boot {qcpm}

Inference on QC-PM model parameters (i.e., loadings and path coefficients)

Description

boot returns in order the estimates, std. errors, t-values, p-values, and confidence interval at the specified confidence level for loadings and path coefficients for each quantile.

Usage

boot(qcpm, conf.level = 0.95, br = 200)

Arguments

qcpm

is an object of class qcpm.

conf.level

is the value used to fix the confidence level to use for the confidence interval. It is equal to 0.95 by default.

br

specifies the number of bootstrap replications. It is fixed to 200 by default.

Details

The argument qcpm is an object of class qcpm returned by qcpm function. Std. errors are calculated by using the bootstrap method implemented in the tidy.rq function of the broom package (Robinson, 2014). When fix.quantile=TRUE, the function boot returns only loading results for the quantile 0.5.

Value

boot.loadings

the outer loading results for each considered quantile.

boot.path

the path coefficient results for each considered quantile.

Author(s)

Cristina Davino, Pasquale Dolce, Giuseppe Lamberti, Domenico Vistocco

References

Davino, C., Dolce, P., Taralli, S. and Vistocco, D. (2020). Composite-based path modeling for conditional quantiles prediction. An application to assess health differences at local level in a well-being perspective. Social Indicators Research, doi:10.1007/s11205-020-02425-5.

Davino, C. and Esposito Vinzi, V. (2016). Quantile composite-based path modeling. Advances in Data Analysis and Classification, 10 (4), pp. 491–520, doi:10.1007/s11634-015-0231-9.

Dolce, P., Davino, C. and Vistocco, D. (2021). Quantile composite-based path modeling: algorithms, properties and applications. Advances in Data Analysis and Classification, doi:10.1007/s11634-021-00469-0.

Robinson, D. (2014). broom: An R package for converting statistical analysis objects into tidy data frames. Available at https://CRAN.R-project.org/package=broom.

See Also

qcpm, assessment, summary, and reliability

Examples

# Example of QC-PM in Well-Being analysis
# model with three LVs and reflective indicators

# load library and dataset province
library(qcpm)
data(province)

# Define the model using laavan sintax. Use a set of regression formulas defining 
# firstly the structural model and then the measurement model
model <- "
ECOW ~ EDU
HEALTH ~ EDU + ECOW

# Reflective measurement model
EDU =~ EDU1 + EDU2 + EDU3 + EDU4 + EDU5 + EDU6 + EDU7
ECOW =~ ECOW1 + ECOW2 + ECOW3 + ECOW4 + ECOW5 + ECOW6
HEALTH =~  HEALTH1 + HEALTH2 + HEALTH3
"


# Apply qcpm
well.qcpm = qcpm(model,province)
well.boot = boot(well.qcpm)   
well.boot