d.rel.conn.multiple {ConnMatTools} R Documentation

## Functions for estimating the probability distribution of relative connectivity values as a weighted sum over possible input parameters

### Description

These functions calculate the probability density function (`d.rel.conn.multiple`), the probability distribution function (aka the cumulative distribution function; `p.rel.conn.multiple`) and the quantile function (`q.rel.conn.multiple`) for the relative (to all settlers at the destination site) connectivity value for larval transport between a source and destination site. This version allows one to input multiple possible fractions of individuals (i.e., eggs) marked at the source site, multiple possible numbers of settlers collected and multiple possible marked individuals observed in the sample. This gives one the possibility to produce ensemble averages over different input parameter values with different probabilities of being correct.

### Usage

```d.rel.conn.multiple(
phi,
ps,
ks,
ns,
weights = 1,
d.rel.conn = d.rel.conn.beta.prior,
...
)

p.rel.conn.multiple(
phi,
ps,
ks,
ns,
weights = 1,
p.rel.conn = p.rel.conn.beta.prior,
...
)

q.rel.conn.multiple.func(
ps,
ks,
ns,
weights = 1,
p.rel.conn = p.rel.conn.beta.prior,
N = 1000,
...
)

q.rel.conn.multiple(
q,
ps,
ks,
ns,
weights = 1,
p.rel.conn = p.rel.conn.beta.prior,
N = 1000,
...
)
```

### Arguments

 `phi` Vector of fractions of individuals (i.e., eggs) from the source population settling at the destination population `ps` Vector of fractions of individuals (i.e., eggs) marked in the source population `ks` Vector of numbers of marked settlers found in sample `ns` Vector of total numbers of settlers collected `weights` Vector of weights for each set of p, k and n values `d.rel.conn` Function to use to calculate probability density for individual combinations of `ps`, `ks` and `ns`. Defaults to `d.rel.conn.beta.prior`. Could also be `d.rel.conn.unif.prior`. `...` Additional arguments for the function `d.rel.conn` or `p.rel.conn` `p.rel.conn` Function to use to calculate cumulative probability distribution for individual combinations of `ps`, `ks` and `ns`. Defaults to `p.rel.conn.beta.prior`. Could also be `p.rel.conn.unif.prior`. `N` Number of points at which to estimate cumulative probability function for reverse approximation of quantile distribution. Defaults to `1000`. `q` Vector of quantiles

### Details

If `ps`, `ks`, `ns` and `weights` can be scalars or vectors of the same length (or lengths divisible into that of the largest input parameter). `weights` are normalized to sum to 1 before being used to sum probabilities from each individual set of input parameters.

### Value

Vector of probabilities or quantiles, or a function in the case of `q.rel.conn.multiple.func`

### Functions

• `d.rel.conn.multiple`: Estimates quantiles for the probability distribution function for relative connectivity between a pair of sites for multiple possible `p`, `k` and `n` values.

• `p.rel.conn.multiple`: Estimates the cumulative probability distribution for relative connectivity between a paire of sites for multiple possible `p`, `k` and `n` values.

• `q.rel.conn.multiple.func`: Returns a function to estimate quantiles for the probability distribution function for relative connectivity between a pair of sites for multiple possible `p`, `k` and `n` values.

• `q.rel.conn.multiple`: Estimates quantiles for the probability distribution function for relative connectivity between a pair of sites for multiple possible `p`, `k` and `n` values.

### Author(s)

David M. Kaplan dmkaplan2000@gmail.com

### References

Kaplan DM, Cuif M, Fauvelot C, Vigliola L, Nguyen-Huu T, Tiavouane J and Lett C (in press) Uncertainty in empirical estimates of marine larval connectivity. ICES Journal of Marine Science. doi:10.1093/icesjms/fsw182.

Other connectivity estimation: `d.rel.conn.beta.prior()`, `d.rel.conn.dists.func()`, `d.rel.conn.finite.settlement()`, `d.rel.conn.multinomial.unnorm()`, `d.rel.conn.unif.prior()`, `dual.mark.transmission()`, `optim.rel.conn.dists()`, `r.marked.egg.fraction()`

### Examples

```library(ConnMatTools)

# p values have uniform probability between 0.1 and 0.4
p <- seq(0.1,0.8,length.out=100)

# Weights the same for all except first and last, which are halved
w <- rep(1,length(p))
w<-0.5
w[length(w)]<-0.5

n <- 20 # Sample size
k <- 2 # Marked individuals in sample

# phi values to use for plotting distribution
phi <- seq(0,1,0.01)

prior.shape1 = 1 # Uniform prior
# prior.shape1 = 0.5 # Jeffreys prior

# Plot distribution
plot(phi,d.rel.conn.multiple(phi,p,k,n,w,prior.shape1=prior.shape1),
main="Probability density for relative connectivity",
xlab=expression(phi),
ylab="Probability density",
type="l")

# Add standard distributions for max and min p values
lines(phi,d.rel.conn.beta.prior(phi,min(p),k,n,prior.shape1=prior.shape1),
col="red",lty="dashed")
lines(phi,d.rel.conn.beta.prior(phi,max(p),k,n,prior.shape1=prior.shape1),
col="red",lty="dashed")