pop_data
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a data.frame with cross-sectional serology data per antibody and age, and additional columns
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sr_params
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a data.frame() containing MCMC samples of parameters from the Bayesian posterior distribution of a longitudinal decay curve model. The parameter columns must be named:
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antigen_iso: a character() vector indicating antigen-isotype combinations
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iter: an integer() vector indicating MCMC sampling iterations
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y0: baseline antibody level at $t=0$ ($y(t=0)$)
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y1: antibody peak level (ELISA units)
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t1: duration of infection
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alpha: antibody decay rate (1/days for the current longitudinal parameter sets)
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r: shape factor of antibody decay
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noise_params
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a data.frame() (or tibble::tibble()) containing the following variables, specifying noise parameters for each antigen isotype:
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antigen_iso: antigen isotype whose noise parameters are being specified on each row
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nu: biological noise
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eps: measurement noise
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y.low: lower limit of detection for the current antigen isotype
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y.high: upper limit of detection for the current antigen isotype
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antigen_isos
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Character vector with one or more antibody names. Must match pop_data
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lambda_start
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starting guess for incidence rate, in events/year.
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stepmin
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A positive scalar providing the minimum allowable relative step length.
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stepmax
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a positive scalar which gives the maximum allowable scaled step length. stepmax is used to prevent steps which would cause the optimization function to overflow, to prevent the algorithm from leaving the area of interest in parameter space, or to detect divergence in the algorithm. stepmax would be chosen small enough to prevent the first two of these occurrences, but should be larger than any anticipated reasonable step.
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verbose
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logical: if TRUE, print verbose log information to console
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build_graph
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whether to graph the log-likelihood function across a range of incidence rates (lambda values)
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print_graph
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whether to display the log-likelihood curve graph in the course of running est_seroincidence()
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cluster_var
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optional name(s) of the variable(s) in pop_data containing cluster identifiers for clustered sampling designs (e.g., households, schools). Can be a single variable name (character string) or a vector of variable names for multi-level clustering (e.g., c(“school”, “classroom”)). When provided, standard errors will be adjusted for within-cluster correlation using cluster-robust variance estimation.
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stratum_var
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optional name of the variable in pop_data containing stratum identifiers. Used in combination with cluster_var for stratified cluster sampling designs.
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sampling_weights
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optional data.frame containing sampling weights with columns for cluster/stratum identifiers and their sampling probabilities. Currently not implemented; reserved for future use.
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…
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Arguments passed on to stats::nlm
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typsize
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an estimate of the size of each parameter at the minimum.
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fscale
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an estimate of the size of
f at the minimum.
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ndigit
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the number of significant digits in the function
f.
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gradtol
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a positive scalar giving the tolerance at which the scaled gradient is considered close enough to zero to terminate the algorithm. The scaled gradient is a measure of the relative change in
f in each direction p[i] divided by the relative change in p[i].
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iterlim
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a positive integer specifying the maximum number of iterations to be performed before the program is terminated.
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check.analyticals
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a logical scalar specifying whether the analytic gradients and Hessians, if they are supplied, should be checked against numerical derivatives at the initial parameter values. This can help detect incorrectly formulated gradients or Hessians.
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