estimate_accuracy()Utility function to generate accuracy metrics, for use with estimate_accuracy()
acc_sim(
n,
method,
p,
dat,
model,
eta,
nsample,
outcome,
power,
effect_size,
powersims,
alpha,
split,
...
)A data frame giving performance metrics for the specified sample size.
An integer giving the desired sample size for which the target function is to be calculated.
An optional string stating the distribution from which data is to be generated. Default is i.i.d. uniform sampling. Currently also supports "Class Imbalance". Can also take a function outputting a vector of probabilities if the user wishes to specify a custom distribution.
If method is 'Class Imbalance', gives the degree of weight placed on the positive class.
A rectangular data.frame or matrix-like object giving the full data from which samples are to be drawn. If left unspecified, gendata() is called to produce synthetic data with an appropriate structure.
A function giving the model to be estimated
A real number between 0 and 1 giving the probability of misclassification error in the training data.
A positive integer giving the number of samples to be generated for each value of $n$. Larger values give more accurate results.
A string giving the name of the outcome variable.
A logical indicating whether experimental power based on the predictions should also be reported
If power is TRUE, a real number indicating the scaled effect size the user would like to be able to detect.
If power is TRUE, an integer indicating the number of simulations to be conducted at each step to calculate power.
If power is TRUE, a real number between 0 and 1 indicating the probability of Type I error to be used for hypothesis testing. Default is 0.05.
A logical indicating whether the data was passed as a single data frame or separately.
Additional model parameters to be specified by the user.