StrainRanking (version 1.2)

ranking.strains: 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 \(K(u)=(1-u^2)I(0\le u\le1)\), where \(I\) is the indicator function. Other possible kernel types: Linear \(K(u)=(1-u)I(0\le u\le1)\), Power3 \(K(u)=(1-u^3)I(0\le u\le1)\), and Power4 \(K(u)=(1-u^4)I(0\le u\le1)\).

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.

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

# NOT RUN {
## 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")
# }