On the detection of dynamic responses in a drought-perturbed tropical rainforest in Borneo

The dynamics of aseasonal lowland dipterocarp forest in Borneo is influenced by perturbation from droughts. These events might be increasing in frequency and intensity in the future. This paper describes drought-affected dynamics between 1986 and 2001 in Sabah, Malaysia, and considers how it is possible, reliably and accurately, to measure both coarse- and fine-scale responses of the forest. Some fundamental concerns about methodology and data analysis emerge. In two plots forming 8 ha, mortality, recruitment, and stem growth rates of trees ≥10 cm gbh (girth at breast height) were measured in a ‘pre-drought’ period (1986–1996), and in a period (1996–2001) including the 1997–1998 ENSO-drought. For 2.56 ha of subplots, mortality and growth rates of small trees (10–<50 cm gbh) were found also for two sub-periods (1996–1999, 1999–2001). A total of c. 19 K trees were recorded. Mortality rate increased by 25% while both recruitment and relative growth rates increased by 12% for all trees at the coarse scale. For small trees, at the fine scale, mortality increased by 6% and 9% from pre-drought to drought and on to ‘post-drought’ sub-periods. Relative growth rates correspondingly decreased by 38% and increased by 98%. Tree size and topography interacted in a complex manner with between-plot differences. The forest appears to have been sustained by off-setting elevated tree mortality by highly resilient stem growth. This last is seen as the key integrating tree variable which links the external driver (drought causing water stress) and population dynamics recorded as mortality and recruitment. Suitably sound measurements of stem girth, leading to valid growth rates, are needed to understand and model tree dynamic responses to perturbations. The proportion of sound data, however, is in part determined by the drought itself.

Plotting regression coefficients and other estimates in Stata

Graphical presentation of regression results has become increasingly popular in the scientific literature, as graphs are much easier to read than tables in many cases. In Stata such plots can be produced by the -marginsplot- command. However, while -marginsplot- is very versatile and flexible, it has two major limitations: it can only process results left behind by -margins- and it can only handle one set of results at the time. In this article I introduce a new command called -coefplot- that overcomes these limitations. It plots results from any estimation command and combines results from several models into a single graph. The default behavior of -coefplot- is to plot markers for coefficients and horizontal spikes for confidence intervals. However, -coefplot- can also produce various other types of graphs. The capabilities of -coefplot- are illustrated in this article using a series of examples.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

Plotting regression coefficients and other estimates

Graphical display of regression results has become increasingly popular in presentations and in scientific literature because graphs are often much easier to read than tables. Such plots can be produced in Stata by the marginsplot command (see [R] marginsplot). However, while marginsplot is versatile and flexible, it has two major limitations: it can only process results left behind by margins (see [R] margins), and it can handle only one set of results at a time. In this article, I introduce a new command called coefplot that overcomes these limitations. It plots results from any estimation command and combines results from several models into one graph. The default behavior of coefplot is to plot markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce other types of graphs. I illustrate the capabilities of coefplot by using a series of examples.

Diffusion-weighted MR imaging of upper abdominal organs: field strength and intervendor variability of apparent diffusion coefficients

PURPOSE To determine the variability of apparent diffusion coefficient (ADC) values in various anatomic regions in the upper abdomen measured with magnetic resonance (MR) systems from different vendors and with different field strengths. MATERIALS AND METHODS Ten healthy men (mean age, 36.6 years ± 7.7 [standard deviation]) gave written informed consent to participate in this prospective ethics committee-approved study. Diffusion-weighted (DW) MR imaging was performed in each subject with 1.5- and 3.0-T MR systems from each of three vendors at two institutions. Two readers independently measured ADC values in seven upper abdominal regions (left and right liver lobe, gallbladder, pancreas, spleen, and renal cortex and medulla). ADC values were tested for interobserver differences, as well as for differences related to field strength and vendor, with repeated-measures analysis of variance; coefficients of variation (CVs) and variance components were calculated. RESULTS Interreader agreement was excellent (intraclass coefficient, 0.876). ADC values were (77.5-88.8) ×10(-5) mm(2)/sec in the spleen and (250.6-278.5) ×10(-5) mm(2)/sec in the gallbladder. There were no significant differences between ADC values measured at 1.5 T and those measured at 3.0 T in any anatomic region (P >.10 for all). In two of seven regions at 1.5 T (left and right liver lobes, P < .023) and in four of seven regions at 3.0 T (left liver lobe, pancreas, and renal cortex and medulla, P < .008), intervendor differences were significant. CVs ranged from 7.0% to 27.1% depending on the anatomic location. CONCLUSION Despite significant intervendor differences in ADC values of various anatomic regions of the upper abdomen, ADC values of the gallbladder, pancreas, spleen, and kidney may be comparable between MR systems from different vendors and between different field strengths.

Saddlepoint Approximations to the Probability of Ruin in Finite Time for the Compound Poisson Risk Process Perturbed by Diffusion

A large deviations type approximation to the probability of ruin within a finite time for the compound Poisson risk process perturbed by diffusion is derived. This approximation is based on the saddlepoint method and generalizes the approximation for the non-perturbed risk process by Barndorff-Nielsen and Schmidli (Scand Actuar J 1995(2):169–186, 1995). An importance sampling approximation to this probability of ruin is also provided. Numerical illustrations assess the accuracy of the saddlepoint approximation using importance sampling as a benchmark. The relative deviations between saddlepoint approximation and importance sampling are very small, even for extremely small probabilities of ruin. The saddlepoint approximation is however substantially faster to compute.