At each step, the function searches for terms to add to the model The stepwiselm function uses forward and backward stepwise regression toĭetermine a final model. The method begins with an initial model, specified using modelspec,Īnd then compares the explanatory power of incrementally larger and Model based on their statistical significance in explaining the response Stepwise regression is a systematic methodįor adding and removing terms from a linear or generalized linear Stepwiselm function removes the redundant term, regardless ofįor more information, see the Criterion name-value pair Is linearly dependent with other terms in the current model, the Is redundant (linearly dependent) with other terms in the current model. Than PRemove, remove the term from the model.Īt each step, the stepwiselm function also checks whether a term If the increase in the adjusted R-squared value of the model is less If the increase in the R-squared value of the model is less than If the change in the BIC of the model is greater than If the change in the AIC of the model is greater than Matrix is convenient when the number of predictors is large and you want to generateĪ character vector or string scalar Formula in the form Number of predictor variables, and +1 accounts for the response variable. Where t is the number of terms and p is the For example,Ī t-by-( p + 1) matrix, or a Terms Matrix, specifying terms in the model, The model contains interaction terms, but the degree of each interaction termĭoes not exceed the maximum value of the specified degrees. Specify the maximum degree for each predictor by using numerals 0 though 9. Predictor, degree j in the second predictor, and so Model is a polynomial with all terms up to degree i in the first Products of pairs of distinct predictors. Model contains an intercept term, linear and squared terms for each predictor, and all Model contains an intercept term and linear and squared terms for each predictor. Model contains an intercept, linear term for each predictor, and all products of pairs of Model contains an intercept and linear term for each predictor. Model contains only a constant (intercept) term. To treat the two indicator variables as two distinct predictor variables, use dummyvar to create separate categorical variables. Stepwiselm treats the two indicator variables as one predictor variable and adds Year in one step. Because the p-value is less than the default threshold value of 0.10, stepwiselm does not remove the term.Īlthough the maximum allowed number of steps is 5, stepwiselm terminates the process after two steps because the model does not improve by adding or removing a term. stepwiselm already examined Weight^2, so it computes only the p-value for removing Year. stepwiselm does not examine adding Weight^3 because of the upper bound specified by the 'Upper' name-value pair argument. Therefore, stepwiselm does not add the term to the model. Because the p-value for Weight^2 is less than the p-value for Weight:Year, the stepwiselm function adds Weight^2 to the model.Īfter adding the quadratic term, stepwiselm computes the p-value for adding Weight:Year again, but the p-value is greater than the threshold value. Stepwiselm computes the p-values for adding Weight:Year or Weight^2. The p-value for Year is less than both the p-value for Weight^2 and the default threshold value of 0.05 therefore, stepwiselm adds Year to the model. Stepwiselm computes the p-values for adding Year or Weight^2. Stepwiselm creates a model as a function of Weight.
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