Package 'logicFS'

Title:	Identification of SNP Interactions
Description:	Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification.
Authors:	Holger Schwender, Tobias Tietz
Maintainer:	Holger Schwender <[email protected]>
License:	LGPL (>= 2)
Version:	2.27.0
Built:	2024-11-29 06:40:29 UTC
Source:	https://github.com/bioc/logicFS

data.logicfs contains two objects: a simulated matrix data.logicfs of 400 observations (rows) and 15 variables (columns) and a vector cl.logicfs of length 400 containing the class labels of the observations.

Each variable is categorical with realizations 1, 2 and 3. The first 200 observations are cases, the remaining are controls. If one of the following expression is TRUE, then the corresponding observation is a case:

SNP1 == 3

SNP2 == 1 AND SNP4 == 3

SNP3 == 3 AND SNP5 == 3 AND SNP6 == 1

where SNP1 is in the first column of data.logicfs, SNP2 in the second, and so on.

Evaluate Prime Implicants

Description

Computes the values of prime implicants for observations for which the values of the variables composing the prime implicants are available.

Usage

getMatEval(data, vec.primes, check = TRUE)
getMatEval(data, vec.primes, check = TRUE)

Arguments

`data`	a data frame in which each row corrsponds to an observation, and each column to a binary variable.
`vec.primes`	a character vector naming the prime implicants that should be evaluated. Each of the variables composing these prime implicants must be represented by one column of `data`.
`check`	should some checks be done before the evaluation is performed? It is highly recommended not to change the default `check = TRUE`.

Value

a matrix in which each row corresponds to an observation (the same observations in the same order as in data, and each column to one of the prime implicants.

Author(s)

Holger Schwender, [email protected]

Bagged Logic Regression

Description

A bagging and subsampling version of logic regression. Currently available for the classification, the linear regression, and the logistic regression approach of logreg. Additionally, an approach based on multinomial logistic regressions as implemented in mlogreg can be used if the response is categorical.

Usage

## Default S3 method:
logic.bagging(x, y, B = 100, useN = TRUE, ntrees = 1, nleaves = 8, 
  glm.if.1tree = FALSE, replace = TRUE, sub.frac = 0.632,
  anneal.control = logreg.anneal.control(), oob = TRUE, 
  onlyRemove = FALSE, prob.case = 0.5, importance = TRUE,
	score = c("DPO", "Conc", "Brier", "PL"), addMatImp = FALSE, fast = FALSE, 
	neighbor = NULL, adjusted = FALSE, ensemble = FALSE, rand = NULL, ...)
  
## S3 method for class 'formula'
logic.bagging(formula, data, recdom = TRUE, ...)
## Default S3 method:
logic.bagging(x, y, B = 100, useN = TRUE, ntrees = 1, nleaves = 8, 
  glm.if.1tree = FALSE, replace = TRUE, sub.frac = 0.632,
  anneal.control = logreg.anneal.control(), oob = TRUE, 
  onlyRemove = FALSE, prob.case = 0.5, importance = TRUE,
	score = c("DPO", "Conc", "Brier", "PL"), addMatImp = FALSE, fast = FALSE, 
	neighbor = NULL, adjusted = FALSE, ensemble = FALSE, rand = NULL, ...)
  
## S3 method for class 'formula'
logic.bagging(formula, data, recdom = TRUE, ...)

Arguments

`x`	a matrix consisting of 0's and 1's. Each column must correspond to a binary variable and each row to an observation. Missing values are not allowed.
`y`	a numeric vector, a factor, or a vector of class `Surv` specifying the values of a response for all the observations represented in `x`, where no missing values are allowed in `y`. If a numeric vector, then `y` either contains the class labels (coded by 0 and 1) or the values of a continuous response depending on whether the classification or logistic regression approach of logic regression, or the linear regression approach, respectively, should be used. If the response is categorical, then `y` must be a factor naming the class labels of the observations. If the response is a (right-censored survival time), then `y` must be vector of class `Surv` (generated, e.g., with the function `Surv` from the `R` package `survival`.
`B`	an integer specifying the number of iterations.
`useN`	logical specifying if the number of correctly classified out-of-bag observations should be used in the computation of the importance measure. If `FALSE`, the proportion of correctly classified oob observations is used instead. Ignored if `importance = FALSE`. Also ignored in the survival case.
`ntrees`	an integer indicating how many trees should be used. For a binary response: If `ntrees` is larger than 1, the logistic regression approach of logic regreesion will be used. If `ntrees` is 1, then by default the classification approach of logic regression will be used (see `glm.if.1tree`.) For a continuous response: A linear regression model with `ntrees` trees is fitted in each of the `B` iterations. For a categorical response: $n.lev-1$ logic regression models with `ntrees` trees are fitted, where $n.lev$ is the number of levels of the response (for details, see `mlogreg`). For a response of class `Surv`: A Cox proportional hazards regression model with `ntrees` trees is fitted in each of the `B` iterations.
`nleaves`	a numeric value specifying the maximum number of leaves used in all trees combined. See the help page of the function `logreg` of the package `LogicReg` for details.
`glm.if.1tree`	if `ntrees` is 1 and `glm.if.1tree` is `TRUE` the logistic regression approach of logic regression is used instead of the classification approach. Ignored if `ntrees` is not 1 or the response is not binary.
`replace`	should sampling of the cases be done with replacement? If `TRUE`, a bootstrap sample of size `length(cl)` is drawn from the `length(cl)` observations in each of the `B` iterations. If `FALSE`, `ceiling(sub.frac * length(cl))` of the observations are drawn without replacement in each iteration.
`sub.frac`	a proportion specifying the fraction of the observations that are used in each iteration to build a classification rule if `replace = FALSE`. Ignored if `replace = TRUE`.
`anneal.control`	a list containing the parameters for simulated annealing. See the help page of `logreg.anneal.control` in the `LogicReg` package.
`oob`	should the out-of-bag error rate (classification and logistic regression) or the out-of-bag root mean square prediction error (linear regression), respectively, be computed?
`onlyRemove`	should in the single tree case the multiple tree measure be used? If `TRUE`, the prime implicants are only removed from the trees when determining the importance in the single tree case. If `FALSE`, the original single tree measure is computed for each prime implicant, i.e.\ a prime implicant is not only removed from the trees in which it is contained, but also added to the trees that do not contain this interaction. Ignored in all other than the classification case.
`prob.case`	a numeric value between 0 and 1. If the outcome of the logistic regression, i.e.\ the class probability, for an observation is larger than `prob.case`, this observations will be classified as case (or 1).
`importance`	should the measure of importance be computed?
`score`	a character string naming the score that should be used in the computation of the importance measure for a survival time analysis. By default, the distance between predicted outcomes (`score = "DPO"`) proposed by Tietz et al.\ (2018) is used in the determination of the importance of the variables. Alternatively, Harrell's C-Index (`"Conc"`), the Brier score (`"Brier"`), or the predictive partial log-likelihood (`"PL"`) can be used.
`addMatImp`	should the matrix containing the improvements due to the prime implicants in each of the iterations be added to the output? (For each of the prime implicants, the importance is computed by the average over the `B` improvements.) Must be set to `TRUE`, if standardized importances should be computed using `vim.norm`, or if permutation based importances should be computed using `vim.signperm`. If `ensemble = TRUE` and `addMatImp = TRUE` in the survival case, the respective score of the full model is added to the output instead of an improvement matrix.
`fast`	should a greedy search (as implemented in `logreg`) be used instead of simulated annealing?
`neighbor`	a list consisting of character vectors specifying SNPs that are in LD. If specified, all SNPs need to occur exactly one time in this list. If specified, the importance measures are adjusted for LD by considering the SNPs within a LD block as exchangable.
`adjusted`	logical specifying whether the measures should be adjusted for noise. Often, the interaction actually associated with the response is not exactly found in some iterations of logic bagging, but an interaction is identified that additionally contains one (or seldomly more) noise SNPs. If `adjusted` is set to `TRUE`, the values of the importance measure is corrected for this behaviour.
`ensemble`	in the case of a survival outcome, should `ensemble` importance measures (as, e.g., in `randomSurvivalSRC` be used? If `FALSE`, importance measures analogous to the ones in the logicFS analysis of other outcomes are used (see Tietz et al., 2018).
`rand`	numeric value. If specified, the random number generator will be set into a reproducible state.
`formula`	an object of class `formula` describing the model that should be fitted.
`data`	a data frame containing the variables in the model. Each row of `data` must correspond to an observation, and each column to a binary variable (coded by 0 and 1) or a factor (for details, see `recdom`) except for the column comprising the response, where no missing values are allowed in `data`. The response must be either binary (coded by 0 and 1), categorical, continuous, or a right-censored survival time. If a survival time, i.e. an object of class `Surv`, a Cox propotional hazard model is fitted in each of the `B` iterations of `logicFS`. If continuous, a linear model is fitted in each iterations. If categorical, the column of `data` specifying the response must be a factor. In this case, multinomial logic regressions are performed as implemented in `mlogreg`. Otherwise, depending on `ntrees` (and `glm.if.1tree`) the classification or the logistic regression approach of logic regression is used.
`recdom`	a logical value or vector of length `ncol(data)` comprising whether a SNP should be transformed into two binary dummy variables coding for a recessive and a dominant effect. If `recdom` is `TRUE` (and a logical value), then all factors/variables with three levels will be coded by two dummy variables as described in `make.snp.dummy`. Each level of each of the other factors (also factors specifying a SNP that shows only two genotypes) is coded by one indicator variable. If `recdom` is`FALSE` (and a logical value), each level of each factor is coded by an indicator variable. If `recdom` is a logical vector, all factors corresponding to an entry in `recdom` that is `TRUE` are assumed to be SNPs and transformed into two binary variables as described above. All variables corresponding to entries of `recdom` that are `TRUE` (no matter whether `recdom` is a vector or a value) must be coded either by the integers 1 (coding for the homozygous reference genotype), 2 (heterozygous), and 3 (homozygous variant), or alternatively by the number of minor alleles, i.e. 0, 1, and 2, where no mixing of the two coding schemes is allowed. Thus, it is not allowed that some SNPs are coded by 1, 2, and 3, and others are coded by 0, 1, and 2.
`...`	for the `formula` method, optional parameters to be passed to the low level function `logic.bagging.default`. Otherwise, ignored.

