vim.set: VIM for SNPs and Sets of Variables

Description Usage Arguments Value Author(s) References See Also


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


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



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


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.


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.


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.


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.


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.


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.

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 Otherwise, is ignored.


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.


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).


an integer for setting the random number generator in a reproducible state.


An object of class logicFS containing


the importances of the sets of variables,




the names of the sets of variables,


the type of model (1: classification, 2:linear regression, 3: logistic regression, 4: Cox regression),


further parameters (if addInfo = TRUE in the previous call of logic.bagging), or NULL (otherwise),


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),


the name of the used importance measure,


the value of useN,


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 (if standardize = TRUE), or NULL (otherwise),






Holger Schwender,; Tobias Tietz,


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

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.

See Also

logic.bagging, logicFS, vim.logicFS, vim.input, vim.ebam, vim.chisq

logicFS documentation built on Nov. 8, 2020, 5:23 p.m.