*Authors:* J. Coelho, S. Ammer

```
library(ggplot2)
library(Momocs) # package for outline and curve analysis
```

`## This is Momocs 1.1.0`

```
##
## Attaching package: 'Momocs'
```

```
## The following object is masked from 'package:stats':
##
## filter
```

```
## The following object is masked from 'package:base':
##
## table
```

```
setwd("/Users/del/ACADEMY/WIP/HumerusProject/Photos_final/") # location of my data
rawLPF <- import_tps(tps.path = "LPF/LPF.TPS", curves = T) # read data Left, Posterior, Females
rawLPM <- import_tps(tps.path = "LPM/LPM.TPS", curves = T) # read data Left, Posterior, Males
```

For some reason, I canâ€™t use `Momocs::Out()`

directly on my imported TPS. Tried many ways, always obtaining different errors. I thought this might be because the **p** of coordinates is not consistent. So I resample them. In my understanding `coo_sample()`

works well for open curves, while I preferred `coo_interpolate()`

for my closed outlines, since they had very inconsistent **p**.

```
# Rebuild datasets
# For females
for (i in seq_along(rawLPF$cur)){
# open (curves)
rawLPF$cur[[i]][[1]] <- coo_sample(rawLPF$cur[[i]][[1]], n = 16)
# closed (outlines)
rawLPF$cur[[i]][[2]] <- coo_interpolate(rawLPF$cur[[i]][[2]], n = 24)
# keep the IDs
names(rawLPF$cur)[[i]] <- names(rawLPF$cur)[[i]]
}
## For males
for (i in seq_along(rawLPM$cur)){
# open (curves)
rawLPM$cur[[i]][[1]] <- coo_sample(rawLPM$cur[[i]][[1]], n = 16)
# closed (outlines)
rawLPM$cur[[i]][[2]] <- coo_interpolate(rawLPM$cur[[i]][[2]], n = 24)
# keep the IDs
names(rawLPM$cur)[[i]] <- names(rawLPM$cur)[[i]]
}
```

I tried to work with my curves together but for some reason, I also got errors trying to `Out()`

these. So first I will split my open curve (TE: Throclear Extension) from my closed outlines (OF: Olecranon Fossa), but combine my males and females datasets to see the differences among these groups.

```
TE.f <- list()
for (i in seq_along(rawLPF$cur)){
TE.f[[i]] <- rawLPF$cur[[i]][[1]]
names(TE.f)[[i]] <- names(rawLPF$cur)[[i]]
}
TE.m <- list()
for (i in seq_along(rawLPM$cur)){
TE.m[[i]] <- rawLPM$cur[[i]][[1]]
names(TE.m)[[i]] <- names(rawLPM$cur)[[i]]
}
Sex <- rep("Female", length(TE.f))
TE.f <- Out(TE.f, fac = as.data.frame(Sex))
Sex <- rep("Male", length(TE.m))
TE.m <- Out(TE.m, fac = as.data.frame(Sex))
TE <- combine(TE.f, TE.m)
###
OF.f <- list()
for (i in seq_along(rawLPF$cur)){
OF.f[[i]] <- rawLPF$cur[[i]][[2]]
names(OF.f)[[i]] <- names(rawLPF$cur)[[i]]
}
OF.m <- list()
for (i in seq_along(rawLPM$cur)){
OF.m[[i]] <- rawLPM$cur[[i]][[2]]
names(OF.m)[[i]] <- names(rawLPM$cur)[[i]]
}
Sex <- rep("Female", length(OF.f))
OF.f <- Out(OF.f, fac = as.data.frame(Sex))
Sex <- rep("Male", length(OF.m))
OF.m <- Out(OF.m, fac = as.data.frame(Sex))
OF <- combine(OF.f, OF.m)
```

My pics were (anatomically) upside-down. So I use the very useful `coo_rotate()`

by Pi radians (180Âº). I have done some landmarks-based analysis before were Procrustes was a mandatory step. I am not sure if it is with open curves and closed outlines, however I am also doing a superimposition step here.

```
TE.aligned <- TE %>% coo_rotate(.,theta = pi) %>% fgProcrustes() %>% Opn()
OF.aligned <- OF %>% coo_rotate(.,theta = pi) %>% fgProcrustes() %>% coo_close()
```

`TE.aligned %>% stack(., title = "Trochlear Extension (H. sapiens)", fac = "Sex")`