Parameters from multiply imputed repeated analyses — model

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

Usage

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

Arguments

model: An object of class mira or mipo.
ci: Confidence Interval (CI) level. Default to 0.95 (95%).
exponentiate: Logical, indicating whether or not to exponentiate the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. It is also recommended to use exponentiate = TRUE for models with log-transformed response values. For models with a log-transformed response variable, when exponentiate = TRUE, a one-unit increase in the predictor is associated with multiplying the outcome by that predictor's coefficient. 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: String value, if not NULL, indicates the method to adjust p-values. See stats::p.adjust() for details. Further possible adjustment methods are "tukey", "scheffe", "sidak", "sup-t", and "none" to explicitly disable adjustment for emmGrid objects (from emmeans). "sup-t" computes simultaneous confidence bands, also called sup-t confidence band (Montiel Olea & Plagborg-Møller, 2019).
keep: Character containing a regular expression pattern that describes the parameters that should be included (for keep) or excluded (for drop) in the returned data frame. keep may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If keep is a character vector, every parameter name in the "Parameter" column that matches the regular expression in keep will be selected from the returned data frame (and vice versa, all parameter names matching drop will be excluded). Furthermore, if keep has more than one element, these will be merged with an OR operator into a regular expression pattern like this: "(one|two|three)". If keep is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where model_parameters() returns multiple columns with parameter components, like in model_parameters.lavaan(). Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the $Parameter column of the parameters table to get the exact parameter names.
drop: See keep.
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)
data(nhanes2, package = "mice")
imp <- mice::mice(nhanes2)
#> 
#>  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
fit <- with(data = imp, exp = lm(bmi ~ age + hyp + chl))
model_parameters(fit)
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |          95% CI | Statistic |   df |      p
#> ------------------------------------------------------------------------------
#> (Intercept) |       19.00 | 3.86 | [ 10.39, 27.61] |      4.92 | 9.95 | < .001
#> age40-59    |       -4.97 | 2.25 | [-10.16,  0.22] |     -2.20 | 8.05 | 0.058 
#> age60-99    |       -7.12 | 2.60 | [-13.36, -0.87] |     -2.74 | 6.50 | 0.031 
#> hypyes      |        2.52 | 2.18 | [ -2.39,  7.44] |      1.16 | 9.28 | 0.277 
#> chl         |        0.05 | 0.02 | [ -0.00,  0.11] |      2.26 | 6.61 | 0.061 
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed)
#>   computed using a Wald distribution approximation.
# \donttest{
# model_parameters() also works for models that have no "tidy"-method in mice
data(warpbreaks)
set.seed(1234)
warpbreaks$tension[sample(1:nrow(warpbreaks), size = 10)] <- NA
imp <- mice::mice(warpbreaks)
#> 
#>  iter imp variable
#>   1   1  tension
#>   1   2  tension
#>   1   3  tension
#>   1   4  tension
#>   1   5  tension
#>   2   1  tension
#>   2   2  tension
#>   2   3  tension
#>   2   4  tension
#>   2   5  tension
#>   3   1  tension
#>   3   2  tension
#>   3   3  tension
#>   3   4  tension
#>   3   5  tension
#>   4   1  tension
#>   4   2  tension
#>   4   3  tension
#>   4   4  tension
#>   4   5  tension
#>   5   1  tension
#>   5   2  tension
#>   5   3  tension
#>   5   4  tension
#>   5   5  tension
fit <- with(data = imp, expr = gee::gee(breaks ~ tension, id = wool))
#> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
#> running glm to get initial regression estimate
#> (Intercept)    tensionM    tensionH 
#>    36.04762   -12.26984   -13.71429 
#> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
#> running glm to get initial regression estimate
#> (Intercept)    tensionM    tensionH 
#>    35.04545   -10.29545   -12.98295 
#> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
#> running glm to get initial regression estimate
#> (Intercept)    tensionM    tensionH 
#>   35.150000   -8.973529  -13.267647 
#> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
#> running glm to get initial regression estimate
#> (Intercept)    tensionM    tensionH 
#>    36.66667   -13.26667   -14.50000 
#> Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
#> running glm to get initial regression estimate
#> (Intercept)    tensionM    tensionH 
#>    36.15000   -11.37222   -14.21250 

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

model_parameters(fit)
#> New names:
#> • `` -> `...6`
#> New names:
#> • `` -> `...6`
#> New names:
#> • `` -> `...6`
#> New names:
#> • `` -> `...6`
#> New names:
#> • `` -> `...6`
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |          95% CI | Statistic |      df |      p
#> ---------------------------------------------------------------------------------
#> (Intercept) |       35.81 | 2.71 | [ 30.49, 41.13] |     13.21 |  646.80 | < .001
#> tensionM    |      -11.24 | 4.31 | [-19.76, -2.71] |     -2.61 |  121.44 | 0.010 
#> tensionH    |      -13.74 | 3.98 | [-21.54, -5.93] |     -3.45 | 4380.62 | < .001
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed)
#>   computed using a Wald distribution approximation.
# }

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

model_parameters(pooled)
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |   SE |          95% CI | Statistic |    df |      p
#> -------------------------------------------------------------------------------
#> (Intercept) |       19.05 | 3.39 | [ 11.82, 26.28] |      5.61 | 15.19 | < .001
#> age40-59    |       -4.97 | 1.86 | [ -9.02, -0.92] |     -2.67 | 12.12 | 0.020 
#> age60-99    |       -6.14 | 1.89 | [-10.15, -2.12] |     -3.25 | 15.35 | 0.005 
#> hypyes      |        2.11 | 2.29 | [ -3.38,  7.59] |      0.92 |  6.60 | 0.390 
#> chl         |        0.05 | 0.02 | [  0.01,  0.09] |      2.83 | 15.74 | 0.012 
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed)
#>   computed using a Wald distribution approximation.