[R-sig-ME] Predictions from zero-inflated or hurdle models
Jarrod Hadfield
j.hadfield at ed.ac.uk
Wed Mar 18 07:03:50 CET 2015
Hi,
1/ I have no problem with it - I'm just lazy about it.
2/ This is the case for all GLMM. In your case you have two sources of
variation to average over: family effects and units
(observation-level) effects. However, they are independent of each
other and so you can replace (for example) u_1 with family_1+units_1
and v_1 with v_family+v_units.
3/ In your model the zero-alteration and the Poisson process are
treated as independent as you use an idh structure. Here, the
covariance is set to zero. You could use an us structure which would
then allow a non-zero covariance between family effects (the
covariance is not identifiable at the units level). I did not post a
double integration example, the integration was of the form
int_{u_{1}} 1-prob d_{u_{1}} * int_{u_{2}} meanc d_{u_{2}}
Double integration would involve
int_{u_{1}}int_{u_{2}} (1-prob)*meanc d_{u_{2}}d_{u_{1}}
For example with have a = [a_{1}, a_{2}]' and V the covariance matrix
of random effects.
library(mvtnorm)
library(cubature)
normal.zap<-function(x, mu, V){
(1-exp(-exp(x[2])))*dmvnorm(x, mu, V)*exp(x[1])/(1-exp(-exp(x[1])))
}
pred2<-function(a,V){
low1<-qnorm(0.0001, a[1],sqrt(V[1,1]))
upp1<-qnorm(0.9999, a[1],sqrt(V[1,1]))
low2<-qnorm(0.0001, a[2],sqrt(V[2,2]))
upp2<-qnorm(0.9999, a[2],sqrt(V[2,2]))
adaptIntegrate(normal.zap, c(low1,low2),c(upp1,upp2), mu=a, V=V)$integral
}
a<-runif(2)
V<-diag(2)
pred2(a,V)
should give the same answer as pred when V is diagonal (after fixing
my mistakes in the last post). The forthcoming version of MCMCglmm
just has the single integration method, but I guess I could also put
this into the next version too.
4/ Currently you have zero covariances, but if you think they exist I
would model them. Remember, that a nice feature of zap models is that
the standard Poisson model is a special case. In terms of random
effects, this occurs when the correlation between family effects for
the two processes is 1 (i.e. fit ~family, rather than
idh(trait):family).
5/ I'm not sure I understand the question. The uncertainty here is
arising because of uncertainty in the parameters of the model? If so,
possibilities are that i) there is more information for the reference
level because other levels are more confounded with other predictors
in the model ii) the reference level is associated with larger
outcomes and so the same change on the link scale generates larger
differences on the data scale.
Cheers,
Jarrod
Quoting Ruben Arslan <rubenarslan at gmail.com> on Tue, 17 Mar 2015
18:53:33 +0000:
> Hi Jarrod,
>
> thanks for the extensive reply! This helps a lot, though it sounds like I
> was hubristic to attempt this myself.
> I tried using the approach you mapped out in the function gist
> <https://gist.github.com/rubenarslan/aeacdd306b3d061819a6> I posted. I
> simply put the pred function in a loop, so that I wouldn't make any
> mistakes while vectorising and since I don't care about performance at this
> point.
>
> Of course, I have some follow up questions though.. I'm sorry if I'm being
> a pain, I really appreciate the free advice, but understand of course if
> other things take precedence.
>
> 1. You're not "down with" developing publicly are you? Because I sure would
> like to test-drive the newdata prediction and simulation functions..
>
> 2. Could you make sure that I got this right: "When predictions are to be
> taken after marginalising the random effects (including the `residual'
> over-dispersion) it is not possible to obtain closed form expressions."
> That is basically my scenario, right? In the example I included, I also had
> a group-level random effect (family). Ore are you talking about the "trait"
> as the random effect (as in your example) and my scenario is different and
> I cannot apply the numerical double integration procedure you posted?
