R/subgroup_tests_homo.R
In kshimotsu/subgroupLRT:
#' @description Performs the LRT statistic given the data for y and x on
#' the null hypothesis H_0: m = m_0. Using this function is equivalent to
#' calling normalmixLRT with regressors specified by x as a parameter.
#' @export
#' @title subgroupLRT_homo_2
#' @name subgroupLRT_homo_2
#' @param y n by 1 vector of data for y.
#' @param x n by q1 matrix of data for x.
#' @param v n by q2 matrix of data for v
#' @param m number of components in the mixture defined by a null hypothesis, m_0.
#' @param z n by p matrix of regressor associated with gamma.
#' @param ninits number of randomly drawn initial values.
#' @param crit.method method used to compute the critical values, one of \code{"none"},
#' \code{"asy"}, and \code{"boot"}. The default option is \code{"none"}. When \code{method = "asy"},
#' the p-values are computed using the asymptotic critical values. When \code{method = "boot"},
#' the p-values are computed by bootstrap.
#' @param nbtsp The number of bootstrap replicates. Default is 199.
#' @param cl cluster used for parallelization; if it is \code{NULL}, the system
#' will automatically generate a new one for computation accordingly.
#' @param parallel Determines what percentage of available cores are used,
#' represented by a double in [0,1]. Default is 1.
#' @return A list of class \code{normalregMix} with items:
#' \item{lrtstat}{the value of the LRT statistic.}
#' \item{pvals}{p-value.}
#' \item{crit}{vector of critical values at the 0.1, 0.05, 0.01 level.}
#' \item{crit.method}{method used to compute the variance-covariance matrix.}
#' \item{parlist}{parameter estimates as a list containing alpha, mubeta, and sigma (and gam if z is included in the model).}
#' \item{call}{The matched call.}
#' \item{m}{number of components in the mixture defined by the null hypothesis, \eqn{m_0}.}
#' @examples
#' data(faithful)
#' attach(faithful)
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 1, crit.method = "none")}
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 2, crit.method = "none")}
subgroupLRT_homo_2 <- function (y, x, v, m = 1, z = NULL, ninits = 10, k = 100,
crit.method = c("none", "boot"), nbtsp = 199,
cl = NULL, parallel = 1) {
y <- as.vector(y)
x <- as.matrix(x)
v <- as.matrix(v)
n <- length(y)
if (!is.null(z))
z <- as.matrix(z)
subgroup.mle.result <- subgroupMLE_homo(y=y, x=x, v=v, m=m, z=z, vcov.method="none", ninits=ninits)
loglik0 <- subgroup.mle.result$loglik
ninits2 = floor(ninits/3)
subgroup.mle.result1 <- subgroupMLE_homo_2(y=y, x=x, v=v, m=m+1, z=z, k = k,
tauinit=c(1,-2), vcov.method="none", ninits=ninits2)
loglik1 <- subgroup.mle.result1$loglik
subgroup.mle.result2 <- subgroupMLE_homo_2(y=y, x=x, v=v, m=m+1, z=z, k = k,
tauinit=c(1,2), vcov.method="none", ninits=ninits2)
loglik2 <- subgroup.mle.result2$loglik
subgroup.mle.result3 <- subgroupMLE_homo_2(y=y, x=x, v=v, m=m+1, z=z, k = k,
tauinit=c(1,0), vcov.method="none", ninits=ninits2)
loglik3 <- subgroup.mle.result3$loglik
loglikem <- max(c(loglik1, loglik2, loglik3))
lrtstat <- 2*(loglikem - loglik0)
if (crit.method == "boot") {
result <-subgroupCritBoot_homo_2(y=y, x=x, v=v, parlist=subgroup.mle.result$parlist, z=z, values=lrtstat,
k=k, ninits=ninits, nbtsp=nbtsp, parallel=parallel, cl=cl)
} else {
result <- list()
result$crit <- result$pvals <- NA
}
a <- list(lrtstat = lrtstat, pvals = result$pvals, crit = result$crit, parlist = subgroup.mle.result$parlist,
call = match.call(), m = m, crit.method = crit.method, nbtsp = nbtsp)
# ,label = "MEMtest")
# class(a) <- "normalregMix"
a
} # end subgroupLRT_homo_2
#' @description Performs the LRT statistic given the data for y and x on
#' the null hypothesis H_0: m = m_0. Using this function is equivalent to
#' calling normalmixLRT with regressors specified by x as a parameter.
