SampleStatistics             package:cmm             R Documentation

_S_a_m_p_l_e_S_t_a_t_i_s_t_i_c_s

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

     Gives sample values, standard errors and z-scores of one or more
     coefficients.

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

     SampleStatistics(dat, coeff, CoefficientDimensions="Automatic",
         Labels="Automatic", ShowCoefficients=TRUE, ShowParameters=FALSE, ParameterCoding="Effect", ShowCorrelations=FALSE,  Title="")

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

     dat: observed data as a list of frequencies or as a data frame

   coeff: list of coefficients, can be obtained using
          'SpecifyCoefficient'

CoefficientDimensions: numeric vector of dimensions of the table in
          which the coefficient vector is to be arranged

  Labels: list of characters or numbers indicating labels for
          dimensions of table in which the coefficient vector is to be
          arranged

ShowCoefficients: boolean, indicating whether or not the coefficients
          are to be displayed

ShowParameters: boolean, indicating whether or not the parameters
          (computed from the coefficients) are to be displayed

ParameterCoding: Coding to be used for parameters, choice of
          '"Effect"', '"Dummy"' and  '"Polynomial"'

ShowCorrelations: boolean, indicating whether or not to show the
          correlation matrix for the estimated coefficients

   Title: title of computation to appear at top of screen output

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

     The data can be a data frame or vector of frequencies.
     'MarginalModelFit' converts a data frame 'dat' using
     'c(t(ftable(dat)))'.

     For 'ParameterCoding', the default for '"Dummy"' is that the first
     cell in the table is the reference cell. Cell (i, j, k, ...) can
     be made reference cell using 'list("Dummy",c(i,j,k,...))'. For
     '"Polynomial"' the default is to use centralized scores based on
     equidistant (distance 1) linear scores, for example, if for i = 1,
     2, 3, 4,

           mu_i = alpha + q_i beta + r_i gamma + s_i delta

     where beta is a quadratic, gamma a cubic and delta a quartic
     effect, then q_i takes the values (-1.5, -.5, .5, 1.5), r_i takes
     the values (1, -1, -1, 1) (centralized squares of the q_i), and
     s_i takes the values (-3.375, -.125, .125, 3.375) (cubes of the
     q_i).

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

     NA. Only output to the screen is provided

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

     W. P. Bergsma w.p.bergsma@lse.ac.uk

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

     Bergsma, W. P. (1997). _Marginal models for categorical data_.
     Tilburg, The Netherlands: Tilburg University Press. <URL:
     http://stats.lse.ac.uk/bergsma/pdf/bergsma_phdthesis.pdf>

     Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009).
     Marginal models for dependent, clustered, and longitudunal
     categorical data. Berlin: Springer.

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

     'ModelStatistics', 'MarginalModelFit'

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

      data(BodySatisfaction)

      ## Table 2.6 in Bergsma, Croon and Hagenaars (2009). Loglinear parameters for marginal table IS
      ## We provide two to obtain the parameters

      dat   <- BodySatisfaction[,2:8]        # omit first column corresponding to gender

      # matrix producing 1-way marginals, ie the 7x5 table IS
      at75 <- MarginalMatrix( c(1, 2, 3, 4, 5, 6, 7), list(c(1),c(2),c(3),c(4),c(5),c(6),c(7)), c(5, 5, 5, 5, 5, 5, 5) )

      # First method: the "coefficients" are the log-probabilities, from which all the (loglinear) parameters are calculated
      SampleStatistics(dat, list("log",at75), CoefficientDimensions=c(7,5),Labels=c("I","S"),ShowCoefficients=FALSE,ShowParameters=TRUE)

      # Second method: the "coefficients" are explicitly specified as being the (highest-order) loglinear parameters
      loglinpar75 <- SpecifyCoefficient("LoglinearParameters", c(7,5) )
      SampleStatistics(dat, list(loglinpar75, at75), CoefficientDimensions=c(7,5), Labels=c("I","S"))

