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Results tagged with r
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user 24515
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`.
3
votes
Accepted
Given probability x, how likely is it that probability y is due to chance?
Now we need
$$P(X \ge 33|H_0) \approx 4.68*10^{-11}$$
As you can verify using R pbinom(32,44,.27,lower.tail=FALSE). …
- 6,714
0
votes
Conditional/normalized multiple imputation
I think one option would follow by analogy of predict on newdata in R. This supposes using mice to single-impute and then access the final regression model after burn in and convergence. …
- 6,714
2
votes
2
answers
2k
views
What is the relationship between the normal and the t-distribution in R (pt and pnorm)?
In R, is it true that:
pt(q,df=Inf) $=$ pnorm(q)? … Or in words, can I supply df=Inf in the cummulative t-distribution to get to the standard normal distribution function in R, as I should in theory? …
- 6,714
4
votes
0
answers
803
views
Avoid numerical overflow problem in likelihood due to $\exp$
Is there a way to prevent this from happening in R? …
- 6,714
8
votes
1
answer
1k
views
Which adaptive Metropolis Hastings algorithm is implemented in R package MHadaptive?
One is implemented in the function Metro_Hastings of R package MHadaptive, see here. …
- 6,714
0
votes
Accepted
How to correctly predict from orthogonalized covariates on new data in polynomial regression?
The answer is to use the coefs argument of poly. The coefficients used in the orthogonal transformation are saved as attributes in a poly object. They have to be saved for later use.
In the implemen …
- 6,714
10
votes
Accepted
"the leading minor of order 1 is not positive definite" error using 2l.norm in mice
I have had a similar problem in MICE, see my self-discussion here. The problem occurs because you have overfitted your model (too many parameters, variables), some variables are highly colinear or you …
- 6,714
0
votes
0
answers
680
views
How can I use the sufficient statistics (variances, covariances, means) to estimate a linear...
My question is simple: is there a function in R which estimates the linear regresion model in a similar fashion as lm, but only using the means, variances, and covariance (correlations), i.e. the sufficient …
- 6,714
2
votes
How to measure association of nominal values for few observations?
I think you clearly have too many cells (240) relative to the number of observations (181). In fact, even if there was only one observation in each cell, you would still have 59 empty cells. Analyses …
- 6,714
5
votes
1
answer
3k
views
How is the optimal probability cut-off in a ROC defined by the R package Epi?
The plot below was created by R package Epi::ROC for a binary classification problem. …
- 6,714
0
votes
1
answer
1k
views
How to correctly predict from orthogonalized covariates on new data in polynomial regression? [closed]
The function poly in R orthogonalize the columns of this matrix or leave them as they are after polynomial extension (raw). …
- 6,714
20
votes
How to obtain p-values of coefficients from bootstrap regression?
The community and @BrianDiggs may correct me if I am wrong, but I believe you can get a p-value for your problem as follows. A p-value for a two sided test is defined as
$$2*\text{min}[P(X \le x|H_0) …
- 6,714
4
votes
Accepted
How should I interpret this residual plot?
The plot is very dense so it is not easy to see all trends there may be. You could run alternative tests for hetoroscedasticity and autocorrelation to get additional diagnostics.
What is visible is t …
- 6,714
5
votes
Comparing nested binary logistic regression models when $n$ is large
One option is to use pseudo R-square measures for both models. A strong difference in pseudo R-square would suggest that the model fit strongly decreases by omitting V17. … There are different kinds of Pseudo R-squares available. …
- 6,714
2
votes
0
answers
2k
views
Using MICE in R: is it possible to impute only sub-sections of the data?
When using the mice library in R to impute data I encounter the following problem. I have a data matrix with missing information on y1 and y2 and predictor variable x. …
- 6,714