http://www.yeast.ib.k.u-tokyo.ac.jp/SCMD/datasheet.php
Showing posts with label RLS. Show all posts
Showing posts with label RLS. Show all posts
Monday, February 6, 2023
Thursday, March 11, 2021
leaf nodes, branching nodes in tree-graph, and RLS estimation
in a tree-graph: leaf nodes have degree=1, branching nodes have degree >= 3, and continuous nodes have degree=2. So, we can tabulate the leaf nodes, branching nodes, their time-intervals to estimate RLS, --Hong
Thursday, November 30, 2017
RLS pairwise difference, P-value < 1/10K using ms02 permutation
RLS pairwise difference, P-value evaluation using ms02 permutation
H Qin
11/27/2017
rm(list=ls())
#setwd("~/github/0.network.aging.ms02/1.Fraser02")
setwd("/home/hqin/github/network.aging.configuration/1.Fraser02")
source("../network.r")
set.seed(2017)
debug = 1;
start_time = Sys.time();list.files(path="../data/")## [1] "ken-RLS-byORF.csv"
## [2] "SummaryRegressionHetHomFactorized2015Oct13.csv"
## [3] "unique_biogrid_ScePPI.csv"rls = read.csv("../data/ken-RLS-byORF.csv");
biogrid = read.csv("../data/unique_biogrid_ScePPI.csv");
fit = read.csv("../data/SummaryRegressionHetHomFactorized2015Oct13.csv")ppi = biogrid[, c("Systematic.Name.Interactor.A","Systematic.Name.Interactor.B")];
names(ppi) = c("ORF1", "ORF2" )#First, define a function to calculate V difference in pairs of proteins
diff.RLS = function( inpairs ) {
inpairs$rls1 = rls$avgLS[match( inpairs$ORF1, rls$ORF ) ];
inpairs$rls2 = rls$avgLS[match( inpairs$ORF2, rls$ORF ) ];
inpairs$essen1 = fit$essenflag[match(inpairs$ORF1, fit$orf)];
inpairs$essen2 = fit$essenflag[match(inpairs$ORF2, fit$orf)];
inpairs$rls1 = ifelse( inpairs$essen1=='essential', 0, inpairs$rls1);
inpairs$rls2 = ifelse( inpairs$essen2=='essential', 0, inpairs$rls2);
ret = mean( abs( inpairs$rls1 - inpairs$rls2 ), na.rm=T );
} # calculate the observed difference in RLS
diff.RLS.obs = diff.RLS ( ppi );
paste( "Observed deltaRLS = ", diff.RLS.obs); ## [1] "Observed deltaRLS = 12.759632586095"#permutation of pairs, and their difference in KaNsims = 10000; #number of permutations permutated.diff.RLS = numeric( Nsims ); #empty vector to store calculations library(foreach) library(doMC)
## Loading required package: iterators## Loading required package: parallelregisterDoMC(cores=8) #Intel i7 has 6 cores, Xeon E5-2603 @ridgeside has 8 cores
permutated.diff.RLS = foreach(i=1:Nsims) %dopar% {
new.pairs = ms02_singlerun(ppi ) #generate a new MS02 random network
new.pairs = new.pairs[,1:2] #reformating into two-columns
names(new.pairs) = c("ORF1", "ORF2")
diff.RLS( new.pairs );
}
p-value
permutated.diff.RLS = unlist(permutated.diff.RLS)
summary(permutated.diff.RLS)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 14.03 14.20 14.22 14.22 14.25 14.37sub = permutated.diff.RLS[ permutated.diff.RLS < diff.RLS.obs ]
paste("pvalue = ", length(sub)/Nsims)## [1] "pvalue = 0"hist(permutated.diff.RLS)
stop_time = Sys.time()
start_time;## [1] "2017-11-29 10:01:30 EST"stop_time;## [1] "2017-11-29 13:04:53 EST"paste( "running time = ", stop_time - start_time) ## [1] "running time = 3.05638415979015"Tuesday, December 6, 2016
time lapsed image analysis for RLS inference
image data: images produced by HYAA
Dang lab uses FIJI http://fiji.sc/http://fiji.sc/
In Dang lab, a person plays video of time lapsed images in IJ at a speed of 5 frames per second. Typically, each cell is counted twice.
Dang lab uses FIJI http://fiji.sc/http://fiji.sc/
In Dang lab, a person plays video of time lapsed images in IJ at a speed of 5 frames per second. Typically, each cell is counted twice.
Wednesday, July 6, 2016
*** Fit Qinlab strains with binomial aging model
Summary: 'n' are mostly 5-7 and 't0' are often large than average lifespan. A encouraging results.
The QIN-RLS data suggested that noisy system signal perfer Gompertz model, based on GG01 theory. Notice that CV measures distrubition of system signals and are different from white noises (errors)
Show the results
0.fit_qinlabrls.Rmd
h qin
July 6, 2016
rm(list=ls())
setwd("~/github/0.network.aging.prj.bmc/0a.rls.fitting")
library('flexsurv')## Warning: package 'flexsurv' was built under R version 3.2.4## Loading required package: survival## Warning: package 'survival' was built under R version 3.2.5source("lifespan.r")Fit with Flexsurv.
files = list.files(path="../qinlab_rls/", pattern="rls.tab")
report = data.frame(cbind(files))
report$R=NA; report$t0=NA; report$n=NA;
i=2
tb = read.table( paste("../qinlab_rls/",files[i],sep=''), sep="\t")
GompFlex = flexsurvreg(formula = Surv(tb[,1]) ~ 1, dist = 'gompertz')
WeibFlex = flexsurvreg(formula = Surv(tb[,1]) ~ 1, dist = 'weibull')
str(GompFlex)## List of 28
## $ call : language flexsurvreg(formula = Surv(tb[, 1]) ~ 1, dist = "gompertz")
## $ dlist :List of 6
## ..$ name : chr "gompertz"
## ..$ pars : chr [1:2] "shape" "rate"
## ..$ location : chr "rate"
## ..$ transforms :List of 2
## .. ..$ :function (x)
## .. ..$ :function (x, base = exp(1))
## ..$ inv.transforms:List of 2
## .. ..$ :function (x)
## .. ..$ :function (x)
## ..$ inits :function (t, mf, mml, aux)
## $ aux : NULL
## $ ncovs : int 0
## $ ncoveffs : int 0
## $ mx :List of 2
## ..$ rate : int(0)
## ..$ shape: NULL
## $ basepars : int [1:2] 1 2
## $ covpars : NULL
## $ AIC : num 213
## $ data :List of 3
## ..$ Y : num [1:30, 1:6] 10 10 11 14 15 15 16 22 24 25 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:30] "1" "2" "3" "4" ...
## .. .. ..$ : chr [1:6] "time" "status" "start" "stop" ...
## ..$ m :'data.frame': 30 obs. of 2 variables:
## .. ..$ Surv(tb[, 1]): Surv [1:30, 1:2] 10 10 11 14 15 15 16 22 24 25 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "time" "status"
## .. .. ..- attr(*, "type")= chr "right"
## .. ..$ (weights) : num [1:30] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 Surv(tb[, 1]) ~ 1
## .. .. .. ..- attr(*, "variables")= language list(Surv(tb[, 1]))
## .. .. .. ..- attr(*, "factors")= int(0)
## .. .. .. ..- attr(*, "term.labels")= chr(0)
## .. .. .. ..- attr(*, "order")= int(0)
## .. .. .. ..- attr(*, "intercept")= int 1
## .. .. .. ..- attr(*, "response")= int 1
## .. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. .. .. ..- attr(*, "predvars")= language list(Surv(tb[, 1]))
## .. .. .. ..- attr(*, "dataClasses")= Named chr "nmatrix.2"
## .. .. .. .. ..- attr(*, "names")= chr "Surv(tb[, 1])"
## .. ..- attr(*, "covnames")= chr(0)
## .. ..- attr(*, "covnames.orig")= chr(0)
## ..$ mml:List of 2
## .. ..$ rate : num [1:30, 1] 1 1 1 1 1 1 1 1 1 1 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:30] "1" "2" "3" "4" ...
