tests/testthat/_snaps/calculate.md

In infer: Tidy Statistical Inference

x is a tibble

Code
  calculate(vec, stat = "mean")
Condition
  Error in `calculate()`:
  ! `x` must be 'tibble', not 'integer'.

calculate checks stat argument

Code
  calculate(gss_tbl, stat = 3)
Condition
  Error in `calculate()`:
  ! `stat` must be 'string', not 'double'.

Code
  calculate(gen_gss_slope, stat = "slopee")
Condition
  Error in `check_calculate_stat()`:
  ! `stat` must be one of "mean", "median", "sum", "sd", "prop", "count", "diff in means", "diff in medians", "diff in props", "Chisq", "F", "slope", "correlation", "t", "z", "ratio of props", "ratio of means", or "odds ratio", not "slopee".
  i Did you mean "slope"?

Code
  calculate(gen_gss_slope, stat = "stdev")
Condition
  Error in `check_calculate_stat()`:
  ! `stat` must be one of "mean", "median", "sum", "sd", "prop", "count", "diff in means", "diff in medians", "diff in props", "Chisq", "F", "slope", "correlation", "t", "z", "ratio of props", "ratio of means", or "odds ratio", not "stdev".

Code
  calculate(gen_gss_slope, stat = "stat")
Condition
  Error in `check_calculate_stat()`:
  ! `stat` must be one of "mean", "median", "sum", "sd", "prop", "count", "diff in means", "diff in medians", "diff in props", "Chisq", "F", "slope", "correlation", "t", "z", "ratio of props", "ratio of means", or "odds ratio", not "stat".

Code
  calculate(gen_gss_slope, stat = "chi sq")
Condition
  Error in `check_calculate_stat()`:
  ! `stat` must be one of "mean", "median", "sum", "sd", "prop", "count", "diff in means", "diff in medians", "diff in props", "Chisq", "F", "slope", "correlation", "t", "z", "ratio of props", "ratio of means", or "odds ratio", not "chi sq".
  i Did you mean "Chisq"?

errors informatively with incompatible stat vs hypothesis

Code
  gss %>% specify(college ~ sex, success = "degree") %>% hypothesise(null = "point",
    p = 0.4) %>% calculate(stat = "diff in props", order = c("female", "male"))
Condition
  Error in `calculate()`:
  ! The supplied statistic `stat = "diff in props"` is incompatible with the supplied hypothesis `null = "point"`.

Code
  gss %>% specify(college ~ sex, success = "degree") %>% hypothesise(null = "point",
    p = 0.4) %>% generate(reps = 10, type = "draw") %>% calculate(stat = "diff in props",
    order = c("female", "male"))
Condition
  Error in `calculate()`:
  ! The supplied statistic `stat = "diff in props"` is incompatible with the supplied hypothesis `null = "point"`.

response attribute has been set

Code
  tibble::as_tibble(gss) %>% calculate(stat = "median")
Condition
  Error in `dplyr::filter()`:
  i In argument: `resp == response_type & exp == explanatory_type`.
  Caused by error:
  ! `..1` must be of size 10 or 1, not size 0.

variable chosen is of appropriate class (one var problems)

Code
  calculate(gen_gss1, stat = "mean")
Condition
  Error in `calculate()`:
  ! A mean is not well-defined for a multinomial categorical response variable (partyid) and no explanatory variable.

Code
  calculate(gen_gss_num, stat = "prop")
Condition
  Error in `calculate()`:
  ! A proportion is not well-defined for a numeric response variable (hours) and no explanatory variable.

Code
  calculate(gen_gss_num, stat = "median")
Condition
  Error in `calculate()`:
  ! `"mu"` does not correspond to `stat = "median"`.

Code
  calculate(gen_gss_num, stat = "sd")
Condition
  Error in `calculate()`:
  ! `"mu"` does not correspond to `stat = "sd"`.

Code
  calculate(gen_gss_num2, stat = "prop")
Condition
  Error in `calculate()`:
  ! A proportion is not well-defined for a numeric response variable (hours) and no explanatory variable.

Code
  calculate(gen_gss_num2, stat = "mean")
Condition
  Error in `calculate()`:
  ! `stat == "mean"` requires `"mu"` to be set in `hypothesize()`.

