subset(data,!country%in%drops)

`data`

is a dataframe with multiple columns one of which is named `country`

. While `drops`

is the list of values in `country`

whose rows in ` data `

will be dropped.

subset(data,!country%in%drops)

`data`

is a dataframe with multiple columns one of which is named `country`

. While `drops`

is the list of values in `country`

whose rows in ` data `

will be dropped.

Replace multiple items in an object

```
recoderFunc <- function(data, oldvalue, newvalue) {
# convert any factors to characters
if (is.factor(data)) data <- as.character(data)
if (is.factor(oldvalue)) oldvalue <- as.character(oldvalue)
if (is.factor(newvalue)) newvalue <- as.character(newvalue)
# create the return vector
newvec <- data
# put recoded values into the correct position in the return vector
for (i in unique(oldvalue)) newvec[data == i] <- newvalue[oldvalue == i]
newvec
}
```

http://susanejohnston.wordpress.com/2012/10/01/find-and-replace-in-r-part-2-how-to-recode-many-values-simultaneously/

`load("C:/Dropbox/SESYNC.Macroevolution.ES.Trees/Taxonomy/FAO/lookup.RData")`

**Note**

1. commonslash (/) instead of backslash (\)

2. Dropbox is at the root.

I have been using the MuMIn package for model averaging. Below is a function that gives the average sum of squares for each predictor variable across the candidate models (i.e., the sum of squares for the model average).

Download this code as a pdf:

model_averaging_SS`#This code calculates model averaged Sum of Squares as a weighted mean of the SS for each predictor across the candidate set of models. #The protocol was that used to average coefficients across the candidate set. #See Johnson J.B. & Omland K.S. (2004). Model selection in ecology and evolution. Trends in Ecology & Evolution, 19, 101-108.`

`require(MuMIn) require(car)`

`getSS<-function(x) { #x is a standard glm model object if(length(coef(x))>1){ An<-Anova(x,test.statistic="F") SS<-as.matrix(An$S) rownames(SS)<-rownames(An) return(t(SS)) } }`

`getSSs<-function(m.s,data=NULL){ if(is.null(data)){stop("must include the matching data object for the model selection object, m.s")} m.fit<-lapply(lapply(get.models(m.s),formula),glm,data=data)`

`hold<-matrix(data=0,length(m.fit),length(attr(m.s,"global")$coefficients)) colnames(hold)<-c("Residuals",names(attr(m.s,"global")$coefficients)[-1]) for(i in 1:(length(m.fit))) { SS<-getSS(m.fit[[i]]) hold[i,colnames(SS)]<-SS } return(hold) }`

`avgSS<-function(m.s,data=NULL,w.min=0){ w<-m.s$weight hold<-as.matrix(getSSs(m.s,data)) hold<-hold[w>w.min,] w<-w[w>w.min] if(is.null(dim(hold))){ warning(paste("There was only one model with a weight > ",w.min," and returned the SS for this one model.",sep="")) return(hold) } else { return(apply(hold,2,weighted.mean,w=w,na.rm=TRUE)) } }`

`#EXAMPLE CorrNorm <- function(rho = 0.4, X1 = rnorm(100)) { #produces correlated Gaussian variables X2<-rnorm(length(X1)) Z = data.frame(Y=rho*X1+sqrt(1-rho^2)*X2,X1) return(Z) } #http://r.789695.n4.nabble.com/generate-two-sets-of-random-numbers-that-are-correlated-td3736161.html y<-CorrNorm(.8) y<-data.frame(y,X2=CorrNorm(.2,X1 = y[,1])[,1]) lm.D <- lm(Y ~ X1 + X2,data=y) #Global Model (m.s<-dredge(lm.D)) #All submodels of the global model (a model.selection object) avgSS(m.s,y) #Average Sum of Squares for all the models avgSS(m.s,y,w.min=0.5) #Average Sum of Squares for all models with weights greater than w.min`

I used the insert headers and footers plugin and pasted my unique script in the header box once this plugin was installed on my site.

The WordPress theme I am using is tiny forge

There is a nice R package googleVis that integrates google motion charts and chart tools with R

A tutorial here