pgleissberg             package:pastecs             R Documentation

_G_l_e_i_s_s_b_e_r_g _d_i_s_t_r_i_b_u_t_i_o_n _p_r_o_b_a_b_i_l_i_t_y

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

     The Gleissberg distribution gives the probability to have k
     extrema in a series of n observations. This distribution is used
     in the turnogram to determine if monotony indices are significant
     (see `turnogram()')

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

     pgleissberg(n, k, lower.tail=TRUE, two.tailed=FALSE)

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

       n: the number of observations in the series 

       k: the number of extrema in the series, as calculated by
          `turnpoints()' 

lower.tail: if `lower.tail=TRUE' (by default) and `two.tailed=FALSE',
          the left-side probability is returned. If it is FALSE, the
          right-side probability is returned 

two.tailed: if `two.tailed=TRUE', the two-sided probability is
          returned. By default, it is FALSE and a one-sided probability
          is returned (left or right, depending on the value of
          `lower.tail' 

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

     a value giving the probability to have `k' extrema in a series of
     `n' observations

_N_o_t_e:

     The Gleissberg distribution is asymptotically normal. For `n' >
     50, the distribution is approximated by a Gaussian curve. For
     lower `n' values, the exact probability is returned (using data in
     the variable `.gleissberg.table'

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

     Frdric Ibanez (ibanez@obs-vlfr.fr), Philippe Grosjean
     (phgrosjean@sciviews.org)

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

     Dallot, S. & M. Etienne, 1990. Une mthode non paramtrique
     d'analyse des sries en ocanographie biologique: les
     tournogrammes. Biomtrie et ocanographie - Socit de biomtrie,
     6, Lille, 26-28 mai 1986. IFREMER, Actes de colloques, 10:13-31.

     Johnson, N.L. & Kotz, S., 1969. Discrete distributions. J. Wiley &
     sons, New York, 328 pp.

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

     `.gleissberg.table', `turnpoints', `turnogram'

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

     # Until n=50, the exact probability is returned
     pgleissberg(20, 10, lower.tail=TRUE, two.tailed=FALSE)
     # For higher n values, it is approximated by a normal distribution
     pgleissberg(60, 33, lower.tail=TRUE, two.tailed=FALSE)

