如何在cftool中自动设置线性拟合方程，或用fit剔除异常值？

Great question! Let's tackle both of your issues with MATLAB's fitting tools one by one:

You don't have to manually switch the model every time you open cftool—you can specify the linear model directly when calling the function. Here are two straightforward ways:

• Quick method with built-in model name:
  Use the third input argument to pass the 'linear' model type, which corresponds to y = a*x + b:

  cftool(avg_strain_values, avg_stress_values, 'linear');
  

  This will launch the Curve Fitting Tool already set to linear regression, no manual adjustments needed.

• Custom fit type (for more control):
  If you want to explicitly define the linear model (e.g., to set parameter names or constraints), create a fittype object first and pass it to cftool:

  % Define the linear model y = a*x + b
  linear_model = fittype('a*x + b');
  % Launch cftool with the pre-defined model
  cftool(avg_strain_values, avg_stress_values, linear_model);
  

The fit function absolutely supports outlier handling—you have two main approaches depending on whether you want automatic robust fitting or manual outlier exclusion:

Option 1: Robust Fitting (Automatic Outlier Downweighting) #

Instead of manually removing outliers, use MATLAB's robust fitting options to reduce the influence of extreme points. This is great if you don't want to manually identify outliers:

% Create fit options with robust regression enabled
opts = fitoptions(...
    'Method', 'LinearLeastSquares', ...
    'Robust', 'Bisquare'); % 'LAR' (Least Absolute Residuals) is another option

% Perform robust linear fit
robust_fit = fit(avg_strain_values, avg_stress_values, 'linear', opts);

The Bisquare method downweights points with large residuals, effectively minimizing their impact on the fit without deleting them.

Option 2: Manual Outlier Detection & Exclusion #

If you prefer to explicitly remove outliers, you can first identify them using residual analysis or statistical methods, then refit the cleaned data:

% Step 1: Perform an initial linear fit
initial_fit = fit(avg_strain_values, avg_stress_values, 'linear');

% Step 2: Calculate residuals (difference between observed and predicted values)
residuals = avg_stress_values - initial_fit(avg_strain_values);

% Step 3: Identify outliers (e.g., residuals beyond 3 standard deviations)
res_std = std(residuals);
outlier_mask = abs(residuals) > 3 * res_std; % Adjust threshold as needed

% Step 4: Filter out outliers from your data
clean_strain = avg_strain_values(~outlier_mask);
clean_stress = avg_stress_values(~outlier_mask);

% Step 5: Refit with cleaned data
final_fit = fit(clean_strain, clean_stress, 'linear');

You can also use other outlier detection methods (like Z-scores for your raw strain/stress data) if residuals aren't the best metric for your dataset.

内容的提问来源于stack exchange，提问作者enea19