Description Usage Arguments Details Value Author(s) References See Also Examples
This function modelaverages the estimate of a parameter of interest among a set of candidate models, computes the unconditional standard error and unconditional confidence intervals as described in Buckland et al. (1997) and Burnham and Anderson (2002). This modelaveraged estimate is also referred to as a natural average of the estimate by Burnham and Anderson (2002, p. 152).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187  modavg(cand.set, parm, modnames = NULL, second.ord = TRUE, nobs = NULL,
uncond.se = "revised", conf.level = 0.95, exclude = NULL, warn =
TRUE, ...)
## S3 method for class 'AICaov.lm'
modavg(cand.set, parm, modnames = NULL, second.ord =
TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICbetareg'
modavg(cand.set, parm, modnames = NULL, second.ord =
TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICsclm.clm'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICclm'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICclmm'
modavg(cand.set, parm, modnames = NULL, second.ord
= TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICcoxme'
modavg(cand.set, parm, modnames = NULL, second.ord
= TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICcoxph'
modavg(cand.set, parm, modnames = NULL, second.ord
= TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICglm.lm'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
gamdisp = NULL, ...)
## S3 method for class 'AICgls'
modavg(cand.set, parm, modnames = NULL, second.ord =
TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AIChurdle'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AIClm'
modavg(cand.set, parm, modnames = NULL, second.ord =
TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AIClme'
modavg(cand.set, parm, modnames = NULL, second.ord =
TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AIClmekin'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICmaxlikeFit.list'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
...)
## S3 method for class 'AICmer'
modavg(cand.set, parm, modnames = NULL, second.ord =
TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AIClmerMod'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICglmerMod'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICmultinom.nnet'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
...)
## S3 method for class 'AICpolr'
modavg(cand.set, parm, modnames = NULL, second.ord
= TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICrlm.lm'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICsurvreg'
modavg(cand.set, parm, modnames = NULL, second.ord =
TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICvglm'
modavg(cand.set, parm, modnames = NULL, second.ord
= TRUE, nobs = NULL, uncond.se = "revised", conf.level = 0.95,
exclude = NULL, warn = TRUE, c.hat = 1, ...)
## S3 method for class 'AICzeroinfl'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, ...)
## S3 method for class 'AICunmarkedFitOccu'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitColExt'
modavg(cand.set, parm, modnames =
NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitOccuRN'
modavg(cand.set, parm, modnames =
NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitPCount'
modavg(cand.set, parm, modnames =
NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitPCO'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitDS'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitGDS'
modavg(cand.set, parm, modnames = NULL,
second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitOccuFP'
modavg(cand.set, parm, modnames =
NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitMPois'
modavg(cand.set, parm, modnames =
NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitGMM'
modavg(cand.set, parm, modnames =
NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)
## S3 method for class 'AICunmarkedFitGPC'
modavg(cand.set, parm, modnames =
NULL, second.ord = TRUE, nobs = NULL, uncond.se = "revised",
conf.level = 0.95, exclude = NULL, warn = TRUE, c.hat = 1,
parm.type = NULL, ...)

cand.set 
a list storing each of the models in the candidate model set. 
parm 
the parameter of interest, enclosed between quotes, for which a modelaveraged estimate is required. For a categorical variable, the label of the estimate must be included as it appears in the output (see 'Details' below). 
modnames 
a character vector of model names to facilitate the identification of
each model in the model selection table. If 
second.ord 
logical. If 
nobs 
this argument allows to specify a numeric value other than total sample
size to compute the AICc (i.e., 
uncond.se 
either, 
conf.level 
the confidence level (1  α) requested for the computation of unconditional confidence intervals. 
exclude 
this argument excludes models based on the terms specified for the
computation of a modelaveraged estimate of 
warn 
logical. If 
c.hat 
value of overdispersion parameter (i.e., variance inflation factor) such
as that obtained from 
gamdisp 
if gamma GLM is used, the dispersion parameter should be specified here to apply the same value to each model. 
parm.type 
this argument specifies the parameter type on which the effect size
will be computed and is only relevant for models of

... 
additional arguments passed to the function. 
