diff --git a/R/methods-utils.R b/R/methods-utils.R
index cd9f060..ab8e3f6 100644
--- a/R/methods-utils.R
+++ b/R/methods-utils.R
@@ -1,12 +1,9 @@
 #' Methods for FAERS class
 #'
-#' Utils function for [FAERS] class.
-#' @param object A [FAERS] object.
-#' @param ... Other arguments passed to specific methods.
-#'  - `faers_filter`: other arguments passed to `.fn`.
-#'  - `faers_phv_table`: other arguments passed to `faers_filter` and `...` is
-#'    solely used when `interested` is `NULL`.
-#'  - `faers_phv_signal`: other arguments passed to `faers_phv_table`.
+#' Utils function for [FAERSascii] class.
+#' @param object A [FAERSascii] object.
+#' @param ... Other arguments passed to specific methods. For `faers_filter`:
+#' other arguments passed to `.fn`.
 #' @details
 #'  - `faers_get`, `[[`, `$`, and `[`: Extract a specific field
 #'    [data.table][data.table::data.table] or a list of field
@@ -18,11 +15,6 @@
 #'    better to run [faers].
 #'  - `faers_filter`: apply a function to extract wanted `primaryid`, then use
 #'    `faers_keep` to filter.
-#'  - `faers_phv_table`: build a contingency table for all events in
-#'    `interested_event`.
-#'    column.
-#'  - `faers_phv_signal`: Pharmacovigilance Analysis used contingency table
-#'    constructed with `faers_phv_table`. Details see [phv_signal].
 #' @export
 #' @rdname FAERS-methods
 methods::setGeneric("faers_get", function(object, ...) {
@@ -154,165 +146,6 @@ methods::setMethod("faers_filter", "FAERSascii", function(object, .fn, ..., fiel
     faers_keep(object, primaryid = ids)
 })
 
-##############################################################
-#' @export
-#' @rdname FAERS-methods
-methods::setGeneric(
-    "faers_phv_table",
-    function(object, ..., interested, object2) {
-        methods::makeStandardGeneric("faers_phv_table")
-    }
-)
-
-#' @param interested_field A string indicates the interested FAERS fields to
-#' use. Only values "demo", "drug", "indi", "ther", "reac", "rpsr", and "outc"
-#' can be used.
-#' @param interested_event A character specify the events column(s?) in field of
-#' object specified in `interested_field`. If multiple columns were selected,
-#' the unique combination will define the interested events.
-#' @param interested A [FAERSascii] object with data from interested drug, must
-#' be a subset of `object`. If `interested` and `object2` are both `missing`,
-#' the `faers_filter` function will be employed to extract data for the drug of
-#' interest from the `object`. The value `n11` or `a` will be calculated from
-#' `interested` .
-#' @rdname FAERS-methods
-methods::setMethod(
-    "faers_phv_table",
-    c(object = "FAERSascii", interested = "missing", object2 = "missing"),
-    function(object, interested_field = "reac", interested_event = "soc_name", ..., interested, object2) {
-        if (!object@standardization) {
-            cli::cli_abort("{.arg object} must be standardized using {.fn faers_standardize}")
-        }
-        interested <- faers_filter(object, ...)
