coefFrame {gmodels}

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",
  ...
)

Arguments

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.

Value

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.

Author(s)

Jim Rogers [e-mail]

Examples

# 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 )