The parameter for which a modelaveraged estimate is requested must be
specified with the parm
argument and must be identical to its
label in the model output (e.g., from summary
). For factors, one
must specify the name of the variable and the level of interest.
modavg
includes checks to find variations of interaction terms
specified in the parm
and exclude
arguments. However, to
avoid problems, one should specify interaction terms consistently for
all models: e.g., either a:b
or b:a
for all models, but
not a mixture of both.
You must exercise caution when some models include interaction or
polynomial terms, because main effect terms do not have the same
interpretation when they also appear in an interaction/polynomial term
in the same model. In such cases, one should exclude models containing
interaction terms where the main effect is involved with the
exclude
argument of modavg
. Note that modavg
checks for potential cases of multiple instances of a variable appearing
more than once in a given model (presumably in an interaction) and
issues a warning. To correctly compute the modelaveraged estimate of a
main effect involved in interaction/polynomial terms, specify the
interaction terms(s) that should not appear in the same model with the
exclude
argument. This will effectively exclude models from the
computation of the modelaveraged estimate.
When warn = TRUE
, modavg
looks for matches among the
labels of the estimates with identical
. It then compares the
results to partial matches with regexpr
, and issues a warning
whenever they are different. As a result, modavg
may issue a
warning when some variables or levels of categorical variables have
nested names (e.g., treat
, treat10
; L
, TL
).
When this warning is only due to the presence of similarly named
variables in the models (and NOT due to interaction terms), you can
suppress this warning by setting warn = FALSE
.
The modelaveraging estimator implemented in modavg
is known to
be biased away from 0 when there is substantial model selection
uncertainty (Cade 2015). In such instances, it is recommended to use
the modelaveraging shrinkage estimator (i.e., modavgShrink
) for
inference on beta estimates or to focus on modelaveraged effect sizes
(modavgEffect
) and modelaveraged predictions
(modavgPred
).
modavg
is implemented for a list containing objects of
aov
, betareg
, clm
, clmm
, clogit
,
coxme
, coxph
, glm
, gls
, hurdle
,
lm
, lme
, lmekin
, maxlikeFit
, mer
,
glmerMod
, lmerMod
, multinom
, polr
,
rlm
, survreg
, vglm
, zeroinfl
classes as well
as various models of unmarkedFit
classes.
modavg
creates an object of class modavg
with the following
components:
Parameter 
the parameter for which a modelaveraged estimate was obtained. 
Mod.avg.table 
the reduced model selection table based on models including the parameter of interest. 
Mod.avg.beta 
the modelaveraged estimate based on all models including the parameter of interest (see 'Details' above regarding the exclusion of models where parameter of interest is involved in an interaction). 
Uncond.SE 
the unconditional standard error for the modelaveraged estimate (as opposed to the conditional SE based on a single model). 
Conf.level 
the confidence level used to compute the confidence interval. 
Lower.CL 
the lower confidence limit. 
Upper.CL 
the upper confidence limit. 
Marc J. Mazerolle
Anderson, D. R. (2008) Modelbased Inference in the Life Sciences: a primer on evidence. Springer: New York.
Buckland, S. T., Burnham, K. P., Augustin, N. H. (1997) Model selection: an integral part of inference. Biometrics 53, 603–618.
Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical informationtheoretic approach. Second edition. Springer: New York.
Burnham, K. P., Anderson, D. R. (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods and Research 33, 261–304.
Cade, B. S. (2015) Model averaging and muddled multimodel inferences. Ecology 96, 2370–2382.
Dail, D., Madsen, L. (2011) Models for estimating abundance from repeated counts of an open population. Biometrics 67, 577–587.
Lebreton, J.D., Burnham, K. P., Clobert, J., Anderson, D. R. (1992) Modeling survival and testing biological hypotheses using marked animals: a unified approach with casestudies. Ecological Monographs 62, 67–118.
MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Royle, J. A., Langtimm, C. A. (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255.
MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G., Franklin, A. B. (2003) Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207.
Mazerolle, M. J. (2006) Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses. AmphibiaReptilia 27, 169–180.
Royle, J. A. (2004) Nmixture models for estimating population size from spatially replicated counts. Biometrics 60, 108–115.
