glmerSLMADS.assign {dsBase}

R Documentation

Fitting generalized linear mixed effect models - serverside function

Description

glmerSLMADS.assign is the same as glmerSLMADS2 which fits a generalized linear mixed effects model (glme) per study and saves the outcomes in each study

Usage

glmerSLMADS.assign(
  formula,
  offset,
  weights,
  dataName,
  family,
  control_type = NULL,
  control_value.transmit = NULL,
  nAGQ = 1L,
  verbose = 0,
  theta = NULL,
  fixef = NULL
)

Arguments

formula	see help for ds.glmerSLMA
offset	see help for ds.glmerSLMA
weights	see help for ds.glmerSLMA
dataName	see help for ds.glmerSLMA
family	see help for ds.glmerSLMA
control_type	see help for ds.glmerSLMA
control_value.transmit	see help for argument <control_value> for function ds.glmerSLMA
nAGQ	integer scalar, defaulting to 1L. IN PRACTICE, IT MAY BE NECESSARY TO SET nAGQ TO 0L when the model appears to converge perfectly well (e.g. verbose=2 demonstrates good initial convergence of both the log-likelihood and regression coefficients) but formal convergence does not get declared - so no output is produced - despite running the model for many iterations. The nAGQ argument is set by the nAGQ argument for ds.glmerSLMA and further details can be found in help(ds.glmerSLMA) and in the native R help for glmer()
verbose	see help for ds.glmerSLMA
theta	see help for argument <start_theta> for function ds.glmerSLMA
fixef	see help for argument <start_fixef> for function ds.glmerSLMA

Details

glmerSLMADS.assign is a serverside function called by ds.glmerSLMA on the clientside. The analytic work engine is the glmer function in R which sits in the lme4 package. glmerSLMADS.assign fits a generalized linear mixed effects model (glme) - e.g. a logistic or Poisson regression model including both fixed and random effects - on data from each single data source and saves the regression outcomes on the serveside.

Value

writes glmerMod object summarising the fitted model to the serverside. For more detailed information see help for ds.glmerSLMA.

Author(s)

Demetris Avraam for DataSHIELD Development Team

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