# Estimation of parameters of autologistic regression model for data on a grid ```r estima(data = 0, covariate1 = NULL, covariate2 = NULL, covariate3 = NULL, norm = "euclidean", vxpresent = 3, vypresent = 3, vxpast = 3, vypast = 3, dx = 1, dy = 1, swpresent = TRUE, swpast = TRUE, graph = FALSE, pastcov = FALSE, buildpres = NULL, buildpast = NULL) ``` ## Arguments - `data`: dataset with the coordinates in the two first columns. - `covariate1`: spatio-temporal covariate. The covariate dataframe must have `dim(data)[1] = dim(covariate)[1]` (same numbers of individuals) and `dim(data)[1] = dim(covariate)[1] + 3` as the covaiate dataset must not contain coordinates, but must match the coodinates of the dataset; and `T-1` years (`T` is the number of years in the dataset `"data"`) as the model needs the first year to initialize. See `"User guides, package vignettes and other documentation"` the `"estima"` vignette. - `covariate2`: spatio-temporal covariate. The covariate dataframe must have `dim(data)[1] = dim(covariate)[1]` (same numbers of individuals) and `dim(data)[1] = dim(covariate)[1] + 3` as the covaiate dataset must not contain coordinates, but must match the coodinates of the dataset; and `T-1` years (`T` is the number of years in the dataset `"data"`) as the model needs the first year to initialize. See `"User guides, package vignettes and other documentation"` the `"estima"` vignette. - `covariate3`: spatio-temporal covariate. The covariate dataframe must have `dim(data)[1] = dim(covariate)[1]` (same numbers of individuals) and `dim(data)[1] = dim(covariate)[1] + 3` as the covaiate dataset must not contain coordinates, but must match the coodinates of the dataset; and `T-1` years (`T` is the number of years in the dataset `"data"`) as the model needs the first year to initialize. See `"User guides, package vignettes and other documentation"` the `"estima"` vignette. - `norm`: `"euclidean"`, `"inf"`, `"abs"`, `"lin"`. `norm = "euclidean"` by default. See vignette `Build`. - `vxpresent`: positive real. Parameter of the ellipse for the tested neighborhood on x-axes in norm `"norm"` if `swpresent = FALSE`. If `swpresent = TRUE`, `vxpresent` will be the upper bound of the tested neighborhoods on x-axes in norm `norm`. See `swpresent`. - `vypresent`: positive real. Parameter of the ellipse for the tested neighborhood on y-axes in norm `"norm"` if `swpresent = FALSE`. If `swpresent = TRUE`, `vypresent` will be the upper bound of the tested neighborhoods on y-axes in norm `norm`. See `swpresent`. - `vxpast`: positive real. Parameter of the ellipse for the tested neighborhood on x-axes in norm `"norm"` if `swpast = FALSE`. If `swpast = TRUE`, `vxpast` will be the upper bound of the tested neighborhoods on x-axes in norm `norm`. See `swpast`. Only use if `pastcov = TRUE`. - `vypast`: positive real. Parameter of the ellipse for the tested neighborhood on y-axes in norm `"norm"` if `swpast = FALSE`. If `swpast = TRUE`, `vypast` will be the upper bound of the tested neighborhoods on y-axes in norm `norm`. See `swpast`. Only use if `pastcov = TRUE`. - `dx`: positive real : distance between sites on x-axis. `dx = 1` by default. - `dy`: positive real : distance between sites on y-axis. `dy = 1` by default. - `swpresent`: if `TRUE` the programm will test all possible neighborhood for the spatial autocorrelation (coefficient `rho1`) with parameters `vxmaxpresent`, `vymaxpresent`, `dx` and `dy`, otherwise the programm will test the neighborhood with the parameters `vxpresent` and `vypresent`. `swpresent = TRUE` by default. - `swpast`: if `TRUE` the programm will test all possible neighborhood for the autoregression on on the sum of the `Zi,t-1` (coefficient Betapast) with parameters `vxmaxpast`, `vymaxpast`, `dx` and `dy`, otherwise the programm will test the neighborhood with the parameters `vxpast` and `vypast`. `swpast = TRUE` by default. - `graph`: if `graph = TRUE`, the program will also return the plot of the dataset for the last time (and the year before if `estima = 3`). `graph = FALSE` by default. - `pastcov`: boolen. If `pastcov = TRUE`, the function will use the past neighborhood as a covariate. See `"User guides, package vignettes and other documentation"` the `"estima"` vignette. `pastcov = FALSE` by default. - `buildpres`: boolean which allow the use of a custom neighborhood matrix. `buildpres = NULL` by default. - `buildpast`: boolean which allow the use of a custom neighborhood matrix. `buildpast = NULL` by default. ## Returns list : estimate parameters using the pseudo-likelihood. ## Details See `"User guides, package vignettes and other documentation"` the `"estima"` vignette. ## Examples ```r data <- plantillness v <- which(data$NRang <= 10) data <- data[v,] v <- which(data$NCep <= 10) data<-data[v,] result <- estima(data = data) #Example in "lin" norm, with a fixed neighborhood : result <- estima(data = plantillness, norm = "lin",swpresent = FALSE,vxpresent = 3, vypresent = 4) #Example with a spatial covariate (adapted to the dimension of the dataset) : cov <- covplant[,1] for (i in (1:(dim(plantillness)[2] - 4))){ cov <- cbind(cov,covplant[,1]) } result <- estima(data = plantillness,covariate1 = cov) #Example with the past neighborhood as covariate: result <- estima(data = plantillness,pastcov = TRUE) #Exemple with a custom neighborhood matrix custompres <- build(data = plantillness) custompast <- build(data = plantillness, vx = 5,vy = 6) result <- estima(data = plantillness,pastcov = TRUE,buildpres = custompres,buildpast = custompast) ```

estima function