,
Sonam Bhatia [ctb] ,
Thomas Quinn [dtc],
Aniket Shetty [rev],
Keith Ligon [fnd] ,
Center for Patient-Derived Models Dana-Farber Cancer Institute [cph]| Title: | Mixed-Effects Modeling for Growth Data |
|---|---|
| Description: | Simple and user-friendly wrappers to the 'saemix' package for performing linear and non-linear mixed-effects regression modeling for growth data to account for clustering or longitudinal analysis via repeated measurements. The package allows users to fit a variety of growth models, including linear, exponential, logistic, and 'Gompertz' functions. For non-linear models, starting values are automatically calculated using initial least-squares estimates. The package includes functions for summarizing models, visualizing data and results, calculating doubling time and other key statistics, and generating model diagnostic plots and residual summary statistics. It also provides functions for generating publication-ready summary tables for reports. Additionally, users can fit linear and non-linear least-squares regression models if clustering is not applicable. The mixed-effects modeling methods in this package are based on Comets, Lavenu, and Lavielle (2017) <doi:10.18637/jss.v080.i03> as implemented in the 'saemix' package. Please contact us at [email protected] with any questions. |
| Authors: | Anand Panigrahy [aut, cre] ,
Sonam Bhatia [ctb] ,
Thomas Quinn [dtc],
Aniket Shetty [rev],
Keith Ligon [fnd] ,
Center for Patient-Derived Models Dana-Farber Cancer Institute [cph] |
| Maintainer: | Anand Panigrahy <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.0.9000 |
| Built: | 2024-12-20 18:29:59 UTC |
| Source: | https://github.com/cancermodels-org/growthcurveme |
A dataset containing the minimum required variables needed to input data into the GrowthCurveME package functions
exp_mixed_dataexp_mixed_data
A data frame with 240 rows and 3 variables:
A character type variable used to specify the clustering of values by a particular metric. Note even when selecting a least-squares, do not leave this variable NA or empty. Instead fill in these values with a single repetitive dummy variable (e.g., '1') for the package to run properly
A numeric type variable for any measurement in time such as minutes, hours, or days
A numeric type variable for measuring growth such as confluency or cell count
Created through simulation to serve as an example
data(exp_mixed_data)data(exp_mixed_data)
This function is utilized within the
growth_curve_model_fit function for fitting a mono-exponential
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.
exponential_mixed_model( data_frame, model_type = "mixed", fixed_rate = TRUE, num_chains = 1, seed = NULL )exponential_mixed_model( data_frame, model_type = "mixed", fixed_rate = TRUE, num_chains = 1, seed = NULL )
data_frame |
A data frame object that at minimum contains three variables:
|
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. |
Returns an exponential 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.
# Load example data (exponential data from GrowthCurveME package) data(exp_mixed_data) # Fit an exponential mixed-effects growth model exp_mixed_model <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential" ) # Fit an exponential mixed-effected model using exponential_mixed_model() exp_mixed_model <- exponential_mixed_model(data_frame = exp_mixed_data)# Load example data (exponential data from GrowthCurveME package) data(exp_mixed_data) # Fit an exponential mixed-effects growth model exp_mixed_model <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential" ) # Fit an exponential mixed-effected model using exponential_mixed_model() exp_mixed_model <- exponential_mixed_model(data_frame = exp_mixed_data)
A dataset containing the minimum required variables needed to input data into the GrowthCurveME package functions
gomp_mixed_datagomp_mixed_data
A data frame with 210 rows and 3 variables:
A character type variable used to specify the clustering of values by a particular metric. Note even when selecting a least-squares, do not leave this variable NA or empty. Instead fill in these values with a single repetitive dummy variable (e.g., '1') for the package to run properly
A numeric type variable for any measurement in time such as minutes, hours, or days
A numeric type variable for measuring growth such as confluency or cell count
Created through simulation to serve as an example
data(gomp_mixed_data)data(gomp_mixed_data)
This function is utilized within the
growth_curve_model_fit function for fitting a Gompertz
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.
gompertz_mixed_model( data_frame, model_type = "mixed", fixed_rate = TRUE, num_chains = 1, seed = NULL )gompertz_mixed_model( data_frame, model_type = "mixed", fixed_rate = TRUE, num_chains = 1, seed = NULL )
data_frame |
A data frame object that at minimum contains three variables:
|
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. |
Returns a Gompertz model object of class 'SaemixObject' if a mixed-effects model is specified or a model object of class 'nls' if a least-squares model is specified.
