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#' Generate a contact matrix from diary survey data
#'
#' Samples a contact survey
#'
#' @param survey a [survey()] object
#' @param countries limit to one or more countries; if not given, will use all countries in the survey; these can be given as country names or 2-letter (ISO Alpha-2) country codes
#' @param survey.pop survey population -- either a data frame with columns 'lower.age.limit' and 'population', or a character vector giving the name(s) of a country or countries from the list that can be obtained via `wpp_countries`; if not given, will use the country populations from the chosen countries, or all countries in the survey if `countries` is not given
#' @param age.limits lower limits of the age groups over which to construct the matrix
#' @param filter any filters to apply to the data, given as list of the form (column=filter_value) - only contacts that have 'filter_value' in 'column' will be considered. If multiple filters are given, they are all applied independently and in the sequence given.
#' @param counts whether to return counts (instead of means)
#' @param symmetric whether to make matrix symmetric, such that \eqn{c_{ij}N_i = c_{ji}N_j}.
#' @param split whether to split the contact matrix into the mean number of contacts, in each age group (split further into the product of the mean number of contacts across the whole population (`mean.contacts`), a normalisation constant (`normalisation`) and age-specific variation in contacts (`contacts`)), multiplied with an assortativity matrix (`assortativity`) and a population multiplier (`demograpy`). For more detail on this, see the "Getting Started" vignette.
#' @param sample.participants whether to sample participants randomly (with replacement); done multiple times this can be used to assess uncertainty in the generated contact matrices. See the "Bootstrapping" section in the vignette for how to do this..
#' @param estimated.participant.age if set to "mean" (default), people whose ages are given as a range (in columns named "..._est_min" and "..._est_max") but not exactly (in a column named "..._exact") will have their age set to the mid-point of the range; if set to "sample", the age will be sampled from the range; if set to "missing", age ranges will be treated as missing
#' @param estimated.contact.age if set to "mean" (default), contacts whose ages are given as a range (in columns named "..._est_min" and "..._est_max") but not exactly (in a column named "..._exact") will have their age set to the mid-point of the range; if set to "sample", the age will be sampled from the range; if set to "missing", age ranges will be treated as missing
#' @param missing.participant.age if set to "remove" (default), participants without age information are removed; if set to "keep", participants with missing age are kept and treated as a separate age group
#' @param missing.contact.age if set to "remove" (default), participants that have contacts without age information are removed; if set to "sample", contacts without age information are sampled from all the contacts of participants of the same age group; if set to "keep", contacts with missing age are kept and treated as a separate age group; if set to "ignore", contact with missing age are ignored in the contact analysis
#' @param weights column names(s) of the participant data of the [survey()] object with user-specified weights (default = empty vector)
#' @param weigh.dayofweek whether to weigh social contacts data by the day of the week (weight (5/7 / N_week / N) for weekdays and (2/7 / N_weekend / N) for weekends)
#' @param weigh.age whether to weigh social contacts data by the age of the participants (vs. the populations' age distribution)
#' @param weight.threshold threshold value for the standardized weights before running an additional standardisation (default 'NA' = no cutoff)
#' @param sample.all.age.groups what to do if sampling participants (with `sample.participants = TRUE`) fails to sample participants from one or more age groups; if FALSE (default), corresponding rows will be set to NA, if TRUE the sample will be discarded and a new one taken instead
#' @param return.part.weights boolean to return the participant weights
#' @param return.demography boolean to explicitly return demography data that corresponds to the survey data (default 'NA' = if demography data is requested by other function parameters)
#' @param per.capita whether to return a matrix with contact rates per capita (default is FALSE and not possible if 'counts=TRUE' or 'split=TRUE')
#' @param ... further arguments to pass to [get_survey()], [check()] and [pop_age()] (especially column names)
#' @return a contact matrix, and the underlying demography of the surveyed population
#' @importFrom stats xtabs runif median
#' @importFrom utils data globalVariables
#' @importFrom countrycode countrycode
#' @import data.table
#' @export
#' @autoglobal
#' @examples
#' data(polymod)
#' contact_matrix(polymod, countries = "United Kingdom", age.limits = c(0, 1, 5, 15))
#' @author Sebastian Funk
contact_matrix <- function(survey, countries = NULL, survey.pop, age.limits, filter, counts = FALSE, symmetric = FALSE, split = FALSE, sample.participants = FALSE, estimated.participant.age = c("mean", "sample", "missing"), estimated.contact.age = c("mean", "sample", "missing"), missing.participant.age = c("remove", "keep"), missing.contact.age = c("remove", "sample", "keep", "ignore"), weights = NULL, weigh.dayofweek = FALSE, weigh.age = FALSE, weight.threshold = NA, sample.all.age.groups = FALSE, return.part.weights = FALSE, return.demography = NA, per.capita = FALSE, ...) {
surveys <- c("participants", "contacts")
dot.args <- list(...)
