f3_script_lorena.R
Ronaldo Alves
Wed Dec 19 07:52:41 2018
##############################################
############## CARREGAR PACOTES ##############
##############################################
library(tidyverse)## -- Attaching packages ---------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 3.1.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.8
## v tidyr 0.8.1 v stringr 1.3.1
## v readr 1.1.1 v forcats 0.3.0## -- Conflicts ------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transposelibrary(readstata13)
library(ggplot2)
library(corrplot) # https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html## corrplot 0.84 loadedlibrary(agricolae)
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.5-3library(BiodiversityR)## Loading required package: tcltk## BiodiversityR 2.10-1: Use command BiodiversityRGUI() to launch the Graphical User Interface;
## to see changes use BiodiversityRGUI(changeLog=TRUE, backward.compatibility.messages=TRUE)library(NCStats) # para instalar: source("http://www.rforge.net/NCStats/InstallNCStats.R")## Loading required package: FSA## ## FSA v0.8.22. See citation('FSA') if used in publication.
## ## Run fishR() for related website and fishR('IFAR') for related book.## ## NCStats v0.4.7 by Derek H. Ogle, Northland College.##
## Attaching package: 'NCStats'## The following object is masked from 'package:FSA':
##
## rSquaredlibrary(qwraps2)
library(summarytools)##
## Attaching package: 'summarytools'## The following object is masked from 'package:NCStats':
##
## viewlibrary(knitr)
library(kableExtra) # https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html#overview
library(Rmisc)## Loading required package: plyr## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:purrr':
##
## compactlibrary(nortest)
##############################################
########## CARREGAR BANCO DE DADOS ###########
##############################################
banco = readstata13::read.dta13(file.choose()) # bancofinalwide281118.dta
##############################################
############ RENOMEAR VARIÁVEIS ##############
##############################################
banco=dplyr::rename(banco,
parity=nativivoss1,
schooling=anos_de_estudo1,
mother_age=idade_mae_calc1,
total_energy=new_kcal_total2,
total_protein=prottotal2,
total_fat=liptotal2,
total_carbohydrates=carbtotal2,
total_fiber=fibrtotal2,
saturated_fat=sattotal2,
unsaturated_fat=instotal2,
calcium=calciototal2,
iron=ferrototal2,
tryptophan=triptofanototal2,
HMOSecretor=HMOSecretor3,
Diversity_7d=Diversity3,
Evenness_7d=Evenness3,
"2'FL_7d"=FL2nmolmL3,
"3FL_7d"=FL3nmolmL3,
LNnT_7d=LNnTnmolmL3,
"3'SL_7d"=SL3nmolmL3,
DFLac_7d=DFLacnmolmL3,
"6'SL_7d"=SL6nmolmL3,
LNT_7d=LNTnmolmL3,
LNFPI_7d=LNFPInmolmL3,
LNFPII_7d=LNFPIInmolmL3,
LNFPIII_7d=LNFPIIInmolmL3,
LSTb_7d=LSTbnmolmL3,
LSTc_7d=LSTcnmolmL3,
DFLNT_7d=DFLNTnmolmL3,
LNH_7d=LNHnmolmL3,
DSLNT_7d=DSLNTnmolmL3,
FLNH_7d=FLNHnmolmL3,
DFLNH_7d=DFLNHnmolmL3,
FDSLNH_7d=FDSLNHnmolmL3,
DSLNH_7d=DSLNHnmolmL3,
SUM_7d=SUMnmolmL3,
Sia_7d=SianmolmL3,
Fuc_7d=FucnmolmL3,
HMOSecretor_1m=HMOSecretor4,
Diversity_1m=Diversity4,
Evenness_1m=Evenness4,
"2'FL_1m"=FL2nmolmL4,
"3FL_1m"=FL3nmolmL4,
LNnT_1m=LNnTnmolmL4,
"3'SL_1m"=SL3nmolmL4,
DFLac_1m=DFLacnmolmL4,
"6'SL_1m"=SL6nmolmL4,
LNT_1m=LNTnmolmL4,
LNFPI_1m=LNFPInmolmL4,
LNFPII_1m=LNFPIInmolmL4,
LNFPIII_1m=LNFPIIInmolmL4,
LSTb_1m=LSTbnmolmL4,
LSTc_1m=LSTcnmolmL4,
DFLNT_1m=DFLNTnmolmL4,
LNH_1m=LNHnmolmL4,
DSLNT_1m=DSLNTnmolmL4,
FLNH_1m=FLNHnmolmL4,
DFLNH_1m=DFLNHnmolmL4,
FDSLNH_1m=FDSLNHnmolmL4,
DSLNH_1m=DSLNHnmolmL4,
SUM_1m=SUMnmolmL4,
Sia_1m=SianmolmL4,
Fuc_1m=FucnmolmL4,
HMOSecretor_3m=HMOSecretor5,
Diversity_3m=Diversity5,
Evenness_3m=Evenness5,
"2'FL_3m"=FL2nmolmL5,
"3FL_3m"=FL3nmolmL5,
LNnT_3m=LNnTnmolmL5,
"3'SL_3m"=SL3nmolmL5,
DFLac_3m=DFLacnmolmL5,
"6'SL_3m"=SL6nmolmL5,
LNT_3m=LNTnmolmL5,
LNFPI_3m=LNFPInmolmL5,
LNFPII_3m=LNFPIInmolmL5,
LNFPIII_3m=LNFPIIInmolmL5,
LSTb_3m=LSTbnmolmL5,
LSTc_3m=LSTcnmolmL5,
DFLNT_3m=DFLNTnmolmL5,
LNH_3m=LNHnmolmL5,
DSLNT_3m=DSLNTnmolmL5,
FLNH_3m=FLNHnmolmL5,
DFLNH_3m=DFLNHnmolmL5,
FDSLNH_3m=FDSLNHnmolmL5,
DSLNH_3m=DSLNHnmolmL5,
SUM_3m=SUMnmolmL5,
Sia_3m=SianmolmL5,
Fuc_3m=FucnmolmL5)
##############################################
########## CRIAR/EDITAR VARIÁVEIS ############
##############################################
banco$peso_pg1[banco$peso_pg1==9999] = NA
# ver outras vars dietéticas = 0
banco = banco %>% mutate(
hmo_status = case_when(
HMOSecretor == 1 | HMOSecretor_1m == 1 | HMOSecretor_3m == 1 ~ 1,
HMOSecretor == 0 | HMOSecretor_1m == 0 | HMOSecretor_3m == 0 ~ 0),
bmi_pg = (peso_pg1/(estatuta_mat_aferi_m4 * estatuta_mat_aferi_m4)*10000),
gwg_3t = peso_1_aferi1 - peso_pg1,
small_7d = `2'FL_7d` + `3FL_7d` + `3'SL_7d` + `6'SL_7d`,
type_1_7d = LNT_7d + LNFPI_7d + LNFPII_7d + LSTb_7d + DSLNT_7d,
type_2_7d = LNnT_7d + LNFPIII_7d + LSTc_7d,
alfa_12_7d = LNFPI_7d + `2'FL_7d`,
alfa_13_7d = LNFPIII_7d + `3FL_7d`,
alfa_26_7d = LSTb_7d + LSTc_7d + `6'SL_7d`,
small_1m = `2'FL_1m` + `3FL_1m` + `3'SL_1m` + `6'SL_1m`,
type_1_1m = LNT_1m + LNFPI_1m + LNFPII_1m + LSTb_1m + DSLNT_1m,
type_2_1m = LNnT_1m + LNFPIII_1m + LSTc_1m,
alfa_12_1m = LNFPI_1m + `2'FL_1m`,
alfa_13_1m = LNFPIII_1m + `3FL_1m`,
alfa_26_1m = LSTb_1m + LSTc_1m + `6'SL_1m`,
small_3m = `2'FL_3m` + `3FL_3m` + `3'SL_3m` + `6'SL_3m`,
type_1_3m = LNT_3m + LNFPI_3m + LNFPII_3m + LSTb_3m + DSLNT_3m,
type_2_3m = LNnT_3m + LNFPIII_3m + LSTc_3m,
alfa_12_3m = LNFPI_3m + `2'FL_3m`,
alfa_13_3m = LNFPIII_3m + `3FL_3m`,
alfa_26_3m = LSTb_3m + LSTc_3m + `6'SL_3m`)
# y = data.frame(banco$HMOSecretor, banco$HMOSecretor_1m, banco$HMOSecretor_3m, banco$hmo_status)
# banco$difdata_sgparto = banco$data_nascimentobb3-banco$data_1usg1
# banco$sg_parto = round(banco$difdata_sgparto / 7 + banco$ig_1usg1, 3)
######################################################
########### OVERVIEW - SELECTED VARIABLES ############
######################################################
# x = data.frame(names(banco))
print(
summarytools::dfSummary(banco[,c(8,29,104,141,143,164:173,235:259,580:604,925:949,1701:1722)],
graph.magnif = 0.75),
method = "viewer") # incluir summary(banco[,c(146,1701)]) #substituir render por viewer para abrir na janela. Mas para aparecer no relatório ## Warning in if (grepl("^\\w+$", data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl("^\\w+\\[.*,\\s*\\-.+\\]", data_str, perl = TRUE)) {:
## a condição tem comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re1, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re2, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re3, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## unable to identify var names: banco[, c(8, 29, 104, 141, 143, 164:173, 235:259, 580:604, 925:949, 1701:1722)]## Warning in if (grepl(re1, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re2, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re3, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re4, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Method 'viewer' only valid within RStudio. Switching method to 'browser'.## Output file written: C:\Users\RONALD~1\AppData\Local\Temp\RtmpmoAZ4h\file9201d6d4d59.htmlprint(
descr(banco[,c(8,29,104,141,143,164:173,235:259,580:604,925:949,1701:1722)],
stats = c("mean","sd","med","iqr","min","max","skewness","kurtosis","n.valid","pct.valid"),
transpose = TRUE, omit.headings = TRUE, style = "rmarkdown"),
method = "viewer") # incluir summary(banco[,c(146,1701)]) # # 1702:1721## Warning in if (grepl("^\\w+$", data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl("^\\w+\\[.*,\\s*\\-.+\\]", data_str, perl = TRUE)) {:
## a condição tem comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re1, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re2, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re3, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## unable to identify var names: banco[, c(8, 29, 104, 141, 143, 164:173, 235:259, 580:604, 925:949, 1701:1722)]## Warning in if (grepl(re1, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re2, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re3, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Warning in if (grepl(re4, data_str, perl = TRUE)) {: a condição tem
## comprimento > 1 e somente o primeiro elemento será usado## Method 'viewer' only valid within RStudio. Switching method to 'browser'.## Output file written: C:\Users\RONALD~1\AppData\Local\Temp\RtmpmoAZ4h\file9206ed95d39.html# names(banco[,c(238:259,1705:1710,583:604,1711:1716,928:949,1717:1722)])
D = matrix(c(
round(lillie.test(banco[,238])$p.value, 4),
round(lillie.test(banco[,239])$p.value, 4),
round(lillie.test(banco[,240])$p.value, 4),
round(lillie.test(banco[,241])$p.value, 4),
round(lillie.test(banco[,242])$p.value, 4),
round(lillie.test(banco[,243])$p.value, 4),
round(lillie.test(banco[,244])$p.value, 4),
round(lillie.test(banco[,245])$p.value, 4),
round(lillie.test(banco[,246])$p.value, 4),
round(lillie.test(banco[,247])$p.value, 4),
round(lillie.test(banco[,248])$p.value, 4),
round(lillie.test(banco[,249])$p.value, 4),
round(lillie.test(banco[,250])$p.value, 4),
round(lillie.test(banco[,251])$p.value, 4),
round(lillie.test(banco[,252])$p.value, 4),
round(lillie.test(banco[,253])$p.value, 4),
round(lillie.test(banco[,254])$p.value, 4),
round(lillie.test(banco[,255])$p.value, 4),
round(lillie.test(banco[,256])$p.value, 4),
round(lillie.test(banco[,257])$p.value, 4),
round(lillie.test(banco[,258])$p.value, 4),
round(lillie.test(banco[,259])$p.value, 4),
round(lillie.test(banco[,1705])$p.value, 4),
round(lillie.test(banco[,1706])$p.value, 4),
round(lillie.test(banco[,1707])$p.value, 4),
round(lillie.test(banco[,1708])$p.value, 4),
round(lillie.test(banco[,1709])$p.value, 4),
round(lillie.test(banco[,1710])$p.value, 4),
round(shapiro.test(banco[,238])$p.value, 4),
round(shapiro.test(banco[,239])$p.value, 4),
round(shapiro.test(banco[,240])$p.value, 4),
round(shapiro.test(banco[,241])$p.value, 4),
round(shapiro.test(banco[,242])$p.value, 4),
round(shapiro.test(banco[,243])$p.value, 4),
round(shapiro.test(banco[,244])$p.value, 4),
round(shapiro.test(banco[,245])$p.value, 4),
round(shapiro.test(banco[,246])$p.value, 4),
round(shapiro.test(banco[,247])$p.value, 4),
round(shapiro.test(banco[,248])$p.value, 4),
round(shapiro.test(banco[,249])$p.value, 4),
round(shapiro.test(banco[,250])$p.value, 4),
round(shapiro.test(banco[,251])$p.value, 4),
round(shapiro.test(banco[,252])$p.value, 4),
round(shapiro.test(banco[,253])$p.value, 4),
round(shapiro.test(banco[,254])$p.value, 4),
round(shapiro.test(banco[,255])$p.value, 4),
round(shapiro.test(banco[,256])$p.value, 4),
round(shapiro.test(banco[,257])$p.value, 4),
round(shapiro.test(banco[,258])$p.value, 4),
round(shapiro.test(banco[,259])$p.value, 4),
round(shapiro.test(banco[,1705])$p.value, 4),
round(shapiro.test(banco[,1706])$p.value, 4),
round(shapiro.test(banco[,1707])$p.value, 4),
round(shapiro.test(banco[,1708])$p.value, 4),
round(shapiro.test(banco[,1709])$p.value, 4),
round(shapiro.test(banco[,1710])$p.value, 4),
round(lillie.test(banco[,583])$p.value, 4),
round(lillie.test(banco[,584])$p.value, 4),
round(lillie.test(banco[,585])$p.value, 4),
round(lillie.test(banco[,586])$p.value, 4),
round(lillie.test(banco[,587])$p.value, 4),
round(lillie.test(banco[,588])$p.value, 4),
round(lillie.test(banco[,589])$p.value, 4),
round(lillie.test(banco[,590])$p.value, 4),
round(lillie.test(banco[,591])$p.value, 4),
