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| \name{h2o.glm} | ||
| \alias{h2o.glm} | ||
| \title{ | ||
| H2O: Generalized Linear Models | ||
| } | ||
| \description{ | ||
| Fit a generalized linear model, specified by a response variable, a set of | ||
| predictors, and a description of the error distribution. | ||
| } | ||
| \usage{ | ||
| h2o.glm(x, y, data, key = "", offset = NULL, family, link, | ||
| tweedie.p = ifelse(family == "tweedie", 1.5, NA_real_), | ||
| prior = NULL, nfolds = 0, alpha = 0.5, lambda = 1e-5, | ||
| lambda_search = FALSE, nlambda = -1, lambda.min.ratio = -1, | ||
| max_predictors = -1, return_all_lambda = FALSE, | ||
| strong_rules = TRUE, standardize = TRUE, intercept = TRUE, | ||
| non_negative = FALSE, use_all_factor_levels = FALSE, | ||
| variable_importances = FALSE, epsilon = 1e-4, iter.max = 100, | ||
| higher_accuracy = FALSE, beta_constraints=NULL) | ||
| } | ||
| \arguments{ | ||
| \item{x}{A character vector containing the column names of the predictors in | ||
| the model.} | ||
| \item{y}{A character string representing the response variable in the model.} | ||
| \item{data}{An \code{\linkS4class{H2OParsedData}} object containing the | ||
| variables in the model.} | ||
| \item{key}{An optional unique hex key assigned to the resulting model. | ||
| If none is given, a key will automatically be generated.} | ||
| \item{offset}{An optional character string representing the offset term in | ||
| the model.} | ||
| \item{family}{A character string specifying the error distribution of the | ||
| model; one of \code{"gaussian"}, \code{"binomial"}, \code{"poisson"}, | ||
| \code{"gamma"}, and \code{"tweedie"}.} | ||
| \item{link}{A character string specifying the link function. The default is | ||
| the canonical link for the \code{family}. The supported links for each of | ||
| the \code{family} specifications are:\cr | ||
| \cr | ||
| \code{"gaussian"}: \code{"identity"}, \code{"log"}, \code{"inverse"}\cr | ||
| \code{"binomial"}: \code{"logit"}, \code{"log"}\cr | ||
| \code{"poisson"}: \code{"log"}, \code{"identity"}\cr | ||
| \code{"gamma"}: \code{"inverse"}, \code{"log"}, \code{"identity"}\cr | ||
| \code{"tweedie"}: \code{"tweedie"}\cr | ||
| } | ||
| \item{tweedie.p}{A numeric specifying the power for the variance function | ||
| when \code{family = "tweedie"}.} | ||
| \item{prior}{An optional numeric specifying the prior probability of class 1 | ||
| in the response when \code{family = "binomial"}. The default prior is the | ||
| observational frequency of class 1.} | ||
| \item{nfolds}{A non-negative integer specifying the number of folds for | ||
| cross-validation and \code{nfolds = 0} indicates no cross-validation.} | ||
| \item{alpha}{A numeric in [0, 1] specifying the elastic-net mixing parameter. | ||
| The elastic-net penalty is defined to be | ||
| \deqn{P(\alpha,\beta) = (1-\alpha)/2||\beta||_2^2 + \alpha||\beta||_1 = \sum_j [(1-\alpha)/2 \beta_j^2 + \alpha|\beta_j|]}, | ||
| making \code{alpha = 1} the lasso penalty and \code{alpha = 0} the ridge | ||
| penalty.} | ||
| \item{lambda}{A non-negative shrinkage parameter for the elastic-net, which | ||
| multiplies \eqn{P(\alpha,\beta)} in the objective. When \code{lambda = 0}, | ||
| then no elastic-net penalty is applied and ordinary generalized linear | ||
| models are fit.} | ||
| \item{lambda_search}{A logical value indicating whether to conduct a search | ||
| over the space of lambda values starting from the \code{lambda} argument | ||
| to \code{lambda.min.ratio} times the smallest lambda that produces zeros | ||
| for all the coefficient estimates.} | ||
| \item{nlambda}{The number of lambda values to use when | ||
| \code{lambda_search = TRUE}.} | ||
| \item{lambda.min.ratio}{A non-negative number that specifies the minimum | ||
