worstErrors              package:qvcalc              R Documentation

_A_c_c_u_r_a_c_y _o_f _a _Q_u_a_s_i-_v_a_r_i_a_n_c_e _A_p_p_r_o_x_i_m_a_t_i_o_n

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

     Computes the worst relative error, among all contrasts, for the
     standard error as derived from a set of quasi-variances. For
     details of the method see Menezes (1999) or Firth and Menezes
     (2002).

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

     worstErrors(qv.object)

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

qv.object: An object of class `qv'

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

     A numeric vector of length 2, the worst negative relative error
     and the worst positive relative error.

_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. 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:

     `qvcalc'

_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)
     worstErrors(shiptype.qvs)