round(lillie.test(banco[,592])$p.value, 4),
round(lillie.test(banco[,593])$p.value, 4),
round(lillie.test(banco[,594])$p.value, 4),
round(lillie.test(banco[,595])$p.value, 4),
round(lillie.test(banco[,596])$p.value, 4),
round(lillie.test(banco[,597])$p.value, 4),
round(lillie.test(banco[,598])$p.value, 4),
round(lillie.test(banco[,599])$p.value, 4),
round(lillie.test(banco[,600])$p.value, 4),
round(lillie.test(banco[,601])$p.value, 4),
round(lillie.test(banco[,602])$p.value, 4),
round(lillie.test(banco[,603])$p.value, 4),
round(lillie.test(banco[,604])$p.value, 4),
round(lillie.test(banco[,1711])$p.value, 4),
round(lillie.test(banco[,1712])$p.value, 4),
round(lillie.test(banco[,1713])$p.value, 4),
round(lillie.test(banco[,1714])$p.value, 4),
round(lillie.test(banco[,1715])$p.value, 4),
round(lillie.test(banco[,1716])$p.value, 4),
round(shapiro.test(banco[,583])$p.value, 4),
round(shapiro.test(banco[,584])$p.value, 4),
round(shapiro.test(banco[,585])$p.value, 4),
round(shapiro.test(banco[,586])$p.value, 4),
round(shapiro.test(banco[,587])$p.value, 4),
round(shapiro.test(banco[,588])$p.value, 4),
round(shapiro.test(banco[,589])$p.value, 4),
round(shapiro.test(banco[,590])$p.value, 4),
round(shapiro.test(banco[,591])$p.value, 4),
round(shapiro.test(banco[,592])$p.value, 4),
round(shapiro.test(banco[,593])$p.value, 4),
round(shapiro.test(banco[,594])$p.value, 4),
round(shapiro.test(banco[,595])$p.value, 4),
round(shapiro.test(banco[,596])$p.value, 4),
round(shapiro.test(banco[,597])$p.value, 4),
round(shapiro.test(banco[,598])$p.value, 4),
round(shapiro.test(banco[,599])$p.value, 4),
round(shapiro.test(banco[,600])$p.value, 4),
round(shapiro.test(banco[,601])$p.value, 4),
round(shapiro.test(banco[,602])$p.value, 4),
round(shapiro.test(banco[,603])$p.value, 4),
round(shapiro.test(banco[,604])$p.value, 4),
round(shapiro.test(banco[,1711])$p.value, 4),
round(shapiro.test(banco[,1712])$p.value, 4),
round(shapiro.test(banco[,1713])$p.value, 4),
round(shapiro.test(banco[,1714])$p.value, 4),
round(shapiro.test(banco[,1715])$p.value, 4),
round(shapiro.test(banco[,1716])$p.value, 4),
round(lillie.test(banco[,928])$p.value, 4),
round(lillie.test(banco[,929])$p.value, 4),
round(lillie.test(banco[,930])$p.value, 4),
round(lillie.test(banco[,931])$p.value, 4),
round(lillie.test(banco[,932])$p.value, 4),
round(lillie.test(banco[,933])$p.value, 4),
round(lillie.test(banco[,934])$p.value, 4),
round(lillie.test(banco[,935])$p.value, 4),
round(lillie.test(banco[,936])$p.value, 4),
round(lillie.test(banco[,937])$p.value, 4),
round(lillie.test(banco[,938])$p.value, 4),
round(lillie.test(banco[,939])$p.value, 4),
round(lillie.test(banco[,940])$p.value, 4),
round(lillie.test(banco[,941])$p.value, 4),
round(lillie.test(banco[,942])$p.value, 4),
round(lillie.test(banco[,943])$p.value, 4),
round(lillie.test(banco[,944])$p.value, 4),
round(lillie.test(banco[,945])$p.value, 4),
round(lillie.test(banco[,946])$p.value, 4),
round(lillie.test(banco[,947])$p.value, 4),
round(lillie.test(banco[,948])$p.value, 4),
round(lillie.test(banco[,949])$p.value, 4),
round(lillie.test(banco[,1717])$p.value, 4),
round(lillie.test(banco[,1718])$p.value, 4),
round(lillie.test(banco[,1719])$p.value, 4),
round(lillie.test(banco[,1720])$p.value, 4),
round(lillie.test(banco[,1721])$p.value, 4),
round(lillie.test(banco[,1722])$p.value, 4),
round(shapiro.test(banco[,928])$p.value, 4),
round(shapiro.test(banco[,929])$p.value, 4),
round(shapiro.test(banco[,930])$p.value, 4),
round(shapiro.test(banco[,931])$p.value, 4),
round(shapiro.test(banco[,932])$p.value, 4),
round(shapiro.test(banco[,933])$p.value, 4),
round(shapiro.test(banco[,934])$p.value, 4),
round(shapiro.test(banco[,935])$p.value, 4),
round(shapiro.test(banco[,936])$p.value, 4),
round(shapiro.test(banco[,937])$p.value, 4),
round(shapiro.test(banco[,938])$p.value, 4),
round(shapiro.test(banco[,939])$p.value, 4),
round(shapiro.test(banco[,940])$p.value, 4),
round(shapiro.test(banco[,941])$p.value, 4),
round(shapiro.test(banco[,942])$p.value, 4),
round(shapiro.test(banco[,943])$p.value, 4),
round(shapiro.test(banco[,944])$p.value, 4),
round(shapiro.test(banco[,945])$p.value, 4),
round(shapiro.test(banco[,946])$p.value, 4),
round(shapiro.test(banco[,947])$p.value, 4),
round(shapiro.test(banco[,948])$p.value, 4),
round(shapiro.test(banco[,949])$p.value, 4),
round(shapiro.test(banco[,1717])$p.value, 4),
round(shapiro.test(banco[,1718])$p.value, 4),
round(shapiro.test(banco[,1719])$p.value, 4),
round(shapiro.test(banco[,1720])$p.value, 4),
round(shapiro.test(banco[,1721])$p.value, 4),
round(shapiro.test(banco[,1722])$p.value, 4)),
nrow=28, ncol=6, byrow=F)
dimnames(D) = list(
c("2'FL, nmol/mL", "3FL, nmol/mL", "LNnT, nmol/mL", "3'SL, nmol/mL", "DFLac, nmol/mL",
"6'SL, nmol/mL", "LNT, nmol/mL", "LNFP I, nmol/mL", "LNFP II, nmol/mL", "LNFP III, nmol/mL",
"LSTb, nmol/mL", "LSTc, nmol/mL", "DFLNT, nmol/mL", "LNH, nmol/mL", "DSLNT, nmol/mL",
"FLNH, nmol/mL", "DFLNH, nmol/mL", "FDSLNH, nmol/mL", "DSLNH, nmol/mL", "Total, nmol/mL",
"HMO-bound sialic acid, nmol/mL","HMO-bound fucose, nmol/mL", "Small HMOs, nmol/mL",
"Type 1, nmol/mL", "Type 2, nmol/mL", "alpha 1,2, nmol/mL", "alpha 1,3, nmol/mL",
"alpha 2,6, nmol/mL"),
c("Lilliefors (K-S) test", "S-W test", "Lilliefors (K-S) test", "S-W test",
"Lilliefors (K-S) test", "S-W test"))
knitr::kable(D, format="html",
caption="Table 0. Kolmogorov-Smirnov test with Lilliefors correction and Shapiro-Wilk normality test for outcomes.") %>%
kable_styling("striped",full_width = T) %>%
add_header_above(c("HMO" = 1, "1-7 days postpartum" = 2, "30-45 day postpartum" = 2,
"3 months postpartum" = 2)) %>%
footnote(general="K-S: Kolmogorov-Smirnov normality test. S-W: Shapiro-Wilk normality test. HMO: human milk oligosaccharide. 2'FL: 2'-fucosyllactose. 3FL: 3-fucosyllactose. LNnT: lacto-N-neotetraose. 3'SL: 3'-sialyllactose. DFLac: difucosyllactose. 6'SL: 6'-sialyllactose. LNT: lacto-N-tetrose. LNFP: lacto-N-fucopentaose. LSTb: sialyl-lacto-N-tetraose b. LSTc: sialyl-lacto-N-tetraose c. DFLNT: difucosyllacto-N-tetrose. LNH: lacto-N-hexaose. DSLNT: disialyllacto-Ntetraose. FLNH: fucosyllacto-N-hexaose. DFLNH: difucosyllacto-N-hexaose. FDSLNH: fucodisialyllacto-N-hexaose. DSLNH: disialyllacto-N-hexaose.") | Lilliefors (K-S) test | S-W test | Lilliefors (K-S) test | S-W test | Lilliefors (K-S) test | S-W test | |
|---|---|---|---|---|---|---|
| 2’FL, nmol/mL | 0.0744 | 0.0140 | 0.0142 | 0.0114 | 0.3655 | 0.6087 |
| 3FL, nmol/mL | 0.5067 | 0.2827 | 0.0050 | 0.0000 | 0.4255 | 0.2609 |
| LNnT, nmol/mL | 0.2762 | 0.0178 | 0.1155 | 0.0003 | 0.5035 | 0.0580 |
| 3’SL, nmol/mL | 0.0010 | 0.0064 | 0.0480 | 0.0117 | 0.0200 | 0.0036 |
| DFLac, nmol/mL | 0.3223 | 0.1714 | 0.3452 | 0.1888 | 0.1480 | 0.4020 |
| 6’SL, nmol/mL | 0.0441 | 0.0000 | 0.0866 | 0.0282 | 0.2294 | 0.1890 |
| LNT, nmol/mL | 0.0809 | 0.0002 | 0.0232 | 0.0086 | 0.1284 | 0.0687 |
| LNFP I, nmol/mL | 0.1464 | 0.0266 | 0.0694 | 0.0029 | 0.0364 | 0.0405 |
| LNFP II, nmol/mL | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0532 | 0.0694 |
| LNFP III, nmol/mL | 0.0022 | 0.0000 | 0.0312 | 0.0000 | 0.3343 | 0.0799 |
| LSTb, nmol/mL | 0.0000 | 0.0000 | 0.1220 | 0.0038 | 0.0967 | 0.4146 |
| LSTc, nmol/mL | 0.4276 | 0.3513 | 0.4395 | 0.1599 | 0.4427 | 0.0091 |
| DFLNT, nmol/mL | 0.0258 | 0.0261 | 0.0807 | 0.0207 | 0.1862 | 0.0174 |
| LNH, nmol/mL | 0.0002 | 0.0000 | 0.0009 | 0.0000 | 0.0467 | 0.0227 |
| DSLNT, nmol/mL | 0.9204 | 0.9180 | 0.0048 | 0.0001 | 0.0000 | 0.0000 |
| FLNH, nmol/mL | 0.1870 | 0.0172 | 0.1957 | 0.0192 | 0.1034 | 0.0751 |
| DFLNH, nmol/mL | 0.0176 | 0.0000 | 0.2806 | 0.0037 | 0.0018 | 0.0011 |
| FDSLNH, nmol/mL | 0.0005 | 0.0000 | 0.0004 | 0.0000 | 0.0177 | 0.0052 |
| DSLNH, nmol/mL | 0.9490 | 0.6095 | 0.2371 | 0.1383 | 0.5065 | 0.8150 |
| Total, nmol/mL | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0203 | 0.0067 |
| HMO-bound sialic acid, nmol/mL | 0.9025 | 0.7689 | 0.7179 | 0.8401 | 0.2171 | 0.5690 |
| HMO-bound fucose, nmol/mL | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.3444 | 0.1627 |
| Small HMOs, nmol/mL | 0.0997 | 0.0236 | 0.0154 | 0.0216 | 0.2340 | 0.5046 |
| Type 1, nmol/mL | 0.3546 | 0.1389 | 0.6105 | 0.4314 | 0.1266 | 0.6721 |
| Type 2, nmol/mL | 0.2554 | 0.0174 | 0.2819 | 0.1428 | 0.1872 | 0.0642 |
| alpha 1,2, nmol/mL | 0.0024 | 0.0010 | 0.1530 | 0.0175 | 0.4714 | 0.6801 |
| alpha 1,3, nmol/mL | 0.2153 | 0.3505 | 0.0105 | 0.0000 | 0.1238 | 0.2002 |
| alpha 2,6, nmol/mL | 0.4102 | 0.6461 | 0.0206 | 0.1427 | 0.1203 | 0.5162 |
| Note: | ||||||
| K-S: Kolmogorov-Smirnov normality test. S-W: Shapiro-Wilk normality test. HMO: human milk oligosaccharide. 2’FL: 2’-fucosyllactose. 3FL: 3-fucosyllactose. LNnT: lacto-N-neotetraose. 3’SL: 3’-sialyllactose. DFLac: difucosyllactose. 6’SL: 6’-sialyllactose. LNT: lacto-N-tetrose. LNFP: lacto-N-fucopentaose. LSTb: sialyl-lacto-N-tetraose b. LSTc: sialyl-lacto-N-tetraose c. DFLNT: difucosyllacto-N-tetrose. LNH: lacto-N-hexaose. DSLNT: disialyllacto-Ntetraose. FLNH: fucosyllacto-N-hexaose. DFLNH: difucosyllacto-N-hexaose. FDSLNH: fucodisialyllacto-N-hexaose. DSLNH: disialyllacto-N-hexaose. |
##############################################
########## ANALISES DESCRITIVAS ##############
##############################################
A = matrix(c(
mean_sd(banco$mother_age[!is.na(banco$HMOSecretor)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$schooling[!is.na(banco$HMOSecretor)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$parity[!is.na(banco$HMOSecretor)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$peso_pg1[!is.na(banco$HMOSecretor)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$estatura_referi1[!is.na(banco$HMOSecretor)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$bmi_pg[!is.na(banco$HMOSecretor)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$sg_parto[!is.na(banco$HMOSecretor)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$gwg_3t[!is.na(banco$HMOSecretor)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$mother_age[!is.na(banco$HMOSecretor_1m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$schooling[!is.na(banco$HMOSecretor_1m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$parity[!is.na(banco$HMOSecretor_1m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$peso_pg1[!is.na(banco$HMOSecretor_1m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$estatura_referi1[!is.na(banco$HMOSecretor_1m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$bmi_pg[!is.na(banco$HMOSecretor_1m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$sg_parto[!is.na(banco$HMOSecretor_1m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$gwg_3t[!is.na(banco$HMOSecretor_1m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$mother_age[!is.na(banco$HMOSecretor_3m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$schooling[!is.na(banco$HMOSecretor_3m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$parity[!is.na(banco$HMOSecretor_3m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$peso_pg1[!is.na(banco$HMOSecretor_3m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$estatura_referi1[!is.na(banco$HMOSecretor_3m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$bmi_pg[!is.na(banco$HMOSecretor_3m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$sg_parto[!is.na(banco$HMOSecretor_3m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$gwg_3t[!is.na(banco$HMOSecretor_3m)], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$mother_age, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$schooling, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$parity, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$peso_pg1, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$estatura_referi1, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$bmi_pg, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$sg_parto, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$gwg_3t, na_rm=T, show_n="never", denote_sd = "paren", digits = 1)),
