effectsize | effect size and standardized parameters | Data Visualization library

I am new on dash and plotly solutions. I am wondering if it is possible to join both approaches in the same application. Selecting the points, update the checkbox and then unselect some checkbox updating the row color. I have this code that i found here: https://community.plotly.com/t/dash-how-to-select-data-points-either-in-the-table-which-updates-the-plot-or-on-the-graph-which-updates-the-selection-on-the-table/46674, but I don't know how to do. Thanks.

I have the following dataset (of which this is a subset):

I am trying to graph my matrix columns using plot but it looks like I made a mistake somewhere. I found the example code online so I'm not entirely sure how to troubleshoot it.

I'm facing a strange situation when building a function that wraps lm(). Specifically, I get an error that pertains to stats::model.frame() when passing the function's model output to effectsize::effectsize().

In the following example there are two scenarios, A and B. In A, I define a function that first builds a formula object my_formula <- as.formula(paste0(y, "~", x)) and then passes it to lm(). This function returns an object of class "lm". When I pass that object to effectsize::effectsize(), I get an error:

Error in stats::model.frame(formula = my_formula, data = data_std, drop.unused.levels = TRUE) : object 'my_formula' not found

Strangely, when I pass the same object to stats::model.frame(), it works.

In scenario B, I build a function in which the formula is specified within lm(), rather than a preemptive object. In that scenario, passing the output to effectsize() works.

I am working on a variant calling Format(.vcf) file. I am separating genotype Allele from individual genome data and calculating the chance of having the specific disease. Based on my analysis I need to implement a condition like : I have two different panda data frame which are extracted from .vcf file

1. Diseases

2.IndividualSNPs

my condition will be like: if number 1.dataframe (Diseases) column 'SNP' is match with number 2. dataframe (individualSNPs) column 'SNP'. then I will check from individualSNPs dataframe cola and colb column.

My statistical analysis design is multiple 2-way mixed ANOVAs. Independent variables are Team (i.e., between factor) and Box (i.e., within factor). Since the number of participants doesn't match with each other, I would like to use aov_car or car::Anova instead of aov or rstatix::anova_test to calculate omega squared. When I ran aov_car individually, it returned the result without error (a warning message appears though) as follows:

I am currently comparing the size of 159 regions (ROI) in the brain between an at-risk and normal population on R. I originally calculated lm model p-values using this loop:

I can choose the color of my confidence intervals in ggplot, but it fails when I try to plot these intervals in different facets.

This works as expected

Once again I would like to thank Daniel Sjoberg and other collaborators for the constant implementation of functionality in the gtsummary package. For me, one of the most efficient suites in R for processing and reporting results in tables / in line.

A while ago I asked for help on including effect size [90%CI] in the analytical tables generated by the gtsummary package. However, in this new post I intend to change the ES calculation package for giving me a vast repertoire of indexes and also for having their qualitative magnitude. I tried to implement this other package in new code. However, this message is returned:

There was an error for variable 'age': Error in .deal_with_cohens_d_arguments (x, y, data): Please provide data argument.

I believe I am not able to configure the function (CohenD object). Could someone please help me with my code?

I copied it below: