Depending on the type
, this function plots coefficients (estimates)
of linear regressions (including panel models fitted with the plm
function
from the plmpackage and generalized least squares models fitted with
the gls
function from the nlmepackage) 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, ...)
fit  Fitted linear regression model (of class 

type  Type of plot. Use one of following:

vars  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 
group.estimates  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'. 
remove.estimates  Character vector with coefficient names that indicate
which estimates should be removed from the plot.

sort.est  Logical, determines whether estimates should be sorted according to their values.
If 
poly.term  name of a polynomial term in 
title  character vector, used as plot title. Depending on plot type and function,
will be set automatically. If 
legend.title  Character vector, used as title for the plot legend. Note that
only some plot types have legends (e.g. 
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 
resp.label  Name of dependent variable, as string. Only
used if fitted model has only one predictor and 
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.colors  User defined color palette for geoms. If 
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. 
axis.lim  Numeric vector of length 2, defining the range of the plot axis.
Depending on plot type, may effect either x or yaxis, or both.
For multiple plot outputs (e.g., from 
grid.breaks  numeric; sets the distance between breaks for the axis,
i.e. at every 
show.values  Logical, whether values should be plotted or not. 
show.p  Logical, adds significance levels to values, or value and variable labels. 
show.ci  Logical, if 
show.legend  logical, if 
show.loess  logical, if 
show.loess.ci  logical, if 
show.summary  Logical, if 
show.scatter  Logical, if 
point.alpha  Alpha value of pointgeoms in the scatter plots. Only applies,
if 
point.color  Color of of pointgeoms in the scatter plots. Only applies,
if 
jitter.ci  Logical, if 
digits  Numeric, amount of digits after decimal point when rounding estimates and values. 
vline.type  Linetype of the vertical "zero point" line. Default is 
vline.color  Color of the vertical "zero point" line. Default value is 
coord.flip  logical, if 
y.offset  numeric, offset for text labels when their alignment is adjusted
to the top/bottom of the geom (see 
facet.grid 

complete.dgns  Logical, if 
prnt.plot  logical, if 
...  Other arguments passed down to further functions. Currently, following arguments are supported:

Depending on the type
, in most cases (insisibily)
returns the ggplotobject with the complete plot (plot
)
as well as the data frame that was used for setting up the
ggplotobject (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 loesssmoothed
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 armpackage.
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 xaxis 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 covariates 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: 2865–2873. 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.
sjPlot manual: sjp.lm for
more details and examples of this function; use sjp.poly
to see which polynomial degree fits best for possible polynomial terms.