View source: R/PredictorResponseFunctions.R
PredictorResponseUnivar | R Documentation |
Plot univariate predictor-response function on a new grid of points
PredictorResponseUnivar( fit, y = NULL, Z = NULL, X = NULL, which.z = 1:ncol(Z), method = "approx", ngrid = 50, q.fixed = 0.5, sel = NULL, min.plot.dist = Inf, center = TRUE, z.names = colnames(Z), ... )
fit |
An object containing the results returned by a the |
y |
a vector of outcome data of length |
Z |
an |
X |
an |
which.z |
vector identifying which predictors (columns of |
method |
method for obtaining posterior summaries at a vector of new points. Options are "approx" and "exact"; defaults to "approx", which is faster particularly for large datasets; see details |
ngrid |
number of grid points to cover the range of each predictor (column in |
q.fixed |
vector of quantiles at which to fix the remaining predictors in |
sel |
logical expression indicating samples to keep; defaults to keeping the second half of all samples |
min.plot.dist |
specifies a minimum distance that a new grid point needs to be from an observed data point in order to compute the prediction; points further than this will not be computed |
center |
flag for whether to scale the exposure-response function to have mean zero |
z.names |
optional vector of names for the columns of |
... |
other arguments to pass on to the prediction function |
For guided examples, go to https://jenfb.github.io/bkmr/overview.html
a long data frame with the predictor name, predictor value, posterior mean estimate, and posterior standard deviation
## First generate dataset set.seed(111) dat <- SimData(n = 50, M = 4) y <- dat$y Z <- dat$Z X <- dat$X ## Fit model with component-wise variable selection ## Using only 100 iterations to make example run quickly ## Typically should use a large number of iterations for inference set.seed(111) fitkm <- kmbayes(y = y, Z = Z, X = X, iter = 100, verbose = FALSE, varsel = TRUE) pred.resp.univar <- PredictorResponseUnivar(fit = fitkm)
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