4.1 Linear predictor (LP)

library(INLA)

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## This is INLA_22.12.16 built 2022-12-23 13:24:10 UTC.
##  - See www.r-inla.org/contact-us for how to get help.
##  - To enable PARDISO sparse library; see inla.pardiso()

library(ggplot2)

1. step one: writing linear predictor as an R formula

$$Response \quad variable \sim fixed + random \quad effect$$

Random effect are put inside f() with a name of model arguments (“iid”)

$$\eta_i = \beta_o + \beta_1x_1 + \beta_2x_2 + u_i$$

$$u_i \sim N(0, \sigma^2_u)$$

y ~ x1 + x2 + f(i, model = "iid")

y ~ 0 = b0 + x1 + x2 + f(i, model = "iid")