Created
December 28, 2010 15:45
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A function to obtain predictions from a fitted lmer object.
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#A function to obtain predictions from a fitted lmer object. | |
#If to_predict is null, the function will attempt to build a | |
#design matrix for prediction from the information in the fit | |
#itself. If a .() object is passed to as to_predict, variables | |
#listed in to_return are used to create the design matrix. A | |
#data frame may also be passed as to_predict. | |
ezPredict <- | |
function( | |
fit | |
, to_predict = NULL | |
, numeric_res = 0 | |
){ | |
data = attr(fit,'frame') | |
vars = as.character(attr(attr(data,'terms'),'variables')) | |
dv = as.character(vars[2]) | |
if(!is.data.frame(to_predict)){ | |
if(length(grep('poly(',vars,fixed=TRUE))>0){ | |
stop('Cannot auto-create "to_predict" when the fitted model contains poly(). Please provide a non-null value to the "to_return" argument.') | |
} | |
if(is.null(to_predict)){ | |
vars = vars[3:length(vars)] | |
}else{ | |
vars = vars[vars%in%to_predict] | |
temp = attr(attr(fit,'X'),'contrasts') | |
if(!all(temp[names(temp)%in%vars]%in%c('contr.sum','contr.helmert','contr.poly'))){ | |
#stop('When using ezPredict to obtain predictions for a subset of variables in a model, the model must be fit after setting either "options(contrasts=c(\'contr.sum\',\'contr.poly\')" or "options(contrasts=c(\'contr.helmert\',\'contr.poly\')".') | |
} | |
} | |
temp = list() | |
for(i in 1:length(vars)){ | |
this_fixed_data = data[,names(data)==vars[i]] | |
if(is.numeric(this_fixed_data)&(numeric_res>0)){ | |
temp[[i]] = seq( | |
min(this_fixed_data) | |
, max(this_fixed_data) | |
, length.out=numeric_res | |
) | |
}else{ | |
temp[[i]] = unique(this_fixed_data) | |
} | |
} | |
to_return = data.frame(expand.grid(temp)) | |
names(to_return) = vars | |
}else{ | |
to_return = to_predict | |
} | |
to_return$ezDV = 0 | |
names(to_return)[ncol(to_return)] = dv | |
requested_terms = terms(eval(parse(text=paste(dv,'~',paste(vars,collapse='*'))))) | |
mm = model.matrix(requested_terms,to_return) | |
f = fixef(fit) | |
indicies = (1:length(names(f)))[names(f) %in% dimnames(mm)[[2]]] | |
f = f[indicies] | |
value = mm %*% f | |
to_return$value = as.numeric(value[,1]) | |
vf = vcov(fit)[indicies,indicies] | |
tc = Matrix::tcrossprod(vf,mm) | |
to_return$var = Matrix::diag(mm %*% tc) | |
to_return = to_return[,names(to_return)!=dv] | |
return(to_return) | |
} |
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