lmrob.control: Tuning Parameters for lmrob() and Auxiliaries
In robustbase: Basic Robust Statistics
lmrob.control R Documentation
Tuning Parameters for lmrob() and Auxiliaries
Description
Tuning parameters for lmrob, the MM-type regression
estimator and the associated S-, M- and D-estimators. Using
setting="KS2011" sets the defaults as suggested by
Koller and Stahel (2011) and analogously for "KS2014".
The .M*.default functions and
.M*.defaults lists contain default tuning
parameters for all the predefined \psi functions, see also
Mpsi, etc.
Usage
lmrob.control(setting, seed = NULL, nResample = 500,
tuning.chi = NULL, bb = 0.5, tuning.psi = NULL,
max.it = 50, groups = 5, n.group = 400,
k.fast.s = 1, best.r.s = 2,
k.max = 200, maxit.scale = 200, k.m_s = 20,
refine.tol = 1e-7, rel.tol = 1e-7, scale.tol = 1e-10, solve.tol = 1e-7,
zero.tol = 1e-10,
trace.lev = 0,
mts = 1000, subsampling = c("nonsingular", "simple"),
compute.rd = FALSE, method = "MM", psi = "bisquare",
numpoints = 10, cov = NULL,
split.type = c("f", "fi", "fii"), fast.s.large.n = 2000,
# only for outlierStats() :
eps.outlier = function(nobs) 0.1 / nobs,
eps.x = function(maxx) .Machine$double.eps^(.75)*maxx,
compute.outlier.stats = method,
warn.limit.reject = 0.5,
warn.limit.meanrw = 0.5, ...)
## S3 method for class 'lmrobCtrl'
update(object, ...)
.Mchi.tuning.defaults
.Mchi.tuning.default(psi)
.Mpsi.tuning.defaults
.Mpsi.tuning.default(psi)
Arguments
setting
a string specifying alternative default values. Leave
empty for the defaults or use "KS2011" or "KS2014"
for the defaults suggested by Koller and Stahel (2011, 2017).
See Details.
seed
NULL or an integer vector compatible with
.Random.seed: the seed to be used for random
re-sampling used in obtaining candidates for the initial
S-estimator. The current value of .Random.seed will be
preserved if seed is set, i.e. non-NULL;
otherwise, as by default, .Random.seed will be used and
modified as usual from calls to runif() etc.
nResample
number of re-sampling candidates to be
used to find the initial S-estimator. Currently defaults to 500
which works well in most situations (see references).
tuning.chi
tuning constant vector for the S-estimator. If
NULL, as by default, sensible defaults are set (depending on
psi) to yield a 50% breakdown estimator. See Details.
bb
expected value under the normal model of the
“chi” (rather \rho (rho)) function with tuning
constant equal to tuning.chi. This is used to compute the
S-estimator.
tuning.psi
tuning constant vector for the redescending
M-estimator. If NULL, as by default, this is set (depending
on psi) to yield an estimator with asymptotic efficiency of
95% for normal errors. See Details.
max.it
integer specifying the maximum number of IRWLS iterations.
groups
(for the fast-S algorithm): Number of
random subsets to use when the data set is large.
n.group
(for the fast-S algorithm): Size of each of the
groups above. Note that this must be at least p.
k.fast.s
(for the fast-S algorithm): Number of
local improvement steps (“I-steps”) for each
re-sampling candidate.
k.m_s
(for the M-S algorithm): specifies after how many
unsuccessful refinement steps the algorithm stops.
best.r.s
(for the fast-S algorithm): Number of
of best candidates to be iterated further (i.e.,
“refined”); is denoted t in
Salibian-Barrera & Yohai(2006).
k.max
(for the fast-S algorithm): maximal number of
refinement steps for the “fully” iterated best candidates.
maxit.scale
integer specifying the maximum number of C level
find_scale() iterations (in fast-S and M-S algorithms).
refine.tol
(for the fast-S algorithm): relative convergence
tolerance for the fully iterated best candidates.
rel.tol
(for the RWLS iterations of the MM algorithm): relative
convergence tolerance for the parameter vector.
scale.tol
(for the scale estimation iterations of the S algorithm): relative
convergence tolerance for the scale \sigma(.).
solve.tol
(for the S algorithm): relative
tolerance for inversion. Hence, this corresponds to
solve.default()'s tol.
