140 research outputs found
A multigroup extension to piecewise path analysis
No full textAbstract Path analysis allows one to test the consistency of data to hypothesized causal relationships between variables. Often, interest lies in how the hypothesized dependencies differ between groups. Multigroup comparisons can be made by imposing various constraints: constraints on the topology, the path coefficients, the residual variances, and more. To date, only classical path analysis and structural equation modeling can account for differences between groups. These techniques have assumptions that are often not appropriate for ecological studies. The d‐sep test and the recently developed generalized chi‐squared test relax many of these assumptions for path models that can be represented as directed acyclic graphs (DAGs), but are currently lacking a multigroup test. In this paper, we develop a multigroup extension to the d‐sep test. Furthermore, we show how a recently developed generalized chi‐squared test and AIC for DAGs can be used for multigroup testing. The approaches are illustrated by a worked example and implemented in the commonly used statistical package, R. Practical recommendations for multigroup modeling are made, and advantages and disadvantages of the multigroup d‐sep and the chi‐squared test are discussed
Generalized AIC and chi-squared statistics for path models consistent with directed acyclic graphs
No full textWe explain how to obtain a generalized maximum-likelihood chi-square statistic, X2 ML, and a full-model Akaike Information Criterion (AIC) statistic for piecewise structural equation modeling (SEM); that is, structural equations without latent variables whose causal topology can be represented as a directed acyclic graph (DAG). The full piecewise SEM is decomposed into submodels as a Markov network, each of which can have different distributional assumptions or functional links and that can be modeled by any method that produces maximum-likelihood parameter estimates. The generalized X2 ML is a function of the difference in the maximum likelihoods of the model and its saturated equivalent and the full-model AIC is calculated by summing the AIC statistics of each of the submodels.</p
Modelling the propagation of electromagnetic fields in proton beam radiotherapy
No full textDe begeleider en/of auteur heeft geen toestemming gegeven tot het openbaar maken van de scriptie.
The supervisor and/or the author did not authorize public publication of the thesis.
Testing piecewise structural equations models in the presence of latent variables and including correlated errors
No full textPath models, expressed as Directed Acyclic Graphs (DAGs), and the testing of such DAGs via a d-sep test, have become popular because they can incorporate complicated data structures that are difficult or impossible to accommodate in classical structural equation modeling. However, d-sep tests cannot accommodate DAGs that include unmeasured (latent) variables. We describe (i) how to convert a DAG with latent variables into an observationally equivalent graph without latents (a Mixed Acyclic Graph, MAG), (ii) how this MAG identifies which latents can/cannot be ignored without changing the causal meaning of the original DAG, and (iii) how to perform the MAG equivalent of a d-sep test
Testing Model Fit in Path Models with Dependent Errors Given Non-Normality, Non-Linearity and Hierarchical Data
No full textWe provide a generic method of testing path models that include dependent errors, nonlinear functional relationships and using nonnormal, hierarchically structured data. First, we provide a decomposition of the causal model into smaller, independent sets. These sets can be modeled independently of each other with methods that respect the type of data in these sets. Second, we introduce copulas to model the dependent errors between non-normally distributed variables. Our method yields identical results as classical covariance-based path modelling when meeting its assumptions of linearity and normality, outperforms classical SEM given nonlinear functional relationships, and can easily accommodate any parametric probability function and nonlinear functional relationships
Monotonicity and stability of periodic polling models
Get PDFPolling Systems;Stability;operations research
No evidence of flowering synchronization upon floral volatiles for a short lived annual plant species: Revisiting an appealing hypothesis
No full textBackground: Self-incompatible plants require simultaneous flowering mates for crosspollination and reproduction. Though the presence of flowering conspecifics and pollination agents are important for reproductive success, so far no cues that signal the flowering state of potential mates have been identified. Here, we empirically tested the hypothesis that plant floral volatiles induce flowering synchrony among self-incompatible conspecifics by acceleration of flowering and flower opening rate of non-flowering conspecifics. We exposed Brassica rapa Maarssen, a self-incompatible, in rather dense patches growing annual, to (1) flowering or non-flowering conspecifics or to (2) floral volatiles of conspecifics by isolating plants in separate containers with a directional airflow. In the latter, odors emitted by non-flowering conspecifics were used as control. Results: Date of first bud, duration of first flower bud, date of first flower, maximum number of open flowers and flower opening rate were not affected by the presence of conspecific flowering neighbors nor by floral volatiles directly. Conclusions: This study presents a compelling approach to empirically test the role of flower synchronization by floral volatiles and challenges the premises that are underlying this hypothesis. We argue that the life history of the plant as well as its interaction with pollinators and insect herbivores, as well as the distance over which volatiles may serve as synchronization cue, set constraints on the fitness benefits of synchronized flowering which needs to be taken into account when testing the role of floral volatiles in synchronized flowering.</p
Touch and plant defence : volatile communication with neighbours
No full textPlants use many cues to get the latest news on their environment, from different parts of the light spectrum predicting future shading by neighbours, to volatiles released by insect-infested plants preparing neighbouring plants for future attack, or touch providing information about impending mechanical stress or herbivore attacks. Markovic et al. (2019) have now shown that gentle touching of leaves leads to emission of volatiles that can activate the same set of defence genes in neighbouring plants as were up-regulated in the touched plant
Testing Model Fit in Path Models with Dependent Errors Given Non-Normality, Non-Linearity and Hierarchical Data
No full textWe provide a generic method of testing path models that include dependent errors, nonlinear functional relationships and using nonnormal, hierarchically structured data. First, we provide a decomposition of the causal model into smaller, independent sets. These sets can be modeled independently of each other with methods that respect the type of data in these sets. Second, we introduce copulas to model the dependent errors between non-normally distributed variables. Our method yields identical results as classical covariance-based path modelling when meeting its assumptions of linearity and normality, outperforms classical SEM given nonlinear functional relationships, and can easily accommodate any parametric probability function and nonlinear functional relationships.</p
- …
