rebuild main analysis output document

Main_analyse.Rmd

@@ -3,9 +3,9 @@ title: "Main_analyse.Rmd"
 author: "Isabelle Boulangeat"
 date: "23/01/2020"
 output: 
-  md_document:
-    variant: markdown_github
   html_document:
+    keep_md: yes
+    variant: markdown_github
 editor_options: 
   chunk_output_type: console
 always_allow_html: true
@@ -97,7 +97,7 @@ res.tab.eval = as.data.frame(do.call(rbind, lapply(eval.models, function(x)unlis
 res.tab.vars = as.data.frame(do.call(rbind, lapply(eval.models, function(x)unlist(x[5:6]))))
   
 kable(res.tab.eval, caption = "model evaluation", digits = 2) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), full_width=FALSE)
-kable(res.tab.vars, caption = "selected variables")%>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 10)
+# kable(res.tab.vars, caption = "selected variables")%>% kable_styling(bootstrap_options = c("striped", "hover", "responsive"), font_size = 10)
 ```
 N.B. Some models do not fit well. The variables explain more or less depending on species but also on processes (extinction and colonisation).
 
@@ -133,7 +133,7 @@ data.frame(unclass(table(coeffs.all$var, coeffs.all$process)))  %>%
   kable_styling("striped")
 ```
 
-## Variables effect coefficients
+## Variable effect coefficients
 
 This gives a summary of selected variables with significant impact on colonisation or extinction and their estimated coefficients.
 
@@ -153,18 +153,15 @@ coeffs.all[which(coeffs.all$process == "extinction" & coeffs.all$Pr...z..<0.1),]
 ## Lambda (1-ext/colo)
 An overview of the performance of populations by site of by species
 
-```{r lambda, fig-TRUE, fig.width=10}
+```{r lambda, fig-TRUE, fig.width=10, warning=FALSE, fig.height=7}
 all.sites = unique(DATA$cover[,c("SemiUniquePLOT", "YEAR", "SUBSITE", "SoilMoist", "TotalCover")])
 pDATA = proj.dat.shape(all.sites, DATA$climato)
-str(pDATA)
-summary(pDATA)
 selectVars = c("snowDays", "GSL", "GST", "SoilMoist", "TotalCover")
 pDATA.all = na.omit(pDATA[,c(selectVars,"SemiUniquePLOT", "YEAR", "SUBSITE") ])
 
 pred.all = lapply(spDATA.fit, predictions, pred.data = pDATA.all, selectVars=selectVars)
 pred.tab = lapply(pred.all, function(x)data.frame(sp = x$sp, lambda = x$lambda, YEAR = x$YEAR, SUBSITE = x$SUBSITE))
 pred.all.together = do.call(rbind, pred.tab)
-head(pred.all.together)
 
 pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
   ggplot(aes(YEAR, lambda)) +
@@ -180,7 +177,7 @@ pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
   ggplot(aes(SITE, lambda, sp)) + 
   theme(axis.text.x = element_text(angle=90, hjust=1)) +
   geom_boxplot() + geom_hline(yintercept = 0) +
-  facet_wrap( ~ sp,scales = "free_x", ncol = 4)
+  facet_wrap( ~ sp,scales = "free_x", ncol = 7)
 
 pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
   ggplot(aes(SITE, lambda, sp)) +
@@ -190,7 +187,7 @@ pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%
 ```
 
 Simplified version focusing only on the sign of lambda
-```{r lambda_sign, fig=TRUE, fig.width=10}
+```{r lambda_sign, fig=TRUE, fig.width=10, warning=FALSE}
 pred.all.together$lambdaSign = ifelse(pred.all.together$lambda>0, "+", "-")
 
 pred.all.together[-which(pred.all.together$sp == "Rubus chamaemorus"), ] %>%