#' @export
#' @title subgroupEM_homo
#' @name subgroupEM_homo
#' @param y n by 1 vector of data for y.
#' @param x n by q1 matrix of data for x.
#' @param v n by q2 matrix of data for v
#' @param m number of components in the mixture defined by a null hypothesis, m_0.
#' @param z n by p matrix of regressor associated with gamma.
#' @param ninits number of randomly drawn initial values.
#' @param crit.method method used to compute the critical values, one of \code{"none"},
#' \code{"asy"}, and \code{"boot"}. The default option is \code{"none"}. When \code{method = "asy"},
#' the p-values are computed using the asymptotic critical values. When \code{method = "boot"},
#' the p-values are computed by bootstrap.
#' @param nbtsp The number of bootstrap replicates. Default is 199.
#' @param cl cluster used for parallelization; if it is \code{NULL}, the system
#' will automatically generate a new one for computation accordingly.
#' @param parallel Determines what percentage of available cores are used,
#' represented by a double in [0,1]. Default is 1.
#' @return A list of class \code{normalregMix} with items:
#' \item{lrtstat}{the value of the LRT statistic.}
#' \item{pvals}{p-value.}
#' \item{crit}{vector of critical values at the 0.1, 0.05, 0.01 level.}
#' \item{crit.method}{method used to compute the variance-covariance matrix.}
#' \item{parlist}{parameter estimates as a list containing alpha, mubeta, and sigma (and gam if z is included in the model).}
#' \item{call}{The matched call.}
#' \item{m}{number of components in the mixture defined by the null hypothesis, \eqn{m_0}.}
#' @examples
#' data(faithful)
#' attach(faithful)
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 1, crit.method = "none")}
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 2, crit.method = "none")}
subgroupEM_homo <- function (y, x, v, m = 1, z = NULL, ninits = 10, k = 9,
crit.method = c("none", "boot"), nbtsp = 199,
cl = NULL, parallel = 1) {
y <- as.vector(y)
x <- as.matrix(x)
v <- as.matrix(v)
n <- length(y)
if (!is.null(z))
z <- as.matrix(z)
subgroup.mle.result <- subgroupMLE_homo(y=y, x=x, v=v, m=m, z=z, vcov.method="none", ninits=ninits)
loglik0 <- subgroup.mle.result$loglik
ninits2 = floor(ninits/2)
subgroup.em.result1 <- subgroupMLE_homo_EM(y=y, x=x, v=v, m=m+1, z=z, k = k, tauinit=c(1,-2),
vcov.method="none", ninits=ninits2)
loglik1 <- subgroup.em.result1$loglik
subgroup.em.result2 <- subgroupMLE_homo_EM(y=y, x=x, v=v, m=m+1, z=z, k = k, tauinit=c(1,2),
vcov.method="none", ninits=ninits2)
loglik2 <- subgroup.em.result2$loglik
loglikem <- max(c(loglik1, loglik2))
lrtstat <- 2*(loglikem - loglik0)
if (crit.method == "boot") {
result <-subgroupCritBoot_homo_EM(y=y, x=x, v=v, parlist=subgroup.mle.result$parlist, z=z, values=lrtstat,
k=k, ninits=ninits, nbtsp=nbtsp, parallel=parallel, cl=cl)
} else {
result <- list()
result$crit <- result$pvals <- NA
}
a <- list(lrtstat = lrtstat, pvals = result$pvals, crit = result$crit, parlist = subgroup.mle.result$parlist,
call = match.call(), m = m, crit.method = crit.method, nbtsp = nbtsp)
# ,label = "MEMtest")
# class(a) <- "normalregMix"
a
} # end subgroupEM_homo
#' @description Performs the LRT statistic given the data for y and x on
#' the null hypothesis H_0: m = m_0. Using this function is equivalent to
#' calling normalmixLRT with regressors specified by x as a parameter.
