Questions tagged [r]

Use this tag for any *on-topic* question that (a) involves `R` either as a critical part of the question or expected answer, & (b) is not *just* about how to use `R`.

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320
votes
10answers
301k views

Difference between logit and probit models

What is the difference between Logit and Probit model? I'm more interested here in knowing when to use logistic regression, and when to use Probit. If there is any literature which defines it using ...
176
votes
9answers
173k views

How to deal with perfect separation in logistic regression?

If you have a variable which perfectly separates zeroes and ones in target variable, R will yield the following "perfect or quasi perfect separation" warning message: ...
53
votes
1answer
40k views

How to interpret type I, type II, and type III ANOVA and MANOVA?

My primary question is how to interpret the output (coefficients, F, P) when conducting a Type I (sequential) ANOVA? My specific research problem is a bit more complex, so I will break my example ...
170
votes
3answers
93k views

R's lmer cheat sheet

There's a lot of discussion going on on this forum about the proper way to specify various hierarchical models using lmer. I thought it would be great to have all ...
186
votes
4answers
291k views

How to interpret a QQ plot

I am working with a small dataset (21 observations) and have the following normal QQ plot in R: Seeing that the plot does not support normality, what could I infer about the underlying distribution? ...
254
votes
2answers
179k views

Interpretation of R's lm() output

The help pages in R assume I know what those numbers mean, but I don't. I'm trying to really intuitively understand every number here. I will just post the output and comment on what I found out. ...
41
votes
4answers
31k views

Does the sign of scores or of loadings in PCA or FA have a meaning? May I reverse the sign?

I performed principal component analysis (PCA) with R using two different functions (prcomp and princomp) and observed that the ...
132
votes
6answers
249k views

Correlations with unordered categorical variables

I have a dataframe with many observations and many variables. Some of them are categorical (unordered) and the others are numerical. I'm looking for associations between these variables. I've been ...
100
votes
1answer
55k views

Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4?

Here is how I have understood nested vs. crossed random effects: Nested random effects occur when a lower level factor appears only within a particular level of an upper level factor. For ...
57
votes
4answers
70k views

Using principal component analysis (PCA) for feature selection

I'm new to feature selection and I was wondering how you would use PCA to perform feature selection. Does PCA compute a relative score for each input variable that you can use to filter out ...
39
votes
2answers
17k views

Simulation of logistic regression power analysis - designed experiments

This question is in response to an answer given by @Greg Snow in regards to a question I asked concerning power analysis with logistic regression and SAS ...
111
votes
2answers
53k views

Removal of statistically significant intercept term increases $R^2$ in linear model

In a simple linear model with a single explanatory variable, $\alpha_i = \beta_0 + \beta_1 \delta_i + \epsilon_i$ I find that removing the intercept term improves the fit greatly (value of $R^2$ ...
56
votes
3answers
32k views

Box-Cox like transformation for independent variables?

Is there a Box-Cox like transformation for independent variables? That is, a transformation that optimizes the $x$ variable so that the y~f(x) will make a more ...
57
votes
1answer
15k views

Logistic regression in R resulted in perfect separation (Hauck-Donner phenomenon). Now what?

I'm trying to predict a binary outcome using 50 continuous explanatory variables (the range of most of the variables is $-\infty$ to $\infty$). My data set has almost 24,000 rows. When I run ...
37
votes
1answer
38k views

Alternatives to one-way ANOVA for heteroskedastic data

I have data from 3 groups of algae biomass ($A$, $B$, $C$) which contain unequal sample sizes ($n_A=15$, $n_B=13$, $n_C=12$) and I would like compare if these groups are from the same population. One-...
96
votes
1answer
76k views

Interpreting plot.lm()

I had a question about interpreting the graphs generated by plot(lm) in R. I was wondering if you guys could tell me how to interpret the scale-location and leverage-residual plots? Any comments ...
43
votes
1answer
11k views

Why do my p-values differ between logistic regression output, chi-squared test, and the confidence interval for the OR?

