GrowthCurveME (version 0.1.0)

logistic_mixed_model: Fit a logistic mixed-effects regression model

Description

This function is utilized within the growth_curve_model_fit function for fitting a logistic mixed-effects regression model to growth data utilizing the saemix package. Starting values are derived from an initial least-squares model using the nlsLM function.

Usage

logistic_mixed_model(
  data_frame,
  model_type = "mixed",
  fixed_rate = TRUE,
  num_chains = 1,
  seed = NULL
)

Value

Returns a logistic model object of class 'SaemixObject' when a mixed-effects model is specified or a model object of class 'nls' if a least-squares model is specified.

Arguments

data_frame

A data frame object that at minimum contains three variables:

• cluster - a character type variable used to specify how observations are nested or grouped by a particular cluster. Note if using a least-squares model, please fill in all values of cluster with a single placeholder character string, do NOT leave blank.

• time - a numeric type variable used for measuring time such as minutes, hours, or days

• growth_metric - a numeric type variable used for measuring growth over time such as cell count or confluency

model_type

A character string specifying the type of regression model to be used. If 'mixed', a mixed-effects regression model will be used with fixed and random-effects to account for clustering. For 'least-squares', a least-squares regression model with only fixed-effects is applied. Defaults to "mixed".

fixed_rate

A logical value specifying whether the rate constant of the function should be treated as a fixed effect (TRUE) or random effect (FALSE). Defaults to TRUE

num_chains

A numeric value specifying the number of chains to run in parallel in the MCMC algorithm of saemix. Increasing the number of chains may improve convergence but may also increase the computational time. Defaults to 1.

seed

A numeric value specifying a seed number to reproduce the random starting values sampled within the function. Defaults to NULL.

See Also

growth_curve_model_fit

Examples

# \donttest{
# Load example data (logistic data from GrowthCurveME package)
data(log_mixed_data)
# Fit a logistic mixed-effects growth model to the data
log_mixed_model <- growth_curve_model_fit(data_frame = log_mixed_data,
function_type = "logistic")
# Fit a logistic mixed-effects model using logistic_mixed_model()
log_mixed_model <- logistic_mixed_model(data_frame = log_mixed_data)
# }