4_diseños
F.Villatoro
10/15/2020
packages
if(!require(googlesheets4)){install.packages("googlesheets4")}
if(!require(AICcmodavg)){install.packages("AICcmodavg")}
if(!require(tidyverse)){install.packages("tidyverse")}
if(!require(DescTools)){install.packages("DescTools")}
if(!require(car)){install.packages("car")}
if(!require(lme4)){install.packages("lme4")}
if(!require(ggpubr)){install.packages("ggpubr")}
if(!require(gridExtra)){install.packages("gridExtra")}
#
library(googlesheets4)
library(AICcmodavg)
library(tidyverse)
library(DescTools)
library(car)
library(lme4)
library(ggpubr)
library(gridExtra)Cuatro tipos de diseño
Según Gotelli & Ellison (2013)
Cuatro tipos de Diseño
Ejemplos
Regresión
Import data
ss="https://docs.google.com/spreadsheets/d/1CMPY0Bjm6Kc3XUq0mZtgIoyXAacdWVkv2KONh0T-inU/edit?usp=sharing"
hoja="Hoja1"
rango="A1:E18"
latsp <- data.frame(read_sheet(ss=ss,
sheet=hoja,
range=rango,
col_names = TRUE,
na= "NA")
)
latsp$group <- factor(latsp$group)Explore
modelo respuesta -> species
predictora -> latitude
head(latsp)## town state latitude species group
## 1 Seaford-Nanticoke DE 38.583 94 a
## 2 Denton MD 38.900 96 a
## 3 Elkton MD 39.533 98 a
## 4 Middletown DE 39.467 108 a
## 5 Salisbury MD 38.333 108 a
## 6 Wilmington DE 39.733 113 asummary(latsp)## town state latitude species group
## Length:17 Length:17 Min. :37.20 Min. : 94 a:9
## Class :character Class :character 1st Qu.:38.32 1st Qu.:108 b:8
## Mode :character Mode :character Median :38.60 Median :118
## Mean :38.64 Mean :120
## 3rd Qu.:39.13 3rd Qu.:128
## Max. :39.73 Max. :157# ggplot
ggplot(data= latsp, aes(x=latitude, y= species)) +
geom_point()
#### Analysis
reg1 <- lm(data=latsp, species ~ latitude)
summary(reg1)##
## Call:
## lm(formula = species ~ latitude, data = latsp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.635 -11.198 -1.993 14.569 28.162
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 585.145 230.024 2.544 0.0225 *
## latitude -12.039 5.953 -2.022 0.0613 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.37 on 15 degrees of freedom
## Multiple R-squared: 0.2143, Adjusted R-squared: 0.1619
## F-statistic: 4.09 on 1 and 15 DF, p-value: 0.06134ggplot
p.reg <- ggplot(data= latsp, aes(x=latitude, y= species, col=)) +
geom_point() +
geom_smooth(method="lm")
p.reg
Supuestos
plot(reg1, which=c(1))
#
hist(reg1$residuals)
shapiro.test(reg1$residuals)##
## Shapiro-Wilk normality test
##
## data: reg1$residuals
## W = 0.97504, p-value = 0.899ANDEVA
data
head(latsp)## town state latitude species group
## 1 Seaford-Nanticoke DE 38.583 94 a
## 2 Denton MD 38.900 96 a
## 3 Elkton MD 39.533 98 a
## 4 Middletown DE 39.467 108 a
## 5 Salisbury MD 38.333 108 a
## 6 Wilmington DE 39.733 113 aExplore
modelo respuesta -> species
predictora -> group
summary(latsp)## town state latitude species group
## Length:17 Length:17 Min. :37.20 Min. : 94 a:9
## Class :character Class :character 1st Qu.:38.32 1st Qu.:108 b:8
## Mode :character Mode :character Median :38.60 Median :118
## Mean :38.64 Mean :120
## 3rd Qu.:39.13 3rd Qu.:128
## Max. :39.73 Max. :157# ggplot
p.aov<- ggplot(data= latsp, aes(x=group, y= species)) +
geom_boxplot() +
geom_point(col=6,
position = position_jitter(width=0.1, height=0)) +
geom_hline(yintercept = mean(latsp$species), lty="dashed", lwd=1)
p.aov
Analysis
aov1 <- aov(data=latsp, species ~ group)
summary(aov1)## Df Sum Sq Mean Sq F value Pr(>F)
## group 1 3015 3014.9 21.5 0.000322 ***
## Residuals 15 2103 140.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Supuestos
y= latsp$species
binwidth=4
ggplot(data=latsp, aes(x=y)) + geom_histogram(aes(y=..density..), binwidth=binwidth,colour="black", fill="white") + geom_density(alpha=.2, fill="#FF6666")
shapiro.test(latsp$species)##