unknown.args <- setdiff(names(dot.args), union(names(formals(check.survey)), names(formals(pop_age))))
if (length(unknown.args) > 0) {
stop("Unknown argument(s): ", paste(unknown.args, sep = ", "), ".")
}
## record if 'missing.participant.age' and 'missing.contact.age' are set, for later
missing.participant.age.set <- !missing(missing.participant.age)
missing.contact.age.set <- !missing(missing.contact.age)
## read arguments
estimated.participant.age <- match.arg(estimated.participant.age)
estimated.contact.age <- match.arg(estimated.contact.age)
missing.participant.age <- match.arg(missing.participant.age)
missing.contact.age <- match.arg(missing.contact.age)
if (!inherits(survey, "survey")) {
stop(
"`survey` must be a survey object (created using `survey()` ",
"or `get_survey()`)"
)
}
if (!missing(age.limits)) {
age.limits <- as.integer(age.limits)
if (anyNA(age.limits) || any(diff(age.limits) <= 0)) {
stop("'age.limits' must be an increasing integer vector of lower age limits.")
}
}
## clean the survey
survey <- clean(survey)
## check and get columns
columns <- suppressMessages(check(survey, ...))
## check if specific countries are requested (if a survey contains data from multiple countries)
if (length(countries) > 0 && columns[["country"]] %in% colnames(survey$participants)) {
if (all(nchar(countries) == 2)) {
corrected_countries <- suppressWarnings(
countrycode(countries, "iso2c", "country.name"))
} else {
corrected_countries <- suppressWarnings(
countrycode(countries, "country.name", "country.name"))
}
present_countries <- unique(as.character(survey$participants[[columns[["country"]]]]))
missing_countries <- countries[which(is.na(corrected_countries))]
if (length(missing_countries) > 0) {
stop("Survey data not found for ", paste(missing_countries, sep = ", "), ".")
}
countries <- corrected_countries
survey$participants <- survey$participants[get(columns[["country"]]) %in% countries]
if (nrow(survey$participants) == 0) {
stop("No participants left after selecting countries.")
