| Type: | Package |
| Title: | Proper Scoring Rules for Missing Value Imputation |
| Version: | 1.1.0 |
| Author: | Loris Michel, Meta-Lina Spohn, Jeffrey Naef |
| Maintainer: | Loris Michel <michel@stat.math.ethz.ch> |
| Description: | Implementation of a KL-based scoring rule to assess the quality of different missing value imputations in the broad sense as introduced in Michel et al. (2021) <doi:10.48550/arXiv.2106.03742>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| Depends: | parallel, stats, ranger, kernlab |
| NeedsCompilation: | no |
| Packaged: | 2021-12-24 08:29:47 UTC; lorismichel |
| Repository: | CRAN |
| Date/Publication: | 2022-01-05 00:00:08 UTC |
Iscores: compute the imputation KL-based scoring rules
Description
Iscores: compute the imputation KL-based scoring rules
Usage
Iscores(
imputations,
methods,
X.NA,
m = length(imputations[[1]]),
num.proj = 100,
num.trees.per.proj = 5,
min.node.size = 10,
n.cores = 1,
projection.function = NULL,
rescale = TRUE
)
Arguments
imputations |
a list of list of imputations matrices containing no missing values of the same size as X.NA |
methods |
a vector of characters indicating which methods are considered for imputations. It should have the same length as the list imputations. |
X.NA |
a matrix containing missing values, the data to impute. |
m |
the number of multiple imputation to consider, defaulting to the number of provided multiple imputations. |
num.proj |
an integer specifying the number of projections to consider for the score. |
num.trees.per.proj |
an integer, the number of trees per projection. |
min.node.size |
the minimum number of nodes in a tree. |
n.cores |
an integer, the number of cores to use. |
projection.function |
a function providing the user-specific projections. |
rescale |
a boolean, TRUE if the scores should be rescaled such that the max score is 0. |
Value
a vector made of the scores for each imputation method.
Examples
n <- 100
X <- cbind(rnorm(n),rnorm(n))
X.NA <- X
X.NA[,1] <- ifelse(stats::runif(n)<=0.2, NA, X[,1])
imputations <- list()
imputations[[1]] <- lapply(1:5, function(i) {
X.loc <- X.NA
X.loc[is.na(X.NA[,1]),1] <- mean(X.NA[,1],na.rm=TRUE)
return(X.loc)
})
imputations[[2]] <- lapply(1:5, function(i) {
X.loc <- X.NA
X.loc[is.na(X.NA[,1]),1] <- sample(X.NA[!is.na(X.NA[,1]),1],
size = sum(is.na(X.NA[,1])), replace = TRUE)
return(X.loc)
})
methods <- c("mean","sample")
Iscores(imputations,
methods,
X.NA,
num.proj=5
)
Balancing of Classes
Description
Balancing of Classes
Usage
class.balancing(X.proj.complete, Y.proj, drawA, Xhat, ids.with.missing, vars)
Arguments
X.proj.complete |
matrix with complete projected observations. |
Y.proj |
matrix with projected imputed observations. |
drawA |
vector of indices corresponding to current missingness pattern. |
Xhat |
matrix of full imputed observations. |
ids.with.missing |
vector of indices of observations with missing values. |
vars |
vectors of variables in projection. |
Value
a list of new X.proj.complete and Y.proj.
Combining projection forests
Description
Combining projection forests
Usage
combine2Forests(mod1, mod2)
Arguments
mod1 |
first forest |
mod2 |
second forest |
Value
a new forest combining the first and the second forest
Combining a list of forest
Description
Combining a list of forest
Usage
combineForests(list.rf)
Arguments
list.rf |
a list of forests |
Value
a forest combination of the forests stored in list.rf
compute the density ratio score
Description
compute the density ratio score
Usage
compute_drScore(object, Z = Z, num.trees.per.proj, num.proj)
Arguments
object |
a crf object. |
Z |
a matrix of candidate points. |
num.trees.per.proj |
an integer, the number of trees per projection. |
num.proj |
an integer specifying the number of projections. |
Value
a numeric value, the DR I-Score.
Computation of the density ratio score
Description
Computation of the density ratio score
Usage
densityRatioScore(
X,
Xhat,
x = NULL,
num.proj = 10,
num.trees.per.proj = 1,
projection.function = NULL,
min.node.size = 1,
normal.proj = T
)
Arguments
X |
a matrix of the observed data containing missing values. |
Xhat |
a matrix of imputations having same size as X. |
x |
pattern of missing values. |
num.proj |
an integer specifying the number of projections. |
num.trees.per.proj |
an integer, the number of trees per projection. |
projection.function |
a function providing the user-specific projections. |
min.node.size |
the minimum number of observations in a leaf of a tree. |
normal.proj |
a boolean, if TRUE, sample from the NA of the pattern and additionally from the non NA. If FALSE, sample only from the NA of the pattern. |
Value
a fitted random forest based on random projections
doevaluation: compute the imputation KL-based scoring rules
Description
doevaluation: compute the imputation KL-based scoring rules
Usage
doevaluation(
imputations,
methods,
X.NA,
m,
num.proj,
num.trees.per.proj,
min.node.size,
n.cores = 1,
projection.function = NULL
)
Arguments
imputations |
a list of list of imputations matrices containing no missing values of the same size as X.NA |
methods |
a vector of characters indicating which methods are considered for imputations. It should have the same length as the list imputations. |
X.NA |
a matrix containing missing values, the data to impute. |
m |
the number of multiple imputation to consider, defaulting to the number of provided multiple imputations. |
num.proj |
an integer specifying the number of projections to consider for the score. |
num.trees.per.proj |
an integer, the number of trees per projection. |
min.node.size |
the minimum number of nodes in a tree. |
n.cores |
an integer, the number of cores to use. |
projection.function |
a function providing the user-specific projections. |
Value
a vector made of the scores for each imputation method.
Sampling of Projections
Description
Sampling of Projections
Usage
sample.vars.proj(ids.x.na, X, projection.function = NULL, normal.proj = T)
Arguments
ids.x.na |
a vector of indices corresponding to NA in the given missingness pattern. |
X |
a matrix of the observed data containing missing values. |
projection.function |
a function providing the user-specific projections. |
normal.proj |
a boolean, if TRUE, sample from the NA of the pattern and additionally from the non NA. If FALSE, sample only from the NA of the pattern. |
Value
a vector of variables corresponding to the projection.
Truncation of probability
Description
Truncation of probability
Usage
truncProb(p)
Arguments
p |
a numeric value between 0 and 1 to be truncated |
Value
a numeric value, the truncated probability.