setwd("~/Google Drive/Agrosavia/colaboraciones/Laura")
datos6<-read.table("sanluisflo.csv", header=T, sep=',')
datos6$gen<-as.factor(datos6$gen)
datos6$bloque<-as.factor(datos6$bloque)
datos6$forestal<-as.factor(datos6$forestal)
attach(datos6)
library(ggplot2)
library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
#Gráfica cojines por planta
ggplot(datos6, aes(fill=gen, y=cojplanta, x=forestal)) +
geom_bar(position="dodge", stat="identity") +
labs(x="Forestal", y="Cojines por planta")
# Gráfica frutos por planta
ggplot(datos6, aes(fill=gen, y=frutos, x=forestal)) +
geom_bar(position="dodge", stat="identity") +
labs(x="Forestal", y="Frutos por planta")
#modelos
gfit1<-glm(cojplanta~forestal*gen, data=datos6, family = poisson (link=log))
gfit2<-glm(frutos~forestal*gen, data=datos6, family = poisson (link=log))
# resultado cojines por planta
summary(gfit1)
##
## Call:
## glm(formula = cojplanta ~ forestal * gen, family = poisson(link = log),
## data = datos6)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -9.428 -5.720 -1.716 2.256 15.621
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.44113 0.04218 81.577 < 2e-16 ***
## forestalRoble -0.42614 0.06711 -6.350 2.16e-10 ***
## forestalTerminalia 0.09499 0.05829 1.630 0.103184
## genTCS 01 -0.93334 0.07940 -11.755 < 2e-16 ***
## genTCS 06 -0.38870 0.06636 -5.857 4.71e-09 ***
## genTCS13 -0.66508 0.07238 -9.188 < 2e-16 ***
## genTCS19 -0.06241 0.06061 -1.030 0.303168
## forestalRoble:genTCS 01 0.79317 0.11029 7.192 6.39e-13 ***
## forestalTerminalia:genTCS 01 0.64458 0.10041 6.420 1.37e-10 ***
## forestalRoble:genTCS 06 0.09281 0.10388 0.893 0.371629
## forestalTerminalia:genTCS 06 0.64683 0.08528 7.585 3.32e-14 ***
## forestalRoble:genTCS13 0.66508 0.10339 6.433 1.25e-10 ***
## forestalTerminalia:genTCS13 0.33666 0.09542 3.528 0.000418 ***
## forestalRoble:genTCS19 -0.03481 0.09696 -0.359 0.719553
## forestalTerminalia:genTCS19 -0.67711 0.09317 -7.267 3.67e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 8665.4 on 269 degrees of freedom
## Residual deviance: 7914.4 on 255 degrees of freedom
## AIC: 8861.2
##
## Number of Fisher Scoring iterations: 6
anova(gfit1, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: cojplanta
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 269 8665.4
## forestal 2 226.23 267 8439.2 < 2.2e-16 ***
## gen 4 175.31 263 8263.9 < 2.2e-16 ***
## forestal:gen 8 349.44 255 7914.4 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# resultado frutos por planta
summary(gfit2)
##
## Call:
## glm(formula = frutos ~ forestal * gen, family = poisson(link = log),
## data = datos6)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4157 -2.0276 -1.4907 0.4377 15.5293
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.6314 0.1043 15.648 < 2e-16 ***
## forestalRoble -0.6716 0.1793 -3.746 0.000180 ***
## forestalTerminalia 0.1322 0.1428 0.926 0.354686
## genTCS 01 -1.1896 0.2158 -5.512 3.56e-08 ***
## genTCS 06 -1.3029 0.2255 -5.777 7.61e-09 ***
## genTCS13 -1.1545 0.2130 -5.421 5.92e-08 ***
## genTCS19 -0.4443 0.1668 -2.664 0.007732 **
## forestalRoble:genTCS 01 1.6702 0.2846 5.868 4.42e-09 ***
## forestalTerminalia:genTCS 01 1.1408 0.2571 4.437 9.13e-06 ***
## forestalRoble:genTCS 06 0.4485 0.3495 1.283 0.199395
## forestalTerminalia:genTCS 06 0.2599 0.2957 0.879 0.379444
## forestalRoble:genTCS13 -0.2978 0.3969 -0.750 0.453138
## forestalTerminalia:genTCS13 -3.4995 1.0271 -3.407 0.000656 ***
## forestalRoble:genTCS19 0.4008 0.2671 1.501 0.133434
## forestalTerminalia:genTCS19 -0.5988 0.2537 -2.360 0.018269 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2072.0 on 269 degrees of freedom
## Residual deviance: 1744.5 on 255 degrees of freedom
## AIC: 2114.1
##
## Number of Fisher Scoring iterations: 7
anova(gfit2, test = "Chisq")
## Analysis of Deviance Table
##
## Model: poisson, link: log
##
## Response: frutos
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 269 2072.0
## forestal 2 13.882 267 2058.1 0.0009671 ***
## gen 4 214.073 263 1844.0 < 2.2e-16 ***
## forestal:gen 8 99.524 255 1744.5 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Contrastes para Cojines
library(emmeans)
contrast <- emmeans(gfit1, ~ forestal * gen)
plot(contrast, comparisons = TRUE, xlab ="LN(Cojines por planta)")
#Contrastes para frutos
contrast2 <- emmeans(gfit2, ~ forestal * gen)
plot(contrast2, comparisons = TRUE, xlab = "LN(Frutos por planta)")
detach(datos6)