# Load example data (Gompertz data from GrowthCurveME package) data(gomp_mixed_data) # Fit a Gompertz mixed-effects growth model gomp_mixed_model <- growth_curve_model_fit( data_frame = gomp_mixed_data, function_type = "gompertz" ) # Fit a Gompertz mixed-effected model using gompertz_mixed_model() gomp_mixed_model <- gompertz_mixed_model(data_frame = gomp_mixed_data)# Load example data (Gompertz data from GrowthCurveME package) data(gomp_mixed_data) # Fit a Gompertz mixed-effects growth model gomp_mixed_model <- growth_curve_model_fit( data_frame = gomp_mixed_data, function_type = "gompertz" ) # Fit a Gompertz mixed-effected model using gompertz_mixed_model() gomp_mixed_model <- gompertz_mixed_model(data_frame = gomp_mixed_data)
This function fits a mixed-effects model to a data frame based on a user-defined function to account for clustering.
growth_curve_model_fit( data_frame, function_type = "exponential", model_type = "mixed", fixed_rate = TRUE, num_chains = 1, time_unit = "hours", return_summary = TRUE, seed = NULL, verbose = TRUE )growth_curve_model_fit( data_frame, function_type = "exponential", model_type = "mixed", fixed_rate = TRUE, num_chains = 1, time_unit = "hours", return_summary = TRUE, seed = NULL, verbose = TRUE )
data_frame |
A data frame object that at minimum contains three variables:
|
function_type |
A character string specifying the function for modeling the shape of the growth. Options include "exponential", "linear", "logistic", or "gompertz". |
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. |
time_unit |
A character string specifying the units in which time is measured in. Defaults to "hours" |
return_summary |
A logical value specifying whether to return the growth_model_summary_list when TRUE (list object containing summarized data) or the object model object when FALSE. Defaults to TRUE. |
seed |
A numeric value specifying a seed number to reproduce the random starting values sampled within the function. Defaults to NULL. |
verbose |
A logical value specifying whether print statements will print in the console. Defaults to TRUE. |
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)
simulated_data - a data frame with the 95% prediction intervals
calculated using the median for the estimate and the 2.5th and 97.5th
percentiles of the simulated data at each time point (not to be
confused with the 95% confidence intervals calculated from the model
estimates). See summarize_growth_model.
Note when return_summary is FALSE, will return a 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.
# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data and return summary exp_mixed_model_summary <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", verbose = FALSE) # Create flextable object from the summary list object for documentation exp_model_table <- growth_model_summary_table( growth_model_summary_list = exp_mixed_model_summary) exp_model_table # Create growth vs time plot of data with fitted values (plot_type = 2), # adjust aesthetics and parameters as desired exp_growth_plot <- growth_vs_time_plot( growth_model_summary_list = exp_mixed_model_summary, plot_type = 2) print(exp_growth_plot) # Check residuals and model assumptions residual_diag_plot <- growth_model_residual_plots( growth_model_summary_list = exp_mixed_model_summary) print(residual_diag_plot)# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data and return summary exp_mixed_model_summary <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", verbose = FALSE) # Create flextable object from the summary list object for documentation exp_model_table <- growth_model_summary_table( growth_model_summary_list = exp_mixed_model_summary) exp_model_table # Create growth vs time plot of data with fitted values (plot_type = 2), # adjust aesthetics and parameters as desired exp_growth_plot <- growth_vs_time_plot( growth_model_summary_list = exp_mixed_model_summary, plot_type = 2) print(exp_growth_plot) # Check residuals and model assumptions residual_diag_plot <- growth_model_residual_plots( growth_model_summary_list = exp_mixed_model_summary) print(residual_diag_plot)
This function provides a wrapper to ggplot2 for generating residual
diagnostic plots and summary statistics for a growth model summary list
object produced by growth_curve_model_fit.
growth_model_residual_plots( growth_model_summary_list, residual_type = "cluster", weighted = TRUE )growth_model_residual_plots( growth_model_summary_list, residual_type = "cluster", weighted = TRUE )
growth_model_summary_list |
A list object created by the
|
residual_type |
A character string specifying the type of residuals to be displayed in the plot. Options include "population" for the fixed-effects residuals for mixed-effects and least-squares models and "cluster" for fixed and random-effects residuals for mixed-effects regression models. Defaults to "cluster". |
weighted |
A logical value, when TRUE displays weighted residuals for mixed-effects models or standardized residuals for least-squares models, when FALSE displays the raw residuals for mixed-effects and least-squares models. Defaults to "TRUE". |
Returns a patchwork collage of ggplot2 model diagnostic plots with the following plots displayed:
Residual vs Fitted Values - a model diagnostic plot for assessing the distribution of the residuals vs the model fitted values, useful in detecting improper function specification, homogeneity of variance, and outlier detection.
