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library(shiny) | |
library(terra) | |
library(mgcv) | |
# load the trained model | |
# GAM, predictor: ni ( r425/(r655+r965) ) | |
# R2 = 0.79 | |
model_gam <- readRDS(url("https://raw.githubusercontent.com/arsilva87/misc/main/gam_model_ntotal.rds")) | |
# set the max file size to 200 MB |
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install.packages(c('rgdal', 'sp', 'raster', 'mapview', 'spdep', 'gstat')) |
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library(shiny) | |
library(EBImage) | |
# ---------------------------------------- | |
load(url("https://raw.githubusercontent.com/arsilva87/misc/main/model_nnet.rda")) | |
ANALYZEPAPER <- function(x, | |
paper_dim = c(76, 26), distance = 0.7, | |
display.it = FALSE, align.x = FALSE) | |
{ |
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library(shiny) | |
library(shinydashboard) | |
library(MASS) | |
model <- readRDS(url("https://github.com/arsilva87/misc/raw/main/model_PQ.rds"), "rb") | |
# UI | |
about <- mainPanel( | |
helpText("Este é apenas um SIMULADOR do resultado de julgamento de propostas |
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install.packages(c("PMCMRplus", "dunn.test", "agricolae", "readxl", "ExpDes")) |
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library(rhandsontable) | |
library(shiny) | |
library(shinydashboard) | |
# ------------------------------------------------------------------- | |
# automatically find the best fit WRC using maximum likelihood | |
findWRC = function(w, h, | |
mod2fit = c(TRUE, TRUE, TRUE, TRUE, TRUE)) | |
{ | |
y = w; x = h |
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install.packages(c("learnr", "rmarkdown")) |
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# Script R para simular distâncias sistemáticas entre pontos amostrais em grid espacial misto | |
dists <- c(5, 10, 20, 40) # metros | |
props <- c(0.2, 0.2, 0.3, 0.3) | |
npts <- 90 | |
pts <- npts * props | |
x <- rep(dists, times = pts) | |
table(x)/npts |
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