emmean chapter changes

chapters/means-and-contrasts.qmd

@@ -61,12 +61,12 @@ Now that we have a good model, let’s use the emmeans() function to obtain EMMs
 Now that we have fitted a linear mixed model (model1) and it meets the model assumption. Let's use the `emmeans()` function to obtain estimated marginal means for main (variety and nitrogen) and interaction (variety x nitrogen) effects. 
 
 #### Main effects
-```{r}
+```{r, warning=FALSE}
 m1 <- emmeans(model1, ~V, level = 0.95)
 m1
 ```
 
-```{r}
+```{r, warning=FALSE}
 m2 <- emmeans(model1, ~N)
 m2
 ```
@@ -97,36 +97,20 @@ pairs(m2)
 Here if we look at the results from code chunk above, it's easy to interpret results from `pairs()` function in case of varietey comparison becuase there were only 3 groups. It's bit confusing in case of Nitrogen treatments where we had 4 groups. We can further simplify it by using custom contrasts.
 
 
+::: {.callout-caution}
+## `pairs()`
+Remember!! 
+The `pairs()` function can be used to calculate pairwise comparison when treatment groups are less than equal to 3. 
+:::
 ### Custom contrasts 
 
-1. Treatment 
-
-Firstly, let's run emmean object 'm1' for variety compariosn.
+Firstly, let's run emmean object 'm2' for nitrogen treatment comparison.
 
 ```{r}
 m2
 ```
-```{r, echo=FALSE}
-#emmeans(model1, specs = trt.vs.ctrlk ~ N)
-```
-
-
-
-```{r}
-coef(pairs(m1))
-```
-
-Now, lets a create a vector for each variety in the same order as presented in output from m1. 
-
+Now, lets a create a vector for each nitrogen treatment in the same order as presented in output from m2. 
 ```{r}
-A1 = c(1, 0, 0)
-A2 = c(0, 1, 0)
-A3 = c(0, 0, 1)
-
-```
-
-
-```{r, echo =FALSE}
 A1 = c(1, 0, 0, 0)
 A2 = c(0, 1, 0, 0)
 A3 = c(0, 0, 1, 0)
@@ -138,7 +122,6 @@ Let's create custom contrasts for comparing '0.0cwt' (A1) treatment to  '0.2cwt'
 This can be evaluated as shown below:
 Here the output shows the difference in mean yield between these two varieties
 ```{r}
-pairs(m2, adjust = "tukey")
 contrast(m2, method = list(A1 - A2) )
 contrast(m2, method = list(A1 - A3) )
 contrast(m2, method = list(A1 - A4) )
@@ -149,13 +132,6 @@ Using custom contrasts is strongly recommended instead of `pairs()` when you are
 :::
 
 
-::: {.callout-caution}
-## `pairs()`
-Remember!! 
-The `pairs()` function can be used to calculate pairwise comparison when treatment groups are less than equal to 3. 
-:::
-
-
 ## Compact letter displays
 Compact letter displays (CLDs) are a popular way to display multiple comparisons when there are more than few group means to compare. However, they are problematic as they are more prone to misinterpretation. 
 The R package `multcompView` (Graves et al., 2019) provides an implementation of CLDs creating a display where any two means associated with same symbol are not statistically different.
@@ -179,9 +155,49 @@ cld(m3, alpha=0.05, Letters=letters)
 ```
 Interpretation of these letters is: Here we have a significant difference in grain yield with varieties "victory", with N treatments of 0.0cwt, 0.2cwt, 0.4cwt, and 0.6wt. Grain yield for Golden.rain variety was significantly lower with 0.0cwt N treatment compared to the 0.2cwt, 0.4cwt, and 0.6wt treatments.
 
-
+In the data set we used for demonstration here, we had equal number of observations in each group. However, this might not be a case every time as it is common to have missing values in the data set. In such cases, readers usually struggle to interpret significant differences among groups. For example, estimated means of two groups are substantially different but they are no statistically different. This normally happens when SE of one group is large due to its small sample size, so it's hard for it to be statistically different from other groups. In such cases, we can use alternatives to CLDs as shown below.
 
 ## Alternatives to CLD
+
+1. Equivalence test 
+
+Let's assume based on subject matter considerations, if mean yield of two groups differ by less than 30 can be considered equivalent. 
+Let's try equivalence test on clds of nitrogen treatment emmeans (m2)
+```{r}
+cld(m2, delta = 30, adjust = "none")
+```
+Here, two treatment groups '0.0cwt' and '0.2cwt',  '0.4cwt' and  '0.6cwt' can be considered equivalent.
+
+2. Significance Sets 
+
+Another alternative is to simply reverse all the boolean flags we used in constructing CLDs for m3 first time.
+
+
+```{r}
+cld(m2, signif = TRUE)
+```
+## Export emmeans to excel sheet
+
+The emmean objects can be directly exported 
+
+### start from here 
+```{r}
+result_n <- as.data.frame(summary(m1))
+```
+
+
+```{r}
+library(writexl)
+write_xlsx(result_n)
+```
+
+
+```{r}
+
+```
+
+
+
 ```{r, eval=FALSE, echo = FALSE}
 One-way estimated marginal means and plot
 
@@ -198,14 +214,6 @@ CLD = cld(marginal,
 CLD
 
 
-Location        emmean        SE df  lower.CL upper.CL .group
- Olympia      8.333333 0.6718548 16  6.449596 10.21707  a   
- Northampton 11.833333 0.6718548 16  9.949596 13.71707   b  
- Ventura     13.333333 0.6718548 16 11.449596 15.21707   b  
- Burlington  21.833333 0.6718548 16 19.949596 23.71707    c 
-
-
-
 ### Order the levels for printing
 
 CLD$Location = factor(CLD$Location,
@@ -258,6 +266,12 @@ ggplot(CLD,
 ```
 
 
+
+
+## creating plots with emmeans
+
+
+
 ### Interactions using Emmeans
 
 ```{r, eval=FALSE, echo=FALSE}