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Showing results 1 to 10 of 18.


Function pointsCovarModel [GmAMisc v1.1.1]
keywords
covariate
title
R function to model (and test) the dependence of a point pattern on a spatial numeric covariate
description
The function is a wrapper for a number of functions out of the extremely useful 'spatstat' package (specifically, ppm(), cdf.test(), auc(), roc(), effectfun()). It allows to test if there is a significant dependence of the input point pattern on a underlying spatial numeric covariate (first-order effect). The function takes as input three datasets: a point patter ('SpatialPointsDataFrame' class), a covariate layer (of 'RasterLayer' class), and a polygon feature ('SpatialPolygonsDataFrame' class) representing the study area and exactly matching the extent of the covariate layer. If the latter is not provided, it is internally worked out from the covariate raster and may make the whole function take a while to complete.
Function covbalance [approxmatch v2.0]
keywords
covariate balance
title
Check covariate balance of a design.
description
For a given match, this function evaluates the balance of variables before and after matching. Balance is evaluated using standardized differences.
Function parse_formula [MatchItEXT v0.0.1]
keywords
covariate
title
Parse formula to obtain grouping variable and covariate vector
description
This function parses the formula used in matchit() to obtain the grouping variable and the covariate vector.
Function paths [BTLLasso v0.1-11]
keywords
covariate
title
Plot covariate paths for BTLLasso
description
Plots paths for every covariate of a BTLLasso object or a cv.BTLLasso object. In contrast to plot.BTLLasso, only one plot is created, every covariate is illustrated by one path. For cv.BTLLasso objects, the optimal model according to the cross-validation is marked by a vertical dashed line.
Function CMatchBalance [CMatching v2.3.0]
keywords
covariate balance
title
Analyze covariate balance before and after matching.
description
Generic function for analyzing covariate balance. If match.out is NULL only balance statistics for the unmatched data are returned otherwise both before and after matching balance are given. The function is simply a wrapper calling MatchBalance, possibly after coercing the class of match.out. See MatchBalance for more detailed description.
Function cvrcov [cvcrand v0.1.0]
keywords
covariate-by-covariate-constrained-randomization
title
Covariate-by-covariate constrained randomization for cluster randomized trials
description
cvrcov performs covariate-by-covariate constrained randomization for cluster randomized trials (CRTs), especially suited for CRTs with a small number of clusters. In constrained randomization, a randomization scheme is randomly sampled from a subset of all possible randomization schemes based on the constraints on each covariate. The cvrcov function enumerates all randomization schemes or simulates a fixed size of unique randomization schemes as specified by the user. A subset of the randomization schemes is chosen based on user-specified covariate-by-covariate constraints. cvrcov treats the subset as the constrained space of randomization schemes and samples one scheme from the constrained space as the final chosen scheme.
Function compound.reg [compound.Cox v3.20]
keywords
compound covariate
title
Compound shrinkage estimation under the Cox model
description
This function implements the "compound shrinkage estimator" to calculate the regression coefficients of the Cox model, which was proposed by Emura, Chen & Chen (2012). The method is a variant of the Cox partial likelihood estimator such that the regression coefficients are mixed with the univariate Cox regression estimators. The resultant estimator is applicable even when the number of covariates is greater than the number of samples (the p>n setting). The standard errors (SEs) are calculated based on the asymptotic theory (see Emura et al., 2012).
Function getCovMeanDiffs [ivmodel v1.9.0]
keywords
Covariate Mean Differences
title
Get Covariate Mean Differences
description
getCovMeanDiffs returns the covariate mean differences between two groups.
Function getStandardizedCovMeanDiffs [ivmodel v1.9.0]
keywords
Covariate Mean Differences
title
Get Standardized Covariate Mean Differences
description
getStandardizedCovMeanDiffs returns the standardized covariate mean differences between two groups.
Function getMD [ivmodel v1.9.0]
keywords
Covariate Mean Differences
title
Get Mahalanobis Distance
description
getMD returns the Mahalanobis distance between two groups.