mcfa                   package:cfa                   R Documentation

_R_e_p_e_a_t_e_d _m_e_a_s_u_r_e_s _a_n_a_l_y_s_i_s _o_f _c_o_n_f_i_g_u_r_a_t_i_o_n _f_r_e_q_u_e_n_c_i_e_s

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

     Determines the frequency of all combinations of variable values
     (their  configurations) over time or with and without treatment in
     comparison with their expected frequency and displays them in the
     order of decreasing chi-squared. In addition, a global chi squared
     is calculated.

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

     mcfa(configmatrix, cntmatrix, descending=TRUE, sortonchisq=TRUE,
          ignore.na=FALSE, verbose=FALSE)

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

configmatrix: Dataframe with the variables to be analyzed

cntmatrix: Matrix with >=2 columns of counts (containing 1 if the data
          are not aggregated) 

descending: Output in the order of decreasing chi squared 

sortonchisq: Sort output on chi squared

ignore.na: Ignore (casewise) missing data in the configurations

 verbose: Long output

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

     Each variable must have at least two different values and may have
     more  (extension of the classical CFA). The `configmatrix' must
     consist of at least  two variables (columns). Factors and numbers
     are both accepted (the numbers  are internally converted to
     factors). The `cntmatrix' must be  numeric. Counts should be at
     least = 5 for the chi squared test to be reliable but when  using
     the CFA as a purely heuristic tool counts of 0 are possible.

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

     A list with class `"mcfa"' contains the tabular results and the
     overall parameters 

Row names: Configuration

n(1)..n(configs): Frequency (count) of this configuration

expected(1)..expected(config): Expected Frequency (count) of this
          configuration

  chi.sq: Chi squared for the given configuration

p(chisq): p(chi squared) for the given configuration

Overall chi squared : Overall chi squared 

p(chi squared)  : p(overall chi squared) 

Degrees of freedom : Overall degrees of freedom 

_W_A_R_N_I_N_G:

     The program is implemented in R itself rather than a  compiled
     library and therefore slow. In most cases the input is a 
     pre-aggregated table and speed is no problem because the
     `configmatrix'  is small. There are no hard-coded limits in the
     program so even large tables  can be processed but this will take
     time and memory. 

     The outout table can be very wide if the levels of the factors
     variables are  long so `options(width=..)' may need to be adjusted

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

     Stefan Funke <funke@attglobal.net>

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

     Krauth J., Lienert G. A. (1973, Reprint 1995) Die
     Konfigurationsfrequenzanalyse (KFA) und ihre Anwendung in
     Psychologie und Medizin Beltz Psychologie Verlagsunion

     Eye, A. von (1990) Introduction to configural frequency analysis.
     The search for types and anti-types in cross-classification. 
     Cambride 1990

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

     `cfa', `hier.cfa', `boot.cfa'

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

     library(cfa)
     data(cfa2dat)
     mcfa(cfa2dat[1:3],cfa2dat[4:5],verbose=T)

