But we really don’t have continuous curves yet, we just have points. Nevertheless, our eyes extrapolate, and we imagine seeing two sets of continuous curves with one set clearly rising to its maximum value faster than the other set. So far we just have plots based on a relatively small number of data points. Scale_color_manual(values=c("navy blue", "dark grey")) + P % ggplot(aes(time,value, color = treat)) + before = "treat", treat = as.factor(treat)) %>% Now, I reformat the data into a long form data frame and plot both sets of curves. The first link above also points to basic references that should help you to get started with your data.
#Dynamics curves tabledit how to#
For example, look here to see how to work with different time points, and here for some ideas for working with sparse data. I have explored some of these situations in previous posts. But please be assured that FDA can deal with considerably more complexity, including a variable number of measurements for each subject, different measurement times for each subject, and situations where you have far fewer than 50 points for each subject. However, if you work with real longitudinal data you know that things are rarely this simple. This is probably the simplest case possible, and it is good enough to show how to explore the dynamics of curves. There are 50 curves for each treatment and 50 points for each curve. N_curves % mutate(treat = if_else((index %in% index1),1,2))
#Dynamics curves tabledit code#
The following code generates two different sets of longitudinal data from Model 4 which is described in the vignette to the fdaoutlier package. \(\alpha\), \(\beta\), and \(\nu\) are coefficients in the covariance function.\(e_i(t)\) is a Gaussian process with zero mean and covariance function of the form: \.While those associated with treatment 1 are of the form: In the example below, the curves associated with treatment 2 are of the form: \ Instead of thinking of normal curves and outliers, I imagine the two related sets of curves to be the results of two different treatments influencing some measured concentration curve. THe curves produced by model 4 look like they can serve plausible synthetic concentration curves. The fdaoutlier package contains functions to generate a number of stochastic models with a mechanism to generate reasonable outliers for each type of model.
0 kommentar(er)
