qvcalc                package:qvcalc                R Documentation

_Q_u_a_s_i-_v_a_r_i_a_n_c_e_s _f_o_r _M_o_d_e_l _C_o_e_f_f_i_c_i_e_n_t_s

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

     Computes a set of `quasi-variances' (and corresponding `quasi
     standard errors') for estimated model coefficients relating to the
     levels of a categorical (i.e., factor) explanatory variable.  For
     details of the method see Firth (2000) or Firth and Menezes
     (2002).

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

     qvcalc(object, factorname=NULL, labels = NULL, dispersion = NULL,
           estimates=NULL,  modelcall=NULL)

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

  object: A model (of class lm, glm, etc.), or the covariance
          (sub)matrix for the estimates of interest

factorname: If `object' is a model, the name of the factor of interest

  labels: An optional vector of row names for the `qvframe' component
          of the result (redundant if `object' is a model)

dispersion: an optional scalar multiplier for the covariance matrix, to
          cope with overdispersion for example

estimates: an optional vector of estimated coefficients (redundant if
          `object' is a model)

modelcall: optional, the call expression for the model of interest
          (redundant if `object' is a model)

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

     A list of class `qv', with components 

  covmat: the full variance-covariance matrix for the estimated 
          coefficients corresponding to the factor of interest

 qvframe: a data frame with variables `estimate', `SE', `quasiSE' and
          `quasiVar', the last two being a quasi standard error and
          quasi-variance for each level of the factor of interest

 relerrs: relative errors for approximating the standard errors of all 
          simple contrasts

factorname: the factor name if given

modelcall: if `object' is a model, `object$call'; otherwise `NULL'

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

     David Firth, david.firth@nuffield.ox.ac.uk

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

     Firth, D. (2000)  Quasi-variances in Xlisp-Stat and on the web.  
     Journal of Statistical Software 5.4, 1-13.   At <URL:
     http://www.jstatsoft.org>

     Firth, D. and Mezezes, R. X. de (2002)  Quasi-variances. 
     Submitted for publication.  At <URL:
     http://www.stats.ox.ac.uk/~firth/papers/>.

     McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models.
     London: Chapman and Hall.

     Menezes, R. X. (1999)  More useful standard errors for group and
     factor effects in generalized linear models.  D.Phil. Thesis,
     Department of Statistics, University of Oxford.

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

     `worstErrors'

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

     ##  Overdispersed Poisson loglinear model for ship damage data
     ##  from McCullagh and Nelder (1989), Sec 6.3.2 
     library(MASS)
     data(ships)
     ships$year <- as.factor(ships$year)
     ships$period <- as.factor(ships$period)
     shipmodel <- glm(formula = incidents ~ type + year + period,
         family = quasipoisson, 
         data = ships, subset = (service > 0), offset = log(service))
     shiptype.qvs <- qvcalc(shipmodel, "type")
     summary(shiptype.qvs, digits=4)
     plot(shiptype.qvs)