#' @export
#' @title subgroupLRT_homo
#' @name subgroupLRT_homo
#' @param y n by 1 vector of data for y.
#' @param x n by q1 matrix of data for x.
#' @param v n by q2 matrix of data for v
#' @param m number of components in the mixture defined by a null hypothesis, m_0.
#' @param z n by p matrix of regressor associated with gamma.
#' @param ninits number of randomly drawn initial values.
#' @param crit.method method used to compute the critical values, one of \code{"none"},
#' \code{"asy"}, and \code{"boot"}. The default option is \code{"none"}. When \code{method = "asy"},
#' the p-values are computed using the asymptotic critical values. When \code{method = "boot"},
#' the p-values are computed by bootstrap.
#' @param nbtsp The number of bootstrap replicates. Default is 199.
#' @param cl cluster used for parallelization; if it is \code{NULL}, the system
#' will automatically generate a new one for computation accordingly.
#' @param parallel Determines what percentage of available cores are used,
#' represented by a double in [0,1]. Default is 1.
#' @return A list of class \code{normalregMix} with items:
#' \item{lrtstat}{the value of the LRT statistic.}
#' \item{pvals}{p-value.}
#' \item{crit}{vector of critical values at the 0.1, 0.05, 0.01 level.}
#' \item{crit.method}{method used to compute the variance-covariance matrix.}
#' \item{parlist}{parameter estimates as a list containing alpha, mubeta, and sigma (and gam if z is included in the model).}
#' \item{call}{The matched call.}
#' \item{m}{number of components in the mixture defined by the null hypothesis, \eqn{m_0}.}
#' @examples
#' data(faithful)
#' attach(faithful)
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 1, crit.method = "none")}
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 2, crit.method = "none")}
subgroupLRT_homo <- function (y, x, v, m = 1, z = NULL, ninits = 10,
crit.method = c("none", "boot"), nbtsp = 199,
tauinit = c(0,0), cl = NULL, parallel = 1) {
y <- as.vector(y)
x <- as.matrix(x)
v <- as.matrix(v)
n <- length(y)
if (!is.null(z))
z <- as.matrix(z)
subgroup.mle.result <- subgroupMLE_homo(y=y, x=x, v=v, m=m, z=z, vcov.method="none", ninits=ninits)
loglik0 <- subgroup.mle.result$loglik
subgroup.mle.result1 <- subgroupMLE_homo(y=y, x=x, v=v, m=m+1, z=z,
tauinit = tauinit,
vcov.method="none", ninits=ninits)
loglik1 <- subgroup.mle.result1$loglik
lrtstat <- 2*(loglik1-loglik0)
if (crit.method == "boot") {
result <-subgroupCritBoot_homo(y=y, x=x, v=v, parlist=subgroup.mle.result$parlist, z=z, values=lrtstat,
tauinit = tauinit, ninits=ninits, nbtsp=nbtsp, parallel=parallel, cl=cl)
} else {
result <- list()
result$crit <- result$pvals <- NA
}
a <- list(lrtstat = lrtstat, pvals = result$pvals, crit = result$crit, parlist = subgroup.mle.result$parlist,
call = match.call(), m = m, crit.method = crit.method, nbtsp = nbtsp)
# ,label = "MEMtest")
# class(a) <- "normalregMix"
a
} # end subgroupLRT_homo
kshimotsu/subgroupLRT documentation built on Feb. 8, 2023, 1:49 p.m.
