Description Usage Arguments Details Value Note Author(s) References Examples

Of limited interest to most users, separable1fc() is called by the main function, submax().

1 | ```
separable1fc(ymat, gamma = 1)
``` |

`ymat` |
A matrix of scores produced by mscoref. |

`gamma` |
The sensitivity parameter |

See Gastwirth, Krieger and Rosenbaum (2000) and Rosenbaum (2007, section 4) for discussion of the separable approximation.

`tstat ` |
Vector of length I = dim(ymat)[1] giving the values of the test statistic in the I matched sets. |

`expect ` |
Vector of length I giving the maximum expectations in the I matched sets. |

`vari ` |
Vector of length I giving the maximum variances at the maximum expectations in the I matched sets. |

This function is similar to the separable1f() function in the sensitivityfull package. Unlike that function, separable1fc() returns the I components for the I matched sets, rather than computing a summary statistic from them.

Paul R. Rosenbaum

Gastwirth, J. L., Krieger, A. M. and Rosenbaum, P. R. (2000). Asymptotic separability in sensitivity analysis. J. Roy. Statist. Soc. B. 62 545-555. <doi:10.1111/1467-9868.00249>

Rosenbaum, P. R. (2007). Sensitivity analysis for m-estimates, tests and confidence intervals in matched observational studies. Biometrics 63 456-64. (See section 4.) <doi:10.1111/j.1541-0420.2006.00717.x>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# The following artificial example computes mscores for a
# full matching, then applies separable1fc() to
# perform a sensitivity analysis. Compare with
# the example below from the sensitivityfull package.
# The artificial example that follows has I=9
# matched sets. The first 3 sets have one treated
# individual and two controls with treated subjects
# in column 1. The next 3 sets are
# matched pairs, with treated subjects in column 1.
# The next 3 sets have one control and two treated
# subjects, with the control in column 1. Simulated
# from a Normal distribution with an additive effect
# of tau=1.
y<-c(2.2, 1.4, 1.6, 2.4, 0.7, 1.3, 1.2, 0.6, 0.3,
0.5, -0.1, -1.3, -0.3, 0.1, 0.4, 3.0, 1.1, 1.4, -0.8,
0.1, 0.8, NA, NA, NA, 1.1, 0.5, 1.8)
y<-matrix(y,9,3)
treated1<-c(rep(TRUE,6),rep(FALSE,3))
s<-separable1fc(sensitivityfull::mscoref(y,treated1),gamma=2)
1-pnorm((sum(s$tstat)-sum(s$expect))/sqrt(sum(s$vari)))
sensitivityfull::senfm(y,treated1,gamma=2)
s
``` |

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