Estimates bootstrap confidence intervals for the mitigated fraction from clustered or stratified data.
1 2 3 4 
formula 
Formula of the form 
data 
Data frame. See 
compare 
Text vector stating the factor levels 

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 
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.
a mfbootclusterclass
data object
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
mfbootclusterclass
or use trace.it
to identify excluded clusters.
David Siev david.siev@aphis.usda.gov
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  ## 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)

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