Penalized Spline model

Penalized Spline model

Usage

penalized_spline_model(
  data,
  s = "bs",
  link = "logit",
  framework = "pl",
  sp = NULL
)

Arguments

data

the input data frame, must either have `age`, `pos`, `tot` column for aggregated data OR `age`, `status` for linelisting data

s

smoothing basis to use

link

link function to use

framework

which approach to fit the model ("pl" for penalized likelihood framework, "glmm" for generalized linear mixed model framework)

sp

smoothing parameter

Value

a list of class penalized_spline_model with 6 attributes

datatype

type of datatype used for model fitting (aggregated or linelisting)

df

the dataframe used for fitting the model

framework

either pl or glmm

info

fitted "gam" model when framework is pl or "gamm" model when framework is glmm

sp

seroprevalence

foi

force of infection

See also

[mgcv::gam()], [mgcv::gamm()] for more information the fitted gam and gamm model

Examples

data <- parvob19_be_2001_2003
data$status <- data$seropositive
model <- penalized_spline_model(data, framework="glmm")
#> 
#>  Maximum number of PQL iterations:  20 
#> iteration 1
#> iteration 2
#> iteration 3
#> iteration 4
model$info$gam
#> 
#> Family: binomial 
#> Link function: logit 
#> 
#> Formula:
#> spos ~ s(age, bs = s, sp = sp)
#> 
#> Estimated degrees of freedom:
#> 5.8  total = 6.8 
#> 
plot(model)