Great video. Imputation handles the missing values. So for all the missing data it will calculate scores. LPA uses a couple of algorithms but you could also handle missing values with a common package like ‘mice’ before running the model. I like this approach, it’s not so much black box like the kmeans and other clustering…
wow! this is useful. do u think R is easier to use than Mplus in LPA? I find it interesting that the tutorial on doing LPA thru MPlus is scarce (in contrast to LCA in MPlus or LPA in R)
Great video. Imputation handles the missing values. So for all the missing data it will calculate scores. LPA uses a couple of algorithms but you could also handle missing values with a common package like ‘mice’ before running the model. I like this approach, it’s not so much black box like the kmeans and other clustering…
this is useful. but i don't really understand what CPROB1mean
Really wonderful to see this instructional video!
Great presentation, however the link to the data shown at 26:39 might be broken. It does not link to the dataset.
Nevermind! Just figured out the last digit isn't a "1", it's a lowercase L
Thank you for this! This saved my butt, thanks for posting this!!!!@@teakrose5296
wow! this is useful. do u think R is easier to use than Mplus in LPA? I find it interesting that the tutorial on doing LPA thru MPlus is scarce (in contrast to LCA in MPlus or LPA in R)
dunno, but R is cheaper.