-        faers_phv_table(
-            object = object, interested_field = interested_field,
-            interested_event = interested_event,
-            interested = interested
-        )
-    }
-)
-
-#' @rdname FAERS-methods
-methods::setMethod(
-    "faers_phv_table",
-    c(object = "FAERSascii", interested = "FAERSascii", object2 = "missing"),
-    function(object, interested_field = "reac", interested_event = "soc_name", interested, object2) {
-        if (!object@standardization) {
-            cli::cli_abort("{.arg object} must be standardized using {.fn faers_standardize}")
-        }
-        if (!interested@standardization) {
-            cli::cli_abort("{.arg interested} must be standardized using {.fn faers_standardize}")
-        }
-        full_primaryids <- faers_primaryid(object)
-        interested_primaryids <- faers_primaryid(interested)
-        if (!all(interested_primaryids %in% full_primaryids)) {
-            cli::cli_abort("Provided {.arg interested} data must be a subset of {.arg object}")
-        }
-        full_data <- faers_get(object, field = interested_field)
-        interested_data <- faers_get(interested, field = interested_field)
-
-        n <- nrow(full_data) # scalar
-        n1. <- nrow(interested_data) # scalar
-        out <- merge(
-            eval(substitute(
-                full_data[, list(n.1 = .N), by = interested_event],
-                list(interested_event = interested_event)
-            )),
-            eval(substitute(
-                interested_data[, list(a = .N), by = interested_event],
-                list(interested_event = interested_event)
-            )),
-            by = interested_event, all = TRUE, allow.cartesian = TRUE
-        )
-        out[, a := data.table::fifelse(is.na(a), 0L, a)] # nolint
-        out[, b := n1. - a] # nolint
-        out[, c := n.1 - a] # nolint
-        out[, d := n - (n1. + n.1 - a)] # nolint
-        out <- out[, !"n.1"]
-        data.table::setcolorder(out, c(interested_event, "a", "b", "c", "d"))[]
-    }
-)
-
-#' @param object2 A [FAERSascii] object with data from another interested drug,
-#' In this way, `object` and `object2` should be not overlapped. The value `n11`
-#' or `a` will be calculated from `object`
-#' @rdname FAERS-methods
-methods::setMethod(
-    "faers_phv_table",
-    c(object = "FAERSascii", interested = "missing", object2 = "FAERSascii"),
-    function(object, interested_event = "soc_name", interested, object2) {
-        if (!object@standardization) {
-            cli::cli_abort("{.arg object} must be standardized using {.fn faers_standardize}")
-        }
-        if (!object2@standardization) {
-            cli::cli_abort("{.arg object2} must be standardized using {.fn faers_standardize}")
-        }
-        primaryids <- faers_primaryid(object)
-        primaryids2 <- faers_primaryid(object2)
-        overlapped_idx <- primaryids %in% primaryids2
-        if (any(overlapped_idx)) {
-            cli::cli_warn("{.val {overlapped_idx}} report{?s} are overlapped between {.arg object} and {.arg object2}")
-        }
-        interested_reac <- faers_get(object, field = "reac")
-        interested_reac2 <- faers_get(object2, field = "reac")
-        n1. <- nrow(interested_reac)
-        n0. <- nrow(interested_reac2)
-        out <- merge(
-            eval(substitute(
-                interested_reac[, list(a = .N), by = interested_event],
-                list(interested_event = interested_event)
-            )),
-            eval(substitute(
-                interested_reac2[, list(c = .N), by = interested_event],
-                list(interested_event = interested_event)
-            )),
-            by = interested_event, all = TRUE, allow.cartesian = TRUE
-        )
-        out[, c("a", "c") := lapply(.SD, function(x) {
-            data.table::fifelse(is.na(x), 0L, x)
-        }), .SDcols = c("a", "c")]
-        out[, b := n1. - a] # nolint
-        out[, d := n0. - c] # nolint
-        data.table::setcolorder(out, c(interested_event, "a", "b", "c", "d"))[]
-    }
-)
-
-utils::globalVariables(c("a", "b", "d", "n.1"))
-
-#' @rdname FAERS-methods
-methods::setMethod(
-    "faers_phv_table",
-    c(object = "FAERSascii", interested = "FAERSascii", object2 = "FAERSascii"),
-    function(object, interested, object2) {
-        cli::cli_abort("{.arg interested} and {.arg object2} are both exclusive, must be provided only one or none")
-    }
-)
-
-##############################################################
-#' @export
-#' @rdname FAERS-methods
-methods::setGeneric("faers_phv_signal", function(object, ...) {
-    methods::makeStandardGeneric("faers_phv_signal")
-})
-
-#' @inheritParams phv_signal
-#' @seealso [phv_signal]
-#' @method faers_phv_signal FAERSascii
-#' @rdname FAERS-methods
-methods::setMethod("faers_phv_signal", "FAERSascii", function(object, ..., methods = NULL, alpha = 0.05, correct = TRUE, n_mcmc = 1e5L, alpha1 = 0.5, alpha2 = 0.5) {
-    out <- faers_phv_table(object, ...)
