Description Usage Arguments Details Value Note References Examples
Calculates Confidence Intervals for Global and Small-Area Estimations
1 2 3 4 5 6 7 8 |
object |
object of class |
parm |
ignored. |
level |
the confidence level required. |
adjust.method |
correction method to obtain simultaneous confidence intervals
for a set of estimates (thus restricted to objects of class |
... |
additional arguments, so far ignored. |
Depending on the estimation method specified, confint()
computes confidence intervals as follows:
onephase
:
Two-sided confidence intervals are computed based on the t-distribution with n2 - 1
degrees of freedom,
where n2
is the number of terrestrial data in the respective inventory domain.
twophase
:
The calculation of the two-sided confidence intervals for global twophase estimates
(objects of class global
) are calculated based on the quantiles of the t-distribution
with n2 - p
degrees of freedom, where p
is the number of parameters used in
the regression model, and n2
is the number of terrestrial observations (i.e. local densities)
in the inventory domain.
The calculation of the two-sided confidence intervals for smallarea twophase estimates
(objects of class smallarea
) are calculated based on the quantiles of the t-distribution
with n2G - 1
degrees of freedom, where n2G
is the number of
terrestrial observations (i.e. local densities) in the smallarea.
threephase
:
The calculation of the two-sided confidence intervals for global threephase estimates
(objects of class global
) are calculated based on the quantiles of the t-distribution
with n2 - p
degrees of freedom, where p
is the number of parameters used in
the full regression model, and n2
is the number of terrestrial observations
(i.e. local densities) in the inventory domain (note: in notation used here n0, n1 and n2
correspond to the zero, first and second phase sample sizes respectively).
The calculation of the two-sided confidence intervals for smallarea theephase estimates
(objects of class smallarea
) are calculated based on the quantiles of the t-distribution
with n2G - 1
degrees of freedom, where n2G
is the number of
terrestrial observations (i.e. local densities) in the smallarea.
confint
returns a list of the following 3 components:
ci |
a
|
level |
the applied confidence level |
adjust.method |
the adjustment method applied to retrieve simultaneous confidence intervals |
In the special case of synthetic smallarea estimations, the two-sided confidence intervals
are calculated based on the quantiles of the t-distribution
with n2 - p
degrees of freedom, i.e. based on the global sample size.
The confidence intervals for synthetic smallarea estimations do not account for the potential bias of a linear model that was fit in a large forest area and applied to a small area. Thus, the coverage rates for confidence intervals produced by synthetic estimators may be less than the nominal level of confidence.
In case of cluster-sampling, n2G
is the number of terrestrial clusters
(a cluster constitutes the sample unit). This is automatically considered by confint
.
The adjustment methods passed to adjust.method
are designed to achieve
simultaneous confidence intervals by correcting the confidence level given by level
.
The use of this option is recommended if a set of estimates contained in a onephase
- or
smallarea
-object should be compared by their confidence intervals. It ensures that the
percentage of confidence intervals containing the true value will correspond to
the nominal confidence level.
Hill, A., Massey, A. F. (2021). The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories. Journal of Statistical Software, 97(4), 1-40.
Mandallaz, D. (2013). Design-based properties of some small-area estimators in forest inventory with two-phase sampling. Canadian Journal of Forest Research, 43(5), 441-449.
Mandallaz, D., Breschan, J., & Hill, A. (2013). New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based monte carlo approach with applications to small-area estimation. Canadian Journal of Forest Research, 43(11), 1023-1031.
Mandallaz, D. (2013). A three-phase sampling extension of the generalized regression estimator with partially exhaustive information. Canadian Journal of Forest Research, 44(4), 383-388.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57, 289-300.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## Calculate twophase estimations by extended pseudosynthetic estimator
# for 4 small areas ("A", "B", "C", "D") using the grisons-dataset:
sae.est <- twophase(formula = tvol ~ mean + stddev + max + q75,
data = grisons,
phase_id = list(phase.col = "phase_id_2p", terrgrid.id = 2),
small_area = list(sa.col = "smallarea",
areas = c("A", "B","C", "D"),
unbiased = TRUE))
## calculate 95%-confidence intervals for each small area:
confint(sae.est)
## calculate simultaneous 95%-confidence intervals using 'bonferroni'-method:
confint(sae.est, adjust.method = "bonferroni")
|
95% Confidence Intervals for twophase small area estimation
area estimate ci_lower_ext ci_upper_ext ci_lower_g ci_upper_g
1 A 391.1605 324.8712 457.4499 324.1627 458.1584
2 B 419.6746 345.7934 493.5558 351.9945 487.3547
3 C 328.0117 263.0906 392.9327 259.0078 397.0155
4 D 371.0596 295.0201 447.0990 299.9594 442.1598
95% Simultaneous Confidence Intervals for twophase small area estimation
area estimate ci_lower_ext ci_upper_ext ci_lower_g ci_upper_g
1 A 391.1605 303.6172 478.7038 302.6815 479.6395
2 B 419.6746 321.6363 517.7130 329.8650 509.4843
3 C 328.0117 241.3207 414.7026 235.8689 420.1544
4 D 371.0596 269.8629 472.2563 276.4363 465.6829
Confidence Interval adjustment by method: bonferroni
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