## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup, output=FALSE, warning=FALSE, message=FALSE------------------------
library(serosv)
library(dplyr)
library(magrittr)

## -----------------------------------------------------------------------------
data <- parvob19_fi_1997_1998[order(parvob19_fi_1997_1998$age), ]

## -----------------------------------------------------------------------------
# Fit a Muench model
muench <- polynomial_model(data, k = 1, status_col="seropositive")
summary(muench$info)
plot(muench) 

## -----------------------------------------------------------------------------
# Provide a range of values for k
best_param <- polynomial_model(data, k = 1:5, status_col = "seropositive")
plot(best_param)
# View the best model here which suggests k = 4 is the best parameter value
best_param

## -----------------------------------------------------------------------------
hav <- hav_be_1993_1994
model <- fp_model(hav, p=c(1, 1.5), link="cloglog")
model
plot(model)

## ----warning=FALSE------------------------------------------------------------
model <- fp_model(hav, 
                  p=list(
                    p_range=seq(-2,3,0.1),
                    degree=2
                  ), 
                  monotonic=FALSE,
                  link="cloglog")
plot(model)
# the best set of powers for this dataset is 1.5 and 1.6
model

## ----warning=FALSE------------------------------------------------------------
# ---- Best model with the monotonic constraint -----
model <- fp_model(hav, 
                  p=list(
                    p_range=seq(-2,3,0.1),
                    degree=2
                  ), 
                  monotonic=TRUE,
                  link="cloglog")
plot(model)
# the best set of powers with the monotonic constraint is 0.5 and 1.1
model

## ----warning=FALSE------------------------------------------------------------
farrington_md <- farrington_model(
   rubella_uk_1986_1987,
   start=list(alpha=0.07,beta=0.1,gamma=0.03)
   )
farrington_md
plot(farrington_md)

## -----------------------------------------------------------------------------
hcv <- hcv_be_2006[order(hcv_be_2006$dur), ]

wb_md <- hcv %>% weibull_model(t_lab = "dur", status_col="seropositive")
wb_md
plot(wb_md) 

## ----message=FALSE, output=FALSE, warning=FALSE-------------------------------
df <- mumps_uk_1986_1987
model <- hierarchical_bayesian_model(df, type="far3")

## -----------------------------------------------------------------------------
model
plot(model)

## ----message=FALSE, output=FALSE, warning=FALSE-------------------------------
df <- rubella_uk_1986_1987
model <- hierarchical_bayesian_model(df, type="log_logistic")

## -----------------------------------------------------------------------------
model
plot(model)