Value

logic.bagging returns an object of class logicBagg containing

`logreg.model`	a list containing the `B` logic regression models,
`inbagg`	a list specifying the `B` Bootstrap samples,
`vim`	an object of class `logicFS` (if `importance = TRUE`),
`oob.error`	the out-of-bag error (if `oob = TRUE`),
`...`	further parameters of the logic regression.

Author(s)

Holger Schwender, [email protected]; Tobias Tietz, [email protected]

References

Ruczinski, I., Kooperberg, C., LeBlanc M.L. (2003). Logic Regression. Journal of Computational and Graphical Statistics, 12, 475-511.

Schwender, H., Ickstadt, K. (2007). Identification of SNP Interactions Using Logic Regression. Biostatistics, 9(1), 187-198.

Tietz, T., Selinski, S., Golka, K., Hengstler, J.G., Gripp, S., Ickstadt, K., Ruczinski, I., Schwender, H. (2018). Identification of Interactions of Binary Variables Associated with Survival Time Using survivalFS. Submitted.

Examples

## Not run: 
 # Load data.
   data(data.logicfs)
   
   # For logic regression and hence logic.bagging, the variables must
   # be binary. data.logicfs, however, contains categorical data 
   # with realizations 1, 2 and 3. Such data can be transformed 
   # into binary data by
   bin.snps<-make.snp.dummy(data.logicfs)
   
   # To speed up the search for the best logic regression models
   # only a small number of iterations is used in simulated annealing.
   my.anneal<-logreg.anneal.control(start=2,end=-2,iter=10000)
   
   # Bagged logic regression is then performed by
   bagg.out<-logic.bagging(bin.snps,cl.logicfs,B=20,nleaves=10,
       rand=123,anneal.control=my.anneal)
   
   # The output of logic.bagging can be printed
   bagg.out
   
   # By default, also the importances of the interactions are 
   # computed
   bagg.out$vim
   
   # and can be plotted.
   plot(bagg.out)
   
   # The original variable names are displayed in
   plot(bagg.out,coded=FALSE)
   
   # New observations (here we assume that these observations are
   # in data.logicfs) are assigned to one of the classes by
   predict(bagg.out,data.logicfs)

## End(Not run)## Not run: 
 # Load data.
   data(data.logicfs)
   
   # For logic regression and hence logic.bagging, the variables must
   # be binary. data.logicfs, however, contains categorical data 
   # with realizations 1, 2 and 3. Such data can be transformed 
   # into binary data by
   bin.snps<-make.snp.dummy(data.logicfs)
   
   # To speed up the search for the best logic regression models
   # only a small number of iterations is used in simulated annealing.
   my.anneal<-logreg.anneal.control(start=2,end=-2,iter=10000)
   
   # Bagged logic regression is then performed by
   bagg.out<-logic.bagging(bin.snps,cl.logicfs,B=20,nleaves=10,
       rand=123,anneal.control=my.anneal)
   
   # The output of logic.bagging can be printed
   bagg.out
   
   # By default, also the importances of the interactions are 
   # computed
   bagg.out$vim
   
   # and can be plotted.
   plot(bagg.out)
   
   # The original variable names are displayed in
   plot(bagg.out,coded=FALSE)
   
   # New observations (here we assume that these observations are
   # in data.logicfs) are assigned to one of the classes by
   predict(bagg.out,data.logicfs)

## End(Not run)

Prime Implicants

Description

Computes the out-of-bag error of the classification rule comprised by a logicBagg object.

Usage

logic.oob(log.out, prob.case = 0.5)
logic.oob(log.out, prob.case = 0.5)

Arguments

`log.out`	an object of class `logicBagg`, i.e.\ the output of `logic.bagging`.
`prob.case`	a numeric value between 0 and 1. If the logic regression models are logistic regression models, i.e.\ if in `logic.bagging` `ntree` is set to a value larger than 1, or `glm.if.1tree` is set to `TRUE`, then an observation will be classified as case (or more exactly, as 1) if the class probability is larger than `prob.case`.

Value

The out-of-bag error estimate.

Author(s)

Holger Schwender, [email protected]

Prime Implicants

Description

Determines the prime implicants contained in the logic regression models comprised in an object of class logicBagg.

Usage

logic.pimp(log.out)
logic.pimp(log.out)

Arguments

log.out

an object of class logicBagg, i.e.\ the output of logic.bagging.

Details

Since we are interested in all potentially interested interactions and not in a minimum set of them, logic.pimp and returns all prime implicants and not a minimum number of them.

Value

A list consisting of the prime implicants for each of the B logic regression models of log.out.

Author(s)

Holger Schwender, [email protected]

Feature Selection with Logic Regression

Description

Identification of interesting interactions between binary variables using logic regression. Currently available for the classification, the linear regression and the logistic regression approach of logreg and for a multinomial logic regression as implemented in mlogreg.

Usage

## Default S3 method:
logicFS(x, y, B = 100, useN = TRUE, ntrees = 1, nleaves = 8, 
  glm.if.1tree = FALSE, replace = TRUE, sub.frac = 0.632, 
  anneal.control = logreg.anneal.control(), onlyRemove = FALSE,
  prob.case = 0.5, score = c("DPO", "Conc", "Brier", "PL"), 
	addMatImp = TRUE, fast = FALSE, neighbor = NULL, 
	adjusted = FALSE, ensemble = FALSE, rand = NULL, ...)
  
## S3 method for class 'formula'
logicFS(formula, data, recdom = TRUE, ...)

## S3 method for class 'logicBagg'
logicFS(x, neighbor = NULL, adjusted = FALSE, 
  prob.case = 0.5, score = c("DPO", "Conc", "Brier", "PL"), 
	ensemble = FALSE, addMatImp = TRUE, ...)
## Default S3 method:
logicFS(x, y, B = 100, useN = TRUE, ntrees = 1, nleaves = 8, 
  glm.if.1tree = FALSE, replace = TRUE, sub.frac = 0.632, 
  anneal.control = logreg.anneal.control(), onlyRemove = FALSE,
  prob.case = 0.5, score = c("DPO", "Conc", "Brier", "PL"), 
	addMatImp = TRUE, fast = FALSE, neighbor = NULL, 
	adjusted = FALSE, ensemble = FALSE, rand = NULL, ...)
  
## S3 method for class 'formula'
logicFS(formula, data, recdom = TRUE, ...)

## S3 method for class 'logicBagg'
logicFS(x, neighbor = NULL, adjusted = FALSE, 
  prob.case = 0.5, score = c("DPO", "Conc", "Brier", "PL"), 
	ensemble = FALSE, addMatImp = TRUE, ...)