zero.tol
for checking 0-residuals in the S algorithm, non-negative number
\epsilon_z such that
\{i; \left|\tilde{R}_i\right| \le \epsilon_z\}
correspond to 0-residuals, where \tilde{R}_i are standardized residuals,
\tilde{R}_i = R_i/s_y and
s_y = \frac{1}{n} \sum_{i=1}^n \left|y_i\right|.
trace.lev
integer indicating if the progress of the MM-algorithm
and the fast-S algorithms, see lmrob.S,
should be traced (increasingly); default trace.lev = 0 does
no tracing.
mts
maximum number of samples to try in subsampling
algorithm.
subsampling
type of subsampling to be used, a string:
"simple" for simple subsampling (default prior to version 0.9),
"nonsingular" for nonsingular subsampling. See also
lmrob.S.
compute.rd
logical indicating if robust distances (based on
the MCD robust covariance estimator covMcd) are to be
computed for the robust diagnostic plots. This may take some
time to finish, particularly for large data sets, and can lead to
singularity problems when there are factor explanatory
variables (with many levels, or levels with “few”
observations). Hence, is FALSE by default.
method
string specifying the estimator-chain. MM
is interpreted as SM. See Details of
lmrob for a description of the possible values.
psi
string specifying the type \psi-function
used. See Details of lmrob. Defaults to
"bisquare" for S and MM-estimates, otherwise "lqq".
numpoints
number of points used in Gauss quadrature.
cov
function or string with function name to be used to
calculate covariance matrix estimate. The default is
if(method %in% c('SM', 'MM')) ".vcov.avar1" else ".vcov.w".
See Details of lmrob.
split.type
determines how categorical and continuous variables
are split. See splitFrame.
fast.s.large.n
minimum number of observations required to
switch from ordinary “fast S” algorithm to an efficient
“large n” strategy.
eps.outlier
limit on the robustness weight below which an observation
is considered to be an outlier.
Either a numeric(1) or a function that takes the number of observations as
an argument. Used only in summary.lmrob and
outlierStats.
eps.x
limit on the absolute value of the elements of the design matrix
below which an element is considered zero.
Either a numeric(1) or a function that takes the maximum absolute value in
the design matrix as an argument.
compute.outlier.stats
vector of character
strings, each valid to be used as method argument. Used to
specify for which estimators outlier statistics (and warnings)
should be produced. Set to empty (NULL or character(0))
if none are required.
Note that the default is method which by default is either
"MM", "SM", or "SMDM"; hence using
compute.outlier.stats = "S" provides outlierStats()
to a lmrob.S() result.
warn.limit.reject
limit of ratio
\#\mbox{rejected} / \#\mbox{obs in level}
above (\geq) which a warning is produced.
Set to NULL to disable warning.
warn.limit.meanrw
limit of the mean robustness per factor level
below which (\leq) a warning is produced.
Set to NULL to disable warning.
object
an "lmrobCtrl" object, as resulting from a
lmrob.control(*) or an update(<lmrobCtrl>, *) call.
...
for
lmrob.control():further arguments to be added as
list components to the result, e.g., those to be used in
.vcov.w().
update(object, *):(named) components from
object, to be modified, not setting = *.
Details
The option setting="KS2011" alters the default
arguments. They are changed to method = "SMDM", psi = "lqq",
max.it = 500, k.max = 2000, cov = ".vcov.w".
The defaults of all the remaining arguments are not changed.
The option setting="KS2014" builds upon setting="KS2011".
More arguments are changed to best.r.s = 20, k.fast.s = 2,
nResample = 1000. This setting should produce more stable estimates
for designs with factors.
By default, and in .Mpsi.tuning.default() and .Mchi.tuning.default(),
tuning.chi and tuning.psi are set to yield an
MM-estimate with breakdown point 0.5 and efficiency of 95% at
the normal.
If numeric tuning.chi or tuning.psi are specified, say
cc, for psi = "ggw" or "lqq",
.psi.const(cc, psi) is used, see its help page.
To get the defaults, e.g., .Mpsi.tuning.default(psi) is
equivalent to but more efficient than the formerly widely used
lmrob.control(psi = psi)$tuning.psi.