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#' @description Performs the LRT statistic given the data for y and x on
#' the null hypothesis H_0: m = m_0. Using this function is equivalent to
#' calling normalmixLRT with regressors specified by x as a parameter.
#' @export
#' @title subgroupLRT_homo_2
#' @name subgroupLRT_homo_2
#' @param y n by 1 vector of data for y.
#' @param x n by q1 matrix of data for x.
#' @param v n by q2 matrix of data for v
#' @param m number of components in the mixture defined by a null hypothesis, m_0.
#' @param z n by p matrix of regressor associated with gamma.
#' @param ninits number of randomly drawn initial values.
#' @param crit.method method used to compute the critical values, one of \code{"none"},
#' \code{"asy"}, and \code{"boot"}. The default option is \code{"none"}. When \code{method = "asy"},
#' the p-values are computed using the asymptotic critical values. When \code{method = "boot"},
#' the p-values are computed by bootstrap.
#' @param nbtsp The number of bootstrap replicates. Default is 199.
#' @param cl cluster used for parallelization; if it is \code{NULL}, the system
#' will automatically generate a new one for computation accordingly.
#' @param parallel Determines what percentage of available cores are used,
#' represented by a double in [0,1]. Default is 1.
#' @return A list of class \code{normalregMix} with items:
#' \item{lrtstat}{the value of the LRT statistic.}
#' \item{pvals}{p-value.}
#' \item{crit}{vector of critical values at the 0.1, 0.05, 0.01 level.}
#' \item{crit.method}{method used to compute the variance-covariance matrix.}
#' \item{parlist}{parameter estimates as a list containing alpha, mubeta, and sigma (and gam if z is included in the model).}
#' \item{call}{The matched call.}
#' \item{m}{number of components in the mixture defined by the null hypothesis, \eqn{m_0}.}
#' @examples
#' data(faithful)
#' attach(faithful)
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 1, crit.method = "none")}
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 2, crit.method = "none")}
subgroupLRT_homo_2 <- function (y, x, v, m = 1, z = NULL, ninits = 10, k = 100,
crit.method = c("none", "boot"), nbtsp = 199,
cl = NULL, parallel = 1) {
y <- as.vector(y)
x <- as.matrix(x)
v <- as.matrix(v)
n <- length(y)
if (!is.null(z))
z <- as.matrix(z)
subgroup.mle.result <- subgroupMLE_homo(y=y, x=x, v=v, m=m, z=z, vcov.method="none", ninits=ninits)
loglik0 <- subgroup.mle.result$loglik
ninits2 = floor(ninits/3)
subgroup.mle.result1 <- subgroupMLE_homo_2(y=y, x=x, v=v, m=m+1, z=z, k = k,
tauinit=c(1,-2), vcov.method="none", ninits=ninits2)
loglik1 <- subgroup.mle.result1$loglik
subgroup.mle.result2 <- subgroupMLE_homo_2(y=y, x=x, v=v, m=m+1, z=z, k = k,
tauinit=c(1,2), vcov.method="none", ninits=ninits2)
loglik2 <- subgroup.mle.result2$loglik
subgroup.mle.result3 <- subgroupMLE_homo_2(y=y, x=x, v=v, m=m+1, z=z, k = k,
tauinit=c(1,0), vcov.method="none", ninits=ninits2)
loglik3 <- subgroup.mle.result3$loglik
loglikem <- max(c(loglik1, loglik2, loglik3))
lrtstat <- 2*(loglikem - loglik0)
if (crit.method == "boot") {
result <-subgroupCritBoot_homo_2(y=y, x=x, v=v, parlist=subgroup.mle.result$parlist, z=z, values=lrtstat,
k=k, ninits=ninits, nbtsp=nbtsp, parallel=parallel, cl=cl)
} else {
result <- list()
result$crit <- result$pvals <- NA
}
a <- list(lrtstat = lrtstat, pvals = result$pvals, crit = result$crit, parlist = subgroup.mle.result$parlist,
call = match.call(), m = m, crit.method = crit.method, nbtsp = nbtsp)
# ,label = "MEMtest")
# class(a) <- "normalregMix"
a
} # end subgroupLRT_homo_2
#' @description Performs the LRT statistic given the data for y and x on
#' the null hypothesis H_0: m = m_0. Using this function is equivalent to
#' calling normalmixLRT with regressors specified by x as a parameter.