Q-Q Plot - a model diagnostic plot (quantile-quantile) plot for comparing the residuals vs their theoretical quantiles, useful in assessing normality assumptions and outlier detection.
Residual Density Plot - a model diagnostic showing the distribution of the residuals (histogram) with a normal distribution curve overlaid based on the residuals mean and standard deviation, useful in assessing normality assumptions and skewness.
Residual Summary Statistics - a list of descriptive statistics of the the residuals including: mean, median, minimum, maximum, skewness, and kurtosis.
# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data and produce summary list exp_mixed_model_summary <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", verbose = FALSE) # Check residuals and model assumptions residual_diag_plot <- growth_model_residual_plots( growth_model_summary_list = exp_mixed_model_summary) print(residual_diag_plot)# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data and produce summary list exp_mixed_model_summary <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", verbose = FALSE) # Check residuals and model assumptions residual_diag_plot <- growth_model_residual_plots( growth_model_summary_list = exp_mixed_model_summary) print(residual_diag_plot)
This function creates a flextable object that can be used
for documentation or Rmarkdown reports from the list object created
by growth_curve_model_fit.
The 'model_summary_long' data frame from the list object is used to
generate the table.
growth_model_summary_table( growth_model_summary_list, font_name = "Albany AMT", font_size_header = 14, font_size_body = 12, use_knit_print = FALSE )growth_model_summary_table( growth_model_summary_list, font_name = "Albany AMT", font_size_header = 14, font_size_body = 12, use_knit_print = FALSE )
growth_model_summary_list |
A list object created by the
|
font_name |
A character string specifying the name of the font to use
when rendering the table. Defaults to "Albany AMT".
See |
font_size_header |
A numeric value specifying the size of the font
for the header of the table. Defaults to 14.
See |
font_size_body |
A numeric value specifying the size of the font
for the body of the table. Defaults to 12.
See |
use_knit_print |
A logical value to specify whether the flextable
should be printed using |
A flextable object of the 'model_summary_long' data frame.
# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data exp_mixed_model_summary <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", verbose = FALSE) # Create flextable object of the growth model results exp_model_table <- growth_model_summary_table( growth_model_summary_list = exp_mixed_model_summary) # Print the table in the view pane exp_model_table# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data exp_mixed_model_summary <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", verbose = FALSE) # Create flextable object of the growth model results exp_model_table <- growth_model_summary_table( growth_model_summary_list = exp_mixed_model_summary) # Print the table in the view pane exp_model_table
This function provides a wrapper to ggplot2 in order to generate
different plots from a growth curve model summary list object generated by
the growth_curve_model_fit function.
Please refer to the documentation for the 'plot_type' parameter for the
different plot options.
growth_vs_time_plot( growth_model_summary_list, plot_type = 2, growth_metric_name = "growth_metric", time_name = "time", cluster_name = "cluster", plot_title = "Growth vs Time", x_axis_breaks = ggplot2::waiver(), x_limits = c(NA, NA), n_x_axis_breaks = NULL, y_axis_breaks = ggplot2::waiver(), y_limits = c(NA, NA), n_y_axis_breaks = NULL, x_axis_text_size = 8, y_axis_text_size = 12, x_axis_title_size = 14, y_axis_title_size = 14, plot_title_size = 20, geom_point_size = 2, geom_line_width = 0.5, pred_plot_annotate_value = "double_time", annotate_value_text_size = 5 )growth_vs_time_plot( growth_model_summary_list, plot_type = 2, growth_metric_name = "growth_metric", time_name = "time", cluster_name = "cluster", plot_title = "Growth vs Time", x_axis_breaks = ggplot2::waiver(), x_limits = c(NA, NA), n_x_axis_breaks = NULL, y_axis_breaks = ggplot2::waiver(), y_limits = c(NA, NA), n_y_axis_breaks = NULL, x_axis_text_size = 8, y_axis_text_size = 12, x_axis_title_size = 14, y_axis_title_size = 14, plot_title_size = 20, geom_point_size = 2, geom_line_width = 0.5, pred_plot_annotate_value = "double_time", annotate_value_text_size = 5 )
growth_model_summary_list |
A list object created by the
|
plot_type |
A numeric value used to specify the plot type to graph. Values include 1, 2, 3, 4 with descriptions of each below (defaults to 2):
|
growth_metric_name |
A character string for specifying the name of the growth metric (y-axis title) to be displayed on the plot. Defaults to "growth_metric". |
time_name |
A character string for specifying the name of the time variable (x-axis title) to be displayed on the plot. Defaults to "time". |
cluster_name |
A character string for specifying the name of the cluster variable (legend title) to be displayed on the plot. Defaults to "cluster". |
plot_title |
A character string for specifying the title to be displayed over the plot. Defaults to "Growth vs Time". |
x_axis_breaks |
A numeric vector specifying manual numeric breaks.