AICc
, aictab
, c_hat
,
confset
, evidence
, importance
,
modavgCustom
, modavgEffect
,
modavgShrink
, modavgPred
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198  ##anuran larvae example modified from Mazerolle (2006)
##these are different models than in the paper
data(min.trap)
##assign "UPLAND" as the reference level as in Mazerolle (2006)
min.trap$Type < relevel(min.trap$Type, ref = "UPLAND")
##set up candidate models
Cand.mod < list( )
##global model
Cand.mod[[1]] < glm(Num_anura ~ Type + log.Perimeter +
Type:log.Perimeter + Num_ranatra,
family = poisson, offset = log(Effort),
data = min.trap)
##interactive model
Cand.mod[[2]] < glm(Num_anura ~ Type + log.Perimeter +
Type:log.Perimeter, family = poisson,
offset = log(Effort), data = min.trap)
##additive model
Cand.mod[[3]] < glm(Num_anura ~ Type + log.Perimeter, family = poisson,
offset = log(Effort), data = min.trap)
##Predator model
Cand.mod[[4]] < glm(Num_anura ~ Type + Num_ranatra, family = poisson,
offset = log(Effort), data = min.trap)
##check chat for global model
c_hat(Cand.mod[[1]]) #uses Pearson's chisquare/df
##note the very low overdispersion: in this case, the analysis could be
##conducted without correcting for chat as its value is reasonably close
##to 1
##assign names to each model
Modnames < c("global model", "interactive model",
"additive model", "invertpred model")
##model selection
aictab(Cand.mod, Modnames)
##compute modelaveraged estimates for parameters appearing in top
##models
modavg(parm = "Num_ranatra", cand.set = Cand.mod, modnames = Modnames)
##round to 4 digits after decimal point
print(modavg(parm = "Num_ranatra", cand.set = Cand.mod,
modnames = Modnames), digits = 4)
##modelaveraging a variable involved in an interaction
##the following produces an error  because the variable is involved
##in an interaction in some candidate models
## Not run: modavg(parm = "TypeBOG", cand.set = Cand.mod,
modnames = Modnames)
## End(Not run)
##exclude models where the variable is involved in an interaction
##to get modelaveraged estimate of main effect
modavg(parm = "TypeBOG", cand.set = Cand.mod, modnames = Modnames,
exclude = list("Type:log.Perimeter"))
##to get modelaveraged estimate of interaction
modavg(parm = "TypeBOG:log.Perimeter", cand.set = Cand.mod,
modnames = Modnames)
##beware of variables that have similar names
set.seed(seed = 4)
resp < rnorm(n = 40, mean = 3, sd = 1)
size < rep(c("small", "medsmall", "high", "medhigh"), times = 10)
set.seed(seed = 4)
mass < rnorm(n = 40, mean = 2, sd = 0.1)
mass2 < mass^2
age < rpois(n = 40, lambda = 3.2)
agecorr < rpois(n = 40, lambda = 2)
sizecat < rep(c("a", "ab"), times = 20)
data1 < data.frame(resp = resp, size = size, sizecat = sizecat,
mass = mass, mass2 = mass2, age = age,
agecorr = agecorr)
##set up models in list
Cand < list( )
Cand[[1]] < lm(resp ~ size + agecorr, data = data1)
Cand[[2]] < lm(resp ~ size + mass + agecorr, data = data1)
Cand[[3]] < lm(resp ~ age + mass, data = data1)
Cand[[4]] < lm(resp ~ age + mass + mass2, data = data1)
Cand[[5]] < lm(resp ~ mass + mass2 + size, data = data1)
Cand[[6]] < lm(resp ~ mass + mass2 + sizecat, data = data1)
Cand[[7]] < lm(resp ~ sizecat, data = data1)
Cand[[8]] < lm(resp ~ sizecat + mass + sizecat:mass, data = data1)
Cand[[9]] < lm(resp ~ agecorr + sizecat + mass + sizecat:mass,
data = data1)
##create vector of model names
Modnames < paste("mod", 1:length(Cand), sep = "")
aictab(cand.set = Cand, modnames = Modnames, sort = TRUE) #correct
##as expected, issues warning as mass occurs sometimes with "mass2" or