| value for lambda as a fraction of smallest lambda that yields the zero | ||
| vector for the coefficient estimates.} | ||
| \item{max_predictors}{When \code{lambda_search = TRUE}, a non-negative | ||
| integer specifying an early stopping rule for the maximum number of | ||
| predictors in the model.} | ||
| \item{return_all_lambda}{A logical value indicating whether to return every | ||
| model built during the lambda search. If \code{return_all_lambda = FALSE}, | ||
| then only the model corresponding to the optimal lambda will be returned.} | ||
| \item{strong_rules}{A logical value indicating whether to use strong rules to | ||
| remove predictors with gradients near zero at the starting solution | ||
| \emph{prior} to model training.} | ||
| \item{standardize}{A logical value indicating whether the numeric predictors | ||
| should be standardized to have a mean of 0 and a variance of 1 prior to | ||
| training the models.} | ||
| \item{intercept}{A logical value indicating whether to include the intercept | ||
| term in the models. This will only have a practical effect in the presence | ||
| of all numeric predictors.} | ||
| \item{non_negative}{A logical value indicating whether the coefficient | ||
| estimates will be constrained to be non-negative.} | ||
| \item{use_all_factor_levels}{A logical value indicating whether dummy | ||
| variables should be used for all factor levels of the categorical predictors. | ||
| When \code{TRUE}, results in an over parameterized models.} | ||
| \item{variable_importances}{A logical value indicating whether the variable | ||
| importances should be computed.} | ||
| \item{epsilon}{A non-negative number specifying the magnitude of the maximum | ||
| difference between the coefficient estimates from successive iterations. | ||
| Defines the convergence criterion for \code{h2o.glm}.} | ||
| \item{iter.max}{A non-negative integer specifying the maximum number of | ||
| iterations.} | ||
| \item{higher_accuracy}{A logical value indicating whether to use line search | ||
| to produce more accurate estimates.} | ||
| \item{beta_constraints}{ | ||
| A data.frame or H2OParsedData object with the columns ["names", "lower_bounds", "upper_bounds", "beta_given"], | ||
| where each row corresponds to a predictor in the GLM. "names" contains the predictor names, "lower"/"upper_bounds", | ||
| are the lower and upper bounds of beta, and "beta_given" is some supplied starting values for the coefficients. | ||
| } | ||
| } | ||
| \value{ | ||
| An object of class \code{\linkS4class{H2OGLMModel}} with slots \code{key}, | ||
| \code{data}, \code{model}, and \code{xval}. The \code{model} slot is a list of | ||
| the following components: | ||
| \item{coefficients}{A named vector of the coefficients estimated in the model.} | ||
| \item{rank}{The numeric rank of the fitted linear model.} | ||
| \item{family}{The family of the error distribution.} | ||
| \item{deviance}{The deviance of the fitted model.} | ||
| \item{aic}{Akaike's Information Criterion for the final computed model.} | ||
| \item{null.deviance}{The deviance for the null model.} | ||
| \item{iter}{Number of algorithm iterations to compute the model.} | ||
| \item{df.residual}{The residual degrees of freedom.} | ||
| \item{df.null}{The residual degrees of freedom for the null model.} | ||
| \item{y}{The response variable in the model.} | ||
| \item{x}{A vector of the predictor variable(s) in the model.} | ||
| \item{auc}{Area under the curve.} | ||
| \item{training.err}{Average training error.} | ||
| \item{threshold}{Best threshold.} | ||
| \item{confusion}{Confusion matrix.} | ||
| The \code{xval} slot is a list of \code{\linkS4class{H2OGLMModel}} objects | ||
| representing the cross-validation models. (Each of these objects themselves | ||
| has xval equal to an empty list). | ||
| } | ||
| \seealso{ | ||
