glmerSLMADS.assing {dsBase} | R Documentation |
glmerSLMADS.assing is the same as glmerSLMADS2 which fits a generalized linear mixed effects model (glme) per study and saves the outcomes in each study
glmerSLMADS.assing( formula, offset, weights, dataName, family, control_type = NULL, control_value.transmit = NULL, nAGQ = 1L, verbose = 0, theta = NULL, fixef = NULL )
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 |
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.
writes glmerMod object summarising the fitted model to the serverside. For more detailed information see help for ds.glmerSLMA.
Demetris Avraam for DataSHIELD Development Team