Here is a reproducible example of the data set:

```
>dput(head(turtledata.HRmean, 10))
structure(list(season = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("beginning", "middle", "end"), class = "factor"),
Turtle = c("R3L1", "R3L1", "R3L1", "R3L1", "R3L1", "R3L1",
"R3L1", "R3L1", "R3L1", "R3L1"), Date = c("2015-05-24", "2015-05-25",
"2015-05-26", "2015-05-27", "2015-05-28", "2015-05-29", "2015-05-30",
"2015-05-31", "2015-06-01", "2015-06-02"), Cycle = 1:10,
Mean.HR = c(22.068574015748, 15.421802020202, 29.1950055335968,
28.754163099631, 22.1920775423729, 32.5265151832461, 26.0038786046512,
19.6938880769231, 10.6471096774194, 9.14997920792079), SD = c(16.8350435805238,
4.53600228857138, 16.38215204534, 14.8497129255194, 9.49196460499246,
17.1430413978331, 5.15496125729505, 4.53101262759239, 2.00900311400501,
1.59621326146081)), row.names = c(NA, -10L), groups = structure(list(
season = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("beginning", "middle", "end"), class = "factor"),
Turtle = c("R3L1", "R3L1", "R3L1", "R3L1", "R3L1", "R3L1",
"R3L1", "R3L1", "R3L1", "R3L1"), Date = c("2015-05-24", "2015-05-25",
"2015-05-26", "2015-05-27", "2015-05-28", "2015-05-29", "2015-05-30",
"2015-05-31", "2015-06-01", "2015-06-02"), .rows = structure(list(
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -10L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))
```

Basically, each Turtle has an average HR taken per day (this is what the Mean.HR value is). Each turtle was measured anywhere from 9-14 days. Here is the code for the double model that I used to run the brm function:

```
double_model = bf(Mean.HR ~ season + (1|Date), sigma ~ (1|Turtle))
```

Here is the output of the function brm:

```
summary(m1double)
Family: gaussian
Links: mu = identity; sigma = log
Formula: Mean.HR ~ season + (1 | Date)
sigma ~ (1 | Turtle)
Data: turtledata.HRmean (Number of observations: 119)
Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup samples = 4000
Group-Level Effects:
~Date (Number of levels: 78)
Estimate Est.Error l-95% CI u-95% CI Rhat
sd(Intercept) 3.17 0.64 1.67 4.31 1.02
Bulk_ESS Tail_ESS
sd(Intercept) 171 442
~Turtle (Number of levels: 11)
Estimate Est.Error l-95% CI u-95% CI Rhat
sd(sigma_Intercept) 1.02 0.41 0.43 2.02 1.03
Bulk_ESS Tail_ESS
sd(sigma_Intercept) 125 160
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat
Intercept 16.51 1.51 13.15 19.15 1.01
sigma_Intercept 1.26 0.41 0.30 1.92 1.02
seasonmiddle -0.08 1.72 -3.18 3.46 1.01
seasonend -1.71 1.88 -5.15 2.17 1.01
Bulk_ESS Tail_ESS
Intercept 530 589
sigma_Intercept 183 227
seasonmiddle 498 285
seasonend 524 806
```

I could use that information for my data analysis, but I’m just not sure if it’s correct because of all the error messages (which were listed above). Does this help?