Chapter 10 Methods
Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise - John W. Tukey, 1962
We describe our methods in this chapter.
10.1 Linear Regression
10.2 Logistic Regression
10.3 Hierarchical Modeling
library(lme4)
library(arm)
data("sleepstudy")
fit_lme <- lmer(extra ~ group + (1 | ID), sleep)
display(fit_lme)## lmer(formula = extra ~ group + (1 | ID), data = sleep)
## coef.est coef.se
## (Intercept) 0.75 0.60
## group2 1.58 0.39
##
## Error terms:
## Groups Name Std.Dev.
## ID (Intercept) 1.69
## Residual 0.87
## ---
## number of obs: 20, groups: ID, 10
## AIC = 78, DIC = 71.1
## deviance = 70.5ranef(fit_lme)## $ID
## (Intercept)
## 1 -0.2118668
## 2 -1.7125900
## 3 -0.9622284
## 4 -1.8450067
## 5 -1.4477565
## 6 2.0833569
## 7 2.7013017
## 8 -0.3001446
## 9 0.6709115
## 10 1.0240229library(brms)
library(ggplot2)
library(ggeffects)
library(sjPlot)
library(sjlabelled)
data(efc)
m <- brm(cbind(c82cop1, c83cop2, c84cop3) ~ c161sex + e42dep, data = efc)
plot_model(m)10.3.1 Bayesian Hierarchical Modeling
data("sleep")
library(rjags)
#model_string <- 10.4 Being Certain about What we can
Uncertainity reflects the lack of complete knolwedge about a paramet; variability refers to underlying differences among individuals or groups (Gelman and Hill 457)