regr_ind {CARRoT}  R Documentation 
Indices of the best regressions
Description
One of the two main functions of the package. Identifies the predictors included into regressions with the highest average predictive power
Usage
regr_ind(
vari,
outi,
crv,
cutoff = NULL,
part = 10,
mode,
cmode = "det",
predm = "exact",
objfun = "acc",
parallel = FALSE,
cores,
minx = 1,
maxx = NULL,
nr = NULL,
maxw = NULL,
st = NULL,
rule = 10,
corr = 1,
Rsq = F,
marg = 0
)
Arguments
vari 
set of predictors 
outi 
array of outcomes 
crv 
number of crossvalidations 
cutoff 
cutoff value for mode 
part 
for each crossvalidation partitions the dataset into training and test set in a proportion 
mode 

cmode 

predm 

objfun 

parallel 
TRUE if using parallel toolbox, FALSE if not. Defaults to FALSE 
cores 
number of cores to use in case of parallel=TRUE 
minx 
minimum number of predictors to be included in a regression, defaults to 1 
maxx 
maximum number of predictors to be included in a regression, defaults to maximum feasible number according to one in ten rule 
nr 
a subset of the dataset, such that 
maxw 
maximum weight of predictors to be included in a regression, defaults to maximum weight according to one in ten rule 
st 
a subset of predictors to be always included into a predictive model,defaults to empty set 
rule 
an Events per Variable (EPV) rule, defaults to 10' 
corr 
maximum correlation between a pair of predictors in a model 
Rsq 
whether the Rsquared statistics constraint is introduced 
marg 
margin of error for Rsquared statistics constraint 
Value
Prints the best predictive power provided by a regression, predictive accuracy of the empirical prediction (value of emp
computed by cross_val
for logistic and linear regression). Returns indices of the predictors included into regressions with the highest predictive power written in a list. For mode='linear'
outputs a list of two lists. First list corresponds to the smallest absolute error, second corresponds to the smallest relative error
See Also
Uses compute_weights
, make_numeric
, compute_max_weight
, compute_weights
, compute_max_length
, cross_val
,av_out
, get_indices
Examples
#creating variables for linear regression mode
variables_lin<matrix(c(rnorm(56,0,1),rnorm(56,1,2)),ncol=2)
#creating outcomes for linear regression mode
outcomes_lin<rnorm(56,2,1)
#running the function
regr_ind(variables_lin,outcomes_lin,100,mode='linear',parallel=TRUE,cores=2)
#creating variables for binary mode
vari<matrix(c(1:100,seq(1,300,3)),ncol=2)
#creating outcomes for binary mode
out<rbinom(100,1,0.3)
#running the function
regr_ind(vari,out,20,cutoff=0.5,part=10,mode='binary',parallel=TRUE,cores=2,nr=c(1,10,20),maxx=1)