83394390 0.41697195
[146,] 0.60508699 0.78982602 0.39491301
[147,] 0.99474922 0.01050156 0.00525078
[148,] 0.98544015 0.02911970 0.01455985
[149,] 0.96390308 0.07219383 0.03609692
[150,] 0.92383269 0.15233461 0.07616731
[151,] 0.82389023 0.35221955 0.17610977
> postscript(file="/var/www/html/freestat/rcomp/tmp/18z0r1290505458.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2j9zc1290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/freestat/rcomp/tmp/3j9zc1290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4j9zc1290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5j9zc1290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 162
Frequency = 1
1 2 3 4 5 6
6.32307579 3.62467074 -4.07247815 -3.35997859 -0.41324548 -0.31081544
7 8 9 10 11 12
4.96821395 -0.74143802 1.03557535 2.22911680 2.71862974 0.27265649
13 14 15 16 17 18
4.15050847 4.13515781 -2.43984307 -2.54789658 1.45210342 2.78314355
19 20 21 22 23 24
4.19404816 -2.92935601 -3.63338452 -3.07247815 4.32307579 2.76904904
25 26 27 28 29 30
3.78314355 6.92189839 0.94161636 -1.48338276 -1.70636938 -0.13699197
31 32 33 34 35 36
-4.18741127 -7.32490995 1.71862974 -0.89991083 -0.61241039 -3.25192509
37 38 39 40 41 42
-4.36844964 1.16460298 -6.28137026 0.35252097 1.97106154 -0.86484219
43 44 45 46 47 48
2.74807491 0.76904904 3.57550759 2.86300803 2.97106154 -1.89991083
49 50 51 52 53 54
1.04119881 3.41703478 -3.12289746 -2.54789658 -1.81442289 -0.23095096
55 56 57 58 59 60
-2.42449241 2.16460298 4.07064399 -6.02893846 2.51099378 0.83356286
61 62 63 64 65 66
-3.20997683 3.48154860 -0.20150578 -3.54789658 -0.77088320 0.89245321
67 68 69 70 71 72
2.12106330 -4.35997859 4.22911680 0.29363062 3.38758961 -3.80032838
73 74 75 76 77 78
-0.77088320 3.03557535 -0.58296522 5.09161812 1.91217118 -0.19588232
79 80 81 82 83 84
-6.77088320 2.28800715 -1.89991083 1.87710254 -0.02893846 2.00613018
85 86 87 88 89 90
-2.83539702 -1.61241039 3.22911680 -1.67692421 -5.32490995 0.07752361
91 92 93 94 95 96
-4.08782882 1.67509005 0.22911680 -2.48338276 -5.64747903 3.94161636
97 98 99 100 101 102
2.89807667 0.25856198 1.78314355 -2.96442465 2.71862974 2.89807667
103 104 105 106 107 108
1.00613018 -2.26601960 -1.57734175 6.56703654 2.94161636 6.42265825
109 110 111 112 113 114
2.15050847 9.73113281 -4.70636938 -3.00796434 -2.58296522 -2.48338276
115 116 117 118 119 120
1.68918456 1.87710254 -1.54789658 -0.61241039 -1.87046566 3.51661724
121 122 123 124 125 126
-1.16643714 -3.54789658 3.10008917 1.90654772 -1.89991083 -3.58296522
127 128 129 130 131 132
3.58960210 8.71862974 -2.53380206 0.22911680 -2.58296522 -5.36844964
133 134 135 136 137 138
5.03557535 -7.64185557 2.16460298 -2.86484219 -5.49185381 -0.54789658
139 140 141 142 143 144
-3.94345052 0.10008917 -3.51845140 -2.96442465 -4.86484219 -4.67692421
145 146 147 148 149 150
-3.83539702 4.94161636 -2.99386982 1.86300803 -2.42449241 1.56703654
151 152 153 154 155 156
3.00613018 7.80411768 -1.18741127 0.64002141 1.56703654 -1.13136851
157 158 159 160 161 162
1.67509005 -2.54789658 -2.53380206 -0.71199285 -1.97289570 -0.26039614
