stapdnd_glmer.Rd
Bayesian inference for stapglms with groupspecific coefficients that have unknown covariance matrices with flexible priors.
stapdnd_glmer(formula, family = gaussian(), subject_data = NULL, distance_data = NULL, time_data = NULL, subject_ID = NULL, group_ID = NULL, max_distance = NULL, max_time = NULL, weights, offset, contrasts = NULL, ..., prior = normal(), prior_intercept = normal(), prior_stap = normal(), prior_theta = log_normal(location = 1L, scale = 1L), prior_aux = exponential(), prior_covariance = decov(), adapt_delta = NULL)
formula  Same as for 


family  Same as for 

subject_data  a data.frame that contains data specific to the subject or subjects on whom the outcome is measured. Must contain one column that has the subject_ID on which to join the distance and time_data 

distance_data  a (minimum) three column data.frame that contains (1) an id_key (2) The sap/tap/stap features and (3) the distances between subject with a given id and the built environment feature in column (2), the distance column must be the only column of type "double" and the sap/tap/stap features must be specified in the dataframe exactly as they are in the formula. 

time_data  same as distance_data except with time that the subject has been exposed to the built environment feature, instead of distance 

subject_ID  name of column to join on between subject_data and bef_data 

group_ID  name of column to join on between 

max_distance  the upper bound on any and all distances included in the model 

max_time  the upper bound on any and all times included in the model 

weights, offset  Same as 

contrasts  Same as 

...  For 

prior  The prior distribution for the regression coefficients.
See the priors help page for details on the families and
how to specify the arguments for all of the functions in the table above.
To omit a prior i.e., to use a flat (improper) uniform prior
Note: If 

prior_intercept  The prior distribution for the intercept.
Note: The prior distribution for the intercept is set so it
applies to the value when all predictors are centered. If you prefer
to specify a prior on the intercept without the predictors being
autocentered, then you have to omit the intercept from the


prior_theta, prior_stap  priors for the spatial scale and spatial effect parameters, respectively 

prior_aux  The prior distribution for the "auxiliary" parameter (if
applicable). The "auxiliary" parameter refers to a different parameter
depending on the


prior_covariance  Cannot be 

adapt_delta  See the adapt_delta help page for details. 
A stapreg object is returned
for stap_glmer, stap_lmer
.
The stap_glmer
function is similar in syntax to
glmer
but rather than performing (restricted) maximum
likelihood estimation of generalized linear models, Bayesian estimation is
performed via MCMC. The Bayesian model adds priors on the
regression coefficients (in the same way as stap_glm
) and
priors on the terms of a decomposition of the covariance matrices of the
groupspecific parameters. See priors
for more information
about the priors.
The stap_lmer
function is equivalent to stap_glmer
with
family = gaussian(link = "identity")
.
Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge, UK.
Muth, C., Oravecz, Z., and Gabry, J. (2018) Userfriendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology. 14(2), 99119. https://www.tqmp.org/RegularArticles/vol142/p099/p099.pdf
stapregmethods
and
glmer
.
The Longituinal Vignette for stap_glmer
and the preprint article available through arXiv.
if (FALSE) { ## subset to only include id, class name and distance variables distdata < homog_longitudinal_bef_data[,c("subj_ID","measure_ID","class","dist")] timedata < homog_longitudinal_bef_data[,c("subj_ID","measure_ID","class","time")] ## distance or time column must be numeric timedata$time < as.numeric(timedata$time) fit < stap_glmer(y_bern ~ centered_income + sex + centered_age + stap(Coffee_Shop) + (1subj_ID), family = binomial(link='logit'), subject_data = homog_longitudinal_subject_data, distance_data = distdata, time_data = timedata, subject_ID = 'subj_ID', group_ID = 'measure_ID', prior_intercept = normal(location = 25, scale = 4, autoscale = F), prior = normal(location = 0, scale = 4, autoscale=F), prior_stap = normal(location = 0, scale = 4), prior_theta = list(Coffee_Shop = list(spatial = log_normal(location = 1, scale = 1), temporal = log_normal(location = 1, scale = 1))), max_distance = 3, max_time = 50, chains = 4, refresh = 1, verbose = FALSE, iter = 1E3, cores = 1) }