slope               package:segmented               R Documentation

_S_l_o_p_e _e_s_t_i_m_a_t_e_s _f_r_o_m _s_e_g_m_e_n_t_e_d _r_e_l_a_t_i_o_n_s_h_i_p_s

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

     Computes slopes of each `segmented' relationship in the fitted
     model.

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

     slope(ogg, conf.level = 0.95)

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

     ogg: an object of class '"segmented"', returned by any 'segmented'
          method. 

conf.level: the confidence level required. 

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

     To fit broken-line relationships, 'segmented' uses a
     parameterization whose coefficients are not  the slopes. Therefore
     given an object '"segmented"', 'slope' computes point estimates,
     standard errors, t-values and confidence intervals of the slopes
     of each segmented relationship in the fitted model.

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

     'slope' returns a list of matrices. Each matrix represents a
     segmented relationship and its number of rows equal  to the number
     of segments, while five columns summarize the results.

_N_o_t_e:

     The returned summary is based on limiting Gaussian distribution
     for the model parameters involved  in the computations. Sometimes,
     even with large sample sizes such approximations are questionable 
     (e.g., with small difference-in-slope parameters) and the results
     returned by 'slope'  might be unreliable. Therefore is
     responsability of the user to gauge the applicability of such
     asymptotic  approximations. Anyway, the t values may be not
     assumed for testing purposes  and they should be used just as
     guidelines to assess the estimate uncertainty.

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

     Vito M. R. Muggeo, vmuggeo@dssm.unipa.it

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

     Muggeo, V.M.R. (2003) Estimating regression models with unknown
     break-points.  _Statistics in Medicine_ *22*, 3055-3071.

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

     See also 'davies.test' to test for a nonzero differece-in-slope
     parameter.

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

     set.seed(16)
     x<-1:100
     y<-2+1.5*pmax(x-35,0)-1.5*pmax(x-70,0)+rnorm(100,0,3)
     out<-glm(y~1)
     out.seg<-segmented(out,seg.Z=~x,psi=list(x=c(20,80)))
     ## the slopes of the three segments....
     slope(out.seg)
     rm(x,y,out,out.seg)

