Day 12 - 09/16/2024

From last class

• Comments about the projects.
  
• Different options for statistical notation.

Modeling

• Clover data
  
• Remember the data from Assignment 1?

library(tidyverse)
url <- "https://raw.githubusercontent.com/jlacasa/stat705_fall2024/main/classes/data/lotus_part2.csv"
dd <- read.csv(url)

dd %>% 
  filter(species %in% c("A")) %>% 
  ggplot(aes(doy, crown_g))+
  geom_point(aes(fill = trt), shape = 21, size =3.5)+
  scale_fill_manual(values = c("#DB504A", "#43AA8B"))+
  labs(x = "Day of the Year", 
       y = "Crown biomass (g)", 
       fill = "Treatment")+
  theme_classic()+
  theme(aspect.ratio = 1)

str(dd)

'data.frame':   84 obs. of  13 variables:
 $ species      : chr  "A" "A" "A" "A" ...
 $ trt          : chr  "ctrl" "ctrl" "ctrl" "ctrl" ...
 $ doy          : int  155 155 155 155 155 155 155 155 155 155 ...
 $ stm.length_cm: num  31.6 30.7 25.2 20.8 27.1 ...
 $ flowers_g    : num  0 0 0 0 0 0 0 0 0 0 ...
 $ gr.leaves_g  : num  0.442 0.364 0.337 0.196 0.324 ...
 $ dead.leaves_g: num  0 0 0 0 0 0 0 0 0 0 ...
 $ stm_g        : num  0.666 0.605 0.587 0.222 0.45 ...
 $ crown_g      : num  0.0935 0.1625 0.1145 0.0472 0.0959 ...
 $ agb_g        : num  1.108 0.968 0.924 0.417 0.774 ...
 $ main.roots_g : num  0.256 0.269 0.258 0.182 0.273 ...
 $ adv.roots_g  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ moment       : chr  "pre-flooding" "pre-flooding" "pre-flooding" "pre-flooding" ...

Possible models?

m <- lm(crown_g ~ trt + doy, data = dd)
summary(m)

Call:
lm(formula = crown_g ~ trt + doy, data = dd)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.40020 -0.13449 -0.00894  0.09488  0.78319 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -1.359492   0.137887  -9.859  1.6e-15 ***
trtflood    -0.030954   0.043359  -0.714    0.477    
doy          0.009197   0.000692  13.291  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1967 on 81 degrees of freedom
Multiple R-squared:  0.6861,    Adjusted R-squared:  0.6784 
F-statistic: 88.53 on 2 and 81 DF,  p-value: < 2.2e-16

m <- lm(crown_g ~ trt * doy, data = dd)
summary(m)

Call:
lm(formula = crown_g ~ trt * doy, data = dd)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.35287 -0.13701 -0.01415  0.09433  0.73550 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -1.5809069  0.1908532  -8.283 2.25e-12 ***
trtflood      0.4123418  0.2706453   1.524    0.132    
doy           0.0103322  0.0009681  10.673  < 2e-16 ***
trtflood:doy -0.0022714  0.0013692  -1.659    0.101    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1946 on 80 degrees of freedom
Multiple R-squared:  0.6966,    Adjusted R-squared:  0.6852 
F-statistic: 61.21 on 3 and 80 DF,  p-value: < 2.2e-16