}
}
## check maximum participant age in the data
part_exact.column <- paste(columns[["participant.age"]], "exact", sep = "_")
part_min.column <- paste(columns[["participant.age"]], "est_min", sep = "_")
part_max.column <- paste(columns[["participant.age"]], "est_max", sep = "_")
if (part_exact.column %in% colnames(survey$participants)) {
survey$participants[,
paste(columns[["participant.age"]]) := as.integer(get(part_exact.column))
]
} else if (!(columns[["participant.age"]] %in% colnames(survey$participants))) {
survey$participants[, paste(columns[["participant.age"]]) := NA_integer_]
}
## sample estimated participant ages
if (part_min.column %in% colnames(survey$participants) &&
part_max.column %in% colnames(survey$participants)) {
if (estimated.participant.age == "mean") {
survey$participants[
is.na(get(part_exact.column)) &
!is.na(get(part_min.column)) & !is.na(get(part_max.column)),
paste(columns[["participant.age"]]) :=
as.integer(rowMeans(.SD)),
.SDcols = c(part_min.column, part_max.column)
]
} else if (estimated.participant.age == "sample") {
survey$participants[
is.na(get(columns[["participant.age"]])) &
!is.na(get(part_min.column)) & !is.na(get(part_max.column)) &
get(part_min.column) <= get(part_max.column),
paste(columns[["participant.age"]]) :=
as.integer(runif(
.N,
get(part_min.column),
get(part_max.column)
))
]
}
# note: do nothing when "missing" is specified
}
if (part_max.column %in% colnames(survey$participants)) {
max.age <- max(
c(
survey$participants[, get(part_exact.column)],
survey$participants[, get(part_max.column)]
),
na.rm = TRUE
) + 1
} else {
max.age <- max(
survey$participants[, get(columns[["participant.age"]])], na.rm = TRUE
) + 1
}
if (missing(age.limits)) {
all.ages <-
unique(as.integer(survey$participants[, get(columns[["participant.age"]])]))
all.ages <- all.ages[!is.na(all.ages)]
all.ages <- sort(all.ages)
age.limits <- union(0, all.ages)
}
if (missing.participant.age == "remove" &&
nrow(survey$participants[is.na(get(columns[["participant.age"]])) |
get(columns[["participant.age"]]) < min(age.limits)]) > 0) {
if (!missing.participant.age.set) {
message(
"Removing participants without age information. ",
"To change this behaviour, set the 'missing.participant.age' option"
)
}
survey$participants <-
survey$participants[!is.na(get(columns[["participant.age"]])) &
get(columns[["participant.age"]]) >= min(age.limits)]
}
exact.column <- paste(columns[["contact.age"]], "exact", sep = "_")
min.column <- paste(columns[["contact.age"]], "est_min", sep = "_")
max.column <- paste(columns[["contact.age"]], "est_max", sep = "_")
## set contact age if it's not in the data
if (exact.column %in% colnames(survey$contacts)) {
survey$contacts[,
paste(columns[["contact.age"]]) := as.integer(get(exact.column))
]
} else {
survey$contacts[, paste(columns[["contact.age"]]) := NA_integer_]
}
## convert factors to integers
for (age_column in
c(columns[["contact.age"]], min.column, max.column, exact.column)) {
if (age_column %in% colnames(survey$contacts) &&
is.factor(survey$contacts[[age_column]])) {
survey$contacts[, paste(age_column) :=
as.integer(levels(get(age_column)))[get(age_column)]]
}
}
## sample estimated contact ages
if (min.column %in% colnames(survey$contacts) &&
max.column %in% colnames(survey$contacts)) {
if (estimated.contact.age == "mean") {
survey$contacts[
is.na(get(columns[["contact.age"]])) &
!is.na(get(min.column)) & !is.na(get(max.column)),
paste(columns[["contact.age"]]) :=
as.integer(rowMeans(.SD)),
.SDcols = c(min.column, max.column)
]
} else if (estimated.contact.age == "sample") {
survey$contacts[
is.na(get(columns[["contact.age"]])) &
!is.na(get(min.column)) & !is.na(get(max.column)) &
get(min.column) <= get(max.column),
paste(columns[["contact.age"]]) :=
as.integer(runif(
.N,
get(min.column),
get(max.column)
))
]
}
# note: do nothing when "missing" is specified
}
if (missing.contact.age == "remove" &&
nrow(survey$contacts[is.na(get(columns[["contact.age"]])) |
get(columns[["contact.age"]]) < min(age.limits)]) > 0) {
if (!missing.contact.age.set) {
message(
"Removing participants that have contacts without age information. ",
"To change this behaviour, set the 'missing.contact.age' option"
)
}
missing.age.id <-
survey$contacts[
