![]() ![]() Log(scale(us_gross)) which would cause an error since you Words, it is similar to the result you would get if you did The function will scale the already-transformed variable. If you have transformed variables (e.g., log(us_gross)), R² = 0.55 # Standard errors: OLS # - # Est. deleted) # Dependent Variable: metascore # Type: OLS linear regression # MODEL FIT: # F(6,824) = 169.37, p = 0.00 # R² = 0.55 # Adj. Summ ( fit, scale = TRUE ) # MODEL INFO: # Observations: 831 (10 missing obs. Variable in the input data or a vector of clusters to get cluster-robust You may also specify with cluster argument the name of a Package calculates are already robust to heteroskedasticity, so anyĪrgument to robust will be ignored with a warning. In the case of svyglm, the standard errors that Whether they should be used for models fit iteratively with non-normalĮrrors. Models (i.e., glm objects) though there is some debate Robust standard errors can also be calculated for generalized linear R² = 0.55 # Standard errors: Robust, type = HC1 # - # Est. Summ ( fit, robust = "HC1" ) # MODEL INFO: # Observations: 831 (10 missing obs. Looks in the R console, but if you are generating your own RMarkdownĭocuments and have kableExtra installed, you’ll instead get Note: The output in this vignette will mimic how it Set_summ_defaults() to reduce the need to do redundant
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