VCAinference {mcradds}

Inferential Statistics for VCA-Results

Description

A copy from VCA::VCAinference in VCA package

Usage

VCAinference(...)

Arguments

...

Arguments passed on to VCA::VCAinference

obj

(object) of class 'VCA' or, alternatively, a list of 'VCA' objects, where all other argument can be specified as vectors, where the i-th vector element applies to the i-th element of 'obj' (see examples)

alpha

(numeric) value specifying the significance level for 100*(1-alpha)% confidence intervals.

total.claim

(numeric) value specifying the claim-value for the Chi-Squared test for the total variance (SD or CV, see claim.type).

error.claim

(numeric) value specifying the claim-value for the Chi-Squared test for the error variance (SD or CV, see claim.type).

claim.type

(character) one of "VC", "SD", "CV" specifying how claim-values have to be interpreted:
"VC" (Default) = claim-value(s) specified in terms of variance(s),
"SD" = claim-values specified in terms of standard deviations (SD),
"CV" = claim-values specified in terms of coefficient(s) of variation (CV) and are specified as percentages.
If set to "SD" or "CV", claim-values will be converted to variances before applying the Chi-Squared test (see examples).

VarVC

(logical) TRUE = if element "Matrices" exists (see anovaVCA), the covariance matrix of the estimated VCs will be computed (see vcovVC, which is used in CIs for intermediate VCs if 'method.ci="sas"'. Note, this might take very long for larger datasets, since there are many matrix operations involved. FALSE (Default) = computing covariance matrix of VCs is omitted, as well as CIs for intermediate VCs.

excludeNeg

(logical) TRUE = confidence intervals of negative variance estimates will not be reported.
FALSE = confidence intervals for all VCs will be reported including those with negative VCs.
See the details section for a thorough explanation.

constrainCI

(logical) TRUE = CI-limits for all variance components are constrained to be >= 0.
FALSE = unconstrained CIs with potentially negative CI-limits will be reported.
which will preserve the original width of CIs. See the details section for a thorough explanation.

ci.method

(character) string or abbreviation specifying which approach to use for computing confidence intervals of variance components (VC). "sas" (default) uses Chi-Squared based CIs for total and error and normal approximation for all other VCs (Wald-limits, option "NOBOUND" in SAS PROC MIXED); "satterthwaite" will approximate DFs for each VC using the Satterthwaite approach (see SattDF for models fitted by ANOVA) and all Cis are based on the Chi-Squared distribution. This approach is conservative but avoids negative values for the lower bounds.

quiet

(logical) TRUE = will suppress any warning, which will be issued otherwise

Value

object of VCAinference contains a series of statistics.

See Also

VCA::VCAinference()

Examples

data(glucose)
fit <- anovaVCA(value ~ day / run, glucose)
VCAinference(fit)