glm predict on new data

Dear Dirk,

You should avoid indexing in the glm call so that the name of the terms
will not contain the indexing part. (Check str(lg) in your example.)
A more preferred solution uses predefined data frames in the original calls:
n <- 250
x <- rnorm(n)
noise <- rnorm(n,0,0.3)
y <- round(exp(x+noise)/(1+exp(x+noise)),digits=0)
datfr <- data.frame(x=x,y=y)
lg <- glm(y~x,data=datfr[1:200,],family="binomial")
pred <- predict(lg,newdata=datfr[201:n,],type="response")

HTH,
  Denes

I am aware this has been asked before but I could not find a resolution.

I am doing a logit

lg <- glm(y[1:200] ~ x[1:200,1],family=binomial)

Then I want to predict a new set

pred <- predict(lg,x[201:250,1],type="response")

But I get varying error messages or warnings about the different number of
rows. I  have tried data/newdata and also to wrap in data.frame() but
cannot
get to work.

Help would be appreciated.

Dirk.

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I am aware this has been asked before but I could not find a resolution.

I am doing a logit

lg<- glm(y[1:200] ~ x[1:200,1],family=binomial)

glm (and most modeling functions) are designed to work with data frames, 
not raw vectors.

Then I want to predict a new set

pred<- predict(lg,x[201:250,1],type="response")

But I get varying error messages or warnings about the different number of
rows. I  have tried data/newdata and also to wrap in data.frame() but cannot
get to work.

I'll made up some data, show the way you approached it, show where it 
went wrong, and then how it works more easily.

# data like what I think you had:
y <- rbinom(200, 1, prob=.8)
x <- data.frame(x=rnorm(250))

# your glm call:
lg <- glm(y[1:200]~x[1:200,1],family=binomial)

# take a look at print(lg).  Notice that your independent variable
# name is "x[1:200, 1]", which is what you would need to match in
# a call to predict.

# Make data.frames of the given and testing data.
DF <- data.frame(y=y, x=x[1:200,1])
DF.new <- data.frame(x=x[200:250,1])
# Notice DF.new has the same name (x) as DF.

lg <- glm(y~x, data=DF, family=binomial)
pred <- predict(lg, newdata=DF.new, type="response")
summary(pred)

Help would be appreciated.

Dirk.

Brian S. Diggs, PhD
Senior Research Associate, Department of Surgery
Oregon Health & Science University