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Data Management code for VMAC-MAP study
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Epoch 3 not included in invalidate_raw_neuropsych_items() and invalidate_derived_neuropsych_items() #21

Closed liud4 closed 5 years ago

liud4 commented 5 years ago

@fullstackstatistician

Epoch 3 invalidation of neuropsych items are not included in these two functions. I added invalidation code below for Epoch 3 in the old invalidNeuropsychItems.R last year. 

#Epoch 3: MAP 001 Color-word interference, TMTA, & TMT B invalid

x13<-Cs(
  np.inhibit,
  np.inhibit.ss,
  np.inhibit.scerr,
  np.inhibit.cumpercscerr,
  np.inhibit.ucerr,
  np.inhibit.cumpercucerr,
  np.inhibit.err,
  np.inhibit.err.ss,
  np.tmta,
  np.tmta.ss,
  np.tmtb,
  np.tmtb.ss,
  np.tmt.contrastdiff.ss,
  np.tmta.seqerr,
  np.tmta.cumperc.seqerr,
  np.tmta.seterr,
  np.tmta.cumperc.seterr,
  np.tmtb.seqerr,
  np.tmtb.cumperc.seqerr,
  np.tmtb.seterr,
  np.tmtb.cumperc.seterr
)
dat[dat$map.id %in% c("001") & dat$epoch==3, x13] <- NA

#Epoch 3: MAP 011 Color-word interference, TMTA, & TMT B invalid
x14<-Cs(
  np.inhibit,
  np.inhibit.ss,
  np.inhibit.scerr,
  np.inhibit.cumpercscerr,
  np.inhibit.ucerr,
  np.inhibit.cumpercucerr,
  np.inhibit.err,
  np.inhibit.err.ss,
  np.tmta,
  np.tmta.ss,
  np.tmtb,
  np.tmtb.ss,
  np.tmt.contrastdiff.ss,
  np.tmta.seqerr,
  np.tmta.cumperc.seqerr,
  np.tmta.seterr,
  np.tmta.cumperc.seterr,
  np.tmtb.seqerr,
  np.tmtb.cumperc.seqerr,
  np.tmtb.seterr,
  np.tmtb.cumperc.seterr
)
dat[dat$map.id %in% c("011") & dat$epoch==3, x14] <- NA

