Description Usage Arguments Value References Examples
Fit ROlogit model and obtain heuristic residuals
1 2 
yvar 
string. Name of outcome variable. 
evar 
string (or vector of strings). Name of exposure(s). 
cfdr 
string (or vector of strings). Names of confounder(s). Default is

emod 
string (or vector of strings). Name of effect modifier(s).
Default is 
svar 
string. Name of stratum variable. Use 
dat 

method 
string. Use Efron ( 
initial.res.par 
The initial values of the intercept and log(scale), to
be passed to the 
plot 
logic. To plot the QQ plot for the heuristic residuals. Default
is 
... 
Other parameters to be passed to the 
Returns a list containing obj
(the ROLogit model fitted using
coxph
), hresid
(the vector of heuristic residuals),
logscale
(log of scale parameter of the heuristic residuals), and
coefficients
(a data.frame with estimated coefficients before and
after scaling).
Allison PD, Christakis NA. Logitmodels for sets of ranked items. Sociological Methodology 1994, Vol 24. 1994;24:199228.
Beggs S, Cardell S, Hausman J. Assessing the Potential Demand for Electric Cars. J Econometrics. 1981;17:119.
Tan CS, Støer NC, Chen Y, Andersson M, Ning Y, Wee HL, Khoo EY, Tai ES, Kao SL, Reilly M. A stratification approach using logitbased models for confounder adjustment in the study of continuous outcomes. Statistical methods in medical research. 2017 Jan 1:0962280217747309.
Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model: Springer New York; 2000.
1 2 3 4 5 6 7 8 9 10 11 12  # Fit an ROlogit model to determine whether the glycaemic control of
# patients differs between medical and surgical wards.
data(inpat_bg)
# Divide patients into strata based on age, gender, duration of monitoring
# episodes, and frequency of daily BG measurements.
inpat_bg$group < paste(inpat_bg$age_group, inpat_bg$sex, inpat_bg$los_group,
inpat_bg$bg_freq_group, sep = "")
# Fit an ROlogit model with mean BG reading as the outcome and ward as the
# exposure:
obj < rologit(yvar = "bg_mean", evar = "ward", svar = "group",
dat = inpat_bg, initial.res.par = c(2, 2))
summary(obj)

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