PoolReg | R Documentation |

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).

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

`formula` |
A |

`data` |
A |

`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 |

An object of class `glmerMod`

(or `glm`

if there are no
random/group effects)

`getPrevalence`

,
`PoolRegBayes`

# 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)

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