I think, values in component matrix are loadings. And in next matrix in excel, you have calculated eigen vectors since eigenvector *sqrt(eigenvalue) =loading. Eigen vectors *original data = new data in reduced dimensions
THANK YOU SO MUCH! Kindly help me pls I want identify a major climate-smart agriculture practices used (adopted) by household in the study area. Accordingly I want to group into heterogeneous principal clusters through principal component analysis (PCA). So can you please help me how it is work? How Binary/dummy option is used in PCA? For example: lets the following are e.g. of climate smart agriculture used in the study area, but not equally adopted by HH. 1. Conservation Agriculture (Reduced tillage, Crop residue management-mulching, Crop-rotation/intercropping with cereals and legumes) 2. Agroforestry (Tree-based conservation agriculture, Practiced both traditionally and as improved practice Farmer-managed natural regeneration) 3. Improved livestock feed and feeding practices (Reduced open grazing/zero grazing, Forage development and rangeland management )
Thankyou sir🙏
Thank you sharing this article in You tube.
In video 21:40, how to fix the formula, it’s so unclear can’t see
I think, values in component matrix are loadings. And in next matrix in excel, you have calculated eigen vectors since eigenvector *sqrt(eigenvalue) =loading.
Eigen vectors *original data = new data in reduced dimensions
Very nicely explained but sir what about biplot how can we interpretate it
where i can find this data ?
Sir make a video on path diagram & systems of mating
When i try to calculate p1 p2 p3 p4 there arise value error though my formula is correct
there is problem showing in mmult formula. how we can fix it. it just depicted #values only.
please post your xlxs file data so that we can replicate the calculations
THANK YOU SO MUCH!
Kindly help me pls
I want identify a major climate-smart agriculture practices used (adopted) by household in the study area. Accordingly I want to group into heterogeneous principal clusters through principal component analysis (PCA). So can you please help me how it is work? How Binary/dummy option is used in PCA?
For example: lets the following are e.g. of climate smart agriculture used in the study area, but not equally adopted by HH.
1. Conservation Agriculture (Reduced tillage, Crop residue management-mulching, Crop-rotation/intercropping with cereals and legumes)
2. Agroforestry (Tree-based conservation agriculture, Practiced both traditionally and as improved practice Farmer-managed natural regeneration)
3. Improved livestock feed and feeding practices (Reduced open grazing/zero grazing, Forage development and rangeland management )
hi,
useful video,I am getting chi square value 1.00E4 ,in KMO and Bartlett test table can someone help me to figure out the error/problem