rms (version 5.1-2)

Predict: Compute Predicted Values and Confidence Limits


Predict allows the user to easily specify which predictors are to vary. When the vector of values over which a predictor should vary is not specified, the range will be all levels of a categorical predictor or equally-spaced points between the datadist "Low:prediction" and "High:prediction" values for the variable (datadist by default uses the 10th smallest and 10th largest predictor values in the dataset). Predicted values are the linear predictor (X beta), a user-specified transformation of that scale, or estimated probability of surviving past a fixed single time point given the linear predictor. Predict is usually used for plotting predicted values but there is also a print method.

When the first argument to Predict is a fit object created by bootcov with coef.reps=TRUE, confidence limits come from the stored matrix of bootstrap repetitions of coefficients, using bootstrap percentile nonparametric confidence limits, basic bootstrap, or BCa limits. Such confidence intervals do not make distributional assumptions. You can force Predict to instead use the bootstrap covariance matrix by setting usebootcoef=FALSE. If coef.reps was FALSE, usebootcoef=FALSE is the default.

There are ggplot, plotp, and plot methods for Predict objects that makes it easy to show predicted values and confidence bands.

The rbind method for Predict objects allows you to create separate sets of predictions under different situations and to combine them into one set for feeding to plot.Predict, ggplot.Predict, or plotp.Predict. For example you might want to plot confidence intervals for means and for individuals using ols, and have the two types of confidence bands be superposed onto one plot or placed into two panels. Another use for rbind is to combine predictions from quantile regression models that predicted three different quantiles.

If conf.type="simultaneous", simultaneous (over all requested predictions) confidence limits are computed. See the predictrms function for details.


Predict(x, ..., fun,
        type = c("predictions", "model.frame", "x"),
        np = 200, conf.int = 0.95,
        conf.type = c("mean", "individual","simultaneous"),
        usebootcoef=TRUE, boot.type=c("percentile","bca","basic"),
        adj.zero = FALSE, ref.zero = FALSE,
        kint=NULL, time = NULL, loglog = FALSE, digits=4, name,
        factors=NULL, offset=NULL)

# S3 method for Predict print(x, …)

# S3 method for Predict rbind(…, rename)



an rms fit object, or for print the result of Predict. options(datadist="d") must have been specified (where d was created by datadist), or it must have been in effect when the the model was fitted.

One or more variables to vary, or single-valued adjustment values. Specify a variable name without an equal sign to use the default display range, or any range you choose (e.g. seq(0,100,by=2),c(2,3,7,14)). The default list of values for which predictions are made is taken as the list of unique values of the variable if they number fewer than 11. For variables with \(>10\) unique values, np equally spaced values in the range are used for plotting if the range is not specified. Variables not specified are set to the default adjustment value limits[2], i.e. the median for continuous variables and a reference category for non-continuous ones. Later variables define adjustment settings. For categorical variables, specify the class labels in quotes when specifying variable values. If the levels of a categorical variable are numeric, you may omit the quotes. For variables not described using datadist, you must specify explicit ranges and adjustment settings for predictors that were in the model. If no variables are specified in …, predictions will be made by separately varying all predictors in the model over their default range, holding the other predictors at their adjustment values. This has the same effect as specifying name as a vector containing all the predictors. For rbind, … represents a series of results from Predict. If you name the results, these names will be taken as the values of the new .set. variable added to the concatenated data frames. See an example below.


an optional transformation of the linear predictor. Specify fun='mean' if the fit is a proportional odds model fit and you ran bootcov with coef.reps=TRUE. This will let the mean function be re-estimated for each bootstrap rep to properly account for all sources of uncertainty in estimating the mean response.


defaults to providing predictions. Set to "model.frame" to return a data frame of predictor settings used. Set to "x" to return the corresponding design matrix constructed from the predictor settings.


the number of equally-spaced points computed for continuous predictors that vary, i.e., when the specified value is . or NA


confidence level. Default is 0.95. Specify FALSE to suppress.


type of confidence interval. Default is "mean" which applies to all models. For models containing a residual variance (e.g, ols), you can specify conf.type="individual" instead, to obtain limits on the predicted value for an individual subject. Specify conf.type="simultaneous" to obtain simultaneous confidence bands for mean predictions with family-wise coverage of conf.int.


set to FALSE to force the use of the bootstrap covariance matrix estimator even when bootstrap coefficient reps are present


set to 'bca' to compute BCa confidence limits or 'basic' to use the basic bootstrap. The default is to compute percentile intervals


Set to TRUE to adjust all non-plotted variables to 0 (or reference cell for categorical variables) and to omit intercept(s) from consideration. Default is FALSE.


Set to TRUE to subtract a constant from \(X\beta\) before plotting so that the reference value of the x-variable yields y=0. This is done before applying function fun. This is especially useful for Cox models to make the hazard ratio be 1.0 at reference values, and the confidence interval have width zero.


This is only useful in a multiple intercept model such as the ordinal logistic model. There to use to second of three intercepts, for example, specify kint=2. The default is 1 for lrm and the middle intercept corresponding to the median y for orm.


Specify a single time u to cause function survest to be invoked to plot the probability of surviving until time u when the fit is from cph or psm.


Specify loglog=TRUE to plot log[-log(survival)] instead of survival, when time is given.


Controls how ``adjust-to'' values are plotted. The default is 4 significant digits.


