[R] For each entry type in column?

How can I do a for loop that does to a data.frame column what: for x in xs: does in Python? Obviously the data.frame column in question holds "levels". What if the data.frame is in matrix form? BR, Matti __ R-help@r-project.org mailing list -- To

[R] How to solve "nlm() non-finite value supplied by 'nlm'" and how to constrain parameters to (0, 1)?

So I'm getting: Error in nlm(neglikhood, p = c(0.1, 0.1), hessian = T, x = elinajat) : non-finite value supplied by 'nlm' In addition: There were 50 or more warnings (use warnings() to see the first 50) with the following (neglikelihood of 1-param. Weibull): neglikhood <- function(theta,x)

[R] How is label parameter used in text() when passing it a vector?

I have a slight doubt with using text() with the label parameter having to contain a vector of of integers (specifically integers in the range [1, 21] corresponding to factors of my categorical variable that I want to numbers to tell). What I'm currently plotting is the following command:

[R] How to use predict() so that one retains the rows that they're associated with?

I'm using predict() for my glm() logistic model, but I'm having trouble relating the predicted results to the rows that produced them. I want to be able to plot predictions along some categorical variables. So what can I do in order to get predicted values but also know what variable values

[R] How to read prediction intervals given by predict()?

My conception of prediction intervals is the following: "a prediction interval gives an interval within which we expect next y_i to lie with a specified probability" So when using predict() for my model: predict(fit4, interval="prediction")[1:20,] I get: fit lwr upr 1

[R] What does lm() output coefficient mean when it's been given a categorical predictor of string values?

I'm using lm() for a model that has a predictor that has two values {poika, tyttö} (boy and girl in Finnish). I make a model with this categorical variable: fit1 <- lm(dta$X.U.FEFF..mpist. ~ dta$sukup + dta$HISEI + dta$SES) and while the variable/vector is here named as dta$sukup, what lm()

[R] Can I get odd ratios from lm() model or only glm()?

I'm doing logistic regression and I need to infer the coefficients as odds ratios. I first did my model using lm(), but now that I need odd ratios, then should I have used glm() like displayed here: http://r.789695.n4.nabble.com/Odds-ratio-from-Logistic-model-in-R-td2630277.html

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

-reproducible-example -- Sent from my phone. Please excuse my brevity. On September 25, 2016 8:36:49 AM PDT, mviljamaa <mvilja...@kapsi.fi> wrote: On 2016-09-25 18:30, Duncan Murdoch wrote: On 25/09/2016 9:10 AM, Matti Viljamaa wrote: Writing: bs["(Intercept)"]+bs["mies&quo

Re: [R] curve() doesn't seem to use the whole range of x? And Error: longer object length is not a multiple of shorter object length

On 2016-09-25 18:30, Duncan Murdoch wrote: On 25/09/2016 9:10 AM, Matti Viljamaa wrote: Writing: bs["(Intercept)"]+bs["mies"]*0+bs["kouluB"]+bs["lka"]*lka+bs["kouluB:clka"]*clka i.e. without that being inside curve produces a vector of length 375. So now it seems that curve() is really

[R] Why removing the (Intercept) from lm is done by adding -1?

So I found out that to remove the (Intercept) term from lm's model one can add -1 to the predictors. I.e. do lm(resp ~ x1 + x2 - 1) Another way is to add 0, e.g. lm(resp ~ 0 + x1 + x2). Adding (or setting the (Intercept) term) zero seems more logical than subtracting one, but why is there the

[R] Using lm's subset parameter results in Error in xj[i] : invalid subscript type 'list'

I'm trying to take lm on a subset of my dataset and to do this I believe I need to pass my subset of the data as the subset parameter of lm. So I do my subsetting: firstkids <- kidmomhsage[0:234,], i.e. the first 234 rows of the data frame. Then construct the model: fit4 <-

[R] Overlapping axis numbering and labels when using par(new=TRUE)?

