Perguntas com a marcação «normality-assumption»

27
Os graus de liberdade podem ser um número não inteiro?

Quando uso o GAM, o DF residual é (última linha do código). O que isso significa? Indo além do exemplo do GAM, em geral, o número de graus de liberdade pode ser um número não inteiro?26,626.626.6 > library(gam) > summary(gam(mpg~lo(wt),data=mtcars)) Call: gam(formula = mpg ~ lo(wt), data =...

r degrees-of-freedom gam machine-learning pca lasso probability self-study bootstrap expected-value regression machine-learning linear-model probability simulation random-generation machine-learning distributions svm libsvm classification pca multivariate-analysis feature-selection archaeology r regression dataset simulation r regression time-series forecasting predictive-models r mean sem lavaan machine-learning regularization regression conv-neural-network convolution classification deep-learning conv-neural-network regression categorical-data econometrics r confirmatory-factor scale-invariance self-study unbiased-estimator mse regression residuals sampling random-variable sample probability random-variable convergence r survival weibull references autocorrelation hypothesis-testing distributions correlation regression statistical-significance regression-coefficients univariate categorical-data chi-squared regression machine-learning multiple-regression categorical-data linear-model pca factor-analysis factor-rotation classification scikit-learn logistic p-value regression panel-data multilevel-analysis variance bootstrap bias probability r distributions interquartile time-series hypothesis-testing normal-distribution normality-assumption kurtosis arima panel-data stata clustered-standard-errors machine-learning optimization lasso multivariate-analysis ancova machine-learning cross-validation
21
Como projetar um novo vetor no espaço PCA?

Depois de executar a análise de componentes principais (PCA), quero projetar um novo vetor no espaço do PCA (ou seja, encontrar suas coordenadas no sistema de coordenadas do PCA). Eu calculei o PCA na linguagem R usando prcomp. Agora eu devo poder multiplicar meu vetor pela matriz de rotação PCA....

r pca r variance heteroscedasticity misspecification distributions time-series data-visualization modeling histogram kolmogorov-smirnov negative-binomial likelihood-ratio econometrics panel-data categorical-data scales survey distributions pdf histogram correlation algorithms r gpu parallel-computing approximation mean median references sample-size normality-assumption central-limit-theorem rule-of-thumb confidence-interval estimation mixed-model psychometrics random-effects-model hypothesis-testing sample-size dataset large-data regression standard-deviation variance approximation hypothesis-testing variance central-limit-theorem kernel-trick kernel-smoothing error sampling hypothesis-testing normality-assumption philosophical confidence-interval modeling model-selection experiment-design hypothesis-testing statistical-significance power asymptotics information-retrieval anova multiple-comparisons ancova classification clustering factor-analysis psychometrics r sampling expectation-maximization markov-process r data-visualization correlation regression statistical-significance degrees-of-freedom experiment-design r regression curve-fitting change-point loess machine-learning classification self-study monte-carlo markov-process references mathematical-statistics data-visualization python cart boosting regression classification robust cart survey binomial psychometrics likert psychology asymptotics multinomial
20
Quais são os valores corretos para precisão e rechamada em casos extremos?

Precisão é definida como: p = true positives / (true positives + false positives) É verdade que, como true positivese false positivesabordagem 0, a precisão se aproxima de 1? Mesma pergunta para recall: r = true positives / (true positives + false negatives) No momento, estou implementando...

precision-recall data-visualization logarithm references r networks data-visualization standard-deviation probability binomial negative-binomial r categorical-data aggregation plyr survival python regression r t-test bayesian logistic data-transformation confidence-interval t-test interpretation distributions data-visualization pca genetics r finance maximum probability standard-deviation probability r information-theory references computational-statistics computing references engineering-statistics t-test hypothesis-testing independence definition r censoring negative-binomial poisson-distribution variance mixed-model correlation intraclass-correlation aggregation interpretation effect-size hypothesis-testing goodness-of-fit normality-assumption small-sample distributions regression normality-assumption t-test anova confidence-interval z-statistic finance hypothesis-testing mean model-selection information-geometry bayesian frequentist terminology type-i-and-ii-errors cross-validation smoothing splines data-transformation normality-assumption variance-stabilizing r spss stata python correlation logistic logit link-function regression predictor pca factor-analysis r bayesian maximum-likelihood mcmc conditional-probability statistical-significance chi-squared proportion estimation error shrinkage application steins-phenomenon
19
Como testar as diferenças entre duas médias de grupos quando os dados não são normalmente distribuídos?

Eliminarei todos os detalhes e experimentos biológicos e citarei apenas o problema em questão e o que fiz estatisticamente. Gostaria de saber se está certo e, se não, como proceder. Se os dados (ou minha explicação) não forem claros o suficiente, tentarei explicar melhor editando. Suponha que eu...

hypothesis-testing t-test normality-assumption wilcoxon-mann-whitney central-limit-theorem