Los ANOVAs de medidas repetidas con animales o forestales emplean análisis más avanzados. Este y este otro sitio
ofrecen discusiones.
 |
|
El primer sitio aporta ejemplos en R. Los códigos siguientes, los ajustamos del eNotes-11 para resolver el ejemplo “13.7 Repeated measures”: tratamientos de enrofloxacina en cerdos. El manual del segundo sitio ofrece códigos en SAS para forestales.
## Course : 02429 Analysis of correlated data: Mixed Linear Models
## Teacher : Per Bruun Brockhoff
## DTU Compute - Section for Statistics and Data Analysis
## Technical University of Denmark (DTU); E-mail:perbb@dtu.dk
## Repeated measures, part 1, simple methods
## http://02429.compute.dtu.dk/enote/afsnit/NUID191/
## 13.7 Repeated measures
## Enrofloxacin treatmentents of pigs
## http://02429.compute.dtu.dk/enote/afsnit/NUID193/
# Importando datos
enrofloxacin <- read.table("C:/Users/Administrator/Desktop/enrofloxacin.txt", header = TRUE, sep = ",", dec=".")
attach(enrofloxacin)
# Factores para variables independientes del ANOVA
position <- factor(position)
treatment <- factor(treatment)
pig <- factor(pig)
enrofloxacin## treatment pig position conc
## 1 PO 1 e1 0.4
## 2 PO 2 e1 1.3
## 3 PO 3 e1 0.8
## 4 PO 4 e1 4.2
## 5 PO 5 e1 1.4
## 6 PO 6 e1 1.4
## 7 IM 7 e1 2.3
## 8 IM 8 e1 1.7
## 9 IM 9 e1 2.2
## 10 IM 10 e1 1.8
## 11 IM 11 e1 1.7
## 12 IM 12 e1 1.7
## 13 PO 1 e2 0.3
## 14 PO 2 e2 2.1
## 15 PO 3 e2 0.8
## 16 PO 4 e2 4.4
## 17 PO 5 e2 2.5
## 18 PO 6 e2 0.8
## 19 IM 7 e2 2.9
## 20 IM 8 e2 2.1
## 21 IM 9 e2 2.1
## 22 IM 10 e2 1.9
## 23 IM 11 e2 1.6
## 24 IM 12 e2 1.8
## 25 PO 1 e3 0.2
## 26 PO 2 e3 0.9
## 27 PO 3 e3 0.6
## 28 PO 4 e3 1.4
## 29 PO 5 e3 1.0
## 30 PO 6 e3 0.7
## 31 IM 7 e3 1.7
## 32 IM 8 e3 2.0
## 33 IM 9 e3 2.0
## 34 IM 10 e3 1.5
## 35 IM 11 e3 1.5
## 36 IM 12 e3 1.5
## 37 PO 1 e4 0.1
## 38 PO 2 e4 0.8
## 39 PO 3 e4 0.4
## 40 PO 4 e4 1.1
## 41 PO 5 e4 0.8
## 42 PO 6 e4 0.6
## 43 IM 7 e4 1.5
## 44 IM 8 e4 1.3
## 45 IM 9 e4 1.2
## 46 IM 10 e4 1.2
## 47 IM 11 e4 1.2
## 48 IM 12 e4 1.0
## 49 PO 1 e5 0.2
## 50 PO 2 e5 0.7
## 51 PO 3 e5 0.5
## 52 PO 4 e5 1.1
## 53 PO 5 e5 0.6
## 54 PO 6 e5 0.5
## 55 IM 7 e5 1.2
## 56 IM 8 e5 1.2
## 57 IM 9 e5 1.3
## 58 IM 10 e5 1.0
## 59 IM 11 e5 0.9
## 60 IM 12 e5 1.0# Gráfico de líneas para cada cerdo
with(enrofloxacin, interaction.plot(position, pig, conc, legend = T,
lty = rep(1:12), col = rep(1:12),
leg.bty = "o", lwd = 2))
# Gráfico de líneas empleando ggplot2
library(ggplot2)enrofloxacin$positionQ <- as.numeric(enrofloxacin$position)
p <- qplot(positionQ, conc, data = enrofloxacin)
p <- p + geom_line(aes(x=positionQ, y = conc, group = pig, colour = treatment));print(p)
# Gráfico de líneas agrupadas por medias
library(doBy)## Warning: package 'doBy' was built under R version 3.3.2mns <- summaryBy(conc ~ position + treatment, id=~positionQ, keep.names=TRUE, data=enrofloxacin)
