Do you want to learn more about R packages and hang out from the nice people at BRAG?
Great! Me too.
Let's hang out this afternoon and discuss R packages from 5-7 in Y801.
We aren't aiming to put anything on CRAN, or make packages that are going to change the world and fabric of the universe.
| samvif <- function(mod){ | |
| # mod is an mgcv object | |
| # this function calculates the variance inflation factors for GAM as no one else has written code to do it properly | |
| # this is used to summarise how well the GAM performed | |
| mod.sum <- summary(mod) | |
| s2 <- mod$sig2 # estimate of standard deviation of residuals | |
| X <- mod$model # data used to fit the model | |
| n <- nrow(X) # how many observations were used in fitting? |
| library(igraph) | |
| library(tidyverse) | |
| library(magrittr) | |
| dat <- data.frame(A = c(1,1,1,2,3), | |
| B = c(2,3,4,5,7)) | |
| Adj <- dat %>% | |
| as.matrix %>% | |
| graph.edgelist(directed=FALSE) %>% |
| library(raster) | |
| library(tidyverse) | |
| get.nearest.neighbour <- function(point_x, | |
| point_y, | |
| grid){ | |
| dists <- pointDistance( | |
| matrix(c(point_x,point_y), nrow = 1), | |
| as.matrix(grid[, c('longitude','latitude')]), | |
| lonlat = F) |
| confint.gam <- function(mod, level = 0.95) { | |
| # a method for extracting confidence intervals and returning a tidy data frame | |
| require(mgcv) | |
| require(dplyr) | |
| mod.s <- summary(mod) | |
| E <- data.frame(Estimate = mod.s$p.coeff) %>% | |
| mutate(Term = row.names(.)) %>% |