https://www.daiphys.com/portal/ 2026-05-01T17:53:56+00:00 https://www.daiphys.com/portal/ https://www.daiphys.com/portal/_media/wiki/dokuwiki.svg text/html 2023-04-07T00:52:15+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.daiphys.com/portal/statistics/tools/index.html?rev=1680828735&do=diff Tools * Lifelines * R * SAS * Stata * Statsmodels text/html 2023-04-07T00:52:53+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.daiphys.com/portal/statistics/tools/lifelines.html?rev=1680828773&do=diff Lifelines Sample import lifelines data = pd.read_csv('sample.csv') duration = 'DUR' event = 'EVT' formula = 'EXP1 + EXP2 + EXP3' model = lifelines.CoxPHFitter(baseline_estimation_method='breslow') model.fit(data, duration_col=duration, event_col=event, formula=formula, fit_options={'step_size':0.001}, show_progress=True) model.print_summary(decimals=5) text/html 2023-04-07T00:53:35+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.daiphys.com/portal/statistics/tools/r.html?rev=1680828815&do=diff R Sample list.files() setwd('project') library(survival) df <- read.csv('sample.csv', header=TRUE) data <- df[, c(1, 2, 3, 4, 5)] model <- glm(data$OBJ ~ ., data=data, family=binomial(link='logit')) model <- coxph(Surv(data$DUR, data$EVT) ~ ., data=data, method='breslow') summary <- summary(model) coef <- summary$coefficient text/html 2023-04-07T00:55:19+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.daiphys.com/portal/statistics/tools/sas.html?rev=1680828919&do=diff SAS References SAS <https://www.sas.com/> text/html 2023-04-07T00:56:33+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.daiphys.com/portal/statistics/tools/stata.html?rev=1680828993&do=diff Stata References Stata <https://www.stata.com/> text/html 2023-04-07T00:57:07+00:00 Anonymous (anonymous@undisclosed.example.com) https://www.daiphys.com/portal/statistics/tools/statsmodels.html?rev=1680829027&do=diff Statsmodels Sample import statsmodels.api as sm import statsmodels.formula.api as smf sm.show_versions() data = pd.read_csv('sample.csv') formula = 'OBJ ~ EXP1 + EXP2 + EXP3' model = smf.glm(formula=formula, data=data, family=sm.families.Binomial()).fit() print(model.summary()) print(model.conf_int(alpha=0.05))