RiskCalc.Rd
This function estimates the production risk of an experimental treatment compared to the control treatment using time series data. We suggest applying the ERA.Prepare
function to
data before using with this function.
RiskCalc(Data, PLevel = "PrName", Out.Codes = 101, MinYear = 3)
A prepared (ERA) dataset, see the ERA.Prepare
function.
The column name of the variable in Data
that describes the practice. Use this parameter to choose different levels of the practice hierarchy. Default = PrName
(Practice).
A vector of outcome codes to consider in the analysis. Default = 101
(Crop Yield).
An integer value for the minimum length of a MYO sequence. Sequences with fewer growing season than this number are excluded from analysis. Default = 3
.
A list of two data.tables Risk
and Risk.Averages
.
Risk
contains statistics calculated for each value of UID
and has the fields:
UID
= a unique identifier based on the field Outcome
,Practice
,Practice.Base
,Practice.Code
,Code
,ID
,Site.ID
,EU
,T.Descrip
,C.Descrip
,T.NI
,T.NO
,C.NI
,C.NO
,Tree
,Variety
,Diversity
, and Rep
.
N.Years
= the number of unique growing seasons reported for each value of UID
.
N.Obs
= the total number of observation for a value of UID
.
Diff.Mean
= mean difference between experimental and control treatments (mean(MeanT-MeanC)
)
Diff.SD
= standard deviation of mean difference (sd(MeanT-MeanC)
)
Diff.t.stat
= t-statistic of mean difference (.Mean/(.SD/N.Obs^0.5)
)
Diff.p.val
= probability of mean difference being <0 (pt(Diff.t.stat,N.Obs-1,lower.tail = T)
)
Mean.C
= mean of control treatment
Mean.T
= mean of experimental treatment
Mean.T.SD
= standard deviation of experimental treatment
Mean.t.stat
= t-statistic for MeanT
< MeanC
(Mean.T-Mean.C)/(Mean.T.SD/N.Obs^0.5
)
Mean.p.val
= probability of MeanT
< MeanC
(pt(Mean.t.stat,N.Obs-1,lower.tail = T)
)
N.Obs.Study
= number of observations a study (Code
column) contributes to a combination of practice x outcome
Risk.Averages
is the data in the Risk
table averaged with weighting across practice x outcome combinations. Additional fields are:
Mean.Seq.Len
= the mean value of Risk$N.Years
Median.Seq.Len
= the median value of Risk$N.Years
N.Studies
= the number of studies contributing MYOs
Total.Obs
= the total number of observations contributing to MYOs
N.Obs
= the total number of MYOs
Diff.p.val.se
= the standard error of Risk$Diff.p.val
Mean.p.val.se
= the standard error of Risk$Mean.p.val
Diff.CI95low
= lower 95% confidence interval of Diff.p.val
Diff.CI95high
= upper 95% confidence interval of Diff.p.val
Mean.CI95low
= lower 95% confidence interval of Mean.p.val
Mean.CI95high
= upper 95% confidence interval of Mean.p.val
Method: We adapted a lower confidence limit (LCL) approach (e.g. Hildebrand1996, Yamoah2000, Sirrine2010) to estimate production risk as:
Risk.Means
= the probability of the mean experimental treatment yielding lower than the control treatment; and
Risk.Diff
= the probability of the mean yield difference between experimental and control treatments being less than 0.
A minimum of three seasons of yield data from the same experimental treatments are required to calculate risk and this is the default threshold for the analyses presented here.
The minimum number of season for a multi-year observation (MYO) to be included in analyses can be adjusted using the MinYear
parameter.
Statistics are reported aggregated to the level of practice hierarchy specified in the PLevel
parameter.
Outcomes are analyzed at the subindicator level. Whilst any outcome(s) can be used with this function, it's primary purpose is to analyze productivity outcomes such as crop yield or net returns.
Weightings are applied to mean and error estimates as per the methods described in the ERA.Analyze
function.