| \code{\link{h2o.gbm}}, \code{\link{h2o.randomForest}} | ||
| } | ||
| \examples{ | ||
| # -- CRAN examples begin -- | ||
| library(h2o) | ||
| localH2O = h2o.init() | ||
| # Run GLM of CAPSULE ~ AGE + RACE + PSA + DCAPS | ||
| prostatePath = system.file("extdata", "prostate.csv", package = "h2o") | ||
| prostate.hex = h2o.importFile(localH2O, path = prostatePath, key = "prostate.hex") | ||
| h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), data = prostate.hex, | ||
| family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE, | ||
| use_all_factor_levels = FALSE, variable_importances = FALSE, | ||
| higher_accuracy = FALSE) | ||
| # Run GLM of VOL ~ CAPSULE + AGE + RACE + PSA + GLEASON | ||
| myX = setdiff(colnames(prostate.hex), c("ID", "DPROS", "DCAPS", "VOL")) | ||
| h2o.glm(y = "VOL", x = myX, data = prostate.hex, family = "gaussian", | ||
| nfolds = 0, alpha = 0.1, lambda_search = FALSE, | ||
| use_all_factor_levels = FALSE, variable_importances = FALSE, | ||
| higher_accuracy = FALSE) | ||
| # -- CRAN examples end -- | ||
| \dontrun{ | ||
| # GLM variable importance | ||
| # Also see: | ||
| # https://github.com/0xdata/h2o/blob/master/R/tests/testdir_demos/runit_demo_VI_all_algos.R | ||
| data.hex = h2o.importFile( | ||
| localH2O, | ||
| path = "https://raw.github.com/0xdata/h2o/master/smalldata/bank-additional-full.csv", | ||
| key = "data.hex") | ||
| myX = 1:20 | ||
| myY="y" | ||
| my.glm = h2o.glm(x=myX, y=myY, data=data.hex, family="binomial", | ||
| standardize=TRUE, use_all_factor_levels=TRUE, | ||
| higher_accuracy=TRUE, lambda_search=TRUE, | ||
| return_all_lambda=TRUE, variable_importances=TRUE) | ||
| best_model = my.glm@best_model | ||
| n_coeff = abs(my.glm@models[[best_model]]@model$normalized_coefficients) | ||
| VI = abs(n_coeff[-length(n_coeff)]) | ||
| glm.VI = VI[order(VI,decreasing=T)] | ||
| print(glm.VI) | ||
| } | ||
| } | ||
| \name{h2o.glm} | ||
| \alias{h2o.glm} | ||
| \title{ | ||
| H2O: Generalized Linear Models | ||
| } | ||
| \description{ | ||
| Fit a generalized linear model, specified by a response variable, a set of | ||
| predictors, and a description of the error distribution. | ||
| } | ||
| \usage{ | ||
| h2o.glm(x, y, data, key = "", offset = NULL, family, link, | ||
| tweedie.p = ifelse(family == "tweedie", 1.5, NA_real_), | ||
| prior = NULL, nfolds = 0, alpha = 0.5, lambda = 1e-5, | ||
| lambda_search = FALSE, nlambda = -1, lambda.min.ratio = -1, | ||
| max_predictors = -1, return_all_lambda = FALSE, | ||
| strong_rules = TRUE, standardize = TRUE, intercept = TRUE, | ||
| non_negative = FALSE, use_all_factor_levels = FALSE, | ||
| variable_importances = FALSE, epsilon = 1e-4, iter.max = 100, | ||
| higher_accuracy = FALSE, beta_constraints = NULL, | ||
| disable_line_search = FALSE) | ||
| } | ||
| \arguments{ | ||
| \item{x}{A character vector containing the column names of the predictors in | ||
| the model.} | ||
| \item{y}{A character string representing the response variable in the model.} | ||
| \item{data}{An \code{\linkS4class{H2OParsedData}} object containing the | ||
| variables in the model.} | ||
| \item{key}{An optional unique hex key assigned to the resulting model. | ||
| If none is given, a key will automatically be generated.} | ||
| \item{offset}{An optional character string representing the offset term in | ||
| the model.} | ||
| \item{family}{A character string specifying the error distribution of the | ||
| model; one of \code{"gaussian"}, \code{"binomial"}, \code{"poisson"}, | ||
| \code{"gamma"}, and \code{"tweedie"}.} | ||
| \item{link}{A character string specifying the link function. The default is | ||
| the canonical link for the \code{family}. The supported links for each of | ||
| the \code{family} specifications are:\cr | ||
| \cr | ||
| \code{"gaussian"}: \code{"identity"}, \code{"log"}, \code{"inverse"}\cr | ||