I have built a logistic regression where the outcome variable is being cured after receiving treatment (Cure vs. No Cure). All ...
64
votes
8answers
40k views

Is PCA followed by a rotation (such as varimax) still PCA?

I have tried to reproduce some research (using PCA) from SPSS in R. In my experience, principal() function from package psych ...
30
votes
4answers
46k views

What is the difference between McNemar's test and the chi-squared test, and how do you know when to use each?

I have tried reading up on different sources, but I am still not clear what test would be the appropriate in my case. There are three different questions I am asking about my dataset: The subjects ...
16
votes
2answers
2k views

What is happening here, when I use squared loss in logistic regression setting?

I am trying to use squared loss to do binary classification on a toy data set. I am using mtcars data set, use mile per gallon and weight to predict transmission ...
20
votes
3answers
7k views

Obtaining a formula for prediction limits in a linear model (i.e.: prediction intervals)

Let's take the following example: set.seed(342) x1 <- runif(100) x2 <- runif(100) y <- x1+x2 + 2*x1*x2 + rnorm(100) fit <- lm(y~x1*x2) This creates a ...
128
votes
3answers
148k views

How are the standard errors of coefficients calculated in a regression?

For my own understanding, I am interested in manually replicating the calculation of the standard errors of estimated coefficients as, for example, come with the output of the ...
17
votes
3answers
22k views

Continuous dependent variable with ordinal independent variable

Given a continuous dependent variable y and independent variables including an ordinal variable X1, how do I fit a linear model in R? Are there papers about this ...
30
votes
1answer
20k views

Comparing hierarchical clustering dendrograms obtained by different distances & methods

[The initial title "Measurement of similarity for hierarchical clustering trees" was later changed by @ttnphns to better reflect the topic] I am performing a number of hierarchical cluster analyses ...
66
votes
4answers
19k views

How should tiny $p$-values be reported? (and why does R put a minimum on 2.22e-16?)

For some tests in R, there is a lower limit on the p-value calculations of $2.22 \cdot 10^{-16}$. I'm not sure why it's this number, if there is a good reason for ...
23
votes
2answers
40k views

How does the inverse transform method work?

How does the inversion method work? Say I have a random sample $X_1,X_2,...,X_n$ with density $f(x;\theta)={1\over \theta} x^{(1-\theta)\over \theta}$ over $0<x<1$ and therefore with cdf $F_X(x)=...
65
votes
1answer
62k views

Understanding ROC curve

I'm having trouble understanding the ROC curve. Is there any advantage / improvement in area under the ROC curve if I build different models from each unique subset of the training set and use it to ...
44
votes
1answer
35k views

Regression: Transforming Variables

When transforming variables, do you have to use all of the same transformation? For example, can I pick and choose differently transformed variables, as in: Let, $x_1,x_2,x_3$ be age, length of ...
82
votes
8answers
61k views

Generate a random variable with a defined correlation to an existing variable(s)

For a simulation study I have to generate random variables that show a predefined (population) correlation to an existing variable $Y$. I looked into the R ...
39
votes
2answers
31k views

How to find a good fit for semi-sinusoidal model in R?

I want to assume that the sea surface temperature of the Baltic Sea is the same year after year, and then describe that with a function / linear model. The idea I had was to just input year as a ...
26
votes
5answers
9k views

Time taken to hit a pattern of heads and tails in a series of coin-tosses

Inspired by Peter Donnelly's talk at TED, in which he discusses how long it would take for a certain pattern to appear in a series of coin tosses, I created the following script in R. Given two ...
62
votes
8answers
100k views

How to simulate data that satisfy specific constraints such as having specific mean and standard deviation?

This question is motivated by my question on meta-analysis. But I imagine that it would also be useful in teaching contexts where you want to create a dataset that exactly mirrors an existing ...
27
votes
7answers
30k views

Testing for linear dependence among the columns of a matrix

I have a correlation matrix of security returns whose determinant is zero. (This is a bit surprising since the sample correlation matrix and the corresponding covariance matrix should theoretically be ...
24
votes
2answers
18k views

Why do lme and aov return different results for repeated measures ANOVA in R?

