StabCalc.Rd
StabCalc
calculates relative and absolute outcome stability for MYOs based on the methods of Knapp el al. (2018). This
function is designed for use within the ERAg::StabCalc2
function.
A data.table output by the ERAg::PrepareStabData
function
logical, if TRUE
coefficient estimates are weighted acccording to the supplied weightings in the Data
object supplied(default = T)
logical, depreciated (default = T
)
logical, if TRUE
extreme outliers are removed withing each Practice x Outcome combination as per the method detailed in ERAg::OutCalc
(default = T
)
logical, if TRUE
back-transformed coefficient estimates and confidence intervals are appended to outputs (default = T
)
list, optional list of control values for the rma.mv
estimation algorithms. If unspecified, default values are defined inside the function (default = list(optimizer="optim",optmethod="Nelder-Mead",maxit=10000)
)
character vector, this argument is depreciated do edit (default=c("lnRR","lnVR","lnCVR")
)
logical, if TRUE
scale-adjusted coefficient of variation, acv, is substituted for the coefficient of variation (cv).
StabCalc
returns a list is containing following data:
[[Coefs]]
A data.table
of model coefficients, test statistics, confidence intervals:
Mean
numeric, response variable test coefficient (see Response
field) given the model used (see Model
field)
CI.low
numeric, response variable test coefficient lower confidence limit see confint.rma
*CI.high
numeric, response variable test coefficient upper confidence limit see confint.rma
Mean.Jen
numeric, back-transformed response variable test coefficient correcting for the Jensen inequality as exp(model$b[,1] + sigma.sq / 2)
CI.low.Jen
numeric, back-transformed response variable test coefficient lower confidence limit correcting for the Jensen inequality as exp(model$ci.lb[,1] + sigma.sq / 2)
CI.high.Jen
numeric, back-transformed response variable test coefficient upper confidence limit correcting for the Jensen inequality as exp(model$ci.ub[,1] + sigma.sq / 2)
Sigma
numeric, estimated sigma^2 value(s)
Mean.Smear
numeric, back-transformed response variable test coefficient correcting for the Jensen inequality using the Smearing estimate as exp(model$b[,1] * (1 / nobs(model) * sum(exp(resid(model)))))
CI.low.Smear
numeric, back-transformed response variable test coefficient lower confidence limit correcting for the Jensen inequality using the Smearing estimate as exp(model$ci.lb[,1] * (1 / nobs(model) * sum(exp(resid(model)))))
CI.high.Smear
numeric, back-transformed response variable test coefficient upper confidence limit correcting for the Jensen inequality using the Smearing estimate as exp(model$ci.ub[,1] * (1 / nobs(model) * sum(exp(resid(model)))))
P.Vals
numeric, p-value for test statistic
SE
numeric, standard error of the coefficients
Mean.exp
numeric, back-transformed response variable test coefficient not correcting for the Jensen inequality as exp(model$b[,1])
CI.low.exp
numeric, back-transformed response variable test coefficient lower confidence limit not correcting for the Jensen inequality as exp(model$ci.lb[,1])
CI.high.exp
numeric, back-transformed response variable test coefficient upper confidence limit not correcting for the Jensen inequality as exp(model$ci.ub[,1])
Z.val
numeric, test statistic of the coefficient
Model
numeric, test used to generate test statistics and confidence intervals rma.mv
= Multivariate/Multilevel Linear (Mixed-Effects) Model see rma.mv or robust.rma
= Cluster-Robust Tests and Confidence Intervals for 'rma' objects see robust
Robust
logical, when T
robust tests are used, when F
they are not
Response
character, lnRR
= natural log of response ratio, lnVR
= natural log of absolute variability ratio, lnCVR
= natural log of relative variability ratio see ERAg::PrepareStabData
function for more information.
N.Studies
integer, number of studies contributing to the analysis
N.Seq
integer, number of unique temporal sequences contributing to the analysis
N.Obs
integer, depreciated field
Practice
character, ERA practice
Outcome
character, ERA outcome
EU
character, ERA EU (product) code
**[[Models]]
** Multivariate Meta-Analysis Model objects
lnRR
= model where response variable is lnRR
= natural log of response ratio
lnVR
= model where response variable is lnVR
= natural log of absolute variability ratio
lnCVR
= model where response variable is lnCVR
= natural log of relative variability ratio
**[[R.Models]]
** Cluster-Robust Tests and Confidence Intervals applied to the Multivariate Meta-Analysis objects
lnRR
= model where repose variable is lnRR
= naturl log of response ratio
lnVR
= model where response variable is lnVR
= natural log of absolute variability ratio
lnCVR
= model where response variable is lnCVR
= natural log of relative variability ratio
**[[Tests]]
** A data.table
containing the results of a weighted linear model of form ln(y) = a + b × ln(x)
where y
is a stability ratio and
x
is the mean yield ratio. Robust results use a weighted robust linear model, see rlm
Estimate
numeric, coefficient estimate
Std.Error
numeric, standard error of the coefficient estimate
t value
numeric, test statistic of the coefficient estimate
Pr(>|t|)
numeric, p-value for test statistic
Coefficient
character, coefficient (intercept or beta)
Variable
character, lnRR
= natural log of response ratio, lnVR
= natural log of absolute variability ratio, lnCVR
= natural log of relative variability ratio see ERAg::PrepareStabData
function for more information.
Robust
logical, when T
robust tests are used, when F
they are not
Sigma.sq
numeric, estimated sigma^2 value(s)
Practice
character, ERA practice
Outcome
character, ERA outcome
EU
character, ERA EU (product) code
PSymbol
character, *
P<=0.05, **
P<=0.01, ***
P<=0.001, N.S. P>0.05.
