compare.relimp {DECIDE} | R Documentation |
Compare estimates of log odds, log odds ratios and relative importance obtained by two datasets
Description
Computes 95% confidence intervals for the differences in log odds of transition, log odds ratios and relative importance estimates between the two datasets. Also calculates chi-squared test statistics and p-values for testing whether the differences are different from zero.
Usage
compare.relimp(dataset1, dataset2)
Arguments
dataset1 |
is the first dataset; a data frame with 4 columns, in the following order: 1: student's ID, 2: class, 3: transition (0 if not, 1 if yes) and 4: performance score. |
dataset2 |
is the second dataset; a data frame with 4 columns, in the following order: 1: student's ID, 2: class, 3: transition (0 if not, 1 if yes) and 4: performance score. |
Value
ci.diff.lo |
95% confidence intervals for differences in log odds of transition |
test.diff.lo |
Test statistic for differences in log odds |
test.diff.lo.pvalue |
p-value for testing for differences in log odds |
ci.diff.lor |
95% confidence intervals for differences in log odds ratios |
test.diff.lo |
Test statistic for differences in log odds ratios |
test.diff.lo.pvalue |
p-value for testing for differences in log odds ratios |
ci.diff.ri.1 |
95% confidence intervals for relative importance estimates - 1 |
ci.diff.ri.2 |
95% confidence intervals for relative importance estimates - 2 |
ci.diff.ri.avg |
95% confidence intervals for relative importance estimates - average |
Author(s)
Christiana Kartsonaki
References
Kartsonaki, C., Jackson, M. and Cox, D. R. (2013). Primary and secondary effects: Some methodological issues, in Jackson, M. (ed.) Determined to succeed?, Stanford: Stanford University Press.
Erikson, R., Goldthorpe, J. H., Jackson, M., Yaish, M. and Cox, D. R. (2005) On Class Differentials in Educational Attainment. Proceedings of the National Academy of Sciences, 102: 9730–9733
Jackson, M., Erikson, R., Goldthorpe, J. H. and Yaish, M. (2007) Primary and secondary effects in class differentials in educational attainment: The transition to A-level courses in England and Wales. Acta Sociologica, 50 (3): 211–229
Examples
# generate two datasets
set.seed(1)
data1 <- data.frame(seq(1:10), rep(c(1, 2), length.out = 10),
c(rep(0, times = 3), rep(1, times = 7)),
c(rnorm(4, 0, 1), rnorm(4, 0.5, 1), NA, NA))
data2 <- data.frame(seq(1:10), rep(c(1, 2), length.out = 10),
c(rep(0, times = 5), rep(1, times = 5)),
c(rnorm(5, 1, 1), rnorm(5, 0.5, 1)))
# run function
compare.relimp(data1, data2)