Preguntas etiquetadas con interactive-visualization

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Calcular la repetibilidad de los efectos de un modelo más antiguo

Acabo de encontrar este artículo , que describe cómo calcular la repetibilidad (también conocida como confiabilidad, también conocida como correlación intraclase) de una medición a través del modelado de efectos mixtos. El código R sería: #fit the model fit =

mixed-model reliability intraclass-correlation repeatability spss factor-analysis survey modeling cross-validation error curve-fitting mediation correlation clustering sampling machine-learning probability classification metric r project-management optimization svm python dataset quality-control checking clustering distributions anova factor-analysis exponential poisson-distribution generalized-linear-model deviance machine-learning k-nearest-neighbour r hypothesis-testing t-test r variance levenes-test bayesian software bayesian-network regression repeated-measures least-squares change-scores variance chi-squared variance nonlinear-regression regression-coefficients multiple-comparisons p-value r statistical-significance excel sampling sample r distributions interpretation goodness-of-fit normality-assumption probability self-study distributions references theory time-series clustering econometrics binomial hypothesis-testing variance t-test paired-comparisons statistical-significance ab-test r references hypothesis-testing t-test normality-assumption wilcoxon-mann-whitney central-limit-theorem t-test data-visualization interactive-visualization goodness-of-fit
9
Bootstrapping paramétrico, semiparamétrico y no paramétrico para modelos mixtos

Los siguientes injertos se toman de este artículo . Soy novato en bootstrap e intento implementar el bootstrapping paramétrico, semiparamétrico y no paramétrico para el modelo mixto lineal con R bootpaquete. Código R Aquí está mi

r mixed-model bootstrap central-limit-theorem stable-distribution time-series hypothesis-testing markov-process r correlation categorical-data association-measure meta-analysis r anova confidence-interval lm r bayesian multilevel-analysis logit regression logistic least-squares eda regression notation distributions random-variable expected-value distributions markov-process hidden-markov-model r variance group-differences microarray r descriptive-statistics machine-learning references r regression r categorical-data random-forest data-transformation data-visualization interactive-visualization binomial beta-distribution time-series forecasting logistic arima beta-regression r time-series seasonality large-data unevenly-spaced-time-series correlation statistical-significance normalization population group-differences demography