r - Doing a linear fit for each group of a data frame, check heteroscedasticity -
i have data frame this:
ord exp type mu 1 combi ph=7 exp_f mu 0.15637365 2 combi ph=7 exp_f mu 0.12817901 3 combi ph=7 exp_f mu 0.13392221 4 combi ph=7 exp_f mu 0.09683254 5 combi ph=7 exp_f mu 0.11249738 6 combi ph=7 exp_f mu 0.10878719 7 combi ph=7 exp_f mu 0.11019295 8 combi ph=7 exp_f mu 0.12100511 9 combi ph=7 exp_f mu 0.09803942 10 combi ph=7 exp_f mu 0.13842086 11 combi ph=7 exp_f mu 0.12778964 12 ord0793 exp_f mu 0.13910441 13 ord0793 exp_f mu 0.12603702 14 ord0793 exp_f mu 0.12670842 15 ord0795 exp_f mu 0.12982122 16 ord0795 exp_f mu 0.13648100 17 ord0795 exp_f mu 0.13593685 18 ord0799 exp_f mu 0.13906691 continues...
i linear adjust lm(mu~ord, data=df)
each group of type , exp. have tried following not working..:
intsl <- df %>% group_by(exp,type) %>% fortify(lm(mu~ord)) %>% select(exp,type, .fitted, .resid)
i need use fortify because need .fitted , .resid fields later on multiplot sorting plots type , exp using facet_grid
contained in ggplot in order check if there's heteroscedasticity in each fitted model.. in orgnanized multiplot:
any suggestions? :<
the documentation fortify()
in ggplot2
package says method deprecated , broom
package should used instead. based on info here, should this:
library(dplyr) library(broom) intsl <- df %>% group_by(exp, type) %>% do(fit = lm(mu ~ ord, .) intsl %>% augment(fit)
this should give data frame variables used group regressions, regression variables, , output each observation such .fitted
, .resid
, can move on plot them ggplot
directly.
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