# R/lm.R In mice: Multivariate Imputation by Chained Equations

#### Documented in glm.midslm.mids

#' Linear regression for \code{mids} object
#'
#' Applies \code{lm()} to multiply imputed data set
#'
#' This function is included for backward compatibility with V1.0. The function
#'
#' @param formula a formula object, with the response on the left of a ~
#' operator, and the terms, separated by + operators, on the right. See the
#' @param data An object of type 'mids', which stands for 'multiply imputed data
#' set', typically created by a call to function \code{mice()}.
#' @return An objects of class \code{mira}, which stands for 'multiply imputed
#' repeated analysis'.  This object contains \code{data$m} distinct #' \code{lm.objects}, plus some descriptive information. #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 #' @seealso \code{\link{lm}}, \code{\link[=mids-class]{mids}}, \code{\link[=mira-class]{mira}} #' @references Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: #' Multivariate Imputation by Chained Equations in \code{R}. \emph{Journal of #' Statistical Software}, \bold{45}(3), 1-67. #' \doi{10.18637/jss.v045.i03} #' @keywords multivariate #' @examples #' imp <- mice(nhanes) #' fit <- lm.mids(bmi ~ hyp + chl, data = imp) #' fit #' @export lm.mids <- function(formula, data, ...) { .Deprecated("with", msg = "Use with(imp, lm(yourmodel)." ) # adapted 28/1/00 repeated complete data regression (lm) on a mids data set call <- match.call() if (!is.mids(data)) { stop("The data must have class mids") } analyses <- lapply(seq_len(data$m), function(i) lm(formula, data = complete(data, i), ...))
# return the complete data analyses as a list of length nimp
object <- list(call = call, call1 = data$call, nmis = data$nmis, analyses = analyses)
oldClass(object) <- c("mira", "lm") ## FEH
object
}

#' Generalized linear model for \code{mids} object
#'
#' Applies \code{glm()} to a multiply imputed data set
#'
#' This function is included for backward compatibility with V1.0. The function
#'
#' @param formula a formula expression as for other regression models, of the
#' form response ~ predictors. See the documentation of \code{\link{lm}} and
#' @param family The family of the glm model
#' @param data An object of type \code{mids}, which stands for 'multiply imputed
#' data set', typically created by function \code{mice()}.
#' @return An objects of class \code{mira}, which stands for 'multiply imputed
#' repeated analysis'.  This object contains \code{data$m} distinct #' \code{glm.objects}, plus some descriptive information. #' @author Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 #' @seealso \code{\link{with.mids}}, \code{\link{glm}}, \code{\link[=mids-class]{mids}}, #' \code{\link[=mira-class]{mira}} #' @references Van Buuren, S., Groothuis-Oudshoorn, C.G.M. (2000) #' \emph{Multivariate Imputation by Chained Equations: MICE V1.0 User's manual.} #' Leiden: TNO Quality of Life. #' @keywords multivariate #' @examples #' #' imp <- mice(nhanes) #' #' # logistic regression on the imputed data #' fit <- glm.mids((hyp == 2) ~ bmi + chl, data = imp, family = binomial) #' fit #' @export glm.mids <- function(formula, family = gaussian, data, ...) { .Deprecated("with", msg = "Use with(imp, glm(yourmodel)." ) # adapted 04/02/00 repeated complete data regression (glm) on a mids data set call <- match.call() if (!is.mids(data)) { stop("The data must have class mids") } analyses <- lapply( seq_len(data$m),
function(i) glm(formula, family = family, data = complete(data, i), ...)
)
# return the complete data analyses as a list of length nimp
object <- list(call = call, call1 = data$call, nmis = data$nmis, analyses = analyses)
oldClass(object) <- c("mira", "glm", "lm") ## FEH
object
}


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mice documentation built on Nov. 19, 2022, 5:06 p.m.