confint-MASS              package:MASS              R Documentation

_C_o_n_f_i_d_e_n_c_e _I_n_t_e_r_v_a_l_s _f_o_r _M_o_d_e_l _P_a_r_a_m_e_t_e_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computes confidence intervals for one or more parameters in a
     fitted model.  Package 'MASS' adds methods for 'glm' and 'nls'
     fits.

_U_s_a_g_e:

     ## S3 method for class 'glm':
     confint(object, parm, level = 0.95, trace = FALSE, ...)

     ## S3 method for class 'nls':
     confint(object, parm, level = 0.95, ...)

_A_r_g_u_m_e_n_t_s:

  object: a fitted model object. Methods currently exist for the
          classes '"glm"', '"nls"' and for profile objects from these
          classes. 

    parm: a specification of which parameters are to be given
          confidence intervals, either a vector of numbers or a vector
          of names. If missing, all parameters are considered. 

   level: the confidence level required. 

   trace: logical.  Should profiling be traced? 

     ...: additional argument(s) for methods. 

_D_e_t_a_i_l_s:

     'confint' is a generic function in package 'base'.

     These 'confint' methods calls the appropriate profile method, then
     finds the confidence intervals by interpolation in the profile
     traces. If the profile object is already available it should be
     used as the main argument rather than the fitted model object
     itself.

_V_a_l_u_e:

     A matrix (or vector) with columns giving lower and upper
     confidence limits for each parameter. These will be labelled as
     (1-level)/2 and 1 - (1-level)/2 in % (by default 2.5% and 97.5%).

_R_e_f_e_r_e_n_c_e_s:

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_S_e_e _A_l_s_o:

     'profile'

_E_x_a_m_p_l_e_s:

     expn1 <- deriv(y ~ b0 + b1 * 2^(-x/th), c("b0", "b1", "th"),
                    function(b0, b1, th, x) {})

     wtloss.gr <- nls(Weight ~ expn1(b0, b1, th, Days),
        data = wtloss, start = c(b0=90, b1=95, th=120))

     expn2 <- deriv(~b0 + b1*((w0 - b0)/b1)^(x/d0),
              c("b0","b1","d0"), function(b0, b1, d0, x, w0) {})

     wtloss.init <- function(obj, w0) {
       p <- coef(obj)
       d0 <-  - log((w0 - p["b0"])/p["b1"])/log(2) * p["th"]
       c(p[c("b0", "b1")], d0 = as.vector(d0))
     }

     out <- NULL
     w0s <- c(110, 100, 90)
     for(w0 in w0s) {
         fm <- nls(Weight ~ expn2(b0, b1, d0, Days, w0),
                   wtloss, start = wtloss.init(wtloss.gr, w0))
         out <- rbind(out, c(coef(fm)["d0"], confint(fm, "d0")))
       }
     dimnames(out) <- list(paste(w0s, "kg:"),  c("d0", "low", "high"))
     out

     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.lg0 <- glm(SF ~ sex + ldose - 1, family = binomial)
     confint(budworm.lg0)
     confint(budworm.lg0, "ldose")

