robustbase 0.93-9 - en to en translation catalogue (126 entries)

en: '%s' is not a valid family (see ?family)
en: ‘%s’ is not a valid family (see ?family)

en: '%s' must be a function, numeric vector of length p, or NULL
en: ‘%s’ must be a function, numeric vector of length p, or NULL

en: '%s' must be character string or function, but is "%s"
en: ‘%s’ must be character string or function, but is "%s"

en: ', 'parm' must be specified as an integer
en: ‘, ’parm' must be specified as an integer

en: 'Anova Table' for a single model not yet implemented
en: ‘Anova Table’ for a single model not yet implemented

en: 'Anova Table' for a single model object not yet implemented
en: ‘Anova Table’ for a single model object not yet implemented

en: 'X' must have at least two columns
en: ‘X’ must have at least two columns

en: 'coef' must not be negative
en: ‘coef’ must not be negative

en: 'control$n.group' is not much larger than 'p', probably too small
en: ‘control$n.group’ is not much larger than ‘p’, probably too small

en: 'control$n.group' must be larger than 'p' for 'large_n' algorithm
en: ‘control$n.group’ must be larger than ‘p’ for ‘large_n’ algorithm

en: 'control' is missing
en: ‘control’ is missing

en: 'cov' must be a p x p matrix
en: ‘cov’ must be a p x p matrix

en: 'epsw' must be numeric(1) or a function of nobs(obj.) which returns a numeric(1)
en: ‘epsw’ must be numeric(1) or a function of nobs(obj.) which returns a numeric(1)

en: 'epsx' must be numeric(1) or a function of max(abs(x)) which returns a numeric(1)
en: ‘epsx’ must be numeric(1) or a function of max(abs(x)) which returns a numeric(1)

en: 'formula' missing or incorrect
en: ‘formula’ missing or incorrect

en: 'formula' should be a formula of the type 'y ~ f(x, alpha)'
en: ‘formula’ should be a formula of the type ‘y ~ f(x, alpha)’

en: 'full.h' is true, but 'hsets.init' has less than n rows
en: ‘full.h’ is true, but ‘hsets.init’ has less than n rows

en: 'groups * n.group' must be smaller than 'n' for 'large_n' algorithm
en: ‘groups * n.group’ must be smaller than ‘n’ for ‘large_n’ algorithm

en: 'hsets.init' must be a  h' x L  matrix (h' >= h) of observation indices
en: ‘hsets.init’ must be a  h' x L  matrix (h' >= h) of observation indices

en: 'hsets.init' must be in {1,2,...,n}; n =
en: ‘hsets.init’ must be in {1,2,...,n}; n =

en: 'id.n' must be in {1,..,
en: ‘id.n’ must be in {1,..,

en: 'id.n' must be in {1,..,%d}
en: ‘id.n’ must be in {1,..,%d}

en: 'mf' is unused and deprecated
en: ‘mf’ is unused and deprecated

en: 'nsamp = "best"' allows maximally
en: ‘nsamp = "best"’ allows maximally

en: 'nsamp' options 'best' and 'exact' not allowed for n greater than
en: ‘nsamp’ options ‘best’ and ‘exact’ not allowed for n greater than

en: 'offset' is not yet implemented for "BY"
en: ‘offset’ is not yet implemented for "BY"

en: 'offset' not fully implemented
en: ‘offset’ not fully implemented

en: 'psi' should be one of %s
en: ‘psi’ should be one of %s

en: 'qr.out = TRUE' for univariate location is disregarded
en: ‘qr.out = TRUE’ for univariate location is disregarded

en: 'reflect part' in
en: ‘reflect part’ in

en: 'scale' must be NULL or a positive number
en: ‘scale’ must be NULL or a positive number

en: 'shortMain' is deprecated and will get defunct.
en: ‘shortMain’ is deprecated and will get defunct.

en: 'start' cannot yet be passed to glmrobBY()
en: ‘start’ cannot yet be passed to glmrobBY()

en: 'start' must be a named list or numeric vector
en: ‘start’ must be a named list or numeric vector

en: 'start' must be a numeric vector, NULL, or a character string
en: ‘start’ must be a numeric vector, NULL, or a character string

en: 'start' must be an initial estimate of beta, of length %d
en: ‘start’ must be an initial estimate of beta, of length %d

en: 'start' or 'lower' or 'upper' must be fully named (list or numeric vector)
en: ‘start’ or ‘lower’ or ‘upper’ must be fully named (list or numeric vector)

en: 'weights' as formula should be one-sided
en: ‘weights’ as formula should be one-sided

en: 'weights' must be a numeric vector
en: ‘weights’ must be a numeric vector

en: 'weights' must be non-negative
en: ‘weights’ must be non-negative

en: 'weights' must be nonnegative and not contain NAs
en: ‘weights’ must be nonnegative and not contain NAs

en: 'weights' must have same length as 'x'
en: ‘weights’ must have same length as ‘x’

en: 'weights.on.x = "%s"' is not implemented
en: ‘weights.on.x = "%s"’ is not implemented

en: 'weights.on.x' must be a string, function, list or numeric n-vector
en: ‘weights.on.x’ must be a string, function, list or numeric n-vector

en: 'wgtFUN' must be a function or a string specifying one
en: ‘wgtFUN’ must be a function or a string specifying one

en: 'wgtFUN' must be a function or a string specifying such a function
en: ‘wgtFUN’ must be a function or a string specifying such a function

en: 'wgtFUN' must be a function or one of the strings %s.
en: ‘wgtFUN’ must be a function or one of the strings %s.

