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SDI_PD_Stats.R
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library(dplyr)
library(data.table)
library(ggplot2)
library(magrittr)
library(knitr)
library(tidyverse)
library(vroom)
library(viridis)
library(pwr)
require(GGally)
require(CCA)
require(CCP)
library(scales)
library(car)
library(plotrix)
library(lubridate)
X=read.csv(file="SDI_PD.csv", header=T)
summary(X)
dim(X)
PdDB=as.data.frame(X)
# Anotar las variables categóricas como factores
PdDB$Sex <-as.factor(PdDB$Sex)
PdDB$SDI_stratum <-as.factor(PdDB$SDI_stratum)
PdDB$SDI_level <-as.factor(PdDB$SDI_level)
PdDB$living_rank <-as.factor(PdDB$Living_rank)
PdDB$health_security_rank <-as.factor(PdDB$Health_security_rank)
PdDB$edu_lag_rank <-as.factor(PdDB$Edu_lag_rank)
PdDB$durable_goods_rank <-as.factor(PdDB$Durable_goods_rank)
PdDB$sanitary_rank <-as.factor(PdDB$Sanitary_rank)
PdDB$energetic_rank <-as.factor(PdDB$Energetic_rank)
PdDB$Education_level <-as.factor(PdDB$Education_level)
PdDB$Physical_activity <-as.factor(PdDB$Physical_activity)
PdDB$Sleep_quality <-as.factor(PdDB$Sleep_quality)
PdDB$smoked <-as.factor(PdDB$Smoked)
PdDB$cigarettes <-as.factor(PdDB$Cigarettes)
PdDB$smokes <-as.factor(PdDB$Smokes)
PdDB$Smoking<-as.factor(PdDB$Smoking)
PdDB$SDI_stratum <-as.factor(PdDB$SDI_stratum)
PdDB$Brush_your_teeth <-as.factor(PdDB$Brush_your_teeth)
PdDB$Brush_before_bed <-as.factor(PdDB$Brush_before_bed)
PdDB$Floss_use_after <-as.factor(PdDB$Floss_use_after)
PdDB$Method_clean <-as.factor(PdDB$Method_clean)
PdDB$Use_dental_prostheses <-as.factor(PdDB$Use_dental_prostheses)
PdDB$Dental_care_12 <-as.factor(PdDB$Dental_care_12)
PdDB$Implant_placed <-as.factor(PdDB$Implant_placed)
PdDB$Gum_treatment <-as.factor(PdDB$Gum_treatment)
PdDB$Lost_bone <-as.factor(PdDB$Lost_bone)
PdDB$Lost_tooth <-as.factor(PdDB$Lost_tooth)
PdDB$Gum_health <-as.factor(PdDB$Gum_health)
PdDB$Tooth_does_not_look_right <-as.factor(PdDB$Tooth_does_not_look_right)
PdDB$Loose_tooth <-as.factor(PdDB$Loose_tooth)
PdDB$Loose_tooth_later <-as.factor(PdDB$Loose_tooth_later)
PdDB$Have_gum_disease <-as.factor(PdDB$Have_gum_disease)
PdDB$Had_bleeding_gums <-as.factor(PdDB$Had_bleeding_gums)
PdDB$Gum_infected <-as.factor(PdDB$Gum_infected)
PdDB$Bad_breath<-as.factor(PdDB$Bad_breath)
PdDB$Abscessed_teeth <-as.factor(PdDB$Abscessed_teeth)
PdDB$Gum_surgery <-as.factor(PdDB$Gum_surgery)
PdDB$Reason_surgery <-as.factor(PdDB$Reason_surgery)
PdDB$Satisfaction_level <-as.factor(PdDB$Satisfaction_level)
PdDB$COVID19 <-as.factor(PdDB$COVID19)
PdDB$MetS <-as.factor(PdDB$MetS)
PdDB$Severe_periodontitis <- as.factor(PdDB$Severe_periodontitis)
PdDB$Gingivitis <- as.factor(PdDB$Gingivitis)
PdDB$Periodontal_care <-as.factor(PdDB$Periodontal_care)
summary(PdDB)
# PdDB$Oral_hygiene <-as.factor(PdDB$Oral_hygiene)
#PdDB$Regular_hygiene <-as.factor(PdDB$Regular_hygiene)
Age.test <- shapiro.test(Age)
print(Age.test)
PdDB$Height
#Weight.test <- shapiro.test(Weight)
#print(Weight.test)
#Height.test <- shapiro.test(Height)
#print(Height.test)
#prueba de normalidad shapiro
#p > 0.05 una distribución normal en p < 0.05 una distribución que no es normal.
