knitr::opts_chunk$set(comment = NA, fig.path = '04_figures_aim3_lmm/', fig.width = 20, fig.height = 12, results = 'markup', tidy = F, message = F, warning = F, echo = F)
alpha <- 0.05
library(moonBook) library(devtools)
# mac load_all('~/Documents/git/chmitools') # home # source('~/git/myR_setup/myR.profile')
load data
section.
chmi.phen()
load data by group and aim.
duplicate & relevel t2_point
to obtain t2_pont_lm
.
# load data dat_lm <- chmi.phen(group = 'mfi', aim_chmi = 'aim_3') # double variable `t2_point` dat_lm <- dat_lm %>% mutate(t2_point_lm = as.numeric( ifelse(t2_point == 'C-1', 0, ifelse(t2_point == 'D7', 7, ifelse(t2_point == 'D13', 13, ifelse(t2_point == 'D19', 19, ifelse(t2_point == 'D28', 28, NA))))))) # table descriptive # tab_1 <- mytable(t2_point ~ dataset + isotype_igg + gr_hbs, data = dat_lm) # tab_2 <- mytable(isotype_igg ~ dataset + t2_point + gr_hbs, data = dat_lm)
betas
& p_values
in lmm
modelssummary()
section of lmm
models.
chmi.stat.lmm_mods()
obtain raw and adjusted p-values.
we are used p.adjust()
function to adjust p-values by Benjamini & Hochberg
method.
# list l_vars <- c('isotype_igg', 'antigen') # lmm models tab_3 <- chmi.stat.lmm_mods(dat_lm, group_by = l_vars, n_levels = 3, format_pval = T) # list for 'tab_3' l_estim <- tab_3[[1]] %>% select(contains('est')) %>% names() # l_pvals <- tab_3[[1]] %>% select(contains('pval')) %>% names() # 'tab_3' datatable datatable(tab_3[[1]], class = c('compact cell-border stripe hover'), extensions = c('Buttons', 'FixedColumns'), filter = 'top', options = list(dom = 'Blftip', pageLength = 30, buttons = c('copy', 'csv'), scrollX = TRUE, text = 'Download')) %>% formatRound(l_estim, 3)
r_data
folder# save save.image(file = '/Users/migvazquez/Documents/git/chmitools/projects/03_aim3/r_data/03_aim3_lmm.RData')
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