limdil                package:statmod                R Documentation

_L_i_m_i_t_i_n_g _D_i_l_u_t_i_o_n _A_n_a_l_y_s_i_s

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

     Fit single-hit model to a dilution series using complementary
     log-log binomial regression.

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

     limdil(response,dose,tested=rep(1,length(response)),observed=FALSE,confidence=0.95,test.unit.slope=FALSE)

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

response: numeric of integer counts of positive cases, out of 'tested'
          trials

    dose: numeric vector of expected number of cells in assay

  tested: numeric vector giving number of trials at each dose

observed: logical, is the actual number of cells observed?

confidence: numeric level for confidence interval

test.unit.slope: logical, should the adequacy of the single-hit model
          be tested?

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

     A binomial generalized linear model is fitted with cloglog link
     and offset 'log(dose)'. If 'observed=FALSE', a classic Poisson
     single-hit model is assumed, and the Poisson frequency of the stem
     cells is the 'exp' of the intercept. If 'observed=TRUE', the
     values of 'dose' are treated as actual cell numbers rather than
     expected values. This doesn't changed the generalized linear model
     fit but changes how the frequencies are extracted from the
     estimated model coefficient.

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

     List with components 

      CI: numeric vector giving estimated frequency and lower and upper
          limits of Wald confidence interval

test.unit.slope: numeric vector giving chisquare likelihood ratio test
          statistic and p-value for testing the slope of the offset
          equal to one

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

     Gordon Smyth

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

     Bonnefoix T, Bonnefoix P, Verdiel P, Sotto JJ. (1996). Fitting
     limiting dilution experiments with generalized linear models
     results in a test of the single-hit Poisson assumption. _J Immunol
     Methods_ 194, 113-119.

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

     Dose <- c(50,100,200,400,800)
     Responses <- c(2,6,9,15,21)
     Tested <- c(24,24,24,24,24)
     limdil(Responses,Dose,Tested,test.unit.slope=TRUE)

