Nothing
R/00Package.r
In CLME: Constrained Inference for Linear Mixed Effects Models
##
## This is the PACKAGE documentation
##
#' Constrained inference for linear mixed models.
#'
#' @docType package
#' @name CLME-package
#' @rdname CLME-package
#'
#' @description
#' Constrained inference on linear fixed and mixed models using residual bootstrap.
#' Covariates and random effects are permitted but not required.
#'
#' Appropriate credit should be given when publishing results obtained using \pkg{CLME}, or when
#' developing other programs/packages based off of this one. Use \code{citation(package="CLME")}
#' for Bibtex information.
#'
#' The work was produced in part with funding from the Intramural Research Program of the NIH,
#' National Institute of Environmental Health Sciences (Z01 ES101744).
#'
#'
#' @details
#'
#' This package was introduced in Jelsema and Peddada (2016). The primary function is \code{\link{clme}}.
#' The other functions in this package may be run separately, but in general are designed for use by \code{\link{clme}}.
#'
#' The method which is implemented is the constrained linear mixed effects model described in
#' Farnan, Ivanova, and Peddada (2014). See that paper for more details regarding the method.
#' Here we give a brief overview of the assumed model:
#'
#' \deqn{ Y = X_{1}\theta_{1} + X_{2}\theta_{2} + U\xi + \epsilon }{Y = X1*theta1 + X2*theta2 + U*xi + e}
#'
#' where
#'
#' \itemize{
#' \item \eqn{X_1}{X1} is a \eqn{N \times p_1}{N x p1} design matrix.
#' \item \eqn{\theta_1}{theta1} are the coefficients (often treatment effects).
#' \item \eqn{X_2}{X2} is a \eqn{N \times p_2}{N x p2} matrix of fixed covariates.
#' \item \eqn{\theta_1}{theta2} are the coefficients for the covariates.
#' \item \eqn{U}{U} is a \eqn{N \times c}{N x c} matrix of random effects.
#' \item \eqn{\xi}{xi} is a zero-mean random vector with covariance \eqn{T}{T} (see below).
#' \item \eqn{\epsilon}{e} is a zero-mean random vector with covariance \eqn{\Sigma}{Sigma} (see below).
#' }
#'
#' Neither covariates (\eqn{X_2}{X2}) nor random effects (\eqn{U}{U}) are required by the model or \pkg{CLME}. The covariance matrix of \eqn{\xi}{xi} is given by:
#'
#' \deqn{ T = diag\left( \tau^{2}_{1}I_{c_{1}}, \tau^{2}_{2}I_{c_{2}} , \dots , \tau^{2}_{q}I_{c_{q}} \right) }{ T = diag( tau1^2 I_c1, tau2^2 I_c2 , ... , tauq^2 I_cq) }
#'
#' The first \eqn{c_{1}}{c1} random effects will share a common variance, \eqn{\tau^{2}_{1}}{tau1^2}, the next \eqn{c_{2}}{c2} random effects will share a common variance, and so on. Note that \eqn{c = \sum_{i=1}^{q} c_i}{c = SUM(ci), i=1,...q}. Homogeneity of variances in the random effects can be induced by letting \eqn{q=1}{q=1} (hence \eqn{c_{1}=c=ncol(U)}{c1=c=ncol(U)}).
#'
#' Similarly, the covariance matrix of \eqn{\epsilon}{e} is given by:
#'
#' \deqn{ \Sigma = diag\left( \sigma^{2}_{1}I_{n_{1}}, \sigma^{2}_{2}I_{n_{2}} , \dots , \sigma^{2}_{q}I_{n_{k}} \right) }{ Sigma = diag( sigma1^2 I_n1, sigma2^2 I_n2 , ... , sigmak^2 I_nk)}
#'
#' Again, the first \eqn{n_{1}}{n1} observations will share a common variance, \eqn{\sigma^{2}_{1}}{sigma1^2}, the next \eqn{n_{2}}{n2} will share a common variance, and so on. Note that \eqn{N = \sum_{i=1}^{k} n_i}{N = SUM(n_i), i=1,...k}. Homogeneity of variances in the residuals can be induced by letting \eqn{k=1}{k=1}.
