Description Usage Arguments Value Author(s) References See Also Examples
Identification of interesting interactions between binary variables
using logic regression. Currently available for the classification, the linear
regression and the logistic regression approach of logreg
and for
a multinomial logic regression as implemented in mlogreg
.
1 2 3 4 5 6 7 8  ## Default S3 method:
logicFS(x, y, B = 100, useN = TRUE, ntrees = 1, nleaves = 8,
glm.if.1tree = FALSE, replace = TRUE, sub.frac = 0.632,
anneal.control = logreg.anneal.control(), onlyRemove = FALSE,
prob.case = 0.5, addMatImp = TRUE, fast = FALSE, rand = NULL, ...)
## S3 method for class 'formula'
logicFS(formula, data, recdom = TRUE, ...)

x 
a matrix consisting of 0's and 1's. Each column must correspond to a binary variable and each row to an observation. Missing values are not allowed. 
y 
a numeric vector or a factor specifying the values of a response for all the observations
represented in 
B 
an integer specifying the number of iterations. 
useN 
logical specifying if the number of correctly classified outofbag observations should
be used in the computation of the importance measure. If 
ntrees 
an integer indicating how many trees should be used. For a binary response: If For a continuous response: A linear regression model with For a categorical response: n.lev1 logic regression models with 
nleaves 
a numeric value specifying the maximum number of leaves used
in all trees combined. For details, see the help page of the function 
glm.if.1tree 
if 
replace 
should sampling of the cases be done with replacement? If

sub.frac 
a proportion specifying the fraction of the observations that
are used in each iteration to build a classification rule if 
anneal.control 
a list containing the parameters for simulated annealing.
See the help of the function 
onlyRemove 
should in the single tree case the multiple tree measure be used? If 
prob.case 
a numeric value between 0 and 1. If the outcome of the
logistic regression, i.e.\ the predicted probability, for an observation is
larger than 
addMatImp 
should the matrix containing the improvements due to the prime implicants
in each of the iterations be added to the output? (For each of the prime implicants,
the importance is computed by the average over the 
fast 
should a greedy search (as implemented in 
rand 
numeric value. If specified, the random number generator will be set into a reproducible state. 
formula 
an object of class 
data 
a data frame containing the variables in the model. Each row of 
recdom 
a logical value or vector of length 
... 
for the 
An object of class logicFS
containing
primes 
the prime implicants, 
vim 
the importance of the prime implicants, 
prop 
the proportion of logic regression models that contain the prime implicants, 
type 
the type of model (1: classification, 2: linear regression, 3: logistic regression), 
param 
further parameters (if 
mat.imp 
the matrix containing the improvements if 
measure 
the name of the used importance measure, 
useN 
the value of 
threshold 
NULL, 
mu 
NULL. 
Holger Schwender, [email protected]
Ruczinski, I., Kooperberg, C., LeBlanc M.L. (2003). Logic Regression. Journal of Computational and Graphical Statistics, 12, 475511.
Schwender, H., Ickstadt, K. (2007). Identification of SNP Interactions Using Logic Regression. Biostatistics, 9(1), 187198.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  ## Not run:
# Load data.
data(data.logicfs)
# For logic regression and hence logic.fs, the variables must
# be binary. data.logicfs, however, contains categorical data
# with realizations 1, 2 and 3. Such data can be transformed
# into binary data by
bin.snps<make.snp.dummy(data.logicfs)
# To speed up the search for the best logic regression models
# only a small number of iterations is used in simulated annealing.
my.anneal<logreg.anneal.control(start=2,end=2,iter=10000)
# Feature selection using logic regression is then done by
log.out<logicFS(bin.snps,cl.logicfs,B=20,nleaves=10,
rand=123,anneal.control=my.anneal)
# The output of logic.fs can be printed
log.out
# One can specify another number of interactions that should be
# printed, here, e.g., 15.
print(log.out,topX=15)
# The variable importance can also be plotted.
plot(log.out)
# And the original variable names are displayed in
plot(log.out,coded=FALSE)
## End(Not run)

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