knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
This vignette describes how survival data should be formatted for use with the functions in this package.
There is a general data format that works for most functions (here), but some functions require data to be in a specific format, these are;
The negative log-likelihood (nll) functions in this package require the survival data to be analysed to be in a data frame.
The default assumption is each row contains data for an individual host.
Data can be grouped, where a row contains data on the frequency of individuals from a particular treatment or population experiencing the same event, in the same sampling interval. In this case, the frequency data must be in a column named, 'fq'. This column will be automatically detected and nll calculations adjusted accordingly; frequencies of zero ('0') are allowed.
By default, most nll functions assume a data frame will contain three columns named as follows,
containing the following information;
These columns can be renamed when specifying parameters for the nll function to be sent for estimation by maximum likelihood. Columns with the default names above to not need to be specified, but the contents of their rows must be specified as above, i.e., data from an infected treatment must be specified as '1' and not 'infected', '+ve', etc.
All nll functions assume individuals in an uninfected treatment are uninfected.
Not all functions assume all individuals in an infected treatment are infected.
Some nll functions have specific data formatting requirements.
This function applies to cases where two distinct subpopulations of hosts have been identified ('observed') within an infected population or treatment. In addition to the columns above, this function requires the data frame to be analysed to have a column identifying the two infected subpopulations;
The column can be renamed when specifying the nll function, but it must contain values of '1' or '2' for the two subpopulations.
The data frame required by this function has a specific structure. In this case, whether an event was death or right-censoring is not coded in the rows of a data frame, but in columns.
The data frame needs six columns with the following column names and these columns need to be filled with binary [0/1] data as follows;
Each of these six columns needs an individual row for every sampling interval between the first and last sampling interval, i.e., from time t = 1 to time t = tmax, where tmax is the last sampling interval.
For example, if survival data was sampled each day from days 1 to 20 of an experiment, the data frame will need to have; 6 x tmax = 6 x 20 = 120 rows.
NB it is assumed sampling intervals are equally spaced throughout the experiment.
There also needs to the following columns with the following names and contents,
For example, the first few lines of the data frame data_recovery are given below;
they are for the population control.d, that is control individuals dying during the experiment (control.d = 1), and show these individuals were not censored (censor = 0), and for times 1, 2, 3, the frequency of individuals dying was 1, 4, 11, respectively.
The last few lines of the same data frame are,
for the population of hosts that recovered and were right-censored, recovered.c = 1, censor = 1, and for times 18, 19, 20, the frequency of individauls censored in this population was 0, 0, 41, respectively. NB all rows between t = 1 and t = tmax need to be included and in ascending order, even if the frequency of individuals involved is zero.
The data for this function needs to be in the same format as for nll_recovery and needs to include the two columns, control.d, control.c, along with the frequency of individuals dying at each interval (= 0), and the number censored during or at the end of the experiment, even though they do not contribute towards the calculation of the negative log-likelihood.
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