% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_residuals.R
\name{plot_residuals}
\alias{plot_residuals}
\title{Plot predicted values and their residuals}
\usage{
plot_residuals(
  fit,
  geom.size = 2,
  remove.estimates = NULL,
  show.lines = TRUE,
  show.resid = TRUE,
  show.pred = TRUE,
  show.ci = FALSE
)
}
\arguments{
\item{fit}{Fitted linear (mixed) regression model (including objects of class
\code{\link[nlme]{gls}} or \code{plm}).}

\item{geom.size}{size resp. width of the geoms (bar width, line thickness or point size,
depending on plot type and function). Note that bar and bin widths mostly
need smaller values than dot sizes.}

\item{remove.estimates}{Numeric vector with indices (order equals to row index of \code{coef(fit)})
or character vector with coefficient names that indicate which estimates should be removed
from the table output. The first estimate is the intercept, followed by the model predictors.
\emph{The intercept cannot be removed from the table output!} \code{remove.estimates = c(2:4)}
would remove the 2nd to the 4th estimate (1st to 3rd predictor after intercept) from the output.
\code{remove.estimates = "est_name"} would remove the estimate \emph{est_name}. Default
is \code{NULL}, i.e. all estimates are printed.}

\item{show.lines}{Logical, if \code{TRUE}, a line connecting predicted and
residual values is plotted. Set this argument to \code{FALSE}, if
plot-building is too time consuming.}

\item{show.resid}{Logical, if \code{TRUE}, residual values are plotted.}

\item{show.pred}{Logical, if \code{TRUE}, predicted values are plotted.}

\item{show.ci}{Logical, if \code{TRUE)}, adds notches to the box plot, which are
used to compare groups; if the notches of two boxes do not overlap,
medians are considered to be significantly different.}
}
\value{
A ggplot-object.
}
\description{
This function plots observed and predicted values of the response
             of linear (mixed) models for each coefficient and highlights the
             observed values according to their distance (residuals) to the
             predicted values. This allows to investigate how well actual and
             predicted values of the outcome fit across the predictor variables.
}
\note{
The actual (observed) values have a coloured fill, while the predicted
      values have a solid outline without filling.
}
\examples{
data(efc)
# fit model
fit <- lm(neg_c_7 ~ c12hour + e17age + e42dep, data = efc)

# plot residuals for all independent variables
plot_residuals(fit)

# remove some independent variables from output
plot_residuals(fit, remove.estimates = c("e17age", "e42dep"))

}