By default, this function plots estimates (coefficients) with confidence
intervalls of either fixed effects or random effects of linear mixed
effects models (that have been fitted with the lmer-function
of the lme4-package). Furhermore, this function also plot
predicted values or diagnostic plots.
sjp.lmer(fit, type = "re", vars = NULL, ri.nr = NULL,
group.estimates = NULL, remove.estimates = NULL, emph.grp = NULL,
sample.n = NULL, poly.term = NULL, sort.est = NULL, title = NULL,
legend.title = NULL, axis.labels = NULL, axis.title = NULL,
geom.size = NULL, geom.colors = "Set1", show.values = TRUE,
show.p = TRUE, show.ci = FALSE, show.legend = FALSE,
show.loess = FALSE, show.loess.ci = FALSE, show.intercept = FALSE,
string.interc = "(Intercept)", p.kr = TRUE, show.scatter = TRUE,
point.alpha = 0.2, point.color = NULL, jitter.ci = FALSE,
fade.ns = FALSE, axis.lim = NULL, digits = 2, vline.type = 2,
vline.color = "grey70", facet.grid = TRUE, free.scale = FALSE,
y.offset = 0.1, prnt.plot = TRUE, ...)a fitted model as returned by the lmer-function.
type of plot. Use one of following:
"re"(default) for conditional modes of random effects as forest plot
"fe"for estimates of fixed effects as forest plot
"fe.std"for standardized estimates of fixed effects as forest plot
"fe.resid"to plot regression lines (slopes) with confidence intervals for each single fixed effect (against residuals), i.e. all fixed terms are extracted and each is plotted against the model residuals (linear relationship between each fixed term and residuals)
"fe.cor"for correlation matrix of fixed effects
"re.qq"for a QQ-plot of random effects (random effects quantiles against standard normal quantiles)
"ri.slope"for fixed effects slopes depending on the random intercept.
"rs.ri"for fitted regression lines indicating the random slope-intercept pairs. Use this to visualize the random effects of random slope-intercept (or repeated measure) models. When having too many groups, use sample.n argument.
"coef"for joint (sum of) random and fixed effects coefficients for each explanatory variable for each level of each grouping factor as forest plot.
"pred"to plot predicted values for the response, related to specific model predictors and conditioned on random effects. See 'Details'.
"pred.fe"to plot predicted values for the response, related to specific model predictors and conditioned on fixed effects only. See 'Details'.
"eff"to plot marginal effects of all fixed terms in fit. Note that interaction terms are excluded from this plot; use sjp.int to plot effects of interaction terms. See also 'Details' of sjp.lm.
"eff.ri"to plot marginal effects of all fixed terms in fit, varying by the random intercepts.
"poly"to plot predicted values (marginal effects) of polynomial terms in fit. Use poly.term to specify the polynomial term in the fitted model (see 'Examples' here and 'Details' of sjp.lm).
"ma"to check model assumptions. Note that no further arguments except fit are relevant for this option. All other arguments are ignored.
Numeric vector with column indices of selected variables or a character vector with
variable names of selected variables from the fitted model, which should be used to plot
- depending on type - estimates, fixed effects slopes or predicted values
(mean, probabilities, incidents rates, ...). See 'Examples'.
Numeric vector. If type = "re" or type = "ri.slope",
and fitted model has more than one random intercept, ri.nr indicates
which random effects of which random intercept (or: which list elements
of ranef) will be plotted. Default is NULL,
so all random effects will be plotted.
Numeric or character vector, indicating a group identifier for each estimate. Dots and confidence intervals of estimates are coloured according to their group association. See 'Examples'.
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.
Numeric vector with index numbers of grouping levels (from random effect).
If type = "ri.slope" and facet.grid = FALSE,
an integrated plot of predicted probabilities of fixed effects resp. fixed
effects slopes for each grouping level is plotted. To better find
certain groups, use this argument to emphasize these groups in the plot.
See 'Examples'.
Numeric vector. only applies, if type = "rs.ri". If
plot has many random intercepts (grouping levels), overplotting of
regression lines may occur. In this case, consider random sampling of
grouping levels. If sample.n is of length 1, a random sample
of sample.n observation is selected to plot random intercepts.
If sample.n is of length > 1, random effects indicated by
the values in sample.n are selected to plot random effects.
Use the latter option to always select a fixed, identical set of
random effects for plotting (useful when ecomparing multiple models).
name of a polynomial term in fit as string. Needs to be
specified, if type = "poly", in order to plot marginal effects
for polynomial terms. See 'Examples'.
Determines in which way estimates are sorted in the plot:
If NULL (default), no sorting is done and estimates are sorted in order of model coefficients.
If sort.est = "sort.all", estimates are re-sorted for each coefficient (only applies if type = "re" and facet.grid = FALSE), i.e. the estimates of the random effects for each predictor are sorted and plotted to an own plot.
If type = "fe" or type = "fe.std", TRUE will sort estimates
If type = "re", specify a predictor's / coefficient's name to sort estimates according to this coefficient.
See 'Examples'.
Character vector with one or more labels that are used as plot title.
character vector, used as title for the plot legend.
Character vector with labels for the model terms, used as axis labels.
For mixed models, should either be vector of fixed effects variable labels
(if type = "fe" or type = "fe.std") or a vector of group (value)
labels from the random intercept's categories (if type = "re").
