est_p_detect.RdEstimate the probability of detection
est_p_detect(
variable_levels,
betas,
ln_eDNA_sd,
std_curve_alpha,
std_curve_beta,
n_rep = 1:12,
prob_zero = 0.08,
model_fit = NULL,
upper_Cq = 40
)numeric vector, with each element corresponding to the condition to estimate the probability of detection.
numeric vector, the effect sizes for each of the variable level
the measurement error on ln[eDNA]. If a model_fit is provided and this is missing, the estimated sd(ln_eDNA) from the model will be used.
the alpha for the std. curve formula for conversion between log(concentration) and CQ
the alpha for the std. curve formula for conversion between log(concentration) and CQ
the number of replicate measurements at the levels specified
the probability of seeing a non-detection, i.e. zero, from a zero-inflated process. Defaults to 8 is the rate of inflated zeros in a large sampling experiment.
optional, a model fit from eDNA_lm or
eDNA_lmer. If this is provided, an estimate derived
from the posterior estimates of beta is calculated.
the upper limit on detection. Converted to the lower_bound of detection internally
object of class "eDNA_p_detect" with the estimates of the probability of detection for the variable levels provided.
This function estimates the probability of getting a positive detection for an eDNA survey given a set of predictors. This can be useful when trying to take the estimates from a preliminary study and use those estimates to inform the deployment of future sampling schemes. The function assumes that you have either an idea of the effects of the various predictors, for example from a previous study, or a fit model with estimates of the effect sizes.
This function takes one circumstance at a time, and calculates the range of outcomes given a number of repeated sampling attempts. The probability calculated is the probability of getting at least one positive detection. For details on the underlying model and assumptions for this calculation, please refer to the package vignette.
This function deals with random effects in two different
ways. First, when we desire to see the probability of detection
for a specific instance of a random effect, users can specify the
random effect as just another effect by specifying the random
effect = 1 in the variable list, and then the size of the random
effect. However, when users wish to estimate the probability of
detection in cases where random effects are generated from a
distribution of random effects, this can be accomplished by adding
the standard deviation of the random effect to the
Cq_sd. This takes advantage of the fact that random effects
are just another source of variation, and that sum of random
normal distributions is itself a random normal distribution.
est_p_detect(variable_levels = c(Intercept = 1, Distance = 100, Volume = 20),
betas = c(Intercept = -10.5, Distance = -0.05, Volume = 0.001),
ln_eDNA_sd = 1, std_curve_alpha = 21.2, std_curve_beta = -1.5,
n_rep = 1:12)
#> Variable levels:
#> Intercept Distance Volume
#> 1 100 20
#>
#> n_reps p_detect
#> 1 1 0.001477609
#> 2 2 0.002953034
#> 3 3 0.004426279
#> 4 4 0.005897347
#> 5 5 0.007366242
#> 6 6 0.008832966
#> 7 7 0.010297523
#> 8 8 0.011759916
#> 9 9 0.013220148
#> 10 10 0.014678222
#> 11 11 0.016134142
#> 12 12 0.017587911