Conditional predicted value and average marginal effect plots for models
Draw one or more conditional effects plots reflecting predictions or marginal effects from a model, conditional on a covariate. Currently methods exist for lm , glm , loess class models.
cplot(object, ...) ## Default S3 method: cplot( object, x = attributes(terms(object))[["term.labels"]][1L], dx = x, what = c("prediction", "effect"), data = prediction::find_data(object), type = c("response", "link"), vcov = stats::vcov(object), at, n = 25L, xvals = prediction::seq_range(data[[x]], n = n), level = 0.95, draw = TRUE, xlab = x, ylab = if (match.arg(what) == "prediction") paste0("Predicted value") else paste0("Marginal effect of ", dx), xlim = NULL, ylim = NULL, lwd = 1L, col = "black", lty = 1L, se.type = c("shade", "lines", "none"), se.col = "black", se.fill = grDevices::gray(0.5, 0.5), se.lwd = lwd, se.lty = if (match.arg(se.type) == "lines") 1L else 0L, factor.lty = 0L, factor.pch = 19L, factor.col = se.col, factor.fill = factor.col, factor.cex = 1L, xaxs = "i", yaxs = xaxs, las = 1L, scatter = FALSE, scatter.pch = 19L, scatter.col = se.col, scatter.bg = scatter.col, scatter.cex = 0.5, rug = TRUE, rug.col = col, rug.size = -0.02, ... ) ## S3 method for class 'clm' cplot( object, x = attributes(terms(object))[["term.labels"]][1L], dx = x, what = c("prediction", "classprediction", "stackedprediction", "effect"), data = prediction::find_data(object), type = c("response", "link"), vcov = stats::vcov(object), at, n = 25L, xvals = seq_range(data[[x]], n = n), level = 0.95, draw = TRUE, xlab = x, ylab = if (match.arg(what) == "effect") paste0("Marginal effect of ", dx) else paste0("Predicted value"), xlim = NULL, ylim = if (match.arg(what) %in% c("prediction", "stackedprediction")) c(0, 1.04) else NULL, lwd = 1L, col = "black", lty = 1L, factor.lty = 1L, factor.pch = 19L, factor.col = col, factor.fill = factor.col, factor.cex = 1L, xaxs = "i", yaxs = xaxs, las = 1L, scatter = FALSE, scatter.pch = 19L, scatter.col = factor.col, scatter.bg = scatter.col, scatter.cex = 0.5, rug = TRUE, rug.col = col, rug.size = -0.02, ... ) ## S3 method for class 'glm' cplot( object, x = attributes(terms(object))[["term.labels"]][1L], dx = x, what = c("prediction", "effect"), data = prediction::find_data(object), type = c("response", "link"), vcov = stats::vcov(object), at, n = 25L, xvals = prediction::seq_range(data[[x]], n = n), level = 0.95, draw = TRUE, xlab = x, ylab = if (match.arg(what) == "prediction") paste0("Predicted value") else paste0("Marginal effect of ", dx), xlim = NULL, ylim = NULL, lwd = 1L, col = "black", lty = 1L, se.type = c("shade", "lines", "none"), se.col = "black", se.fill = grDevices::gray(0.5, 0.5), se.lwd = lwd, se.lty = if (match.arg(se.type) == "lines") 1L else 0L, factor.lty = 0L, factor.pch = 19L, factor.col = se.col, factor.fill = factor.col, factor.cex = 1L, xaxs = "i", yaxs = xaxs, las = 1L, scatter = FALSE, scatter.pch = 19L, scatter.col = se.col, scatter.bg = scatter.col, scatter.cex = 0.5, rug = TRUE, rug.col = col, rug.size = -0.02, ... ) ## S3 method for class 'lm' cplot( object, x = attributes(terms(object))[["term.labels"]][1L], dx = x, what = c("prediction", "effect"), data = prediction::find_data(object), type = c("response", "link"), vcov = stats::vcov(object), at, n = 25L, xvals = prediction::seq_range(data[[x]], n = n), level = 0.95, draw = TRUE, xlab = x, ylab = if (match.arg(what) == "prediction") paste0("Predicted value") else paste0("Marginal effect of ", dx), xlim = NULL, ylim = NULL, lwd = 1L, col = "black", lty = 1L, se.type = c("shade", "lines", "none"), se.col = "black", se.fill = grDevices::gray(0.5, 0.5), se.lwd = lwd, se.lty = if (match.arg(se.type) == "lines") 1L else 0L, factor.lty = 0L, factor.pch = 19L, factor.col = se.col, factor.fill = factor.col, factor.cex = 1L, xaxs = "i", yaxs = xaxs, las = 1L, scatter = FALSE, scatter.pch = 19L, scatter.col = se.col, scatter.bg = scatter.col, scatter.cex = 0.5, rug = TRUE, rug.col = col, rug.size = -0.02, ... ) ## S3 method for class 'loess' cplot( object, x = attributes(terms(object))[["term.labels"]][1L], dx = x, what = c("prediction", "effect"), data = prediction::find_data(object), type = c("response", "link"), vcov = stats::vcov(object), at, n = 25L, xvals = prediction::seq_range(data[[x]], n = n), level = 0.95, draw = TRUE, xlab = x, ylab = if (match.arg(what) == "prediction") paste0("Predicted value") else paste0("Marginal effect of ", dx), xlim = NULL, ylim = NULL, lwd = 1L, col = "black", lty = 1L, se.type = c("shade", "lines", "none"), se.col = "black", se.fill = grDevices::gray(0.5, 0.5), se.lwd = lwd, se.lty = if (match.arg(se.type) == "lines") 1L else 0L, factor.lty = 0L, factor.pch = 19L, factor.col = se.col, factor.fill = factor.col, factor.cex = 1L, xaxs = "i", yaxs = xaxs, las = 1L, scatter = FALSE, scatter.pch = 19L, scatter.col = se.col, scatter.bg = scatter.col, scatter.cex = 0.5, rug = TRUE, rug.col = col, rug.size = -0.02, ... ) ## S3 method for class 'polr' cplot( object, x = attributes(terms(object))[["term.labels"]][1L], dx = x, what = c("prediction", "classprediction", "stackedprediction", "effect"), data = prediction::find_data(object), type = c("response", "link"), vcov = stats::vcov(object), at, n = 25L, xvals = seq_range(data[[x]], n = n), level = 0.95, draw = TRUE, xlab = x, ylab = if (match.arg(what) == "effect") paste0("Marginal effect of ", dx) else paste0("Predicted value"), xlim = NULL, ylim = if (match.arg(what) %in% c("prediction", "stackedprediction")) c(0, 1.04) else NULL, lwd = 1L, col = "black", lty = 1L, factor.lty = 1L, factor.pch = 19L, factor.col = col, factor.fill = factor.col, factor.cex = 1L, xaxs = "i", yaxs = xaxs, las = 1L, scatter = FALSE, scatter.pch = 19L, scatter.col = factor.col, scatter.bg = scatter.col, scatter.cex = 0.5, rug = TRUE, rug.col = col, rug.size = -0.02, ... ) ## S3 method for class 'multinom' cplot( object, x = attributes(terms(object))[["term.labels"]][1L], dx = x, what = c("prediction", "classprediction", "stackedprediction", "effect"), data = prediction::find_data(object), type = c("response", "link"), vcov = stats::vcov(object), at, n = 25L, xvals = seq_range(data[[x]], n = n), level = 0.95, draw = TRUE, xlab = x, ylab = if (match.arg(what) == "effect") paste0("Marginal effect of ", dx) else paste0("Predicted value"), xlim = NULL, ylim = if (match.arg(what) %in% c("prediction", "stackedprediction")) c(0, 1.04) else NULL, lwd = 1L, col = "black", lty = 1L, factor.lty = 1L, factor.pch = 19L, factor.col = col, factor.fill = factor.col, factor.cex = 1L, xaxs = "i", yaxs = xaxs, las = 1L, scatter = FALSE, scatter.pch = 19L, scatter.col = factor.col, scatter.bg = scatter.col, scatter.cex = 0.5, rug = TRUE, rug.col = col, rug.size = -0.02, ... )
object: A model object....: Additional arguments passed to plot.x: A character string specifying the name of variable to use as the x-axis dimension in the plot.dx: If what = "effect", the variable whose conditional marginal effect should be displayed. By default it is x (so the plot displays the marginal effect of x across values of x); ignored otherwise. If dx is a factor with more than 2 levels, an error will be issued.what: A character string specifying whether to draw a prediction (fitted values from the model, calculated using predict) or an effect (average marginal effect of dx conditional on x, using margins). Methods for classes other than lm or glm may provided additional options (e.g., cplot.polr() provides stackedprediction and class alternatives).data: A data frame to override the default value offered in object[["model"]].type: A character string specifying whether to calculate predictions on the response scale (default) or link (only relevant for non-linear models).vcov: A matrix containing the variance-covariance matrix for estimated model coefficients, or a