Semana 4 (20/marzo)

Ejercicio

var:223

N=30

IC97%

x=18.5

Resolver z

qnorm(0.985,30) #Z teorico  32.17009
#r=18.5+-(  32.17009)*(raiz(:223)/(raiz (30)))

18.5+ 32.17009* (sqrt(223)/(sqrt (30))) #106.209
18.5- 32.17009*(sqrt(223)/(sqrt (30))) #-69.20898

Anovas (Analisis de varianza )

Se usa qf para determinar el teorico

data("InsectSprays")  
conteo<-InsectSprays$count
spray<-InsectSprays$spray
a<-aov(conteo~spray)
summary(a)
plot(a)
boxplot(InsectSprays$count~InsectSprays$spray)
length(spray)

Regresiones y correlaciones

El coeficiente de variacion se llama R, es util para determinar si hay relacion lineal entre dos variales.

El parametro coficiente de correlacion lineal va de -1 a 1. Es adimencional

Practica

library(readr)
geo <- read_delim("base de datos/geo.csv", 
    delim = ";", escape_double = FALSE, trim_ws = TRUE)
str(geo)
View(geo)

#Cargar desde web:
link <- "https://raw.githubusercontent.com/entomolab/bases_datos/master/geo.csv"
geo_2<-read.csv(link, header = T, sep= ";")

#correlacion 
#Total_Dist vs DOC
cor(geo$Total_Diss,geo$DOC_mg_L)
plot(geo$Total_Diss,geo$DOC_mg_L)
cor.test(geo$Total_Diss,geo$DOC_mg_L)

#NO3 vs temp
cor.test(geo$NO3_mg_L,geo$Temperatur)
plot(geo$NO3_mg_L,geo$Temperatur)
0.4541717^2 #coeficiente de determinacion 
shapiro.test(geo$NO3_mg_L)#"asimetrico"
shapiro.test(geo$Temperatur)#"asimetrico"
#como los datos son asimetricos se utiliza el metodo spearman
cor.test(geo$NO3_mg_L,geo$Temperatur,method= "spearman")
install.packages("corrplot")
library(corrplot)

grafica_corr<-cor(geo[,-c(2,17,38,39)])
corrplot(grafica_corr)
library(PerformanceAnalytics)
dat<-data.frame(geo$NO3_mg_L,geo$Temperatur)
chart.Correlation(dat,method = "s")

Regresiones:

model<-lm(geo$Fe_ug_L~ geo$Hg_ug_L)

summary(model)
anova(model)
 #F value: calculado (0.1125)
#F teorico :

qf(0.95,1,38)# 4.098172
windows()
par(mfrow=c(2,2))
plot(model)

#eliminando valores atipicos
#model<-lm(y~x,data)
model1<-lm(Fe_ug_L~ Hg_ug_L, geo[-c(11,20,40), ])
summary(model)
anova(model)

windows()
par(mfrow=c(2,2))
plot(model)

#Library GGplot
windows()
grafico <- ggplot(geo, aes(x=Hg_ug_L, y=Fe_ug_L )) +
        geom_point(size=7) +
        geom_smooth(method = lm) +
        annotate("text", label = "R2=25",
    x = 2, y = 500, size = 8, colour = "red") +
    theme_bw()
grafico

Practica

modelo1<-lm(geo$Temperatur~geo$pH)
summary(modelo1)
anova(modelo1)
windows()
par(mfrow=c(2,2))
plot(modelo1)
#y=mx+b
y= 2.916(pH)+2.425
y= 2.916*8+2.425

grafico1 <- ggplot(geo, aes(x=geo$pH, y=geo$Temperatur )) +
        geom_point(size=3) +
        geom_smooth(method = lm) 
#+
        #annotate("text", label = "R2=25",
    #x = 2, y = 500, size = 8, colour = "red") +
    theme_bw()
grafico1