You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_P_Bonf.m
    path of file: ./repos/malini/Codes/deconvolution - model/spm8
    
    the code of the file until where you have to start completion: function P = spm_P_Bonf(Z,df,STAT,S,n)
% Returns the corrected P value using Bonferroni
% FORMAT P = spm_P_Bonf(Z,df,STAT,S,n)
%
% Z     - height {minium over n values}
% df    - [df{interest} df{error}]
% STAT  - Statistical field
%       'Z' - Gaussian field
%       'T' - T - field
%       'X' - Chi squared field
%       'F' - F - field
% n     - number of conjoint SPMs
% S     - Voxel count
%
% P     - corrected   P value  - P(STAT > Z)
%
%___________________________________________________________________________
%
% spm_P_Bonf returns the p-value of Z corrected by the Bonferroni
% inequality. 
%
% If n > 1 a conjunction probility over the n values of the statistic
% is returned
%
%___________________________________________________________________________

    
function P = spm_P_Bonf(Z,df,STAT,S,n)
% Returns the corrected P value using Bonferroni
% FORMAT P = spm_P_Bonf(Z,df,STAT,S,n)
%
% Z     - height {minium over n values}
% df    - [df{interest} df{error}]
% STAT  - Statistical field
%       'Z' - Gaussian field
%       'T' - T - field
%       'X' - Chi squared field
%       'F' - F - field
% n     - number of conjoint SPMs
% S     - Voxel count
%
% P     - corrected   P value  - P(STAT > Z)
%
%___________________________________________________________________________
%
% spm_P_Bonf returns the p-value of Z corrected by the Bonferroni
% inequality. 
%
% If n > 1 a conjunction probility over the n values of the statistic
% is returned
%
%___________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging

% Thomas Nichols
% $Id: spm_P_Bonf.m 1143 2008-02-07 19:33:33Z spm $


if      STAT == 'Z'
  P = 1 - spm_Ncdf(Z);
elseif  STAT == 'T'
  P = 1 - spm_Tcdf(Z,df(2));
elseif  STAT == 'X'
  P = 1 - spm_Xcdf(Z,df(2));
elseif  STAT == 'F'
  P = 1 - spm_Fcdf(Z,df);
end