> postscript(file="/var/www/html/freestat/rcomp/tmp/6uihf1290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 6.32307579 NA
1 3.62467074 6.32307579
2 -4.07247815 3.62467074
3 -3.35997859 -4.07247815
4 -0.41324548 -3.35997859
5 -0.31081544 -0.41324548
6 4.96821395 -0.31081544
7 -0.74143802 4.96821395
8 1.03557535 -0.74143802
9 2.22911680 1.03557535
10 2.71862974 2.22911680
11 0.27265649 2.71862974
12 4.15050847 0.27265649
13 4.13515781 4.15050847
14 -2.43984307 4.13515781
15 -2.54789658 -2.43984307
16 1.45210342 -2.54789658
17 2.78314355 1.45210342
18 4.19404816 2.78314355
19 -2.92935601 4.19404816
20 -3.63338452 -2.92935601
21 -3.07247815 -3.63338452
22 4.32307579 -3.07247815
23 2.76904904 4.32307579
24 3.78314355 2.76904904
25 6.92189839 3.78314355
26 0.94161636 6.92189839
27 -1.48338276 0.94161636
28 -1.70636938 -1.48338276
29 -0.13699197 -1.70636938
30 -4.18741127 -0.13699197
31 -7.32490995 -4.18741127
32 1.71862974 -7.32490995
33 -0.89991083 1.71862974
34 -0.61241039 -0.89991083
35 -3.25192509 -0.61241039
36 -4.36844964 -3.25192509
37 1.16460298 -4.36844964
38 -6.28137026 1.16460298
39 0.35252097 -6.28137026
40 1.97106154 0.35252097
41 -0.86484219 1.97106154
42 2.74807491 -0.86484219
43 0.76904904 2.74807491
44 3.57550759 0.76904904
45 2.86300803 3.57550759
46 2.97106154 2.86300803
47 -1.89991083 2.97106154
48 1.04119881 -1.89991083
49 3.41703478 1.04119881
50 -3.12289746 3.41703478
51 -2.54789658 -3.12289746
52 -1.81442289 -2.54789658
53 -0.23095096 -1.81442289
54 -2.42449241 -0.23095096
55 2.16460298 -2.42449241
56 4.07064399 2.16460298
57 -6.02893846 4.07064399
58 2.51099378 -6.02893846
59 0.83356286 2.51099378
60 -3.20997683 0.83356286
61 3.48154860 -3.20997683
62 -0.20150578 3.48154860
63 -3.54789658 -0.20150578
64 -0.77088320 -3.54789658
65 0.89245321 -0.77088320
66 2.12106330 0.89245321
67 -4.35997859 2.12106330
68 4.22911680 -4.35997859
69 0.29363062 4.22911680
70 3.38758961 0.29363062
71 -3.80032838 3.38758961
72 -0.77088320 -3.80032838
73 3.03557535 -0.77088320
74 -0.58296522 3.03557535
75 5.09161812 -0.58296522
76 1.91217118 5.09161812
77 -0.19588232 1.91217118
78 -6.77088320 -0.19588232
79 2.28800715 -6.77088320
80 -1.89991083 2.28800715
81 1.87710254 -1.89991083
82 -0.02893846 1.87710254
83 2.00613018 -0.02893846
84 -2.83539702 2.00613018
85 -1.61241039 -2.83539702
86 3.22911680 -1.61241039
87 -1.67692421 3.22911680
88 -5.32490995 -1.67692421
89 0.07752361 -5.32490995
90 -4.08782882 0.07752361
91 1.67509005 -4.08782882
92 0.22911680 1.67509005
93 -2.48338276 0.22911680
94 -5.64747903 -2.48338276
95 3.94161636 -5.64747903
96 2.89807667 3.94161636
97 0.25856198 2.89807667
98 1.78314355 0.25856198
99 -2.96442465 1.78314355
100 2.71862974 -2.96442465
101 2.89807667 2.71862974
102 1.00613018 2.89807667
103 -2.26601960 1.00613018
104 -1.57734175 -2.26601960
105 6.56703654 -1.57734175
106 2.94161636 6.56703654
107 6.42265825 2.94161636
108 2.15050847 6.42265825
109 9.73113281 2.15050847
110 -4.70636938 9.73113281
111 -3.00796434 -4.70636938
112 -2.58296522 -3.00796434
