s_NLS: Nonlinear Least Squares (NLS) [R]
In egenn/rtemis: Machine Learning and Visualization

s_NLS

R Documentation

Nonlinear Least Squares (NLS) [R]

Description

Build a NLS model

Usage

s_NLS(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  formula = NULL,
  weights = NULL,
  start = NULL,
  control = nls.control(maxiter = 200),
  .type = NULL,
  default.start = 0.1,
  algorithm = "default",
  nls.trace = FALSE,
  x.name = NULL,
  y.name = NULL,
  save.func = TRUE,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  question = NULL,
  verbose = TRUE,
  verbosity = 0,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  ...
)

Arguments

`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 `x`
`y.test`	Numeric vector of testing set outcome
`formula`	Formula for the model. If NULL, a model is built with all predictors.
`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`.
`start`	List of starting values for the parameters in the model.
`control`	Control parameters for `nls` created by `nls.control`
`.type`	Type of model to build. If NULL, a linear model is built. If "sig", a sigmoid model is built.
`default.start`	Numeric: Default starting value for all parameters
`algorithm`	Character: Algorithm to use for `nls`. See `nls` for details.
`nls.trace`	Logical: If TRUE, trace information is printed during the optimization process.
`x.name`	Character: Name for feature set
`y.name`	Character: Name for outcome
`save.func`	Logical: If TRUE, save model as character string
`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.
`verbose`	Logical: If TRUE, print summary to screen.
`verbosity`	Integer: If > 0, print model summary
`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 `nls`

Value

Object of class rtemis

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_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_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()