Last updated on 2024-03-27 22:57:53 CET.
Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
---|---|---|---|---|---|---|
r-devel-linux-x86_64-debian-clang | 1.1.2 | 2.65 | 26.16 | 28.81 | NOTE | |
r-devel-linux-x86_64-debian-gcc | 1.1.2 | 2.21 | 20.29 | 22.50 | NOTE | |
r-devel-linux-x86_64-fedora-clang | 1.1.2 | 37.22 | NOTE | |||
r-devel-linux-x86_64-fedora-gcc | 1.1.2 | 36.12 | NOTE | |||
r-devel-windows-x86_64 | 1.1.2 | 3.00 | 42.00 | 45.00 | NOTE | |
r-patched-linux-x86_64 | 1.1.2 | 4.24 | 25.58 | 29.82 | OK | |
r-release-linux-x86_64 | 1.1.2 | 2.45 | 25.36 | 27.81 | OK | |
r-release-macos-arm64 | 1.1.2 | 17.00 | OK | |||
r-release-macos-x86_64 | 1.1.2 | 22.00 | OK | |||
r-release-windows-x86_64 | 1.1.2 | 3.00 | 40.00 | 43.00 | OK | |
r-oldrel-macos-arm64 | 1.1.2 | 19.00 | OK | |||
r-oldrel-windows-x86_64 | 1.1.2 | 4.00 | 45.00 | 49.00 | OK |
Version: 1.1.2
Check: Rd files
Result: NOTE
checkRd: (-1) pampe.Rd:54: Lost braces; missing escapes or markup?
54 | The way they propose to estimate the outcome of the treated unit under no treatment, Y^0_{1t}, is to use the following modeling strategy: use R^2 (or likelihood values) in order to select the best OLS estimator for Y^0_{1t} using j out of the J units in the donor pool, denoted by M(j)* for j=1, ..., J; then choose M(m)* from M(1)*, ..., M(J)* in terms of a model selection criterion, like AICc, AIC or BIC. Note that the method calculates OLS models of up to J+1 parameters; so that if the length of the pre-treatment period t=1, 2, ..., T'-1 is not of a much higher order than that, the regressions M(J-1)*, M(J)* can not be calculated because there are not enough degrees of freedom.
| ^
checkRd: (-1) pampe.Rd:54: Lost braces; missing escapes or markup?
54 | The way they propose to estimate the outcome of the treated unit under no treatment, Y^0_{1t}, is to use the following modeling strategy: use R^2 (or likelihood values) in order to select the best OLS estimator for Y^0_{1t} using j out of the J units in the donor pool, denoted by M(j)* for j=1, ..., J; then choose M(m)* from M(1)*, ..., M(J)* in terms of a model selection criterion, like AICc, AIC or BIC. Note that the method calculates OLS models of up to J+1 parameters; so that if the length of the pre-treatment period t=1, 2, ..., T'-1 is not of a much higher order than that, the regressions M(J-1)*, M(J)* can not be calculated because there are not enough degrees of freedom.
| ^
checkRd: (-1) pampe.Rd:56: Lost braces; missing escapes or markup?
56 | To avoid this problem, the pampe package proposes the following slight modification to the previously outlined modeling strategy: use R^2 in order to select the best OLS estimator for Y^0_{1t} using j out of the J units in the donor pool, denoted by M(j)* for j=1, ..., T_0-4; then choose M(m)* from M(1)*, ..., M(T_0-4)* in terms of a model selection criterion (in our case AICc). Note that the key difference is that while we allowed models up to M(J)*, this is now modified to allow models up to M(T_0-4)*, with T_0-4<J, which allows for at least 3 degrees of freedom. This is implemented through the default value of nvmax, which is equal to J, or if not possible, to J-4. The user can of course override this default.
| ^
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64