#'
#' The order constraints are defined by the matrix \eqn{A}{A}. This is an \eqn{r \times p}{r x p} matrix where \eqn{r}{r} is the number of constraints, and \eqn{p=p_{1}+p_{2}}{p = p1 + p2} is the dimension of \eqn{ \theta = ( \theta_{1}' , \theta_{2}')'}{ theta = ( theta1' , theta2')'}. Formally the hypothesis being tested is:
#'
#' \deqn{ H_{a}: A\theta > 0 }{Ha: A*theta > 0 }
#'
#' For several default orders (simple, umbrella, simple tree) the \eqn{A}{A} matrix can be automatically generated. Alternatively, the user may define a custom \eqn{A}{A} matrix to test other patterns among the elements of \eqn{\theta}{theta}. See \code{\link{create.constraints}} and \code{\link{clme}} for more details.
#'
#' For computational reasons, the implementation is not identical to the model expressed. Particularly, the fixed-effects matrix (or matrices) and the random effects matrix are assumed to be columns in a data frame, not passed as matrices. The \eqn{A}{A} matrix is not \eqn{r\ times p}{r x p}, but \eqn{r\ times 2}{r x 2}, where each row gives the indices of the constrained coefficients. See \code{\link{create.constraints}} for further explanation.
#'
#'
#' The creation of this package \pkg{CLME}, this manual, and the vignette were all supported by the Intramural Research Program of the United States' National Institutes of Health (Z01 ES101744).
#'
#' @references
#' Jelsema, C. M. and Peddada, S. D. (2016).
#' CLME: An R Package for Linear Mixed Effects Models under Inequality Constraints.
#' \emph{Journal of Statistical Software}, 75(1), 1-32. doi:10.18637/jss.v075.i01
#'
#' Farnan, L., Ivanova, A., and Peddada, S. D. (2014).
#' Linear Mixed Efects Models under Inequality Constraints with Applications.
#' \emph{PLOS ONE}, 9(1). e84778. doi: 10.1371/journal.pone.0084778
#'
#'
#' @import methods
#' @import stats
#'
#'
#'
"_PACKAGE"
#' @title
#' Fibroid Growth Study
#'
#' @description
#' This data set contains a subset of the data from the Fibroid Growth Study.
#'
#' \tabular{rll}{
#' [,1] \tab ID \tab ID for subject. \cr
#' [,2] \tab fid \tab ID for fibroid (each women could have multiple fibroids). \cr
#' [,3] \tab lfgr \tab log fibroid growth rate. See details. \cr
#' [,4] \tab age \tab age category Younger, Middle, Older. \cr
#' [,5] \tab loc \tab location of fibroid, corpus, fundus, or lower segment. \cr
#' [,6] \tab bmi \tab body mass index of subject. \cr
#' [,7] \tab preg \tab parity, whether the subject had delivered a child. \cr
#' [,8] \tab race \tab race of subject (Black or White only). \cr
#' [,9] \tab vol \tab initial volume of fibroid. \cr
#' }
#'
#' @details
#' The response variable \code{lfgr} was calculated as the change in log fibroid volume,
#' divided by the length of time between measurements. The growth rates were averaged to produce
#' a single value for each fibroid, which was scaled to represent a 6-month percent change in volume.
#'
#' @references
#' Peddada, Laughlin, Miner, Guyon, Haneke, Vahdat, Semelka, Kowalik, Armao, Davis, and Baird(2008).
#' Growth of Uterine Leiomyomata Among Premenopausal Black and White Women.
#' Proceedings of the National Academy of Sciences of the United States of America, 105(50),
#' 19887-19892. URL \url{http://www.pnas.org/content/105/50/19887.full.pdf}.
#'
#'
#' @docType data
#' @keywords datasets
#' @name fibroid
#' @usage data(fibroid)
#' @format A data frame containing 240 observations on 9 variables.
"fibroid"
#' @title
#' Experiment on mice
#'
#' @description
#' This data set contains the data from an experiment on 24 Sprague-Dawley rats from Cora et al (2012).
