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NBA_PLS_path.R
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NBA_PLS_path.R
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# Partial Least Squares Path Modeling analysis of NBA team success
library(plspm)
library(colortools)
# save team data pre Game 7 Finals
# save(teamData, file="Team_data_pre_game_7_finals.RData")
# load team data pre pre game 7
load("Team_data_pre_game_7_finals.RData")
# rows of the inner model matrix
Offense = c(0, 0, 0)
Defense = c(0, 0, 0)
Success = c(1, 1, 0)
# path matrix created by row binding
nba_path <- rbind(Offense, Defense, Success)
# add column names (optional)
colnames(nba_path) <- rownames(nba_path)
# plot the path matrix
innerplot(nba_path)
# define list of indicators: what variables are associated with offense, defense and success
nba_blocks <- list(c(8, 11, 14, 15, 18, 24),
c(16, 20, 21),
c(2, 4, 25, 26))
# all latent variables are measured in a reflective way
nba_modes <- c("A", "A", "A")
# run plspm analysis
nba_pls <- plspm(teamData, nba_path, nba_blocks, modes = nba_modes)
summary(nba_pls)
## Model Results
# path coefficients
nba_pls$path_coefs
# inner model
nba_pls$inner_model
# plotting results (inner model)
plot(nba_pls)
# plotting loadings of the outer model
plot(nba_pls, what = "loadings", arr.width = 0.1)
# index of success
View(nba_pls$scores)
# unidimensionality
nba_pls$unidim