infer (version 1.0.7)

fit.infer: Fit linear models to infer objects

Description

Given the output of an infer core function, this function will fit a linear model using stats::glm() according to the formula and data supplied earlier in the pipeline. If passed the output of specify() or hypothesize(), the function will fit one model. If passed the output of generate(), it will fit a model to each data resample, denoted in the replicate column. The family of the fitted model depends on the type of the response variable. If the response is numeric, fit() will use family = "gaussian" (linear regression). If the response is a 2-level factor or character, fit() will use family = "binomial" (logistic regression). To fit character or factor response variables with more than two levels, we recommend parsnip::multinom_reg().

infer provides a fit "method" for infer objects, which is a way of carrying out model fitting as applied to infer output. The "generic," imported from the generics package and re-exported from this package, provides the general form of fit() that points to infer's method when called on an infer object. That generic is also documented here.

Learn more in vignette("infer").

Usage

# S3 method for infer
fit(object, ...)

Value

A tibble containing the following columns:

• replicate: Only supplied if the input object had been previously passed to generate(). A number corresponding to which resample of the original data set the model was fitted to.

• term: The explanatory variable (or intercept) in question.

• estimate: The model coefficient for the given resample (replicate) and explanatory variable (term).

Arguments

object

Output from an infer function---likely generate() or specify()---which specifies the formula and data to fit a model to.

...

Any optional arguments to pass along to the model fitting function. See stats::glm() for more information.

Reproducibility

When using the infer package for research, or in other cases when exact reproducibility is a priority, be sure the set the seed for R’s random number generator. infer will respect the random seed specified in the set.seed() function, returning the same result when generate()ing data given an identical seed. For instance, we can calculate the difference in mean age by college degree status using the gss dataset from 10 versions of the gss resampled with permutation using the following code.

set.seed(1)
gss %>%
  specify(age ~ college) %>%
  hypothesize(null = "independence") %>%
  generate(reps = 5, type = "permute") %>%
  calculate("diff in means", order = c("degree", "no degree"))

## Response: age (numeric)
## Explanatory: college (factor)
## Null Hypothesis: independence
## # A tibble: 5 x 2
##   replicate   stat
##       <int>  <dbl>
## 1         1 -0.531
## 2         2 -2.35 
## 3         3  0.764
## 4         4  0.280
## 5         5  0.350

Setting the seed to the same value again and rerunning the same code will produce the same result.

# set the seed
set.seed(1)
gss %>%
  specify(age ~ college) %>%
  hypothesize(null = "independence") %>%
  generate(reps = 5, type = "permute") %>%
  calculate("diff in means", order = c("degree", "no degree"))

## Response: age (numeric)
## Explanatory: college (factor)
## Null Hypothesis: independence
## # A tibble: 5 x 2
##   replicate   stat
##       <int>  <dbl>
## 1         1 -0.531
## 2         2 -2.35 
## 3         3  0.764
## 4         4  0.280
## 5         5  0.350

Please keep this in mind when writing infer code that utilizes resampling with generate().

Details

Randomization-based statistical inference with multiple explanatory variables requires careful consideration of the null hypothesis in question and its implications for permutation procedures. Inference for partial regression coefficients via the permutation method implemented in generate() for multiple explanatory variables, consistent with its meaning elsewhere in the package, is subject to additional distributional assumptions beyond those required for one explanatory variable. Namely, the distribution of the response variable must be similar to the distribution of the errors under the null hypothesis' specification of a fixed effect of the explanatory variables. (This null hypothesis is reflected in the variables argument to generate(). By default, all of the explanatory variables are treated as fixed.) A general rule of thumb here is, if there are large outliers in the distributions of any of the explanatory variables, this distributional assumption will not be satisfied; when the response variable is permuted, the (presumably outlying) value of the response will no longer be paired with the outlier in the explanatory variable, causing an outsize effect on the resulting slope coefficient for that explanatory variable.

More sophisticated methods that are outside of the scope of this package requiring fewer---or less strict---distributional assumptions exist. For an overview, see "Permutation tests for univariate or multivariate analysis of variance and regression" (Marti J. Anderson, 2001), tools:::Rd_expr_doi("10.1139/cjfas-58-3-626").

Examples

# fit a linear model predicting number of hours worked per
# week using respondent age and degree status.
observed_fit <- gss %>%
  specify(hours ~ age + college) %>%
  fit()

observed_fit

# fit 100 models to resamples of the gss dataset, where the response
# `hours` is permuted in each. note that this code is the same as
# the above except for the addition of the `generate` step.
null_fits <- gss %>%
  specify(hours ~ age + college) %>%
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute") %>%
  fit()

null_fits

# for logistic regression, just supply a binary response variable!
# (this can also be made explicit via the `family` argument in ...)
gss %>%
  specify(college ~ age + hours) %>%
  fit()

# more in-depth explanation of how to use the infer package
if (FALSE) {
vignette("infer")
}