resources:lme

<2014.2.10>

AC's attempts at learning to do this in R.

Added package **lme4** to R.
http://cran.r-project.org/web/packages/lme4/lme4.pdf

Also added **plyr** package, because it has a rename() function for renaming columns in a data frame:
(http://www.cookbook-r.com/Manipulating_data/Renaming_columns_in_a_data_frame/)

However, I found it easier to do this:

colnames(x) <- c("name1","name2"...)

based on guidance from:

https://stat.ethz.ch/R-manual/R-devel/library/base/html/colnames.html

and from

http://cran.r-project.org/doc/manuals/r-devel/R-data.html

Column names can be given explicitly via the col.names; explicit names override the header line (if present).

Added **arm** package:
http://cran.r-project.org/web/packages/arm/index.html

`arm: Data Analysis Using Regression and Multilevel/Hierarchical Models`

R functions for processing lm, glm, svy.glm, merMod and polr outputs.

**arm** package provides the `coefplot`

function for plotting the regression coefficient values and std. errors.

Need to specify that subjects is a nominal variable, using the `factor`

command:

http://www.statmethods.net/input/datatypes.html

Tell R that a variable is nominal by making it a factor. The factor stores the nominal values as a vector of integers in the range [ 1… k ] (where k is the number of unique values in the nominal variable), and an internal vector of character strings (the original values) mapped to these integers.

A nice description from:

https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/015590.html

In this case the coefficient corresponds to one of the

terms in the model and I would advocate performing a likelihood ratio

test comparing the two models

>fm <- glmer(SameSite~BreedSuc1+Sex+(1|Bird), family="binomial") >fm0 <- glmer(SameSite~Sex+(1|Bird), family="binomial") # the null >hypothesis model >anova(fm0, fm) >

Even though the function is called anova it will, in this case,

perform a likelihood ratio test (LRT). It also prints the values of

AIC and BIC if you prefer to compare models according to one of those

criteria but I prefer using the likelihood ratio for nested models.

However, before doing that comparison you should ask yourself whether

you want to compare models that have the, apparently unnecessary term

for Sex in them. The way I would approach the model building is first

to reduce the model to

>fm1 <- lmer(SameSite~BreedSuc1+(1|Bird), family="binomial") >

You could then compare

>anova(fm1, fm) >

which I presume will give a large p-value for the LRT, so we prefer

the simpler model, fm1. After that, I would compare

>fm2 <- lmer(SameSite ~ 1 + (1|Bird), family="binomial") >

resources/lme.txt · Last modified: 2019/05/22 16:08 (external edit)

Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Share Alike 4.0 International