PoolLink: Link Function for Logistic Regression with Presence/Absence...

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

Link Function for Logistic Regression with Presence/Absence Tests on Pooled Samples

Description

A custom link function for the binomial family to be used with glm

Usage

PoolLink(PoolSize = 1)

Arguments

PoolSize

The number of specimens/isolates/insects in each pool. When used with glm, the length must either be 1 if all the pools are the same size, but the same length as the data otherwise

Value

An object of class link-glm

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/Site),
                   data = SimpleExampleData,
                   poolSize = NumInPool)
summary(HierMod)
#Extract fitted prevalence 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)


### 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)
  getPrevalence(BayesMod) #Extract fitted prevalence for each combination of region and year

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


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

# Extract fitted prevalence for each combination of region and year
getPrevalence(Mod)

  getPrevalence(BayesMod)


#Extract fitted prevalence 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)

  getPrevalence(BayesHierMod)


# You can also use getPrevalence to predict at prevalence for other values of
# the covariates (e.g. predict prevalence in year 4)

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

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

  getPrevalence(BayesMod, newdata = DataFuture)
  getPrevalence(BayesHierMod, newdata = DataFuture)

PoolTestR documentation built on July 1, 2022, 9:06 a.m.