Compute the prevalence estimates of wasting on the basis of the PROBIT method.

This approach is applied when the standard deviation of WFHZ is problematic. The PROBIT method estimates the prevalence of wasting indirectly by calculating the area under the tail of the curve, from negative infinitive to the given threshold, using the cumulative normal distribution function with the mean and standard deviation as inputs.

Usage

apply_probit_approach(x, .status = c("gam", "sam"))

compute_probit_prevalence(df, .summary_by = NULL, .for = c("wfhz", "mfaz"))

Arguments

x

A vector of class double of WFHZ or MFAZ values.

.status

A choice of the form of wasting for which the prevalence should be estimated.

df

An already wrangled dataset object of class data.frame to use.

.summary_by

A vector of class character of the geographical areas where the data was collected and for which the analysis should be performed.

.for

A choice between "wfhz" and "mfaz" for the anthropometric index.

Value

A summarised table of class data.frame of the prevalence estimates. No confidence intervals are yielded.