Parameters from multiply imputed repeated analyses

Format models of class mira, obtained from mice::width.mids().

# S3 method for mira
model_parameters(
  model,
  ci = 0.95,
  exponentiate = FALSE,
  p_adjust = NULL,
  verbose = TRUE,
  ...
)

Arguments

model	An object of class mira.
ci	Confidence Interval (CI) level. Default to 0.95 (95%).
exponentiate	Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. Note: Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For compare_parameters(), exponentiate = "nongaussian" will only exponentiate coefficients from non-Gaussian families.
p_adjust	Character vector, if not NULL, indicates the method to adjust p-values. See p.adjust for details. Further possible adjustment methods are "tukey", "scheffe", "sidak" and "none" to explicitly disable adjustment for emmGrid objects (from emmeans).
verbose	Toggle warnings and messages.
...	Arguments passed to or from other methods.

Details

model_parameters() for objects of class mira works similar to summary(mice::pool()), i.e. it generates the pooled summary of multiple imputed repeated regression analyses.

Examples

library(parameters)
if (require("mice", quietly = TRUE)) {
  data(nhanes2)
  imp <- mice(nhanes2)
  fit <- with(data = imp, exp = lm(bmi ~ age + hyp + chl))
  model_parameters(fit)
}
#> 
#> Attaching package: ‘mice’
#> The following object is masked from ‘package:stats’:
#> 
#>     filter
#> The following objects are masked from ‘package:base’:
#> 
#>     cbind, rbind
#> 
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   1   3  bmi  hyp  chl
#>   1   4  bmi  hyp  chl
#>   1   5  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl
#>   2   3  bmi  hyp  chl
#>   2   4  bmi  hyp  chl
#>   2   5  bmi  hyp  chl
#>   3   1  bmi  hyp  chl
#>   3   2  bmi  hyp  chl
#>   3   3  bmi  hyp  chl
#>   3   4  bmi  hyp  chl
#>   3   5  bmi  hyp  chl
#>   4   1  bmi  hyp  chl
#>   4   2  bmi  hyp  chl
#>   4   3  bmi  hyp  chl
#>   4   4  bmi  hyp  chl
#>   4   5  bmi  hyp  chl
#>   5   1  bmi  hyp  chl
#>   5   2  bmi  hyp  chl
#>   5   3  bmi  hyp  chl
#>   5   4  bmi  hyp  chl
#>   5   5  bmi  hyp  chl
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |          95% CI | Statistic |    df |      p
#> -------------------------------------------------------------------------------
#> (Intercept) |       19.20 | 3.60 | [ 11.51, 26.89] |      5.33 | 14.67 | < .001
#> age40-59    |       -5.06 | 2.22 | [-10.23,  0.11] |     -2.28 |  7.59 | 0.054 
#> age60-99    |       -7.49 | 2.09 | [-11.91, -3.07] |     -3.59 | 16.18 | 0.002 
#> hypyes      |        2.21 | 1.83 | [ -1.69,  6.12] |      1.21 | 14.94 | 0.245 
#> chl         |        0.05 | 0.02 | [  0.01,  0.09] |      2.72 | 16.19 | 0.015 
if (FALSE) {
# model_parameters() also works for models that have no "tidy"-method in mice
if (require("mice", quietly = TRUE) && require("gee", quietly = TRUE)) {
  data(warpbreaks)
  set.seed(1234)
  warpbreaks$tension[sample(1:nrow(warpbreaks), size = 10)] <- NA
  imp <- mice(warpbreaks)
  fit <- with(data = imp, expr = gee(breaks ~ tension, id = wool))

  # does not work:
  # summary(pool(fit))

  model_parameters(fit)
}
}



# and it works with pooled results
if (require("mice")) {
  data("nhanes2")
  imp <- mice(nhanes2)
  fit <- with(data = imp, exp = lm(bmi ~ age + hyp + chl))
  pooled <- pool(fit)

  model_parameters(pooled)
}
#> 
#>  iter imp variable
#>   1   1  bmi  hyp  chl
#>   1   2  bmi  hyp  chl
#>   1   3  bmi  hyp  chl
#>   1   4  bmi  hyp  chl
#>   1   5  bmi  hyp  chl
#>   2   1  bmi  hyp  chl
#>   2   2  bmi  hyp  chl
#>   2   3  bmi  hyp  chl
#>   2   4  bmi  hyp  chl
#>   2   5  bmi  hyp  chl
#>   3   1  bmi  hyp  chl
#>   3   2  bmi  hyp  chl
#>   3   3  bmi  hyp  chl
#>   3   4  bmi  hyp  chl
#>   3   5  bmi  hyp  chl
#>   4   1  bmi  hyp  chl
#>   4   2  bmi  hyp  chl
#>   4   3  bmi  hyp  chl
#>   4   4  bmi  hyp  chl
#>   4   5  bmi  hyp  chl
#>   5   1  bmi  hyp  chl
#>   5   2  bmi  hyp  chl
#>   5   3  bmi  hyp  chl
#>   5   4  bmi  hyp  chl
#>   5   5  bmi  hyp  chl
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |          95% CI | Statistic |    df |      p
#> -------------------------------------------------------------------------------
#> (Intercept) |       19.49 | 3.99 | [ 10.43, 28.54] |      4.88 |  8.88 | < .001
#> age40-59    |       -6.36 | 2.07 | [-11.08, -1.64] |     -3.07 |  8.60 | 0.014 
#> age60-99    |       -8.13 | 2.68 | [-14.45, -1.81] |     -3.03 |  7.08 | 0.019 
#> hypyes      |        2.43 | 2.47 | [ -3.61,  8.47] |      0.98 |  6.04 | 0.364 
#> chl         |        0.05 | 0.02 | [  0.01,  0.10] |      2.64 | 12.01 | 0.021