slm                  package:assist                  R Documentation

_F_i_t _a _S_e_m_i-_p_a_r_a_m_e_t_r_i_c _L_i_n_e_a_r _M_i_x_e_d _E_f_f_e_c_t_s _M_o_d_e_l

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

     Returns an object of class 'slm' that represents a semi-parametric
     linear mixed effects model fit.

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

     slm(formula, rk, data=sys.parent(), random, weights=NULL, 
     correlation=NULL, control=list(apVar=FALSE))

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

 formula: a formula object, with the response on the left of a ~
          operator, and the bases  of the null space H_0 of the
          non-parametric function and other terms, separated by +
          operators, on the right. 

      rk: a list of expressions that specify the reproducing kernels of
          the spline function(s), R^1,...,R^p for spaces H_1,...,H_p.
          See the help file of ssr for more details. 

    data: An optional data frame containing the variables appearing in
          'formula', 'random', 'rk', 'correlation', 'weights'.  By
          default, the variables are taken from the environment from
          which 'slm' is called. 

  random: A named list of formulae, lists of formulae, or pdMat
          objects, which defines nested random effects structures. See
          help file of lme for more details. 

 weights: An optional 'varFun' object or one-sided formula describing
          the within-group heteroscedasticity stucture.  See the help
          file of 'lme' for more details. 

correlation: An optional 'corStruct' object specifying the within-group
          correlation structure. See 'lme' for more details. 

 control: an optional list of any applicable control parameters from
          'lme'. 

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

     This generic function fits a semi-parametric linear mixed effects
     model (or non-parametric mixed effects models)  as described in
     Wang (1998), but allowing for general random and correlation
     structures. Because the connection to a linear mixed effects model
     is adopted, only GML is available to choose smoothing parameters.

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

     An object of class 'slm' is returned. Generic functions such as
     print, summary, predict and intervals have methods to show the
     results of the fit.

     Note: output from earlier versions of 'slm' shows incorrect
     smoothing spline parameters for SSANOVA, which is corrected in
     this version.

_A_u_t_h_o_r(_s):

     Chunlei Ke chunlei_ke@yahoo.com and Yuedong Wang
     yuedong@pstat.ucsb.edu.

_R_e_f_e_r_e_n_c_e_s:

     Wang, Y. (1998) Mixed Effects Smoothing Spline ANOVA. JRSS, Series
     B, 60:159-174.

     Pinherio, J. C. and Bates, D. M. (2000) Mixed-effects Models in S
     and S-Plus. Springer.

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

     'ssr', 'predict.slm', 'intervals.slm', 'print.slm','summary.slm'

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

     ## SS ANOVA is used to model "time" and "group" 
     ## with random intercept for "dog".
     data(dog)

     dog.fit<- slm(y~group*time, rk=list(cubic(time), shrink1(group),
             rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time), 
             shrink1(group))), random=list(dog=~1), data=dog)

