Perspective and heatmap/contour plots for models
Draw one or more perspectives plots reflecting predictions or marginal effects from a model, or the same using a flat heatmap or filled contour (image) representation. Currently methods exist for lm , glm , and loess models.
## S3 method for class 'lm' image( x, xvar = attributes(terms(x))[["term.labels"]][1], yvar = attributes(terms(x))[["term.labels"]][2], dx = xvar, what = c("prediction", "effect"), type = c("response", "link"), vcov = stats::vcov(x), nx = 25L, ny = nx, nz = 20, xlab = xvar, ylab = yvar, xaxs = "i", yaxs = xaxs, bty = "o", col = gray(seq(0.05, 0.95, length.out = nz), alpha = 0.75), contour = TRUE, contour.labels = NULL, contour.drawlabels = TRUE, contour.cex = 0.6, contour.col = "black", contour.lty = 1, contour.lwd = 1, ... ) ## S3 method for class 'glm' image( x, xvar = attributes(terms(x))[["term.labels"]][1], yvar = attributes(terms(x))[["term.labels"]][2], dx = xvar, what = c("prediction", "effect"), type = c("response", "link"), vcov = stats::vcov(x), nx = 25L, ny = nx, nz = 20, xlab = xvar, ylab = yvar, xaxs = "i", yaxs = xaxs, bty = "o", col = gray(seq(0.05, 0.95, length.out = nz), alpha = 0.75), contour = TRUE, contour.labels = NULL, contour.drawlabels = TRUE, contour.cex = 0.6, contour.col = "black", contour.lty = 1, contour.lwd = 1, ... ) ## S3 method for class 'loess' image( x, xvar = attributes(terms(x))[["term.labels"]][1], yvar = attributes(terms(x))[["term.labels"]][2], dx = xvar, what = c("prediction", "effect"), type = c("response", "link"), vcov = stats::vcov(x), nx = 25L, ny = nx, nz = 20, xlab = xvar, ylab = yvar, xaxs = "i", yaxs = xaxs, bty = "o", col = gray(seq(0.05, 0.95, length.out = nz), alpha = 0.75), contour = TRUE, contour.labels = NULL, contour.drawlabels = TRUE, contour.cex = 0.6, contour.col = "black", contour.lty = 1, contour.lwd = 1, ... ) ## S3 method for class 'lm' persp( x, xvar = attributes(terms(x))[["term.labels"]][1], yvar = attributes(terms(x))[["term.labels"]][2], dx = xvar, what = c("prediction", "effect"), type = c("response", "link"), vcov = stats::vcov(x), nx = 25L, ny = nx, theta = 45, phi = 10, shade = 0.75, xlab = xvar, ylab = yvar, zlab = if (match.arg(what) == "prediction") "Predicted value" else paste0("Marginal effect of ", dx), ticktype = c("detailed", "simple"), ... ) ## S3 method for class 'glm' persp( x, xvar = attributes(terms(x))[["term.labels"]][1], yvar = attributes(terms(x))[["term.labels"]][2], dx = xvar, what = c("prediction", "effect"), type = c("response", "link"), vcov = stats::vcov(x), nx = 25L, ny = nx, theta = 45, phi = 10, shade = 0.75, xlab = xvar, ylab = yvar, zlab = if (match.arg(what) == "prediction") "Predicted value" else paste0("Marginal effect of ", dx), ticktype = c("detailed", "simple"), ... ) ## S3 method for class 'loess' persp( x, xvar = attributes(terms(x))[["term.labels"]][1], yvar = attributes(terms(x))[["term.labels"]][2], dx = xvar, what = c("prediction", "effect"), type = c("response", "link"), vcov = stats::vcov(x), nx = 25L, ny = nx, theta = 45, phi = 10, shade = 0.75, xlab = xvar, ylab = yvar, zlab = if (match.arg(what) == "prediction") "Predicted value" else paste0("Marginal effect of ", dx), ticktype = c("detailed", "simple"), ... )
x: A model object.xvar: A character string specifying the name of variable to use as the x dimension in the plot. See persp for details.yvar: A character string specifying the name of variable to use as the y dimension in the plot. See persp for details.dx: A character string specifying the name of the variable for which the conditional average marginal effect is desired when what = "effect". By default this is xvar.what: A character string specifying whether to draw prediction (fitted values from the model, calculated using predict) or effect (marginal effect of dx, using margins).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.nx: An integer specifying the number of points across x at which to calculate the predicted value or marginal effect.ny: An integer specifying the number of points across y at which to calculate the predicted value or marginal effect.nz: An integer specifying, for image, the number of breakpoints to use when coloring the plot.xlab: A character string specifying the value of xlab in persp or image.ylab: A character string specifying the value of ylab in persp or image.xaxs: A character string specifying the x-axis type (see par).yaxs: A character string specifying the y-axis type (see par).bty: A character string specifying the box type (see par).col: A character vector specifying colors to use when coloring the contour plot.contour: For image, a logical specifying whether to overlay contour lines onto the heatmap using contour.contour.labels: For image, if contour = TRUE a logical specifying whether to overlay contour lines onto the heatmap.contour.drawlabels: For image, if contour = TRUE a logical specifying whether to overlay contour lines onto the heatmap.contour.cex: For image, if contour = TRUE and contour.drawlabels = TRUE a numeric specifying the label size for contour line labels (see par).contour.col: For image, if contour = TRUE a character string specifying a color for contour lines.contour.lty: For image, if contour = TRUE an integer specifying a line type for contour lines (see par).contour.lwd: For image, if contour = TRUE an integer specifying a line width for contour lines (see par)....: Additional arguments passed to persp or image.theta: For persp, an integer vector specifying the value of theta in persp. If length greater than 1, multiple subplots are drawn with different rotations.phi: For persp, an integer vector specifying the value of phi in persp. If length greater than 1, multiple subplots are drawn with different rotations.shade: For persp, an integer vector specifying the value of shade in persp.zlab: A character string specifying the value of zlab (vertical axis label) in persp.ticktype: A character string specifying one of: detailed (the default) or simple . See persp.## Not run: require('datasets') # prediction from several angles m <- lm(mpg ~ wt*drat, data = mtcars) persp(m, theta = c(45, 135, 225, 315)) # flat/heatmap representation image(m) # marginal effect of 'drat' across drat and wt m <- lm(mpg ~ wt*drat*I(drat^2), data = mtcars) persp(m, xvar = "drat", yvar = "wt", what = "effect", nx = 10, ny = 10, ticktype = "detailed") # a non-linear model m <- glm(am ~ wt*drat, data = mtcars, family = binomial) persp(m, theta = c(30, 60)) # prediction # flat/heatmap representation image(m) # effects on linear predictor and outcome persp(m, xvar = "drat", yvar = "wt", what = "effect", type = "link") persp(m, xvar = "drat", yvar = "wt", what = "effect", type = "response") ## End(Not run)
plot.margins, cplot