Predicted Values from Model Fit

The predict function is used to obtain a variety of values or predicted values from either the data used to fit the model (if type="adjto" or "" or if x=TRUE or linear.predictors=TRUE were specified to the modeling function), or from a new dataset. Parameters such as knots and factor levels used in creating the design matrix in the original fit are "remembered". See the Function function for another method for computing the linear predictors.

models, regression
## S3 method for class 'bj':
predict(object, newdata,
        type=c("lp", "x", "data.frame",
                 "terms", "adjto", "", "model.frame"),,, conf.type=c('mean','individual'),
        non.slopes, kint=1, na.action=na.keep,,
        center.terms=TRUE, ...) # for bj

## S3 method for class 'cph': predict(object, newdata, type=c("lp", "x", "data.frame", "terms", "adjto", "", "model.frame"),,, conf.type=c('mean','individual'), incl.non.slopes=NULL, non.slopes=NULL, kint=1, na.action=na.keep,, center.terms=TRUE, ...) # cph

## S3 method for class 'Glm': predict(object, newdata, type= c("lp", "x", "data.frame", "terms", "adjto", "", "model.frame"),,, conf.type=c('mean','individual'), incl.non.slopes, non.slopes, kint=1, na.action=na.keep,, center.terms=TRUE, ...) # Glm

## S3 method for class 'Gls': predict(object, newdata, type=c("lp", "x", "data.frame", "terms", "adjto", "", "model.frame"),,, conf.type=c('mean','individual'), incl.non.slopes, non.slopes, kint=1, na.action=na.keep,, center.terms=TRUE, ...) # Gls

## S3 method for class 'ols': predict(object, newdata, type=c("lp", "x", "data.frame", "terms", "adjto", "", "model.frame"),,, conf.type=c('mean','individual'), incl.non.slopes, non.slopes, kint=1, na.action=na.keep,, center.terms=TRUE, ...) # ols

## S3 method for class 'psm': predict(object, newdata, type=c("lp", "x", "data.frame", "terms", "adjto", "", "model.frame"),,, conf.type=c('mean','individual'), incl.non.slopes, non.slopes, kint=1, na.action=na.keep,, center.terms=TRUE, ...) # psm

a fit object with an rms fitting function
An S data frame, list or a matrix specifying new data for which predictions are desired. If newdata is a list, it is converted to a matrix first. A matrix is converted to a data frame. For the matrix form, categorical variables (catg<
Type of output desired. The default is "lp" to get the linear predictors - predicted $X\beta$. For Cox models, these predictions are centered. You may specify "x" to get an expanded design matrix at the desired combinations of
Defaults to FALSE. If type="linear.predictors", set to return a list with components linear.predictors and instead of just a vector of fitted values.
Specify as a positive fraction to obtain upper and lower confidence intervals (e.g., The $t$-distribution is used in the calculation for ols fits. Otherwise, the normal critical value is us
specifies the type of confidence interval. Default is for the mean. For ols fits there is the option of obtaining confidence limits for individual predicted values by specifying conf.type="individual".
Default is TRUE if non.slopes or kint is specified, the model has a scale parameter (e.g., a parametric survival model), or type!="x". Otherwise the default is FALSE. Set to TRUE
For models such as the ordinal logistic models containing more than one intercept, this specifies dummy variable values to pick off intercept(s) to use in computing predictions. For example, if there are 3 intercepts, use non.slopes=c(0,1,0)
a single integer specifying the number of the intercept to use in multiple-intercept models
Function to handle missing values in newdata. For predictions "in data", the same na.action that was used during model fitting is used to define an naresid function to possibly restore rows of the data matrix that w
set to FALSE to keep the naresid from having any effect, i.e., to keep from adding back observations removed because of NAs in the returned object. If, the na.action attribute will be add
set to FALSE to suppress subtracting the mean from columns of the design matrix before computing terms with type="terms".

datadist and options(datadist=) should be run before predictrms if using type="adjto", type="", or type="terms", or if the fit is a Cox model fit and you are requesting For these cases, the adjustment values are needed (either for the returned result or for the correct covariance matrix computation).

