Topic
Carpet plot
About: Carpet plot is a research topic. Over the lifetime, 60 publications have been published within this topic receiving 742 citations.
Papers published on a yearly basis
Papers
TL;DR: In this article, the authors proposed the normal quantile plot as a preferred alternative to the funnel plot for meta-analysis, which can be used to check statistical assumptions on which numerical analyses are based.
Abstract: In a meta-analysis, graphical displays can be used to check statistical assumptions for numerical procedures and they can be used to discover important patterns in the data. The authors propose the normal quantile plot as a preferred alternative to the funnel plot for such purposes. The normal quantile plot, like the funnel plot, can be used to investigate whether all studies come from a single population and to search for publication bias. However, the normal quantile plot is easier to interpret than the funnel plot, especially when it includes 95% confidence bands. In addition, the normal quantile plot can be used to check the normality assumption for numerical procedures. The funnel plot cannot be used for this latter purpose. Most people have heard the phrase, "A picture is worth a thousand words." This phrase seems especially applicable to producers and consumers of metaanalytic reviews. In a meta-analysis, graphical displays (figures, plots) can be used to enhance numerical analyses in at least two ways. First, graphical displays can be used to discover patterns and relations among variables in a meta-analysis. Second, graphical displays can be used to check statistical assumptions on which numerical analyses are based. One of the most popular graphical displays for exploring meta-analytic data sets is the funnel plot (Light & Pillemer, 1984). In this article, we begin by describing the funnel plot and its uses. We then propose the normal quantile plot as a preferred alternative to the funnel plot.
183 citations
TL;DR: In this paper, the authors examined the use of the probability probability probability plot (p-p plot) as a method for comparing treatment effects and compared it with the quantile-quantile plot (q-q plot), which is an alternative means of describing treatment effects.
Abstract: This article examines the use of the probability-probability plot (p-p plot) as a method for comparing treatment effects. To begin in the context of three examples the p-p plot is contrasted with the quantile-quantile plot (q-q plot), which is an alternative means of describing treatment effects. In these examples it is shown that p-p plots representing different experimental conditions or patient populations allow scale-invariant comparisons of treatment effects but q-q plots do not; that the presentation of the treatment effect by the p-p plot is not obscured by outliers, whereas it may be in the q-q plot; and that the p-p plot encompasses information in the control distributions that is important for the assessment of treatment effects but that is not incorporated in the q-q plot. Theoretical considerations are presented that show that under appropriate assumptions, the p-p plot is a maximal invariant and contains all the information necessary to make scale-invariant comparisons of treatment e...
81 citations
Posted Content•
27 Sep 2007TL;DR: A model where spikes are effective at times multiple of a characteristic time scale, which can be arbitrary small (in particular, well beyond the numerical precision) is proposed, and an order parameter is introduced, providing a relevant characterization of the computational capabilities of the network.
Abstract: We present a mathematical analysis of a networks with Integrate-and-Fire neurons with conductance based synapses. Taking into account the realistic fact that the spike time is only known within some finite precision, we propose a model where spikes are effective at times multiple of a characteristic time scale, which can be arbitrary small (in particular, well beyond the numerical precision). We make a complete mathematical characterization of the model-dynamics and obtain the following results. The asymptotic dynamics is composed by finitely many stable periodic orbits, whose number and period can be arbitrary large and can diverge in a region of the synaptic weights space, traditionally called the ``edge of chaos'', a notion mathematically well defined in the present paper. Furthermore, except at the edge of chaos, there is a one-to-one correspondence between the membrane potential trajectories and the raster plot. This shows that the neural code is entirely ``in the spikes'' in this case. As a key tool, we introduce an order parameter, easy to compute numerically, and closely related to a natural notion of entropy, providing a relevant characterization of the computational capabilities of the network. This allows us to compare the computational capabilities of leaky and Integrate-and-Fire models and conductance based models. The present study considers networks with constant input, and without time-dependent plasticity, but the framework has been designed for both extensions.
39 citations
Journal Article•
TL;DR: In this paper, a mathematical analysis of a network with Integrate-and-fire neurons with conductance-based synapses is presented, where spikes are effective at times multiple of a characteristic time scale, which can be arbitrary small.
Abstract: We present a mathematical analysis of a networks with Integrate-and-Fire neurons with conductance based synapses. Taking into account the realistic fact that the spike time is only known within some finite precision, we propose a model where spikes are effective at times multiple of a characteristic time scale, which can be arbitrary small (in particular, well beyond the numerical precision). We make a complete mathematical characterization of the model-dynamics and obtain the following results. The asymptotic dynamics is composed by finitely many stable periodic orbits, whose number and period can be arbitrary large and can diverge in a region of the synaptic weights space, traditionally called the ``edge of chaos'', a notion mathematically well defined in the present paper. Furthermore, except at the edge of chaos, there is a one-to-one correspondence between the membrane potential trajectories and the raster plot. This shows that the neural code is entirely ``in the spikes'' in this case. As a key tool, we introduce an order parameter, easy to compute numerically, and closely related to a natural notion of entropy, providing a relevant characterization of the computational capabilities of the network. This allows us to compare the computational capabilities of leaky and Integrate-and-Fire models and conductance based models. The present study considers networks with constant input, and without time-dependent plasticity, but the framework has been designed for both extensions.
38 citations
TL;DR: In this paper, an empirical equation for characterizing the heterogeneity of non-isotropic fields is proposed, which is an extension of Fairfield Smith's (1938) empirical law describing heterogeneity in isotropic fields.
36 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2016 | 1 |
| 2015 | 2 |
| 2014 | 5 |
| 2012 | 3 |
| 2011 | 6 |