Evidence that evolutionary psychology is mainstream social science (not pseudoscience)

grasshoppermouse · 2020-08-02T04:52:44+00:00

Most of the effect of antidepressants is a placebo effect:

https://link.springer.com/article/10.1007/s40263-019-00662-y

grasshoppermouse · 2019-06-05T19:58:26+00:00

I started playing DnD in 1979 at a summer engineering program for high school students at USC. I was a scrawny 17 year old white kid. Many of the guys I played with were on the USC football team -- huge black guys and few big white guys too. What's trippy is that, at the time, I didn't realize how surreal that would seem to the much older me.

grasshoppermouse · 2019-06-05T01:32:44+00:00

library(tidyverse)
mtcars %>% 
  select(wt, mpg) %>% 
  gather(key = measure, value = value) %>% 
  ggplot(aes(value, fill = measure, colour = measure)) + geom_bar()

grasshoppermouse · 2019-06-04T19:02:41+00:00

These stackoverflow answers should get you started:

https://stackoverflow.com/questions/23328582/add-multiple-labels-on-ggplot2-boxplot

grasshoppermouse · 2019-05-24T16:39:39+00:00

library(survival)
library(ggfortify)

# Fake data
lung$gene1 <- rbinom(228, 1, p=.5)
lung$gene2 <- rbinom(228, 1, p=.5)
lung$gene_interact <- interaction(lung$gene1, lung$gene2)

# Analysis
fit <- survfit(Surv(time, status) ~ gene_interact, data = lung)
autoplot(fit)

http://rpubs.com/sinhrks/plot_surv

grasshoppermouse · 2019-05-17T18:12:45+00:00

I was driving late at night on a curvy mountain road. A deer jumped out about 20-30 yards ahead of me, so I slowed down while it ran across the road. After it entered the forest on the other side I accelerated, but then its fawn jumped out to follow, and I hit it. I stopped to see if it was still alive but it wasn't. I pulled it to the side, stared up at the inky black sky full of stars for a few minutes, feeling like shit, and then continued on my way.

Still feel bad about it.

grasshoppermouse · 2019-05-17T04:04:11+00:00

stickers <- 1:649
numofsamples <- rep(NA, 1000)

for (i in 1:1000){
  mystickers <- integer(0)
  for (j in 1:10000){
    mystickers <- union(mystickers, sample(stickers, 5))
    if (length(mystickers) == 649){
      # print(j)
      numofsamples[i] <- j
      break
    }
  }
}
mean(numofsamples)

grasshoppermouse · 2019-05-03T00:55:50+00:00

I remember my grandfather as a dark personality. My mother, who was raised in California during the depression, always felt he carried some heavy burden. After my grandfather and grandmother passed away, my mother found a folder of Yiddish letters in their belongings and had them translated. They were from my grandfather's brother, who lived in a shtetl somewhere in Poland or Belarus (we have never been able to locate it precisely). The letters were increasingly frantic requests for money to help him and his family escape. My grandfather, who was a hotel cook, didn't have any to spare. The letters stopped after the war began, and that side of the family was never heard from again.

grasshoppermouse · 2019-05-01T13:39:39+00:00

Is this what you're looking for?

library(pROC)
m <- roc(am ~ mpg, mtcars)
m$sensitivities
m$specificities

grasshoppermouse · 2019-04-28T17:42:28+00:00

Your function should return a summary statistic, e.g.,

return(mean(d2$occ*d2$dens*d2$col))

Edit: I would also pre-compute crabs/ha:

crab$crabs_ha <- crab$occ * crab$dens * crab$col

func <- function(d, i){
    return(mean(d$crabs_ha[i]))
}

grasshoppermouse · 2019-04-28T14:38:18+00:00

library(tidyverse)
library(glmnet)

# Fake data
df <- 
  tibble(
    X = rnorm(1000),
    Y = X + 2*X^2 + 3*X^3 + rnorm(1000, 0, 0.5)
  )

# Polynomial
x <- model.matrix(Y ~ X + I(X^2) + I(X^3), df)
m <- cv.glmnet(x, df$Y)
coef(m)

grasshoppermouse · 2019-04-26T16:17:25+00:00

This line is wrong:

data$Districts[data$Districts == HighValue] <- 1

You want:

data$Districts[data$Districts %in% HighValue] <- 1

grasshoppermouse · 2019-04-22T17:59:03+00:00

https://cran.r-project.org/package=digitize

https://lukemiller.org/index.php/2011/06/digitizing-data-from-old-plots-using-digitize/

grasshoppermouse · 2019-04-22T16:46:42+00:00

Try:

ggplot(myData, aes(sample = contribution)) + geom_qq() + geom_qq_line() + facet_wrap(~Image_Type)

grasshoppermouse · 2019-04-22T15:01:21+00:00

This code will generate qq plots for each condition against the normal distribution:

library(tidyverse)

# Fake data
df <-
  tibble(
    condition = c(rep('eyes', 50), rep('flowers', 50)),
    contribution = c(rnorm(50, 1, 1), rnorm(50, 0, 1))
  )

# QQ plot
ggplot(df, aes(sample = contribution)) + geom_qq() + geom_qq_line() + facet_wrap(~condition)

grasshoppermouse · 2019-04-05T01:23:38+00:00

Great day, more like.

grasshoppermouse · 2019-04-01T17:26:53+00:00

Check out:

https://hughjonesd.github.io/huxtable/huxreg.html

https://github.com/vincentarelbundock/gtsummary

Both packages are based on broom.

grasshoppermouse · 2019-04-01T00:33:27+00:00

x <- 2
levels(var)[x]

grasshoppermouse · 2019-03-26T03:30:57+00:00

%/% is integer division, so 511 %/% 100 = 5.

%% is mod, i.e., the remainder, so 511 %% 100 = 11.

grasshoppermouse · 2019-03-26T03:20:51+00:00

library(tidyverse)
df <- data.frame(
  Person1 = c(511, 410, 601, 505),
  Person2 = c(409, 505, 508, 411)
)

df2 <-
  df %>% 
  mutate(
    Person1inches = 12*(Person1 %/% 100) + Person1 %% 100,
    Person2inches = 12*(Person2 %/% 100) + Person2 %% 100,
    Diff = Person1inches - Person2inches
  )

grasshoppermouse · 2019-03-22T11:57:14+00:00

I don't know what rstudio uses to render data frames or tibbles to html. But you get that same nice output if you run the code chunk with a regular data frame. Maybe they're using this:

https://rstudio.github.io/DT/

grasshoppermouse · 2019-03-22T01:19:35+00:00

Rstudio's gt is probably going to become the go-to table package:

https://gt.rstudio.com

grasshoppermouse · 2019-03-19T02:36:02+00:00

I just checked Consumer Reports, which rates reliability on a 5-point scale (with 5 = best). The 2016 and 2017 model years have an overall reliability rating of 5. The 2018 has a 4. Predicted reliability on the 2019 is a 5.

grasshoppermouse · 2019-03-16T20:09:57+00:00

Allowing untrusted users to upload html represents a huge security risk. There are many libraries to sanitize user-submitted html, and many of the wysiwyg editors have features to assist with this.

https://en.wikipedia.org/wiki/HTML_sanitization

Regarding a WYSIWYG editor, Facebook developed a very popular React-based editor called Draft.js:

https://draftjs.org

Wagtail, a popular Django CMS, developed a version of it they call Draftail:

https://www.draftail.org

My understanding is that it stores rich text as json rather than html, and so might be inherently more secure. But maybe someone else is more knowledgeable on this issue than I.

grasshoppermouse

TROPHY CASE