Mixed distribution
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In the football data there was some reason to use a mixed distribution in the football data (ref) so I tried doing that. It was more difficult than I expected. Not only is a mixture of distributions fairly difficult, also the system was over parameterized, causing grief.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
mixed distribution
data from a mixed distribution is simply said data which is from a combination of distributions. Most obvious example is length of people; males are longer than females. Combine their lengths and a joint non normal distribution appears. Separate them, two normal distributions. This problem is equivalent to the eyes problem in winbugs. Except that I use JAGS and it was supposed to be part of a larger model. The example section in JAGS shows two ways how to do this. In the end I chose to use the dnormmix distribution in the mix module. To quote the JAGS manual: ‘The mix module defines a novel distribution dnormmix(mu,tau,pi) representing a finite mixture of normal distributions‘ and ‘If you want to use the dnormmix distribution but do not care about label switching, then you can disable the tempered transition sampler withset factory “mix::TemperedMix” off, type(sampler)’
That is me, so here is how to do that in R:
library(R2jags)
Loading required package: coda
Loading required package: lattice
Loading required package: R2WinBUGS
Loading required package: rjags
linking to JAGS 3.3.0
module basemod loaded
module bugs loaded
Loading required package: abind
Loading required package: parallel
Attaching package: ‘R2jags’
The following object(s) are masked from ‘package:coda’:
traceplot
load.module(“mix”)
module mix loaded
set.factory(“mix::TemperedMix”, ‘sampler’, FALSE)
NULL
list.factories(‘sampler’)
factory status
1 mix::TemperedMix FALSE
2 bugs::DSum TRUE
3 bugs::Conjugate TRUE
4 bugs::Dirichlet TRUE
5 bugs::MNormal TRUE
6 base::Finite TRUE
7 base::Slice TRUE
football model
This is the previous model with dnormmix grafted in and overall strength with attack/defense removed in an attempt to simplify and understand the estimation problems . Note that Astr[1] is fixed to 1, Dstr[1] is almost fixed to 0 and all other AStr and DStr are free. I am not 100% sure about those parts. Astr needs to be fixed because otherwise the whole set of Astr[] and Dstr[] changes in a similar way over the MCMC samples. It is their relative numbers that count. Somehow, there is still a need to fix Dstr[1], as there is still an overall change. Yet, I have the feeling fixing Astr[1] gives information to determine Dstr[2:nclub] hence Astr[2:nclub] hence Dstr[1]. Yet, they all move similar, and Dstr[1] is clearly not 0 as suggested by the prior.
fbmodel1 <- function() {
for (i in 1:N) {
HomeMadeGoals[i] ~ dpois(HS[i])
OutMadeGoals[i] ~ dpois(OS[i])
log(HS[i]) <- Home + Dstr[OutClub[i]] + Astr[HomeClub[i]]
log(OS[i]) <- Dstr[HomeClub[i]] + Astr[OutClub[i]]
}
Astr[1] <- 1
Dstr[1] ~ dnorm(0,10)
for (i in 2:nclub) {
Astr[i] ~ dnormmix(MAStr,tauAStr1,EtaAStr1)
Dstr[i] ~ dnormmix(MDStr,tauDStr1,EtaDStr1)
}
for (i in 1:3) {
MAStr[i] ~ dnorm(0,.01)
MDStr[i] ~ dnorm(0,.01)
tauDStr1[i] <- tauDStr
tauAStr1[i] <- tauAStr
eee[i] <- 3
}
EtaAStr1[1:3] ~ ddirch(eee[1:3])
EtaDStr1[1:3] ~ ddirch(eee[1:3])
sigmaAstr <- 1/sqrt(tauAStr)
tauAStr ~ dgamma(.001,.001)
sigmaDstr <- 1/sqrt(tauDStr)