113 -2.48338276 -2.58296522
114 1.68918456 -2.48338276
115 1.87710254 1.68918456
116 -1.54789658 1.87710254
117 -0.61241039 -1.54789658
118 -1.87046566 -0.61241039
119 3.51661724 -1.87046566
120 -1.16643714 3.51661724
121 -3.54789658 -1.16643714
122 3.10008917 -3.54789658
123 1.90654772 3.10008917
124 -1.89991083 1.90654772
125 -3.58296522 -1.89991083
126 3.58960210 -3.58296522
127 8.71862974 3.58960210
128 -2.53380206 8.71862974
129 0.22911680 -2.53380206
130 -2.58296522 0.22911680
131 -5.36844964 -2.58296522
132 5.03557535 -5.36844964
133 -7.64185557 5.03557535
134 2.16460298 -7.64185557
135 -2.86484219 2.16460298
136 -5.49185381 -2.86484219
137 -0.54789658 -5.49185381
138 -3.94345052 -0.54789658
139 0.10008917 -3.94345052
140 -3.51845140 0.10008917
141 -2.96442465 -3.51845140
142 -4.86484219 -2.96442465
143 -4.67692421 -4.86484219
144 -3.83539702 -4.67692421
145 4.94161636 -3.83539702
146 -2.99386982 4.94161636
147 1.86300803 -2.99386982
148 -2.42449241 1.86300803
149 1.56703654 -2.42449241
150 3.00613018 1.56703654
151 7.80411768 3.00613018
152 -1.18741127 7.80411768
153 0.64002141 -1.18741127
154 1.56703654 0.64002141
155 -1.13136851 1.56703654
156 1.67509005 -1.13136851
157 -2.54789658 1.67509005
158 -2.53380206 -2.54789658
159 -0.71199285 -2.53380206
160 -1.97289570 -0.71199285
161 -0.26039614 -1.97289570
162 NA -0.26039614
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.62467074 6.32307579
[2,] -4.07247815 3.62467074
[3,] -3.35997859 -4.07247815
[4,] -0.41324548 -3.35997859
[5,] -0.31081544 -0.41324548
[6,] 4.96821395 -0.31081544
[7,] -0.74143802 4.96821395
[8,] 1.03557535 -0.74143802
[9,] 2.22911680 1.03557535
[10,] 2.71862974 2.22911680
[11,] 0.27265649 2.71862974
[12,] 4.15050847 0.27265649
[13,] 4.13515781 4.15050847
[14,] -2.43984307 4.13515781
[15,] -2.54789658 -2.43984307
[16,] 1.45210342 -2.54789658
[17,] 2.78314355 1.45210342
[18,] 4.19404816 2.78314355
[19,] -2.92935601 4.19404816
[20,] -3.63338452 -2.92935601
[21,] -3.07247815 -3.63338452
[22,] 4.32307579 -3.07247815
[23,] 2.76904904 4.32307579
[24,] 3.78314355 2.76904904
[25,] 6.92189839 3.78314355
[26,] 0.94161636 6.92189839
[27,] -1.48338276 0.94161636
[28,] -1.70636938 -1.48338276
[29,] -0.13699197 -1.70636938
[30,] -4.18741127 -0.13699197
[31,] -7.32490995 -4.18741127
[32,] 1.71862974 -7.32490995
[33,] -0.89991083 1.71862974
[34,] -0.61241039 -0.89991083
[35,] -3.25192509 -0.61241039
[36,] -4.36844964 -3.25192509
[37,] 1.16460298 -4.36844964
[38,] -6.28137026 1.16460298
[39,] 0.35252097 -6.28137026
[40,] 1.97106154 0.35252097
[41,] -0.86484219 1.97106154
[42,] 2.74807491 -0.86484219
[43,] 0.76904904 2.74807491
[44,] 3.57550759 0.76904904
[45,] 2.86300803 3.57550759
[46,] 2.97106154 2.86300803
[47,] -1.89991083 2.97106154
[48,] 1.04119881 -1.89991083
[49,] 3.41703478 1.04119881
[50,] -3.12289746 3.41703478
[51,] -2.54789658 -3.12289746
[52,] -1.81442289 -2.54789658
[53,] -0.23095096 -1.81442289
[54,] -2.42449241 -0.23095096
[55,] 2.16460298 -2.42449241
[56,] 4.07064399 2.16460298
[57,] -6.02893846 4.07064399
