predictrms {rms} | R Documentation

## Predicted Values from Model Fit

### Description

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 `"adjto.data.frame"` 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.

### Usage

    ## S3 method for class 'bj'
    predict(object, newdata,
            type=c("lp", "x", "data.frame", "terms", "cterms", "ccterms",
                   "adjto", "adjto.data.frame", "model.frame"),
            se.fit=FALSE, conf.int=FALSE,
            conf.type=c('mean','individual','simultaneous'),
            kint=1,
            na.action=na.keep, expand.na=TRUE,
            center.terms=type=="terms", ...) # for bj

    ## S3 method for class 'cph'
    predict(object, newdata=NULL,
            type=c("lp", "x", "data.frame", "terms", "cterms", "ccterms",
                   "adjto", "adjto.data.frame", "model.frame"),
            se.fit=FALSE, conf.int=FALSE,
            conf.type=c('mean','individual','simultaneous'),
            kint=1, na.action=na.keep, expand.na=TRUE,
            center.terms=type=="terms", ...) # cph

    ## S3 method for class 'Glm'
    predict(object, newdata,
            type= c("lp", "x", "data.frame", "terms", "cterms", "ccterms",
                    "adjto", "adjto.data.frame", "model.frame"),
            se.fit=FALSE, conf.int=FALSE,
            conf.type=c('mean','individual','simultaneous'),
            kint=1, na.action=na.keep, expand.na=TRUE,
            center.terms=type=="terms", ...) # Glm

    ## S3 method for class 'Gls'
    predict(object, newdata,
            type=c("lp", "x", "data.frame", "terms", "cterms", "ccterms",
                   "adjto", "adjto.data.frame", "model.frame"),
            se.fit=FALSE, conf.int=FALSE,
            conf.type=c('mean','individual','simultaneous'),
            kint=1, na.action=na.keep, expand.na=TRUE,
            center.terms=type=="terms", ...) # Gls

    ## S3 method for class 'ols'
    predict(object, newdata,
            type=c("lp", "x", "data.frame", "terms", "cterms", "ccterms",
                   "adjto", "adjto.data.frame", "model.frame"),
            se.fit=FALSE, conf.int=FALSE,
            conf.type=c('mean','individual','simultaneous'),
            kint=1, na.action=na.keep, expand.na=TRUE,
            center.terms=type=="terms", ...) # ols

    ## S3 method for class 'psm'
    predict(object, newdata,
            type=c("lp", "x", "data.frame", "terms", "cterms", "ccterms",
                   "adjto", "adjto.data.frame", "model.frame"),
            se.fit=FALSE, conf.int=FALSE,
            conf.type=c('mean','individual','simultaneous'),
            kint=1, na.action=na.keep, expand.na=TRUE,
            center.terms=type=="terms", ...) # psm

### Arguments

`object` | a fit object with an `rms` fitting function
`newdata` | 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` or `strat`) must be coded as integer category numbers corresponding to the order in which value labels were stored. For list or matrix forms, `matrx` factors must be given a single value. If this single value is the S missing value `NA`, the adjustment values of matrx (the column medians) will later replace this value. If the single value is not `NA`, it is propagated throughout the columns of the `matrx` factor. For `factor` variables having numeric levels, you can specify the numeric values in `newdata` without first converting the variables to factors. These numeric values are checked to make sure they match a level, then the variable is converted internally to a `factor`. It is most typical to use a data frame for newdata, and the S function `expand.grid` is very handy here. For example, one may specify `newdata=expand.grid(age=c(10,20,30),` `race=c("black","white","other"),` `chol=seq(100,300,by=25))`.
`type` | 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 values, `"data.frame"` to get an S data frame of the combinations, `"model.frame"` to get a data frame of the transformed predictors, `"terms"` to get a matrix with each column being the linear combination of variables making up a factor (with separate terms for interactions), `"cterms"` ("combined terms") to not create separate terms for interactions but to add all interaction terms involving each predictor to the main terms for each predictor, `"ccterms"` to combine all related terms (related through interactions) and their interactions into a single column, `"adjto"` to return a vector of `limits[2]` (see `datadist`) in coded form, and `"adjto.data.frame"` to return a data frame version of these central adjustment values. Use of `type="cterms"` does not make sense for a `strat` variable that does not interact with another variable. If `newdata` is not given, `predict` will attempt to return information stored with the fit object if the appropriate options were used with the modeling function (e.g., `x, y, linear.predictors, se.fit`).
`se.fit` | Defaults to `FALSE`. If `type="linear.predictors"`, set `se.fit=TRUE` to return a list with components `linear.predictors` and `se.fit` instead of just a vector of fitted values. For Cox model fits, standard errors of linear predictors are computed after subtracting the original column means from the new design matrix.
`conf.int` | Specify `conf.int` as a positive fraction to obtain upper and lower confidence intervals (e.g., `conf.int=0.95`). The t-distribution is used in the calculation for `ols` fits. Otherwise, the normal critical value is used.
`conf.type` | 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"`.
`kint` | a single integer specifying the number of the intercept to use in multiple-intercept models. The default is 1 for `lrm` and the reference median intercept for `orm`.
`na.action` | 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 were deleted due to NAs. For predictions "out of data", the default `na.action` is `na.keep`, resulting in NA predictions when a row of `newdata` has an NA. Whatever `na.action` is in effect at the time for "out of data" predictions, the corresponding `naresid` is used also.
`expand.na` | 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 `expand.na=FALSE`, the `na.action` attribute will be added to the returned object.
`center.terms` | set to `FALSE` to suppress subtracting adjust-to values from columns of the design matrix before computing terms with `type="terms"`.
`...` | ignored