Code
  calculate(gen_gss_num2, stat = "sd")
Condition
  Error in `calculate()`:
  ! `"med"` does not correspond to `stat = "sd"`.

Code
  calculate(gen_gss_num3, stat = "prop")
Condition
  Error in `calculate()`:
  ! A proportion is not well-defined for a numeric response variable (hours) and no explanatory variable.

Code
  calculate(gen_gss_num3, stat = "mean")
Condition
  Error in `calculate()`:
  ! `stat == "mean"` requires `"mu"` to be set in `hypothesize()`.

Code
  calculate(gen_gss_num3, stat = "median")
Condition
  Error in `calculate()`:
  ! `stat == "median"` requires `"med"` to be set in `hypothesize()`.

grouping (explanatory) variable is a factor (two var problems)

Code
  calculate(gen_gss2, stat = "diff in means")
Condition
  Error in `calculate()`:
  ! A difference in means is not well-defined for a numeric response variable (hours) and a numeric explanatory variable (age).

Code
  calculate(gen_gss2, stat = "diff in medians")
Condition
  Error in `calculate()`:
  ! A difference in medians is not well-defined for a numeric response variable (hours) and a numeric explanatory variable (age).

grouping (explanatory) variable is numeric (two var problems)

Code
  calculate(gen_gss2a, stat = "slope")
Condition
  Error in `calculate()`:
  ! The infer team has not implemented test statistics for the supplied variable types.

Code
  calculate(gen_gss2a, stat = "t")
Condition
  Error in `calculate()`:
  ! The infer team has not implemented test statistics for the supplied variable types.

Code
  calculate(gen_gss2a, stat = "diff in medians")
Condition
  Error in `calculate()`:
  ! The infer team has not implemented test statistics for the supplied variable types.

response variable is a factor (two var problems)

Code
  calculate(gen_gss3, stat = "Chisq")
Condition
  Error in `calculate()`:
  ! A chi-square statistic is not well-defined for a numeric response variable (hours) and a multinomial categorical explanatory variable (partyid).

Code
  calculate(gen_gss4, stat = "diff in props")
Condition
  Error in `calculate()`:
  ! A difference in proportions is not well-defined for a dichotomous categorical response variable (sex) and a multinomial categorical explanatory variable (partyid).

Code
  calculate(gen_gss4, stat = "ratio of props")
Condition
  Error in `calculate()`:
  ! A ratio of proportions is not well-defined for a dichotomous categorical response variable (sex) and a multinomial categorical explanatory variable (partyid).

Code
  calculate(gen_gss4, stat = "odds ratio")
Condition
  Error in `calculate()`:
  ! An odds ratio is not well-defined for a dichotomous categorical response variable (sex) and a multinomial categorical explanatory variable (partyid).

Code
  calculate(gen_gss4, stat = "t")
Condition
  Error in `calculate()`:
  ! A t statistic is not well-defined for a dichotomous categorical response variable (sex) and a multinomial categorical explanatory variable (partyid).

Code
  res_ <- calculate(gen_gss4a, stat = "z")
Condition
  Warning:
  The statistic is based on a difference or ratio; by default, for difference-based statistics, the explanatory variable is subtracted in the order "male" - "female", or divided in the order "male" / "female" for ratio-based statistics. To specify this order yourself, supply `order = c("male", "female")` to the calculate() function.

response variable is numeric (two var problems)

Code
  calculate(gen_gss5, stat = "F")
Condition
  Error in `calculate()`:
  ! The infer team has not implemented test statistics for the supplied variable types.

two sample mean-type problems are working

Code
  res_ <- calculate(gen_gss5a, stat = "diff in means")
Condition
  Warning:
  The statistic is based on a difference or ratio; by default, for difference-based statistics, the explanatory variable is subtracted in the order "no degree" - "degree", or divided in the order "no degree" / "degree" for ratio-based statistics. To specify this order yourself, supply `order = c("no degree", "degree")` to the calculate() function.