is.na(get(columns[["contact.age"]])) |
get(columns[["contact.age"]]) < min(age.limits),
get(columns[["id"]])
]
survey$participants <- survey$participants[!(get(columns[["id"]]) %in% missing.age.id)]
}
if (missing.contact.age == "ignore" &&
nrow(survey$contacts[is.na(get(columns[["contact.age"]])) |
get(columns[["contact.age"]]) < min(age.limits)]) > 0) {
if (!missing.contact.age.set) {
message(
"Ignore contacts without age information. ",
"To change this behaviour, set the 'missing.contact.age' option"
)
}
survey$contacts <- survey$contacts[!is.na(get(columns[["contact.age"]])) &
get(columns[["contact.age"]]) >= min(age.limits), ]
}
## check if any filters have been requested
if (!missing(filter)) {
missing_columns <- list()
for (table in surveys) {
if (nrow(survey[[table]]) > 0) {
missing_columns <-
c(missing_columns, list(setdiff(names(filter), colnames(survey[[table]]))))
## filter contact data
for (column in names(filter)) {
if (column %in% colnames(survey[[table]])) {
survey[[table]] <- survey[[table]][get(column) == filter[[column]]]
}
}
}
}
missing_all <- do.call(intersect, missing_columns)
if (length(missing_all) > 0) {
warning("filter column(s) ", toString(missing_all), " not found")
}
}
# adjust age.group.brakes to the lower and upper ages in the survey
survey$participants[, lower.age.limit := reduce_agegroups(
get(columns[["participant.age"]]),
age.limits[age.limits < max.age]
)]
part.age.group.breaks <- c(age.limits[age.limits < max.age], max.age)
part.age.group.present <- age.limits[age.limits < max.age]
survey$participants[, age.group :=
cut(survey$participants[, get(columns[["participant.age"]])],
breaks = part.age.group.breaks,
right = FALSE
)]
age.groups <- survey$participants[, levels(age.group)]
age.groups[length(age.groups)] <-
sub("\\[([0-9]+),.*$", "\\1+", age.groups[length(age.groups)])
survey$participants[, age.group :=
factor(age.group, levels = levels(age.group), labels = age.groups)]
## add upper age limits
lower.upper.age.limits <- data.table(
lower.age.limit = part.age.group.present,
upper.age.limit = part.age.group.breaks[-1]
)
survey$participants <-
merge(survey$participants, lower.upper.age.limits, by = "lower.age.limit", all.x = TRUE)
## if split, symmetric or age weights are requested, get demographic data (survey population)
need.survey.pop <- split || symmetric || weigh.age ||
(!is.na(return.demography) && return.demography) || per.capita
if (need.survey.pop) {
## check if survey population is either not given or given as a vector of countries
if (missing(survey.pop) || is.character(survey.pop)) {
survey.representative <- FALSE
if (!missing(survey.pop)) {
## survey population is given as vector of countries
survey.countries <- survey.pop
} else if (!missing(countries)) {
## survey population not given but countries requested from
## survey - get population data from those countries
survey.countries <- countries
} else {
## neither survey population nor country names given - try to
## guess country or countries surveyed from participant data
if (columns[["country"]] %in% colnames(survey$participants)) {
survey.countries <- unique(survey$participants[, get(columns[["country"]])])
} else {
warning(
"No 'survey.pop' or 'countries' given, and no '", columns[["country"]],
"' column found in the data. ",
"I don't know which population this is from. ",
"Assuming the survey is representative"
)
survey.representative <- TRUE
}
}
if (!survey.representative) {
## get population data for countries from 'wpp' package
country.pop <- data.table(wpp_age(survey.countries))
# !! warning: spelling can differ between wpp_age and wpp_countries (e.g. Viet Nam vs Vietnam)
# fix: rename countries using the same approach as in clean(survey,...)
country.pop$country <- suppressWarnings(countrycode(country.pop$country, "country.name", "country.name"))
## check if survey data are from a specific year - in that case
## use demographic data from that year, otherwise latest
if (columns[["year"]] %in% colnames(survey$participants)) {
survey.year <-
survey$participants[, median(get(columns[["year"]]), na.rm = TRUE)]
} else {
survey.year <- country.pop[, max(year, na.rm = TRUE)]
warning(
"No '", columns[["year"]], "' column found in the data. Will use ",
survey.year, " population data."
