Preguntas etiquetadas con contingency-tables

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¿Por qué Anova () y drop1 () proporcionaron diferentes respuestas para GLMM?

Tengo un GLMM de la forma: lmer(present? ~ factor1 + factor2 + continuous + factor1*continuous + (1 | factor3), family=binomial) Cuando lo uso drop1(model, test="Chi"), obtengo resultados diferentes a los que uso Anova(model, type="III")del paquete del automóvil o summary(model). Estos dos...

r anova glmm r mixed-model bootstrap sample-size cross-validation roc auc sampling stratification random-allocation logistic stata interpretation proportion r regression multiple-regression linear-model lm r cross-validation cart rpart logistic generalized-linear-model econometrics experiment-design causality instrumental-variables random-allocation predictive-models data-mining estimation contingency-tables epidemiology standard-deviation mean ancova psychology statistical-significance cross-validation synthetic-data poisson-distribution negative-binomial bioinformatics sequence-analysis distributions binomial classification k-means distance unsupervised-learning euclidean correlation chi-squared spearman-rho forecasting excel exponential-smoothing binomial sample-size r change-point wilcoxon-signed-rank ranks clustering matlab covariance covariance-matrix normal-distribution simulation random-generation bivariate standardization confounding z-statistic forecasting arima minitab poisson-distribution negative-binomial poisson-regression overdispersion probability self-study markov-process estimation maximum-likelihood classification pca group-differences chi-squared survival missing-data contingency-tables anova proportion
8
¿Por qué un modelo estadístico se sobreajusta si se le da un gran conjunto de datos?

Mi proyecto actual puede requerir que construya un modelo para predecir el comportamiento de un determinado grupo de personas. el conjunto de datos de entrenamiento contiene solo 6 variables (la identificación es solo para fines de identificación): id, age, income, gender, job category, monthly...

modeling large-data overfitting clustering algorithms error spatial r regression predictive-models linear-model average measurement-error weighted-mean error-propagation python standard-error weighted-regression hypothesis-testing time-series machine-learning self-study arima regression correlation anova statistical-significance excel r regression distributions statistical-significance contingency-tables regression optimization measurement-error loss-functions image-processing java panel-data probability conditional-probability r lme4-nlme model-comparison time-series probability probability conditional-probability logistic multiple-regression model-selection r regression model-based-clustering svm feature-selection feature-construction time-series forecasting stationarity r distributions bootstrap r distributions estimation maximum-likelihood garch references probability conditional-probability regression logistic regression-coefficients model-comparison confidence-interval r regression r generalized-linear-model outliers robust regression classification categorical-data r association-rules machine-learning distributions posterior likelihood r hypothesis-testing normality-assumption missing-data convergence expectation-maximization regression self-study categorical-data regression simulation regression self-study self-study gamma-distribution modeling microarray synthetic-data