glmerSLMADS.assign.RdglmerSLMADS.assign is the same as glmerSLMADS2 which fits a generalized linear mixed effects model (glme) per study and saves the outcomes in each study
glmerSLMADS.assign(
formula,
offset,
weights,
dataName,
family,
control_type = NULL,
control_value.transmit = NULL,
nAGQ = 1L,
verbose = 0,
theta = NULL,
fixef = NULL
)see help for ds.glmerSLMA
see help for ds.glmerSLMA
see help for ds.glmerSLMA
see help for ds.glmerSLMA
see help for ds.glmerSLMA
see help for ds.glmerSLMA
see help for argument <control_value> for function ds.glmerSLMA
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()
see help for ds.glmerSLMA
see help for argument <start_theta> for function ds.glmerSLMA
see help for argument <start_fixef> for function ds.glmerSLMA
writes glmerMod object summarising the fitted model to the serverside. For more detailed information see help for ds.glmerSLMA.
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.