ranking.strains {StrainRanking} | R Documentation |
Method for ranking pathogen strains
Description
Ranking pathogen strains based on demographic and genetic data collected during an epidemic.
Usage
ranking.strains(DGobject, bw, nb.mcsimul, plots = FALSE, kernel.type = "Quadratic")
Arguments
DGobject |
Object of the DG class. |
bw |
[Positive numeric] Smoothing bandwidth of the kernel used to estimate strain proportions. |
nb.mcsimul |
[Positive integer] Number of permutations to assess the significance of the ranking. |
plots |
[Logical] If TRUE, plots are produced. The plots show the growth variable in space, the sampling sites, the estimated values of the fitness coefficients and the corresponding permutation-based distributions obtained under the null hypothesis of coefficient equality. |
kernel.type |
[Character string] Type of kernel. Default: Quadratic kernel |
Value
permutation.estimates |
Estimates of the fitness coefficients obtained for the permutations (one row for each permutation). |
estimates |
Estimates of the fitness coefficients obtained for the raw data. |
p.values |
p.values of pairwise permutation tests of equality of the coefficients. |
Author(s)
Soubeyrand, S., Tollenaere, C., Haon-Lasportes, E. and Laine, A.-L.
References
Soubeyrand S., Tollenaere C., Haon-Lasportes E. & Laine A.-L. (2014). Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics. PLOS ONE 9(1): e86591.
See Also
DGobj-class, DGobj.rawdata, DGobj.simul.mechanistic, DGobj.simul.regression
Examples
## Application of the ranking method to a real data set
data(powderymildew)
DGdata=DGobj.rawdata(demographic.coord=powderymildew$demographic.coord,
genetic.coord=powderymildew$genetic.coord,
demographic.measures=powderymildew$demographic.measures,
genetic.frequencies=powderymildew$genetic.frequencies)
ranking.strains(DGobject=DGdata, bw=sqrt(2), nb.mcsimul=10^3, plots=TRUE,
kernel.type="Power4")
## Application of the ranking method to a data set simulated under the
## mechanistic model
DGmech=DGobj.simul.mechanistic(sqrtn=10, size1=30, size2=10, theta=c(1.5,2,3),
beta=c(5,5), M=7, delta=0.2)
ranking.strains(DGobject=DGmech, bw=sqrt(2), nb.mcsimul=10^3, plots=TRUE,
kernel.type="Power4")
## Application of the ranking method to a data set simulated under the
## regression model
DGreg=DGobj.simul.regression(sqrtn=10, size1=30, size2=10, theta=c(1.5,2,3),
alpha.function=generation.alpha.3strains, sigma=0.1)
ranking.strains(DGobject=DGreg, bw=sqrt(2), nb.mcsimul=10^3, plots=TRUE,
kernel.type="Power4")