nrow=8, ncol=4, byrow=F)
dimnames(A) = list(
c("Age, y", "Education, y", "Parity, n", "Pre-gestational weight, Kg", "Height, cm",
"Pre-gestational BMI, Kg/m²", "Gestational age at delivery, w", "GWG at third-trimester, Kg"),
c("1-7 day postpartum (n=43)", "30-45 day postpartum (n=55)", "3 months postpartum (n=21)", "All women (N=148)"))
knitr::kable(A, format="html",
caption="Table 1. Characteristics of women participating in the study.") %>%
kable_styling(full_width = T) %>%
footnote(general="All values are means (±SD). BMI: body mass index. GWG: gestational weight gain")| 1-7 day postpartum (n=43) | 30-45 day postpartum (n=55) | 3 months postpartum (n=21) | All women (N=148) | |
|---|---|---|---|---|
| Age, y | 27.1 (5.1) | 26.7 (5.5) | 27.7 (5.6) | 27.2 (5.5) |
| Education, y | 10.9 (2.4) | 10.9 (2.5) | 10.8 (2.3) | 10.8 (2.7) |
| Parity, n | 1.5 (0.6) | 1.4 (0.6) | 1.4 (0.7) | 1.5 (0.7) |
| Pre-gestational weight, Kg | 65.6 (14.7) | 65.6 (14.8) | 70.3 (17.6) | 65.0 (14.7) |
| Height, cm | 160.3 (5.6) | 160.0 (6.0) | 160.3 (6.1) | 160.3 (5.7) |
| Pre-gestational BMI, Kg/m² | 26.3 (6.5) | 26.0 (6.1) | 27.2 (5.5) | 25.8 (5.7) |
| Gestational age at delivery, w | 41.1 (7.9) | 41.9 (9.4) | 39.8 (1.1) | 41.0 (6.9) |
| GWG at third-trimester, Kg | 9.1 (4.0) | 9.3 (4.1) | 9.7 (4.4) | 9.4 (5.3) |
| Note: | ||||
| All values are means (±SD). BMI: body mass index. GWG: gestational weight gain |
#A2 = matrix(c(
# n_perc0(banco$uso_antibiotico_mae1 %in% "Sim", na_rm=T),
# n_perc0(banco$vitaminas_suple1 %in% "Sim", na_rm=T), # epiDisplay::tab1(banco$vitaminas_suple1[!is.na(banco$HMOSecretor)], graph=F)
# n_perc0(banco$ferro1 %in% "Está tomando", na_rm=T),
# n_perc0(banco$acidofolico1 %in% "Está tomando", na_rm=T),
# n_perc0(banco$tipo_aleitamentoo4o53 %in% "Está tomando", na_rm=T), # epiDisplay::tab1(banco$tipo_aleitamentoo4o53)
# n_perc0(banco$tipo_aleitamentoo4o54 %in% "Está tomando", na_rm=T),
# n_perc0(banco$tipo_aleitamentoo4o55 %in% "Está tomando", na_rm=T)))
B = matrix(c(
mean_sd(banco$`2'FL_7d`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[238:259])
mean_sd(banco$`3FL_7d`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_7d`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_7d`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`2'FL_7d`[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[238:259])
mean_sd(banco$`3FL_7d`[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_7d`[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_7d`[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`2'FL_7d`[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[238:259])
mean_sd(banco$`3FL_7d`[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_7d`[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_7d`[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`2'FL_1m`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[583:604])
mean_sd(banco$`3FL_1m`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_1m`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_1m`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`2'FL_1m`[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[580:604])
mean_sd(banco$`3FL_1m`[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_1m`[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_1m`[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`2'FL_1m`[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[580:604])
mean_sd(banco$`3FL_1m`[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_1m`[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_1m`[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`2'FL_3m`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[928:949])
mean_sd(banco$`3FL_3m`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_3m`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_3m`, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`2'FL_3m`[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[925:949])
mean_sd(banco$`3FL_3m`[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_3m`[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_3m`[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`2'FL_3m`[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1), # names(banco[925:949])
mean_sd(banco$`3FL_3m`[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNnT_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`3'SL_3m`[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLac_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$`6'SL_3m`[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNT_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPI_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPII_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNFPIII_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTb_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LSTc_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNT_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$LNH_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNT_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FLNH_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DFLNH_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$FDSLNH_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$DSLNH_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$SUM_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1)),
nrow=20, ncol=9, byrow=F)
dimnames(B) = list(
c("2'FL, nmol/mL","3FL, nmol/mL","LNnT, nmol/mL","3'SL, nmol/mL","DFLac, nmol/mL","6'SL, nmol/mL",
"LNT, nmol/mL","LNFP I, nmol/mL","LNFP II, nmol/mL","LNFP III, nmol/mL","LSTb, nmol/mL",
"LSTc, nmol/mL","DFLNT, nmol/mL","LNH, nmol/mL","DSLNT, nmol/mL","FLNH, nmol/mL",
"DFLNH, nmol/mL","FDSLNH, nmol/mL","DSLNH, nmol/mL","Total, nmol/mL"),
c("All women (n=43)", "Secretors (n=37)", "Nonsecretors (n=6)",
"All women (n=55)", "Secretors (n=49)", "Nonsecretors (n=6)",
"All women (n=21)", "Secretors (n=19)", "Nonsecretors (n=2)"))
knitr::kable(B, format="html",
caption="Table 2. Variation in total and individual HMOs among women
participating in the study.") %>%
kable_styling("striped", full_width = T) %>%
add_header_above(c("HMOs" = 1, "1-7 days postpartum" = 3, "30-45 day postpartum" = 3,
"3 months postpartum" = 3)) %>%
footnote(general="HMO: human milk oligosaccharide. 2'FL: 2'-fucosyllactose. 3FL: 3-fucosyllactose. LNnT: lacto-N-neotetraose. 3'SL: 3'-sialyllactose. DFLac: difucosyllactose. 6'SL: 6'-sialyllactose. LNT: lacto-N-tetrose. LNFP: lacto-N-fucopentaose. LSTb: sialyl-lacto-N-tetraose b. LSTc: sialyl-lacto-N-tetraose c. DFLNT: difucosyllacto-N-tetrose. LNH: lacto-N-hexaose. DSLNT: disialyllacto-Ntetraose. FLNH: fucosyllacto-N-hexaose. DFLNH: difucosyllacto-N-hexaose. FDSLNH: fucodisialyllacto-N-hexaose. DSLNH: disialyllacto-N-hexaose.")| All women (n=43) | Secretors (n=37) | Nonsecretors (n=6) | All women (n=55) | Secretors (n=49) | Nonsecretors (n=6) | All women (n=21) | Secretors (n=19) | Nonsecretors (n=2) | |
|---|---|---|---|---|---|---|---|---|---|
| 2’FL, nmol/mL | 4,575.3 (2,394.3) | 5,314.7 (1,631.4) | 15.9 (12.9) | 5,001.3 (2,487.4) | 5,607.8 (1,875.8) | 48.5 (54.9) | 5,937.3 (3,257.1) | 6,555.2 (2,748.8) | 67.3 (28.4) |
| 3FL, nmol/mL | 235.1 (116.8) | 259.5 (105.3) | 85.2 (57.6) | 335.9 (174.3) | 355.4 (172.3) | 177.2 (96.5) | 373.6 (155.3) | 350.8 (141.0) | 590.1 (144.5) |
| LNnT, nmol/mL | 814.5 (486.7) | 787.3 (470.6) | 982.2 (596.4) | 352.3 (224.3) | 343.7 (215.8) | 423.1 (299.0) | 672.6 (287.3) | 706.6 (280.8) | 349.3 (35.0) |
| 3’SL, nmol/mL | 342.5 (187.2) | 365.5 (187.4) | 200.2 (115.7) | 440.5 (219.6) | 452.9 (224.0) | 339.2 (159.0) | 672.9 (434.0) | 707.4 (439.9) | 345.3 (216.7) |
| DFLac, nmol/mL | 253.3 (134.8) | 281.5 (120.8) | 79.2 (73.1) | 385.9 (186.4) | 426.1 (154.3) | 57.4 (32.6) | 425.3 (177.8) | 463.5 (136.5) | 61.8 (72.2) |
| 6’SL, nmol/mL | 350.2 (224.2) | 330.7 (177.7) | 470.8 (417.9) | 578.3 (227.1) | 571.0 (205.1) | 638.1 (385.0) | 344.5 (131.9) | 342.7 (132.7) | 361.4 (174.3) |
| LNT, nmol/mL | 1,617.8 (892.8) | 1,378.8 (558.8) | 3,091.3 (1,191.5) | 1,496.8 (650.2) | 1,401.3 (564.1) | 2,276.8 (831.3) | 1,413.0 (675.0) | 1,444.1 (703.2) | 1,117.5 (137.1) |
| LNFP I, nmol/mL | 2,639.0 (1,377.3) | 3,052.6 (976.1) | 88.2 (26.3) | 1,721.1 (1,233.4) | 1,917.6 (1,161.9) | 116.2 (18.2) | 1,164.3 (838.9) | 1,273.2 (806.5) | 129.8 (6.7) |
| LNFP II, nmol/mL | 1,047.3 (653.1) | 892.6 (487.4) | 2,001.6 (776.1) | 1,308.2 (721.9) | 1,167.3 (575.6) | 2,458.6 (815.5) | 1,745.2 (808.2) | 1,574.3 (628.2) | 3,368.8 (366.0) |
| LNFP III, nmol/mL | 99.2 (50.3) | 100.7 (53.0) | 89.7 (30.9) | 77.1 (39.1) | 76.8 (40.8) | 79.8 (22.3) | 52.0 (24.7) | 49.0 (23.8) | 80.6 (12.5) |
| LSTb, nmol/mL | 120.8 (108.5) | 99.3 (39.3) | 253.3 (251.6) | 123.1 (49.7) | 117.0 (41.6) | 172.6 (82.0) | 95.0 (38.7) | 94.1 (38.4) | 103.7 (57.0) |
| LSTc, nmol/mL | 825.3 (321.2) | 867.7 (307.5) | 563.5 (300.5) | 295.5 (128.7) | 301.4 (123.3) | 248.1 (172.4) | 131.6 (78.5) | 140.5 (76.8) | 46.7 (32.7) |
| DFLNT, nmol/mL | 1,451.2 (791.0) | 1,640.5 (680.9) | 284.1 (131.3) | 1,434.7 (836.7) | 1,567.0 (783.4) | 354.6 (312.3) | 1,327.0 (718.0) | 1,370.4 (740.8) | 915.0 (240.1) |
| LNH, nmol/mL | 88.9 (93.2) | 75.7 (65.4) | 170.1 (181.4) | 108.4 (78.9) | 107.4 (78.0) | 116.6 (93.4) | 70.6 (40.9) | 69.2 (41.3) | 83.5 (49.9) |
| DSLNT, nmol/mL | 635.7 (218.9) | 620.4 (209.8) | 730.2 (270.2) | 264.6 (221.5) | 268.0 (216.7) | 237.3 (279.8) | 57.8 (74.3) | 62.5 (76.7) | 13.2 (7.7) |
| FLNH, nmol/mL | 119.3 (82.0) | 122.8 (76.0) | 98.3 (119.5) | 135.7 (86.4) | 141.6 (86.6) | 87.2 (74.2) | 88.6 (52.3) | 89.8 (54.6) | 77.3 (27.8) |
| DFLNH, nmol/mL | 160.8 (147.0) | 183.4 (146.3) | 21.1 (16.8) | 172.8 (125.0) | 191.7 (119.2) | 18.2 (9.2) | 65.2 (55.6) | 69.9 (56.4) | 19.9 (1.7) |
| FDSLNH, nmol/mL | 110.3 (102.0) | 94.7 (84.9) | 206.8 (150.3) | 283.9 (220.8) | 246.6 (191.1) | 589.2 (224.4) | 355.1 (259.8) | 300.0 (190.4) | 878.3 (303.1) |
| DSLNH, nmol/mL | 237.3 (104.7) | 240.1 (105.6) | 220.3 (106.4) | 287.1 (123.2) | 279.3 (115.4) | 351.0 (174.7) | 128.3 (54.5) | 130.5 (55.9) | 107.2 (46.5) |
| Total, nmol/mL | 15,723.8 (2,636.8) | 16,708.4 (959.8) | 9,651.8 (595.0) | 14,803.4 (2,402.6) | 15,539.8 (1,183.3) | 8,789.5 (429.7) | 15,119.7 (2,555.0) | 15,793.8 (1,488.7) | 8,716.6 (187.7) |
| Note: | |||||||||
| HMO: human milk oligosaccharide. 2’FL: 2’-fucosyllactose. 3FL: 3-fucosyllactose. LNnT: lacto-N-neotetraose. 3’SL: 3’-sialyllactose. DFLac: difucosyllactose. 6’SL: 6’-sialyllactose. LNT: lacto-N-tetrose. LNFP: lacto-N-fucopentaose. LSTb: sialyl-lacto-N-tetraose b. LSTc: sialyl-lacto-N-tetraose c. DFLNT: difucosyllacto-N-tetrose. LNH: lacto-N-hexaose. DSLNT: disialyllacto-Ntetraose. FLNH: fucosyllacto-N-hexaose. DFLNH: difucosyllacto-N-hexaose. FDSLNH: fucodisialyllacto-N-hexaose. DSLNH: disialyllacto-N-hexaose. |
C = matrix(c(
