Arguments Sample Clauses

Arguments. ▇▇▇▇ 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 ▇▇▇▇.▇▇▇▇ ▇▇▇▇.▇▇▇ 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 ▇▇▇▇.▇▇▇▇.

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 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 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. This optional subsection describes input arguments to a function or macro.

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 formula symbolic description of the model of type y1 + y2 ~ x1 + ... + xk. The left- hand side should specify a pair of measurements (y1 and y2) for the ▇▇▇▇▇- ▇▇▇▇▇▇ analysis. The right-hand side can specify any number of potential split variables for the tree. data, subset, na.action arguments controlling the formula processing via model.frame. weights optional numeric vector of weights (case/frequency weights, by default). means logical. Should the intra-individual mean values of measurements be included as potential split variable? type character string specifying the type of tree to be fit. Either "ctree" (default) or "mob". minsize, minbucket

Arguments formula symbolic description of the model used to perform the ▇▇▇▇▇-▇▇▇▇▇▇ test of type y1 + y2 ~ x. The left-hand side should specify a pair of measurements (y1 and y2) to assess the agreement. The right-hand side should specify a factor with two levels indicating two independent groups or samples to be compared. Al- ternatively, multilevel factors or continuously scaled variables can be specified to perform a ▇▇▇▇▇-▇▇▇▇▇▇ test of association, followed by binary splitting into two subgroups. data, subset, na.action arguments controlling the formula processing via model.frame. weights optional numeric vector of weights (case/frequency weights, by default).

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 ▇▇▇▇▇▇▇▇, S., ▇▇▇▇▇▇, 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])