## Loading required package: Matrix
## Loading required package: mvtnorm
## Loading required package: TH.data
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lmer(formula = log(TrailsAtestSec) ~ Age + IPVstatus + PovStat +
## Sex + CES1 + (Age | HNDid) + (1 | subclass) + Age:IPVstatus +
## Age:PovStat + Age:Sex + Age:CES1 + IPVstatus:PovStat + IPVstatus:Sex +
## IPVstatus:CES1 + PovStat:Sex + PovStat:CES1 + Sex:CES1 +
## Age:IPVstatus:PovStat + Age:IPVstatus:Sex + Age:IPVstatus:CES1 +
## Age:PovStat:Sex + Age:Sex:CES1 + IPVstatus:PovStat:Sex +
## IPVstatus:Sex:CES1 + PovStat:Sex:CES1 + Age:IPVstatus:PovStat:Sex +
## Age:IPVstatus:Sex:CES1, data = IPVandCognitionDataSet2, na.action = na.omit)
##
## Linear Hypotheses:
## Estimate Std. Error z value
## (Intercept) == 0 3.30551 0.15427 21.43
## Age == 0 0.01561 0.01076 1.45
## IPVstatus1 == 0 0.54121 0.27496 1.97
## PovStatBelow == 0 0.31640 0.22467 1.41
## SexMen == 0 0.15118 0.20136 0.75
## CES11 == 0 0.45066 0.19599 2.30
## Age:IPVstatus1 == 0 0.05086 0.02336 2.18
## Age:PovStatBelow == 0 -0.01324 0.01615 -0.82
## Age:SexMen == 0 -0.00178 0.01519 -0.12
## Age:CES11 == 0 0.01273 0.01530 0.83
## IPVstatus1:PovStatBelow == 0 0.98871 0.42255 2.34
## IPVstatus1:SexMen == 0 -0.66280 0.54993 -1.21
## IPVstatus1:CES11 == 0 -1.03006 0.34485 -2.99
## PovStatBelow:SexMen == 0 -0.08332 0.29852 -0.28
## PovStatBelow:CES11 == 0 -0.52057 0.16620 -3.13
## SexMen:CES11 == 0 -0.79333 0.34596 -2.29
## Age:IPVstatus1:PovStatBelow == 0 0.11065 0.03737 2.96
## Age:IPVstatus1:SexMen == 0 -0.07175 0.03836 -1.87
## Age:IPVstatus1:CES11 == 0 -0.09548 0.02960 -3.23
## Age:PovStatBelow:SexMen == 0 0.01042 0.02638 0.39
## Age:SexMen:CES11 == 0 -0.02649 0.02998 -0.88
## IPVstatus1:PovStatBelow:SexMen == 0 -0.64647 0.58127 -1.11
## IPVstatus1:SexMen:CES11 == 0 1.56956 0.53900 2.91
## PovStatBelow:SexMen:CES11 == 0 0.60334 0.35877 1.68
## Age:IPVstatus1:PovStatBelow:SexMen == 0 -0.07537 0.05057 -1.49
## Age:IPVstatus1:SexMen:CES11 == 0 0.15571 0.04565 3.41
## Pr(>|z|)
## (Intercept) == 0 < 2e-16
## Age == 0 0.14670
## IPVstatus1 == 0 0.04903
## PovStatBelow == 0 0.15905
## SexMen == 0 0.45278
## CES11 == 0 0.02148
## Age:IPVstatus1 == 0 0.02945
## Age:PovStatBelow == 0 0.41233
## Age:SexMen == 0 0.90698
## Age:CES11 == 0 0.40512
## IPVstatus1:PovStatBelow == 0 0.01929
## IPVstatus1:SexMen == 0 0.22811
## IPVstatus1:CES11 == 0 0.00282
## PovStatBelow:SexMen == 0 0.78017
## PovStatBelow:CES11 == 0 0.00173
## SexMen:CES11 == 0 0.02184
## Age:IPVstatus1:PovStatBelow == 0 0.00307
## Age:IPVstatus1:SexMen == 0 0.06139
## Age:IPVstatus1:CES11 == 0 0.00126
## Age:PovStatBelow:SexMen == 0 0.69293
## Age:SexMen:CES11 == 0 0.37687
## IPVstatus1:PovStatBelow:SexMen == 0 0.26607
## IPVstatus1:SexMen:CES11 == 0 0.00359
## PovStatBelow:SexMen:CES11 == 0 0.09262
## Age:IPVstatus1:PovStatBelow:SexMen == 0 0.13612
## Age:IPVstatus1:SexMen:CES11 == 0 0.00065
## (Univariate p values reported)
Age/IPV/Sex/Depression