Instead of specifying the variables to vary in the variables (…) list, you can specify one or more variables by specifying a vector of character string variable names in the name argument. Using this mode you cannot specify a list of variable values to use; prediction is done as if you had said e.g. age without the equal sign. Also, interacting factors can only be set to their reference values using this notation.


an alternate way of specifying …, mainly for use by survplot or gendata. This must be a list with one or more values for each variable listed, with NA values for default ranges.


a list containing one value for one variable, which is mandatory if the model included an offset term. The variable name must match the innermost variable name in the offset term. The single offset is added to all predicted values.


If you are concatenating predictor sets using rbind and one or more of the variables were renamed for one or more of the sets, but these new names represent different versions of the same predictors (e.g., using or not using imputation), you can specify a named character vector to rename predictors to a central name. For example, specify rename=c(age.imputed='age', corrected.bp='bp') to rename from old names age.imputed, corrected.bp to age, bp. This happens before concatenation of rows.


a data frame containing all model predictors and the computed values yhat, lower, upper, the latter two if confidence intervals were requested. The data frame has an additional class "Predict". If name is specified or no predictors are specified in …, the resulting data frame has an additional variable called .predictor. specifying which predictor is currently being varied. .predictor. is handy for use as a paneling variable in lattice or ggplot2 graphics.


When there are no intercepts in the fitted model, plot subtracts adjustment values from each factor while computing variances for confidence limits.

Specifying time will not work for Cox models with time-dependent covariables. Use survest or survfit for that purpose.

See Also

plot.Predict, ggplot.Predict, plotp.Predict, datadist, predictrms, contrast.rms, summary.rms, rms, rms.trans, survest, survplot, rmsMisc, transace, rbind, bootcov, bootBCa, boot.ci


Run this code
n <- 1000    # 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'))
# 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)

fit <- lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4)))
Predict(fit, age, cholesterol, np=4)
Predict(fit, age=seq(20,80,by=10), sex, conf.int=FALSE)
Predict(fit, age=seq(20,80,by=10), sex='male')  # works if datadist not used
# Get simultaneous confidence limits accounting for making 7 estimates
# Predict(fit, age=seq(20,80,by=10), sex='male', conf.type='simult')
# (this needs the multcomp package)

ddist$limits$age[2] <- 30    # make 30 the reference value for age
# Could also do: ddist$limits["Adjust to","age"] <- 30
fit <- update(fit)   # make new reference value take effect
Predict(fit, age, ref.zero=TRUE, fun=exp)

# Make two curves, and plot the predicted curves as two trellis panels
w <- Predict(fit, age, sex)
xyplot(yhat ~ age | sex, data=w, type='l')
# To add confidence bands we need to use the Hmisc xYplot function in
# place of xyplot
xYplot(Cbind(yhat,lower,upper) ~ age | sex, data=w, 
       method='filled bands', type='l', col.fill=gray(.95))
# If non-displayed variables were in the model, add a subtitle to show
# their settings using title(sub=paste('Adjusted to',attr(w,'info')$adjust),adj=0)
# Easier: feed w into plot.Predict, ggplot.Predict, plotp.Predict
# }
# Predictions form a parametric survival model
n <- 1000
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n, 
              rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
t <- -log(runif(n))/h
label(t) <- 'Follow-up Time'
e <- ifelse(t<=cens,1,0)
t <- pmin(t, cens)
units(t) <- "Year"
ddist <- datadist(age, sex)
Srv <- Surv(t,e)

# Fit log-normal survival model and plot median survival time vs. age
f <- psm(Srv ~ rcs(age), dist='lognormal')
med <- Quantile(f)       # Creates function to compute quantiles
                         # (median by default)
Predict(f, age, fun=function(x)med(lp=x))
# Note: This works because med() expects the linear predictor (X*beta)
#       as an argument.  Would not work if use 
#       ref.zero=TRUE or adj.zero=TRUE.
# Also, confidence intervals from this method are approximate since
# they don't take into account estimation of scale parameter

# Fit an ols model to log(y) and plot the relationship between x1
# and the predicted mean(y) on the original scale without assuming
# normality of residuals; use the smearing estimator.  Before doing
# that, show confidence intervals for mean and individual log(y),
# and for the latter, also show bootstrap percentile nonparametric
# pointwise confidence limits
x1 <- runif(300)
x2 <- runif(300)
ddist <- datadist(x1,x2); options(datadist='ddist')
y  <- exp(x1+ x2 - 1 + rnorm(300))
f  <- ols(log(y) ~ pol(x1,2) + x2, x=TRUE, y=TRUE)  # x y for bootcov
fb <- bootcov(f, B=100)
pb <- Predict(fb, x1, x2=c(.25,.75))
p1 <- Predict(f,  x1, x2=c(.25,.75))
p <- rbind(normal=p1, boot=pb)

p1 <- Predict(f, x1, conf.type='mean')
p2 <- Predict(f, x1, conf.type='individual')
p  <- rbind(mean=p1, individual=p2)
plot(p, label.curve=FALSE)   # uses superposition
plot(p, ~x1 | .set.)         # 2 panels

r <- resid(f)
smean <- function(yhat)smearingEst(yhat, exp, res, statistic='mean')
formals(smean) <- list(yhat=numeric(0), res=r[!is.na(r)])
#smean$res <- r[!is.na(r)]   # define default res argument to function
Predict(f, x1, fun=smean)

## Example using offset
g <- Glm(Y ~ offset(log(N)) + x1 + x2, family=poisson)
Predict(g, offset=list(N=100))
# }
# }

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