Description Usage Arguments Value Methods (by class) References See Also Examples
View source: R/driver_estimate_transmissision_flows.r
This function estimates c_hat
, the probability that a randomly
selected pathogen sequence in one population group links to at least
one pathogen sequence in another population group.
1 2 3 4  estimate_c_hat(df_counts_and_p_hat, ...)
## Default S3 method:
estimate_c_hat(df_counts_and_p_hat, ...)

df_counts_and_p_hat 
A data.frame returned by the function: 
... 
Further arguments. 
Returns a data.frame containing:
H1_group, Name of population group 1
H2_group, Name of population group 2
number_hosts_sampled_group_1, Number of individuals sampled from population group 1
number_hosts_sampled_group_2, Number of individuals sampled from population group 2
number_hosts_population_group_1, Estimated number of individuals in population group 1
number_hosts_population_group_2, Estimated number of individuals in population group 2
max_possible_pairs_in_sample, Number of distinct possible transmission pairs between individuals sampled from population groups 1 and 2
max_possible_pairs_in_population, Number of distinct possible transmission pairs between individuals in population groups 1 and 2
num_linked_pairs_observed, Number of observed directed transmission pairs between samples from population groups 1 and 2
p_hat, Probability that pathogen sequences from two individuals randomly sampled from their respective population groups are linked
c_hat, Probability that a randomly selected pathogen sequence in one population group links to at least one pathogen sequence in another population group i.e. probability of clustering
default
: Estimates probability of clustering
Magosi LE, et al., Deepsequence phylogenetics to quantify patterns of HIV transmission in the context of a universal testing and treatment trial – BCPP/ Ya Tsie trial. To submit for publication, 2021.
Carnegie, N.B., et al., Linkage of viral sequences among HIVinfected village residents in Botswana: estimation of linkage rates in the presence of missing data. PLoS Computational Biology, 2014. 10(1): p. e1003430.
See estimate_p_hat
to prepare input data to estimate c_hat
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  library(bumblebee)
library(dplyr)
# Estimate the probability of clustering between individuals from two population groups of interest
# We shall use the data of HIV transmissions within and between intervention and control
# communities in the BCPP/Ya Tsie HIV prevention trial. To learn more about the data
# ?counts_hiv_transmission_pairs, ?sampling_frequency and ?estimated_hiv_transmission_flows
# Load and view data
#
# The input data comprises counts of observed directed HIV transmission pairs within and
# between intervention and control communities in the BCPP/Ya Tsie trial, sampling
# information and the probability of linkage between individuals sampled from
# intervention and control communities (i.e. \code{p_hat})
#
# See ?estimate_p_hat() for details on estimating p_hat
results_estimate_p_hat < estimated_hiv_transmission_flows[, c(1:10)]
results_estimate_p_hat
# Estimate c_hat
results_estimate_c_hat < estimate_c_hat(df_counts_and_p_hat = results_estimate_p_hat)
# View results
results_estimate_c_hat

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