00_chmi_r_readme.md
In mvazquezs/chmitools: Package to manage & obtain results of the CHMI Study

`chmi.phen()` function

The chmi.phen() is the main chmitools function manages and loads data.
The chmi.phen() allows several arguments to load different subset of data.
study_data: type of the study to analyse (ab_data, cyto_data, pbmc_data, hemo_data, none).
aim_ab_chmi: data subset, in case of the study_chmi == 'antibodies' (aim_1, aim_2, aim_3, aim_4).
fold_change: allows to load fold_change data, for each aim (TRUE / FALSE).
fc_traits: allows to show all fold_change columns.
group_tr: allows to load only group of related variables (ab_full, ab_select, pbmc_full, none).
traits: allows to load only one or more specific variables.

`chmi.phen()` r code exemple


> dat <- chmi.phen(
    data_type = 'ab_data',
    aim_ab_data = 'aim_3',
    fold_change = FALSE,
    group_tr = 'ab_select',
    traits = 'mean_ratio')

> dat
# A tibble: 13,542 x 15
   original_id dataset gender gr_hbs gr2_hbs hb_status_ori ppp_time_ori status_ori mean_ratio antigen   log10_mfi mfi_corr t_igg t_point t2_point
   <fct>       <fct>   <fct>  <fct>  <fct>   <fct>                <dbl> <fct>           <dbl> <fct>         <dbl>    <dbl> <fct> <fct>   <fct>   
 1 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.947 ama1_3d71      4.24   17406. IgG   C_1     C_1     
 2 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.947 ama1_3d71      4.44   27590. IgG   D7      D7      
 3 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.947 ama1_3d71      5.86  731660. IgG   D13     D11_D13 
 4 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.947 ama1_3d71      5.93  858952. IgG   D28     D28     
 5 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.938 ama1_fvo       4.22   16642. IgG   C_1     C_1     
 6 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.938 ama1_fvo       4.40   25081. IgG   D7      D7      
 7 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.938 ama1_fvo       5.77  591384. IgG   D13     D11_D13 
 8 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.938 ama1_fvo       5.61  403398. IgG   D28     D28     
 9 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.657 cel_tos        5.00  100257. IgG   C_1     C_1     
10 L1_001      L1      M      naive  naive   AA                    14.1 naive           0.657 cel_tos        5.07  118665. IgG   D7      D7      
# ... with 13,532 more rows

main `chmi.stat_summary.R` functions

chmi.stat.oneway_test()
chmi.stat.oneway_testfold()
The chmi.stat.p_adjust() give adjust p_values by p.adjust()
the phen argument is a dataset.
the method argument manages type of statistic adjust gave by p.adjust() function.

main `chmi.stat_lm.R` functions

chmi.stat.stepwise()
chmi.stat.glm_or()
chmi.stat.lm_beta()
chmi.stat.lmm_mods()

main `chmi.stat_plots.R` functions

main `chmi.stat_tools.R` functions

The chmi.stat.select_traits() manages different outputs of the datasets by several arguments in the chmi.phen().
the phen argument is a dataset.
the l_vars argument is a list of fix variables (or covariables) to load.
the group_tr argument selects different groups of variables.
the traits argument selects individual variables.
The chmi.stat.rmarkdown_plot() manages a list of objects (mainly plots).
by for() loop, to run it, and cat(), to print title of the element.