This is an R package for estimation and prediction of the cosinor model for periodic data. It allows for covariate effects and provides tools for inference on the mean shift, amplitude, and acrophase.

The package can be installed from CRAN:

`install.packages("cosinor")`

To install from github, you must first have the `devtools`

package installed. Then run this command to install the latest
version:

`::install_github("cosinor", "sachsmc") devtools`

Load the package into your library:

`library(cosinor)`

The package comes with an example dataset called
`vitamind`

to illustrate the usage. Fit a basic cosinor model
with covariates:

`<- cosinor.lm(Y ~ time(time) + X + amp.acro(X), data = vitamind, period = 12) fit `

The time variable is indicated by the `time`

function in
the formula. By default, the period length is 12. Covariates can be
included directly in the formula to allow for mean shifts. To allow for
differences in amplitude and acrophase by covariate values, include the
covariate wrapped in the `amp.acro`

function in the formula.
Let’s summarize the model.

`summary(fit)`

```
## Raw model coefficients:
## estimate standard.error lower.CI upper.CI p.value
## (Intercept) 29.6898 0.4654 28.7776 30.6020 0.0000
## X 1.9019 0.8041 0.3258 3.4779 0.0180
## rrr 0.9308 0.6357 -0.3151 2.1767 0.1431
## sss 6.2010 0.6805 4.8673 7.5347 0.0000
## X:rrr 5.5795 1.1386 3.3479 7.8111 0.0000
## X:sss -1.3825 1.1364 -3.6097 0.8447 0.2237
##
## ***********************
##
## Transformed coefficients:
## estimate standard.error lower.CI upper.CI p.value
## (Intercept) 29.6898 0.4654 28.7776 30.6020 0.000
## [X = 1] 1.9019 0.8041 0.3258 3.4779 0.018
## amp 6.2705 0.6799 4.9378 7.6031 0.000
## [X = 1]:amp 8.0995 0.9095 6.3170 9.8820 0.000
## acr 1.4218 0.1015 1.2229 1.6207 0.000
## [X = 1]:acr 0.6372 0.1167 0.4084 0.8659 0.000
```

This prints the raw coefficients, and the transformed coefficients. The transformed coefficients display the amplitude and acrophase for the covariate = 1 group and the covariate = 0 group. Beware when using continuous covariates!

Testing is easy to do, tell the function which covariate to test, and whether to test the amplitude or acrophase. The global test is useful when you have multiple covariates in the model.

`test_cosinor(fit, "X", param = "amp")`

```
## Global test:
## Statistic:
## [1] 2.59
##
##
## P-value:
## [1] 0.1072
##
## Individual tests:
## Statistic:
## [1] 1.61
##
##
## P-value:
## [1] 0.1072
##
## Estimate and confidence interval[1] "1.83 (-0.4 to 4.05)"
```

The predict function allows you to estimate the mean value of your outcome for individuals. This is useful for adjusting for the seasonal effects on some measurement. Let’s compare the raw values to the seasonally adjusted values of the vitamin D dataset.

`summary(vitamind$Y)`

```
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 13.27 25.59 29.79 30.09 34.85 51.30
```

`summary(predict(fit))`

```
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.10 25.98 29.26 29.69 33.59 46.48
```

Plotting the fitted curves is also easy to do. Currently only
`ggplot2`

is supported.

```
library(ggplot2)
ggplot_cosinor.lm(fit, x_str = "X")
```

The package comes with an interactive web app that can be used to
analyze your own data. If you have your data loaded in R as a
`data.frame`

, run the command

`cosinor_analyzer(vitamind)`

to run the Shiny application.