By default the (amazing) `broom` package uses the `confint()` function to calculate CIs. For GLMs this calculates confidence intervals via profile likelihood by default. When using large datasets this takes a long time and does not meaningfully alter the CIs compared to simply calculating using 1.96*SE. This function `tidy_ci()` runs `broom::tidy()` and returns the tidy estimates with CIs calculated as EST +/- 1.96*SE. (Well, actually 1.959964)
The function also does a few other nice/useful things to the output: hides the intercept by default, automatically detects logistic/CoxPH/CRR models and exponentiates the estimates, and if p=0 returns the extreme p as a string. Other optional outputs include -log10 p-values.
Not tested for models other than `glm()` and `survival::coxph()` where it seems to work very well and produces consistent CIs. Also works well for `cmprsk::crr()`.
For linear mixed-effects models `lmer()` from package lme4 this can display the fixed-effects with p-values and Wald CIs.
Usage
tidy_ci(
x,
ci = TRUE,
ci_denominator = 1.959964,
exp = FALSE,
intercept = FALSE,
tidy_factors = TRUE,
extreme_ps = TRUE,
neglog10p = FALSE,
check_model = TRUE,
n = NA,
get_r2 = TRUE,
conf.int = FALSE,
quiet = FALSE,
...
)Arguments
- x
object containing model output to be tidied e.g., from a `glm()` or `survival::coxph()`.
- ci
Logical. Default is TRUE. Calculate CIs using 1.96*SE method - where 1.96 can be modified using `ci_denominator`.
- ci_denominator
Numeric. Default is 1.96 (actually 1.959964). The standard error of the sample mean.
- exp
Logical. Default is FALSE. Exponentiate estimate and CIs -- also see `check_model`.
- intercept
Logical. Default is FALSE. Exclude intercept for tidier output.
- tidy_factors
Logical. Default is TRUE. Tidy `as.factor(x_var)#` terms to `x_var-#`.
- extreme_ps
Logical. Default is TRUE. If p=0 then return "extreme p-values" as strings.
- neglog10p
Logical. Default is FALSE. Provides negative log10 p-values (if input is class `glm` or `coxph` or `crr` -- user can provide sample size `n=#` to override).
- check_model
Logical. Default is TRUE. Set `exp=TRUE` if `glm(family=binomial)` or `survival::coxph()` or `cmprsk::crr()` was performed.
- n
Numeric. Default is NA. The N for `neglog10p` is extracted automatically for `glm` or `coxph` objects - override here if required.
- get_r2
Logical. Default is TRUE. get R^2 value if model is linear, or C-statistic if model is CoxPH
- quiet
Logical. Default is FALSE. Suppress text output
- ...
Other `tidy()` options
Examples
fit_linear = glm(sbp ~ age + sex + as.factor(bmi_cat), data = example_data)
tidy_ci(fit_linear)
#> Linear model (estimate=coefficient) :: N=4690 :: R2=0.21
#> # A tibble: 4 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 age 0.951 0.0353 26.9 1.45e-148 0.882 1.02
#> 2 sex -1.48 0.606 -2.44 1.48e- 2 -2.67 -0.291
#> 3 bmi_cat-1 7.40 0.652 11.4 1.70e- 29 6.13 8.68
#> 4 bmi_cat-2 16.3 0.940 17.3 2.78e- 65 14.4 18.1
fit_logistic = glm(event ~ age + sex + bmi, data = example_data, family = binomial)
tidy_ci(fit_logistic) # detect model and exponentiate automatically
#> Binomial model (estimate=Odds Ratio) :: N=4690, Ncases=1472
#> # A tibble: 3 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 age 1.04 0.00388 8.95 3.47e-19 1.03 1.04
#> 2 sex 2.10 0.0654 11.3 1.02e-29 1.84 2.38
#> 3 bmi 1.07 0.00801 8.66 4.74e-18 1.06 1.09