Arguments

`x`	a matrix consisting of 0's and 1's. Alternatively, `x` can also be an object of class `logicBagg`, i.e. the output of `logic.bagging`. If a matrix, each column must correspond to a binary variable and each row to an observation. Missing values are not allowed.
`y`	a numeric vector, a factor, or a vector of class `Surv` specifying the values of a response for all the observations represented in `x`, where no missing values are allowed in `y`. If a numeric vector, then `y` either contains the class labels (coded by 0 and 1) or the values of a continuous response depending on whether the classification or logistic regression approach of logic regression, or the linear regression approach, respectively, should be used. If the response is categorical, then `y` must be a factor naming the class labels of the observations. If the response is a (right-censored survival time), then `y` must be vector of class `Surv` (generated, e.g., with the function `Surv` from the `R` package `survival`.
`B`	an integer specifying the number of iterations.
`useN`	logical specifying if the number of correctly classified out-of-bag observations should be used in the computation of the importance measure. If `FALSE`, the proportion of correctly classified oob observations is used instead. Ignored in the survival case.
`ntrees`	an integer indicating how many trees should be used. For a binary response: If `ntrees` is larger than 1, the logistic regression approach of logic regreesion will be used. If `ntrees` is 1, then by default the classification approach of logic regression will be used (see `glm.if.1tree`.) For a continuous response: A linear regression model with `ntrees` trees is fitted in each of the `B` iterations. For a categorical response: $n.lev-1$ logic regression models with `ntrees` trees are fitted, where $n.lev$ is the number of levels of the response (for details, see `mlogreg`). For a response of class `Surv`: A Cox proportional hazards regression model with `ntrees` trees is fitted in each of the `B` iterations.
`nleaves`	a numeric value specifying the maximum number of leaves used in all trees combined. For details, see the help page of the function `logreg` of the package `LogicReg`.
`glm.if.1tree`	if `ntrees` is 1 and `glm.if.1tree` is `TRUE` the logistic regression approach of logic regression is used instead of the classification approach. Ignored if `ntrees` is not 1, or the response is not binary.
`replace`	should sampling of the cases be done with replacement? If `TRUE`, a Bootstrap sample of size `length(y)` is drawn from the `length(y)` observations in each of the `B` iterations. If `FALSE`, `ceiling(sub.frac * length(y))` of the observations are drawn without replacement in each iteration.
`sub.frac`	a proportion specifying the fraction of the observations that are used in each iteration to build a classification rule if `replace = FALSE`. Ignored if `replace = TRUE`.
`anneal.control`	a list containing the parameters for simulated annealing. See the help of the function `logreg.anneal.control` in the `LogicReg` package.
`onlyRemove`	should in the single tree case the multiple tree measure be used? If `TRUE`, the prime implicants are only removed from the trees when determining the importance in the single tree case. If `FALSE`, the original single tree measure is computed for each prime implicant, i.e.\ a prime implicant is not only removed from the trees in which it is contained, but also added to the trees that do not contain this interaction. Ignored in all other than the classification case.
`prob.case`	a numeric value between 0 and 1. If the outcome of the logistic regression, i.e.\ the predicted probability, for an observation is larger than `prob.case` this observations will be classified as case (or 1).
`score`	a character string naming the score that should be used in the computation of the importance measure for a survival time analysis. By default, the distance between predicted outcomes (`score = "DPO"`) proposed by Tietz et al.\ (2018) is used in the determination of the importance of the variables. Alternatively, Harrell's C-Index (`"Conc"`), the Brier score (`"Brier"`), or the predictive partial log-likelihood (`"PL"`) can be used.
`addMatImp`	should the matrix containing the improvements due to the prime implicants in each of the iterations be added to the output? (For each of the prime implicants, the importance is computed by the average over the `B` improvements.) Must be set to `TRUE`, if standardized importances should be computed using `vim.norm`, or if permutation based importances should be computed using `vim.signperm`. If `ensemble = TRUE` and `addMatImp = TRUE` in the survival case, the respective score of the full model is added to the output instead of an improvement matrix.
`fast`	should a greedy search (as implemented in `logreg`) be used instead of simulated annealing?
`neighbor`	a list consisting of character vectors specifying SNPs that are in LD. If specified, all SNPs need to occur exactly one time in this list. If specified, the importance measures are adjusted for LD by considering the SNPs within a LD block as exchangable.
`adjusted`	logical specifying whether the measures should be adjusted for noise. Often, the interaction actually associated with the response is not exactly found in some iterations of logic bagging, but an interaction is identified that additionally contains one (or seldomly more) noise SNPs. If `adjusted` is set to `TRUE`, the values of the importance measure is corrected for this behaviour.
`ensemble`	in the case of a survival outcome, should `ensemble` importance measures (as, e.g., in `randomSurvivalSRC` be used? If `FALSE`, importance measures analogous to the ones in the logicFS analysis of other outcomes are used (see Tietz et al., 2018).
`rand`	numeric value. If specified, the random number generator will be set into a reproducible state.
`formula`	an object of class `formula` describing the model that should be fitted.
`data`	a data frame containing the variables in the model. Each row of `data` must correspond to an observation, and each column to a binary variable (coded by 0 and 1) or a factor (for details, see `recdom`) except for the column comprising the response, where no missing values are allowed in `data`. The response must be either binary (coded by 0 and 1), categorical, continuous, or a right-censored survival time. If a survival time, i.e. an object of class `Surv`, a Cox propotional hazard model is fitted in each of the `B` iterations of `logicFS`. If continuous, a linear model is fitted in each iterations. If categorical, the column of `data` specifying the response must be a factor. In this case, multinomial logic regressions are performed as implemented in `mlogreg`. Otherwise, depending on `ntrees` (and `glm.if.1tree`) the classification or the logistic regression approach of logic regression is used.
`recdom`	a logical value or vector of length `ncol(data)` comprising whether a SNP should be transformed into two binary dummy variables coding for a recessive and a dominant effect. If `recdom` is `TRUE` (and a logical value), then all factors/variables with three levels will be coded by two dummy variables as described in `make.snp.dummy`. Each level of each of the other factors (also factors specifying a SNP that shows only two genotypes) is coded by one indicator variable. If `recdom` is`FALSE` (and a logical value), each level of each factor is coded by an indicator variable. If `recdom` is a logical vector, all factors corresponding to an entry in `recdom` that is `TRUE` are assumed to be SNPs and transformed into two binary variables as described above. All variables corresponding to entries of `recdom` that are `TRUE` (no matter whether `recdom` is a vector or a value) must be coded either by the integers 1 (coding for the homozygous reference genotype), 2 (heterozygous), and 3 (homozygous variant), or alternatively by the number of minor alleles, i.e. 0, 1, and 2, where no mixing of the two coding schemes is allowed. Thus, it is not allowed that some SNPs are coded by 1, 2, and 3, and others are coded by 0, 1, and 2.
`...`	for the `formula` method, optional parameters to be passed to the low level function `logicFS.default`. Otherwise, ignored.

Value

An object of class logicFS containing

`primes`	the prime implicants,
`vim`	the importance of the prime implicants,
`prop`	the proportion of logic regression models containing the prime implicants (or the neighbors of the prime implicants, if `neighbor != NULL`; or the extended primes of the prime implicants, if `adjusted = TRUE`; or the extended primes of the neighbors of the prime implicants, if `neighbor != NULL` and `adjusted = TRUE`),
`type`	the type of model (1: classification, 2: linear regression, 3: logistic regression, 4: Cox regression),
`param`	further parameters (if `addInfo = TRUE`),
`mat.imp`	either the matrix containing the improvements if `addMatImp = TRUE` and `ensemble = FALSE`, or the respective score of the full model if `addMatImp = TRUE` and `ensemble = TRUE`, or `NULL` if `addMatImp = FALSE`,
`measure`	the name of the used importance measure,
`neighbor`	`neighbor`,
`useN`	the value of `useN`,
`threshold`	NULL,
`mu`	NULL.

Author(s)

Holger Schwender, [email protected]; Tobias Tietz, [email protected]

References

Ruczinski, I., Kooperberg, C., LeBlanc M.L. (2003). Logic Regression. Journal of Computational and Graphical Statistics, 12, 475-511.

Schwender, H., Ickstadt, K. (2007). Identification of SNP Interactions Using Logic Regression. Biostatistics, 9(1), 187-198.

Examples

## Not run: 
   # Load data.
   data(data.logicfs)
   
   # For logic regression and hence logic.fs, the variables must
   # be binary. data.logicfs, however, contains categorical data 
   # with realizations 1, 2 and 3. Such data can be transformed 
   # into binary data by
   bin.snps<-make.snp.dummy(data.logicfs)
   
   # To speed up the search for the best logic regression models
   # only a small number of iterations is used in simulated annealing.
   my.anneal<-logreg.anneal.control(start=2,end=-2,iter=10000)
   
   # Feature selection using logic regression is then done by
   log.out<-logicFS(bin.snps,cl.logicfs,B=20,nleaves=10,
       rand=123,anneal.control=my.anneal)
   
   # The output of logic.fs can be printed
   log.out
   
   # One can specify another number of interactions that should be
   # printed, here, e.g., 15.
   print(log.out,topX=15)
   
   # The variable importance can also be plotted.
   plot(log.out)
   
   # And the original variable names are displayed in
   plot(log.out,coded=FALSE)

## End(Not run)## Not run: 
   # Load data.
   data(data.logicfs)
   
   # For logic regression and hence logic.fs, the variables must
   # be binary. data.logicfs, however, contains categorical data 
   # with realizations 1, 2 and 3. Such data can be transformed 
   # into binary data by
   bin.snps<-make.snp.dummy(data.logicfs)
   
   # To speed up the search for the best logic regression models
   # only a small number of iterations is used in simulated annealing.
   my.anneal<-logreg.anneal.control(start=2,end=-2,iter=10000)
   
   # Feature selection using logic regression is then done by
   log.out<-logicFS(bin.snps,cl.logicfs,B=20,nleaves=10,
       rand=123,anneal.control=my.anneal)
   
   # The output of logic.fs can be printed
   log.out
   
   # One can specify another number of interactions that should be
   # printed, here, e.g., 15.
   print(log.out,topX=15)
   
   # The variable importance can also be plotted.
   plot(log.out)
   
   # And the original variable names are displayed in
   plot(log.out,coded=FALSE)

## End(Not run)

SNPs to Dummy Variables

Description

Transforms SNPs into binary dummy variables.

Usage

  make.snp.dummy(data)
make.snp.dummy(data)

Arguments

data

a matrix in which each column corresponds to a SNP and each row to an observation. The genotypes of all SNPs must be either coded by 1 (for the homozygous reference genotype), 2 (heterozygous), and 3 (homozygous variant) or by 0, 1, 2. It is not allowed that some SNPs following the 1, 2, 3 coding scheme and some SNPs the 0, 1, 2 coding. Missing values are allowed, but please note that neither logic.bagging nor logicFS can handle missing values so that the missing values need to be imputed (preferably before an application of make.snp.dummy.

Details

make.snp.dummy assumes that the homozygous dominant genotype is coded by 1, the heterozygous genotype by 2, and the homozygous recessive genotype by 3. Alternatively, the three genotypes can be coded by the number of minor alleles, i.e. by 0, 1, and 2. For each SNP, two dummy variables are generated:

SNP.1: At least one of the bases explaining the SNP are of the recessive type.
SNP.2: Both bases are of the recessive type.

Value

A matrix with 2*ncol(data) columns containing 2 dummy variables for each SNP.

Note

See the R package scrime for more general functions for recoding SNPs.

Author(s)

Holger Schwender, [email protected]

Multinomial Logic Regression

Description

Performs a multinomial logic regression for a nominal response by fitting a logic regression model (with logit as link function) for each of the levels of the response except for the level with the smallest value which is used as reference category.

Usage

## S3 method for class 'formula'
mlogreg(formula, data, recdom = TRUE, ...)

## Default S3 method:
mlogreg(x, y, ntrees = 1, nleaves = 8, anneal.control = logreg.anneal.control(), 
    select = 1, rand = NA, ...)
## S3 method for class 'formula'
mlogreg(formula, data, recdom = TRUE, ...)

## Default S3 method:
mlogreg(x, y, ntrees = 1, nleaves = 8, anneal.control = logreg.anneal.control(), 
    select = 1, rand = NA, ...)

