updated test-coverage yaml

.Rhistory

@@ -1,104 +1,3 @@
-)
-# constants
-nsite <- 20
-nobs_count <- 100
-nobs_pcr <- 8
-# params
-mu <- rlnorm(nsite,meanlog=log(1),sdlog=1)
-alpha <- c(0.5, 0.1, -0.4)
-log_p10 <- -4.5
-phi <- 1.2
-# count
-count <- matrix(NA,nrow=nsite,ncol=nobs_count)
-for(i in 1:nsite){
-count[i,] <- rnbinom(n=nobs_count,mu=mu[i],size=phi)
-}
-# site-level covariates
-mat_site <- matrix(NA,nrow=nsite,ncol=length(alpha))
-mat_site[,1] <- 1 # intercept
-for(i in 2:length(alpha)){
-mat_site[,i] <- rnorm(nsite,0,1)
-}
-colnames(mat_site) <- c('int','var_a','var_b')
-# p11 (probability of true positive eDNA detection) and p (probability
-# of eDNA detection)
-p11 <- rep(NA,nsite)
-p <- rep(NA,nsite)
-for (i in 1:nsite){
-p11[i] <- mu[i] / (mu[i] + exp(sum(mat_site[i,]*alpha)))
-p[i] <- min(p11[i] + exp(log_p10),1)
-}
-# qPCR.N (# qPCR observations)
-qPCR.N <- matrix(NA,nrow=nsite,ncol=nobs_pcr)
-for(i in 1:nsite){
-qPCR.N[i,] <- rep(3,nobs_pcr)
-}
-# qPCR.K (# positive qPCR detections)
-qPCR.K <- matrix(NA,nrow=nsite,ncol=nobs_pcr)
-for (i in 1:nsite){
-qPCR.K[i,] <- rbinom(nobs_pcr, qPCR.N[i,], rep(p[i],nobs_pcr))
-}
-# collect data
-data <- list(
-qPCR.N = qPCR.N,
-qPCR.K = qPCR.K,
-count = count,
-site.cov = mat_site
-)
-# initial values
-inits <- list()
-inits[[1]] <- list(
-mu = mu,
-p10 = log_p10,
-alpha = alpha,
-phi = phi
-)
-names(inits[[1]]) <- c('mu','p10','alpha',
-'phi')
-# run model
-fit <- jointModel(data=data, family = 'negbin',
-cov=c('var_a','var_b'),
-n.chain=1, multicore=FALSE, seed = 10,
-initial_values=inits)
-# constants
-nsite <- 20
-nobs_count <- 100
-nobs_pcr <- 8
-# params
-mu <- rlnorm(nsite,meanlog=log(1),sdlog=1)
-alpha <- c(0.5, 0.1, -0.4)
-log_p10 <- -4.5
-phi <- 10
-# count
-count <- matrix(NA,nrow=nsite,ncol=nobs_count)
-for(i in 1:nsite){
-count[i,] <- rnbinom(n=nobs_count,mu=mu[i],size=phi)
-}
-# site-level covariates
-mat_site <- matrix(NA,nrow=nsite,ncol=length(alpha))
-mat_site[,1] <- 1 # intercept
-for(i in 2:length(alpha)){
-mat_site[,i] <- rnorm(nsite,0,1)
-}
-colnames(mat_site) <- c('int','var_a','var_b')
-# p11 (probability of true positive eDNA detection) and p (probability
-# of eDNA detection)
-p11 <- rep(NA,nsite)
-p <- rep(NA,nsite)
-for (i in 1:nsite){
-p11[i] <- mu[i] / (mu[i] + exp(sum(mat_site[i,]*alpha)))
-p[i] <- min(p11[i] + exp(log_p10),1)
-}
-# qPCR.N (# qPCR observations)
-qPCR.N <- matrix(NA,nrow=nsite,ncol=nobs_pcr)
-for(i in 1:nsite){
-qPCR.N[i,] <- rep(3,nobs_pcr)
-}
-# qPCR.K (# positive qPCR detections)
-qPCR.K <- matrix(NA,nrow=nsite,ncol=nobs_pcr)
-for (i in 1:nsite){
-qPCR.K[i,] <- rbinom(nobs_pcr, qPCR.N[i,], rep(p[i],nobs_pcr))
-}
 # collect data
 data <- list(
 qPCR.N = qPCR.N,
@@ -510,3 +409,104 @@ initial_values=inits)
 output_params <- rownames(as.data.frame(jointSummarize(fit$model)))
 # test expectation
 expect_true(all(!c('p10','q','phi') %in% output_params))
+roxygen2::roxygenise()
+library(covr)
+package_coverage()
+getwd()
+cov <- package_coverage("/home/abby/eDNAjoint")
+posterior <- read.csv('../eDNA_presentation/Joint_posterior.csv')
+library(tidyverse)
+ggplot()+
+geom_density(aes(x=posterior$p10))
+ggplot()+
+geom_density(aes(x=exp(posterior$p10)))
