More rainfall calculations – REML

August 4, 2013
By

(This article was first published on Wiekvoet, and kindly contributed to R-bloggers)

I wanted to have a look at various REML methods for a long time. The rainfall data seemed a nice example. On top of that, FreshBiostats had a blog post ‘Mixed Models in R: lme4, nlme, or both?’. So lme4 it is.

The data  

As described before; the data are from KNMI; ROYAL NETHERLANDS METEOROLOGICAL INSTITUTE. The actual page for the data is http://www.knmi.nl/klimatologie/monv/reeksen/select_rr.html.  The data selected were from 6 locations; De Bilt, Groningen, Heerde, Kerkwerve, Ter Apel and Winterswijk (Sibinkweg). The blog starts with data entered in a data.frame named all which was derived a few weeks ago.

Analysis

A simple analysis would ignore years. The data are plotted below, the figure indicates a clear effect.  

sel1 <- all[(all$year <1916 | all$year>2003) 
        & all$Month==’Jan’ ,]
sel1$decade <- factor(c(‘1906-1915′,’2004-2013’)[1+(sel1$year>1950)]) 
sel1$Rain <- as.numeric(sel1$RD>0)

ag1 <- aggregate(sel1$Rain,list(Year=sel1$Year,
        location=sel1$location,
        decade=sel1$decade),FUN=sum)
ag1$Year <- factor(ag1$Year)
p <- ggplot(ag1, aes(decade, x/31))
p + geom_boxplot(notch=TRUE)

Analysis part 2

The problem appears once years are plotted; It seems more of the years in the first decade have a low proportion days with rain, but some of the last decade also have less days with rain.
p <- ggplot(ag1, aes(decade, x/31,col=Year))
p + geom_boxplot()

As written lme4 was my package of choice. This does give a significant effect, but it only at 3%.
library(lme4)
l1 <- glmer(cbind(31-x,x) ~  (1|Year)  ,data=ag1,family=binomial)
l1
Generalized linear mixed model fit by the Laplace approximation 
Formula: cbind(31 – x, x) ~ (1 | Year) 
   Data: ag1 
   AIC   BIC logLik deviance
 231.3 236.9 -113.7    227.3
Random effects:
 Groups Name        Variance Std.Dev.
 Year   (Intercept) 0.38069  0.617   
Number of obs: 120, groups: Year, 20

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)   
(Intercept)  -0.3950     0.1423  -2.775  0.00552 **

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1


l2 <- glmer(cbind(31-x,x) ~  decade + (1|Year) ,data=ag1,family=binomial)
l2
Generalized linear mixed model fit by the Laplace approximation 
Formula: cbind(31 – x, x) ~ decade + (1 | Year) 
   Data: ag1 
   AIC   BIC logLik deviance
 228.5 236.9 -111.3    222.5
Random effects:
 Groups Name        Variance Std.Dev.
 Year   (Intercept) 0.29496  0.5431  
Number of obs: 120, groups: Year, 20

Fixed effects:
                Estimate Std. Error z value Pr(>|z|)  
(Intercept)      -0.1019     0.1783  -0.572   0.5674  
decade2004-2013  -0.5862     0.2528  -2.319   0.0204 *

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1

Correlation of Fixed Effects:
            (Intr)

dc2004-2013 -0.705

anova(l1,l2)
Data: ag1
Models:
l1: cbind(31 – x, x) ~ (1 | Year)
l2: cbind(31 – x, x) ~ decade + (1 | Year)
   Df    AIC    BIC  logLik Chisq Chi Df Pr(>Chisq)  
l1  2 231.31 236.88 -113.65                          
l2  3 228.53 236.90 -111.27 4.772      1    0.02893 *
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1

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