PoolReg: Frequentist Mixed or Fixed Effect Logistic Regression with...

In PoolTestR: Prevalence and Regression for Pool-Tested (Group-Tested) Data

Frequentist Mixed or Fixed Effect Logistic Regression with Presence/Absence Tests on Pooled Samples

Description

It can be useful to do mixed effects logistic regression on the presence/absence results from pooled samples, however one must adjust for the size of each pool to correctly identify trends and associations. This can done by using a custom link function [PoolTestR::PoolLink()], defined in this package, in conjunction with using glm from the stats package (fixed effect models) or glmer from the lme4 package (mixed effect models).

Usage

PoolReg(formula, data, poolSize, link = "logit", ...)

Arguments

formula	A formula of the kind used to define models in lme4, which are generalisation of the formulae used in lm or glm that allow for random/group effects. The left-hand side of the formula should be the name of column in data with the result of the test on the pooled samples. The result must be encoded with 1 indicating a positive test result and 0 indicating a negative test result.
data	A data.frame with one row for each pooled sampled and columns for the size of the pool (i.e. the number of specimens / isolates / insects pooled to make that particular pool), the result of the test of the pool and any number of columns to be used as the dependent variables in the logistic regression
poolSize	The name of the column with number of specimens/isolates/insects in each pool
link	link function. There are two options ''logit'' (logistic regression, the default) and ''cloglog'' (complementary log log regression).
...	Arguments to be passed on to stats::glm or lme4::glmer e.g. weights

Value

An object of class glmerMod (or glm if there are no random/group effects)

See Also

getPrevalence, PoolRegBayes

Examples

# Perform logistic-type regression modelling for a synthetic dataset consisting
# of pools (sizes 1, 5, or 10) taken from 4 different regions and 3 different
# years. Within each region specimens are collected at 4 different villages,
# and within each village specimens are collected at 8 different sites.


### Models in a frequentist framework
#ignoring hierarchical sampling frame within each region
Mod <- PoolReg(Result ~ Region + Year,
               data = SimpleExampleData,
               poolSize = NumInPool)
summary(Mod)

#accounting hierarchical sampling frame within each region
HierMod <- PoolReg(Result ~ Region + Year + (1|Village) + (1|Site),
                   data = SimpleExampleData,
                   poolSize = NumInPool)
summary(HierMod)


### Models in a Bayesian framework with default (non-informative) priors
#ignoring hierarchical sampling frame within each region

  BayesMod <- PoolRegBayes(Result ~ Region + Year,
                           data = SimpleExampleData,
                           poolSize = NumInPool)
  summary(BayesMod)

  #we could also account for hierarchical sampling frame within each region but
  #note that this is more complex and slower)

  # BayesHierMod <- PoolRegBayes(Result ~ Region + Year + (1|Village) + (1|Site),
  #                              data = SimpleExampleData,
  #                              poolSize = NumInPool)


### Calculate adjusted estimates of prevalence
# We use the same function for all four models, but the outputs are slightly different

#For models without hierarchical sampling structure there is an estimate of
#prevalence for every combination of population (fixed) effects: e.g. Region and
#Year
getPrevalence(Mod) #Frequentist model

  getPrevalence(BayesMod) #Bayesian model


#For models without hierarchical sampling structure, there is a prevalence
#estimate for each combination of region and year and then at each level of the
#hierarchical sampling frame (i.e. for each village in each region and each site
#in each village)
getPrevalence(HierMod)

# You can also use getPrevalence to predict prevalence for other values of the
# covariates (e.g. predict prevalence in year 4 based on linear trend on the
# logit scale)

#Making a data frame containing data make predictions on
DataFuture <- unique(data.frame(Region = SimpleExampleData$Region,
                                Village = SimpleExampleData$Village,
                                Site = SimpleExampleData$Site,
                                Year = 4))

getPrevalence(Mod, newdata = DataFuture)
getPrevalence(HierMod, newdata = DataFuture)

PoolTestR documentation built on April 3, 2025, 9:28 p.m.