Bootstrap analysis

The key practical question is How can the sample size be quantified Initial approaches to answering this question were provided by Grossfield et al. [1] and by Lyman and Zuckerman [10]. Grossfield et al. employed a bootstrap analysis to a set of 26 independent trajectories for rhodopsin, extending the previous... 

Fig. 7. Bootstrap analysis among characters in a parsimony analysis. The tree to the right of each matrix is the most parsimonious tree for that matrix. The final results of the bootstrap analysis are shown in the tree at the bottom. The number of times each branch was supported in the bootstrap replication is shown as a percentage. Outgroup rooting carries the assumption of ingroup monophyly, so no confidence interval can be assigned to the branch that unites the ingroup.

Disadvantages are that these response surface models are not available in standard software packages. Like all nonlinear statistical methods, the methodology is still subject to research, which has 2 important consequences. First, correlation structure of the parameters in these nonlinear models is usually not addressed. Second, the assessment of the test statistic is based on approximate statistical procedures. The statistical analyses can probably be improved through bootstrap analysis or permutation tests. 

Figure 5.2 Neighbor-joining tree of sequence similarity in the 7TM domains of human monoamine-related GPCRs. The receptors are coded according to the SwissProt nomenclature scheme orphan receptors are coded with the prefix GPR followed by an index number. Distance corresponds to percent sequence identity, scale is indicated by a 5% bar. The tree is rooted by outgrouping the node of the H, and muscarinic receptors. The numbers on the branches are the result of bootstrap analysis (1 OCX) replicates). For further details see [60].

Table 9.16 Results of bootstrap analysis of tobramycin model presented in Eq. (9.14) with questionable observations and influential patients removed.

Bootstrapping is a resampling tree evaluation method that works with distance, parsimony, likelihood, and just about any other tree derivation method. It was invented in 1979 (Efron, 1979) and introduced as a tree evaluation method in phylogenetic analysis by Felsenstein (1985). The result of bootstrap analysis is typically a number associated with a particular branch in the phylogenetic tree that gives the proportion of bootstrap replicates that supports the monophyly of the clade. 

The efficiency and validity of methods to correct bias in parameter and strength estimates have been evaluated by carrying out simulations of the input data to the bootstrap analysis. Since the bootstrap itself is a simulation, the check of validity in bias correction is then a simulation of a simulation therefore, the computer execution time mushrooms quickly. 

ABSTRACT Soil hydraulic properties parameters are the crucial input parameters in water and solute transport modeling in the vadose zone. Pedotransfer functions are an alternative to direct measurement for obtaining soil hydraulic properties. In this study, Pedotransfer functions were established from particle-size distribution, bulk density, and organic matter using linear regression method with bootstrap analysis, then its prediction performance were further compared to artificial neural network and Vereecken with the Mean Error (ME) and the Mean Absolute Errors (MAE). The developed models were ranked the best models for estimation of hydraulic parameters with ME and MAE were less than O.lOcm /cm whereas Vereecken performed the worse results for hydraulic parameters, especially ME and MAE for parameters of a reached 0.74 1/cm and 0.63 1/cm, respectively. Function uncertainty evaluation was performed in Hydrus-ID model to simulate soil water, the developed pedotransfer functions and artificial neural network provide similar higher level of accuracy and precision with ME. 

FIGURE 4.23 Cladogram of a bootstrap analysis (1000 iterations) with PAUP. Numbers above branches are bootstrap values (%). 

The sequence data from the wild species and the cultivated peanut was subjected to PAUP computer analysis (Swofford 3.0), which infers phylogenies based on maximum parsimony. One phylogenetic tree was generated. The data was also subjected to bootstrap analysis to determine the confidence level of the branches. 

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