VEwaningVariant (version 1.4)

plot: Plot Analysis Results

Description

Plot the Estimated Vaccine Efficacy

Usage

# S3 method for VEwaningVariant
plot(x, y, ..., times = NULL, xlim = NULL, nTimes = 20L, ylim = NULL)

Value

A gg object.

Arguments

x

An object of class VEwaningVariant. The object returned by a call to veWaningVariant()

y

Ignored

...

Ignored

times

A numeric vector object or NULL. The times since full efficacy at which the vaccine efficacy is to be estimated. If NULL, the times will be generated internally as a vector of length nTimes spanning the range [0, maxTime], where maxTime is the maximum time since vaccination present in the original analysis. Values provided outside of [0, maxTime] are ignored.

xlim

A numeric vector object of length 2 or NULL. The extrema of the times values at which estimates are to be calculated. A vector of length nTimes spanning the range [xlim[1], xlim[2]] is generated. Note that the specified limits must lie in the range [0, maxTime]. If input times is a vector object, this input is ignored.

nTimes

An integer object. The number of time values at which estimates are obatined. The default is 20. If input times is a vector object, this input is ignored.

ylim

A numeric vector object or NULL. The y-axis limits for the plots. If NULL, the y-axis limits are taken from the estimated values.

Details

When the variant under analysis is present only in the unblinded phase, vaccine efficacy cannot be estimated. In this case, plot() shows the relative infection rate at times t since full efficacy reached, defined as infection rate at time t = time since full efficacy reached divided by the infection rate at the time full efficacy is reached (t=0).

Examples

# \dontshow{
  RcppArmadillo::armadillo_throttle_cores(2)
# }
data(variantData)

set.seed(1234)

ind <- sample(1:nrow(x = variantData), 2000, FALSE)

# NOTE: This sample size is chosen for example only -- larger data sets
# should be used.

res <- veWaningVariant(data = variantData[ind,], 
                       L = 52,  
                       lag = 6,
                       gFunc = 'piece', 
                       v = c(15,30))

plot(x = res)