abr_densfunctions: Kernel Density Estimate for ZD and MIC data

In tpetzoldt/antibioticR: Analysis of Antibiotic Resistance Data

Description

Estimate kernel density for antibiotic resistance data using either standard density estimation with Gaussian kernels or boundary corrected density estimation with function pbckden from package evmix.

Usage

abr_density(
  x,
  cutoff = 5.5,
  method = c("density", "evmix"),
  control = abr_density.control()
)

abr_cumdens(
  x,
  cutoff = 5.5,
  method = c("evmix", "density", "spline", "fmm"),
  control = abr_density.control()
)

abr_density.control(bw = 1, cut = 3,  n=512, bcmethod = "renorm", proper = TRUE)

abr_cumdensquant(
  x,
  p = 0.5,
  cutoff = 5.5,
  method = c("density", "evmix", "spline", "fmm"),
  control = abr_density.control()
)

maxdens(
  x,
  cutoff = 5.5,
  from = min(x),
  to = max(x),
  method = c("density", "evmix"),
  control = abr_density.control()
)

Arguments

x	vector of antibiotic resistance observations, given as zone diameter (zd) or log of minimum inhibitory concentration (mic). Note that for abr_density x must contain raw observations, not frequencies, cf. unbin, while for abr_cumdens x and y contain binned data. If y is omitted, x must contain a two-column matrix with value and frequency.
cutoff	cutoff value (disc diameter) in case of zd data
method	character which density method to use: 'density', 'evmix' (function pbckden), 'spline', or 'fmm' (monotone spline)
control	options passed to the density estimation methods, see abr_density.control
bw	the smoothing bandwidth to be used, see density for details.
cut	by default, the values of from and to are cut bandwidths beyond the extremes of the data, see density for details
n	the number of equally spaced points at which the density is to be estimated, see density for details
bcmethod	character, boundary correction method
proper	logical, whether density is renormalised to integrate to unity
p	vector of probability quantiles
from, to	search range for maximum (= mode) search

Value

data frame with

• x zone diameter resp. mic value

• y density estimate

See Also

density, dbckden

Examples

opar <- par(no.readonly = TRUE)
## ===== minimum inhibitory concentrations (MIC) data =====
data("micdata")
micdata$freq <- with(micdata, ifelse(is.na(freq), 0, freq))

par(las = 2, mar=c(9,4,4,2)+.1)

##' relative frequencies
y <- micdata$freq / sum(micdata$freq)
x <- log2(micdata$conc)

plot(y ~ x, type = "h", axes = FALSE, xlab="", ylab="rel. frequency")
axis(2)
axis(1)
axis(1, line=3, at = log2(micdata$conc), label = round(micdata$conc, 4))
mtext("log2(conc)", side=1, line=1.5, las=1)
mtext("conc", side=1, line=6, las=1)

##' standard R function with weights
dens <- density(x, weights=y, bw=2/3)
lines(dens)

## ===== zone diameter (ZD) data =====
freq <- c(36, 0, 2, 3, 4, 8, 9, 14, 10, 9, 3, 1, 8, 2, 4, 8, 20,
          45, 40, 54, 41, 22, 8, 3, 3, 0, 0, 0, 0, 2)

relfreq <- freq/sum(freq)
bins <- 5 +(1:length(freq))
mids <- bins + 0.5

plot(mids, relfreq, type="h")

## ----- standard R function -----
edf <- density(mids, weights=relfreq, bw=2/3, from=0, to=40)
lines(edf)

## ----- same, but with different interface -----
edf2 <- abr_density(unbin(mids, freq), control=list(bw=2/3))
lines(edf2, col="red", lty="dotted")

## ----- boundary corrected density from package evmix -----
bcd <- abr_density(unbin(mids, freq), method="evmix")
lines(bcd, col="blue", lty="dotted")

## ===== maximum of density function (uses optimizer) =====
maxdens(unbin(mids, freq))

## ----- cumulative density -----
plot(abr_cumdens(unbin(mids, freq), method="fmm"), type="l")

## ----- quantiles -----
prob <- c(0.5, 0.95, 0.99)
abline(h=prob, lty="dotted")
abline(v=abr_cumdensquant(unbin(mids, freq),
                          p=prob, method="fmm"), lty="dotted")

par(opar)

tpetzoldt/antibioticR documentation built on Sept. 25, 2021, 1:17 p.m.