summary_c2c {cat2cat} | R Documentation |
transforming summary.lm object according to real number of d.f. The standard errors, t statistics and p values have to be adjusted because of replicated rows.
summary_c2c(x, df_old, df_new = x$df.residual)
x |
lm object |
df_old |
integer number of d.f in original dataset. For bigger datasets 'nrow' should be sufficient. |
df_new |
integer number of d.f in dataset with replicated rows, Default: x$df.residual |
The size of the correction is equal to sqrt(df_new / df_old).
Where standard errors are multiplied and t statistics divided by it.
In most cases the default df_new
value should be used.
data.frame with additional columns over a regular summary.lm output like correct and statistics adjusted by it.
data(occup_small) data(trans) occup_old <- occup_small[occup_small$year == 2008, ] occup_new <- occup_small[occup_small$year == 2010, ] occup_2 <- cat2cat( data = list(old = occup_old, new = occup_new, cat_var = "code", time_var = "year"), mappings = list(trans = trans, direction = "backward"), ml = list( method = "knn", features = c("age", "sex", "edu", "exp", "parttime", "salary"), args = list(k = 10) ) ) # Regression # we have to adjust size of std as we artificialy enlarge degrees of freedom lms <- lm(I(log(salary)) ~ age + sex + factor(edu) + parttime + exp, occup_2$old, weights = multiplier * wei_freq_c2c ) summary_c2c(lms, df_old = nrow(occup_old))