Fit robust row-column models to a matrix
These functions fit row-column effect models to matrices using PLM-d
rcModelPLMd(y,group.labels)
y |
A numeric matrix |
group.labels |
A vector of group labels. Of length |
This functions first tries to fit row-column models to the specified input matrix. Specifically the model
y_ij = r_i + c_j + e_ij
with r_i and c_j as row and column effects respectively. Note that these functions treat the row effect as the parameter to be constrained using sum to zero.
Next the residuals for each row are compared to the group variable. In cases where there appears to be a significant relationship, the row-effect is "split" and separate row-effect parameters, one for each group, replace the single row effect.
A list with following items:
Estimates |
The parameter estimates. Stored in column effect then row effect order |
Weights |
The final weights used |
Residuals |
The residuals |
StdErrors |
Standard error estimates. Stored in column effect then row effect order |
WasSplit |
An indicator variable indicating whether or not a row was split with separate row effects for each group |
B. M. Bolstad bmb@bmbolstad.com
col.effects <- c(10,11,10.5,12,9.5)
row.effects <- c(seq(-0.5,-0.1,by=0.1),seq(0.1,0.5,by=0.1))
y <- outer(row.effects, col.effects,"+")
y <- y + rnorm(50,sd=0.1)
rcModelPLMd(y,group.labels=c(1,1,2,2,2))
row.effects <- c(4,3,2,1,-1,-2,-3,-4)
col.effects <- c(8,9,10,11,12,10)
y <- outer(row.effects, col.effects,"+") + rnorm(48,0,0.25)
y[8,4:6] <- c(11,12,10)+ 2.5 + rnorm(3,0,0.25)
y[5,4:6] <- c(11,12,10)+-2.5 + rnorm(3,0,0.25)
rcModelPLMd(y,group.labels=c(1,1,1,2,2,2))
par(mfrow=c(2,2))
matplot(y,type="l",col=c(rep("red",3),rep("blue",3)),ylab="residuals",xlab="probe",main="Observed Data")
matplot(rcModelPLM(y)$Residuals,col=c(rep("red",3),rep("blue",3)),ylab="residuals",xlab="probe",main="Residuals (PLM)")
matplot(rcModelPLMd(y,group.labels=c(1,1,1,2,2,2))$Residuals,col=c(rep("red",3),rep("blue",3)),xlab="probe",ylab="residuals",main="Residuals (PLM-d)")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.