> To be clear about my prediction goal without using language that I might be
> using incorrectly: I want to show what the average effect in the response
> unit, number of children, is in my population(s). I have data on whole
> populations and am using all of it (except individuals that don't have
> completed fertility yet, because I have yet to find a way to model both
> zero-inflation and right censoring).
>
> 3. "Numerical integration could be extended to double integration in which
> case covariance between the Poisson part and the binary part could be
> handled." That is what you posted an example of and it applies to my
> scenario, because I specified a prior R=list(V=diag(2), nu=1.002, fix=2)
> and rcov=~idh(trait):units, random=~idh(trait):idParents?
> But this double integration approach is something you just wrote
> off-the-cuff and I probably shouldn't use it in a publication? Or is this
> in the forthcoming MCMCglmm release and I might actually be able to refer
> to it once I get to submitting?
>
> 4. Could I change my model specification to forbid covariance between the
> two parts and not shoot myself in the foot? Would this allow for a more
> valid/tested approach to prediction?
>
> 5. When I use your method on my real data, I get less variation around the
> prediction "for the reference level" than for all other factor levels.
> My reference level actually has fewer cases than the others, so this isn't
> "right" in a way.
> Is this because I'm not doing newdata prediction? I get the "right" looking
> uncertainty if I bootstrap newdata predictions in lme4,
> Sorry if this is children's logic :-)
> Here's an image of the prediction
> <http://rpubs.com/rubenarslan/mcmcglmm_pred> and the raw data
> <http://rpubs.com/rubenarslan/raw>.
>
> Many thanks for any answers that you feel inclined to give.
>
> Best wishes,
>
> Ruben
>
> On Tue, Mar 17, 2015 at 5:31 PM Jarrod Hadfield <j.hadfield at ed.ac.uk> wrote:
>
>> Hi,
>>
>> Sorry - I should have replied to this post earlier. I've been working
>> on predict/simulate methods for all MCMcglmm models (including
>> zero-inflated/altered/hurdle/truncated models) and these are now
>> largely complete (together with newdata options).
>>
>> When predictions are to be taken after marginalising the random
>> effects (including the `residual' over-dispersion) it is not possible
>> to obtain closed form expressions. The options that will be available
>> in MCMCglmm are:
>>
>> 1) algebraic approximation
>> 2) numerical integration
>> 3) simulation
>>
>> 1) and 2) are currently only accurate when the random/residual effect
>> structure implies no covariance between the Poisson part and the
>> binary part.
>>
>> 1) is reasonably accurate for zero-inflated distributions, but can be
>> pretty poor for the remainder because they all involve zero-truncated
>> Poisson log-normal distributions and my taylor approximation for the
>> mean is less than ideal (any suggestions would be helpful).
>>
>> 2) could be extended to double integration in which case covariance
>> between the Poisson part and the binary part could be handled.
>>
>> In your code, part of the problem is that you have fitted a zapoisson,
>> but the prediction is based on a zipoisson (with complementary log-log
>> link rather than logt link).
