Soybean | R Documentation |
The Soybean
data frame has 412 rows and 5 columns.
This data frame contains the following columns:
a factor giving a unique identifier for each plot.
a factor indicating the variety; Forrest (F) or Plant Introduction #416937 (P).
a factor indicating the year the plot was planted.
a numeric vector giving the time the sample was taken (days after planting).
a numeric vector giving the average leaf weight per plant (g).
These data are described in Davidian and Giltinan (1995, 1.1.3, p.7) as “Data from an experiment to compare growth patterns of two genotypes of soybeans: Plant Introduction #416937 (P), an experimental strain, and Forrest (F), a commercial variety.” In order to fit the Nonlinear data we sugest to use the three parameter logistic model as in Pinheiro & Bates (1995).
Pinheiro, J. C. and Bates, D. M. (2000), Mixed-Effects Models in S and S-PLUS, Springer, New York. (Appendix A.27)
Davidian, M. and Giltinan, D. M. (1995), Nonlinear Models for Repeated Measurement Data, Chapman and Hall, London.
## Not run: data(Soybean) attach(Soybean) ################################# #A full model (no covariate) y = weight #response x = Time #time #Expression for the three parameter logistic curve exprNL = expression((fixed[1]+random[1])/(1 + exp(((fixed[2]+random[2])- x)/(fixed[3]+random[3])))) #Initial values for fixed effects initial = c(max(y),0.6*max(y),0.73*max(y)) #A median regression (by default) median_reg = QRNLMM(y,x,Plot,initial,exprNL) #Assing the fit fxd = median_reg$res$beta nlmodel = median_reg$res$nlmodel seqc = seq(min(x),max(x),length.out = 500) group.plot(x = Time,y = weight,groups = Plot,type="l", main="Soybean profiles",xlab="time (days)", ylab="mean leaf weight (gr)",col="gray") lines(seqc,nlmodel(x = seqc,fixed = fxd,random = rep(0,3)), lwd=2,col="blue") #Histogram for residuals hist(median_reg$res$residuals) ######################################### #A model for comparing the two genotypes (with covariates) y = weight #response x = Time #time covar = c(Variety)-1 #factor genotype (0=Forrest, 1=Plan Introduction) #Expression for the three parameter logistic curve with a covariate exprNL = expression((fixed[1]+(fixed[4]*covar[1])+random[1])/ (1 + exp(((fixed[2]+random[2])- x)/(fixed[3]+random[3])))) #Initial values for fixed effects initial = c(max(y),0.6*max(y),0.73*max(y),3) # A quantile regression for the three quartiles box_reg = QRNLMM(y,x,Plot,initial,exprNL,covar,p=c(0.25,0.50,0.75)) #Assing the fit for the median (second quartile) fxd = box_reg[[2]]$res$beta nlmodel = box_reg[[2]]$res$nlmodel seqc = seq(min(x),max(x),length.out = 500) group.plot(x = Time[Variety=="P"],y = weight[Variety=="P"], groups = Plot[Variety=="P"],type="l",col="light blue", main="Soybean profiles by genotype",xlab="time (days)", ylab="mean leaf weight (gr)") group.lines(x = Time[Variety=="F"],y = weight[Variety=="F"], groups = Plot[Variety=="F"],col="gray") lines(seqc,nlmodel(x = seqc,fixed = fxd,random = rep(0,3),covar=1), lwd=2,col="blue") lines(seqc,nlmodel(x = seqc,fixed = fxd,random = rep(0,3),covar=0), lwd=2,col="black") ## End(Not run)
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