sreg                 package:fields                 R Documentation

_S_m_o_o_t_h_i_n_g _s_p_l_i_n_e _r_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Fits a cubic smoothing spline to univariate data. The amount of
     smoothness can be specified or estimated from the data by GCV. 
     <!-brief description->

_U_s_a_g_e:

     sreg(x, y, lam = NA, df = NA, offset = 0, wt = rep(1, length(x)), cost = 1, 
     nstep.cv = 80, find.diagA = TRUE, trmin = 2.01,
     trmax = length(unique(x)) * 0.95, lammin = NA, lammax = NA, verbose = FALSE,
     do.cv = TRUE, method = "GCV", rmse = NA, lambda = NA)

_A_r_g_u_m_e_n_t_s:

       x: Vector of x value 

       y: Vector of y values 

     lam: Single smoothing parameter or a vector of values . If omitted
           smoothing parameter estimated by GCV. 

      df: Amount of smoothing in term of effective degrees of freedom
          for the spline  

  offset: an offset added to the term cost*degrees of freedom in the
          denominator of the GCV function. (This would be used for
          adjusting the df from fitting other models such as in
          back-fitting additive models.) 

      wt: A vector that is proportional to the reciprocal variances of
          the errors. 

    cost: Cost value to be used in the GCV criterion. 

nstep.cv : Number of grid points of smoothing parameter for GCV grid
          search 

find.diagA : If true calculate the diagonal elements of the smoothing
          matrix. The effective number of degrees of freedom is the sum
          of these diagonal elements. Default is true. This requires
          more stores if a grid of smoothing parameters is passed. (
          See returned values below.)

   trmin: Sets the minimum of the smoothing parameter range  for the
          GCV grid search in terms of effective degrees of freedom. 

   trmax: Sets the maximum of the smoothing parameter range  for the
          GCV grid search in terms of effective degrees of freedom. 

  lammin: Same function as trmin but in the lambda scale. 

  lammax: Same function as trmax but in the lambda scale. 

 verbose: Print out all sorts of debugging info. Default is false!  

  do.cv : Evaluate the spline at the GCV minimum. Default is true. 

  method: A character string giving the  method for determining the
          smoothing parameter. Choices are "GCV", "GCV.one",
          "GCV.model", "pure error", "RMSE". Default is "GCV" 

    rmse: Value of the root mean square error to match by varying
          lambda.  

  lambda: Another name for lam. This is just for consistency with Krig,
          Tps.  

_D_e_t_a_i_l_s:

     MODEL: The assumed model is Y.k=f(x.k) +e.k where e.k should be
     approximately normal and independent errors with variances
     sigma**2/w.k

     ESTIMATE: A smoothing spline is a locally weighted average of the
     y's based  on the relative locations of the x values. Formally the
     estimate is  the curve that minimizes the criterion: 

     (1/n) sum(k=1,n) w.k( Y.k - f( X.k))**2  + lambda R(f) 

     where R(f) is the integral of the squared second derivative of f
     over  the range of the X values. The solution is a piecewise cubic
      polynomial with the join points at the unique set of X values.
     The  polynomial segments are constructed so that the entire curve
     has  continuous first and second derivatives and the second and
     third  derivatives are zero at the boundaries.  The smoothing has
     the range  [0,infinity]. Lambda equal to  zero gives a cubic
     spline interpolation  of  the data. As lambda diverges to infinity
     ( e.g lambda =1e20) the   estimate will converge to the straight
     line estimated by least squares.

     The values of the estimated function at the data points can be
     expressed in the matrix form:

     predicted.values= A(lambda)Y 

     where A is an nXn symmetric matrix that does NOT depend on Y.  The
     diagonal elements are the leverage values for the estimate and the
      sum of these  (trace(A(lambda)) can be interpreted as the
     effective  number of parameters that are used to define the spline
     function.  IF there are replicate points the A matrix is the
     result of finding group averages and applying a weighted spline to
     the means.  The A matrix is also used to find "Bayesian"
     confidence intervals for the  estimate, see the example below. 

     CROSS-VALIDATION:The GCV criterion with no replicate points for a
     fixed value of lambda is

     (1/n)(Residual sum of squares)/((1-(tr(A)-offset)*cost +
     offset)/n)**2, 

     Usually offset =0 and cost =1. Variations on GCV with replicate
     points are described in the documentation help file for Krig. 
     With an appropriate choice for the smoothing parameter, the
     estimate of sigma**2 is found by (Residual sum of squares)/tr(A).

