fit_models {edge} | R Documentation

## Linear regression of the null and full models

### Description

`fit_models` fits a model matrix to each gene by using the least squares method. Model fits can be either statistic type "odp" (optimal discovery procedure) or "lrt" (likelihood ratio test).

### Usage

    fit_models(object, stat.type = c("lrt", "odp"), weights = NULL)

    ## S4 method for signature 'deSet'
    fit_models(object, stat.type = c("lrt", "odp"),
      weights = NULL)

### Arguments

`object` | `S4 object`: `deSet`.
`stat.type` | `character`: type of statistic to be used. Either "lrt" or "odp". Default is "lrt".
`weights` | `matrix`: weights for each observation. Default is NULL.

### Details

If "odp" method is implemented then the null model is removed from the full model (see Storey 2007). Otherwise, the statistic type has no affect on the model fit.

### Value

`deFit` object

### Note

`fit_models` does not have to be called by the user to use `odp`, `lrt` or `kl_clust` as it is an optional input and is implemented in the methods. The `deFit` object can be created by the user if a different statistical implementation is required.

### Author(s)

John Storey

### References

Storey JD. (2007) The optimal discovery procedure: A new approach to simultaneous significance testing. Journal of the Royal Statistical Society, Series B, 69: 347-368.

Storey JD, Dai JY, and Leek JT. (2007) The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments. Biostatistics, 8: 414-432.

Storey JD, Xiao W, Leek JT, Tompkins RG, and Davis RW. (2005) Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences, 102: 12837-12842.

### See Also

`deFit`, `odp` and `lrt`

### Examples

    # import data
    library(splines)
    data(kidney)
    age <- kidney$age
    sex <- kidney$sex
    kidexpr <- kidney$kidexpr
    cov <- data.frame(sex = sex, age = age)

    # create models
    null_model <- ~sex
    full_model <- ~sex + ns(age, df = 4)

    # create deSet object from data
    de_obj <- build_models(data = kidexpr, cov = cov, null.model = null_model,
    full.model = full_model)

    # retrieve statistics from linear regression for each gene
    fit_lrt <- fit_models(de_obj, stat.type = "lrt") # lrt method
    fit_odp <- fit_models(de_obj, stat.type = "odp") # odp method

    # summarize object
    summary(fit_odp)

[Package edge version 2.19.0 Index]