lmerSLMADS2 {dsBase} | R Documentation |
lmerSLMADS2 is a serverside function which fits a linear mixed effects model (lme) - i.e. can include both fixed and random effects - on data from one or multiple sources with pooling via SLMA (study level meta-analysis)
lmerSLMADS2(
formula,
offset,
weights,
dataName,
REML = TRUE,
control_type,
control_value.transmit,
optimizer,
verbose = 0
)
formula |
see help for ds.lmerSLMA |
offset |
see help for ds.lmerSLMA |
weights |
see help for ds.lmerSLMA |
dataName |
see help for ds.lmerSLMA |
REML |
see help for ds.lmerSLMA |
control_type |
see help for ds.lmerSLMA |
control_value.transmit |
see help for argument <control_value> for function ds.lmerSLMA |
optimizer |
see help for ds.lmerSLMA |
verbose |
see help for ds.lmerSLMA |
lmerSLMADS2 is a serverside function called by ds.lmerSLMA on the clientside. The analytic work engine is the lmer function in R which sits in the lme4 package. ds.lmerSLMA fits a linear mixed effects model (lme) - can include both fixed and random effects - on data from a single or multiple sources. When there are multiple data sources, the lme is fitted to convergence in each data source independently and the estimates and standard errors returned to the client thereby enabling cross-study pooling using study level meta-analysis (SLMA). By default the SLMA is undertaken using the metafor package, but as the SLMA occurs on the clientside which, as far as the user is concerned is just a standard R environment, the user can choose to use any approach to meta-analysis they choose. For more detailed help about any aspect of lmerSLMDS2 please see the extensive help for ds.lmerSLMA. Additional information about fitting lmes using the lmer engine can be obtained using R help for lmer and the lme4 package
all key model components see help for ds.lmerSLMA
Tom Bishop, with some additions by Paul Burton