MFClusBoot: Bootstrap MF CI from clustered data

In ABS-dev/MF: Mitigated Fraction

Bootstrap MF CI from clustered data

Description

Estimates bootstrap confidence intervals for the mitigated fraction from clustered or stratified data.

Usage

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

Arguments

formula	Formula of the form y ~ x + cluster(w), where y is a continuous response, x is a factor with two levels of treatment, and w is a factor indicating the clusters.
data	Data frame. See Note for handling of input data with more than two levels.
compare	Text vector stating the factor levels - compare[1] is the control or reference group to which compare[2] is compared
boot.cluster	Boolean whether to resample the clusters.
boot.unit	Boolean whether to resample the units within cluster.
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	Boolean whether to estimate highest density intervals.
return.boot	Boolean whether to save the bootstrap sample of the MF statistic.
trace.it	Boolean whether to display verbose tracking of the cycles.
seed	to initialize random number generator for reproducibility. Passed to set.seed.

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.

Value

a mfbootcluster data object

Note

If input data contains more than two levels of treatment, rows associated with unused treatment levels will be removed.

Factor levels for treatments not present in the input data will be ignored.

Clusters with missing treatments will be excluded. See mfbootcluster or use trace.it to identify excluded clusters.

Author(s)

MF-package

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.

Examples

## Not run: 
MFClusBoot(lesion ~ group + cluster(litter), piglung, seed = 12345)
# Bootstrapping clusters. . . . . . . . . . . . . . . . .
#
# Bootstrapping units. . . . . . . . . . . . . . . . . .
#
# 10000 bootstrap samples of clusters and units in treatment in cluster
# Comparing vac to con
#
# 95% confidence interval
#
# observed    median       lower     upper
# Equal Tailed    0.3533835 0.3648649 -0.01409471 0.7109966
# Highest Density 0.3533835 0.3648649  0.00000000 0.7236842
#
# Excluded Clusters
# M, Q, R, B, O, V, I, C

## End(Not run)

ABS-dev/MF documentation built on Oct. 14, 2024, 7:28 p.m.