ds.gamlss: Generalized Additive Models for Location Scale and Shape
In datashield/dsBaseClient: DataSHIELD Client Functions

ds.gamlss

R Documentation

Generalized Additive Models for Location Scale and Shape

Description

This function calls the gamlssDS that is a wrapper function from the gamlss R package. The function returns an object of class "gamlss", which is a generalized additive model for location, scale and shape (GAMLSS). The function also saves the residuals as an object on the server-side with a name specified by the newobj argument. In addition, if the argument centiles is set to TRUE, the function calls the centiles function from the gamlss package and returns the sample percentages below each centile curve.

Usage

ds.gamlss(
  formula = NULL,
  sigma.formula = "~1",
  nu.formula = "~1",
  tau.formula = "~1",
  family = "NO()",
  data = NULL,
  method = "RS",
  mu.fix = FALSE,
  sigma.fix = FALSE,
  nu.fix = FALSE,
  tau.fix = FALSE,
  control = c(0.001, 20, 1, 1, 1, 1, Inf),
  i.control = c(0.001, 50, 30, 0.001),
  centiles = FALSE,
  xvar = NULL,
  newobj = NULL,
  datasources = NULL
)

Arguments

`formula`	a formula object, with the response on the left of an ~ operator, and the terms, separated by + operators, on the right. Nonparametric smoothing terms are indicated by pb() for penalised beta splines, cs for smoothing splines, lo for loess smooth terms and random or ra for random terms, e.g. 'y~cs(x,df=5)+x1+x2*x3'.
`sigma.formula`	a formula object for fitting a model to the sigma parameter, as in the formula above, e.g. sigma.formula='~cs(x,df=5)'.
`nu.formula`	a formula object for fitting a model to the nu parameter, e.g. nu.formula='~x'.
`tau.formula`	a formula object for fitting a model to the tau parameter, e.g. tau.formula='~cs(x,df=2)'.
`family`	a gamlss.family object, which is used to define the distribution and the link functions of the various parameters. The distribution families supported by gamlss() can be found in gamlss.family. Functions such as 'BI()' (binomial) produce a family object. Also can be given without the parentheses i.e. 'BI'. Family functions can take arguments, as in 'BI(mu.link=probit)'.
`data`	a data frame containing the variables occurring in the formula. If this is missing, the variables should be on the parent environment.
`method`	a character indicating the algorithm for GAMLSS. Can be either 'RS', 'CG' or 'mixed'. If method='RS' the function will use the Rigby and Stasinopoulos algorithm, if method='CG' the function will use the Cole and Green algorithm, and if method='mixed' the function will use the RS algorithm twice before switching to the Cole and Green algorithm for up to 10 extra iterations.
`mu.fix`	logical, indicate whether the mu parameter should be kept fixed in the fitting processes.
`sigma.fix`	logical, indicate whether the sigma parameter should be kept fixed in the fitting processes.
`nu.fix`	logical, indicate whether the nu parameter should be kept fixed in the fitting processes.
`tau.fix`	logical, indicate whether the tau parameter should be kept fixed in the fitting processes.
`control`	this sets the control parameters of the outer iterations algorithm using the gamlss.control function. This is a vector of 7 numeric values: (i) c.crit (the convergence criterion for the algorithm), (ii) n.cyc (the number of cycles of the algorithm), (iii) mu.step (the step length for the parameter mu), (iv) sigma.step (the step length for the parameter sigma), (v) nu.step (the step length for the parameter nu), (vi) tau.step (the step length for the parameter tau), (vii) gd.tol (global deviance tolerance level). The default values for these 7 parameters are set to c(0.001, 20, 1, 1, 1, 1, Inf).
`i.control`	this sets the control parameters of the inner iterations of the RS algorithm using the glim.control function. This is a vector of 4 numeric values: (i) cc (the convergence criterion for the algorithm), (ii) cyc (the number of cycles of the algorithm), (iii) bf.cyc (the number of cycles of the backfitting algorithm), (iv) bf.tol (the convergence criterion (tolerance level) for the backfitting algorithm). The default values for these 4 parameters are set to c(0.001, 50, 30, 0.001).
`centiles`	logical, indicating whether the function centiles() will be used to tabulate the sample percentages below each centile curve. Default is set to FALSE.
`xvar`	the unique explanatory variable used in the centiles() function. This variable is used only if the centiles argument is set to TRUE. A restriction in the centiles function is that it applies to models with one explanatory variable only.
`newobj`	a character string that provides the name for the output object that is stored on the data servers. Default `gamlss_res`.
`datasources`	a list of `DSConnection-class` objects obtained after login. If the `datasources` argument is not specified the default set of connections will be used: see `datashield.connections_default`.

Details

For additional details see the help header of gamlss and centiles functions in native R gamlss package.

Value

a gamlss object with all components as in the native R gamlss function. Individual-level information like the components y (the response response) and residuals (the normalised quantile residuals of the model) are not disclosed to the client-side.