Analyzing FBD Parameters

## Import all log (.p) files from all runs and combine them, with burn-in = 25% 
## and downsampling to 2.5k trees in each log file
posterior3p <- combine_log("LogFiles3p", burnin = 0.25, downsample = 1000)

data(posterior3p)

## Reshape imported combined log file from wide to long with FBD_reshape
posterior3p_long <- FBD_reshape(posterior3p, variables = NULL, log.type = "MrBayes")

## Show first 5 lines of combined log file
head(posterior3p_long, 5)

## Summarize parameters by time bin and analysis
t3.1 <- FBD_summary(posterior3p_long)
t3.1

## Export the table
write.csv(t3.1, file = "FBD_summary.csv")

## Plot distribution of the desired FBD parameter by time bin with 
## kernel density plot
FBD_dens_plot(posterior3p_long, parameter = "net_speciation",
              type = "density", stack = FALSE)

## Plot distribution of the desired FBD parameter by time bin with 
## stacked kernel density plot
FBD_dens_plot(posterior3p_long, parameter = "net_speciation",
              type = "density", stack = TRUE)

## Plot distribution of the desired FBD parameter by time bin with 
## a violin plot
FBD_dens_plot(posterior3p_long, parameter = "net_speciation",
              type = "violin", stack = FALSE, color = "red")

## Plot distribution of all FBD parameter by time bin with a violin plot
p1 <- FBD_dens_plot(posterior3p_long, parameter = "net_speciation",
                    type = "violin", stack = FALSE, color = "red")
p2 <- FBD_dens_plot(posterior3p_long, parameter = "relative_extinction",
                    type = "violin", stack = FALSE, color = "cyan3")
p3 <- FBD_dens_plot(posterior3p_long, parameter = "relative_fossilization",
                    type = "violin", stack = FALSE, color = "green3")

library(patchwork)
p1 + p2 + p3 + plot_layout(nrow = 1)

## Save your plot to your working directory as a PDF
ggplot2::ggsave("Plot_regs.pdf", width = 12, height = 4)

##### Tests for normality and homoscedasticity for each FBD parameter for all time bins
t3.2 <- FBD_tests1(posterior3p_long)

### Export the output table for all tests
write.csv(t3.2, file = "FBD_Tests1_Assum.csv")

# Output as separate tables 
t3.2$shapiro

# OR as single merged table
t3.2$shapiro$net_speciation$bin <- row.names(t3.2$shapiro$net_speciation)  
t3.2$shapiro$relative_extinction$bin <- row.names(t3.2$shapiro$relative_extinction)  
t3.2$shapiro$relative_fossilization$bin <- row.names(t3.2$shapiro$relative_fossilization)  

k1all <- rbind(t3.2$shapiro$net_speciation,
               t3.2$shapiro$relative_extinction,
               t3.2$shapiro$relative_fossilization,
               make.row.names = FALSE)

k1all

## Bartlett's test for homogeneity of variance 
t3.2$bartlett

## Fligner-Killeen test for homogeneity of variance 
t3.2$fligner

## Visualize deviations from normality and similarity of variances
FBD_normality_plot(posterior3p_long)

## Save your plot to your working directory as a PDF
ggplot2::ggsave("Plot_normTests.pdf", width = 8, height = 6)

##### Test for significant differences between each time bin for each FBD parameter
t3.3 <- FBD_tests2(posterior3p_long)

### Export the output table for all tests
write.csv(t3.3, file = "FBD_Tests2_Sign.csv")

## Pairwise t-tests
# Output as separate tables 
t3.3$t_tests

# OR as single merged table
k3.3a <- rbind(t3.3$t_tests$net_speciation,
               t3.3$t_tests$relative_extinction,
               t3.3$t_tests$relative_fossilization,
               make.row.names = FALSE)

k3.3a

## Mann-Whitney tests (use if Tests in step #4 fail assumptions)
# Output as separate tables 
t3.3$mwu_tests

# OR as single merged table
k3.3b <- rbind(t3.3$mwu_tests$net_speciation,
               t3.3$mwu_tests$relative_extinction,
               t3.3$mwu_tests$relative_fossilization,
               make.row.names = FALSE)

k3.3b