Hi,
I am trying to generate a metabolic syndrome score for the children aged 11 to 12 years using PCA. I run the PCA with the standardised z-score of all five metabolic components But I confused on one statement mentioned in the article that I am following "Our PCA identified
2 principal components that were summed, with weights determined by the relative amount of variance, explained to generate a total MetSrisk score".
I am also missing Stata command for final analysis " we internally standardise the final met score to get mean 0 and SD 1 so that regression coefficient can be explained by SMD
I USED THE FOLLOWING COMMAND. It will be highly ap[appreciated if you can help me out with missing command lines. I am badly stuck at this point.
global xlist Z2TotChol Z2Glucose Z2HDL Z2TG fcbmizc
global ncomp 2
** PCA
pca $xlist
screeplot, yline (1)
** PCA Component
pca $xlist, comp($ncomp)
pca $xlist, comp($ncomp) blanks(0.3)
rotate, varimax
rotate, varimax blanks(0.3)
rotate, promax
rotate, promax blanks(0.3)
estat loadings
estat residuals, fitted
estat loadings, cnorm(eigen)
predict Pc1 Pc2, score
gen PCAScore=Pc1 + Pc2
Thanks in advance
I am trying to generate a metabolic syndrome score for the children aged 11 to 12 years using PCA. I run the PCA with the standardised z-score of all five metabolic components But I confused on one statement mentioned in the article that I am following "Our PCA identified
2 principal components that were summed, with weights determined by the relative amount of variance, explained to generate a total MetSrisk score".
I am also missing Stata command for final analysis " we internally standardise the final met score to get mean 0 and SD 1 so that regression coefficient can be explained by SMD
I USED THE FOLLOWING COMMAND. It will be highly ap[appreciated if you can help me out with missing command lines. I am badly stuck at this point.
global xlist Z2TotChol Z2Glucose Z2HDL Z2TG fcbmizc
global ncomp 2
** PCA
pca $xlist
screeplot, yline (1)
** PCA Component
pca $xlist, comp($ncomp)
pca $xlist, comp($ncomp) blanks(0.3)
rotate, varimax
rotate, varimax blanks(0.3)
rotate, promax
rotate, promax blanks(0.3)
estat loadings
estat residuals, fitted
estat loadings, cnorm(eigen)
predict Pc1 Pc2, score
gen PCAScore=Pc1 + Pc2
Thanks in advance
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