Given the output of specify() and/or hypothesize(), this function will return the observed statistic specified with the stat argument. Some test statistics, such as Chisq, t, and z, require a null hypothesis. If provided the output of generate(), the function will calculate the supplied stat for each replicate.

Learn more in vignette("infer").

calculate(
x,
stat = c("mean", "median", "sum", "sd", "prop", "count", "diff in means",
"diff in medians", "diff in props", "Chisq", "F", "slope", "correlation", "t", "z",
"ratio of props", "odds ratio"),
order = NULL,
...
)

## Arguments

x The output from generate() for computation-based inference or the output from hypothesize() piped in to here for theory-based inference. A string giving the type of the statistic to calculate. Current options include "mean", "median", "sum", "sd", "prop", "count", "diff in means", "diff in medians", "diff in props", "Chisq" (or "chisq"), "F" (or "f"), "t", "z", "ratio of props", "slope", "odds ratio", and "correlation". infer only supports theoretical tests on one or two means via the "t" distribution and one or two proportions via the "z". A string vector of specifying the order in which the levels of the explanatory variable should be ordered for subtraction (or division for ratio-based statistics), where order = c("first", "second") means ("first" - "second"), or the analogue for ratios. Needed for inference on difference in means, medians, proportions, ratios, t, and z statistics. To pass options like na.rm = TRUE into functions like mean(), sd(), etc. Can also be used to supply hypothesized null values for the "t" statistic.

## Value

A tibble containing a stat column of calculated statistics.

## Missing levels in small samples

In some cases, when bootstrapping with small samples, some generated bootstrap samples will have only one level of the explanatory variable present. For some test statistics, the calculated statistic in these cases will be NaN. The package will omit non-finite values from visualizations (with a warning) and raise an error in p-value calculations.

## 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().

visualize(), get_p_value(), and get_confidence_interval() to extract value from this function's outputs.

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

## Examples


# calculate a null distribution of hours worked per week under
# the null hypothesis that the mean is 40
gss %>%
specify(response = hours) %>%
hypothesize(null = "point", mu = 40) %>%
generate(reps = 200, type = "bootstrap") %>%
calculate(stat = "mean")
#> Response: hours (numeric)
#> Null Hypothesis: point
#> # A tibble: 200 × 2
#>    replicate  stat
#>        <int> <dbl>
#>  1         1  39.2
#>  2         2  39.4
#>  3         3  40.1
#>  4         4  39.6
#>  5         5  40.8
#>  6         6  39.9
#>  7         7  39.9
#>  8         8  40.8
#>  9         9  39.6
#> 10        10  41.0
#> # … with 190 more rows

# calculate the corresponding observed statistic
gss %>%
specify(response = hours) %>%
calculate(stat = "mean")
#> Response: hours (numeric)
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1  41.4

# calculate a null distribution assuming independence between age
# of respondent and whether they have a college degree
gss %>%
specify(age ~ college) %>%
hypothesize(null = "independence") %>%
generate(reps = 200, type = "permute") %>%
calculate("diff in means", order = c("degree", "no degree"))
#> Response: age (numeric)
#> Explanatory: college (factor)
#> Null Hypothesis: independence
#> # A tibble: 200 × 2
#>    replicate     stat
#>        <int>    <dbl>
#>  1         1 -2.48
#>  2         2 -0.699
#>  3         3 -0.0113
#>  4         4  0.579
#>  5         5  0.553
#>  6         6  1.84
#>  7         7 -2.31
#>  8         8 -0.320
#>  9         9 -0.00250
#> 10        10 -1.78
#> # … with 190 more rows

# calculate the corresponding observed statistic
gss %>%
specify(age ~ college) %>%
calculate("diff in means", order = c("degree", "no degree"))
#> Response: age (numeric)
#> Explanatory: college (factor)
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1 0.941

# some statistics require a null hypothesis
gss %>%
specify(response = hours) %>%
hypothesize(null = "point", mu = 40) %>%
calculate(stat = "t")
#> Response: hours (numeric)
#> Null Hypothesis: point
#> # A tibble: 1 × 1
#>    stat
#>   <dbl>
#> 1  2.09

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