Last chance! 50% off unlimited learning
Sale ends in
For a dataset containing a time variable, a scalar response variable,
and an optional subject identification variable, obtains least squares
estimates of the coefficients of a restricted cubic spline function or
a linear regression in time after adjusting for subject effects
through the use of subject dummy variables. Then the fit is
bootstrapped B times, either by treating time and subject ID as
fixed (i.e., conditioning the analysis on them) or as random
variables. For the former, the residuals from the original model fit
are used as the basis of the bootstrap distribution. For the latter,
samples are taken jointly from the time, subject ID, and response
vectors to obtain unconditional distributions.
If a subject id variable is given, the bootstrap sampling will
be based on samples with replacement from subjects rather than from
individual data points. In other words, either none or all of a given
subject's data will appear in a bootstrap sample. This cluster
sampling takes into account any correlation structure that might exist
within subjects, so that confidence limits are corrected for
within-subject correlation. Assuming that ordinary least squares
estimates, which ignore the correlation structure, are consistent
(which is almost always true) and efficient (which would not be true
for certain correlation structures or for datasets in which the number
of observation times vary greatly from subject to subject), the
resulting analysis will be a robust, efficient repeated measures
analysis for the one-sample problem.
Predicted values of the fitted models are evaluated by default at a
grid of 100 equally spaced time points ranging from the minimum to
maximum observed time points. Predictions are for the average subject
effect. Pointwise confidence intervals are optionally computed
separately for each of the points on the time grid. However,
simultaneous confidence regions that control the level of confidence
for the entire regression curve lying within a band are often more
appropriate, as they allow the analyst to draw conclusions about
nuances in the mean time response profile that were not stated
apriori. The method of Tibshirani (1997) is used to easily
obtain simultaneous confidence sets for the set of coefficients of the
spline or linear regression function as well as the average intercept
parameter (over subjects). Here one computes the objective criterion
(here both the -2 log likelihood evaluated at the bootstrap estimate
of beta but with respect to the original design matrix and response
vector, and the sum of squared errors in predicting the original
response vector) for the original fit as well as for all of the
bootstrap fits. The confidence set of the regression coefficients is
the set of all coefficients that are associated with objective
function values that are less than or equal to say the 0.95 quantile
of the vector of
By default, the log likelihoods that are computed for obtaining the
simultaneous confidence band assume independence within subject. This
will cause problems unless such log likelihoods have very high rank
correlation with the log likelihood allowing for dependence. To allow
for correlation or to estimate the correlation function, see the
cor.pattern argument below.
rm.boot(time, y, id=seq(along=time), subset,
plot.individual=FALSE,
bootstrap.type=c('x fixed','x random'),
nk=6, knots, B=500, smoother=supsmu,
xlab, xlim, ylim=range(y),
times=seq(min(time), max(time), length=100),
absorb.subject.effects=FALSE,
rho=0, cor.pattern=c('independent','estimate'), ncor=10000,
…)
# S3 method for rm.boot
plot(x, obj2, conf.int=.95,
xlab=x$xlab, ylab=x$ylab,
xlim, ylim=x$ylim,
individual.boot=FALSE,
pointwise.band=FALSE,
curves.in.simultaneous.band=FALSE,
col.pointwise.band=2,
objective=c('-2 log L','sse','dep -2 log L'), add=FALSE, ncurves,
multi=FALSE, multi.method=c('color','density'),
multi.conf =c(.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,.95,.99),
multi.density=c( -1,90,80,70,60,50,40,30,20,10, 7, 4),
multi.col =c( 1, 8,20, 5, 2, 7,15,13,10,11, 9, 14),
subtitles=TRUE, …)
numeric time vector
continuous numeric response vector of length the same as time.
Subjects having multiple measurements have the measurements strung out.
an object returned from rm.boot
subject ID variable. If omitted, it is assumed that each time-response pair is measured on a different subject.
subset of observations to process if not all the data
set to TRUE to plot nonparametrically smoothed time-response
curves for each subject
specifies whether to treat the time and subject ID variables as fixed or random
number of knots in the restricted cubic spline function fit. The
number of knots may be 0 (denoting linear regression) or an integer
greater than 2 in which k knots results in
vector of knot locations. May be specified if nk is
omitted.
number of bootstrap repetitions. Default is 500.
a smoothing function that is used if plot.individual=TRUE.
