predict.glm -> which class does it predict?

predict.glm -> which class does it predict?
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Hi,
I have a question about logistic regression in R. Suppose I have a small list of proteins P1, P2, P3 that predict a model <- glm(T ~ ., data=d.f(Y), family=binomial) (Y is the dataset of This works fine. T is a factored vector with levels cancer, noncancer. Now, I want to use predict.glm to predict a new data. predict(model, newdata=testsamples, type="response") (testsamples is The result is a vector of the probabilites for each sample in Is this fallowing expression Thank you, Peter ______________________________________________ |
Re: predict.glm -> which class does it predict?
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On Jul 10, 2009, at 9:46 AM, Peter Schüffler wrote:
> Hi,
> > I have a question about logistic regression in R. > > Suppose I have a small list of proteins P1, P2, P3 that predict a > two-class target T, say cancer/noncancer. Lets further say I know > that I can build a simple logistic regression model in R > > model <- glm(T ~ ., data=d.f(Y), family=binomial) (Y is the > dataset of the Proteins). > > This works fine. T is a factored vector with levels cancer, > noncancer. Proteins are numeric. > > Now, I want to use predict.glm to predict a new data. > > predict(model, newdata=testsamples, type="response") (testsamples > is a small set of new samples). > > The result is a vector of the probabilites for each sample in > testsamples. But probabilty WHAT for? To belong to the first level > in T? To belong to second level in T? > > Is this fallowing expression > factor(predict(model, newdata=testsamples, type="response") >= 0.5) > TRUE, when the new sample is classified to Cancer or when it's > classified to Noncancer? And why not the other way around? > > Thank you, > > Peter ... [show rest of quote]
As per the Details section of ?glm: A typical predictor has the form response ~ terms where response is So, given your description above, you are predicting If you want to predict "cancer", alter the factor levels thusly: T <- factor(T, levels = c("noncancer", "cancer")) By default, R will alpha sort the factor levels, so "cancer" would be Think of it in terms of using a 0,1 integer code for absence,presence, BTW, using 'T' as the name of the response vector is not a good habit: > T 'T' is shorthand for the built in R constant TRUE. R is generally HTH, Marc Schwartz ______________________________________________ |
Re: predict.glm -> which class does it predict?
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In reply to this post by Peter Schüffler-2
Peter Schüffler wrote:
> Hi,
> > I have a question about logistic regression in R. > > Suppose I have a small list of proteins P1, P2, P3 that predict a > two-class target T, say cancer/noncancer. Lets further say I know that I > can build a simple logistic regression model in R > > model <- glm(T ~ ., data=d.f(Y), family=binomial) (Y is the dataset of > the Proteins). > > This works fine. T is a factored vector with levels cancer, noncancer. > Proteins are numeric. > > Now, I want to use predict.glm to predict a new data. > > predict(model, newdata=testsamples, type="response") (testsamples is > a small set of new samples). > > The result is a vector of the probabilites for each sample in > testsamples. But probabilty WHAT for? To belong to the first level in T? > To belong to second level in T? > > Is this fallowing expression > factor(predict(model, newdata=testsamples, type="response") >= 0.5) > TRUE, when the new sample is classified to Cancer or when it's > classified to Noncancer? And why not the other way around? ... [show rest of quote]
It's the probability of the 2nd level of a factor response (termed I find it easiest to sort ut this kind of issue by experimentation in > x <- sample(c("A","B"),10,replace=TRUE) (notice that the relative frequency of B is 0.6) > glm(x~1,binomial) (OK, so it won't go without conversion to factor. This is a good thing.) > glm(factor(x)~1,binomial) Call: glm(formula = factor(x) ~ 1, family = binomial) Coefficients: Degrees of Freedom: 9 Total (i.e. Null); 9 Residual (The intercept is positive, corresponding to log odds for a probability > predict(glm(factor(x)~1,binomial)) As for why it's not the other way around, well, if it had been, then you -- ______________________________________________ |
Re: predict.glm -> which class does it predict?
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2009/7/10 Peter Dalgaard <[hidden email]>:
> Peter Schüffler wrote:
>> >> Hi, >> >> I have a question about logistic regression in R. >> >> Suppose I have a small list of proteins P1, P2, P3 that predict a >> two-class target T, say cancer/noncancer. Lets further say I know that I can >> build a simple logistic regression model in R >> >> model <- glm(T ~ ., data=d.f(Y), family=binomial) (Y is the dataset of >> the Proteins). >> >> This works fine. T is a factored vector with levels cancer, noncancer. >> Proteins are numeric. >> >> Now, I want to use predict.glm to predict a new data. >> >> predict(model, newdata=testsamples, type="response") (testsamples is a >> small set of new samples). >> >> The result is a vector of the probabilites for each sample in testsamples. >> But probabilty WHAT for? To belong to the first level in T? To belong to >> second level in T? >> >> Is this fallowing expression >> factor(predict(model, newdata=testsamples, type="response") >= 0.5) >> TRUE, when the new sample is classified to Cancer or when it's classified >> to Noncancer? And why not the other way around? > > It's the probability of the 2nd level of a factor response (termed "success" > in the documentation, even when your modeling the probability of disease or > death...), just like when interpreting the logistic regression itself. > > I find it easiest to sort ut this kind of issue by experimentation in > simplified situations. E.g. > >> x <- sample(c("A","B"),10,replace=TRUE) >> x > [1] "B" "A" "B" "B" "A" "B" "B" "A" "B" "A" >> table(x) > x > A B > 4 6 > > (notice that the relative frequency of B is 0.6) > >> glm(x~1,binomial) > Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1 > In addition: Warning message: > In model.matrix.default(mt, mf, contrasts) : > variable 'x' converted to a factor > > (OK, so it won't go without conversion to factor. This is a good thing.) > >> glm(factor(x)~1,binomial) > > Call: glm(formula = factor(x) ~ 1, family = binomial) > > Coefficients: > (Intercept) > 0.4055 > > Degrees of Freedom: 9 Total (i.e. Null); 9 Residual > Null Deviance: 13.46 > Residual Deviance: 13.46 AIC: 15.46 > > (The intercept is positive, corresponding to log odds for a probability > > 0.5 ; i.e., must be that "B": 0.4055==log(6/4)) > >> predict(glm(factor(x)~1,binomial)) > 1 2 3 4 5 6 7 8 > 0.4054651 0.4054651 0.4054651 0.4054651 0.4054651 0.4054651 0.4054651 > 0.4054651 > 9 10 > 0.4054651 0.4054651 >> predict(glm(factor(x)~1,binomial),type="response") > 1 2 3 4 5 6 7 8 9 10 > 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 > > As for why it's not the other way around, well, if it had been, then you > could have asked the same question.... > ... [show rest of quote]
Or more specifically: > resp <- factor(c("cancer", "noncancer", "noncancer", "noncancer")) and since noncancer occurs 75% of the time in the sample clearly ______________________________________________ |
Re: predict.glm -> which class does it predict?
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In reply to this post by Peter Dalgaard
> As for why it's not the other way around, well, if it had been, then you
> could have asked the same question.... ...and come to think about it, it is rather convenient that it meshes -- ______________________________________________ |
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