# Plot Effects of Variables Estimated by a Regression Model Fit Using plotly Uses `plotly` graphics (without using ggplot2) to plot the effect of one or two predictors on the linear predictor or X beta scale, or on some transformation of that scale. The first argument specifies the result of the `Predict` function. The predictor is always plotted in its original coding. Hover text shows point estimates, confidence intervals, and on the leftmost x-point, adjustment variable settings. If `Predict` was run with no variable settings, so that each predictor is varied one at a time, the result of `plotp.Predict` is a list with two elements. The first, named `Continuous`, is a `plotly` object containing a single graphic with all the continuous predictors varying. The second, named `Categorical`, is a `plotly` object containing a single graphic with all the categorical predictors varying. If there are no categorical predictors, the value returned by by `plotp.Predict` is a single `plotly` object and not a list of objects. If `rdata` is given, a spike histogram is drawn showing the location/density of data values for the $x$-axis variable. If there is a superposition variable that generated separate curves, the data density specific to each class of points is shown. The histograms are drawn by `histSpikeg`. To plot effects instead of estimates (e.g., treatment differences as a function of interacting factors) see `contrast.rms` and `summary.rms`. Unlike `ggplot.Predict`, `plotp.Predict` does not handle `groups`, `anova`, or `perim` arguments. ```r ## S3 method for class 'Predict' plotp(data, subset, xlim, ylim, xlab, ylab, rdata=NULL, nlevels=3, vnames=c('labels','names'), histSpike.opts=list(frac=function(f) 0.01 + 0.02 * sqrt(f - 1)/sqrt(max(f, 2) - 1), side=1, nint=100), ncols=3, width=800, ...) ``` ## Arguments - `data`: a data frame created by `Predict` - `subset`: a subsetting expression for restricting the rows of `data` that are used in plotting. For example, predictions may have been requested for males and females but one wants to plot only females. - `xlim`: ignored unless predictors were specified to `Predict`. Specifies the x-axis limits of the single plot produced. - `ylim`: Range for plotting on response variable axis. Computed by default and includes the confidence limits. - `xlab`: Label for `x`-axis when a single plot is made, i.e., when a predictor is specified to `Predict`. Default is one given to `asis, rcs`, etc., which may have been the `"label"` attribute of the variable. - `ylab`: Label for `y`-axis. If `fun` is not given, default is `"log Odds"` for `lrm`, `"log Relative Hazard"` for `cph`, name of the response variable for `ols`, `TRUE` or `log(TRUE)` for `psm`, or `"X * Beta"` otherwise. Specify `ylab=NULL` to omit `y`-axis labels. - `rdata`: a data frame containing the original raw data on which the regression model were based, or at least containing the $x$-axis and grouping variable. If `rdata` is present and contains the needed variables, the original data are added to the graph in the form of a spike histogram using `histSpikeg` in the Hmisc package. - `nlevels`: A non-numeric x-axis variable with `nlevels` or fewer unique values will cause a horizontal dot plot to be drawn instead of an x-y plot. - `vnames`: applies to the case where multiple plots are produced separately by predictor. Set to `'names'` to use variable names instead of labels for these small plots. - `histSpike.opts`: a list containing named elements that specifies parameters to `histSpikeg` when `rdata` is given. The `col` parameter is usually derived from other plotting information and not specified by the user. - `ncols`: number of columns of plots to use when plotting multiple continuous predictors - `width`: width in pixels for `plotly` graphics - ``...``: ignored ## Returns a `plotly` object or a list containing two elements, each one a `plotly` object ## Author(s) Frank Harrell Department of Biostatistics, Vanderbilt University fh@fharrell.com ## References Fox J, Hong J (2009): Effect displays in R for multinomial and proportional-odds logit models: Extensions to the effects package. J Stat Software 32 No. 1. ## See Also `Predict`, `rbind.Predict`, `datadist`, `predictrms`, `contrast.rms`, `summary.rms`, `rms`, `rmsMisc`, `plot.Predict`, `ggplot.Predict`, `histSpikeg`, `Overview` ## Examples ```r ## Not run: n <- 350 # define sample size set.seed(17) # so can reproduce the results age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'), n,TRUE)) label(age) <- 'Age' # label is in Hmisc label(cholesterol) <- 'Total Cholesterol' label(blood.pressure) <- 'Systolic Blood Pressure' label(sex) <- 'Sex' units(cholesterol) <- 'mg/dl' # uses units.default in Hmisc units(blood.pressure) <- 'mmHg' # Specify population model for log odds that Y=1 L <- .4*(sex=='male') + .045*(age-50) + (log(cholesterol - 10)-5.2)*(-2*(sex=='female') + 2*(sex=='male')) + .01 * (blood.pressure - 120) # Simulate binary y to have Prob(y=1) = 1/[1+exp(-L)] y <- ifelse(runif(n) < plogis(L), 1, 0) ddist <- datadist(age, blood.pressure, cholesterol, sex) options(datadist='ddist') fit <- lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4)), x=TRUE, y=TRUE) p <- plotp(Predict(fit)) p$Continuous p$Categorical # When using Rmarkdown html notebook, best to use # prList(p) to render the two objects plotp(Predict(fit), rdata=llist(blood.pressure, age))$Continuous # spike histogram plot for two of the predictors p <- Predict(fit, name=c('age','cholesterol')) # Make 2 plots plotp(p) p <- Predict(fit, age, sex) plotp(p, rdata=llist(age,sex)) # rdata= allows rug plots (1-dimensional scatterplots) # on each sex's curve, with sex- # specific density of age # If data were in data frame could have used that p <- Predict(fit, age=seq(20,80,length=100), sex='male', fun=plogis) # works if datadist not used plotp(p, ylab='P') # plot predicted probability in place of log odds # Compute predictions for three predictors, with superpositioning or # conditioning on sex, combined into one graph p1 <- Predict(fit, age, sex) p2 <- Predict(fit, cholesterol, sex) p3 <- Predict(fit, blood.pressure, sex) p <- rbind(age=p1, cholesterol=p2, blood.pressure=p3) plotp(p, ncols=2, rdata=llist(age, cholesterol, sex)) ## End(Not run) ```

plotp.Predict function