Spec cplot4/1/2023 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" ) # predicted values for each factor level cplot(m, x = "am" ) # 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 ), Also, if set to value add, then the resulting data is added to the existing plot. This might be useful if you want to plot using an alternative plotting package (e.g., ggplot2). If FALSE, the data used in drawing are returned as a list of ames. # prediction from several angles m <- lm(Sepal.Length ~ Sepal.Width, data = iris) A logical (default TRUE ), specifying whether to draw the plot. 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"), Incorporating Navionics charting capabilities, the system links with new cartography for ease of management and navigation. ![]() Se.lty = if (match.arg(se.type) = "lines") 1L else 0L, C-Plot Nav+ is a reliable, computer-based plotting system from TMQ designed for both recreational and professional applications. This avoids streaks of colors occurring with other color spaces, e.g., HSL. ![]() Ylab = if (match.arg(what) = "prediction") paste0("Predicted value") else Features of this software: cplot uses OKLAB, a perceptually uniform color space for the argument colors. Xvals = prediction::seq_range(data], n = n), Currently methods exist for “lm”, “glm”, “loess” class models. 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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