# 1. Modelos estadisticos para medidas repetidas. ```{r Importar datos, eval=TRUE} ranas <- read.table("rana.txt", header=T) head(ranas) levels(ranas$Sustrato)<- c("Turba", "Suelo", "Musgo") ``` ```{r graficar datos, eval=TRUE} layout(mat=matrix(1:4, nrow=2)) plot(ranas$perdidasqr~ranas$Pesoi, ylab="Perdida de agua (transformacion raiz cuadrada)", xlab="Peso inicial(g)") plot(ranas$perdidasqr~ranas$TemAire, ylab="Perdida de agua (transformacion raiz cuadrada)", xlab="Temperatura del aire(C)") plot(ranas$perdidasqr~ranas$Sombra, ylab="Perdida de agua (transformacion raiz cuadrada)", xlab="Sombra") plot(ranas$perdidasqr~ranas$Sustrato, ylab="Perdida de agua (transformacion raiz cuadrada)", xlab="Tipo de sustrato") ``` ```{r correr gee, eval=TRUE} ##cargar paquete gee library(gee) mod_gee_ar1<-gee(perdidasqr~Pesoi+TemAire+Sombra+Sustrato, data=ranas, family=gaussian, corstr="AR-M", Mv=1, id=ID) mod_gee_unstr<-gee(perdidasqr~Pesoi+TemAire+Sombra+Sustrato, data=ranas, family=gaussian corstr="unstructured", id=ID) ``` Resumen ```{r resumen gee, eval=TRUE} summary(mod_gee_ar1) ``` Verificar ajuste ```{r verificar ajuste, eval=TRUE} ##parametro de dispersion glm1<-glm(perdidasqr~Pesoi+TemAire+Sombra+Sustrato, data=ranas, , family=gaussian) sum(residuals(glm1, type="pearson")^2)/glm1$df.residual ##residuales plot(mod_gee_ar1$residuals~mod_gee_ar1$fitted, ylab="Residuales", xlab="Valores predichos") ##SCE sse.ar1<-sum((ranas$perdidasqr-fitted(mod_gee_ar1))^2) ##valores p tmp <- summary(mod_gee_ar1) pvalue2sided <- 2*pnorm(-abs(tmp$coefficients[,5])) pvalue2sided ``` Datos de bacteria ```{r datos bacteria, eval=TRUE} bacteria <-read.table("bacteria.txt", header = T) head(bacteria) bacteria$Presence1<-ifelse(bacteria$Presence=="y", 1, 0) hist(bacteria$Presence1) ``` ```{r correr modelos, eval=TRUE} mod_bact_ar1<-gee(Presence1 ~ Treat_combo + Week + Treat_combo:Week, data = bacteria, id = ID, family = binomial,corstr = "AR-M", Mv = 1) ##No hay efecto significativo de la interaccion, por lo que podemos correr modelo sin interaccion mod_bactb_ar1<-gee(Presence1 ~ Treat_combo + Week, data = bacteria, id = ID, family = binomial, corstr = "AR-M", Mv = 1) summary(mod_bact_ar1) tmp2 <- summary(mod_bact_ar1) pvalue2sideda <- 2*pnorm(-abs(tmp2$coefficients[,5])) pvalue2sideda summary(mod_bactb_ar1) tmp3 <- summary(mod_bactb_ar1) pvalue2sidedb <- 2*pnorm(-abs(tmp3$coefficients[,5])) pvalue2sidedb ``` ```{r residuales, eval=TRUE} plot(mod_bactb_ar1$residuals~mod_bactb_ar1$fitted, ylab="Residuales", xlab="Valores predichos") ```