#' @export
#' @title subgroupEM_homo
#' @name subgroupEM_homo
#' @param y n by 1 vector of data for y.
#' @param x n by q1 matrix of data for x.
#' @param v n by q2 matrix of data for v
#' @param m number of components in the mixture defined by a null hypothesis, m_0.
#' @param z n by p matrix of regressor associated with gamma.
#' @param ninits number of randomly drawn initial values.
#' @param crit.method method used to compute the critical values, one of \code{"none"},
#' \code{"asy"}, and \code{"boot"}. The default option is \code{"none"}. When \code{method = "asy"},
#' the p-values are computed using the asymptotic critical values. When \code{method = "boot"},
#' the p-values are computed by bootstrap.
#' @param nbtsp The number of bootstrap replicates. Default is 199.
#' @param cl cluster used for parallelization; if it is \code{NULL}, the system
#' will automatically generate a new one for computation accordingly.
#' @param parallel Determines what percentage of available cores are used,
#' represented by a double in [0,1]. Default is 1.
#' @return A list of class \code{normalregMix} with items:
#' \item{lrtstat}{the value of the LRT statistic.}
#' \item{pvals}{p-value.}
#' \item{crit}{vector of critical values at the 0.1, 0.05, 0.01 level.}
#' \item{crit.method}{method used to compute the variance-covariance matrix.}
#' \item{parlist}{parameter estimates as a list containing alpha, mubeta, and sigma (and gam if z is included in the model).}
#' \item{call}{The matched call.}
#' \item{m}{number of components in the mixture defined by the null hypothesis, \eqn{m_0}.}
#' @examples
#' data(faithful)
#' attach(faithful)
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 1, crit.method = "none")}
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 2, crit.method = "none")}
subgroupEM_homo <- function (y, x, v, m = 1, z = NULL, ninits = 10, k = 9,
crit.method = c("none", "boot"), nbtsp = 199,
cl = NULL, parallel = 1) {
y <- as.vector(y)
x <- as.matrix(x)
v <- as.matrix(v)
n <- length(y)
if (!is.null(z))
z <- as.matrix(z)
subgroup.mle.result <- subgroupMLE_homo(y=y, x=x, v=v, m=m, z=z, vcov.method="none", ninits=ninits)
loglik0 <- subgroup.mle.result$loglik
ninits2 = floor(ninits/2)
subgroup.em.result1 <- subgroupMLE_homo_EM(y=y, x=x, v=v, m=m+1, z=z, k = k, tauinit=c(1,-2),
vcov.method="none", ninits=ninits2)
loglik1 <- subgroup.em.result1$loglik
subgroup.em.result2 <- subgroupMLE_homo_EM(y=y, x=x, v=v, m=m+1, z=z, k = k, tauinit=c(1,2),
vcov.method="none", ninits=ninits2)
loglik2 <- subgroup.em.result2$loglik
loglikem <- max(c(loglik1, loglik2))
lrtstat <- 2*(loglikem - loglik0)
if (crit.method == "boot") {
result <-subgroupCritBoot_homo_EM(y=y, x=x, v=v, parlist=subgroup.mle.result$parlist, z=z, values=lrtstat,
k=k, ninits=ninits, nbtsp=nbtsp, parallel=parallel, cl=cl)
} else {
result <- list()
result$crit <- result$pvals <- NA
}
a <- list(lrtstat = lrtstat, pvals = result$pvals, crit = result$crit, parlist = subgroup.mle.result$parlist,
call = match.call(), m = m, crit.method = crit.method, nbtsp = nbtsp)
# ,label = "MEMtest")
# class(a) <- "normalregMix"
a
} # end subgroupEM_homo
#' @description Performs the LRT statistic given the data for y and x on
#' the null hypothesis H_0: m = m_0. Using this function is equivalent to
#' calling normalmixLRT with regressors specified by x as a parameter.