[58,] 2.51099378 -6.02893846
[59,] 0.83356286 2.51099378
[60,] -3.20997683 0.83356286
[61,] 3.48154860 -3.20997683
[62,] -0.20150578 3.48154860
[63,] -3.54789658 -0.20150578
[64,] -0.77088320 -3.54789658
[65,] 0.89245321 -0.77088320
[66,] 2.12106330 0.89245321
[67,] -4.35997859 2.12106330
[68,] 4.22911680 -4.35997859
[69,] 0.29363062 4.22911680
[70,] 3.38758961 0.29363062
[71,] -3.80032838 3.38758961
[72,] -0.77088320 -3.80032838
[73,] 3.03557535 -0.77088320
[74,] -0.58296522 3.03557535
[75,] 5.09161812 -0.58296522
[76,] 1.91217118 5.09161812
[77,] -0.19588232 1.91217118
[78,] -6.77088320 -0.19588232
[79,] 2.28800715 -6.77088320
[80,] -1.89991083 2.28800715
[81,] 1.87710254 -1.89991083
[82,] -0.02893846 1.87710254
[83,] 2.00613018 -0.02893846
[84,] -2.83539702 2.00613018
[85,] -1.61241039 -2.83539702
[86,] 3.22911680 -1.61241039
[87,] -1.67692421 3.22911680
[88,] -5.32490995 -1.67692421
[89,] 0.07752361 -5.32490995
[90,] -4.08782882 0.07752361
[91,] 1.67509005 -4.08782882
[92,] 0.22911680 1.67509005
[93,] -2.48338276 0.22911680
[94,] -5.64747903 -2.48338276
[95,] 3.94161636 -5.64747903
[96,] 2.89807667 3.94161636
[97,] 0.25856198 2.89807667
[98,] 1.78314355 0.25856198
[99,] -2.96442465 1.78314355
[100,] 2.71862974 -2.96442465
[101,] 2.89807667 2.71862974
[102,] 1.00613018 2.89807667
[103,] -2.26601960 1.00613018
[104,] -1.57734175 -2.26601960
[105,] 6.56703654 -1.57734175
[106,] 2.94161636 6.56703654
[107,] 6.42265825 2.94161636
[108,] 2.15050847 6.42265825
[109,] 9.73113281 2.15050847
[110,] -4.70636938 9.73113281
[111,] -3.00796434 -4.70636938
[112,] -2.58296522 -3.00796434
[113,] -2.48338276 -2.58296522
[114,] 1.68918456 -2.48338276
[115,] 1.87710254 1.68918456
[116,] -1.54789658 1.87710254
[117,] -0.61241039 -1.54789658
[118,] -1.87046566 -0.61241039
[119,] 3.51661724 -1.87046566
[120,] -1.16643714 3.51661724
[121,] -3.54789658 -1.16643714
[122,] 3.10008917 -3.54789658
[123,] 1.90654772 3.10008917
[124,] -1.89991083 1.90654772
[125,] -3.58296522 -1.89991083
[126,] 3.58960210 -3.58296522
[127,] 8.71862974 3.58960210
[128,] -2.53380206 8.71862974
[129,] 0.22911680 -2.53380206
[130,] -2.58296522 0.22911680
[131,] -5.36844964 -2.58296522
[132,] 5.03557535 -5.36844964
[133,] -7.64185557 5.03557535
[134,] 2.16460298 -7.64185557
[135,] -2.86484219 2.16460298
[136,] -5.49185381 -2.86484219
[137,] -0.54789658 -5.49185381
[138,] -3.94345052 -0.54789658
[139,] 0.10008917 -3.94345052
[140,] -3.51845140 0.10008917
[141,] -2.96442465 -3.51845140
[142,] -4.86484219 -2.96442465
[143,] -4.67692421 -4.86484219
[144,] -3.83539702 -4.67692421
[145,] 4.94161636 -3.83539702
[146,] -2.99386982 4.94161636
[147,] 1.86300803 -2.99386982
[148,] -2.42449241 1.86300803
[149,] 1.56703654 -2.42449241
[150,] 3.00613018 1.56703654
[151,] 7.80411768 3.00613018
[152,] -1.18741127 7.80411768
[153,] 0.64002141 -1.18741127
[154,] 1.56703654 0.64002141
[155,] -1.13136851 1.56703654
[156,] 1.67509005 -1.13136851
[157,] -2.54789658 1.67509005
[158,] -2.53380206 -2.54789658
[159,] -0.71199285 -2.53380206
[160,] -1.97289570 -0.71199285
