A Multivariate FIML Regression Fit

Broom-style tidy() and glance() for mlmr_mv fits. tidy() returns one row per coefficient per outcome with the broom dotted columns plus a leading response column identifying the outcome; glance() returns a one-row model-level summary with the per-outcome \(R^2\) averaged and the number of responses reported.

Usage

# S3 method for class 'mlmr_mv'
tidy(x, conf.int = FALSE, conf_level = NULL, standardized = FALSE, ...)

# S3 method for class 'mlmr_mv'
glance(x, ...)

Arguments

x

An mlmr_mv fit.

conf.int

Logical: include ci_lower / ci_upper?

conf_level

Ignored (the interval level is fixed at fit time and stored on the object); present for broom signature compatibility.

standardized

Logical: include std_estimate?

...

Unused.

Value

For tidy.mlmr_mv, a data.frame with columns response, term, estimate, se, statistic, p_value, and optionally ci_lower, ci_upper, std_estimate. For glance.mlmr_mv, a one-row data.frame with R2 (mean across outcomes), df, logLik, AIC, BIC, deviance, nobs, and n.responses.

See also

mlmr_mv; mlmr for the univariate methods these mirror.

Author

Ken Kelley kkelley@nd.edu