These defaults are:
psi tuning.chi tuning.psi
bisquare 1.54764 4.685061
welsh 0.5773502 2.11
ggw c(-0.5, 1.5, NA, 0.5) c(-0.5, 1.5, 0.95, NA)
lqq c(-0.5, 1.5, NA, 0.5) c(-0.5, 1.5, 0.95, NA)
optimal 0.4047 1.060158
hampel c(1.5, 3.5, 8)*0.2119163 c(1.5, 3.5, 8)*0.9014
The values for the tuning constant for the ggw and lqq
psi functions are specified differently here by a vector with four
elements: minimal slope, b (controlling the bend at the maximum of the curve),
efficiency, breakdown point.
Use NA for an unspecified value of either efficiency or
breakdown point, see examples in the tables (above and below).
For these table examples, the respective “inner constants” are
stored precomputed, see .psi.lqq.findc for more.
The constants for the "hampel" psi function are chosen to have a
redescending slope of -1/3. Constants for a slope of -1/2
would be
psi tuning.chi tuning.psi
"hampel" c(2, 4, 8) * 0.1981319 c(2, 4, 8) * 0.690794
Alternative coefficients for an efficiency of 85%
at the normal are given in the table below.
psi tuning.psi
bisquare 3.443689
welsh 1.456
ggw, lqq c(-0.5, 1.5, 0.85, NA)
optimal 0.8684
hampel (-1/3) c(1.5, 3.5, 8)* 0.5704545
hampel (-1/2) c( 2, 4, 8) * 0.4769578
Value
.Mchi.tuning.default(psi) and .Mpsi.tuning.default(psi)
return a short numeric vector of tuning constants which
are defaults for the corresponding psi-function, see the Details.
They are based on the named lists
.Mchi.tuning.defaults and .Mpsi.tuning.defaults,
respectively.
lmrob.control() returns a named list with over
twenty components, corresponding to the arguments, where
tuning.psi and tuning.chi are typically computed, as
.Mpsi.tuning.default(psi) or .Mchi.tuning.default(psi),
respectively.
It is of class "lmrobCtrl" and we provide
print(), update() and within methods.
update(<lmrobCtrl>, ....) does not allow a
setting="<...>" in .....
Author(s)
Matias Salibian-Barrera, Martin Maechler and Manuel Koller
References
Koller, M. and Stahel, W.A. (2011)
Sharpening Wald-type inference in robust regression for small samples.
Computational Statistics & Data Analysis 55(8), 2504–2515.
Koller, M. and Stahel, W.A. (2017)
Nonsingular subsampling for regression S estimators with categorical predictors,
Computational Statistics 32(2): 631–646.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00180-016-0679-x")}.
Referred as "KS2014" everywhere in robustbase; A shorter first
version, Koller (2012) has been available from https://arxiv.org/abs/1208.5595.
See Also
Mpsi, etc, for the (fast!) psi function computations;
lmrob, also for references and examples.
Examples
## Show the default settings:
str(lmrob.control())
## Artificial data for a simple "robust t test":
set.seed(17)
y <- y0 <- rnorm(200)
y[sample(200,20)] <- 100*rnorm(20)
gr <- as.factor(rbinom(200, 1, prob = 1/8))
lmrob(y0 ~ 0+gr)
## Use Koller & Stahel(2011)'s recommendation but a larger 'max.it':
str(ctrl <- lmrob.control("KS2011", max.it = 1000))
str(.Mpsi.tuning.defaults)
stopifnot(identical(.Mpsi.tuning.defaults,
sapply(names(.Mpsi.tuning.defaults),
.Mpsi.tuning.default)))
## Containing (names!) all our (pre-defined) redescenders:
str(.Mchi.tuning.defaults)
## Difference between settings:
Cdef <- lmrob.control()
C11 <- lmrob.control("KS2011")
C14 <- lmrob.control("KS2014")
str(C14)
## Differences:
diffD <- names(which(!mapply(identical, Cdef,C11, ignore.environment=TRUE)))
diffC <- names(which(!mapply(identical, C11, C14, ignore.environment=TRUE)))
## KS2011 vs KS2014: Apart from `setting` itself, they only differ in three places:
cbind(KS11 = unlist(C11[diffC[-1]]),
KS14 = unlist(C14[diffC[-1]]))
## KS11 KS14
## nResample 500 1000
## best.r.s 2 20
## k.fast.s 1 2
## default vs KS2011: a bit more: setting + 8
str2simpLang <- function(x) {
r <- if(is.null(x)) quote((NULL)) else str2lang(deparse(x))
if(is.call(r)) format(r) else r
}
cbind(deflt= lapply(Cdef[diffD], str2simpLang),
KS11 = lapply(C11 [diffD], str2simpLang))
## update()ing a lmrob.control() , e.g.,
C14mod <- update(C14, trace.lev = 2) # the same as
C14m.d <- C14; C14m.d$trace.lev <- 2
stopifnot(identical(C14mod, C14m.d))
## changing psi --> updates tuning.{psi,chi}:
C14mp <- update(C14, psi = "hampel", seed=101)
## updating 'method' is "smart" :
C.SMDM <- update(Cdef, method="SMDM")
all.equal(Cdef, C.SMDM) # changed also psi, tuning.{psi,chi} and cov !