function to perform the estimation with model as its only argument.at: Currently ignored.n: An integer specifying the number of points across x at which to calculate the predicted value or marginal effect, when x is numeric. Ignored otherwise.xvals: A numeric vector of values at which to calculate predictions or marginal effects, if x is numeric. By default, it is calculated from the data using seq_range. If x is a factor, this is ignored, as is n.level: The confidence level required (used to draw uncertainty bounds).draw: A logical (default TRUE), specifying whether to draw the plot. If FALSE, the data used in drawing are returned as a list of data.frames. This might be useful if you want to plot using an alternative plotting package (e.g., ggplot2). Also, if set to value add , then the resulting data is added to the existing plot.xlab: A character string specifying the value of xlab in plot.ylab: A character string specifying the value of ylab in plot.xlim: A two-element numeric vector specifying the x-axis limits. Set automatically if missing.ylim: A two-element numeric vector specifying the y-axis limits. Set automatically if missing.lwd: An integer specifying the width of the prediction or marginal effect line. See lines. If x is a factor variable in the model, this is used to set the line width of the error bars.col: A character string specifying the color of the prediction or marginal effect line. If x is a factor variable in the model, this is used to set the color of the error bars.lty: An integer specifying the line type of the prediction or marginal effect line. See par. If x is a factor variable in the model, this is used to set the line type of the error bars.se.type: A character string specifying whether to draw the confidence interval as lines (the default, using lines) or a shade (using polygon).se.col: If se.type = "lines", a character string specifying the color of the confidence interval lines. If se.type = "shade", the color of the shaded region border.se.fill: If se.type = "shade", the color of the shaded region. Ignored otherwise.se.lwd: If se.type = "lines", the width of the confidence interval lines. See lines.se.lty: If se.type = "lines", an integer specifying the line type of the confidence interval lines; if se.type = "shade", the line type of the shaded polygon border. See par.factor.lty: If x is a factor variable in the model, this is used to set the line type of an optional line connecting predictions across factor levels. If factor.lty = 0L (the default), no line is drawn.. See par.factor.pch: If x is a factor variable in the model, the shape to use when drawing points. See points.factor.col: If x is a factor variable in the model, the color to use for the border of the points. See points.factor.fill: If x is a factor variable in the model, the color to use for the fill of the points. See points.factor.cex: If x is a factor variable in the model, the expansion factor to use for the point size. See points.xaxs: A character string specifying xaxs. See par.yaxs: A character string specifying xaxs. See par.las: An integer string specifying las. See par.scatter: A logical indicating whether to plot the observed data in data as a scatterplot.scatter.pch: If scatter = TRUE, an integer specifying a shape to use for plotting the data. See points.scatter.col: If scatter = TRUE, a character string specifying a color to use for plotting the data. See points.scatter.bg: If scatter = TRUE, a character string specifying a color to use for plotting the data. See points.scatter.cex: If scatter = TRUE, an integer specifying the size of the points. See points.rug: A logical specifying whether to include an x-axis rug (see rug).rug.col: A character string specifying col to rug.rug.size: A numeric value specifying ticksize to rug.A tidy data frame containing the data used to draw the plot. Use draw = FALSE to simply generate the data structure for use elsewhere.
