s_LOESS | R Documentation |
Fits a LOESS curve or surface
s_LOESS(
x,
y = NULL,
x.test = NULL,
y.test = NULL,
x.name = NULL,
y.name = NULL,
print.plot = FALSE,
plot.fitted = NULL,
plot.predicted = NULL,
plot.theme = rtTheme,
question = NULL,
verbose = TRUE,
trace = 0,
outdir = NULL,
save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
...
)
x |
Numeric vector or matrix / data frame of features i.e. independent variables |
y |
Numeric vector of outcome, i.e. dependent variable |
x.test |
Numeric vector or matrix / data frame of testing set features
Columns must correspond to columns in |
y.test |
Numeric vector of testing set outcome |
x.name |
Character: Name for feature set |
y.name |
Character: Name for outcome |
print.plot |
Logical: if TRUE, produce plot using |
plot.fitted |
Logical: if TRUE, plot True (y) vs Fitted |
plot.predicted |
Logical: if TRUE, plot True (y.test) vs Predicted.
Requires |
plot.theme |
Character: "zero", "dark", "box", "darkbox" |
question |
Character: the question you are attempting to answer with this model, in plain language. |
verbose |
Logical: If TRUE, print summary to screen. |
trace |
Integer: If higher than 0, will print more information to the console. |
outdir |
Path to output directory.
If defined, will save Predicted vs. True plot, if available,
as well as full model output, if |
save.mod |
Logical: If TRUE, save all output to an RDS file in |
... |
Additional arguments to |
A maximum of 4 features are allowed in this implementation (stats::loess
)
The main use for this algorithm would be fitting curves in bivariate plots,
where GAM or similar is preferable anyway. It is included in rtemis mainly for academic purposes -
not for building predictive models.
Object of class rtemis
E.D. Gennatas
train_cv
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