View source: R/Selbal_Functions.R
selbal  R Documentation 
Looks for a highlyassociated balance with a response variable
selbal(x, y, th.imp = 0, covar = NULL, logit.acc = "AUC", logt = T,
col = c("steelblue1", "tomato1"), tab = T, draw = T,
maxV = 1e+10, zero.rep = "bayes")
x 
a 
y 
the response variable, either continuous or dichotomous. 
th.imp 
a numeric value indicating the minimum increment required in the association parameter between two consecutive steps in order to continue with the variable addition into the balance. 
covar 

logit.acc 
when 
logt 

col 

tab 

draw 

maxV 

zero.rep 
a value defining the method to use for zero  replacement.

opt.cri 
parameter for selecting the method to determine the optimal
number of variables. 
A list
with the following objects:
FINAL.BAL
the numeric values of the selected balance for each
sample.
POS
a vector with the variables appearing in the numerator of
the balance.
NEG
a vector with the variables appearing in the denominator of
the balance.
INC.VAR
a vector with both POS
and NUM
variables
(included variables).
ACC.Bal
a vector with the association value after each step of
the algorithm.
EVOL
a data.frame
with the variables sorted as they have
been added into the balance with the corresponding association value after
their inclusion. Only returned if tab
is TRUE
.
FINAL.P
the graphical representation of the results. Only
showed if draw = T
.
FIT.Final
the regression model taking covariates and the final
balance as the explanatory variables and y
as the response variable.
# Load data set
load("HIV.rda")
# Define x and y
x < HIV[,1:60]
y < HIV[,62]
# Run the algorithm
Bal < selbal(x,y)
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