Estimate survival

Estimate survival rates based on the Kaplan-Meier survival rate estimator (Pollock et al. 1989).

Usage

bbr_survival(
  x,
  include_uncertain_morts = TRUE,
  variance = "greenwood",
  year_start = 4L
)

Format

The return object has these columns:

PopulationName

Population name

Year

Year sampled

estimate

Survival estimate

se

SE

lower

Confidence limit

upper

Confidence limit

mean_monitored

Mean number of caribou monitored each month

sum_dead

Total number of mortalities in a year

sum_alive

Total number of caribou-months in a year

status

Indicates less than 12 months monitored or if there were 0 mortalities in a given year

Arguments

x

A data frame that has survival data.

include_uncertain_morts

A flag indicating whether to include uncertain mortalities in total mortalities. The default value is TRUE.

variance

Variance type to estimate. Can be the Greenwood estimator "greenwood" or Cox Oakes estimator "cox_oakes". The default is "greenwood".

year_start

A whole number between 1 and 12 indicating the month of the start of the caribou (i.e., biological) year. By default, April is set as the start of the caribou year.

Value

A data frame. The columns are listed in the format section.

Details

x needs to be formatted in a certain manner. To confirm the input data frame is in the right format you can use the bbd_chk_data_survival function. See the vignette("methods", package = "bbouretro") for the equations used in this function.

References

Pollock, K. H., S. R. Winterstein, C. M. Bunck, and P. D. Curtis. 1989. Survival analysis in telemetry studies: the staggered entry design. Journal of Wildlife Management 53:7-15.

Examples

survival_est <- bbr_survival(
  bboudata::bbousurv_a,
  include_uncertain_morts = TRUE,
  variance = "greenwood"
)
survival_est <- bbr_survival(
  bboudata::bbousurv_b,
  include_uncertain_morts = FALSE,
  variance = "cox_oakes"
)