Default is supsmu.
label for x-axis. Default is "units" attribute of the
original time variable, or "Time" if no such
attribute was defined using the units function.
specifies x-axis plotting limits. Default is to use range of times
specified to rm.boot.
for rm.boot this is a vector of y-axis limits used if
plot.individual=TRUE. It is also passed along for later use
by plot.rm.boot. For plot.rm.boot, ylim can
be specified, to override the value stored in the object stored by
rm.boot. The default is the actual range of y in the
input data.
a sequence of times at which to evaluated fitted values and
confidence limits. Default is 100 equally spaced points in the
observed range of time.
If TRUE, adjusts the response vector y before
re-sampling so that the subject-specific effects in the initial
model fit are all zero. Then in re-sampling, subject effects are
not used in the models. This will downplay one of the sources of
variation. This option is used mainly for checking for consistency
of results, as the re-sampling analyses are simpler when
absort.subject.effects=TRUE.
The log-likelihood function that is used as the basis of
simultaneous confidence bands assumes normality with independence
within subject. To check the robustness of this assumption, if
rho is not zero, the log-likelihood under multivariate
normality within subject, with constant correlation rho
between any two time points, is also computed. If the two
log-likelihoods have the same ranks across re-samples, alllowing
the correlation structure does not matter. The agreement in ranks
is quantified using the Spearman rank correlation coefficient. The
plot method allows the non-zero intra-subject
correlation log-likelihood to be used in deriving the simultaneous
confidence band. Note that this approach does assume
homoscedasticity.
More generally than using an equal-correlation structure, you can
specify a function of two time vectors that generates as many
correlations as the length of these vectors. For example,
cor.pattern=function(time1,time2) 0.2^(abs(time1-time2)/10)
would specify a dampening serial correlation pattern.
cor.pattern can also be a list containing vectors x
(a vector of absolute time differences) and y (a
corresponding vector of correlations). To estimate the correlation
function as a function of absolute time differences within
subjects, specify cor.pattern="estimate". The products of
all possible pairs of residuals (or at least up to ncor of
them) within subjects will be related to the absolute time
difference. The correlation function is estimated by computing the
sample mean of the products of standardized residuals, stratified
by absolute time difference. The correlation for a zero time
difference is set to 1 regardless of the lowess
estimate. NOTE: This approach fails in the presence of large
subject effects; correcting for such effects removes too much of
the correlation structure in the residuals.
the maximum number of pairs of time values used in estimating the
correlation function if cor.pattern="estimate"
other arguments to pass to smoother if plot.individual=TRUE
a second object created by rm.boot that can also be passed
to plot.rm.boot. This is used for two-sample problems for
which the time profiles are allowed to differ between the two
groups. The bootstrapped predicted y values for the second fit are
subtracted from the fitted values for the first fit so that the
predicted mean response for group 1 minus the predicted mean
response for group 2 is what is plotted. The confidence bands that
are plotted are also for this difference. For the simultaneous
confidence band, the objective criterion is taken to be the sum of
the objective criteria (-2 log L or sum of squared errors) for the
separate fits for the two groups. The times vectors must
have been identical for both calls to rm.boot, although
NAs can be inserted by the user of one or both of the time
vectors in the rm.boot objects so as to suppress certain
sections of the difference curve from being plotted.
the confidence level to use in constructing simultaneous, and optionally pointwise, bands. Default is 0.95.
label for y-axis. Default is the "label" attribute of the
original y variable, or "y" if no label was assigned
to y (using the label function, for example).
set to TRUE to plot the first 100 bootstrap regression fits
set to TRUE to draw a pointwise confidence band in addition
to the simultaneous band
set to TRUE to draw all bootstrap regression fits that had a
sum of squared errors (obtained by predicting the original y
vector from the original time vector and id vector)
that was less that or equal to the conf.int quantile of all
bootstrapped models (plus the original model). This will show how
the point by point max and min were computed to form the
simultaneous confidence band.
color for the pointwise confidence band. Default is 2, which defaults to red for default Windows S-PLUS setups.
the default is to use the -2 times log of the Gaussian likelihood
for computing the simultaneous confidence region. If neither
cor.pattern nor rho was specified to rm.boot,
the independent homoscedastic Gaussian likelihood is
used. Otherwise the dependent homoscedastic likelihood is used
according to the specified or estimated correlation
pattern. Specify objective="sse" to instead use the sum of
squared errors.
set to TRUE to add curves to an existing plot. If you do
this, titles and subtitles are omitted.
