Estimating the IQs of my family members

Update: whiffed the GRE (60-80th pctile scores) so my IQ has been updated to 129 with no family estimate and 132 with family estimates. Also going to fix these with the new multicolinear method when I get the chance.

For opsec purposes, I am not publicly posting how I got these estimates, but I can say that a SAT/GRE score or an IQ test was involved in generating all of the estimates. Regardless, I will be posting the code I used to generate the estimates, which should hint at which method was used without revealing the exact details.

Given that all of my family members are 115+, I took advantage of the fact that I estimated the IQs of all of my family members and used those figures to make the estimates more accurate. Given that two related family members correlate in IQ at about .45, a person with lots of high IQ family members will be more intelligent than what would be expected from the results of IQ tests.

Here are the initial estimates without considering the average family IQ, with standard errors in parenthesis:

Father: 127 (5.4) (IIRC he recalled he scored ~150 on a test he was given as a preteen in Mexico. Didn’t take this into account because of computational constraints and potential unreliability of recall)

Mother: 118 (8.2)

Older brother: 131 (7.7)

Older sister: 118 (6.3)

Self: 129 (5.3)

Younger sister: 115 (6.6)

Adjusted for the IQs of the family members, these are the new estimates:

Father: 130

Mother: 124

Older brother: 134

Older sister: 122

Self: 132 (assuming same improvement of older brother)

Younger sister: 120

(due to computational constraints, I removed the most intelligent and least intelligent family member when doing the estimates)

Here are my own subjective estimates of our IQs:

Father: 130

Mother: 122

Older brother: 127

Older sister: 121

Self: 131

Younger sister: 116

code:

set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.8*g + rnorm(70000000)*sqrt(1-0.8^2)
d <- 0.78*g + rnorm(70000000)*sqrt(1-0.78^2)
e <- 0.7*g + rnorm(70000000)*sqrt(1-0.7^2)

subby1 <- data.frame(g, d)
subby1$c <- c
subby1$e <- e
subby2 <- subset(subby1, (subby1$d > 40/15) & (subby1$c > 1.8 & subby1$c < 2) & (subby1$e > 1.6 & subby1$e < 2))
mean(subby2$g)*15
sd(subby2$g)*15

#############
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)
d <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)

subby1 <- data.frame(g, d)
subby1$c <- c
subby1$e <- e
subby2 <- subset(subby1, (subby1$d > 1.2 & subby1$d < 1.25) & (subby1$c > 1.15 & subby1$c < 1.25))
mean(subby2$g)*15
sd(subby2$g)*15

##########
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)

subby1 <- data.frame(g, d)
subby1$c <- c
subby1$e <- e
subby2 <- subset(subby1, (subby1$c > 1.35 & subby1$c < 1.45))
mean(subby2$g)*15
sd(subby2$g)*15

##########
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)
e <- 0.8*g + rnorm(70000000)*sqrt(1-0.8^2)
f <- 0.78*g + rnorm(70000000)*sqrt(1-0.78^2)
h <- 0.35*g + rnorm(70000000)*sqrt(1-0.35^2)

subby1 <- data.frame(g, d)
subby1$c <- c
subby1$e <- e
subby1$f <- f
subby1$h <- h
subby2 <- subset(subby1, (subby1$c > 1.5 & subby1$c < 1.6) & (subby1$e > 1.65 & subby1$e < 1.75) & (subby1$f > 1.65 & subby1$f < 1.75) & (subby1$h > 2 & subby1$h < 2.5))
mean(subby2$g)*15
sd(subby2$g)*15

##########
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.9*g + rnorm(70000000)*sqrt(1-0.9^2)

subby1 <- data.frame(g, c)
subby2 <- subset(subby1, (subby1$c > 1 & subby1$c < 1.2))
mean(subby2$g)*15
sd(subby2$g)*15

##########
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.9*g + rnorm(70000000)*sqrt(1-0.9^2)
z <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
y <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
x <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
v <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
u <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)

subby1 <- data.frame(g, c)
subby1$z <- z
subby1$y <- y
subby1$x <- x
subby1$v <- v
subby1$u <- u

subby2 <- subset(subby1, (subby1$c > 1 & subby1$c < 1.2) & (subby1$z > 25/15 & subby1$z < 30/15) & (subby1$x > 25/15 & subby1$x < 35/15) & (subby1$v > 15/15 & subby1$v < 20/15))
mean(subby2$g)*15
sd(subby2$g)*15

