- Referenced in 194 articles
- MEMSS: Data sets from Mixed-effects Models in S .Data sets and sample analyses from...
- Referenced in 93 articles
- seek to separate out or ’un-mix’ data that is assumed to arise...
- Referenced in 73 articles
- Approach of linear mixed model in longitudinal data analysis using SAS. Linear mixed models ... analysis of the longitudinal (repeated measures) data. One major advantage of this methodology is that ... complexities of typical longitudinal data sets. The analysis of linear mixed model methodology ... analysis of repeated measurements data using the SAS Software. PROC MIXED in SAS provides...
- Referenced in 419 articles
- analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis ... nonparametric analysis. A few examples include mixed models, generalized linear models, correspondence analysis, and structural...
- Referenced in 60 articles
- Data sets from ”SAS System for Mixed Models”. Data sets and sample lmer analyses corresponding ... Stroup and Wolfinger (1996), ”SAS System for Mixed Models”, SAS Institute...
- Referenced in 67 articles
- response data, longitudinal and clustered data using generalized linear mixed models, and regression data using...
- Referenced in 96 articles
- methods appropriate for the mix of continuous, discrete, and categorical data often found in applied...
- Referenced in 23 articles
- Bayesian Gaussian copula factor models for mixed data. Gaussian factor models have proven widely useful ... multivariate data. There is rich literature on their extension to mixed categorical and continuous variables...
- Referenced in 26 articles
- MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary ... clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within ... data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts...
- Referenced in 30 articles
- values particularly in the case of mixed-type data. It uses a random forest trained...
- Referenced in 116 articles
- chosen. Finally, ABACUS provides many basic data structures and useful tools for the implementation ... algorithms. It is designed both for general mixed integer optimization problems and for combinatorial optimization ... code and the design of abstract data structures and algorithms are met by object oriented...
- Referenced in 299 articles
- Data for ”GAMs: An Introduction with R”. Data sets and scripts used in the book ... linear, generalized linear, generalized additive and mixed models. Moreover, the book contains explanations...
- Referenced in 11 articles
- package clustMD: Model Based Clustering for Mixed Data. Model-based clustering of mixed data...
- Referenced in 31 articles
- fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression ... meta-analyses of binomial and person-time data, the package also provides functions that implement ... variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects (conditional) logistic and Poisson...
- Referenced in 8 articles
- type (numerical or nominal) or with mix data type (numerical and nominal) and only ... real-world applications data to handle both feature types and their mix. In this paper ... high-dimensional data that contains numerical or nominal or mix of attributes. We suggest...
- Referenced in 44 articles
- Gamma latent variables. We propose a new data-augmentation strategy for fully Bayesian inference ... binomial regression, nonlinear mixed-effect models, and spatial models for count data. In each case...
- Referenced in 5 articles
- Multivariate analysis of mixed data: The PCAmixdata R package. Mixed data type arise when observations ... analysis methods to incorporate this type of data. The key techniques included in the package ... MFAmix (multiple factor analysis with mixed type data within a dataset). This paper gives...
- Referenced in 7 articles
- rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group ... groups of categorical variables or even mixed data by a likelihood ratio approach. Closed...
- Referenced in 24 articles
- Regression Models for Ordinal Data. Implementation of cumulative link (mixed) models also known as ordered...
- Referenced in 65 articles
- NLMIXED procedure fits nonlinear mixed models—that is, models in which both fixed and random ... most common being pharmacokinetics and overdispersed binomial data. PROC NLMIXED enables you to specify ... conditional distribution for your data (given the random effects) having either a standard form (normal ... programming statements. PROC NLMIXED fits nonlinear mixed models by maximizing an approximation to the likelihood...