     COMPUTATIONS: The computations for 1-d splines exploit the banded
     structure of the matrices needed to solve for the spline
     coefficients. Banded structure also makes it possible to get the
     diagonal elements of A quickly. This approach is different from
     the algorithms in Tps and tremendously more efficient for larger
     numbers of unique x values ( say > 200). The advantage of Tps is
     getting "Bayesian" standard errors at predictions different from
     the observed x values. This function is similar to the S-Plus
     smooth.spline. The main advantages are more information and
     control over the choice of lambda and also the FORTRAN source code
     is available (css.f).

_V_a_l_u_e:

     Returns a list of class sreg.  Some of the returned components are 

    call: Call to the function  

       y: Vector of dependent variables. If replicated data is given
          these are the replicate group means.  

       x: Unique x values matching the y's.  

      wt: Reciprocal variances. If replicated data is given these are
          the results of adding all combining the  weights in each
          replicate group.   

    xraw: Original  x   data.  

    yraw: Original  y   data.  

  method: Method used to find the smoothing parameter.  

 pure.ss: Pure error sum of squares from replicate groups.  

shat.pure.error: Estimate of sigma from replicate groups. 

shat.GCV: Estimate of sigma using estimated lambda from GCV
          minimization  

   trace: Effective degrees of freedom for the spline estimate(s) 

gcv.grid: Values of trace, GCV, shat. etc. for a grid of smoothing
          parameters. If lambda ( or df) is specified those values are
          used.   

lambda.est: Summary of various estimates of the smoothing parameter 

  lambda: If lambda is specified this vector. If missing this the
          estimated value. 

residuals: Residuals from spline(s). If lambda or df is specified the
          residuals from these values. If lambda and df are omitted
          then the spline having estimated lambda. This will be a
          matrix with as many columns as the values of lambda.  

fitted.values: Matrix of fitted values. See notes on residuals.  

predicted: A list with components  x and y. x is the unique values of
          xraw in sorted order. y is a matrix of the spline estimates
          at these values.  

  eff.df: Same as trace. 

   diagA: Matrix containing diagonal elements of the smoothing matrix.
          Number of columns is the number of lambda values.  WARNING:
          If there is replicated data the diagonal elements are those
          for the smoothing the group means at the unique x locations.  

_S_e_e _A_l_s_o:

     Krig, Tps

_E_x_a_m_p_l_e_s:

     # fit a GCV spline to  
     # control group of rats.  
     fit<- sreg(rat.diet$t,rat.diet$con)
     summary( fit)

     plot(fit)                       # diagnostic plots of  fit 
     predict( fit) # predicted values at data points 

     xg<- seq(0,110,,50) 
     sm<-predict( fit, xg) # spline fit at 50 equally spaced points 
     der.sm<- predict( fit, xg, deriv=1) # derivative of spline fit 
     set.panel( 2,1) 
     plot( fit$x, fit$y) # the data 
     lines( xg, sm) # the spline 
     plot( xg,der.sm, type="l") # plot of estimated derivative 
     set.panel() # reset panel to 1 plot

     # the same fit using  the thin plate spline numerical algorithms 
     # (NOTE: sreg is more efficient for 1-d problems) 
     fit.tps<-Tps( rat.diet$t,rat.diet$con)
     summary( fit.tps) 

     # finding approximate standard errors at observations

     SE<- fit$shat.GCV*sqrt(fit$diagA)

     # compare to predict.se( fit.tps) differences are due to 
     # slightly different lambda values and using shat.MLE instad of shat.GCV
     #

     # 95
     Zvalue<-  qnorm(.0975)
     upper<- fit$fitted.values + Zvalue* SE
     lower<- fit$fitted.values - Zvalue* SE
     #
     # conservative, simultaneous Bonferroni bounds
     #
     ZBvalue<-  qnorm(1- .025/fit$N)
     upperB<- fit$fitted.values + ZBvalue* SE
     lowerB<- fit$fitted.values - ZBvalue* SE
     #
     # take a look

     plot( fit$x, fit$y)
     lines( fit$predicted, lwd=2)
     matlines( fit$x, 
     cbind( lower, upper, lowerB, upperB), type="l", col=c( 2,2,4,4), lty=1)
     title( "95 pct pointwise  and simultaneous intervals")
     # or try the more visually  honest:
     plot( fit$x, fit$y)
     lines( fit$predicted, lwd=2)
     segments(  fit$x, lowerB, fit$x, upperB, col=4)
     segments(  fit$x, lower, fit$x, upper, col=2, lwd=2)
     title( "95 pct pointwise  and simultaneous intervals")





     # replicated data
     # this is a simulated case. Find lambda by matching rmse to be .2
     # and use this estimate of lambda
     sreg( test.data2$x, test.data2$y, rmse=.2, method="RMSE")-> fit

     set.panel( 1,1)