tauDStr ~ dgamma(.001,.001)
Home ~ dnorm(0,.0001)
}
params <- c("Dstr","Astr","sigmaAstr","sigmaDstr","Home")
inits <- function(){
list(TopStr=rnorm(JAGSData$nclub),
AD=rnorm(JAGSData$nclub),
sigmaAD=runif(1),
sigmaStr=runif(0,.5),
Home=rnorm(1)
)
}
jagsfit <- jags(JAGSData, model=fbmodel1, inits=inits,
parameters=params,progress.bar=”gui”,
n.iter=5000)
jagsfit
Inference for Bugs model at “C:/Users/…/RtmpSGssTD/modeld94a9b3849.txt”, fit using jags,
3 chains, each with 15000 iterations (first 7500 discarded), n.thin = 7
n.sims = 3216 iterations saved
mu.vect sd.vect 2.5% 25% 50% 75% 97.5% Rhat n.eff
Astr[1] 1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1
Astr[2] 1.641 0.160 1.338 1.533 1.639 1.748 1.971 1.033 69
Astr[3] 1.328 0.196 0.943 1.196 1.324 1.463 1.710 1.023 91
Astr[4] 0.878 0.185 0.513 0.753 0.880 1.007 1.228 1.021 120
Astr[5] 0.753 0.207 0.326 0.617 0.755 0.894 1.143 1.017 140
Astr[6] 0.949 0.177 0.598 0.836 0.950 1.065 1.304 1.021 110
Astr[7] 1.559 0.158 1.257 1.450 1.559 1.669 1.875 1.032 73
Astr[8] 1.167 0.193 0.804 1.034 1.161 1.293 1.566 1.022 120
Astr[9] 1.426 0.180 1.078 1.304 1.429 1.551 1.756 1.029 84
Astr[10] 1.117 0.187 0.764 0.990 1.114 1.237 1.499 1.024 99
Astr[11] 1.006 0.179 0.660 0.889 1.003 1.121 1.368 1.024 98
Astr[12] 0.955 0.181 0.603 0.830 0.957 1.073 1.312 1.024 91
Astr[13] 1.599 0.159 1.296 1.490 1.599 1.712 1.919 1.034 71
Astr[14] 0.927 0.179 0.566 0.809 0.928 1.053 1.277 1.025 90
Astr[15] 1.178 0.195 0.810 1.046 1.174 1.308 1.574 1.016 160
Astr[16] 1.539 0.164 1.216 1.429 1.538 1.656 1.854 1.034 70
Astr[17] 1.040 0.183 0.693 0.917 1.039 1.154 1.414 1.019 130
Astr[18] 0.968 0.180 0.614 0.849 0.971 1.090 1.315 1.029 91
Dstr[1] -0.640 0.162 -0.977 -0.747 -0.638 -0.530 -0.331 1.031 79
Dstr[2] -1.183 0.179 -1.533 -1.301 -1.183 -1.062 -0.844 1.034 66
Dstr[3] -1.203 0.181 -1.568 -1.323 -1.196 -1.083 -0.853 1.023 97
Dstr[4] -0.709 0.160 -1.011 -0.814 -0.710 -0.604 -0.400 1.040 58
Dstr[5] -0.697 0.162 -1.020 -0.804 -0.697 -0.591 -0.375 1.050 46
Dstr[6] -0.840 0.177 -1.208 -0.956 -0.833 -0.719 -0.508 1.031 70
Dstr[7] -1.054 0.189 -1.413 -1.191 -1.055 -0.927 -0.675 1.039 62
Dstr[8] -0.869 0.181 -1.237 -0.987 -0.862 -0.743 -0.538 1.023 91
Dstr[9] -1.177 0.177 -1.533 -1.293 -1.178 -1.059 -0.832 1.022 100
Dstr[10] -0.821 0.169 -1.174 -0.929 -0.812 -0.704 -0.503 1.022 98
Dstr[11] -0.944 0.188 -1.307 -1.074 -0.935 -0.812 -0.593 1.023 95
Dstr[12] -1.092 0.177 -1.433 -1.216 -1.092 -0.972 -0.739 1.024 87
Dstr[13] -1.030 0.188 -1.386 -1.162 -1.036 -0.900 -0.650 1.023 100
Dstr[14] -1.031 0.189 -1.384 -1.160 -1.028 -0.899 -0.665 1.035 64
Dstr[15] -0.735 0.164 -1.065 -0.843 -0.734 -0.624 -0.418 1.042 54
Dstr[16] -0.836 0.178 -1.212 -0.952 -0.830 -0.714 -0.500 1.025 88
Dstr[17] -1.116 0.178 -1.456 -1.237 -1.121 -0.998 -0.758 1.042 55
Dstr[18] -0.681 0.163 -1.005 -0.791 -0.681 -0.571 -0.367 1.041 54
Home 0.334 0.062 0.210 0.293 0.335 0.376 0.453 1.012 170
sigmaAstr 0.185 0.103 0.042 0.102 0.169 0.254 0.410 1.014 220
sigmaDstr 0.135 0.079 0.028 0.067 0.123 0.190 0.301 1.033 68
deviance 1889.263 8.560 1873.823 1883.430 1888.635 1894.484 1907.580 1.001 2400
For each parameter, n.eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
DIC info (using the rule, pD = var(deviance)/2)
pD = 36.6 and DIC = 1925.9
DIC is an estimate of expected predictive error (lower deviance is better).
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