Code
  res_ <- calculate(gen_gss5a, stat = "t")
Condition
  Warning:
  The statistic is based on a difference or ratio; by default, for difference-based statistics, the explanatory variable is subtracted in the order "no degree" - "degree", or divided in the order "no degree" / "degree" for ratio-based statistics. To specify this order yourself, supply `order = c("no degree", "degree")` to the calculate() function.

properties of tibble passed-in are correct

Code
  calculate(gen_gss6)
Condition
  Error in `calculate()`:
  ! `stat` must be 'string', not 'character'.

chi-square matches chisq.test value

Code
  dat %>% specify(action ~ sex, success = "promote") %>% calculate(stat = "Chisq",
    order = c("male", "female"), correct = "boop")
Condition
  Error in `dplyr::summarise()`:
  i In argument: `stat = chisq_indep(data)`.
  i In row 1.
  Caused by error in `correct && nrow(x) == 2L`:
  ! invalid 'x' type in 'x && y'

chi-square works with factors with unused levels

Code
  out <- test_tbl %>% specify(y ~ x) %>% calculate(stat = "Chisq") %>% pull()
Message
  Dropping unused factor levels d from the supplied explanatory variable 'x'.

Code
  out <- test_tbl %>% specify(y ~ x) %>% calculate(stat = "Chisq") %>% pull()
Message
  Dropping unused factor levels g from the supplied response variable 'y'.

order is working

Code
  calculate(gen_gss_tbl10, stat = "diff in means", order = c(TRUE, FALSE))
Condition
  Error in `calculate()`:
  ! TRUE is not a level of the explanatory variable.

Code
  calculate(gen_gss_tbl11, stat = "diff in medians", order = "no degree")
Condition
  Error in `calculate()`:
  ! Only one level specified in `order`. Both levels need to be specified.

Code
  calculate(gen_gss_tbl11, stat = "diff in medians", order = c(NA, "no degree"))
Condition
  Error in `calculate()`:
  ! Only one level specified in `order`. Both levels need to be specified.

Code
  calculate(gen_gss_tbl11, stat = "diff in medians", order = c("no degree",
    "other"))
Condition
  Error in `calculate()`:
  ! other is not a level of the explanatory variable.

Code
  calculate(gen_gss_tbl11, stat = "diff in means", order = c("no degree",
    "degree", "the last one"))
Condition
  Error in `calculate()`:
  ! `order` is expecting only two entries.

Code
  res_ <- calculate(gen_gss_tbl11, stat = "diff in means")
Condition
  Warning:
  The statistic is based on a difference or ratio; by default, for difference-based statistics, the explanatory variable is subtracted in the order "no degree" - "degree", or divided in the order "no degree" / "degree" for ratio-based statistics. To specify this order yourself, supply `order = c("no degree", "degree")` to the calculate() function.

NULL response gives error

Code
  gss_tbl_improp %>% calculate(stat = "mean")
Condition
  Error in `dplyr::filter()`:
  i In argument: `resp == response_type & exp == explanatory_type`.
  Caused by error:
  ! `..1` must be of size 10 or 1, not size 0.

order being given when not needed gives warning

Code
  res_ <- calculate(gen_gss_tbl15, stat = "Chisq", order = c("dem", "ind"))
Condition
  Warning:
  Statistic is not based on a difference or ratio; the `order` argument will be ignored. Check `calculate()` (`?infer::calculate()`) for details.

specify() %>% calculate() works

Code
  res_ <- gss_tbl %>% specify(hours ~ NULL) %>% hypothesize(null = "point", mu = 4) %>%
    calculate(stat = "mean")
Message
  Message: The point null hypothesis `mu = 4` does not inform calculation of the observed statistic (a mean) and will be ignored.

Code
  res_ <- gss_tbl %>% specify(partyid ~ NULL) %>% calculate(stat = "Chisq")
Condition
  Warning:
  A chi-square statistic requires a null hypothesis to calculate the observed statistic.
  Output assumes the following null values: `p = c(dem = 0.333333333333333, ind = 0.333333333333333, rep = 0.333333333333333)`.

One sample t hypothesis test is working

Code
  res_ <- gss_tbl %>% specify(hours ~ NULL) %>% hypothesize(null = "point", mu = 1) %>%
    generate(reps = 10) %>% calculate(stat = "t")
Message
  Setting `type = "bootstrap"` in `generate()`.

