Generate a Generalized Linear Model (GLM) Response modelMaarten Speekenbrink
Ensemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. However, forward...
Ensemble predictors such as the random forest are known to have superior accuracy but their black-box predictions are difficult to interpret. In contrast, a generalized linear model (GLM) is very interpretable especially when forward feature selection is used to construct the model. However, forward...
This example loads therpartpackage and then pushes thekyphosisdata set to a temporary database table that has the proxyore.frameobjectKYPHOSIS. The example builds a Generalized Linear Model using theore.glmfunction and one using theglmfunction and calls thesummaryfunction on the models. ...
We evaluated the selectivity of individual cells in a more systematic manner using a generalized linear model (GLM)39,74. The GLM included each task variable (sample cue, test cue, choice) and their interactions (including interactions between sample cue and test cue to allow for XOR selectivity...
We used a generalized linear model (GLM) to deal with the correlations arising from nested data (see Methods and Statistical Supplement, p. 1). Generalized linear models account for correlated data without inflating Type I error rates or loss of statistical power (problems that accompany ...
The purpose of this article is to find the settings of the factors which simultaneously optimize several mean responses in a multivariate generalized linear model (GLM) environment. The generalized distance approach, initially developed for the simultaneous optimization of several linear response surface ...
GLM: general linear model GSES: The General Self-Efficacy Scale HAQ: Health assessment questionnaire HbA1c: glycosylated hemoglobin, type A1c HR-QoL: health-related quality of life ICER: incremental cost-effectiveness ratios IgM-RF: rheumatoid factor of the immunoglobulin M class ...
To address these limitations, we modeled the non-Gaussian neural spike train observations by using a generalized linear model (GLM) that encapsulates covariates of neural activity, including the neurons ' own spiking history, concurrent ensemble activity, and extrinsic covariates (EMG signals). In ...
A generalized linear model (GLM) assuming a Gaussian distribution was utilized, and the likelihood ratio test was used to determine the statistical significance of the models. To create figures, ggplot in R was used [51]. 2.2.6. Beta Diversity/Dissimilarity Analyses Prior to the beta-diversity...