s_GAM.formula: Generalized Additive Model (GAM) C, R
In egenn/rtemis: Machine Learning and Visualization

s_GAM.formula

R Documentation

Generalized Additive Model (GAM) C, R

Description

Trains a GAM using mgcv::gam and validates it. Input will be used to create a formula of the form:

y = s(x_{1}, k = gam.k) + s(x_{2}, k = gam.k) + ... + s(x_{n}, k = gam.k)

Usage

## S3 method for class 'formula'
s_GAM(
  formula,
  data,
  data.test = NULL,
  x.name = NULL,
  y.name = NULL,
  k = 6,
  family = gaussian(),
  weights = NULL,
  method = "REML",
  select = FALSE,
  verbose = TRUE,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  na.action = na.exclude,
  question = NULL,
  n.cores = rtCores,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  ...
)

Arguments

`formula`	Formula: A formula of the form `y ~ s(x1) + s(x2) + ... + s(xn)`
`data`	data.frame: Training data
`data.test`	data.frame: Testing data
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`k`	Integer. Number of bases for smoothing spline
`family`	Family: Distribution and link function to be used in the model
`weights`	Numeric vector: Weights for cases. For classification, `weights` takes precedence over `ifw`, therefore set `weights = NULL` if using `ifw`. Note: If `weight` are provided, `ifw` is not used. Leave NULL if setting `ifw = TRUE`.
`method`	Character: "auto", "anova", "poisson", "class" or "exp".
`verbose`	Logical: If TRUE, print summary to screen.
`print.plot`	Logical: if TRUE, produce plot using `mplot3` Takes precedence over `plot.fitted` and `plot.predicted`.
`plot.fitted`	Logical: if TRUE, plot True (y) vs Fitted
`plot.predicted`	Logical: if TRUE, plot True (y.test) vs Predicted. Requires `x.test` and `y.test`
`plot.theme`	Character: "zero", "dark", "box", "darkbox"
`question`	Character: the question you are attempting to answer with this model, in plain language.
`n.cores`	Integer: Number of cores to use.
`outdir`	Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if `save.mod` is TRUE
`save.mod`	Logical: If TRUE, save all output to an RDS file in `outdir` `save.mod` is TRUE by default if an `outdir` is defined. If set to TRUE, and no `outdir` is defined, outdir defaults to `paste0("./s.", mod.name)`
`...`	Additional arguments to be passed to `mgcv::gam`

Details

s_GAM.default is the preferred way to train GAMs

Value

rtMod

Author(s)

E.D. Gennatas

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GAM.default(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_QRNN(), s_RF(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()