Normal priors on the logit scale. The defaults are weakly informative for standardized covariates.
Usage
fp_priors(
intercept_sd = 1.5,
slope_sd = 2.5,
gap_mean = 2,
gap_sd = 1.5,
recorder_sd = 2,
fp_site_sd = 1
)Arguments
- intercept_sd
Standard deviation of the Normal(0, sd) priors on the intercepts of occupancy, true detection, and verified detection.
- slope_sd
Standard deviation of the Normal(0, sd) priors on all slopes, including those of the false-positive rate.
- gap_mean, gap_sd
Mean and standard deviation of the Normal prior, truncated at zero, on the gap between the true-detection and false-positive intercepts. The default, Normal(2, 1.5), puts the false-positive rate at the reference roughly an order of magnitude below true detection, while leaving room for the data.
- recorder_sd
Scale of the half-Normal(0, sd) prior on the standard deviation of the recorder random intercept.
- fp_site_sd
Scale of the half-Normal(0, sd) prior on the standard deviation of the site random intercept on the false-positive rate (
fp_occu(random_fp = TRUE)).
Examples
fp_priors()
#> <fp_priors> (logit scale)
#> intercepts ~ Normal(0, 1.5)
#> slopes ~ Normal(0, 2.5)
#> fp_gap ~ Normal(2, 1.5), truncated at 0
#> sd_recorder ~ half-Normal(0, 2)
#> sd_fp_site ~ half-Normal(0, 1)
fp_priors(slope_sd = 1)
#> <fp_priors> (logit scale)
#> intercepts ~ Normal(0, 1.5)
#> slopes ~ Normal(0, 1)
#> fp_gap ~ Normal(2, 1.5), truncated at 0
#> sd_recorder ~ half-Normal(0, 2)
#> sd_fp_site ~ half-Normal(0, 1)