genderizeTrain: Training genderize function
In kalimu/genderizeR: Gender Prediction Based on First Names

Description Usage Arguments Value See Also Examples

THe genderizeTrain function predicts gender and checks different combinations of probability and count parameters.

1 2	genderizeTrain(x, y, givenNamesDB, probs, counts, parallel = FALSE, cores = NULL)

`x`	A text vector that we want to genderize.
`y`	A text vector of true gender labels for the x vector.
`givenNamesDB`	A dataset with gender data (could be an output of `findGivenNames` function).
`probs`	A numeric vector of different probability values. Used to subseting a givenNamesDB dataset.
`counts`	A numeric vector of different count values. Used to subseting a givenNamesDB dataset.
`parallel`	If TRUE it computes errors with the use of `parallel` package and available cores. Default is FALSE.
`cores`	A integer value for number of cores designated to parallel processing or NULL (default). If `parallel` argument is TRUE and `cores` is NULL, than the available number of cores will be detected automatically.

A data frame with prediction indicators for each combination of parameters:

`errorCoded`	The classification error for predicted and unpredicted gender.
`errorCodedWithoutNA`	The classification error for items with predicted gender only.
`naCoded`	The proportion of items with manually coded gender and with unpredicted gender.
`errorGenderBias`	The net gender bias error.

Implementation of parallel mclapply on Windows machines by Nathan VanHoudnos http://edustatistics.org/nathanvan/setup/mclapply.hack.R.

## Not run: 

x = c('Alex', 'Darrell', 'Kale', 'Lee', 'Robin', 'Terry', 'John', 'Tom')
y = c(rep('male', length(x)))

givenNamesDB = findGivenNames(x)
probs = seq(from =  0.5, to = 0.9, by = 0.1)
counts = c(1, 10)

genderizeTrain(x = x, y = y, 
               givenNamesDB = givenNamesDB, 
               probs = probs, counts = counts, 
               parallel = TRUE) 

#     prob count errorCoded errorCodedWithoutNA naCoded errorGenderBias
#  1:  0.5     1      0.125               0.125   0.000           0.125
#  2:  0.6     1      0.125               0.000   0.125           0.000
#  3:  0.7     1      0.125               0.000   0.125           0.000
#  4:  0.8     1      0.375               0.000   0.375           0.000
#  5:  0.9     1      0.500               0.000   0.500           0.000
#  6:  0.5    10      0.125               0.125   0.000           0.125
#  7:  0.6    10      0.125               0.000   0.125           0.000
#  8:  0.7    10      0.125               0.000   0.125           0.000
#  9:  0.8    10      0.375               0.000   0.375           0.000
# 10:  0.9    10      0.500               0.000   0.500           0.000


## End(Not run)