Thank you Sir for the nice tutorial. How can I convert pairwise distance matrix data (Bray-Curtis pairwise dissimilarity matrix) into continues variable Please ? I have collected plant species diversity and biomss from 25 sites. I want to assess relationship between Biomass and Beta diversity in r. As you know the biomass data from 25 sites is continues variable, while the Bray-Curtise pairwise distance data is a matrix. So how can I make linear regression using these Biomass as response variable and Bray-Curtis dissimilarity (beta diversity) as predictor Please ? Your help will be highly appreciated!
I am confused as to which package *pivot_** functions belong to. I thought that should be *tidyr* but I realized the functions still work fine with only *dplyr* loaded.
@@Riffomonas Yeah, that might be the case then. I strive not to load the the whole *tidyverse* thing; I like to keep my environment lean that way. So, I have to know where every function I call comes from.
Thank you so much, Pat!!!! As a beginner to R, this video was very easy to follow & helped me transform my data into a dist matrix. I am so appreciative!!
Amazing video again! It took longer this time , didn't it? :)) Dear Pat! What happens if we want to repeat a multiple linear regression model like this lm(y ~ SNP[i] + biomarker[p]) for all the combinations of (i, p) -> (e.g if we have i=200 SNPs' dosage level and p=100 different quantitative traits like biochemical lab tests), so that we want to do sth like sensitivity analysis and take the Regression coefficients and their corresponding Pvalues in a neat and tidy dataframe for our publication. Map2_df does not work because it does not work like 2x for loop! Any idea will be appreciated, or even it's a great exercise for next videos to scale up more our advanced functioning and object oriented programming skills. Many thanks! Dariush
Couldn't you use the broom package? You could run the 200 lms in a listed df and extract all the values you need. After that, you can use gtable:: to create a neat table
@@sven9r Thank you so much for your suggestion. I've seen it in map functions manual in the help section, but I did not try it. I actually tried to run it by using subsequentially lapply and map, and then write another function to make the raw output in my desired format. I can later share my solution in my Github. Many thanks :)
@@Riffomonas I think you meant this talk by Hadley Wickham on "Managing many models with R". Sure, I'll have look again, maybe it's better to make up my mind again and apply their approach.
Thanks a bunch for this series. Your videos are really clear, and practical. I’ve recommended your videos to everyone on my team!
Wonderful! Thanks for spreading the word 😊
The MANNNNNN
Thank you Sir for the nice tutorial. How can I convert pairwise distance matrix data (Bray-Curtis pairwise dissimilarity matrix) into continues variable Please ? I have collected plant species diversity and biomss from 25 sites. I want to assess relationship between Biomass and Beta diversity in r. As you know the biomass data from 25 sites is continues variable, while the Bray-Curtise pairwise distance data is a matrix. So how can I make linear regression using these Biomass as response variable and Bray-Curtis dissimilarity (beta diversity) as predictor Please ? Your help will be highly appreciated!
Thank you for this video! And of course for advice and help earlier. The video was already shared with the other people in my department.
Fantastic!
good ideas for matrix diagonal extraction for PCA
Thanks for watching!
I love the trick of comparing two character strings using < or >. Very nice.
You have to think a little about what’s going on but it’s pretty slick. Thanks for watching!
I am confused as to which package *pivot_** functions belong to. I thought that should be *tidyr* but I realized the functions still work fine with only *dplyr* loaded.
Hi Caty! It is tidyr but I’m sure dplyr uses tidyr as a dependency to work with tibbles
@@Riffomonas Yeah, that might be the case then. I strive not to load the the whole *tidyverse* thing; I like to keep my environment lean that way. So, I have to know where every function I call comes from.
@@caty863 I think you could do tidyr::pivot_longer to load only that function without calling library(dplyr) or library(tidyr)
Thank you so much, Pat!!!! As a beginner to R, this video was very easy to follow & helped me transform my data into a dist matrix. I am so appreciative!!
Amazing video again! It took longer this time , didn't it? :)) Dear Pat! What happens if we want to repeat a multiple linear regression model like this lm(y ~ SNP[i] + biomarker[p]) for all the combinations of (i, p) -> (e.g if we have i=200 SNPs' dosage level and p=100 different quantitative traits like biochemical lab tests), so that we want to do sth like sensitivity analysis and take the Regression coefficients and their corresponding Pvalues in a neat and tidy dataframe for our publication. Map2_df does not work because it does not work like 2x for loop! Any idea will be appreciated, or even it's a great exercise for next videos to scale up more our advanced functioning and object oriented programming skills. Many thanks! Dariush
Couldn't you use the broom package? You could run the 200 lms in a listed df and extract all the values you need. After that, you can use gtable:: to create a neat table
I think Hadley or David Robinson have a video somewhere talking about this.
@@sven9r Thank you so much for your suggestion. I've seen it in map functions manual in the help section, but I did not try it. I actually tried to run it by using subsequentially lapply and map, and then write another function to make the raw output in my desired format. I can later share my solution in my Github. Many thanks :)
@@Riffomonas I'm seeking for it in David's channel but I don't think I can find it :( Thanks BTW
@@Riffomonas I think you meant this talk by Hadley Wickham on "Managing many models with R". Sure, I'll have look again, maybe it's better to make up my mind again and apply their approach.