Function reference
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Collins - Collins data
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DVTipd - Hypothetical dataset for diagnosis of Deep Vein Thrombosis (DVT)
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DVTmodels - Risk prediction models for diagnosing Deep Venous Thrombosis (DVT)
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Daniels - Daniels and Hughes data
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EuroSCORE - Predictive performance of EuroSCORE II
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Fibrinogen - Meta-analysis of the association between plasma fibrinogen concentration and the risk of coronary heath disease
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Framingham - Predictive performance of the Framingham Risk Score in male populations
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Kertai - Kertai data
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Roberts - Roberts data
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Scheidler - Diagnostic accuracy data
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Tzoulaki - The incremental value of cardiovascular risk factors
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Zhang - Meta-analysis of the prognostic role of hormone receptors in endometrial cancer
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acplot() - Plot the autocorrelation of a Bayesian meta-analysis model
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acplot(<mcmc.list>) - Plot the autocorrelation of a Bayesian meta-analysis model
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acplot(<uvmeta>) - Plot the autocorrelation of a Bayesian meta-analysis model
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acplot(<valmeta>) - Plot the autocorrelation of a Bayesian meta-analysis model
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ccalc() - Calculate the concordance statistic
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cor2cov() - Convert a correlation matrix into a covariance matrix
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dplot() - Posterior distribution of estimated model parameters
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dplot(<mcmc.list>) - Posterior distribution of estimated model parameters
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dplot(<uvmeta>) - Plot the prior and posterior distribution of a meta-analysis model
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dplot(<valmeta>) - Plot the prior and posterior distribution of a meta-analysis model
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fat() - Regression tests for detecting funnel plot asymmetry
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fitted(<metapred>) - Extract Model Fitted Values
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forest() - Forest plot
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forest(<default>) - Forest plot
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forest(<metapred>) - Forest plot of a metapred fit
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forest(<mp.cv.val>) - Forest plot of a validation object.
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gelmanplot() - Gelman-Rubin-Brooks plot
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gelmanplot(<mcmc.list>) - Gelman-Rubin-Brooks plot
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gelmanplot(<uvmeta>) - Gelman-Rubin-Brooks plot
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gelmanplot(<valmeta>) - Gelman-Rubin-Brooks plot
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gen()generalizability() - Generalizability estimates
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impact - IMPACT data
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impute_conditional_mean() - Impute missing values by their conditional mean
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inv.logit() - Apply the inverse logit tranformation
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logLik(<riley>) - Print the log-likelihood
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logit() - Apply logit tranformation
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ma() - Random effects meta-analysis
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metamisc-package - Meta-Analysis of Diagnosis and Prognosis Research Studies
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metapred() - Generalized Stepwise Regression for Prediction Models in Clustered Data
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oecalc() - Calculate the total O:E ratio
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perf()performance() - Performance estimates
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plot(<fat>) - Display results from the funnel plot asymmetry test
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plot(<mm_perf>) - Forest Plots
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plot(<riley>) - Plot the summary of the bivariate model from Riley et al. (2008).
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plot(<uvmeta>) - Forest Plots
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plot(<valmeta>) - Forest Plots
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predict(<riley>) - Prediction Interval
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recalibrate() - Recalibrate a Prediction Model
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riley() - Fit the alternative model for bivariate random-effects meta-analysis
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rmplot() - Plot the running means of a Bayesian meta-analysis model
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rmplot(<mcmc.list>) - Plot the running means of a Bayesian meta-analysis model
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rmplot(<uvmeta>) - Plot the running means of a Bayesian meta-analysis model
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rmplot(<valmeta>) - Plot the running means of a Bayesian meta-analysis model
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se()variances()tau()tau2() - Standard errors and variances
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stackedglm() - Stacked Regression
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subset(<metapred>) - Subsetting metapred fits
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summary(<riley>) - Parameter summaries Provides the summary estimates of the alternative model for bivariate random-effects meta-analysis by Riley et al. (2008) with their corresponding standard errors (derived from the inverse Hessian). For confidence intervals, asymptotic normality is assumed.
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summary(<uvmeta>) - Summarizing Univariate Meta-Analysis Models
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uvmeta-clas - Class "uvmeta". Result of a univariate meta-analysis.
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uvmeta() - Univariate meta-analysis
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valmeta() - Meta-analysis of prediction model performance
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vcov(<riley>) - Calculate Variance-Covariance Matrix for a Fitted Riley Model Object