


computes the F-statistic for sine wave in locally-white noise (continuous data).
[Fval,A,f,sig,sd] = ftestc(data,params,p)
Inputs:
data (data in [N,C] i.e. time x channels/trials or a single vector) - required.
params structure containing parameters - params has the
following fields: tapers, Fs, fpass, pad
tapers (parameters for calculating tapers [NW,K]) - optional. Defaults to [3 5]
Fs (sampling frequency) -- optional. Defaults to 1.
fpass (frequency band to be used in the calculation in the form
[fmin fmax])- optional.
Default all frequencies between 0 and Fs/2
pad (padding factor for the FFT) - optional. Defaults to 0.
e.g. For N = 500, if PAD = 0, we pad the FFT
to 512 points; if PAD = 2, we pad the FFT
to 2048 points, etc.
p (P-value to calculate error bars for) - optional. Defaults to 0.05/N where N is the number of samples which
corresponds to a false detect probability of approximately 0.05.
plt (y/n for plot and no plot respectively)
Outputs:
Fval (F-statistic in frequency x channels/trials form)
A (Line amplitude for X in frequency x channels/trials form)
f (frequencies of evaluation)
sig (F distribution (1-p)% confidence level)
sd (standard deviation of the amplitude C)

0001 function [Fval,A,f,sig,sd] = ftestc(data,params,p,plt) 0002 % computes the F-statistic for sine wave in locally-white noise (continuous data). 0003 % 0004 % [Fval,A,f,sig,sd] = ftestc(data,params,p) 0005 % 0006 % Inputs: 0007 % data (data in [N,C] i.e. time x channels/trials or a single vector) - required. 0008 % params structure containing parameters - params has the 0009 % following fields: tapers, Fs, fpass, pad 0010 % tapers (parameters for calculating tapers [NW,K]) - optional. Defaults to [3 5] 0011 % Fs (sampling frequency) -- optional. Defaults to 1. 0012 % fpass (frequency band to be used in the calculation in the form 0013 % [fmin fmax])- optional. 0014 % Default all frequencies between 0 and Fs/2 0015 % pad (padding factor for the FFT) - optional. Defaults to 0. 0016 % e.g. For N = 500, if PAD = 0, we pad the FFT 0017 % to 512 points; if PAD = 2, we pad the FFT 0018 % to 2048 points, etc. 0019 % p (P-value to calculate error bars for) - optional. Defaults to 0.05/N where N is the number of samples which 0020 % corresponds to a false detect probability of approximately 0.05. 0021 % plt (y/n for plot and no plot respectively) 0022 % 0023 % Outputs: 0024 % Fval (F-statistic in frequency x channels/trials form) 0025 % A (Line amplitude for X in frequency x channels/trials form) 0026 % f (frequencies of evaluation) 0027 % sig (F distribution (1-p)% confidence level) 0028 % sd (standard deviation of the amplitude C) 0029 if nargin < 1; error('Need data'); end; 0030 if nargin < 2 || isempty(params); params=[]; end; 0031 [tapers,pad,Fs,fpass,err,trialave,params]=getparams(params); 0032 clear err trialave 0033 data=change_row_to_column(data); 0034 [N,C]=size(data); 0035 if nargin<3|| isempty(p);p=0.05/N;end; 0036 tapers=dpsschk(tapers,N,Fs); % calculate the tapers 0037 [N,K]=size(tapers); 0038 nfft=2^(nextpow2(N)+pad);% number of points in fft 0039 [f,findx]=getfgrid(Fs,nfft,fpass);% frequency grid to be returned 0040 % errorchk = 0; % set error checking to default (no errors calculated) 0041 % if nargout <= 3 % if called with 4 output arguments, activate error checking 0042 % errorchk = 0; 0043 % else 0044 % errorchk = 1; 0045 % end 0046 Kodd=1:2:K; 0047 Keven=2:2:K; 0048 J=mtfftc(data,tapers,nfft,Fs);% tapered fft of data - f x K x C 0049 Jp=J(findx,Kodd,:); % drop the even ffts and restrict fft to specified frequency grid - f x K x C 0050 tapers=tapers(:,:,ones(1,C)); % add channel indices to the tapers - t x K x C 0051 H0 = squeeze(sum(tapers(:,Kodd,:),1)); % calculate sum of tapers for even prolates - K x C 0052 if C==1;H0=H0';end; 0053 Nf=length(findx);% number of frequencies 0054 H0 = H0(:,:,ones(1,Nf)); % add frequency indices to H0 - K x C x f 0055 H0=permute(H0,[3 1 2]); % permute H0 to get dimensions to match those of Jp - f x K x C 0056 H0sq=sum(H0.*H0,2);% sum of squares of H0^2 across taper indices - f x C 0057 JpH0=sum(Jp.*squeeze(H0),2);% sum of the product of Jp and H0 across taper indices - f x C 0058 A=squeeze(JpH0./H0sq); % amplitudes for all frequencies and channels 0059 Kp=size(Jp,2); % number of even prolates 0060 Ap=A(:,:,ones(1,Kp)); % add the taper index to C 0061 Ap=permute(Ap,[1 3 2]); % permute indices to match those of H0 0062 Jhat=Ap.*H0; % fitted value for the fft 0063 0064 num=(K-1).*(abs(A).^2).*squeeze(H0sq);%numerator for F-statistic 0065 den=squeeze(sum(abs(Jp-Jhat).^2,2)+sum(abs(J(findx,Keven,:)).^2,2));% denominator for F-statistic 0066 Fval=num./den; % F-statisitic 0067 if nargout > 3 0068 sig=finv(1-p,2,2*K-2); % F-distribution based 1-p% point 0069 var=den./(K*squeeze(H0sq)); % variance of amplitude 0070 sd=sqrt(var);% standard deviation of amplitude 0071 end; 0072 if nargout==0 || strcmp(plt,'y'); 0073 [S,f]=mtspectrumc(detrend(data),params);subplot(211); plot(f,10*log10(S));xlabel('frequency Hz'); ylabel('Spectrum dB'); 0074 subplot(212);plot(f,Fval); line(get(gca,'xlim'),[sig sig],'Color','r');xlabel('frequency Hz'); 0075 ylabel('F ratio'); 0076 end 0077 A=A*Fs;