![]() ![]() ![]() Geom_line(aes(y = effect + 1.96 *se. # use ggplot2 instead of base graphics ggplot(tmp, aes(x = Petal.Width, y = "effect" )) + What = "effect", n = 10, draw = FALSE ) # marginal effect of 'Petal.Width' across 'Sepal.Width' # without drawing the plot # this might be useful for using, e.g., ggplot2 for plotting tmp <- cplot(m, x = "Sepal.Width", dx = "Petal.Width" , # marginal effect of each factor level across numeric variable cplot(m, x = "wt", dx = "am", what = "effect" ) By voting up you can indicate which examples are most useful and appropriate. # predicted values for each factor level cplot(m, x = "am" ) mpmath.cplot - python examples Here are the examples of the python api mpmath.cplot taken from open source projects. # factor independent variables mtcars] <- factor(mtcars]) # marginal effect of 'Petal.Width' across 'Petal.Width' cplot(m, x = "Petal.Width", what = "effect", n = 10 ) # more complex model m <- lm(Sepal.Length ~ Sepal.Width * Petal.Width * I(Petal.Width ^ 2 ), # prediction from several angles m <- lm(Sepal.Length ~ Sepal.Width, data = iris) Ylim = if (match.arg(what) %in% c("prediction", "stackedprediction")) c(0, 1.04) Ylab = if (match.arg(what) = "effect") paste0("Marginal effect of ", dx) else What = c("prediction", "classprediction", "stackedprediction", "effect"), Se.lty = if (match.arg(se.type) = "lines") 1L else 0L, Ylab = if (match.arg(what) = "prediction") paste0("Predicted value") else ![]() Xvals = prediction::seq_range(data], n = n), Until I've figured out the details, I'll share a couple of test plots. Currently methods exist for “lm”, “glm”, “loess” class models. There will probably be a 3D plot function in a future version of mpmath (or two functions for two-variable real, and complex functions), similar in style to the existing matplotlib wrappers plot and cplot. Cplot: Conditional predicted value and average marginal effect plots for models Descriptionĭraw one or more conditional effects plots reflecting predictions or marginal effects from a model, conditional on a covariate. ![]()
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