when using individual.boot=TRUE or
curves.in.simultaneous.band=TRUE, you can plot a random
sample of ncurves of the fitted curves instead of plotting
up to B of them.
set to TRUE to draw multiple simultaneous confidence bands
shaded with different colors. Confidence levels vary over the
values in the multi.conf vector.
specifies the method of shading when multi=TRUE. Default is
to use colors, with the default colors chosen so that when the
graph is printed under S-Plus for Windows 4.0 to an HP LaserJet
printer, the confidence regions are naturally ordered by darkness
of gray-scale. Regions closer to the point estimates (i.e., the
center) are darker. Specify multi.method="density" to
instead use densities of lines drawn per inch in the confidence
regions, with all regions drawn with the default color. The
polygon function is used to shade the regions.
vector of confidence levels, in ascending order. Default is to use 12 confidence levels ranging from 0.05 to 0.99.
vector of densities in lines per inch corresponding to
multi.conf. As is the convention in the
polygon function, a density of -1 indicates a solid
region.
vector of colors corresponding to multi.conf. See
multi.method for rationale.
set to FALSE to suppress drawing subtitles for the plot
an object of class rm.boot is returned by rm.boot. The
principal object stored in the returned object is a matrix of
regression coefficients for the original fit and all of the bootstrap
repetitions (object Coef), along with vectors of the
corresponding -2 log likelihoods are sums of squared errors. The
original fit object from lm.fit.qr is stored in
fit. For this fit, a cell means model is used for the
id effects.
plot.rm.boot returns a list containing the vector of times used
for plotting along with the overall fitted values, lower and upper
simultaneous confidence limits, and optionally the pointwise
confidence limits.
Observations having missing time or y are excluded from
the analysis.
As most repeated measurement studies consider the times as design
points, the fixed covariable case is the default. Bootstrapping the
residuals from the initial fit assumes that the model is correctly
specified. Even if the covariables are fixed, doing an unconditional
bootstrap is still appropriate, and for large sample sizes
unconditional confidence intervals are only slightly wider than
conditional ones. For moderate to small sample sizes, the
bootstrap.type="x random" method can be fairly conservative.
If not all subjects have the same number of observations (after
deleting observations containing missing values) and if
bootstrap.type="x fixed", bootstrapped residual vectors may
have a length m that is different from the number of original
observations n. If bootstrap.type="x fixed" can still be invalid, as this
method assumes that a vector (over subjects) of all residuals can be
added to the original yhats, and varying number of points will cause
mis-alignment.
For bootstrap.type="x random" in the presence of significant
subject effects, the analysis is approximate as the subjects used in
any one bootstrap fit will not be the entire list of subjects. The
average (over subjects used in the bootstrap sample) intercept is used
from that bootstrap sample as a predictor of average subject effects
in the overall sample.
Once the bootstrap coefficient matrix is stored by rm.boot,
plot.rm.boot can be run multiple times with different options
(e.g, different confidence levels).
See bootcov in the rms library for a general
approach to handling repeated measurement data for ordinary linear
models, binary and ordinal models, and survival models, using the
unconditional bootstrap. bootcov does not handle bootstrapping
residuals.
Feng Z, McLerran D, Grizzle J (1996): A comparison of statistical methods for clustered data analysis with Gaussian error. Stat in Med 15:1793--1806.
Tibshirani R, Knight K (1997):Model search and inference by bootstrap "bumping". Technical Report, Department of Statistics, University of Toronto.
http://statweb.stanford.edu/~tibs/. Presented at the Joint Statistical Meetings, Chicago, August 1996.
Efron B, Tibshirani R (1993): An Introduction to the Bootstrap. New York: Chapman and Hall.
Diggle PJ, Verbyla AP (1998): Nonparametric estimation of covariance structure in logitudinal data. Biometrics 54:401--415.
Chapman IM, Hartman ML, et al (1997): Effect of aging on the sensitivity of growth hormone secretion to insulin-like growth factor-I negative feedback. J Clin Endocrinol Metab 82:2996--3004.
