library(tidyverse) library(circumplex) library(lavaan) library(correlation) jz2017
rmat <- correlation( data = select(jz2017, PARPD), data2 = select(jz2017, PA:NO), method = "Pearson" ) print_md(rmat, digits = 3)
model <- ' PA ~~ BC + DE + FG + HI + JK + LM + NO BC ~~ DE + FG + HI + JK + LM + NO DE ~~ FG + HI + JK + LM + NO FG ~~ HI + JK + LM + NO HI ~~ JK + LM + NO JK ~~ LM + NO LM ~~ NO PA ~ m1*1 BC ~ m2*1 DE ~ m3*1 FG ~ m4*1 HI ~ m5*1 JK ~ m6*1 LM ~ m7*1 NO ~ m8*1 PARPD ~~ s1*PA + s2*BC + s3*DE + s4*FG + s5*HI + s6*JK + s7*LM + s8*NO elev := mean(c(s1, s2, s3, s4, s5, s6, s7, s8)) xval := 0.25 * sum(s2*-0.7071068 - s3 + s4*-0.7071068 + s6*0.7071068 + s7 + s8*0.7071068) yval := 0.25 * sum(s1 + s2*0.7071068 + s4*-0.7071068 - s5 + s6*-0.7071068 + s8*0.7071068) ampl := sqrt(xval^2 + yval^2) disp := atan2(yval, xval) * 57.29578 ' fit <- sem(model, data = jz2017, std.ov = TRUE, parameterization = "delta") summary(fit)
fit2 <- ssm_analyze( .data = jz2017, angles = octants(), scales = PA:NO, measures = PARPD ) fit2
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