powerAnalysis {unmarked} | R Documentation |
Conduct a power analysis for an unmarked
model
Description
This function uses a simulation-based approach to estimate power for parameters
in unmarked models. At a minimum, users must provide an unmarkedFrame
object describing the experimental design and a list of effect sizes for each
parameter in the model. See the unmarkedPower
vignette for more details and
examples.
Usage
## S4 method for signature 'unmarkedFrame'
powerAnalysis(object, model = NULL, effects = NULL,
alpha=0.05, nsim = 100, parallel = FALSE, nulls=NULL, ...)
## S4 method for signature 'list'
powerAnalysis(object, model = NULL, effects = NULL,
alpha=0.05, nsim = length(object), parallel = FALSE, nulls=NULL, ...)
Arguments
object |
An |
model |
The model to use when the |
effects |
A list containing the desired effect sizes/parameter values
for which you want to estimate power. This list must follow a specific format.
There is one named entry in the list per submodel (e.g., occupancy, detection).
Each list element should be a numeric vector with length equal to the number
of parameters in that submodel. Parameter values are on the inverse link
scale. You can leave |
alpha |
Desired Type I error rate. |
nsim |
Number of simulations to conduct. |
parallel |
Logical; run simulations in parallel? |
nulls |
If provided, a list matching the structure of |
... |
Arguments to send to the fitting function for the model.
Most importantly this will include formula argument(s), but could also include
distributions, key functions, etc. For example, for simulating occupancy data,
you must also supply the argument |
Value
unmarkedPower
object containing the results of the power analysis.
For information on interpretation of the output, see the power analysis
vignette.
Author(s)
Ken Kellner contact@kenkellner.com
See Also
Examples
## Not run:
# Create experimental design
M <- 50
J <- 3
y <- matrix(NA, M, J)
sc <- data.frame(x=rnorm(M))
umf <- unmarkedFrameOccu(y, siteCovs=sc)
# Power analysis
p <- powerAnalysis(umf, model=occu, formula=~1~x,
effects = list(state = c(-0.2, 0.3), det = 0))
p
summary(p, alpha=0.3)
plot(p, ylim=c(-3, 3))
plot(p, ylim=c(-3, 3))
# Simulate your own datasets first and pass to power analysis
cf <- list(state=c(0,1), det=0)
s <- simulate(umf, model = occu, formula=~1~x, coefs=cf, nsim = 100)
p2 <- powerAnalysis(s, model=occu, formula=~1~x, effects=cf)
p2
## End(Not run)