Function to run generalized linear model (glm) across multiple grouping variables.

Source: R/grouped_glm.R

Function to run generalized linear model (glm) across multiple grouping variables.

grouped_glm(
  data,
  grouping.vars,
  ...,
  output = "tidy",
  tidy.args = list(conf.int = TRUE, conf.level = 0.95),
  augment.args = list()
)

Arguments

data

Dataframe (or tibble) from which variables are to be taken.
grouping.vars

Grouping variables.
...

Additional arguments to broom::tidy, broom::glance, or broom::augment S3 method.
output

A character describing what output is expected. Two possible options: "tidy" (default), which will return the results, or "glance", which will return model summaries.
tidy.args

A list of arguments to be used in the relevant S3 method.
augment.args

A list of arguments to be used in the relevant S3 method.

Value

A tibble dataframe with tidy results from linear model.

See also

grouped_lm, grouped_lmer, grouped_glmer

Examples

groupedstats::grouped_glm(
  data = mtcars,
  formula = am ~ wt,
  grouping.vars = cyl,
  family = stats::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: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> 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
#> Warning: collapsing to unique 'x' values
#> Warning: collapsing to unique 'x' values
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> # A tibble: 6 x 9
#>     cyl term        estimate std.error statistic p.value      conf.low conf.high
#>   <dbl> <chr>          <dbl>     <dbl>     <dbl>   <dbl>         <dbl>     <dbl>
#> 1     4 (Intercept)    13.8       7.79  1.77      0.0773       3.30       38.4  
#> 2     4 wt             -5.12      2.98 -1.71      0.0865     -14.5        -1.04 
#> 3     6 (Intercept)   417.   871566.    0.000478  1.00   -113315.         NA    
#> 4     6 wt           -137.   284315.   -0.000481  1.00        NA       25376.   
#> 5     8 (Intercept)    28.3      21.4   1.32      0.187        0.00542    99.8  
#> 6     8 wt             -8.46      6.17 -1.37      0.171      -29.1        -0.563
#> # … with 1 more variable: significance <chr>