Estimates bootstrap confidence intervals for MF, HL, and Qdif.
1 2 3 
formula 
Formula of the form 
data 
Data frame 
compare 
Text vector stating the factor levels 

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 for MF and HL? Default TRUE. 
bca 
Estimate BCa intervals for MF? Default FALSE. 
return.boot 
Save the bootstrap samples of the statistics? Default FALSE. 
trace.it 
Verbose tracking of the cycles? Default FALSE. 
seed 
initial seed value. Ignored. 
Estimates bootstrap confidence intervals for the mitigated fraction (MF), HodgeLehmann estimator (HL), and the difference of medians and quartiles (Qdif). The HodgesLehmann estimator is the media difference; it assumes that the two distributions have the same shape and differ by a constant shift.
a mfhlbootclass
data object
David Siev david.siev@aphis.usda.gov
Hodges JL, Lehmann EL, (1963). Estimates of location
based on rank tests. Annals of Mathematical
Statistics. 34:598–611.
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  HLBoot(lesion~group,calflung)
# Bootstrapping . . . . . . . . . . . .
#
# 10000 bootstrap samples
# 95% confidence intervals
# Comparing vac to con
#
#
# Mitigated Fraction
#
# observed median lower upper
# Equal Tailed 0.44 0.4464 0.1264 0.7056
# Highest Density 0.44 0.4464 0.1392 0.7120
#
#
# HodgesLehmann
#
# observed median lower upper
# Equal Tailed 0.07335 0.07125 0.1720537 0.01430
# Highest Density 0.07335 0.07125 0.1563500 0.00555
#
#
# Quartile Differences (quartiles of vac  quartiles of con)
#
# observed median lower upper
# Q25 0.041500 0.041300 0.1034000 0.000905
# Q50 0.112525 0.111175 0.2811688 0.023200
# Q75 0.168000 0.168000 0.3858500 0.023975

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