Tidy the parameter estimates from an msm model
# S3 method for msm
tidy(x, ...)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.
Object returned by msm, representing a fitted
multi-state model.
Other arguments (currently unused).