Reposted by Russell Pierce
Can confirm. A student told me about a an LLM-based tool that is supposed to help with your lit review. I asked it a question where my work should have been prominent. None of my work came up. Lots of different men did.
Russell Pierce
@russellpierce.bsky.social
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Cognitive Psychology PhD turned Data Scientist turned Data Engineering turned Engineering Leader... Everyday We Stray Further From Science's Light... but also closer somehow.
![# Hold onto the mean of the outcome
outcome_mean <- mean(mtcars$mpg)
# mean center the outcome and predictors
# n.b., you only need to mean center (no need to scale any of the variables)
mean_centered_variables <- scale(mtcars, center=TRUE, scale=FALSE)
model <- lm(mpg~0+am+wt,data=data.frame(mean_centered_variables))
# The adjustment is to just take the unexplained variance from the model
# i.e. residuals and restore their original mean
adj <- outcome_mean + model$residuals
# Original Variable
mean(mtcars$mpg)
#> [1] 20.09062
var(mtcars$mpg)
#> [1] 36.3241
# Adjusted variable
mean(adj)
#> [1] 20.09062
var(adj)
#> [1] 8.978055
# Note 1: We mean centered, of course we didn't need to estimate an intercept
model_w_intercept <- lm(mpg~1+am+wt,data=data.frame(mean_centered_variables))
zero <- 0
names(zero) <- "(Intercept)"
all.equal(0,model_w_intercept$coefficients["(Intercept)"],check.names=FALSE)
#> [1] TRUE](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:cpcrno5vsyeqxletrl6dpjse/bafkreib5ip46evrzuaxqdu6l2twul4wiewk3jeil7r77odg4a47cg6sjbq)