SDI_1=filter(PdDB, SDI_stratum=="1")
summary(SDI_1)
SDI_2=filter(PdDB, SDI_stratum=="2")
summary(SDI_2)
SDI_3=filter(PdDB, SDI_stratum=="3")
summary(SDI_3)
SDI_4=filter(PdDB, SDI_stratum=="4")
summary(SDI_4)
summary(SDI_1$Anxiety_trait)
shapiro.test(SDI_1$Anxiety_trait)
summary(SDI_2$Anxiety_trait)
shapiro.test(SDI_2$Anxiety_trait)
summary(SDI_3$Anxiety_trait)
shapiro.test(SDI_3$Anxiety_trait)
summary(SDI_4$Anxiety_trait)
shapiro.test(SDI_4$Anxiety_trait)
######
data_hist_bucal_semana <-data.frame(PdDB$Times_mouthwash_use, PdDB$SDI_stratum)
data_hist_dia_cepilla <-data.frame(PdDB$Times_brush_teeth, PdDB$SDI_stratum)
data_hist_hilo_semana <-data.frame(PdDB$Times_floss_use, PdDB$SDI_stratum)
data_hist_implantes <-data.frame(PdDB$Num_implant, PdDB$SDI_stratum)
data_hist_edad <-data.frame(PdDB$Age, PdDB$SDI_stratum)
library(ggplot2)
ggplot(data_hist_bucal_semana, aes(x=PdDB$Times_mouthwash_use, fill=PdDB$SDI_stratum))+geom_histogram(position="identity",alpha=0.2, bins=10)
ggplot(data_hist_dia_cepilla, aes(x=PdDB$Times_brush_teeth, fill=PdDB$SDI_stratum))+geom_histogram(position="identity",alpha=0.2, bins=30)
ggplot(data_hist_hilo_semana, aes(x=PdDB$Times_floss_use, fill=PdDB$SDI_stratum))+geom_histogram(position="identity",alpha=0.2, bins=10)
ggplot(data_hist_implantes, aes(x=PdDB$Num_implant, fill=PdDB$SDI_stratum))+geom_histogram(position="identity",alpha=0.2, bins=10)
ggplot(data_hist_edad, aes(x=PdDB$Age, fill=PdDB$SDI_stratum))+geom_histogram(position="identity",alpha=0.2, bins=10)
#Calculos de edades
library(lubridate)
#' Calculate Age
#'
#' By default, calculates the typical "Age in years", with a
#' \code{floor} applied so that you are, e.g., 5 years old from
#' 5th birthday through the day before your 6th birthday. Set
#' \code{floor = FALSE} to return decimal ages, and change \code{units}
#' for units other than years.
#' @param dob date-of-birth, the day to start calculating Age.
#' @param Age.day the date on which Age is to be calculated.
#' @param units unit to measure Age in. Defaults to \code{"years"}. Passed to \link{\code{duration}}.
#' @param floor boolean for whether or not to floor the result. Defaults to \code{TRUE}.
#' @return Age in \code{units}. Will be an integer if \code{floor = TRUE}.
#' @examples
#' my.dob <- as.Date('1983-10-20')
#' age(my.dob)
#' age(my.dob, units = "minutes")
#' age(my.dob, floor = FALSE)
age <- function(dob, age.day = today(), units = "years", floor = TRUE) {
calc.age = interval(dob, age.day) / duration(num = 1, units = units)
if (floor) return(as.integer(floor(calc.age)))
return(calc.age)
}
head(PdDB$FECHANACIMIENTO)
# Poner años en 4 digitos (agregas 19 al principio)
PdDB$nacim <- as.Date(format(as.Date(PdDB$FECHANACIMIENTO,format="%d/%m/%y"), "19%y%m%d"), "%Y%m%d")
head(PdDB$nacim)
# Calculamos age con el codigo que puse arriba 'Calculate Age'
PdDB$Age <-Age(PdDB$nacim)
PdDB$Age
age.test <- shapiro.test(Age)
print(Age.test)
#prueba de normalidad shapiro
#p > 0.05 una distribución normal en p < 0.05 una distribución que no es normal.
