Savgolcv: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
Cross-validation for Savitzky-Golay smoothing and differentiation. | Cross-validation for Savitzky-Golay smoothing and differentiation. | ||
===Synopsis=== | ===Synopsis=== | ||
:cumpress = savgolcv(x,y,lv,width,order,deriv,ind,rm,cvi,pre); %for x class "double" | :cumpress = savgolcv(x,y,lv,width,order,deriv,ind,rm,cvi,pre); %for x class "double" | ||
:cumpress = savgolcv(x,y,lv,width,order,deriv,[],rm,cvi,pre); %for x class "dataset" | :cumpress = savgolcv(x,y,lv,width,order,deriv,[],rm,cvi,pre); %for x class "dataset" | ||
===Description=== | ===Description=== | ||
SAVGOLCV performs cross-validation of Savitzky-Golay parameters: filter width, polynomial order, and derviative order. | SAVGOLCV performs cross-validation of Savitzky-Golay parameters: filter width, polynomial order, and derviative order. | ||
====INPUTS==== | ====INPUTS==== | ||
* '''x''' = ''M'' by ''N'' matrix of predictor variables with ROW vectors to be smoothed (''e.g.'' spectra), and | * '''x''' = ''M'' by ''N'' matrix of predictor variables with ROW vectors to be smoothed (''e.g.'' spectra), and | ||
* '''y''' = ''M'' by ''P'' matrix of predicted variables. | * '''y''' = ''M'' by ''P'' matrix of predicted variables. | ||
====OPTIONAL INPUTS==== | ====OPTIONAL INPUTS==== | ||
* '''''ind''''' = indices of columns of x to be used for calibration {default ind = [1:n] ''i.e.'' all x columns}. | * '''''ind''''' = indices of columns of x to be used for calibration {default ind = [1:n] ''i.e.'' all x columns}. | ||
The following are optional Savitzky-Golay parameters (calls SAVGOL). By entering a vector, instead of a scalar, these variables are cross-validated. | The following are optional Savitzky-Golay parameters (calls SAVGOL). By entering a vector, instead of a scalar, these variables are cross-validated. | ||
* '''''width''''' = number of points in filter {default width = [11 17 23]}. | * '''''width''''' = number of points in filter {default width = [11 17 23]}. | ||
* '''''order''''' = polynomial order {default order = [2 3]}. | * '''''order''''' = polynomial order {default order = [2 3]}. | ||
* '''''deriv''''' = derivative order {default deriv = [0 1 2]}. | * '''''deriv''''' = derivative order {default deriv = [0 1 2]}. | ||
The following are optional cross-validation parameters (calls CROSSVAL). | The following are optional cross-validation parameters (calls CROSSVAL). | ||
* '''''lv''''' = maximum number of LVs {default lv = min(size(x))}. | * '''''lv''''' = maximum number of LVs {default lv = min(size(x))}. | ||
* '''''rm''''' = regression method. Options are: rm = 'nip', PLS via NIPALS algorithm; rm = 'sim', PLS via SIMPLS algorithm {default}, and rm = 'pcr', uses PCR. | * '''''rm''''' = regression method. Options are: rm = 'nip', PLS via NIPALS algorithm; rm = 'sim', PLS via SIMPLS algorithm {default}, and rm = 'pcr', uses PCR. | ||
* '''''cvi''''' = cross-validation method. Options are: cvi = 'loo', leave-one-out, cvi = 'vet', venetian blinds {default}, cvi = 'con', contiguous blocks, and cvi = 'rnd', repeated random test sets. | * '''''cvi''''' = cross-validation method. Options are: cvi = 'loo', leave-one-out, cvi = 'vet', venetian blinds {default}, cvi = 'con', contiguous blocks, and cvi = 'rnd', repeated random test sets. | ||
* '''''split''''' = number of subsets to split the data into {default = 5} and is required for cvi = 'vet', 'con', or 'rnd'. | * '''''split''''' = number of subsets to split the data into {default = 5} and is required for cvi = 'vet', 'con', or 'rnd'. | ||
