imputeFAMD

Impute the missing values of a mixed dataset (with continuous and categorical variables) using the principal component method "factorial analysis for mixed data" (FAMD). Can be used as a preliminary step before performing FAMD on an incomplete dataset.

This function is nearly identical with imputeFAMD function in 'missMDA' package. The only difference is that in this function imputeMFA is used, which then calls impute_mod. In impute, some changes have been made to avoid the convergence error.

imputeFAMD(
  X,
  ncp = 2,
  method = c("Regularized", "EM"),
  row.w = NULL,
  coeff.ridge = 1,
  threshold = 1e-06,
  ind.sup = NULL,
  sup.var = NULL,
  seed = NULL,
  maxiter = 1000,
  ...
)

Arguments

X

a data.frame with continuous and categorical variables containing missing values.

ncp

integer corresponding to the number of components used to predict the missing entries.

method

"Regularized" by default or "EM".

row.w

row weights (by default, uniform row weights).

coeff.ridge

1 by default to perform the regularized imputeFAMD algorithm; useful only if method="Regularized". Other regularization terms can be implemented by setting the value to less than 1 in order to regularized less (to get closer to the results of the EM method) or more than 1 to regularized more.

threshold

the threshold for assessing convergence

ind.sup

a vector indicating the indexes of the supplementary individuals.

sup.var

a vector indicating the indexes of the supplementary variables (quantitative and categorical).

seed

integer, by default seed = NULL implies that missing values are initially imputed by the mean of each variable for the continuous variables and by the proportion of the category for the categorical variables coded with indicator matrices of dummy variables. Other values leads to a random initialization.

maxiter

max iteration number for imputeFAMD

...

further arguments passed to or from other methods.

Value

tab.disj the imputed matrix; the observed values are kept for the non-missing entries and the missing values are replaced by the predicted ones. The categorical variables are coded with the indicator matrix of dummy variables. In this indicator matrix, the imputed values are real numbers but they met the constraint that the sum of the entries corresponding to one individual and one variable is equal to one. Consequently they can be seen as degree of membership to the corresponding category. completeObs the mixed imputed dataset; the observed values are kept for the non-missing entries and the missing values are replaced by the predicted ones. For the continuous variables, the values are the same as in the tab.disj output; for the categorical variables missing values are imputed with the most plausible categories according to the values in the tab.disj output. call the matched call.

References

Audigier, V., Husson, F. & Josse, J. (2013). A principal components method to impute mixed data. Advances in Data Analysis and Classification, 10(1), 5-26. https://arxiv.org/abs/1301.4797#'