mean_sd(banco$Sia_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_7d, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Sia_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_7d[banco$HMOSecretor==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Sia_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_7d[banco$HMOSecretor==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Sia_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_1m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Sia_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_1m[banco$HMOSecretor_1m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Sia_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_1m[banco$HMOSecretor_1m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Sia_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_3m, na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Sia_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_3m[banco$HMOSecretor_3m==1], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Sia_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$Fuc_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$small_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_1_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$type_2_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_12_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_13_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1),
mean_sd(banco$alfa_26_3m[banco$HMOSecretor_3m==0], na_rm=T, show_n="never", denote_sd = "paren", digits = 1)),
nrow=8, ncol=9, byrow=F)
dimnames(C) = list(
c("HMO-bound sialic acid¹, nmol/mL","HMO-bound fucose², nmol/mL", "Small HMOs³, nmol/mL",
"Type 1, nmol/mL", "Type 2, nmol/mL", "alpha 1,2, nmol/mL", "alpha 1,3, nmol/mL",
"alpha 2,6, nmol/mL"),
c("All women (n=43)","Secretors (n=37)","Nonsecretors (n=6)",
"All women (n=55)","Secretors (n=49)","Nonsecretors (n=6)",
"All women (n=21)","Secretors (n=19)","Nonsecretors (n=2)"))
knitr::kable(C, format="html",
caption="Table 3. Variation in HMO groupings among women
participating in the study.") %>%
kable_styling("striped",full_width = T) %>%
add_header_above(c("Variable" = 1, "1-7 days postpartum" = 3, "30-45 day postpartum" = 3,
"3 months postpartum" = 3)) %>%
footnote(general="All values are means (SD). HMO: human milk oligosaccharide.",
number = c("Calculated as the sum of all sialic acid moieties bound to each HMO.",
"Calculated as the sum of all fucose moieties bound to each HMO.",
"Calculated as 2'FL + 3FL + 3'SL + 6'SL.",
"Calculated as LNT + LNFP I + LNFP II + LSTb + DSLNT.",
"Calculated as LNnT + LNFP III + LSTc.",
"Calculated as LNFP I + 2'FL.",
"Calculated as LNFP III + 3FL.",
"Calculated as LSTb + LSTc + 6'SL."))| All women (n=43) | Secretors (n=37) | Nonsecretors (n=6) | All women (n=55) | Secretors (n=49) | Nonsecretors (n=6) | All women (n=21) | Secretors (n=19) | Nonsecretors (n=2) | |
|---|---|---|---|---|---|---|---|---|---|
| HMO-bound sialic acid¹, nmol/mL | 3,605.4 (712.6) | 3,573.5 (650.8) | 3,802.1 (1,078.1) | 3,108.8 (861.9) | 3,029.9 (845.5) | 3,753.1 (775.8) | 2,326.2 (750.6) | 2,270.6 (757.7) | 2,854.6 (564.4) |
| HMO-bound fucose², nmol/mL | 12,556.1 (3,987.8) | 14,048.3 (1,433.0) | 3,354.4 (843.6) | 12,850.1 (3,435.8) | 13,882.8 (1,768.3) | 4,416.9 (1,296.5) | 13,351.0 (2,776.4) | 14,000.0 (1,969.5) | 7,185.5 (566.3) |
| Small HMOs³, nmol/mL | 5,503.1 (2,467.9) | 6,270.3 (1,653.8) | 772.1 (503.5) | 6,356.1 (2,581.1) | 6,987.1 (1,933.8) | 1,202.9 (531.3) | 7,328.3 (3,292.8) | 7,956.1 (2,770.9) | 1,364.1 (158.4) |
| Type 1, nmol/mL | 6,060.5 (1,050.7) | 6,043.7 (1,064.9) | 6,164.6 (1,045.8) | 4,913.8 (1,195.5) | 4,871.3 (1,236.8) | 5,261.6 (766.4) | 4,475.3 (1,116.6) | 4,448.2 (1,172.7) | 4,733.0 (186.4) |
| Type 2, nmol/mL | 1,739.0 (546.3) | 1,755.8 (540.6) | 1,635.4 (622.8) | 725.0 (260.1) | 721.8 (258.7) | 750.9 (295.2) | 856.1 (329.1) | 896.1 (319.8) | 476.6 (80.2) |
| alpha 1,2, nmol/mL | 7,214.3 (3,579.7) | 8,367.3 (2,271.1) | 104.1 (29.7) | 6,722.4 (3,286.7) | 7,525.4 (2,473.6) | 164.7 (47.4) | 7,101.6 (3,747.7) | 7,828.4 (3,122.6) | 197.1 (35.1) |
| alpha 1,3, nmol/mL | 334.3 (127.5) | 360.2 (115.3) | 174.9 (73.5) | 413.0 (175.0) | 432.1 (172.5) | 256.9 (110.7) | 425.6 (156.1) | 399.8 (136.8) | 670.7 (131.9) |
| alpha 2,6, nmol/mL | 1,296.3 (346.9) | 1,297.7 (337.3) | 1,287.5 (437.8) | 997.0 (304.6) | 989.4 (259.0) | 1,058.8 (594.1) | 571.1 (197.5) | 577.3 (204.1) | 511.8 (150.0) |
| Note: | |||||||||
| All values are means (SD). HMO: human milk oligosaccharide. | |||||||||
| 1 Calculated as the sum of all sialic acid moieties bound to each HMO. | |||||||||
| 2 Calculated as the sum of all fucose moieties bound to each HMO. | |||||||||
| 3 Calculated as 2’FL + 3FL + 3’SL + 6’SL. | |||||||||
| 4 Calculated as LNT + LNFP I + LNFP II + LSTb + DSLNT. | |||||||||
| 5 Calculated as LNnT + LNFP III + LSTc. | |||||||||
| 6 Calculated as LNFP I + 2’FL. | |||||||||
| 7 Calculated as LNFP III + 3FL. | |||||||||
| 8 Calculated as LSTb + LSTc + 6’SL. |
##############################################
############ ANALISES GRAFICAS ###############
##############################################
############ Correlogram with the significance test ############
bd_w1 = banco[,c(8,29,104,164:173,235:259)]
res1 = cor.mtest(bd_w1, conf.level = .99, method="spearman", exact=F, use = "pairwise.complete.obs")
corrplot(cor(bd_w1,
method = "spearman", use = "pairwise.complete.obs"),
method = "circle", type = "upper", diag = F, tl.col = "black", number.cex = .3,
p.mat = res1$p, sig.level = 0.01, tl.srt = 55,
insig = "blank", # leave blank on no significant coefficient
# title = "Figure. Spearman rank correlations \n between selected maternal anthropometric, demographic, and reproductive variables and HMO types and groupings \n at 3-7 days postpartum.",
mar=c(0,0,0,0))
bd_w2 = banco[,c(8,29,104,164:173,580:604)]
res2 = cor.mtest(bd_w2, conf.level = .99, method="spearman", exact=F, use = "pairwise.complete.obs")
corrplot(cor(bd_w2,
method = "spearman", use = "pairwise.complete.obs"),
method = "circle", type = "upper", diag = F, tl.col = "black", number.cex = .3,
p.mat = res1$p, sig.level = 0.01, tl.srt = 55,
insig = "blank") # leave blank on no significant coefficient
bd_w3 = banco[,c(8,29,104,164:173,925:949)]
res3 = cor.mtest(bd_w3, conf.level = .99, method="spearman", exact=F, use = "pairwise.complete.obs")
corrplot(cor(bd_w3,
method = "spearman", use = "pairwise.complete.obs"),
method = "circle", type = "upper", diag = F, tl.col = "black", number.cex = .3,
p.mat = res1$p, sig.level = 0.01, tl.srt = 55,
insig = "blank") # leave blank on no significant coefficient
############## GGPLOTS #################
tb3 = banco %>% dplyr::select("record_id", "hmo_status", 238:256, 583:601, 928:946) # dim = 148 x 58 (=19 x 3)
tb3 = melt(tb3, id=c("record_id","hmo_status"))
headtail(tb3)## record_id hmo_status variable value
## 1 1 NA 2'FL_7d NA
## 2 2 1 2'FL_7d NA
## 3 3 NA 2'FL_7d NA
## 8434 146 NA DSLNH_3m NA
## 8435 147 NA DSLNH_3m NA
## 8436 148 NA DSLNH_3m NAtb3$onda=rep(c("w1","w2","w3"), each=2812) # 19 vars x 148 ind
tb3$onda=as.factor(tb3$onda)
headtail(tb3)## record_id hmo_status variable value onda
## 1 1 NA 2'FL_7d NA w1
## 2 2 1 2'FL_7d NA w1
## 3 3 NA 2'FL_7d NA w1
## 8434 146 NA DSLNH_3m NA w3
## 8435 147 NA DSLNH_3m NA w3
## 8436 148 NA DSLNH_3m NA w3tb3$variable = as.factor(sub("[_].*", "", tb3$variable)) # levels(tb3$variable)
headtail(tb3)## record_id hmo_status variable value onda
## 1 1 NA 2'FL NA w1
## 2 2 1 2'FL NA w1
## 3 3 NA 2'FL NA w1
## 8434 146 NA DSLNH NA w3
## 8435 147 NA DSLNH NA w3
## 8436 148 NA DSLNH NA w3sum_hmo = Rmisc::summarySE(tb3, measurevar="value", groupvars=c("onda","variable"),
na.rm = T)
headtail(sum_hmo)## onda variable N value sd se ci
## 1 w1 2'FL 43 4575.30384 2394.26944 365.122666 736.84737
## 2 w1 3'SL 43 342.45873 187.23204 28.552619 57.62152
## 3 w1 3FL 43 235.13986 116.77472 17.807977 35.93795
## 55 w3 LNT 21 1412.98045 674.98590 147.293998 307.24990
## 56 w3 LSTb 21 94.99957 38.70427 8.445964 17.61797
## 57 w3 LSTc 21 131.57653 78.46418 17.122288 35.71647sum_hmo_secretor = Rmisc::summarySE(tb3 %>% filter(hmo_status==1),
measurevar="value", groupvars=c("onda","variable"),
na.rm = T)
headtail(sum_hmo_secretor)## onda variable N value sd se ci
## 1 w1 2'FL 37 5314.65922 1631.40076 268.200632 543.93609
## 2 w1 3'SL 37 365.53364 187.39560 30.807646 62.48080
## 3 w1 3FL 37 259.45728 105.31856 17.314264 35.11496
## 55 w3 LNT 19 1444.08485 703.18017 161.320596 338.92199
## 56 w3 LSTb 19 94.08463 38.40074 8.809734 18.50857
## 57 w3 LSTc 19 140.50920 76.79080 17.617019 37.01198sum_hmo_nonsecretor = Rmisc::summarySE(tb3 %>% filter(hmo_status==0),
measurevar="value", groupvars=c("onda","variable"),
na.rm = T)
headtail(sum_hmo_nonsecretor)## onda variable N value sd se ci
## 1 w1 2'FL 6 15.94564 12.92597 5.277004 13.56497
## 2 w1 3'SL 6 200.16341 115.70718 47.237260 121.42724
## 3 w1 3FL 6 85.18241 57.63888 23.530976 60.48830
## 55 w3 LNT 2 1117.48867 137.05416 96.911927 1231.38279
## 56 w3 LSTb 2 103.69156 57.01139 40.313139 512.22700
## 57 w3 LSTc 2 46.71613 32.72651 23.141136 294.03601# ABSOLUTE GGPLOT
# <title> Figure. Mean +/- SEM absolute total and HMO isoform concentrations of all women combined (A), nonsecretors (B), and secretors (C).
library(RColorBrewer)
colourCount = length(unique(sum_hmo$variable))
getPalette = colorRampPalette(brewer.pal(12, "Paired")) # "Set3" "Accent" "Set1"
#dev.new()
ggplot(data=sum_hmo, aes(x=onda, y=value, group=variable, fill=variable)) +
geom_bar(stat="identity", position=position_stack(reverse=T)) +
scale_fill_manual(values=getPalette(colourCount),
guide = guide_legend(reverse=T)) +
labs(title="A) All women",
x="Postpartum period",
y="HMO concentration (nmol/mL)") +
scale_x_discrete(labels=c("w1"="1-7 days","w2"="30-45 days","w3"="3 months")) +
scale_y_continuous(limits=c(0, 18000)) +
theme_minimal() +
theme(panel.grid = element_blank(),
legend.position="right",
legend.title=element_blank(),
text = element_text(size=13),
axis.text=element_text(size = 13))
ggplot(data=sum_hmo_secretor, aes(x=onda, y=value, group=variable, fill=variable)) +
geom_bar(stat="identity", position=position_stack(reverse=T)) +
scale_fill_manual(values=getPalette(colourCount),
guide = guide_legend(reverse=T)) +
labs(title="B) Secretors",
x="Postpartum period",
y="HMO concentration (nmol/mL)") +
scale_x_discrete(labels=c("w1"="1-7 days","w2"="30-45 days","w3"="3 months")) +
scale_y_continuous(limits=c(0, 18000)) +
theme_minimal() +
theme(panel.grid = element_blank(),
legend.position="right",
legend.title=element_blank(),
text = element_text(size=13),
axis.text=element_text(size = 13))
ggplot(data=sum_hmo_nonsecretor, aes(x=onda, y=value, group=variable, fill=variable)) +
geom_bar(stat="identity", position=position_stack(reverse=T)) +
scale_fill_manual(values=getPalette(colourCount),
guide = guide_legend(reverse=T)) +
labs(title="C) Nonsecretors",
x="Postpartum period",
y="HMO concentration (nmol/mL)") +
scale_x_discrete(labels=c("w1"="1-7 days","w2"="30-45 days","w3"="3 months")) +
scale_y_continuous(limits=c(0, 18000)) +
theme_minimal() +
theme(panel.grid = element_blank(),
legend.position="right",
legend.title=element_blank(),
text = element_text(size=13),
axis.text=element_text(size = 13))
# RELATIVE GGPLOT
# <title> Figure. Mean +/- SEM relative total and HMO isoform concentrations of all women combined (A), nonsecretors (B), and secretors (C).