## Shapiro-Wilk normality test
##
## data: latsp$species
## W = 0.95152, p-value = 0.4809shapiro.test(reg1$residuals)##
## Shapiro-Wilk normality test
##
## data: reg1$residuals
## W = 0.97504, p-value = 0.899leveneTest(aov1)## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 1.2805 0.2756
## 15===============================================
Análisis de Frecuencias (tablas de contingencia)
Import Data
ss="https://docs.google.com/spreadsheets/d/1sd5Bkkhs2CBl-5mpRUNPv0WfqZr3KQwjbgScY27Tf-s/edit?usp=sharing"
hoja="Sheet3"
rango="A2:C5"
dogs <- read_sheet(ss=ss,
sheet=hoja,
range=rango,
col_names = TRUE,
na= "NA"
)## Reading from "TresDiseños_Clase"## Range "'Sheet3'!A2:C5"dogs$total <- dogs$imig+dogs$local
dogs$Pr.imig <- dogs$imig/dogs$total
obs <- cbind(dogs$imig, dogs$local)Explore
modelo
respuesta -> origen
predictora -> edad
dogs## # A tibble: 3 x 5
## edad imig local total Pr.imig
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 adulto 20 19 39 0.513
## 2 Juv 3 16 19 0.158
## 3 cachorro 4 203 207 0.0193# ggplot
pr.gen <- sum(dogs$imig)/sum(dogs$total)
p.tc <- ggplot(data= dogs,
aes(x=edad, y= Pr.imig, fill=edad)) +
geom_col() +
geom_hline(yintercept = pr.gen, lty="dashed", lwd=1) +
ylim(0,0.8)
p.tc
Analysis
chi2 <- chisq.test(obs)## Warning in chisq.test(obs): Chi-squared approximation may be incorrectchi2##
## Pearson's Chi-squared test
##
## data: obs
## X-squared = 88.043, df = 2, p-value < 2.2e-16chi2$stdres## [,1] [,2]
## [1,] 9.1864627 -9.1864627
## [2,] 0.8376399 -0.8376399
## [3,] -8.3937442 8.3937442chi2$expected## [,1] [,2]
## [1,] 3.973585 35.02642
## [2,] 1.935849 17.06415
## [3,] 21.090566 185.90943dogs[, c(1,2,3)]## # A tibble: 3 x 3
## edad imig local
## <chr> <dbl> <dbl>
## 1 adulto 20 19
## 2 Juv 3 16
## 3 cachorro 4 203==============================================================
Regresión logística
Import Data
ss="https://docs.google.com/spreadsheets/d/1CMPY0Bjm6Kc3XUq0mZtgIoyXAacdWVkv2KONh0T-inU/edit?usp=sharing"
hoja="Hoja3"
rango="A1:D32"
fever <- data.frame(read_sheet(ss=ss,
sheet=hoja,
range=rango,
col_names = TRUE,
na= "NA")
)## Reading from "Regres_datos"## Range "'Hoja3'!A1:D32"Explore
modelo respuesta -> fever
predictora -> Pulse
summary(fever)## Species Temp Pulse fever
## Length:31 Min. :17.20 Min. : 44.30 Min. :0.0000
## Class :character 1st Qu.:20.80 1st Qu.: 59.45 1st Qu.:0.0000
## Mode :character Median :24.00 Median : 76.20 Median :0.0000
## Mean :23.76 Mean : 72.89 Mean :0.3871
## 3rd Qu.:26.35 3rd Qu.: 85.25 3rd Qu.:1.0000
## Max. :30.40 Max. :101.70 Max. :1.0000# ggplot
ggplot(data= fever,
aes(x=Pulse, y= fever)) +
geom_point(col=6,
position = position_jitter(width=0.1, height=0.02)) +
geom_hline(yintercept = 0.5, lty="dashed", lwd=0.6)
#### Analysis
reglog <- glm(data=fever,
fever ~ Pulse,
family= binomial)
summary(reglog)##
## Call:
## glm(formula = fever ~ Pulse, family = binomial, data = fever)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.38513 -0.16483 -0.00855 0.14584 1.62815
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -27.4351 13.0573 -2.101 0.0356 *
## Pulse 0.3472 0.1657 2.095 0.0362 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 41.381 on 30 degrees of freedom
## Residual deviance: 14.002 on 29 degrees of freedom
## AIC: 18.002
##
## Number of Fisher Scoring iterations: 8ggplot
p.rlog <- ggplot(data= fever,
aes(x=Pulse, y= fever)) +
geom_point(col=6,
position = position_jitter(width=0.1, height=0.02)) +
geom_hline(yintercept = 0.5, lty="dashed", lwd=0.6) +
geom_smooth(method="glm",method.args=list(family=binomial))
p.rlog## `geom_smooth()` using formula 'y ~ x'
===============================================
Referencias
- Gotelli, N. J., & Ellison, M. (2013). A Primer of Ecological Statistics (2nd Editio). Sinauer Associates, Inc.