## .. .. .. ..$ : chr "(Intercept)"
## .. .. ..- attr(*, "assign")= int 0
## .. ..$ shape: NULL
## $ datameans : num(0)
## $ N : int 30
## $ events : int 30
## $ trisk : num 810
## $ concat.formula:Class 'formula' length 3 Surv(tb[, 1]) ~ 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "covnames")= chr(0)
## .. ..- attr(*, "covnames.orig")= chr(0)
## $ all.formulae :List of 1
## ..$ rate:Class 'formula' length 3 Surv(tb[, 1]) ~ 1
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## $ dfns :List of 8
## ..$ p :function (q, shape, rate = 1, lower.tail = TRUE, log.p = FALSE)
## ..$ d :function (x, shape, rate = 1, log = FALSE)
## ..$ h :function (x, shape, rate = 1, log = FALSE)
## ..$ H :function (x, shape, rate = 1, log = FALSE)
## ..$ r :function (n, shape = 1, rate = 1)
## ..$ DLd :function (t, shape, rate)
## ..$ DLS :function (t, shape, rate)
## ..$ deriv: logi TRUE
## $ res : num [1:2, 1:4] 0.146413 0.001603 0.098752 0.000421 0.194074 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "shape" "rate"
## .. ..$ : chr [1:4] "est" "L95%" "U95%" "se"
## $ res.t : num [1:2, 1:4] 0.1464 -6.4361 0.0988 -7.7719 0.1941 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "shape" "rate"
## .. ..$ : chr [1:4] "est" "L95%" "U95%" "se"
## $ cov : num [1:2, 1:2] 0.000591 -0.015967 -0.015967 0.464473
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "shape" "rate"
## .. ..$ : chr [1:2] "shape" "rate"
## $ coefficients : Named num [1:2] 0.146 -6.436
## ..- attr(*, "names")= chr [1:2] "shape" "rate"
## $ npars : int 2
## $ fixedpars : NULL
## $ optpars : int [1:2] 1 2
## $ loglik : num -104
## $ logliki : num [1:30] -5.01 -5.01 -4.87 -4.46 -4.33 ...
## $ cl : num 0.95
## $ opt :List of 6
## ..$ par : Named num [1:2] 0.146 -6.436
## .. ..- attr(*, "names")= chr [1:2] "shape" "rate"
## ..$ value : num 104
## ..$ counts : Named int [1:2] 35 10
## .. ..- attr(*, "names")= chr [1:2] "function" "gradient"
## ..$ convergence: int 0
## ..$ message : NULL
## ..$ hessian : num [1:2, 1:2] 23564 810 810 30
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "shape" "rate"
## .. .. ..$ : chr [1:2] "shape" "rate"
## - attr(*, "class")= chr "flexsurvreg"GompFlex$res## est L95% U95% se
## shape 0.146412756 0.0987519623 0.194073550 0.024317178
## rate 0.001602638 0.0004214271 0.006094643 0.001092234GompFlex$res.t## est L95% U95% se
## shape 0.1464128 0.09875196 0.1940735 0.02431718
## rate -6.4361044 -7.77186373 -5.1003451 0.68152238GompFlex$opt$hessian## shape rate
## shape 23564.3361 810.0597
## rate 810.0597 30.0000str(WeibFlex)## List of 28
## $ call : language flexsurvreg(formula = Surv(tb[, 1]) ~ 1, dist = "weibull")
## $ dlist :List of 6
## ..$ name : chr "weibull.quiet"
## ..$ pars : chr [1:2] "shape" "scale"
## ..$ location : chr "scale"
## ..$ transforms :List of 2
## .. ..$ :function (x, base = exp(1))
## .. ..$ :function (x, base = exp(1))
## ..$ inv.transforms:List of 2
## .. ..$ :function (x)
## .. ..$ :function (x)
## ..$ inits :function (t, mf, mml, aux)
## $ aux : NULL
## $ ncovs : int 0
## $ ncoveffs : int 0
## $ mx :List of 2
## ..$ scale: int(0)
## ..$ shape: NULL
## $ basepars : int [1:2] 1 2
## $ covpars : NULL
## $ AIC : num 218
## $ data :List of 3
## ..$ Y : num [1:30, 1:6] 10 10 11 14 15 15 16 22 24 25 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:30] "1" "2" "3" "4" ...
## .. .. ..$ : chr [1:6] "time" "status" "start" "stop" ...
## ..$ m :'data.frame': 30 obs. of 2 variables:
## .. ..$ Surv(tb[, 1]): Surv [1:30, 1:2] 10 10 11 14 15 15 16 22 24 25 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : NULL
## .. .. .. ..$ : chr [1:2] "time" "status"
## .. .. ..- attr(*, "type")= chr "right"
## .. ..$ (weights) : num [1:30] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 Surv(tb[, 1]) ~ 1
## .. .. .. ..- attr(*, "variables")= language list(Surv(tb[, 1]))
## .. .. .. ..- attr(*, "factors")= int(0)
## .. .. .. ..- attr(*, "term.labels")= chr(0)
## .. .. .. ..- attr(*, "order")= int(0)
## .. .. .. ..- attr(*, "intercept")= int 1
## .. .. .. ..- attr(*, "response")= int 1
## .. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. .. .. ..- attr(*, "predvars")= language list(Surv(tb[, 1]))
## .. .. .. ..- attr(*, "dataClasses")= Named chr "nmatrix.2"
## .. .. .. .. ..- attr(*, "names")= chr "Surv(tb[, 1])"
## .. ..- attr(*, "covnames")= chr(0)
## .. ..- attr(*, "covnames.orig")= chr(0)
## ..$ mml:List of 2
## .. ..$ scale: num [1:30, 1] 1 1 1 1 1 1 1 1 1 1 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:30] "1" "2" "3" "4" ...
## .. .. .. ..$ : chr "(Intercept)"
## .. .. ..- attr(*, "assign")= int 0
## .. ..$ shape: NULL
## $ datameans : num(0)
## $ N : int 30
## $ events : int 30
## $ trisk : num 810
## $ concat.formula:Class 'formula' length 3 Surv(tb[, 1]) ~ 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "covnames")= chr(0)
## .. ..- attr(*, "covnames.orig")= chr(0)
## $ all.formulae :List of 1
## ..$ scale:Class 'formula' length 3 Surv(tb[, 1]) ~ 1
## .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## $ dfns :List of 8
## ..$ p :function (q, shape, scale = 1, lower.tail = TRUE, log.p = FALSE)
## ..$ d :function (x, shape, scale = 1, log = FALSE)
## ..$ h :function (x, ...)
## ..$ H :function (x, ...)
## ..$ r :function (n, shape, scale = 1)
## ..$ DLd :function (t, shape, scale)
## ..$ DLS :function (t, shape, scale)
## ..$ deriv: logi TRUE
## $ res : num [1:2, 1:4] 3.77 30 2.76 27.17 5.13 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "shape" "scale"
## .. ..$ : chr [1:4] "est" "L95%" "U95%" "se"
## $ res.t : num [1:2, 1:4] 1.33 3.4 1.02 3.3 1.64 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "shape" "scale"
## .. ..$ : chr [1:4] "est" "L95%" "U95%" "se"
## $ cov : num [1:2, 1:2] 0.02502 0.00225 0.00225 0.00255
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:2] "shape" "scale"
