Pertanyaan yang diberi tag generalized-linear-model

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R / mgcv: Mengapa produk tensor () dan ti () menghasilkan permukaan yang berbeda?

The mgcvpaket untuk Rmemiliki dua fungsi untuk pas interaksi produk tensor: te()dan ti(). Saya memahami pembagian kerja dasar antara keduanya (menyesuaikan interaksi non-linear vs menguraikan interaksi ini menjadi efek utama dan interaksi). Yang tidak saya mengerti adalah mengapa te(x1, x2)dan...

r gam mgcv conditional-probability mixed-model references bayesian estimation conditional-probability machine-learning optimization gradient-descent r hypothesis-testing wilcoxon-mann-whitney time-series bayesian inference change-point time-series anova repeated-measures statistical-significance bayesian contingency-tables regression prediction quantiles classification auc k-means scikit-learn regression spatial circular-statistics t-test effect-size cohens-d r cross-validation feature-selection caret machine-learning modeling python optimization frequentist correlation sample-size normalization group-differences heteroscedasticity independence generalized-least-squares lme4-nlme references mcmc metropolis-hastings optimization r logistic feature-selection separation clustering k-means normal-distribution gaussian-mixture kullback-leibler java spark-mllib data-visualization categorical-data barplot hypothesis-testing statistical-significance chi-squared type-i-and-ii-errors pca scikit-learn conditional-expectation statistical-significance meta-analysis intuition r time-series multivariate-analysis garch machine-learning classification data-mining missing-data cart regression cross-validation matrix-decomposition categorical-data repeated-measures chi-squared assumptions contingency-tables prediction binary-data trend test-for-trend matrix-inverse anova categorical-data regression-coefficients standard-error r distributions exponential interarrival-time copula log-likelihood time-series forecasting prediction-interval mean standard-error meta-analysis meta-regression network-meta-analysis systematic-review normal-distribution multiple-regression generalized-linear-model poisson-distribution poisson-regression r sas cohens-kappa
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Mengapa Anova () dan drop1 () memberikan jawaban berbeda untuk GLMM?

Saya memiliki GLMM formulir: lmer(present? ~ factor1 + factor2 + continuous + factor1*continuous + (1 | factor3), family=binomial) Ketika saya menggunakan drop1(model, test="Chi"), saya mendapatkan hasil yang berbeda daripada jika saya menggunakan Anova(model, type="III")dari paket mobil atau...

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
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Cara mendapatkan interval kepercayaan pada perubahan populasi r-square

Demi contoh sederhana berasumsi bahwa ada dua model regresi linier Model 1 memiliki tiga prediktor, x1a, x2b, danx2c Model 2 memiliki tiga prediktor dari model 1 dan dua prediktor tambahan x2adanx2b Ada persamaan regresi populasi di mana varians populasi yang dijelaskan adalah untuk Model 1 dan...

regression confidence-interval estimation r-squared shrinkage anova t-test references tukey-hsd machine-learning boosting r clustering fishers-exact generalized-linear-model model probit link-function r survival probability distributions dice logistic lme4-nlme glmm meta-analysis distributions distributions factor-analysis r anova repeated-measures post-hoc
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Catat Kemungkinan untuk GLM

Dalam kode berikut ini saya melakukan regresi logistik pada data yang dikelompokkan menggunakan glm dan "dengan tangan" menggunakan mle2. Mengapa fungsi logLik di R memberi saya kemungkinan logLik (fit.glm) = - 2.336 yang berbeda dari yang logLik (fit.ml) = - 5.514 saya dapatkan dengan...

r self-study generalized-linear-model