lavaSearch2 (version 1.0.0)

extractData: Extract Data From a Model

Description

Extract data from a model using nlme::getData, riskRegression::coxDesign or model.frame.. If it fails it will try to extract it by its name according to model$call$data.

Usage

extractData(object, design.matrix, as.data.frame)
# S3 method for lm
extractData(object, design.matrix = FALSE,
  as.data.frame = TRUE)
# S3 method for coxph
extractData(object, design.matrix = FALSE,
  as.data.frame = TRUE)
# S3 method for cph
extractData(object, design.matrix = FALSE,
  as.data.frame = TRUE)
# S3 method for lvmfit
extractData(object, design.matrix = FALSE,
  as.data.frame = TRUE)
# S3 method for gls
extractData(object, design.matrix = FALSE,
  as.data.frame = TRUE)
# S3 method for lme
extractData(object, design.matrix = FALSE,
  as.data.frame = TRUE)

Arguments

object

the fitted model.

design.matrix

should the data be extracted after transformation (e.g. conversion of categorical variables to dummy variables)? Otherwise the original data will be returned.

as.data.frame

should the output be converted into a dataa.frame object?

Examples

# NOT RUN {
set.seed(10)
n <- 101

#### linear regression ####
Y1 <- rnorm(n, mean = 0)
Y2 <- rnorm(n, mean = 0.3)
Id <- findInterval(runif(n), seq(0.1,1,0.1))
data.df <- rbind(data.frame(Y=Y1,G="1",Id = Id),
           data.frame(Y=Y2,G="2",Id = Id)
           )
m.lm <- lm(Y ~ G, data = data.df)
a <- extractData(m.lm, design.matrix = TRUE)
b <- extractData(m.lm, design.matrix = FALSE)

library(nlme)
m.gls <- gls(Y ~ G, weights = varIdent(form = ~ 1|Id), data = data.df)
c <- extractData(m.gls)
m.lme <- lme(Y ~ G, random = ~ 1|Id, data = data.df)
d <- extractData(m.lme)

library(lava)
e.lvm <- estimate(lvm(Y ~ G), data = data.df)
e <- extractData(e.lvm)
e <- extractData(e.lvm, design.matrix = TRUE)

#### survival #### 
library(survival)

# }
# NOT RUN {
  library(riskRegression) ## needs version >=1.4.3
  library(data.table)
  dt.surv <- sampleData(n, outcome = "survival")
  m.cox <- coxph(Surv(time, event) ~ X1 + X2, data = dt.surv, x = TRUE, y = TRUE)
  f <- extractData(m.cox, design.matrix = FALSE)
  f <- extractData(m.cox, design.matrix = TRUE)
  m.cox <- coxph(Surv(time, event) ~ strata(X1) + X2, data = dt.surv, x = TRUE, y = TRUE)
  f <- extractData(m.cox, design.matrix = TRUE)
# }
# NOT RUN {
#### nested fuuctions ####
fct1 <- function(m){
   fct2(m)
}
fct2 <- function(m){ 
   extractData(m)
}
g <- fct1(m.gls)
# }