![]() 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 ), Originally, CPlot was meant to be an expanded version of MyPlot that should support many more features. I, ColinHDev, started the development of CPlot way back in 2019 after using MyPlot on my server for a few years. #CPLOT PREMIUM UCSD SOFTWARE## prediction from several angles m <- lm(Sepal.Length ~ Sepal.Width, data = iris) CPlot is a land and world management plugin for the Minecraft: Bedrock Edition server software PocketMine-MP. 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), 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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