Performs parameter stability test (Kundu and Harezlak, 2019) with categorical partitioning variable to determine whether the parameters of linear mixed effects model remains same across all distinct values of given categorical partitioning variable.
StabCat(data, patid, fixed, splitvar)name of the dataset. It must contain variable specified for patid (indicating subject id) and all the variables specified in the formula and the caterogrical partitioning variable of interest specified in splitvar. Note that, only numerically coded categorical variable should be specified.
name of the subject id variable.
a two-sided linear formula object describing the fixed-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. Model with -1 to the end of right side indicates no intercept. For model with no fixed effect beyond intercept, please specify only 1 right to the ~ operator.
the categorical partitioning variable of interest. It's value should not change over time.
It returns the p-value for parameter instability test
The categorical 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 categorical partitioning
variable of interest. StabCat() 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.
Kundu, M. G., and Harezlak, J. (2019). Regression trees for longitudinal data with baseline covariates. Biostatistics & Epidemiology, 3(1):1-22.
# NOT RUN {
#--- Get the data
data(ACTG175)
#--- Run StabCat()
out<- StabCat(data=ACTG175, patid="pidnum", fixed=cd4~time, splitvar="gender")
out$pval
# }
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