semireg: Fitting Semi Parametric Models Using lme4 Ecosystem

In predictmeans: Predicted Means for Linear and Semiparametric Models

Fitting Semi Parametric Models Using lme4 Ecosystem

Description

Fit a semi parametric model based on lme4 ecosystem including lmer, glmer and glmer.nb.

Usage

semireg(formula, data, family = NULL, ngbinomial=FALSE, REML = TRUE, 
        smoothZ = list(), ncenter=TRUE, nscale=TRUE, resp_scale=FALSE, 
        control = lmerControl(optimizer="bobyqa"), start = NULL, 
        verbose = FALSE, drop.unused.levels=TRUE, subset, weights, 
        offset, contrasts = NULL,  prt=TRUE, ...)

Arguments

formula	A two-sided linear formula object describing both the fixed-effects and random-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. Random-effects terms are distinguished by vertical bars ("|") separating expressions for design matrices from grouping factors.
data	A data frame or list containing the model response variable and covariates required by the formula. By default the variables are taken from environment(formula and smoothZ), typically the environment from which semireg is called.
family	A GLM family, see glm and family.
ngbinomial	Logical scalar - Should a negative binomial GLMMs be used? .
REML	Logical scalar - Should the estimates be chosen to optimize the REML criterion (as opposed to the log-likelihood)?
smoothZ	A list includes a set of smooth Z matrixs (called 'smooth term') used in the mixed effects model, the name of 'smooth term' should be different any variables in the model, each 'smooth term' is the result of function smZ. e.g. smoothZ=list(sm1=smZ(x1), sm2=smZ(x2, by=f1), sm3=smZ(x3, by=f2, group=TRUE), ...) where 'sm1' to 'sm3' should be new variable names in the data, and x1 to x3 are covariates, and f1, f2 are factors.
ncenter	Logical scalar - Should the numeric predictors to be centered or not?
nscale	Logical scalar - Should the numeric predictors to be scaled or not?
resp_scale	Logical scalar - Should the response be involved in the scaling action or not?
control	A list (of correct class, resulting from lmerControl() or glmerControl() respectively) containing control parameters, including the nonlinear optimizer to be used and parameters to be passed through to the nonlinear optimizer, see the *lmerControl documentation for details.
start	Starting value list as used by lmer or glmer.
verbose	Passed on to fitting lme4 fitting routines.
drop.unused.levels	By default unused levels are dropped from factors before fitting. For some smooths involving factor variables you might want to turn this off. Only do so if you know what you are doing.
subset	An optional expression indicating the subset of the rows of data that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
weights	An optional vector of ‘prior weights’ to be used in the fitting process. Should be NULL or a numeric vector.
offset	This can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See model.offset.
contrasts	An optional list. See the contrasts.arg of model.matrix.default.
prt	Logical scalar - Should the info to be print on screen in the middle of the process or not?
...	Further arguments for passing on to model setup routines.

Details

A semi parametric model can be parameterized as a linear (or generalized linear) mixed model in which its random effects are smooth functions of some covariates (named ‘smooth term’). semireg follows the approach suggested by Wand and Ormerod (2008) and represents the 'smooth term' using O'Sullivan-type of Z.

Value

semer	A mer model used in the fitting.
data	A data.frame with generated variables in the fitting.
fomul_vars	Name of variables in the formula of semireg model.
sm_vars	Name of variables in the smoothZ list.
smoothZ_call	A call used to produce smooth terms in the fitting.
knots_lst	Knots used in each smooth term in the fitting.
range_lst	Range of covariate used in each smooth term in the fitting.
cov_lst	Covariance matrix list for each smooth term.
u_lst	Random effects list for each smooth term.
type_lst	Smooth type list of smooth terms.
CovMat	Covariance matrix for all smooth terms.
Cov_ind	Covariance matrix index for each smooth term.
Cov_indN	Covariance matrix index for each smooth term when group=TRUE in smoothZ argument.
df	Degree of freedom of all random terms.
lmerc	Call used in the mer model in the fitting.

Author(s)

Dongwen Luo, Siva Ganesh and John Koolaard

References

Wand, M.P. and Ormerod, J.T. (2008). On semiparametric regression with O'Sullivan penalized splines. Australian and New Zealand Journal of Statistics. 50, 179-198.

Examples

## Not run
# library(predictmeans)
# library(HRW) 
# data(WarsawApts)
# help(WarsawApts)
# str(WarsawApts)
# fit1 <- semireg(areaPerMzloty ~ construction.date,
#                smoothZ=list(
#                  grp=smZ(construction.date, k=25)
#                ),
#                data = WarsawApts)
# sp_out1 <- semipred(fit1, "construction.date", "construction.date")
# 
# WarsawApts$district <- factor(WarsawApts$district)
# fit2 <- semireg(areaPerMzloty ~ construction.date*district, resp_scale = TRUE,
#                 smoothZ=list(group=smZ(construction.date, k=15,
#                                        by = district, group=TRUE)), 
#                 data=WarsawApts)
# sp_out2_1 <- semipred(fit2, "district", "construction.date")
# sp_out2_2 <- semipred(fit2, "district", "construction.date", contr=c(2,1))
# 
# data(indonRespir)
# help(indonRespir)
# str(indonRespir)
# fit3 <- semireg(respirInfec ~ age+vitAdefic + female + height
#                + stunted + visit2 + visit3 + visit4  + visit5 + visit6+(1|idnum),
#                smoothZ=list(
#                  grp=smZ(age)
#                ),
#                family = binomial,
#                data = indonRespir)
# sp_out3 <- semipred(fit3, "age", "age")
# library(ggplot2)
# sp_out3$plt+
#   geom_rug(data = subset(indonRespir, respirInfec==0), sides = "b", col="deeppink") +
#   geom_rug(data = subset(indonRespir, respirInfec==1), sides = "t", col="deeppink")+
#   ylim(0, 0.2)                     

predictmeans documentation built on Oct. 20, 2023, 5:07 p.m.