Applies a modeling function to every combination of a set of formulas and a set of data subsets.
A data frame
A list of formulas to apply to each subset of the data.
If named, these names will be used in the model column of the output.
Otherwise, the formulas will be converted to strings in the model column.
Columns to subset the data.
Can be any expression supported by
<tidy-select>.
If NULL, the data is not subset into columns.
Defaults to NULL.
A list of columns passed to weights in fn.
If one of the elements is NULL or NA, that model will not
be weighted.
Defaults to NULL.
A list of columns passed to clusters if supported by fn.
If one of the elements is NULL or NA, that model will not
be clustered.
Defaults to NULL.
A list of glm model families passed to family if
supported by fn.
Defaults to gaussian("identity"), the equivalent of lm().
See family for examples.
The modeling function.
Either an unquoted function name or a purrr-style lambda
function with two arguments.
To use multiple modeling functions, see cross_fit_glm().
Defaults to lm.
A list of additional arguments to fn.
A logical or function to use to tidy model output into
data.frame columns.
If TRUE, uses the default tidying function: tidy_glance().
If FALSE, NA, or NULL, the untidied model output will be returned in
a list column named fit.
An alternative function can be specified with an unquoted function name or
a purrr-style lambda function with one argument (see usage
with broom::tidy(conf.int = TRUE) in examples).
Defaults to tidy_glance.
A list of additional arguments to the tidy function
If "stop", the default, the function will stop and return an
error if any subset produces an error.
If "warn", the function will produce a warning for subsets that produce
an error and return results for all subsets that do not.
A tibble with a column for the model formula,
columns for subsets,
columns for the model family and type (if applicable),
columns for the weights and clusters (if applicable),
and columns of tidy model output or a list column of models
(if tidy = FALSE)
cross_fit_glm() to map a model across multiple model types.
cross_fit_robust() to map robust linear models.
xmap() to apply any function to combinations of inputs.
cross_fit(mtcars, mpg ~ wt, cyl)
#> # A tibble: 6 × 19
#> model cyl term estim…¹ std.e…² stati…³ p.value r.squ…⁴ adj.r…⁵ sigma
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg ~ wt 4 (Interce… 39.6 4.35 9.10 7.77e-6 0.509 0.454 3.33
#> 2 mpg ~ wt 4 wt -5.65 1.85 -3.05 1.37e-2 0.509 0.454 3.33
#> 3 mpg ~ wt 6 (Interce… 28.4 4.18 6.79 1.05e-3 0.465 0.357 1.17
