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POplugin()
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Fits a proportional odds model via pooled logistic regression. |
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bca_interval()
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Compute a BCa confidence interval |
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bca_logodds()
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Compute a BCa bootstrap confidence interval for the weighted mean. The code is
based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf |
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bca_mannwhitney()
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Compute a BCa bootstrap confidence interval for the Mann-Whitney parameter. The code is
based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf |
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bca_marg_dist()
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Compute a BCa bootstrap confidence interval for the weighted mean. The code is
based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf |
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bca_wmean()
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Compute a BCa bootstrap confidence interval for the weighted mean. The code is
based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf |
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compute_trt_spec_bca_intervals()
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Used to compute treatment-specific BCa intervals for
the CDF and PMF |
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compute_trt_spec_marg_dist_ptwise_ci()
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Compute simultaneous confidence interval for treatment-specific marginal distribution |
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compute_trt_spec_marg_dist_simul_ci()
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Compute simultaneous confidence interval for treatment-specific marginal distribution |
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covid19
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Simulated COVID-19 outcomes for hospitalized patients. |
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drord()
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Doubly robust estimates of for evaluating effects
of treatments on ordinal outcomes. |
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eif_pmf_k()
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Get EIF estimates for treatment-specific PMF at a particular
level of the outcome |
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eif_theta_k()
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Get EIF estimates for treatment-specific CDF at a particular
level of the outcome |
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estimate_cdf()
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Map an estimate of the conditional PMF into an estimate of the conditional CDF |
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estimate_ci_logodds()
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Compute confidence interval/s for the log-odds parameters |
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estimate_ci_mannwhitney()
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Compute confidence interval/s for the Mann-Whitney parameter |
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estimate_ci_marg_dist()
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Compute confidence interval/s for the treatment specific
PMF and CDF. |
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estimate_ci_wmean()
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Compute confidence interval/s for the weight mean parameters |
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estimate_cond_mean()
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Map an estimate of treatment-specific PMF into an estimate of
treatment specific conditional mean for each observation. |
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estimate_eif_wmean()
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Obtain an estimate of the efficient influence function for the
treatment-specific weighted mean parameter |
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estimate_logodds()
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implements a plug-in estimator of equation (2) in Diaz et al |
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estimate_mannwhitney()
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Compute the estimate of Mann-Whitney based on conditional CDF and PMF |
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estimate_pmf()
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Get a treatment-specific estimate of the conditional PMF.
Essentially this is a wrapper function for fit_trt_spec_reg, which
fits the proportion odds model in a given treatment arm. |
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estimate_treat_prob()
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Estimate probability of receiving each level of treatment |
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estimate_wmean()
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Compute the estimate of the weighted mean parameter based on
estimated PMF in each treatment arm. |
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evaluate_beta_cov()
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Get the covariance matrix for beta |
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evaluate_mannwhitney_gradient()
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Compute the estimated gradient of the Mann-Whitney parameter. Needed to derive
standard error for Wald confidence intervals. |
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evaluate_marg_cdf_eif()
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Get eif estimates for treatment-specific CDF |
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evaluate_marg_cdf_ptwise_ci()
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Evaluate pointwise confidence interval for marginal CDF. |
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evaluate_marg_dist_simul_ci()
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Evaluate simultaneous confidence interval for marginal PMF or CDF. |
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evaluate_marg_pmf_eif()
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Get eif estimates for treatment-specific PMF |
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evaluate_marg_pmf_ptwise_ci()
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Evaluate pointwise confidence interval for marginal PMF. |
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evaluate_theta_cov()
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get a covariance matrix for the estimated CDF |
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evaluate_trt_spec_pmf_eif()
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Get a matrix of eif estimates for treatment-specific PMF |
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evaluate_trt_spec_theta_eif()
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get a matrix of eif estimates for the treatment-specific CDF estimates |
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fit_trt_spec_reg()
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Helper function to fit a treatment specific outcome regression.
If there are more than 2 observed levels of the outcome for the
specified treatment arm, then polr is used from the MASS
package. Otherwise logistic regression is used. In both cases,
inverse probability of treatment weights are included in the regression.
If there are levels of the outcome that are not observed in this treatment
group, then 0's are added in. The function returns a matrix with named columns
corresponding to each outcome (ordered numerically). The entries
represent the estimated covariate-conditional treatment-specific PMF. |
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getResponseFromFormula()
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Get a response from model formula |
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get_one_logodds()
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Compute one log odds based on a given data set. |
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get_one_mannwhitney()
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Compute one estimate of Mann-Whitney parameter based on a given data set. |
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get_one_marg_dist()
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Compute one estimate of the marginal CDF/PMF on a given data set. |
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get_one_wmean()
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Compute one weighted mean based on a given data set. |
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jack_logodds()
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Compute jackknife log-odds estimates. |
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jack_mannwhitney()
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Compute Mann-Whitney log-odds estimates. |
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jack_marg_cdf()
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Compute jackknife distribution estimates. |
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jack_wmean()
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Compute jackknife weighted mean estimates. |
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marginalize_cdf()
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Marginalize over empirical distribution to obtain marginal
treatment-specific CDF estimate. |
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marginalize_pmf()
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Marginalize over empirical distribution to obtain marginal
treatment-specific PMF estimate. |
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one_boot_logodds()
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Get one bootstrap computation of the log odds parameters. |
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one_boot_mannwhitney()
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Get one bootstrap computation of the Mann-Whitney parameter. |
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one_boot_marg_dist()
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Get one bootstrap computation of the CDF and PMF estimates |
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one_boot_wmean()
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Get one bootstrap computation of the weighted mean parameters. |
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plot(<drord>)
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Print the output of a "drord" object. |
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predict(<POplugin>)
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Predict method for a POplugin object |
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print(<drord>)
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Print the output of a "drord" object. |
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trimmed_logit()
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Trimmed logistic function |
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wald_ci_wmean()
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Compute a Wald confidence interval for the weighted mean |