single.index {mgcv} | R Documentation

## Single index models with mgcv

### Description

Single index models contain smooth terms with arguments that are linear combinations of other covariates. e.g. s(Xa) where a has to be estimated. For identifiability, assume ||a||=1 with positive first element. One simple way to fit such models is to use `gam` to profile out the smooth model coefficients and smoothing parameters, leaving only the a to be estimated by a general purpose optimizer.

Example code is provided below, which can be easily adapted to include multiple single index terms, parametric terms and further smooths. Note the initialization strategy. First estimate a without penalization to get starting values and then do the full fit. Otherwise it is easy to get trapped in a local optimum in which the smooth is linear. An alternative is to initialize using fixed penalization (via the `sp` argument to `gam`).

### Author(s)

Simon N. Wood simon.wood@r-project.org

### Examples

    require(mgcv)

    si <- function(theta,y,x,z,opt=TRUE,k=10,fx=FALSE) {
    ## Fit single index model using gam call, given theta (defines alpha).
    ## Return ML is opt==TRUE and fitted gam with theta added otherwise.
    ## Suitable for calling from 'optim' to find optimal theta/alpha.
      alpha <- c(1,theta) ## constrained alpha defined using free theta
      kk <- sqrt(sum(alpha^2))
      alpha <- alpha/kk  ## so now ||alpha||=1
      a <- x%*%alpha     ## argument of smooth
      b <- gam(y~s(a,fx=fx,k=k)+s(z),family=poisson,method="ML") ## fit model
      if (opt) return(b$gcv.ubre) else {
        b$alpha <- alpha  ## add alpha
        J <- outer(alpha,-theta/kk^2) ## compute Jacobian
        for (j in 1:length(theta)) J[j+1,j] <- J[j+1,j] + 1/kk
        b$J <- J ## dalpha_i/dtheta_j
        return(b)
      }
    } ## si

    ## simulate some data from a single index model...

    set.seed(1)
    f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 *
                (10 * x)^3 * (1 - x)^10
    n <- 200;m <- 3
    x <- matrix(runif(n*m),n,m) ## the covariates for the single index part
    z <- runif(n) ## another covariate
    alpha <- c(1,-1,.5); alpha <- alpha/sqrt(sum(alpha^2))
    eta <- as.numeric(f2((x%*%alpha+.41)/1.4)+1+z^2*2)/4
    mu <- exp(eta)
    y <- rpois(n,mu) ## Poi response

    ## now fit to the simulated data...

    th0 <- c(-.8,.4) ## close to truth for speed
    ## get initial theta, using no penalization...
    f0 <- nlm(si,th0,y=y,x=x,z=z,fx=TRUE,k=5)
    ## now get theta/alpha with smoothing parameter selection...
    f1 <- nlm(si,f0$estimate,y=y,x=x,z=z,hessian=TRUE,k=10)
    theta.est <-f1$estimate

    ## Alternative using 'optim' ('Not run' simply to keep
    ## CRAN check time down)...
    ## Not run:
    th0 <- rep(0,m-1)
    ## get initial theta, using no penalization...
    f0 <- optim(th0,si,y=y,x=x,z=z,fx=TRUE,k=5)
    ## now get theta/alpha with smoothing parameter selection...
    f1 <- optim(f0$par,si,y=y,x=x,z=z,hessian=TRUE,k=10)
    theta.est <-f1$par

    ## End(Not run)
    ## extract and examine fitted model...

    b <- si(theta.est,y,x,z,opt=FALSE) ## extract best fit model
    plot(b,pages=1)
    b
    b$alpha
    ## get sd for alpha...
    Vt <- b$J%*%solve(f1$hessian,t(b$J))
    diag(Vt)^.5

[Package mgcv version 1.8-12 Index]