lrp | R Documentation |
Longitudinal Recursive Partitioning
lrp( method, nlme.model = NULL, randomFormula, fixedFormula = NULL, data, start, group, rPartFormula, weight = NULL, use_parallel = FALSE, R = NULL, min.dev = NULL, control = rpart.control() )
method |
Whether to use lme() or nlme(). Use either method="lme" or method="nlme". This changes what additional arguments need to be passed. |
nlme.model |
Necessary to specify if method="nlme" |
randomFormula |
Random effects to include for nlme() or lme() |
fixedFormula |
Fixed effects to include for nlme() or lme() |
data |
Dataset |
start |
Starting values for nlme() |
group |
Grouping for nlme() |
rPartFormula |
Not sure yet |
weight |
Sample weights to be passed to rpart |
use_parallel |
Whether to parallelize the split models |
R |
Correlation matrix to use for nlme. this is correlation= |
min.dev |
The minimum decrease in deviance to choose a split. Note that this overrides the default cp criterion in rpart.control() |
control |
Control function to be passed to rpart() |
library(longRPart2) data(ex.data.3) model.0 = nlme(y~b0i+b1i*time, data=ex.data.3, fixed=b0i+b1i~1, random=b0i+b1i~1, group=~id, start=c(10,5)) lcart.mod1 <- lrp(method="nlme", nlme.model=y~b0i+b1i*time, fixedFormula=b0i+b1i~1, rPartFormula = ~ z, group= ~ id, randomFormula=b0i+b1i~1, data=ex.data.3, start=c(10,5)) data(lcart.mod1) summary(lcart.mod1) plot(lcart.mod1) # for smooth_method, "loess" is recommend but "gam" faster lrp2Plot(lcart.mod1,smooth_method="gam")
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