en: 'which' must be in 1:5
en: ‘which’ must be in 1:5

en: 'x' must be a numeric matrix
en: ‘x’ must be a numeric matrix

en: (tau / hybrid / tauold): tau not found in 'obj'
en: (tau / hybrid / tauold): tau not found in ‘obj’

en: ** direction sampling iterations were not sufficient. Maybe try increasing 'maxit.mult'
en: ** direction sampling iterations were not sufficient. Maybe try increasing ‘maxit.mult’

en: .vcov.avar1: negative diag(<vcov>) fixed up; consider 'cov=".vcov.w."' instead
en: .vcov.avar1: negative diag(<vcov>) fixed up; consider ‘cov=".vcov.w."’ instead

en: As "sigma" is in 'pnames', do not use it as variable or parameter name in 'formula'
en: As "sigma" is in ‘pnames’, do not use it as variable or parameter name in ‘formula’

en: Currently, only family 'poisson' is supported for the "MT" estimator
en: Currently, only family ‘poisson’ is supported for the "MT" estimator

en: Do not use '%s' as a variable name or as a parameter name
en: Do not use ‘%s’ as a variable name or as a parameter name

en: For method 'cubif', use glmRob() from package 'robust'
en: For method ‘cubif’, use glmRob() from package ‘robust’

en: For method = "%s", currently 'psi' must be specified via 'control'
en: For method = "%s", currently ‘psi’ must be specified via ‘control’

en: For test = 'Deviance', the estimator chain has to end with 'M'
en: For test = ‘Deviance’, the estimator chain has to end with ‘M’

en: For tolerance ellipses the dimension 'p' must be 2!
en: For tolerance ellipses the dimension ‘p’ must be 2!

en: Initial estimator '%s' not supported; using S-estimator instead
en: Initial estimator ‘%s’ not supported; using S-estimator instead

en: Initialization 'init = "%s"' not supported (yet)
en: Initialization ‘init = "%s"’ not supported (yet)

en: Invalid  scalefn='%s': must be function or a valid string
en: Invalid  scalefn=‘%s’: must be function or a valid string

en: Matrix 'x' is not of full rank: rankM(x) =
en: Matrix ‘x’ is not of full rank: rankM(x) =

en: Maximum 5000 subsets allowed for option 'best'.
en: Maximum 5000 subsets allowed for option ‘best’.

en: Only 'test.acc = "coef"' is currently implemented
en: Only ‘test.acc = "coef"’ is currently implemented

en: Options 'best' and 'exact' not allowed for n greater than  2*nmini-1 =
en: Options ‘best’ and ‘exact’ not allowed for n greater than  2*nmini-1 =

en: Provide 'upper' or 'lower' with names()
en: Provide ‘upper’ or ‘lower’ with names()

en: Psi function '%s' not supported yet
en: Psi function ‘%s’ not supported yet

en: Std.error computation not yet available for the case of 'weights'
en: Std.error computation not yet available for the case of ‘weights’

en: The 'method' argument is different from 'control$method'
en: The ‘method’ argument is different from ‘control$method’

en: The argument 'acc' has been renamed to 'tol'; do adapt your code.
en: The argument ‘acc’ has been renamed to ‘tol’; do adapt your code.

en: Unexpected 'exactfit' code
en: Unexpected ‘exactfit’ code

en: Use 'main = "short"' instead of 'shortMain = TRUE'
en: Use ‘main = "short"’ instead of ‘shortMain = TRUE’

en: Use lmrob argument 'setting="KS2014"' to avoid this problem.
en: Use lmrob argument ‘setting="KS2014"’ to avoid this problem.

