Last active
July 8, 2019 21:56
-
-
Save rasmusab/cd84b63d13c8280ec91cdf576c8a0f52 to your computer and use it in GitHub Desktop.
UseR 2019 tutorial - Get up to speed with Bayes test script
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # Prior to the tutorial make sure that the script below runs without error on your R installation. | |
| # You first need to install the follwoing packages: | |
| # install.packages(c("rstanarm", "prophet", "CausalImpact")) | |
| library(rstanarm) | |
| library(prophet) | |
| library(CausalImpact) | |
| # This will test that rstanarm works | |
| # Don't be alarmed if you get a warning about "divergent transitions " | |
| fit <- stan_lm(mpg ~ wt + qsec + am, data = mtcars, prior = R2(0.75)) | |
| plot(fit, prob = 0.8) | |
| # This tests that prophet is working | |
| history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'), | |
| y = sin(1:366/200) + rnorm(366)/10) | |
| m <- prophet(history) | |
| future <- make_future_dataframe(m, periods = 365) | |
| forecast <- predict(m, future) | |
| plot(m, forecast) | |
| # This tests that CausalImpact is working | |
| # First simulating some data | |
| x1 <- 100 + arima.sim(model = list(ar = 0.999), n = 52) | |
| y <- 1.2 * x1 + rnorm(52) | |
| y[41:52] <- y[41:52] + 10 | |
| data <- cbind(y, x1) | |
| pre.period <- c(1, 40) | |
| post.period <- c(41, 52) | |
| # Then running CausalImpact | |
| impact <- CausalImpact(data, pre.period, post.period) | |
| plot(impact) |
@rasmusab Is this the expected output? There is some output regarding "Disabling yearly seasonality." and warnings regarding "Removed 52 rows containing missing values (geom_path)."
> library(rstanarm)
> library(prophet)
> library(CausalImpact)
>
> # This will test that rstanarm works
> # Don't be alarmed if you get a warning about "divergent transitions "
> fit <- stan_lm(mpg ~ wt + qsec + am, data = mtcars, prior = R2(0.75))
SAMPLING FOR MODEL 'lm' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 3.998 seconds (Warm-up)
Chain 1: 2.404 seconds (Sampling)
Chain 1: 6.402 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'lm' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 2:
Chain 2: Elapsed Time: 4.17 seconds (Warm-up)
Chain 2: 5.554 seconds (Sampling)
Chain 2: 9.724 seconds (Total)
Chain 2:
SAMPLING FOR MODEL 'lm' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 3:
Chain 3: Elapsed Time: 3.83 seconds (Warm-up)
Chain 3: 2.556 seconds (Sampling)
Chain 3: 6.386 seconds (Total)
Chain 3:
SAMPLING FOR MODEL 'lm' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 4:
Chain 4: Elapsed Time: 4 seconds (Warm-up)
Chain 4: 5.935 seconds (Sampling)
Chain 4: 9.935 seconds (Total)
Chain 4:
Warning messages:
1: There were 13 divergent transitions after warmup. Increasing adapt_delta above 0.99 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
2: Examine the pairs() plot to diagnose sampling problems
> plot(fit, prob = 0.8)
>
> # This tests that prophet is working
> history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'),
+ y = sin(1:366/200) + rnorm(366)/10)
> m <- prophet(history)
Disabling yearly seasonality. Run prophet with yearly.seasonality=TRUE to override this.
Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.
> future <- make_future_dataframe(m, periods = 365)
> forecast <- predict(m, future)
> plot(m, forecast)
>
> # This tests that CausalImpact is working
> # First simulating some data
> x1 <- 100 + arima.sim(model = list(ar = 0.999), n = 52)
> y <- 1.2 * x1 + rnorm(52)
> y[41:52] <- y[41:52] + 10
> data <- cbind(y, x1)
> pre.period <- c(1, 40)
> post.period <- c(41, 52)
> # Then running CausalImpact
> impact <- CausalImpact(data, pre.period, post.period)
> plot(impact)
Warning messages:
1: Removed 52 rows containing missing values (geom_path).
2: Removed 104 rows containing missing values (geom_path).
>
Yeah, that should be fine. As long as it runs! :)
/R
On Thu, 4 Jul 2019 at 13:28, Johan Rex ***@***.***> wrote:
@rasmusab <https://github.com/rasmusab> Is this the expected output?
There is some output regarding "Disabling yearly seasonality." and warnings
regarding "Removed 52 rows containing missing values (geom_path)."
