ReadingTime

Data frame of 547 observations in 4 variables

datasets

Bayesian inference under log-normality assumption must be performed very carefully. In fact, under the common priors for the variance, useful quantities in the original data scale (like mean and quantiles) do not have posterior moments that are finite (Fabrizi et al. 2012 <doi:10.1214/12-BA733>). This package allows to easily carry out a proper Bayesian inferential procedure by fixing a suitable distribution (the generalized inverse Gaussian) as prior for the variance. Functions to estimate several kind of means (unconditional, conditional and conditional under a mixed model) and quantiles (unconditional and conditional) are provided.

Aldo Gardini

BayesLN

Bayesian Inference for Log-Normal Data

Enrico Fabrizi

Carlo Trivisano

ReadingTime function

Dataframe with variables:<code>subj</code>: label indicating the subject.<code>item</code>: label indicating the item read.<code>so</code>: variable assuming value 1 (object relative condition) and -1 (subject relative condition).<code>log_rt</code>: logarithm of the reading time measured.

ReadingTime: Reading Times data

Description

Usage

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