)
}
## check if any survey countries are not in wpp
missing.countries <- setdiff(survey.countries, unique(country.pop$country))
if (length(missing.countries) > 0) {
stop(
"Could not find population data for ",
toString(missing.countries), ". ",
" Use wpp_countries() to get a list of country names."
)
}
## get demographic data closest to survey year
country.pop.year <- unique(country.pop[, year])
survey.year <-
min(country.pop.year[which.min(abs(survey.year - country.pop.year))])
survey.pop <-
country.pop[year == survey.year][, list(population = sum(population)),
by = "lower.age.limit"
]
}
if (survey.representative) {
survey.pop <-
survey$participants[, lower.age.limit :=
reduce_agegroups(
get(columns[["participant.age"]]),
age.limits
)]
survey.pop <- survey.pop[, list(population = .N), by = lower.age.limit]
survey.pop <- survey.pop[!is.na(lower.age.limit)]
if (columns[["year"]] %in% colnames(survey$participants)) {
survey.year <-
survey$participants[, median(get(columns[["year"]]), na.rm = TRUE)]
}
}
} else {
# if survey.pop is a data frame with columns 'lower.age.limit' and 'population'
survey.pop <- data.table(survey.pop)
# make sure the maximum survey.pop age exceeds the participant age group breaks
if (max(survey.pop$lower.age.limit) < max(part.age.group.present)) {
survey.pop <- rbind(
survey.pop,
list(max(part.age.group.present + 1), 0)
)
}
# add dummy survey.year
survey.year <- NA_integer_
}
# add upper.age.limit after sorting the survey.pop ages (and add maximum age > given ages)
survey.pop <- survey.pop[order(lower.age.limit), ]
# if any lower age limits are missing remove them
survey.pop <- survey.pop[!is.na(population)]
survey.pop$upper.age.limit <- unlist(c(
survey.pop[-1, "lower.age.limit"],
1 + max(
survey.pop$lower.age.limit,
part.age.group.present
)
))
if (weigh.age) {
## keep reference of survey.pop
survey.pop.full <-
data.table(pop_age(
survey.pop,
seq(
min(survey.pop$lower.age.limit),
max(survey.pop$upper.age.limit)
), ...
))
}
## adjust age groups by interpolating, in case they don't match between
## demographic and survey data
survey.pop.max <- max(survey.pop$upper.age.limit)
survey.pop <- data.table(pop_age(survey.pop, part.age.group.present, ...))
## set upper age limits
survey.pop[, upper.age.limit := c(part.age.group.present[-1], survey.pop.max)]
}
## weights
survey$participants[, weight := 1]
## assign weights to participants to account for weekend/weekday variation
if (weigh.dayofweek) {
found.dayofweek <- FALSE
if ("dayofweek" %in% colnames(survey$participants)) {
## Add column sum_weight: Number of entries on weekdays / weekends
survey$participants[, sum_weight := nrow(.SD),
by = (dayofweek %in% 1:5),
]
## The sum of the weights on weekdays is 5
survey$participants[dayofweek %in% 1:5, weight := 5 / sum_weight]
## The sum of the weights on weekend is 2
survey$participants[!(dayofweek %in% 1:5), weight := 2 / sum_weight]
survey$participants[, sum_weight := NULL]
found.dayofweek <- TRUE
# add boolean for "weekday"
survey$participants[, is.weekday := dayofweek %in% 1:5]
}
if (!found.dayofweek) {
warning(
"'weigh.dayofweek' is TRUE, but no 'dayofweek' column in the data. ",
"Will ignore."