#> 4 mpg ~ wt 6 wt -2.78 1.33 -2.08 9.18e-2 0.465 0.357 1.17
#> 5 mpg ~ wt 8 (Interce… 23.9 3.01 7.94 4.05e-6 0.423 0.375 2.02
#> 6 mpg ~ wt 8 wt -2.19 0.739 -2.97 1.18e-2 0.423 0.375 2.02
#> # … with 9 more variables: model.statistic <dbl>, model.p.value <dbl>,
#> # df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
#> # df.residual <int>, nobs <int>, and abbreviated variable names ¹estimate,
#> # ²std.error, ³statistic, ⁴r.squared, ⁵adj.r.squared
cross_fit(mtcars, list(mpg ~ wt, mpg ~ hp), cyl)
#> # A tibble: 12 × 19
#> model cyl term estimate std.e…¹ stati…² p.value r.squ…³ adj.r.…⁴ sigma
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg ~ wt 4 (Inte… 39.6 4.35 9.10 7.77e-6 0.509 0.454 3.33
#> 2 mpg ~ wt 4 wt -5.65 1.85 -3.05 1.37e-2 0.509 0.454 3.33
#> 3 mpg ~ wt 6 (Inte… 28.4 4.18 6.79 1.05e-3 0.465 0.357 1.17
#> 4 mpg ~ wt 6 wt -2.78 1.33 -2.08 9.18e-2 0.465 0.357 1.17
#> 5 mpg ~ wt 8 (Inte… 23.9 3.01 7.94 4.05e-6 0.423 0.375 2.02
#> 6 mpg ~ wt 8 wt -2.19 0.739 -2.97 1.18e-2 0.423 0.375 2.02
#> 7 mpg ~ hp 4 (Inte… 36.0 5.20 6.92 6.93e-5 0.274 0.193 4.05
#> 8 mpg ~ hp 4 hp -0.113 0.0612 -1.84 9.84e-2 0.274 0.193 4.05
#> 9 mpg ~ hp 6 (Inte… 20.7 3.30 6.26 1.53e-3 0.0161 -0.181 1.58
#> 10 mpg ~ hp 6 hp -0.00761 0.0266 -0.286 7.86e-1 0.0161 -0.181 1.58
#> 11 mpg ~ hp 8 (Inte… 18.1 2.99 6.05 5.74e-5 0.0804 0.00382 2.56
#> 12 mpg ~ hp 8 hp -0.0142 0.0139 -1.02 3.26e-1 0.0804 0.00382 2.56
#> # … with 9 more variables: model.statistic <dbl>, model.p.value <dbl>,
#> # df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
#> # df.residual <int>, nobs <int>, and abbreviated variable names ¹std.error,
#> # ²statistic, ³r.squared, ⁴adj.r.squared
cross_fit(mtcars, list(wt = mpg ~ wt, hp = mpg ~ hp), cyl)
#> # A tibble: 12 × 19
#> model cyl term estimate std.e…¹ stati…² p.value r.squ…³ adj.r.…⁴ sigma
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 wt 4 (Interce… 39.6 4.35 9.10 7.77e-6 0.509 0.454 3.33
#> 2 wt 4 wt -5.65 1.85 -3.05 1.37e-2 0.509 0.454 3.33
#> 3 wt 6 (Interce… 28.4 4.18 6.79 1.05e-3 0.465 0.357 1.17
#> 4 wt 6 wt -2.78 1.33 -2.08 9.18e-2 0.465 0.357 1.17
#> 5 wt 8 (Interce… 23.9 3.01 7.94 4.05e-6 0.423 0.375 2.02
#> 6 wt 8 wt -2.19 0.739 -2.97 1.18e-2 0.423 0.375 2.02
#> 7 hp 4 (Interce… 36.0 5.20 6.92 6.93e-5 0.274 0.193 4.05
#> 8 hp 4 hp -0.113 0.0612 -1.84 9.84e-2 0.274 0.193 4.05
#> 9 hp 6 (Interce… 20.7 3.30 6.26 1.53e-3 0.0161 -0.181 1.58
#> 10 hp 6 hp -0.00761 0.0266 -0.286 7.86e-1 0.0161 -0.181 1.58
#> 11 hp 8 (Interce… 18.1 2.99 6.05 5.74e-5 0.0804 0.00382 2.56
#> 12 hp 8 hp -0.0142 0.0139 -1.02 3.26e-1 0.0804 0.00382 2.56
#> # … with 9 more variables: model.statistic <dbl>, model.p.value <dbl>,
