Internal Logic Forest Functions

Description

INTERNAL FUNCTION TO CREATE PERMUTATIONS OF N VARIABLES This function is called by TTab. It is not intended to be used independently of this function.

Usage

Perms(n)

Arguments

Value

mat matrix with each combination of n variables, where inclusion/exclusion is denoted by 0/1

Truth table

Description

Internal function to evaluate importance of predictor combinations

Usage

TTab(data, tree, Xs, mtype)

Arguments

Details

Creates a matrix of all interactions contained in one sample's logic tree.

Value

mat.truth: Matrix of all interactions contained in one sample's logic tree

Author(s)

Bethany J. Wolf wolfb@musc.edu

References

Wolf BJ, Hill EG, Slate EH. Logic Forest: an ensemble classifier for discovering logical combinations of binary markers. Bioinformatics. 2010;26(17):2183-2189. doi:10.1093/bioinformatics/btq354

See Also

prime.imp

Function to find complement of the original tree and find complement PIs ### Internal Logic Forest Functions

Description

Function to find complement of the original tree and find complement PIs This function is called by pimp.import. It is not intended to be used independently of this function.

Usage

find.ctree(tree)

Arguments

Value

ctree An object of class "logregtree" that is the complement of tree

INTERNAL FUNCTION TO EVALUATE IMPORTANCE OF PREDICTOR COMBINATIONS

Description

INTERNAL FUNCTION TO EVALUATE IMPORTANCE OF PREDICTOR COMBINATIONS

Usage

frame.logreg2(fit, msz, ntr, newbin, newresp, newsep, newcens, newweight)

Arguments

Details

This function is called by predict.logreg2. It is not intended to be used independently.

Value

outframe

Logic Forest & Logic Survival Forest

Description

Constructs an ensemble of logic regression models using bagging for classification or regression, and identifies important predictors and interactions. Logic Forest (LF) efficiently searches the space of logical combinations of binary variables using simulated annealing. It has been extended to support linear and survival regression.

Usage

logforest(
  resp.type,
  resp,
  resp.time = data.frame(X = rep(1, nrow(resp))),
  Xs,
  nBSXVars,
  anneal.params,
  nBS = 100,
  h = 0.5,
  norm = TRUE,
  numout = 5,
  nleaves
)

Arguments

Details

Logic Forest is designed to identify interactions between binary predictors without requiring their pre-specification. Using simulated annealing, it searches the space of all possible logical combinations (e.g., AND, OR, NOT) among predictors. Originally developed for binary outcomes in gene-environment interaction studies, it has since been extended to linear and time-to-event outcomes (Logic Survival Forest).

Value

A logforest object containing:

Predictor.frequency

Frequency of each predictor across trees.

Predictor.importance

Importance of each predictor.

PI.frequency

Frequency of each interaction across trees.

PI.importance

Importance of each interaction.

Note

Development of Logic Forest was supported by NIH/NCATS UL1RR029882. Logic Survival Forest development was supported by NIH/NIA R01AG082873.

Author(s)

Bethany J. Wolf wolfb@musc.edu
J. Madison Hyer madison.hyer@osumc.edu

References

Wolf BJ, Hill EG, Slate EH. (2010). Logic Forest: An ensemble classifier for discovering logical combinations of binary markers. Bioinformatics, 26(17):2183–2189. doi:10.1093/bioinformatics/btq354
Wolf BJ et al. (2012). LBoost: A boosting algorithm with application for epistasis discovery. PLoS One, 7(11):e47281. doi:10.1371/journal.pone.0047281
Hyer JM et al. (2019). Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery. JAMA Surg, 154(11):1014–1021. doi:10.1001/jamasurg.2019.2979

See Also

pimp.import, logreg.anneal.control

Examples

## Not run: 
set.seed(10051988)
N_c <- 50
N_r <- 200
init <- as.data.frame(matrix(0, nrow = N_r, ncol = N_c))
colnames(init) <- paste0("X", 1:N_c)
for(n in 1:N_c){
  p <- runif(1, min = 0.2, max = 0.6)
  init[,n] <- rbinom(N_r, 1, p)
}

X3X4int <- as.numeric(init$X3 == init$X4)
X5X6int <- as.numeric(init$X5 == init$X6)
y_p <- -2.5 + init$X1 + init$X2 + 2 * X3X4int + 2 * X5X6int
p <- 1 / (1 + exp(-y_p))
init$Y.bin <- rbinom(N_r, 1, p)

# Classification
LF.fit.bin <- logforest("bin", init$Y.bin, NULL, init[,1:N_c], nBS=10, nleaves=8, numout=10)
print(LF.fit.bin)

# Continuous
init$Y.cont <- rnorm(N_r, mean = 0) + init$X1 + init$X2 + 5 * X3X4int + 5 * X5X6int
LF.fit.lin <- logforest("lin", init$Y.cont, NULL, init[,1:N_c], nBS=10, nleaves=8, numout=10)
print(LF.fit.lin)

# Time-to-event
shape <- 1 - 0.05*init$X1 - 0.05*init$X2 - 0.2*init$X3*init$X4 - 0.2*init$X5*init$X6
scale <- 1.5 - 0.05*init$X1 - 0.05*init$X2 - 0.2*init$X3*init$X4 - 0.2*init$X5*init$X6
init$TIME_Y <- rgamma(N_r, shape = shape, scale = scale)
LF.fit.surv <- logforest("exp_surv", init$Y.bin, init$TIME_Y, init[,1:N_c],
  nBS=10, nleaves=8, numout=10)
print(LF.fit.surv)

## End(Not run)

Internal Logic Forest Functions

Description

INTERNAL FUNCTION TO CREATE COMBINATIONS OF N.PAIR VARIABLES This function is called by prime.imp. It is not intended to be used independently of this function.

