Arguments result agdex result returned by function agdex gset.ids a vector of gene-set IDs. If NULL, the result will return gene level details for all significant gene-sets at a chosen significant level alpha. alpha significance level of gene-set, default set to 0.01
Arguments xxxx an ExpressionSet object carries the gene expression data (Exprs) and Phenotype data (pData) comp.var the column name or numeric index for group labels in pData of object Xxxx.xxxx xxxx.xxx a string definition of comparison, group labels connected by "-" gset.collection an object belongs to class GeneSetCollection The ExpressionSet includes two components: exprs: a matrix of expression values pData: a data frame contains the sample IDs and their assigned group labels. gset.collection contains a GeneSetCollection object defined in the Bioconductor package GSEABase. The gset.collection object must use the same identifiers for probe-sets as that used in the exprs com- ponent of Xxxx.xxxx.
Arguments. Each advocate will be allowed approximately ten (10) minutes to present argument(s) supporting their position. In hearing disciplinary grievances, the Hospital will present first. In all other grievances, the Union will present first. There can be only one (1) spokesperson for each party, in each case.
Arguments. This optional subsection describes input arguments to a function or macro.
Arguments data a matrix of scores. Each row corresponds to a unit, each column a coder. level the level of measurement, one of "nominal", "ordinal", "interval", or "ratio"; or a user-defined distance function. confint logical; if TRUE, a bootstrap sample is produced. verbose logical; if TRUE, various messages are printed to the console. Note that if confint = TRUE a progress bar (pblapply) is displayed (if possible) during the bootstrap computation. control a list of control parameters. bootit the size of the bootstrap sample. This applies when confint = TRUE. Defaults to 1,000. nodes the desired number of nodes in the cluster. parallel logical; if TRUE (the default), bootstrapping is done in parallel. type one of the supported cluster types for makeCluster. Defaults to "SOCK".
Arguments x an object of class "krippendorffsalpha", the result of a call to krippendorffs.alpha. y always ignored. level the desired confidence level for the interval. The default is 0.95. type the method used to compute sample quantiles. This argument is passed to quantile. The default is 7. density logical; if TRUE, a kernel density estimate is plotted. lty.density the line type for the kernel density estimate. The default is 1. lty.estimate the line type for the estimate of alpha. The default is 1. lty.interval the line type for the confidence limits. The default is 2. col.density the color for the kernel density estimate. The default is black. col.estimate the color for the estimate of alpha. The default is orange. col.interval the color for the confidence limits. The default is blue. lwd.density the line width for the kernel density estimate. The default is 3. ratio.dist 9 lwd.estimate the line width for the estimate of alpha. The default is 3. lwd.interval the line width for the confidence limits. The default is 3.
Arguments model a fitted model object, the result of a call to krippendorffs.alpha. units a vector of integers. A DFBETA will be computed for each of the corresponding units. coders a vector of integers. A DFBETA will be computed for each of the corresponding coders. ... additional arguments. These are ignored.
Arguments. (Please note: The arguments contained in this analysis originate from sources outside the Senate Fiscal Agency. The Senate Fiscal Agency neither supports nor opposes legislation.)
Arguments ratings matrix (subjects by raters), containing the ratings variant Which variant of kappa? Default is Fleiss (1971). Other options are Xxxxxx (1980) or robust variant. detail Should category-wise Kappas be computed? Only available for the Fleiss (1971) variant. ratingScale Specify possible levels for the rating. Default NULL means to use all unique levels from the sample. Different variants of Fleiss’ kappa are implemented. By default (variant="fleiss"), the original Fleiss Kappa (1971) is calculated, together with an asymptotic standard error and test for kappa=0. It assumes that the raters involved are not assumed to be the same (one-way ANOVA setting). The marginal category proportions determine the chance agreement. Setting variant="xxxxxx" gives the variant of Xxxxxx (1980) that reduces to Xxxxx’x kappa when m=2 raters. It assumes identical raters for the different subjects (two-way ANOVA setting). The chance agreement is based on the category proportions of each rater separately. Typically, the Xxxxxx variant yields slightly higher values than Fleiss kappa. variant="robust" assumes a chance agreement of two raters to be simply 1/q, where q is the number of categories (uniform model).
Arguments ratingsGr1 matrix of subjects x raters for 1st group of raters ratingsGr2 matrix of subjects x raters for 2nd group of raters ratingScale character vector of the levels for the rating. Or NULL. weights optional weighting schemes: "unweighted", "linear","quadratic" conf.level confidence level for interval estimation Data need to be stored with raters in columns. list. kappa agreement between two groups of raters Xxxxxxxx, S., Xxxxxx, A. Agreement between Two Independent Groups of Raters. Psychometrika 74, 477–491 (2009). doi:10.1007/s1133600991161 # compare rater1-rater2 vs rater3-rater6 from diagnoses-data # (there is no systematic difference between both groups #+as the raters are randomly selected per subject) kappam_vanbelle(diagnoses[,1:2], diagnoses[,3:6])
