confint                 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.  Methods currently exist for `lm', `glm' and `nls'
     fits.

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

     confint(object, parm = <<see below>>, 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. 

     ...: additional argument(s) for methods - the `glm' methods allows
          `trace'. 

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

     `confint' 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.

     For objects of class `"lm"' the direct formulae based on t values
     are used.

_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%).

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

     `profile'

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

     library(nls)
     data(wtloss)
     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