I am trying to move from using the ez package to lme for repeated measures ANOVA (as I hope I will be able to use custom ...
29
votes
3answers
37k views

Computing p-value using bootstrap with R

I use "boot" package to compute an approximated 2-sided bootstrapped p-value but the result is too far away from p-value of using t.test. I can't figure out what I did wrong in my R code. Can someone ...
39
votes
9answers
9k views

How can I efficiently model the sum of Bernoulli random variables?

I am modeling a random variable ($Y$) which is the sum of some ~15-40k independent Bernoulli random variables ($X_i$), each with a different success probability ($p_i$). Formally, $Y=\sum X_i$ where $\...
33
votes
2answers
48k views

Interpretation of plot (glm.model)

Can anyone tell me how to interpret the 'residuals vs fitted', 'normal q-q', 'scale-location', and 'residuals vs leverage' plots? I am fitting a binomial GLM, saving it and then plotting it.
38
votes
4answers
13k views

Good methods for density plots of non-negative variables in R?

plot(density(rexp(100)) Obviously all density to the left of zero represents bias. I'm looking to summarize some data for non-statisticians, and I want to avoid ...
146
votes
2answers
200k views

How to determine which distribution fits my data best?

I have a dataset and would like to figure out which distribution fits my data best. I used the fitdistr() function to estimate the necessary parameters to ...
70
votes
1answer
76k views

How to interpret coefficients in a Poisson regression?

How can I interpret the main effects (coefficients for dummy-coded factor) in a Poisson regression? Assume the following example: ...
20
votes
2answers
47k views

Significance of categorical predictor in logistic regression

I am having trouble interpreting the z values for categorical variables in logistic regression. In the example below I have a categorical variable with 3 classes and according to the z value, CLASS2 ...
48
votes
4answers
97k views

How to statistically compare two time series?

I have two time series, shown in the plot below: The plot is showing the full detail of both time series, but I can easily reduce it to just the coincident observations if needed. My question is: ...
29
votes
3answers
24k views

How to perform orthogonal regression (total least squares) via PCA?

I always use lm() in R to perform linear regression of $y$ on $x$. That function returns a coefficient $\beta$ such that $$y = \beta x.$$ Today I learned about ...
27
votes
3answers
39k views

Why is nls() giving me “singular gradient matrix at initial parameter estimates” errors?

I have some basic data on emission reductions and cost per car: ...
64
votes
4answers
17k views

Why does including latitude and longitude in a GAM account for spatial autocorrelation?

I have produced generalized additive models for deforestation. To account for spatial-autocorrelation, I have included latitude and longitude as a smoothed, interaction term (i.e. s(x,y)). I've based ...
20
votes
3answers
6k views

I'm getting “jumpy” loadings in rollapply PCA in R. Can I fix it?

I have 10 years of daily returns data for 28 different currencies. I wish to extract the first principal component, but rather than operate PCA on the whole 10 years, I want to rollapply a 2 year ...
16
votes
3answers
51k views

Test a significant difference between two slope values

The data I have are a regression slope value of y~time, a standard error, an n value and a p value, for a particular species in two different areas. I want to check whether the the regression slope ...
25
votes
3answers
23k views

Interpreting plot of residuals vs. fitted values from Poisson regression

I am trying to fit data with a GLM (poisson regression) in R. When I plotted the residuals vs the fitted values, the plot created multiple (almost linear with a slight concave curve) "lines". What ...
11
votes
3answers
20k views

Can the scaling values in a linear discriminant analysis (LDA) be used to plot explanatory variables on the linear discriminants?

Using a biplot of values obtained through principal component analysis, it is possible to explore the explanatory variables that make up each principle component. Is this also possible with Linear ...
91
votes
4answers
162k views

What is rank deficiency, and how to deal with it?

Fitting a logistic regression using lme4 ends with Error in mer_finalize(ans) : Downdated X'X is not positive definite. A likely cause of this error is ...

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