pheno.mlm.fit {pheno}R Documentation

Fits a two-way linear mixed model

Description

Fits a two-way linear mixed model. The model assumes the first factor f1 to be fixed and the second factor f2 to be random. Errors are assumed to be i.i.d. No general mean and sum of f2 is constrained to be zero.

Usage

pheno.mlm.fit(D)

Arguments

D Data frame with three columns (x, f1, f2) or a matrix where rows are ranks of factor f1 levels and columns are ranks of factor f2 levels and missing values are set to 0.

Details

This function is basically a wrapper for the lme() function of the nlme package, adapted for the estimation of combined phenological time series. Estimation method: restricted maximum likelihood (REML) In phenological application, x should be the julian day of observation of a certain phase, factor f1 should be the observation year and factor f2 should be a station-id.

Value

fixed Estimated fixed effects, in phenology this is precisely the combined time series.
fixed.lev Levels of fixed effects. Should be the same order as fixed effects.
random Estimated random effects, in phenology these are the station effects.
random.lev Levels of random effects. Should be the same order as random effects.
SEf1 Standard error group f1, i.e. square root of variance component fixed effect.
SEf2 Standard error group f2, i.e. square root of variance component random effect.
lclf Lower 95 percent confidence limit of fixed effects.
uclf Upper 95 percent confidence limit of fixed effects.
fit The fitted lme model object.

Author(s)

Joerg Schaber

References

Searle (1997) 'Linear Models'. Wiley. Schaber J, Badeck F-W (2002) 'Evaluation of methods for the combination of phenological time series and outlier detection'. Tree Physiology 22:973-982

See Also

lme

Examples

        data(DWD)
        R <- pheno.mlm.fit(DWD)                                                         # parameter estimation
        plot(levels(factor(DWD[[2]])),R$fixed,type="l")         # plot combined time series
        tr <- lm(R$fixed~rank(levels(factor(DWD[[2]]))))        # trend estimation
        summary(tr)$coef[2]                                                                     # slope of trend
        summary(tr)$coef[4]                                                                     # standard error of trend

[Package pheno version 1.4 Index]