这种情况下,使用潜增长曲线模型拟合我们的数据,或者说在我们的数据中建立潜增长模型将不合适,应该考虑到样本异质性问题,而能解决这一问题的模型之一便是潜类别增长模型(Latent Class Growth Model, LCGM)。 潜类别增长模型可以简单理解为:先将样本分成不同的潜类别组,然后在每个潜类别组中建立潜增长曲线模型去描述...
潜类别增长模型（LCGM）旨在解决在一些情况下，群体内个体间存在显著差异，不能满足同质性假设的问题。相比潜增长曲线模型（LGCM），LCGM通过将样本分为不同的潜类别组，并在各组内建立特定的潜增长曲线模型，以此来描述各组内个体特征随时间变化的过程。LCGM实际上是潜在类别模型和潜增长曲线模型的组合...
To identify common drinking trajectories, we used latent class growth modeling (LCGM), an analytic approach based on finite mixture modeling (Muthen and Muthen, 2000, Nagin, 1999). We sought to characterize profiles of drinkers over time by constructing prototypical trajectories of the variable of...
Latent class growth model (LCGM) Latent class trajectories We performed an unconditional LCGM. Table 4 compares the resulting model fit indices. They were beginning with the most parsimonious one-class model through to the four- class model (Fig. 1), according to BIC, a-BIC, Entropy, ...
Pedometer-based step-count data across 18 consecutive months were fitted to a latent growth model (LGM) and a latent class growth model (LCGM). Baseline characteristics were regressed on latent class membership. The longitudinal change in PA was best fit to a piecewise LGM with seasonal ...
Can I assume that my growth model, which has a TOTAL of 4 time points, would NOT be appropriate for piecewise modeling ? I am unsure of the interpretation for the estimate -2.287 for Intercepts C#1. Is this the inverse natural log (or odds) of being in class #1 vs class 2, adjusted...
latent class variables [cross-sectional factor mixture models, longitudinal growth mixture models (GMMs)], whether the data were collected cross-sectionally or longitudinally (latent class vs. latent transition), and whether variability is allowed within the LCs [latent class growth modeling (LCGM) ...
Deciding on the number of classes in latent class analysis and growth mixture modeling: a monte carlo simulation study. Str Eq Mod. 2007;14(4):535–69. Google Scholar Woo SE, Jebb AT, Tay L, Parrigon S. Putting the person in the center: review and synthesis of person-centered ...
PRR pattern recognition receptor, Th cells helper T cells, TGF transforming growth factor, DC dendritic cell, NK cell natural kill cell, Fas-FasL Fas and Fas ligand, TNF tumor necrosis factor, MTBMycobacterium tuberculosis, GM-CSF granulocyte–macrophage colony-stimulating factor, M1 type I macroph...
SupirFactor can distinguish contextual networks by embedding context-specific assigned data and computing ERV only within that data set. We explore learning context-dependent GRNs here; we evaluate this ability on the single-cell RNA expression data set, which has samples annotated by growth conditio...