Description Usage Arguments Value References Examples
Fit RO-logit 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 Q-Q plot for the heuristic residuals. Default
is |
... |
Other parameters to be passed to the |
Returns a list containing obj
(the RO-Logit 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. Logit-models for sets of ranked items. Sociological Methodology 1994, Vol 24. 1994;24:199-228.
Beggs S, Cardell S, Hausman J. Assessing the Potential Demand for Electric Cars. J Econometrics. 1981;17:1-19.
Tan CS, Støer NC, Chen Y, Andersson M, Ning Y, Wee HL, Khoo EY, Tai ES, Kao SL, Reilly M. A stratification approach using logit-based 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 RO-logit 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 RO-logit 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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