#'
#' \tabular{rll}{
#' [,1] \tab id \tab ID for rat (factor). \cr
#' [,2] \tab time \tab time period (in order, 0 , 6, 24, 48, 72, 96 hours). \cr
#' [,3] \tab temp \tab storage temperature reference (\code{''Ref''}) vs. room temperature (\code{''RT''}). \cr
#' [,4] \tab sex \tab sex, male (\code{''Male''}) vs. female (\code{''Female''}). Coded as \code{''Female''=1}. \cr
#' [,5] \tab wbc \tab white blood cell count (\eqn{10^3 / \mu L}{10^3 / mu L}). \cr
#' [,6] \tab rbc \tab red blood cell count )\eqn{10^6 / \mu L}{10^6 / mu L}). \cr
#' [,7] \tab hgb \tab hemoglobin concentration (g/dl). \cr
#' [,8] \tab hct \tab hematocrit (\%). \cr
#' [,9] \tab spun \tab (HCT \%). \cr
#' [,10] \tab mcv \tab MCV, a measurement of erythrocyte volume (fl). \cr
#' [,11] \tab mch \tab mean corpuscular hemoglobin (pg). \cr % ????
#' [,12] \tab mchc \tab mean corpuscular hemoglobin concentration (g/dl). \cr
#' [,13] \tab plts \tab platelet count (\eqn{10^3 / \mu L}{10^3 / mu L}). \cr
#' }
#'
#' @details
#' The response variable \code{lfgr} was calculated as the change in log fibroid volume,
#' divided by the length of time between measurements. The growth rates were averaged to produce
#' a single value for each fibroid, which was scaled to represent a 6-month percent change in volume.
#'
#' @references
#' Cora M, King D, Betz L, Wilson R, and Travlos G (2012).
#' Artifactual changes in Sprauge-Dawley rat hematologic parameters after storage of samples at 3 C and 21 C.
#' Journal of the American Association for Laboratory Animal Science, 51(5), 616-621.
#' URL \url{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3447451/}.
#'
#'
#' @docType data
#' @keywords datasets
#' @name rat.blood
#' @usage data(rat.blood)
#' @format A data frame containing 241 observations on 13 variables.
"rat.blood"
# library("shiny")
# library("lme4")
# library("isotone")
# library("stringr")
# library("prettyR")
# library("openxlsx")
# devtools::check( devtools::as.package("../CLME_2.0-12.tar.gz") )
# devtools::check( devtools::as.package("C:/Users/statman/Documents/00-Packages/CLME_2.0-12.tar.gz") )
Try the CLME package in your browser
Any scripts or data that you put into this service are public.
CLME documentation built on July 8, 2020, 5:49 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
##
## This is the PACKAGE documentation
##
#' Constrained inference for linear mixed models.
#'
#' @docType package
#' @name CLME-package
#' @rdname CLME-package
#'
#' @description
#' Constrained inference on linear fixed and mixed models using residual bootstrap.
#' Covariates and random effects are permitted but not required.
#'
#' Appropriate credit should be given when publishing results obtained using \pkg{CLME}, or when
#' developing other programs/packages based off of this one. Use \code{citation(package="CLME")}
#' for Bibtex information.
#'
#' The work was produced in part with funding from the Intramural Research Program of the NIH,
#' National Institute of Environmental Health Sciences (Z01 ES101744).
#'
#'
#' @details
#'
#' This package was introduced in Jelsema and Peddada (2016). The primary function is \code{\link{clme}}.
#' The other functions in this package may be run separately, but in general are designed for use by \code{\link{clme}}.
#'
#' The method which is implemented is the constrained linear mixed effects model described in
#' Farnan, Ivanova, and Peddada (2014). See that paper for more details regarding the method.
#' Here we give a brief overview of the assumed model:
#'
#' \deqn{ Y = X_{1}\theta_{1} + X_{2}\theta_{2} + U\xi + \epsilon }{Y = X1*theta1 + X2*theta2 + U*xi + e}
#'
#' where
#'
#' \itemize{
#' \item \eqn{X_1}{X1} is a \eqn{N \times p_1}{N x p1} design matrix.
#' \item \eqn{\theta_1}{theta1} are the coefficients (often treatment effects).
#' \item \eqn{X_2}{X2} is a \eqn{N \times p_2}{N x p2} matrix of fixed covariates.
#' \item \eqn{\theta_1}{theta2} are the coefficients for the covariates.
#' \item \eqn{U}{U} is a \eqn{N \times c}{N x c} matrix of random effects.
#' \item \eqn{\xi}{xi} is a zero-mean random vector with covariance \eqn{T}{T} (see below).
#' \item \eqn{\epsilon}{e} is a zero-mean random vector with covariance \eqn{\Sigma}{Sigma} (see below).
