View source: R/Selbal_Functions.R
selbal.cv  R Documentation 
Cross  validation process for the selection of the optimal number of variables and robustness evaluation
selbal.cv(x, y, n.fold = 5, n.iter = 10, seed = 31415,
covar = NULL, col = c("steelblue1", "tomato1"),
col2 = c("darkgreen", "steelblue4", "tan1"), logit.acc = "AUC",
maxV = 20, zero.rep = "bayes", opt.cri = "1se",
user_numVar = NULL)
x 
a 
y 
the response variable, either continuous or dichotomous. 
n.fold 
number of folds in which to divide the whole data set. 
n.iter 
number of iterations for the cross  validation process. 
seed 
a seed to make the results reproducible. 
covar 

col 

col2 

logit.acc 
when 
maxV 

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

opt.cri 
parameter indicating the method to determine the optimal
number of variables. 
user_numVar 
parameter to modify the choosen optimal number of variables. If it is used, it is the final number of variables used in the method. 
th.imp 
the minimum increment needed when adding a new variable into the balance in order to consider an improvement. 
A list
with the following objects:
a boxplot with the mean squared errors (numeric responses) or AUC
values (dichotomous responses) for the test data sets using the balances
resulted in the cross  validation. Branches represent the standard error and
the optimal number of components according with the opt.cri
criteria
is highlighted with a dashed line.
barplot with the proportion of times a variable appears in the cross  validation balances.
a graphical representation of the Global Balance (draw it using
grid.draw
function).
a table with the infromation of Global Balance, CV Balance and the
three most repeated balances in the cross  validation process (draw it using
plot.tab
function).
a vector with the accuracy values (MSE for continuous variables and AUC for dichotomous variables) obtained in the cross  validation procedure.
a table with the variables appearing in the Global Balance in a useful
format for bal.value
function in order to get the balance score for
new datasets.
the regression model object where the covariates and the final balance
are the explanatory variables and y
the response variable.
the optimal number of variables estimated in the cross  validation.
# Load data set
load("HIV.rda")
# Define x and y
x < HIV[,1:60]
y < HIV[,62]
# Run the algorithm
CV.Bal < selbal.cv(x,y)
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