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spark.lm fits a linear regression model against a SparkDataFrame. Users can call summary to print a summary of the fitted model, predict to make predictions on new data, and write.ml/read.ml to save/load fitted models.

## Usage

spark.lm(data, formula, ...)

# S4 method for SparkDataFrame,formula
spark.lm(
data,
formula,
maxIter = 100L,
regParam = 0,
elasticNetParam = 0,
tol = 1e-06,
standardization = TRUE,
solver = c("auto", "l-bfgs", "normal"),
weightCol = NULL,
aggregationDepth = 2L,
loss = c("squaredError", "huber"),
epsilon = 1.35,
stringIndexerOrderType = c("frequencyDesc", "frequencyAsc", "alphabetDesc",
"alphabetAsc")
)

# S4 method for LinearRegressionModel
summary(object)

# S4 method for LinearRegressionModel
predict(object, newData)

# S4 method for LinearRegressionModel,character
write.ml(object, path, overwrite = FALSE)

## Arguments

data

a SparkDataFrame of observations and labels for model fitting.

formula

a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'.

...

additional arguments passed to the method.

maxIter

maximum iteration number.

regParam

the regularization parameter.

elasticNetParam

the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.

tol

convergence tolerance of iterations.

standardization

whether to standardize the training features before fitting the model.

solver

The solver algorithm for optimization. Supported options: "l-bfgs", "normal" and "auto".

weightCol

weight column name.

aggregationDepth

suggested depth for treeAggregate (>= 2).

loss

the loss function to be optimized. Supported options: "squaredError" and "huber".

epsilon

the shape parameter to control the amount of robustness.

stringIndexerOrderType

how to order categories of a string feature column. This is used to decide the base level of a string feature as the last category after ordering is dropped when encoding strings. Supported options are "frequencyDesc", "frequencyAsc", "alphabetDesc", and "alphabetAsc". The default value is "frequencyDesc". When the ordering is set to "alphabetDesc", this drops the same category as R when encoding strings.

object

a Linear Regression Model model fitted by spark.lm.

newData

a SparkDataFrame for testing.

path

The directory where the model is saved.

overwrite

Overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.

## Value

spark.lm returns a fitted Linear Regression Model.

summary returns summary information of the fitted model, which is a list.

predict returns the predicted values based on a LinearRegressionModel.

## Note

spark.lm since 3.1.0

summary(LinearRegressionModel) since 3.1.0

predict(LinearRegressionModel) since 3.1.0

write.ml(LinearRegressionModel, character) since 3.1.0

## Examples

if (FALSE) {
df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")

# fit Linear Regression Model
model <- spark.lm(df, label ~ features, regParam = 0.01, maxIter = 1)

# get the summary of the model
summary(model)

# make predictions
predictions <- predict(model, df)

# save and load the model
path <- "path/to/model"
write.ml(model, path)
savedModel <- read.ml(path)
summary(savedModel)
}