glmgam.fit              package:statmod              R Documentation

_G_a_m_m_a _G_e_n_e_r_a_l_i_z_e_d _L_i_n_e_a_r _M_o_d_e_l _w_i_t_h _I_d_e_n_t_i_t_y _L_i_n_k

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

     Estimates a gamma generalized linear model with identity link
     using Fisher scoring with Levenberg damping.

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

     glmgam.fit(X, y, start=NULL, tol=1e-6, maxit=50, trace=FALSE)

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

       X: design matrix, assumed to be of full column rank.  Missing
          values not allowed.

       y: numeric vector of responses. Negative or missing values not
          allowed.

   start: numeric vector of starting values for the regression
          coefficients

     tol: small positive numeric value giving convergence tolerance

   maxit: maximum number of iterations allowed

   trace: logical value. If 'TRUE' then output diagnostic information
          at each iteration.

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

     This function is similar to
     'glm.fit(X,y,family=Gamma(link="identity"))' but has more secure
     convergence.

     This function is used by 'randomizedBlockFit'.

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

     List with the following components: 

coefficients: numeric vector of regression coefficients

  fitted: numeric vector of fitted values

deviance: residual deviance

   maxit: input maximum number of iterations

    iter: number of iterations used to convergence. If convergence was
          not achieved then 'iter' is set to 'maxit+1'.

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

     Gordon Smyth

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

     y <- rgamma(10,shape=5)
     X <- cbind(1,1:10)
     fit <- glmgam.fit(X,y,trace=TRUE)