>>
>> In all zero-inflated/altered/hurdle/truncated models
>>
>> E[y] = E[(1-prob)*meanc]
>>
>> where prob is the probabilty of a zero in the binary part and meanc is
>> the mean of a Poisson distribution (zipoisson) or a zero-truncated
>> poisson (zapoisson and hupoisson). If we have eta_1 as the linear
>> predictor for the poisson part and eta_2 as the linear predictor for
>> the binary part:
>>
>> In zipoisson: prob = plogis(eta_2) and meanc = exp(eta_1)
>> In zapoisson: prob = exp(-exp(eta_2)) and meanc =
>> exp(eta_1)/(1-exp(-exp(eta_1)))
>> In hupoisson: prob = plogis(eta_2) and meanc =
>> exp(eta_1)/(1-exp(-exp(eta_1)))
>> In ztpoisson: prob = 0 and meanc =
>> exp(eta_1)/(1-exp(-exp(eta_1)))
>>
>> In each case the linear predictor has a `fixed' part and a `random'
>> part which I'll denote as `a' and `u' respectively. Ideally we would
>> want
>>
>> E[(1-prob)*meanc] taken over u_1 & u_2
>>
>> if prob and meanc are independent this is a bit easier as
>>
>> E[y] = E[1-prob]E[meanc]
>>
>> and the two expectations ony need to taken with repsect to their
>> respective random effects. If we have sd_1 and sd_2 as the standard
>> deviations of the two sets of random effects then for the zapoisson:
>>
>> normal.evd<-function(x, mu, v){
>> exp(-exp(x))*dnorm(x, mu, sqrt(v))
>> }
>> normal.zt<-function(x, mu, v){
>> exp(x)/(1-exp(-exp(x)))*dnorm(x, mu, sqrt(v))
>> }
>>
>> pred<-function(a_1, a_2, sd_1, sd_2){
>> prob<-1-integrate(normal.evd, qnorm(0.0001, a_2,sd_2),
>> qnorm(0.9999, a_2,sd_2), a_2,sd_2)[[1]]
>> meanc<-integrate(normal.zt, qnorm(0.0001, a_1,sd_1),
>> qnorm(0.9999, a_1,sd_1), a_1,sd_1)[[1]]
>> prob*meanc
>> }
>>
>> # gives the expected value with reasonable accuracy. As an example:
>>
>> x<-rnorm(300)
>> l1<-rnorm(300, 1/2+x, sqrt(1))
>> l2<-rnorm(300, 1-x, sqrt(1))
>>
>> y<-rbinom(300, 1, 1-exp(-exp(l2)))
>> y[which(y==1)]<-qpois(runif(sum(y==1), dpois(0,
>> exp(l1[which(y==1)])), 1), exp(l1[which(y==1)]))
>> # cunning sampler from Peter Dalgaard (R-sig-mixed)
>>
>> data=data.frame(y=y, x=x)
>> prior=list(R=list(V=diag(2), nu=0.002, fix=2))
>>
>> m1<-MCMCglmm(y~trait+trait:x-1, rcov=~idh(trait):units, data=data,
>> family="zapoisson", prior=prior)
>>
>> b_1<-colMeans(m1$Sol)[c(1,3)]
>> b_2<-colMeans(m1$Sol)[c(2,4)]
>> sd_1<-mean(sqrt(m1$VCV[,1]))
>> sd_2<-mean(sqrt(m1$VCV[,2]))
>>
>> # note it is more accurate to take the posterior mean prediction
>> rather than the prediction from the posterior means as I've done here,
>> but for illustration:
>>
>> x.pred<-seq(-3,3,length=100)
>> p<-1:100
>> for(i in 1:100){
>> p[i]<-pred(a_1 = b_1[1]+x.pred[i]*b_1[2], a_2 =
>> b_2[1]+x.pred[i]*b_2[2], sd_1=sd_1, sd_2=sd_2)
>> }
>>
>> plot(y~x)
>> lines(p~x.pred)
>>
>> Cheers,
>>
>> Jarrod
>>
>>
>>
>>
>> Quoting Ruben Arslan <rubenarslan at gmail.com> on Tue, 17 Mar 2015
>> 13:33:25 +0000:
>>
>> > Dear list,
>> >
>> > I've made a reproducible example of the zero-altered prediction,
>> > in the hope that someone will have a look and reassure me that I'm going
>> > about this the right way.
>> > I was a bit confused by the point about p_i and n_i being correlated
>> (they
>> > are in my case), but I think this was a red herring for me
>> > since I'm not deriving the variance analytically.