## .. ..$ : chr [1:2] "shape" "scale"
## $ coefficients : Named num [1:2] 1.33 3.4
## ..- attr(*, "names")= chr [1:2] "shape" "scale"
## $ npars : int 2
## $ fixedpars : NULL
## $ optpars : int [1:2] 1 2
## $ loglik : num -107
## $ logliki : Named num [1:30] -5.13 -5.13 -4.87 -4.24 -4.06 ...
## ..- attr(*, "names")= chr [1:30] "1" "2" "3" "4" ...
## $ cl : num 0.95
## $ opt :List of 6
## ..$ par : Named num [1:2] 1.33 3.4
## .. ..- attr(*, "names")= chr [1:2] "shape" "scale"
## ..$ value : num 107
## ..$ counts : Named int [1:2] 2 1
## .. ..- attr(*, "names")= chr [1:2] "function" "gradient"
## ..$ convergence: int 0
## ..$ message : NULL
## ..$ hessian : num [1:2, 1:2] 43.4 -38.3 -38.3 425.3
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "shape" "scale"
## .. .. ..$ : chr [1:2] "shape" "scale"
## - attr(*, "class")= chr "flexsurvreg"for( i in 1:length(report[,1])){
#for( i in 3:4){
tb = read.table( paste("../qinlab_rls/",files[i],sep=''), sep="\t")
GompFlex = flexsurvreg(formula = Surv(tb[,1]) ~ 1, dist = 'gompertz')
WeibFlex = flexsurvreg(formula = Surv(tb[,1]) ~ 1, dist = 'weibull')
report$avgLS[i] = mean(tb[,1])
report$stdLS[i] = sd(tb[,1])
report$CV[i] = report$stdLS[i] / report$avgLS[i]
report$GompGFlex[i] = GompFlex$res[1,1]
report$GompRFlex[i] = GompFlex$res[2,1]
report$GompLogLikFlex[i] = round(GompFlex$loglik, 1)
report$GompAICFlex[i] = round(GompFlex$AIC)
report$WeibShapeFlex[i] = WeibFlex$res[1,1]
report$WeibRateFlex[i] = WeibFlex$res[2,1]
report$WeibLogLikFlex[i] = round(WeibFlex$loglik, 1)
report$WeibAICFlex[i] = round(WeibFlex$AIC)
Rhat = report$GompRFlex[i]; # 'i' was missing. a bug costed HQ a whole afternoon.
Ghat = report$GompGFlex[i];
nhat = 5;
t0= (nhat-1)/Ghat;
fitBinom = optim ( c(Rhat, t0, nhat), llh.binomialMortality.single.run,
lifespan=tb[,1], method="L-BFGS-B",
lower=c(1E-10,0.05, 1), upper=c(10,100,20) );
report[i, c("R", "t0", "n")] = fitBinom$par[1:3]
}report$BestModel = ifelse(report$GompAICFlex < report$WeibAICFlex, "Gomp", "Weib")
report$BestModel = ifelse(abs(report$GompAICFlex - report$WeibAICFlex)<2, "<2", report$BestModel)report## files R t0 n avgLS
## 1 010305.BY4743.rls.tab 0.003361728 51.84926 6.986795 32.56667
## 2 010305.M34.rls.tab 0.002281149 27.19448 6.315472 27.00000
## 3 010305.YPS128.rls.tab 0.004208781 35.91839 5.003790 34.30000
## 4 010305.YPS163.rls.tab 0.001973314 29.91767 6.129599 32.40000
## 5 011705.BY4743.rls.tab 0.003199692 56.68345 7.020039 34.80000
## 6 011705.M2-8.rls.tab 0.004309832 40.19223 6.888514 24.96429
## 7 020105.M2-8.rls.tab 0.002100836 24.96629 6.782559 24.63265
## 8 020105.YPS128.rls.tab 0.003198883 31.92006 5.003532 35.53846
## 9 020205.M5.rls.tab 0.003607006 69.39799 6.950697 38.10811
## 10 020905.M32.rls.tab 0.004377059 25.52056 5.004739 28.03333
## 11 020905.YPS163.rls.tab 0.001118296 16.35315 5.001654 38.06667
## 12 030105.M34.rls.tab 0.003961421 23.40804 5.004505 27.03571
## 13 030105.YPS163.rls.tab 0.001921115 22.98321 5.002596 36.06667
## 14 030205.M13.rls.tab 0.002041629 24.67678 6.151194 27.79310
## 15 030205.M8.rls.tab 0.002059049 24.20832 5.002678 35.70000
## 16 030905.M1-2.rls.tab 0.006986271 35.56665 5.003291 27.33333
## 17 030905.M14.rls.tab 0.002795597 31.69570 5.003190 36.83333
## 18 030905.M22.rls.tab 0.005151605 39.94654 5.003192 32.86667
## 19 030905.M8.rls.tab 0.002583361 26.17782 5.003157 34.16667
## 20 032105.M14.rls.tab 0.003135334 53.10912 6.399874 36.26667
## 21 032105.RM112N.rls.tab 0.003270878 45.59994 5.003008 44.27586
## 22 040805.M5.rls.tab 0.009506987 74.89056 5.001534 34.57500
## 23 040805.YPS163.rls.tab 0.006231753 42.59927 5.004125 32.00000
## 24 041505.M5.rls.tab 0.002107127 43.50023 6.667947 36.80000
## 25 042805.BY4716.rls.tab 0.003931803 55.09461 6.888677 31.76667
## 26 042805.RM112N.rls.tab 0.003034956 43.87268 5.003003 43.86667
## 27 042805.SGU57.rls.tab 0.009287234 53.31357 6.523759 22.70000
## 28 050905.BY4742.rls.tab 0.015187375 75.50504 6.345264 22.09677
## 29 050905.sir2D.4742.rls.tab 0.011968830 25.04875 6.929371 12.54839
## 30 050905.sir2DSIR2.4742.rls.tab 0.014314338 39.37424 6.568399 15.80000
## 31 051704.M13.rls.tab 0.003702579 38.12647 6.828330 25.65854
## 32 051704.S288c.rls.tab 0.004622237 45.92097 6.953311 26.26829
## 33 051805.sir2D.4742.rls.tab 0.003326837 14.51431 7.435202 12.83784
## 34 051805.sir2DSIR2.4742.rls.tab 0.009062209 30.52449 6.920478 15.77500
## 35 051805.SK1.rls.tab 0.016208627 62.59829 6.297356 19.46341
## 36 051805.W303.rls.tab 0.004902812 14.81653 5.006306 19.20000
## 37 052604.sir2D.4741a.rls.tab 0.006931647 19.30151 7.256420 12.48000
## 38 052604.W303.rls.tab 0.004830018 27.36559 7.252533 17.93333
## 39 053104.BY4741.rls.tab 0.003897944 55.09940 6.865935 32.03333
## 40 053104.BY4742.rls.tab 0.013531169 61.72988 6.492899 20.90000
## 41 053104.JSBY4741.rls.tab 0.002301507 41.09508 6.395818 35.40000
## 42 060805.101S.rls.tab 0.003189098 26.89393 5.003689 32.20690
## 43 060805.SK1.rls.tab 0.003586238 35.64948 6.775244 25.06667
## 44 061004.BY4741.rls.tab 0.007837809 55.44565 6.952965 24.00000
## 45 061004.BY4742.rls.tab 0.005960546 16.88916 5.006873 18.96667
## 46 090104.M22.rls.tab 0.002029863 30.79522 6.436227 30.80000
## 47 090104.M5.rls.tab 