#' }
#'
#' Neither covariates (\eqn{X_2}{X2}) nor random effects (\eqn{U}{U}) are required by the model or \pkg{CLME}. The covariance matrix of \eqn{\xi}{xi} is given by:
#'
#' \deqn{ T = diag\left( \tau^{2}_{1}I_{c_{1}}, \tau^{2}_{2}I_{c_{2}} , \dots , \tau^{2}_{q}I_{c_{q}} \right) }{ T = diag( tau1^2 I_c1, tau2^2 I_c2 , ... , tauq^2 I_cq) }
#'
#' The first \eqn{c_{1}}{c1} random effects will share a common variance, \eqn{\tau^{2}_{1}}{tau1^2}, the next \eqn{c_{2}}{c2} random effects will share a common variance, and so on. Note that \eqn{c = \sum_{i=1}^{q} c_i}{c = SUM(ci), i=1,...q}. Homogeneity of variances in the random effects can be induced by letting \eqn{q=1}{q=1} (hence \eqn{c_{1}=c=ncol(U)}{c1=c=ncol(U)}).
#'
#' Similarly, the covariance matrix of \eqn{\epsilon}{e} is given by:
#'
#' \deqn{ \Sigma = diag\left( \sigma^{2}_{1}I_{n_{1}}, \sigma^{2}_{2}I_{n_{2}} , \dots , \sigma^{2}_{q}I_{n_{k}} \right) }{ Sigma = diag( sigma1^2 I_n1, sigma2^2 I_n2 , ... , sigmak^2 I_nk)}
#'
#' Again, the first \eqn{n_{1}}{n1} observations will share a common variance, \eqn{\sigma^{2}_{1}}{sigma1^2}, the next \eqn{n_{2}}{n2} will share a common variance, and so on. Note that \eqn{N = \sum_{i=1}^{k} n_i}{N = SUM(n_i), i=1,...k}. Homogeneity of variances in the residuals can be induced by letting \eqn{k=1}{k=1}.
#'
#' The order constraints are defined by the matrix \eqn{A}{A}. This is an \eqn{r \times p}{r x p} matrix where \eqn{r}{r} is the number of constraints, and \eqn{p=p_{1}+p_{2}}{p = p1 + p2} is the dimension of \eqn{ \theta = ( \theta_{1}' , \theta_{2}')'}{ theta = ( theta1' , theta2')'}. Formally the hypothesis being tested is:
#'
#' \deqn{ H_{a}: A\theta > 0 }{Ha: A*theta > 0 }
#'
#' For several default orders (simple, umbrella, simple tree) the \eqn{A}{A} matrix can be automatically generated. Alternatively, the user may define a custom \eqn{A}{A} matrix to test other patterns among the elements of \eqn{\theta}{theta}. See \code{\link{create.constraints}} and \code{\link{clme}} for more details.
#'
#' For computational reasons, the implementation is not identical to the model expressed. Particularly, the fixed-effects matrix (or matrices) and the random effects matrix are assumed to be columns in a data frame, not passed as matrices. The \eqn{A}{A} matrix is not \eqn{r\ times p}{r x p}, but \eqn{r\ times 2}{r x 2}, where each row gives the indices of the constrained coefficients. See \code{\link{create.constraints}} for further explanation.
#'
#'
#' The creation of this package \pkg{CLME}, this manual, and the vignette were all supported by the Intramural Research Program of the United States' National Institutes of Health (Z01 ES101744).
#'
#' @references
#' Jelsema, C. M. and Peddada, S. D. (2016).
#' CLME: An R Package for Linear Mixed Effects Models under Inequality Constraints.
#' \emph{Journal of Statistical Software}, 75(1), 1-32. doi:10.18637/jss.v075.i01
#'
#' Farnan, L., Ivanova, A., and Peddada, S. D. (2014).
#' Linear Mixed Efects Models under Inequality Constraints with Applications.
#' \emph{PLOS ONE}, 9(1). e84778. doi: 10.1371/journal.pone.0084778
#'
#'
#' @import methods
#' @import stats
#'
#'
#'
"_PACKAGE"
#' @title
#' Fibroid Growth Study
#'
#' @description
#' This data set contains a subset of the data from the Fibroid Growth Study.
