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Hello Matlab experts,
I have a doubt on calling the field names. I have a program, and it needs to call fopts =fieldnames(options). I already have the "options" defined. But i am having an error.
function [X, M, C, Xerr] = regem(X, options)
%REGEM Imputation of missing values with regularized EM algorithm.
%
% [X, M, C, Xerr] = REGEM(X, OPTIONS) replaces missing values
% (NaNs) in the data matrix X with imputed values. REGEM
% returns
%
% X, the data matrix with imputed values substituted for NaNs,
% M, the estimated mean of X,
% C, the estimated covariance matrix of X,
% Xerr, an estimated standard error of the imputed values.
%
% Missing values are imputed with a regularized expectation
% maximization (EM) algorithm. In an iteration of the EM algorithm,
% given estimates of the mean and of the covariance matrix are
% revised in three steps. First, for each record X(i,:) with
% missing values, the regression parameters of the variables with
% missing values on the variables with available values are
% computed from the estimates of the mean and of the covariance
% matrix. Second, the missing values in a record X(i,:) are filled
% in with their conditional expectation values given the available
% values and the estimates of the mean and of the covariance
% matrix, the conditional expectation values being the product of
% the available values and the estimated regression
% coefficients. Third, the mean and the covariance matrix are
% re-estimated, the mean as the sample mean of the completed
% dataset and the covariance matrix as the sum of the sample
% covariance matrix of the completed dataset and an estimate of the
% conditional covariance matrix of the imputation error.
%
% In the regularized EM algorithm, the parameters of the regression
% models are estimated by a regularized regression method. By
% default, the parameters of the regression models are estimated by
% an individual ridge regression for each missing value in a
% record, with one regularization parameter (ridge parameter) per
% missing value. Optionally, the parameters of the regression
% models can be estimated by a multiple ridge regression for each
% record with missing values, with one regularization parameter per
% record with missing values. The regularization parameters for the
% ridge regressions are selected as the minimizers of the
% generalized cross-validation (GCV) function. As another option,
% the parameters of the regression models can be estimated by
% truncated total least squares. The truncation parameter, a
% discrete regularization parameter, is fixed and must be given as
% an input argument. The regularized EM algorithm with truncated
% total least squares is faster than the regularized EM algorithm
% with with ridge regression, requiring only one eigendecomposition
% per iteration instead of one eigendecomposition per record and
% iteration. But an adaptive choice of truncation parameter has not
% been implemented for truncated total least squares. So the
% truncated total least squares regressions can be used to compute
% initial values for EM iterations with ridge regressions, in which
% the regularization parameter is chosen adaptively.
%
% As default initial condition for the imputation algorithm, the
% mean of the data is computed from the available values, mean
% values are filled in for missing values, and a covariance matrix
% is estimated as the sample covariance matrix of the completed
% dataset with mean values substituted for missing
% values. Optionally, initial estimates for the missing values and
% for the covariance matrix estimate can be given as input
% arguments.
%
% The OPTIONS structure specifies parameters in the algorithm:
%
% Field name Parameter Default
%
% OPTIONS.regress Regression procedure to be used: 'mridge'
% 'mridge': multiple ridge regression
% 'iridge': individual ridge regressions
% 'ttls': truncated total least squares
% regression
%
% OPTIONS.stagtol Stagnation tolerance: quit when 5e-3
% consecutive iterates of the missing
% values are so close that
% norm( Xmis(it)-Xmis(it-1) )
% <= stagtol * norm( Xmis(it-1) )
%
% OPTIONS.maxit Maximum number of EM iterations. 30
%
% OPTIONS.inflation Inflation factor for the residual 1
% covariance matrix. Because of the
% regularization, the residual covariance
% matrix underestimates the conditional
% covariance matrix of the imputation
% error. The inflation factor is to correct
% this underestimation. The update of the
% covariance matrix estimate is computed
% with residual covariance matrices
% inflated by the factor OPTIONS.inflation,
% and the estimates of the imputation error
% are inflated by the same factor.
%
% OPTIONS.disp Diagnostic output of algorithm. Set to 1
% zero for no diagnostic output.
%
% OPTIONS.regpar Regularization parameter. not set
% For ridge regression, set regpar to
% sqrt(eps) for mild regularization; leave
% regpar unset for GCV selection of
% regularization parameters.
% For TTLS regression, regpar must be set
% and is a fixed truncation parameter.
%
% OPTIONS.relvar_res Minimum relative variance of residuals. 5e-2
% From the parameter OPTIONS.relvar_res, a
% lower bound for the regularization
% parameter is constructed, in order to
% prevent GCV from erroneously choosing
% too small a regularization parameter.
%
% OPTIONS.minvarfrac Minimum fraction of total variation in 0
% standardized variables that must be
% retained in the regularization.
