ss.maxT(null, obs, alternative, get.cutoff, get.cr,
get.adjp, alpha = 0.05)
ss.minP(null, obs, rawp, alternative, get.cutoff, get.cr,
get.adjp, alpha=0.05)
sd.maxT(null, obs, alternative, get.cutoff, get.cr,
get.adjp, alpha = 0.05)
sd.minP(null, obs, rawp, alternative, get.cutoff, get.cr,
get.adjp, alpha=0.05)
boot.resample
.meanX
. These are stored as a matrix with numerator (possibly absolute value or negative, depending on the value of alternative) in the first row, denominator in the second row, and a 1 or -1 in the third row (depending on the value of alternative). The observed test statistics are obs[1,]*obs[3,]/obs[2,]."matrix"
, for each nominal (i.e. target) level for the test, a vector of threshold values for the vector of test statistics."array"
, for each nominal (i.e. target) level for the test, a matrix of lower and upper confidence bounds for the parameter of interest for each hypothesis. Not available for f-tests."numeric"
, adjusted p-values for each hypothesis.In step-down procedures, the hypotheses corresponding to the most significant test statistics (i.e., largest absolute test statistics or smallest unadjusted p-values) are considered successively, with further tests depending on the outcome of earlier ones. As soon as one fails to reject a null hypothesis, no further hypotheses are rejected. In contrast, for step-up procedures, the hypotheses corresponding to the least significant test statistics are considered successively, again with further tests depending on the outcome of earlier ones. As soon as one hypothesis is rejected, all remaining more significant hypotheses are rejected.
These functions perform the following procedures: ss.maxT: single-step, common cut-off (maxima of test statistics) ss.minP: single-step, common quantile (minima of p-values) sd.maxT: step-down, common cut-off (maxima of test statistics) sd.minP: step-down, common quantile (minima of p-values)
M.J. van der Laan, S. Dudoit, K.S. Pollard (2004), Multiple Testing. Part II. Step-Down Procedures for Control of the Family-Wise Error Rate, Statistical Applications in Genetics and Molecular Biology, 3(1). http://www.bepress.com/sagmb/vol3/iss1/art14/
S. Dudoit, M.J. van der Laan, K.S. Pollard (2004), Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates, Statistical Applications in Genetics and Molecular Biology, 3(1). http://www.bepress.com/sagmb/vol3/iss1/art13/
Katherine S. Pollard and Mark J. van der Laan, "Resampling-based Multiple Testing: Asymptotic Control of Type I Error and Applications to Gene Expression Data" (June 24, 2003). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 121. http://www.bepress.com/ucbbiostat/paper121
MTP
## These functions are used internally by the MTP function
## See MTP function: ? MTP
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