regression_g: Generalized additive model function gam() fits the observed...
In acweisha/gFPCAClassif: Classification of Binary Functional Data
Description
Usage
Arguments
Value
Description
Generalized additive model function gam() fits the observed binary-valued functional data by fitting a GLMM using a set of known basis functions
Usage
1
regression_g(z, Curves, tt, k = 10, method = "REML", bs0 = "cr")
Arguments
z
index z = 1,...,N
Curves
N x D matrix of observed binary series
tt
grid of timepoints going from 0 to 1 with D observations
k
number of basis functions
method
method used to evaluate the gam
bs0
type of basis functions used in the gam
Value
Fitted values from the game function for subject z
acweisha/gFPCAClassif documentation built on Dec. 18, 2021, 10:23 p.m.
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Description Usage Arguments Value
Description
Generalized additive model function gam() fits the observed binary-valued functional data by fitting a GLMM using a set of known basis functions
Usage
1 | regression_g(z, Curves, tt, k = 10, method = "REML", bs0 = "cr")
|
Arguments
z |
index z = 1,...,N |
Curves |
N x D matrix of observed binary series |
tt |
grid of timepoints going from 0 to 1 with D observations |
k |
number of basis functions |
method |
method used to evaluate the gam |
bs0 |
type of basis functions used in the gam |
Value
Fitted values from the game function for subject z
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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