Defaults to ggplot2::waiver(). See |
x_limits |
A numeric vector of length two providing limits for
the x-axis. Use NA to refer to the existing minimum or maximum.
Defaults to c(NA, NA). See |
n_x_axis_breaks |
An integer specifying the number of major breaks
for the x-axis. Defaults to NULL.
See |
y_axis_breaks |
A numeric vector specifying manual numeric breaks.
Defaults to ggplot2::waiver(). See |
y_limits |
A numeric vector of length two providing limits for
the y-axis. Use NA to refer to the existing minimum or maximum.
Defaults to c(NA, NA). See |
n_y_axis_breaks |
An integer specifying the number of major breaks
for the x-axis. Defaults to NULL.
See |
x_axis_text_size |
A numeric value specifying the size of the
x-axis text. Defaults to 8. See |
y_axis_text_size |
A numeric value specifying the size of the
y-axis text. Defaults to 12. See |
x_axis_title_size |
A numeric value specifying the size of the
x-axis title. Defaults to 14. See |
y_axis_title_size |
A numeric value specifying the size of the
y-axis title. Defaults to 14. See |
plot_title_size |
A numeric value specifying the size of the plot
title. Defaults to 20. See |
geom_point_size |
A numeric value specifying the size of the points
on the graph. Defaults to 2. See |
geom_line_width |
A numeric value specifying the width of the line (applicable only for plot_type = 2, 3, or 4). Defaults to 0.5. |
pred_plot_annotate_value |
A character string specifying whether to add the doubling time or rate estimates from the model to plot 4. Options include "double_time" for the doubling time with 95% CI, "rate" for the rate estimate with 95% CI, or "none" for no annotation. Defaults to "double_time" |
annotate_value_text_size |
A numeric value specifying the size of
the annotation text. Defaults to 5. See |
Returns a ggplot2 plot
# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data exp_mixed_model_summary <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", verbose = FALSE ) # Create growth vs time plot of data with fitted values (plot_type = 2) exp_growth_plot <- growth_vs_time_plot( growth_model_summary_list = exp_mixed_model_summary, plot_type = 2 ) print(exp_growth_plot)# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data exp_mixed_model_summary <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", verbose = FALSE ) # Create growth vs time plot of data with fitted values (plot_type = 2) exp_growth_plot <- growth_vs_time_plot( growth_model_summary_list = exp_mixed_model_summary, plot_type = 2 ) print(exp_growth_plot)
A dataset containing the minimum required variables needed to input data into the GrowthCurveME package functions
lin_mixed_datalin_mixed_data
A data frame with 110 rows and 3 variables:
A character type variable used to specify the clustering of values by a particular metric. Note even when selecting a least-squares, do not leave this variable NA or empty. Instead fill in these values with a single repetitive dummy variable (e.g., '1') for the package to run properly
A numeric type variable for any measurement in time such as minutes, hours, or days
A numeric type variable for measuring growth such as confluency or cell count
Created through simulation to serve as an example
data(lin_mixed_data)data(lin_mixed_data)
This function is utilized within the
growth_curve_model_fit function for fitting a linear
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.
linear_mixed_model( data_frame, model_type = "mixed", fixed_rate = TRUE, num_chains = 1, seed = NULL )linear_mixed_model( data_frame, model_type = "mixed", fixed_rate = TRUE, num_chains = 1, seed = NULL )
data_frame |
A data frame object that at minimum contains three variables:
|
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. |
Returns a linear 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.
# Load example data (linear data from GrowthCurveME package) data(lin_mixed_data) # Fit a linear mixed-effects growth model lin_mixed_model <- growth_curve_model_fit( data_frame = lin_mixed_data, function_type = "linear") # Fit a linear mixed-effects model using linear_mixed_model() lin_mixed_model <- linear_mixed_model(data_frame = lin_mixed_data)# Load example data (linear data from GrowthCurveME package) data(lin_mixed_data) # Fit a linear mixed-effects growth model lin_mixed_model <- growth_curve_model_fit( data_frame = lin_mixed_data, function_type = "linear") # Fit a linear mixed-effects model using linear_mixed_model() lin_mixed_model <- linear_mixed_model(data_frame = lin_mixed_data)
A dataset containing the minimum required variables needed to input data into the GrowthCurveME package functions
log_mixed_datalog_mixed_data
A data frame with 800 rows and 3 variables:
A character type variable used to specify the clustering of values by a particular metric. Note even when selecting a least-squares, do not leave this variable NA or empty. Instead fill in these values with a single repetitive dummy variable (e.g., '1') for the package to run properly
A numeric type variable for any measurement in time such as minutes, hours, or days
A numeric type variable for measuring growth such as confluency or cell count
Created through simulation to serve as an example
data(log_mixed_data)data(log_mixed_data)
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.