0.002727043 26.24409 5.003288 33.44828
## 48 091904.101S.rls.tab 0.003274548 23.70610 5.003873 29.11538
## 49 091904.M1-2.rls.tab 0.002276024 29.17914 6.373190 28.33333
## 50 091904.SGU57.rls.tab 0.003780261 35.99534 6.702348 25.10714
## 51 122004.101S.rls.tab 0.004350692 32.05298 5.004511 32.43333
## 52 122004.BY4743.rls.tab 0.004414673 34.99516 5.003808 32.83333
## 53 122004.M32.rls.tab 0.002369105 31.09866 6.678197 27.90000
## 54 122004.M5.rls.tab 0.004086084 47.11631 5.002861 40.43333
## stdLS CV GompGFlex GompRFlex GompLogLikFlex GompAICFlex
## 1 10.666361 0.3275239 0.07697842 4.520672e-03 -116.8 238
## 2 8.870719 0.3285451 0.14641276 1.602638e-03 -104.5 213
## 3 10.034939 0.2925638 0.11136263 1.483120e-03 -110.8 226
## 4 8.528249 0.2632176 0.13323490 1.056716e-03 -106.1 216
## 5 11.244616 0.3231211 0.07043570 4.463297e-03 -118.8 242
## 6 9.566921 0.3832243 0.09917977 5.775350e-03 -206.0 416
## 7 6.197089 0.2515802 0.15850580 2.018556e-03 -161.7 327
## 8 9.566515 0.2691877 0.12531128 8.467668e-04 -141.2 286
## 9 13.817830 0.3625955 0.05756788 4.894747e-03 -152.7 309
## 10 5.833810 0.2081026 0.15673277 1.227489e-03 -99.3 203
## 11 4.968406 0.1305185 0.24459288 1.249275e-05 -89.4 183
## 12 7.593770 0.2808792 0.17087720 9.632527e-04 -93.1 190
## 13 6.410839 0.1777497 0.17403638 1.910733e-04 -98.6 201
## 14 7.866653 0.2830434 0.16127313 1.059086e-03 -97.7 199
## 15 6.170844 0.1728528 0.16522928 2.747861e-04 -99.0 202
## 16 9.876156 0.3613228 0.11246404 3.283654e-03 -98.4 201
## 17 10.683804 0.2900580 0.12619877 6.691071e-04 -109.8 224
## 18 12.249513 0.3727032 0.10013325 2.220664e-03 -114.6 233
## 19 7.598018 0.2223810 0.15279846 4.799010e-04 -102.6 209
## 20 14.806647 0.4082715 0.07521646 3.146350e-03 -121.0 246
## 21 12.900433 0.2913649 0.08771906 1.080074e-03 -114.5 233
## 22 15.311949 0.4428619 0.05341122 6.731125e-03 -166.2 336
## 23 10.940398 0.3418874 0.09389769 3.011183e-03 -152.0 308
## 24 10.179086 0.2766056 0.09173172 2.061227e-03 -114.3 233
## 25 12.082142 0.3803403 0.07246963 5.281817e-03 -118.2 240
## 26 13.325457 0.3037718 0.09117251 9.902588e-04 -117.5 239
## 27 10.948626 0.4823183 0.07490550 1.132399e-02 -113.4 231
## 28 11.736992 0.5311632 0.05293360 1.766844e-02 -119.9 244
## 29 5.371770 0.4280845 0.15825671 1.688167e-02 -96.0 196
## 30 7.480549 0.4734525 0.10126119 1.775066e-02 -102.9 210
## 31 9.946381 0.3876441 0.10454519 4.730819e-03 -150.5 305
## 32 10.254327 0.3903690 0.08686882 6.451205e-03 -154.2 312
## 33 3.452161 0.2689052 0.26717114 5.645344e-03 -101.6 207
## 34 6.454923 0.4091869 0.13022750 1.273026e-02 -132.4 269
## 35 10.892882 0.5596594 0.06383451 1.868936e-02 -153.2 310
## 36 3.188119 0.1660479 0.26993030 9.700421e-04 -110.2 224
## 37 4.371709 0.3502972 0.20362687 1.126646e-02 -147.2 298
## 38 6.085219 0.3393245 0.14491620 7.578039e-03 -98.5 201
## 39 12.385986 0.3866593 0.07246639 5.139799e-03 -118.5 241
## 40 10.178917 0.4870295 0.06471182 1.635804e-02 -112.8 230
## 41 11.352047 0.3206793 0.09711885 1.921016e-03 -114.5 233
## 42 7.710750 0.2394130 0.14872996 7.326872e-04 -99.4 203
## 43 9.645200 0.3847819 0.11178011 4.413639e-03 -108.6 221
## 44 10.230673 0.4262780 0.07199986 1.096505e-02 -117.3 239
## 45 5.061575 0.2668669 0.23682017 1.550829e-03 -89.3 183
## 46 7.897621 0.2564163 0.12936774 1.492113e-03 -105.7 215
## 47 7.953374 0.2377813 0.15241271 5.365022e-04 -99.5 203
## 48 7.778570 0.2671636 0.16872944 6.950097e-04 -87.3 179
## 49 8.633566 0.3047141 0.13650874 1.691235e-03 -94.9 194
## 50 10.130041 0.4034725 0.11074093 4.400827e-03 -102.1 208
## 51 6.891691 0.2124879 0.12479182 1.444821e-03 -104.8 214
## 52 10.680576 0.3252967 0.11430051 1.577003e-03 -111.0 226
## 53 7.906130 0.2833738 0.12802240 2.259762e-03 -105.6 215
## 54 14.204621 0.3513097 0.08489598 1.638453e-03 -119.4 243
## WeibShapeFlex WeibRateFlex WeibLogLikFlex WeibAICFlex BestModel
## 1 3.261288 36.32122 -113.0 230 Weib
## 2 3.765034 29.99508 -106.8 218 Gomp
## 3 4.054009 37.86399 -110.8 226 <2
## 4 4.548007 35.57140 -105.8 216 <2
## 5 3.280775 38.76066 -114.8 234 Weib
## 6 2.887681 28.00442 -205.0 414 Weib
## 7 4.455201 26.99855 -158.5 321 Weib
## 8 4.514794 39.02985 -142.0 288 Gomp
## 9 2.992231 42.69465 -148.7 301 Weib
## 10 5.027798 30.41238 -96.3 197 Weib
## 11 9.442320 40.16692 -89.2 182 <2
## 12 4.269533 29.49942 -96.7 197 Gomp
## 13 6.562842 38.70434 -97.7 199 Weib
## 14 3.867366 30.11810 -103.8 212 Gomp
## 15 6.405254 38.26809 -97.3 199 Weib
## 16 3.211352 30.47489 -99.7 203 Gomp
## 17 4.375519 40.57677 -112.1 228 Gomp
## 18 3.146918 36.73987 -117.2 238 Gomp
## 19 5.392529 37.10399 -102.3 209 <2
## 20 2.810727 40.81549 -122.5 249 Gomp
## 21 4.045783 48.91572 -114.4 233 <2
## 22 2.470620 39.09320 -163.9 332 Weib
## 23 3.360932 35.71519 -151.4 307 <2
## 24 4.004311 40.53145 -111.9 228 Weib
## 25 2.909731 35.65364 -116.4 237 Weib
## 26 4.036826 48.37379 -119.3 243 Gomp
## 27 2.250964 25.65845 -112.8 230 <2
## 28 1.954315 24.80903 -118.7 241 Weib
## 29 2.506113 14.06274 -96.1 196 <2
## 30 2.280870 17.85698 -101.2 206 Weib
## 31 2.760441 28.54233 -153.9 312 Gomp
## 32 2.792964 29.42942 -153.3 311 <2
## 33 4.043804 14.14174 -98.2 200 Weib
## 34 2.681618 17.78053 -129.8 264 Weib
## 35 1.743683 21.63084 -155.6 315 Gomp
## 36 5.940896 20.56914 -105.8 216 Weib
## 37 3.146271 13.96559 -143.6 291 Weib
## 38 3.215089 19.97318 -96.4 197 Weib
## 39 2.869141 35.94135 -117.3 239 Weib
## 40 2.228588 23.68764 -109.7 223 Weib
## 41 3.661056 39.35057 -114.5 233 <2
## 42 5.018631 35.06636 -99.3 203 <2
## 43 2.982129 28.05650 -110.1 224 Gomp
## 44 2.437297 26.79805 -116.6 237 Weib
## 45 4.501854 20.75329 -90.5 185 Gomp
## 46 4.432582 33.80532 -104.0 212 Weib
## 47 5.195892 36.42391 -99.8 204 <2
## 48 4.758210 31.89759 -88.8 182 Gomp
## 49 3.948049 31.35905 -95.5 195 <2
## 50 2.767603 28.06295 -104.8 214 Gomp
## 51 4.912670 35.14447 -101.2 206 Weib
## 52 3.710021 36.43191 -112.7 229 Gomp
## 53 3.965354 30.79124 -104.2 212 Weib
## 54 3.418825 45.13994 -121.1 246 Gompreport[,c("files", "BestModel","CV", "GompAICFlex", "WeibAICFlex", "GompLogLikFlex", "WeibLogLikFlex")]## files BestModel CV GompAICFlex
## 1 010305.BY4743.rls.tab Weib 0.3275239 238
## 2 010305.M34.rls.tab Gomp 0.3285451 213
## 3 010305.YPS128.rls.tab <2 0.2925638 226
## 4 010305.YPS163.rls.tab <2 0.2632176 216
## 5 011705.BY4743.rls.tab Weib 0.3231211 242
## 6 011705.M2-8.rls.tab Weib 0.3832243 416
## 7 020105.M2-8.rls.tab Weib 0.2515802 327
## 8 020105.YPS128.rls.tab Gomp 0.2691877 286
## 9 020205.M5.rls.tab Weib 0.3625955 309
## 10 020905.M32.rls.tab Weib 0.2081026 203
## 11 020905.YPS163.rls.tab <2 0.1305185 183
## 12 030105.M34.rls.tab Gomp 0.2808792 190
## 13 030105.YPS163.rls.tab Weib 0.1777497 201
## 14 030205.M13.rls.tab Gomp 0.2830434 199
## 15 030205.M8.rls.tab Weib 0.1728528 202
## 16 030905.M1-2.rls.tab Gomp 0.3613228 201
## 17 030905.M14.rls.tab Gomp 0.2900580 224
## 18 030905.M22.rls.tab Gomp 0.3727032 233
## 19 030905.M8.rls.tab <2 0.2223810 209
## 20 032105.M14.rls.tab Gomp 0.4082715 246
## 21 032105.RM112N.rls.tab <2 0.2913649 233
## 22 040805.M5.rls.tab Weib 0.4428619 336
## 23 040805.YPS163.rls.tab <2 0.3418874 308
## 24 041505.M5.rls.tab Weib 0.2766056 233
## 25 042805.BY4716.rls.tab Weib 0.3803403 240
## 26 042805.RM112N.rls.tab Gomp 0.3037718 239
## 27 042805.SGU57.rls.tab <2 0.4823183 231
## 28 050905.BY4742.rls.tab Weib 0.5311632 244
## 29 050905.sir2D.4742.rls.tab <2 0.4280845 196
## 30 050905.sir2DSIR2.4742.rls.tab Weib 0.4734525 210
## 31 051704.M13.rls.tab Gomp 0.3876441 305
## 32 051704.S288c.rls.tab <2 0.3903690 312
## 33 051805.sir2D.4742.rls.tab Weib 0.2689052 207
## 34 051805.sir2DSIR2.4742.rls.tab Weib 0.4091869 269
## 35 051805.SK1.rls.tab Gomp 0.5596594 310
## 36 051805.W303.rls.tab Weib 0.1660479 224
## 37 052604.sir2D.4741a.rls.tab Weib 0.3502972 298
## 38 052604.W303.rls.tab Weib 0.3393245 201
## 39 053104.BY4741.rls.tab Weib 0.3866593 241
## 40 053104.BY4742.rls.tab Weib 0.4870295 230
## 41 053104.JSBY4741.rls.tab <2 0.3206793 233
## 42 060805.101S.rls.tab <2 0.2394130 203
## 43 060805.SK1.rls.tab Gomp 0.3847819 221
## 44 061004.BY4741.rls.tab Weib 0.4262780 239
## 45 061004.BY4742.rls.tab Gomp 0.2668669 183
## 46 090104.M22.rls.tab Weib 0.2564163 215
## 47 090104.M5.rls.tab <2 0.2377813 203
## 48 091904.101S.rls.tab Gomp 0.2671636 179
## 49 091904.M1-2.rls.tab <2 0.3047141 194
## 50 091904.SGU57.rls.tab Gomp 0.4034725 208
## 51 122004.101S.rls.tab Weib 0.2124879 214
## 52 122004.BY4743.rls.tab Gomp 0.3252967 226
## 53 122004.M32.rls.tab Weib 0.2833738 215
## 54 122004.M5.rls.tab Gomp 0.3513097 243
## WeibAICFlex GompLogLikFlex WeibLogLikFlex
## 1 230 -116.8 -113.0
## 2 218 -104.5 -106.8
## 3 226 -110.8 -110.8
## 4 216 -106.1 -105.8
## 5 234 -118.8 -114.8
## 6 414 -206.0 -205.0
## 7 321 -161.7 -158.5
## 8 288 -141.2 -142.0
## 9 301 -152.7 -148.7
## 10 197 -99.3 -96.3
## 11 182 -89.4 -89.2
## 12 197 -93.1 -96.7
## 13 199 -98.6 -97.7
## 14 212 -97.7 -103.8
## 15 199 -99.0 -97.3
## 16 203 -98.4 -99.7
## 17 228 -109.8 -112.1
## 18 238 -114.6 -117.2
## 19 209 -102.6 -102.3
## 20 249 -121.0 -122.5
## 21 233 -114.5 -114.4
## 22 332 -166.2 -163.9
## 23 307 -152.0 -151.4
## 24 228 -114.3 -111.9
## 25 237 -118.2 -116.4
## 26 243 -117.5 -119.3
## 27 230 -113.4 -112.8
## 28 241 -119.9 -118.7
## 29 196 -96.0 -96.1
## 30 206 -102.9 -101.2
## 31 312 -150.5 -153.9
## 32 311 -154.2 -153.3
## 33 200 -101.6 -98.2
## 34 264 -132.4 -129.8
## 35 315 -153.2 -155.6
## 36 216 -110.2 -105.8
## 37 291 -147.2 -143.6
## 38 197 -98.5 -96.4
## 39 239 -118.5 -117.3
## 40 223 -112.8 -109.7
## 41 233 -114.5 -114.5
## 42 203 -99.4 -99.3
## 43 224 -108.6 -110.1
## 44 237 -117.3 -116.6
## 45 185 -89.3 -90.5
## 46 212 -105.7 -104.0
## 47 204 -99.5 -99.8
## 48 182 -87.3 -88.8
## 49 195 -94.9 -95.5
## 50 214 -102.1 -104.8
## 51 206 -104.8 -101.2
## 52 229 -111.0 -112.7
## 53 212 -105.6 -104.2
## 54 246 -119.4 -121.1report[,c("files", "BestModel", "GompAICFlex", "WeibAICFlex", "GompLogLikFlex", "WeibLogLikFlex")]## files BestModel GompAICFlex WeibAICFlex
## 1 010305.BY4743.rls.tab Weib 238 230
## 2 010305.M34.rls.tab Gomp 213 218
## 3 010305.YPS128.rls.tab <2 226 226
## 4 010305.YPS163.rls.tab <2 216 216
## 5 011705.BY4743.rls.tab Weib 242 234
## 6 011705.M2-8.rls.tab Weib 416 414
## 7 020105.M2-8.rls.tab Weib 327 321
## 8 020105.YPS128.rls.tab Gomp 286 288
## 9 020205.M5.rls.tab Weib 309 301
## 10 