Title: Functions for medical statistics book with some demographic data
Description: Several utility functions for the book entitled "Practices
of Medical and Health Data Analysis using R" (Pearson Education

Description: Several utility functions for the book entitled
"Practices of Medical and Health Data Analysis using R"

(Pearson Education Japan, 2007) with Japanese demographic
data and some demographic analysis related functions.

Japan, 2007) with Japanese demographic data and some

Title: Functions for Medical Statistics Book with some Demographic Data

Packaged: 2019-12-14 02:55:14 UTC; MINATO NAKAZAWA

Packaged: 2021-05-10 22:15:34 UTC; MINATO NAKAZAWA

Packaged: 2021-12-11 14:30:52 UTC; MINATO NAKAZAWA

Packaged: 2022-03-01 11:33:31 UTC; MINATO NAKAZAWA

Package: fmt
Type: Package
Title: Variance estimation of FMT method (Fully Moderated t-statistic)
Version: 1.0
Date: 2012-08-30
Author: Lianbo Yu, The Ohio State University
Maintainer: Lianbo Yu <Lianbo.Yu@osumc.edu>
Description: This package computes posterior residual variances to be
used in the denominator of a moderated t-statistic from a
linear model analysis of microarray data.  It is an extension
of the moderated t-statistic original proposed by Smyth (2004).
LOESS local regression and empirical Bayesian method are used
to estimate gene specific prior degrees of freedom and prior
variance based on average gene intensity level. The posterior
residual variance in the denominator is a weighted average of
prior and residual variance and the weights are prior degrees
of freedom and residual variance degrees of freedom. The
degrees of freedom of the moderated t-statistic is simply the
sum of prior and residual variance degrees of freedom.
Suggests: limma
License: GPL-2
Packaged: 2012-08-30 18:57:52 UTC; yu18
Repository: CRAN
Date/Publication: 2012-08-30 19:46:07

Title: Variance Estimation of FMT Method (Fully Moderated T-Statistic)
Description: The FMT method computes posterior residual variances to be used in
the denominator of a moderated t-statistic from a linear model analysis of
gene expression data. It is an extension of the moderated t-statistic
LOESS local regression and empirical Bayesian method are used to estimate
gene specific prior degrees of freedom and prior variance based on average
gene intensity levels. The posterior residual variance in the denominator is
a weighted average of prior and residual variance and the weights are prior
degrees of freedom and residual variance degrees of freedom. The degrees of
freedom of the moderated t-statistic is simply the sum of prior and residual