Description Usage Arguments Value Note Examples
compute_WMS
computes the WMS gradient based on the fit of step 3 for a
given study. The WMS gradient is produced by predicting the gradient in each
year during the study period of ALL studies (accounting for the realization
of the yearly random effect). The predictions are then averaged accounting
for their uncertainty as measured by the prediction variance of the fit.
1 | compute_WMS(mod_sel)
|
mod_sel |
An object produced by the function |
A list with two elements: the Weighted Mean Selection (WMS) gradient, and the weighted standard error associated with WMS (se).
The weighted standard error is the square root of a prediction variance. It represents the prediction uncertainty of a weighted average of yearly selection gradients, taking into account the prediction covariance of yearly selection gradients. The weights used for averaging are the inverse of the yearly prediction variances, to minimize the variance of the average.
1 2 3 4 | data_for_Trait <- prepare_data(data = dat_Trait, temperature = TRUE,
phenology = TRUE, morphology = FALSE)
mod3 <- fit_cond_id(data = data_for_Trait, id = "82", condition = "3")
compute_WMS(mod_sel = mod3)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.