View source: R/pheno.mlm.fit.R
pheno.mlm.fit | R Documentation |
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.
pheno.mlm.fit(D)
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. |
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.
Note that the input data is sorted before fitting, such that subsequent
analyses using the input data should be done using the sorted output data frame.
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. |
D |
The input as ordered data frame, ordered first by f2 then by f1 |
fit |
The fitted lme model object. |
Joerg Schaber
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
lme
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
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