gmodels: coefFrame – R documentation

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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", ...)

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 james.a.rogers@pfizer.com

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 )

gmodels

Various R Programming Tools for Model Fitting

v2.18.1

GPL-2

Authors

Gregory R. Warnes, Ben Bolker, Thomas Lumley, and Randall C Johnson. Contributions from Randall C. Johnson are Copyright (2005) SAIC-Frederick, Inc. Funded by the Intramural Research Program, of the NIH, National Cancer Institute, Center for Cancer Research under NCI Contract NO1-CO-12400.

Initial release

2018-06-25