infer 1.0.0

v1.0.0 is the first major release of the {infer} package! By and large, the core verbs specify(), hypothesize(), generate(), and calculate() will interface as they did before. This release makes several improvements to behavioral consistency of the package and introduces support for theory-based inference as well as randomization-based inference with multiple explanatory variables.

Behavioral consistency

A major change to the package in this release is a set of standards for behavorial consistency of calculate() (#356). Namely, the package will now

supply a consistent error when the supplied stat argument isn't well-defined
for the variables specify()d

gss %>%
  specify(response = hours) %>%
  calculate(stat = "diff in means")
#> Error: A difference in means is not well-defined for a 
#> numeric response variable (hours) and no explanatory variable.

gss %>%
  specify(college ~ partyid, success = "degree") %>%
  calculate(stat = "diff in props")
#> Error: A difference in proportions is not well-defined for a dichotomous categorical 
#> response variable (college) and a multinomial categorical explanatory variable (partyid).

supply a consistent message when the user supplies unneeded information via hypothesize() to calculate() an observed statistic

# supply mu = 40 when it's not needed
gss %>%
  specify(response = hours) %>%
  hypothesize(null = "point", mu = 40) %>%
  calculate(stat = "mean")
#> Message: The point null hypothesis `mu = 40` does not inform calculation of 
#> the observed statistic (a mean) and will be ignored.
#> # A tibble: 1 x 1
#>    stat
#>   <dbl>
#> 1  41.4

and

supply a consistent warning and assume a reasonable null value when the user does not supply sufficient information to calculate an observed statistic

# don't hypothesize `p` when it's needed
gss %>%
    specify(response = sex, success = "female") %>%
    calculate(stat = "z")
#> # A tibble: 1 x 1
#>    stat
#>   <dbl>
#> 1 -1.16
#> Warning message:
#> A z statistic requires a null hypothesis to calculate the observed statistic. 
#> Output assumes the following null value: `p = .5`.

# don't hypothesize `p` when it's needed
gss %>%
  specify(response = partyid) %>%
  calculate(stat = "Chisq")
#> # A tibble: 1 x 1
#>    stat
#>  <dbl>
#> 1  334.
#> Warning message:
#> A chi-square statistic requires a null hypothesis to calculate the observed statistic. 
#> Output assumes the following null values: `p = c(dem = 0.2, ind = 0.2, rep = 0.2, other = 0.2, DK = 0.2)`.

To accommodate this behavior, a number of new calculate methods were added or improved. Namely:

Implemented the standardized proportion $z$ statistic for one categorical variable
Extended calculate() with stat = "t" by passing mu to the calculate() method for stat = "t" to allow for calculation of t statistics for one numeric variable with hypothesized mean
Extended calculate() to allow lowercase aliases for stat arguments (#373).
Fixed bugs in calculate() for to allow for programmatic calculation of statistics

This behavorial consistency also allowed for the implementation of observe(), a wrapper function around specify(), hypothesize(), and calculate(), to calculate observed statistics. The function provides a shorthand alternative to calculating observed statistics from data:

# calculating the observed mean number of hours worked per week
gss %>%
  observe(hours ~ NULL, stat = "mean")
#> # A tibble: 1 x 1
#>    stat
#>   <dbl>
#> 1  41.4

# equivalently, calculating the same statistic with the core verbs
gss %>%
  specify(response = hours) %>%
  calculate(stat = "mean")
#> # A tibble: 1 x 1
#>    stat
#>   <dbl>
#> 1  41.4

# calculating a t statistic for hypothesized mu = 40 hours worked/week
gss %>%
  observe(hours ~ NULL, stat = "t", null = "point", mu = 40)
#> # A tibble: 1 x 1
#>    stat
#>   <dbl>
#> 1  2.09

# equivalently, calculating the same statistic with the core verbs
gss %>%
  specify(response = hours) %>%
  hypothesize(null = "point", mu = 40) %>%
  calculate(stat = "t")
#> # A tibble: 1 x 1
#>    stat
#>   <dbl>
#> 1  2.09

We don't anticipate that these changes are "breaking" in the sense that code that previously worked will continue to, though it may now message or warn in a way that it did not used to or error with a different (and hopefully more informative) message.