#dev.new()
ggplot(data=sum_hmo, aes(x=onda, y=value, group=variable, fill=variable)) +
geom_bar(stat="identity", position=position_fill(reverse = TRUE)) +
scale_y_continuous(labels=scales::percent) +
scale_fill_manual(values=getPalette(colourCount),
guide = guide_legend(reverse=T)) +
labs(title="A) All women",
x="Postpartum period",
y="Relative HMO concentration (% of total)") +
scale_x_discrete(labels=c("w1"="1-7 days","w2"="30-45 days","w3"="3 months")) +
theme_minimal() +
theme(panel.grid = element_blank(),
legend.position="right",
legend.title=element_blank(),
text = element_text(size=13),
axis.text=element_text(size = 13))
ggplot(data=sum_hmo_secretor, aes(x=onda, y=value, group=variable, fill=variable)) +
geom_bar(stat="identity", position=position_fill(reverse = TRUE)) +
scale_y_continuous(labels=scales::percent) +
scale_fill_manual(values=getPalette(colourCount),
guide = guide_legend(reverse=T)) +
labs(title="B) Secretors",
x="Postpartum period",
y="Relative HMO concentration (% of total)") +
scale_x_discrete(labels=c("w1"="1-7 days","w2"="30-45 days","w3"="3 months")) +
theme_minimal() +
theme(panel.grid = element_blank(),
legend.position="right",
legend.title=element_blank(),
text = element_text(size=13),
axis.text=element_text(size = 13))
ggplot(data=sum_hmo_nonsecretor, aes(x=onda, y=value, group=variable, fill=variable)) +
geom_bar(stat="identity", position=position_fill(reverse = TRUE)) +
scale_y_continuous(labels=scales::percent) +
scale_fill_manual(values=getPalette(colourCount),
guide = guide_legend(reverse=T)) +
labs(title="C) Nonsecretors",
x="Postpartum period",
y="Relative HMO concentration (% of total)") +
scale_x_discrete(labels=c("w1"="1-7 days","w2"="30-45 days","w3"="3 months")) +
theme_minimal() +
theme(panel.grid = element_blank(),
legend.position="right",
legend.title=element_blank(),
text = element_text(size=13),
axis.text=element_text(size = 13))
##############################################
#### 1-FACTOR ANOVA + Bonferroni #############
##############################################
## PREPARAR...
tb2 = banco %>% dplyr::select("record_id", 238:257, 583:602, 928:947) #dim = 148 x 60 (=20 x 3)
tb2 = melt(tb2, id="record_id")
headtail(tb2)## record_id variable value
## 1 1 2'FL_7d NA
## 2 2 2'FL_7d NA
## 3 3 2'FL_7d NA
## 8878 146 SUM_3m NA
## 8879 147 SUM_3m NA
## 8880 148 SUM_3m NAtb2$onda=rep(c("w1","w2","w3"), each=2960)
tb2$onda=as.factor(tb2$onda)
headtail(tb2)## record_id variable value onda
## 1 1 2'FL_7d NA w1
## 2 2 2'FL_7d NA w1
## 3 3 2'FL_7d NA w1
## 8878 146 SUM_3m NA w3
## 8879 147 SUM_3m NA w3
## 8880 148 SUM_3m NA w3tb2$variable = as.factor(sub("[_].*", "", tb2$variable)) # levels(tb2$variable)
headtail(tb2)## record_id variable value onda
## 1 1 2'FL NA w1
## 2 2 2'FL NA w1
## 3 3 2'FL NA w1
## 8878 146 SUM NA w3
## 8879 147 SUM NA w3
## 8880 148 SUM NA w3## APONTAR...
mean_sd_w1 = tapply(tb2$value[tb2$onda=="w1"], tb2$variable[tb2$onda=="w1"], mean_sd, na_rm=T, show_n="never")
mean_sd_w2 = tapply(tb2$value[tb2$onda=="w2"], tb2$variable[tb2$onda=="w2"], mean_sd, na_rm=T, show_n="never")
mean_sd_w3 = tapply(tb2$value[tb2$onda=="w3"], tb2$variable[tb2$onda=="w3"], mean_sd, na_rm=T, show_n="never")
knitr::kable(mean_sd_w1, format="markdown", col.names=c("Mean (SD)"))| Mean (SD) | |
|---|---|
| 2’FL | 4,575.30 \(\pm\) 2,394.27 |
| 3’SL | 342.46 \(\pm\) 187.23 |
| 3FL | 235.14 \(\pm\) 116.77 |
| 6’SL | 350.21 \(\pm\) 224.24 |
| DFLac | 253.30 \(\pm\) 134.79 |
| DFLNH | 160.79 \(\pm\) 147.04 |
| DFLNT | 1,451.19 \(\pm\) 790.97 |
| DSLNH | 237.30 \(\pm\) 104.67 |
| DSLNT | 635.73 \(\pm\) 218.85 |
| FDSLNH | 110.29 \(\pm\) 102.04 |
| FLNH | 119.35 \(\pm\) 82.04 |
| LNFPI | 2,638.96 \(\pm\) 1,377.29 |
| LNFPII | 1,047.32 \(\pm\) 653.09 |
| LNFPIII | 99.20 \(\pm\) 50.33 |
| LNH | 88.89 \(\pm\) 93.16 |
| LNnT | 814.53 \(\pm\) 486.69 |
| LNT | 1,617.76 \(\pm\) 892.83 |
| LSTb | 120.78 \(\pm\) 108.50 |
| LSTc | 825.28 \(\pm\) 321.24 |
| SUM | 15,723.77 \(\pm\) 2,636.81 |
knitr::kable(mean_sd_w2, format="markdown", col.names=c("Mean (SD)"))| Mean (SD) | |
|---|---|
| 2’FL | 5,001.34 \(\pm\) 2,487.45 |
| 3’SL | 440.48 \(\pm\) 219.58 |
| 3FL | 335.93 \(\pm\) 174.35 |
| 6’SL | 578.32 \(\pm\) 227.09 |
| DFLac | 385.91 \(\pm\) 186.37 |
| DFLNH | 172.78 \(\pm\) 124.98 |
| DFLNT | 1,434.74 \(\pm\) 836.71 |
| DSLNH | 287.09 \(\pm\) 123.18 |
| DSLNT | 264.65 \(\pm\) 221.52 |
| FDSLNH | 283.94 \(\pm\) 220.80 |
| FLNH | 135.71 \(\pm\) 86.44 |
| LNFPI | 1,721.06 \(\pm\) 1,233.38 |
| LNFPII | 1,308.19 \(\pm\) 721.88 |
| LNFPIII | 77.10 \(\pm\) 39.09 |
| LNH | 108.42 \(\pm\) 78.93 |
| LNnT | 352.32 \(\pm\) 224.29 |
| LNT | 1,496.85 \(\pm\) 650.19 |
| LSTb | 123.09 \(\pm\) 49.69 |
| LSTc | 295.55 \(\pm\) 128.66 |
| SUM | 14,803.45 \(\pm\) 2,402.59 |
knitr::kable(mean_sd_w3, format="markdown", col.names=c("Mean (SD)"))| Mean (SD) | |
|---|---|
| 2’FL | 5,937.33 \(\pm\) 3,257.14 |
| 3’SL | 672.92 \(\pm\) 434.00 |
| 3FL | 373.56 \(\pm\) 155.32 |
| 6’SL | 344.50 \(\pm\) 131.90 |
| DFLac | 425.27 \(\pm\) 177.84 |
| DFLNH | 65.18 \(\pm\) 55.60 |
| DFLNT | 1,327.04 \(\pm\) 718.03 |
| DSLNH | 128.27 \(\pm\) 54.49 |
| DSLNT | 57.79 \(\pm\) 74.31 |
| FDSLNH | 355.06 \(\pm\) 259.80 |
| FLNH | 88.59 \(\pm\) 52.32 |
| LNFPI | 1,164.30 \(\pm\) 838.89 |
| LNFPII | 1,745.22 \(\pm\) 808.22 |
| LNFPIII | 52.00 \(\pm\) 24.67 |
| LNH | 70.60 \(\pm\) 40.95 |
| LNnT | 672.56 \(\pm\) 287.32 |
| LNT | 1,412.98 \(\pm\) 674.99 |
| LSTb | 95.00 \(\pm\) 38.70 |
| LSTc | 131.58 \(\pm\) 78.46 |
| SUM | 15,119.75 \(\pm\) 2,554.96 |
#by(tb2$value[tb2$onda=="w1"], tb2$variable[tb2$onda=="w1"], mean_sd, na_rm=T, show_n="never")
## FOGOOO !!!
levels(tb2$variable)## [1] "2'FL" "3'SL" "3FL" "6'SL" "DFLac" "DFLNH" "DFLNT"
## [8] "DSLNH" "DSLNT" "FDSLNH" "FLNH" "LNFPI" "LNFPII" "LNFPIII"
## [15] "LNH" "LNnT" "LNT" "LSTb" "LSTc" "SUM"model_1=aov(value[variable=="2'FL"] ~ onda[variable=="2'FL"], data=tb2)
out_1=LSD.test(model_1, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_1 ~ "onda"
##
## LSD t Test for value[variable == "2'FL"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 6785050
##
## onda[variable == "2'FL"], means and individual ( 95 %) CI
##
## value.variable.....2.FL.. std r LCL UCL Min
## w1 4575.304 2394.269 43 3788.539 5362.068 8.873210
## w2 5001.336 2487.450 55 4305.675 5696.997 1.473807
## w3 5937.334 3257.144 21 4811.513 7063.155 47.255995
## Max
## w1 9436.263
## w2 10619.831
## w3 12839.160
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -426.0320 1.0000 -1714.092 862.0281
## w1 - w3 -1362.0298 0.1557 -3046.583 322.5237
## w2 - w3 -935.9978 0.4918 -2559.132 687.1367#out_1
model_2=aov(value[variable=="3'SL"] ~ onda[variable=="3'SL"], data=tb2)
out_2=LSD.test(model_2, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_2 ~ "onda"
##
## LSD t Test for value[variable == "3'SL"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 67611.51
##
## onda[variable == "3'SL"], means and individual ( 95 %) CI
##
## value.variable.....3.SL.. std r LCL UCL Min
## w1 342.4587 187.2320 43 263.9210 420.9965 49.48814
## w2 440.4754 219.5762 55 371.0319 509.9189 141.66936
## w3 672.9190 433.9960 21 560.5353 785.3026 192.10786
## Max
## w1 965.1158
## w2 1014.8000
## w3 1771.0216
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -98.01669 0.1998 -226.5957 30.56227
## w1 - w3 -330.46024 0.0000 *** -498.6186 -162.30185
## w2 - w3 -232.44355 0.0021 ** -394.4709 -70.41623#out_2
model_3=aov(value[variable=="3FL"] ~ onda[variable=="3FL"], data=tb2)
out_3=LSD.test(model_3, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_3 ~ "onda"
##
## LSD t Test for value[variable == "3FL"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 23246.63
##
## onda[variable == "3FL"], means and individual ( 95 %) CI
##
## value.variable.....3FL.. std r LCL UCL Min
## w1 235.1399 116.7747 43 189.0879 281.1918 15.47513
## w2 335.9340 174.3458 55 295.2147 376.6534 102.52644
## w3 373.5552 155.3181 21 307.6572 439.4533 145.13431
## Max
## w1 531.6593
## w2 1090.3054
## w3 708.7105
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -100.7942 0.0046 ** -176.1887 -25.39970
## w1 - w3 -138.4154 0.0027 ** -237.0179 -39.81282
## w2 - w3 -37.6212 1.0000 -132.6287 57.38630#out_3
model_4=aov(value[variable=="6'SL"] ~ onda[variable=="6'SL"], data=tb2)
out_4=LSD.test(model_4, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_4 ~ "onda"
##
## LSD t Test for value[variable == "6'SL"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 45213.68
##
## onda[variable == "6'SL"], means and individual ( 95 %) CI
##
## value.variable.....6.SL.. std r LCL UCL Min
## w1 350.2109 224.2430 43 285.9861 414.4358 66.30064
## w2 578.3153 227.0937 55 521.5274 635.1033 193.59157
## w3 344.5038 131.9007 21 252.6012 436.4064 100.23206
## Max
## w1 1253.9314
## w2 1168.9649
## w3 532.4109
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -228.104400 0e+00 *** -333.2508 -122.9580
## w1 - w3 5.707099 1e+00 -131.8057 143.2199
## w2 - w3 233.811499 1e-04 *** 101.3124 366.3106#out_4
model_5=aov(value[variable=="DFLac"] ~ onda[variable=="DFLac"], data=tb2)
out_5=LSD.test(model_5, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_5 ~ "onda"
##
## LSD t Test for value[variable == "DFLac"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 28200.08
##
## onda[variable == "DFLac"], means and individual ( 95 %) CI
##
## value.variable.....DFLac.. std r LCL UCL Min
## w1 253.2968 134.7861 43 202.5751 304.0184 8.383564
## w2 385.9064 186.3712 55 341.0580 430.7547 24.976855
## w3 425.2714 177.8392 21 352.6912 497.8515 10.772385
## Max
## w1 601.4064
## w2 872.8305
## w3 759.9184
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -132.6096 5e-04 *** -215.6491 -49.57005
## w1 - w3 -171.9746 6e-04 *** -280.5755 -63.37366
## w2 - w3 -39.3650 1e+00 -144.0063 65.27630#out_5
model_6=aov(value[variable=="DFLNH"] ~ onda[variable=="DFLNH"], data=tb2)
out_6=LSD.test(model_6, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_6 ~ "onda"
##
## LSD t Test for value[variable == "DFLNH"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 15632.47
##
## onda[variable == "DFLNH"], means and individual ( 95 %) CI
##
## value.variable.....DFLNH.. std r LCL UCL Min
## w1 160.78542 147.03719 43 123.02107 198.5498 7.736311
## w2 172.77919 124.98161 55 139.38776 206.1706 7.324023
## w3 65.17786 55.60009 21 11.13895 119.2168 17.595097
## Max
## w1 793.9381
## w2 606.3908
## w3 206.9968
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -11.99377 1.0000 -73.82008 49.83255
## w1 - w3 95.60756 0.0145 * 14.74974 176.46538
## w2 - w3 107.60132 0.0032 ** 29.69159 185.51106#out_6
model_7=aov(value[variable=="DFLNT"] ~ onda[variable=="DFLNT"], data=tb2)
out_7=LSD.test(model_7, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_7 ~ "onda"
