R/codalm_em.R
codalm_ci.RdImplements percentile based bootstrapping to estimate the confidence intervals for the regression coefficients when doing linear regression for compositional outcomes and predictors
codalm_ci( y, x, accelerate = TRUE, nboot = 500, conf = 0.95, parallel = FALSE, ncpus = NULL, strategy = NULL, init.seed = 123 )
| y | A matrix of compositional outcomes. Each row is an observation, and must sum to 1. If any rows do not sum to 1, they will be renormalized |
|---|---|
| x | A matrix of compositional predictors. Each row is an observation, and must sum to 1. If any rows do not sum to 1, they will be renormalized |
| accelerate | A logical variable, indicating whether or not to use the Squarem algorithm for acceleration of the EM algorithm. Default is TRUE |
| nboot | The number of bootstrap repetitions to use. Default is 500 |
| conf | A scalar between 0 and 1 containing the confidence level of the required intervals. Default is .95. |
| parallel | A logical variable, indicating whether or not to use a parallel operation for computing the permutation statistics |
| ncpus | Optional argument. When provided, is an integer giving the number of clusters to be used in parallelization. Defaults to the number of cores, minus 1. |
| strategy | Optional argument. When provided, this will be the evaluation function
(or name of it) to use for parallel computation (if parallel = TRUE). Otherwise,
if parallel = TRUE, then this will default to multisession. See |
| init.seed | The initial seed for the permutations. Default is 123. |
A list, with ci_L and ci_U, giving the lower and upper bounds
of each element of the B matrix
# \donttest{ require(ggtern) data("WhiteCells", package = 'ggtern') image <- subset(WhiteCells, Experiment == "ImageAnalysis") image_mat <- as.matrix(image[,c("G", "L", "M")]) microscopic <- subset(WhiteCells, Experiment == "MicroscopicInspection") microscopic_mat <- as.matrix(microscopic[,c("G", "L", "M")]) x <- image_mat / rowSums(image_mat) y <- microscopic_mat / rowSums(microscopic_mat) codalm_ci(y, x, nboot = 50, conf = .95)#> $ci_L #> [,1] [,2] [,3] #> [1,] 9.576089e-01 0.01399186 1.156448e-03 #> [2,] 2.512029e-15 0.97220006 9.764192e-17 #> [3,] 6.197244e-21 0.01165546 9.145955e-01 #> #> $ci_U #> [,1] [,2] [,3] #> [1,] 9.837854e-01 0.03981767 4.560504e-03 #> [2,] 2.779953e-02 1.00000000 4.198096e-07 #> [3,] 3.143104e-06 0.08540455 9.868145e-01 #># }