Re: [R] mgcv: relative risk from GAM with distributed lag

;, "f3", "time") > > > > # lag function based on Wood (book 2017, p.349 and gamair package > > documentation p.54 > > # https://cran.rstudio.com/web/packages/gamair/gamair.pdf) > > lagard <- function(x,n.lag=7) { > > n <- length(x); X <- ma

Re: [R] mgcv: relative risk from GAM with distributed lag

(dat$humidity), N) pred_rain <- rep(median(dat$rain), N) pd <- data.frame(temp = pred_temp, lag = pred_lag, humidity = pred_humidity, rain = pred_rain) # make predictions predictions <- predict(mod, pd, type = "terms") On Fri, 22 Jul 2022 at 09:54, Simon Wood wrote: > >

Re: [R] mgcv: relative risk from GAM with distributed lag

Hello everyone (incl. Simon Wood?), I'm not sure that my original question (see below, including reproducible example) was as clear as it could have been. To clarify, what I would to like to get is: 1) a perspective plot of temperature x lag x relative risk. I know how to use plot.gam and vis.ga

[R] mgcv: relative risk from GAM with distributed lag

Dear list members, Does anyone know how to obtain a relative risk/ risk ratio from a GAM with a distributed lag model implemented in mgcv? I have a GAM predicting daily deaths from time series data consisting of daily temperature, humidity and rainfall. The GAM includes a distributed lag model bec

Re: [R] Geom ribbon

I'm not sure if geom_ribbon works with categorical data. It didn't work for me, so I have coded location as a numeric, which works. You can then manuall re-label the tick marks, as per the code below. Others may be able to add to the code to add a legend, or propose a different solution altogether,

[R] Space between axis title and tick labels in persp plot in R (using vis.gam)

Dear list, I am making a perspective plot of my generalised additive model (GAM) named a1b, using vis.gam() in mgcv, which in turn makes use of the persp function in base R. Code is as follows: library(mgcv) vis.gam(x = a1b, view = c("wbgt_max", "lag"), plot.type = "persp",

Re: [R] High concurvity/ collinearity between time and temperature in GAM predicting deaths but low ACF. Does this matter?

ke it a binary predictor or a categorical one with fewer > > levels, perhaps 14 (for heaping in each year) or 12 (for each calendar > > month). I have no idea whether that would help but it seems worth a try. > > > > Michael > > > > On 08/06/2022 18:15, jade.shoda

Re: [R] High concurvity/ collinearity between time and temperature in GAM predicting deaths but low ACF. Does this matter?

; > -Original Message- > From: R-help On Behalf Of jade.shodan--- via > R-help > Sent: Sunday, June 5, 2022 3:02 PM > To: r-help@r-project.org > Subject: [R] High concurvity/ collinearity between time and temperature in > GAM predicting deaths but low ACF. Does this matter? >