generate_antigenic_map_flexible | R Documentation |
Fits a smoothing spline through a set of antigenic coordinates, and uses this to predict antigenic coordinates for all potential infection time points. This version is more flexible than generate_antigenic_map
, and allows the user to specify "clusters" to assume that strains circulating in a given period are all identical, rather than on a continuous path through space as a function of time.
generate_antigenic_map_flexible(
antigenic_distances,
buckets = 1,
clusters = NULL,
use_clusters = FALSE,
spar = 0.3,
year_min = 1968,
year_max = 2016
)
antigenic_distances |
a data frame of antigenic coordinates, with columns labelled X, Y and Strain for x coord, y coord and Strain label respectively. "Strain" should be a single number giving the year of circulation of that strain. See |
buckets |
= 1 the number of epochs per year. 1 means that each year has 1 strain; 12 means that each year has 12 strains (monthly resolution) |
clusters |
= NULL a data frame of cluster labels, indicating which cluster each circulation year belongs to. Note that each row (year) gets repeated by the number of buckets. Column names "year" and "cluster_used" |
use_clusters |
= FALSE if TRUE, uses the clusters data frame above, otherwise just returns as normal |
spar |
= 0.3 to be passed to smooth.spline |
year_min |
= 1968 first year in the antigenic map (usually 1968) |
year_max |
= 2016 last year in the antigenic map |
a fitted antigenic map
generate_antigenic_map
Other antigenic_maps:
create_start_level_data()
## Not run:
antigenic_coords_path <- system.file("extdata", "fonville_map_approx.csv", package = "serosolver")
antigenic_coords <- read.csv(antigenic_coords_path, stringsAsFactors=FALSE)
antigenic_coords$Strain <- c(68,72,75,77,79,87,89,92,95,97,102,104,105,106) + 1900
antigenic_map <- generate_antigenic_map_flexible(antigenic_coords, buckets=1, year_min=1968, year_max=2015,spar=0.3)
times <- 1968:2010
n_times <- length(times)
clusters <- rep(1:5, each=10)
clusters <- clusters[1:n_times]
clusters <- data.frame(year=times, cluster_used=clusters)
antigenic_map <- generate_antigenic_map_flexible(antigenic_coords, buckets=1,
clusters=clusters,use_clusters=TRUE,
year_min=1968, year_max=2010,spar=0.5)
## End(Not run)
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