Li Y, Wang YG (2008): Smooth bootstrap methods for analysis of longitudinal data. Stat in Med 27:937-953. (potential improvements to cluster bootstrap; not implemented here)
rcspline.eval, lm, lowess,
supsmu, bootcov,
units, label, polygon,
reShape
# NOT RUN {
# Generate multivariate normal responses with equal correlations (.7)
# within subjects and no correlation between subjects
# Simulate realizations from a piecewise linear population time-response
# profile with large subject effects, and fit using a 6-knot spline
# Estimate the correlation structure from the residuals, as a function
# of the absolute time difference
# Function to generate n p-variate normal variates with mean vector u and
# covariance matrix S
# Slight modification of function written by Bill Venables
# See also the built-in function rmvnorm
mvrnorm <- function(n, p = 1, u = rep(0, p), S = diag(p)) {
Z <- matrix(rnorm(n * p), p, n)
t(u + t(chol(S)) %*% Z)
}
n <- 20 # Number of subjects
sub <- .5*(1:n) # Subject effects
# Specify functional form for time trend and compute non-stochastic component
times <- seq(0, 1, by=.1)
g <- function(times) 5*pmax(abs(times-.5),.3)
ey <- g(times)
# Generate multivariate normal errors for 20 subjects at 11 times
# Assume equal correlations of rho=.7, independent subjects
nt <- length(times)
rho <- .7
set.seed(19)
errors <- mvrnorm(n, p=nt, S=diag(rep(1-rho,nt))+rho)
# Note: first random number seed used gave rise to mean(errors)=0.24!
# Add E[Y], error components, and subject effects
y <- matrix(rep(ey,n), ncol=nt, byrow=TRUE) + errors +
matrix(rep(sub,nt), ncol=nt)
# String out data into long vectors for times, responses, and subject ID
y <- as.vector(t(y))
times <- rep(times, n)
id <- sort(rep(1:n, nt))
# Show lowess estimates of time profiles for individual subjects
f <- rm.boot(times, y, id, plot.individual=TRUE, B=25, cor.pattern='estimate',
smoother=lowess, bootstrap.type='x fixed', nk=6)
# In practice use B=400 or 500
# This will compute a dependent-structure log-likelihood in addition
# to one assuming independence. By default, the dep. structure
# objective will be used by the plot method (could have specified rho=.7)
# NOTE: Estimating the correlation pattern from the residual does not
# work in cases such as this one where there are large subject effects
# Plot fits for a random sample of 10 of the 25 bootstrap fits
plot(f, individual.boot=TRUE, ncurves=10, ylim=c(6,8.5))
# Plot pointwise and simultaneous confidence regions
plot(f, pointwise.band=TRUE, col.pointwise=1, ylim=c(6,8.5))
# Plot population response curve at average subject effect
ts <- seq(0, 1, length=100)
lines(ts, g(ts)+mean(sub), lwd=3)
# }
# NOT RUN {
#
# Handle a 2-sample problem in which curves are fitted
# separately for males and females and we wish to estimate the
# difference in the time-response curves for the two sexes.
# The objective criterion will be taken by plot.rm.boot as the
# total of the two sums of squared errors for the two models
#
knots <- rcspline.eval(c(time.f,time.m), nk=6, knots.only=TRUE)
# Use same knots for both sexes, and use a times vector that
# uses a range of times that is included in the measurement
# times for both sexes
#
tm <- seq(max(min(time.f),min(time.m)),
min(max(time.f),max(time.m)),length=100)
f.female <- rm.boot(time.f, bp.f, id.f, knots=knots, times=tm)
f.male <- rm.boot(time.m, bp.m, id.m, knots=knots, times=tm)
plot(f.female)
plot(f.male)
# The following plots female minus male response, with
# a sequence of shaded confidence band for the difference
plot(f.female,f.male,multi=TRUE)
# Do 1000 simulated analyses to check simultaneous coverage
# probability. Use a null regression model with Gaussian errors
n.per.pt <- 30
n.pt <- 10
null.in.region <- 0
for(i in 1:1000) {
y <- rnorm(n.pt*n.per.pt)
time <- rep(1:n.per.pt, n.pt)
# Add the following line and add ,id=id to rm.boot to use clustering
# id <- sort(rep(1:n.pt, n.per.pt))
# Because we are ignoring patient id, this simulation is effectively
# using 1 point from each of 300 patients, with times 1,2,3,,,30
f <- rm.boot(time, y, B=500, nk=5, bootstrap.type='x fixed')
g <- plot(f, ylim=c(-1,1), pointwise=FALSE)
null.in.region <- null.in.region + all(g$lower<=0 & g$upper>=0)
prn(c(i=i,null.in.region=null.in.region))
}
# Simulation Results: 905/1000 simultaneous confidence bands
# fully contained the horizontal line at zero
# }
Run the code above in your browser using DataLab