Package index

Package overview

skmle-package

skmle: Sieve Kernel Maximum Likelihood Estimation

Survival models with sparse longitudinal covariates

skmle()

Fit a Transformed Hazards Model by SMKLE

skmle_cv() print(<cv.skmle>)

Select the Bandwidth by Cross-Validation

kee_cox()

Fit a Cox-Type KEE Model

kee_additive()

Fit an Additive Hazards KEE Model

Asynchronous longitudinal regression

Kernel-weighted estimating equations of Cao, Zeng and Fine (2015) for a response and a covariate observed on different time grids.

kee_async()

Asynchronous longitudinal regression with time-invariant coefficients

kee_async_cv() print(<cv.kee_async>)

Choose the bandwidth for an asynchronous longitudinal fit

kee_async_td() print(<kee_td>)

Asynchronous longitudinal regression with time-dependent coefficients

Simulation

sim_skmle_data()

Simulate Sparse Longitudinal Survival Data

sim_async_data()

Simulate asynchronous longitudinal data

Model summaries and plots

plot(<skmle>)

Plot the estimated baseline function for skmle model

plot(<kee_td>)

Plot estimated coefficient curves

vcov(<skmle>) vcov(<kee>) vcov(<kee_td>)

Extract the covariance matrix of a fitted model

nobs(<skmle>) nobs(<kee>) nobs(<kee_td>)

Number of subjects contributing to a fit

confint(<kee_td>)

Wald confidence intervals for a coefficient curve

plot(<cv.kee_async>) plot(<cv.skmle>)

Plot a cross-validation curve

Tidy output

broom generics returning tibbles, so fits compose with the rest of a tidy workflow.

tidy(<skmle>) tidy(<kee>) tidy(<kee_td>)

Summarise a fit as a tibble

glance(<skmle>) glance(<kee>) glance(<kee_td>)

One-row summary of a fit

augment(<kee_async>)

Add fitted means to the covariate table

summary(<skmle>)

Summary for skmle object

summary(<kee>)

Summary for kee object

print(<skmle>)

Print skmle object

print(<kee>)

Print kee object

print(<summary.skmle>)

Print summary of skmle object

print(<summary.kee>)

Print summary of kee object