##"sizecatab:mass" in some of the models
## Not run: modavg(cand.set = Cand, parm = "mass", modnames = Modnames)
##no warning issued, because "age" and "agecorr" never appear in same model
modavg(cand.set = Cand, parm = "age", modnames = Modnames)
##as expected, issues warning because warn=FALSE, but it is a very bad
##idea in this example since "mass" occurs with "mass2" and "sizecat:mass"
##in some of the models  results are INCORRECT
## Not run: modavg(cand.set = Cand, parm = "mass", modnames = Modnames,
warn = FALSE)
## End(Not run)
##correctly excludes models with quadratic term and interaction term
##results are CORRECT
modavg(cand.set = Cand, parm = "mass", modnames = Modnames,
exclude = list("mass2", "sizecat:mass"))
##correctly computes modelaveraged estimate because no other parameter
##occurs simultaneously in any of the models
modavg(cand.set = Cand, parm = "sizesmall", modnames = Modnames) #correct
##as expected, issues a warning because "sizecatab" occurs sometimes in
##an interaction in some models
## Not run: modavg(cand.set = Cand, parm = "sizecatab",
modnames = Modnames)
## End(Not run)
##exclude models with "sizecat:mass" interaction  results are CORRECT
modavg(cand.set = Cand, parm = "sizecatab", modnames = Modnames,
exclude = list("sizecat:mass"))
##example with multipleseason occupancy model modified from ?colext
##this is a bit longer
## Not run:
require(unmarked)
data(frogs)
umf < formatMult(masspcru)
obsCovs(umf) < scale(obsCovs(umf))
siteCovs(umf) < rnorm(numSites(umf))
yearlySiteCovs(umf) < data.frame(year = factor(rep(1:7,
numSites(umf))))
##set up model with constant transition rates
fm < colext(psiformula = ~ 1, gammaformula = ~ 1, epsilonformula = ~ 1,
pformula = ~ JulianDate + I(JulianDate^2), data = umf,
control = list(trace=1, maxit=1e4))
##model with with yeardependent transition rates
fm.yearly < colext(psiformula = ~ 1, gammaformula = ~ year,
epsilonformula = ~ year,
pformula = ~ JulianDate + I(JulianDate^2),
data = umf)
##store in list and assign model names
Cand.mods < list(fm, fm.yearly)
Modnames < c("psi1(.)gam(.)eps(.)p(Date + Date2)",
"psi1(.)gam(Year)eps(Year)p(Date + Date2)")
##compute modelaveraged estimate of occupancy in the first year
modavg(cand.set = Cand.mods, modnames = Modnames, parm = "(Intercept)",
parm.type = "psi")
##compute modelaveraged estimate of Julian Day squared on detectability
modavg(cand.set = Cand.mods, modnames = Modnames,
parm = "I(JulianDate^2)", parm.type = "detect")
## End(Not run)
##example of modelaveraged estimate of area from distance model
##this is a bit longer
## Not run:
data(linetran) #example modified from ?distsamp
ltUMF < with(linetran, {
unmarkedFrameDS(y = cbind(dc1, dc2, dc3, dc4),
siteCovs = data.frame(Length, area, habitat),
dist.breaks = c(0, 5, 10, 15, 20),
tlength = linetran$Length * 1000, survey = "line", unitsIn = "m")
})
## Halfnormal detection function. Density output (log scale). No covariates.
fm1 < distsamp(~ 1 ~ 1, ltUMF)
## Halfnormal. Covariates affecting both density and detection.
fm2 < distsamp(~ area + habitat ~ area + habitat, ltUMF)
## Hazard function. Covariates affecting both density and detection.
fm3 < distsamp(~ habitat ~ area + habitat, ltUMF, keyfun="hazard")
##assemble model list
Cands < list(fm1, fm2, fm3)
Modnames < paste("mod", 1:length(Cands), sep = "")
##modelaverage estimate of area on abundance
modavg(cand.set = Cands, modnames = Modnames, parm = "area", parm.type = "lambda")
detach(package:unmarked)
## End(Not run)

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