#> # df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
#> # df.residual <int>, nobs <int>, and abbreviated variable names ¹std.error,
#> # ²statistic, ³r.squared, ⁴adj.r.squared
cross_fit(mtcars, list(mpg ~ wt, mpg ~ hp), c(cyl, vs))
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: essentially perfect fit: summary may be unreliable
#> Warning: essentially perfect fit: summary may be unreliable
#> # A tibble: 20 × 20
#> model cyl vs term estimate std.error statistic p.value r.squ…¹
#> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg ~ wt 4 0 (Inte… 26 NaN NaN NaN 0
#> 2 mpg ~ wt 4 0 wt NA NA NA NA 0
#> 3 mpg ~ wt 4 1 (Inte… 39.9 4.61e+ 0 8.66e+ 0 2.47e- 5 0.520
#> 4 mpg ~ wt 4 1 wt -5.72 1.95e+ 0 -2.94e+ 0 1.87e- 2 0.520
#> 5 mpg ~ wt 6 0 (Inte… 22.2 1.61e+ 1 1.38e+ 0 3.99e- 1 0.0103
#> 6 mpg ~ wt 6 0 wt -0.594 5.83e+ 0 -1.02e- 1 9.35e- 1 0.0103
#> 7 mpg ~ wt 6 1 (Inte… 63.6 1.19e+ 1 5.36e+ 0 3.30e- 2 0.876
#> 8 mpg ~ wt 6 1 wt -13.1 3.50e+ 0 -3.75e+ 0 6.42e- 2 0.876
#> 9 mpg ~ wt 8 0 (Inte… 23.9 3.01e+ 0 7.94e+ 0 4.05e- 6 0.423
#> 10 mpg ~ wt 8 0 wt -2.19 7.39e- 1 -2.97e+ 0 1.18e- 2 0.423
#> 11 mpg ~ hp 4 0 (Inte… 26 NaN NaN NaN 0
#> 12 mpg ~ hp 4 0 hp NA NA NA NA 0
#> 13 mpg ~ hp 4 1 (Inte… 36.0 5.52e+ 0 6.52e+ 0 1.85e- 4 0.273
#> 14 mpg ~ hp 4 1 hp -0.113 6.55e- 2 -1.73e+ 0 1.21e- 1 0.273
#> 15 mpg ~ hp 6 0 (Inte… 23.2 1.98e-14 1.17e+15 5.43e-16 1
#> 16 mpg ~ hp 6 0 hp -0.0200 1.46e-16 -1.37e+14 4.66e-15 1
#> 17 mpg ~ hp 6 1 (Inte… 24.2 1.41e+ 1 1.72e+ 0 2.28e- 1 0.0613
#> 18 mpg ~ hp 6 1 hp -0.0440 1.22e- 1 -3.61e- 1 7.52e- 1 0.0613
#> 19 mpg ~ hp 8 0 (Inte… 18.1 2.99e+ 0 6.05e+ 0 5.74e- 5 0.0804
#> 20 mpg ~ hp 8 0 hp -0.0142 1.39e- 2 -1.02e+ 0 3.26e- 1 0.0804
#> # … with 11 more variables: adj.r.squared <dbl>, sigma <dbl>,
#> # model.statistic <dbl>, model.p.value <dbl>, df <dbl>, logLik <dbl>,
#> # AIC <dbl>, BIC <dbl>, deviance <dbl>, df.residual <int>, nobs <int>, and
#> # abbreviated variable name ¹r.squared
cross_fit(mtcars, list(mpg ~ wt, mpg ~ hp), dplyr::starts_with("c"))
#> # A tibble: 36 × 20
#> model cyl carb term estim…¹ std.e…² stati…³ p.value r.squ…⁴ adj.r…⁵
#> <chr> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg ~ wt 4 1 (Inte… 62.0 17.2 3.60 3.67e-2 0.574 0.432
#> 2 mpg ~ wt 4 1 wt -16.0 7.95 -2.01 1.38e-1 0.574 0.432
#> 3 mpg ~ wt 4 2 (Inte… 36.8 2.83 13.0 2.01e-4 0.802 0.752
#> 4 mpg ~ wt 4 2 wt -4.56 1.13 -4.02 1.59e-2 0.802 0.752
#> 5 mpg ~ wt 6 1 (Inte… 64.7 NaN NaN NaN 1 NaN
#> 6 mpg ~ wt 6 1 wt -13.5 NaN NaN NaN 1 NaN
#> 7 mpg ~ wt 6 4 (Inte… 30.2 3.61 8.37 1.40e-2 0.810 0.714
