sjPlot (version 2.1.0)

sjp.glmm: Plot estimates of multiple fitted glm(er)'s

Description

Plot and compare odds or incidents ratios (forest plots) of multiple fitted generalized linear (mixed effects) models with confidence intervals in one plot.

Usage

sjp.glmm(..., remove.estimates = NULL, title = NULL, depvar.labels = NULL, legend.title = "Dependent Variables", legend.pval.title = "p-level", axis.labels = NULL, axis.title = "Estimates", axis.lim = NULL, wrap.title = 50, wrap.labels = 25, wrap.legend.title = 20, grid.breaks = 0.5, trns.ticks = TRUE, geom.size = 3, geom.spacing = 0.4, geom.colors = "Set1", show.values = TRUE, show.legend = TRUE, show.intercept = FALSE, show.p = TRUE, fade.ns = FALSE, p.shape = FALSE, vline.type = 2, vline.color = "grey70", digits = 2, facet.grid = FALSE, coord.flip = TRUE, prnt.plot = TRUE)

Arguments

...

one or more fitted glm- or glmerMod-objects. May also be a list-object with fitted models, instead of separating each model with comma. See 'Examples'.

remove.estimates

character vector with coefficient names that indicate which estimates should be removed from the plot. remove.estimates = "est_name" would remove the estimate est_name. Default is NULL, i.e. all estimates are printed.

title

character vector, used as plot title. Depending on plot type and function, will be set automatically. If title = "", no title is printed.

depvar.labels

character vector with labels of dependent variables of all fitted models. See 'Examples'.

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.

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.

trns.ticks

logical, if TRUE, the grid lines have exponential distances (equidistant), i.e. they visually have the same distance from one panel grid to the next. If FALSE, grids are plotted on every grid.breaks's position, thus the grid lines become narrower with higher odds ratio values.

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

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. For glm's, please note that due to exponential transformation of estimates, the intercept in some cases can not be calculated, thus the function call is interrupted and no plot printed.

show.p

logical, adds significance levels to values, or value and variable labels.

fade.ns

if TRUE, non significant estimates will be printed in slightly faded colors.

p.shape

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.

coord.flip

logical, if TRUE, the x and y axis are swapped.

prnt.plot

logical, if TRUE (default), plots the results as graph. Use FALSE if you don't want to plot any graphs. In either case, the ggplot-object will be returned as value.

Value

(Insisibily) returns the ggplot-object with the complete plot (plot) as well as the data frame that was used for setting up the ggplot-object (data).

Examples

# prepare dummy variables for binary logistic regression
y1 <- ifelse(swiss$Fertility < median(swiss$Fertility), 0, 1)
y2 <- ifelse(swiss$Infant.Mortality < median(swiss$Infant.Mortality), 0, 1)
y3 <- ifelse(swiss$Agriculture<median(swiss$Agriculture), 0, 1)

# Now fit the models. Note that all models share the same predictors
# and only differ in their dependent variable (y1, y2 and y3)
fitOR1 <- glm(y1 ~ swiss$Education + swiss$Examination + swiss$Catholic,
              family = binomial(link = "logit"))
fitOR2 <- glm(y2 ~ swiss$Education + swiss$Examination + swiss$Catholic,
              family = binomial(link = "logit"))
fitOR3 <- glm(y3 ~ swiss$Education + swiss$Examination + swiss$Catholic,
              family = binomial(link = "logit"))

# plot multiple models
sjp.glmm(fitOR1, fitOR2, fitOR3, facet.grid = TRUE)

# plot multiple models with legend labels and point shapes instead of value  labels
sjp.glmm(fitOR1, fitOR2, fitOR3,
         depvar.labels = c("Fertility", "Infant Mortality", "Agriculture"),
         show.values = FALSE, show.p = FALSE, fade.ns = TRUE, p.shape = TRUE)

# plot multiple models from nested lists argument
all.models <- list()
all.models[[1]] <- fitOR1
all.models[[2]] <- fitOR2
all.models[[3]] <- fitOR3

sjp.glmm(all.models)

# -------------------------------
# Predictors for negative impact
# of care. Data from the EUROFAMCARE
# sample dataset
# -------------------------------
library(sjmisc)
data(efc)

# create binary response
y <- ifelse(efc$neg_c_7 < median(na.omit(efc$neg_c_7)), 0, 1)
# create dummy variables for educational status
mydf <- data.frame(y = as.factor(y),
                   sex = efc$c161sex,
                   dep = to_factor(efc$e42dep),
                   barthel = efc$barthtot,
                   education = to_factor(efc$c172code))

fit1 <- glm(y ~ sex + education,  data = mydf, family = binomial(link = "logit"))
fit2 <- update(fit1, . ~ . + barthel)
fit3 <- update(fit2, . ~ . + dep)

sjp.glmm(fit1, fit2, fit3)