>> > The script is here: https://gist.github.com/rubenarslan/
>> aeacdd306b3d061819a6
>> > and if you don't want to run the simulation fit yourself, I've put an RDS
>> > file of the fit here:
>> > https://dl.dropboxusercontent.com/u/1078620/m1.rds
>> >
>> > Best regards,
>> >
>> > Ruben Arslan
>> >
>> > On Tue, Mar 10, 2015 at 1:22 PM Ruben Arslan <rubenarslan at gmail.com>
>> wrote:
>> >
>> >> Dear Dr Bolker,
>> >>
>> >> I'd thought about something like this, one point of asking was to
>> >> see whether
>> >> a) it's implemented already, because I'll probably make dumb mistakes
>> while
>> >> trying b) it's not implemented because it's a bad idea.
>> >> Your response and the MCMCglmm course notes make me hope that it's c)
>> not
>> >> implemented because nobody did yet or d) it's so simple that everybody
>> does
>> >> it on-the-fly.
>> >>
>> >> So I tried my hand and would appreciate corrections. I am sure there is
>> >> some screw-up or an inelegant approach in there.
>> >> I included code for dealing with mcmc.lists because that's what I have
>> and
>> >> I'm not entirely sure how I deal with them is correct either.
>> >>
>> >> I started with a zero-altered model, because those fit fastest and
>> >> according to the course notes have the least complex likelihood.
>> >> Because I know not what I do, I'm not dealing with my random effects at
>> >> all.
>> >>
>> >> I pasted a model summary below to show what I've applied the below
>> >> function to. The function gives the following quantiles when applied to
>> 19
>> >> chains of that model.
>> >> 5% 50% 95%
>> >> 5.431684178 5.561211207 5.690655200
>> >> 5% 50% 95%
>> >> 5.003974382 5.178192327 5.348246558
>> >>
>> >> Warm regards,
>> >>
>> >> Ruben Arslan
>> >>
>> >> HPDpredict_za = function(object, predictor) {
>> >>
>> >> if(class(object) != "MCMCglmm") {
>> >> if(length( object[[1]]$Residual$nrt )>1) {
>> >> object = lapply(object,FUN=function(x) { x$Residual$nrt<-2;x })
>> >> }
>> >> Sol = mcmc.list(lapply(object,FUN=function(x) { x$Sol}))
>> >> vars = colnames(Sol[[1]])
>> >> } else {
>> >> Sol = as.data.frame(object$Sol)
>> >> vars = names(Sol)
>> >> }
>> >> za_predictor = vars[ vars %ends_with% predictor & vars %begins_with%
>> >> "traitza_"]
>> >> za_intercept_name = vars[ ! vars %contains% ":" & vars %begins_with%
>> >> "traitza_"]
>> >> intercept = Sol[,"(Intercept)"]
>> >> za_intercept = Sol[, za_intercept_name]
>> >> l1 = Sol[, predictor ]
>> >> l2 = Sol[, za_predictor ]
>> >> if(is.list(object)) {
>> >> intercept = unlist(intercept)
>> >> za_intercept = unlist(za_intercept)
>> >> l1 = unlist(l1)
>> >> l2 = unlist(l2)
>> >> }
>> >> py_0 = dpois(0, exp(intercept + za_intercept))
>> >> y_ygt0 = exp(intercept)