#> 8 mpg ~ wt 6 4 wt -3.38 1.16 -2.92 1.00e-1 0.810 0.714
#> 9 mpg ~ wt 6 6 (Inte… 19.7 NaN NaN NaN 0 0
#> 10 mpg ~ wt 6 6 wt NA NA NA NA 0 0
#> # … with 26 more rows, 10 more variables: sigma <dbl>, model.statistic <dbl>,
#> # model.p.value <dbl>, df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>,
#> # deviance <dbl>, df.residual <int>, nobs <int>, and abbreviated variable
#> # names ¹estimate, ²std.error, ³statistic, ⁴r.squared, ⁵adj.r.squared
cross_fit(mtcars, list(hp = mpg ~ hp), cyl, weights = wt)
#> # A tibble: 6 × 20
#> model weights cyl term estimate std.e…¹ stati…² p.value r.squ…³ adj.r.…⁴
#> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 hp wt 4 (Interc… 36.2 5.14 7.03 6.10e-5 0.316 0.240
#> 2 hp wt 4 hp -0.123 0.0601 -2.04 7.17e-2 0.316 0.240
#> 3 hp wt 6 (Interc… 20.4 3.49 5.85 2.07e-3 0.0107 -0.187
#> 4 hp wt 6 hp -0.00657 0.0283 -0.232 8.26e-1 0.0107 -0.187
#> 5 hp wt 8 (Interc… 18.0 3.36 5.36 1.72e-4 0.0724 -0.00491
#> 6 hp wt 8 hp -0.0151 0.0156 -0.968 3.52e-1 0.0724 -0.00491
#> # … with 10 more variables: sigma <dbl>, model.statistic <dbl>,
#> # model.p.value <dbl>, df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>,
#> # deviance <dbl>, df.residual <int>, nobs <int>, and abbreviated variable
#> # names ¹std.error, ²statistic, ³r.squared, ⁴adj.r.squared
cross_fit(mtcars, list(hp = mpg ~ hp), cyl, weights = c(wt, NA))
#> # A tibble: 12 × 20
#> model weights cyl term estimate std.e…¹ stati…² p.value r.squ…³ adj.r.…⁴
#> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 hp NA 4 (Inter… 36.0 5.20 6.92 6.93e-5 0.274 0.193
#> 2 hp NA 4 hp -0.113 0.0612 -1.84 9.84e-2 0.274 0.193
#> 3 hp NA 6 (Inter… 20.7 3.30 6.26 1.53e-3 0.0161 -0.181
#> 4 hp NA 6 hp -0.00761 0.0266 -0.286 7.86e-1 0.0161 -0.181
#> 5 hp NA 8 (Inter… 18.1 2.99 6.05 5.74e-5 0.0804 0.00382
#> 6 hp NA 8 hp -0.0142 0.0139 -1.02 3.26e-1 0.0804 0.00382
#> 7 hp wt 4 (Inter… 36.2 5.14 7.03 6.10e-5 0.316 0.240
#> 8 hp wt 4 hp -0.123 0.0601 -2.04 7.17e-2 0.316 0.240
#> 9 hp wt 6 (Inter… 20.4 3.49 5.85 2.07e-3 0.0107 -0.187
#> 10 hp wt 6 hp -0.00657 0.0283 -0.232 8.26e-1 0.0107 -0.187
#> 11 hp wt 8 (Inter… 18.0 3.36 5.36 1.72e-4 0.0724 -0.00491
#> 12 hp wt 8 hp -0.0151 0.0156 -0.968 3.52e-1 0.0724 -0.00491
#> # … with 10 more variables: sigma <dbl>, model.statistic <dbl>,
#> # model.p.value <dbl>, df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>,
#> # deviance <dbl>, df.residual <int>, nobs <int>, and abbreviated variable
#> # names ¹std.error, ²statistic, ³r.squared, ⁴adj.r.squared
cross_fit(
mtcars, list(vs ~ cyl, vs ~ hp), am,
fn = glm, fn_args = list(family = binomial(link = logit))
