fit.method: Methods to fit models

In lampk/R306:

Description

Methods to fit models

Usage

fit.method(model, data, type = c("lm", "glm", "glm.step.AIC", "glm.step.BIC",
  "glm.best.AIC", "glm.best.BIC", "gam", "glmnet", "glmnet.1se", "glmnet.cv",
  "glmnet.boot", "adaptiveLASSO", "SCAD", "penalized", "stability",
  "stability.step.AIC", "cox", "cox.step.AIC", "cox.step.BIC", "cox.best.AIC",
  "cox.best.BIC", "glmnet.cox1", "glmnet.cox2", "rpart1", "rpart2",
  "randomForest", "gbm", "gbm.cox", "coxtv", "coxtv.step.AIC",
  "coxtv.step.BIC"), pi = 0.8, rep = 100, size = 0.5, seed = NULL, ...)

fit.method.lm(model, data, ...)

fit.method.lm.step.AIC(model, data, ...)

fit.method.lm.step.BIC(model, data, ...)

fit.method.glm(model, data, ...)

fit.method.glm.step.AIC(model, data, ...)

fit.method.stab.step.AIC(model, data, pi = 0.8, B = 100, size = 0.5,
  seed = NULL, parallel = FALSE, ...)

fit.method.glm.step.BIC(model, data, ...)

fit.method.glm.step(model, data, ...)

fit.method.cox(model, data, ...)

fit.method.cox.step.AIC(model, data, ...)

fit.method.cox.step.BIC(model, data, ...)

fit.method.cox.step(model, data, ...)

fit.method.coxtv(model, data, ...)

fit.method.coxtv.step(model, data, ...)

fit.method.glmnet(model, data, family = "binomial", lambda = NULL,
  nfolds = 10, type.measure = "deviance", standardize = TRUE, alpha = 1,
  ...)

fit.method.glmnet.cv(model, data, family = "binomial", lambda = NULL,
  refit = TRUE, nfolds = 10, type.measure = "deviance",
  standardize = TRUE, alpha = 1, krepeat = 10, nse = 1, nlambda = 100,
  lambda.min.ratio = 1e-04, parallel = FALSE, ...)

fit.method.glmnet.boot(model, data, family = "binomial", lambda = NULL,
  refit = TRUE, krepeat = 100, type.measure = "deviance",
  standardize = TRUE, alpha = 1, nse = 1, nlambda = 100,
  lambda.min.ratio = 1e-04, parallel = FALSE, ...)

fit.method.penalized(model, data, ...)

fit.method.alasso(model, data, family = "binomial", lambda = NULL,
  nfolds = 10, type.measure = "deviance", standardize = TRUE, alpha = 1,
  ...)

fit.method.scad(model, data, family = "binomial", penalty = "SCAD",
  gamma = 3.7, alpha = 1, lambda.min = 0.001, nlambda = 100)

fit.method.stability(model, data, size = 0.632, steps = 100, weakness = 1,
  error = 0.05, pi_thr = 0.6, error.type = "pfer", family = "binomial",
  standardize = TRUE, intercept = TRUE, alpha = 1, ...)

fit.method.gam(model, data, family = "binomial", ...)

fit.method.rpart1(model, data, ...)

fit.method.rpart2(model, data, method = "anova",
  control = rpart.control(minsplit = 20, xval = 10, cp = 0), ...)

fit.method.randomForest(model, data, ntree = 500, ...)

fit.method.gbm(model, data, distribution = "bernoulli", cv.folds = 10,
  n.trees = 3000, interaction.depth = 2, verbose = FALSE,
  shrinkage = 0.001, n.cores = 1, ...)

fit.method.glm.glmulti.AIC(model, data, ...)

fit.method.glm.glmulti.BIC(model, data, ...)

fit.method.cox.glmulti.AIC(model, data, ...)

fit.method.cox.glmulti.BIC(model, data, ...)

fit.method.glm.glmulti(model, data, ...)

fit.method.cox.glmulti(model, data, ...)

Arguments

model	Model formula to fit
data	Data to fit model
...	Other arguments

Value

fitted model

Functions

• fit.method.lm: Linear regression

• fit.method.lm.step.AIC: Linear regression with stepwise variable selection using AIC as stopping rule

• fit.method.lm.step.BIC: Linear regression with stepwise variable selection using BIC as stopping rule

• fit.method.glm: Logistic regression

• fit.method.glm.step.AIC: Logistic regression with stepwise variable selection using AIC as stopping rule

• fit.method.stab.step.AIC: Logistic regression with stepwise variable selection using AIC as stopping rule + stability selection

• fit.method.glm.step.BIC: Logistic regression with stepwise variable selection using BIC as stopping rule

• fit.method.glm.step: Logistic regression with stepwise variable selection

• fit.method.cox: Cox regression

• fit.method.cox.step.AIC: Cox regression with stepwise variable selection using AIC as stopping rule

• fit.method.cox.step.BIC: Cox regression with stepwise variable selection using BIC as stopping rule

• fit.method.cox.step: Cox regression with stepwise variable selection

• fit.method.coxtv: Cox regression with time-dependent variable

• fit.method.coxtv.step: Cox regression with time-dependent variable using stepwise variable selection

• fit.method.glmnet: Logistic regression with LASSO (using glmnet)

• fit.method.glmnet.cv: Logistic regression with LASSO (using glmnet) and refit (only for logistic regression)

• fit.method.glmnet.boot: Logistic regression with LASSO (using glmnet) and refit (only for logistic regression) ## use bootstrap

• fit.method.penalized: Logistic regression with LASSO (using penalized)

• fit.method.alasso: Logistic regression with adaptive LASSO

• fit.method.scad: Logistic regression with SCAD

• fit.method.stability: GLM LASSO with stability selection

• fit.method.gam: GAM Logistic regression

• fit.method.rpart1: CART: build and prune tree at the same time

• fit.method.rpart2: CART: build, then prune tree

• fit.method.randomForest: Random forest

• fit.method.gbm: GLM Boosting

• fit.method.glm.glmulti.AIC: Logistic regression with best subset using AIC as criterion

• fit.method.glm.glmulti.BIC: Logistic regression with best subset using BIC as criterion

• fit.method.cox.glmulti.AIC: Cox regression with best subset using AIC as criterion

• fit.method.cox.glmulti.BIC: Cox regression with best subset using BIC as criterion

• fit.method.glm.glmulti: Logistic regression with best subset

• fit.method.cox.glmulti: Cox regression with best subset

lampk/R306 documentation built on May 20, 2019, 7:34 p.m.