# si quieres la age con fracciones de año
#PdDB$age_fraction <-age(PdDB$nacim, floor=FALSE)
#PdDB$age_fraction
# write.csv(PdDB, file="Base_limpia_periodontitis_Abril_2022.csv")
#para prueba de hipóteisis entre proporciones
sexf<-c(83,299,199,262)
sexm<-c(51,163,106,131)
ene<-c(134,462,305,393)
prop.test(sexf,ene)
prop.test(sexm,ene)
smoke<-c(80,269,179,240)
prop.test(smoke,ene)
phy_act_low<-c(22,91,59,67)
prop.test(phy_act_low,ene)
phy_act_med<-c(67,213,137,192)
prop.test(phy_act_med,ene)
phy_act_high<-c(45,158,109,134)
prop.test(phy_act_high,ene)
coviddx<-c(29,115,58,55)
prop.test(coviddx,ene)
metsdx<-c(9,20,11,18)
prop.test(metsdx,ene)
sleep_bad<-c(82,247,170,205)
prop.test(sleep_bad,ene)
lost_tooth<-c(60,193,124,161)
prop.test(lost_tooth,ene)
notlookright<-c(38.06,31.60,33.44,30.28)
prop.test(notlookright,ene)
loose_tooth<-c(15,42,31,40)
prop.test(loose_tooth,ene)
loose_tooth_lost<-c(11,26,20,27)
prop.test(loose_tooth_lost,ene)
gum_disease<-c(19.40,25.54, 31.80,25.45)
prop.test(gum_disease,ene)
gum_bleeding<-c(48.51,52.38,50.82,47.84)
prop.test(gum_bleeding,ene)
gum_infected<-c(16.42,20.56,19.67,22.14)
prop.test(gum_infected,ene)
bad_breath<-c(23.13,25.97,27.54,21.88)
prop.test(bad_breath,ene)
abscessed<-c(14.93,15.80,12.13,12.47)
prop.test(abscessed,ene)
gum_surgery<-c(15.67,14.72,15.41,20.10)
prop.test(gum_surgery,ene)
gingiv_dx<-c(7,37,21,38)
prop.test(gingiv_dx,ene)
perio_dx_1<-c(5,8,8,13)
prop.test(perio_dx_1,ene)
perio_dx_0<-c(129,454,297,380)
prop.test(perio_dx_0,ene)
perio_tx_1<-c(1,4,3,9)
prop.test(perio_tx_1,ene)
perio_tx_0<-c(133,458,302,384)
prop.test(perio_tx_0,ene)
dental_tx_0<-c(61,242,153,213)
prop.test(dental_tx_0,ene)
Implant_placed_si<-c(19,53,46,51)
prop.test(Implant_placed_si,ene)
gum_tx_1<-c(17.16,22.73,25.25,21.88)
prop.test(gum_tx_1,ene)
lost_bone_yes<-c(12,42,41,57)
prop.test(lost_bone_yes,ene)
brush_teeth<-c(132,451,297,385)
prop.test(brush_teeth,ene)
brush_bef_sleep<-c(102,361,245,339)
prop.test(brush_bef_sleep,ene)
floos_aft_brus<-c(6.72,16.23,20.33,20.61)
prop.test(floos_aft_brus,ene)
dental_prostheses<-c(14.18,11.26,12.46,12.47)
prop.test(dental_prostheses,ene)
satisfaction_very_unsat<-c(2.24,1.30,1.97,0.76)
prop.test(satisfaction_very_unsat,ene)
satisfaction_unsat<-c(8.96,9.96,9.51,8.40)
prop.test(satisfaction_unsat,ene)
satisfaction_satisfied<-c(55.97,55.19,47.87,57)
prop.test(satisfaction_satisfied,ene)
satisfaction_very_satisfied<-c(32.84,33.55,40.66,33.84)
prop.test(satisfaction_very_satisfied,ene)
gum_excellent<-c(6.72,9.31,6.23,11.70)
prop.test(gum_excellent,ene)
gum_verygood<-c(25.37,24.46,28.85,22.90)
prop.test(gum_verygood,ene)
gum_good<-c(45.52,44.37,42.95,41.73)
prop.test(gum_good,ene)
gum_regular<-c(21.64,18.18,19.02,20.87)
prop.test(gum_regular,ene)
gum_bad<-c(0.75,3.68,2.95,2.80)
prop.test(gum_bad,ene)
Elementary<-c(0.74,0.22,0.66,0.25)
prop.test(Elementary,ene)
Middle<-c(11.94,5.19,4.92,2.54 )
prop.test(Middle,ene)
High<-c(40.30,36.80,29.18,21.12)
prop.test(High,ene)
College<-c(38.81,47.62,50.82,52.42)
prop.test(College,ene)
Postgraduate<-c(8.20,10.17,14.42,23.66)
prop.test(Postgraduate,ene)
# Codigo para hacer correlogramas
summary(PdDB)
Oral_Health_IDS <- PdDB[c("Gingivitis","Had_bleeding_gums", "Severe_periodontitis", "Times_mouthwash_use",
"Times_brush_teeth","SDI_value")]
theme_set(theme_minimal())
opts <- options()
options(ggplot2.continuous.colour="viridis")
options(ggplot2.discrete.colour="viridis")
options(ggplot2.continuous.fill = "viridis")
options(ggplot2.discrete.fill = "viridis")
ggpairs(Oral_Health_IDS, title="Correlograms", upper = list(continuous = "smooth_loess", combo = "box_no_facet"), lower = list(continuous = "density", combo = "facethist"), diag = list(continuous = "barDiag", discrete = "barDiag", na = "naDiag"), ggplot2::aes(color=Times_brush_teeth))