### Details

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

### Author(s)

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

### See Also

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

### Examples

    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)
    options(datadist='ddist')

    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),
                       age=c(20,40,60),blood.pressure=120,
                       cholesterol=seq(100,300,length=10))
    # Add variables linear.predictors and se.fit to dat
    dat <- cbind(dat, predict(fit, dat, se.fit=TRUE))
    # This is much easier with Predict
    # xYplot in Hmisc extends xyplot to allow error bars
    xYplot(Cbind(linear.predictors,linear.predictors-1.96*se.fit,
                 linear.predictors+1.96*se.fit) ~ 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),
                     cholesterol=median(cholesterol),
                     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),
                      blood.pressure=c(80,120,160))
    #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)
    Key()

    # Add upper and lower 0.95 confidence limits for individuals
    combos <- cbind(combos, predict(fit, combos, conf.int=.95))
    xYplot(Cbind(linear.predictors, lower, upper) ~ age | cholesterol,
           groups=blood.pressure, data=combos, type='b')
    Key()

    # 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   <- vcov(fit, intercepts='none')

    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 <- matxv(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',conf.int=.99))
      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')
        title(paste("Sex:", sx))
        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

    # Demonstrate type="terms", "cterms", "ccterms"
    set.seed(1)
    n <- 40
    x <- 1:n
    w <- factor(sample(c('a', 'b'), n, TRUE))
    u <- factor(sample(c('A', 'B'), n, TRUE))
    y <- .01*x + .2*(w=='b') + .3*(u=='B') + .2*(w=='b' & u=='B') + rnorm(n)/5
    ddist <- datadist(x, w, u)
    f <- ols(y ~ x*w*u, x=TRUE, y=TRUE)
    f
    anova(f)
    z <- predict(f, type='terms', center.terms=FALSE)
    z[1:5,]
    k <- coef(f)
    ## Manually compute combined terms
    wb <- w=='b'
    uB <- u=='B'
    h  <- k['x * w=b * u=B']*x*wb*uB
    tx <- k['x']  *x  + k['x * w=b']*x*wb + k['x * u=B']  *x*uB  + h
    tw <- k['w=b']*wb + k['x * w=b']*x*wb + k['w=b * u=B']*wb*uB + h
    tu <- k['u=B']*uB + k['x * u=B']*x*uB + k['w=b * u=B']*wb*uB + h
    h   <- z[,'x * w * u'] # highest order term is present in all cterms
    tx2 <- z[,'x']+z[,'x * w']+z[,'x * u']+h
    tw2 <- z[,'w']+z[,'x * w']+z[,'w * u']+h
    tu2 <- z[,'u']+z[,'x * u']+z[,'w * u']+h
    ae <- function(a, b) all.equal(a, b, check.attributes=FALSE)
    ae(tx, tx2)
    ae(tw, tw2)
    ae(tu, tu2)

    zc <- predict(f, type='cterms')
    zc[1:5,]
    ae(tx, zc[,'x'])
    ae(tw, zc[,'w'])
    ae(tu, zc[,'u'])

    zc <- predict(f, type='ccterms')
    # As all factors are indirectly related, ccterms gives overall linear
    # predictor except for the intercept
    zc[1:5,]
    ae(as.vector(zc + coef(f)[1]), f$linear.predictors)

    ## Not run:
    #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

    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

    ## End(Not run)
    options(datadist=NULL)

[Package rms version 5.1-1 Index]