Description Usage Arguments Details Value Author(s) References See Also Examples
Predictions including variance for basic nlme and gnls models.
1 | varPredictNlmeGnls(object, newdata, ...)
|
object |
the model fit object used for predictions, treated by |
newdata |
dataframe of new predictors and covariates |
... |
further arguments to |
Variance calculation is based on Taylor series expansion as described in appendix A1 by Wutzler08.
Fitted object
needs to be prepared by function attachVarPrep
.
If not done before, this function is called automatically within varPredictNlmeGnls
.
However, for finetuning or avoiding overhead in repeated calls, it
is recommended to explicitely call attachVarPrep
before calling varPredictNlmeGnls
.
numeric matrix with columns
fit |
predictions |
varFix |
variance component due to uncertainty in fixed effects |
varRan |
variance component due to uncertainty in random effects |
varResid |
variance component due to residual error |
sdPop |
standard deviation of prediction of a new population |
sdInd |
standard deviation of prediction of a new individual |
Thomas Wutzler
Wutzler, T.; Wirth, C. & Schumacher, J. (2008)
Generic biomass functions for Common beech (Fagus sylvatica L.) in Central Europe - predictions and components of uncertainty.
Canadian Journal of Forest Research, 38, 1661-1675
varSumPredictNlmeGnls
, twNlme-package
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | #---- fit a nlme and gnls model to data of stem weights
data(Wutzler08BeechStem)
lmStart <- lm(log(stem) ~ log(dbh) + log(height), Wutzler08BeechStem )
nlmeFit <- nlme( stem~b0*dbh^b1*height^b2, data=Wutzler08BeechStem
,fixed=list(b0 ~ si + age + alt, b1+b2 ~ 1)
,random= b0 ~ 1 | author
,start=c( b0=c(as.numeric(exp(coef(lmStart)[1])),0,0,0), b1=as.numeric(coef(lmStart)[2]), b2=as.numeric(coef(lmStart)[3]) )
,weights=varPower(form=~fitted(.))
,method='REML' # for unbiased error estimates
)
summary(nlmeFit)
x3 <- update(nlmeFit, fixed=list(b0 ~ si * log(age), b1+b2 ~ 1))
gnlsFit <- gnls( stem~b0*dbh^b1*height^b2, data=Wutzler08BeechStem
,params = list(b0 ~ si + age + alt, b1~1, b2 ~ 1)
,start=c( b0=c(as.numeric(exp(coef(lmStart)[1])),0,0,0), b1=as.numeric(coef(lmStart)[2]), b2=as.numeric(coef(lmStart)[3]) )
,weights=varPower(form=~fitted(.))
)
summary(gnlsFit)
fixef(gnlsFit) # note the usage of fixef.gnls.
ranef(gnlsFit) # note the usage of ranef.gnls.
#---- some artificial data for new prediction
nData <- data.frame( dbh=tmp <- seq(2,80,length.out=40), alt=median(Wutzler08BeechStem$alt), si=median(Wutzler08BeechStem$si) )
lmHeight <- lm( height ~ dbh, Wutzler08BeechStem)
nData$height <- predict(lmHeight, nData)
lmAge <- lm( age ~ dbh, Wutzler08BeechStem)
nData$age <- predict(lmAge, nData)
#---- do the prediction including variance calculation
# automatic derivation with accounting for residual variance model
nlmeFit <- attachVarPrep(nlmeFit, fVarResidual=varResidPower)
resNlme <- varPredictNlmeGnls(nlmeFit,nData)
# plotting prediction and standard errors
plot( resNlme[,"fit"] ~ dbh, nData, type="l", xlim=c(40,80), lty="dashed")
lines( resNlme[,"fit"]+resNlme[,"sdPop"] ~ dbh, nData, col="maroon", lty="dashed" )
lines( resNlme[,"fit"]-resNlme[,"sdPop"] ~ dbh, nData, col="maroon", lty="dashed" )
lines( resNlme[,"fit"]+resNlme[,"sdInd"] ~ dbh, nData, col="orange", lty="dashed" )
lines( resNlme[,"fit"]-resNlme[,"sdInd"] ~ dbh, nData, col="orange", lty="dashed" )
#---- handling special model of residual weights
# here we fit different power coefficients for authors
# for the prediction we take the mean, but because it appears in a nonlinear term
# we need a correction term
.tmp.f <- function(){ # takes long, so do not execute each test time
nlmeFitAuthor <- nlme( stem~b0*dbh^b1*height^b2, data=Wutzler08BeechStem
,fixed=list(b0 ~ si + age + alt, b1+b2 ~ 1)
,random= b0 ~ 1 | author
,start=c( b0=c(as.numeric(exp(coef(lmStart)[1])),0,0,0), b1=as.numeric(coef(lmStart)[2]), b2=as.numeric(coef(lmStart)[3]) )
,weights=varPower(form=~fitted(.)|author)
)
#pred <- predict(modExampleStem, newdata, level=0)
varResidPowerAuthor <- function(object,newdata,pred ){
sigma <- object$sigma
deltaAuthor <- coef(object$modelStruct$varStruct, allCoef = TRUE)
delta <- mean(deltaAuthor)
varDelta <- var(deltaAuthor)
sigma^2 * abs(pred)^(2*delta) * (1+2*log(abs(pred))^2*varDelta)
}
#mtrace(varResidPowerAuthor)
nlmeFitResidAuthor <- attachVarPrep(nlmeFitAuthor, fVarResidual=varResidPowerAuthor)
resNlmeAuthor <- varPredictNlmeGnls(nlmeFitResidAuthor,nData)
AIC(nlmeFitResidAuthor)
}
nlmeFit #return for creating modExampleStem.RData
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