chgd <- c("method", "psi", "tuning.chi", "tuning.psi", "cov")
str(Cdef [chgd])
str(C.SMDM[chgd])
C14m <- update(C14, method="SMM")
(ae <- all.equal(C14, C14mp))# changed tuning.psi & tuning.chi, too
stopifnot(exprs = {
identical(C14, update(C14, method="SMDM")) # no change!
identical(c("psi", "seed", "tuning.chi", "tuning.psi"),
sort(gsub("[^.[:alpha:]]", "", sub(":.*", "", sub("^Component ", "", ae)))))
identical(C14m, local({C <- C14; C$method <- "SMM"; C}))
})
##
try( update(C14, setting="KS2011") ) #--> Error: .. not allowed
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| lmrob.control | R Documentation |
Tuning Parameters for lmrob() and Auxiliaries
Description
Tuning parameters for lmrob, the MM-type regression
estimator and the associated S-, M- and D-estimators. Using
setting="KS2011" sets the defaults as suggested by
Koller and Stahel (2011) and analogously for "KS2014".
The .M*.default functions and
.M*.defaults lists contain default tuning
parameters for all the predefined \psi functions, see also
Mpsi, etc.
Usage
lmrob.control(setting, seed = NULL, nResample = 500,
tuning.chi = NULL, bb = 0.5, tuning.psi = NULL,
max.it = 50, groups = 5, n.group = 400,
k.fast.s = 1, best.r.s = 2,
k.max = 200, maxit.scale = 200, k.m_s = 20,
refine.tol = 1e-7, rel.tol = 1e-7, scale.tol = 1e-10, solve.tol = 1e-7,
zero.tol = 1e-10,
trace.lev = 0,
mts = 1000, subsampling = c("nonsingular", "simple"),
compute.rd = FALSE, method = "MM", psi = "bisquare",
numpoints = 10, cov = NULL,
split.type = c("f", "fi", "fii"), fast.s.large.n = 2000,
# only for outlierStats() :
eps.outlier = function(nobs) 0.1 / nobs,
eps.x = function(maxx) .Machine$double.eps^(.75)*maxx,
compute.outlier.stats = method,
warn.limit.reject = 0.5,
warn.limit.meanrw = 0.5, ...)
## S3 method for class 'lmrobCtrl'
update(object, ...)
.Mchi.tuning.defaults
.Mchi.tuning.default(psi)
.Mpsi.tuning.defaults
.Mpsi.tuning.default(psi)
Arguments
setting |
a string specifying alternative default values. Leave
empty for the defaults or use |
seed |
|
nResample |
number of re-sampling candidates to be used to find the initial S-estimator. Currently defaults to 500 which works well in most situations (see references). |
tuning.chi |
tuning constant vector for the S-estimator. If
|
bb |
expected value under the normal model of the
“chi” (rather |
tuning.psi |
tuning constant vector for the redescending
M-estimator. If |
max.it |
integer specifying the maximum number of IRWLS iterations. |
groups |
(for the fast-S algorithm): Number of random subsets to use when the data set is large. |
n.group |
(for the fast-S algorithm): Size of each of the
|
k.fast.s |
(for the fast-S algorithm): Number of local improvement steps (“I-steps”) for each re-sampling candidate. |
k.m_s |
(for the M-S algorithm): specifies after how many unsuccessful refinement steps the algorithm stops. |
best.r.s |
(for the fast-S algorithm): Number of
of best candidates to be iterated further (i.e.,
“refined”); is denoted |
k.max |
(for the fast-S algorithm): maximal number of refinement steps for the “fully” iterated best candidates. |
maxit.scale |
integer specifying the maximum number of C level
|
refine.tol |
(for the fast-S algorithm): relative convergence tolerance for the fully iterated best candidates. |
rel.tol |
(for the RWLS iterations of the MM algorithm): relative convergence tolerance for the parameter vector. |
scale.tol |
(for the scale estimation iterations of the S algorithm): relative
convergence tolerance for the |
solve.tol |
(for the S algorithm): relative
tolerance for inversion. Hence, this corresponds to
|
zero.tol |
for checking 0-residuals in the S algorithm, non-negative number
|
trace.lev |
integer indicating if the progress of the MM-algorithm
and the fast-S algorithms, see |
mts |
maximum number of samples to try in subsampling algorithm. |
subsampling |
type of subsampling to be used, a string:
|
compute.rd |
logical indicating if robust distances (based on
the MCD robust covariance estimator |
method |
string specifying the estimator-chain. |
psi |
string specifying the type |
numpoints |
number of points used in Gauss quadrature. |
cov |
function or string with function name to be used to
calculate covariance matrix estimate. The default is
|
split.type |
determines how categorical and continuous variables
are split. See |
fast.s.large.n |
minimum number of observations required to switch from ordinary “fast S” algorithm to an efficient “large n” strategy. |
eps.outlier |
limit on the robustness weight below which an observation
is considered to be an outlier.