Code
  res_ <- gss_tbl %>% specify(response = hours) %>% calculate(stat = "t")
Condition
  Warning:
  A t statistic requires a null hypothesis to calculate the observed statistic.
  Output assumes the following null value: `mu = 0`.

specify done before calculate

Code
  calculate(gss_tbl_mean, stat = "mean")
Condition
  Error in `dplyr::filter()`:
  i In argument: `resp == response_type & exp == explanatory_type`.
  Caused by error:
  ! `..1` must be of size 10 or 1, not size 0.

Code
  calculate(gss_tbl_prop, stat = "prop")
Condition
  Error in `dplyr::filter()`:
  i In argument: `resp == response_type & exp == explanatory_type`.
  Caused by error:
  ! `..1` must be of size 10 or 1, not size 0.

Code
  calculate(gss_tbl_prop, stat = "count")
Condition
  Error in `dplyr::filter()`:
  i In argument: `resp == response_type & exp == explanatory_type`.
  Caused by error:
  ! `..1` must be of size 10 or 1, not size 0.

chisq GoF has params specified for observed stat

Code
  res_ <- calculate(no_params, stat = "Chisq")
Condition
  Warning:
  A chi-square statistic requires a null hypothesis to calculate the observed statistic.
  Output assumes the following null values: `p = c(dem = 0.333333333333333, ind = 0.333333333333333, rep = 0.333333333333333)`.

One sample t bootstrap is working

Code
  res_ <- gss_tbl %>% specify(hours ~ NULL) %>% generate(reps = 10, type = "bootstrap") %>%
    calculate(stat = "t")
Condition
  Warning:
  A t statistic requires a null hypothesis to calculate the observed statistic.
  Output assumes the following null value: `mu = 0`.

calculate warns informatively with insufficient null

Code
  res_ <- gss %>% specify(response = sex, success = "female") %>% calculate(stat = "z")
Condition
  Warning:
  A z statistic requires a null hypothesis to calculate the observed statistic.
  Output assumes the following null value: `p = .5`.

Code
  res_ <- gss %>% specify(hours ~ NULL) %>% calculate(stat = "t")
Condition
  Warning:
  A t statistic requires a null hypothesis to calculate the observed statistic.
  Output assumes the following null value: `mu = 0`.

Code
  res_ <- gss %>% specify(response = partyid) %>% calculate(stat = "Chisq")
Message
  Dropping unused factor levels DK from the supplied response variable 'partyid'.
Condition
  Warning:
  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)`.

calculate messages informatively with excessive null

Code
  res_ <- gss %>% specify(hours ~ NULL) %>% hypothesize(null = "point", mu = 40) %>%
    calculate(stat = "mean")
Message
  Message: The point null hypothesis `mu = 40` does not inform calculation of the observed statistic (a mean) and will be ignored.

Code
  res_ <- gss %>% specify(hours ~ NULL) %>% hypothesize(null = "point", sigma = 10) %>%
    calculate(stat = "sd")
Message
  Message: The point null hypothesis `sigma = 10` does not inform calculation of the observed statistic (a standard deviation) and will be ignored.

Code
  res_ <- gss %>% specify(hours ~ college) %>% hypothesize(null = "independence") %>%
    calculate("diff in means", order = c("no degree", "degree"))
Message
  Message: The independence null hypothesis does not inform calculation of the observed statistic (a difference in means) and will be ignored.

calculate errors out with multiple explanatory variables

Code
  gss %>% specify(hours ~ age + college) %>% hypothesize(null = "independence") %>%
    calculate(stat = "t")
Condition
  Error in `calculate()`:
  ! Multiple explanatory variables are not supported in `calculate()`.
  i When working with multiple explanatory variables, use `fit()` (`?infer::fit.infer()`) instead.

Code
  gss %>% specify(hours ~ age + college) %>% hypothesize(null = "independence") %>%
    generate(reps = 3, type = "permute") %>% calculate(stat = "t")
Condition
  Error in `calculate()`:
  ! Multiple explanatory variables are not supported in `calculate()`.
  i When working with multiple explanatory variables, use `fit()` (`?infer::fit.infer()`) instead.

infer documentation built on May 29, 2024, 11:54 a.m.