% From the parameter OPTIONS.minvarfrac,
% an approximate upper bound for the
% regularization parameter is constructed.
% The default value OPTIONS.minvarfrac = 0
% essentially corresponds to no upper bound
% for the regularization parameter.
%
% OPTIONS.Xmis0 Initial imputed values. Xmis0 is a not set
% (possibly sparse) matrix of the same
% size as X with initial guesses in place
% of the NaNs in X.
%
% OPTIONS.C0 Initial estimate of covariance matrix. not set
% If no initial covariance matrix C0 is
% given but initial estimates Xmis0 of the
% missing values are given, the sample
% covariance matrix of the dataset
% completed with initial imputed values is
% taken as an initial estimate of the
% covariance matrix.
%
% OPTIONS.Xcmp Display the weighted rms difference not set
% between the imputed values and the
% values given in Xcmp, a matrix of the
% same size as X but without missing
% values. By default, REGEM displays
% the rms difference between the imputed
% values at consecutive iterations. The
% option of displaying the difference
% between the imputed values and reference
% values exists for testing purposes.
%
% OPTIONS.neigs Number of eigenvalue-eigenvector pairs not set
% to be computed for TTLS regression.
% By default, all nonzero eigenvalues and
% corresponding eigenvectors are computed.
% By computing fewer (neigs) eigenvectors,
% the computations can be accelerated, but
% the residual covariance matrices become
% inaccurate. Consequently, the residual
% covariance matrices underestimate the
% imputation error conditional covariance
% matrices more and more as neigs is
% decreased.
% References:
% [1] T. Schneider, 2001: Analysis of incomplete climate data:
% Estimation of mean values and covariance matrices and
% imputation of missing values. Journal of Climate, 14,
% 853--871.
% [2] R. J. A. Little and D. B. Rubin, 1987: Statistical
% Analysis with Missing Data. Wiley Series in Probability
% and Mathematical Statistics. (For EM algorithm.)
% [3] P. C. Hansen, 1997: Rank-Deficient and Discrete Ill-Posed
% Problems: Numerical Aspects of Linear Inversion. SIAM
% Monographs on Mathematical Modeling and Computation.
% (For regularization techniques, including the selection of
% regularization parameters.)
%if plo; set(gcf,'Double','on'); end
[J, B]=xlsread('Data_ChinaJustin1.xls');%%%%%Reading the input data. A contains numerical values and B contains text values
A1(:,:,1)=J(2:101,6:45); %%%%%%%%%%%%%%storing required values from input data to the array A1, which contains 100 responses for each of 40 questions
ARow=A1;
for i=1:100
for j=1:40
if ARow(i,j)==-9
ARow(i,j)=NaN;
end
end
end
X=ARow
options='regress';
% if nargin<2
% options=1;
% end
% error(nargchk(1, 2, nargin)) % check number of input arguments
if ndims(X) > 2, error('X must be vector or 2-D array.'); end
% if X is a vector, make sure it is a column vector (a single variable)
if length(X)==prod(size(X))
X = X(:);
end
[n, p] = size(X);
% number of degrees of freedom for estimation of covariance matrix
dofC = n - 1; % use degrees of freedom correction
optreg = [];
% ============== process options ========================
if nargin ==1 || isempty(options)
fopts = [];
else
fopts = fieldnames(options);
end
% initialize options structure for regression modules
optreg = [];
if strmatch('regress', fopts)
regress = lower(options.regress);
switch regress
case {'mridge', 'iridge'}
% OK
case {'ttls'}
if isempty(strmatch('regpar', fopts))
error('Truncation parameter for TTLS regression must be given.')
else
trunc = min([options.regpar, n-1, p]);
end
if strmatch('neigs', fopts)
neigs = options.neigs;
else
neigs = min(n-1, p);
end
otherwise
error(['Unknown regression method ', regress])
end
else
regress = 'mridge';
end
if strmatch('stagtol', fopts)
stagtol = options.stagtol;
else
stagtol = 5e-3;
end
if strmatch('maxit', fopts)
maxit = options.maxit;
else
maxit = 30;
end
if strmatch('inflation', fopts)
inflation = options.inflation;
else
inflation = 1;
end
if strmatch('relvar_res', fopts)
optreg.relvar_res = options.relvar_res;
else
optreg.relvar_res = 5e-2;
end
if strmatch('minvarfrac', fopts)
optreg.minvarfrac = options.minvarfrac;
else
optreg.minvarfrac = 0;
end
h_given = 0;
if strmatch('regpar', fopts)
h_given = 1;
optreg.regpar = options.regpar;
if strmatch(regress, 'iridge')
regress = 'mridge';
end
end
if strmatch('disp', fopts);
dispon = options.disp;
else
dispon = 1;
end
if strmatch('Xmis0', fopts);
Xmis0_given= 1;
Xmis0 = options.Xmis0;
if any(size(Xmis0) ~= [n,p])
error('OPTIONS.Xmis0 must have the same size as X.')
