HLBoot: Bootstrap CI for MF, HL, and Qdif

In MF: Mitigated Fraction

Description

Estimates bootstrap confidence intervals for MF, HL, and Qdif.

Usage

  HLBoot(formula, data, compare = c("con", "vac"), b = 100,
    B = 100, alpha = 0.05, hpd = TRUE, bca = FALSE,
    return.boot = FALSE, trace.it = FALSE, seed = NULL)

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
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 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.

Details

Estimates bootstrap confidence intervals for the mitigated fraction (MF), Hodge-Lehmann estimator (HL), and the difference of medians and quartiles (Qdif). The Hodges-Lehmann estimator is the media difference; it assumes that the two distributions have the same shape and differ by a constant shift.

Value

a mfhlboot-class data object

Author(s)

David Siev [e-mail]

References

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.

See Also

mfhlboot-class

Examples

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
#
#
#  Hodges-Lehmann
#
#                  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

Example output

Bootstrapping
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

10000 bootstrap samples
95% confidence intervals
Comparing vac to con 


Mitigated Fraction

                observed median  lower   upper
Equal Tailed        0.44 0.4496 0.1328 0.70248
Highest Density     0.44 0.4496 0.1456 0.71200


Hodges-Lehmann

                observed   median   lower    upper
Equal Tailed    -0.07335 -0.07335 -0.1722 -0.01445
Highest Density -0.07335 -0.07335 -0.1605 -0.00985


Quartile Differences (quartiles of vac - quartiles of con)

     observed    median    lower     upper
Q25 -0.041500 -0.041500 -0.10305 -0.000905
Q50 -0.112525 -0.111175 -0.28115  0.019350
Q75 -0.168000 -0.170425 -0.38585  0.030000


Quartiles of con 
    observed   median    lower   upper
Q25 0.054000 0.054000 0.021005 0.11275
Q50 0.139275 0.139275 0.061400 0.31000
Q75 0.315000 0.315000 0.173000 0.45250


Quartiles of vac 
    observed  median   lower    upper
Q25  0.01250 0.01250 0.00125 0.026000
Q50  0.02675 0.02675 0.01665 0.144575
Q75  0.14700 0.14700 0.02810 0.219250

MF documentation built on May 2, 2019, 1:46 p.m.