-    cbind(
-        out,
-        do.call(
-            phv_signal,
-            c(out[, .SD, .SDcols = c("a", "b", "c", "d")], list(
-                methods = methods, alpha = alpha, correct = correct,
-                n_mcmc = n_mcmc, alpha1 = alpha1, alpha2 = alpha2
-            ))
-        )
-    )
-})
-
 #########################################################
 use_indices <- function(i, names, arg = rlang::caller_arg(i), call = rlang::caller_env()) {
     if (anyNA(i)) {
diff --git a/R/table.R b/R/table.R
new file mode 100644
index 0000000..6fe3ca0
--- /dev/null
+++ b/R/table.R
@@ -0,0 +1,201 @@
+#' Create contingency table and run disproportionality analysis
+#' @details
+#'  - `faers_phv_table`: build a contingency table for all events in
+#'    `interested_event`.
+#'  - `faers_phv_signal`: Pharmacovigilance Analysis used contingency table
+#'    constructed with `faers_phv_table`. Details see [phv_signal].
+#' @param object A [FAERSascii] object.
+#' @param ... Other arguments passed to specific methods.
+#'  - `faers_phv_table`: other arguments passed to `interested_fn`.
+#'  - `faers_phv_signal`: other arguments passed to `faers_phv_table`.
+#' @export
+#' @aliases faers_phv_table
+#' @name faers_phv_signal
+methods::setGeneric(
+    "faers_phv_table",
+    function(object, ..., interested, object2) {
+        methods::makeStandardGeneric("faers_phv_table")
+    }
+)
+
+
+#' @param interested_field A string indicates the interested FAERS fields to
+#' use. Only values "demo", "drug", "indi", "ther", "reac", "rpsr", and "outc"
+#' can be used.
+#' @param interested_event A character specify the events column(s?) in field of
+#' object specified in `interested_field`. If multiple columns were selected,
+#' the unique combination will define the interested events.
+#' @param filter_params Other arguments passed to [faers_filter], solely used
+#' when `interested` and `object2` are both `missing`
+#' @param interested_fn A function or formula defined the preprocessing function
+#' before creating contingency table, with the `interested_field` data as the
+#' input and return a [data.table][data.table::data.table].
+#'
+#'   If a **function**, it is used as is.
+#'
+#'   If a **formula**, e.g. `~ .x + 2`, it is converted to a function with up to
+#'   two arguments: `.x` (single argument) or `.x` and `.y` (two arguments). The
+#'   `.` placeholder can be used instead of `.x`.  This allows you to create
+#'   very compact anonymous functions (lambdas) with up to two inputs.
+#'
+#'   If a **string**, the function is looked up in `globalenv()`.
+#' @param interested A [FAERSascii] object with data from interested exposure
+#' (usually drug), must be a subset of `object`. If `interested` and `object2`
+#' are both `missing`, the [faers_filter] function will be employed to extract
+#' data for the exposure of interest from the `object`. Then the extracted
+#' `interested` will be passed again to `faers_phv_table` in the method of
+#' [FAERSascii] object in `interested`. The value `n11` or `a` will be
+#' calculated from `interested`.
+#' @rdname faers_phv_signal
+methods::setMethod(
+    "faers_phv_table",
+    c(object = "FAERSascii", interested = "missing", object2 = "missing"),
+    function(object, ..., filter_params = list(), interested, object2) {
+        if (!object@standardization) {
+            cli::cli_abort("{.arg object} must be standardized using {.fn faers_standardize}")
+        }
+        interested <- do.call(
+            faers_filter,
+            c(list(object = object), filter_params)
+        )
+        faers_phv_table(object = object, ..., interested = interested)
+    }
+)
+
+#' @rdname faers_phv_signal
+methods::setMethod(
+    "faers_phv_table",
+    c(object = "FAERSascii", interested = "FAERSascii", object2 = "missing"),
+    function(object, interested_field = "reac", interested_event = "soc_name", interested_fn = NULL, ..., interested, object2) {
+        if (!object@standardization) {
+            cli::cli_abort("{.arg object} must be standardized using {.fn faers_standardize}")
+        }
+        if (!interested@standardization) {
+            cli::cli_abort("{.arg interested} must be standardized using {.fn faers_standardize}")
+        }
+        full_primaryids <- faers_primaryid(object)
+        interested_primaryids <- faers_primaryid(interested)
+        if (!all(interested_primaryids %in% full_primaryids)) {
+            cli::cli_abort("Provided {.arg interested} data must be a subset of {.arg object}")
+        }
+        full_data <- faers_get(object, field = interested_field)
+        interested_data <- faers_get(interested, field = interested_field)
+        if (!is.null(interested_fn)) {
+            interested_fn <- rlang::as_function(interested_fn)
+            full_data <- interested_fn(full_data, ...)
