Bootstrap MF CI

Estimates bootstrap confidence intervals for the mitigated fraction.

Usage

MFBoot(
  formula,
  data,
  compare = c("con", "vac"),
  b = 100,
  B = 100,
  alpha = 0.05,
  hpd = TRUE,
  bca = FALSE,
  return.boot = FALSE,
  trace.it = FALSE,
  seed = sample(1:1e+05, 1)
)

Arguments

formula: Formula of the form y ~ x, where y is a continuous response and x is a factor with two levels.
data: Data frame
compare: Text vector stating the factor levels - compare[1] is the control or reference group to which compare[2] is compared
b: Number of bootstrap samples to take with each cycle
B: Number of cycles, giving the total number of samples = B * b
alpha: Complement of the confidence level
hpd: Estimate highest density intervals?
bca: Estimate BCa intervals?
return.boot: Save the bootstrap sample of the MF statistic?
trace.it: Verbose tracking of the cycles?
seed: to initialize random number generator for reproducibility. Passed to set.seed.

Value

a mfboot data object

Details

Resamples the data and produces bootstrap confidence intervals. Equal tailed intervals are estimated by the percentile method. Highest density intervals are estimated by selecting the shortest of all possible intervals. For BCa intervals, see Efron and Tibshirani section 14.3.

References

Siev D. (2005). An estimator of intervention effect on disease severity. Journal of Modern Applied Statistical Methods. 4:500–508

Efron B, Tibshirani RJ. An Introduction to the Bootstrap. Chapman and Hall, New York, 1993.

Author

MF-package

Examples


MFBoot(lesion ~ group, calflung, seed = 12345)
#> 10000 bootstrap samples
#>  95% confidence interval
#> Seed =  12345
#> 
#> Comparing vac to con 
#>                 observed median  lower  upper
#> Equal Tailed        0.44 0.4464 0.1328 0.7120
#> Highest Density     0.44 0.4464 0.1456 0.7184
#> 

# 10000 bootstrap samples
# 95% confidence interval
# Seed = 12345
#
# Comparing vac to con
# observed median lower  upper
# Equal Tailed        0.44 0.4496 0.152 0.7088
# Highest Density     0.44 0.4496 0.152 0.7088