Either a |
eps.x |
limit on the absolute value of the elements of the design matrix
below which an element is considered zero.
Either a |
compute.outlier.stats |
vector of |
warn.limit.reject |
limit of ratio
|
warn.limit.meanrw |
limit of the mean robustness per factor level
below which ( |
object |
an |
... |
for
|
Details
The option setting="KS2011" alters the default
arguments. They are changed to method = "SMDM", psi = "lqq",
max.it = 500, k.max = 2000, cov = ".vcov.w".
The defaults of all the remaining arguments are not changed.
The option setting="KS2014" builds upon setting="KS2011".
More arguments are changed to best.r.s = 20, k.fast.s = 2,
nResample = 1000. This setting should produce more stable estimates
for designs with factors.
By default, and in .Mpsi.tuning.default() and .Mchi.tuning.default(),
tuning.chi and tuning.psi are set to yield an
MM-estimate with breakdown point 0.5 and efficiency of 95% at
the normal.
If numeric tuning.chi or tuning.psi are specified, say
cc, for psi = "ggw" or "lqq",
.psi.const(cc, psi) is used, see its help page.
To get the defaults, e.g., .Mpsi.tuning.default(psi) is
equivalent to but more efficient than the formerly widely used
lmrob.control(psi = psi)$tuning.psi.
These defaults are:
psi | tuning.chi | tuning.psi |
bisquare | 1.54764 | 4.685061 |
welsh | 0.5773502 | 2.11 |
ggw | c(-0.5, 1.5, NA, 0.5) | c(-0.5, 1.5, 0.95, NA) |
lqq | c(-0.5, 1.5, NA, 0.5) | c(-0.5, 1.5, 0.95, NA) |
optimal | 0.4047 | 1.060158 |
hampel | c(1.5, 3.5, 8)*0.2119163 | c(1.5, 3.5, 8)*0.9014
|
The values for the tuning constant for the ggw and lqq
psi functions are specified differently here by a vector with four
elements: minimal slope, b (controlling the bend at the maximum of the curve),
efficiency, breakdown point.
Use NA for an unspecified value of either efficiency or
breakdown point, see examples in the tables (above and below).
For these table examples, the respective “inner constants” are
stored precomputed, see .psi.lqq.findc for more.
The constants for the "hampel" psi function are chosen to have a
redescending slope of -1/3. Constants for a slope of -1/2
would be
psi | tuning.chi | tuning.psi |
"hampel" | c(2, 4, 8) * 0.1981319 | c(2, 4, 8) * 0.690794
|
Alternative coefficients for an efficiency of 85% at the normal are given in the table below.
psi | tuning.psi |
bisquare | 3.443689 |
welsh | 1.456 |
ggw, lqq | c(-0.5, 1.5, 0.85, NA) |
optimal | 0.8684 |
hampel (-1/3) | c(1.5, 3.5, 8)* 0.5704545 |
hampel (-1/2) | c( 2, 4, 8) * 0.4769578
|
Value
.Mchi.tuning.default(psi) and .Mpsi.tuning.default(psi)
return a short numeric vector of tuning constants which
are defaults for the corresponding psi-function, see the Details.