#'
#' \tabular{rll}{
#' [,1] \tab ID \tab ID for subject. \cr
#' [,2] \tab fid \tab ID for fibroid (each women could have multiple fibroids). \cr
#' [,3] \tab lfgr \tab log fibroid growth rate. See details. \cr
#' [,4] \tab age \tab age category Younger, Middle, Older. \cr
#' [,5] \tab loc \tab location of fibroid, corpus, fundus, or lower segment. \cr
#' [,6] \tab bmi \tab body mass index of subject. \cr
#' [,7] \tab preg \tab parity, whether the subject had delivered a child. \cr
#' [,8] \tab race \tab race of subject (Black or White only). \cr
#' [,9] \tab vol \tab initial volume of fibroid. \cr
#' }
#'
#' @details
#' The response variable \code{lfgr} was calculated as the change in log fibroid volume,
#' divided by the length of time between measurements. The growth rates were averaged to produce
#' a single value for each fibroid, which was scaled to represent a 6-month percent change in volume.
#'
#' @references
#' Peddada, Laughlin, Miner, Guyon, Haneke, Vahdat, Semelka, Kowalik, Armao, Davis, and Baird(2008).
#' Growth of Uterine Leiomyomata Among Premenopausal Black and White Women.
#' Proceedings of the National Academy of Sciences of the United States of America, 105(50),
#' 19887-19892. URL \url{http://www.pnas.org/content/105/50/19887.full.pdf}.
#'
#'
#' @docType data
#' @keywords datasets
#' @name fibroid
#' @usage data(fibroid)
#' @format A data frame containing 240 observations on 9 variables.
"fibroid"
#' @title
#' Experiment on mice
#'
#' @description
#' This data set contains the data from an experiment on 24 Sprague-Dawley rats from Cora et al (2012).
#'
#' \tabular{rll}{
#' [,1] \tab id \tab ID for rat (factor). \cr
#' [,2] \tab time \tab time period (in order, 0 , 6, 24, 48, 72, 96 hours). \cr
#' [,3] \tab temp \tab storage temperature reference (\code{''Ref''}) vs. room temperature (\code{''RT''}). \cr
#' [,4] \tab sex \tab sex, male (\code{''Male''}) vs. female (\code{''Female''}). Coded as \code{''Female''=1}. \cr
#' [,5] \tab wbc \tab white blood cell count (\eqn{10^3 / \mu L}{10^3 / mu L}). \cr
#' [,6] \tab rbc \tab red blood cell count )\eqn{10^6 / \mu L}{10^6 / mu L}). \cr
#' [,7] \tab hgb \tab hemoglobin concentration (g/dl). \cr
#' [,8] \tab hct \tab hematocrit (\%). \cr
#' [,9] \tab spun \tab (HCT \%). \cr
#' [,10] \tab mcv \tab MCV, a measurement of erythrocyte volume (fl). \cr
#' [,11] \tab mch \tab mean corpuscular hemoglobin (pg). \cr % ????
#' [,12] \tab mchc \tab mean corpuscular hemoglobin concentration (g/dl). \cr
#' [,13] \tab plts \tab platelet count (\eqn{10^3 / \mu L}{10^3 / mu L}). \cr
#' }
#'
#' @details
#' The response variable \code{lfgr} was calculated as the change in log fibroid volume,
#' divided by the length of time between measurements. The growth rates were averaged to produce
#' a single value for each fibroid, which was scaled to represent a 6-month percent change in volume.
#'
#' @references
#' Cora M, King D, Betz L, Wilson R, and Travlos G (2012).
#' Artifactual changes in Sprauge-Dawley rat hematologic parameters after storage of samples at 3 C and 21 C.
#' Journal of the American Association for Laboratory Animal Science, 51(5), 616-621.
#' URL \url{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3447451/}.
#'
#'
#' @docType data
#' @keywords datasets
#' @name rat.blood
#' @usage data(rat.blood)
#' @format A data frame containing 241 observations on 13 variables.
"rat.blood"
# library("shiny")
# library("lme4")
# library("isotone")
# library("stringr")
# library("prettyR")
# library("openxlsx")
# devtools::check( devtools::as.package("../CLME_2.0-12.tar.gz") )
# devtools::check( devtools::as.package("C:/Users/statman/Documents/00-Packages/CLME_2.0-12.tar.gz") )
Try the CLME package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.