# ccc <- filter(PdDB, MetS=="1")
# summary(ccc)
#
# ddd <- filter(PdDB, Gingivitis=="1")
#
# summary(ddd)
#
# eee <- filter(PdDB, Severe_periodontitis=="1")
#
# summary(eee)
#
#Lista Completa de variables para correr los modelos
# Periodon <- glm(Severe_periodontitis ~ Sex + Weight + Height + Body_mass_index + Waist
# + Systolic_average + Diastolic_average + SDI_value
# + SDI_stratum + SDI_level + Living_value + Health_security_value
# + Edu_lag_value + Durable_goods_value + Sanitary_value + Energetic_value
# + Living_rank + Health_security_rank + Edu_lag_rank + Durable_goods_rank
# + Sanitary_rank + Energetic_rank + Education_level + Physical_activity
# + Anxiety_trait + Anxiety_trait + Sleep_quality + Smoked
# + Cigarettes + Smokes + Smoking + Brush_your_teeth + Times_brush_teeth
# + Brush_before_bed + Floss_use_after + Times_floss_use
# + Times_mouthwash_use + Method_clean + Use_dental_prostheses
# + Dental_care_12 + Implant_placed + Num_implant
# + Gum_treatment + Lost_bone + Lost_tooth
# + Gum_health + Tooth_does_not_look_right + Loose_tooth
# + Num_loose_teeth + Loose_tooth_later + Have_gum_disease
# + Had_bleeding_gums + Gum_infected + Bad_breath
# + Abscessed_teeth + Gum_surgery + Reason_surgery
# + Satisfaction_level + COVID19 + MetS
# + Gingivitis + Periodontal_care,
# data = PdDB, family = "binomial")
#
# summary(Periodon)
# Conteo de número de valores en un factor
#sapply(lapply(PdDB, unique), length)
# Variables selectas generales
# Periodon <- glm( Severe_periodontitis ~ Sex + SDI_value
# + Living_value + Health_security_value
# + Edu_lag_value + Durable_goods_value + Sanitary_value
# + Education_level + Sleep_quality + Smoking
# + Brush_your_teeth + Times_brush_teeth
# + Brush_before_bed + Floss_use_after + Times_floss_use
# + Times_mouthwash_use + Use_dental_prostheses
# + Dental_care_12 + Implant_placed + Num_implant
# + Gum_treatment + Lost_bone + Lost_tooth
# + Gum_health + Tooth_does_not_look_right + Loose_tooth
# + Num_loose_teeth + Loose_tooth_later + Have_gum_disease
# + Had_bleeding_gums + Gum_infected + Bad_breath
# + Abscessed_teeth + Gum_surgery
# + Satisfaction_level + COVID19 + MetS
# + Gingivitis,
# data = PdDB, family = "binomial")
#
# summary(Periodon)
# Variables selectas generales no redundantes
#Salud_Peri <- glm(Severe_periodontitis ~ Sex + Living_value + Physical_activity
#+ Anxiety_trait + Health_security_value
# + Edu_lag_value + Durable_goods_value + Sanitary_value + Education_level
# + Sleep_quality + Smoking + Brush_your_teeth+ Times_brush_teeth+ Brush_before_bed
#+ Floss_use_after + Times_floss_use + Times_mouthwash_use
#+ Use_dental_prostheses + Dental_care_12 + Implant_placed + Num_implant +Gum_treatment
#+ Gum_health + Tooth_does_not_look_right
#+ Bad_breath + Abscessed_teeth + Gum_surgery + Satisfaction_level
#+ COVID19 + MetS + Gingivitis,
#data = PdDB, family = "binomial")
#summary(Periodon)
# Variables selectas generales no relacionadas directamente con el padecimiento
# + Education_level
#Periodon2 <- glm(Severe_periodontitis ~ Sex + Living_value + Health_security_value
# + Edu_lag_value + Durable_goods_value + Sanitary_value
# + Sleep_quality + Smoking + Brush_your_teeth + Times_brush_teeth + Brush_before_bed
# + Floss_use_after+ Times_floss_use + Times_mouthwash_use
# + Use_dental_prostheses + Dental_care_12 + Implant_placed + Num_implant +Gum_treatment
# + Gum_health + Tooth_does_not_look_right
# + Bad_breath + Abscessed_teeth + Gum_surgery + Satisfaction_level
# + COVID19 + MetS + Gingivitis,
# data = PdDB, family = "binomial")
#summary(Periodon2)
#step(Periodon2, test="LRT")
# Modelo final segun LRT
#Periodon_final<- glm(formula = Severe_periodontitis ~ Sex + Living_value + Edu_lag_value +
# Education_level + Times_brush_teeth + Brush_before_bed + Use_dental_prostheses +
# Gum_health + Tooth_does_not_look_right + Abscessed_teeth +
# Gum_surgery + Satisfaction_level + COVID19,
# family = "binomial", data = PdDB)
#summary(Periodon_final)
