GrowthCurveME (version 0.1.0)

summarize_growth_model_ls: Summarize least-squares growth model object and data

Description

This function is used within the summarize_growth_model function to create a list object of data frames based on a user's input data frame and output least-squares growth model object from growth_curve_model_fit. The list object (referred to in this package as 'growth_model_summary_list') can be used to extract model predicted values, residuals, and can be inputted into supporting functions from GrowthCurveME to generate plots and perform model diagnostics.

Usage

summarize_growth_model_ls(
  data_frame,
  ls_model,
  function_type = "exponential",
  time_unit = "hours"
)

Value

A list object with the following data frames within the list:

• model_summary_wide - a data frame with 1 row containing key model estimates, doubling-time, and model metrics depending on the model_type and function_type specified

• model_summary_long - a data frame that is a long dataset version of 'model_summary_wide' that can be used to generate a table of the model results (see function growth_model_summary_table)

• model_residual_data - a data frame containing the original data frame values as well as predicted values, residuals, and theoretical quantiles of the residuals depending on the model_type selected (see functions growth_model_residual_plots and growth_vs_time_plot)

• model_sim_pred_data - a data frame with estimates and 95% prediction intervals (not to be confused with the 95% confidence intervals calculated from the model estimates), for mixed-effects models, values are calculated as the median estimate and the 2.5th and 97.5th percentiles of the simulated data from the saemix model at each time point (see compute.sres and plot with plot.type = "vpc"). For least-squares models, prediction intervals are calculated through Taylor-series approximations using the predFit function.

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 cluster values with a single repetitive dummy variable (e.g., '1'), 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

ls_model

The least-squares model object that is created using the growth_curve_model_fit

function_type

A character string specifying the function for modeling the shape of the growth. Options include "exponential", "linear", "logistic", or "gompertz".

time_unit

A character string specifying the units in which time is measured in. Defaults to "hours"

See Also

growth_curve_model_fit summarize_growth_model

Examples

# Load example data (exponential data)
data(exp_mixed_data)
# Fit an mixed-effects growth model to the data
exp_ls_model <- growth_curve_model_fit(
data_frame = exp_mixed_data,
function_type = "exponential",
model_type = "least-squares",
return_summary = FALSE)
# Summarize the data by creating a summary list object
exp_ls_model_summary <- summarize_growth_model_ls(
data_frame = exp_mixed_data,
ls_model = exp_ls_model,
function_type = "exponential",
time_unit = "hours")