)
}
}
## assign weights to participants, to account for age variation
if (weigh.age) {
# get number and proportion of participants by age
survey$participants[, age.count := .N, by = eval(columns[["participant.age"]])]
survey$participants[, age.proportion := age.count / .N]
# get reference population by age (absolute and proportional)
part.age.all <- range(unique(survey$participants[, get(columns[["participant.age"]])]))
survey.pop.detail <- data.table(pop_age(survey.pop.full, seq(part.age.all[1], part.age.all[2] + 1)))
names(survey.pop.detail) <- c(columns[["participant.age"]], "population.count")
survey.pop.detail[, population.proportion := population.count / sum(population.count)]
# merge reference and survey population data
survey$participants <- merge(survey$participants, survey.pop.detail, by = eval(columns[["participant.age"]]))
# calculate age-specific weights
survey$participants[, weight.age := population.proportion / age.proportion]
# merge 'weight.age' into 'weight'
survey$participants[, weight := weight * weight.age]
## Remove the additional columns
survey$participants[, age.count := NULL]
survey$participants[, age.proportion := NULL]
survey$participants[, population.count := NULL]
survey$participants[, population.proportion := NULL]
survey$participants[, weight.age := NULL]
}
## option to weigh the contact data with user-defined participant weights
if (length(weights) > 0) {
for (i in seq_along(weights)) {
if (weights[i] %in% colnames(survey$participants)) {
## Compute the overall weight
survey$participants[, weight := weight * get(weights[i])]
}
}
}
# post-stratification weight standardisation: by age.group
survey$participants[, weight := weight / sum(weight) * .N,
by = age.group
]
# option to truncate overall participant weights (if not NULL or NA)
if (!is.null(weight.threshold) && !is.na(weight.threshold)) {
survey$participants[weight > weight.threshold, weight := weight.threshold]
# re-normalise
survey$participants[, weight := weight / sum(weight) * .N,
by = age.group
]
}
## merge participants and contacts into a single data table
setkeyv(survey$participants, columns[["id"]])
participant_ids <- unique(survey$participants[[columns[["id"]]]])
survey$contacts <-
merge(survey$contacts, survey$participants,
by = columns[["id"]], all = FALSE,
allow.cartesian = TRUE, suffixes = c(".cont", ".part")
)
setkeyv(survey$contacts, columns[["id"]])
## sample contacts
if (missing.contact.age == "sample" &&
nrow(survey$contacts[is.na(get(columns[["contact.age"]]))]) > 0) {
for (this.age.group in
unique(survey$contacts[is.na(get(columns[["contact.age"]])), age.group])) {
## first, deal with missing age
if (nrow(survey$contacts[!is.na(get(columns[["contact.age"]])) &
age.group == this.age.group]) > 0) {
## some contacts in the age group have an age, sample from these
survey$contacts[
is.na(get(columns[["contact.age"]])) &
age.group == this.age.group,
paste(columns[["contact.age"]]) :=
sample(
survey$contacts[
!is.na(get(columns[["contact.age"]])) &
age.group == this.age.group,
get(columns[["contact.age"]])
],
size = .N,
replace = TRUE
)
]
} else {
## no contacts in the age group have an age, sample uniformly between limits
min.contact.age <-
survey$contacts[, min(get(columns[["contact.age"]]), na.rm = TRUE)]
max.contact.age <-
survey$contacts[, max(get(columns[["contact.age"]]), na.rm = TRUE)]
survey$contacts[
is.na(get(columns[["contact.age"]])) &
age.group == this.age.group,
paste(columns[["contact.age"]]) :=
as.integer(floor(runif(.N,
min = min.contact.age,
max = max.contact.age + 1
)))
]
}
}
}
## set contact age groups
max.contact.age <-
survey$contacts[, max(get(columns[["contact.age"]]), na.rm = TRUE) + 1]
contact.age.group.breaks <- part.age.group.breaks