>> >> at_intercept = (1-py_0) * y_ygt0
>> >>
>> >> py_0 = dpois(0, exp(intercept + za_intercept + l2))
>> >> y_ygt0 = exp(intercept + l1)
>> >> at_predictor_1 = (1-py_0) * y_ygt0
>> >> print(qplot(at_intercept))
>> >> print(qplot(at_predictor_1))
>> >> df = data.frame("intercept" = at_intercept)
>> >> df[, predictor] = at_predictor_1
>> >> print(qplot(x=variable,
>> >> y=value,data=suppressMessages(melt(df)),fill=variable,alpha=I(0.40),
>> geom =
>> >> 'violin'))
>> >> print(quantile(at_intercept, probs = c(0.05,0.5,0.95)))
>> >> print(quantile(at_predictor_1, probs = c(0.05,0.5,0.95)))
>> >> invisible(df)
>> >> }
>> >>
>> >>
>> >> > summary(object[[1]])
>> >>
>> >> Iterations = 100001:299901
>> >> Thinning interval = 100
>> >> Sample size = 2000
>> >>
>> >> DIC: 349094
>> >>
>> >> G-structure: ~idh(trait):idParents
>> >>
>> >> post.mean l-95% CI u-95% CI eff.samp
>> >> children.idParents 0.0189 0.0164 0.0214 1729
>> >> za_children.idParents 0.2392 0.2171 0.2622 1647
>> >>
>> >> R-structure: ~idh(trait):units
>> >>
>> >> post.mean l-95% CI u-95% CI eff.samp
>> >> children.units 0.144 0.139 0.148 1715
>> >> za_children.units 1.000 1.000 1.000 0
>> >>
>> >> Location effects: children ~ trait * (maternalage.factor +
>> paternalloss +
>> >> maternalloss + center(nr.siblings) + birth.cohort + urban + male +
>> >> paternalage.mean + paternalage.diff)
>> >>
>> >> post.mean l-95% CI u-95% CI
>> >> eff.samp pMCMC
>> >> (Intercept) 2.088717 2.073009 2.103357
>> >> 2000 <0.0005 ***
>> >> traitza_children -1.933491 -1.981945 -1.887863
>> >> 2000 <0.0005 ***
>> >> maternalage.factor(14,20] 0.007709 -0.014238 0.027883
>> >> 1500 0.460
>> >> maternalage.factor(35,50] 0.006350 -0.009634 0.024107
>> >> 2000 0.462
>> >> paternallossTRUE 0.000797 -0.022716 0.025015
>> >> 2000 0.925
>> >> maternallossTRUE -0.015542 -0.040240 0.009549
>> >> 2000 0.226
>> >> center(nr.siblings) 0.005869 0.004302 0.007510
>> >> 2000 <0.0005 ***
>> >> birth.cohort(1703,1722] -0.045487 -0.062240 -0.028965
>> >> 2000 <0.0005 ***
>> >> birth.cohort(1722,1734] -0.055872 -0.072856 -0.036452
>> >> 2000 <0.0005 ***
>> >> birth.cohort(1734,1743] -0.039770 -0.056580 -0.020907
>> >> 2000 <0.0005 ***
>> >> birth.cohort(1743,1750] -0.030713 -0.048301 -0.012214
>> >> 2000 0.002 **
>> >> urban -0.076748 -0.093240 -0.063002
>> >> 2567 <0.0005 ***
>> >> male 0.106074 0.095705 0.115742
>> >> 2000 <0.0005 ***
>> >> paternalage.mean -0.024119 -0.033133 -0.014444
>> >> 2000 <0.0005 ***
>> >> paternalage.diff -0.018367 -0.032083 -0.005721
>> >> 2000 0.007 **
>> >> traitza_children:maternalage.factor(14,20] -0.116510 -0.182432
>> -0.051978
>> >> 1876 0.001 ***
>> >> traitza_children:maternalage.factor(35,50] -0.045196 -0.094485
>> 0.002640
>> >> 2000 0.075 .