Arguments

`formula`	an object of class `formula` describing the model that should be fitted.
`data`	a data frame containing the variables in the model. Each column of `data` must correspond to a binary variable (coded by 0 and 1) or a factor (for details on factors, see `recdom`) except for the column comprising the response, and each row to an observation. The response must be a categorical variable with less than 10 levels. This response can be either a factor or of type `numeric` or `character`.
`recdom`	a logical value or vector of length `ncol(data)` comprising whether a SNP should be transformed into two binary dummy variables coding for a recessive and a dominant effect. If `TRUE` (logical value), then all factors (variables) with three levels will be coded by two dummy variables as described in `make.snp.dummy`. Each level of each of the other factors (also factors specifying a SNP that shows only two genotypes) is coded by one indicator variable. If `FALSE` (logical value), each level of each factor is coded by an indicator variable. If `recdom` is a logical vector, all factors corresponding to an entry in `recdom` that is `TRUE` are assumed to be SNPs and transformed into the two binary variables described above. Each variable that corresponds to an entry of `recdom` that is `TRUE` (no matter whether `recdom` is a vector or a value) must be coded by the integers 1 (coding for the homozygous reference genotype), 2 (heterozygous), and 3 (homozygous variant).
`x`	a matrix consisting of 0's and 1's. Each column must correspond to a binary variable and each row to an observation.
`y`	either a factor or a numeric or character vector specifying the values of the response. The length of `y` must be equal to the number of rows of `x`.
`ntrees`	an integer indicating how many trees should be used in the logic regression models. For details, see `logreg` in the `LogicReg package`.
`nleaves`	a numeric value specifying the maximum number of leaves used in all trees combined. See the help page of the function `logreg` in the `LogicReg` package for details.
`anneal.control`	a list containing the parameters for simulated annealing. For details, see the help page of `logreg.anneal.control` in the `LogicReg` package.
`select`	numeric value. Either 0 for a stepwise greedy selection (corresponds to `select = 6` in `logreg`) or 1 for simulated annealing.
`rand`	numeric value. If specified, the random number generator will be set into a reproducible state.
`...`	for the `formula` method, optional parameters to be passed to the low level function `mlogreg.default`. Otherwise, ignored.

Value

An object of class mlogreg composed of

`model`	a list containing the logic regression models,
`data`	a matrix containing the binary predictors,
`cl`	a vector comprising the class labels,
`ntrees`	a numeric value naming the maximum number of trees used in the logic regressions,
`nleaves`	a numeric value comprising the maximum number of leaves used in the logic regressions,
`fast`	a logical value specifying whether the faster search algorithm, i.e.\ the greedy search, has been used.

Author(s)

Holger Schwender, [email protected]

References

Schwender, H., Ruczinski, I., Ickstadt, K. (2011). Testing SNPs and Sets of SNPs for Importance in Association Studies. Biostatistics, 12, 18-32.

Variable Importance Plot

Description

Generates a dotchart of the importance of the most important interactions for an object of class logicFS or logicBagg.

Usage

## S3 method for class 'logicFS'
plot(x, topX = 15, cex = 0.9, pch = 16, col = 1, show.prop = FALSE, 
   force.topX = FALSE, coded = TRUE, add.thres = TRUE, thres = NULL, 
   include0 = TRUE, add.v0 = TRUE, v0.col = "grey50", main = NULL, ...)

## S3 method for class 'logicBagg'
plot(x, topX = 15, cex = 0.9, pch = 16, col = 1, show.prop = FALSE, 
   force.topX = FALSE, coded = TRUE, include0 = TRUE, add.v0 = TRUE,
   v0.col = "grey50", main = NULL, ...)
## S3 method for class 'logicFS'
plot(x, topX = 15, cex = 0.9, pch = 16, col = 1, show.prop = FALSE, 
   force.topX = FALSE, coded = TRUE, add.thres = TRUE, thres = NULL, 
   include0 = TRUE, add.v0 = TRUE, v0.col = "grey50", main = NULL, ...)

## S3 method for class 'logicBagg'
plot(x, topX = 15, cex = 0.9, pch = 16, col = 1, show.prop = FALSE, 
   force.topX = FALSE, coded = TRUE, include0 = TRUE, add.v0 = TRUE,
   v0.col = "grey50", main = NULL, ...)

Arguments

`x`	an object of either class `logicFS` or `logicBagg`.
`topX`	integer specifying how many interactions should be shown. If `topX` is larger than the number of interactions contained in `x` all the interactions are shown. For further information, see `force.topX`.
`cex`	a numeric value specifying the relative size of the text and symbols.
`pch`	specifies the used symbol. See the help of `par` for details.
`col`	the color of the text and the symbols. See the help of `par` for how colors can be specified.
`show.prop`	if `TRUE` the proportions of models that contain the interactions of interest are shown. If `FALSE` (default) the importances of the interactions are shown.
`force.topX`	if `TRUE` exactly `topX` interactions are shown. If `FALSE` (default) all interactions up to the `topX`th most important one and all interactions having the same importance as the `topX`th most important one are shown.
`coded`	should the coded variable names be displayed? Might be useful if the actual variable names are pretty long. The coded variable name of the j-th variable is `Xj`.
`add.thres`	should a vertical line marking the threshold for a prime implicant to be called important be drawn in the plot? If `TRUE`, this vertical line will be drawn at `NULL`.
`thres`	non-negative numeric value specifying the threshold for a prime implicant to be called important. If `NULL` and `add.thres = TRUE`, the suggested threshold from `x` will be used.
`include0`	should the x-axis include zero regardless whether the importances of the shown interactions are much higher than 0?
`add.v0`	should a vertical line be drawn at $x = 0$ ? Ignored if `include0 = FALSE` and all importances are larger than zero.
`v0.col`	the color of the vertical line at $x = 0$ . See the help page of `par` for how colors can be specified.
`main`	character string naming the title of the plot. If `NULL`, the name of the importance measure is used.
`...`	Ignored.

Author(s)

Holger Schwender, [email protected]

Survival and Cumulative Hazard Function Plot

Description

Plots predicted survival or cumulative hazard curves of new observations for an object of class predict.survivalFS.

Usage

## S3 method for class 'predict.survivalFS'
plot(x, select_obs, xlab = "time", ylab = NULL, 
              ylim = NULL, type = "l", main = NULL, sub = NULL, 
              vec_col = NULL, vec_lty = NULL, addLegend = TRUE, ...)
## S3 method for class 'predict.survivalFS'
plot(x, select_obs, xlab = "time", ylab = NULL, 
              ylim = NULL, type = "l", main = NULL, sub = NULL, 
              vec_col = NULL, vec_lty = NULL, addLegend = TRUE, ...)

Arguments

`x`	an object of class `predict.survivalFS` as generated by the function `predict.logicBagg`.
`select_obs`	a numeric vector identifying the observations whose survival curves should be plotted. If `is.missing(select.obs)` the first five observations, or, if the number of observations is less than five, all observations are chosen.
`xlab`	a title for the x axis: see `title`.
`ylab`	a title for the y axis: see `title`. If `NULL`, the title is generated automatically.
`ylim`	a numeric vector of length 2 that sets the limits of the y axis. If `NULL`, the limits are generated automatically.
`type`	character indicating the type of plotting; actually any of the types as in `plot.default`.
`main`	an overall title for the plot: see `title`. If `NULL`, the main title is generated automatically.
`sub`	a sub title for the plot: see `title`. If `NULL`, the sub title is generated automatically.
`vec_col`	a numeric or character vector that specifies the plotting colors of the survival curves (see `par`). Vector must have the same length as `select_obs`.
`vec_lty`	a numeric or character vector that specifies the line types of the survival curves (see `par`). Vector must have the same length as `select_obs`.
`addLegend`	should a legend be added to the plot automatically?
`...`	Ignored.

Author(s)

Tobias Tietz, [email protected]

Predict Method for logicBagg objects

Description

Prediction for test data using an object of class logicBagg.

Usage

## S3 method for class 'logicBagg'
predict(object, newData, prob.case = 0.5, 
    type = c("class", "prob"), score = c("DPO", "Conc", "Brier"), ...)
## S3 method for class 'logicBagg'
predict(object, newData, prob.case = 0.5, 
    type = c("class", "prob"), score = c("DPO", "Conc", "Brier"), ...)

Arguments

`object`	an object of class `logicBagg`.
`newData`	a matrix or data frame containing new data. If omitted `object\$data`, i.e.\ the original training data, are used. Each row of `newData` must correspond to a new observation. Each row of `newData` must contain the same variable as the corresponding column of the data matrix used in `logic.bagging`, i.e.\ `x` if the default method of `logic.bagging` has been used, or `data` without the column containing the response if the `formula` method has been used.
`prob.case`	a numeric value between 0 and 1. A new observation will be classified as case (or more exactly, as 1) if the class probability, i.e.\ the average of the predicted probabilities of the models (if the logistic regression approach of logic regression has been used), or the percentage of votes for class 1 (if the classification approach of logic regression has been used) is larger than `prob.case`. Ignored if `type = "prob"` or the response is either quantitative or an object of class `Surv`.
`type`	character vector indicating the type of output. If `"class"`, a numeric vector of zeros and ones containing the predicted classes of the observations (using the specification of `prob.case`) will be returned. If `"prob"`, the class probabilities or percentages of votes for class 1, respectively, for all observations are returned. Ignored if the response is quantitative or an object of class `Surv`.
`score`	a character string naming the score that should be used to assess the performance of the prediction model in the survival case. By default, the distance between predicted outcomes (`score = "DPO"`) proposed by Tietz et al.\ (2018) is used in the assessment of the prediction performance. Alternatively, Harrell's C-Index (`"Conc"`), or the Brier score (`"Brier"`) can be used. Furthermore, `score` determines whether a prediction for the cumulative hazard function (`score = "DPO"` or `score = "Conc"`) or the survival function (`score = "Brier"`) of the new observations should be made. Ignored in all other than the survival case.
`...`	Ignored.

Value

A numeric vector containing the predicted classes (if type = "class") or the class probabilities (if type = "prob") of the new observations if the classification or the logistic regression approach of logic regression is used. If the response is quantitative, the predicted value of the response for all observations in the test data set is returned. If the response is of class Surv, an object of class predict.survivalFS with either an prediction for the cumulative hazard function or the survival function of the new observations is returned.

Author(s)

Holger Schwender, [email protected], Tobias Tietz, [email protected]

Predict Method for mlogreg Objects

Description

Prediction for test data using an object of class mlogreg.

