dlogspline             package:polspline             R Documentation

_L_o_g_s_p_l_i_n_e _D_e_n_s_i_t_y _E_s_t_i_m_a_t_i_o_n

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

     Density ('dlogspline'), cumulative probability ('plogspline'),
     quantiles ('qlogspline'), and  random samples ('rlogspline') from 
     a logspline density that was fitted using the 1997 knot addition
     and deletion algorithm ('logspline').  The 1992 algorithm is
     available using the 'oldlogspline' function.

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

     dlogspline(q, fit) 
     plogspline(q, fit) 
     qlogspline(p, fit) 
     rlogspline(n, fit) 

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

       q: vector of quantiles. Missing values (NAs) are allowed.  

       p: vector of probabilities. Missing values (NAs) are allowed.  

       n: sample size. If 'length(n)' is larger than 1, then 
          'length(n)' random values are returned. 

     fit: 'logspline' object, typically the result of 'logspline'. 

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

     Elements of 'q' or 'p' that are missing will cause the 
     corresponding elements of the result to be missing.

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

     Densities ('dlogspline'), probabilities ('plogspline'), quantiles
     ('qlogspline'),  or a random sample ('rlogspline') from a
     'logspline' density that was fitted using knot addition and
     deletion.

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

     Charles Kooperberg clk@fhcrc.org.

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

     Charles Kooperberg and Charles J. Stone.  Logspline density
     estimation for censored data (1992). _Journal of Computational and
     Graphical Statistics_, *1*, 301-328.

     Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K.
     Truong.  The use of polynomial splines and their tensor products
     in extended linear modeling (with discussion) (1997).  _Annals of
     Statistics_, *25*, 1371-1470.

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

     'logspline',       'plot.logspline', 'summary.logspline',
     'oldlogspline'.

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

     x <- rnorm(100)
     fit <- logspline(x)
     qq <- qlogspline((1:99)/100, fit)
     plot(qnorm((1:99)/100), qq)                  # qq plot of the fitted density
     pp <- plogspline((-250:250)/100, fit)
     plot((-250:250)/100, pp, type = "l")
     lines((-250:250)/100,pnorm((-250:250)/100))  # asses the fit of the distribution
     dd <- dlogspline((-250:250)/100, fit)
     plot((-250:250)/100, dd, type = "l")
     lines((-250:250)/100, dnorm((-250:250)/100)) # asses the fit of the density
     rr <- rlogspline(100, fit)                   # random sample from fit

