R Graphical Manual

Browse All

Last data update: 2014.03.03

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

smoothed

R Documentation

A function for smoothing under multivariate normal response distribution

Description

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

Usage

smoothed(formula, data = NULL, id, process = "bm", timeVar, estimate, 
subj.id = NULL, fine = NULL, eq.forec = NULL, uneq.forec = NULL)

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
`estimate`	a vector for the maximum likelihood estimates
`fine`	a numerical value for smoothing at fine intervals within the follow-up period
`subj.id`	a vector of IDs of the subject for whom smoothing is to be carried out
`eq.forec`	a two element vector for equally spaced forecasting
`uneq.forec`	a two-column data frame or matrix for forecasting at desired time points

Details

For 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

Asar O, Ritchie J, Kalra P, Diggle PJ (2015) Acute kidney injury amongst chronic kidney disease patients: a case-study in statistical modelling. To be submitted.

Diggle PJ (1988) An approach to the analysis of repeated measurements. Biometrics, 44, 959-971.

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

# smoothing for subject with ID=1 and 2
subj.id <- c(1, 2)
smo.res <- smoothed(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)
smo.res

# smoothing with fine interval of 0.01 within the follow-up period
smo.within <- smoothed(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, fine = 0.01)
smo.within

# one, two and three month forecasting for patients with IDs = 1 and 2
eq.forecast <- smoothed(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, 
    eq.forec = c(1/12, 3))
eq.forecast

# forecasting at arbitrary time points for patients with IDs = 1 and 2
uneq.forec <- data.frame(c(1, 1, 1, 2, 2), c(1/12, 2/12, 6/12, 1/12, 3/12))
uneq.forecast <- smoothed(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], uneq.forec = uneq.forec)
uneq.forecast

## smoothing for a new (hypothetical) patient 
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))
new.id     <-  501

# at observed time points
smo.501 <- smoothed(formula = formula, data = data.501, 
    id = data.501$id, process = "ibm", timeVar = data.501$fu, 
    estimate = fit.ibm$estimate[, 1], subj.id = new.id)
smo.501

# at fine interval of 0.01 within the follow-up period
smo.within.501 <- smoothed(formula = formula, data = data.501, 
    id = data.501$id, process = "ibm", timeVar = data.501$fu,  
    estimate = fit.ibm$estimate[, 1], subj.id = new.id, fine = 0.01)
smo.within.501

# one, two and three month forecasting 
eq.forecast.501 <- smoothed(formula = formula, data = data.501, 
    id = data.501$id, process = "ibm", timeVar = data.501$fu, 
    estimate = fit.ibm$estimate[, 1], subj.id = new.id, 
    eq.forec = c(1/12, 3))
eq.forecast.501

# forecasting at arbitrary time points 
uneq.forec.501 <- data.frame(c(501, 501, 501), c(1/12, 2/12, 4/12))
uneq.forecast.501 <- smoothed(formula = formula, data = data.501, 
    id = data.501$id, process = "ibm", timeVar = data.501$fu, 
    estimate = fit.ibm$estimate[, 1], uneq.forec = uneq.forec.501)
uneq.forecast.501