[161,] -0.26039614 -1.97289570
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.62467074 6.32307579
2 -4.07247815 3.62467074
3 -3.35997859 -4.07247815
4 -0.41324548 -3.35997859
5 -0.31081544 -0.41324548
6 4.96821395 -0.31081544
7 -0.74143802 4.96821395
8 1.03557535 -0.74143802
9 2.22911680 1.03557535
10 2.71862974 2.22911680
11 0.27265649 2.71862974
12 4.15050847 0.27265649
13 4.13515781 4.15050847
14 -2.43984307 4.13515781
15 -2.54789658 -2.43984307
16 1.45210342 -2.54789658
17 2.78314355 1.45210342
18 4.19404816 2.78314355
19 -2.92935601 4.19404816
20 -3.63338452 -2.92935601
21 -3.07247815 -3.63338452
22 4.32307579 -3.07247815
23 2.76904904 4.32307579
24 3.78314355 2.76904904
25 6.92189839 3.78314355
26 0.94161636 6.92189839
27 -1.48338276 0.94161636
28 -1.70636938 -1.48338276
29 -0.13699197 -1.70636938
30 -4.18741127 -0.13699197
31 -7.32490995 -4.18741127
32 1.71862974 -7.32490995
33 -0.89991083 1.71862974
34 -0.61241039 -0.89991083
35 -3.25192509 -0.61241039
36 -4.36844964 -3.25192509
37 1.16460298 -4.36844964
38 -6.28137026 1.16460298
39 0.35252097 -6.28137026
40 1.97106154 0.35252097
41 -0.86484219 1.97106154
42 2.74807491 -0.86484219
43 0.76904904 2.74807491
44 3.57550759 0.76904904
45 2.86300803 3.57550759
46 2.97106154 2.86300803
47 -1.89991083 2.97106154
48 1.04119881 -1.89991083
49 3.41703478 1.04119881
50 -3.12289746 3.41703478
51 -2.54789658 -3.12289746
52 -1.81442289 -2.54789658
53 -0.23095096 -1.81442289
54 -2.42449241 -0.23095096
55 2.16460298 -2.42449241
56 4.07064399 2.16460298
57 -6.02893846 4.07064399
58 2.51099378 -6.02893846
59 0.83356286 2.51099378
60 -3.20997683 0.83356286
61 3.48154860 -3.20997683
62 -0.20150578 3.48154860
63 -3.54789658 -0.20150578
64 -0.77088320 -3.54789658
65 0.89245321 -0.77088320
66 2.12106330 0.89245321
67 -4.35997859 2.12106330
68 4.22911680 -4.35997859
69 0.29363062 4.22911680
70 3.38758961 0.29363062
71 -3.80032838 3.38758961
72 -0.77088320 -3.80032838
73 3.03557535 -0.77088320
74 -0.58296522 3.03557535
75 5.09161812 -0.58296522
76 1.91217118 5.09161812
77 -0.19588232 1.91217118
78 -6.77088320 -0.19588232
79 2.28800715 -6.77088320
80 -1.89991083 2.28800715
81 1.87710254 -1.89991083
82 -0.02893846 1.87710254
83 2.00613018 -0.02893846
84 -2.83539702 2.00613018
85 -1.61241039 -2.83539702
86 3.22911680 -1.61241039
87 -1.67692421 3.22911680
88 -5.32490995 -1.67692421
89 0.07752361 -5.32490995
90 -4.08782882 0.07752361
91 1.67509005 -4.08782882
92 0.22911680 1.67509005
93 -2.48338276 0.22911680
94 -5.64747903 -2.48338276
95 3.94161636 -5.64747903
96 2.89807667 3.94161636
97 0.25856198 2.89807667
98 1.78314355 0.25856198
99 -2.96442465 1.78314355
100 2.71862974 -2.96442465
101 2.89807667 2.71862974
102 1.00613018 2.89807667
103 -2.26601960 1.00613018
104 -1.57734175 -2.26601960
105 6.56703654 -1.57734175
106 2.94161636 6.56703654
107 6.42265825 2.94161636
108 2.15050847 6.42265825
109 9.73113281 2.15050847
110 -4.70636938 9.73113281
111 -3.00796434 -4.70636938
112 -2.58296522 -3.00796434
113 -2.48338276 -2.58296522
114 1.68918456 -2.48338276