##
## LSD t Test for value[variable == "DFLNT"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 641314.1
##
## onda[variable == "DFLNT"], means and individual ( 95 %) CI
##
## value.variable.....DFLNT.. std r LCL UCL Min
## w1 1451.191 790.9748 43 1209.3087 1693.073 148.66005
## w2 1434.740 836.7051 55 1220.8670 1648.614 13.26986
## w3 1327.039 718.0333 21 980.9174 1673.160 64.93601
## Max
## w1 3532.190
## w2 3379.887
## w3 2270.841
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 16.45048 1 -379.5494 412.4503
## w1 - w3 124.15215 1 -393.7452 642.0495
## w2 - w3 107.70168 1 -391.3130 606.7164#out_7
model_8=aov(value[variable=="DSLNH"] ~ onda[variable=="DSLNH"], data=tb2)
out_8=LSD.test(model_8, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_8 ~ "onda"
##
## LSD t Test for value[variable == "DSLNH"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 11541.81
##
## onda[variable == "DSLNH"], means and individual ( 95 %) CI
##
## value.variable.....DSLNH.. std r LCL UCL Min
## w1 237.2962 104.67281 43 204.84685 269.7454 57.39176
## w2 287.0922 123.17568 55 258.40041 315.7841 89.36123
## w3 128.2675 54.48875 21 81.83412 174.7008 14.77203
## Max
## w1 479.5164
## w2 556.1867
## w3 247.4448
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -49.79608 0.0739 . -102.92080 3.328639
## w1 - w3 109.02869 0.0007 *** 39.55101 178.506373
## w2 - w3 158.82477 0.0000 *** 91.88026 225.769290#out_8
model_9=aov(value[variable=="DSLNT"] ~ onda[variable=="DSLNT"], data=tb2)
out_9=LSD.test(model_9, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_9 ~ "onda"
##
## LSD t Test for value[variable == "DSLNT"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 41138.35
##
## onda[variable == "DSLNT"], means and individual ( 95 %) CI
##
## value.variable.....DSLNT.. std r LCL UCL Min
## w1 635.72806 218.85386 43 574.46599 696.9901 25.979887
## w2 264.64741 221.52380 55 210.47919 318.8156 3.194394
## w3 57.79312 74.31118 21 -29.86987 145.4561 7.785786
## Max
## w1 1130.3700
## w2 736.7102
## w3 336.1953
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 371.0806 0e+00 *** 270.78478 471.3765
## w1 - w3 577.9349 0e+00 *** 446.76579 709.1041
## w2 - w3 206.8543 4e-04 *** 80.46759 333.2410#out_9
model_10=aov(value[variable=="FDSLNH"] ~ onda[variable=="FDSLNH"], data=tb2)
out_10=LSD.test(model_10, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_10 ~ "onda"
##
## LSD t Test for value[variable == "FDSLNH"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 38102.92
##
## onda[variable == "FDSLNH"], means and individual ( 95 %) CI
##
## value.variable.....FDSLNH.. std r LCL UCL Min
## w1 110.2931 102.0421 43 51.33442 169.2517 20.18971
## w2 283.9391 220.8017 55 231.80755 336.0706 37.33255
## w3 355.0608 259.8006 21 270.69389 439.4276 87.96471
## Max
## w1 478.1588
## w2 992.3354
## w3 1092.6768
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -173.6460 0.0001 *** -270.1708 -77.12124
## w1 - w3 -244.7677 0.0000 *** -371.0049 -118.53050
## w2 - w3 -71.1217 0.4745 -192.7563 50.51288#out_10
model_11=aov(value[variable=="FLNH"] ~ onda[variable=="FLNH"], data=tb2)
out_11=LSD.test(model_11, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_11 ~ "onda"
##
## LSD t Test for value[variable == "FLNH"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 6387.157
##
## onda[variable == "FLNH"], means and individual ( 95 %) CI
##
## value.variable.....FLNH.. std r LCL UCL Min
## w1 119.3451 82.03619 43 95.20598 143.4843 10.23279
## w2 135.7129 86.44340 55 114.36893 157.0568 15.16136
## w3 88.5876 52.31639 21 54.04566 123.1295 17.50512
## Max
## w1 363.1713
## w2 408.1923
## w3 237.2466
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -16.36775 0.9494 -55.887434 23.15193
## w1 - w3 30.75754 0.4529 -20.927170 82.44225
## w2 - w3 47.12529 0.0699 . -2.674985 96.92557#out_11
model_12=aov(value[variable=="LNFPI"] ~ onda[variable=="LNFPI"], data=tb2)
out_12=LSD.test(model_12, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_12 ~ "onda"
##
## LSD t Test for value[variable == "LNFPI"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 1516313
##
## onda[variable == "LNFPI"], means and individual ( 95 %) CI
##
## value.variable.....LNFPI.. std r LCL UCL Min
## w1 2638.964 1377.2946 43 2267.0327 3010.895 57.73936
## w2 1721.061 1233.3797 55 1392.1979 2049.925 92.17351
## w3 1164.302 838.8868 21 632.0865 1696.517 125.01014
## Max
## w1 5000.461
## w2 5171.549
## w3 2965.465
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 917.9027 0.0011 ** 308.9914 1526.814
## w1 - w3 1474.6623 0.0000 *** 678.3147 2271.010
## w2 - w3 556.7596 0.2418 -210.5530 1324.072#out_12
model_13=aov(value[variable=="LNFPII"] ~ onda[variable=="LNFPII"], data=tb2)
out_13=LSD.test(model_13, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_13 ~ "onda"
##
## LSD t Test for value[variable == "LNFPII"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 509643.2
##
## onda[variable == "LNFPII"], means and individual ( 95 %) CI
##
## value.variable.....LNFPII.. std r LCL UCL Min
## w1 1047.319 653.0938 43 831.6926 1262.945 267.5823
## w2 1308.185 721.8783 55 1117.5278 1498.843 328.8927
## w3 1745.219 808.2214 21 1436.6687 2053.769 735.7669
## Max
## w1 2921.199
## w2 3433.966
## w3 3627.576
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -260.8667 0.2257 -613.8813 92.147940
## w1 - w3 -697.9002 0.0011 ** -1159.5805 -236.219908
## w2 - w3 -437.0335 0.0559 . -881.8809 7.813869#out_13
model_14=aov(value[variable=="LNFPIII"] ~ onda[variable=="LNFPIII"], data=tb2)
out_14=LSD.test(model_14, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_14 ~ "onda"
##
## LSD t Test for value[variable == "LNFPIII"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 1733.484
##
## onda[variable == "LNFPIII"], means and individual ( 95 %) CI
##
## value.variable.....LNFPIII.. std r LCL UCL Min
## w1 99.20448 50.32939 43 86.62889 111.78006 45.24402
## w2 77.09704 39.09277 55 65.97765 88.21643 23.76174
## w3 51.99749 24.66874 21 34.00245 69.99253 17.37602
## Max
## w1 300.21614
## w2 248.89678
## w3 89.47754
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 22.10744 0.0309 * 1.5191818 42.69569
## w1 - w3 47.20699 0.0001 *** 20.2812139 74.13276
## w2 - w3 25.09955 0.0614 . -0.8445058 51.04361#out_14
model_15=aov(value[variable=="LNH"] ~ onda[variable=="LNH"], data=tb2)
out_15=LSD.test(model_15, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_15 ~ "onda"
##
## LSD t Test for value[variable == "LNH"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 6331.485
##
## onda[variable == "LNH"], means and individual ( 95 %) CI
##
## value.variable.....LNH.. std r LCL UCL Min
## w1 88.89172 93.16362 43 64.85800 112.9254 9.598484
## w2 108.42429 78.92617 55 87.17356 129.6750 21.820551
## w3 70.59994 40.94547 21 36.20887 104.9910 16.490096
## Max
## w1 525.6633
## w2 428.2573
## w3 175.0604
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -19.53257 0.6909 -58.87964 19.81450
## w1 - w3 18.29178 1.0000 -33.16718 69.75075
## w2 - w3 37.82435 0.1992 -11.75841 87.40712#out_15
model_16=aov(value[variable=="LNnT"] ~ onda[variable=="LNnT"], data=tb2)
out_16=LSD.test(model_16, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_16 ~ "onda"
##
## LSD t Test for value[variable == "LNnT"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 123413.3
##
## onda[variable == "LNnT"], means and individual ( 95 %) CI
##
## value.variable.....LNnT.. std r LCL UCL Min
## w1 814.5270 486.6904 43 708.4187 920.6353 154.00172
## w2 352.3151 224.2884 55 258.4937 446.1366 67.55773
## w3 672.5642 287.3165 21 520.7286 824.3999 324.57999
## Max
## w1 2048.859
## w2 1118.779
## w3 1498.809
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 462.2119 0.0000 *** 288.49560 635.9282
## w1 - w3 141.9628 0.3953 -85.22723 369.1528
## w2 - w3 -320.2491 0.0017 ** -539.15574 -101.3425#out_16
model_17=aov(value[variable=="LNT"] ~ onda[variable=="LNT"], data=tb2)
out_17=LSD.test(model_17, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_17 ~ "onda"
##
## LSD t Test for value[variable == "LNT"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 563970.9
##
## onda[variable == "LNT"], means and individual ( 95 %) CI
##
## value.variable.....LNT.. std r LCL UCL Min
## w1 1617.760 892.8264 43 1390.932 1844.588 286.4739
## w2 1496.848 650.1947 55 1296.286 1697.410 542.2827
## w3 1412.980 674.9859 21 1088.401 1737.560 519.0140
## Max
## w1 5211.305
## w2 3721.284
## w3 3167.202
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 120.91225 1.0000 -250.4416 492.2661
## w1 - w3 204.77988 0.9235 -280.8849 690.4447
## w2 - w3 83.86763 1.0000 -384.0898 551.8250#out_17
model_18=aov(value[variable=="LSTb"] ~ onda[variable=="LSTb"], data=tb2)
out_18=LSD.test(model_18, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_18 ~ "onda"
##
## LSD t Test for value[variable == "LSTb"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 5669.823
##
## onda[variable == "LSTb"], means and individual ( 95 %) CI
##
## value.variable.....LSTb.. std r LCL UCL Min
## w1 120.77566 108.49924 43 98.03239 143.5189 37.49274
## w2 123.09114 49.68634 55 102.98143 143.2009 38.22470
## w3 94.99957 38.70427 21 62.45507 127.5441 31.99769
## Max
## w1 760.9990
## w2 308.9165
## w3 167.3872
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 -2.315477 1.0000 -39.54988 34.91892
## w1 - w3 25.776090 0.6032 -22.91988 74.47206
## w2 - w3 28.091567 0.4456 -18.82894 75.01208#out_18
model_19=aov(value[variable=="LSTc"] ~ onda[variable=="LSTc"], data=tb2)
out_19=LSD.test(model_19, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_19 ~ "onda"
##
## LSD t Test for value[variable == "LSTc"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 46129.97
##
## onda[variable == "LSTc"], means and individual ( 95 %) CI
##
## value.variable.....LSTc.. std r LCL UCL Min
## w1 825.2800 321.23687 43 760.40764 890.1524 201.83792
## w2 295.5469 128.65637 55 238.18646 352.9074 79.63516
## w3 131.5765 78.46418 21 38.74736 224.4057 23.57499
## Max
## w1 1692.6517
## w2 668.2915
## w3 384.6444
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 529.7331 0.0000 *** 423.52655 635.9396
## w1 - w3 693.7035 0.0000 *** 554.80426 832.6027
## w2 - w3 163.9704 0.0107 * 30.13546 297.8054#out_19
model_20=aov(value[variable=="SUM"] ~ onda[variable=="SUM"], data=tb2)
out_20=LSD.test(model_20, "onda", p.adj="bonferroni", console=T, group=F)##
## Study: model_20 ~ "onda"
##
## LSD t Test for value[variable == "SUM"]
## P value adjustment method: bonferroni
##
## Mean Square Error: 6330049
##
## onda[variable == "SUM"], means and individual ( 95 %) CI
##
## value.variable.....SUM.. std r LCL UCL Min
## w1 15723.77 2636.812 43 14963.84 16483.70 9099.194
## w2 14803.45 2402.593 55 14131.52 15475.38 8241.586
## w3 15119.75 2554.962 21 14032.33 16207.16 8583.907
## Max
## w1 18503.51
## w2 18243.79
## w3 19008.19
##
## Alpha: 0.05 ; DF Error: 116
## Critical Value of t: 2.429194
##
## Comparison between treatments means
##
## difference pvalue signif. LCL UCL
## w1 - w2 920.3232 0.2248 -323.7992 2164.446
## w1 - w3 604.0234 1.0000 -1023.0674 2231.114
## w2 - w3 -316.2999 1.0000 -1884.0667 1251.467#out_20
print(out_1$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -426.0320 1.0000 -1714.092 862.0281
