setwd("~/Dropbox/Fernando 2018/archives/data")
library("lme4", lib.loc="/Library/Frameworks/R.framework/Versions/3.5/Resources/library")
## Loading required package: Matrix
enemy<- read.table("invas_fam_out_2.csv", sep = ",", header = T)
attach(enemy)
damage<-as.factor(damage)
fam<-as.factor(fam)
##Modelo sin interacciónes
fit1<-lmer(rel.fitness~damage*origen+(1|pop))
summary(fit1)
## Linear mixed model fit by REML ['lmerMod']
## Formula: rel.fitness ~ damage * origen + (1 | pop)
##
## REML criterion at convergence: 452.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3955 -0.4856 0.0592 0.5255 5.2419
##
## Random effects:
## Groups Name Variance Std.Dev.
## pop (Intercept) 0.0000 0.0000
## Residual 0.1885 0.4342
## Number of obs: 376, groups: pop, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.86770 0.05345 16.235
## damage1 -0.12391 0.07779 -1.593
## origenMEX 0.27112 0.06635 4.086
## damage1:origenMEX 0.04528 0.09518 0.476
##
## Correlation of Fixed Effects:
## (Intr) damag1 orgMEX
## damage1 -0.687
## origenMEX -0.806 0.553
## dmg1:rgnMEX 0.562 -0.817 -0.697
##Probando efectos fijos (damage) con la correción Kenward-Roger para datos desbalanceados
library(pbkrtest)
modtodos<-lmer(rel.fitness~damage+origen+(1|pop)+damage:origen, REML = F)
modsindamage<-lmer(rel.fitness~origen+(1|pop), REML = F)
p.damage<-KRmodcomp(modtodos,modsindamage)
p.damage
## F-test with Kenward-Roger approximation; computing time: 0.13 sec.
## large : rel.fitness ~ damage + origen + (1 | pop) + damage:origen
## small : rel.fitness ~ origen + (1 | pop)
## stat ndf ddf F.scaling p.value
## Ftest 2.2964 2.0000 370.2336 1 0.1021
modsinorigen<-lmer(rel.fitness~damage+(1|pop), REML = F)
p.origen<-KRmodcomp(modtodos,modsinorigen)
p.origen
## F-test with Kenward-Roger approximation; computing time: 0.08 sec.
## large : rel.fitness ~ damage + origen + (1 | pop) + damage:origen
## small : rel.fitness ~ damage + (1 | pop)
## stat ndf ddf F.scaling p.value
## Ftest 16.915 2.000 6.356 0.90687 0.00285 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
modsininter<-lmer(rel.fitness~damage+origen+(1|pop), REML=F)
p.inter<-KRmodcomp(modtodos,modsininter)
p.inter
## F-test with Kenward-Roger approximation; computing time: 0.08 sec.
## large : rel.fitness ~ damage + origen + (1 | pop) + damage:origen
## small : rel.fitness ~ damage + origen + (1 | pop)
## stat ndf ddf F.scaling p.value
## Ftest 0.2263 1.0000 370.2284 1 0.6346
##Probando efectos aleatorios
library(lmerTest)
##
## Attaching package: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
ranova(fit1)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## rel.fitness ~ damage + origen + (1 | pop) + damage:origen
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 6 -226.47 464.94
## (1 | pop) 5 -226.47 462.94 0 1 1
detach("package:lmerTest", unload=TRUE)
#Finalmente el diagnostico de residuales del modelo...
plot(residuals(fit1)~predict(fit1,type="link"), xlab=expression(hat(eta)), ylab="Deviance residuals")
#...y los intervalos de confianza para las familias y poblaciones
library(lattice)
ranef(fit1)
## $pop
## (Intercept)
## morelos 0
## teotihuacan 0
## valdeflores 0
## zubia 0
dput(cl<-ranef(fit1,condVar=T))
## structure(list(pop = structure(list(`(Intercept)` = c(0, 0, 0,
## 0)), class = "data.frame", row.names = c("morelos", "teotihuacan",
## "valdeflores", "zubia"), postVar = structure(c(0, 0, 0, 0), .Dim = c(1L,
## 1L, 4L)))), class = "ranef.mer")
dotplot(cl)
## $pop