I'm trying to plot two data sets on the same plot by using par(new=TRUE). However this results in the axis numbering and labels being plotted twice as seen in the following picture: http://i.imgur.com/4b1sNIc.png How can I get the axis numbering and labels to not overlap? I could also

Re: [R] What are the red line and cut line in lm's Residuals vs Fitted plot?

Do you mean that the red line is a regression line? Why is the regression (line) weighted? On 2016-09-19 14:41, S Ellison wrote: What are the red line and cut line in lm's Residuals vs Fitted plot? The dotted line is at 0 and the red line is a locally weighted regression calculated using

Re: [R] How are interaction terms computed in lm's result / problems with interaction terms in lm?

On Sep 18, 2016, at 11:01 AM, mviljamaa <mvilja...@kapsi.fi> wrote: Also if you, rather than doing what's done below, do: fit3 <- lm(kidmomhsage$kid_score ~ kidmomhsage$mom_age + kidmomhsage$mom_hs + kidmomhsage$mom_age * kidmomhsage$mom_hs) Then this gives the result: Call: l

Re: [R] How are interaction terms computed in lm's result / problems with interaction terms in lm?

391? On 2016-09-18 20:41, mviljamaa wrote: I'm trying to use interaction terms in lm and for the following types of models: fit3_hs <- lm(kidmomhsage$kid_score ~ kidmomhsage$mom_age + kidmomhsage$mom_hs + kidmomhsage$mom_age * 1) fit3_nohs <- lm(kidmomhsage$kid_score ~ kidmomhsage$mom_age + ki

[R] How are interaction terms computed in lm's result / problems with interaction terms in lm?

I'm trying to use interaction terms in lm and for the following types of models: fit3_hs <- lm(kidmomhsage$kid_score ~ kidmomhsage$mom_age + kidmomhsage$mom_hs + kidmomhsage$mom_age * 1) fit3_nohs <- lm(kidmomhsage$kid_score ~ kidmomhsage$mom_age + kidmomhsage$mom_hs + kidmomhsage$mom_age *

[R] How to plot the regression line of multivariable linear model?

I'm having a bit of trouble plotting the regression line of multivariable linear model. Specifically my model has one response and two predictors, i.e. it's of the form Y = b_0+b_1*X_1+b_2*X_2 Plotting the regression line for a single predictor model Y = b_0+b_1*X_1 is simple enough, just

[R] What are the red line and cut line in lm's Residuals vs Fitted plot?

What are the red line and cut line in lm's Residuals vs Fitted plot? As seen in e.g.: http://i.imgur.com/QvZ6oeT.png __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the

[R] Is this kind of removing of elements from data.frame (in)efficient?

So I'm a beginner in R and I was testing the removal of elements from a data.frame. The way I remove the element(s) with the minimum value in kid_score variable is to do: kidmomhs <- data[kidmomhs$kid_score != min(kidmomhs$kid_score),] So now kidmomhs is the same data, but without the

[R] Where to get quantiles (1st parameter q) for pnorm? (Normal approximation to binomial distribution)

I'm doing Normal approximation to binomial distribution. My variables are generated by rbinom. Here: http://msemac.redwoods.edu/~darnold/math15/spring2013/R/Activities/ApproxBinomWithNorm.html it's claimed that normal approximation is done using the command pnorm. Question: Where to get

[R] Separating lines in ts.plot()?

I'm using ts.plot() to plot a matrix of time series (each column is a ts). What I noticed is that ts.plot() creates a lot of overlapping lines which makes it difficult to distinguish different series. What options exist for making the series more easily read?

[R] Using uniroot on c(0, 1) when the function does not change sign in this interval?

How can I use uniroot to find a root in the interval (0,1) when my function does not change sign in this interval. I've tried plugging in some values and seems like e.g. f(50) < 0 and then I can pick c(0,50). But this sounds really weird, given that I need to find a root in (0,1). So what