end
else
Xmis0_given= 0;
end
if strmatch('C0', fopts);
C0_given = 1;
C0 = options.C0;
if any(size(C0) ~= [p, p])
error('OPTIONS.C0 has size incompatible with X.')
end
else
C0_given = 0;
end
if strmatch('Xcmp', fopts);
Xcmp_given = 1;
Xcmp = options.Xcmp;
if any(size(Xcmp) ~= [n,p])
error('OPTIONS.Xcmp must have the same size as X.')
end
sXcmp = std(Xcmp);
else
Xcmp_given = 0;
end
% ================= end options =========================
% get indices of missing values and initialize matrix of imputed values
indmis = find(isnan(X));
nmis = length(indmis);
if nmis == 0
warning('No missing value flags found.')
return % no missing values
end
[jmis,kmis] = ind2sub([n, p], indmis);
Xmis = sparse(jmis, kmis, NaN, n, p); % matrix of imputed values
Xerr = sparse(jmis, kmis, Inf, n, p); % standard error imputed vals.
% for each row of X, assemble the column indices of the available
% values and of the missing values
kavlr = cell(n,1);
kmisr = cell(n,1);
for j=1:n
kavlr{j} = find(~isnan(X(j,:)));
kmisr{j} = find(isnan(X(j,:)));
end
if dispon
disp(sprintf('\nREGEM:'))
disp(sprintf('\tPercentage of values missing: %5.2f', nmis/(n*p)*100))
disp(sprintf('\tStagnation tolerance: %9.2e', stagtol))
disp(sprintf('\tMaximum number of iterations: %3i', maxit))
if (inflation ~= 1)
disp(sprintf('\tResidual (co-)variance inflation: %6.3f ', inflation))
end
if Xmis0_given & C0_given
disp(sprintf(['\tInitialization with given imputed values and' ...
' covariance matrix.']))
elseif C0_given
disp(sprintf(['\tInitialization with given covariance' ...
' matrix.']))
elseif Xmis0_given
disp(sprintf(['\tInitialization with given imputed values.']))
else
disp(sprintf('\tInitialization of missing values by mean substitution.'))
end
switch regress
case 'mridge'
disp(sprintf('\tOne multiple ridge regression per record:'))
disp(sprintf('\t==> one regularization parameter per record.'))
case 'iridge'
disp(sprintf('\tOne individual ridge regression per missing value:'))
disp(sprintf('\t==> one regularization parameter per missing value.'))
case 'ttls'
disp(sprintf('\tOne total least squares regression per record.'))
disp(sprintf('\tFixed truncation parameter: %4i', trunc))
end
if h_given
disp(sprintf('\tFixed regularization parameter: %9.2e', optreg.regpar))
end
if Xcmp_given
disp(sprintf(['\n\tIter \tmean(peff) \t|X-Xcmp|/std(Xcmp) ' ...
'\t|D(Xmis)|/|Xmis|']))
else
disp(sprintf(['\n\tIter \tmean(peff) \t|D(Xmis)| ' ...
'\t|D(Xmis)|/|Xmis|']))
end
end
% initial estimates of missing values
if Xmis0_given
% substitute given guesses for missing values
X(indmis) = Xmis0(indmis);
[X, M] = center(X); % center data to mean zero
else
[X, M] = center(X); % center data to mean zero
X(indmis) = zeros(nmis, 1); % fill missing entries with zeros
end
if C0_given
C = C0;
else
C = X'*X / dofC; % initial estimate of covariance matrix
end
it = 0;
rdXmis = Inf;
while (it < maxit & rdXmis > stagtol)
it = it + 1;
% initialize for this iteration ...
CovRes = zeros(p,p); % ... residual covariance matrix
peff_ave = 0; % ... average effective number of variables
% scale variables to unit variance
D = sqrt(diag(C));
const = (abs(D) < eps); % test for constant variables
nconst = ~const;
if sum(const) ~= 0 % do not scale constant variables
D = D .* nconst + 1*const;
end
X = X ./ repmat(D', n, 1);
% correlation matrix
C = C ./ repmat(D', p, 1) ./ repmat(D, 1, p);
if strmatch(regress, 'ttls')
% compute eigendecomposition of correlation matrix
[V, d] = peigs(C, neigs);
peff_ave = trunc;
end
for j=1:n % cycle over records
pm = length(kmisr{j}); % number of missing values in this record
if pm > 0
pa = p - pm; % number of available values in this record
% regression of missing variables on available variables
switch regress
case 'mridge'
% one multiple ridge regression per record
[B, S, h, peff] = mridge(C(kavlr{j},kavlr{j}), ...
C(kmisr{j},kmisr{j}), ...
