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 p-values while those with more contributions will have low p-values.
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
)(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.
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.
Id of the training data frame.
Destination id for this model; auto-generated if not specified.
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 (time-based random number).
Logical. Ignore constant columns. Defaults to TRUE.
Logical. Whether to score during each iteration of model training. Defaults to FALSE.
Offset column. This will be added to the combination of columns before applying the link function.
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 per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. 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. 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 Defaults to 0.
Tweedie link power Defaults to 1.
Theta Defaults to 0.
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 lambda-search 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.
Handling of missing values. Either MeanImputation, Skip or PlugValues. Must be one of: "MeanImputation", "Skip", "PlugValues". Defaults to MeanImputation.
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)
Logical. Request p-values computation, p-values work only with IRLSM solver and no regularization
Defaults to TRUE.
Logical. Standardize numeric columns to have zero mean and unit variance Defaults to TRUE.
Logical. Restrict coefficients (not intercept) to be non-negative Defaults to FALSE.
Maximum number of iterations Defaults to 0.
Link function. Must be one of: "family_default", "identity", "logit", "log", "inverse", "tweedie", "ologit". Defaults to family_default.
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.
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 = 'L-BFGS'; 0.5 otherwise.
Regularization strength Defaults to c(0.0).
Logical. Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
Defaults to FALSE.
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.
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.
Logical. Stop early when there is no more relative improvement on train or validation (if provided).
Defaults to FALSE.
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.001.
Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to
FALSE.
Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0.
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
Logical. true to save the keys of transformed predictors and interaction column. Defaults to FALSE.
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.
Number of models to build in parallel. Default to 4. Adjust according to your system. Defaults to 4.
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)
}
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