p <- qplot(positionQ, conc, data = mns)
p <- p + geom_line(aes(x=positionQ, y=conc, group=treatment, colour=treatment));print(p)
head(enrofloxacin)## treatment pig position conc positionQ
## 1 PO 1 e1 0.4 1
## 2 PO 2 e1 1.3 1
## 3 PO 3 e1 0.8 1
## 4 PO 4 e1 4.2 1
## 5 PO 5 e1 1.4 1
## 6 PO 6 e1 1.4 1tail(enrofloxacin)## treatment pig position conc positionQ
## 55 IM 7 e5 1.2 5
## 56 IM 8 e5 1.2 5
## 57 IM 9 e5 1.3 5
## 58 IM 10 e5 1.0 5
## 59 IM 11 e5 0.9 5
## 60 IM 12 e5 1.0 5library(plyr)lmbyposition <- lmBy(conc~treatment|position, data=enrofloxacin)
ldaov <- ldply(lmbyposition, anova) # presenta data frame de resultados
ldaov[(1:10)*2-1,5:6]## F value Pr(>F)
## 1 0.3204332 0.5838261303
## 3 0.1487898 0.7077732524
## 5 21.6964286 0.0008971212
## 7 14.4642857 0.0034676215
## 9 13.3928571 0.0043918058# ANOVA
sumMeasure <- summaryBy(exp(conc) ~ pig, id=~treatment,
keep.names=TRUE, FUN = sum, data=enrofloxacin)
sumMeasure$conc <- log(sumMeasure$"exp(conc)")
anova(lm(conc ~ treatment, data = sumMeasure))## Analysis of Variance Table
##
## Response: conc
## Df Sum Sq Mean Sq F value Pr(>F)
## treatment 1 0.3568 0.35681 0.5251 0.4853
## Residuals 10 6.7948 0.67948library(lmerTest)modellmer1 <- lmer(conc ~ position + treatment + position:treatment
+(1 | pig), data = enrofloxacin); anova(modellmer1)## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## position 11.2423 2.81058 4 40 12.7377 8.945e-07 ***
## treatment 0.5594 0.55941 1 10 2.5353 0.1424
## position:treatment 0.7957 0.19892 4 40 0.9015 0.4722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1VarCorr(modellmer1)## Groups Name Std.Dev.
## pig (Intercept) 0.51738
## Residual 0.46973-2*logLik(modellmer1)## 'log Lik.' 103.8052 (df=12)# ANOVA con nlme y la funcion lme
library(nlme)model1 <- lme(conc ~ position + treatment + position:treatment,
random = ~1 | pig, data = enrofloxacin); anova(model1)## numDF denDF F-value p-value
## (Intercept) 1 40 69.44710 <.0001
## position 4 40 12.73774 <.0001
## treatment 1 10 2.53528 0.1424
## position:treatment 4 40 0.90150 0.4722VarCorr(model1)## pig = pdLogChol(1)
## Variance StdDev
## (Intercept) 0.2676833 0.5173812
## Residual 0.2206500 0.4697340-2*logLik(model1)## 'log Lik.' 103.8052 (df=12)intervals(model1, which = "var-cov")## Approximate 95% confidence intervals
##
## Random Effects:
## Level: pig
## lower est. upper
## sd((Intercept)) 0.3101369 0.5173812 0.8631134
##
## Within-group standard error:
## lower est. upper
## 0.3772990 0.4697340 0.5848146ins <- intervals(model1, which = "var-cov")
lins <- log(ins$sigma)
lins[2]-lins[1]## est.
## 0.2191284lins[3]-lins[2]## upper
## 0.2191284Datos y códigos adjuntos:
cerdos ANOVA medidas repetidas.r, enrofloxacin.txt
No hay comentarios:
Publicar un comentario