+            interested_data <- interested_fn(interested_data, ...)
+            if (!(data.table::is.data.table(interested_data) ||
+                data.table::is.data.table(full_data))) {
+                cli::cli_abort("{.fn interested_fn} must return an {.cls data.table}")
+            }
+        }
+        n <- nrow(full_data) # scalar
+        n1. <- nrow(interested_data) # scalar
+        out <- merge(
+            eval(substitute(
+                full_data[, list(n.1 = .N), by = interested_event],
+                list(interested_event = interested_event)
+            )),
+            eval(substitute(
+                interested_data[, list(a = .N), by = interested_event],
+                list(interested_event = interested_event)
+            )),
+            by = interested_event, all = TRUE, allow.cartesian = TRUE
+        )
+        out[, a := data.table::fifelse(is.na(a), 0L, a)] # nolint
+        out[, b := n1. - a] # nolint
+        out[, c := n.1 - a] # nolint
+        out[, d := n - (n1. + n.1 - a)] # nolint
+        out <- out[, !"n.1"]
+        data.table::setcolorder(out, c(interested_event, "a", "b", "c", "d"))[]
+    }
+)
+
+#' @param object2 A [FAERSascii] object with data from another interested drug,
+#' In this way, `object` and `object2` should be not overlapped. The value `n11`
+#' or `a` will be calculated from `object`
+#' @rdname faers_phv_signal
+methods::setMethod(
+    "faers_phv_table",
+    c(object = "FAERSascii", interested = "missing", object2 = "FAERSascii"),
+    function(object, interested_field = "reac", interested_event = "soc_name", interested_fn = NULL, ..., interested, object2) {
+        if (!object@standardization) {
+            cli::cli_abort("{.arg object} must be standardized using {.fn faers_standardize}")
+        }
+        if (!object2@standardization) {
+            cli::cli_abort("{.arg object2} must be standardized using {.fn faers_standardize}")
+        }
+        primaryids <- faers_primaryid(object)
+        primaryids2 <- faers_primaryid(object2)
+        overlapped_idx <- primaryids %in% primaryids2
+        if (any(overlapped_idx)) {
+            cli::cli_warn("{.val {overlapped_idx}} report{?s} are overlapped between {.arg object} and {.arg object2}")
+        }
+        interested_reac <- faers_get(object, field = interested_field)
+        interested_reac2 <- faers_get(object2, field = interested_field)
+        if (!is.null(interested_fn)) {
+            interested_fn <- rlang::as_function(interested_fn)
+            interested_reac <- interested_fn(interested_reac, ...)
+            interested_reac2 <- interested_fn(interested_reac2, ...)
+            if (!(data.table::is.data.table(interested_reac) ||
+                data.table::is.data.table(interested_reac2))) {
+                cli::cli_abort("{.arg interested_fn} must return an {.cls data.table}")
+            }
+        }
+        n1. <- nrow(interested_reac)
+        n0. <- nrow(interested_reac2)
+        out <- merge(
+            eval(substitute(
+                interested_reac[, list(a = .N), by = interested_event],
+                list(interested_event = interested_event)
+            )),
+            eval(substitute(
+                interested_reac2[, list(c = .N), by = interested_event],
+                list(interested_event = interested_event)
+            )),
+            by = interested_event, all = TRUE, allow.cartesian = TRUE
+        )
+        out[, c("a", "c") := lapply(.SD, function(x) {
+            data.table::fifelse(is.na(x), 0L, x)
+        }), .SDcols = c("a", "c")]
+        out[, b := n1. - a] # nolint
+        out[, d := n0. - c] # nolint
+        data.table::setcolorder(out, c(interested_event, "a", "b", "c", "d"))[]
+    }
+)
+
+utils::globalVariables(c("a", "b", "d", "n.1"))
+
+#' @rdname faers_phv_signal
+methods::setMethod(
+    "faers_phv_table",
+    c(object = "FAERSascii", interested = "FAERSascii", object2 = "FAERSascii"),
+    function(object, interested, object2) {
+        cli::cli_abort("{.arg interested} and {.arg object2} are both exclusive, must be provided only one or none")
+    }
+)
+
+##############################################################
+#' @export
+#' @rdname faers_phv_signal
+methods::setGeneric("faers_phv_signal", function(object, ...) {
+    methods::makeStandardGeneric("faers_phv_signal")
+})
+
+#' @inheritParams phv_signal
+#' @seealso [phv_signal]
+#' @method faers_phv_signal FAERSascii
+#' @rdname faers_phv_signal
+methods::setMethod("faers_phv_signal", "FAERSascii", function(object, ..., methods = NULL, alpha = 0.05, correct = TRUE, n_mcmc = 1e5L, alpha1 = 0.5, alpha2 = 0.5) {
+    out <- faers_phv_table(object, ...)
