Repeatedly fits the specified model, leaving out one study at a time.
# S3 method for nsue
leave1out(x, data = data.frame(), formula = ~1, hypotheses = NULL,
n.imp = 50, n.bins = 200, maxiter = 200, tol = 1e-06, ...)an object of class "nsue".
an optional data frame containing variables to be used as regressors in the maximum likelihood step.
an object of class "formula": a symbolic description of the model to be fitted.
a list of hypotheses, or NULL to simply test the coefficients of the model.
number of imputations of NSUEs per study.
number of bins used in the imputations.
maximum number of iterations in the REML estimation of \(\tau^2\).
tolerance in the REML estimation of \(\tau^2\).
other arguments (currently ignored).
leave1out.nsue returns an object of class "leave1out.nsue", which is a list containing a list for each iteration with the component study (the study discarded) and the component meta.nsue, which is an object of class "meta.nsue".
The functions print and summary may be used to print the details or a summary of the results.
Use smc_from_t, smd_from_t, z_from_r or r_in_smd_from_t_means_and_sds1 to create "nsue" objects.
Models for meta.nsue, leave1out.nsue and metalm.meta.nsue are specified symbolically. The formula is a series of terms which specify a linear predictor for x. A formula specification of the form first + second indicates a multiple regression by first and second. A specification of the form first:second indicates the interaction of first with second. The specification first*second is the same as first + second + first:second.
Each hypothesis must be a matrix (or vector) giving linear combinations of coefficients by rows.
Radua, J., Schmidt, A., Borgwardt, S., Heinz, A., Schlagenhauf, F., McGuire, P., Fusar-Poli, P. (2015) Ventral striatal activation during reward processing in psychosis. A neurofunctional meta-analysis. JAMA Psychiatry, 72, 1243--51.
smc_from_t, smd_from_t, z_from_r and r_in_smd_from_t_means_and_sds1 for creating "nsue" objects.
meta for conducting a meta-analysis.
# NOT RUN {
t <- c(3.4, NA, NA, NA, NA, 2.8, 2.1, 3.1, 2.0, 3.4)
n <- c(40, 20, 22, 24, 18, 30, 25, 30, 16, 22)
leave1out(smc_from_t(t, n))
# }
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