Search Results

Similarly, model selection is troublesome because almost any variable and any interaction you might contemplate is going to look significant. … Part of the solution was to write a program that generated the SQL commands directly from the model estimates. …

Also, sparse PCA might be used to perform dimension reduction and variable selection based on the resulting variable loadings. … A last point: If you intend to perform feature selection before applying a classification or regression model, be sure to cross-validate the whole process (see §7.10.2 of the Elements of Statistical Learning …

The classic example of this is adding polynomial terms and interaction effects to a regression: In the degenerate case, the prediction equation will interpolate data points, but probably be terrible when … Likewise, for something like an SVM, you can include more predictors than features because the kernel trick lets you operate solely on the inner product of those feature vectors. …

For the first, you might include an interaction between the treatment and each confounder, allowing for heterogeneous treatment effects while estimating the marginal effect. … You decide to use full matching to relax the requirement of 1:1 matching to include more units in the analysis (10). …

studies suffer from small sample size, my understanding is that there will be a lot of false positives in the literature; this also makes me less likely to trust the literature for potential variables to include … Gelman + Hill propose the following: In my Stats course, I also recall using F tests or Analysis of Deviance to compare full and nested models to do model/variable selection variable by variable …

but is just shifted along the axis of the independent variable (this is an oversimplification, as any practical basis will also include an intercept and a linear term, but hopefully you get the idea). … Our new two-variable basis should then have dimension $ij$, and therefore the same number of columns in its model matrix. …

However, before doing anything, do you have a good reason for wanting X, Condition and their interaction, all to vary by subject in the first place ? … Only include them if they make sound theoretical sense AND they are supported by the data. …

. loess has several capabilities that lowess doesn't: It accepts a formula specifying the model rather than the x and y matrices The model can include multiple predictors, factors and interactions. … The gam CRAN package provides the ability to include lowess curves in generalized linear model fits. …

Nonetheless, your response rates require us to include both squared terms and interaction terms in our model. … Specifically, your model will need to include: $var1^2$, $var1*var2$, and $var1^2*var2$, beyond the basic terms. …

An instructive remark: When we do a regression by binary predictor variables, the parameter of such a variable says about the difference in Y between variable=1 and variable=0 groups. … =1 and (not just variable=0 but even) reference_variable=1 groups. …

I would suggest we use domain knowledge to decide on likely interaction terms, thresholds, categorical variable coding strategies, etc. … Or if you have a date, maybe you'll include the S&P 500 closing price for that day. …

I am looking to use LASSO variable selection for a multiple linear regression model in R. I have 15 predictors, one of which is categorical(will that cause a problem?). … Is there any way to include interaction terms in a LASSO procedure? …

The model that will be used to analyze the results will be a logistic regression, with main effects and interaction (response is 0 or 1). … When the real analysis is conducted, Var1 will be used a numeric (and we will include a polynomial term Var1*Var1) to account for any curvature. …

Note: There may be special circumstances where you would only want to include the interaction, if the $x_i z_i$ has some particular substantive meaning or if you only observe the product and not the individual … But, in that case, one may as well think of the predictor $a_i = x_i z_i$ and proceed with the model $$ y_i = \alpha_0 + \alpha_1 a_i + \varepsilon_i $$ rather than thinking of $a_i$ as an interaction …

You include the interactions of the time dummies and the treatment indicator for the first two pre-treatment periods and you leave out the one interaction for the last pre-treatment period due to the dummy … variable trap. …