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  • Interpretability
  • But how do we capture the quantitative details of the dynamics of complex systems with models simple enough to offer substantial interpretability? (pnas.org)
  • describe
  • Transparency is also a key feature: model details describe key characteristics of the coefficients, and global details provide high-level statistics. (oracle.com)
  • predictors
  • The algorithm can build and score quality models that use a virtually limitless number of predictors (attributes). (oracle.com)
  • The best regression models are those in which the predictors correlate highly with the target, but there is very little correlation between the predictors themselves. (oracle.com)
  • syntax to display the non-linear formula including predictors (i.e. (r-project.org)
  • specification
  • Non-linear models are incredibly flexible and powerful, but require much more care with respect to model specification and priors than typical generalized linear models. (r-project.org)
  • More complex uses of % model are discussed below % The output of encode is a complete model specification stored as a % structure that I usually call glm. (mathworks.com)
  • Distribution-free specification tests for dynamic linear models ," Econometrics Journal , Royal Economic Society, vol. 12(s1), pages 105-134, January. (repec.org)
  • Bounds
  • Bounds in Competing Risks Models and the War on Cancer ," Econometrica , Econometric Society, vol. 74(6), pages 1675-1698, November. (repec.org)
  • SQL function to obtain the confidence bounds of a model prediction. (oracle.com)
  • subset
  • Many genes may show significant change when only a subset of the model terms are considered. (warwick.ac.uk)
  • Including more variables into the analysis when using ANOVA may result in missing a lot of interesting effects for genes whose expression changes in response to only a subset of the experiment variables, so a method must be used to fit a model to each gene individually. (warwick.ac.uk)
  • simple
  • Next, a representation of every log-linear model as the intersection of several log-linear models is discussed, where all of the latter models belong to one of two classes of simple log-linear models. (springer.com)
  • The link function transforms the target range to potentially -infinity to +infinity so that the simple form of linear models can be maintained. (oracle.com)
  • We introduce a general method based on the simple idea that even complicated time series are locally linear. (pnas.org)
  • anova
  • Once built the same model can be analyzed in many ways including least-squares regression, fit and lack-of-fit statistics, ANOVA (or ANACOVA), MANOVA (or MANACOVA) This tutorial will use several examples to show how to build different types of models. (mathworks.com)
  • functions
  • Functions that work with the model will be illustrated as needed, but will also be explained in more detail elsewhere. (mathworks.com)
  • variables
  • Further, these models admit a representation using graphs, where the nodes are the variables in the model. (springer.com)
  • One is the model of conditional joint independence of a group of variables, given all other variables (and graphical log-linear models) may be represented as intersections of such models only and (in the case of non-graphical models) no highest-order conditional interaction among a group of variables. (springer.com)
  • to a data frame) containing the variables in the model. (ethz.ch)
  • The problem of fitting a saturated model to all genes becomes larger as more variables are included in the model. (warwick.ac.uk)
  • Linear models allow extension ofANOVA to allow inclusion of numeric variables. (warwick.ac.uk)
  • data
  • Further topics related to the use of log-linear models in data analysis are also considered. (springer.com)
  • First, the selection and interpretation of log-linear models are illustrated in regression type and non-regression type problems, using real data sets. (springer.com)
  • Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data ," Review of Economic Studies , Oxford University Press, vol. 75(3), pages 835-864. (repec.org)
  • Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data ," Post-Print halshs-00754272, HAL. (repec.org)
  • To address these difficulties, we detail an approach based on local linear models within windows determined adaptively from data. (pnas.org)
  • Motivated by the remarkable increase in data quantity and quality as well as growing computational power, one approach is to fit a single global model to the dynamics with properties extracted from data. (pnas.org)
  • Summarise data with an appropriate statistical model. (southampton.ac.uk)
  • time
  • Setting priors is a non-trivial task in all kinds of models, especially in non-linear models, so you should always invest some time to think of appropriate priors. (r-project.org)
  • Quite often, you may be forced to change your priors after fitting a non-linear model for the first time, when you observe different MCMC chains converging to different posterior regions. (r-project.org)
  • Also, short-time brain oscillations were studied by using jPCA ( 8 ), a method that approximates the dynamics as a linear model with skew-symmetric couplings. (pnas.org)