Title: | Crash Course On Bayesian Regression Modelling |
---|---|
Description: | Code, data and vignettes for a short (< 1 day) practical course on Bayesian Statistics. |
Authors: | Facundo Muñoz [aut, cre] |
Maintainer: | Facundo Muñoz <[email protected]> |
License: | GPL (>= 3) |
Version: | 0.1.0 |
Built: | 2024-09-30 03:05:45 UTC |
Source: | https://forgemia.inra.fr/umr-astre/training/crashbayes |
Example data from Kruschke (2013) used to compare the quantitative means of two groups using Null-Hypothesis Significance Testing and Bayesian Estimation
best
best
best
A data frame with 89 rows and 2 columns:
Factor with levels 'drug' or 'placebo'
Observed outcome of the IQ test for an individual
Outcomes from two groups of people who take an IQ test. Group 1 (N1 = 47) consumes a "smart drug" and Group 2 (N2 = 42) is a control group that consumes a placebo
The data was simulated randomly from t distributions, in order to generate some outliers.
https://jkkweb.sitehost.iu.edu/BEST/
John K. Kruschke, Journal of Experimental Psychology: General, 2013, v.142(2), pp.573-603. (doi: 10.1037/a0029146)
Probability density function of a variable whose logit is Gaussian.
dlogitnorm(x, mu, sd)
dlogitnorm(x, mu, sd)
x |
Numeric vector. Evaluation value. |
mu |
Numeric vector. Mean of the latent Gaussian. |
sd |
Numeric vector. Standard deviation of the latent Gaussian. |
https://en.wikipedia.org/wiki/Logit-normal_distribution
dlogitnorm(seq(0.1, 0.9, by = 0.1), 1, 1)
dlogitnorm(seq(0.1, 0.9, by = 0.1), 1, 1)
Compute a Confidence Interval
sigma_ci(x, alpha)
sigma_ci(x, alpha)
x |
An object of class |
alpha |
A data.frame with 1 line and variables parameter
, point_est
,
ll
and hh
.
See, for instance,
sigma_ci(lm(y ~ 1, data.frame(y = rnorm(1e3, sd = 2))), alpha = 0.05)
sigma_ci(lm(y ~ 1, data.frame(y = rnorm(1e3, sd = 2))), alpha = 0.05)