# Intervalos de confianza
# confint(Periodon_final)
# Odds ratios
# lreg.or <-exp(cbind(OR = coef(Periodon_final)))
##############################################################
#No se incluyeron: Weight + Height + Waist + SDI_stratum + SDI_level + Living_rank + Health_security_rank + Edu_lag_rank + Durable_goods_rank
#+ Sanitary_rank + Energetic_rank + Num_implant
#+ Education_level + Anxiety_trait
#se quitaron por ser las que construyeron la variable de gingivitis + Have_gum_disease
#se queja de:+ Reason_surgery+ Gum_surgery + Abscessed_teeth + Gum_infected + Cigarettes + Smokes + Method_clean + Gum_health +Had_bleeding_gums
############################Gingivitis1 es el modelo con SDI global#################
Gingivitis1<- glm(formula = Gingivitis ~ Sex
+ Body_mass_index
+ Systolic_average
+ Diastolic_average
+ Smoked
+ SDI_value
+ Sleep_quality
+ Physical_activity
+ Smoking
+ Brush_your_teeth
+ Times_brush_teeth
+ Brush_before_bed
+ Floss_use_after
+ Times_floss_use
+ Times_mouthwash_use
+ Use_dental_prostheses
+ Dental_care_12
+ Implant_placed
+ Gum_treatment
+ Lost_bone
+ Lost_tooth
+ Tooth_does_not_look_right
+ Loose_tooth
+ Num_loose_teeth
+ Loose_tooth_later
+ Bad_breath
+ Periodontal_care
+ Satisfaction_level
+ COVID19
+ MetS
+ Periodontal_care
, family = "binomial",
data = PdDB)
step(Gingivitis1, test="LRT")
# Modelo final segun LRT
Gingivitis1_final<- glm(formula = Gingivitis ~ SDI_value + Brush_before_bed + Lost_bone +
Tooth_does_not_look_right + Loose_tooth_later + Bad_breath +
Satisfaction_level + COVID19, family = "binomial", data = PdDB)
summary(Gingivitis1_final)
vif(Gingivitis1_final)
# Intervalos de confianza
confint(Gingivitis1_final)
# Odds ratios Cálculo de razones de momios
lreg.or <-exp(cbind(OR = coef(Gingivitis1_final)))
round(lreg.or, digits=4)
# Pruebas de sensibilidad/especificidad del modelo por Curvas ROC
library(pROC)
test_prob = predict(Gingivitis1_final, newdata = PdDB, type = "response")
test_roc = roc(PdDB$Gingivitis ~ test_prob, plot = TRUE, print.auc = TRUE)
as.numeric(test_roc$auc)
library(LogisticDx)
GoodFit<-gof(Gingivitis1_final, plotROC=TRUE,g=8)
# Pas0 10: Pruebas de bondad de ajuste
# Medidas pseudo-R-cuadrada para la bondad de ajuste por máxima verosimilitud
library(pscl)
#pR2(Gingivitis1_final)
pR2(Gingivitis1_final)
# Prueba de Hosmer Lemeshow
library(generalhoslem)
logitgof(PdDB$Gingivitis, fitted(Gingivitis1_final), g = 10)
# Prueba de Stukel
# Definimos la función stukel.test,
#una vez definida se puede comentar el código que la define
################ Una vez definida stukel.test se puede comentar desde aquí
stukel.test = function(obj) {
# first, check to see if we fed in the right kind of object
stopifnot(family(obj)$family == "binomial" && family(obj)$link == "logit")
high.prob <- (obj$fitted.values >= 0.5)
logit2 <- obj$linear.predictors^2
z1 = 0.5*logit2*high.prob
z2 = 0.5*logit2*(1-high.prob)
mf <- obj$model
trials = rep(1, times = nrow(mf))
if(any(colnames(mf) == "(weights)"))
trials <- mf[[ncol(mf)]]
prop = mf[[1]]
# the double bracket (above) gets the index of items within an object
if (is.factor(prop))
prop = (as.numeric(prop) == 2) # Converts 1-2 factor levels to logical 0/1 values
pi.hat = obj$fitted.values
y <- trials*prop
exclude <- which(colnames(mf) == "(weights)")
vars <- data.frame(z1, z2, y, mf[,-c(1,exclude)])
full <- glm(formula = y/trials ~ ., family = binomial(link = logit), weights = trials, data = vars)
null <- glm(formula = y/trials ~ ., family = binomial(link = logit), weights = trials, data = vars[,-c(1,2)])
LRT <- anova(null,full)
p.value <- 1 - pchisq(LRT$Deviance[[2]], LRT$Df[[2]])
cat("Stukel Test Stat = ", LRT$Deviance[[2]], "with p-value = ", p.value, "\n")