###########

##########
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)
z <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
y <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
x <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)

subby1 <- data.frame(g, c)
subby1$z <- z
subby1$y <- y
subby1$x <- x
subby2 <- subset(subby1, (subby1$c > 1.35 & subby1$c < 1.45) & (subby1$z > 10/15 & subby1$z < 25/15) & (subby1$x > 20/15 & subby1$x < 40/15) & (subby1$y > 20/15 & subby1$y < 35/15))
mean(subby2$g)*15
sd(subby2$g)*15

##################

set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)
d <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)
z <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
y <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
x <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)

subby1 <- data.frame(g, d)
subby1$c <- c
subby1$e <- e
subby1$z <- z
subby1$y <- y
subby1$x <- x
subby2 <- subset(subby1, (subby1$d > 1.15 & subby1$d < 1.3) & (subby1$c > 1.15 & subby1$c < 1.3) & (subby1$z > 10/15 & subby1$z < 25/15) & (subby1$x > 20/15 & subby1$x < 40/15) & (subby1$y > 20/15 & subby1$y < 35/15))
mean(subby2$g)*15
sd(subby2$g)*15

#########
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.8*g + rnorm(70000000)*sqrt(1-0.8^2)
d <- 0.78*g + rnorm(70000000)*sqrt(1-0.78^2)
e <- 0.7*g + rnorm(70000000)*sqrt(1-0.7^2)
z <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
y <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
x <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)

subby1 <- data.frame(g, d)
subby1$c <- c
subby1$e <- e
subby1$z <- z
subby1$y <- y
subby1$x <- x
subby2 <- subset(subby1, (subby1$d > 40/15) & (subby1$c > 1.8 & subby1$c < 2) & (subby1$e > 1.6 & subby1$e < 2) & (subby1$z > 10/15 & subby1$z < 25/15) & (subby1$x > 10/15 & subby1$x < 25/15) & (subby1$y > 20/15 & subby1$y < 35/15))
mean(subby2$g)*15
sd(subby2$g)*15

##########
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)
e <- 0.8*g + rnorm(70000000)*sqrt(1-0.8^2)
f <- 0.78*g + rnorm(70000000)*sqrt(1-0.78^2)
h <- 0.35*g + rnorm(70000000)*sqrt(1-0.35^2)
z <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
y <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
x <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)

subby1 <- data.frame(g, c)
subby1$c <- c
subby1$e <- e
subby1$f <- f
subby1$h <- h
subby1$z <- z
subby1$y <- y
subby1$x <- x
subby2 <- subset(subby1, (subby1$c > 1.4 & subby1$c < 1.8) & (subby1$e > 1.5 & subby1$e < 1.9) & (subby1$f > 1.5 & subby1$f < 1.9) & (subby1$h > 2 & subby1$h < 2.5) & (subby1$z > 10/15 & subby1$z < 25/15) & (subby1$x > 10/15 & subby1$x < 25/15) & (subby1$y > 20/15 & subby1$y < 40/15))
mean(subby2$g)*15
sd(subby2$g)*15

subby2$g

##########
set.seed(25)
g <- rnorm(70000000, 0, 1)
c <- 0.84*g + rnorm(70000000)*sqrt(1-0.84^2)
f <- 0.6*g + rnorm(70000000)*sqrt(1-0.6^2)
z <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
y <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)
x <- 0.45*g + rnorm(70000000)*sqrt(1-0.45^2)

subby1 <- data.frame(g, c)
subby1$c <- c
subby1$f <- f
subby1$z <- z
subby1$y <- y
subby1$x <- x
subby2 <- subset(subby1, (subby1$c > 2 & subby1$c < 2.2) & (subby1$f > 2) & (subby1$z > 10/15 & subby1$z < 25/15) & (subby1$x > 10/15 & subby1$x < 25/15) & (subby1$y > 20/15 & subby1$y < 35/15))
mean(subby2$g)*15
sd(subby2$g)*15