m_summary | R Documentation |
A dataframe corresponding to a univariate or multivariate regression table. If for_plot = TRUE, estimates and confidence interval bounds will also be displayed separately for easy plotting.
m_summary(
model,
CIwidth = 0.95,
digits = 2,
vif = FALSE,
whichp = "levels",
for_plot = FALSE
)
model |
model fit |
CIwidth |
width for confidence intervals, defaults to 0.95 |
digits |
number of digits to round estimates to, does not affect p-values |
vif |
boolean indicating if the variance inflation factor should be included. See details |
whichp |
string indicating whether you want to display p-values for levels within categorical data ("levels"), global p values ("global"), or both ("both"). Irrelevant for continuous predictors. When for_plot = TRUE, global p values will be displayed in a separate column from p values. If whichp = "levels", global p values will not be included in the outputted table. |
for_plot |
boolean indicating whether or not the function will be used for plotting. Default is FALSE |
Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned. For negative binomial models a deviance test is used.
If the variance inflation factor is requested (VIF=T) then a generalised VIF will be calculated in the same manner as the car package.
As of R 4.4.0 the likelihood profiles are included in base R.
The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.
## Not run: data("pembrolizumab")
uv_lm <- lm(age~sex,data=pembrolizumab)
m_summary(uv_lm, digits = 3, for_plot = FALSE)
mv_binom <- glm(orr~age+sex+cohort,family = 'binomial',data = pembrolizumab)
m_summary(mv_binom, whichp = "both", for_plot = TRUE)
## End(Not run)
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