Sentence examples for multiple correspondence analyses from inspiring English sources

Groups of defining characteristics with greater differentiation capacity were identified by multiple correspondence analyses.

Cluster analysis was used to define a well typology, while principal component and multiple correspondence analyses were used to relate the nitrate pollution to well characteristics and land use properties.

Then multiple correspondence analyses is used through two main stages: 1) with the membership value table coming from healthy drivers and 2) with the table from disabled drivers, the table rows being considered as supplementary points.

Clustering and multiple correspondence analyses showed that the DLN pathologies induced by the two species were statistically significantly different and identified the most discriminating elementary lesions.

Multiple correspondence analyses were run for the entire dataset, and several vetted subsets that removed potential problematic taxa (see Supporting Information Text S1).

Final multiple correspondence analyses were then conducted using the coordinates of each organization for the significant dimensions obtained by multiple correspondence analyses of each organizational component.

Firstly, multiple correspondence and cluster analyses allowed identification of sufficiently robust clinical and treatment subgroups.

Multivariate analyses using multiple correspondence analysis (MCA) and ascendant hierarchical clustering on clinical, biological and therapeutic characteristics of SHPT were performed to identify subgroups of patients [ 22].

To obtain a preliminary architecture of the data, we conducted classical descriptive multivariate analyses using multiple correspondence analysis (MCA), clustering and principal component analysis (PCA) [17] as a first step to evaluate the data structure, reveal unknown relationships and reveal clusters of genes potentially involved in immune responses.

Multiple correspondence analysis can be an alternative tool when analysing relationships between different variables in terms of multicollinearity [ 30].

We used the multiple correspondence analysis (MCA) of SPSS version 16.0 to analyse the relationships among four variables, i.e., gene GC% groups (GC%), substitution, the 5' (-1) immediately adjacent nucleotides (5' adjacent base) and 3' (+1) immediately adjacent nucleotides (3' adjacent base).