dlm: Distributed lag models
In asw221/dlm: Distributed Lag Models for Built Environment Applications

Description Usage Arguments Details Value References See Also Examples

Fit distributed lag models using lme4 to penalize smooth terms. Other random effects terms and generalized mixed models supported.

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dlm(formula, data, subset, na.action, weights, offset,
    method = c("REML", "MLE"), family = gaussian(),
    control = list(), ...)

`formula`	an object of class `stats::formula`: a symbolic description of the model to be fitted. See Details
`data`	an optional data frame, list, or environment containing the variables of the model to be fitted
`subset`	optional vector specifying a subset of observations to be used in the fitting process
`na.action`	optional function that indicates what should happen when the data contains `NA`'s. The default is set by the `na.action` setting of `base::options`, usually `stats::na.omit`
`weights`	optional vector of weights to be used in the fitting process. Should be `NULL` or a numeric vector
`offset`	a known offset term to include in the model, e.g. for `poisson()` family models
`method`	algorithm used to fit the DLM. Partial matching and capitalization allowed. The default is `"REML"` for linear/`gaussian(link = "identity")` family models, and `"MLE"` otherwise
`family`	a description of the error distribution and link function to be used in the model. The default is `gaussian(link = "identity")`. See `stats::family` for possible family functions and details
`control`	either a `list` object with arguments to be passed to the `lme4::lmerControl` sequence, or the output of `[g]lmerConrol` directly
`...`	Additional parameters passed to `lme4::lFormula`

Models are specified using typical lme4 formula syntax with at least one set of lag terms returned by a given smoothing function (e.g. see cr). The smoothing function can be any that returns a SmoothLag basis object. See Examples for a basic call to dlm using the formula interface, and a cubic radial lag basis specified via cr, and the dlmBE package documentation for a discussion of the types of models dlm is designed to handle.

An S4 object that inherits from dlMod (and lme4::merMod, by extension) containing the results of the fitted model. Many standard model summary methods are available for these object types

Baek J, Sanchez BN, Berrocal VJ, & Sanchez-Vaznaugh EV (2016) Epidemiology 27(1):116-24. (PubMed)

Baek J, Hirsch JA, Moore K, Tabb LP, et al. (2017) Epidemiology 28(3):403-11. (PubMed)

Bates D, Maechler M, Bolker BM, & Walker SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1). (jstatsoft.org)

lme4::lmer, cr, dlMod

data (simdata)

## Setup distance count matrix and corresponding lag distances
X <- as.matrix(simdata[, -(1:3)])
lag <- seq(0.1, 10, length.out = ncol(X))

fit <- dlm(Y ~ Age + Gender + cr(lag, X), data = simdata)
summary (fit)