gamlssDS1 called by ds.gamlss — gamlssDS1 • dsGamlss

This is the first server-side aggregate function called by ds.gamlss.

Usage

gamlssDS1(
  formula = formula,
  sigma.formula = sigma.formula,
  nu.formula = nu.formula,
  tau.formula = tau.formula,
  family = family,
  data = data,
  mu.fix = mu.fix,
  sigma.fix = sigma.fix,
  nu.fix = nu.fix,
  tau.fix = tau.fix,
  global.mean = global.mean,
  global.sd = global.sd,
  control = control,
  i.control = i.control,
  autostep = autostep
)

Arguments

formula: A formula string in the legal transmission format for DataSHIELD, specifying the model for the mu distribution parameter. The DataSHIELD legal transmission format means that special characters, like '(' are replaced with the corresponding verbal descriptions, e.g. 'left_parenthesis'.
sigma.formula: A formula string in the legal transmission format for DataSHIELD, specifying the model for the sigma distribution parameter. The DataSHIELD legal transmission format means that special characters, like '(' are replaced with the corresponding verbal descriptions, e.g. 'left_parenthesis'.
nu.formula: A formula string in the legal transmission format for DataSHIELD, specifying the model for the nu distribution parameter. The DataSHIELD legal transmission format means that special characters, like '(' are replaced with the corresponding verbal descriptions, e.g. 'left_parenthesis'.
tau.formula: A formula string in the legal transmission format for DataSHIELD, specifying the model for the tau distribution parameter. The DataSHIELD legal transmission format means that special characters, like '(' are replaced with the corresponding verbal descriptions, e.g. 'left_parenthesis'.
family: A family string in the legal transmission format for DataSHIELD, which is used to define the distribution of the response variable. The DataSHIELD legal transmission format means that special characters, like '(' are replaced with the corresponding verbal descriptions, e.g. 'left_parenthesis'. Currently, only the following families are supported: family=c('NOleft_parenthesisright_parenthesis', 'NO2left_parenthesisright_parenthesis', 'BCCGleft_parenthesisright_parenthesis', 'BCPEleft_parenthesisright_parenthesis').
data: A character string specifying a data.frame object holding the data to be analysed under the specified model.
mu.fix: Logical, indicating whether the mu parameter should be kept fixed in the fitting processes. Default mu.fix=FALSE.
sigma.fix: Logical, indicating whether the sigma parameter should be kept fixed in the fitting processes. Default sigma.fix=FALSE.
nu.fix: Logical, indicating whether the nu parameter should be kept fixed in the fitting processes. Default nu.fix=FALSE.
tau.fix: Logical, indicating whether the tau parameter should be kept fixed in the fitting processes. Default tau.fix=FALSE.
global.mean: Numeric value, giving the global mean of the outcome variable, which is necessary to initialize the distribution parameters for some families. Otherwise global.mean=NULL.
global.sd: Numeric value, giving the global sd of the outcome variable which is necessary to initialize the distribution parameters for some families. Otherwise global.sd=NULL.
control: This sets the control parameters of the outer iterations algorithm using the using the gamlss.control function. This is a comma-separated string 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 control='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 using the glim.control function. This is a comma-separated string 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 i.control='0.001,50,30,0.001'.
autostep: Logical, indicating whether the steps should be halved automatically if the new global deviance is greater than the old one. The default is autostep=TRUE.

Value

A list with the following elements.

mod.gamlss.ds: A gamlss object with all components as in the native R gamlss function. Individual-level information like the components y (the response) and residuals (the normalised quantile residuals of the model) are not disclosed to the client-side.
G.dev: Numeric value for the initial deviance on the server.
dim.mu.x: Numeric vector with two elements, specifying the dimension of the design matrix for mu.
dim.sigma.x: Numeric vector with two elements, specifying the dimension of the design matrix for sigma.
dim.nu.x: Numeric vector with two elements, specifying the dimension of the design matrix for nu.
dim.tau.x: Numeric vector with two elements, specifying the dimension of the design matrix for tau.
smoother.names: String vector with the unique variable names that are used for smoothing.
smoother.xmin: Numeric vector with anononymized minima for the variables in smoother.names.
smoother.xmax: Numeric vector with anononymized maxima for the variables in smoother.names.
y.invalid: Numeric value, either 0 or 1, whereby 1 indicates a disclosure risk in the response variable.
mu.par.invalid: Numeric vector with elements 0 or 1, whereby 1 indicates a disclosure risk in the corresponding explanatory variable for mu.
sigma.par.invalid: Numeric vector with elements 0 or 1, whereby 1 indicates a disclosure risk in the corresponding explanatory variable for sigma.
nu.par.invalid: Numeric vector with elements 0 or 1, whereby 1 indicates a disclosure risk in the corresponding explanatory variable for nu.
tau.par.invalid: Numeric vector with elements 0 or 1, whereby 1 indicates a disclosure risk in the corresponding explanatory variable for tau.
gamlss.saturation.invalid: Numeric value, either 0 or 1, whereby 1 indicates a disclosure risk from an oversaturated model.
errorMessage: String for the disclosure risk. errorMessage='Study data or applied model invalid for this source' indicates a disclosure risk, whereas errorMessage='No errors' means that no disclosure risk was identified.

Details

It is an aggregation function that sets up the appropriate model structure and dimensions to fit a ds.gamlss model. This function is not intended for direct use by the user. For more details please see the extensive header of ds.gamlss.

Author

Annika Swenne