mean_ci: Obtain a (bootstrapped) confidence interval for the mean of a...

In bcgov/elucidate: Convenience Functions to Help Researchers Elucidate Patterns in Their Data

Obtain a (bootstrapped) confidence interval for the mean of a numeric vector.

Description

mean_ci returns confidence intervals for the mean of a numeric vector. One might want to use bootstrapping to obtain robust estimates for a confidence interval of the mean if the sample size is small (e.g. n = 10) or calculate them from a theoretical normal distribution otherwise. Note that the usual calculation based on quantiles of the theoretical distribution can be obtained with this function using the default ci_type = "norm". This function provides a simplified user interface to the boot and boot.ci functions similarly to the one.boot function but retains more of the boot package's functionality, most notably including options for parallelization. For convenience, when operating in parallel the user's operating system is automatically detected so that the appropriate parallelization engine is used (e.g. snow for Windows, multicore otherwise) by the parallel package. Since the mean and median are common descriptive statistics for which confidence intervals are estimated, these have their own dedicated functions. To obtain bootstrapped confidence intervals for other summary statistics use codestat_ci instead.

Usage

mean_ci(
  y,
  replicates = 2000,
  ci_level = 0.95,
  ci_type = c("norm", "perc", "bca", "basic"),
  parallel = FALSE,
  cores = NULL,
  na.rm = TRUE
)

Arguments

y	A vector/variable (required).
replicates	The number of bootstrap replicates to use. Default is 2,000, as recommended by Efron & Tibshirani (1993). For publications, or if you need more precise estimates, more replications (e.g. >= 5,000) are recommended. N.B. more replications will of course take longer to run. If you get the error: "estimated adjustment 'a' is NA" when ci_type is set to "bca" then try again with more replications.
ci_level	The confidence level to use for constructing confidence intervals. Default is set to ci_level = 0.95 for 95 percent CIs.
ci_type	The type of confidence intervals to calculate from the bootstrap samples. Most of the options available in the underlying boot.ci function are implemented (except for studentized intervals): "norm" for calculation based on a theoretical normal distribution, "perc" for percentile, "basic" for basic, and "bca" for bias-corrected and accelerated. See boot.ci for details regarding options other than "norm". Since the normal confidence intervals for the mean can be directly calculation based quantiles from the theoretical gaussian distribution this method is used for this unique case (CIs for the mean) instead of bootstrapping when the ci_type is set to "norm" (the default), since it is MUCH faster.
parallel	set to TRUE if you want to use multiple cores or FALSE if you don't (the default). Note that there is some processing overhead involved when operating in parallel so speed gains may not be very noticeable for smaller samples (and may even take longer than sequential processing). Due to the nature of the underlying parallelization architecture, performance gains will likely be greater on non-Windows machines that can use the "multicore" implementation instead of "snow". For obvious reasons this option only works on machines with more than 1 logical processing core.
cores	If parallel is set to TRUE, this determines the number of cores to use. To see how many cores are available on your machine, use parallel::detectCores()
na.rm	should missing values be removed before attempting to calculate the mean and confidence intervals? Default is TRUE.

Author(s)

Craig P. Hutton, [e-mail]

References

Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman & Hall.

See Also

boot, boot.ci, median_ci, stat_ci, describe_ci, describe_ci_all

Examples

y1 <- rnorm(1:10000, 100, 10)

#using a single core (sequential processing)
mean_ci(y1)

mean_ci(y1, ci_type = "perc")

#using multiple cores (parallel processing)
mean_ci(y1, parallel = TRUE, cores = 2, ci_type = "perc")

bcgov/elucidate documentation built on Sept. 3, 2022, 7:16 p.m.