varPartConfInf {variancePartition} | R Documentation

## Linear mixed model confidence intervals

### Description

Fit linear mixed model to estimate contribution of multiple sources of variation while simultaneously correcting for all other variables. Then perform parametric bootstrap sampling to get a 95% confidence intervals for each variable for each gene.

### Usage

    varPartConfInf(
      exprObj,
      formula,
      data,
      REML = FALSE,
      useWeights = TRUE,
      weightsMatrix = NULL,
      showWarnings = TRUE,
      colinearityCutoff = 0.999,
      control = lme4::lmerControl(calc.derivs = FALSE, check.rankX = "stop.deficient"),
      nsim = 1000,
      ...
    )

### Arguments

`exprObj` | matrix of expression data (g genes x n samples), or `ExpressionSet`, or `EList` returned by `voom()` from the `limma` package
`formula` | specifies variables for the linear (mixed) model. Must only specify covariates, since the rows of exprObj are automatically used a a response. e.g.: `~ a + b + (1|c)`
`data` | `data.frame` with columns corresponding to formula
`REML` | use restricted maximum likelihood to fit linear mixed model. default is FALSE. Strongly discourage against changing this option, but here for compatibility.
`useWeights` | if TRUE, analysis uses heteroskedastic error estimates from `voom()`. Value is ignored unless exprObj is an `EList` from `voom()` or `weightsMatrix` is specified
`weightsMatrix` | matrix the same dimension as exprObj with observation-level weights from `voom()`. Used only if useWeights is TRUE
`showWarnings` | show warnings about model fit (default TRUE)
`colinearityCutoff` | cutoff used to determine if model is computationally singular
`control` | control settings for `lmer()`
`nsim` | number of bootstrap datasets
`...` | Additional arguments for `lmer()` or l`m()`

### Details

A linear mixed model is fit for each gene, and `bootMer()` is used to generate parametric bootstrap confidence intervals. `use.u=TRUE` is used so that the

\hat{u}

values from the random effects are used as estimated and are not re-sampled. This gives confidence intervals as if additional data were generated from these same current samples. Conversely, `use.u=FALSE` assumes that this dataset is a sample from a larger population. Thus it simulates

\hat{u}

based on the estimated variance parameter. This approach gives confidence intervals as if additional data were collected from the larger population from which this dataset is sampled. Overall, `use.u=TRUE` gives smaller confidence intervals that are appropriate in this case.

### Value

`list()` of where each entry is the result for a gene. Each entry is a matrix of the 95% confidence interval of the variance fraction for each variable

### Examples

    # load library
    # library(variancePartition)

    # Intialize parallel backend with 4 cores
    library(BiocParallel)
    register(SnowParam(4))

    # load simulated data:
    # geneExpr: matrix of gene expression values
    # info: information/metadata about each sample
    data(varPartData)

    # Specify variables to consider
    # Age is continuous so we model it as a fixed effect
    # Individual and Tissue are both categorical, so we model them as random effects
    form <- ~ Age + (1|Individual) + (1|Tissue)

    # Compute bootstrap confidence intervals for each variable for each gene
    resCI <- varPartConfInf( geneExpr[1:5,], form, info, nsim=100 )

[Package variancePartition version 1.20.0 Index]