if (max.contact.age > max(contact.age.group.breaks)) {
contact.age.group.breaks[length(contact.age.group.breaks)] <- max.contact.age
}
survey$contacts[, contact.age.group :=
cut(get(columns[["contact.age"]]),
breaks = contact.age.group.breaks,
labels = age.groups,
right = FALSE
)]
ret <- list()
if (sample.participants) {
good.sample <- FALSE
while (!good.sample) {
## take a sample from the participants
part.sample <- sample(participant_ids, replace = TRUE)
part.age.limits <-
unique(survey$participants[
get(columns[["id"]]) %in% part.sample,
lower.age.limit
])
good.sample <- !sample.all.age.groups ||
(length(setdiff(age.limits, part.age.limits)) == 0)
sample.table <-
data.table(id = part.sample, weight = 1)
sample.table <-
sample.table[, list(bootstrap.weight = sum(weight)), by = id]
setnames(sample.table, "id", columns[["id"]])
setkeyv(sample.table, columns[["id"]])
sampled.contacts <- merge(survey$contacts, sample.table)
sampled.contacts[, sampled.weight := weight * bootstrap.weight]
sampled.participants <-
merge(survey$participants, sample.table)
sampled.participants[, sampled.weight := weight * bootstrap.weight]
}
} else {
## just use all participants
sampled.contacts <- survey$contacts
sampled.contacts[, sampled.weight := weight]
sampled.participants <- survey$participants
sampled.participants[, sampled.weight := weight]
}
## calculate weighted contact matrix
weighted.matrix <-
xtabs(
data = sampled.contacts,
formula = sampled.weight ~ age.group + contact.age.group,
addNA = TRUE
)
dims <- dim(weighted.matrix)
dim.names <- dimnames(weighted.matrix)
if (!counts) { ## normalise to give mean number of contacts
## calculate normalisation vector
norm.vector <-
xtabs(
data = sampled.participants,
formula = sampled.weight ~ age.group, addNA = TRUE
)
## normalise contact matrix
weighted.matrix <-
array(apply(weighted.matrix, 2, function(x) x / norm.vector),
dim = dims,
dimnames = dim.names
)
## set non-existent data to NA
weighted.matrix[is.nan(weighted.matrix)] <- NA_real_
}
## construct a warning in case there are NAs
na.headers <- anyNA(dimnames(weighted.matrix), recursive = TRUE)
na.content <- anyNA(weighted.matrix)
na.present <- na.headers || na.content
if (na.present) {
warning.suggestion <- " Consider "
if (na.headers) {
warning.suggestion <- paste0(warning.suggestion, "setting ")
suggested.options <- NULL
if (anyNA(rownames(weighted.matrix))) {
suggested.options <- c(suggested.options, "'missing.participant.age'")
}
if (anyNA(colnames(weighted.matrix))) {
suggested.options <- c(suggested.options, "'missing.contact.age'")
}
warning.suggestion <-
paste0(warning.suggestion, paste(suggested.options, collapse = " and "))
if (na.content) {
warning.suggestion <- paste0(warning.suggestion, ", and ")
} else {
warning.suggestion <- paste0(warning.suggestion, ".")
}
}
if (na.content) {
warning.suggestion <- paste0(warning.suggestion, "adjusting the age limits.")
}
}
if (symmetric && prod(dim(as.matrix(weighted.matrix))) > 1) {
if (counts) {
warning(
"'symmetric=TRUE' does not make sense with 'counts=TRUE'; ",
"will not make matrix symmetric."
)
} else if (na.present) {
warning(
"'symmetric=TRUE' does not work with missing data; ",
"will not make matrix symmetric\n",
warning.suggestion
)
} else {
## set c_{ij} N_i and c_{ji} N_j (which should both be equal) to
## 0.5 * their sum; then c_{ij} is that sum / N_i
normalised.weighted.matrix <- diag(survey.pop$population) %*% weighted.matrix
weighted.matrix <- 0.5 * diag(1 / survey.pop$population) %*%
(normalised.weighted.matrix + t(normalised.weighted.matrix))
}
}
if (split) {
if (counts) {
warning(
"'split=TRUE' does not make sense with 'counts=TRUE'; ",
"will not split the contact matrix."