Usage

p.combos(n.pair, conj = 0)

Arguments

Value

mat matrix with each combination of n.pair variables

Predictor Importance - Variables and Interactions

Description

Measures the predictor importance for each predictor and interaction importance for each iteration.

Usage

pimp.import(fit, data, testdata, BSpred, pred, Xs, mtype)

Arguments

Details

This function is called to calculate importance measures for each bootstrapped sample. Importance measures are calculated as differences between the original out-of-bag sample and a permuted out-of-bag sample. Model fit for both samples is evaluated using:

• Concordance for classification,

• Mean squared error for linear regression,

• Harrell's C-index for survival regression.

Value

A list with:

single.vimp

Vector of predictor importance estimates. One estimate per predictor used in the tree of that sample.

pimp.vimp

Vector of interaction importance estimates. One estimate per interaction detected in that sample.

Ipimat

Matrix updated for each sample. Contains all predictors (and their NOT-ed versions if used) for each interaction.

vec.Xvars

Vector of predictors used in the tree of that sample.

Xids

Vector of predictor IDs used in the tree of that sample.

Author(s)

Bethany J. Wolf wolfb@musc.edu
J. Madison Hyer madison.hyer@osumc.edu

References

Wolf BJ, Hill EG, Slate EH. Logic Forest: an ensemble classifier for discovering logical combinations of binary markers. Bioinformatics. 2010;26(17):2183-2189. doi:10.1093/bioinformatics/btq354

See Also

logforest

Predictor Importance Matrix - Classification

Description

Internal function called in pimp.import to construct a binary/logical matrix of predictors used (columns) in each of the interactions of a sample (rows).

Usage

pimp.mat.bin(pimps.out, testdata)

Arguments

Details

NOTE: For regression models, pimp.mat.nonbin is used to accommodate complements of logic trees.

Value

A list with:

pimp.names

Vector of predictor names.

pimp.datamat

Logical matrix of predictors used (columns) in each of the interactions of a sample (rows).

Author(s)

Bethany J. Wolf wolfb@musc.edu

References

Wolf BJ, Hill EG, Slate EH. Logic Forest: an ensemble classifier for discovering logical combinations of binary markers. Bioinformatics. 2010;26(17):2183-2189. doi:10.1093/bioinformatics/btq354

See Also

pimp.import

Predictor Importance Matrix - Regression

Description

Internal function called in pimp.import to construct a binary/logical matrix of predictors used (columns) in each of the interactions of a sample (rows).

Usage

pimp.mat.nonbin(pimps.out, testdata)

Arguments

Details

NOTE: For classification models, pimp.mat.bin is used.

Value

A list with:

pimp.names

Vector of predictor names.

pimp.datamat

Logical matrix of predictors used (columns) in each of the interactions of a sample (rows).

Author(s)

Bethany J. Wolf wolfb@musc.edu

References

Wolf BJ, Hill EG, Slate EH. Logic Forest: an ensemble classifier for discovering logical combinations of binary markers. Bioinformatics. 2010;26(17):2183-2189. doi:10.1093/bioinformatics/btq354

See Also

pimp.import

Predict method for logic forest models

Description

Predicts outcomes for new observations using a fitted logic forest model.

Usage

## S3 method for class 'logforest'
predict(object, newdata, cutoff, ...)

Arguments

Value

An object of class "LFprediction" containing the predicted outcomes.

Internal Logic Forest Functions

Description

INTERNAL FUNCTION TO EVALUATE IMPORTANCE OF PREDICTOR COMBINATIONS

Usage

## S3 method for class 'logreg2'
predict(object, msz, ntr, newbin, newsep, newcens, ...)

Arguments

Value

outframe

Internal Logic Forest Functions

Description

This function is called by pimp.import. It is not intended to be used independently of these functions.

Usage

prime.imp(tree, data, Xs, mtype)

Arguments

Value

ListPI An object of class "primeImp" containing all variables and variable interactions

Print method for logic forest predictions

Description

Displays the results of a prediction from a logic forest model.

Usage

## S3 method for class 'LFprediction'
print(x, ...)

Arguments

Value

No return value. This function is called for its side effects (printing).

Print method for logic forest models

Description

Prints the important predictors from a fitted logic forest model.

Usage

## S3 method for class 'logforest'
print(x, sortby = "importance", ...)

Arguments

Value

No return value. This function is called for its side effects (printing).

Internal Logic Forest Functions

Description

This function is called by predict.logforest. It is not intended to be used independently of this function. It determined the proportion of logic regression trees within a logic forest that predict a class of one for new observations. Additionally it returns predicted values for new observations.

Usage

proportion.positive(predictmatrix, cutoff)

Arguments

Value

ans list of predicted values and proportion of trees that predict class of one