Usage

 ## S3 method for class 'mlogreg'
predict(object, newData, type = c("class", "prob"), ...)
## S3 method for class 'mlogreg'
predict(object, newData, type = c("class", "prob"), ...)

Arguments

`object`	an object of class `mlogreg`, i.e.\ the output of the function `mlogreg`.
`newData`	a matrix or data frame containing new data. If omitted `object\$data`, i.e.\ the original training data, are used. Each row of `newData` must correspond to a new observation. Each row of `newData` must contain the same variable as the corresponding column of the data matrix used in `mlogreg`, i.e.\ `x` if the default method of `mlogreg` has been used, or `data` without the column containing the response if the `formula` method has been used.
`type`	character vector indicating the type of output. If `"class"`, a vector containing the predicted classes of the observations will be returned. If `"prob"`, the class probabilities for each level and all observations are returned.
`...`	Ignored.

Value

A numeric vector containing the predicted classes (if type = "class"), or a matrix composed of the class probabilities (if type = "prob").

Author(s)

Holger Schwender, [email protected]

Print a logicFS object

Description

Prints an object of class logicFS.

Usage

## S3 method for class 'logicFS'
print(x, topX = 5, show.prop = TRUE, coded = FALSE, digits = 2, ...)
## S3 method for class 'logicFS'
print(x, topX = 5, show.prop = TRUE, coded = FALSE, digits = 2, ...)

Arguments

`x`	an object of either class `logicFS`.
`topX`	integer indicating how many interactions should be shown. Additionally to the `topX` most important interactions, any interaction having the same importance as the `topX` most important one are also shown.
`show.prop`	should the proportions of models containing the interactions of interest also be shown?
`coded`	should the coded variable names be displayed? Might be useful if the actual variable names are pretty long. The coded variable name of the j-th variable is `Xj`.
`digits`	number of digits used in the output.
`...`	Ignored.

Author(s)

Holger Schwender, [email protected]

Logic Feature Selection for Survival Data

Description

Identification of interactions of binary variables associated with survival time using logic regression.

Usage

## Default S3 method:
survivalFS(x, y, B = 20, replace = FALSE, 
  sub.frac = 0.632, score = c("DPO", "Conc", "Brier", "PL"), 
	addMatImp = TRUE, adjusted = FALSE, neighbor = NULL, 
	ensemble = FALSE, rand = NULL, ...)
  
## S3 method for class 'formula'
survivalFS(formula, data, recdom = TRUE, ...)

## S3 method for class 'logicBagg'
survivalFS(x, score = c("DPO", "Conc", "Brier", "PL"),
  adjusted = FALSE, neighbor = NULL, ensemble = FALSE,
	addMatImp = TRUE, rand = NULL, ...)
## Default S3 method:
survivalFS(x, y, B = 20, replace = FALSE, 
  sub.frac = 0.632, score = c("DPO", "Conc", "Brier", "PL"), 
	addMatImp = TRUE, adjusted = FALSE, neighbor = NULL, 
	ensemble = FALSE, rand = NULL, ...)
  
## S3 method for class 'formula'
survivalFS(formula, data, recdom = TRUE, ...)

## S3 method for class 'logicBagg'
survivalFS(x, score = c("DPO", "Conc", "Brier", "PL"),
  adjusted = FALSE, neighbor = NULL, ensemble = FALSE,
	addMatImp = TRUE, rand = NULL, ...)

Arguments

`x`	a matrix consisting of 0's and 1's. Alternatively, `x` can also be an object of class `logicBagg`, i.e. the output of `logic.bagging`. If a matrix, each column must correspond to a binary variable and each row to an observation. Missing values are not allowed.
`y`	a vector of class `Surv` specifying the right-censored survival time for all observations represented in `x`, where no missing values are allowed in `y`. This vector can, e.g., be generated using the function `Surv` from the `R` package `survival`.
`B`	an integer specifying the number of iterations.
`replace`	should sampling of the cases be done with replacement? If `TRUE`, a Bootstrap sample of size `length(y)` is drawn from the `length(y)` observations in each of the `B` iterations. If `FALSE`, `ceiling(sub.frac * length(y))` of the observations are drawn without replacement in each iteration.
`sub.frac`	a proportion specifying the fraction of the observations that are used in each iteration to build a classification rule if `replace = FALSE`. Ignored if `replace = TRUE`.
`score`	a character string naming the score that should be used in the computation of the importance measure for a survival time analysis. By default, the distance between predicted outcomes (`score = "DPO"`) proposed by Tietz et al.\ (2018) is used in the determination of the importance of the variables. Alternatively, Harrell's C-Index (`"Conc"`), the Brier score (`"Brier"`), or the predictive partial log-likelihood (`"PL"`) can be used.
`addMatImp`	should the matrix containing the improvements due to the prime implicants in each of the iterations be added to the output if `ensemble = FALSE`? (For each of the prime implicants, the importance is computed by the average over the `B` improvements.) If `ensemble = TRUE` and `addMatImp = TRUE`, the respective score of the full model is added to the output instead of an improvement matrix.
`adjusted`	logical specifying whether the measures should be adjusted for noise. Often, the interaction actually associated with the response is not exactly found in some iterations of logic bagging, but an interaction is identified that additionally contains one (or seldomly more) noise SNPs. If `adjusted` is set to `TRUE`, the values of the importance measure is corrected for this behaviour.
`neighbor`	a list consisting of character vectors specifying SNPs that are in LD. If specified, all SNPs need to occur exactly one time in this list. If specified, the importance measures are adjusted for LD by considering the SNPs within a LD block as exchangable.
`ensemble`	in the case of a survival outcome, should `ensemble` importance measures (as, e.g., in `randomSurvivalSRC` be used? If `FALSE`, importance measures analogous to the ones in the logicFS analysis of other outcomes are used (see Tietz et al., 2018).
`rand`	numeric value. If specified, the random number generator will be set into a reproducible state.
`formula`	an object of class `formula` describing the model that should be fitted.
`data`	a data frame containing the variables in the model. Each row of `data` must correspond to an observation, and each column to a binary variable (coded by 0 and 1) or a factor (for details, see `recdom`) except for the column comprising the response, where no missing values are allowed in `data`. The response must be an object of class `Surv`.
`recdom`	a logical value or vector of length `ncol(data)` comprising whether a SNP should be transformed into two binary dummy variables coding for a recessive and a dominant effect. If `recdom` is `TRUE` (and a logical value), then all factors/variables with three levels will be coded by two dummy variables as described in `make.snp.dummy`. Each level of each of the other factors (also factors specifying a SNP that shows only two genotypes) is coded by one indicator variable. If `recdom` is`FALSE` (and a logical value), each level of each factor is coded by an indicator variable. If `recdom` is a logical vector, all factors corresponding to an entry in `recdom` that is `TRUE` are assumed to be SNPs and transformed into two binary variables as described above. All variables corresponding to entries of `recdom` that are `TRUE` (no matter whether `recdom` is a vector or a value) must be coded either by the integers 1 (coding for the homozygous reference genotype), 2 (heterozygous), and 3 (homozygous variant), or alternatively by the number of minor alleles, i.e. 0, 1, and 2, where no mixing of the two coding schemes is allowed. Thus, it is not allowed that some SNPs are coded by 1, 2, and 3, and others are coded by 0, 1, and 2.
`...`	further arguments of `logicFS`. Ignored, if `x` is an object of class `logicBagg`.

Value

An object of class logicFS containing

`primes`	the prime implicants,
`vim`	the importance of the prime implicants,
`prop`	the proportion of logic regression models containing the prime implicants, (or the neighbors of the prime implicants, if `neighbor != NULL`; or the extended primes of the prime implicants, if `adjusted = TRUE`; or the extended primes of the neighbors of the prime implicants, if `neighbor != NULL` and `adjusted = TRUE`),
`type`	the type of model (1: classification, 2: linear regression, 3: logistic regression, 4: Cox regression),
`param`	further parameters (if `addInfo = TRUE`),
`mat.imp`	either the matrix containing the improvements if `addMatImp = TRUE` and `ensemble = FALSE`, or the respective score of the full model if `addMatImp = TRUE` and `ensemble = TRUE`, or `NULL` if `addMatImp = FALSE`,
`measure`	the name of the used importance measure,
`neighbor`	`neighbor`,
`useN`	the value of `useN`,
`threshold`	NULL,
`mu`	NULL.

Author(s)

Tobias Tietz, [email protected]

References

Approximate P-Value Based Importance Measure

Description

Computes the importances based on an approximation to a t- or F-distribution.

Usage

vim.approxPval(object, version = 1, adjust = "bonferroni")
vim.approxPval(object, version = 1, adjust = "bonferroni")

Arguments

`object`	an object of class `logicFS` which contains the values of standardized importances. Only in the linear regression case, the importances in `object` are allowed to be non-standardized.
`version`	either `1` or `2`. If `1`, then the importance measure is computed by 1 - padj, where padj is the adjusted p-value. If `2`, the importance measure is determined by -log10(padj), where a raw p-value equal to 0 is set to 1 / (10 * `n.perm`) to avoid infinitive importances.
`adjust`	character vector naming the method with which the raw permutation based p-values are adjusted for multiplicity. If `"qvalue"`, the function `qvalue.cal` from the package `siggenes` is used to compute q-values. Otherwise, `p.adjust` is used to adjust for multiple comparisons. See `p.adjust` for all other possible specifications of `adjust`. If `"none"`, the raw p-values will be used.

Value

An object of class logicFS containing the same object as object except for

`vim`	the values of the importance measure based on an approximation to the t- or F-distribution,
`measure`	the name of the used importance measure,
`threshold`	0.95 if `version = 1`, and -log10(0.05) if `version = 2`.

Author(s)

Holger Schwender, [email protected]

References

Schwender, H., Ruczinski, I., Ickstadt, K. (2011). Testing SNPs and Sets of SNPs for Importance in Association Studies. Biostatistics, 12, 18-32.