+    cbind(
+        out,
+        do.call(
+            phv_signal,
+            c(out[, .SD, .SDcols = c("a", "b", "c", "d")], list(
+                methods = methods, alpha = alpha, correct = correct,
+                n_mcmc = n_mcmc, alpha1 = alpha1, alpha2 = alpha2
+            ))
+        )
+    )
+})
diff --git a/man/FAERS-methods.Rd b/man/FAERS-methods.Rd
index 1bf0b95..d7b46de 100644
--- a/man/FAERS-methods.Rd
+++ b/man/FAERS-methods.Rd
@@ -13,13 +13,6 @@
 \alias{faers_keep,FAERSascii-method}
 \alias{faers_filter}
 \alias{faers_filter,FAERSascii-method}
-\alias{faers_phv_table}
-\alias{faers_phv_table,FAERSascii,missing,missing-method}
-\alias{faers_phv_table,FAERSascii,FAERSascii,missing-method}
-\alias{faers_phv_table,FAERSascii,missing,FAERSascii-method}
-\alias{faers_phv_table,FAERSascii,FAERSascii,FAERSascii-method}
-\alias{faers_phv_signal}
-\alias{faers_phv_signal,FAERSascii-method}
 \title{Methods for FAERS class}
 \usage{
 faers_get(object, ...)
@@ -43,53 +36,12 @@ faers_keep(object, ...)
 faers_filter(object, ...)
 
 \S4method{faers_filter}{FAERSascii}(object, .fn, ..., field = NULL)
-
-faers_phv_table(object, ..., interested, object2)
-
-\S4method{faers_phv_table}{FAERSascii,missing,missing}(
-  object,
-  interested_field = "reac",
-  interested_event = "soc_name",
-  ...,
-  interested,
-  object2
-)
-
-\S4method{faers_phv_table}{FAERSascii,FAERSascii,missing}(
-  object,
-  interested_field = "reac",
-  interested_event = "soc_name",
-  interested,
-  object2
-)
-
-\S4method{faers_phv_table}{FAERSascii,missing,FAERSascii}(object, interested_event = "soc_name", interested, object2)
-
-\S4method{faers_phv_table}{FAERSascii,FAERSascii,FAERSascii}(object, interested, object2)
-
-faers_phv_signal(object, ...)
-
-\S4method{faers_phv_signal}{FAERSascii}(
-  object,
-  ...,
-  methods = NULL,
-  alpha = 0.05,
-  correct = TRUE,
-  n_mcmc = 100000L,
-  alpha1 = 0.5,
-  alpha2 = 0.5
-)
 }
 \arguments{
-\item{object}{A \link{FAERS} object.}
+\item{object}{A \link{FAERSascii} object.}
 
-\item{...}{Other arguments passed to specific methods.
-\itemize{
-\item \code{faers_filter}: other arguments passed to \code{.fn}.
-\item \code{faers_phv_table}: other arguments passed to \code{faers_filter} and \code{...} is
-solely used when \code{interested} is \code{NULL}.
-\item \code{faers_phv_signal}: other arguments passed to \code{faers_phv_table}.
