Description Usage Arguments Details Value Author(s) Examples
Runs a combined GLM analysis of non-pooled data
1 |
opals |
a list of opal object(s) obtained after
login in to opal servers; these objects hold also the
data assign to R, as |
formula |
an object of class |
family |
a description of the error distribution function to use in the model |
maxit |
the number of iterations of IWLS used |
It enables a parallelized analysis of individual-level data sitting on distinct servers by sending instructions to each computer requesting non-disclosing summary statistics. The sumaries are then combined to estimate the parameters of the model; these parameters are the same as those obtained if the data were 'physically' pooled.
coefficients a named vector of coefficients
residuals the 'working' residuals, that is the residuals in the final iteration of the IWLS fit.
fitted.values the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function.
rank the numeric rank of the fitted linear model.
family the family
object used.
linear.predictors the linear fit on link scale.
aic A version of Akaike's An Information Criterion, which tells how well the model fits
Burton, P.; Laflamme, P.; Gaye, A.
1 2 3 4 5 6 7 8 9 10 11 | {
# load the file that contains the login details
data(logindata)
# login and assign some variables to R
myvar <- list("DIS_DIAB","PM_BMI_CONTINUOUS","LAB_HDL")
opals <- ag.ds.login(logins=logindata,assign=TRUE,variables=myvar)
# run a GLM (e.g. diabetes prediction using BMI and HDL level)
mod <- ag.ds.glm(opals=opals,formula=D$DIS_DIAB~D$PM_BMI_CONTINUOUS+D$LAB_HDL,family=quote(binomial))
}
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