ChiSquare Based Importance

Description

Determining the importance of interactions found by logic.bagging or logicFS by Pearson's ChiSquare Statistic. Only available for the classification and the logistic regression approach of logic regression.

Usage

vim.chisq(object, data = NULL, cl = NULL)
vim.chisq(object, data = NULL, cl = NULL)

Arguments

`object`	either an object of class `logicFS` or the output of an application of `logic.bagging` with `importance = TRUE`.
`data`	a data frame or matrix consisting of 0's and 1's in which each column corresponds to one of the explanatory variables used in the original analysis with `logic.bagging` or `logicFS`, and each row corresponds to an observation. Must be specified if `object` is an object of class `logicFS`, or `cl` is specified. If `object` is an object of class `logicBagg` and neither `data` nor `cl` is specified, `data` and `cl` stored in `object` is used to compute the ChiSquare statistics. It is, however, highly recommended to use new `data` to test the interactions contained in `object`, as they have been found using the `data` stored in `object`, and it is very likely that most of them will show up as interesting if they are tested on the same data set.
`cl`	a numeric vector of 0's and 1's specifying the class labels of the observations in `data`. Must be specified either if `object` is an object of class `logicFS`, or if `data` is specified.

Details

Currently Pearson's ChiSquare statistic is computed without continuity correction.

Contrary to vim.logicFS (and vim.norm and vim.signperm), vim.chisq does neither take the logic regression models into acount nor uses the out-of-bag observations for computing the importances of the identified interactions. It "just" tests each of the found interactions on the whole data set by calculating Pearson's ChiSquare statistic for each of these interactions. It is, therefore, highly recommended to use an independent data set for specifying the importances of these interactions with vim.chisq.

Value

An object of class logicFS containing

`primes`	the prime implicants
`vim`	the values of Pearson's ChiSquare statistic,
`prop`	NULL,
`type`	NULL,
`param`	further parameters (if `object` is the output of `logicFS` or `vim.logicFS` with `addInfo = TRUE`),
`mat.imp`	NULL,
`measure`	"ChiSquare Based",
`threshold`	the 1 - 0.05/m quantile of the ChiSquare distribution with one degree of freedom,
`mu`	NULL.

Author(s)

Holger Schwender, [email protected]

EBAM Based Importance

Description

Determines the importance of interactions found by logic.bagging or logicFS by an Empirical Bayes Analysis of Microarrays (EBAM). Only available for the classification and the logistic regression approach of logic regression.

Usage

vim.ebam(object, data = NULL, cl = NULL, storeEBAM = FALSE, ...)
vim.ebam(object, data = NULL, cl = NULL, storeEBAM = FALSE, ...)

Arguments

`object`	either an object of class `logicFS` or the output of an application of `logic.bagging` with `importance = TRUE`.
`data`	a data frame or matrix consisting of 0's and 1's in which each column corresponds to one of the explanatory variables used in the original analysis with `logic.bagging` or `logicFS`, and each row corresponds to an observation. Must be specified if `object` is an object of class `logicFS`, or `cl` is specified. If `object` is an object of class `logicBagg` and neither `data` nor `cl` is specified, `data` and `cl` stored in `object` is used to compute the ChiSquare statistics. It is, however, highly recommended to use new `data` to test the interactions contained in `object`, as they have been found using the `data` stored in `object`, and it is very likely that most of them will show up as interesting if they are tested on the same data set.
`cl`	a numeric vector of 0's and 1's specifying the class labels of the observations in `data`. Must be specified either if `object` is an object of class `logicFS`, or if `data` is specified.
`storeEBAM`	logical specifying whether the output of the EBAM analysis should be stored in the output of `vim.ebam`.
`...`	further arguments of `ebam` and `cat.ebam`. For details, see the help files of these functions from the package `siggenes`.

Details

For each interaction found by logic.bagging or logicFS, the posterior probability that this interaction is significant is computed using the Empirical Bayes Analysis of Microarrays (EBAM). These posterior probabilities are used as the EBAM based importances of the interactions.

The test statistic underlying this EBAM analysis is Pearson's ChiSquare statistic. Currently, the value of this statistic is computed without continuity correction.

Contrary to vim.logicFS (and vim.norm and vim.signperm), vim.ebam does neither take the logic regression models into acount nor uses the out-of-bag observations for computing the importances of the identified interactions. It "just" tests each of the found interactions on the whole data set by calculating Pearson's ChiSquare statistic for each of these interactions and performing an EBAM analysis. It is, therefore, highly recommended to use an independent data set for specifying the importances of these interactions with vim.ebam.

Value

An object of class logicFS containing

`primes`	the prime implicants,
`vim`	the posterior probabilities of the interactions,
`prop`	NULL,
`type`	NULL,
`param`	further parameters (if `object` is the output of `logicFS` or `vim.logicFS` with `addInfo = TRUE`),
`mat.imp`	NULL,
`measure`	"EBAM Based",
`threshold`	the value of `delta` used in the EBAM analysis (see help files for `ebam`); by default: 0.9,
`mu`	NULL,
`ebam`	an object of class `EBAM` (only available if `storeEBAM = TRUE`).

Author(s)

Holger Schwender, [email protected]

References

Schwender, H. and Ickstadt, K. (2008). Empirical Bayes Analysis of Single Nucleotide Polymorphisms. BMC Bioinformatics, 9:144.

VIM for Inputs

Description

Quantifies the importance of each input variable occuring in at least one of the logic regression models found in the application of logic.bagging.

Usage

vim.input(object, useN = NULL, iter = NULL, prop = TRUE,
   standardize = NULL, mu = 0, addMatImp = FALSE, 
   prob.case = 0.5, rand = NA)
vim.input(object, useN = NULL, iter = NULL, prop = TRUE,
   standardize = NULL, mu = 0, addMatImp = FALSE, 
   prob.case = 0.5, rand = NA)

Arguments

`object`	an object of class `logicBagg`, i.e.\ the output of `logic.bagging`
`useN`	logical specifying if the number of correctly classified out-of-bag observations should be used in the computation of the importance measure. If `FALSE`, the proportion of correctly classified oob observations is used instead. If `NULL` (default), then the specification of `useN` in `object` is used.
`iter`	integer specifying the number of times the values of the considered variable are permuted in the computation of its importance. If `NULL` (default), the values of the variable are not permuted, but the variable is removed from the model.
`prop`	should the proportion of logic regression models containing the respective variable also be computed?
`standardize`	should a standardized version of the importance measure for a set of variables be returned? By default, `standardize = TRUE` is used in the classification and the (multinomial) logistic regression case, and `standarize` is set to `FALSE` in the linear regression case. For details, see `mu`.
`mu`	a non-negative numeric value. Ignored if `standardize = FALSE`. Otherwise, a t-statistic for testing the null hypothesis that the importance of the respective variable is equal to `mu` is computed.
`addMatImp`	should the matrix containing the improvements due to each of the variables in each of the logic regression models be added to the output?
`prob.case`	a numeric value between 0 and 1. If the logistic regression approach of logic regression has been used in `logic.bagging`, then an observation will be classified as a case (or more exactly, as 1), if the class probability of this observation is larger than `prob.case`. Otherwise, `prob.case` is ignored.
`rand`	an integer for setting the random number generator in a reproducible case.

Value

An object of class logicFS containing

`vim`	the importances of the variables,
`prop`	the proportion of logic regression models containing the respective variable (if `prop = TRUE`) or `NULL` (if `prop = FALSE`),
`primes`	the names of the variables,
`type`	the type of model (1: classification, 2:linear regression, 3: logistic regression),
`param`	further parameters (if `addInfo = TRUE` in the previous call of `logic.bagging`),
`mat.imp`	either a matrix containing the improvements due to the variables for each of the models (if `addMatImp = TRUE`), or `NULL` (if `addMatImp = FALSE`),
`measure`	the name of the used importance measure,
`useN`	the value of `useN`,
`threshold`	`NULL` if `standardize = FALSE`, otherwise the $1-0.05/m$ quantile of the t-distribution with $B-1$ degrees of freedom, where $m$ is the number of variables and $B$ is the number of logic regression models composing `object`,
`mu`	`mu` (if `standardize = TRUE`), or `NULL` (otherwise),
`iter`	`iter`.

Author(s)

Holger Schwender, [email protected]

References

Schwender, H., Ruczinski, I., Ickstadt, K. (2011). Testing SNPs and Sets of SNPs for Importance in Association Studies. Biostatistics, 12, 18-32.

Importance Measures

Description

Computes the value of the single or the multiple tree measure, respectively, for each prime implicant contained in a logic bagging model to specify the importance of the prime implicant for classification, if the response is binary. If the response is quantitative, the importance is specified by a measure based on the log2-transformed mean square prediction error. If the response is a time to an event, performance measures for time-to-event models are employed to determine the importance measures.

