Analyze linear selection gradients (beta) — analyze_linear

Estimates linear selection gradients by OLS on relative fitness. Binary fitness gets its p-values from a logistic GLM and count fitness from a Poisson GLM (negative binomial if overdispersed), since the OLS tests are not valid for either.

Usage

analyze_linear_selection(
  data,
  fitness_col,
  trait_cols,
  fitness_type,
  binary_response_col = NULL,
  group = NULL,
  se_type = c("ols", "hc3")
)

Arguments

data: A data frame containing fitness and trait measurements.
fitness_col: A string specifying the response column for the OLS gradient model (relative fitness).
trait_cols: A character vector of trait column names.
fitness_type: A string indicating the fitness type: "binary", "continuous", "count", or "proportion".
binary_response_col: Optional string naming the raw fitness column (0/1 for binary, counts for count fitness) used for the GLM that supplies p-values. If NULL, fitness_col is treated as the raw outcome and relativised internally.
group: Optional grouping column. With two or more groups the GLM that supplies the p-values gets an intercept for each, to match the standardising within groups; the least-squares fit has no group term.
se_type: Standard errors of the least-squares gradients: "ols" (the default) or "hc3", heteroscedasticity-consistent; see selection_coefficients().

Value

A list containing the fitted models, summaries, ANOVA tables, and VIFs.

Examples

prep <- prepare_selection_data(bumpus, "survival", c("total_length", "weight"))
fit <- analyze_linear_selection(prep, "survival", c("total_length", "weight"), "binary")
extract_linear_coefficients(c("total_length", "weight"), fit)
#>           Term   Type Beta_Coefficient Standard_Error    P_Value
#> 1 total_length Linear      -0.17811220        0.09748 0.07276336
#> 2       weight Linear      -0.09992466        0.09748 0.30287778