ggfan (version 0.1.0)

gp_model_fit: A stan_fit object used in the ggfan_stan vignette, containing posterior samples from a latent gaussian process model. This is provided as data to avoid having to conduct computationally expensive sampling when producing the vignettes.

Description

The code needed to recreate the object is included in the examples, as well as in the vignette code chunks.

Usage

gp_model_fit

Format

A `stan_fit` object containing samples of the following parameters.

eta_sq

Gaussian process variance parameter

rho_sq

Gaussian process roughness parameter

z

Latent poisson rate

y_gen

Posterior predictive sample of counts `y`

See the help page for stanfit for more details.

Examples

# NOT RUN {
# generate mean and variance for sequence of samples over time
library(rstan)
library(dplyr)
library(magrittr)
library(tidyr)
library(tibble)

library(ggfan)
seed <- 34526
set.seed(seed)

# data 
x <- seq(-5,5,0.1)
N <- length(x)
y <- cbind(rpois(N, exp(sin(x)+2)),rpois(N, exp(sin(x)+2)))

stan_data <- list(N=N, x=x, y=y)


compiled_model <- stan_model(file=file.path(path.package("ggfan"), 
                                            "stan","latent_gp_pois.stan"))
gp_model_fit <- sampling(compiled_model, data=stan_data, iter=3000,thin=6)
#devtools::use_data(gp_model_fit, internal=FALSE)
# }

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