Usage

vim.logicFS(log.out, neighbor = NULL, adjusted = FALSE, useN = TRUE, 
   onlyRemove = FALSE, prob.case = 0.5, addInfo = FALSE, 
	 score = c("DPO", "Conc", "Brier", "PL"), ensemble = FALSE, 
	 addMatImp = TRUE)
vim.logicFS(log.out, neighbor = NULL, adjusted = FALSE, useN = TRUE, 
   onlyRemove = FALSE, prob.case = 0.5, addInfo = FALSE, 
	 score = c("DPO", "Conc", "Brier", "PL"), ensemble = FALSE, 
	 addMatImp = TRUE)

Arguments

`log.out`	an object of class `logicBagg`, i.e.\ the output of `logic.bagging`.
`neighbor`	a list consisting of character vectors specifying SNPs that are in LD. If specified, all SNPs need to occur exactly one time in this list. If specified, the importance measures are adjusted for LD by considering the SNPs within a LD block as exchangable.
`adjusted`	logical specifying whether the measures should be adjusted for noise. Often, the interaction actually associated with the response is not exactly found in some iterations of logic bagging, but an interaction is identified that additionally contains one (or seldomly more) noise SNPs. If `adjusted` is set to `TRUE`, the values of the importance measure is corrected for this behaviour.
`useN`	logical specifying if the number of correctly classified out-of-bag observations should be used in the computation of the importance measure. If `FALSE`, the proportion of correctly classified oob observations is used instead. Ignored in the survival case.
`onlyRemove`	should in the single tree case the multiple tree measure be used? If `TRUE`, the prime implicants are only removed from the trees when determining the importance in the single tree case. If `FALSE`, the original single tree measure is computed for each prime implicant, i.e.\ a prime implicant is not only removed from the trees in which it is contained, but also added to the trees that do not contain this interaction. Ignored in all other than the classification case.
`prob.case`	a numeric value between 0 and 1. If the logistic regression approach of logic regression is used (i.e.\ if the response is binary, and in `logic.bagging` `ntrees` is set to a value larger than 1, or `glm.if.1tree` is set to `TRUE`), then an observation will be classified as a case (or more exactly as 1), if the class probability of this observation estimated by the logic bagging model is larger than `prob.case`.
`addInfo`	should further information on the logic regression models be added?
`score`	a character string naming the score that should be used in the computation of the importance measure for a survival time analysis. By default, the distance between predicted outcomes (`score = "DPO"`) proposed by Tietz et al.\ (2018) is used in the determination of the importance of the variables. Alternatively, Harrell's C-Index (`"Conc"`), the Brier score (`"Brier"`), or the predictive partial log-likelihood (`"PL"`) can be used.
`ensemble`	in the case of a survival outcome, should `ensemble` importance measures (as, e.g., in `randomSurvivalSRC` be used? If `FALSE`, importance measures analogous to the ones in the logicFS analysis of other outcomes are used (see Tietz et al., 2018).
`addMatImp`	should the matrix containing the improvements due to the prime implicants in each of the iterations be added to the output? (For each of the prime implicants, the importance is computed by the average over the `B` improvements.) Must be set to `TRUE`, if standardized importances should be computed using `vim.norm`, or if permutation based importances should be computed using `vim.signperm`. If `ensemble = TRUE` and `addMatImp = TRUE` in the survival case, the respective score of the full model is added to the output instead of an improvement matrix.

Value

An object of class logicFS containing

`primes`	the prime implicants,
`vim`	the importance of the prime implicants,
`prop`	the proportion of logic regression models containing the prime implicants (or the neighbors of the prime implicants, if `neighbor != NULL`; or the extended primes of the prime implicants, if `adjusted = TRUE`; or the extended primes of the neighbors of the prime implicants, if `neighbor != NULL` and `adjusted = TRUE`),
`type`	the type of model (1: classification, 2: linear regression, 3: logistic regression, 4: Cox regression),
`param`	further parameters (if `addInfo = TRUE`),
`mat.imp`	either the matrix containing the improvements if `addMatImp = TRUE` and `ensemble = FALSE`, or the respective score of the full model if `addMatImp = TRUE` and `ensemble = TRUE`, or `NULL` if `addMatImp = FALSE`,
`measure`	the name of the used importance measure,
`neighbor`	`neighbor`,
`useN`	the value of `useN`,
`threshold`	NULL,
`mu`	NULL.

Author(s)

Holger Schwender, [email protected]; Tobias Tietz, [email protected]

References

Schwender, H., Ickstadt, K. (2007). Identification of SNP Interactions Using Logic Regression. Biostatistics, 9(1), 187-198.

Standardized and Sign-Permutation Based Importance Measure

Description

Computes a standarized or a sign-permutation based version of either the Single Tree Measure, the Quantitative Response Measure, or the Multiple Tree Measure.

Usage

vim.norm(object, mu = 0)

vim.signperm(object, mu = 0, n.perm = 10000, n.subset = 1000, 
  version = 1, adjust = "bonferroni", rand = NA)
vim.norm(object, mu = 0)

vim.signperm(object, mu = 0, n.perm = 10000, n.subset = 1000, 
  version = 1, adjust = "bonferroni", rand = NA)

Arguments

`object`	either the output of `logicFS` or `vim.logicFS` with `addMatImp = TRUE`, or the output of `logic.bagging` with `importance = TRUE` and `addMatImp = TRUE`.
`mu`	a non-negative numeric value against which the importances are tested. See `Details`.
`n.perm`	the number of sign permutations used in `vim.signperm`.
`n.subset`	an integer specifying how many permutations should be considered at once.
`version`	either `1` or `2`. If `1`, then the importance measure is computed by 1 - padj, where padj is the adjusted p-value. If `2`, the importance measure is determined by -log10(padj), where a raw p-value equal to 0 is set to 1 / (10 * `n.perm`) to avoid infinitive importances.
`adjust`	character vector naming the method with which the raw permutation based p-values are adjusted for multiplicity. If `"qvalue"`, the function `qvalue.cal` from the package `siggenes` is used to compute q-values. Otherwise, `p.adjust` is used to adjust for multiple comparisons. See `p.adjust` for all other possible specifications of `adjust`. If `"none"`, the raw p-values will be used. For more details, see `Details`.
`rand`	an integer for setting the random number generator in a reproducible case.

Details

In both vim.norm and vim.signperm, a paired t-statistic is computed for each prime implicant, where the numerator is given by $VIM -$ mu with VIM being the single or the multiple tree importance, and the denominator is the corresponding standard error computed by employing the B improvements of the considered prime implicant in the B logic regression models, where VIM is the mean over these B improvements.

Note that in the case of a quantitative response, such a standardization is not necessary. Thus, vim.norm returns a warning when the response is quantitative, and vim.signperm does not divide $VIM -$ mu by its sample standard error.

Using mu = 0 might lead to calling a prime implicant important, even though it actually shows only improvements of 1 or 0. When considering the prime implicants, it might be therefore be helpful to set mu to a value slightly larger than zero.

In vim.norm, the value of this t-statistic is returned as the standardized importance of a prime implicant. The larger this value, the more important is the prime implicant. (This applies to all importance measures – at least for those contained in this package.) Assuming normality, a possible threshold for a prime implicant to be considered as important is the $1 - 0.05 / m$ quantile of the t-distribution with $B - 1$ degrees of freedom, where $m$ is the number of prime implicants.

In vim.signperm, the sign permutation is used to determine n.perm permuted values of the one-sample t-statistic, and to compute the raw p-values for each of the prime implicants. Afterwards, these p-values are adjusted for multiple comparisons using the method specified by adjust. The permutation based importance of a prime implicant is then given by $1 -$ these adjusted p-values. Here, a possible threshold for calling a prime implicant important is 0.95.

Value

An object of class logicFS containing

`primes`	the prime implicants,
`vim`	the respective importance of the prime implicants,
`prop`	NULL,
`type`	the type of model (1: classification, 2: linear regression, 3: logistic regression),
`param`	further parameters (if `addInfo = TRUE`),
`mat.imp`	NULL,
`measure`	the name of the used importance measure,
`useN`	the value of `useN` from the original analysis with, e.g., `logicFS`,
`threshold`	the threshold suggested in `Details`,
`mu`	`mu`.

Author(s)

Holger Schwender, [email protected]

References

Schwender, H., Ruczinski, I., Ickstadt, K. (2011). Testing SNPs and Sets of SNPs for Importance in Association Studies. Biostatistics, 12, 18-32.

Permutation Based Importance Measures

Description

Computes the importances of input variables, SNPs, or sets of SNPs, respectively, based on permutations of the response. Currently only available for the classification and the logistic regression approach of logic regression.

Usage

  vim.permInput(object, n.perm = NULL, standardize = TRUE, 
    rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1, 
    adjust = "bonferroni", addMatPerm = FALSE, rand=NA)

  vim.permSNP(object, n.perm = NULL, standardize = TRUE,
     rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1,
     adjust = "bonferroni", addMatPerm = FALSE, rand = NA)

  vim.permSet(object, set = NULL, n.perm = NULL, standardize = TRUE,
     rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1,
     adjust = "bonferroni", addMatPerm = FALSE, rand = NA)
vim.permInput(object, n.perm = NULL, standardize = TRUE, 
    rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1, 
    adjust = "bonferroni", addMatPerm = FALSE, rand=NA)

  vim.permSNP(object, n.perm = NULL, standardize = TRUE,
     rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1,
     adjust = "bonferroni", addMatPerm = FALSE, rand = NA)

  vim.permSet(object, set = NULL, n.perm = NULL, standardize = TRUE,
     rebuild = FALSE, prob.case = 0.5, useAll = FALSE, version = 1,
     adjust = "bonferroni", addMatPerm = FALSE, rand = NA)

