Generation creates a simulated distribution from specify(). In the context of confidence intervals, this is a bootstrap distribution based on the result of specify(). In the context of hypothesis testing, this is a null distribution based on the result of specify() and hypothesize().

Learn more in vignette("infer").

generate(x, reps = 1, type = NULL, variables = !!response_expr(x), ...)

Arguments

x

A data frame that can be coerced into a tibble.

reps

The number of resamples to generate.

type

The method used to generate resamples of the observed data reflecting the null hypothesis. Currently one of "bootstrap", "permute", or "draw" (see below).

variables

If type = "permute", a set of unquoted column names in the data to permute (independently of each other). Defaults to only the response variable. Note that any derived effects that depend on these columns (e.g., interaction effects) will also be affected.

...

Currently ignored.

Value

A tibble containing reps generated datasets, indicated by the replicate column.

Generation Types

The type argument determines the method used to create the null distribution.

  • bootstrap: A bootstrap sample will be drawn for each replicate, where a sample of size equal to the input sample size is drawn (with replacement) from the input sample data.

  • permute: For each replicate, each input value will be randomly reassigned (without replacement) to a new output value in the sample.

  • draw: A value will be sampled from a theoretical distribution with parameter p specified in hypothesize() for each replicate. This option is currently only applicable for testing on one proportion. This generation type was previously called "simulate", which has been superseded.

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 × 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 × 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().

See also

Other core functions: calculate(), hypothesize(), specify()

Examples

# generate a null distribution by taking 200 bootstrap samples
gss %>%
 specify(response = hours) %>%
 hypothesize(null = "point", mu = 40) %>%
 generate(reps = 200, type = "bootstrap")
#> Response: hours (numeric)
#> Null Hypothesis: point
#> # A tibble: 100,000 × 2
#> # Groups:   replicate [200]
#>    replicate hours
#>        <int> <dbl>
#>  1         1 48.6 
#>  2         1 38.6 
#>  3         1 38.6 
#>  4         1  8.62
#>  5         1 38.6 
#>  6         1 38.6 
#>  7         1 18.6 
#>  8         1 38.6 
#>  9         1 38.6 
#> 10         1 58.6 
#> # … with 99,990 more rows

# generate a null distribution for the independence of
# two variables by permuting their values 200 times
gss %>%
 specify(partyid ~ age) %>%
 hypothesize(null = "independence") %>%
 generate(reps = 200, type = "permute")
#> Dropping unused factor levels DK from the supplied response variable 'partyid'.
#> Response: partyid (factor)
#> Explanatory: age (numeric)
#> Null Hypothesis: independence
#> # A tibble: 100,000 × 3
#> # Groups:   replicate [200]
#>    partyid   age replicate
#>    <fct>   <dbl>     <int>
#>  1 rep        36         1
#>  2 ind        34         1
#>  3 dem        24         1
#>  4 dem        42         1
#>  5 ind        31         1
#>  6 dem        32         1
#>  7 ind        48         1
#>  8 rep        36         1
#>  9 ind        30         1
#> 10 ind        33         1
#> # … with 99,990 more rows

# generate a null distribution via sampling from a
# binomial distribution 200 times
gss %>%
specify(response = sex, success = "female") %>%
  hypothesize(null = "point", p = .5) %>%
  generate(reps = 200, type = "draw") %>%
  calculate(stat = "z")
#> Response: sex (factor)
#> Null Hypothesis: point
#> # A tibble: 200 × 2
#>    replicate    stat
#>    <fct>       <dbl>
#>  1 1          0.537 
#>  2 2          0.447 
#>  3 3         -0.447 
#>  4 4         -0.984 
#>  5 5          1.70  
#>  6 6          1.52  
#>  7 7          0.0894
#>  8 8         -1.25  
#>  9 9         -0.268 
#> 10 10        -0.805 
#> # … with 190 more rows

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