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I am trying to perform a Kernel Logistic Regression in R. Is there a package that does this?
stats::ksmooth gives you the Nadaraya–Watson kernel regression:
with(cars, {
plot(speed, dist>40)
lines(ksmooth(speed, dist>40, "normal", bandwidth = 2), col = 2)
lines(ksmooth(speed, dist>40, "normal", bandwidth = 6), col = 3)
})
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I have dataframe In R wanted to plot pairwise corelation plot currently using package "PerformanceAnalytics" but I am not able get nice colored graph any one suggest me a nice package i tried "psych" not able install to my system. is there any way i can plot
below mention my data
test1 test2 test3 test4 test4 test5 test6 test7
1.4367 1.6589 2.0227 2.2793 159.91 144 18910.1 19137.1902803603
0.2403 0.3404 0.9538 1.1041 226.1 317.61 6275.68 12618.0982994217
0.1737 0.2882 0.8039 3.1088 372.12 177.59 7912.73 6529.72939693551
library("PerformanceAnalytics")
chart.Correlation(df, histogram=TRUE, pch=19)
output
how I wanted a nice colured plot like below any one suggest me how can I plot
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Which R's package with arima modeling capabilities is considered being the best ? I would like to simulate new time series from fitted arima model in straightforward way ?
Personally, I like rugarch package. It gives a lot of possibilities and models, easiness of use, maybe It's a bit too "black box".
#example ARMA GARCH 1,1 need rugarch package
spec <- ugarchspec(variance.model = list(model = "sGARCH", garchOrder = c(1, 1)),mean.model = list(armaOrder = c(1, 1)))
garch <- ugarchfit(spec = spec, data = timeseries)
forc<-ugarchforecast(garch,n.ahead=200)
plot(forc#forecast$sigmaFor)
plot(forc#forecast$seriesFor)
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I was wondering if there is a way to calculate Nagelkerke R-square based upon the output produced. I know that I can calculate McFadden R-square directly. But Nagelkerke produces what we feel is a more accurate strength of the model.
I am not having luck with adding on packages to my setup, if that is the line of thought that you have.
Thanks.
This question is underdefined so I'll do my best assuming that "the output produced" is a glm object. This function should produce the appropriate pseudo-R square you want when applied to a glm object.
Nagelkerke <- function(mod) {
l_full <- exp(logLik(mod))
l_intercept <- exp(logLik( update(mod, . ~ 1) ))
N <- length(mod$y)
r_2 <- (1 - (l_intercept / l_full)^(2/N)) / (1 - l_intercept^(2/N))
return( as.numeric(r_2) )
}
Example:
model <- glm(formula = vs ~ mpg + disp, family = binomial("logit"), data = mtcars);
Nagelkerke(model);
#[1] 0.6574295
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Each co-ordinate(x,y) corresponds to an index. For example: (0,0) has index 0 AND (1,0) has index 1 .
X <-c(0,1,1,0,-1,-1,-1,0,1,2,2,2,2,1,0,-1,-2)
Y <- c(0,0,1,1,1,0,-1,-1,-1,-1,0,1,2,2,2,2,2)
Z<- as.factor(0:16)
df <- data.frame(X,Y,Z)
library(e1071)
model <- svm(Z ~ X+Y, xyz, kernel = "radial")
predict(model, xyz[1:4,1:2]) #1.647681 3.859354 5.908940 4.151374
# From the last code, it appears that it does not predict well for its own data
i need help in building a predictive model which can predict the index.
What is the index corresponding to (12,-22) ?
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Does anyone know of a package or method in R of carrying out a MANOVA whilst controlling for phylogenetic non-independence?
Thank you!
sos package is your friend:
library('sos')
findFn('phylogenetic MANOVA')
seems like geiger package and more precisely aov.phylo performs phylogenetic ANOVA or MANOVA.
Here an example from the help :
library(geiger)
geo=get(data(geospiza))
dat=geo$dat
d1=dat[,1]
grp<-as.factor(c(rep(0, 7), rep(1, 6)))
names(grp)=rownames(dat)
## MANOVA
x=aov.phylo(dat~grp, geo$phy, nsim=50, test="Wilks")
Multivariate Analysis of Variance Table
Response: dat
Df Wilks approx-F num-Df den-Df Pr(>F) Pr(phy)
group 1 0.27872 3.6229 5 7 0.061584 0.549
Residuals 11