SES.temporal(target, reps, group, dataset, max_k = 3, threshold = 0.05,
test = NULL, ini = NULL, user_test = NULL, hash = FALSE, hashObject = NULL,
slopes = FALSE, ncores = 1)
MMPC.temporal(target, reps, group, dataset, max_k = 3, threshold = 0.05,
test = NULL, ini = NULL, user_test = NULL, hash = FALSE, hashObject = NULL,
slopes = FALSE, ncores = 1)testIndGLMM, which fits linear mixed models.
Important: the generated hashObjects should be used only when the same dataset is re-analyzed, possibly with different values of max_k and threshold.
http://www.mensxmachina.org/publications/discovering-multiple-equivalent-biomarker-signatures/
The MMPC function mplements the MMPC algorithm as presented in "Tsamardinos, Brown and Aliferis. The max-min hill-climbing Bayesian network structure learning algorithm" adapted to longitudinal data. http://www.dsl-lab.org/supplements/mmhc_paper/paper_online.pdf
For faster computations in the internal SES functions, install the suggested package "gRbase". In addition, the output value "univ" along with the output value "hashObject" can speed up the computations of subesequent runs of SES and MMPC. The first run with a specific pair of hyper-parameters (threshold and max_k) the univariate associations tests and the conditional independence tests (test statistic and logarithm of their corresponding p-values) are stored and returned. In the next run(s) with different pair(s) of hyper-parameters you can use this information to save time. With a few thousands of variables you will see the difference, which can be up to 50%.
The max_k option: the maximum size of the conditioning set to use in the conditioning independence test. Larger values provide more accurate results, at the cost of higher computational times. When the sample size is small (e.g., $<50$ observations)="" the="" max_k="" parameter="" should="" be="" $\leq="" 5$,="" otherwise="" conditional="" independence="" test="" may="" not="" able="" to="" provide="" reliable="" results.<="" p="">
If the dataset contains missing (NA) values, they will automatically be replaced by the current variable (column) mean value with an appropriate warning to the user after the execution.
If the target is a single integer value or a string, it has to corresponds to the column number or to the name of the target feature in the dataset. In any other case the target is a variable that is not contained in the dataset.
If the current 'test' argument is defined as NULL or "auto" and the user_test argument is NULL then the algorithm automatically selects only available, which is testIndGLMM.
Conditional independence test functions to be pass through the user_test argument should have the same signature of the included test. See "?testIndFisher" for an example.
For all the available conditional independence tests that are currently included on the package, please see "?CondIndTests".
If two or more p-values are below the machine epsilon (.Machine$double.eps which is equal to 2.220446e-16), all of them are set to 0. To make the comparison or the ordering feasible we use the logarithm of the p-value. The max-min heuristic though, requires comparison and an ordering of the p-values. Hence, all conditional independence tests calculate the logarithm of the p-value.
If there are missing values in the dataset (predictor variables) columnwise imputation takes place. The median is used for the continuous variables and the mode for categorical variables. It is a naive and not so clever method. For this reason the user is encouraged to make sure his data contain no missing values.
If you have percentages, in the (0, 1) interval, they are automatically mapped into $R$ by using the logit transformation and a linear mixed model is fitted. If you have binary data, logistic mixed regression is applied and if you have discrete data (counts), Poisson mixed regression is applied.
Tsamardinos, Brown and Aliferis (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning, 65(1), 31-78.
I. Tsamardinos, M. Tsagris and V. Lagani (2015). Feature selection for longitudinal data. Proceedings of the 10th conference of the Hellenic Society for Computational Biology & Bioinformatics (HSCBB15)
Pinheiro J. and D. Bates. Mixed-effects models in S and S-PLUS. Springer Science \& Business Media, 2006.
CondIndTests, testIndGLMM
## require(gRbase) #for faster computations in the internal functions
## require(lme4)
## data(sleepstudy)
## attach(sleepstudy)
## x <- matrix(rnorm(180 * 100),ncol = 100) ## unrelated preidctor variables
## m1 <- SES.temporal(Reaction, Days, Subject, x)
## m2 <- MMPC.temporal(Reaction, Days, Subject, x)
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