Description Usage Arguments Value Examples
extract_effects_all_ids
retrieves the effect size and SE for each study
by calling the function extract_effects
on each study.
1 | extract_effects_all_ids(data, condition, nb_cores)
|
data |
A dataframe containing per each study the time series of relevant variables (i.e. yearly climate values, yearly trait values or yearly selection differentials) to be analyzed. |
condition |
A character specifying which condition is to be tested (for more details see Radchuk et al. (in review)):
|
nb_cores |
An integer indicating how many cores should be used for assessing the LRT by bootstrap (see spaMM documentation for more details). |
A dataframe containing all effect sizes and SE for the meta-analysis.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## Not run:
nb_cores <- 2L ## increase the number for using more cores
dat_clim <- prepare_data(data = dat_Clim, temperature = TRUE,
precipitation = FALSE,
phenology = TRUE, morphology = TRUE)
test <- extract_effects_all_ids(data = dat_clim,
condition = "1", nb_cores = nb_cores)
head(test)
dat_sel_phen <- prepare_data(data = dat_Sel, temperature = TRUE,
precipitation = FALSE,
phenology = TRUE, morphology = FALSE)
test_sel <- extract_effects_all_ids(data = dat_sel_phen,
condition = "3", nb_cores = nb_cores)
dat_T_Trait <- prepare_data(data = dat_Trait, temperature = TRUE,
precipitation = FALSE,
phenology = TRUE, morphology = FALSE)
test_Trait <- extract_effects_all_ids(data = dat_T_Trait,
condition = "2", nb_cores = nb_cores)
## End(Not run)
|
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