C(kavlr{j},kmisr{j}), n-1, optreg);
peff_ave = peff_ave + peff*pm/nmis; % add up eff. number of variables
dofS = dofC - peff; % residual degrees of freedom
% inflation of residual covariance matrix
S = inflation * S;
% bias-corrected estimate of standard error in imputed values
Xerr(j, kmisr{j}) = dofC/dofS * sqrt(diag(S))';
case 'iridge'
% one individual ridge regression per missing value in this record
[B, S, h, peff] = iridge(C(kavlr{j},kavlr{j}), ...
C(kmisr{j},kmisr{j}), ...
C(kavlr{j},kmisr{j}), n-1, optreg);
peff_ave = peff_ave + sum(peff)/nmis; % add up eff. number of variables
dofS = dofC - peff; % residual degrees of freedom
% inflation of residual covariance matrix
S = inflation * S;
% bias-corrected estimate of standard error in imputed values
Xerr(j, kmisr{j}) = ( dofC * sqrt(diag(S)) ./ dofS)';
case 'ttls'
% truncated total least squares with fixed truncation parameter
[B, S] = pttls(V, d, kavlr{j}, kmisr{j}, trunc);
dofS = dofC - trunc; % residual degrees of freedom
% inflation of residual covariance matrix
S = inflation * S;
% bias-corrected estimate of standard error in imputed values
Xerr(j, kmisr{j}) = dofC/dofS * sqrt(diag(S))';
end
% missing value estimates
Xmis(j, kmisr{j}) = X(j, kavlr{j}) * B;
% add up contribution from residual covariance matrices
CovRes(kmisr{j}, kmisr{j}) = CovRes(kmisr{j}, kmisr{j}) + S;
end
end % loop over records
% rescale variables to original scaling
X = X .* repmat(D', n, 1);
Xerr = Xerr .* repmat(D', n, 1);
Xmis = Xmis .* repmat(D', n, 1);
C = C .* repmat(D', p, 1) .* repmat(D, 1, p);
CovRes = CovRes .* repmat(D', p, 1) .* repmat(D, 1, p);
% rms change of missing values
dXmis = norm(Xmis(indmis) - X(indmis)) / sqrt(nmis);
% relative change of missing values
nXmis_pre = norm(X(indmis) + M(kmis)') / sqrt(nmis);
if nXmis_pre < eps
rdXmis = Inf;
else
rdXmis = dXmis / nXmis_pre;
end
% update data matrix X
X(indmis) = Xmis(indmis);
% re-center data and update mean
[X, Mup] = center(X); % re-center data
M = M + Mup; % updated mean vector
% update covariance matrix estimate
C = (X'*X + CovRes)/dofC;
if dispon
if Xcmp_given
% imputed values in original scaling
Xmis(indmis) = X(indmis) + M(kmis)';
% relative error of imputed values (relative to values in Xcmp)
dXmis = norm( (Xmis(indmis)-Xcmp(indmis))./sXcmp(kmis)' ) ...
/ sqrt(nmis);
disp(sprintf(' \t%3i \t %8.2e \t %10.3e \t %10.3e', ...
it, peff_ave, dXmis, rdXmis))
else
disp(sprintf(' \t%3i \t %8.2e \t%9.3e \t %10.3e', ...
it, peff_ave, dXmis, rdXmis))
end
end % display of diagnostics
end % EM iteration
% add mean to centered data matrix
X = X + repmat(M, n, 1);
As you can see from the program, I need to call the regress. But I am having an error at this line fopts = fieldnames(options); the error is ??? Undefined function or method 'fieldnames' for input arguments of type 'char'. Help me
采纳的回答
Walter Roberson
2011-10-12
Don't go around hacking up programs like that. Instead of ignoring the input arguments and overwriting them with hard-coded values (or values read in from a hard-coded file name), restore your routine to what it was before and write a second routine that does the xlsread for you and calls regem() passing in that data and the appropriate regress option.
If you prefer to place sticky jam upon the hacked program in hopes it will adhere together until the first time an ant happens upon it, then change the test
if nargin == 1 || isempty(options)
to
if nargin < 2 || isempty(options)
更多回答（1 个）
Fangjun Jiang
2011-10-12
function fieldnames() is for structure. If the input argument is not a structure, it will cause the error.
s.a=1;
s.b=2;
fieldnames(s)
a='abc';
fieldnames(a)
4 个评论
manoraaju
2011-10-12
Walter Roberson
2011-10-12
Yup. options is being passed in as a formal parameter, but then there is the line
options='regress';
which overwrites it with a string.
Walter Roberson
2011-10-12
Looking at the code and the way it handles defaults, I would just remove the
options='regress';
line. If no options are passed in then nargin will be 1 and fopts will be assigned [], which will lead to regress being set to 'mridge' which sounds as valid as any default.
manoraaju
2011-10-12
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