115 1.87710254 1.68918456
116 -1.54789658 1.87710254
117 -0.61241039 -1.54789658
118 -1.87046566 -0.61241039
119 3.51661724 -1.87046566
120 -1.16643714 3.51661724
121 -3.54789658 -1.16643714
122 3.10008917 -3.54789658
123 1.90654772 3.10008917
124 -1.89991083 1.90654772
125 -3.58296522 -1.89991083
126 3.58960210 -3.58296522
127 8.71862974 3.58960210
128 -2.53380206 8.71862974
129 0.22911680 -2.53380206
130 -2.58296522 0.22911680
131 -5.36844964 -2.58296522
132 5.03557535 -5.36844964
133 -7.64185557 5.03557535
134 2.16460298 -7.64185557
135 -2.86484219 2.16460298
136 -5.49185381 -2.86484219
137 -0.54789658 -5.49185381
138 -3.94345052 -0.54789658
139 0.10008917 -3.94345052
140 -3.51845140 0.10008917
141 -2.96442465 -3.51845140
142 -4.86484219 -2.96442465
143 -4.67692421 -4.86484219
144 -3.83539702 -4.67692421
145 4.94161636 -3.83539702
146 -2.99386982 4.94161636
147 1.86300803 -2.99386982
148 -2.42449241 1.86300803
149 1.56703654 -2.42449241
150 3.00613018 1.56703654
151 7.80411768 3.00613018
152 -1.18741127 7.80411768
153 0.64002141 -1.18741127
154 1.56703654 0.64002141
155 -1.13136851 1.56703654
156 1.67509005 -1.13136851
157 -2.54789658 1.67509005
158 -2.53380206 -2.54789658
159 -0.71199285 -2.53380206
160 -1.97289570 -0.71199285
161 -0.26039614 -1.97289570
> 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/html/freestat/rcomp/tmp/74ry01290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/84ry01290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9f1xl1290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10f1xl1290505458.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/freestat/rcomp/tmp/110jv91290505458.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/html/freestat/rcomp/tmp/12mkuw1290505458.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/html/freestat/rcomp/tmp/13alr81290505458.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/html/freestat/rcomp/tmp/143uqb1290505458.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/html/freestat/rcomp/tmp/15pc7z1290505458.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/html/freestat/rcomp/tmp/16lm4q1290505458.tab")
+ }
>
> try(system("convert tmp/18z0r1290505458.ps tmp/18z0r1290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/2j9zc1290505458.ps tmp/2j9zc1290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j9zc1290505458.ps tmp/3j9zc1290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j9zc1290505458.ps tmp/4j9zc1290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j9zc1290505458.ps tmp/5j9zc1290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uihf1290505458.ps tmp/6uihf1290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/74ry01290505458.ps tmp/74ry01290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/84ry01290505458.ps tmp/84ry01290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/9f1xl1290505458.ps tmp/9f1xl1290505458.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f1xl1290505458.ps tmp/10f1xl1290505458.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.567 2.705 7.461