Description Usage Arguments Details Value Author(s) References See Also Examples
The method is to estimate the sobol indices under the logit link model.
1 2 3 4 5 6 7 8  LogitSImainsingle(i, xdata, beta)
LogitSImain(xdata, beta)
LogitSIsecpair(pair, xdata, beta)
LogitSIsec(xdata, beta)
LogitSImainsample(i, xdata, beta)
LogitSIfordersample(xdata, beta)
LogitSIkintersample(interaction, xdata, beta)
LogitSIkordersample(k, xdata, beta)

xdata 
A data set of class 'matrix' or 'data.frame' which only includes the variables or features. 
beta 
A vector; the coefficients of the variables estimated by the regression model. 
i 
A positive integer; the index of the variable which is of interest for computing the sobol indices main effect. 
pair 
A vector of length two; the indices of the paired variables which are of interest for computing their interaction sobol indices main effect. 
interaction 
A vector; the indices of the variables which are of interest for computing their interaction (usually high order) sobol indices main effect. 
k 
A positive integer; the order which are of interest
for computing all variables' possible interactions' (of order

This is our proposed method for computing the sobol indices under the logit link. The idea is to use the definition of sobol indices to derive variables' main effect of interest by applying sampling (or integration) approach and determine how important the variables are by computational calculations.
The LogitSImainsingle
function returns a number which is the numerator
of sobol indices for deriving single variable's main effect by using integration
approach.
The LogitSImain
function returns a vector which are the numerators
of sobol indices for deriving all single variables' main effects by using
integration approach.
The LogitSIsecpair
function returns a number which is the numerator
of sobol indices for deriving paired variables interaction's main effect by
using integration approach.
The LogitSIsec
function returns a matrix which are the numerators
of sobol indices for deriving all possible paired variables interactions'
main effects by using integration approach.
The LogitSImainsample
function returns a number which is the numerator
of sobol indices for deriving single variable's main effect by using sampling
approach.
The LogitSIfordersample
function returns a vector which are the numerators
of sobol indices for deriving all single variables' main effects by using sampling
approach.
The LogitSIkintersample
function returns a number which is the numerator
of sobol indices for deriving variables interaction's main effect by using sampling
approach.
The LogitSIkordersample
function returns a list (or numeric object) which
are the numerators of sobol indices for deriving the main effects of all
possible variables interactions of order k
by using sampling
approach.
Min Wang <wang.1807@mbi.osu.edu>
Sobol, I. M. (1990). On sensitivity estimation for nonlinear mathematical models, Matematicheskoe Modelirovanie, 2, 112118.
Lu, R., Rempala, G. and Wang, M. (2016). Sensitivity Analysis of Generalized Linear models, submitted.
Methods to address the other link functions can be found at identitySI2
and
logSI
.
1 2 3 4 5 6  xdata < matrix(rnorm(20*5, 1), ncol=5)
beta < runif(6, min=1, max=1)
LogitSImainsample(1, xdata, beta)
LogitSIfordersample(xdata, beta)
LogitSIkintersample(c(1,2,3), xdata, beta)
LogitSIkordersample(3, xdata, beta)

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