020905.M32.rls.tab Weib 203 197
## 11 020905.YPS163.rls.tab <2 183 182
## 12 030105.M34.rls.tab Gomp 190 197
## 13 030105.YPS163.rls.tab Weib 201 199
## 14 030205.M13.rls.tab Gomp 199 212
## 15 030205.M8.rls.tab Weib 202 199
## 16 030905.M1-2.rls.tab Gomp 201 203
## 17 030905.M14.rls.tab Gomp 224 228
## 18 030905.M22.rls.tab Gomp 233 238
## 19 030905.M8.rls.tab <2 209 209
## 20 032105.M14.rls.tab Gomp 246 249
## 21 032105.RM112N.rls.tab <2 233 233
## 22 040805.M5.rls.tab Weib 336 332
## 23 040805.YPS163.rls.tab <2 308 307
## 24 041505.M5.rls.tab Weib 233 228
## 25 042805.BY4716.rls.tab Weib 240 237
## 26 042805.RM112N.rls.tab Gomp 239 243
## 27 042805.SGU57.rls.tab <2 231 230
## 28 050905.BY4742.rls.tab Weib 244 241
## 29 050905.sir2D.4742.rls.tab <2 196 196
## 30 050905.sir2DSIR2.4742.rls.tab Weib 210 206
## 31 051704.M13.rls.tab Gomp 305 312
## 32 051704.S288c.rls.tab <2 312 311
## 33 051805.sir2D.4742.rls.tab Weib 207 200
## 34 051805.sir2DSIR2.4742.rls.tab Weib 269 264
## 35 051805.SK1.rls.tab Gomp 310 315
## 36 051805.W303.rls.tab Weib 224 216
## 37 052604.sir2D.4741a.rls.tab Weib 298 291
## 38 052604.W303.rls.tab Weib 201 197
## 39 053104.BY4741.rls.tab Weib 241 239
## 40 053104.BY4742.rls.tab Weib 230 223
## 41 053104.JSBY4741.rls.tab <2 233 233
## 42 060805.101S.rls.tab <2 203 203
## 43 060805.SK1.rls.tab Gomp 221 224
## 44 061004.BY4741.rls.tab Weib 239 237
## 45 061004.BY4742.rls.tab Gomp 183 185
## 46 090104.M22.rls.tab Weib 215 212
## 47 090104.M5.rls.tab <2 203 204
## 48 091904.101S.rls.tab Gomp 179 182
## 49 091904.M1-2.rls.tab <2 194 195
## 50 091904.SGU57.rls.tab Gomp 208 214
## 51 122004.101S.rls.tab Weib 214 206
## 52 122004.BY4743.rls.tab Gomp 226 229
## 53 122004.M32.rls.tab Weib 215 212
## 54 122004.M5.rls.tab Gomp 243 246
## GompLogLikFlex WeibLogLikFlex
## 1 -116.8 -113.0
## 2 -104.5 -106.8
## 3 -110.8 -110.8
## 4 -106.1 -105.8
## 5 -118.8 -114.8
## 6 -206.0 -205.0
## 7 -161.7 -158.5
## 8 -141.2 -142.0
## 9 -152.7 -148.7
## 10 -99.3 -96.3
## 11 -89.4 -89.2
## 12 -93.1 -96.7
## 13 -98.6 -97.7
## 14 -97.7 -103.8
## 15 -99.0 -97.3
## 16 -98.4 -99.7
## 17 -109.8 -112.1
## 18 -114.6 -117.2
## 19 -102.6 -102.3
## 20 -121.0 -122.5
## 21 -114.5 -114.4
## 22 -166.2 -163.9
## 23 -152.0 -151.4
## 24 -114.3 -111.9
## 25 -118.2 -116.4
## 26 -117.5 -119.3
## 27 -113.4 -112.8
## 28 -119.9 -118.7
## 29 -96.0 -96.1
## 30 -102.9 -101.2
## 31 -150.5 -153.9
## 32 -154.2 -153.3
## 33 -101.6 -98.2
## 34 -132.4 -129.8
## 35 -153.2 -155.6
## 36 -110.2 -105.8
## 37 -147.2 -143.6
## 38 -98.5 -96.4
## 39 -118.5 -117.3
## 40 -112.8 -109.7
## 41 -114.5 -114.5
## 42 -99.4 -99.3
## 43 -108.6 -110.1
## 44 -117.3 -116.6
## 45 -89.3 -90.5
## 46 -105.7 -104.0
## 47 -99.5 -99.8
## 48 -87.3 -88.8
## 49 -94.9 -95.5
## 50 -102.1 -104.8
## 51 -104.8 -101.2
## 52 -111.0 -112.7
## 53 -105.6 -104.2
## 54 -119.4 -121.1summary(lm(report$CV ~ report$BestModel ))##
## Call:
## lm(formula = report$CV ~ report$BestModel)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.172966 -0.063774 -0.003727 0.053454 0.215896
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.30348 0.02578 11.771 3.73e-16 ***
## report$BestModelGomp 0.04028 0.03425 1.176 0.245
## report$BestModelWeib 0.02557 0.03201 0.799 0.428
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09296 on 51 degrees of freedom
## Multiple R-squared: 0.02662, Adjusted R-squared: -0.01155
## F-statistic: 0.6973 on 2 and 51 DF, p-value: 0.5026summary(lm(report$CV ~ report$WeibLogLikFlex ))##
## Call:
## lm(formula = report$CV ~ report$WeibLogLikFlex)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15227 -0.04636 -0.01465 0.05047 0.19943
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1347941 0.0601566 2.241 0.02934 *
## report$WeibLogLikFlex -0.0016591 0.0005082 -3.265 0.00194 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08501 on 52 degrees of freedom
## Multiple R-squared: 0.1701, Adjusted R-squared: 0.1541
## F-statistic: 10.66 on 1 and 52 DF, p-value: 0.001941summary(lm(report$CV ~ report$GompLogLikFlex ))##
## Call:
## lm(formula = report$CV ~ report$GompLogLikFlex)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15432 -0.04772 -0.01208 0.04978 0.19843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1444593 0.0599704 2.409 0.01958 *
## report$GompLogLikFlex -0.0015703 0.0005046 -3.112 0.00302 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08567 on 52 degrees of freedom
## Multiple R-squared: 0.157, Adjusted R-squared: 0.1408
## F-statistic: 9.685 on 1 and 52 DF, p-value: 0.003017summary(lm(report$CV ~ (report$GompLogLikFlex - report$WeibLogLikFlex)))##
## Call:
## lm(formula = report$CV ~ (report$GompLogLikFlex - report$WeibLogLikFlex))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.15432 -0.04772 -0.01208 0.04978 0.19843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1444593 0.0599704 2.409 0.01958 *
## report$GompLogLikFlex -0.0015703 0.0005046 -3.112 0.00302 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08567 on 52 degrees of freedom
## Multiple R-squared: 0.157, Adjusted R-squared: 0.1408
## F-statistic: 9.685 on 1 and 52 DF, p-value: 0.003017plot( report$GompLogLikFlex ~ report$CV, col="red", pch=3, xlim=c(0, 0.8), ylim=c(-220, -80))
points( report$CV, report$WeibLogLikFlex, col="blue", pch=4)