* '''''iter''''' = number of iterations {default = 5} and is required for cvi = 'rnd'. | * '''''iter''''' = number of iterations {default = 5} and is required for cvi = 'rnd'. | ||
* '''mc''' = 0 supresses mean centering of subsets {default mc = 1}. | * '''mc''' = 0 supresses mean centering of subsets {default mc = 1}. | ||
====OUTPUTS==== | ====OUTPUTS==== | ||
The output is a 4 dimensional array with each dimension corresponding to one of the directions cross-validated over. | The output is a 4 dimensional array with each dimension corresponding to one of the directions cross-validated over. | ||
: cumpress(i,:,:,:) = derivative dimension, | : cumpress(i,:,:,:) = derivative dimension, | ||
: cumpress(:,j,:,:) = latent variable dimension, | : cumpress(:,j,:,:) = latent variable dimension, | ||
: cumpress(:,:,k,:) = window width dimension, and | : cumpress(:,:,k,:) = window width dimension, and | ||
: cumpress(:,:,:,l) = polynomial order dimension. | : cumpress(:,:,:,l) = polynomial order dimension. | ||
===See Also=== | ===See Also=== | ||
[[baseline]], [[crossval]], [[lamsel]], [[mscorr]], [[savgol]], [[specedit]], [[stdfir]] | [[baseline]], [[crossval]], [[lamsel]], [[mscorr]], [[savgol]], [[specedit]], [[stdfir]] | ||
Revision as of 15:26, 3 September 2008
Purpose
Cross-validation for Savitzky-Golay smoothing and differentiation.
Synopsis
- cumpress = savgolcv(x,y,lv,width,order,deriv,ind,rm,cvi,pre); %for x class "double"
- cumpress = savgolcv(x,y,lv,width,order,deriv,[],rm,cvi,pre); %for x class "dataset"
Description
SAVGOLCV performs cross-validation of Savitzky-Golay parameters: filter width, polynomial order, and derviative order.
INPUTS
- x = M by N matrix of predictor variables with ROW vectors to be smoothed (e.g. spectra), and
- y = M by P matrix of predicted variables.
OPTIONAL INPUTS
- ind = indices of columns of x to be used for calibration {default ind = [1:n] i.e. all x columns}.
The following are optional Savitzky-Golay parameters (calls SAVGOL). By entering a vector, instead of a scalar, these variables are cross-validated.
- width = number of points in filter {default width = [11 17 23]}.
- order = polynomial order {default order = [2 3]}.
- deriv = derivative order {default deriv = [0 1 2]}.
The following are optional cross-validation parameters (calls CROSSVAL).
- lv = maximum number of LVs {default lv = min(size(x))}.
- rm = regression method. Options are: rm = 'nip', PLS via NIPALS algorithm; rm = 'sim', PLS via SIMPLS algorithm {default}, and rm = 'pcr', uses PCR.
- cvi = cross-validation method. Options are: cvi = 'loo', leave-one-out, cvi = 'vet', venetian blinds {default}, cvi = 'con', contiguous blocks, and cvi = 'rnd', repeated random test sets.
- split = number of subsets to split the data into {default = 5} and is required for cvi = 'vet', 'con', or 'rnd'.
- iter = number of iterations {default = 5} and is required for cvi = 'rnd'.
- mc = 0 supresses mean centering of subsets {default mc = 1}.
OUTPUTS
The output is a 4 dimensional array with each dimension corresponding to one of the directions cross-validated over.
- cumpress(i,:,:,:) = derivative dimension,
- cumpress(:,j,:,:) = latent variable dimension,
- cumpress(:,:,k,:) = window width dimension, and
- cumpress(:,:,:,l) = polynomial order dimension.
See Also
baseline, crossval, lamsel, mscorr, savgol, specedit, stdfir