Weight utterance lengths by using an ordinal regression model

For each participant, we find their length of longest utterance. We predict this longest utterance length as a nonlinear function of some variable, and we compute the probability of reaching each utterance length at each value of the predictor variable. These probabilities are then normalized to provide weights for each utterance length.

Usage

weight_lengths_with_ordinal_model(
  data_train,
  var_length,
  var_x,
  id_cols,
  spline_df = 2,
  data_join = NULL
)

Arguments

data_train

dataframe used to train the ordinal model. data_train can also be a function. In this case, it is applied to the data_join argument in order to derive (filter) a subset of the data for training.

var_length

bare name of the length variable. For example, tocs_level.

var_x

bare name of the predictor variable. For example, age_months.

id_cols

a selection of variable names that uniquely identify each group of related observations. For example, c(child_id, age_months).

spline_df

number of degrees of freedom to use for the ordinal regression model.

data_join

(optional) dataset to use join the weights onto. This feature is necessary because we want to train a dataset on the observed data but supply the weights to the dataset with missing values imputed.

Value

the probability and weights of each utterance length at each observed value of var_x. These are in the added columns {var_length}_prob_reached and {var_length}_weight, respectively.

References

Hustad, K. C., Mahr, T., Natzke, P. E. M., & Rathouz, P. J. (2020). Development of Speech Intelligibility Between 30 and 47 Months in Typically Developing Children: A Cross-Sectional Study of Growth. Journal of Speech, Language, and Hearing Research, 63(6), 1675–1687. https://doi.org/10.1044/2020_JSLHR-20-00008

Hustad, K. C., Mahr, T., Natzke, P. E. M., & J. Rathouz, P. (2020). Supplemental Material S1 (Hustad et al., 2020). ASHA journals. https://doi.org/10.23641/asha.12330956.v1

Examples

data_weights <- weight_lengths_with_ordinal_model(
  data_example_intelligibility_by_length,
  tocs_level,
  age_months,
  child,
  spline_df = 2
)

if (requireNamespace("ggplot2")) {
  library(ggplot2)
  p1 <- ggplot(data_weights) +
    aes(x = age_months, y = tocs_level_prob_reached) +
    geom_line(aes(color = ordered(tocs_level)), linewidth = 1) +
    scale_color_ordinal(end = .85) +
    labs(y = "Prob. of reaching length", color = "Utterance length")
  print(p1)

  p2 <- p1 +
    aes(y = tocs_level_weight) +
    labs(y = "Weight of utterance length")
  print(p2)
}