# model-visualisations

#### Oliver Jayasinghe and Rex Parsons

Source:`vignettes/model-visualisations.Rmd`

`model-visualisations.Rmd`

`#> all betas were present but beta.group was FALSE. beta.group has been changed to be TRUE.`

## Plotting cglmm objects

The `GLMMcosinor`

package includes two ways to visualise
models from `cglmm()`

. Firstly, the function
`autoplot()`

creates a time-response plot of the fitted
model:

```
library(GLMMcosinor)
object <- cglmm(
vit_d ~ X + amp_acro(time,
group = "X",
period = 12
),
data = vitamind
)
autoplot(object, x_str = "X")
```

This function also allows users to superimpose the data points (that
the fit is based on) over the fitted model, using the
`superimpose.data = TRUE`

argument:

```
object <- cglmm(
vit_d ~ X + amp_acro(time,
group = "X",
period = 12
),
data = vitamind
)
autoplot(object, x_str = "X", superimpose.data = TRUE)
```

If there are multiple factors in the model, the user can specify
which covariate to be plotted using the `x_str`

argument
which accepts a string corresponding to a group name within the original
dataset. By default, `x_str = NULL`

and the intercept is
plotted (all `group levels = 0`

).

The following examples demonstrate how `x_str`

can be used
to produce different plots for the same model. Note how
`predict.ribbon`

can be set to `FALSE`

to remove
the prediction interval from the plots.

`#> all betas were present but beta.group was FALSE. beta.group has been changed to be TRUE.`

```
testdata_two_components <- testdata_two_components
testdata_two_components$X <- rbinom(length(testdata_two_components$group),
2,
prob = 0.5
)
object <- cglmm(
Y ~ group + amp_acro(times,
n_components = 2,
period = c(12, 6),
group = c("group", "X")
),
data = testdata_two_components,
family = poisson()
)
autoplot(object, predict.ribbon = FALSE)
```

```
object <- cglmm(
Y ~ group + amp_acro(times,
n_components = 2,
period = c(12, 6),
group = c("group", "X")
),
data = testdata_two_components,
family = poisson()
)
autoplot(object, x_str = "X", predict.ribbon = FALSE)
```

```
object <- cglmm(
Y ~ group + amp_acro(times,
n_components = 2,
period = c(12, 6),
group = c("group", "X")
),
data = testdata_two_components,
family = poisson()
)
autoplot(object, x_str = "group", predict.ribbon = FALSE)
```

By default, `xmin`

will be set to the minimum time value
in the time vector of the original dataframe, and `xmax`

will
be set to the maximum time value. If we want to focus on a specific
region of the plot, we can define use the `xlims`

argument to
specify the x-bounds.

For example, on the plot above, we can adjust the x-limits:

```
object <- cglmm(
Y ~ group + amp_acro(times,
n_components = 2,
period = c(12, 6),
group = c("group", "X")
),
data = testdata_two_components,
family = poisson()
)
autoplot(object, x_str = "group", predict.ribbon = TRUE, xlims = c(13, 15))
```

To increase the resolution of the plots, the
`pred.length.out`

can be increased. If there are multiple
periods, the function will automatically generate an appropriate number
of points to plot such that the smallest period has sufficient
resolution to appreciate cosinor behaviour. This can be adjusted using
the `points_per_min_cycle_length`

argument which is 20 by
default.

```
testdata_period_diff <- simulate_cosinor(
1000,
n_period = 1,
mesor = 7,
amp = c(0.1, 0.4),
acro = c(1, 1.5),
family = "poisson",
period = c(12, 1000),
n_components = 2
)
```

## Polar plots

In addition to time-response plots, the `GLMMcosinor`

package also allows users to create polar plots. In these plots, the
plotted point represents the acrophase estimate, and the radius
represents the amplitude estimate for a given component. The ellipses
represent confidence regions.

```
model <- cglmm(
vit_d ~ X + amp_acro(time,
group = "X",
period = 12
),
data = vitamind
)
polar_plot(model)
```

The angle units in the plot can be specified with the
`radial_units`

argument. By default, the units are in radians
where a complete revolution of the plot \((2\pi)\) represents the maximum period from
the model. The units can be changed to degrees, or even to be expressed
in the same units as the period specification.

```
model <- cglmm(
vit_d ~ X + amp_acro(time,
group = "X",
period = 12
),
data = vitamind
)
polar_plot(model, radial_units = "degrees")
```

By default, the function creates creates polar plots for all
components and stitches them together using the
`make_cowplot = TRUE`

argument. If the user wishes to plot
just one component, they can specify this by using
`component_index`

, though the `make_cowplot`

argument must be `FALSE`

for this to register.

The direction that the angle increases in can be changed with the
clockwise argument, and the location of the angle = 0 starting point can
be specified with the `start`

argument. Hence, if the user
wishes to create a polar plot that resembles a clock, this can be done
by specifying `clockwise = TRUE`

and
`start = "top"`

.

The argument: `overlay_parameter_info`

can be used to
create a line extending from the origin to the parameter estimate (to
visualise the amplitude estimate), and a circular arc extending from the
angle starting position (at 0) to the acrophase estimate.

```
model <- cglmm(
vit_d ~ X + amp_acro(time,
group = "X",
period = 12
),
data = vitamind
)
polar_plot(model, overlay_parameter_info = TRUE)
```

The background grid can also be customised. The argument
`grid_angle_segments`

is used to specify how many sectors the
polar grid has, and the `n_breaks`

argument can be used to
specify the number of concentric circles.

```
model <- cglmm(
vit_d ~ X + amp_acro(time,
group = "X",
period = 12
),
data = vitamind
)
polar_plot(model,
grid_angle_segments = 12,
clockwise = TRUE,
start = "top",
n_breaks = 5
)
```

If the user wishes to zoom into the confidence ellipses to show
relevant information, they can adjust the view from the default
`full`

(which plots a full view of the polar plot) to
`zoom`

(which enlarges the smallest view window containing
all confidence ellipses), or `zoom_origin`

(which enlarges
the smallest view window containing all confidence ellipses AND the
origin).

```
model <- cglmm(
vit_d ~ X + amp_acro(time,
group = "X",
period = 12
),
data = vitamind
)
polar_plot(model,
grid_angle_segments = 12,
clockwise = TRUE,
start = "top",
view = "zoom_origin"
)
```