en: Use only with 'lmrob' objects
en: Use only with ‘lmrob’ objects

en: Use only with 'lts' objects
en: Use only with ‘lts’ objects

en: Use only with 'mcd' objects
en: Use only with ‘mcd’ objects

en: You cannot specify both 'main' and the deprecated 'shortMain'
en: You cannot specify both ‘main’ and the deprecated ‘shortMain’

en: You should use different 'eps' and/or 'eps1'
en: You should use different ‘eps’ and/or ‘eps1’

en: anova.glmrob() only works for 'glmrob' objects
en: anova.glmrob() only works for ‘glmrob’ objects

en: anova.lmrob() only works for 'lmrob' objects
en: anova.lmrob() only works for ‘lmrob’ objects

en: argument 'm.cov' must have numeric components 'center' and 'cov'
en: argument ‘m.cov’ must have numeric components ‘center’ and ‘cov’

en: argument 'psi' must be a string (denoting a psi function)
en: argument ‘psi’ must be a string (denoting a psi function)

en: family '%s' not yet implemented
en: family ‘%s’ not yet implemented

en: finite sample corrections are not corrected for non-default 'k'
en: finite sample corrections are not corrected for non-default ‘k’

en: ignoring cov.resid == 'final' since est != final
en: ignoring cov.resid == ‘final’ since est != final

en: illegal 'family' argument
en: illegal ‘family’ argument

en: illegal 'singularity$kind'
en: illegal ‘singularity$kind’

en: invalid 'cov.dfcorr':
en: invalid ‘cov.dfcorr’:

en: invalid 'lmrob' object:  no terms component
en: invalid ‘lmrob’ object:  no terms component

en: invalid 'method':
en: invalid ‘method’:

en: invalid 'posdef.meth':
en: invalid ‘posdef.meth’:

en: invalid 'psi'=%s; possibly use .regularize.Mpsi(%s)
en: invalid ‘psi’=%s; possibly use .regularize.Mpsi(%s)

en: invalid 'seed'. Must be compatible with .Random.seed !
en: invalid ‘seed’. Must be compatible with .Random.seed !

en: invalid 'start' string
en: invalid ‘start’ string

en: invalid 'test'
en: invalid ‘test’

en: invalid 'type'
en: invalid ‘type’

en: invalid 'variant':
en: invalid ‘variant’:

en: lmrob object does not have a proper 'qr' component. Rank must be zero
en: lmrob object does not have a proper ‘qr’ component. Rank must be zero

en: lmrob object does not have a proper 'qr' component. Rank zero?
en: lmrob object does not have a proper ‘qr’ component. Rank zero?

en: mc(): not 'converged'
en: mc(): not ‘converged’

en: method='%s' is only applicable for binomial family, but family=""
en: method=‘%s’ is only applicable for binomial family, but family=""

en: need 'model' or 'x' component for robust Mahalanobis distances
en: need ‘model’ or ‘x’ component for robust Mahalanobis distances

en: negative scale 's'
en: negative scale ‘s’

en: neither breakdown point 'bp' nor efficiency 'eff' specified
en: neither breakdown point ‘bp’ nor efficiency ‘eff’ specified

en: no valid subsample found in LTS - set 'nsamp' or rather use lmrob.S()
en: no valid subsample found in LTS - set ‘nsamp’ or rather use lmrob.S()

en: non-trivial 'offset' is not yet implemented
en: non-trivial ‘offset’ is not yet implemented

en: non-trivial prior 'weights' are not yet implemented for "BY"
en: non-trivial prior ‘weights’ are not yet implemented for "BY"

en: number of rows in 'x' and length of 'object$rweights' must be the same
en: number of rows in ‘x’ and length of ‘object$rweights’ must be the same

en: option 'cov.hubercorr' is ignored for cov.corrfact = "asympt"
en: option ‘cov.hubercorr’ is ignored for cov.corrfact = "asympt"

en: parameter 'tuning.psi' is not numeric
en: parameter ‘tuning.psi’ is not numeric

en: parameter names must appear in 'formula'
en: parameter names must appear in ‘formula’

en: saving the robust distances 'MD' as part of
en: saving the robust distances ‘MD’ as part of

en: scale 's' is zero -- returning initial 'mu'
en: scale ‘s’ is zero -- returning initial ‘mu’

en: setting 'y = FALSE' has no longer any effect
en: setting ‘y = FALSE’ has no longer any effect

en: specifying 'weights' is not yet supported for method
en: specifying ‘weights’ is not yet supported for method

en: specifying both 'acc' and 'tol' is invalid
en: specifying both ‘acc’ and ‘tol’ is invalid

en: start 'theta' has still NA's .. badly singular x
en: start ‘theta’ has still NA's .. badly singular x

en: the following arguments to 'anova.glmrob' are invalid and
en: the following arguments to ‘anova.glmrob’ are invalid and

en: the following arguments to 'anova.lmrob' are invalid and
en: the following arguments to ‘anova.lmrob’ are invalid and

en: tuning constant 'cc' is not numeric
en: tuning constant ‘cc’ is not numeric

en: tuning constants for "lqq" psi: both 'eff' and 'bp' specified, ignoring 'bp'
en: tuning constants for "lqq" psi: both ‘eff’ and ‘bp’ specified, ignoring ‘bp’

en: type 'pearson' is not yet implemented
en: type ‘pearson’ is not yet implemented

en: unknown 'wgtFUN' specification:
en: unknown ‘wgtFUN’ specification:

en: unknown setting for 'subsampling': %s
en: unknown setting for ‘subsampling’: %s

en: value of the tuning constant c ('const') must be > 0
en: value of the tuning constant c (‘const’) must be > 0
