mads object contains an amputed data set. The
mads object is
generated by the
ampute function. The
mads class of objects has
methods for the following generic functions:
The function call.
Proportion of cases with missing values. Note: even when
the proportion is entered as the proportion of missing cells (when
bycases == TRUE), this object contains the proportion of missing cases.
A data frame of size #patterns by #variables where
indicates a variable has missing values and
1 indicates a variable remains
A vector of length #patterns containing the relative
frequency with which the patterns occur. For example, if the vector is
c(0.4, 0.4, 0.2), this means that of all cases with missing values,
40 percent is candidate for pattern 1, 40 percent for pattern 2 and 20
percent for pattern 3. The vector sums to 1.
A string specifying the missingness mechanism, either
A data frame of size #patterns by #variables. It contains the weights that were used to calculate the weighted sum scores. The weights may differ between patterns and between variables.
Logical, whether probabilities are based on continuous logit functions or on discrete odds distributions.
A vector of strings containing the type of missingness
for each pattern. Either
"RIGHT". The first type refers to the first pattern, the second type
to the second pattern, etc.
A matrix where #patterns defines the #rows. Each row contains the odds of being missing for the corresponding pattern. The amount of odds values defines in how many quantiles the sum scores were divided. The values are relative probabilities: a quantile with odds value 4 will have a probability of being missing that is four times higher than a quantile with odds 1. The #quantiles may differ between patterns, NA is used for cells remaining empty.
A data frame containing the input data with NAs for the amputed values.
A vector that contains the pattern number for each case. A value between 1 and #patterns is given. For example, a case with value 2 is candidate for missing data pattern 2.
A list containing vectors with weighted sum scores of the candidates. The first vector refers to the candidates of the first pattern, the second vector refers to the candidates of the second pattern, etc. The length of the vectors differ because the number of candidates is different for each pattern.
The complete data set that was entered in
Many of the functions of the
mice package do not use the S4 class
definitions, and instead rely on the S3 list equivalent
oldClass(obj) <- "mads".
Rianne Schouten, 2016
ampute, Vignette titled "Multivariate Amputation using
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