Fit a mixture model to classify serostatus — mixture_model • serosv

Refers to section 11.1 - 11.4

Usage

mixture_model(
  antibody_level,
  breaks = 40,
  pi = c(0.2, 0.8),
  mu = c(2, 6),
  sigma = c(0.5, 1)
)

Arguments

antibody_level: - vector of the corresponding raw antibody level
breaks: - number of intervals which the antibody_level are grouped into
pi: - proportion of susceptible, infected
mu: - a vector of means of component distributions (vector of 2 numbers in ascending order)
sigma: - a vector of standard deviations of component distributions (vector of 2 number)

Value

a list of class mixture_model with the following items

df: the dataframe used for fitting the model
info: list of 3 items parameters, distribution and constraints for the fitted model
susceptible: fitted distribution for susceptible
infected: fitted distribution for infected

Examples

df <- vzv_be_2001_2003[vzv_be_2001_2003$age < 40.5,]
data <- df$VZVmIUml[order(df$age)]
model <- mixture_model(antibody_level = data)
model$info
#> 
#> Parameters:
#>       pi    mu  sigma
#> 1 0.1088 2.349 0.6804
#> 2 0.8912 6.439 0.9437
#> 
#> Distribution:
#> [1] "norm"
#> 
#> Constraints:
#>    conpi    conmu consigma 
#>   "NONE"   "NONE"   "NONE" 
#> 
plot(model)