Forest plot of multiple regression models

Source: R/plot_models.R

plot_models.Rd

Arguments

...	One or more regression models, including glm's or mixed models.May also be a `list` with fitted models. See 'Examples'.
transform	A character vector, naming a function that will be appliedon estimates and confidence intervals. By default, `transform` willautomatically use `"exp"` as transformation for applicable classes of`model` (e.g. logistic or poisson regression). Estimates of linearmodels remain untransformed. Use `NULL` if you want the raw,non-transformed estimates.
std.est	For linear models, choose whether standardized coefficients shouldbe used for plotting. Default is no standardization. `NULL` (default) no standardization, returns original estimates. `"std"` standardized beta values. `"std2"` standardized beta values, however, standardization is done by rescaling estimates by dividing them by two sd (see `std_beta`).
rm.terms	Character vector with names that indicate which terms shouldbe removed from the plot. Counterpart to `terms`. `rm.terms ="t_name"` would remove the term t_name. Default is `NULL`, i.e.all terms are used. Note that this argument does not apply toMarginal Effects plots.
title	Character vector, used as plot title. By default,`get_dv_labels` is called to retrieve the label ofthe dependent variable, which will be used as title. Use `title = ""`to remove title.
m.labels	Character vector, used to indicate the different modelsin the plot's legend. If not specified, the labels of the dependentvariables for each model are used.
legend.title	Character vector, used as title for the plot legend. Note thatonly some plot types have legends (e.g. `type = "pred"` or whengrouping estimates with `group.estimates`).
legend.pval.title	Character vector, used as title of the plot legend thatindicates the p-values. Default is `"p-level"`. Only applies if`p.shape = TRUE`.
axis.labels	Character vector with labels for the model terms, used asaxis labels. By default, `get_term_labels` iscalled to retrieve the labels of the coefficients, which will be used asaxis labels. Use `axis.labels = ""` or `auto.label = FALSE` touse the variable names as labels instead. If `axis.labels` is a namedvector, axis labels (by default, the names of the model's coefficients)will be matched with the names of `axis.label`. This ensures thatlabels always match the related axis value, no matter in which wayaxis labels are sorted.
axis.title	Character vector of length one or two (depending on theplot function and type), used as title(s) for the x and y axis. If notspecified, a default labelling is chosen. Note: Some plot typesmay not support this argument sufficiently. In such cases, use the returnedggplot-object and add axis titles manually with`labs`. Use `axis.title = ""` to remove axistitles.
axis.lim	Numeric vector of length 2, defining the range of the plotaxis. Depending on plot-type, may effect either x- or y-axis. ForMarginal Effects plots, `axis.lim` may also be a list of twovectors of length 2, defining axis limits for both the x and y axis.
wrap.title	Numeric, determines how many chars of the plot title aredisplayed in one line and when a line break is inserted.
wrap.labels	Numeric, determines how many chars of the value, variableor axis labels are displayed in one line and when a line break is inserted.
wrap.legend.title	numeric, determines how many chars of the legend's titleare displayed in one line and when a line break is inserted.
grid.breaks	Numeric value or vector; if `grid.breaks` is asingle value, sets the distance between breaks for the axis at every`grid.breaks`'th position, where a major grid line is plotted. If`grid.breaks` is a vector, values will be used to define theaxis positions of the major grid lines.
dot.size	Numeric, size of the dots that indicate the point estimates.
spacing	Numeric, spacing between the dots and error bars of theplotted fitted models. Default is 0.3.
colors	May be a character vector of color values in hex-format, validcolor value names (see `demo("colors")`) or a name of a pre-definedcolor palette. Following options are valid for the `colors` argument: If not specified, a default color brewer palette will be used, which is suitable for the plot style. If `"gs"`, a greyscale will be used. If `"bw"`, and plot-type is a line-plot, the plot is black/white and uses different line types to distinguish groups (see this package-vignette). If `colors` is any valid color brewer palette name, the related palette will be used. Use `display.brewer.all` to view all available palette names. If wesanderson is installed, you may also specify a name of a palette from that package. If viridis is installed, use `colors = "v"` to get the viridis color palette. There are some pre-defined color palettes in this package, see `sjPlot-themes` for details. Else specify own color values or names as vector (e.g. `colors = "#00ff00"` or `colors = c("firebrick", "blue")`).
show.values	Logical, whether values should be plotted or not.
show.legend	For Marginal Effects plots, shows or hides thelegend.
show.intercept	Logical, if `TRUE`, the intercept of the fittedmodel is also plotted. Default is `FALSE`. If `transform ="exp"`, please note that due to exponential transformation of estimates,the intercept in some cases is non-finite and the plot can not be created.
show.p	Logical, adds asterisks that indicate the significance level ofestimates to the value labels.
p.shape	Logical, if `TRUE`, significant levels are distinguished bydifferent point shapes and a related legend is plotted. Defaultis `FALSE`.
ci.lvl	Numeric, the level of the confidence intervals (error bars).Use `ci.lvl = NA` to remove error bars. For `stanreg`-models,`ci.lvl` defines the (outer) probability for the`hdi` (High Density Interval) that is plotted. Bydefault, `stanreg`-models are printed with two intervals: the "inner"interval, which defaults to the 50%-HDI; and the "outer" interval, whichdefaults to the 89%-HDI. `ci.lvl` affects only the outer interval insuch cases. See `prob.inner` and `prob.outer` under the`...`-argument for more details.
vline.color	Color of the vertical "zero effect" line. Default color isinherited from the current theme.
digits	Numeric, amount of digits after decimal point when roundingestimates or values.
grid	Logical, if `TRUE`, multiple plots are plotted as gridlayout.
auto.label	Logical, if `TRUE` (the default), plot-labels arebased on value and variable labels, if the data is labelled. See`get_label` and`get_term_labels` for details. If `FALSE`,original variable names and value labels (factor levels) are used.
prefix.labels	Indicates whether the value labels of categorical variablesshould be prefixed, e.g. with the variable name or variable label. Seeargument `prefix` in `get_term_labels` fordetails.

Value

A ggplot-object.

Examples

data(efc)# fit three modelsfit1 <- lm(barthtot ~ c160age + c12hour + c161sex + c172code, data = efc)fit2 <- lm(neg_c_7 ~ c160age + c12hour + c161sex + c172code, data = efc)fit3 <- lm(tot_sc_e ~ c160age + c12hour + c161sex + c172code, data = efc)# plot multiple modelsplot_models(fit1, fit2, fit3, grid = TRUE)
See Also
Chapter 6 Forest Plots | Doing Meta-Analysis in R
# plot multiple models with legend labels and# point shapes instead of value labelsplot_models( fit1, fit2, fit3, axis.labels = c( "Carer's Age", "Hours of Care", "Carer's Sex", "Educational Status" ), m.labels = c("Barthel Index", "Negative Impact", "Services used"), show.values = FALSE, show.p = FALSE, p.shape = TRUE)
# plot multiple models from nested lists argumentall.models <- list()all.models[[1]] <- fit1all.models[[2]] <- fit2all.models[[3]] <- fit3plot_models(all.models)
# plot multiple models with different predictors (stepwise inclusion),# standardized estimatesfit1 <- lm(mpg ~ wt + cyl + disp + gear, data = mtcars)fit2 <- update(fit1, . ~ . + hp)fit3 <- update(fit2, . ~ . + am)plot_models(fit1, fit2, fit3, std.est = "std2")

Forest plot of multiple regression models — plot_models (2024)

Arguments

Value

Examples

References