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")`

.

## 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.

## 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, which has been superseded.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 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()`

.

## 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 38.6
#> 2 1 28.6
#> 3 1 38.6
#> 4 1 38.6
#> 5 1 48.6
#> 6 1 48.6
#> 7 1 38.6
#> 8 1 38.6
#> 9 1 87.6
#> 10 1 48.6
#> # ℹ 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 dem 36 1
#> 2 ind 34 1
#> 3 dem 24 1
#> 4 dem 42 1
#> 5 rep 31 1
#> 6 rep 32 1
#> 7 dem 48 1
#> 8 ind 36 1
#> 9 dem 30 1
#> 10 dem 33 1
#> # ℹ 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
#> <int> <dbl>
#> 1 1 -0.358
#> 2 2 0.358
#> 3 3 -1.70
#> 4 4 -0.447
#> 5 5 0.805
#> 6 6 -0.179
#> 7 7 -2.24
#> 8 8 -1.07
#> 9 9 1.34
#> 10 10 0.268
#> # ℹ 190 more rows
# more in-depth explanation of how to use the infer package
if (FALSE) {
vignette("infer")
}
```