m1 = lm( report$GompLogLikFlex ~ report$CV)
m2 = lm( report$WeibLogLikFlex ~ report$CV)
abline( m1, col="red", lty=2)
abline( m2, col='blue', lty=1)
text(0.6, -210, "nearly the same!?")The QIN-RLS data suggested that noisy system signal perfer Gompertz model, based on GG01 theory. Notice that CV measures distrubition of system signals and are different from white noises (errors)
report$DeltaGompWeiLLH = report$GompLogLikFlex - report$WeibLogLikFlex
plot( report$DeltaGompWeiLLH ~ report$CV )
m3 = lm( report$DeltaGompWeiLLH ~ report$CV)
abline(m3, col='red')Show the results
report## files R t0 n avgLS
## 1 010305.BY4743.rls.tab 0.003361728 51.84926 6.986795 32.56667
## 2 010305.M34.rls.tab 0.002281149 27.19448 6.315472 27.00000
## 3 010305.YPS128.rls.tab 0.004208781 35.91839 5.003790 34.30000
## 4 010305.YPS163.rls.tab 0.001973314 29.91767 6.129599 32.40000
## 5 011705.BY4743.rls.tab 0.003199692 56.68345 7.020039 34.80000
## 6 011705.M2-8.rls.tab 0.004309832 40.19223 6.888514 24.96429
## 7 020105.M2-8.rls.tab 0.002100836 24.96629 6.782559 24.63265
## 8 020105.YPS128.rls.tab 0.003198883 31.92006 5.003532 35.53846
## 9 020205.M5.rls.tab 0.003607006 69.39799 6.950697 38.10811
## 10 020905.M32.rls.tab 0.004377059 25.52056 5.004739 28.03333
## 11 020905.YPS163.rls.tab 0.001118296 16.35315 5.001654 38.06667
## 12 030105.M34.rls.tab 0.003961421 23.40804 5.004505 27.03571
## 13 030105.YPS163.rls.tab 0.001921115 22.98321 5.002596 36.06667
## 14 030205.M13.rls.tab 0.002041629 24.67678 6.151194 27.79310
## 15 030205.M8.rls.tab 0.002059049 24.20832 5.002678 35.70000
## 16 030905.M1-2.rls.tab 0.006986271 35.56665 5.003291 27.33333
## 17 030905.M14.rls.tab 0.002795597 31.69570 5.003190 36.83333
## 18 030905.M22.rls.tab 0.005151605 39.94654 5.003192 32.86667
## 19 030905.M8.rls.tab 0.002583361 26.17782 5.003157 34.16667
## 20 032105.M14.rls.tab 0.003135334 53.10912 6.399874 36.26667
## 21 032105.RM112N.rls.tab 0.003270878 45.59994 5.003008 44.27586
## 22 040805.M5.rls.tab 0.009506987 74.89056 5.001534 34.57500
## 23 040805.YPS163.rls.tab 0.006231753 42.59927 5.004125 32.00000
## 24 041505.M5.rls.tab 0.002107127 43.50023 6.667947 36.80000
## 25 042805.BY4716.rls.tab 0.003931803 55.09461 6.888677 31.76667
## 26 042805.RM112N.rls.tab 0.003034956 43.87268 5.003003 43.86667
## 27 042805.SGU57.rls.tab 0.009287234 53.31357 6.523759 22.70000
## 28 050905.BY4742.rls.tab 0.015187375 75.50504 6.345264 22.09677
## 29 050905.sir2D.4742.rls.tab 0.011968830 25.04875 6.929371 12.54839
## 30 050905.sir2DSIR2.4742.rls.tab 0.014314338 39.37424 6.568399 15.80000
## 31 051704.M13.rls.tab 0.003702579 38.12647 6.828330 25.65854
## 32 051704.S288c.rls.tab 0.004622237 45.92097 6.953311 26.26829
## 33 051805.sir2D.4742.rls.tab 0.003326837 14.51431 7.435202 12.83784
## 34 051805.sir2DSIR2.4742.rls.tab 0.009062209 30.52449 6.920478 15.77500
## 35 051805.SK1.rls.tab 0.016208627 62.59829 6.297356 19.46341
## 36 051805.W303.rls.tab 0.004902812 14.81653 5.006306 19.20000
## 37 052604.sir2D.4741a.rls.tab 0.006931647 19.30151 7.256420 12.48000
## 38 052604.W303.rls.tab 0.004830018 27.36559 7.252533 17.93333
## 39 053104.BY4741.rls.tab 0.003897944 55.09940 6.865935 32.03333
## 40 053104.BY4742.rls.tab 0.013531169 61.72988 6.492899 20.90000
## 41 053104.JSBY4741.rls.tab 0.002301507 41.09508 6.395818 35.40000
## 42 060805.101S.rls.tab 0.003189098 26.89393 5.003689 32.20690
## 43 060805.SK1.rls.tab 0.003586238 35.64948 6.775244 25.06667
## 44 061004.BY4741.rls.tab 0.007837809 55.44565 6.952965 24.00000
## 45 061004.BY4742.rls.tab 0.005960546 16.88916 5.006873 18.96667
## 46 090104.M22.rls.tab 0.002029863 30.79522 6.436227 30.80000
## 47 090104.M5.rls.tab 0.002727043 26.24409 5.003288 33.44828
## 48 091904.101S.rls.tab 0.003274548 23.70610 5.003873 29.11538
## 49 091904.M1-2.rls.tab 0.002276024 29.17914 6.373190 28.33333
## 50 091904.SGU57.rls.tab 0.003780261 35.99534 6.702348 25.10714
## 51 122004.101S.rls.tab 0.004350692 32.05298 5.004511 32.43333
## 52 122004.BY4743.rls.tab 0.004414673 34.99516 5.003808 32.83333
## 53 122004.M32.rls.tab 0.002369105 31.09866 6.678197 27.90000
## 54 122004.M5.rls.tab 0.004086084 47.11631 5.002861 40.43333
## stdLS CV GompGFlex GompRFlex GompLogLikFlex GompAICFlex
## 1 10.666361 0.3275239 0.07697842 4.520672e-03 -116.8 238
## 2 8.870719 0.3285451 0.14641276 1.602638e-03 -104.5 213
## 3 10.034939 0.2925638 0.11136263 1.483120e-03 -110.8 226
## 4 8.528249 0.2632176 0.13323490 1.056716e-03 -106.1 216
## 5 11.244616 0.3231211 0.07043570 4.463297e-03 -118.8 242
## 6 9.566921 0.3832243 0.09917977 5.775350e-03 -206.0 416
## 7 6.197089 0.2515802 0.15850580 2.018556e-03 -161.7 327
## 8 9.566515 0.2691877 0.12531128 8.467668e-04 -141.2 286
## 9 13.817830 0.3625955 0.05756788 4.894747e-03 -152.7 309
## 10 5.833810 0.2081026 0.15673277 1.227489e-03 -99.3 203
## 11 4.968406 0.1305185 0.24459288 1.249275e-05 -89.4 183
## 12 7.593770 0.2808792 0.17087720 9.632527e-04 -93.1 190
## 13 6.410839 0.1777497 0.17403638 1.910733e-04 -98.6 201
## 14 7.866653 0.2830434 0.16127313 1.059086e-03 -97.7 199
## 15 6.170844 0.1728528 0.16522928 