}
#### Fin de la definición de la función.
# Hasta aquí se puede comentar después de la primera vez que se use
# The results are similar: if p-value is above the threshold of 0.05,
# there is no statistical evidence of the poor fit of our model
# based on the difference in the occurence of event between
# the fitted and the observed values.
stukel.test(Gingivitis1_final)
# Prueba de Osius-Rojek
# Definimos la función o.r.test,
#una vez definida se puede comentar el código que la define
################ Una vez definida stukel.test se puede comentar desde aquí
o.r.test = function(obj) {
# first, check to see if we fed in the right kind of object
stopifnot(family(obj)$family == "binomial" && family(obj)$link == "logit")
mf <- obj$model
trials = rep(1, times = nrow(mf))
if(any(colnames(mf) == "(weights)"))
trials <- mf[[ncol(mf)]]
prop = mf[[1]]
# the double bracket (above) gets the index of items within an object
if (is.factor(prop))
prop = as.numeric(prop) == 2 # Converts 1-2 factor levels to logical 0/1 values
pi.hat = obj$fitted.values
y <- trials*prop
yhat <- trials*pi.hat
nu <- yhat*(1-pi.hat)
pearson <- sum((y - yhat)^2/nu)
c = (1 - 2*pi.hat)/nu
exclude <- c(1,which(colnames(mf) == "(weights)"))
vars <- data.frame(c,mf[,-exclude])
wlr <- lm(formula = c ~ ., weights = nu, data = vars)
rss <- sum(nu*residuals(wlr)^2 )
J <- nrow(mf)
A <- 2*(J - sum(1/trials))
z <- (pearson - (J - ncol(vars) - 1))/sqrt(A + rss)
p.value <- 2*(1 - pnorm(abs(z)))
cat("z = ", z, "with p-value = ", p.value, "\n")
}
#### Fin de la definición de la función.
# Hasta aquí se puede comentar después de la primera vez que se use
o.r.test(Gingivitis1_final)
# Paso 11: Pruebas de multicolinealidad y residuos
library(car)
car::residualPlots(Gingivitis1_final)
car::marginalModelPlots(Gingivitis1_final)
vif(Gingivitis1_final)
outlierTest(Gingivitis1_final)
influenceIndexPlot(Gingivitis1_final, id.n. = 3)
influencePlot(Gingivitis1_final, col = "red", id.n. = 3)
# Paso 12: Forest plot comparando coeficientes
# https://strengejacke.github.io/sjPlot/
library(sjPlot)
library(glmmTMB)
plot_model(Gingivitis1_finalbis, type="std2",sort.est=TRUE,vline.color="grey80",
show.values=TRUE,value.offset=.3,show.p=TRUE,title="Comparacion de coeficientes")
plot_model(Gingivitis1_finalbis, type="resid",sort.est=TRUE,vline.color="grey80",
show.values=TRUE,value.offset=.3,show.p=TRUE,title="Comparacion de residuos")
plot_model(Gingivitis1_finalbis, type="eff",sort.est=TRUE,vline.color="grey80",
show.values=TRUE,value.offset=.3,show.p=TRUE,title="Efectos en modelo univariado")
plot_model(Gingivitis1_finalbis, type="std2",sort.est=TRUE,vline.color="grey80",
show.values=TRUE,value.offset=.3,show.p=TRUE,title="Comparacion de coeficientes",transform = NULL)
plot_model(bis, type="std2",sort.est=TRUE,vline.color="grey80",
show.values=TRUE,value.offset=.3,show.p=TRUE,title="Comparacion de coeficientes",transform = "plogis")
#######################################Gingivitis2 es el modelo con los componentes del SDI
Gingivitis2<- glm(formula = Gingivitis ~ Sex
+ Body_mass_index
+ Systolic_average
+ Diastolic_average
+ Edu_lag_value
+ Living_value
+ Health_security_value
+ Durable_goods_value
+ Energetic_value
+ Sanitary_value
+ Sleep_quality
+ Physical_activity
+ Smoking
+ Brush_your_teeth
+ Times_brush_teeth
+ Brush_before_bed
+ Floss_use_after
+ Times_floss_use
+ Times_mouthwash_use
+ Use_dental_prostheses
+ Dental_care_12
+ Implant_placed
+ Gum_treatment
+ Lost_bone
+ Lost_tooth
+ Tooth_does_not_look_right
+ Loose_tooth
+ Num_loose_teeth
+ Loose_tooth_later
