# Rank parameter estimates Only parameters that vary by agent/class can be ranked. ```r ## S3 method for class 'mbnma' rank( x, params = NULL, lower_better = TRUE, level = "agent", to.rank = NULL, ... ) ``` ## Arguments - `x`: An object on which to apply the rank method - `params`: A character vector of named parameters in the model that vary by either agent or class (depending on the value assigned to `level`). If left as `NULL` (the default), then ranking will be calculated for all available parameters that vary by agent/class. - `lower_better`: Indicates whether negative responses are better (`TRUE`) or positive responses are better (`FALSE`) - `level`: Can be set to `"agent"` to rank across different agents or `"class"` to rank across different classes. - `to.rank`: A numeric vector containing the codes for the agents/classes you wish to rank. If left `NULL` then all agents/classes (depending on the value assigned to `level`) in the model will be ranked. Included codes must be greater than `2` if placebo has been modelled, since placebo cannot be included in the ranking - `...`: Arguments to be passed to methods ## Returns An object of `class("mbnma.rank")` which is a list containing a summary data frame, a matrix of rankings for each MCMC iteration, a matrix of probabilities that each agent has a particular rank, and a matrix of cumulative ranking probabilities for each agent, for each parameter that has been ranked. ## Details Ranking cannot currently be performed on non-parametric dose-response MBNMA ## Examples ```r # Using the triptans data network <- mbnma.network(triptans) # Rank selected agents from a log-linear dose-response MBNMA loglin <- mbnma.run(network, fun=dloglin()) ranks <- rank(loglin, to.rank=c("zolmitriptan", "eletriptan", "sumatriptan")) summary(ranks) # Rank only ED50 parameters from an Emax dose-response MBNMA emax <- mbnma.run(network, fun=demax(), method="random") ranks <- rank(emax, params="ed50") summary(ranks) #### Ranking by class #### # Generate some classes for the data class.df <- triptans class.df$class <- ifelse(class.df$agent=="placebo", "placebo", "active1") class.df$class <- ifelse(class.df$agent=="eletriptan", "active2", class.df$class) netclass <- mbnma.network(class.df) emax <- mbnma.run(netclass, fun=demax(), method="random", class.effect=list("ed50"="common")) # Rank by class, with negative responses being worse ranks <- rank(emax, level="class", lower_better=FALSE) print(ranks) # Print and generate summary data frame for `mbnma.rank` object summary(ranks) print(ranks) # Plot `mbnma.rank` object plot(ranks) ```

rank.mbnma function