lkappa                  package:psy                  R Documentation

_L_i_g_h_t'_s _k_a_p_p_a _f_o_r _n _r_a_t_e_r_s

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

     Computes Light's Kappa for agreement in the case of n raters. The
     diagnosis (the object of the rating) may have k possible values
     (ordered or not).

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

     lkappa(r, type="Cohen", weights="squared")

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

       r: m*n matrix, m subjects and n raters

    type: type="Cohen" for a categorical diagnosis. If not, the
          diagnosis is supposed to be ordered

 weights: weights="squared" for a weighted kappa with squared weights.
          If not, weigths are computed with absolute values.

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

     Light's Kappa is equal to the mean of the n(n-1)/2 kappas obtained
     from each pair of raters. Missing values are omitted locally when
     considering each pair of raters. If type="Cohen" the diagnosis is
     considered as a categorical variable. If not, the diagnosis is
     considered as an ordered variable and weigthed kappa's are
     computed. In this last situation, the type of weights that is used
     (squared or absolute values) is given by the variable weigths.

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

     kappa (mean of the n(n-1)/2 kappas obtained from each pair of
     raters)

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

     Bruno Falissard

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

     Conger, A.J. (1980), Integration and generalisation of Kappas for
     multiple raters, Psychological Bulletin, 88, 322-328.

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

     data(expsy)
     lkappa(expsy[,c(11,13,15)])       # Light's kappa for non binary diagnosis
     lkappa(expsy[,c(12,14,16)])       # Light's kappa for binary diagnosis
     lkappa(expsy[,c(11,13,15)], type="weighted")      # Light's kappa for non binary ordered diagnosis

     library(boot)
     lkappa.boot <- function(data,x) {lkappa(data[x,], type="weighted")}
     res <- boot(expsy[,c(11,13,15)],lkappa.boot,1000)
     quantile(res$t,c(0.025,0.975))    # Bootstrapped confidence interval of Light's kappa  
     boot.ci(res,type="bca")           # adjusted bootstrap percentile (BCa) confidence interval (better)

