- data
data.frame of the dataset to be used.
- latent_var
list of data.frame. The elements of the list are the datasets used to construct each latent variable. For interpretability and proper convergence, not using the same variable in more than one latent variable is highly recommended. It is recommended to set names to the list elements to prevent confusion because otherwise, the latent variables will be named L1, L2, ... (See examples below for more details)
- formula
Model formula. The names of latent_var can be used in the formula to represent the latent variables. If names(latent_var) is NULL, then L1, L2, ... can be used in the formula to represent the latent variables. Do not manually code interactions, write them in the formula instead (ex: G*E1*E2 or G:E1:E2).
- latent_var_searched
Optional If not null, you must specify a vector containing all indexes of the latent variables you want to use elastic net on. Ex: If latent_var=list(G=genes, E=env), specifying latent_var_search=c(1,2) will use both, latent_var_search=1 will only do it for G, and latent_var_search=2 will only do it for E.
- cross_validation
(Optional) If TRUE, will return cross-validation criterion (slower, but very good criterion).
- alpha
The elasticnet mixing parameter (between 0 and 1). 1 leads to lasso, 0 leads to ridge. See glmnet package manual for more information. We recommend somewhere betwen .50 and 1.
- standardize
If TRUE, standardize all variables inside every latent_var component. Note that if FALSE, glmnet will still standardize and unstandardize, but it will do so for each model (i.e., when at the step of estimating the parameters of latent variable G it standardize them, apply glmnet, then unstandarize them). This means that fixed parameters in the alternating steps are not standardized when standardize=FALSE. In practice, we found that standardize=FALSE leads to weird paths that do not always make sense. In the end, we only care about the order of the variable removal from the glmnet. We highly recommend standardize=TRUE for best results.
- lambda_path
Optional vector of all lambda (penalty term for elastic net, see glmnet package manual). By default, we automatically determine it.
- lambda_mult
scalar which multiplies the maximum lambda (penalty term for elastic net, see glmnet package manual) from the lambda path determined automatically. Sometimes, the maximum lambda found automatically is too big or too small and you may not want to spend the effort to manually set your own lambda path. This is where this comes in, you can simply scale lambda max up or down. (Default = 1)
- lambda_min
minimum lambda (penalty term for elastic net, see glmnet package manual) from the lambda path. (Default = .0001)
- n_lambda
Number of lambda (penalty term for elastic net, see glmnet package manual) in lambda path. Make lower for faster training, or higher for more precision. If you have many variables, make it bigger than 100 (Default = 100).
- start_latent_var
Optional list of starting points for each latent variable (The list must have the same length as the number of latent variables and each element of the list must have the same length as the number of variables of the corresponding latent variable).
- eps
Threshold for convergence (.01 for quick batch simulations, .0001 for accurate results).
- maxiter
Maximum number of iterations.
- family
Outcome distribution and link function (Default = gaussian).
- ylim
Optional vector containing the known min and max of the outcome variable. Even if your outcome is known to be in [a,b], if you assume a Gaussian distribution, predict() could return values outside this range. This parameter ensures that this never happens. This is not necessary with a distribution that already assumes the proper range (ex: [0,1] with binomial distribution).
- cv_iter
Number of cross-validation iterations (Default = 5).
- cv_folds
Number of cross-validation folds (Default = 10). Using cv_folds=NROW(data) will lead to leave-one-out cross-validation.
- folds
Optional list of vectors containing the fold number for each observation. Bypass cv_iter and cv_folds. Setting your own folds could be important for certain data types like time series or longitudinal data.
- Huber_p
Parameter controlling the Huber cross-validation error (Default = 1.345).
- classification
Set to TRUE if you are doing classification (binary outcome).
- print
If FALSE, nothing except warnings will be printed. (Default = TRUE).
- test_only
If TRUE, only uses the first fold for training and predict the others folds; do not train on the other folds. So instead of cross-validation, this gives you train/test and you get the test R-squared as output.