hatIPVcog1 = zMixHat(IPVandCognitionDataSet2, mm2, vary = "Age=pAge, CES1=zQ(0,1),IPVstatus=zQ(0,1),Sex=zQ(Women,Men)",
fixedCov = "PovStat")
head(hatIPVcog1)
## Age CES1 IPVstatus Sex log TrailsAtestSec PovStat hat
## 1 30 0 0 Women 0 0 0.3175 3.748
## 2 31 0 0 Women 0 0 0.3175 3.760
## 3 32 0 0 Women 0 0 0.3175 3.771
## 4 33 0 0 Women 0 0 0.3175 3.782
## 5 34 0 0 Women 0 0 0.3175 3.794
## 6 35 0 0 Women 0 0 0.3175 3.805
par(mar = c(4, 4, 0.5, 2), las = 1, lwd = 2)
HNDcolors = HNDpltColors()
with(hatIPVcog1[hatIPVcog1$Sex == "Women" & hatIPVcog1$CES1 == "0" & hatIPVcog1$IPVstatus ==
"0", ], plot(pAge, hat, lty = 1, col = "red", type = "l", ylim = c(1, 11),
ylab = "log(Trails A)", xlab = "Age"))
with(hatIPVcog1[hatIPVcog1$Sex == "Women" & hatIPVcog1$CES1 == "0" & hatIPVcog1$IPVstatus ==
"1", ], lines(pAge, hat, lty = 2, col = "red"))
with(hatIPVcog1[hatIPVcog1$Sex == "Women" & hatIPVcog1$CES1 == "1" & hatIPVcog1$IPVstatus ==
"0", ], lines(pAge, hat, lty = 3, col = "red"))
with(hatIPVcog1[hatIPVcog1$Sex == "Women" & hatIPVcog1$CES1 == "1" & hatIPVcog1$IPVstatus ==
"1", ], lines(pAge, hat, lty = 4, col = "red"))
with(hatIPVcog1[hatIPVcog1$Sex == "Men" & hatIPVcog1$CES1 == "0" & hatIPVcog1$IPVstatus ==
"0", ], lines(pAge, hat, lty = 1, col = "blue", typ = "l", ylim = c(1, 11),
ylab = "Trails A", xlab = "Age"))
with(hatIPVcog1[hatIPVcog1$Sex == "Men" & hatIPVcog1$CES1 == "0" & hatIPVcog1$IPVstatus ==
"1", ], lines(pAge, hat, lty = 2, col = "blue"))
with(hatIPVcog1[hatIPVcog1$Sex == "Men" & hatIPVcog1$CES1 == "1" & hatIPVcog1$IPVstatus ==
"0", ], lines(pAge, hat, lty = 3, col = "blue"))
with(hatIPVcog1[hatIPVcog1$Sex == "Men" & hatIPVcog1$CES1 == "1" & hatIPVcog1$IPVstatus ==
"1", ], lines(pAge, hat, lty = 4, col = "blue"))
legend(30, 11, zQ(nonDepressednoIPV, nonDepressedIPV, DepressednoIPV, DepressedIPV),
lty = 1:4, col = "black", cex = 0.9, bty = "n")
text(30, 1.7, "Women in red", adj = c(0, 0), col = "red", cex = 0.95)
text(30, 1, "Men in blue", adj = c(0, 0), col = "blue", cex = 0.95)
Age/IPV/Poverty
hatIPVcog1 = zMixHat(IPVandCognitionDataSet2, mm2, vary = "Age=pAge, PovStat=zQ(abovePovStat,belowPovStat),IPVstatus=zQ(0,1)",
fixedCov = c("Sex", "CES1"))
head(hatIPVcog1)
## Age PovStat IPVstatus log TrailsAtestSec Sex CES1 hat
## 1 30 abovePovStat 0 0 0 0.4603 0.4444 3.864
## 2 31 abovePovStat 0 0 0 0.4603 0.4444 3.879
## 3 32 abovePovStat 0 0 0 0.4603 0.4444 3.894
## 4 33 abovePovStat 0 0 0 0.4603 0.4444 3.909
## 5 34 abovePovStat 0 0 0 0.4603 0.4444 3.924
## 6 35 abovePovStat 0 0 0 0.4603 0.4444 3.939
par(mar = c(4, 4, 0.5, 2), las = 1, lwd = 2)
HNDcolors = HNDpltColors()
with(hatIPVcog1[hatIPVcog1$PovStat == "abovePovStat" & hatIPVcog1$IPVstatus ==
"0", ], plot(pAge, hat, lty = 1, col = "black", type = "l", ylim = c(1,
11), ylab = "log(Trails A)", xlab = "Age"))
with(hatIPVcog1[hatIPVcog1$PovStat == "abovePovStat" & hatIPVcog1$IPVstatus ==
