power_dsgn {spsurvey} | R Documentation |
Power calculation for multiple panel designs
Description
Calculates the power for trend detection for one or more variables, for one or more panel designs, for one or more linear trends, and for one or more significance levels. The panel designs create a covariance model where the model includes variance components for units, periods, the interaction of units and periods, and the residual (or index) variance.
Usage
power_dsgn(
ind_names,
ind_values,
unit_var,
period_var,
unitperiod_var,
index_var,
unit_rho = 1,
period_rho = 0,
paneldsgn,
nrepeats = NULL,
trend_type = "mean",
ind_pct = NULL,
ind_tail = NULL,
trend = 2,
alpha = 0.05
)
Arguments
ind_names |
Vector of indicator names |
ind_values |
Vector of indicator mean values |
unit_var |
Vector of variance component estimates for unit variability for the indicators |
period_var |
Vector of variance component estimates for period variability for the indicators |
unitperiod_var |
Vector of variance component estimates for unit by period interaction variability for the indicators |
index_var |
Vector of variance component estimates for index (residual) error for the indicators |
unit_rho |
Correlation across units. Default is |
period_rho |
Correlation across periods. Default is |
paneldsgn |
A list of panel designs each as a matrix. Each element of
the list is a matrix with |
nrepeats |
Either |
trend_type |
Trend type is either |
ind_pct |
When |
ind_tail |
When trend_type is equal to |
trend |
Single value or vector of assumed percent change from
initial value in the indicator for each period. Assumes the trend is
expressed as percent per period. Note that the trend may be either positive
or negative. The default is |
alpha |
Single value or vector of significance level for linear
trend test, alpha, Type I error, level. The default is |
Details
Calculates the power for detecting a change in the mean for different panel design structures. The model incorporates unit, period, unit by period, and index variance components as well as correlation across units and across periods. See references for methods.
Value
A list with components trend_type
, ind_pct
, ind_tail
, trend values
across periods, periods (all periods included in one or more panel
designs), significance levels, a five-dimensional array of power
calculations (dimensions: panel, design names, periods, indicator names,
trend names, alpha_names
), an array of indicator mean values for each trend
and the function call.
Author(s)
Tony Olsen Olsen.Tony@epa.gov
References
Urquhart, N. S., W. S. Overton, et al. (1993) Comparing sampling designs for monitoring ecological status and trends: impact of temporal patterns. In: Statistics for the Environment. V. Barnett and K. F. Turkman. John Wiley & Sons, New York, pp. 71-86.
Urquhart, N. S. and T. M. Kincaid (1999). Designs for detecting trends from repeated surveys of ecological resources. Journal of Agricultural, Biological, and Environmental Statistics, 4(4), 404-414.
Urquhart, N. S. (2012). The role of monitoring design in detecting trend in long-term ecological monitoring studies. In: Design and Analysis of Long-term Ecological Monitoring Studies. R. A. Gitzen, J. J. Millspaugh, A. B. Cooper, and D. S. Licht (eds.). Cambridge University Press, New York, pp. 151-173.
See Also
ppd_plot
to plot power curves for panel designs
Examples
# Power for rotating panel with sample size 60
power_dsgn("Variable_Name",
ind_values = 43, unit_var = 280, period_var = 4,
unitperiod_var = 40, index_var = 90, unit_rho = 1, period_rho = 0,
paneldsgn = list(NoR60 = revisit_dsgn(20,
panels = list(NoR60 = list(
n = 60, pnl_dsgn = c(1, NA),
pnl_n = NA, start_option = "None"
)), begin = 1
)),
nrepeats = NULL, trend_type = "mean", trend = 1.0, alpha = 0.05
)