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The advantage of the above dataset sampler is that a large number of datasets can be created with variable number of samples and cancer types as well as variable level of similarities between the chosen cancer types.
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With the band structures and modal properties of passive dielectric photonic structures largely mapped out, analytic models can be created with only a few dynamic variables based on modal properties.
Based on exploration of variables which showed trends toward significance, a logistic regression model was created with the variable "experience of violence" as dependent variable with no-prevalence as the reference category and eleven independent variables.
A correlation matrix (Additional file 1: Table S1) was created with the variables (metal concentrations) in its rows and its columns.
To investigate whether early and late onset ARF can present different patterns of nonpulmonary associated organ dysfunction (OD), a prospective data bank was created with physiological variables and organ function scores.
The logistic regression model was created with these 4 variables while the ML model was built with 3 extra variables, namely the presence of seizure, confusion and clinical signs of skull fracture.
A Cox proportional hazards regression model was created with the predictor variables and interactions listed above with time to death as the outcome.
A random point generator populated the classifiable area with 1013 points, for which a matrix was created with the four GIS variable values.
The prognosis bands were created with model containing the variables age, GCS, pupillary reactivity and major extracranial injury.
New binary variables were generated, in both Lima and Durban datasets, as follows: (i) for each allelic variant on each of the three HLA loci (HLA-A, HLA-B, and HLA-C) a binary variable was created with value 1 if the subject had that respective variant and 0 otherwise; (ii) for each OLP a binary variable was set with value 1 if the subject has responded to this OLP and 0 otherwise.
We also evaluated single variable associations between candidate variables (which were created with a range of lag times and parameterization techniques) and CFP calls to identify the lag time and weather variable parameterization technique most associated with CFP calls.
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