logistic_mixed_model( data_frame, model_type = "mixed", fixed_rate = TRUE, num_chains = 1, seed = NULL )logistic_mixed_model( data_frame, model_type = "mixed", fixed_rate = TRUE, num_chains = 1, seed = NULL )
data_frame |
A data frame object that at minimum contains three variables:
|
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. |
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.
# 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)# 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)
This function is used to create a list object of
data frames based on a user's input data and output
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.
summarize_growth_model( data_frame, growth_model_object, model_type = "mixed", function_type = "exponential", fixed_rate = TRUE, time_unit = "hours" )summarize_growth_model( data_frame, growth_model_object, model_type = "mixed", function_type = "exponential", fixed_rate = TRUE, time_unit = "hours" )
data_frame |
A data frame object that at minimum contains three variables:
|
growth_model_object |
The model object that is created using
the |
model_type |
A character string specifying the model_type that was
fit using the |
function_type |
A character string specifying the function for modeling the shape of the growth. Options include "exponential", "linear", "logistic", or "gompertz". |
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 |
time_unit |
A character string specifying the units in which time is measured in. Defaults to "hours" |
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.
# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data exp_mixed_model <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", return_summary = FALSE) # Summarize the data by creating a summary list object exp_mixed_model_summary <- summarize_growth_model( data_frame = exp_mixed_data, growth_model_object = exp_mixed_model, model_type = "mixed", function_type = "exponential", time_unit = "hours") # Extracting a data frame from the list object model_summary_wide <- exp_mixed_model_summary[["model_summary_wide"]]# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data exp_mixed_model <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", return_summary = FALSE) # Summarize the data by creating a summary list object exp_mixed_model_summary <- summarize_growth_model( data_frame = exp_mixed_data, growth_model_object = exp_mixed_model, model_type = "mixed", function_type = "exponential", time_unit = "hours") # Extracting a data frame from the list object model_summary_wide <- exp_mixed_model_summary[["model_summary_wide"]]
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.
summarize_growth_model_ls( data_frame, ls_model, function_type = "exponential", time_unit = "hours" )summarize_growth_model_ls( data_frame, ls_model, function_type = "exponential", time_unit = "hours" )
data_frame |
A data frame object that at minimum contains three variables:
|
ls_model |
The least-squares model object that is created using
the |
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" |
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.
growth_curve_model_fit
summarize_growth_model
# 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")# 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")
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 mixed-effects
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.
summarize_growth_model_mixed( data_frame, mixed_growth_model, function_type = "exponential", fixed_rate = TRUE, time_unit = "hours" )summarize_growth_model_mixed( data_frame, mixed_growth_model, function_type = "exponential", fixed_rate = TRUE, time_unit = "hours" )
data_frame |
A data frame object that at minimum contains three variables:
|
mixed_growth_model |
The mixed-effects model object that is created
using the |
function_type |
A character string specifying the function for modeling the shape of the growth. Options include "exponential", "linear", "logistic", or "gompertz". |
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 |
time_unit |
A character string specifying the units in which time is measured in. Defaults to "hours" |
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.
growth_curve_model_fit
summarize_growth_model
# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data exp_mixed_model <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", return_summary = FALSE) # Summarize the data by creating a summary list object exp_mixed_model_summary <- summarize_growth_model_mixed( data_frame = exp_mixed_data, mixed_growth_model = exp_mixed_model, fixed_rate = TRUE, function_type = "exponential", time_unit = "hours") model_summary_wide <- exp_mixed_model_summary[["model_summary_wide"]]# Load example data (exponential data) data(exp_mixed_data) # Fit an mixed-effects growth model to the data exp_mixed_model <- growth_curve_model_fit( data_frame = exp_mixed_data, function_type = "exponential", return_summary = FALSE) # Summarize the data by creating a summary list object exp_mixed_model_summary <- summarize_growth_model_mixed( data_frame = exp_mixed_data, mixed_growth_model = exp_mixed_model, fixed_rate = TRUE, function_type = "exponential", time_unit = "hours") model_summary_wide <- exp_mixed_model_summary[["model_summary_wide"]]