+ Bad_breath
+ Periodontal_care
+ Satisfaction_level
+ COVID19
+ MetS
+ Periodontal_care
, family = "binomial",
data = PdDB)
step(Gingivitis2, test="LRT")
# Modelo final segun LRT
Gingivitis2_final<- glm(formula = Gingivitis ~ Sanitary_value + Brush_before_bed +
Lost_bone + Tooth_does_not_look_right + Loose_tooth_later +
Bad_breath + Satisfaction_level + COVID19, family = "binomial",
data = PdDB)
summary(Gingivitis2_final)
vif(Gingivitis2_final)
# Intervalos de confianza
confint(Gingivitis2_final)
# Odds ratios Cálculo de razones de momios
lreg.or <-exp(cbind(OR = coef(Gingivitis2_final)))
round(lreg.or, digits=4)
# Pruebas de sensibilidad/especificidad del modelo por Curvas ROC
library(pROC)
test_prob = predict(Gingivitis2_final, newdata = PdDB, type = "response")
test_roc = roc(PdDB$Gingivitis ~ test_prob, plot = TRUE, print.auc = TRUE)
as.numeric(test_roc$auc)
################################Lost_tooth_later1##############################
#se eliminaron del modelo + Lost_tooth + Bad_breath + Loose_tooth + Num_loose_teeth + Education_level + Anxiety_trait + Num_implant + Gum_health
#+ Living_value + Health_security_value + Abscessed_teeth + Edu_lag_value + Durable_goods_value + Sanitary_value +Energetic_value + Use_dental_prostheses+ Implant_placed + Gum_treatment + Lost_bone+ Tooth_does_not_look_right +Have_gum_disease+ Had_bleeding_gums + Gum_infected
Loose_tooth_later1 <- glm(Loose_tooth_later ~ Sex
+ Body_mass_index
+ Systolic_average
+ Diastolic_average
+ Physical_activity
+ SDI_value + Sleep_quality
+ Smoking + Brush_your_teeth + Times_brush_teeth + Brush_before_bed
+ Floss_use_after+ Times_floss_use + Times_mouthwash_use
+ Dental_care_12
+ Satisfaction_level + COVID19 + MetS, data = PdDB, family = "binomial")
summary(Loose_tooth_later1)
step(Loose_tooth_later1, test="LRT")
# Modelo final segun LRT
Loose_tooth_later1_final<- glm(formula = Loose_tooth_later ~ Sex + Body_mass_index + Floss_use_after +
Satisfaction_level + COVID19, family = "binomial", data = PdDB)
summary(Loose_tooth_later1_final)
vif(Loose_tooth_later1_final)
# Intervalos de confianza
confint(Loose_tooth_later1_final)
# Odds ratios Cálculo de razones de momios
lreg.or <-exp(cbind(OR = coef(Loose_tooth_later1_final)))
round(lreg.or, digits=4)
test_prob = predict(Loose_tooth_later1_final, newdata = PdDB, type = "response")
test_roc = roc(PdDB$Loose_tooth_later ~ test_prob, plot = TRUE, print.auc = TRUE)
as.numeric(test_roc$auc)
################################Lost_tooth_later2##############################
#se eliminaron del modelo + Bad_breath +Lost_tooth + Loose_tooth + Num_loose_teeth + Education_level + Anxiety_trait + Use_dental_prostheses + Gum_health
Loose_tooth_later2 <- glm(Loose_tooth_later ~ Sex
+ Body_mass_index + Living_value + Health_security_value + Energetic_value
+ Edu_lag_value + Durable_goods_value + Sanitary_value
+ Systolic_average
+ Diastolic_average
+ Physical_activity
+ SDI_value + Sleep_quality
+ Smoking + Brush_your_teeth + Times_brush_teeth + Brush_before_bed
+ Floss_use_after+ Times_floss_use + Times_mouthwash_use
+ Dental_care_12
+ Satisfaction_level + COVID19 + MetS, data = PdDB, family = "binomial")
summary(Loose_tooth_later2)
step(Loose_tooth_later2, test="LRT")
# Modelo final segun LRT
Loose_tooth_later2_final<- glm(formula = Loose_tooth_later ~ Sex + Body_mass_index + Floss_use_after +
Satisfaction_level + COVID19, family = "binomial", data = PdDB)
summary(Loose_tooth_later2_final)
vif(Loose_tooth_later2_final)
# Intervalos de confianza
confint(Loose_tooth_later2_final)
