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visualize the etiology regression with a continuous covariate

Source: R/plot-etiology-regression.R
plot_etiology_regression.Rd

This function visualizes the etiology regression against one continuous covariate, e.g., enrollment date. (NB: dealing with NoA, multiple-pathogen causes, other continuous covariates? also there this function only plots the first slice - so generalization may be useful - give users an option to choose slice s; currently default to the first slice.)

Usage

plot_etiology_regression(
  DIR_NPLCM,
  stratum_bool,
  slice = 1,
  plot_basis = FALSE,
  truth = NULL,
  RES_NPLCM = NULL,
  do_plot = TRUE,
  do_rug = TRUE,
  return_metric = TRUE,
  plot_ma_dots = FALSE
)

Arguments

DIR_NPLCM

File path to the folder containing posterior samples

stratum_bool

a vector of TRUE/FALSE with TRUE indicating the rows of subjects to include

slice

integer; specifies which slice of bronze-standard data to visualize; Default to 1.

plot_basis

TRUE for plotting basis functions; Default to FALSE

truth

a list of truths computed from true parameters in simulations; elements: Eti, FPR, PR_case,TPR; All default to NULL in real data analyses. Currently only works for one slice of bronze-standard measurements (in a non-nested model).

  • Eti matrix of # of rows = # of subjects, # columns: length(cause_list) for Eti

  • FPR matrix of # of rows = # of subjects, # columns: ncol(data_nplcm$Mobs$MBS$MBS1)

  • PR_case matrix of # of rows = # of subjects, # columns: ncol(data_nplcm$Mobs$MBS$MBS1)

  • TPR a vector of length identical to PR_case

RES_NPLCM

pre-read res_nplcm; default to NULL.

do_plot

TRUE for plotting

do_rug

TRUE for plotting

return_metric

TRUE for showing overall mean etiology, quantiles, s.d., and if truth$Eti is supplied, coverage, bias, truth and integrated mean squared errors (IMSE).

plot_ma_dots

plot moving averages among case and controls if TRUE; Default to FALSE.

Value

A figure of etiology regression curves and some marginal positive rate assessment of model fit; See example for the legends.

References

See example figures

See also

Other visualization functions: plot.nplcm(), plot_BrS_panel(), plot_SS_panel(), plot_check_common_pattern(), plot_check_pairwise_SLORD(), plot_etiology_strat(), plot_panels(), plot_pie_panel(), plot_subwt_regression()