View source: R/estConnectivity.R

diffMC | R Documentation |

Estimates mean (and median) differences in MC, and includes measures of uncertainty (SE and CI). For those measures of uncertainty to be accurate, only apply this function to MC estimates where all data sources are independent (e.g., different species).

```
diffMC(estimates, nSamples = 1e+05, alpha = 0.05, returnSamples = FALSE)
diffStrength(estimates, nSamples = 1e+05, alpha = 0.05, returnSamples = FALSE)
```

`estimates` |
List of at least two MC estimates, provided by the estMC function. If this is a named list (recommended), the function will use these names in labeling the differences. |

`nSamples` |
A positive integer, number of samples (with replacement) to draw from each pair of MC estimates (default 100000). If set to NULL, compares all MC samples from each pair. |

`alpha` |
Level for confidence/credible intervals provided. |

`returnSamples` |
Should the function return all the sampled differences? Defaults to FALSE to reduce storage requirements. Change to TRUE to compute your own summary statistics. |

`diffMC`

returns a list with elements:

`meanDiff, medianDiff`

Vectors with mean and medians of sampled differences for each pairwise comparison. Estimates of difference between MC values incorporating parametric uncertainty.

`seDiff`

Vector with standard errors of MC differences for each pairwise comparison, estimated from SD of sampled differences.

`simpleCI`

Matrix of

`1 - alpha`

confidence intervals for MC differences, estimated as`alpha/2`

and`1 - alpha/2`

quantiles of`sampleMC`

.`bcCI`

Matrix of bias-corrected

`1 - alpha`

confidence intervals for MC differences for each pairwise comparison. Preferable to`simpleCI`

when`meanDiff`

is the best estimate of the MC difference.`simpleCI`

is preferred when`medianDiff`

is a better estimator. When`meanDiff==medianDiff`

, these should be identical. Estimated as the`pnorm(2 * z0 + qnorm(alpha / 2))`

and`pnorm(2 * z0 + qnorm(1 - alpha / 2))`

quantiles of sampled differences, where z0 is the proportion of`sampleDiff < meanDiff`

.`sampleDiff`

Only provided if

`returnSamples`

is TRUE. List of sampled values for each pairwise MC difference.

Cohen, E. B., C. S. Rushing, F. R. Moore, M. T. Hallworth, J. A. Hostetler, M. Gutierrez Ramirez, and P. P. Marra. 2019. The strength of migratory connectivity for birds en route to breeding through the Gulf of Mexico.

```
data('OVENdata')
ovenPsi <- estTransition(isGL = OVENdata$isGL, #Logical vector:light-level GL(T)
isTelemetry = !OVENdata$isGL,
geoBias = OVENdata$geo.bias, # Light-level GL location bias
geoVCov = OVENdata$geo.vcov, # Location covariance matrix
targetSites = OVENdata$targetSites, # Non-breeding target sites
originSites = OVENdata$originSites, # Breeding origin sites
originPoints = OVENdata$originPoints, # Capture Locations
targetPoints = OVENdata$targetPoints, # Device target locations
verbose = 0, # output options
nSamples = 100, # This is set low for example
resampleProjection = sf::st_crs(OVENdata$targetSites))
ovenEst <- estStrength(targetDist = OVENdata$targetDist, # targetSites distance matrix
originDist = OVENdata$originDist, # originSites distance matrix
originRelAbund = OVENdata$originRelAbund,#Origin relative abund
psi = ovenPsi,
verbose = 1, # output options
nSamples = 1000)
fm <- getCMRexample()
originPos13 <- matrix(c(rep(seq(-99, -81, 2), each = 10),
rep(seq(49, 31, -2), 10)), 100, 2)
targetPos13 <- matrix(c(rep(seq(-79, -61, 2), each = 10),
rep(seq(9, -9, -2), 10)), 100, 2)
originPosCMR <- rowsum(originPos13, c(rep(1:2, 5, each = 5),
rep(3:4, 5, each = 5))) / 25
targetPosCMR <- rowsum(targetPos13, c(rep(1:2, 5, each = 5),
rep(3:4, 5, each = 5))) / 25
originDist <- distFromPos(originPosCMR, 'ellipsoid')
targetDist <- distFromPos(targetPosCMR, 'ellipsoid')
originRelAbundTrue <- rep(0.25, 4)
theorEst <- estStrength(originRelAbund = originRelAbundTrue, psi = fm,
originDist = originDist, targetDist = targetDist,
originSites = 5:8, targetSites = c(3,2,1,4),
nSamples = 1000, verbose = 0,
sampleSize = length(grep("[2-5]", fm$data$data$ch)))
ovenEst
theorEst
diff1 <- diffMC(estimates = list(Ovenbird = ovenEst, Theorybird = theorEst),
nSamples = 10000, returnSamples = TRUE)
```

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