View source: R/LongCART_subfunctions.R
StabCont | R Documentation |
Performs parameter stability test (Kundu and Harezlak, 2019) with continuous partitioning variable to determine whether the parameters of linear mixed effects model remains same across all distinct values of given continuous partitioning variable.
StabCont(data, patid, fixed, splitvar)
data |
name of the dataset. It must contain variable specified for |
patid |
name of the subject id variable. |
fixed |
a two-sided linear formula object describing the fixed-effects part of the model, with the response on the left of a |
splitvar |
the continuous partitioning variable of interest. It's value should not change over time. |
The continuous partitioning variable of interest. It's value should not change over time.
Y_i(t)= W_i(t)theta + b_i + epsilon_{it}
where W_i(t) is the design matrix, theta
is the parameter associated with
W_i(t) and b_i
is the random intercept. Also, epsilon_{it} ~ N(0,sigma ^2)
and b_i ~ N(0, sigma_u^2). Let X be the baseline continuous partitioning
variable of interest. StabCont()
performs the following omnibus test
H_0:theta_{(g)}=theta_0 vs. H_1: theta_{(g)} ^= theta_0, for all g
where, theta_{(g)} is the true value of theta for subjects with X=C_g where C_g is the any value realized by X.
p |
It returns the p-value for parameter instability test |
Madan Gopal Kundu madan_g.kundu@yahoo.com
Kundu, M. G., and Harezlak, J. (2019). Regression trees for longitudinal data with baseline covariates. Biostatistics & Epidemiology, 3(1):1-22.
StabCont
, LongCART
, plot
, text
#--- Get the data data(ACTG175) #--- Run StabCont() out<- StabCont(data=ACTG175, patid="pidnum", fixed=cd4~time, splitvar="age") out$pval
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