## w1 - w3 -1362.0298 0.1557 -3046.583 322.5237
## w2 - w3 -935.9978 0.4918 -2559.132 687.1367print(out_2$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -98.01669 0.1998 -226.5957 30.56227
## w1 - w3 -330.46024 0.0000 *** -498.6186 -162.30185
## w2 - w3 -232.44355 0.0021 ** -394.4709 -70.41623print(out_3$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -100.7942 0.0046 ** -176.1887 -25.39970
## w1 - w3 -138.4154 0.0027 ** -237.0179 -39.81282
## w2 - w3 -37.6212 1.0000 -132.6287 57.38630print(out_4$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -228.104400 0e+00 *** -333.2508 -122.9580
## w1 - w3 5.707099 1e+00 -131.8057 143.2199
## w2 - w3 233.811499 1e-04 *** 101.3124 366.3106print(out_5$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -132.6096 5e-04 *** -215.6491 -49.57005
## w1 - w3 -171.9746 6e-04 *** -280.5755 -63.37366
## w2 - w3 -39.3650 1e+00 -144.0063 65.27630print(out_6$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -11.99377 1.0000 -73.82008 49.83255
## w1 - w3 95.60756 0.0145 * 14.74974 176.46538
## w2 - w3 107.60132 0.0032 ** 29.69159 185.51106print(out_7$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 16.45048 1 -379.5494 412.4503
## w1 - w3 124.15215 1 -393.7452 642.0495
## w2 - w3 107.70168 1 -391.3130 606.7164print(out_8$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -49.79608 0.0739 . -102.92080 3.328639
## w1 - w3 109.02869 0.0007 *** 39.55101 178.506373
## w2 - w3 158.82477 0.0000 *** 91.88026 225.769290print(out_9$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 371.0806 0e+00 *** 270.78478 471.3765
## w1 - w3 577.9349 0e+00 *** 446.76579 709.1041
## w2 - w3 206.8543 4e-04 *** 80.46759 333.2410print(out_10$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -173.6460 0.0001 *** -270.1708 -77.12124
## w1 - w3 -244.7677 0.0000 *** -371.0049 -118.53050
## w2 - w3 -71.1217 0.4745 -192.7563 50.51288print(out_11$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -16.36775 0.9494 -55.887434 23.15193
## w1 - w3 30.75754 0.4529 -20.927170 82.44225
## w2 - w3 47.12529 0.0699 . -2.674985 96.92557print(out_12$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 917.9027 0.0011 ** 308.9914 1526.814
## w1 - w3 1474.6623 0.0000 *** 678.3147 2271.010
## w2 - w3 556.7596 0.2418 -210.5530 1324.072print(out_13$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -260.8667 0.2257 -613.8813 92.147940
## w1 - w3 -697.9002 0.0011 ** -1159.5805 -236.219908
## w2 - w3 -437.0335 0.0559 . -881.8809 7.813869print(out_14$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 22.10744 0.0309 * 1.5191818 42.69569
## w1 - w3 47.20699 0.0001 *** 20.2812139 74.13276
## w2 - w3 25.09955 0.0614 . -0.8445058 51.04361print(out_15$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -19.53257 0.6909 -58.87964 19.81450
## w1 - w3 18.29178 1.0000 -33.16718 69.75075
## w2 - w3 37.82435 0.1992 -11.75841 87.40712print(out_16$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 462.2119 0.0000 *** 288.49560 635.9282
## w1 - w3 141.9628 0.3953 -85.22723 369.1528
## w2 - w3 -320.2491 0.0017 ** -539.15574 -101.3425print(out_17$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 120.91225 1.0000 -250.4416 492.2661
## w1 - w3 204.77988 0.9235 -280.8849 690.4447
## w2 - w3 83.86763 1.0000 -384.0898 551.8250print(out_18$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 -2.315477 1.0000 -39.54988 34.91892
## w1 - w3 25.776090 0.6032 -22.91988 74.47206
## w2 - w3 28.091567 0.4456 -18.82894 75.01208print(out_19$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 529.7331 0.0000 *** 423.52655 635.9396
## w1 - w3 693.7035 0.0000 *** 554.80426 832.6027
## w2 - w3 163.9704 0.0107 * 30.13546 297.8054print(out_20$comparison)## difference pvalue signif. LCL UCL
## w1 - w2 920.3232 0.2248 -323.7992 2164.446
## w1 - w3 604.0234 1.0000 -1023.0674 2231.114
## w2 - w3 -316.2999 1.0000 -1884.0667 1251.467#############################################################################################
###### Chi-square test with Benjamini and Hochberg false discovery-rate corrections #########
#############################################################################################
df3 = banco %>% dplyr::select("record_id", "HMOSecretor", "HMOSecretor_1m", "HMOSecretor_3m")
headtail(df3)## record_id HMOSecretor HMOSecretor_1m HMOSecretor_3m
## 1 1 NA NA NA
## 2 2 NA 1 NA
## 3 3 NA NA NA
## 146 146 NA NA NA
## 147 147 NA NA NA
## 148 148 NA NA NAdf3 = melt(df3, id="record_id")
headtail(df3)## record_id variable value
## 1 1 HMOSecretor NA
## 2 2 HMOSecretor NA
## 3 3 HMOSecretor NA
## 442 146 HMOSecretor_3m NA
## 443 147 HMOSecretor_3m NA
## 444 148 HMOSecretor_3m NAtab = table(df3$variable, df3$value)
tab##
## 0 1
## HMOSecretor 6 37
## HMOSecretor_1m 6 49
## HMOSecretor_3m 2 19chi_sq = chisq.test(tab) # simulate.p.value = T## Warning in chisq.test(tab): Chi-squared approximation may be incorrectchi_sq## Pearson's Chi-squared test with tab
## X-squared = 0.3388, df = 2, p-value = 0.8442chisqPostHoc(chi_sq, control = "BH")## Warning in stats::chisq.test(tmp): Chi-squared approximation may be
## incorrect## Warning in stats::chisq.test(tmp): Chi-squared approximation may be
## incorrect## Adjusted p-values used the BH method.## comparison raw.p adj.p
## 1 HMOSecretor vs. HMOSecretor_1m 0.8841 1
## 2 HMOSecretor vs. HMOSecretor_3m 0.9198 1
## 3 HMOSecretor_1m vs. HMOSecretor_3m 1.0000 1#chisqPostHoc(chi_sq)
#chisq.test(tab[1,])
#chisq.test(tab[2,])
#chisq.test(tab[3,])
###################################################################
############## HMO diversity and evenness indexes #################
###################################################################
# 1 TO 7 DAYS POSTPARTUM
tb4_w1 = banco %>% dplyr::select(238:256) # 583:601, 928:946
glimpse(tb4_w1)## Observations: 148
## Variables: 19
## $ `2'FL_7d` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9.106348, N...
## $ `3FL_7d` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 42.76633, N...
## $ LNnT_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 802.8967, N...
## $ `3'SL_7d` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 380.7289, N...
## $ DFLac_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 23.430400, ...
## $ `6'SL_7d` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1253.93136,...
## $ LNT_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1725.021, N...
## $ LNFPI_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77.27484, N...
## $ LNFPII_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1805.1432, ...
## $ LNFPIII_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 107.00523, ...
## $ LSTb_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 127.73312, ...
## $ LSTc_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 405.6550, N...
## $ DFLNT_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 148.6601, N...
## $ LNH_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 525.663349,...
## $ DSLNT_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 631.4719, N...
## $ FLNH_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 192.64154, ...
## $ DFLNH_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 51.35774, N...
## $ FDSLNH_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 478.15880, ...
## $ DSLNH_7d <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 310.54825, ...names(tb4_w1)## [1] "2'FL_7d" "3FL_7d" "LNnT_7d" "3'SL_7d" "DFLac_7d"
## [6] "6'SL_7d" "LNT_7d" "LNFPI_7d" "LNFPII_7d" "LNFPIII_7d"
## [11] "LSTb_7d" "LSTc_7d" "DFLNT_7d" "LNH_7d" "DSLNT_7d"
## [16] "FLNH_7d" "DFLNH_7d" "FDSLNH_7d" "DSLNH_7d"summary(tb4_w1)## 2'FL_7d 3FL_7d LNnT_7d 3'SL_7d
## Min. : 8.873 Min. : 15.48 Min. : 154.0 Min. : 49.49
## 1st Qu.:3585.216 1st Qu.:151.60 1st Qu.: 371.3 1st Qu.:229.62
## Median :4951.775 Median :218.91 Median : 731.7 Median :283.74
## Mean :4575.304 Mean :235.14 Mean : 814.5 Mean :342.46
## 3rd Qu.:6234.048 3rd Qu.:289.17 3rd Qu.:1249.5 3rd Qu.:426.47
## Max. :9436.263 Max. :531.66 Max. :2048.9 Max. :965.12
## NA's :105 NA's :105 NA's :105 NA's :105
## DFLac_7d 6'SL_7d LNT_7d LNFPI_7d
## Min. : 8.384 Min. : 66.3 Min. : 286.5 Min. : 57.74
## 1st Qu.:170.296 1st Qu.: 217.0 1st Qu.:1073.5 1st Qu.:2132.52
## Median :241.654 Median : 291.0 Median :1430.7 Median :2813.80
## Mean :253.297 Mean : 350.2 Mean :1617.8 Mean :2638.96
## 3rd Qu.:335.532 3rd Qu.: 422.7 3rd Qu.:2043.9 3rd Qu.:3494.47
## Max. :601.406 Max. :1253.9 Max. :5211.3 Max. :5000.46
## NA's :105 NA's :105 NA's :105 NA's :105
## LNFPII_7d LNFPIII_7d LSTb_7d LSTc_7d
## Min. : 267.6 Min. : 45.24 Min. : 37.49 Min. : 201.8
## 1st Qu.: 594.0 1st Qu.: 69.20 1st Qu.: 76.02 1st Qu.: 628.3
## Median : 767.9 Median : 84.60 Median :101.01 Median : 808.8
## Mean :1047.3 Mean : 99.20 Mean :120.78 Mean : 825.3
## 3rd Qu.:1286.7 3rd Qu.:110.43 3rd Qu.:128.93 3rd Qu.: 981.7
## Max. :2921.2 Max. :300.22 Max. :761.00 Max. :1692.7
## NA's :105 NA's :105 NA's :105 NA's :105
## DFLNT_7d LNH_7d DSLNT_7d FLNH_7d
## Min. : 148.7 Min. : 9.598 Min. : 25.98 Min. : 10.23
## 1st Qu.: 971.5 1st Qu.: 33.010 1st Qu.: 521.63 1st Qu.: 53.82
## Median :1546.4 Median : 61.239 Median : 645.29 Median :121.42
## Mean :1451.2 Mean : 88.892 Mean : 635.73 Mean :119.35
## 3rd Qu.:1857.0 3rd Qu.:117.741 3rd Qu.: 757.72 3rd Qu.:163.56
## Max. :3532.2 Max. :525.663 Max. :1130.37 Max. :363.17
## NA's :105 NA's :105 NA's :105 NA's :105
## DFLNH_7d FDSLNH_7d DSLNH_7d
## Min. : 7.736 Min. : 20.19 Min. : 57.39
## 1st Qu.: 51.522 1st Qu.: 42.56 1st Qu.:160.51
## Median :134.040 Median : 74.22 Median :226.11
## Mean :160.785 Mean :110.29 Mean :237.30
## 3rd Qu.:206.006 3rd Qu.:135.41 3rd Qu.:309.80
## Max. :793.938 Max. :478.16 Max. :479.52
## NA's :105 NA's :105 NA's :105shannon_div_w1 = diversity(tb4_w1, index="shannon") # parece conceitualmente = shannon_ent
mean_sd(shannon_div_w1, na_rm=T, digits=2)## [1] "43; 2.16 $\\pm$ 0.19"simpson_inv_w1 = diversity(tb4_w1, index="invsimpson")
mean_sd(simpson_inv_w1, na_rm=T, digits=2)## [1] "43; 6.00 $\\pm$ 1.53"shannon_even_mean_w1 = diversityresult(tb4_w1[complete.cases(tb4_w1),], index="Jevenness", method="mean", digits=2)
shannon_even_sd_w1 = diversityresult(tb4_w1[complete.cases(tb4_w1),], index="Jevenness", method="sd", digits=2)
shannon_even_mean_w1; shannon_even_sd_w1## Jevenness
## mean 0.73## Jevenness
## sd 0.06pielou_even_w1 = shannon_div_w1/log(specnumber(tb4_w1))
mean_sd(pielou_even_w1, na_rm=T, digits=2)## [1] "43; 0.73 $\\pm$ 0.06"# shannon_ent = ?