"1", ], lines(pAge, hat, lty = 2, col = "red"))
with(hatIPVcog1[hatIPVcog1$PovStat == "belowPovStat" & hatIPVcog1$IPVstatus ==
"0", ], lines(pAge, hat, lty = 3, col = "black"))
with(hatIPVcog1[hatIPVcog1$PovStat == "belowPovStat" & hatIPVcog1$IPVstatus ==
"1", ], lines(pAge, hat, lty = 4, col = "red"))
legend(30, 11, zQ(AbovePovStatnoIPV, AbovePovStatIPV, BelowPovStatnoIPV, belowPovStatIPV),
lty = 1:4, col = "black", cex = 0.95, bty = "n")
text(30, 1.5, "IPV in red", adj = c(0, 0), col = "red", cex = 0.95)
text(30, 1, "No IPV in black", adj = c(0, 0), col = "black", cex = 0.95)
Age/IPV/Depression
hatIPVcog1 = zMixHat(IPVandCognitionDataSet2, mm2, vary = "Age=pAge, CES1=zQ(0,1),IPVstatus=zQ(0,1)",
fixedCov = c("Sex", "PovStat"))
head(hatIPVcog1)
## Age CES1 IPVstatus log TrailsAtestSec Sex PovStat hat
## 1 30 0 0 0 0 0.4603 0.3175 3.827
## 2 31 0 0 0 0 0.4603 0.3175 3.839
## 3 32 0 0 0 0 0.4603 0.3175 3.851
## 4 33 0 0 0 0 0.4603 0.3175 3.863
## 5 34 0 0 0 0 0.4603 0.3175 3.875
## 6 35 0 0 0 0 0.4603 0.3175 3.887
par(mar = c(4, 4, 0.5, 2), las = 1, lwd = 2)
HNDcolors = HNDpltColors()
with(hatIPVcog1[hatIPVcog1$CES1 == "0" & hatIPVcog1$IPVstatus == "0", ], plot(pAge,
hat, lty = 1, col = "black", type = "l", ylim = c(1, 8), ylab = "log(Trails A)",
xlab = "Age"))
with(hatIPVcog1[hatIPVcog1$CES1 == "0" & hatIPVcog1$IPVstatus == "1", ], lines(pAge,
hat, lty = 2, col = "red"))
with(hatIPVcog1[hatIPVcog1$CES1 == "1" & hatIPVcog1$IPVstatus == "0", ], lines(pAge,
hat, lty = 3, col = "black"))
with(hatIPVcog1[hatIPVcog1$CES1 == "1" & hatIPVcog1$IPVstatus == "1", ], lines(pAge,
hat, lty = 4, col = "red"))
legend(30, 8, zQ(nonDepressednoIPV, nonDepressedIPV, DepressednoIPV, DepressedIPV),
lty = 1:4, col = "black", cex = 0.95)
text(30, 1.5, "IPV in red", adj = c(0, 0), col = "red", cex = 0.95)
text(30, 1, "No IPV in black", adj = c(0, 0), col = "black", cex = 0.95)
##IPV/Sex/Depression ##```{r} Depression=(c("No Depression","Depression"))
hatIPVcog1 = zMixHat(IPVandCognitionDataSet2, mm2, vary = "CES1=Depression,IPVstatus=zQ(0,1),Sex=zQ(Women,Men)",fixedCov=c("PovStat","Age"))
head(hatIPVcog1)
par(mar=c(4,4,.5,2),las = 1, lwd = 2)
HNDcolors = HNDpltColors()
with(hatIPVcog1[hatIPVcog1$Sex == "Women" & hatIPVcog1$IPVstatus == "0", ], plot(CES1,hat, lty = 1, col = "red", type = "l",ylim = c(1,4), ylab = "log(Trails A)", xlab = "Depression")) with(hatIPVcog1[hatIPVcog1$Sex == "Women" & hatIPVcog1$IPVstatus == "1", ], lines(CES1,hat, lty = 2, col = "red"))
with(hatIPVcog1[hatIPVcog1$Sex == "Men" & hatIPVcog1$IPVstatus == "0", ], lines(CES1,hat, lty = 1, col = "blue", typ = "l", ylim = c(1,4), ylab = "Trails A", xlab = "Depression")) with(hatIPVcog1[hatIPVcog1$Sex == "Men" & hatIPVcog1$IPVstatus == "1", ], lines(CES1,hat, lty = 2, col = "blue"))
legend(0,3, zQ(NoIPV,IPV), lty = 1:2, col = "black",cex=.90,bty="n") text(0,1.5,"Women in red", adj = c(0,0), col = "red",cex=.95) text(0,1,"Men in blue", adj = c(0,0), col = "blue",cex=.95)