summary.elrm {elrm} | R Documentation |
Summary method for class elrm
that formats and prints out the results of an elrm
object.
## S3 method for class 'elrm': summary(object, ...)
object |
an object of class elrm , resulting from a call to elrm() or a previous call to update() . |
... |
additional arguments to the summary function (currently unused). |
The following results are formatted and printed to the screen: the matched call, coefficient estimates and confidence intervals for each model term of interest, estimated p-value for jointly testing that the parameters of interest are simultaneously equal to zero, full conditional p-values from separately testing each parameter equal to zero, length of the Markov chain that inference was based on, and the Monte Carlo standard error of each reported p-value.
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.
# Drug dataset example with both sex and treatment as the variables of interest data(drugDat); drug.elrm=elrm(formula=recovered/n~sex+treatment,interest=~sex+treatment,r=4,iter=100000,burnIn=1000,dataset=drugDat); # Summarize the results: summary(drug.elrm); # Call: # [[1]] # elrm(formula = recovered/n ~ sex + treatment, interest = ~sex + # treatment, r = 4, iter = 1e+05, dataset = drugDat, burnIn = 1000) # Results: # estimate p-value p-value_se mc_size # joint NA 0.12951 0.00216 99000 # sex 0.29479 0.54092 0.00880 2749 # treatment 0.82389 0.06892 0.00347 13131 # 95% Confidence Intervals for Parameters # lower upper # sex -0.6109481 1.209525 # treatment -0.1042183 2.028083 ## Not run: # Urinary tract dataset example with dia as the variable of interst data(utiDat); uti.elrm=elrm(uti/n~age+current+dia+oc+pastyr+vi+vic+vicl+vis,interest=~dia,r=4,iter=30000,burnIn=1000,dataset=utiDat); # Summarize the results: summary(uti.elrm); # Call: # [[1]] # elrm(formula = uti/n ~ age + current + dia + oc + pastyr + vi + # vic + vicl + vis, interest = ~dia, r = 4, iter = 30000, dataset = uti, # burnIn = 1000) # Results: # estimate p-value p-value_se mc_size # dia 2.07146 0.03286 0.00802 29000 # 95% Confidence Intervals for Parameters # lower upper # dia -0.06231932 Inf ## End(Not run)