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Diffstat (limited to 'R/confint.mkinfit.R')
| -rw-r--r-- | R/confint.mkinfit.R | 100 |
1 files changed, 82 insertions, 18 deletions
diff --git a/R/confint.mkinfit.R b/R/confint.mkinfit.R index 8467a85b..75813360 100644 --- a/R/confint.mkinfit.R +++ b/R/confint.mkinfit.R @@ -22,15 +22,18 @@ #' @param backtransform If we approximate the likelihood in terms of the #' transformed parameters, should we backtransform the parameters with #' their confidence intervals? -#' @param distribution For the quadratic approximation, should we use -#' the student t distribution or assume normal distribution for -#' the parameter estimate -#' @param quiet Should we suppress messages? +#' @param cores The number of cores to be used for multicore processing. This +#' is only used when the \code{cluster} argument is \code{NULL}. On Windows +#' machines, cores > 1 is not supported. +#' @param quiet Should we suppress the message "Profiling the likelihood" #' @return A matrix with columns giving lower and upper confidence limits for #' each parameter. #' @param \dots Not used #' @importFrom stats qnorm -#' @references Pawitan Y (2013) In all likelihood - Statistical modelling and +#' @references +#' Bates DM and Watts GW (1988) Nonlinear regression analysis & its applications +#' +#' Pawitan Y (2013) In all likelihood - Statistical modelling and #' inference using likelihood. Clarendon Press, Oxford. #' #' Venzon DJ and Moolgavkar SH (1988) A Method for Computing @@ -39,15 +42,78 @@ #' @examples #' f <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE) #' confint(f, method = "quadratic") +#' #' \dontrun{ -#' confint(f, method = "profile") +#' confint(f, method = "profile") +#' +#' SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), quiet = TRUE) +#' SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), +#' use_of_ff = "max", quiet = TRUE) +#' f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE) +#' system.time(ci_profile <- confint(f_d_1, cores = 1, quiet = TRUE)) +#' # The following does not save much time, as parent_0 takes up most of the time +#' # system.time(ci_profile <- confint(f_d_1, cores = 5)) +#' # system.time(ci_profile <- confint(f_d_1, +#' # c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = 1)) +#' # If we exclude parent_0 (the confidence of which is often of minor interest), we get a nice +#' # performance improvement from about 30 seconds to about 12 seconds +#' # system.time(ci_profile_no_parent_0 <- confint(f_d_1, c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = 4)) +#' ci_profile +#' ci_quadratic_transformed <- confint(f_d_1, method = "quadratic") +#' ci_quadratic_transformed +#' ci_quadratic_untransformed <- confint(f_d_1, method = "quadratic", transformed = FALSE) +#' ci_quadratic_untransformed +#' # Against the expectation based on Bates and Watts (1988), the confidence +#' # intervals based on the internal parameter transformation are less +#' # congruent with the likelihood based intervals. Note the superiority of the +#' # interval based on the untransformed fit for k_m1_sink +#' rel_diffs_transformed <- abs((ci_quadratic_transformed - ci_profile)/ci_profile) +#' rel_diffs_untransformed <- abs((ci_quadratic_untransformed - ci_profile)/ci_profile) +#' rel_diffs_transformed +#' rel_diffs_untransformed +#' +#' # Set the number of cores for further examples +#' if (identical(Sys.getenv("NOT_CRAN"), "true")) { +#' n_cores <- parallel::detectCores() - 1 +#' } else { +#' n_cores <- 1 +#' } +#' if (Sys.getenv("TRAVIS") != "") n_cores = 1 +#' if (Sys.info()["sysname"] == "Windows") n_cores = 1 +#' +#' # Investigate a case