areg {Hmisc} | R Documentation

## Additive Regression with Optimal Transformations on Both Sides using Canonical Variates

### Description

Expands continuous variables into restricted cubic spline bases and categorical variables into dummy variables and fits a multivariate equation using canonical variates. This finds optimum transformations that maximize R^2. Optionally, the bootstrap is used to estimate the covariance matrix of both left- and right-hand-side transformation parameters, and to estimate the bias in the R^2 due to overfitting and compute the bootstrap optimism-corrected R^2. Cross-validation can also be used to get an unbiased estimate of R^2 but this is not as precise as the bootstrap estimate. The bootstrap and cross-validation may also used to get estimates of mean and median absolute error in predicted values on the original `y` scale. These two estimates are perhaps the best ones for gauging the accuracy of a flexible model, because it is difficult to compare R^2 under different y-transformations, and because R^2 allows for an out-of-sample recalibration (i.e., it only measures relative errors).

Note that uncertainty about the proper transformation of `y` causes an enormous amount of model uncertainty. When the transformation for `y` is estimated from the data a high variance in predicted values on the original `y` scale may result, especially if the true transformation is linear. Comparing bootstrap or cross-validated mean absolute errors with and without restricted the `y` transform to be linear (`ytype='l'`) may help the analyst choose the proper model complexity.

### Usage

    areg(x, y, xtype = NULL, ytype = NULL, nk = 4,
         B = 0, na.rm = TRUE, tolerance = NULL, crossval = NULL)

    ## S3 method for class 'areg'
    print(x, digits=4, ...)

    ## S3 method for class 'areg'
    plot(x, whichx = 1:ncol(x$x), ...)

    ## S3 method for class 'areg'
    predict(object, x, type=c('lp','fitted','x'),
                           what=c('all','sample'), ...)

### Arguments

`x` | A single predictor or a matrix of predictors. Categorical predictors are required to be coded as integers (as `factor` does internally). For `predict`, `x` is a data matrix with the same integer codes that were originally used for categorical variables.
`y` | a `factor`, categorical, character, or numeric response variable
`xtype` | a vector of one-letter character codes specifying how each predictor is to be modeled, in order of columns of `x`. The codes are `"s"` for smooth function (using restricted cubic splines), `"l"` for no transformation (linear), or `"c"` for categorical (to cause expansion into dummy variables). Default is `"s"` if `nk > 0` and `"l"` if `nk=0`.
`ytype` | same coding as for `xtype`. Default is `"s"` for a numeric variable with more than two unique values, `"l"` for a binary numeric variable, and `"c"` for a factor, categorical, or character variable.
`nk` | number of knots, 0 for linear, or 3 or more. Default is 4 which will fit 3 parameters to continuous variables (one linear term and two nonlinear terms)
`B` | number of bootstrap resamples used to estimate covariance matrices of transformation parameters. Default is no bootstrapping.
`na.rm` | set to `FALSE` if you are sure that observations with `NA`s have already been removed
`tolerance` | singularity tolerance. List source code for `lm.fit.qr.bare` for details.
`crossval` | set to a positive integer k to compute k-fold cross-validated R-squared (square of first canonical correlation) and mean and median absolute error of predictions on the original scale
`digits` | number of digits to use in formatting for printing
`object` | an object created by `areg`
`whichx` | integer or character vector specifying which predictors are to have their transformations plotted (default is all). The `y` transformation is always plotted.
`type` | tells `predict` whether to obtain predicted untransformed `y` (`type='lp'`, the default) or predicted `y` on the original scale (`type='fitted'`), or the design matrix for the right-hand side (`type='x'`).
`what` | When the `y`-transform is non-monotonic you may specify `what='sample'` to `predict` to obtain a random sample of `y` values on the original scale instead of a matrix of all `y`-inverses. See `inverseFunction`.
`...` | arguments passed to the plot function.

### Details

`areg` is a competitor of `ace` in the `acepack` package. Transformations from `ace` are seldom smooth enough and are often overfitted. With `areg` the complexity can be controlled with the `nk` parameter, and predicted values are easy to obtain because parametric functions are fitted.

If one side of the equation has a categorical variable with more than two categories and the other side has a continuous variable not assumed to act linearly, larger sample sizes are needed to reliably estimate transformations, as it is difficult to optimally score categorical variables to maximize R^2 against a simultaneously optimally transformed continuous variable.

