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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/emtrends.R
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\title{Estimated marginal means of linear trends}
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emtrends(model, specs, var, delta.var = 0.01 * rng, data,
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transform = c("none", "response"), ...)
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\item{model}{A supported model object (\emph{not} a reference grid)}
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\item{specs}{Specifications for what marginal trends are desired -- as in
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\code{\link{emmeans}}}
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\item{var}{Character value giving the name of a variable with respect to
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which a difference quotient of the linear predictors is computed. In order
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for this to be useful, \code{var} should be a numeric predictor that
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interacts with at least one factor in \code{specs}. Then instead of
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computing EMMs, we compute and compare the slopes of the \code{var} trend
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over levels of the specified other predictor(s). As in EMMs, marginal
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averages are computed for the predictors in \code{specs} and \code{by}.
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See also the \dQuote{Generalizations} section below.}
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\item{delta.var}{The value of \emph{h} to use in forming the difference
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quotient \eqn{(f(x+h) - f(x))/h}. Changing it (especially changing its
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sign) may be necessary to avoid numerical problems such as logs of negative
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numbers. The default value is 1/100 of the range of \code{var} over the
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\item{data}{As in \code{\link{ref_grid}}, you may use this argument to supply
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the dataset used in fitting the model, for situations where it is not
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possible to reconstruct the data. Otherwise, leave it missing.}
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\item{transform}{If \code{object} has a response
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transformation or link function, then specifying
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\code{transform = "response"} will cause
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\code{emtrends} to calculate the trends after back-transforming to the
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response scale. This is done using the chain rule, and standard errors are
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estimated via the delta method. With \code{transform = "none"} (the
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default), the trends are calculated on the scale of the linear predictor,
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without back-transforming it. This argument works similarly to the
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\code{transform} argument in \code{\link{ref_grid}}, in that the returned
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object is re-gridded to the new scale (see also \code{\link{regrid}}).}
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\item{...}{Additional arguments passed to other methods or to
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\code{\link{ref_grid}}}
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An \code{emmGrid} or \code{emm_list} object, according to \code{specs}.
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See \code{\link{emmeans}} for more details on when a list is returned.
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The \code{emtrends} function is useful when a fitted model involves a
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numerical predictor \eqn{x} interacting with another predictor \code{a}
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(typically a factor). Such models specify that \eqn{x} has a different trend
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depending on \eqn{a}; thus, it may be of interest to estimate and compare
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those trends. Analogous to the \code{\link{emmeans}} setting, we construct a
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reference grid of these predicted trends, and then possibly average them over
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some of the predictors in the grid.
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\section{Generalizations}{
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Instead of a single predictor, the user may specify some monotone function of
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one variable, e.g., \code{var = "log(dose)"}. If so, the chain rule is
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applied. Note that, in this example, if \code{model} contains
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\code{log(dose)} as a predictor, we will be comparing the slopes estimated by
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that model, whereas specifying \code{var = "dose"} would perform a
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transformation of those slopes, making the predicted trends vary depending on
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fiber.lm <- lm(strength ~ diameter*machine, data=fiber)
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# Obtain slopes for each machine ...
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( fiber.emt <- emtrends(fiber.lm, "machine", var = "diameter") )
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# ... and pairwise comparisons thereof
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# Suppose we want trends relative to sqrt(diameter)...
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emtrends(fiber.lm, ~ machine | diameter, var = "sqrt(diameter)",
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at = list(diameter = c(20, 30)))
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\code{link{emmeans}}, \code{\link{ref_grid}}
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man/emtrends.Rd