# simpson_even = ?
## diversityresult(tb4[complete.cases(tb4),], index="Eevenness", method="mean", digits=5)
## RAM::evenness(tb4, index = "shannon")
#nota# alfa-diversity metrics (richness, Shannon diversity index, the inverse Simpson)
# 30 TO 45 DAYS POSTPARTUM
tb4_w2 = banco %>% dplyr::select(583:601)
names(tb4_w2)## [1] "2'FL_1m" "3FL_1m" "LNnT_1m" "3'SL_1m" "DFLac_1m"
## [6] "6'SL_1m" "LNT_1m" "LNFPI_1m" "LNFPII_1m" "LNFPIII_1m"
## [11] "LSTb_1m" "LSTc_1m" "DFLNT_1m" "LNH_1m" "DSLNT_1m"
## [16] "FLNH_1m" "DFLNH_1m" "FDSLNH_1m" "DSLNH_1m"shannon_div_w2 = diversity(tb4_w2, index="shannon")
mean_sd(shannon_div_w2, na_rm=T, digits=2)## [1] "55; 2.15 $\\pm$ 0.21"simpson_inv_w2 = diversity(tb4_w2, index="invsimpson")
mean_sd(simpson_inv_w2, na_rm=T, digits=2)## [1] "55; 5.59 $\\pm$ 1.43"shannon_even_mean_w2 = diversityresult(tb4_w2[complete.cases(tb4_w2),], index="Jevenness", method="mean", digits=2)
shannon_even_sd_w2 = diversityresult(tb4_w2[complete.cases(tb4_w2),], index="Jevenness", method="sd", digits=2)
shannon_even_mean_w2; shannon_even_sd_w2## Jevenness
## mean 0.73## Jevenness
## sd 0.07pielou_even_w2 = shannon_div_w2/log(specnumber(tb4_w2))
mean_sd(pielou_even_w2, na_rm=T, digits=2)## [1] "55; 0.73 $\\pm$ 0.07"# 3 MONTHS POSTPARTUM
tb4_w3 = banco %>% dplyr::select(928:946)
names(tb4_w3)## [1] "2'FL_3m" "3FL_3m" "LNnT_3m" "3'SL_3m" "DFLac_3m"
## [6] "6'SL_3m" "LNT_3m" "LNFPI_3m" "LNFPII_3m" "LNFPIII_3m"
## [11] "LSTb_3m" "LSTc_3m" "DFLNT_3m" "LNH_3m" "DSLNT_3m"
## [16] "FLNH_3m" "DFLNH_3m" "FDSLNH_3m" "DSLNH_3m"shannon_div_w3 = diversity(tb4_w3, index="shannon")
mean_sd(shannon_div_w3, na_rm=T, digits=2)## [1] "21; 2.00 $\\pm$ 0.27"simpson_inv_w3 = diversity(tb4_w3, index="invsimpson")
mean_sd(simpson_inv_w3, na_rm=T, digits=2)## [1] "21; 4.90 $\\pm$ 1.51"shannon_even_mean_w3 = diversityresult(tb4_w3[complete.cases(tb4_w3),], index="Jevenness", method="mean", digits=2)
shannon_even_sd_w3 = diversityresult(tb4_w3[complete.cases(tb4_w3),], index="Jevenness", method="sd", digits=2)
shannon_even_mean_w3; shannon_even_sd_w3## Jevenness
## mean 0.68## Jevenness
## sd 0.09pielou_even_w3 = shannon_div_w3/log(specnumber(tb4_w3))
mean_sd(pielou_even_w3, na_rm=T, digits=2)## [1] "21; 0.68 $\\pm$ 0.09"E = matrix(c(mean_sd(shannon_div_w1, na_rm=T, show_n="never", denote_sd = "paren", digits = 2),
mean_sd(simpson_inv_w1, na_rm=T, show_n="never", denote_sd = "paren", digits = 2),
"0.73 (0.06)", # shannon_even_mean_w1; shannon_even_sd_w1
mean_sd(pielou_even_w1, na_rm=T, show_n="never", denote_sd = "paren", digits = 2),
mean_sd(shannon_div_w2, na_rm=T, show_n="never", denote_sd = "paren", digits = 2),
mean_sd(simpson_inv_w2, na_rm=T, show_n="never", denote_sd = "paren", digits = 2),
"0.73 (0.07)", # shannon_even_mean_w2; shannon_even_sd_w2
mean_sd(pielou_even_w2, na_rm=T, show_n="never", denote_sd = "paren", digits = 2),
mean_sd(shannon_div_w3, na_rm=T, show_n="never", denote_sd = "paren", digits = 2),
mean_sd(simpson_inv_w3, na_rm=T, show_n="never", denote_sd = "paren", digits = 2),
"0.68 (0.09)", # shannon_even_mean_w3; shannon_even_sd_w3
mean_sd(pielou_even_w3, na_rm=T, show_n="never", denote_sd = "paren", digits = 2)),
nrow=4, ncol=3, byrow=F)
dimnames(E) = list(c("Shannon diversity", "Inverse Simpson", "Shannon evenness", "Pielou evenness"),
c("1-7 days postpartum (n=43)", "30-45 day postpartum (n=55)",
"3 months postpartum (n=21)"))
knitr::kable(E, format="html",
caption="Table 4. Variation in HMO diversity and evenness indexes among women
participating in the study.") %>%
kable_styling("striped",full_width = T) %>%
footnote(general="All values are means (SD). HMO: human milk oligosaccharide.")| 1-7 days postpartum (n=43) | 30-45 day postpartum (n=55) | 3 months postpartum (n=21) | |
|---|---|---|---|
| Shannon diversity | 2.16 (0.19) | 2.15 (0.21) | 2.00 (0.27) |
| Inverse Simpson | 6.00 (1.53) | 5.59 (1.43) | 4.90 (1.51) |
| Shannon evenness | 0.73 (0.06) | 0.73 (0.07) | 0.68 (0.09) |
| Pielou evenness | 0.73 (0.06) | 0.73 (0.07) | 0.68 (0.09) |
| Note: | |||
| All values are means (SD). HMO: human milk oligosaccharide. |
####################################
############## NMF #################
####################################
library(NMF) # install.packages("NMF", dep=T)## Loading required package: pkgmaker## Loading required package: registry##
## Attaching package: 'pkgmaker'## The following object is masked from 'package:base':
##
## isFALSE## Loading required package: rngtools## Loading required package: cluster## NMF - BioConductor layer [NO: missing Biobase] | Shared memory capabilities [NO: windows] | Cores 3/4## To enable the Bioconductor layer, try: install.extras('
## NMF
## ') [with Bioconductor repository enabled]##
## Attaching package: 'NMF'## The following object is masked from 'package:agricolae':
##
## consensustb5_w1 = banco %>% dplyr::select(238:256) # 583:601, 928:946
tb5_w1=tb5_w1[complete.cases(tb5_w1),]
tb5_w1 = t(tb5_w1)
res_w1 = nmf(tb5_w1, 19, method="brunet")
res_w1## <Object of class: NMFfit>
## # Model:
## <Object of class:NMFstd>
## features: 19
## basis/rank: 19
## samples: 43
## # Details:
## algorithm: brunet
## seed: random
## RNG: 403L, 624L, ..., 1101502046L [c7430444357748f96c00f3d561508567]
## distance metric: 'KL'
## residuals: 386
## Iterations: 2000
## Timing:
## user system elapsed
## 0.58 0.01 0.67summary(res_w1)## rank sparseness.basis sparseness.coef silhouette.coef
## 1.9e+01 7.5e-01 4.9e-01 8.9e-02
## silhouette.basis residuals niter cpu
## 1.3e-01 3.9e+02 2.0e+03 NA
## cpu.all nrun
## NA 1.0e+00featureScore(res_w1) ## 2'FL_7d 3FL_7d LNnT_7d 3'SL_7d DFLac_7d 6'SL_7d
## 0.34 0.31 0.51 0.40 0.37 0.59
## LNT_7d LNFPI_7d LNFPII_7d LNFPIII_7d LSTb_7d LSTc_7d
## 0.32 0.29 0.31 0.28 0.37 0.31
## DFLNT_7d LNH_7d DSLNT_7d FLNH_7d DFLNH_7d FDSLNH_7d
## 0.39 0.57 0.30 0.52 0.52 0.53
## DSLNH_7d
## 0.39tb5_w2 = banco %>% dplyr::select(583:601)
tb5_w2=tb5_w2[complete.cases(tb5_w2),]
tb5_w2 = t(tb5_w2)
res_w2 = nmf(tb5_w2, 19, method="brunet")
res_w2## <Object of class: NMFfit>
## # Model:
## <Object of class:NMFstd>
## features: 19
## basis/rank: 19
## samples: 55
## # Details:
## algorithm: brunet
## seed: random
## RNG: 403L, 554L, ..., 1254425563L [22f05377b2d76ed35ce2425bb0bf8704]
## distance metric: 'KL'
## residuals: 500
## Iterations: 2000
## Timing:
## user system elapsed
## 0.96 0.00 1.03summary(res_w2)## rank sparseness.basis sparseness.coef silhouette.coef
## 1.9e+01 7.8e-01 4.8e-01 1.2e-01
## silhouette.basis residuals niter cpu
## 9.7e-02 5.0e+02 2.0e+03 NA
## cpu.all nrun
## NA 1.0e+00featureScore(res_w2) ## 2'FL_1m 3FL_1m LNnT_1m 3'SL_1m DFLac_1m 6'SL_1m
## 0.24 0.38 0.63 0.40 0.47 0.26
## LNT_1m LNFPI_1m LNFPII_1m LNFPIII_1m LSTb_1m LSTc_1m
## 0.39 0.39 0.35 0.26 0.22 0.37
## DFLNT_1m LNH_1m DSLNT_1m FLNH_1m DFLNH_1m FDSLNH_1m
## 0.47 0.47 0.92 0.36 0.46 0.43
## DSLNH_1m
## 0.25tb5_w3 = banco %>% dplyr::select(928:946)
tb5_w3=tb5_w3[complete.cases(tb5_w3),]
tb5_w3 = t(tb5_w3)
res_w3 = nmf(tb5_w3, 19, method="brunet")
res_w3## <Object of class: NMFfit>
## # Model:
## <Object of class:NMFstd>
## features: 19
## basis/rank: 19
## samples: 21
## # Details:
## algorithm: brunet
## seed: random
## RNG: 403L, 88L, ..., -1414204312L [965688250c7314f2ca6d715f30e54377]
## distance metric: 'KL'
## residuals: 17
## Iterations: 1110
## Timing:
## user system elapsed
## 0.28 0.00 0.33summary(res_w3)## rank sparseness.basis sparseness.coef silhouette.coef
## 1.9e+01 7.3e-01 5.3e-01 9.1e-02
## silhouette.basis residuals niter cpu
## 2.0e-01 1.7e+01 1.1e+03 NA
## cpu.all nrun
## NA 1.0e+00featureScore(res_w3)## 2'FL_3m 3FL_3m LNnT_3m 3'SL_3m DFLac_3m 6'SL_3m
## 0.20 0.29 0.14 0.35 0.24 0.34
## LNT_3m LNFPI_3m LNFPII_3m LNFPIII_3m LSTb_3m LSTc_3m
## 0.17 0.27 0.20 0.35 0.28 0.27
## DFLNT_3m LNH_3m DSLNT_3m FLNH_3m DFLNH_3m FDSLNH_3m
## 0.33 0.34 0.55 0.34 0.50 0.40
## DSLNH_3m
## 0.24F = data.frame(featureScore(res_w1), featureScore(res_w2), featureScore(res_w3))
dimnames(F) = list(
c("2'FL","3FL","LNnT","3'SL","DFLac","6'SL","LNT","LNFP I","LNFP II","LNFP III","LSTb",
"LSTc","DFLNT","LNH","DSLNT","FLNH","DFLNH","FDSLNH","DSLNH"),
c("1-7 days postpartum (n=43)", "30-45 day postpartum (n=55)",
"3 months postpartum (n=21)"))
knitr::kable(F, format="html",
caption="Table 5. Postpartum period-specific NMF scores for each HMO.") %>%
kable_styling("striped", full_width = T) %>%
footnote(general="NMF scores represent the probability of contribution to (and importance of) a speci???ed HMO variable to the basis component.
HMO: human milk oligosaccharide. 2'FL: 2'-fucosyllactose. 3FL: 3-fucosyllactose. LNnT: lacto-N-neotetraose. 3'SL: 3'-sialyllactose. DFLac: difucosyllactose. 6'SL: 6'-sialyllactose. LNT: lacto-N-tetrose. LNFP: lacto-N-fucopentaose. LSTb: sialyl-lacto-N-tetraose b. LSTc: sialyl-lacto-N-tetraose c. DFLNT: difucosyllacto-N-tetrose. LNH: lacto-N-hexaose. DSLNT: disialyllacto-Ntetraose. FLNH: fucosyllacto-N-hexaose. DFLNH: difucosyllacto-N-hexaose. FDSLNH: fucodisialyllacto-N-hexaose. DSLNH: disialyllacto-N-hexaose.")| 1-7 days postpartum (n=43) | 30-45 day postpartum (n=55) | 3 months postpartum (n=21) | |
|---|---|---|---|
| 2’FL | 0.34 | 0.24 | 0.20 |
| 3FL | 0.31 | 0.38 | 0.29 |
| LNnT | 0.51 | 0.63 | 0.14 |
| 3’SL | 0.40 | 0.40 | 0.35 |
| DFLac | 0.37 | 0.47 | 0.24 |
| 6’SL | 0.59 | 0.26 | 0.34 |
| LNT | 0.32 | 0.39 | 0.17 |
| LNFP I | 0.29 | 0.39 | 0.27 |
| LNFP II | 0.31 | 0.35 | 0.20 |
| LNFP III | 0.28 | 0.26 | 0.35 |
| LSTb | 0.37 | 0.22 | 0.28 |
| LSTc | 0.31 | 0.37 | 0.27 |
| DFLNT | 0.39 | 0.47 | 0.33 |
| LNH | 0.57 | 0.47 | 0.34 |
| DSLNT | 0.30 | 0.92 | 0.55 |
| FLNH | 0.52 | 0.36 | 0.34 |
| DFLNH | 0.52 | 0.46 | 0.50 |
| FDSLNH | 0.53 | 0.43 | 0.40 |
| DSLNH | 0.39 | 0.25 | 0.24 |
| Note: | |||
|
NMF scores represent the probability of contribution to (and importance of) a speci???ed HMO variable to the basis component. HMO: human milk oligosaccharide. 2’FL: 2’-fucosyllactose. 3FL: 3-fucosyllactose. LNnT: lacto-N-neotetraose. 3’SL: 3’-sialyllactose. DFLac: difucosyllactose. 6’SL: 6’-sialyllactose. LNT: lacto-N-tetrose. LNFP: lacto-N-fucopentaose. LSTb: sialyl-lacto-N-tetraose b. LSTc: sialyl-lacto-N-tetraose c. DFLNT: difucosyllacto-N-tetrose. LNH: lacto-N-hexaose. DSLNT: disialyllacto-Ntetraose. FLNH: fucosyllacto-N-hexaose. DFLNH: difucosyllacto-N-hexaose. FDSLNH: fucodisialyllacto-N-hexaose. DSLNH: disialyllacto-N-hexaose. |
# heatmap.2
################
#### OTHERS ####
################
library(nlme)##
## Attaching package: 'nlme'## The following object is masked from 'package:NMF':
##
## coef<-## The following object is masked from 'package:dplyr':
##
## collapseheadtail(tb2)## record_id variable value onda
## 1 1 2'FL NA w1
## 2 2 2'FL NA w1
## 3 3 2'FL NA w1
## 8878 146 SUM NA w3
## 8879 147 SUM NA w3
## 8880 148 SUM NA w3data=tb2[complete.cases(tb2),] %>% filter(variable=="2'FL") # <- substituir por vars HMO
headtail(data) # mean(data$value)## record_id variable value onda
## 1 11 2'FL 9.1 w1
## 2 14 2'FL 5489.5 w1
## 3 15 2'FL 7557.6 w1
## 117 98 2'FL 7371.7 w3
## 118 109 2'FL 1878.8 w3
## 119 115 2'FL 4595.9 w3data$record_id = as.factor(data$record_id)
lme.raw = lme(value ~ onda, random = ~1 | record_id, data=data)
summary(lme.raw)## Linear mixed-effects model fit by REML
## Data: data
## AIC BIC logLik
## 2138 2152 -1064
##
## Random effects:
## Formula: ~1 | record_id
## (Intercept) Residual
## StdDev: 2192 1329
##
## Fixed effects: value ~ onda
## Value Std.Error DF t-value p-value
## (Intercept) 4630 346 75 13.4 0.000
## ondaw2 220 328 41 0.7 0.506
## ondaw3 948 442 41 2.1 0.038
## Correlation:
## (Intr) ondaw2
## ondaw2 -0.55
## ondaw3 -0.40 0.46
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.026 -0.423 -0.023 0.371 2.649
##
## Number of Observations: 119
## Number of Groups: 76anova(lme.raw)## numDF denDF F-value p-value
## (Intercept) 1 75 297 <.0001
## onda 2 41 2 0.11plot.lme(lme.raw)
#############
#### FIM ####
#############
#require(graphics) # library(help = "graphics")
#dev.new()
#pairs(bd_w1[17:27]) # matrix of scatterplots
#pairs(bd_w1[28:36]) # matrix of scatterplots
#dev.off()
# banco %>% dplyr::select(HMOSecretor, HMOSecretor_1m, HMOSecretor_3m)
#rm(list=ls())
#q()