| data |
DataFrame
DataFrame containting the data.
|
| key |
character, optional
Name of the ID column.
If not provided, the data is assumed to have no ID column.
No default value.
|
| features |
character of list of characters, optional
Name of feature columns.
If not provided, it defaults all non-key, non-label columns of data.
|
| label |
character, optional
Name of the column which specifies the dependent variable.
Defaults to the last column of data if not provided.
|
| formula |
formula type, optional
Formula to be used for model generation.
format = label~<feature_list>
e.g.: formula=CATEGORY~V1+V2+V3
You can either give the formula,
or a feature and label combination, but do not provide both.
Defaults to NULL.
|
| solver |
{"QR", "SVD", "CD", "Cholesky", "ADMM"}, optional
Algorithms to use to solve the least square problem.
"QR": QR decomposition. "SVD": singular value decomposition. "CD": cyclical coordinate descent method. "Cholesky": Cholesky decomposition. "ADMM": alternating direction method of multipliers. Defaults to "QR". |
| var.select |
c("all", "forward", "backward", "stepwise"), optional
Method to perform variable selection.
"all": all variables are included. "forward": forward selection. "backward": backward selection. "stepwise": stepwise selection. "forward", "backward" and "stepwise" are supported only when solver
is "QR", "SVD" or "Cholesky".
Defaults to "all". |
| features.must.select |
character or list/vector of characters, optional
Specifies the column names of data that needs to be included in the final
training model when executing variable selection.
Only valid when varselect is not "all".
Note: This parameter is a hint.
There are exceptional cases that a specified mandatory feature is excluded in the final model.
For instance, some mandatory features can be represented as a linear combination
of other features, among which some are also mandatory features.
No default value.
|
| intercept |
logical, optional
If TRUE, include the intercept in the model.
Defaults to TRUE.
|
| alpha.to.enter |
double, optional
P-value for forward selection.
When var.select is 'forward', default value is 0.05; when var.select is "stepwise", default value is 0.15.
|
| alpha.to.remove |
double, optional
P-value for backward selection.
When var.select is 'backward', default value is 0.1; when var.select is "stepwise", default value is 0.15.
|
| enet.lambda |
double, optional
Penalized weight. Value should be greater than or equal to 0.
Valid only when solver is "CD" or "ADMM".
|
| enet.alpha |
double, optional
Elastic net mixing parameter.
Ranges from 0 (Ridge penalty) to 1 (LASSO penalty) inclusively.
Valid only when solver is "CD" or "ADMM".
Defaults to 1.0.
|
| max.iter |
integer, optional
Maximum number of passes over training data.
If convergence is not reached after the specified number of
iterations, an error will be generated.
Valid only when solver is "CD" or "ADMM".
Defaults to 1e5.
|
| tol |
double, optional
Convergence threshold for coordinate descent.
Valid only when solver is "CD".
Defaults to 1.0e-7.
|
| pho |
double, optional
Step size for ADMM. Generally, it should be greater than 1.
Valid only when solver is "ADMM".
Defaults to 1.8.
|
| stat.inf |
logical, optional
If TRUE, output t-value and Pr(>|t|) of coefficients.
Defaults to FALSE.
|
| adjusted.r2 |
logical, optional
If TRUE, include the adjusted R^2 value in statistics.
Defaults to FALSE.
|
| dw.test |
logical, optional
If TRUE, conduct Durbin-Watson test under null hypothesis that
errors do not follow a first order autoregressive process.
Not available if elastic net regularization is enabled or
intercept is ignored.
Defaults to FALSE.
|
| reset.test |
integer, optional
Specifies the order of Ramsey RESET test.
Ramsey RESET test with power of variables ranging from 2
to this value (greater than 1) will be conducted.
Value 1 means RESET test will not be conducted.
Not available if elastic net regularization is enabled or
intercept is ignored.
Defaults to 1.
|
| bp.test |
logical, optional
If TRUE, conduct Breusch-Pagan test under null hypothesis that
homoscedasticity is satisfied.
Not available if elastic net regularization is enabled or
intercept is ignored.
Defaults to FALSE.
|
| ks.test |
logical, optional
If TRUE, conduct Kolmogorov-Smirnov normality test under
null hypothesis that errors follow a normal distribution.
Not available if elastic net regularization is enabled or
intercept is ignored.
Defaults to FALSE.
|
| thread.ratio |
double, optional
Controls the proportion of available threads that can be used by this
function.
The value range is from 0 to 1, where 0 indicates a single thread,
and 1 indicates all available threads.
Values between 0 and 1 will use up to
that percentage of available threads.Values outside this
range are ignored.
Defaults to 0.
|
| categorical.variable |
character or list/vector of characters, optional
Indicates features should be treated as categorical variable.
The default behavior is dependent on what input is given:
VALID only for variables of "INTEGER" type, omitted otherwise.
No default value. |
| pmml.export |
{"no", "single-row", "multi-row"}, optional
Controls whether to output a PMML representation of the model,
and how to format the PMML.
"no": No PMML model.
"single-row": Exports a PMML model in a maximum of
one row. Fails if the model doesn't fit in one row.
"multi-row": Exports a PMML model, splitting it
across multiple rows if it doesn't fit in one.
Default to "no". |
| resampling.method |
c("cv", "bootstrap"), optional
Specifies the resampling values form below list.
If no value is specifier, neither model evaluation
nor parameter selection is activated.
|
| evaluation.metric |
character, optional
Specifies the evaluation metric for model evaluation or parameter selection.
Currently the only valid option(also the default value) is "rmse".
|
| fold.num |
integer, optional
Specifies the fold number for the cross-validation(cv).
Mandatory and valid only when resampling.method is "cv".
|
| repeat.times |
numeric, optional
Specifies the number of repeat times for resampling.
Defaults to 1.
|
| param.search.strategy |
c("grid", "random"), optional
Specifies the method to activate parameter selection.
If not specified, model parameter selection shall not be triggered.
|
| random.search.times |
integer, optional
Specifies the number of times to randomly select candidate parameters for selection.
Mandatory and valid only when param.search.strategy is "random".
|
| random.state |
numeric, optional
Specifies the seed for random generation.
Use system time when 0 is specified.
|
| timeout |
integer, optional
Specifies maximum running time for model evaluation or parameter selection in seconds.
No timeout when 0 is specified.
|
| progress.indicator.id |
character, optional
Sets an ID of progress indicator for model evaluation or parameter selection.
No progress indicator is active if no value is provided.
|
| parameter.range |
list, optional
Specifies range of the following parameters for parameter selection:
enet.lambda, enet.alpha.
Parameter range should be specified by 3 numbers in the form of c(start, step, end).
Examples:
parameter.range <- list(enet.lambda = c(0.01, 0.01, 0.1)), which means taking
enet.lambda values from 0.01 to 0.1 with 0.01 being the step size, i.e.
0.01, 0.02, 0.03, ..., 0.09, 0.1.
If param.search.strategy is "random", then the middle term,
i.e. step has no effect and thus can be omitted.
|
| parameter.values |
list, optional
Specifies values of the following parameters for parameter selection:
enet.lambda, enet.alpha.
Example: parameter.values <- list(enet.lambda = c(0.001, 0.003, 0.007, 0.01))
|
Linear regression is an approach to model the linear relationship
between a variable, usually referred to as dependent variable,
and one or more variables, usually referred to as
independent variables, denoted as predictor vector.