#Epoch 3: MAP 059 everything except BNT, Animals, & HVOT invalid
x15<-Cs(
  np.moca,
  np.cvlt1,
  np.cvlt1z,
  np.cvlt2,
  np.cvlt2z,
  np.cvlt3,
  np.cvlt3z,
  np.cvlt4,
  np.cvlt4z,
  np.cvlt5,
  np.cvlt5z,
  np.cvlt1to5,
  np.cvlt1to5.tscore,
  np.cvltb,
  np.cvltbz,
  np.cvlt.sdfr,
  np.cvlt.sdfr.z,
  np.cvlt.sdcr,
  np.cvlt.sdcr.z,
  np.cvlt.ldfr,
  np.cvlt.ldfr.z,
  np.cvlt.ldcr,
  np.cvlt.ldcr.z,
  np.cvlt1to5.semclust,
  np.cvlt1to5.semclust.z,
  np.cvlt1to5.serialclustfwd,
  np.cvlt1to5.serialclustfwd.z,
  np.cvlt1to5.serialclustbirect,
  np.cvlt1to5.serialclustbidirect.z,
  np.cvlt1to5.primacy,
  np.cvlt1to5.primacy.z,
  np.cvlt1to5.middle,
  np.cvlt1to5.middle.z,
  np.cvlt1to5.recency,
  np.cvlt1to5.recency.z,
  np.cvltslope,
  np.cvltslope.z,
  np.cvltslope.t1to2,
  np.cvltslope.t1to2.z,
  np.cvltslope.t2to5,
  np.cvltslope.t2to5.z,
  np.cvlt.learnconsist,
  np.cvlt.learnconsist.z,
  np.cvltcontrast.bvs1,
  np.cvltcontrast.bvs1.z,
  np.cvltcontrast.sdvs5,
  np.cvltcontrast.sdvs5.z,
  np.cvltcontrast.ldvs5,
  np.cvltcontrast.ldvs5.z,
  np.cvltcontrast.ldvssd,
  np.cvltcontrast.ldvssd.z,
  np.cvlt.reps,
  np.cvlt.reps.z,
  np.cvlt.intrus,
  np.cvlt.intrus.z,
  np.cvltrec.hits,
  np.cvltrec.hits.z,
  np.cvltrec.falsepos,
  np.cvltrecog.falsepos.z,
  np.cvltrecog.discrim,
  np.cvltrecog.discrim.z,
  np.cvltrecog.sourcediscrim,
  np.cvltrecog.sourcediscrim.z,
  np.cvltrecog.semanticdiscrim,
  np.cvltrecog.semanticdiscrim.z,
  np.cvltrecog.noveldiscrim,
  np.cvltrecog.noveldiscrim.z,
  np.cvltrecog.responbias,
  np.cvltrecog.responbias.z,
  np.tower01,
  np.tower02,
  np.tower03,
  np.tower04,
  np.tower05,
  np.tower06,
  np.tower07,
  np.tower08,
  np.tower09,
  np.tower.items,
  np.tower.ruleviol,
  np.tower.ruleviol.cumperc,
  np.tower.ss,
  np.digsymb,
  np.digsymb.ss,
  np.color,
  np.color.ss,
  np.word,
  np.word.ss,
  np.inhibit,
  np.inhibit.ss,
  np.colorword.sum,
  np.colorword.comp,
  np.inhibitcolor.diff,
  np.inhibitcolor.contrast,
  np.color.scerr,
  np.color.ucerr,
  np.color.err,
  np.color.cumpercerr,
  np.word.scerr,
  np.word.ucerr,
  np.word.err,
  np.word.cumpercerr,
  np.inhibit.scerr,
  np.inhibit.cumpercscerr,
  np.inhibit.ucerr,
  np.inhibit.cumpercucerr,
  np.inhibit.err,
  np.inhibit.err.ss,
  np.fas.fq1,
  np.fas.fq2,
  np.fas.fq3,
  np.fas.fq4,
  np.fas.f,
  np.fas.f.intrus,
  np.fas.f.reps,
  np.fas.aq1,
  np.fas.aq2,
  np.fas.aq3,
  np.fas.aq4,
  np.fas.a,
  np.fas.a.intrus,
  np.fas.a.reps,
  np.fas.sq1,
  np.fas.sq2,
  np.fas.sq3,
  np.fas.sq4,
  np.fas.s,
  np.fas.s.intrus,
  np.fas.s.reps,
  np.fas.q1,
  np.fas.q2,
  np.fas.q3,
  np.fas.q4,
  np.fas,
  np.fas.tscore,
  np.fas.intrus,
  np.fas.rep,
  np.tmta,
  np.tmta.ss,
  np.tmtb,
  np.tmtb.ss,
  np.tmt.contrastdiff.ss,
  np.tmta.seqerr,
  np.tmta.cumperc.seqerr,
  np.tmta.seterr,
  np.tmta.cumperc.seterr,
  np.tmtb.seqerr,
  np.tmtb.cumperc.seqerr,
  np.tmtb.seterr,
  np.tmtb.cumperc.seterr,
  np.biber1,
  np.biber1.z,
  np.biber2,
  np.biber2.z,
  np.biber3,
  np.biber3.z,
  np.biber4,
  np.biber4.z,
  np.biber5,
  np.biber5.z,
  np.biber.t1to5.z,
  np.biber.t1to5.persev,
  np.biber.t1to5.extra,
  np.biberb,
  np.biberb.z,
  np.biber.sd,
  np.biber.sd.z,
  np.biber.ld,
  np.biber.ld.z,
  np.biber1.figures,
  np.biber2.figures,
  np.biber3.figures,
  np.biber4.figures,
  np.biber5.figures,
  np.biberb.figures,
  np.bibersd.figures,
  np.biberld.figures,
  np.biber.hits,
  np.biber.related.falsealarms,
  np.biber.unrelated.falsealarms,
  np.biber.falsealarms,
  np.biber.recoghitrate,
  np.biber.falsealarm.relatedrate,
  np.biber.falsealarm.unrelatedrate,
  np.biber.falsealarm.totalrate,
  np.biber.discrim,
  np.biber.discrim.related,
  np.biber.discrim.unrelated,
  np.biber.bias,
  np.biber.bias.related,
  np.biber.bias.unrelated
)
dat[dat$map.id %in% c("059") & dat$epoch==3, x15] <- NA

#Epoch 3: MAP 134 Biber recognition invalid 
x16<-Cs(
  np.biber.hits,
  np.biber.related.falsealarms,
  np.biber.unrelated.falsealarms,
  np.biber.falsealarms,
  np.biber.recoghitrate,
  np.biber.falsealarm.relatedrate,
  np.biber.falsealarm.unrelatedrate,
  np.biber.falsealarm.totalrate,
  np.biber.discrim,
  np.biber.discrim.related,
  np.biber.discrim.unrelated,
  np.biber.bias,
  np.biber.bias.related,
  np.biber.bias.unrelated
)
dat[dat$map.id %in% c("134") & dat$epoch==3, x16] <- NA
omair-a-khan commented 5 years ago

@liud4, invalidate_derived_neuropsych_items.R is for derived variables. Are all of these derived variables?

omair-a-khan commented 5 years ago

I added this code to the invalidate_raw_neuropsych_items.R.

Closing.