Linear regression for mids object

Applies lm() to multiply imputed data set

lm.mids(formula, data, ...)

Arguments

formula	a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right. See the documentation of `lm` and `formula` for details.
data	An object of type 'mids', which stands for 'multiply imputed data set', typically created by a call to function `mice()`.
...	Additional parameters passed to `lm`

Value

An objects of class mira, which stands for 'multiply imputed repeated analysis'. This object contains data$m distinct lm.objects, plus some descriptive information.

Details

This function is included for backward compatibility with V1.0. The function is superseded by with.mids.

References

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. https://www.jstatsoft.org/v45/i03/

Author

Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

Examples

imp <- mice(nhanes)
#> 
#>  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 <- lm.mids(bmi ~ hyp + chl, data = imp)
#> Warning: Use with(imp, lm(yourmodel).
fit
#> call :
#> lm.mids(formula = bmi ~ hyp + chl, data = imp)
#> 
#> call1 :
#> mice(data = nhanes)
#> 
#> nmis :
#> age bmi hyp chl 
#>   0   9   8  10 
#> 
#> analyses :
#> [[1]]
#> 
#> Call:
#> lm(formula = formula, data = complete(data, i))
#> 
#> Coefficients:
#> (Intercept)          hyp          chl  
#>    21.97200     -2.10751      0.03717  
#> 
#> 
#> [[2]]
#> 
#> Call:
#> lm(formula = formula, data = complete(data, i))
#> 
#> Coefficients:
#> (Intercept)          hyp          chl  
#>     21.1645      -2.1283       0.0436  
#> 
#> 
#> [[3]]
#> 
#> Call:
#> lm(formula = formula, data = complete(data, i))
#> 
#> Coefficients:
#> (Intercept)          hyp          chl  
#>    22.14878     -0.20111      0.02421  
#> 
#> 
#> [[4]]
#> 
#> Call:
#> lm(formula = formula, data = complete(data, i))
#> 
#> Coefficients:
#> (Intercept)          hyp          chl  
#>    23.21196     -2.15281      0.02989  
#> 
#> 
#> [[5]]
#> 
#> Call:
#> lm(formula = formula, data = complete(data, i))
#> 
#> Coefficients:
#> (Intercept)          hyp          chl  
#>    20.86029     -3.49178      0.05265  
#> 
#> 
#>