View source: R/simulatenplcm.R
simulate_nplcm  R Documentation 
Simulate data from nested partiallylatent class model (npLCM) family
simulate_nplcm(set_parameter)
set_parameter 
True model parameters in an npLCM specification:

A list of diagnostic test measurements, true latent statues:
data_nplcm
a list of structured data (see nplcm()
for
description).
template
a matrix: rows for causes (may comprise a single or multiple causative agents),
columns for measurements; generated as a lookup table to match diseaseclass specific
parameters (true and false positive rates)
latent_cat
integer values to indicate the latent category. The integer
code corresponds to the order specified in set_parameter$etiology
.
Controls are coded as length(set_parameter$etiology)+1
.)
simulate_latent for simulating discrete latent status, given which simulate_brs simulates bronzestandard data.
K.true < 2 # no. of latent subclasses in actual simulation. # If eta = c(1,0), effectively, it is K.true=1. J < 21 # no. of pathogens. N < 600 # no. of cases/controls. eta < c(1,0) # if it is c(1,0),then it is conditional independence model, and # only the first column of parameters in PsiBS, ThetaBS matter! seed_start < 20150202 print(eta) # set fixed simulation sequence: set.seed(seed_start) ThetaBS_withNA < c(.75,rep(c(.75,.75,.75,NA),5)) PsiBS_withNA < c(.15,rep(c(.05,.05,.05,NA),5)) ThetaSS_withNA < c(NA,rep(c(0.15,NA,0.15,0.15),5)) PsiSS_withNA < c(NA,rep(c(0,NA,0,0),5)) set_parameter < list( cause_list = c(LETTERS[1:J]), etiology = c(c(0.36,0.1,0.1,0.1,0.1,0.05,0.05,0.05, 0.05,0.01,0.01,0.01,0.01),rep(0.00,8)), #same length as cause_list. pathogen_BrS = LETTERS[1:J][!is.na(ThetaBS_withNA)], pathogen_SS = LETTERS[1:J][!is.na(ThetaSS_withNA)], meas_nm = list(MBS = c("MBS1"),MSS="MSS1"), Lambda = eta, #ctrl mix Eta = t(replicate(J,eta)), #case mix, row number equal to Jcause. PsiBS = cbind(PsiBS_withNA[!is.na(PsiBS_withNA)], rep(0,sum(!is.na(PsiBS_withNA)))), ThetaBS = cbind(ThetaBS_withNA[!is.na(ThetaBS_withNA)], rep(0,sum(!is.na(ThetaBS_withNA)))), PsiSS = PsiSS_withNA[!is.na(PsiSS_withNA)], ThetaSS = ThetaSS_withNA[!is.na(ThetaSS_withNA)], Nu = N, # control size. Nd = N # case size. ) simu_out < simulate_nplcm(set_parameter) data_nplcm < simu_out$data_nplcm pathogen_display < rev(set_parameter$pathogen_BrS) plot_logORmat(data_nplcm,pathogen_display) # more examples are provided in the vignette, including settings with # covariates.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.