Fit Emax model to data
fit_EmaxModel.Rd
Estimate parameters Emax, EC50 and Hill parameter from x
and y
values provided with a data frame. Error model parameters are
estimated alongside the model parameters via a maximum-
likelihood approach.
fit_EmaxModel( data, Emax = NULL, EC50 = NULL, Hill = NULL, y0 = 0, errmodel = "abs", log10Data = "" )
Arguments
data | data frame with |
---|---|
Emax | assume Emax to be fixed on this value. If |
EC50 | assume EC50 to be fixed on this value. If |
Hill | assume Emax to be fixed on this value. If |
y0 | assume y0 (intercept) to be fixed on this value. If |
errmodel | the error model to be use, either |
log10Data | specifies if |
Value
list with
objval
(final objective function value, -2*log(L))converged
(convergenceTRUE
orFALSE
)iterations
(number of iterations)parameters
(data frame of parameter values, estimated and fixed)prd
(prediction function to evaluate the fitted model at newx
values)prediction
(predicted values at thex
values provded withdata
)plot
(function that produces a plot)table
(parameter estimation table)
Details
The model y = y0 + Emax * x^Hill / (EC50^Hill + x^Hill)
is fitted to the x
and y
values provided with data
.
The fit is based on minimization of twice the negative
log-likelihood (-2*log(L)).
See also
Other Auxiliary:
IQRloadCSVdata()
,
IQRsaveCSVdata()
,
and()
,
aux_explodePC()
,
aux_explode()
,
aux_fileparts()
,
aux_fileread()
,
aux_filewrite()
,
aux_getRelPath()
,
aux_mkdir()
,
aux_na_locf()
,
aux_postFillChar()
,
aux_preFillChar()
,
aux_quantilenumber()
,
aux_rmdir()
,
aux_simplifypath()
,
aux_splitVectorEqualPieces()
,
aux_strFindAll()
,
aux_strrep()
,
aux_strtrim()
,
aux_unlevel()
,
aux_version()
,
calcAICBIC()
,
clusterX()
,
compare_IQRmodel_IQRsysModel_simulation()
,
format_GUM()
,
geocv()
,
geomean()
,
geosd()
,
ge()
,
ginv()
,
gt()
,
interp0()
,
interp1()
,
interpcs()
,
inv_logit()
,
le()
,
logit()
,
lt()
,
mod()
,
mvrnorm()
,
or()
,
piecewise()
,
progressBar()
,
run_silent_IQR()
,
stopIQR()
,
tempdirIQR()
,
tempfileIQR()
,
warningIQR()
Examples
mydata <- data.frame( log10Conc = log10(1:10), Value = 5*(1:10)^5/(4^5 + (1:10)^5) + rnorm(10, 0, .1) ) myfit <- fit_EmaxModel(mydata, errmodel = "abs", log10Data = "x") myfit$table#> Name | Value | SE | RSE | Fixed #> ------------------------------------------- #> EC50 | 3.995 | 0.040 | 0.9996% | FALSE #> Emax | 5.044 | 0.052 | 1.028% | FALSE #> Hill | 5.19 | 0.25 | 4.91% | FALSE #> y0 | 0 | -- | --% | TRUE #> sigma_abs | 0.109 | 0.017 | 15.8% | FALSE #> sigma_rel | 0 | -- | --% | TRUEmyfit$plot()