They are based on the named lists
.Mchi.tuning.defaults and .Mpsi.tuning.defaults,
respectively.
lmrob.control() returns a named list with over
twenty components, corresponding to the arguments, where
tuning.psi and tuning.chi are typically computed, as
.Mpsi.tuning.default(psi) or .Mchi.tuning.default(psi),
respectively.
It is of class "lmrobCtrl" and we provide
print(), update() and within methods.
update(<lmrobCtrl>, ....) does not allow a
setting="<...>" in .....
Author(s)
Matias Salibian-Barrera, Martin Maechler and Manuel Koller
References
Koller, M. and Stahel, W.A. (2011) Sharpening Wald-type inference in robust regression for small samples. Computational Statistics & Data Analysis 55(8), 2504–2515.
Koller, M. and Stahel, W.A. (2017)
Nonsingular subsampling for regression S estimators with categorical predictors,
Computational Statistics 32(2): 631–646.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00180-016-0679-x")}.
Referred as "KS2014" everywhere in robustbase; A shorter first
version, Koller (2012) has been available from https://arxiv.org/abs/1208.5595.
See Also
Mpsi, etc, for the (fast!) psi function computations;
lmrob, also for references and examples.
Examples
## Show the default settings:
str(lmrob.control())
## Artificial data for a simple "robust t test":
set.seed(17)
y <- y0 <- rnorm(200)
y[sample(200,20)] <- 100*rnorm(20)
gr <- as.factor(rbinom(200, 1, prob = 1/8))
lmrob(y0 ~ 0+gr)
## Use Koller & Stahel(2011)'s recommendation but a larger 'max.it':
str(ctrl <- lmrob.control("KS2011", max.it = 1000))
str(.Mpsi.tuning.defaults)
stopifnot(identical(.Mpsi.tuning.defaults,
sapply(names(.Mpsi.tuning.defaults),
.Mpsi.tuning.default)))
## Containing (names!) all our (pre-defined) redescenders:
str(.Mchi.tuning.defaults)
## Difference between settings:
Cdef <- lmrob.control()
C11 <- lmrob.control("KS2011")
C14 <- lmrob.control("KS2014")
str(C14)
## Differences:
diffD <- names(which(!mapply(identical, Cdef,C11, ignore.environment=TRUE)))
diffC <- names(which(!mapply(identical, C11, C14, ignore.environment=TRUE)))
## KS2011 vs KS2014: Apart from `setting` itself, they only differ in three places:
cbind(KS11 = unlist(C11[diffC[-1]]),
KS14 = unlist(C14[diffC[-1]]))
## KS11 KS14
## nResample 500 1000
## best.r.s 2 20
## k.fast.s 1 2
## default vs KS2011: a bit more: setting + 8
str2simpLang <- function(x) {
r <- if(is.null(x)) quote((NULL)) else str2lang(deparse(x))
if(is.call(r)) format(r) else r
}
cbind(deflt= lapply(Cdef[diffD], str2simpLang),
KS11 = lapply(C11 [diffD], str2simpLang))
## update()ing a lmrob.control() , e.g.,
C14mod <- update(C14, trace.lev = 2) # the same as
C14m.d <- C14; C14m.d$trace.lev <- 2
stopifnot(identical(C14mod, C14m.d))
## changing psi --> updates tuning.{psi,chi}:
C14mp <- update(C14, psi = "hampel", seed=101)
## updating 'method' is "smart" :
C.SMDM <- update(Cdef, method="SMDM")
all.equal(Cdef, C.SMDM) # changed also psi, tuning.{psi,chi} and cov !
chgd <- c("method", "psi", "tuning.chi", "tuning.psi", "cov")
str(Cdef [chgd])
str(C.SMDM[chgd])
C14m <- update(C14, method="SMM")
(ae <- all.equal(C14, C14mp))# changed tuning.psi & tuning.chi, too
stopifnot(exprs = {
identical(C14, update(C14, method="SMDM")) # no change!
identical(c("psi", "seed", "tuning.chi", "tuning.psi"),
sort(gsub("[^.[:alpha:]]", "", sub(":.*", "", sub("^Component ", "", ae)))))
identical(C14m, local({C <- C14; C$method <- "SMM"; C}))
})
##
try( update(C14, setting="KS2011") ) #--> Error: .. not allowed
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