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ols Ordinary least squares linear model
lrm Binary and ordinal logistic regression model
psm Accelerated failure time parametric survival model
cph Cox proportional hazards regression
bj Buckley-James censored least squares linear model
specs Detailed specifications of fit
robcov Robust covariance matrix estimates
bootcov Bootstrap covariance matrix estimates
summary Summary of effects of predictors
plot.summary
Plot continuously shaded confidence
bars for results of summary
anova Wald tests of most meaningful hypotheses
contrast General contrasts, C.L., tests
plot.anova Depict results of anova graphically
plot Plot effects of predictors
gendata Generate data frame with predictor
combinations (optionally interactively)
predict Obtain predicted values or design matrix
fastbw Fast backward step-down variable
selection
residuals Residuals, influence statistics from fit
which.influence
Which observations are overly influential
sensuc Sensitivity of one binary predictor in
lrm and cph models to an unmeasured
binary confounder
latex LaTeX representation of fitted
model or anova() table
Function S function analytic representation
of a fitted regression model (X*Beta)
Hazard S function analytic representation
of a fitted hazard function (for psm)
Survival S function analytic representation of
fitted survival function (for psm,cph)
Quantile S function analytic representation of
fitted function for quantiles of
survival time (for psm, cph)
nomogram Draws a nomogram for the fitted model
survest Estimate survival probabilities (for psm, cph)
survplot Plot survival curves (psm, cph)
validate Validate indexes of model fit using resampling
calibrate Estimate calibration curve for model using resampling
vif Variance inflation factors for a fit
naresid Bring elements corresponding to missing
data back into predictions and residuals
naprint Print summary of missing values
pentrace Find optimum penality for penalized MLE
effective.df
Print effective d.f. for each type of
variable in model, for penalized fit or pentrace result
rm.impute Impute repeated measures data with
non-random dropout (experimental function, not working
correctly)
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