similar to: discriminant analysis

Hello Dears. I have a 2 questions about Discriminant Analysis in R. 1- In "ade4" package I perform this analysis for both quantitative and qualitative variables using "discrimin" function. R gives me "Canonical weights/ Loadings" and "Canonical scores" but doesn't give "*F* or *t *or *X-square *statistic" and "P-value" to determine

HI, I am a studient, so don't be surprise if my question seems so simple for you... I have a dataframe with 6 qualitative variables divided in 33 modalities, 2 qualitatives variables and 78 lines. I use a glm to know wich variables have interactions... I would like to know if its normal that one (the first in alphabetical order) of the modalities of each qualitatives variable doesn't

Hello, My name is Ion Andronache and I am a candidate for a doctor's degree on geomorphology in Braila, Romania. I would like to ask for some advice. I would like to make an analysis on the Danube's flow capacity and to determine the circles and periodicity using the Schuster system. I understood that your soft can do these operations but I don't know how. Would you be so kind to

Hi ! I've used the example given in the documentation for the prcomp function both in R and SPAD to compare the results obtained. Surprisingly, I do not obtain the same results for the coordinates of the principal composantes with these two softwares. using USArrests data I obtain with R : > summary(prcomp(USArrests)) Importance of components: PC1 PC2

Dear R users: I want to model mosquito count data based on landcover attributes and meteorological variables using a Poisson GLSM in the geoRglm package. I have monthly mosquito counts over more than 20 years with repeated observations from individual trap sites over time. I have used as.geodata() to successfully read my dataset into the geodata format utilized by geoR and geoRglm,

The ioreadX() helpers have inconsistent interface. On some architectures void *__iomem address argument is a pointer to const, on some not. Implementations of ioreadX() do not modify the memory under the address so they can be converted to a "const" version for const-safety and consistency among architectures. Signed-off-by: Krzysztof Kozlowski <krzk at kernel.org> Reviewed-by:

Dear R-Helpers, I have the following code: data = read.csv("test.csv",header=T,na.strings="-",row.names=1) myData=as.matrix(data) ##myData is a 102x19 matrix myList = alply(myData, 2,function(x){matrix(x,ncol=6,byrow=T)}) ##myList is a list containing 19 matrices with the following names > names(myList) [1] "*ES" "*ET" "*ER"

Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get ?NA? in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached

I have a vector of land cover class data from a GIS: > landcov[1:10,2] #the first ten examples of a large (100k+) object 1 2 3 4 5 6 7 8 9 10 12 12 01 12 01 15 15 15 15 15 etc. I also have a lookup table for the class data that gives the cover type as a string: > cnd.names #the look up table, i.e., landcov[3,2] == 1 == "Montane Meadow" CndVal