Description Usage Arguments Value References Examples
Perform variable selection for the spatial Poisson regression model under adaptive elastic net penalty.
1 2 3 
dat.obj 
List, input data. Must contains:

alpha.vec 
numeric vector, a vector of possible values of regularization parameter. The range is [0,1]. 
lambda.vec 
numeric vector, a vector of positive values of regularization parameter. 
method 
string, the method to be used. Options are:

plots 
bool, if 
intercept 
bool, if 
verbose 
bool, if 
A list of 13 items:
dat.obj
, List, a copy of the dat.obj
input.
start
, Initial values of parameters given by glmmPQL().
L.obj
, Regression coefficients under each alpha.vec
and lambda.vec
, under the adaptive elastic net.
Lout.obj
, AIC and BIC values under each L.obj value
, under the adaptive elastic net.
contour.out.obj
, Object used to generate contour plot as a function of alpha.vec
and lambda.vec
, with AIC or BIC as the output. Used to choose best penalty parameter, under the adaptive elastic net.
L.best.obj
, Model fitting results under the best chosen alpha.vec
and lambda.vec
, under the adaptive elastic net.
Lout.best.obj
, Best BIC value for L.best.obj
.
L.EN.obj, Lout.EN.obj, contour.out.EN.obj, L.EN.best.obj
, Similar items but under the elastic penalty.
lasso.weight
, Numeric, specifies the adaptive Lasso weight.
method
, String, the method used for computing the approximate likelihood function.
Xie, Y., Xu, L., Li, J., Deng, X., Hong, Y., Kolivras, K., and Gaines, D. N. (2018). Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence. Preprint, arXiv:1809.06418 [stat.AP].
1 2 3 4 5 6  #use small.test.dat as the input to fit the spatial Poisson regression model.
#a grid of alpha.vec and lambda.vec is typically used.
#Here one point of alpha.vec and lambda.vec is used for fast illustration.
test.fit<SpatialVS(dat.obj=small.test.dat, alpha.vec=0.5,
lambda.vec=5, method="PQL", intercept=TRUE, verbose=FALSE)

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