prepare_slopes: Prepare the slopes for the forest plots

In radchukv/adRes: Assessing Whether Phenotypic Changes in Response to Climate are Adaptive

Description

prepare_slopes extracts the slopes needed for the forest plots from the fitted model objects.

Usage

prepare_slopes(meta_obj1 = NULL, meta_obj2 = NULL,
  list_extra_meta_obj = NULL, labels_obj1 = NULL, labels_obj2 = NULL)

Arguments

meta_obj1	A fitted meta-analytical model object whose global slopes and per-study effect sizes will be displayed on the plot.
meta_obj2	An (optional) second fitted meta-analytical model object whose global slopes and per-study effect sizes will be displayed on the plot.
list_extra_meta_obj	A list of meta-analytical model objects to be used for displaying the global effects (either across all studies or in response to a fixed predictor).
labels_obj1	A list of labels to be used for slopes extracted from meta_obj1 object. This list is returned by the function prepare_labels.
labels_obj2	A list of labels to be used for slopes extracted from meta_obj2 object. This list is returned by the function prepare_labels

Value

A list containing the slopes and other information required for the forest plot.

See Also

plot_forest

Examples

## Not run: 
nb_cores <- 2L ## increase the number for using more cores
meta_Sel_phen <- fit_all(data = dat_Sel,
                         temperature = TRUE, precipitation = FALSE,
                         phenology = TRUE, morphology = FALSE,
                         condition = '3', nb_cores = nb_cores,
                         rand_trait = FALSE, fixed = NULL, digit = 3)

meta_Sel_morph <- fit_all(data = dat_Sel,
                          temperature = TRUE, precipitation = FALSE,
                          phenology = FALSE, morphology = TRUE,
                          condition = '3', nb_cores = nb_cores,
                          rand_trait = FALSE, fixed = NULL, digit = 3)

meta_Sel_phen_Fitn <- fit_all(data = dat_Sel,
                              temperature = TRUE, precipitation = FALSE,
                              phenology = TRUE, morphology = FALSE,
                              condition = '3', nb_cores = nb_cores,
                              rand_trait = FALSE, fixed = 'Fitness_Categ',
                              digit = 3)

test1 <- prepare_slopes(meta_obj1 = meta_Sel_phen$meta_res)

test2 <- prepare_slopes(meta_obj2 = meta_Sel_phen$meta_res)

test3 <- prepare_slopes(meta_obj1 = meta_Sel_phen$meta_res,
                        meta_obj2 = meta_Sel_morph$meta_res)

test4 <- prepare_slopes(meta_obj1 = meta_Sel_phen$meta_res,
                        list_extra_meta_obj =
                        list(meta_Sel_phen_Fitn$meta_res))

test5 <- prepare_slopes(meta_obj1 = meta_Sel_phen$meta_res,
                        meta_obj2 = meta_Sel_morph$meta_res,
                        list_extra_meta_obj =
                        list(meta_Sel_phen_Fitn$meta_res))

test6 <- prepare_slopes(meta_obj1 = meta_Sel_phen_Fitn$meta_res,
                        list_extra_meta_obj =
                        list(meta_Sel_morph$meta_res,
                        meta_Sel_phen$meta_res))

## End(Not run)

radchukv/adRes documentation built on June 1, 2019, 7:05 p.m.