Description Usage Arguments Details Value Author(s) References See Also Examples
Bootstraps a structural equation model in an sem
object (as returned by
the sem
function).
1 2 3 4 5 6 7 8 |
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 |
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
|
x, object |
an object of class |
digits |
controls the number of digits to print. |
type |
type of bootstrapped confidence intervals to compute; the
default is |
level |
level for confidence intervals; default is |
... |
in |
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 |
statistic |
the function used to produce the bootstrap replications;
this is always the local function |
sim |
always set to |
stype |
always set to |
call |
the call of the |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ## 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)
|
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