plotp.Predict {rms} | R Documentation

## Plot Effects of Variables Estimated by a Regression Model Fit Using plotly

### Description

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.

### Usage

    ## 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, ...)

    plotp(data, ...)

### 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

### Value

a `plotly` object or a list containing two elements, each one a `plotly` object

### Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu

### 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

    ## 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)

[Package rms version 5.1-2 Index]