-}}
+\item{...}{Other arguments passed to specific methods. For \code{faers_filter}:
+other arguments passed to \code{.fn}.}
 
 \item{field}{A string indicates the FAERS fields to use. Only values "demo",
 "drug", "indi", "ther", "reac", "rpsr", and "outc" can be used. For
@@ -114,45 +66,9 @@ two arguments: \code{.x} (single argument) or \code{.x} and \code{.y} (two argum
 very compact anonymous functions (lambdas) with up to two inputs.
 
 If a \strong{string}, the function is looked up in \code{globalenv()}.}
-
-\item{interested}{A \link{FAERSascii} object with data from interested drug, must
-be a subset of \code{object}. If \code{interested} and \code{object2} are both \code{missing},
-the \code{faers_filter} function will be employed to extract data for the drug of
-interest from the \code{object}. The value \code{n11} or \code{a} will be calculated from
-\code{interested} .}
-
-\item{object2}{A \link{FAERSascii} object with data from another interested drug,
-In this way, \code{object} and \code{object2} should be not overlapped. The value \code{n11}
-or \code{a} will be calculated from \code{object}}
-
-\item{interested_field}{A string indicates the interested FAERS fields to
-use. Only values "demo", "drug", "indi", "ther", "reac", "rpsr", and "outc"
-can be used.}
-
-\item{interested_event}{A character specify the events column(s?) in field of
-object specified in \code{interested_field}. If multiple columns were selected,
-the unique combination will define the interested events.}
-
-\item{methods}{An atomic character, specifies the method used to signal
-mining. Currently, only "ror", "prr", "chisq", "bcpnn_norm", "bcpnn_mcmc",
-"obsexp_shrink", and "fisher" are supported. If \code{NULL}, all supported methods
-will be used.}
-
-\item{alpha}{Level of significance, for construction of the confidence
-intervals.}
-
-\item{correct}{A bool indicating whether to apply Yates's continuity
-correction when computing the chi-squared statistic.}
-
-\item{n_mcmc}{Number of MCMC simulations per \verb{(a,b,c,d)}-tuple to calculate
-confidence intervals.}
-
-\item{alpha1}{Numerator shrinkage parameter \verb{>=0}, default \code{0.5}.}
-
-\item{alpha2}{Denominator shrinkage parameter \verb{>=0}, default \code{0.5}.}
 }
 \description{
-Utils function for \link{FAERS} class.
+Utils function for \link{FAERSascii} class.
 }
 \details{
 \itemize{
@@ -166,13 +82,5 @@ didn't filter a whole period FAERS quarterly data, in this way, it's much
 better to run \link[=FAERS]{faers}.
 \item \code{faers_filter}: apply a function to extract wanted \code{primaryid}, then use
 \code{faers_keep} to filter.
-\item \code{faers_phv_table}: build a contingency table for all events in
-\code{interested_event}.
-column.
-\item \code{faers_phv_signal}: Pharmacovigilance Analysis used contingency table
-constructed with \code{faers_phv_table}. Details see \link{phv_signal}.
-}
 }
-\seealso{
-\link{phv_signal}
 }
diff --git a/man/faers_phv_signal.Rd b/man/faers_phv_signal.Rd
new file mode 100644
index 0000000..4c8f2bb
--- /dev/null
+++ b/man/faers_phv_signal.Rd
@@ -0,0 +1,128 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/table.R
+\name{faers_phv_signal}
+\alias{faers_phv_signal}
+\alias{faers_phv_table}
+\alias{faers_phv_table,FAERSascii,missing,missing-method}
+\alias{faers_phv_table,FAERSascii,FAERSascii,missing-method}
+\alias{faers_phv_table,FAERSascii,missing,FAERSascii-method}
+\alias{faers_phv_table,FAERSascii,FAERSascii,FAERSascii-method}
+\alias{faers_phv_signal,FAERSascii-method}
+\title{Create contingency table and run disproportionality analysis}
+\usage{
+faers_phv_table(object, ..., interested, object2)
+
+\S4method{faers_phv_table}{FAERSascii,missing,missing}(object, ..., filter_params = list(), interested, object2)
+
+\S4method{faers_phv_table}{FAERSascii,FAERSascii,missing}(
+  object,
+  interested_field = "reac",
+  interested_event = "soc_name",
+  interested_fn = NULL,
+  ...,
+  interested,
+  object2
+)
+
+\S4method{faers_phv_table}{FAERSascii,missing,FAERSascii}(
+  object,
+  interested_field = "reac",
+  interested_event = "soc_name",
+  interested_fn = NULL,
+  ...,
+  interested,
+  object2
+)
+
+\S4method{faers_phv_table}{FAERSascii,FAERSascii,FAERSascii}(object, interested, object2)
+
+faers_phv_signal(object, ...)
