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Last data update: 2014.03.03

R: A function for filtering under multivariate normal response...

filtered

R Documentation

A function for filtering under multivariate normal response distribution

Description

Filters random components of the mixed model with a stationary or non-stationary stochastic process component, under multivariate normal response distribution

Usage

filtered(formula, data = NULL, id, process = "bm", timeVar, estimate, subj.id)

Arguments

`formula`	a typical `R` formula for the fixed effects component of the model
`data`	a data frame from which the variables are to be extracted
`id`	a vector for subject identification
`process`	a character string, `"bm"` for Brownian motion, `"ibm"` for integrated Brownian motion, `"iou"` for integrated Ornstein-Uhlenbeck process, `"sgp-powered-power-method"` for stationary process with powered correlation function, and `"sgp-matern-kappa"` for stationary process with Matern correlation function
`timeVar`	a vector for the time variable at which filtered values are required
`estimate`	a vector for the maximum likelihood estimates
`subj.id`	a vector for IDs of the subjects for whom filtering will be carried out

Details

For the details of process, see lmenssp.

Value

Returns the results as lists for the random intercept and stochastic process

Author(s)

Ozgur Asar, Peter J. Diggle

References

Diggle PJ, Sousa I, Asar O (2015) Real time monitoring of progression towards renal failure in primary care patients. Biostatistics, 16(3), 522-536.

Examples

# loading the data set and subsetting it for the first 20 patients 
# for the sake illustration of the usage of the functions
data(data.sim.ibm)
data.sim.ibm.short <- data.sim.ibm[data.sim.ibm$id <= 20, ]

# model formula to be used below
formula <- log.egfr ~ sex + bage + fu + pwl

# obtaining the maximum likelihood estimates of the model
# parameters for the model with integrated Brownian motion
fit.ibm <- lmenssp(formula = formula, data = data.sim.ibm.short,
  id = data.sim.ibm.short$id, process = "ibm", timeVar = data.sim.ibm.short$fu, 
  silent = FALSE)
fit.ibm

# filtering for subjects with ID=1 and 2
subj.id <- c(1, 2)
fil.res <- filtered(formula = formula, data = data.sim.ibm.short, 
    id = data.sim.ibm.short$id, process = "ibm",  timeVar = data.sim.ibm.short$fu, 
    estimate = fit.ibm$estimate[, 1], subj.id = subj.id)
fil.res

# filtering for a new (hypothetical) subject
data.501   <- data.frame(id = c(501, 501, 501), sex = c(0, 0, 0),
   bage = c(50, 50, 50), fu = c(0, 0.2, 0.4), 
   pwl = c(0, 0, 0), log.egfr = c(4.3, 2.1, 4.1))
data.501
fil.501 <- filtered(formula = formula, data = data.501, 
   id = data.501$id, process = "ibm", timeVar = data.501$fu,  
   estimate = fit.ibm$estimate[, 1], subj.id = 501)
fil.501