Design matrix of a panglm fit

Returns the model matrix used for the fit (data = NULL) or rebuilt for new data with the stored terms, factor levels, contrasts and spline knots (splines::ns(), splines::bs() and poly() keep the basis of the fit). Its columns match coef(object). With a regression spline, model.matrix(fit, grid) %*% coef(fit) traces the fitted curve, and the covariance vcov(fit) gives pointwise confidence bands; for fixed-effects fits the curve is identified up to a constant, so center it at a reference value.

Usage

# S3 method for class 'panglm'
model.matrix(object, data = NULL, cre_means = NULL, ...)

Arguments

object

a "panglm" fit

data

optional new data containing the regressors

cre_means

for correlated random effects fits with new data: a matrix of the mean_* columns, one row per row of data; when NULL they are recomputed as individual means within data, which then needs the individual index

...

unused

Value

a numeric matrix with one column per coefficient

See also

Other panglm methods: coefplot(), confint.panglm(), effectcurve(), fitted.panglm(), fixef(), glance.panglm(), logLik.panglm(), partialeffects.panordered(), plot.panglm(), plot.panglm_ape(), plot.panglm_effectcurve(), predict.panglm(), predict.panordered(), residuals.panglm(), summary.panglm(), tidy.panglm(), vcov.panglm()

Examples

data(copd)
fit <- panglm(fev1 ~ splines::ns(crp, 3), data = copd,
              index = c("id", "visit"), model = "within")
grid <- data.frame(crp = seq(min(copd$crp), max(copd$crp), length.out = 5))
X <- model.matrix(fit, grid)
X0 <- model.matrix(fit, data.frame(crp = median(copd$crp)))
Xc <- sweep(X, 2, X0)            # centered at the median CRP
cbind(grid, effect = drop(Xc %*% coef(fit)),
      se = sqrt(rowSums((Xc %*% vcov(fit)) * Xc)))
#>    crp      effect         se
#> 1  0.1  0.17651379 0.06824028
#> 2  3.4  0.04928458 0.01512769
#> 3  6.7 -0.04884615 0.01859332
#> 4 10.0 -0.21194570 0.05430171
#> 5 13.3 -0.42355337 0.14824802