Find the uncertainty of values in a data.frame that is derived from a tsv file or similar

given a data.frame, this function will look for a column that contains some kind of standard error and retrieve it. The returned numeric vector will be named. This function is not intended for data, for data, the values function will retrieve both the value and the standard error if it was specified.

Usage

uncertainty(df)

Arguments

df

a data frame with a "value" column

Value

a named numeric vector

Details

This function is for the case that the table specifies a distribution with a mean and an range (of some sort). The type of uncertainty found will be attached as a comment to the returned value: "sd" standard deviation for normal distribution, "se" standard error (for a normal prior), and "range" for a uniform prior. Other priors are not recognised yet.

The distinction between standard-error and standard-deviation doesn't matter much here: either the value is some kind of mean and the uncertainty is the standard-error or standard-deviation of the mean, or it is a raw data-point (not averaged) and we know the standard deviation (noise) of the device that measured it, then uncertainty is the standard deviation of the noise distribution. In either case, the value will be taken at face value and the uncertainty is used as sigma in the default log-likelihood function.

Any entry of prior.distribution other than "uniform", will start a search for some kind of standard deviation or standard error (or sigma). As more priors are added, this function will look for the parameters of those distributions.

This function makes many assumptions specifically that all variables in the table have the same type of prior distribution (but not identically distributed).