R: Spread model coefficients of list-variables into columns

spread_coef {sjmisc} | R Documentation

## Spread model coefficients of list-variables into columns

### Description

This function extracts coefficients (and standard error and p-values) of fitted model objects from (nested) data frames, which are saved in a list-variable, and spreads the coefficients into new colummns.

### Usage

    spread_coef(data, model.column, model.term, se, p.val, append = TRUE,
      ...)

### Arguments

`data` | A (nested) data frame with a list-variable that contains fitted model objects (see 'Details').
`model.column` | Name or index of the list-variable that contains the fitted model objects.
`model.term` | Optional, name of a model term. If specified, only this model term (including p-value) will be extracted from each model and added as new column.
`se` | Logical, if `TRUE`, standard errors for estimates will also be extracted.
`p.val` | Logical, if `TRUE`, p-values for estimates will also be extracted.
`append` | Logical, if `TRUE` (default), this function returns `data` with new columns for the model coefficients; else, a new data frame with model coefficients only are returned.
`...` | Other arguments passed down to the `tidy`-function.

### Details

This function requires a (nested) data frame (e.g. created by the `nest`-function of the tidyr-package), where several fitted models are saved in a list-variable (see 'Examples'). Since nested data frames with fitted models stored as list-variable are typically fit with an identical formula, all models have the same dependent and independent variables and only differ in their subsets of data. The function then extracts all coefficients from each model and saves each estimate in a new column. The result is a data frame, where each row is a model with each model's coefficients in an own column.

### Value

A data frame with columns for each coefficient of the models that are stored in the list-variable of `data`; or, if `model.term` is given, a data frame with the term's estimate. If `se = TRUE` or `p.val = TRUE`, the returned data frame also contains columns for the coefficients' standard error and p-value. If `append = TRUE`, the columns are appended to `data`, i.e. `data` is also returned.

### Examples

    library(dplyr)
    library(tidyr)
    library(purrr)
    data(efc)

    # create nested data frame, grouped by dependency (e42dep)
    # and fit linear model for each group. These models are
    # stored in the list variable "models".
    model.data <- efc %>%
      filter(!is.na(e42dep)) %>%
      group_by(e42dep) %>%
      nest() %>%
      mutate(
        models = map(data, ~lm(neg_c_7 ~ c12hour + c172code, data = .x))
      )

    # spread coefficients, so we can easily access and compare the
    # coefficients over all models. arguments `se` and `p.val` default
    # to `FALSE`, when `model.term` is not specified
    spread_coef(model.data, models)
    spread_coef(model.data, models, se = TRUE)

    # select only specific model term. `se` and `p.val` default to `TRUE`
    spread_coef(model.data, models, c12hour)

    # spread_coef can be used directly within a pipe-chain
    efc %>%
      filter(!is.na(e42dep)) %>%
      group_by(e42dep) %>%
      nest() %>%
      mutate(
        models = map(data, ~lm(neg_c_7 ~ c12hour + c172code, data = .x))
      ) %>%
      spread_coef(models)

    # spread_coef() makes it easy to generate bootstrapped
    # confidence intervals, using the 'bootstrap()' and 'boot_ci()'
    # functions from the 'sjstats' package, which creates nested
    # data frames of bootstrap replicates
    library(sjstats)
    efc %>%
      # generate bootstrap replicates
      bootstrap(100) %>%
      # apply lm to all bootstrapped data sets
      mutate(
        models = map(strap, ~lm(neg_c_7 ~ e42dep + c161sex + c172code, data = .x))
      ) %>%
      # spread model coefficient for all 100 models
      spread_coef(models, se = FALSE, p.val = FALSE) %>%
      # compute the CI for all bootstrapped model coefficients
      boot_ci(e42dep, c161sex, c172code)

[Package sjmisc version 2.7.5 Index]