| \code{"binomial"}: \code{"logit"}, \code{"log"}\cr | ||
| \code{"poisson"}: \code{"log"}, \code{"identity"}\cr | ||
| \code{"gamma"}: \code{"inverse"}, \code{"log"}, \code{"identity"}\cr | ||
| \code{"tweedie"}: \code{"tweedie"}\cr | ||
| } | ||
| \item{tweedie.p}{A numeric specifying the power for the variance function | ||
| when \code{family = "tweedie"}.} | ||
| \item{prior}{An optional numeric specifying the prior probability of class 1 | ||
| in the response when \code{family = "binomial"}. The default prior is the | ||
| observational frequency of class 1.} | ||
| \item{nfolds}{A non-negative integer specifying the number of folds for | ||
| cross-validation and \code{nfolds = 0} indicates no cross-validation.} | ||
| \item{alpha}{A numeric in [0, 1] specifying the elastic-net mixing parameter. | ||
| The elastic-net penalty is defined to be | ||
| \deqn{P(\alpha,\beta) = (1-\alpha)/2||\beta||_2^2 + \alpha||\beta||_1 = \sum_j [(1-\alpha)/2 \beta_j^2 + \alpha|\beta_j|]}, | ||
| making \code{alpha = 1} the lasso penalty and \code{alpha = 0} the ridge | ||
| penalty.} | ||
| \item{lambda}{A non-negative shrinkage parameter for the elastic-net, which | ||
| multiplies \eqn{P(\alpha,\beta)} in the objective. When \code{lambda = 0}, | ||
| then no elastic-net penalty is applied and ordinary generalized linear | ||
| models are fit.} | ||
| \item{lambda_search}{A logical value indicating whether to conduct a search | ||
| over the space of lambda values starting from the \code{lambda} argument | ||
| to \code{lambda.min.ratio} times the smallest lambda that produces zeros | ||
| for all the coefficient estimates.} | ||
| \item{nlambda}{The number of lambda values to use when | ||
| \code{lambda_search = TRUE}.} | ||
| \item{lambda.min.ratio}{A non-negative number that specifies the minimum | ||
| value for lambda as a fraction of smallest lambda that yields the zero | ||
| vector for the coefficient estimates.} | ||
| \item{max_predictors}{When \code{lambda_search = TRUE}, a non-negative | ||
| integer specifying an early stopping rule for the maximum number of | ||
| predictors in the model.} | ||
| \item{return_all_lambda}{A logical value indicating whether to return every | ||
| model built during the lambda search. If \code{return_all_lambda = FALSE}, | ||
| then only the model corresponding to the optimal lambda will be returned.} | ||
| \item{strong_rules}{A logical value indicating whether to use strong rules to | ||
| remove predictors with gradients near zero at the starting solution | ||
| \emph{prior} to model training.} | ||
| \item{standardize}{A logical value indicating whether the numeric predictors | ||
| should be standardized to have a mean of 0 and a variance of 1 prior to | ||
| training the models.} | ||
| \item{intercept}{A logical value indicating whether to include the intercept | ||
| term in the models. This will only have a practical effect in the presence | ||
| of all numeric predictors.} | ||
| \item{non_negative}{A logical value indicating whether the coefficient | ||
| estimates will be constrained to be non-negative.} | ||
| \item{use_all_factor_levels}{A logical value indicating whether dummy | ||
| variables should be used for all factor levels of the categorical predictors. | ||
| When \code{TRUE}, results in an over parameterized models.} | ||
| \item{variable_importances}{A logical value indicating whether the variable | ||
| importances should be computed.} | ||
| \item{epsilon}{A non-negative number specifying the magnitude of the maximum | ||
| difference between the coefficient estimates from successive iterations. | ||
| Defines the convergence criterion for \code{h2o.glm}.} | ||
| \item{iter.max}{A non-negative integer specifying the maximum number of | ||
| iterations.} | ||
| \item{higher_accuracy}{A logical value indicating whether to use line search | ||
