Depending on the type, this function plots coefficients (estimates)
of linear regressions (including panel models fitted with the plm-function
from the plm-package and generalized least squares models fitted with
the gls-function from the nlme-package) with confidence
intervals as dot plot (forest plot),
model assumptions for linear models or slopes and scatter plots for each single
coefficient. See type for details.
sjp.lm(fit, type = "lm", vars = NULL, group.estimates = NULL,
remove.estimates = NULL, sort.est = TRUE, poly.term = NULL,
title = NULL, legend.title = NULL, axis.labels = NULL,
axis.title = NULL, resp.label = NULL, geom.size = NULL,
geom.colors = "Set1", wrap.title = 50, wrap.labels = 25,
axis.lim = NULL, grid.breaks = NULL, show.values = TRUE,
show.p = TRUE, show.ci = TRUE, show.legend = FALSE,
show.loess = FALSE, show.loess.ci = FALSE, show.summary = FALSE,
show.scatter = TRUE, point.alpha = 0.2, point.color = NULL,
jitter.ci = FALSE, digits = 2, vline.type = 2, vline.color = "grey70",
coord.flip = TRUE, y.offset = 0.15, facet.grid = TRUE,
complete.dgns = FALSE, prnt.plot = TRUE, ...)Type of plot. Use one of following:
"lm"(default) for forest-plot of estimates. If the fitted model only contains one predictor, slope-line is plotted.
"pred"to plot predicted values (marginal effects) for specific model terms. See 'Details'.
"eff"to plot marginal effects of all terms in fit. Note that interaction terms are excluded from this plot.
"std"for forest-plot of standardized beta values.
"std2"for forest-plot of standardized beta values, however, standardization is done by dividing by two sd (see 'Details').
"resid"to plot regression lines for each single predictor of the fitted model, against the residuals (linear relationship between each model term and residuals). May be used for model diagnostics.
"vif"to plot Variance Inflation Factors.
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 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.
Logical, determines whether estimates should be sorted according to their values.
If group.estimates is not NULL, estimates are sorted
according to their group assignment.
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'.
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.
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).
character vector with labels used as axis labels. Optional argument, since in most cases, axis labels are set automatically.
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 = ...)
Name of dependent variable, as string. Only
used if fitted model has only one predictor and type = "lm".
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 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'.
numeric, determines how many chars of the plot title are displayed in one line and when a line break is inserted.
numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.
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; sets the distance between breaks for the axis,
i.e. at every grid.breaks'th position a major grid is being printed.
Logical, whether values should be plotted or not.
Logical, adds significance levels to values, or value and variable labels.
Logical, if TRUE, depending on type, a confidence
interval or region is added to the plot. For frequency plots, the
confidence interval for the relative frequencies are shown.
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, a summary with model statistics
is added to the plot.
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.
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".
logical, if TRUE, the x and y axis are swapped.
numeric, offset for text labels when their alignment is adjusted
to the top/bottom of the geom (see hjust and vjust).
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, additional tests are performed. Default is FALSE
Only applies if type = "ma".
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.
Depending on the type, in most cases (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 (df). For type = "ma", an updated model
with removed outliers is returned.
type = "lm"if fitted model only has one predictor, no
forest plot is shown. Instead, a regression line with confidence
interval (in blue) is plotted by default, and a loess-smoothed
line without confidence interval (in red) can be added if argument
show.loess = TRUE.
type = "std2"plots standardized beta values, however,
standardization follows Gelman's (2008) suggestion, rescaling the
estimates by dividing them by two standard deviations instead of
just one. Resulting coefficients are then directly comparable for
untransformed binary predictors. This standardization uses the
standardize-function from the arm-package.
type = "slope"regression lines (slopes) with confidence intervals for each single predictor of the fitted model are plotted, i.e. all predictors of the fitted model are extracted and for each of them, the linear relationship is plotted against the response variable. Other predictors are omitted, so this plot type is intended to check the linear relationship between a predictor and the response.
type = "resid"is similar to the type = "slope" option,
however, each predictor is plotted against the residuals
(instead of response).
type = "pred"plots predicted values of the response, related
to specific model predictors. This plot type calls
predict(fit, newdata = model.frame, type = "response")
and 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'.
type = "eff"computes the marginal effects for all predictors,
using the allEffects function. I.e. for each
predictor, the predicted values towards the response are plotted, with
all remaining co-variates set to the mean. Due to possible different
scales of predictors, a faceted plot is printed (instead of plotting
all lines in one plot).
You can pass further arguments down to allEffects for flexible
function call via the ...-argument.
type = "poly"plots the marginal effects of polynomial terms
in fit, using the effect function, but
only for a selected polynomial term, which is specified with poly.term.
This function helps undertanding the effect of polynomial terms by
plotting the curvilinear relationships of response and quadratic, cubic etc.
terms. This function accepts following argument.
type = "ma"checks model assumptions. Please note that only
three arguments are relevant: fit and complete.dgns.
All other arguments are ignored.
type = "vif"Variance Inflation Factors (check for multicollinearity) are plotted. As a rule of thumb, values below 5 are considered as good and indicate no multicollinearity, values between 5 and 10 may be tolerable. Values greater than 10 are not acceptable and indicate multicollinearity between model's predictors.
Gelman A (2008) "Scaling regression inputs by dividing by two standard deviations." Statistics in Medicine 27: 2865<U+2013>2873. http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf
Hyndman RJ, Athanasopoulos G (2013) "Forecasting: principles and practice." OTexts; accessed from https://www.otexts.org/fpp/5/4.