`
library(rstanarm) > library(prophet) > library(CausalImpact) > > # This
will test that rstanarm works > # Don't be alarmed if you get a warning
about "divergent transitions " > fit <- stan_lm(mpg ~ wt + qsec + am, data
= mtcars, prior = R2(0.75)) SAMPLING FOR MODEL 'lm' NOW (CHAIN 1). Chain 1:
Chain 1: Gradient evaluation took 0 seconds Chain 1: 1000 transitions using
10 leapfrog steps per transition would take 0 seconds. Chain 1: Adjust your
expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 2000 [
0%] (Warmup) Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 1:
Iteration: 400 / 2000 [ 20%] (Warmup) Chain 1: Iteration: 600 / 2000 [ 30%]
(Warmup) Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 1: Iteration:
1000 / 2000 [ 50%] (Warmup) Chain 1: Iteration: 1001 / 2000 [ 50%]
(Sampling) Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 1:
Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 1: Iteration: 1600 / 2000 [
80%] (Sampling) Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 1:
Iteration: 2000 / 2000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time:
3.998 seconds (Warm-up) Chain 1: 2.404 seconds (Sampling) Chain 1: 6.402
seconds (Total) Chain 1: SAMPLING FOR MODEL 'lm' NOW (CHAIN 2). Chain 2:
Chain 2: Gradient evaluation took 0 seconds Chain 2: 1000 transitions using
10 leapfrog steps per transition would take 0 seconds. Chain 2: Adjust your
expectations accordingly! Chain 2: Chain 2: Chain 2: Iteration: 1 / 2000 [
0%] (Warmup) Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 2:
Iteration: 400 / 2000 [ 20%] (Warmup) Chain 2: Iteration: 600 / 2000 [ 30%]
(Warmup) Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 2: Iteration:
1000 / 2000 [ 50%] (Warmup) Chain 2: Iteration: 1001 / 2000 [ 50%]
(Sampling) Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 2:
Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 2: Iteration: 1600 / 2000 [
80%] (Sampling) Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 2:
Iteration: 2000 / 2000 [100%] (Sampling) Chain 2: Chain 2: Elapsed Time:
4.17 seconds (Warm-up) Chain 2: 5.554 seconds (Sampling) Chain 2: 9.724
seconds (Total) Chain 2: SAMPLING FOR MODEL 'lm' NOW (CHAIN 3). Chain 3:
Chain 3: Gradient evaluation took 0 seconds Chain 3: 1000 transitions using
10 leapfrog steps per transition would take 0 seconds. Chain 3: Adjust your
expectations accordingly! Chain 3: Chain 3: Chain 3: Iteration: 1 / 2000 [
0%] (Warmup) Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 3:
Iteration: 400 / 2000 [ 20%] (Warmup) Chain 3: Iteration: 600 / 2000 [ 30%]
(Warmup) Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 3: Iteration:
1000 / 2000 [ 50%] (Warmup) Chain 3: Iteration: 1001 / 2000 [ 50%]
(Sampling) Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 3:
Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 3: Iteration: 1600 / 2000 [
80%] (Sampling) Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 3:
Iteration: 2000 / 2000 [100%] (Sampling) Chain 3: Chain 3: Elapsed Time:
3.83 seconds (Warm-up) Chain 3: 2.556 seconds (Sampling) Chain 3: 6.386
seconds (Total) Chain 3: SAMPLING FOR MODEL 'lm' NOW (CHAIN 4). Chain 4:
Chain 4: Gradient evaluation took 0 seconds Chain 4: 1000 transitions using
10 leapfrog steps per transition would take 0 seconds. Chain 4: Adjust your
expectations accordingly! Chain 4: Chain 4: Chain 4: Iteration: 1 / 2000 [
0%] (Warmup) Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup) Chain 4:
Iteration: 400 / 2000 [ 20%] (Warmup) Chain 4: Iteration: 600 / 2000 [ 30%]
(Warmup) Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup) Chain 4: Iteration:
1000 / 2000 [ 50%] (Warmup) Chain 4: Iteration: 1001 / 2000 [ 50%]
(Sampling) Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling) Chain 4:
Iteration: 1400 / 2000 [ 70%] (Sampling) Chain 4: Iteration: 1600 / 2000 [
80%] (Sampling) Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling) Chain 4:
Iteration: 2000 / 2000 [100%] (Sampling) Chain 4: Chain 4: Elapsed Time: 4
seconds (Warm-up) Chain 4: 5.935 seconds (Sampling) Chain 4: 9.935 seconds
(Total) Chain 4: Warning messages: 1: There were 13 divergent transitions
after warmup. Increasing adapt_delta above 0.99 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
2: Examine the pairs() plot to diagnose sampling problems > plot(fit, prob
= 0.8) > > # This tests that prophet is working > history <- data.frame(ds
= seq(as.Date('2015-01-01'), as.Date('2016-01-01'), by = 'd'), + y =
sin(1:366/200) + rnorm(366)/10) > m <- prophet(history) Disabling yearly
seasonality. Run prophet with yearly.seasonality=TRUE to override this.
Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to
override this. > future <- make_future_dataframe(m, periods = 365) >
forecast <- predict(m, future) > plot(m, forecast) > > # This tests that
CausalImpact is working > # First simulating some data > x1 <- 100 +
arima.sim(model = list(ar = 0.999), n = 52) > y <- 1.2 * x1 + rnorm(52) >
y[41:52] <- y[41:52] + 10 > data <- cbind(y, x1) > pre.period <- c(1, 40) >
post.period <- c(41, 52) > # Then running CausalImpact > impact <-
CausalImpact(data, pre.period, post.period) > plot(impact) Warning
messages: 1: Removed 52 rows containing missing values (geom_path). 2:
Removed 104 rows containing missing values (geom_path).
--
| >
`
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<https://gist.github.com/cd84b63d13c8280ec91cdf576c8a0f52?email_source=notifications&email_token=AAHVUQFFGPKBXASFEUXDML3P5XNHLA5CNFSM4HOEUWY2YY3PNVWWK3TUL52HS4DFVNDWS43UINXW23LFNZ2KUY3PNVWWK3TUL5UWJTQAFUY4S#gistcomment-2961865>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/AAHVUQFU4K3IHGPLMY2T2JTP5XNHLANCNFSM4HOEUWYQ>
.
--
/Rasmus
Thank you Rasmus, my script is working with graphs plotting fine but i understand what am running can you please explain what we are trying to achieve even if there is a document to study.
@serifatf The only purpose of this script is to see if you have correctly installed the three packages, nothing else :)
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
@rasmusab Thanks for your comments!
I finally got
rstanarmto work by before installing it runningsudo apt install libssl-dev(see the comment related to Ubuntu here https://github.com/rocker-org/rocker/issues/124).