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Tidy the parameter estimates from an msm model


# S3 method for msm
tidy(x, ...)



Object returned by msm, representing a fitted multi-state model.


Other arguments (currently unused).


A "tibble", with one row for each parameter and the following columns describing the parameter.

  • parclass: Class of parameters: intens (transition intensities), hr (hazard ratios representing effects of covariates on intensities), and their transformed versions logintens (log intensities) and loghr (log hazard ratios).

    For "misclassification" models fitted with the ematrix argument to msm, other classes of parameters include misc (misclassification probabilities), logitmisc (misclassification log odds), or_misc and logor_misc (effects of covariates on misclassification probabilities, as odds ratios or log odds ratios, with the first state as the reference category).

    For hidden Markov models fitted with the hmodel argument to msm, the parameter class called hmm comprises the parameters of the distributions of the outcome conditionally on the hidden state. Covariates on the location parameter of these distributions are included in class hmmcov. If initial state occupancy probabilities are estimated, these are included in class initp (or initlogodds for the log odds transforms of these), and any covariates on these probabilities are included in class initpcov.

  • state: Starting state of the transition for transition intensities, and true state for misclassification probabilities or hidden Markov model parameters.

  • tostate: Ending state of the transition for transition intensities, and observed state for misclassification probabilities

  • term: Name of the covariate for covariate effects, or "baseline" for the baseline intensity or analogous parameter value. Note that the "baseline" parameters are the parameters with covariates set to their mean values in the data (stored in e.g. x$qcmodel$covmeans), unless msm was called with center=FALSE.

  • estimate, std.error, conf.low, conf.high: Parameter estimate, standard error, and lower and upper confidence limits.

  • statistic, p.value: For covariate effects, the Z-test statistic and p-value for a test of the null hypothesis that the covariate effect is zero, based on the estimate and standard error.