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" , Size: Actual Size Orientation: Auto portrait/landscape. Course List How to View Podcast Your Course About UC San Diego 9500 Gilman Dr. # marginal effect of each factor level across numeric variable cplot(m, x = "wt", dx = "am", what = "effect" ) Select HP DesignJet T1100ps (Remote Printer) or Cplot (as named from the driver installation section for Windows) as your printer. Know of another podcast produced at UC San Diego that might be of interest to others Let us know Podcasts. # predicted values for each factor level cplot(m, x = "am" ) # factor independent variables mtcars] <- factor(mtcars]) 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. # marginal effect of 'Petal.Width' across 'Petal.Width' cplot(m, x = "Petal.Width", what = "effect", n = 10 ) CPlot is a land and world management plugin for the Minecraft: Bedrock Edition server software PocketMine-MP. # 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), We recommend using the UC San Diego Tritons colors for personal. Currently methods exist for “lm”, “glm”, “loess” class models. The color codes: RGB, CYMK for print, Hex for web and the Pantone colors can be seen below. 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |