Package index • metamisc

All functions

Collins: Collins data

DVTipd: Hypothetical dataset for diagnosis of Deep Vein Thrombosis (DVT)

DVTmodels: Risk prediction models for diagnosing Deep Venous Thrombosis (DVT)

Daniels: Daniels and Hughes data

EuroSCORE: Predictive performance of EuroSCORE II

Fibrinogen: Meta-analysis of the association between plasma fibrinogen concentration and the risk of coronary heath disease

Framingham: Predictive performance of the Framingham Risk Score in male populations

Kertai: Kertai data

Roberts: Roberts data

Scheidler: Diagnostic accuracy data

Tzoulaki: The incremental value of cardiovascular risk factors

Zhang: Meta-analysis of the prognostic role of hormone receptors in endometrial cancer

acplot(): Plot the autocorrelation of a Bayesian meta-analysis model

acplot(<mcmc.list>): Plot the autocorrelation of a Bayesian meta-analysis model

acplot(<uvmeta>): Plot the autocorrelation of a Bayesian meta-analysis model

acplot(<valmeta>): Plot the autocorrelation of a Bayesian meta-analysis model

ccalc(): Calculate the concordance statistic

cor2cov(): Convert a correlation matrix into a covariance matrix

dplot(): Posterior distribution of estimated model parameters

dplot(<mcmc.list>): Posterior distribution of estimated model parameters

dplot(<uvmeta>): Plot the prior and posterior distribution of a meta-analysis model

dplot(<valmeta>): Plot the prior and posterior distribution of a meta-analysis model

fat(): Regression tests for detecting funnel plot asymmetry

fitted(<metapred>): Extract Model Fitted Values

forest(): Forest plot

forest(<default>): Forest plot

forest(<metapred>): Forest plot of a metapred fit

forest(<mp.cv.val>): Forest plot of a validation object.

gelmanplot(): Gelman-Rubin-Brooks plot

gelmanplot(<mcmc.list>): Gelman-Rubin-Brooks plot

gelmanplot(<uvmeta>): Gelman-Rubin-Brooks plot

gelmanplot(<valmeta>): Gelman-Rubin-Brooks plot

gen() generalizability(): Generalizability estimates

impact: IMPACT data

impute_conditional_mean(): Impute missing values by their conditional mean

inv.logit(): Apply the inverse logit tranformation

is_metapred(): Check if an object is of class 'metapred'

is_min_cstat_set(): Check if pars$min.cstat is set and valid

logLik(<riley>): Print the log-likelihood

logit(): Apply logit tranformation

ma(): Random effects meta-analysis

metamisc-package: Meta-Analysis of Diagnosis and Prognosis Research Studies

metapred(): Generalized Stepwise Regression for Prediction Models in Clustered Data

oecalc(): Calculate the total O:E ratio

perf() performance(): Performance estimates

plot(<fat>): Display results from the funnel plot asymmetry test

plot(<mm_perf>): Forest Plots

plot(<riley>): Plot the summary of the bivariate model from Riley et al. (2008).

plot(<uvmeta>): Forest Plots

plot(<valmeta>): Forest Plots

predict(<riley>): Prediction Interval

recalibrate(): Recalibrate a Prediction Model

riley(): Fit the alternative model for bivariate random-effects meta-analysis

rmplot(): Plot the running means of a Bayesian meta-analysis model

rmplot(<mcmc.list>): Plot the running means of a Bayesian meta-analysis model

rmplot(<uvmeta>): Plot the running means of a Bayesian meta-analysis model

rmplot(<valmeta>): Plot the running means of a Bayesian meta-analysis model

se() variances() tau() tau2(): Standard errors and variances

stackedglm(): Stacked Regression

subset(<metapred>): Subsetting metapred fits

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.

summary(<uvmeta>): Summarizing Univariate Meta-Analysis Models

uvmeta-clas: Class "uvmeta". Result of a univariate meta-analysis.

uvmeta(): Univariate meta-analysis

valmeta(): Meta-analysis of prediction model performance

vcov(<riley>): Calculate Variance-Covariance Matrix for a Fitted Riley Model Object