#' @export
#' @title subgroupLRT_homo
#' @name subgroupLRT_homo
#' @param y n by 1 vector of data for y.
#' @param x n by q1 matrix of data for x.
#' @param v n by q2 matrix of data for v
#' @param m number of components in the mixture defined by a null hypothesis, m_0.
#' @param z n by p matrix of regressor associated with gamma.
#' @param ninits number of randomly drawn initial values.
#' @param crit.method method used to compute the critical values, one of \code{"none"},
#' \code{"asy"}, and \code{"boot"}. The default option is \code{"none"}. When \code{method = "asy"},
#' the p-values are computed using the asymptotic critical values. When \code{method = "boot"},
#' the p-values are computed by bootstrap.
#' @param nbtsp The number of bootstrap replicates. Default is 199.
#' @param cl cluster used for parallelization; if it is \code{NULL}, the system
#' will automatically generate a new one for computation accordingly.
#' @param parallel Determines what percentage of available cores are used,
#' represented by a double in [0,1]. Default is 1.
#' @return A list of class \code{normalregMix} with items:
#' \item{lrtstat}{the value of the LRT statistic.}
#' \item{pvals}{p-value.}
#' \item{crit}{vector of critical values at the 0.1, 0.05, 0.01 level.}
#' \item{crit.method}{method used to compute the variance-covariance matrix.}
#' \item{parlist}{parameter estimates as a list containing alpha, mubeta, and sigma (and gam if z is included in the model).}
#' \item{call}{The matched call.}
#' \item{m}{number of components in the mixture defined by the null hypothesis, \eqn{m_0}.}
#' @examples
#' data(faithful)
#' attach(faithful)
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 1, crit.method = "none")}
#' \dontrun{regmixLRT_homo(y = eruptions, x = waiting, m = 2, crit.method = "none")}
subgroupLRT_homo <- function (y, x, v, m = 1, z = NULL, ninits = 10,
crit.method = c("none", "boot"), nbtsp = 199,
tauinit = c(0,0), cl = NULL, parallel = 1) {
y <- as.vector(y)
x <- as.matrix(x)
v <- as.matrix(v)
n <- length(y)
if (!is.null(z))
z <- as.matrix(z)
subgroup.mle.result <- subgroupMLE_homo(y=y, x=x, v=v, m=m, z=z, vcov.method="none", ninits=ninits)
loglik0 <- subgroup.mle.result$loglik
subgroup.mle.result1 <- subgroupMLE_homo(y=y, x=x, v=v, m=m+1, z=z,
tauinit = tauinit,
vcov.method="none", ninits=ninits)
loglik1 <- subgroup.mle.result1$loglik
lrtstat <- 2*(loglik1-loglik0)
if (crit.method == "boot") {
result <-subgroupCritBoot_homo(y=y, x=x, v=v, parlist=subgroup.mle.result$parlist, z=z, values=lrtstat,
tauinit = tauinit, ninits=ninits, nbtsp=nbtsp, parallel=parallel, cl=cl)
} else {
result <- list()
result$crit <- result$pvals <- NA
}
a <- list(lrtstat = lrtstat, pvals = result$pvals, crit = result$crit, parlist = subgroup.mle.result$parlist,
call = match.call(), m = m, crit.method = crit.method, nbtsp = nbtsp)
# ,label = "MEMtest")
# class(a) <- "normalregMix"
a
} # end subgroupLRT_homo
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