### Value

a list of class `"areg"` containing many objects

### Author(s)

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

### References

Breiman and Friedman, Journal of the American Statistical Association (September, 1985).

### See Also

`cancor`,`ace`, `transcan`

### Examples

    set.seed(1)

    ns <- c(30,300,3000)
    for(n in ns) {
      y <- sample(1:5, n, TRUE)
      x <- abs(y-3) + runif(n)
      par(mfrow=c(3,4))
      for(k in c(0,3:5)) {
        z <- areg(x, y, ytype='c', nk=k)
        plot(x, z$tx)
    	title(paste('R2=',format(z$rsquared)))
        tapply(z$ty, y, range)
        a <- tapply(x,y,mean)
        b <- tapply(z$ty,y,mean)
        plot(a,b)
    	abline(lsfit(a,b))
        # Should get same result to within linear transformation if reverse x and y
        w <- areg(y, x, xtype='c', nk=k)
        plot(z$ty, w$tx)
        title(paste('R2=',format(w$rsquared)))
        abline(lsfit(z$ty, w$tx))
     }
    }

    par(mfrow=c(2,2))
    # Example where one category in y differs from others but only in variance of x
    n <- 50
    y <- sample(1:5,n,TRUE)
    x <- rnorm(n)
    x[y==1] <- rnorm(sum(y==1), 0, 5)
    z <- areg(x,y,xtype='l',ytype='c')
    z
    plot(z)
    z <- areg(x,y,ytype='c')
    z
    plot(z)

    ## Not run:
    # Examine overfitting when true transformations are linear
    par(mfrow=c(4,3))
    for(n in c(200,2000)) {
      x <- rnorm(n); y <- rnorm(n) + x
        for(nk in c(0,3,5)) {
        z <- areg(x, y, nk=nk, crossval=10, B=100)
        print(z)
        plot(z)
        title(paste('n=',n))
      }
    }
    par(mfrow=c(1,1))

    # Underfitting when true transformation is quadratic but overfitting
    # when y is allowed to be transformed
    set.seed(49)
    n <- 200
    x <- rnorm(n); y <- rnorm(n) + .5*x^2
    #areg(x, y, nk=0, crossval=10, B=100)
    #areg(x, y, nk=4, ytype='l', crossval=10, B=100)
    z <- areg(x, y, nk=4) #, crossval=10, B=100)
    z
    # Plot x vs. predicted value on original scale.  Since y-transform is
    # not monotonic, there are multiple y-inverses
    xx <- seq(-3.5,3.5,length=1000)
    yhat <- predict(z, xx, type='fitted')
    plot(x, y, xlim=c(-3.5,3.5))
    for(j in 1:ncol(yhat)) lines(xx, yhat[,j], col=j)
    # Plot a random sample of possible y inverses
    yhats <- predict(z, xx, type='fitted', what='sample')
    points(xx, yhats, pch=2)

    ## End(Not run)

    # True transformation of x1 is quadratic, y is linear
    n <- 200
    x1 <- rnorm(n); x2 <- rnorm(n); y <- rnorm(n) + x1^2
    z <- areg(cbind(x1,x2),y,xtype=c('s','l'),nk=3)
    par(mfrow=c(2,2))
    plot(z)

    # y transformation is inverse quadratic but areg gets the same answer by
    # making x1 quadratic
    n <- 5000
    x1 <- rnorm(n); x2 <- rnorm(n); y <- (x1 + rnorm(n))^2
    z <- areg(cbind(x1,x2),y,nk=5)
    par(mfrow=c(2,2))
    plot(z)

    # Overfit 20 predictors when no true relationships exist
    n <- 1000
    x <- matrix(runif(n*20),n,20)
    y <- rnorm(n)
    z <- areg(x, y, nk=5)  # add crossval=4 to expose the problem

    # Test predict function
    n <- 50
    x <- rnorm(n)
    y <- rnorm(n) + x
    g <- sample(1:3, n, TRUE)
    z <- areg(cbind(x,g),y,xtype=c('s','c'))
    range(predict(z, cbind(x,g)) - z$linear.predictors)

[Package Hmisc version 3.17-4 Index]