qpCItest               package:qpgraph               R Documentation

_C_o_n_d_i_t_i_o_n_a_l _i_n_d_e_p_e_n_d_e_n_c_e _t_e_s_t

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

     Performs a conditional independence test between two variables
     given a conditioning set.

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

     ## S4 method for signature 'ExpressionSet':
     qpCItest(data, N, i=1, j=2, Q=c(),
                                        long.dim.are.variables=TRUE, R.code.only=FALSE)
     ## S4 method for signature 'data.frame':
     qpCItest(data, N, i=1, j=2, Q=c(),
                                     long.dim.are.variables=TRUE, R.code.only=FALSE)
     ## S4 method for signature 'matrix':
     qpCItest(data, N, i=1, j=2, Q=c(),
                                 long.dim.are.variables=TRUE, R.code.only=FALSE)

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

    data: data set where the test should be performed. It can be either
          an 'ExpressionSet' object, a data frame, or a matrix. If it
          is a matrix and the matrix is squared then this function
          assumes the matrix is the sample covariance matrix of the
          data and the sample size parameter 'N' should be provided.

       N: number of observations in the data set. Only necessary when
          the sample covariance matrix is provided through the 'data'
          parameter.

       i: index or name of one of the two variables.

       j: index or name of the other variable.

       Q: indexes or names of the variables forming the conditioning
          set.

long.dim.are.variables: logical; if TRUE it is assumed that when data
          are in a data frame or in a matrix, the longer dimension is
          the one defining the random variables (default); if FALSE,
          then random variables are assumed to be at the columns of the
          data frame or matrix.

R.code.only: logical; if FALSE then the faster C implementation is used
          (default); if TRUE then only R code is executed.

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

     Note that the size of possible 'Q' sets should be in the range 1
     to 'min(p,n-3)', where 'p' is the number of variables and 'n' the
     number of observations. The computational cost increases linearly
     with the number of variables in 'Q'.

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

     A list with two members, the t-statistic value and the p-value on
     rejecting the null hypothesis of independence.

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

     R. Castelo and A. Roverato

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

     Castelo, R. and Roverato, A. A robust procedure for Gaussian
     graphical model search from microarray data with p larger than n,
     _J. Mach. Learn. Res._, 7:2621-2650, 2006.

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

     'qpNrr' 'qpEdgeNrr'

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

     # in this graph 3 is conditionally independent of 4 given 1 AND 2

     I <- matrix(c(FALSE,  TRUE,  TRUE,  TRUE,
                   TRUE,  FALSE,  TRUE,  TRUE,
                   TRUE,   TRUE, FALSE, FALSE,
                   TRUE,   TRUE, FALSE, FALSE), nrow=4, ncol=4, byrow=TRUE)
     K <- qpI2K(I)

     X <- qpSampleMvnorm(K, N=100)

     qpCItest(X, N=100, i=3, j=4, Q=1, long.dim.are.variables=FALSE)

     qpCItest(X, N=100, i=3, j=4, Q=c(1,2), long.dim.are.variables=FALSE)

