R/anovaglm.R
h2o.anovaglm.Rd
H2O ANOVAGLM is used to calculate Type III SS which is used to evaluate the contributions of individual predictors and their interactions to a model. Predictors or interactions with negligible contributions to the model will have high pvalues while those with more contributions will have low pvalues.
h2o.anovaglm( x, y, training_frame, model_id = NULL, seed = 1, ignore_const_cols = TRUE, score_each_iteration = FALSE, offset_column = NULL, weights_column = NULL, family = c("AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial"), tweedie_variance_power = 0, tweedie_link_power = 1, theta = 0, solver = c("AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR"), missing_values_handling = c("MeanImputation", "Skip", "PlugValues"), plug_values = NULL, compute_p_values = TRUE, standardize = TRUE, non_negative = FALSE, max_iterations = 0, link = c("family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit"), prior = 0, alpha = NULL, lambda = c(0), lambda_search = FALSE, stopping_rounds = 0, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing"), early_stopping = FALSE, stopping_tolerance = 0.001, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_runtime_secs = 0, save_transformed_framekeys = FALSE, highest_interaction_term = 0, nparallelism = 4, type = 0 )
x  (Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. 

y  The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. 
training_frame  Id of the training data frame. 
model_id  Destination id for this model; autogenerated if not specified. 
seed  Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Defaults to 1 (timebased random number). 
ignore_const_cols 

score_each_iteration 

offset_column  Offset column. This will be added to the combination of columns before applying the link function. 
weights_column  Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are perrow observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but noninteger values are supported as well. During training, rows with higher weights matter more, due to the larger loss function prefactor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. 
family  Family. Use binomial for classification with logistic regression, others are for regression problems. Must be one of: "AUTO", "gaussian", "binomial", "fractionalbinomial", "quasibinomial", "poisson", "gamma", "tweedie", "negativebinomial". Defaults to AUTO. 
tweedie_variance_power  Tweedie variance power Defaults to 0. 
tweedie_link_power  Tweedie link power Defaults to 1. 
theta  Theta Defaults to 0. 
solver  AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambdasearch with L1 penalty, L_BFGS scales better for datasets with many columns. Must be one of: "AUTO", "IRLSM", "L_BFGS", "COORDINATE_DESCENT_NAIVE", "COORDINATE_DESCENT", "GRADIENT_DESCENT_LH", "GRADIENT_DESCENT_SQERR". Defaults to IRLSM. 
missing_values_handling  Handling of missing values. Either MeanImputation, Skip or PlugValues. Must be one of: "MeanImputation", "Skip", "PlugValues". Defaults to MeanImputation. 
plug_values  Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) 
compute_p_values 

standardize 

non_negative 

max_iterations  Maximum number of iterations Defaults to 0. 
link  Link function. Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit". Defaults to family_default. 
prior  Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. Defaults to 0. 
alpha  Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'LBFGS'; 0.5 otherwise. 
lambda  Regularization strength Defaults to c(0.0). 
lambda_search 

stopping_rounds  Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 0. 
stopping_metric  Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. 
early_stopping 

stopping_tolerance  Relative tolerance for metricbased stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001. 
balance_classes 

class_sampling_factors  Desired over/undersampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. 
max_after_balance_size  Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. 
max_runtime_secs  Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. 
save_transformed_framekeys 

highest_interaction_term  Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on... Default to 2. Defaults to 0. 
nparallelism  Number of models to build in parallel. Default to 4. Adjust according to your system. Defaults to 4. 
type  Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3 Defaults to 0. 
if (FALSE) { h2o.init() # Run ANOVA GLM of VOL ~ CAPSULE + RACE prostate_path < system.file("extdata", "prostate.csv", package = "h2o") prostate < h2o.uploadFile(path = prostate_path) prostate$CAPSULE < as.factor(prostate$CAPSULE) model < h2o.anovaglm(y = "VOL", x = c("CAPSULE","RACE"), training_frame = prostate) }