| boot.sem {sem} | R Documentation |
Bootstraps a structural equation model in an sem object (as returned by
the sem function).
boot.sem(data, model, R=100, cov=cov, ...)
## S3 method for class 'bootsem':
print(x, digits=getOption("digits"), ...)
## S3 method for class 'bootsem':
summary(object,
type=c("perc", "bca", "norm", "basic", "none"), level=0.95, ...)
data |
a data frame containing the data to which the model was fit; note that the original observations are required, not just the covariance matrix of the observed variables in the model. |
model |
an sem object, produced by the sem function. |
R |
the number of bootstrap replications; the default is 100, which should be enough for computing standard errors, but not confidence intervals (except for the normal-theory intervals). |
cov |
a function to compute the input covariance matrix; the default is
cov. Use cor if the model is fit
to the correlation matrix. The function hetcor in the
polycor package will compute product-moment, polychoric, and
polyserial correlations among mixed continuous and ordinal variables
(see the example below for an illustration). |
x, object |
an object of class bootsem. |
digits |
controls the number of digits to print. |
type |
type of bootstrapped confidence intervals to compute; the
default is "perc" (percentile); see
boot.ci for details. |
level |
level for confidence intervals; default is 0.95. |
... |
in boot.sem, arguments to be passed to
sem; otherwise ignored. |
boot.sem implements the nonparametric bootstrap, assuming an
independent random sample. Convergence failures in the bootstrap resamples
are discarded (and a warning printed); 10 consecutive convergence failures
result in an error. You can use the boot function
in the boot package for more complex sampling schemes and additional options.
Bootstrapping is implemented by resampling the observations in
data, recalculating the input covariance matrix with cov
and refitting the model with sem, using the
parameter estimates from the original sample as start-values.
Warning: the bootstrapping process can be very time-consuming.
boot.sem returns an object of class bootsem, which inherits
from class boot, supported by the boot package. The returned
object contains the following components:
t0 |
the estimated parameters in the model fit to the original data set. |
t |
a matrix containing the bootstrapped estimates, one bootstrap replication per row. |
data |
the data frame containing the data to which the model was fit. |
seed |
the value of .Random.seed when boot.sem was called. |
statistic |
the function used to produce the bootstrap replications;
this is always the local function refit from boot.sem. |
sim |
always set to "ordinary"; see the documentation for the
boot function. |
stype |
always set to "i"; see the documentation for the
boot function. |
call |
the call of the boot.sem function. |
strata |
always a vector of ones. |
John Fox jfox@mcmaster.ca
Davison, A. C., and Hinkley, D. V. (1997) Bootstrap Methods and their Application. Cambridge.
Efron, B., and Tibshirani, R. J. (1993) An Introduction to the Bootstrap. Chapman and Hall.
## Not run:
# A simple confirmatory factor-analysis model using polychoric correlations.
# The polycor package is required for the hetcor function.
library(polycor)
# The following function returns correlations computed by hetcor,
# and is used for the bootstrapping.
hcor <- function(data) hetcor(data, std.err=FALSE)$correlations
model.cnes <- specify.model()
F -> MBSA2, lam1, NA
F -> MBSA7, lam2, NA
F -> MBSA8, lam3, NA
F -> MBSA9, lam4, NA
F <-> F, NA, 1
MBSA2 <-> MBSA2, the1, NA
MBSA7 <-> MBSA7, the2, NA
MBSA8 <-> MBSA8, the3, NA
MBSA9 <-> MBSA9, the4, NA
data(CNES)
R.cnes <- hcor(CNES)
sem.cnes <- sem(model.cnes, R.cnes, N=1529)
summary(sem.cnes)
# Note: this can take a couple of minutes:
system.time(boot.cnes <- boot.sem(CNES, sem.cnes, R=100, cov=hcor), gcFirst=TRUE)
summary(boot.cnes, type="norm")
# cf., standard errors to those computed by summary(sem.cnes)
## End(Not run)