2.747861e-04 -99.0 202
## 16 9.876156 0.3613228 0.11246404 3.283654e-03 -98.4 201
## 17 10.683804 0.2900580 0.12619877 6.691071e-04 -109.8 224
## 18 12.249513 0.3727032 0.10013325 2.220664e-03 -114.6 233
## 19 7.598018 0.2223810 0.15279846 4.799010e-04 -102.6 209
## 20 14.806647 0.4082715 0.07521646 3.146350e-03 -121.0 246
## 21 12.900433 0.2913649 0.08771906 1.080074e-03 -114.5 233
## 22 15.311949 0.4428619 0.05341122 6.731125e-03 -166.2 336
## 23 10.940398 0.3418874 0.09389769 3.011183e-03 -152.0 308
## 24 10.179086 0.2766056 0.09173172 2.061227e-03 -114.3 233
## 25 12.082142 0.3803403 0.07246963 5.281817e-03 -118.2 240
## 26 13.325457 0.3037718 0.09117251 9.902588e-04 -117.5 239
## 27 10.948626 0.4823183 0.07490550 1.132399e-02 -113.4 231
## 28 11.736992 0.5311632 0.05293360 1.766844e-02 -119.9 244
## 29 5.371770 0.4280845 0.15825671 1.688167e-02 -96.0 196
## 30 7.480549 0.4734525 0.10126119 1.775066e-02 -102.9 210
## 31 9.946381 0.3876441 0.10454519 4.730819e-03 -150.5 305
## 32 10.254327 0.3903690 0.08686882 6.451205e-03 -154.2 312
## 33 3.452161 0.2689052 0.26717114 5.645344e-03 -101.6 207
## 34 6.454923 0.4091869 0.13022750 1.273026e-02 -132.4 269
## 35 10.892882 0.5596594 0.06383451 1.868936e-02 -153.2 310
## 36 3.188119 0.1660479 0.26993030 9.700421e-04 -110.2 224
## 37 4.371709 0.3502972 0.20362687 1.126646e-02 -147.2 298
## 38 6.085219 0.3393245 0.14491620 7.578039e-03 -98.5 201
## 39 12.385986 0.3866593 0.07246639 5.139799e-03 -118.5 241
## 40 10.178917 0.4870295 0.06471182 1.635804e-02 -112.8 230
## 41 11.352047 0.3206793 0.09711885 1.921016e-03 -114.5 233
## 42 7.710750 0.2394130 0.14872996 7.326872e-04 -99.4 203
## 43 9.645200 0.3847819 0.11178011 4.413639e-03 -108.6 221
## 44 10.230673 0.4262780 0.07199986 1.096505e-02 -117.3 239
## 45 5.061575 0.2668669 0.23682017 1.550829e-03 -89.3 183
## 46 7.897621 0.2564163 0.12936774 1.492113e-03 -105.7 215
## 47 7.953374 0.2377813 0.15241271 5.365022e-04 -99.5 203
## 48 7.778570 0.2671636 0.16872944 6.950097e-04 -87.3 179
## 49 8.633566 0.3047141 0.13650874 1.691235e-03 -94.9 194
## 50 10.130041 0.4034725 0.11074093 4.400827e-03 -102.1 208
## 51 6.891691 0.2124879 0.12479182 1.444821e-03 -104.8 214
## 52 10.680576 0.3252967 0.11430051 1.577003e-03 -111.0 226
## 53 7.906130 0.2833738 0.12802240 2.259762e-03 -105.6 215
## 54 14.204621 0.3513097 0.08489598 1.638453e-03 -119.4 243
## WeibShapeFlex WeibRateFlex WeibLogLikFlex WeibAICFlex BestModel
## 1 3.261288 36.32122 -113.0 230 Weib
## 2 3.765034 29.99508 -106.8 218 Gomp
## 3 4.054009 37.86399 -110.8 226 <2
## 4 4.548007 35.57140 -105.8 216 <2
## 5 3.280775 38.76066 -114.8 234 Weib
## 6 2.887681 28.00442 -205.0 414 Weib
## 7 4.455201 26.99855 -158.5 321 Weib
## 8 4.514794 39.02985 -142.0 288 Gomp
## 9 2.992231 42.69465 -148.7 301 Weib
## 10 5.027798 30.41238 -96.3 197 Weib
## 11 9.442320 40.16692 -89.2 182 <2
## 12 4.269533 29.49942 -96.7 197 Gomp
## 13 6.562842 38.70434 -97.7 199 Weib
## 14 3.867366 30.11810 -103.8 212 Gomp
## 15 6.405254 38.26809 -97.3 199 Weib
## 16 3.211352 30.47489 -99.7 203 Gomp
## 17 4.375519 40.57677 -112.1 228 Gomp
## 18 3.146918 36.73987 -117.2 238 Gomp
## 19 5.392529 37.10399 -102.3 209 <2
## 20 2.810727 40.81549 -122.5 249 Gomp
## 21 4.045783 48.91572 -114.4 233 <2
## 22 2.470620 39.09320 -163.9 332 Weib
## 23 3.360932 35.71519 -151.4 307 <2
## 24 4.004311 40.53145 -111.9 228 Weib
## 25 2.909731 35.65364 -116.4 237 Weib
## 26 4.036826 48.37379 -119.3 243 Gomp
## 27 2.250964 25.65845 -112.8 230 <2
## 28 1.954315 24.80903 -118.7 241 Weib
## 29 2.506113 14.06274 -96.1 196 <2
## 30 2.280870 17.85698 -101.2 206 Weib
## 31 2.760441 28.54233 -153.9 312 Gomp
## 32 2.792964 29.42942 -153.3 311 <2
## 33 4.043804 14.14174 -98.2 200 Weib
## 34 2.681618 17.78053 -129.8 264 Weib
## 35 1.743683 21.63084 -155.6 315 Gomp
## 36 5.940896 20.56914 -105.8 216 Weib
## 37 3.146271 13.96559 -143.6 291 Weib
## 38 3.215089 19.97318 -96.4 197 Weib
## 39 2.869141 35.94135 -117.3 239 Weib
## 40 2.228588 23.68764 -109.7 223 Weib
## 41 3.661056 39.35057 -114.5 233 <2
## 42 5.018631 35.06636 -99.3 203 <2
## 43 2.982129 28.05650 -110.1 224 Gomp
## 44 2.437297 26.79805 -116.6 237 Weib
## 45 4.501854 20.75329 -90.5 185 Gomp
## 46 4.432582 33.80532 -104.0 212 Weib
## 47 5.195892 36.42391 -99.8 204 <2
## 48 4.758210 31.89759 -88.8 182 Gomp
## 49 3.948049 31.35905 -95.5 195 <2
## 50 2.767603 28.06295 -104.8 214 Gomp
## 51 4.912670 35.14447 -101.2 206 Weib
## 52 3.710021 36.43191 -112.7 229 Gomp
## 53 3.965354 30.79124 -104.2 212 Weib
## 54 3.418825 45.13994 -121.1 246 Gomp
## DeltaGompWeiLLH
## 1 -3.8
## 2 2.3
## 3 0.0
## 4 -0.3
## 5 -4.0
## 6 -1.0
## 7 -3.2
## 8 0.8
## 9 -4.0
## 10 -3.0
## 11 -0.2
## 12 3.6
## 13 -0.9
## 14 6.1
## 15 -1.7
## 16 1.3
## 17 2.3
## 18 2.6
## 19 -0.3
## 20 1.5
## 21 -0.1
## 22 -2.3
## 23 -0.6
## 24 -2.4
## 25 -1.8
## 26 1.8
## 27 -0.6
## 28 -1.2
## 29 0.1
## 30 -1.7
## 31 3.4
## 32 -0.9
## 33 -3.4
## 34 -2.6
## 35 2.4
## 36 -4.4
## 37 -3.6
## 38 -2.1
## 39 -1.2
## 40 -3.1
## 41 0.0
## 42 -0.1
## 43 1.5
## 44 -0.7
## 45 1.2
## 46 -1.7
## 47 0.3
## 48 1.5
## 49 0.6
## 50 2.7
## 51 -3.6
## 52 1.7
## 53 -1.4
## 54 1.7# report$residues ??
Subscribe to:
Posts (Atom)