WebApr 9, 2024 · stan_gamm4 ( formula, random = NULL, family = gaussian (), data, weights = NULL, subset = NULL, na.action, knots = NULL, drop.unused.levels = TRUE, ..., prior = default_prior_coef (family), prior_intercept = default_prior_intercept (family), prior_smooth = exponential (autoscale = FALSE), prior_aux = exponential (autoscale = TRUE), … Web# from example(gamm4, package = "gamm4"), prefixing gamm4() call with stan_ # \donttest{dat <-mgcv:: gamSim (1, n = 400, scale = 2) ## simulate 4 term additive truth
Model selection with beta and quassi families using gamm4
Webmgcv, gamm4 mgcvis a package supplied with R for generalized additive modelling, including generalized additive mixed models. The main GAM fitting routine is gam. bamprovides an alternative for very large datasets. The main GAMM fitting is gammwhich uses PQL based on package nlme. gamm4is an R package available from cran.r … WebR/gamm4.r defines the following functions: gamm4.setup gamm4 print.gamm4.version .onAttach .onUnload dc tde01 ドライバ
gamm4: Generalized Additive Mixed Models using
WebSep 13, 2024 · I'm trying to obtain marginal effects of a smooth in a {gamm4} model. I notice a discrepancy between what {ggeffects} gives me and what I get manually. For a smooth x0, I calcualte the predictions … Weblibrary(mgcv) ## simple examples using gamm as alternative to gam set.seed(0) dat <- gamSim(1,n=200,scale=2) b <- gamm(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat) … Web> summary (data) Object of class SpatialPolygonsDataFrame Coordinates: min max x 670000 780000 y 140000 234000 Is projected: TRUE proj4string : [+proj=tmerc +lat_0=0 +lon_0=19 +k=0.9993 +x_0=500000 +y_0=-5300000 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0] Data attributes: f_edge lat long dam Min. : 0.0 Min. … dc v 2276 ドライバ