update.elrm(elrm) | R Documentation |
An update method for objects created by elrm()
. Extends the Markov chain of an elrm
object by a specified number of iterations.
## S3 method for class 'elrm': update(object, iter, burnIn = 0, alpha = 0.05, ...)
object |
an object of class elrm , resulting from a call to elrm() or a previous call to update() . |
iter |
an integer representing the number of Markov chain iterations to make. |
burnIn |
the burn-in period to use when conducting inference. Values of the Markov chain in the burn-in period are discarded; default=0. |
alpha |
determines the level used for confidence intervals; default=0.05. |
... |
additional arguments to the update function (currently unused). |
Extends the Markov chain of an elrm
object by creating a new Markov chain of the specified length using the last sampled value as the starting point. The newly created chain is then appended to the original. Subsequent inference is based on the extended Markov chain.
An object of class elrm
.
David Zamar, Jinko Graham, Brad McNeney
Zamar David. Monte Carlo Markov Chain Exact Inference for Binomial Regression Models. Master's thesis, Statistics and Actuarial Sciences, Simon Fraser University, 2006.
Zamar D, McNeney B and Graham J. elrm: Software Implementing Exact-like Inference for Logistic Regression Models. Journal of Statistical Software 2007, 21(3).
summary.elrm
, plot.elrm
, elrm
.
# Drug dataset example with sex and treatment as the variables of interest data(drugDat); drug.elrm=elrm(formula=recovered/n~sex+treatment,interest=~sex+treatment,r=4,iter=2000,burnIn=0,dataset=drugDat); # Summarize the results summary(drug.elrm); # Call: # [[1]] # elrm(formula = recovered/n ~ sex + treatment, interest = ~sex + # treatment, r = 4, iter = 2000, dataset = drugDat, burnIn = 0) # Results: # estimate p-value p-value_se mc_size # joint NA 0.517 0.01755 2000 # sex NA NA NA 90 # treatment NA NA NA 275 # 95% Confidence Intervals for Parameters # lower upper # sex NA NA # treatment NA NA # Call update and extend the chain by 50000 iterations and set the burn-in period to 100 iterations drug.elrm = update(drug.elrm, iter=50000, burnIn=100); # Summarize the results summary(drug.elrm); # Call: # [[1]] # elrm(formula = recovered/n ~ sex + treatment, interest = ~sex + # treatment, r = 4, iter = 2000, dataset = drugDat, burnIn = 0) # [[2]] # update.elrm(object = drug.elrm, iter = 50000, burnIn = 100) # Results: # estimate p-value p-value_se mc_size # joint NA 0.14669 0.00314 51900 # sex 0.29431 0.52625 0.01180 1543 # treatment 0.75707 0.07805 0.00512 6842 # 95% Confidence Intervals for Parameters # lower upper # sex -0.6109599 1.230676 # treatment -0.1366174 1.845202 # Now change the burn-in to 5000 drug.elrm = update(drug.elrm, iter=0, burnIn=5000); # Summarize the results summary(drug.elrm); # Call: # [[1]] # elrm(formula = recovered/n ~ sex + treatment, interest = ~sex + # treatment, r = 4, iter = 2000, dataset = drugDat, burnIn = 0) # [[2]] # update.elrm(object = drug.elrm, iter = 50000, burnIn = 100) # [[3]] # update.elrm(object = drug.elrm, iter = 0, burnIn = 5000) # Results: # estimate p-value p-value_se mc_size # joint NA 0.13419 0.00341 47000 # sex 0.28423 0.52774 0.01890 1370 # treatment 0.79565 0.07500 0.00377 6227 # 95% Confidence Intervals for Parameters # lower upper # sex -0.6053313 1.199807 # treatment -0.1240906 1.926238