A framework for theoretical inference

This release also introduces a more complete and principled interface for theoretical inference. While the package previously supplied some methods for visualization of theory-based curves, the interface did not provide any object that was explicitly a "null distribution" that could be supplied to helper functions like get_p_value() and get_confidence_interval(). The new interface is based on a new verb, assume(), that returns a null distribution that can be interfaced in the same way that simulation-based null distributions can be interfaced with.

As an example, we'll work through a full infer pipeline for inference on a mean using infer's gss dataset. Supposed that we believe the true mean number of hours worked by Americans in the past week is 40.

First, calculating the observed t-statistic:

obs_stat <- gss %>%
  specify(response = hours) %>%
  hypothesize(null = "point", mu = 40) %>%
  calculate(stat = "t")

obs_stat
#> Response: hours (numeric)
#> Null Hypothesis: point
#> # A tibble: 1 x 1
#>    stat
#>   <dbl>
#> 1  2.09

The code to define the null distribution is very similar to that required to calculate a theorized observed statistic, switching out calculate() for assume() and replacing arguments as needed.

null_dist <- gss %>%
  specify(response = hours) %>%
  assume(distribution = "t")

null_dist 
#> A T distribution with 499 degrees of freedom.

This null distribution can now be interfaced with in the same way as a simulation-based null distribution elsewhere in the package. For example, calculating a p-value by juxtaposing the observed statistic and null distribution:

get_p_value(null_dist, obs_stat, direction = "both")
#> # A tibble: 1 x 1
#>   p_value
#>     <dbl>
#> 1  0.0376

…or visualizing the null distribution alone:

visualize(null_dist)

…or juxtaposing the two visually:

visualize(null_dist) + 
  shade_p_value(obs_stat, direction = "both")

Confidence intervals lie in data space rather than the standardized scale of the theoretical distributions. Calculating a mean rather than the standardized t-statistic:

obs_mean <- gss %>%
  specify(response = hours) %>%
  calculate(stat = "mean")

The null distribution here just defines the spread for the standard error calculation.

ci <- 
  get_confidence_interval(
    null_dist,
    level = .95,
    point_estimate = obs_mean
  )

ci
#> # A tibble: 1 x 2
#>   lower_ci upper_ci
#>      <dbl>    <dbl>
#> 1     40.1     42.7

Visualizing the confidence interval results in the theoretical distribution being recentered and rescaled to align with the scale of the observed data:

visualize(null_dist) + 
  shade_confidence_interval(ci)

Previous methods for interfacing with theoretical distributions are superseded—they will continue to be supported, though documentation will forefront the assume() interface.

Support for multiple regression

The 2016 "Guidelines for Assessment and Instruction in Statistics Education" [1] state that, in introductory statistics courses, "[s]tudents should gain experience with how statistical models, including multivariable models, are used." In line with this recommendation, we introduce support for randomization-based inference with multiple explanatory variables via a new fit.infer core verb.

If passed an infer object, the method will parse a formula out of the formula or response and explanatory arguments, and pass both it and data to a stats::glm call.

gss %>%
  specify(hours ~ age + college) %>%
  fit()
#> # A tibble: 3 x 2
#>   term          estimate
#>   <chr>            <dbl>
#> 1 intercept     40.6    
#> 2 age            0.00596
#> 3 collegedegree  1.53

Note that the function returns the model coefficients as estimate rather than their associated t-statistics as stat.

