Skip to contents

Extract Coefficients from a beezdemand_nlme Model

Source: R/mixed-methods.R
coef.beezdemand_nlme.Rd

Provides methods to extract fixed effects, random effects, or subject-specific (combined fixed + random) coefficients from a beezdemand_nlme object. This is an S3 method for the generic coef function.

Usage

# S3 method for class 'beezdemand_nlme'
coef(
  object,
  type = "combined",
  report_space = c("internal", "natural", "log10"),
  ...
)

Arguments

object

A beezdemand_nlme object.

type

Character, type of coefficients to extract. One of:

  • "fixed": Returns only fixed effects (equivalent to fixef(object)).

  • "random": Returns only random effects (equivalent to ranef(object)).

  • "combined" (default): Returns subject-specific coefficients, where each subject's coefficient is the sum of the corresponding fixed effect and that subject's random effect deviation. This is equivalent to what stats::coef() on an nlme object returns.

report_space

Character. One of "internal" (default), "natural", or "log10".

...

Additional arguments passed to the underlying nlme coefficient extraction functions (nlme::fixef(), nlme::ranef(), or stats::coef.nlme()).

Value

Depending on type:

  • type="fixed": A named numeric vector of fixed-effect coefficients.

  • type="random": A data frame (or list of data frames if multiple levels of grouping) of random effects, as returned by ranef.nlme().

  • type="combined": A data frame where rows are subjects (from id_var) and columns are the Q0 and alpha parameters, representing subject-specific estimates (on the log10 scale).

See also

fixef.beezdemand_nlme, ranef.beezdemand_nlme

Examples

# \donttest{
data(ko)
fit <- fit_demand_mixed(ko, y_var = "y_ll4", x_var = "x",
                        id_var = "monkey", equation_form = "zben")
#> Generating starting values using method: 'heuristic'
#> Using heuristic method for starting values.
#> --- Fitting NLME Model ---
#> Equation Form: zben
#> Param Space: log10
#> NLME Formula: y_ll4 ~ Q0 * exp(-(10^alpha/Q0) * (10^Q0) * x)
#> Start values (first few): Q0_int=2.27, alpha_int=-3
#> Number of fixed parameters: 2 (Q0: 1, alpha: 1)
coef(fit, type = "fixed")
#>        Q0     alpha 
#>  2.158507 -4.586304 
coef(fit, type = "random")
#>              Q0         alpha
#> A -8.206090e-12 -1.491344e-09
#> B -1.086347e-10 -2.434156e-11
#> C  1.168408e-10  1.515685e-09
coef(fit, type = "combined")
#>         Q0     alpha
#> A 2.158507 -4.586304
#> B 2.158507 -4.586304
#> C 2.158507 -4.586304
# }