predictmeans (version 1.1.1)

semireg: 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=FALSE, resp_scale=FALSE, 
        control = lmerControl(optimizer="bobyqa"), start = NULL, 
        verbose = FALSE, drop.unused.levels=TRUE, subset, weights, 
        offset, contrasts = NULL,  prt=TRUE, predict_info=TRUE, ...)

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.

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?

predict_info

Logical scalar - Should provide the info for function semipred or not?

...

Further arguments for passing on to model setup routines.

Author

Dongwen Luo, Siva Ganesh and John Koolaard

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.

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)