Description Usage Arguments Details Value Author(s) Examples
View source: R/apply.leos.method.R
Function apply.leos.method
applies the following method for the estimates:
Notification delay modelling
by Leo Bastos
1 2 | apply.leos.method(df.in, current.epiyearweek, quantile.target = 0.95,
low.activity = NULL, generate.plots = F)
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df.in |
Data frame with the FIRST THREE columns refering to [,1] location id, [,2] notification date, [,3] digitization date, unless all(c('ID_MUNICIP', 'DT_NOTIFIC', 'DT_DIGITA') %in% names(df.in)) == T |
current.epiyearweek |
Most recent epidemiological week to be considered and estimated. Expected format YYYY*WW, e.g., 2010W03 |
quantile.target |
Quantile to be used to determine Dmax from delay profile. Default: 0.95 |
low.activity |
List of location id's not to be estimated due to low activity. Default: NULL |
generate.plots |
Boolean object to determine wether function should generate and save plots or not. Default: F |
N_t - number of notified cases at time t
Y_t,d - number of notified cases from time t with notification delay d
D - maximum acceptable time delay
N_t = Y_t,0 + sum_d=1^D Y_t,d
Y_0,t is known forall t
If T is today, Y_t,d is unknown for all (t,d) such that t+d > T
Contributtors: Claudia T Codeço and Marcelo F C Gomes
Function apply.leos.method
returns a list containing the following components:
estimated.data.frame |
Data frame containing the weekly aggregate of df.in, plus columns with estimate mean, quantiles 2.5%, 50% and 97.5% and other relevant info |
delay.cutoff |
Data frame with Dmax obtained for each locality, epiyearweek used as cutoff and execution date |
estimated.epiyearweek |
Epidemiological week requested |
model.pars |
List with model's WAIC, DIC and hyperparameters from the INLA, for each locality (as character) |
call |
Function call |
Marcelo F C Gomes marcelo.gomes@fiocruz.br
1 2 3 | data(opportunity.example.data)
res <- apply.leos.method(opportunity.example.data, current.epiyearweek='2014W52',
quantile.target=0.95)
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