with 1,698 more rows, and 5 more variables. se.fit #> #> 1 Africa Algeria 2 Africa Algeria 3 Africa Algeria 4 Africa Algeria 5 Africa Algeria 6 Africa Algeria #. with 278 more rows, and 2 more variables: p.value, augment gapminder_summary %>% unnest(augment) #> # A tibble: 1,704 x 15 #> continent country data model glance tidy lifeExp year1950. with 136 more rows, and 8 more variables: df, logLik, #> # AIC, BIC, deviance, df.residual, tidy, #> # augment gapminder_summary %>% unnest(tidy) #> # A tibble: 284 x 11 #> continent country data model glance term estimate std.error statistic #> #> 1 Africa Algeria 2 Africa Algeria 3 Africa Angola 4 Africa Angola 5 Africa Benin 6 Africa Benin #. # which country has the best fit gapminder_summary %>% unnest(glance) %>% arrange( desc(r.squared)) #> # A tibble: 142 x 17 #> continent country data model r.squared adj.r.squared sigma statistic p.value #> #> 1 Americas Brazil 0.998 0.998 0.326 5111. 10 vroom: Fast reading of delimited files.7.4.2 group_nest、group_split、group_keys、group_data.6.3.1 Example: Managing multiple models.6.2.3 Combining pivot_longer() and pivot_wider().5.2.1 Sorting by frequency, appearance, or numeric order.If we write the centered data in a matrix X, where rows are objects and columns are features, then XT X nV, where V is the covariance matrix of the data. 2.3 Comparing two data frames (tibbles) Throughout, assume that the data have been centered', so that every fea-ture has mean 0.1.5 group_by() combined with other functions.
0 kommentar(er)