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 = ...)
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.
user defined color for geoms. See 'Details' in sjp.grpfrq.
Logical, whether values should be plotted or not.
Logical, adds significance levels to values, or value and variable labels.
Logical, if 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.
logical, if TRUE, and depending on plot type and
function, a legend is added to the plot.
logical, if TRUE, and depending on type, an
additional loess-smoothed line is plotted.
logical, if TRUE, a confidence region for the loess-smoothed line
will be plotted. Default is FALSE. Only applies, if show.loess = TRUE
(and for sjp.lmer, only applies if type = "fe.slope"
or type = "fe.resid").
Logical, if TRUE, the intercept of the fitted
model 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.
String, axis label of intercept estimate. Only applies,
if show.intercept = TRUE and axis.labels is not NULL.
logical, if TRUE, p-value estimation is based on conditional
F-tests with Kenward-Roger approximation for the df. Caution: Computation
may take very long time for large samples!
Logical, if TRUE (default), adds a scatter plot of
data points to the plot. Only applies for slope-type or predictions plots.
For most plot types, dots are jittered to avoid overplotting, hence the
points don't reflect exact values in the data.
Alpha value of point-geoms in the scatter plots. Only applies,
if show.scatter = TRUE.
Color of of point-geoms in the scatter plots. Only applies,
if show.scatter = TRUE.
Logical, if TRUE and show.ci = TRUE and confidence
bands are displayed as error bars, adds jittering to lines and error bars
to avoid overlapping.
Logical, if TRUE, non significant estimates will be printed in slightly faded colors.
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).
Numeric, amount of digits after decimal point when rounding estimates and values.
Linetype of the vertical "zero point" line. Default is 2 (dashed line).
Color of the vertical "zero point" line. Default value is "grey70".
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.
Logical, if TRUE and facet.grid = TRUE, each facet grid
gets its own fitted scale. If free.scale = FALSE, each facet in
the grid has the same scale range.
numeric, offset for text labels when their alignment is adjusted
to the top/bottom of the geom (see hjust and vjust).
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.
Other arguments passed down to further functions. Currently, following arguments are supported:
?effects::effectAny arguments accepted by the effect resp.
allEffects function, for type = "eff".
widthThe width-argument for error bars.
alphaThe alpha-argument for confidence bands.
levelThe level-argument confidence bands.
(Insisibily) returns
the ggplot-object (plot), if type = "fe" or if type = "re" and facet.grid = TRUE). Multiple plots (type = "re" and if facet.grid = FALSE) are returned in the object plot.list.
a list of ggplot-objects (plot.list). see plot for details.
a data frame data with the data used to build the ggplot-object(s).
type = "re"plots the conditional modes of the random
effects, inclduing predicion intervals. It basically does the same
as dotplot(ranef(fit, condVar = TRUE)[[i]]), where i
denotes the random effect index.
type = "fe.slope"plots the linear relationship between
each fixed effect and the response. The regression lines are not
based on the fitted model's fixed effects estimates (though they may
be similar). This plot type just computes a simple linear model for
each fixed effect and response. Hence, it's intended for checking
model assumptions, i.e. if predictor and respone are in a linear relationship.
You may use the show.loess argument to see whether the linear
line differs from the best fitting line.
type = "fe.resid"Similar to type = "fe.slope",
this this type is intended for checking model assumptions. However,
fitted values are plotted against the residuals instead of response.
type = "eff"plots the adjusted (marginal) effects
for each fixed effect, with all co-variates set to the mean, as
returned by the allEffects function.
You can pass further arguments down to allEffects for flexible
function call via the ...-argument.
type = "eff.ri"plots the adjusted (marginal) effects
for each fixed effect, with all co-variates set to the mean, varying
by the random intercepts. This plot type basically does the same
as type = "ri.slope", except that the co-variates are not
set to zero, but adjusted for. This plot type differs from type = "ri.slope"
only in the adjusted y-axis-scale
type = "rs.ri"plots regression lines for the random
effects of the model, i.e. all random slopes for each random intercept.
As the random intercepts describe the deviation from the global intercept,
the regression lines are computed as global intercept + random intercept +
random slope. In case of overplotting,
use the sample.n argument to randomly sample a limited amount
of groups.
type = "ri.slope"plots regression lines for each fixed
effect (slopes) within each random intercept. Lines are based on
the fixed effects intercept, plus each random intercept and
each specific fixed term's estimate. All other fixed effects are
set to zero (i.e. ignored),
which corresponds to b0 + b0[r1-rn] + bi * xi)
(where xi is the estimate of fixed effects, b0 is the intercept of
the fixed effects and b0[r1-rn] are all random intercepts).
type = "coef"forest plot of joint fixed and random
effect coefficients, as retrieved by coef.merMod,
it's simply ranef + fixef.
type = "pred" or type = "pred.fe"predicted
values for response, conditional on fixed effects only or on random
intercept. It's calling
predict(fit, type = "response", re.form = NA) resp.
predict(fit, type = "response", re.form = NULL) to
compute the values.
This plot type requires the vars argument to select specific terms
that should be used for the x-axis and - optional - as grouping factor.
Hence, vars must be a character vector with the names of
one or two model predictors. See 'Examples'.