Description Usage Arguments Details Value References Examples
qrmix
estimates the components of a finite mixture model by using quantile regression to select a group of quantiles that satisfy an optimality criteria chosen by the user.
1 2 |
formula |
an object of class |
data |
an optional data frame that contains the variables in |
k |
number of clusters. |
Ntau |
an optional value that indicates the number of quantiles that will be considered for quantile regression comparison. |
alpha |
an optional value that will determine the minimum separation between the k quantiles that represent each of the k clusters. |
lossFn |
the loss function to be used to select the best combination of k quantiles. The available functions are |
fitMethod |
the method to be used for the final fitting. Use |
xy |
logical. If |
... |
additional arguments to be passed to the function determined in |
The optimality criteria is determined by the lossFn
parameter. If, for example, the default value is used (lossFn = "Squared"
), the k
quantiles selected will minimize the sum of squared residuals. Use "Bisquare"
or "Huber"
to make the method less sensitive to outliers.
qrmix
returns an object of class "qrmix"
coefficients |
a matrix with k columns that represent the coefficients for each cluster. |
clusters |
cluster assignment for each observation. |
quantiles |
the set of k quantiles that minimize the mean loss. |
residuals |
the residuals, response minus fitted values. |
fitted.values |
the fitted values. |
call |
the matched call. |
xy |
the data used if xy is set to |
Emir, B., Willke, R. J., Yu, C. R., Zou, K. H., Resa, M. A., and Cabrera, J. (2017), "A Comparison and Integration of Quantile Regression and Finite Mixture Modeling" (submitted).
1 2 3 4 5 6 7 8 9 10 | data(blood.pressure)
#qrmix model using default function values:
mod1 = qrmix(bmi ~ ., data = blood.pressure, k = 3)
summary(mod1)
#qrmix model using Bisquare loss function and refitted with robust regression:
mod2 = qrmix(bmi ~ age + systolic + diastolic + gender, data = blood.pressure, k = 3,
Ntau = 25, alpha = 0.1, lossFn = "Bisquare", fitMethod = "rlm")
summary(mod2)
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