>> >> traitza_children:paternallossTRUE -0.171957 -0.238218
>> -0.104820
>> >> 2000 <0.0005 ***
>> >> traitza_children:maternallossTRUE -0.499539 -0.566825
>> -0.430637
>> >> 2000 <0.0005 ***
>> >> traitza_children:center(nr.siblings) -0.023723 -0.028676
>> -0.018746
>> >> 1848 <0.0005 ***
>> >> traitza_children:birth.cohort(1703,1722] -0.026012 -0.074250
>> 0.026024
>> >> 2000 0.319
>> >> traitza_children:birth.cohort(1722,1734] -0.279418 -0.329462
>> -0.227187
>> >> 2000 <0.0005 ***
>> >> traitza_children:birth.cohort(1734,1743] -0.260165 -0.312659
>> -0.204462
>> >> 2130 <0.0005 ***
>> >> traitza_children:birth.cohort(1743,1750] -0.481457 -0.534568
>> -0.426648
>> >> 2000 <0.0005 ***
>> >> traitza_children:urban -0.604108 -0.645169 -0.562554
>> >> 1702 <0.0005 ***
>> >> traitza_children:male -0.414988 -0.444589 -0.387005
>> >> 2000 <0.0005 ***
>> >> traitza_children:paternalage.mean 0.006545 -0.018570
>> 0.036227
>> >> 2000 0.651
>> >> traitza_children:paternalage.diff -0.097982 -0.136302
>> -0.060677
>> >> 2000 <0.0005 ***
>> >>
>> >>
>> >> On Mon, Mar 9, 2015 at 10:12 PM Ben Bolker <bbolker at gmail.com> wrote:
>> >>
>> >>> Ruben Arslan <rubenarslan at ...> writes:
>> >>>
>> >>> >
>> >>> > Dear list,
>> >>> >
>> >>> > I wanted to ask: Is there any (maybe just back of the envelope) way
>> to
>> >>> > obtain a response prediction for zero-inflated or hurdle type models?
>> >>> > I've fit such models in MCMCglmm, but I don't work in ecology and my
>> >>> > previous experience with explaining such models to "my audience" did
>> not
>> >>> > bode well. When it comes to humans, the researchers I presented to
>> are
>> >>> not
>> >>> > used to offspring count being zero-inflated (or acquainted with that
>> >>> > concept), but in my historical data with high infant mortality, it is
>> >>> (in
>> >>> > modern data it's actually slightly underdispersed).
>> >>> >
>> >>> > Currently I'm using lme4 and simply splitting my models into two
>> stages
>> >>> > (finding a mate and having offspring).
>> >>> > That's okay too, but in one population the effect of interest is not
>> >>> > clearly visible in either stage, only when both are taken together
>> (but
>> >>> > then the outcome is zero-inflated).
>> >>> > I expect to be given a hard time for this and hence thought I'd use a
>> >>> > binomial model with the outcome offspring>0 as my main model, but
>> that
>> >>> > turns out to be hard to explain too and doesn't
>> >>> > really do the data justice.
>> >>> >
>> >>> > Basically I don't want to be forced to discuss my smallest population
>> >>> as a
>> >>> > non-replication of the effect because I was insufficiently able to
>> >>> explain
>> >>> > the statistics behind my reasoning that the effect shows.
>> >>>
>> >>> I think the back-of-the envelope answer would be that for a two-stage
>> >>> model with a prediction of p_i for the probability of having a non-zero
>> >>> response (or in the case of zero-inflated models, the probability of
>> >>> _not_ having a structural zero) and a prediction of n_i for the
>> >>> conditional
>> >>> part of the model, the mean predicted value is p_i*n_i and the
>> >>> variance is _approximately_ (p_i*n_i)^2*(var(p_i)/p_i^2 +
>> var(n_i)/n_i^2)
>> >>> (this is assuming
>> >>> that you haven't built in any correlation between p_i and n_i, which
>> >>> would be hard in lme4 but _might_ be possible under certain
>> circumstances
>> >>> via a multitype model in MCMCglmm).
>> >>>
>> >>> Does that help?
>> >>>
>> >>> _______________________________________________
>> >>> R-sig-mixed-models at r-project.org mailing list
>> >>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>> >>>
>> >>
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > _______________________________________________
>> > R-sig-mixed-models at r-project.org mailing list
>> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>> >
>> >
>>
>>
>>
>> --
>> The University of Edinburgh is a charitable body, registered in
>> Scotland, with registration number SC005336.
>>
>>
>>
>
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