Arguments

`object`	an object of class `logicBagg`, i.e.\ the output of `logic.bagging`.
`set`	either a list or a character or numeric vector. If `NULL` (default), then it will be assumed that `data`, i.e.\ the data set used in the application of `logic.bagging`, has been generated using `make.snp.dummy` or similar functions for coding variables by binary variables, i.e.\ with a function that splits a variable, say SNPx, into the dummy variables SNPx.1, SNPx.2, ... (where the “." can also be any other sign, e.g., an underscore). If a character or a numeric vector, then the length of `set` must be equal to the number of variables used in `object`, i.e.\ the number of columns of `data` in the `logicBagg` object, and must specify the set to which a variable belongs either by an integer between 1 and the number of sets, or by a set name. If a variable should not be included in any of the sets, set the corresponding entry of `set` to `NA`. Using this specification of `set` it is not possible to assign a variable to more than one sets. For such a case, set `set` to a list (as follows). If `set` is a list, then each object in this list represents a set of variables. Therefore, each object must be either a character or a numeric vector specifying either the names of the variables that belongs to the respective set or the columns of `data` that contains these variables. If `names(set)` is `NULL`, generic names will be employed as names for the sets. Otherwise, `names(set)` are used.
`n.perm`	number of permutations used in the computation of the importances. By default (i.e.\ if `n.perm = NULL`), 100 permutations are used if `rebuild = TRUE` and the regression approach of logic regression has been used in `logic.bagging` (by setting `ntrees` to an integer larger than 1, or `glm.if.1tree = TRUE`). Otherwise, 1000 permutation are employed. Note that actually much more permutations should be used.
`standardize`	should the standardized importance measure be used?
`rebuild`	logical indicating whether the logic regression models should be rebuild (i.e.\ the parameters $\beta$ of the generalized linear models should be recomputed) after removing a variable or a set of variables from the logic trees and for each permutation of the response. Note that setting `rebuild = TRUE` increases the computation time substantially.
`prob.case`	a numeric value between 0 and 1. If the logistic regression approach of logic regression has been used in `logic.bagging`, then an observation will be classified as a case (or more exactly, as 1), if the class probability of this observation is larger than `prob.case`. Otherwise, `prob.case` is ignored.
`useAll`	logical indicating whether all $m *$ `n.perm` permuted values should be used in the computation of the permutation based p-values, where $m$ is the number of variables or sets of variables, respectively. If `FALSE`, the `n.perm` permuted values corresponding to the respective variable (or set of variables) are employed in the determination of the p-value of this variable (or set of variables).
`version`	either `1` or `2`. If `1`, then the importance measure is computed by 1 - padj, where padj is the adjusted p-value. If `2`, the importance measure is determined by -log10(padj), where a raw p-value equal to 0 is set to 1 / (10 * `n.perm`) to avoid infinitive importances.
`adjust`	character vector naming the method with which the raw permutation based p-values are adjusted for multiplicity. If `"qvalue"`, the function `qvalue.cal` from the package `siggenes` is used to compute q-values. Otherwise, `p.adjust` is used to adjust for multiple comparisons. See `p.adjust` for all other possible specifications of `adjust`. If `"none"`, the raw p-values will be used.
`addMatPerm`	should the (`n.perm` + 1) x $m$ matrix containing the original values (first column) and the permuted values (the remaining columns) of the importance measure for the $m$ variables or $m$ sets of variables be added to the output?
`rand`	an integer for setting the random number generator in a reproducible state.

Value

An object of class logicFS containing

`vim`	the values of the importance measure for the input variables, the SNPs, or the sets of SNPs, respectively,
`prop`	`NULL`,
`primes`	the names of the inputs, SNPs, or sets of variables, respectively,
`type`	the type of model (1: classification, 3: logistic regression),
`param`	`NULL`,
`mat.imp`	`NULL`,
`measure`	the name of the used importance measure,
`threshold`	0.95, i.e.\ the suggested threshold for calling an input, SNP or set of SNPs, respectively, important (this is just used as default value when plotting the importances, see argument `thres` of `plot.logicFS`),
`mu`	`NULL`,
`useN`	`TRUE`,
`name`	either `"Variable"`, `"SNP"`, or `"Set"`,
`mat.perm`	if `addMatPerm = FALSE`, `NULL`; otherwise, a matrix containing the original and the permuted values of the respective importance measure.

Author(s)

Holger Schwender, [email protected]

References

Schwender, H., Ruczinski, I., Ickstadt, K. (2011). Testing SNPs and Sets of SNPs for Importance in Association Studies. Biostatistics, 12, 18-32.

VIM for SNPs and Sets of Variables

Description

Quantifies the importances of SNPs or sets of variables, respectively, contained in a logic bagging model.

Usage

  vim.snp(object, useN = NULL, iter = NULL, standardize = NULL, 
     mu = 0, addMatImp = FALSE, prob.case = 0.5, 
     score = c("DPO", "Conc", "Brier", "PL"), ensemble = FALSE, 
     rand = NULL)

  vim.set(object, set = NULL, useN = NULL, iter = NULL, standardize = NULL, 
     mu = 0, addMatImp = FALSE, prob.case = 0.5, 
     score = c("DPO", "Conc", "Brier", "PL"), ensemble = FALSE,
     rand = NULL)
vim.snp(object, useN = NULL, iter = NULL, standardize = NULL, 
     mu = 0, addMatImp = FALSE, prob.case = 0.5, 
     score = c("DPO", "Conc", "Brier", "PL"), ensemble = FALSE, 
     rand = NULL)

  vim.set(object, set = NULL, useN = NULL, iter = NULL, standardize = NULL, 
     mu = 0, addMatImp = FALSE, prob.case = 0.5, 
     score = c("DPO", "Conc", "Brier", "PL"), ensemble = FALSE,
     rand = NULL)

Arguments

`object`	an object of class `logicBagg`, i.e.\ the output of `logic.bagging`.
`set`	either a list or a character or numeric vector. If `NULL` (default), then it will be assumed that `data`, i.e.\ the data set used in the application of `logic.bagging`, has been generated using `make.snp.dummy` or similar functions for coding variables by binary variables, i.e.\ with a function that splits a variable, say SNPx, into the dummy variables SNPx.1, SNPx.2, ... (where the “." can also be any other sign, e.g., an underscore). If a character or a numeric vector, then the length of `set` must be equal to the number of variables used in `object`, i.e.\ the number of columns of `data` in the `logicBagg` object, and must specify the set to which a variable belongs either by an integer between 1 and the number of sets, or by a set name. If a variable should not be included in any of the sets, set the corresponding entry of `set` to `NA`. Using this specification of `set` it is not possible to assign a variable to more than one sets. For such a case, set `set` to a list (as follows). If `set` is a list, then each object in this list represents a set of variables. Therefore, each object must be either a character or a numeric vector specifying either the names of the variables that belongs to the respective set or the columns of `data` that contains these variables. If `names(set)` is `NULL`, generic names will be employed as names for the sets. Otherwise, `names(set)` are used.
`useN`	logical specifying if the number of correctly classified out-of-bag observations should be used in the computation of the importance measure. If `FALSE`, the proportion of correctly classified oob observations is used instead. If `NULL` (default), then the specification of `useN` in `object` is used. In the survival case, `useN` is ignored.
`iter`	integer specifying the number of times the values of the variables in the respective set are permuted in the computation of the importance of this set. If `NULL` (default), the values of the variables are not permuted, but all variables belonging to the set are removed from the model. Permutation of variables is not available in the survival case, i.e. `iter` is set to `NULL`.
`standardize`	should a standardized version of the importance measure for a set of variables be returned? By default, `standardize = TRUE` is used in the classification and the (multinomial) logistic regression case, and `standarize` is set to `FALSE` in the linear regression case. Standardization is not available in the survival case. For details, see `mu`.
`mu`	a non-negative numeric value. Ignored if `standardize = FALSE`. Otherwise, a t-statistic for testing the null hypothesis that the importance of the respective set is equal to `mu` is computed.
`addMatImp`	should the matrix containing the improvements due to each of the sets in each of the logic regression models be added to the output? If `ensemble = TRUE` and `addMatImp = TRUE` in the survival case, the respective score of the full model is added to the output instead of an improvement matrix.
`prob.case`	a numeric value between 0 and 1. If the logistic regression approach of logic regression has been used in `logic.bagging`, then an observation will be classified as a case (or more exactly, as 1), if the class probability of this observation is larger than `prob.case`. Otherwise, `prob.case` is ignored.
`score`	a character string naming the score that should be used in the computation of the importance measure for a survival time analysis. By default, the distance between predicted outcomes (`score = "DPO"`) proposed by Tietz et al.\ (2018) is used in the determination of the importance of the variables. Alternatively, Harrell's C-Index (`"Conc"`), the Brier score (`"Brier"`), or the predictive partial log-likelihood (`"PL"`) can be used.
`ensemble`	in the case of a survival outcome, should `ensemble` importance measures (as, e.g., in `randomSurvivalSRC` be used? If `FALSE`, importance measures analogous to the ones in the logicFS analysis of other outcomes are used (see Tietz et al., 2018).
`rand`	an integer for setting the random number generator in a reproducible state.

Value

An object of class logicFS containing

`vim`	the importances of the sets of variables,
`prop`	`NULL`,
`primes`	the names of the sets of variables,
`type`	the type of model (1: classification, 2:linear regression, 3: logistic regression, 4: Cox regression),
`param`	further parameters (if `addInfo = TRUE` in the previous call of `logic.bagging`), or `NULL` (otherwise),
`mat.imp`	either a matrix containing the improvements due to the sets of variables for each of the models (if `addMatImp = TRUE` and `ensemble = FALSE`), or the respective score of the full model (if `addMatImp = TRUE` and `ensemble = TRUE`, or `NULL` (if `addMatImp = FALSE`),
`measure`	the name of the used importance measure,
`useN`	the value of `useN`,
`threshold`	`NULL` if `standardize = FALSE`, otherwise the $1-0.05/m$ quantile of the t-distribution with $B-1$ degrees of freedom, where $m$ is the number of sets and $B$ is the number of logic regression models composing `object`,
`mu`	`mu` (if `standardize = TRUE`), or `NULL` (otherwise),
`iter`	`iter`,
`name`	`"Set"`.

Author(s)

Holger Schwender, [email protected]; Tobias Tietz, [email protected]

References

Schwender, H., Ruczinski, I., Ickstadt, K. (2011). Testing SNPs and Sets of SNPs for Importance in Association Studies. Biostatistics, 12, 18-32.

Package 'logicFS'

Help Index

Example Data of logicFS

Description

See Also

Evaluate Prime Implicants

Description

Usage

Arguments

Value

Author(s)

Bagged Logic Regression

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Prime Implicants

Description

Usage

Arguments

Value

Author(s)

See Also

Prime Implicants

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Feature Selection with Logic Regression

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

SNPs to Dummy Variables

Description

Usage

Arguments

Details

Value

Note

Author(s)

Multinomial Logic Regression

Description

Usage

Arguments

Value

Author(s)

References

See Also

Variable Importance Plot

Description

Usage

Arguments

Author(s)

See Also

Survival and Cumulative Hazard Function Plot

Description

Usage

Arguments

Author(s)

Predict Method for logicBagg objects

Description

Usage

Arguments

Value

Author(s)

See Also

Predict Method for mlogreg Objects

Description