---
title: "Sampling functions"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Sampling functions}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
editor_options:
  markdown:
    wrap: 80
    canonical: true
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  include = TRUE
)
```

# Introduction

SIMplyBee includes functions to sample various values that are expected to vary
between colonies and events. These functions are used to sample numbers, usually
individuals, and proportions. We can use the functions, pass them to other
functions, or save them in the `SimParamBee` object so they can be used by
default by other functions.

We start by loading the package:

```{r}
library(package = "SIMplyBee")
```

# Functions to sample numbers

First, there are functions to sample the number of caste individuals from either
a Poisson or truncated Poisson distribution: `n*Poisson()` and
`n*TruncPoisson()`, where `*` is either `Workers`, `Drones`, `VirginQueens`, or
`Fathers`. Most SIMplyBee functions that take the number of individuals as an
argument can accept these sampling functions as an input, meaning that the
output of such function calls will be stochastic. These functions are useful
when you want to sample a variable number of individuals around a mean, as for
example when mating virgin queens with a variable number of drones.

Let's start a simulation by creating a DCA and an apiary with 10 virgin
colonies:

```{r}
founderGenomes <- quickHaplo(nInd = 20, nChr = 1, segSites = 100)
SP <- SimParamBee$new(founderGenomes)
basePop <- createVirginQueens(founderGenomes)

# Create a DCA from the first 10 base virgin queens
DCA <- createDrones(x = basePop[1:10], nInd = 100)

# Create an apiary with 10 virgin colonies
apiary <- createMultiColony(basePop[11:20])
```

From the literature we know that virgin queens on average mate to 17 drones, but
the actual number varies around this mean. Some mate with 10 drones, some with
20, etc. To resemble this variation, we can use the function `nFathersPoisson()`
to sample variable number of drones from a DCA. The default average for this
function is 15, but you can use any value you want. Let's use this function to
sample 1,000 values and inspect the distribution and the mean.

```{r, echo = FALSE, fig.height = 3, fig.width = 6}
oldpar <- par(mfrow = c(1,2))
hist(nFathersPoisson(n = 1000), main = "Average = 15", xlab = "nFathers")
# Change the average to 10
hist(nFathersPoisson(n = 1000, average = 10), main = "Average = 10", xlab = "nFathers")
par(oldpar)
```

Let's now use this functionality to sample a variable number of drones from the
DCA to mate with each of the 10 virgin queens.

```{r}
droneGroups <- pullDroneGroupsFromDCA(DCA = DCA, n = 10, nDrones = nFathersPoisson)
apiary <- cross(apiary, drones = droneGroups, checkCross = "warning")
```

And inspect the number of fathers in each of the colony and their mean.

```{r}
nFathers(apiary)
mean(nFathers(apiary))
```

Second, we have a group of functions that will sample the number of individuals
according to the colony phenotype, whatever that might be. These functions are
named `n*ColonyPhenotype()`, where `*` is either is either `Workers`, `Drones`,
or `VirginQueens`. An example of this would be sampling the number of workers
and drones according to queen's fecundity or honey yield. An example of this can
be seen in the quantitative genetics vignette in the "Strength and honey yield"
example.

# Functions that sample proportions

SIMplyBee also includes functions to sample the proportions of workers that
leave or are removed when downsizing, splitting, or swarming a colony from
either a uniform distribution or from a beta distribution that accounts for the
number of individuals in a colony (colony strength). These functions are named
`*PUnif()`, where `*` can be either `swarm`, `split`, or `downsize`. There is an
additional function, `splitPColonyStrength()`, that determines the number of
workers to be removed in a split according to the colony strength.

Let's say we want to swarm all the colonies in our apiary with a variable
percentage of workers that leave. We want to sample this percentage from an
uniform distribution with the mean of 0.6. For this, we use `swarmPUnif()`
function that takes a `min` and a `max` values and sample a value between them.
By default, the `min` is set to 0.4 and `max` to 0.6. Let's use this function to
sample a 1,000 values between 0.5 and 0.7 and inspect the mean.

```{r, echo = FALSE, fig.height = 3, fig.width = 3}
hist(swarmPUnif(n = 1000, min = 0.5, max = 0.7),
     xlab = "swarmP", main = "min=0.5, max=0.7", xlim = c(0, 1))
```

Let's now swarm all the colonies in our apiary with a variable percentage of
workers that leave.

```{r}
apiary <- buildUp(apiary, nWorkers = 1000, nDrones = 100)
tmp <- swarm(apiary, p = swarmPUnif(n = 10, min = 0.5, max = 0.7))
nWorkers(tmp$swarm)
```

We see that each colony swarmed with a different percentage between 0.5 and 0.7.

.