)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> # A tibble: 8 × 15
#> model am term estimate std.e…¹ statis…² p.value null.…³ df.null logLik
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 vs ~ … 0 (Int… 172. 2.52e+5 6.82e-4 0.999 25.0 18 -2.89e-10
#> 2 vs ~ … 0 cyl -24.6 3.57e+4 -6.89e-4 0.999 25.0 18 -2.89e-10
#> 3 vs ~ … 1 (Int… 44.3 1.04e+4 4.24e-3 0.997 17.9 12 -3.01e+ 0
#> 4 vs ~ … 1 cyl -10.6 2.61e+3 -4.06e-3 0.997 17.9 12 -3.01e+ 0
#> 5 vs ~ … 0 (Int… 231. 2.76e+5 8.35e-4 0.999 25.0 18 -5.08e-10
#> 6 vs ~ … 0 hp -1.69 2.00e+3 -8.44e-4 0.999 25.0 18 -5.08e-10
#> 7 vs ~ … 1 (Int… 7.07 4.76e+0 1.49e+0 0.137 17.9 12 -4.79e+ 0
#> 8 vs ~ … 1 hp -0.0663 4.67e-2 -1.42e+0 0.156 17.9 12 -4.79e+ 0
#> # … with 5 more variables: AIC <dbl>, BIC <dbl>, deviance <dbl>,
#> # df.residual <int>, nobs <int>, and abbreviated variable names ¹std.error,
#> # ²statistic, ³null.deviance
cross_fit(
mtcars, list(vs ~ cyl, vs ~ hp), am,
fn = ~ glm(.x, .y, family = binomial(link = logit))
)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> # A tibble: 8 × 15
#> model am term estimate std.e…¹ statis…² p.value null.…³ df.null logLik
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
#> 1 vs ~ … 0 (Int… 172. 2.52e+5 6.82e-4 0.999 25.0 18 -2.89e-10
#> 2 vs ~ … 0 cyl -24.6 3.57e+4 -6.89e-4 0.999 25.0 18 -2.89e-10
#> 3 vs ~ … 1 (Int… 44.3 1.04e+4 4.24e-3 0.997 17.9 12 -3.01e+ 0
#> 4 vs ~ … 1 cyl -10.6 2.61e+3 -4.06e-3 0.997 17.9 12 -3.01e+ 0
#> 5 vs ~ … 0 (Int… 231. 2.76e+5 8.35e-4 0.999 25.0 18 -5.08e-10
#> 6 vs ~ … 0 hp -1.69 2.00e+3 -8.44e-4 0.999 25.0 18 -5.08e-10
#> 7 vs ~ … 1 (Int… 7.07 4.76e+0 1.49e+0 0.137 17.9 12 -4.79e+ 0
#> 8 vs ~ … 1 hp -0.0663 4.67e-2 -1.42e+0 0.156 17.9 12 -4.79e+ 0
#> # … with 5 more variables: AIC <dbl>, BIC <dbl>, deviance <dbl>,
#> # df.residual <int>, nobs <int>, and abbreviated variable names ¹std.error,
#> # ²statistic, ³null.deviance
cross_fit(mtcars, list(mpg ~ wt, mpg ~ hp), cyl, tidy = FALSE)
#> # A tibble: 6 × 3
#> model cyl fit
#> <chr> <dbl> <list>
#> 1 mpg ~ wt 4 <lm>
#> 2 mpg ~ wt 6 <lm>
#> 3 mpg ~ wt 8 <lm>
#> 4 mpg ~ hp 4 <lm>
#> 5 mpg ~ hp 6 <lm>
#> 6 mpg ~ hp 8 <lm>
cross_fit(mtcars, list(mpg ~ wt, mpg ~ hp), cyl, tidy_args = c(conf.int = TRUE))
#> # A tibble: 12 × 21
#> model cyl term estimate std.e…¹ stati…² p.value conf.…³ conf.…⁴ r.squ…⁵
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg ~ wt 4 (Int… 39.6 4.35 9.10 7.77e-6 29.7 49.4 0.509
#> 2 mpg ~ wt 4 wt -5.65 1.85 -3.05 1.37e-2 -9.83 -1.46 0.509
#> 3 mpg ~ wt 6 (Int… 28.4 4.18 6.79 1.05e-3 17.7 39.2 0.465
#> 4 mpg ~ wt 6 wt -2.78 1.33 -2.08 9.18e-2 -6.21 0.651 0.465