)
} else if (na.present) {
warning(
"'split=TRUE' does not work with missing data; ",
"will not split contact.matrix.\n",
warning.suggestion
)
ret[["mean.contacts"]] <- NA
ret[["normalisation"]] <- NA
ret[["contacts"]] <- rep(NA, nrow(weighted.matrix))
} else {
## get rid of name but preserve row and column names
weighted.matrix <- unname(weighted.matrix)
nb.contacts <- rowSums(weighted.matrix)
mean.contacts <- sum(survey.pop$population * nb.contacts) /
sum(survey.pop$population)
spectrum.matrix <- weighted.matrix
spectrum.matrix[is.na(spectrum.matrix)] <- 0
spectrum <- as.numeric(eigen(spectrum.matrix, only.values = TRUE)$values[1])
ret[["mean.contacts"]] <- mean.contacts
ret[["normalisation"]] <- spectrum / mean.contacts
age.proportions <- survey.pop$population / sum(survey.pop$population)
weighted.matrix <-
diag(1 / nb.contacts) %*% weighted.matrix %*% diag(1 / age.proportions)
nb.contacts <- nb.contacts / spectrum
ret[["contacts"]] <- nb.contacts
}
}
# make sure the dim.names are retained after symmetric or split procedure
dimnames(weighted.matrix) <- dim.names
ret[["matrix"]] <- weighted.matrix
# option to add matrix per capita, i.e. the contact rate of age i with one individual of age j in the population.
if (per.capita) {
if (counts) {
warning(
"'per.capita=TRUE' does not make sense with 'counts=TRUE'; ",
"will not return the contact matrix per capita."
)
} else if (split) {
warning(
"'per.capita=TRUE' does not make sense with 'split=TRUE'; ",
"will not return the contact matrix per capita."
)
} else {
survey.pop$population
weighted.matrix.per.capita <- weighted.matrix / matrix(rep(survey.pop$population, nrow(survey.pop)), ncol = nrow(survey.pop), byrow = TRUE)
weighted.matrix.per.capita
ret[["matrix.per.capita"]] <- weighted.matrix.per.capita
}
}
if (exists("survey.year")) {
survey.pop[, year := survey.year]
survey.pop <-
merge(
survey.pop,
unique(survey$participants[, list(lower.age.limit, age.group)])
)
survey.pop <- survey.pop[, list(age.group, population,
proportion = population / sum(population), year
)]
}
## get number of participants in each age group
if (anyNA(survey$participants$age.group)) {
useNA <- "always"
} else {
useNA <- "no"
}
part.pop <- data.table(table(survey$participants[, age.group], useNA = useNA))
setnames(part.pop, c("age.group", "participants"))
part.pop[, proportion := participants / sum(participants)]
if (!is.null(ret)) {
if (need.survey.pop && (is.na(return.demography) || return.demography)) {
# change survey.pop$age.group factors into characters (cfr. part.pop)
survey.pop[, age.group := as.character(age.group)]
ret[["demography"]] <- survey.pop[]
}
ret[["participants"]] <- part.pop[]
}
# option to return participant weights
if (return.part.weights) {
# default
part.weights <- survey$participants[, .N, by = list(age.group, weight)]
part.weights <- part.weights[order(age.group, weight), ]
# add age and/or dayofweek info
if (weigh.age && weigh.dayofweek) {
part.weights <- survey$participants[, .N, by = list(age.group, participant.age = get(columns[["participant.age"]]), is.weekday, weight)]
} else if (weigh.age) {
part.weights <- survey$participants[, .N, by = list(age.group, participant.age = get(columns[["participant.age"]]), weight)]
} else if (weigh.dayofweek) {
part.weights <- survey$participants[, .N, by = list(age.group, is.weekday, weight)]
}
# order (from left to right)
part.weights <- part.weights[order(part.weights), ] # nolint
# set name of last column
names(part.weights)[ncol(part.weights)] <- "participants"
# add proportion and add to ret
part.weights[, proportion := participants / sum(participants)]
ret[["participants.weights"]] <- part.weights[]
}
return(ret)
}
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