impute_mod — impute_mod • MissImp

Impute the missing values of a dataset with Multiple Factor Analysis (MFA).

This function is nearly identical with impute function in 'missMDA' package. The only difference is that in impute_mod, we have forced MM[[g]] to be bigger than one threshold, so the convergence error could be avoided.

impute_mod(
  X,
  group,
  ncp = 2,
  type = rep("s", length(group)),
  method = NULL,
  threshold = 1e-06,
  ind.sup = NULL,
  num.group.sup = NULL,
  seed = NULL,
  maxiter = 1000,
  row.w = NULL,
  coeff.ridge = 1,
  ...
)

Arguments

X: a data.frame with continuous and categorical variables containing missing values.
group: a vector indicating the number of variables in each group
ncp: integer corresponding to the number of components used to predict the missing entries.
type: the type of variables in each group; three possibilities: "c" or "s" for continuous variables (for "c" the variables are centered and for "s" variables are scaled to unit variance), "n" for categorical variables
method: "Regularized" by default or "EM".
threshold: the threshold for assessing convergence
ind.sup: a vector indicating the indexes of the supplementary individuals.
num.group.sup: a vector indicating the group of variables that are supplementary.
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
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.
...: 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

F. Husson, J. Josse (2013) Handling missing values in multiple factor analysis. Food Quality and Preferences, 30 (2), 77-85.

Josse, J. and Husson, F. missMDA (2016). A Package for Handling Missing Values in Multivariate Data Analysis. Journal of Statistical Software, 70 (1), pp 1-31 <doi:10.18637/jss.v070.i01>