#> 5 mpg ~ wt 8 (Int… 23.9 3.01 7.94 4.05e-6 17.3 30.4 0.423
#> 6 mpg ~ wt 8 wt -2.19 0.739 -2.97 1.18e-2 -3.80 -0.582 0.423
#> 7 mpg ~ hp 4 (Int… 36.0 5.20 6.92 6.93e-5 24.2 47.7 0.274
#> 8 mpg ~ hp 4 hp -0.113 0.0612 -1.84 9.84e-2 -0.251 0.0256 0.274
#> 9 mpg ~ hp 6 (Int… 20.7 3.30 6.26 1.53e-3 12.2 29.2 0.0161
#> 10 mpg ~ hp 6 hp -0.00761 0.0266 -0.286 7.86e-1 -0.0759 0.0607 0.0161
#> 11 mpg ~ hp 8 (Int… 18.1 2.99 6.05 5.74e-5 11.6 24.6 0.0804
#> 12 mpg ~ hp 8 hp -0.0142 0.0139 -1.02 3.26e-1 -0.0445 0.0160 0.0804
#> # … with 11 more variables: adj.r.squared <dbl>, sigma <dbl>,
#> # model.statistic <dbl>, model.p.value <dbl>, df <dbl>, logLik <dbl>,
#> # AIC <dbl>, BIC <dbl>, deviance <dbl>, df.residual <int>, nobs <int>, and
#> # abbreviated variable names ¹std.error, ²statistic, ³conf.low, ⁴conf.high,
#> # ⁵r.squared
cross_fit(mtcars, list(mpg ~ wt, mpg ~ hp), cyl, tidy = broom::tidy)
#> # A tibble: 12 × 7
#> model cyl term estimate std.error statistic p.value
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg ~ wt 4 (Intercept) 39.6 4.35 9.10 0.00000777
#> 2 mpg ~ wt 4 wt -5.65 1.85 -3.05 0.0137
#> 3 mpg ~ wt 6 (Intercept) 28.4 4.18 6.79 0.00105
#> 4 mpg ~ wt 6 wt -2.78 1.33 -2.08 0.0918
#> 5 mpg ~ wt 8 (Intercept) 23.9 3.01 7.94 0.00000405
#> 6 mpg ~ wt 8 wt -2.19 0.739 -2.97 0.0118
#> 7 mpg ~ hp 4 (Intercept) 36.0 5.20 6.92 0.0000693
#> 8 mpg ~ hp 4 hp -0.113 0.0612 -1.84 0.0984
#> 9 mpg ~ hp 6 (Intercept) 20.7 3.30 6.26 0.00153
#> 10 mpg ~ hp 6 hp -0.00761 0.0266 -0.286 0.786
#> 11 mpg ~ hp 8 (Intercept) 18.1 2.99 6.05 0.0000574
#> 12 mpg ~ hp 8 hp -0.0142 0.0139 -1.02 0.326
cross_fit(
mtcars, list(mpg ~ wt, mpg ~ hp), cyl,
tidy = ~ broom::tidy(., conf.int = TRUE)
)
#> # A tibble: 12 × 9
#> model cyl term estimate std.error stati…¹ p.value conf.…² conf.…³
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mpg ~ wt 4 (Intercept) 39.6 4.35 9.10 7.77e-6 29.7 49.4
#> 2 mpg ~ wt 4 wt -5.65 1.85 -3.05 1.37e-2 -9.83 -1.46
#> 3 mpg ~ wt 6 (Intercept) 28.4 4.18 6.79 1.05e-3 17.7 39.2
#> 4 mpg ~ wt 6 wt -2.78 1.33 -2.08 9.18e-2 -6.21 0.651
#> 5 mpg ~ wt 8 (Intercept) 23.9 3.01 7.94 4.05e-6 17.3 30.4
#> 6 mpg ~ wt 8 wt -2.19 0.739 -2.97 1.18e-2 -3.80 -0.582
#> 7 mpg ~ hp 4 (Intercept) 36.0 5.20 6.92 6.93e-5 24.2 47.7
#> 8 mpg ~ hp 4 hp -0.113 0.0612 -1.84 9.84e-2 -0.251 0.0256
#> 9 mpg ~ hp 6 (Intercept) 20.7 3.30 6.26 1.53e-3 12.2 29.2
#> 10 mpg ~ hp 6 hp -0.00761 0.0266 -0.286 7.86e-1 -0.0759 0.0607
#> 11 mpg ~ hp 8 (Intercept) 18.1 2.99 6.05 5.74e-5 11.6 24.6
#> 12 mpg ~ hp 8 hp -0.0142 0.0139 -1.02 3.26e-1 -0.0445 0.0160
#> # … with abbreviated variable names ¹statistic, ²conf.low, ³conf.high