I am trying to create a function that calculates the Planck function, given a wavelength and a specific temperature. Then, I want to plot out the range of wavelengths emitted at temperatures of 290K, 1250K, 1800K, 2600K, 5000K, and 6000K. I am using Studio and I have never plotted this before. I will also need to plot lambda max for each of these temperatures using wien's displacement law. Is there a basic script that I can use to help me plot this? I don't have much experience with R or programming softwares.
I am using Plancks equation to solve
Related
I have a vector which I have made a density function for. It is a curve with multiple peaks, and I want to perform a Gaussian deconvolution in order to represent my density curve with multiple Gaussian curves. I am hoping there is a package that will allow me to do this, but I haven't found anything that has worked for me so far. I tried to follow the example given here (https://www.r-bloggers.com/fitting-mixture-distributions-with-the-r-package-mixtools/) but the vector I am working with is ~400 mb and won't play nicely with the mixtools package tools. I need something that will work with the size of the datasets I am working with or be able to work with the density curve directly. Any help appreciated, thank you!
I do not have sample data but I am happy to have it explained with the faithful dataset in R, I know I can find the density of faithful$waiting with density(faithful$waiting) and it plots just fine, but the deconvolution has me stuck.
I'm trying to summarize raster cell values in overlapping polygons in ArcMap. This can be done in Geospatial Modelling Environment (GME), an extension for ArcMap. They have a command called isectpolyrst that calculates for values in overlapping polygons. My problem is, my version of ArcGIS (10.6.2), doesn't support the use of GME, so I can't use this function. I've heard that isectpolyrst can still accomplished in R Studio using R script, but I haven't found it anywhere.
I have a number of GPS points with 10 km buffers around them (these buffers overlap a lot). I'm trying to calculate proportions of different vegetation types within these buffer zones. I'm using ArcMap 10.6.2., and zonal statistics can't calculate for overlapping polygons.
You can use raster::extract for that. As you seem entirely new to R, you will need to study it a bit first. You can start here: https://rspatial.org/
I created a logistic/logit model with a binomial response variable using
model <- glm(response~predictor1+predictor2+...)
and then I used the predict function to create a new data frame
outcome <-data.frame(predict(model,newdata=IndependentDataSet,type="response"),as.numeric(as.character(Independent$ResponseVariable)))
names(outcome) <- c("Pr","Obs")
I can use one of the following functions
plot(verify(data$obs,data$pr),CI=TRUE)
attribute(verify(data$obs,data$pr))
to create a plot that looks like this
or
reliability.plot(verify(data$obs,data$pr))
from
library(verification)
to create a reliability diagram. I am wondering how I can separate the bins based on specific values. For example, the model that I am evaluating is based around a climatology of 19% (0.19) and I want there to be a bin at (1/3)*climatology, climatology, and go up by (2/3) of climatology for the proceeding bins. How can I do this?
Additionally, I have seen the bins represented as circles that are proportional in size to the percent of the data that is at that bin. Does anyone know how to make a more aesthetically pleasing reliability diagram in R? Any recommendations are welcome.
This is how I would like my diagrams to appear
The easiest could be using
trace("attribute.default",edit=TRUE)
or whichever other function.
In this way, you access the source code and edit it. These changes affect only the current R session.
There is histfit function in Matlab would plot histogram and fit the distribution by bin values.
The distribution's parameters have to be estimated.
How to implement histfit in r? I searched for a long time, but it has no lucky.
This post have mentioned this before, but there is no preferable solution. The sn package seems support several distribution, not so much.
I explore the data with hist function, the histogram shows gamma distribution in gerneral.
But if I add up bins and show it again, the graph will show more details, and gamma distribution fails.
fitdistr would fail to find parameters also.
so I want to fit the data just using the coarse data from histogram. This is the question, thank you for your help.
The fitdistr function in the MASS package can be used to find parameters for a given distribution (including gamma). The function density and the logspline package (and others) can be used to estimate the density function of the data without assuming a specific distribution.
The lines and curve functions can be used to add an estimated density curve to a plotted histogram (use prob=TRUE when creating the histogram).
If you want to compare your data to a specific distribution then tools like qqplots (qqplot function or others) or visual tests (vis.test in the TeachingDemos package) will probably be better than a histogram and density plot.
I have to answer it myself, package 'bda' could fit the binned data in several distributions, however it could only binning data by rounding.
I'm not sure if that's the right terminology.
I've entered some data into R, and I've put a smooth.spline through it using the following command.
smoothingSpline = smooth.spline(year, rate, spar=0.35)
plot(x,y)
lines(smoothingSpline)
Now I'd like to measure some limits (or where the curve is at a given x point), and maybe do some predictive analysis on points that extend beyond the graph.
Are there commands in R for measuring (or predicting) the points along a curve?
Is ?predict.smooth.spline what you are looking for?