See Also

plot.Predict, summary.rms, rms, rms.trans, predict.lrm, residuals.cph, datadist, gendata, Function.rms, reShape, xYplot, contrast.rms

  • predictrms
  • predict.rms
  • predict.cph
  • predict.Glm
  • predict.Gls
  • predict.ols
  • predict.psm
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))
treat          <- factor(sample(c('a','b','c'), n,TRUE))

# 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')) +
  .3*sqrt(blood.pressure-60)-2.3 + 1*(treat=='b')
# 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, treat)

fit <- lrm(y ~ rcs(blood.pressure,4) + 
           sex * (age + rcs(cholesterol,4)) + sex*treat*age)

# Use xYplot to display predictions in 9 panels, with error bars,
# with superposition of two treatments

dat <- expand.grid(treat=levels(treat),sex=levels(sex),
# Add variables linear.predictors and to dat
dat <- cbind(dat, predict(fit, dat,
# This is much easier with Predict
# xYplot in Hmisc extends xyplot to allow error bars
             linear.predictors+1.96* ~ cholesterol | sex*age,
       groups=treat, data=dat, type='b')

# Since blood.pressure doesn't interact with anything, we can quickly and
# interactively try various transformations of blood.pressure, taking
# the fitted spline function as the gold standard. We are seeking a
# linearizing transformation even though this may lead to falsely
# narrow confidence intervals if we use this data-dredging-based transformation

bp <- 70:160
logit <- predict(fit, expand.grid(treat="a", sex='male', age=median(age),
                 blood.pressure=bp), type="terms")[,"blood.pressure"]
#Note: if age interacted with anything, this would be the age
#      "main effect" ignoring interaction terms
#Could also use Predict(f, age=ag)$yhat
#which allows evaluation of the shape for any level of interacting
#factors.  When age does not interact with anything, the result from
#predict(f, \dots, type="terms") would equal the result from
#plot if all other terms were ignored

plot(bp^.5, logit)               # try square root vs. spline transform.
plot(bp^1.5, logit)              # try 1.5 power
plot(sqrt(bp-60), logit)

#Some approaches to making a plot showing how predicted values
#vary with a continuous predictor on the x-axis, with two other
#predictors varying

combos <- gendata(fit, age=seq(10,100,by=10), cholesterol=c(170,200,230),
#treat, sex not specified -> set to mode
#can also used expand.grid

combos$pred <- predict(fit, combos)
xyplot(pred ~ age | cholesterol*blood.pressure, data=combos, type='l')
xYplot(pred ~ age | cholesterol, groups=blood.pressure, data=combos, type='l')
Key()   # Key created by xYplot
xYplot(pred ~ age, groups=interaction(cholesterol,blood.pressure),
       data=combos, type='l', lty=1:9)

#Add upper and lower 0.95 confidence limits for individuals
combos <- cbind(combos, predict(fit, combos,
xYplot(Cbind(linear.predictors, lower, upper) ~ age | cholesterol,
       groups=blood.pressure, data=combos, type='b')

# Plot effects of treatments (all pairwise comparisons) vs.
# levels of interacting factors (age, sex)

d <- gendata(fit, treat=levels(treat), sex=levels(sex), age=seq(30,80,by=10))
x <- predict(fit, d, type="x")
betas <- fit$coef
cov   <- fit$var

i <- d$treat=="a"; xa <- x[i,]; Sex <- d$sex[i]; Age <- d$age[i]
i <- d$treat=="b"; xb <- x[i,]
i <- d$treat=="c"; xc <- x[i,]

doit <- function(xd, lab) {
  xb <- xd%*%betas
  se <- apply((xd %*% cov) * xd, 1, sum)^.5
  q <- qnorm(1-.01/2)   # 0.99 confidence limits
  lower <- xb - q * se; upper <- xb + q * se
  #Get odds ratios instead of linear effects
  xb <- exp(xb); lower <- exp(lower); upper <- exp(upper)
  #First elements of these agree with 
  #summary(fit, age=30, sex='female',
  for(sx in levels(Sex)) {
    j <- Sex==sx
    errbar(Age[j], xb[j], upper[j], lower[j], xlab="Age", 
           ylab=paste(lab,"Odds Ratio"), ylim=c(.1,20), log='y')
    abline(h=1, lty=2)

par(mfrow=c(3,2), oma=c(3,0,3,0))
doit(xb - xa, "b:a")
doit(xc - xa, "c:a")
doit(xb - xa, "c:b")

# NOTE: This is much easier to do using contrast.rms

#A variable state.code has levels "1", "5","13"
#Get predictions with or without converting variable in newdata to factor
predict(fit, data.frame(state.code=c(5,13)))
predict(fit, data.frame(state.code=factor(c(5,13))))

#Use gendata function (gendata.rms) for interactive specification of
#predictor variable settings (for 10 observations)
df <- gendata(fit, nobs=10, viewvals=TRUE)
df$predicted <- predict(fit, df)  # add variable to data frame

df <- gendata(fit, age=c(10,20,30))  # leave other variables at ref. vals.
predict(fit, df, type="fitted")

# See reShape (in Hmisc) for an example where predictions corresponding to 
# values of one of the varying predictors are reformatted into multiple
# columns of a matrix
Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)

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