analysis/reports/sd_relationship.md

In diazrenata/BBSsize: Simulate body masses for BBS routes

Size data analysis

sp_raw <- read.csv(here::here("analysis", "species_data", "species_list_working.csv"), stringsAsFactors = F, strip.white = T, na.strings = "")

SD fit

Black line of fit is parameters as estimated from a lm fit to this data; blue line is using the parameters from Thibault (2011).

sp_for_sd <- filter(sp_raw,
                    !is.na(sd)) %>%
  mutate(mass = as.numeric(mass),
         sd = as.numeric(sd)) %>%
  mutate(var = sd^2) %>%
  mutate(log_m = log(mass),
         log_var = log(var))


sd_fit <- lm(sp_for_sd, formula = log_var ~ log_m)

summary(sd_fit)

## 
## Call:
## lm(formula = log_var ~ log_m, data = sp_for_sd)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.1567 -0.4005 -0.0103  0.4502  8.2246 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -5.27312    0.12738  -41.40   <2e-16 ***
## log_m        1.99461    0.03302   60.41   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.065 on 566 degrees of freedom
## Multiple R-squared:  0.8657, Adjusted R-squared:  0.8655 
## F-statistic:  3649 on 1 and 566 DF,  p-value: < 2.2e-16

sp_for_sd <- mutate(sp_for_sd,
                    log_var_est = -5.273 + (log_m * 1.995)) %>%
  mutate(var_est = exp(log_var_est),
         t_var_est = .0055 * (mass ^ 1.98)) %>%
  mutate(log_t_var_est = log(t_var_est))


ggplot(sp_for_sd, aes(x = log_m, y = log_var)) +
  geom_point(alpha = .4) +
  theme_bw() +
  geom_line(aes(x = log_m, y = log_var_est)) +
  geom_line(aes(x = log_m, y = log_t_var_est), color = "blue")

ggplot(sp_for_sd, aes(x = mass, y = var)) +
  geom_point(alpha = .4) +
  theme_bw() +
  geom_line(aes(x = mass, y = var_est)) +
  geom_line(aes(x = mass, y = t_var_est), color = "blue")

diazrenata/BBSsize documentation built on April 21, 2022, 7:39 p.m.