+
+\S4method{faers_phv_signal}{FAERSascii}(
+  object,
+  ...,
+  methods = NULL,
+  alpha = 0.05,
+  correct = TRUE,
+  n_mcmc = 100000L,
+  alpha1 = 0.5,
+  alpha2 = 0.5
+)
+}
+\arguments{
+\item{object}{A \link{FAERSascii} object.}
+
+\item{...}{Other arguments passed to specific methods.
+\itemize{
+\item \code{faers_phv_table}: other arguments passed to \code{interested_fn}.
+\item \code{faers_phv_signal}: other arguments passed to \code{faers_phv_table}.
+}}
+
+\item{interested}{A \link{FAERSascii} object with data from interested exposure
+(usually drug), must be a subset of \code{object}. If \code{interested} and \code{object2}
+are both \code{missing}, the \link{faers_filter} function will be employed to extract
+data for the exposure of interest from the \code{object}. Then the extracted
+\code{interested} will be passed again to \code{faers_phv_table} in the method of
+\link{FAERSascii} object in \code{interested}. The value \code{n11} or \code{a} will be
+calculated from \code{interested}.}
+
+\item{object2}{A \link{FAERSascii} object with data from another interested drug,
+In this way, \code{object} and \code{object2} should be not overlapped. The value \code{n11}
+or \code{a} will be calculated from \code{object}}
+
+\item{filter_params}{Other arguments passed to \link{faers_filter}, solely used
+when \code{interested} and \code{object2} are both \code{missing}}
+
+\item{interested_field}{A string indicates the interested FAERS fields to
+use. Only values "demo", "drug", "indi", "ther", "reac", "rpsr", and "outc"
+can be used.}
+
+\item{interested_event}{A character specify the events column(s?) in field of
+object specified in \code{interested_field}. If multiple columns were selected,
+the unique combination will define the interested events.}
+
+\item{interested_fn}{A function or formula defined the preprocessing function
+before creating contingency table, with the \code{interested_field} data as the
+input and return a \link[data.table:data.table]{data.table}.
+
+If a \strong{function}, it is used as is.
+
+If a \strong{formula}, e.g. \code{~ .x + 2}, it is converted to a function with up to
+two arguments: \code{.x} (single argument) or \code{.x} and \code{.y} (two arguments). The
+\code{.} placeholder can be used instead of \code{.x}.  This allows you to create
+very compact anonymous functions (lambdas) with up to two inputs.
+
+If a \strong{string}, the function is looked up in \code{globalenv()}.}
+
+\item{methods}{An atomic character, specifies the method used to signal
+mining. Currently, only "ror", "prr", "chisq", "bcpnn_norm", "bcpnn_mcmc",
+"obsexp_shrink", and "fisher" are supported. If \code{NULL}, all supported methods
+will be used.}
+
+\item{alpha}{Level of significance, for construction of the confidence
+intervals.}
+
+\item{correct}{A bool indicating whether to apply Yates's continuity
+correction when computing the chi-squared statistic.}
+
+\item{n_mcmc}{Number of MCMC simulations per \verb{(a,b,c,d)}-tuple to calculate
+confidence intervals.}
+
+\item{alpha1}{Numerator shrinkage parameter \verb{>=0}, default \code{0.5}.}
+
+\item{alpha2}{Denominator shrinkage parameter \verb{>=0}, default \code{0.5}.}
+}
+\description{
+Create contingency table and run disproportionality analysis
+}
+\details{
+\itemize{
+\item \code{faers_phv_table}: build a contingency table for all events in
+\code{interested_event}.
+\item \code{faers_phv_signal}: Pharmacovigilance Analysis used contingency table
+constructed with \code{faers_phv_table}. Details see \link{phv_signal}.
+}
+}
+\seealso{
+\link{phv_signal}
+}