# Odds ratios Cálculo de razones de momios
lreg.or <-exp(cbind(OR = coef(Loose_tooth_later2_final)))
round(lreg.or, digits=4)
test_prob = predict(Loose_tooth_later2_final, newdata = PdDB, type = "response")
test_roc = roc(PdDB$Loose_tooth_later ~ test_prob, plot = TRUE, print.auc = TRUE)
as.numeric(test_roc$auc)
###############################Bleeding1##############################
#mod 1 sin componentes mod 2 con componentes del SDI, se eliminaron: + Bad_breath + Use_dental_prostheses + Num_implant + Gum_health
#+ Implant_placed + Gum_treatment + Lost_bone+ Tooth_does_not_look_right + Have_gum_disease+ Lost_tooth + Loose_tooth + Num_loose_teeth + Gum_infected + Abscessed_teeth
Had_bleeding_gums_mod1 <- glm(Had_bleeding_gums ~ Sex
+ Body_mass_index
+ Systolic_average
+ Diastolic_average
+ SDI_value
+ Physical_activity
+ Sleep_quality + Smoking + Brush_your_teeth + Times_brush_teeth + Brush_before_bed
+ Floss_use_after+ Times_floss_use + Times_mouthwash_use
+ Dental_care_12
+ Satisfaction_level + COVID19 + MetS, data = PdDB, family = "binomial")
summary(Had_bleeding_gums_mod1)
step(Had_bleeding_gums_mod1, test="LRT")
# Modelo final segun LRT
Had_bleeding_gums_mod1_final<- glm(formula = Had_bleeding_gums ~ Brush_your_teeth + Times_brush_teeth +
Brush_before_bed + Floss_use_after + Times_mouthwash_use,
family = "binomial", data = PdDB)
summary(Had_bleeding_gums_mod1_final)
vif(Had_bleeding_gums_mod1_final)
# Intervalos de confianza
confint(Had_bleeding_gums_mod1_final)
# Odds ratios Cálculo de razones de momios
lreg.or <-exp(cbind(OR = coef(Had_bleeding_gums_mod1_final)))
round(lreg.or, digits=4)
test_prob = predict(Had_bleeding_gums_mod1_final, newdata = PdDB, type = "response")
test_roc = roc(PdDB$Had_bleeding_gums ~ test_prob, plot = TRUE, print.auc = TRUE)
as.numeric(test_roc$auc)
###############################Blending2##############################
#se eliminaron + Bad_breath + Num_implant + Use_dental_prostheses + Gum_health + Implant_placed + Gum_treatment + Lost_bone+ Tooth_does_not_look_right + Have_gum_disease+ Lost_tooth + Loose_tooth + Num_loose_teeth + Gum_infected + Abscessed_teeth
Had_bleeding_gums_mod2 <- glm(Had_bleeding_gums ~ Sex
+ Body_mass_index
+ Systolic_average
+ Diastolic_average
+ Living_value + Health_security_value +Energetic_value
+ Edu_lag_value + Durable_goods_value + Sanitary_value
+ Physical_activity
+ Sleep_quality + Smoking + Brush_your_teeth + Times_brush_teeth + Brush_before_bed
+ Floss_use_after+ Times_floss_use + Times_mouthwash_use
+ Dental_care_12
+ Satisfaction_level + COVID19 + MetS, data = PdDB, family = "binomial")
summary(Had_bleeding_gums_mod2)
step(Had_bleeding_gums_mod2, test="LRT")
# Modelo final segun LRT
Had_bleeding_gums_mod2_final<- glm(formula = Had_bleeding_gums ~ Brush_your_teeth + Times_brush_teeth +
Brush_before_bed + Floss_use_after + Times_mouthwash_use,
family = "binomial", data = PdDB)
summary(Had_bleeding_gums_mod2_final)
vif(Had_bleeding_gums_mod2_final)
# Intervalos de confianza
confint(Had_bleeding_gums_mod2_final)
# Odds ratios Cálculo de razones de momios
lreg.or <-exp(cbind(OR = coef(Had_bleeding_gums_mod2_final)))
round(lreg.or, digits=4)
test_prob = predict(Had_bleeding_gums_mod2_final, newdata = PdDB, type = "response")
test_roc = roc(PdDB$Had_bleeding_gums ~ test_prob, plot = TRUE, print.auc = TRUE)
as.numeric(test_roc$auc)
############Brush_before_bed#################################################
#No se incluyó este modelo
#se eliminaron del modelo: Education_level + Brush_your_teeth + MetS
Brush_before_bed_mod <- glm(Brush_before_bed ~ Sex + Body_mass_index