If passed a generate()d object, the model will be fitted to each replicate.

gss %>%
  specify(hours ~ age + college) %>%
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute") %>%
  fit()
#> # A tibble: 300 x 3
#> # Groups:   replicate [100]
#>    replicate term          estimate
#>        <int> <chr>            <dbl>
#>  1         1 intercept     44.4    
#>  2         1 age           -0.0767 
#>  3         1 collegedegree  0.121  
#>  4         2 intercept     41.8    
#>  5         2 age            0.00344
#>  6         2 collegedegree -1.59   
#>  7         3 intercept     38.3    
#>  8         3 age            0.0761 
#>  9         3 collegedegree  0.136  
#> 10         4 intercept     43.1    
#> # … with 290 more rows

If type = "permute", a set of unquoted column names in the data to permute (independently of each other) can be passed via the variables argument to generate. It defaults to only the response variable.

gss %>%
  specify(hours ~ age + college) %>%
  hypothesize(null = "independence") %>%
  generate(reps = 100, type = "permute", variables = c(age, college)) %>%
  fit()
#> # A tibble: 300 x 3
#> # Groups:   replicate [100]
#>    replicate term          estimate
#>        <int> <chr>            <dbl>
#>  1         1 intercept      39.4   
#>  2         1 age             0.0748
#>  3         1 collegedegree  -2.98  
#>  4         2 intercept      42.8   
#>  5         2 age            -0.0190
#>  6         2 collegedegree  -1.83  
#>  7         3 intercept      40.4   
#>  8         3 age             0.0354
#>  9         3 collegedegree  -1.31  
#> 10         4 intercept      40.9   
#> # … with 290 more rows

This feature allows for more detailed exploration of the effect of disrupting the correlation structure among explanatory variables on outputted model coefficients.

Each of the auxillary functions get_p_value(), get_confidence_interval(), visualize(), shade_p_value(), and shade_confidence_interval() have methods to handle fit() output! See their help-files for example usage. Note that shade_* functions now delay evaluation until they are added to an existing ggplot (e.g. that outputted by visualize()) with +.

Improvements

Following extensive discussion, the generate() type type = "simulate" has been renamed to the more evocative type = "draw". We will continue to support type = "simulate" indefinitely, though supplying that argument will now prompt a message notifying the user of its preferred alias. (#233, #390)
Fixed several bugs related to factors with unused levels. specify() will now drop unused factor levels and message that it has done so. (#374, #375, #397, #380)
Added two.sided as an acceptable alias for two_sided for the direction argument in get_p_value() and shade_p_value(). (#355)
Various improvements to documentation, including extending example sections in help-files, re-organizing the function reference in the {pkgdown} site, and linking more extensively among help-files.

Breaking changes

We don't anticipate that any changes made in this release are "breaking" in the sense that code that previously worked will continue to, though it may now message or warn in a way that it did not used to or error with a different (and hopefully more informative) message. If you currently teach or research with infer, we recommend re-running your materials and noting any changes in messaging and warning.

Move forward with a number of planned deprecations. Namely, the GENERATION_TYPES object is now fully deprecated, and arguments that were relocated from visualize() to shade_p_value() and shade_confidence_interval() are now fully deprecated in visualize(). If supplied a deprecated argument, visualize() will warn the user and ignore the argument.
Added a prop argument to rep_slice_sample() as an alternative to the n
argument for specifying the proportion of rows in the supplied data to sample
per replicate (#361, #362, #363). This changes order of arguments of
rep_slice_sample() (in order to be more aligned with dplyr::slice_sample())
which might break code if it didn't use named arguments (like
rep_slice_sample(df, 5, TRUE)). To fix this, use named arguments (like
rep_slice_sample(df, 5, replicate = TRUE)).

Other

Added Simon P. Couch as an author. Long deserved for his reliable maintenance and improvements of the package.

[1]: GAISE College Report ASA Revision Committee, "Guidelines for Assessment and Instruction in Statistics Education College Report 2016," http://www.amstat.org/education/gaise.

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First major release