| to produce more accurate estimates.} | ||
| \item{beta_constraints}{ | ||
| A data.frame or H2OParsedData object with the columns ["names", "lower_bounds", "upper_bounds", "beta_given"], | ||
| where each row corresponds to a predictor in the GLM. "names" contains the predictor names, "lower"/"upper_bounds", | ||
| are the lower and upper bounds of beta, and "beta_given" is some supplied starting values for the coefficients. | ||
| } | ||
| \item{disable_line_search}{A logical value indicating whether line search should be disabled.} | ||
| } | ||
| \value{ | ||
| An object of class \code{\linkS4class{H2OGLMModel}} with slots \code{key}, | ||
| \code{data}, \code{model}, and \code{xval}. The \code{model} slot is a list of | ||
| the following components: | ||
| \item{coefficients}{A named vector of the coefficients estimated in the model.} | ||
| \item{rank}{The numeric rank of the fitted linear model.} | ||
| \item{family}{The family of the error distribution.} | ||
| \item{deviance}{The deviance of the fitted model.} | ||
| \item{aic}{Akaike's Information Criterion for the final computed model.} | ||
| \item{null.deviance}{The deviance for the null model.} | ||
| \item{iter}{Number of algorithm iterations to compute the model.} | ||
| \item{df.residual}{The residual degrees of freedom.} | ||
| \item{df.null}{The residual degrees of freedom for the null model.} | ||
| \item{y}{The response variable in the model.} | ||
| \item{x}{A vector of the predictor variable(s) in the model.} | ||
| \item{auc}{Area under the curve.} | ||
| \item{training.err}{Average training error.} | ||
| \item{threshold}{Best threshold.} | ||
| \item{confusion}{Confusion matrix.} | ||
| The \code{xval} slot is a list of \code{\linkS4class{H2OGLMModel}} objects | ||
| representing the cross-validation models. (Each of these objects themselves | ||
| has xval equal to an empty list). | ||
| } | ||
| \seealso{ | ||
| \code{\link{h2o.gbm}}, \code{\link{h2o.randomForest}} | ||
| } | ||
| \examples{ | ||
| # -- CRAN examples begin -- | ||
| library(h2o) | ||
| localH2O = h2o.init() | ||
| # Run GLM of CAPSULE ~ AGE + RACE + PSA + DCAPS | ||
| prostatePath = system.file("extdata", "prostate.csv", package = "h2o") | ||
| prostate.hex = h2o.importFile(localH2O, path = prostatePath, key = "prostate.hex") | ||
| h2o.glm(y = "CAPSULE", x = c("AGE","RACE","PSA","DCAPS"), data = prostate.hex, | ||
| family = "binomial", nfolds = 0, alpha = 0.5, lambda_search = FALSE, | ||
| use_all_factor_levels = FALSE, variable_importances = FALSE, | ||
| higher_accuracy = FALSE) | ||
| # Run GLM of VOL ~ CAPSULE + AGE + RACE + PSA + GLEASON | ||
| myX = setdiff(colnames(prostate.hex), c("ID", "DPROS", "DCAPS", "VOL")) | ||
| h2o.glm(y = "VOL", x = myX, data = prostate.hex, family = "gaussian", | ||
| nfolds = 0, alpha = 0.1, lambda_search = FALSE, | ||
| use_all_factor_levels = FALSE, variable_importances = FALSE, | ||
| higher_accuracy = FALSE) | ||
| # -- CRAN examples end -- | ||
| \dontrun{ | ||
| # GLM variable importance | ||
| # Also see: | ||
| # https://github.com/0xdata/h2o/blob/master/R/tests/testdir_demos/runit_demo_VI_all_algos.R | ||
| data.hex = h2o.importFile( | ||
| localH2O, | ||
| path = "https://raw.github.com/0xdata/h2o/master/smalldata/bank-additional-full.csv", | ||
| key = "data.hex") | ||
| myX = 1:20 | ||
| myY="y" | ||
| my.glm = h2o.glm(x=myX, y=myY, data=data.hex, family="binomial", | ||
| standardize=TRUE, use_all_factor_levels=TRUE, | ||
| higher_accuracy=TRUE, lambda_search=TRUE, | ||
| return_all_lambda=TRUE, variable_importances=TRUE) | ||
| best_model = my.glm@best_model | ||
| n_coeff = abs(my.glm@models[[best_model]]@model$normalized_coefficients) | ||
| VI = abs(n_coeff[-length(n_coeff)]) | ||
| glm.VI = VI[order(VI,decreasing=T)] | ||
| print(glm.VI) | ||
| } | ||
| } |