R/growthrates-package.R

In growthrates: Estimate Growth Rates from Experimental Data

#' \packageTitle{growthrates}
#'
#' @description
#'   \packageDescription{growthrates}
#'
#' The package contains basically three methods:
#' \itemize{
#'   \item fit a linear regression to a subset of data with the steepest
#'     log-linear increase (a method, similar to Hall et al., 2013),
#'   \item fit parametric nonlinear models to the complete data set, where the
#'      model functions can be given either in closed form or as numerically
#'      solved (system of) differential equation(s),
#'   \item use maximum of the 1st derivative of a smoothing spline with
#'     log-transformed y-values (similar to Kahm et al., 2010).
#' }
#'
#' The package can fit data sets of single experiments or complete series
#' containing multiple data sets. Included are functions for extracting
#' estimates and for plotting. The package supports growth models given as
#' numerically solved differential equations. Multi-core computation is used to
#' speed up fitting of parametric models.
#'
#' @name growthrates-package
#' @aliases growthrates growthrates-package
#' @docType package
#' @author Thomas Petzoldt
#'
#' @seealso \code{\link{fit_easylinear}}, \code{\link{fit_spline}}, \code{\link{fit_growthmodel}},
#' \code{\link{all_easylinear}}, \code{\link{all_splines}}, \code{\link{all_growthmodels}}
#'
#'
#' @references
#'
#' Hall, B. G., Acar, H. and Barlow, M. 2013. Growth Rates Made Easy.
#'   Mol. Biol. Evol. 31, 232-238, \doi{10.1093/molbev/mst197}
#'
#' Kahm, M., Hasenbrink, G., Lichtenberg-Frate, H., Ludwig, J., Kschischo, M.
#'   2010. grofit: Fitting Biological Growth Curves with R.
#'   Journal of Statistical Software, 33(7), 1-21,
#'   \doi{10.18637/jss.v033.i07}
#'
#' Soetaert, K. and Petzoldt, T. 2010. Inverse Modelling, Sensitivity and
#'   Monte Carlo Analysis in R Using Package FME.
#'   Journal of Statistical Software, 33(3), 1-28,
#'   \doi{10.18637/jss.v033.i03}
#'
#' Soetaert, K., Petzoldt, T. Setzer, R. W. 2010. Solving Differential Equations
#'   in R: Package deSolve. Journal of Statistical Software, 33(9), 1-25,
#'   \doi{10.18637/jss.v033.i09}
#'
#'
#'
#' @keywords package
#' @examples
#'
#' data(bactgrowth)
#' splitted.data <- multisplit(bactgrowth, c("strain", "conc", "replicate"))
#'
#' ## get table from single experiment
#' dat <- splitted.data[["D:0:1"]]
#'
#' fit1 <- fit_spline(dat$time, dat$value)
#' plot(fit1, log="y")
#' plot(fit1)
#'
#' ## derive start parameters from spline fit
#' p <- coef(fit1)
#'
#' ## subset of first 10 data
#' first10 <-  dat[1:10, ]
#' fit2 <- fit_growthmodel(grow_exponential, p=p, time=first10$time, y=first10$value)
#'
#' ## use parameters from spline fit and take K from the data maximum
#' p <- c(coef(fit1), K = max(dat$value))
#' fit3 <- fit_growthmodel(grow_logistic, p=p, time=dat$time, y=dat$value, transform="log")
#'
#' plot(fit1)
#' lines(fit2, col="green")
#' lines(fit3, col="red")
#'
#' @useDynLib growthrates
#'
#' @import stats graphics methods FME lattice
#' @importFrom deSolve ode
#' @importFrom utils type.convert
#' @import parallel
## @importFrom parallel makeCluster stopCluster parLapply
NULL