fix pb variable not found

vignettes/tutorial.Rmd

@@ -115,7 +115,7 @@ By using the estimated intercept obtained from the previous analysis of publicly
 ```{r design_init, warning = FALSE, message = FALSE}
 ## -- init a design 
 input_var_list <- init_variable(name = "environment", mu = -0.10047682, sd = sqrt(0.3198276), level = 4) %>%
-                init_variable(name = "genotype", mu = -0.01375802, sd = sqrt(0.6892266), level = N_GENOTYPES) %>%
+                init_variable(name = "genotype", mu = -0.01375802, sd = sqrt(0.6892266), level = 2) %>%
                 add_interaction(between_var = c("environment", "genotype"), mu = 0.08256185, sd = sqrt(0.3123347)) %>%
                 set_correlation(between_var = c("environment", "genotype"), corr = 0.7344005) %>%
                 set_correlation(between_var = c("environment", "environment:genotype"), corr = -0.9213156) %>%
@@ -231,10 +231,10 @@ resSimu$performances$byparams
 ```
 
 
-## Preparing and optimizing a futur experimental design
+## Optimizing an analysis
 
 
-Let's consider a scenario where a team is strategizing a new experiment encompassing 100 genotypes and 4 environments. To enhance the precision of their analysis, the team is conducting a power analysis through HTRfit. For this purpose, they are utilizing the experimental design template, `input_var_list`, which draws insights from the publicly available [SRP217588](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135473) dataset. They are adjusting the design levels to align with the specifics of their upcoming study involving 200 genotypes and 4 environments.
+Let's consider a scenario where a team is strategizing a new experiment encompassing 100 genotypes and 4 environments. To enhance the precision of their analysis, the team is conducting a power analysis through HTRfit. For this purpose, they are utilizing the experimental design template, `input_var_list`, which draws insights from the publicly available [SRP217588](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135473) dataset. They are adjusting the design levels to align with the specifics of their upcoming study involving 100 genotypes and 4 environments.
 
 
 #### Simulation
@@ -243,7 +243,7 @@ Let's consider a scenario where a team is strategizing a new experiment encompas
 ```{r design_init_200, warning = FALSE, message = FALSE}
 ## -- init a design 
 input_var_list <- init_variable(name = "environment", mu = -0.10047682, sd = sqrt(0.3198276), level = 4) %>%
-                init_variable(name = "genotype", mu = -0.01375802, sd = sqrt(0.6892266), level = N_GENOTYPES) %>%
+                init_variable(name = "genotype", mu = -0.01375802, sd = sqrt(0.6892266), level = 100) %>%
                 add_interaction(between_var = c("environment", "genotype"), mu = 0.08256185, sd = sqrt(0.3123347)) %>%
                 set_correlation(between_var = c("environment", "genotype"), corr = 0.7344005) %>%
                 set_correlation(between_var = c("environment", "environment:genotype"), corr = -0.9213156) %>%