N.Obs
integer, depreciated field
N.Studies
integer, number of studies contributing to the analysis
Mean.Jen
numeric, back-transformed coefficient estimate correcting for the Jensen inequality as exp(Estimate + sigma.sq / 2)
CI.low.Jen
numeric, back-transformed coefficient estimate less standard error correcting for the Jensen inequality as exp(Estimate - Std.Error + Sigma.sq / 2)
CI.high.Jen
numeric, back-transformed coefficient estimate plus standard error correcting for the Jensen inequality as exp(Estimate + Std.Error + Sigma.sq / 2)
**[[Tests2]]
** = as per the Coeffs
data.table but transformed to be a wide format for Response variables.
Model
numeric, test used to generate test statistics and confidence intervals rma.mv
= Multivariate/Multilevel Linear (Mixed-Effects) Model see rma.mv or robust.rma
= Cluster-Robust Tests and Confidence Intervals for 'rma' objects see robust
Robust
logical, when T
robust tests are used, when F
they are not
N.Studies
integer, number of studies contributing to the analysis
N.Seq
integer, number of unique temporal sequences contributing to the analysis
N.Obs
integer, depreciated field
Mean_lnCVR
numeric, lnCVR test coefficient
Mean_lnRR
numeric, lnRR test coefficient
Mean_lnVR
numeric, lnVR test coefficient
Mean.Jen_lnCVR
numeric, back-transformed lnCVR test coefficient correcting for the Jensen inequality as exp(model$b[,1] + sigma.sq / 2)
Mean.Jen_lnRR
numeric, back-transformed lnRR test coefficient correcting for the Jensen inequality as exp(model$b[,1] + sigma.sq / 2)
Mean.Jen_lnVR
numeric, back-transformed lnVR test coefficient correcting for the Jensen inequality as exp(model$b[,1] + sigma.sq / 2)
SE_lnCVR
numeric, standard error of lnCVR test coefficient
SE_lnRR
numeric, standard error of lnRR test coefficient
SE_lnVR
numeric, standard error of lnVR test coefficient
CI.low_lnCVR
numeric, lnCVR test coefficient lower confidence limit
CI.high_lnCVR
numeric, lnCVR test coefficient upper confidence limit
CI.low_lnRR
numeric, lnRR test coefficient lower confidence limit
CI.high_lnRR
numeric, lnRR test coefficient upper confidence limit
CI.low_lnVR
numeric, lnVR test coefficient lower confidence limit
CI.high_lnVR
numeric, lnVR test coefficient upper confidence limit
CI.low.Jen_lnCVR
numeric, back-transformed lnCVR test coefficient lower confidence limit correcting for the Jensen inequality as exp(model$ci.lb[,1] + sigma.sq / 2)
CI.high.Jen_lnCVR
numeric, back-transformed lnCVR test coefficient lower confidence limit correcting for the Jensen inequality as exp(model$ci.b[,1] + sigma.sq / 2)
CI.low.Jen_lnRR
numeric, back-transformed lnRR test coefficient upper confidence limit correcting for the Jensen inequality as exp(model$ci.lb[,1] + sigma.sq / 2)
CI.high.Jen_lnRR
numeric, back-transformed lnRR test coefficient lower confidence limit correcting for the Jensen inequality as exp(model$ci.b[,1] + sigma.sq / 2)
CI.low.Jen_lnVR
numeric, back-transformed lnVR test coefficient lower confidence limit correcting for the Jensen inequality as exp(model$ci.lb[,1] + sigma.sq / 2)
CI.high.Jen_lnVR
numeric, back-transformed lnVR test coefficient lower confidence limit correcting for the Jensen inequality as exp(model$ci.b[,1] + sigma.sq / 2)
P.Vals_lnCVR
numeric, p-value for lnCVR test statistic
P.Vals_lnRR
numeric, p-value for lnRR test statistic
P.Vals_lnVR
numeric, p-value for lnVR test statistic
PSymbol_lnCVR
character, lnCVR *
P<=0.05, **
P<=0.01, ***
P<=0.001, N.S. P>0.05.
PSymbol_lnRR
character, lnRR *
P<=0.05, **
P<=0.01, ***
P<=0.001, N.S. P>0.05.
PSymbol_lnVR
character, lnVR *
P<=0.05, **
P<=0.01, ***
P<=0.001, N.S. P>0.05.
Sigma_lnCVR
numeric, lnCVR estimated sigma^2 value(s)
Sigma_lnRR
numeric, lnRR estimated sigma^2 value(s)
Sigma_lnVR
numeric, lnVR estimated sigma^2 value(s)
Practice
character, ERA practice
Outcome
character, ERA outcome
EU
character, ERA EU (product) code
In the Coefs
, Models
, Tests2
outputs of this function class estimates of the response variables lnRR
= natural log of response ratio, lnVR
=
natural log of absolute variability ratio, and lnCVR
= natural log of relative variability ratio are obtained with the rma.mv function with
formula response.variable~1
. Confidence intervals at P<0.95 are obtained by default for all (non-fixed) variance and correlation components of
the models. Cluster-robust tests, confidence intervals, and significance of the model coefficients from rma.mv
models are additionally calculated using the
robust and f.robftest functions.
Natural log ratios are back-transformed with and without a corrections for the Jensen inequality. Corrections are applied as per Tandini & Mehrabi 2017 using two methods for back-transformation:
exp(fitted(model) + summary(model)$sigma^2 / 2)
situable for normally distributed data
a smearing estimate as exp(fitted(model) * (1 / nobs(model) * sum(exp(resid(model))))
The Tests
output contains the results of a weighted linear model of form log(y) = a + b × log(x)
where y
is a stability ratio and
x
is the mean yield ratio. The robust results in this table use a weighted robust linear model, see rlm.