cusp.logist {cusp} | R Documentation |
Fit a Logistic Surface Model to Data
Description
This function fits a logistic curve model to data using maximum likelihood under the assumption of normal errors (i.e., nonlinear least squares). Both the response variable may be modelled by a linear combination of variables and design factors, as well as the normal/asymmetry factor alpha
and bifurction/splitting factor beta
.
Usage
cusp.logist(formula, alpha, beta, data, ..., model = TRUE, x =
FALSE, y = TRUE)
Arguments
formula , alpha , beta |
|
data |
|
... |
named arguments that are passed to |
model , x , y |
logicals. If |
Details
A nonlinear regression is carried out of the model
for ,
where
in which the 's, and
's, are estimated. The
's are variables in the data set
and specified by
formula
; the 's are variables in the data set and are specified in
alpha
and beta
. Variables in alpha
and beta
need not be the same. The 's are estimated implicitely
using concentrated likelihood methods, and are not returned explicitely.
Value
List with components
minimum |
Objective function value at minimum |
estimate |
Coordinates of objective function minimum |
gradient |
Gradient of objective function at minimum. |
code |
Convergence |
iterations |
Number of iterations used by |
coefficients |
A named vector of estimates of |
linear.predictors |
Estimates of |
fitted.values |
Predicted values of |
residuals |
Residuals |
rank |
Numerical rank of matrix of predictors for |
deviance |
Residual sum of squares. |
logLik |
Log of the likelihood at the minimum. |
aic |
Akaike's information criterion |
rsq |
R Squared (proportion of explained variance) |
df.residual |
Degrees of freedom for the residual |
df.null |
Degrees of freedom for the Null residual |
rss |
Residual sum of squares |
hessian |
Hessian matrix of objective function at the minimum if |
Hessian |
Hessian matrix of log-likelihood function at the minimum (currently unavailable) |
qr |
QR decomposition of the |
converged |
Boolean indicating if optimization convergence is proper (based on exit code |
weights |
|
call |
the matched call |
y |
If requested (the default), the matrix of response variables used. |
x |
If requested, the model matrix used. |
null.deviance |
The sum of squared deviations from the mean of the estimated |
Author(s)
Raoul Grasman
References
Hartelman PAI (1997). Stochastic Catastrophe Theory. Amsterdam: University of Amsterdam, PhDthesis.