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Provides an S3 generic method for plotting coefficients from a model so it can be extended to other model types.
A graphical display of the coefficients and standard errors from a fitted model
coefplot is the S3 generic method for plotting the coefficients from a fitted model.
This can be extended with new methods for other types of models not currently available.
Coefplot method for workflow objects
Coefplot method for parsnip objects
coefplot(model, ...)# S3 method for default
coefplot(
model,
title = "Coefficient Plot",
xlab = "Value",
ylab = "Coefficient",
innerCI = 1,
outerCI = 2,
lwdInner = 1 + interactive * 2,
lwdOuter = if (interactive) 1 else unname((Sys.info()["sysname"] != "Windows") * 0.5),
pointSize = 3 + interactive * 5,
color = "blue",
shape = 16,
cex = 0.8,
textAngle = 0,
numberAngle = 0,
zeroColor = "grey",
zeroLWD = 1,
zeroType = 2,
facet = FALSE,
scales = "free",
sort = c("natural", "magnitude", "alphabetical"),
decreasing = FALSE,
numeric = FALSE,
fillColor = "grey",
alpha = 1/2,
horizontal = FALSE,
factors = NULL,
only = NULL,
shorten = TRUE,
intercept = TRUE,
interceptName = "(Intercept)",
coefficients = NULL,
predictors = NULL,
strict = FALSE,
trans = identity,
interactive = FALSE,
newNames = NULL,
plot = TRUE,
...
)
# S3 method for lm
coefplot(...)
# S3 method for glm
coefplot(...)
# S3 method for workflow
coefplot(model, ...)
# S3 method for model_fit
coefplot(model, ...)
# S3 method for rxGlm
coefplot(...)
# S3 method for rxLinMod
coefplot(...)
# S3 method for rxLogit
coefplot(...)
A parsnip object
All arguments are passed on to coefplot.lm. Please see that function for argument information.
The name of the plot, if NULL then no name is given
The x label
The y label
How wide the inner confidence interval should be, normally 1 standard deviation. If 0, then there will be no inner confidence interval.
How wide the outer confidence interval should be, normally 2 standard deviations. If 0, then there will be no outer confidence interval.
The thickness of the inner confidence interval
The thickness of the outer confidence interval
Size of coefficient point
The color of the points and lines
The shape of the points
The text size multiplier, currently not used
The angle for the coefficient labels, 0 is horizontal
The angle for the value labels, 0 is horizontal
The color of the line indicating 0
The thickness of the 0 line
The type of 0 line, 0 will mean no line
logical; If the coefficients should be faceted by the variables, numeric coefficients (including the intercept) will be one facet. Currently not available.
The way the axes should be treated in a faceted plot. Can be c("fixed", "free", "free_x", "free_y"). Currently not available.
Determines the sort order of the coefficients. Possible values are c("natural", "magnitude", "alphabetical")
logical; Whether the coefficients should be ascending or descending
logical; If true and factors has exactly one value, then it is displayed in a horizontal graph with continuous confidence bounds. Currently not available.
The color of the confidence bounds for a numeric factor. Currently not available.
The transparency level of the numeric factor's confidence bound. Currently not available.
logical; If the plot should be displayed horizontally. Currently not available.
Vector of factor variables that will be the only ones shown
logical; If factors has a value this determines how interactions are treated. True means just that variable will be shown and not its interactions. False means interactions will be included.
logical or character; If FALSE then coefficients for factor levels will include their variable name. If TRUE coefficients for factor levels will be stripped of their variable names. If a character vector of variables only coefficients for factor levels associated with those variables will the variable names stripped. Currently not available.
logical; Whether the Intercept coefficient should be plotted
Specifies name of intercept it case it is not the default of "(Intercept").
A character vector specifying which factor coefficients to keep. It will keep all levels and any interactions, even if those are not listed.
A character vector specifying which coefficients to keep. Each individual coefficient can be specified. Use predictors to specify entire factors.
If TRUE then predictors will only be matched to its own coefficients, not its interactions
A transformation function to apply to the values and confidence intervals. identity by default. Use invlogit for binary regression.
If TRUE an interactive plot is generated instead of ggplot2
Named character vector of new names for coefficients
logical; If the plot should be drawn, if false then a data.frame of the values will be returned
A ggplot2 object or data.frame. See details in coefplot.lm for more information
If plot is TRUE then a ggplot object is returned. Otherwise a data.frame listing coefficients and confidence bands is returned.
A ggplot object. See coefplot.lm for more information.
A ggplot object. See coefplot.lm for more information.
A ggplot object. See coefplot.lm for more information.
default: Default method
lm: lm
glm: glm
workflow: tidymodels workflows
model_fit: parsnip
rxGlm: rxGlm
rxLinMod: rxLinMod
rxLogit: rxLogit
Currently, methods are available for lm, glm and rxLinMod objects.
For more information on this function and it's arguments see coefplot.default
Pulls model element out of workflow object then calls coefplot.
Pulls model element out of parsnip object then calls coefplot.
# NOT RUN {
data(diamonds)
head(diamonds)
model1 <- lm(price ~ carat + cut*color, data=diamonds)
model2 <- lm(price ~ carat*color, data=diamonds)
model3 <- glm(price > 10000 ~ carat*color, data=diamonds)
coefplot(model1)
coefplot(model2)
coefplot(model3)
coefplot(model1, predictors="color")
coefplot(model1, predictors="color", strict=TRUE)
coefplot(model1, coefficients=c("(Intercept)", "color.Q"))
coefplot(model1, predictors="cut", coefficients=c("(Intercept)", "color.Q"), strict=TRUE)
coefplot(model1, predictors="cut", coefficients=c("(Intercept)", "color.Q"), strict=FALSE)
coefplot(model1, predictors="cut", coefficients=c("(Intercept)", "color.Q"),
strict=TRUE, newNames=c(color.Q="Color", "cut^4"="Fourth"))
coefplot(model1, predictors=c("(Intercept)", "carat"), newNames=c(carat="Size"))
coefplot(model1, predictors=c("(Intercept)", "carat"),
newNames=c(carat="Size", "(Intercept)"="Constant"))
data(diamonds)
head(diamonds)
model1 <- lm(price ~ carat + cut*color, data=diamonds)
model2 <- lm(price ~ carat*color, data=diamonds)
coefplot(model1)
coefplot(model2)
coefplot(model1, predictors="color")
coefplot(model1, predictors="color", strict=TRUE)
coefplot(model1, coefficients=c("(Intercept)", "color.Q"))
model1 <- lm(price ~ carat + cut*color, data=diamonds)
coefplot(model1)
model2 <- glm(price > 10000 ~ carat + cut*color, data=diamonds, family=binomial(link="logit"))
coefplot(model2)
coefplot(model2, trans=invlogit)
# }
# NOT RUN {
mod4 <- rxGlm(price ~ carat + cut + x, data=diamonds)
mod5 <- rxGlm(price > 10000 ~ carat + cut + x, data=diamonds, family="binomial")
coefplot(mod4)
coefplot(mod5)
# }
# NOT RUN {
# }
# NOT RUN {
data(diamonds)
mod3 <- rxLinMod(price ~ carat + cut + x, data=diamonds)
coefplot(mod3)
# }
# NOT RUN {
# }
# NOT RUN {
data(diamonds)
mod6 <- rxLogit(price > 10000 ~ carat + cut + x, data=diamonds)
coefplot(mod6)
# }
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