+rounded <- round(posterior$p10,7)
+head(roudned)
+head(rounded)
+table(rounded)
+rounded <- round(posterior$p10,3)
+table(rounded)
+names(table(rounded))
+names <- as.numeric(names(table(rounded)))
+names
+values <- as.vector(tables(rounded))
+values <- as.vector(table(rounded))
+ggplot()+
+geom_line(aes(x=names,y=values))
+ggplot()+
+geom_line(aes(x=names,y=exp(values)))
+rounded <- round(posterior$p10,7)
+names <- as.numeric(names(table(rounded)))
+values <- as.vector(table(rounded))
+ggplot()+
+geom_line(aes(x=names,y=exp(values)))
+ggplot()+
+geom_line(aes(x=exp(names),y=values))
+rounded <- round(posterior$p10,3)
+names <- as.numeric(names(table(rounded)))
+values <- as.vector(table(rounded))
+ggplot()+
+geom_line(aes(x=exp(names),y=values))
+ggplot()+
+geom_smooth(aes(x=exp(names),y=values))
+ggplot()+
+geom_density(aes(x=exp(posterior$p10)))
+ggplot()+
+geom_density(aes(x=exp(posterior$p10)))+
+labs(x='probability of false positive eDNA detection',
+y='density')+
+theme_minimal()
+ggplot()+
+geom_density(aes(x=exp(posterior$p10)),linewidth=1)+
+labs(x='probability of false positive eDNA detection',
+y='density')+
+theme_minimal()
+ggplot()+
+geom_density(aes(x=exp(posterior$p10)),linewidth=1)+
+labs(x='probability of false positive eDNA detection',
+y='density')+
+theme_minimal()+
+theme(text=element_text(size=12))
+p10_plot <- ggplot()+
+geom_density(aes(x=exp(posterior$p10)),linewidth=1)+
+labs(x='probability of false positive eDNA detection',
+y='density')+
+theme_minimal()+
+theme(text=element_text(size=12))
+ggsave('../eDNA_presentation/p10_plot.png',p10_plot,dpi=400)
+raa_plot <- ggplot()+
+geom_density(aes(x=posterior$mu_crab.15),linewidth=1)+
+labs(x='EGC density (crabs/trap)',
+y='density')+
+theme_minimal()+
+theme(text=element_text(size=12))
+raa_plot
+raa_plot <- ggplot()+
+geom_density(aes(x=posterior$mu.15),linewidth=1)+
+labs(x='EGC density (crabs/trap)',
+y='density')+
+theme_minimal()+
+theme(text=element_text(size=12))
+raa_plot
+raa_plot <- ggplot()+
+geom_density(aes(x=posterior$mu.15),linewidth=1)+
+scale_x_continuous(limits=c(0,1.5))+
+labs(x='EGC density (crabs/trap)',
+y='density')+
+theme_minimal()+
+theme(text=element_text(size=12))
+raa_plot
+ggsave('../eDNA_presentation/raa_plot.png',p10_plot,dpi=400)
+ggsave('../eDNA_presentation/raa_plot.png',raa_plot,dpi=400)
+cov <- package_coverage("/home/abby/eDNAjoint")
+cov$`detectionCalculate.R:429:7:429:70:7:70:431:431`
+dim(cov)
+str(cov)
+cov$`detectionCalculate.R:429:7:429:70:7:70:431:431`
+length(cov)
+test <- as.data.frame(cov)
+View(test)
+table(test$value)
+zero_coverage(cov)
+getwd()
+saveRDS(test,'../eDNAjoint_coverage.rds')

.github/workflows/test-coverage.yaml

@@ -13,6 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
     steps:
       - uses: actions/checkout@v4
@@ -28,11 +29,12 @@ jobs:
 
       - name: Test coverage
         run: |
+          token <- Sys.getenv("CODECOV_TOKEN", "")
           covr::codecov(
             quiet = FALSE,
             clean = FALSE,
             install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package"),
-            paths = c("src/", "R/")
+            token = if (token != "") token
           )
         shell: Rscript {0}