Package 'Iscores'

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.

---

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
)

---

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.

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