Boostrap MF CI from clustered data

Description

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

Usage

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  MFClusBoot(formula, data, compare = c("con", "vac"),
    boot.cluster = TRUE, boot.unit = FALSE, b = 100, B =
    100, alpha = 0.05, hpd = TRUE, return.boot = FALSE,
    trace.it = FALSE)

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

Resample the clusters? Default TRUE

boot.unit

Resample the units within cluster? Default FALSE

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? Default TRUE

return.boot

Save the bootstrap sample of the MF statistic? Default FALSE

trace.it

Verbose tracking of the cycles? Default FALSE

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-class 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-class or use trace.it to identify excluded clusters.

Author(s)

David Siev david.siev@aphis.usda.gov

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

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## Not run: 
MFClusBoot(lesion ~ group + cluster(litter), piglung)

#  Bootstrapping clusters. . . . .
#
#  10000 bootstrap samples of clusters
#  Comparing vac to con
#
#   95% confidence interval
#
#                   observed    median      lower     upper
#  Equal Tailed    0.3533835 0.3630573 0.07382550 0.6567271
#  Highest Density 0.3533835 0.3630573 0.07262462 0.6551724
#
#  Excluded Clusters
#  [1] M, Q, R, B, O, V, I, C

MFClusBoot(lesion ~ group + cluster(litter), piglung, boot.unit = T, b = 12, B = 12)

#### 144 resamples to save time
#
#  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 0.3714286 -0.0138888889 0.7162213
#  Highest Density 0.3533835 0.3714286 -0.0001472081 0.7297387
#
#  Excluded Clusters
#  [1] M, Q, R, B, O, V, I, C

## End(Not run)