fit4plModel: Fitting 4pL models
In logicDT: Identifying Interactions Between Binary Predictors
fit4plModel R Documentation
Fitting 4pL models
Description
Method for fitting four parameter logistic models.
In the fashion of this package, only binary and quantitative
outcomes are supported.
Usage
fit4plModel(y, Z)
Arguments
y
Response vector. 0-1 coding for binary outcomes,
otherwise conventional regression is performed.
Z
Numeric vector of (univariate) input samples.
Details
4pL models are non-linear regression models of the shape
Y = f(x, b, c, d, e) + \varepsilon =
c + \frac{d-c}{1+\exp(b \cdot (x-e))} + \varepsilon
with \varepsilon being a random error term.
Value
An object of class "4pl" which contains a numeric
vector of the fitted parameters b, c, d, and e.
logicDT documentation built on Jan. 14, 2023, 5:06 p.m.
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| fit4plModel | R Documentation |
Fitting 4pL models
Description
Method for fitting four parameter logistic models. In the fashion of this package, only binary and quantitative outcomes are supported.
Usage
fit4plModel(y, Z)
Arguments
y |
Response vector. 0-1 coding for binary outcomes, otherwise conventional regression is performed. |
Z |
Numeric vector of (univariate) input samples. |
Details
4pL models are non-linear regression models of the shape
Y = f(x, b, c, d, e) + \varepsilon = c + \frac{d-c}{1+\exp(b \cdot (x-e))} + \varepsilon
with \varepsilon being a random error term.
Value
An object of class "4pl" which contains a numeric vector of the fitted parameters b, c, d, and e.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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