plotreg(l, file = NULL, custom.model.names = NULL, custom.coef.names = NULL, custom.note = NULL, override.coef = 0, override.se = 0, override.pval = 0, override.ci.low = 0, override.ci.up = 0, omit.coef = NULL, reorder.coef = NULL, ci.level = 0.95, use.se = FALSE, mfrow = TRUE, xlim = NULL, cex = 2.5, lwd.zerobar = 4, lwd.vbars = 1, lwd.inner = 7, lwd.outer = 5, ylab.cex = 1.0, signif.light = "#fbc9b9", signif.medium = "#f7523a", signif.dark = "#bd0017", insignif.light = "#c5dbe9", insignif.medium = "#5a9ecc", insignif.dark = "#1c5ba6", ...)
coefplot(labels, estimates, lower.inner = NULL, upper.inner = NULL, lower.outer = NULL, upper.outer = NULL, signif.outer = TRUE, xlab = "Coefficients and confidence intervals", main = "Coefficient plot", xlim = NULL, cex = 2.5, lwd.zerobar = 4, lwd.vbars = 1, lwd.inner = 7, lwd.outer = 5, ylab.cex = 1.0, signif.light = "#fbc9b9", signif.medium = "#f7523a", signif.dark = "#bd0017", insignif.light = "#c5dbe9", insignif.medium = "#5a9ecc", insignif.dark = "#1c5ba6", ...)
l = list(model.1, model.2, ...)
. Different object types can also be mixed.pdf
, ps
, png
, bmp
, jpg
, and tiff
are supported.model.names = c("My name 1", "My name 2")
etc. overrides the default behavior.plotreg
uses the coefficient names which are stored in the models. The custom.coef.names
argument can be used to replace them by other character strings in the order of appearance. For example, if a model shows a total of three coefficients (including the intercept), the argument custom.coef.names = c("Intercept", "variable 1", "variable 2")
will replace their names in this order.xlab
note below the diagram can be provided. If an empty character object is provided (custom.note = ""
), the note will be omitted completely.override.coef = list(c(0.1, 0.2, 0.3), c(0.05, 0.06, 0.07))
. If there is only one model, custom values can be provided as a plain vector (not embedded in a list). For example: override.coef = c(0.05, 0.06, 0.07)
.override.se = list(c(0.1, 0.2, 0.3), c(0.05, 0.06, 0.07))
. If there is only one model, custom values can be provided as a plain vector (not embedded in a list). For example: override.se = c(0.05, 0.06, 0.07)
. Overriding standard errors can be useful for the implementation of robust SEs, for example.override.pval = list(c(0.1, 0.2, 0.3), c(0.05, 0.06, 0.07))
. If there is only one model, custom values can be provided as a plain vector (not embedded in a list). For example: override.pval = c(0.05, 0.06, 0.07)
. Overriding p values can be useful for the implementation of robust SEs and p values, for example.override.ci.up
argument, the standard errors and p values as well as the ci.force
argument are ignored.override.ci.low
argument, the standard errors and p values as well as the ci.force
argument are ignored.omit.coef = "group"
deletes all coefficient rows from the diagram where the name of the coefficient contains the character sequence "group". More complex regular expressions can be used to filter out several kinds of model terms, for example omit.coef = "(thresh)|(ranef)"
to remove all model terms matching either "thresh" or "ranef".The omit.coef
argument is processed after the custom.coef.names
argument, so the regular expression should refer to the custom coefficient names.reorder.coef = c(3, 2, 1)
will put the third coefficient in the first row and the first coefficient in the third row. Reordering can be sensible because interaction effects are often added to the end of the model output although they were specified earlier in the model formula. Note: Reordering takes place after processing custom coefficient names and after omitting coefficients, so the custom.coef.names
and omit.coef
arguments should follow the original order.0.95
is used (i.e., an alpha value of 0.05).extract
function.l
argument, several plots are produced. If mfrow = TRUE
is set, multiple diagrams are aligned on the same page. If mfrow = FALSE
is set, each diagram per model comes out as a separate plot.coefplot
function, they must be provided as a vector with two numeric, e.g., xlim = c(-5, 5)
for displaying a range from -5
to +5
. In the plotreg
function, they can be provided either as such a vector with two values or as a list of vectors (with each entry corresponding to a model in l
). x
value of 0
. To remove the line, set lwd.zerobar = 0
. lwd.vbars = 0
. signif.outer = TRUE
, the outer CIs are used to evaluate significance, otherwise the inner CIs are used.cex.axis = 1.0
.coefplot
function produces coefficient plots (i.e., forest plots applied to point estimates and confidence intervals). It accepts raw data (the lower and upper bounds of inner and outer confidence intervals as well as the point estimates and their names) as input data. Significant coefficients and intervals can be plotted in a different color.The plotreg
function is a wrapper for the coefplot
function and works much like the
screenreg
, texreg
, and htmlreg
functions. It accepts a single or multiple statistical models as input and internally extracts the relevant data from the models. If confidence intervals are not defined in the extract method of a statistical model (see extract and extract-methods), the default standard errors are converted to confidence intervals. Most of the arguments work either like in the screenreg
, texreg
, and htmlreg
functions, or they work like in the coefplot
function.
texreg-package extract extract-methods texreg
#example from the 'lm' help file:
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2,10,20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
screenreg(lm.D9) # print model output to the R console
plotreg(lm.D9) # plot model output as a diagram
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