sjPlot (version 2.3.3)

plot_models: Forest plot of multiple regression models

Description

Plot and compare regression coefficients with confidence intervals of multiple regression models in one plot.

Usage

plot_models(..., exponentiate, std.est = NULL, rm.terms = NULL,
  title = NULL, m.labels = NULL, legend.title = "Dependent Variables",
  legend.pval.title = "p-level", axis.labels = NULL, axis.title = NULL,
  axis.lim = NULL, wrap.title = 50, wrap.labels = 25,
  wrap.legend.title = 20, grid.breaks = NULL, geom.size = 3,
  geom.spacing = 0.4, geom.colors = "Set1", show.values = FALSE,
  show.legend = TRUE, show.intercept = FALSE, show.p = TRUE,
  p.shape = FALSE, vline.type = 2, vline.color = "grey70", digits = 2,
  facet.grid = FALSE)
sjp.lmm(...)
sjp.glmm(...)

Arguments

...

One or more regression models, including glm's or mixed models. May also be a list with fitted models. See 'Examples'.

exponentiate

Logical, if TRUE and models inherit from generalized linear models, estimates will be exponentiated (e.g., log-odds will be displayed as odds ratios). By default, exponentiate will automatically be set to FALSE or TRUE, depending on the class of fit.

std.est

For linear models, choose whether standardized coefficients should be 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 should be removed from the plot. rm.terms = "t_name" would remove the term t_name. Default is NULL, i.e. all terms are used.

title

character vector, used as plot title. Depending on plot type and function, will be set automatically. If title = "", no title is printed. For effect-plots, may also be a character vector of length > 1, to define titles for each sub-plot or facet.

m.labels

Character vector, used to indicate the different models in the plot's legend. If not specified, the labels of the dependent variables for each model are used.

legend.title

Character vector, used as title for the plot legend. Note that only some plot types have legends (e.g. type = "pred" or when grouping estimates with group.estimates).

legend.pval.title

Character vector, used as title of the plot legend that indicates the p-values. Default is "p-level". Only applies if p.shape = TRUE.

axis.labels

character vector with labels used as axis labels. Optional argument, since in most cases, axis labels are set automatically.

axis.title

Character vector of length one or two (depending on the plot function and type), used as title(s) for the x and y axis. If not specified, a default labelling is chosen. To set multiple axis titles (e.g. with type = "eff" for many predictors), axis.title must be a character vector of same length of plots that are printed. In this case, each plot gets an own axis title (applying, for instance, to the y-axis for type = "eff"). Note: Some plot types do not support this argument. In such cases, use the return value and add axis titles manually with labs, e.g.: $plot.list[[1]] + labs(x = ...)

axis.lim

Numeric vector of length 2, defining the range of the plot axis. Depending on plot type, may effect either x- or y-axis, or both. For multiple plot outputs (e.g., from type = "eff" or type = "slope" in sjp.glm), axis.lim may also be a list of vectors of length 2, defining axis limits for each plot (only if non-faceted).

wrap.title

numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.

wrap.labels

numeric, determines how many chars of the value, variable or 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 title are displayed in one line and when a line break is inserted.

grid.breaks

numeric; sets the distance between breaks for the axis, i.e. at every grid.breaks'th position a major grid is being printed.

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.

geom.spacing

Numeric, spacing between the dots and error bars of the plotted fitted models. Default is 0.3.

geom.colors

User defined color palette for geoms. If group.estimates is not specified, must either be vector with two color values or a specific color palette code (see 'Details' in sjp.grpfrq). Else, if group.estimates is specified, geom.colors must be a vector of same length as groups. See 'Examples'.

show.values

logical, whether values should be plotted or not.

show.legend

logical, if TRUE, and depending on plot type and function, a legend is added to the plot.

show.intercept

Logical, if TRUE, the intercept of the fitted model is also plotted. Default is FALSE. If exponentiate = TRUE, 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 significance levels to values, or value and variable labels.

p.shape

Logical, if TRUE, significant levels are distinguished by different point shapes and a related legend is plotted. Default is FALSE.

vline.type

Linetype of the vertical "zero point" line. Default is 2 (dashed line).

vline.color

Color of the vertical "zero point" line. Default value is "grey70".

digits

Numeric, amount of digits after decimal point when rounding estimates and values.

facet.grid

TRUE to arrange the lay out of of multiple plots in a grid of an integrated single plot. This argument calls facet_wrap or facet_grid to arrange plots. Use plot_grid to plot multiple plot-objects as an arranged grid with grid.arrange.

Value

A ggplot-object.

Examples

# NOT RUN {
library(sjmisc)
data(efc)

# fit three models
fit1 <- 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 models
plot_models(fit1, fit2, fit3, facet.grid = TRUE)

# plot multiple models with legend labels and
# point shapes instead of value labels
plot_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 argument
all.models <- list()
all.models[[1]] <- fit1
all.models[[2]] <- fit2
all.models[[3]] <- fit3

plot_models(all.models)

# plot multiple models with different predictors (stepwise inclusion),
# standardized estimates
fit1 <- lm(mpg ~ wt + cyl + disp + gear, data = mtcars)
fit2 <- update(fit1, . ~ . + hp)
fit3 <- update(fit2, . ~ . + am)

plot_models(fit1, fit2, fit3, std.est = "std2")

# }