TestDimorph (version 0.3.5)

accu_model: Evaluation Of Sex prediction Accuracy

Description

Testing, cross validation and visualization of the accuracy of different sex prediction models using the confusionMatrix and roc curves

Usage

accu_model(
  f,
  x,
  y = NULL,
  method = "lda",
  res_method = "repeatedcv",
  p = 0.75,
  nf = 10,
  nr = 3,
  plot = FALSE,
  Sex = 1,
  Pop = NULL,
  byPop = FALSE,
  ref. = "F",
  post. = "M",
  ...
)

Arguments

f

Formula in the form groups ~ x1 + x2 + .... The grouping factor is placed to the left hand side while the numerical measurements are placed to the right hand side

x

Data frame to be fitted to the model

y

New data frame to be tested, if NULL x is splitted to test and training data seta, Default: NULL

method

A string specifying which classification or regression model to use,

res_method

The resampling method used by trainControl, Default: 'repeatedcv'

p

Percentage of x for testing the model in case y is NULL, Default: 0.75

nf

number of folds or of resampling iterations, Default: 10

nr

Number of repeats for repeated k fold cross validation, Default: 3

plot

Logical; if TRUE returns an roc curve for model accuracy, Default: FALSE

Sex

Number of the column containing sex 'M' for male and 'F' for female, Default: 1

Pop

Number of the column containing populations' names, Default: NULL

byPop

Logical; if TRUE returns the accuracy in different populations of the new data frame, Default: FALSE.

ref.

reference category in the grouping factor, Default: 'F'

post.

positive category in the grouping factor, Default: 'M'

...

additional arguments that can passed to modeling, confusionMatrix function and roc curve generated by plot_roc

Value

Visual and numerical accuracy parameters for the tested model

Details

Data frames to be entered as input need to be arranged in a similar manner to Howells dataset.

See Also

cutpointr::plot_roc() caret::confusionMatrix()

Examples

# NOT RUN {
# Using a single dataset
library(TestDimorph)
accu_model(
  Sex ~ GOL + NOL + BNL,
  x = Howells,
  method = "lda",
  plot = FALSE
)
# }