with formation fractions +#' f_d_2 <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE) +#' ci_profile_ff <- confint(f_d_2, cores = n_cores) +#' ci_profile_ff +#' ci_quadratic_transformed_ff <- confint(f_d_2, method = "quadratic") +#' ci_quadratic_transformed_ff +#' ci_quadratic_untransformed_ff <- confint(f_d_2, method = "quadratic", transformed = FALSE) +#' ci_quadratic_untransformed_ff +#' rel_diffs_transformed_ff <- abs((ci_quadratic_transformed_ff - ci_profile_ff)/ci_profile_ff) +#' rel_diffs_untransformed_ff <- abs((ci_quadratic_untransformed_ff - ci_profile_ff)/ci_profile_ff) +#' # While the confidence interval for the parent rate constant is closer to +#' # the profile based interval when using the internal parameter +#' # transformation, the intervals for the other parameters are 'better +#' # without internal parameter transformation. +#' rel_diffs_transformed_ff +#' rel_diffs_untransformed_ff +#' +#' # The profiling for the following fit does not finish in a reasonable time +#' #m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")), +#' # M1 = mkinsub("SFO"), +#' # M2 = mkinsub("SFO"), +#' # use_of_ff = "max", quiet = TRUE) +#' #DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data +#' #f_tc_2 <- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = "tc", +#' # error_model_algorithm = "direct", quiet = TRUE) +#' #confint(f_tc_2, "parent_0") #' } #' @export confint.mkinfit <- function(object, parm, level = 0.95, alpha = 1 - level, cutoff, method = c("profile", "quadratic"), transformed = TRUE, backtransform = TRUE, - distribution = c("student_t", "normal"), quiet = FALSE, ...) + cores = round(detectCores()/2), quiet = FALSE, ...) { tparms <- parms(object, transformed = TRUE) bparms <- parms(object, transformed = FALSE) @@ -68,11 +134,7 @@ confint.mkinfit <- function(object, parm, if (method == "quadratic") { - distribution <- match.arg(distribution) - - quantiles <- switch(distribution, - student_t = qt(a, object$df.residual), - normal = qnorm(a)) + quantiles <- qt(a, object$df.residual) covar_pnames <- if (missing(parm)) { if (transformed) tpnames else bpnames @@ -99,7 +161,7 @@ confint.mkinfit <- function(object, parm, ses <- sqrt(diag(covar))[covar_pnames] lci <- covar_parms + quantiles[1] * ses uci <- covar_parms + quantiles[2] * ses - if (backtransform) { + if (transformed & backtransform) { lci_back <- backtransform_odeparms(lci, object$mkinmod, object$transform_rates, object$transform_fractions) lci <- c(lci_back, lci[names(object$errparms)]) @@ -108,6 +170,7 @@ confint.mkinfit <- function(object, parm, uci <- c(uci_back, uci[names(object$errparms)]) } } + ci <- cbind(lower = lci, upper = uci) } if (method == "profile") { @@ -125,8 +188,7 @@ confint.mkinfit <- function(object, parm, all_parms <- parms(object) - for (pname in profile_pnames) - { + get_ci <- function(pname) { pnames_free <- setdiff(names(all_parms), pname) profile_ll <- function(x) { @@ -143,12 +205,14 @@ confint.mkinfit <- function(object, parm, (cutoff - (object$logLik - profile_ll(x)))^2 } - lci[pname] <- optimize(cost, lower = 0, upper = all_parms[pname])$minimum - uci[pname] <- optimize(cost, lower = all_parms[pname], upper = 15 * all_parms[pname])$minimum + lci_pname <- optimize(cost, lower = 0, upper = all_parms[pname])$minimum + uci_pname <- optimize(cost, lower = all_parms[pname], + upper = ifelse(grepl("^f_|^g$", pname), 1, 15 * all_parms[pname]))$minimum + return(c(lci_pname, uci_pname)) } + ci <- t(parallel::mcmapply(get_ci, profile_pnames, mc.cores = cores)) } - ci <- cbind(lower = lci, upper = uci) colnames(ci) <- paste0( format(100 * a, trim = TRUE, scientific = FALSE, digits = 3), "%") |