## Loading required package: ggplot2
NOTE: "Low sexMen" = Women; "High sexMen = Men"
NOTE: "Low CES11" = No depression;"High CES1" = with depression
NOTE: "Low RaceAfrm" = Caucasian; "High RaceAfrAm" = African American
Trails B (IPV*Sex*CES1)
model3 <- lmres(TrailsBtestSec ~ IPVstatus * Sex * CES1, data = IPVandCognitionDataSet2)
S_slopes <- simpleSlope(model3, pred = "IPVstatus", mod1 = "Sex", mod2 = "CES1")
Plot <- PlotSlope(S_slopes)
Plot
Word Fluency (Age*Sex*CES1)
model3 <- lmres(FluencyWord ~ Age * Sex * CES1, data = IPVandCognitionDataSet2)
S_slopes <- simpleSlope(model3, pred = "Age", mod1 = "Sex", mod2 = "CES1")
Plot <- PlotSlope(S_slopes)
Plot
Word Fluency (Age*Sex*Race)
model3 <- lmres(FluencyWord ~ Age * Sex * Race, data = IPVandCognitionDataSet2)
S_slopes <- simpleSlope(model3, pred = "Age", mod1 = "Sex", mod2 = "Race")
Plot <- PlotSlope(S_slopes)
Plot
Word Fluency (Sex*Race*CES1)
model3 <- lmres(FluencyWord ~ Sex * Race * CES1, data = IPVandCognitionDataSet2)
S_slopes <- simpleSlope(model3, pred = "Sex", mod1 = "Race", mod2 = "CES1")
Plot <- PlotSlope(S_slopes)
Plot
Word Fluency (IPV*Race*CES1)
model3 <- lmres(FluencyWord ~ IPVstatus * Race * CES1, data = IPVandCognitionDataSet2)
S_slopes <- simpleSlope(model3, pred = "IPVstatus", mod1 = "Race", mod2 = "CES1")
Plot <- PlotSlope(S_slopes)
Plot
Clock Total (IPV*Sex*Race)
model3 <- lmres(ClockTotal ~ IPVstatus * Sex * Race, data = IPVandCognitionDataSet2)
S_slopes <- simpleSlope(model3, pred = "IPVstatus", mod1 = "Sex", mod2 = "Race")
Plot <- PlotSlope(S_slopes)
Plot
Clock Total (Sex*Race*CES1)
model3 <- lmres(ClockTotal ~ Sex * Race * CES1, data = IPVandCognitionDataSet2)
S_slopes <- simpleSlope(model3, pred = "Sex", mod1 = "Race", mod2 = "CES1")
Plot <- PlotSlope(S_slopes)
Plot