Note that when what = "prediction", the plots show predictions holding values of the data at their mean or mode, whereas when what = "effect" average marginal effects (i.e., at observed values) are shown.
When examining generalized linear models (e.g., logistic regression models), confidence intervals for predictions can fall outside of the response scale (again, for logistic regression this means confidence intervals can exceed the (0,1) bounds). This is consistent with the behavior of predict but may not be desired. The examples (below) show ways of constraining confidence intervals to these bounds.
The overall aesthetic is somewhat similar to to the output produced by the marginalModelPlot() function in the car package.
## Not run: require('datasets') # prediction from several angles m <- lm(Sepal.Length ~ Sepal.Width, data = iris) cplot(m) # more complex model m <- lm(Sepal.Length ~ Sepal.Width * Petal.Width * I(Petal.Width ^ 2), data = head(iris, 50)) ## marginal effect of 'Petal.Width' across 'Petal.Width' cplot(m, x = "Petal.Width", what = "effect", n = 10) # factor independent variables mtcars[["am"]] <- factor(mtcars[["am"]]) m <- lm(mpg ~ am * wt, data = mtcars) ## predicted values for each factor level cplot(m, x = "am") ## marginal effect of each factor level across numeric variable cplot(m, x = "wt", dx = "am", what = "effect") # 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", what = "effect", n = 10, draw = FALSE) if (require("ggplot2")) { # use ggplot2 instead of base graphics ggplot(tmp, aes(x = Petal.Width, y = "effect")) + geom_line(lwd = 2) + geom_line(aes(y = effect + 1.96*se.effect)) + geom_line(aes(y = effect - 1.96*se.effect)) } # a non-linear model m <- glm(am ~ wt*drat, data = mtcars, family = binomial) cplot(m, x = "wt") # prediction (response scale) cplot(m, x = "wt") # prediction (link scale) if (require("ggplot2")) { # prediction (response scale, constrained to [0,1]) cplotdat <- cplot(m, x = "wt", type = "link", draw = FALSE) ggplot(cplotdat, aes(x = xvals, y = plogis(yvals))) + geom_line(lwd = 1.5) + geom_line(aes(y = plogis(upper))) + geom_line(aes(y = plotis(lower))) } # effects on linear predictor and outcome cplot(m, x = "drat", dx = "wt", what = "effect", type = "link") cplot(m, x = "drat", dx = "wt", what = "effect", type = "response") # plot conditional predictions across a third factor local({ iris$long <- rbinom(nrow(iris), 1, 0.6) x <- glm(long ~ Sepal.Width*Species, data = iris) cplot(x, x = "Sepal.Width", data = iris[iris$Species == "setosa", ], ylim = c(0,1), col = "red", se.fill = rgb(1,0,0,.5), xlim = c(2,4.5)) cplot(x, x = "Sepal.Width", data = iris[iris$Species == "versicolor", ], draw = "add", col = "blue", se.fill = rgb(0,1,0,.5)) cplot(x, x = "Sepal.Width", data = iris[iris$Species == "virginica", ], draw = "add", col = "green", se.fill = rgb(0,0,1,.5)) }) # ordinal outcome if (require("MASS")) { # x is a factor variable house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) ## predicted probabilities cplot(house.plr) ## cumulative predicted probabilities cplot(house.plr, what = "stacked") ## ggplot2 example if (require("ggplot2")) { ggplot(cplot(house.plr), aes(x = xvals, y = yvals, group = level)) + geom_line(aes(color = level)) } # x is continuous cyl.plr <- polr(factor(cyl) ~ wt, data = mtcars) cplot(cyl.plr, col = c("red", "purple", "blue"), what = "stacked") cplot(cyl.plr, what = "class") } ## End(Not run)
plot.margins, persp.lm