tooth               package:bootstrap               R Documentation

_T_o_o_t_h _S_t_r_e_n_g_t_h _D_a_t_a

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

     Thirteen accident victims have had the strength of their teeth
     measured,  It is desired to predict teeth strength from
     measurements not requiring destructive testing. Four such
     bvariables have been obtained for  each subject, ('D1','D2') are
     difficult to obtain,  ('E1','E2') are easy to obtain.

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

     data(tooth)

_F_o_r_m_a_t:

     A data frame with 13 observations on the following 6 variables.

     _p_a_t_i_e_n_t a numeric vector

     _D_1 a numeric vector

     _D_2 a numeric vector

     _E_1 a numeric vector

     _E_2 a numeric vector

     _s_t_r_e_n_g_t_h a numeric vector

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

     Do the easy to obtain variables give as good prediction as the
     difficult  to obtain ones?

_S_o_u_r_c_e:

     Efron, B. and Tibshirani, R. (1993) An Introduction to the
     Bootstrap.  Chapman and Hall, New York, London.

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

     str(tooth)
     mod.easy <-  lm(strength ~ E1+E2, data=tooth)
     mod.diffi <- lm(strength ~ D1+D2, data=tooth)
     summary(mod.easy)
     summary(mod.diffi)
     if(interactive())par(ask=TRUE)
     theta <- function(ind) {
         easy <- lm(strength ~ E1+E2, data=tooth, subset=ind)
         diffi<- lm(strength ~ D1+D2, data=tooth, subset=ind)
         (sum(resid(easy)^2) - sum(resid(diffi)^2))/13   }
     tooth.boot <- bootstrap(1:13, 2000, theta)
     hist(tooth.boot$thetastar)
     abline(v=0, col="red2") 
     qqnorm(tooth.boot$thetastar)
     qqline(tooth.boot$thetastar, col="red2")

