pheno.ddm {pheno} | R Documentation |
Creation of dense two-way classification design matrix for usage in robust parameter estimation with rq.fit.sfn (package nprq). The sum of the second factor is constrained to be zero. No general mean.
pheno.ddm(D,na.omit=TRUE)
D |
Data frame with three columns: (observations, factor 1, factor 2). |
na.omit |
Determined whether missing values should be omitted or not. Default is TRUE. |
In phenological applications observations should be the julian day
of observation of a certain phase, factor 1 should be the observation year
and factor 2 should be a station-id.
Usually this is much easier created by:
y <- factor(f1)
s <- factor(f2)
ddm <- as.matrix.csr(model.matrix(~ y + s -1, contrasts=list(s=("contr.sum"))))
.
However, this procedure can be quite memory demanding and might exceed storage
capacity for large problems.
This procedure here is much less memory comsuming.
ddm |
Dense roworder matrix, matrix.csr format (see matrix.csr in package SparseM) |
D |
Data frame D sorted first by f2 then by f1 and with rows containing NA's removed. |
na.rows |
Rows in D that were omitted due to missing values. |
Joerg Schaber
data(DWD) ddm1 <- pheno.ddm(DWD) attach(DWD) y <- factor(DWD[[2]]) s <- factor(DWD[[3]]) ddm2 <- as.matrix.csr(model.matrix(~ y + s -1, contrasts=list(s=("contr.sum")))) identical(ddm1$ddm,ddm2)