profile.glm package:MASS R Documentation _M_e_t_h_o_d _f_o_r _P_r_o_f_i_l_i_n_g _g_l_m _O_b_j_e_c_t_s _D_e_s_c_r_i_p_t_i_o_n: Investigates the profile log-likelihood function for a fitted model of class '"glm"'. _U_s_a_g_e: ## S3 method for class 'glm': profile(fitted, which = 1:p, alpha = 0.01, maxsteps = 10, del = zmax/5, trace = FALSE, ...) _A_r_g_u_m_e_n_t_s: fitted: the original fitted model object. which: the original model parameters which should be profiled. This can be a numeric or character vector. By default, all parameters are profiled. alpha: highest significance level allowed for the profile t-statistics. maxsteps: maximum number of points to be used for profiling each parameter. del: suggested change on the scale of the profile t-statistics. Default value chosen to allow profiling at about 10 parameter values. trace: logical: should the progress of profiling be reported? ...: further arguments passed to or from other methods. _D_e_t_a_i_l_s: The profile t-statistic is defined as the square root of change in sum-of-squares divided by residual standard error with an appropriate sign. _V_a_l_u_e: A list of classes '"profile.glm"' and '"profile"' with an element for each parameter being profiled. The elements are data-frames with two variables par.vals: a matrix of parameter values for each fitted model. tau: the profile t-statistics. _A_u_t_h_o_r(_s): Originally, D. M. Bates and W. N. Venables. (For S in 1996.) _S_e_e _A_l_s_o: 'glm', 'profile', 'plot.profile' _E_x_a_m_p_l_e_s: options(contrasts = c("contr.treatment", "contr.poly")) ldose <- rep(0:5, 2) numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) sex <- factor(rep(c("M", "F"), c(6, 6))) SF <- cbind(numdead, numalive = 20 - numdead) budworm.lg <- glm(SF ~ sex*ldose, family = binomial) pr1 <- profile(budworm.lg) plot(pr1) pairs(pr1)