cyclomort (version 1.0.2)

plot.cmfit: Plot cmfit objects

Description

Plot cmfit objects

Usage

# S3 method for cmfit
plot(
  x,
  plotCI = TRUE,
  CI.level = 0.95,
  histogram = TRUE,
  add = FALSE,
  monthlabs = FALSE,
  nreps = 5000,
  hazcolor = "black",
  alpha = 0.3,
  ymax = NULL,
  prediction = NULL,
  yaxt = par()$yaxt,
  ...
)

Arguments

x

a cmfit object

plotCI

whether confidence intervals should also be drawn.

CI.level

confidence level (default 0.95) for CIs (if CI is TRUE)

histogram

boolean dictating whether a histogram of actual mortalities will be included in the plot

add

boolean dictating whether the plot will be added to an existing plot

monthlabs

whether or not to label the x-axis with months - suitable for (common) annual seasonal data. If FALSE, labels are numeric within the period [0,1]

nreps

number of samples from parameter estimates for confidence intervals (see predict.cmfit)

hazcolor

color of lines for hazard function and confidence intervals

alpha

transparency of confidence interval polygon

ymax

maximum value for the y-axis - can be useful for scaling purposes

prediction

an optional predict.cmfit object- otherwise the function will estimate this every time which can be a bit slow.

yaxt

location for y-axis label

...

additional parameters to hist (e.g., number of breaks)

Value

a plot comparing the estimated mortality curve (based on parameter estimates) and the actual results (as a histogram).

See Also

predict.cmfit

Examples

# NOT RUN {
# Simulate data
T.morts1 <- simulate_cycloSurv(1000, period = 365, 
                             meanhazard = 0.3 / 365, 
                             peaks = c(0.25 * 365, 0.75 * 365), 
                             durations = c(0.3 * 365, 0.1 * 365), 
                             weights = c(0.7,0.3), 
                             plotme = FALSE)

# Estimate simulated data
fits <- fit_cyclomort(T.morts1, n.seasons = 2)
fits

# Plot results
plot(fits, nreps = 1000, monthlabs = TRUE)
# NB: `nreps` is for the bootstrap of the confidence interval 
# The default (5000) is slower but smoother

# Actual parameter values from simulated data
attributes(T.morts1)
# }