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Each sample has a score along each model component which shows the location of the sample in this model and can be used to detect sample patterns, groupings, similarities or differences [43, 44].
The time domain basis functions along each model dimension are the complex product of a sinusoid basis function with a damping function.
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You also get a picture of each model along with the suggested retail price.
The process of trust measurement in each model along with the relevant flowchart is also described.
Hazard ratios (HR) and their 95% confidence intervals (CI) are reported for each model along with coefficient p-values.
Lastly, ROC curves were calculated for each model along with an area under each respective curve in order to compare models for accuracy in predicting HCH.
In these analyses, the demographic variables were forced into each model along with those variables that met the 0.15 criterion in the individual analyses of the variable groupings.
Table 3 presents the classification accuracy on the training, validation, and test sets of each model, along with its optimal parameters.
In stage 1, linear mixed-models were constructed with the outcome ERI (or its subcomponents) and a dummy variable for 'survey year' in the fixed effect section of each model along with covariates for confounders [ 43].
The predicted patterns were shown as mean curves calculated from 1000 replicates of each model, along with dashed lines representing the boundaries of zones in which the models predict that real curves should fall 95% of the time.
We discuss autocorrelation issues for each model, below, along with other features of their application.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com