coefFrame: Return model parameters in a data frame

Description

Fits a model to each subgroup defined by by, then returns a data frame with one row for each fit and one column for each parameter.
Usage

coefFrame(mod, data, by = NULL, fit.on = TRUE, fitfun, keep.unused.levels = TRUE, byvar.sep = "<001>", ...)
mod{a model formula, to be passed to by fitfun.}
  data{a data frame, row subsets of which will be used as the
    data argument to fitfun.}
  by{names of columns in x that will be used to define the
    subgroups.}
  fit.on{a logical vector indicating which rows of x are
    to be used to fit the model (like the subset argument in a
    lot of other functions). Can be
    given in terms of variables in x}
  fitfun{a model fitting function (e.g. lm, nls). More
    specifically, a function that expects at least a formula object (as the
    first argument) and a data.frame object (passed as an argument named
    data) and
    returns a model object for which a coef method
    has been defined (e.g. coef.lm, coef.nls) to extract fit values of
    model parameters.}
  keep.unused.levels{Include rows in output for all unique values
    of by, even those which were excluded by
    fit.on. The default value TRUE should be left alone
    if you are going to go on to pass the result to backFit.}
  byvar.sep{passed to frameApply, used to form the
    subsets of the data.}
  ...{other arguments to pass to fitfun.}
a data frame with a row for each unique row of x[by], and
  column for each model paramter, as well as columns specified in by.
# load example data
library(gtools)
data(ELISA)
# Coefficients for four parameter logistic fits:
coefFrame(log(Signal) ~ SSfpl(log(Concentration), A, B, xmid, scal),
           data = ELISA, fitfun = nls,
           by = c("PlateDay", "Read"),
           fit.on = Description == "Standard" & Concentration != 0)
# Coefficients for linear fits:
coefFrame(log(Signal) ~ log(Concentration), 
           data = ELISA, fitfun = lm, 
           by = c("PlateDay", "Read"),
           fit.on = Description == "Standard" & Concentration != 0 )
# Example passing arguments to fitfun, and example of
# error handling during model fitting:
ELISA$Signal[1] <- NA
coefFrame(log(Signal) ~ log(Concentration), 
           data = ELISA, fitfun = lm, na.action = na.fail,
           by = c("PlateDay", "Read"),
           fit.on = Description == "Standard" & Concentration != 0 )
[object Object]
models
Description

Usage

Arguments
