predict.snm: Predictions from a Semiparametric Nonlinear Mixed Effects...
In assist: A Suite of R Functions Implementing Spline Smoothing Techniques
predict.snm R Documentation
Predictions from a Semiparametric Nonlinear Mixed Effects Model Fit
Description
The predictions are obtained on a semiparametric nonlinear mixed effects model object
by replacing the unknown functuons and the unknown parameters with their estimates.
Of note, only a population level of predictions is available.
Usage
## S3 method for class 'snm'
predict(object, newdata, ...)
Arguments
object
a fitted snm object.
newdata
a data frame containing the values at which predictions are required.
Default are data used to fit the object.
...
other arguments, but currently unused.
Details
This function is a method for the generic function predict for class snm.
Value
a vector of prediction values, obtained by evaluating the model in the frame newdata
Author(s)
Chunlei Ke chunlei_ke@yahoo.com and Yuedong Wang yuedong@pstat.ucsb.edu
References
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Ke, C. and Wang, Y. (2001). Semi-parametric Nonlinear Mixed Effects Models and
Their Applications. JASA.
See Also
snm, predict
assist documentation built on Aug. 22, 2023, 9:12 a.m.
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| predict.snm | R Documentation |
Predictions from a Semiparametric Nonlinear Mixed Effects Model Fit
Description
The predictions are obtained on a semiparametric nonlinear mixed effects model object by replacing the unknown functuons and the unknown parameters with their estimates. Of note, only a population level of predictions is available.
Usage
## S3 method for class 'snm'
predict(object, newdata, ...)
Arguments
object |
a fitted |
newdata |
a data frame containing the values at which predictions are required. Default are data used to fit the object. |
... |
other arguments, but currently unused. |
Details
This function is a method for the generic function predict for class snm.
Value
a vector of prediction values, obtained by evaluating the model in the frame newdata
Author(s)
Chunlei Ke chunlei_ke@yahoo.com and Yuedong Wang yuedong@pstat.ucsb.edu
References
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Ke, C. and Wang, Y. (2001). Semi-parametric Nonlinear Mixed Effects Models and Their Applications. JASA.
See Also
snm, predict
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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