Kernel Density Estimate for ZD and MIC data

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.

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))

#> final solution
#> $x
#> [1] 25.24166
#> 
#> $y
#> [1] 0.1259965
#> 

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

#> Warning: collapsing to unique 'x' values

## ----- 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")

#> Warning: collapsing to unique 'x' values

par(opar)