"samplesizeTable"(fit, direction, mmdvec, power = 0.80, alpha = 0.05, nmax = 1000, display = "print", ...)cgOneFactorFit object from a previous one factor study.
character value indicating whether the sample size should be estimated
to detect an "increase" or a "decrease". This only effects
the sample size estimates if the previous study in fit was
analyzed on the log scale, in which case the differences in mmdvec
are relative differences instead of absolute differences.
For detecting relative changes, the sample size
required to detect a relative increase of 25% is not the same as the sample
size to detect a relative decrease of 25%, for example. But for detecting
absolute changes, the sample size required
to detect an absolute increase of 25 is the same as the sample size
to detect an absolute decrease of 25.
numeric vector specifying the minimum meaningful differences
to be detected in the future study. If the previous study
in fit was analyzed on the log scale, then the values in mmdvec
are assumed to be relative percentage increases or decreases, depending
on the value of direction. If the previous study in
fit was not analyzed on the log scale, then the values in mmdvec
are assumed to be absolute increases or decreases, depending on
the value of direction. Each value in mmdvec needs to
be positive.
0.80.
This is equivalent
to $1 - \beta$, where $\beta$ is the probability of
committing a Type II error: accepting the null hypothesis of
no difference when differences truly exist.
0.05.
"print"print method for the
created cgOneFactorSamplesizeTable object, which is
a formatted text output of the table(s).
"none"
"show"showDefault method, which
will just print out the cgOneFactorSamplesizeTable components.
ngrps ngrps = 2.
modelcharacter value indicating which variability estimate
in
fit should be
used to estimate the sample size: the robust model ("rronly"),
the classical model ("olsonly"), or both ("both"). If an estimate
is requested for a model that was not fit, then no sample sizes are returned
for that model but an error is not reported (e.g. if only the classical model
was fitted but "both" are requested, only the classical model estimates
will be returned):
"both"cgOneFactorFit object specified in the fit
argument. If the resistant & robust fit is not available,
this value is not relevant.
"olsonly"olsfit fit is calculated.
"rronly"rrfit fit is calculated.
cgOneFactorSampleSizeTable, with the
following slots:ols.sstableNULL. The matrix has 3 columns and
one row for each element of the mmdvec vector.
The first column specifies the minimum meaningful difference ("mmd").
The second column gives the number of subjects required for each
group ("n"), possibly truncated at nmax.
The third column gives the total number of
subjects required ("N"), also truncated if
nmax is truncated.rr.sstableNULL if
model="olsonly" was specified. See the ols.sstable
slot description above for the analogous layout of the matrix.
settingsdata argument object of class
cgOneFactorData, with the following additional members:
sigmaestols, containing the
estimated spread (sigma, standard deviation) from
the classical model of fit,
and rr,
containing the estimated spread (sigma, standard deviation)
from the robust model of
fit, or
NULL if the robust model was not fit.planningnamecharacter describing the study
or purpose of the sample size analysis. Taken from the
settings$analysisname of the fit object. ngrpsngrps argument. directiondirection argument. alphaalpha argument. powerpower argument. nmaxnmax argument. Sample sizes are estimated for detecting a minimum difference with a global
F test. The algorithm is detailed in Fleiss (1986), Appendix A. When
there are more than 2 groups, the lower bound of possible
noncentrality parameter values is calculated from assuming only two of
the ngrps number of groups differ by the mmdvec/2 amount
from the "grand mean" while the rest of the groups are equal to the grand
mean.
For detecting an absolute difference, the sample size is the
smallest group size n for which 1 - pf (qf (1 - alpha, numdf, dendf), numdf,
dendf, ncp) exceeds power, where ncp = (n * mmdvec ^ 2) / (2 *
sigamest ^ 2), and sigmaest is the residual mean square error from the model in
fit. For detecting a relative difference, the calculations are
the same except ncp = (n * (log (sign * mmdvec / 100 + 1) ) ^ 2) / (2
* sigmaest ^ 2), where sign = -1 if direction="decreasing",
and sign = 1 if direction = "increasing".
data(canine)
canine.data <- prepareCGOneFactorData(canine, format="groupcolumns",
analysisname="Canine",
endptname="Prostate Volume",
endptunits=expression(plain(cm)^3),
digits=1, logscale=TRUE, refgrp="CC")
canine.fit <- fit(canine.data)
canine.samplesize <- samplesizeTable(canine.fit, direction="increasing",
mmdvec=c(10, 25, 50, 75, 100))
samplesizeTable(canine.fit, direction="decreasing",
mmdvec=c(25, 50, 75), model="olsonly")
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