search for: altitude

Hola a todos! Estoy intentando crear un vector (alt) a partir de la repetición de valores provenientes de otro vector (altitud). A cada valor de altitud lo quiero repetir 1247 veces, y de ahi continuar con el siguiente valor de altitud. Probé varias cosas, pero esto me pareció lo más coherente: altitud=read.csv("C:/Users/IER/Dropbox/Pasantia Castelar/YungasLigustroTS/altitud44.csv")

Hi everyone, I have a vector with atmospheric measurements (x-axis) that is obtained/calculated at different altitudes (y-axis). The altitude is uniformly distributed every 7 meters. For example my dataframe is: df <- dataframe( *altitude* = c(1005, 1012, 1019, 1026, 1033, 1040, 1047, 1054, 1061, 1068), *atm_values* = c(1.41, 1.40, 1.39, 1.38, 1.37, 1.37, 1.38, 1.36, 1.33, 1.31) ) How...

Try something like with(df, predict(smooth.spline(x = altitude, y = atm_values), deriv = 1)) Cheers, Andrew -- Andrew Robinson Chief Executive Officer, CEBRA and Professor of Biosecurity, School/s of BioSciences and Mathematics & Statistics University of Melbourne, VIC 3010 Australia Tel: (+61) 0403 138 955 Email: apro at unimelb.edu.au Website: https:/...

...the node - labels) is labeled in a plot with letters a,b,c... instead of the numerical levels. when I run >labels(tree.object) It gives me the correct labels (the problem concerns 'landuse': [1] "root" "landuse:1,2,3,4,5,6,7,8,9,10" [3] "altitude<2883.5" "landuse:1,4,6" [5] "altitude<1909.5" "altitude>1909.5" [7] "landuse:2,3,5,7,8,9,10" "altitude>2883.5" [9] "landuse:1,2,3,5,6,10" "altitude<3526.5" [11] &quot...

...sday, January 31, 2023 10:16 AM > To: r-help mailing list <r-help at r-project.org> > Subject: [R] How to calculate the derivatives at each data point? > > Hi everyone, > > I have a vector with atmospheric measurements (x-axis) that is > obtained/calculated at different altitudes (y-axis). The altitude is uniformly > distributed every 7 meters. > For example my dataframe is: > df <- dataframe( > *altitude* = c(1005, 1012, 1019, 1026, 1033, 1040, 1047, 1054, 1061, 1068), > *atm_values* = c(1.41, 1.40, 1.39, 1.38, 1.37, 1.37, 1.38, 1.36, 1.33, 1.31) >...

Dear friends, I used R to analyze my data with the models of generalized linear models, and found three models were relatively good, but i can't decide which is the best,how should i do ? *Model1:* glm(formula = snail ~ grass + gheight + humidity + altitude + soiltem + airtem + grass:altitude, *family = Gamma(link = inverse*), data = model, na.action = na.exclude, control = list(epsilon = 1e-04, maxit = 50, trace = T)) (Dispersion parameter for Gamma family taken to be 0.2644025) Null deviance: 63.635 on 161 degrees of freedom Resid...

Dear friends, I used R and SAS to analyze my data through generalized linear model, and there is some difference between them. Results from R: glm(formula = snail ~ grass + gheight + humidity + altitude + soiltemr + airtemr, family = Gamma) Deviance Residuals: Min 1Q Median 3Q Max -1.23873 -0.41123 -0.08703 0.24339 1.21435 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.024e-02 1.655e-02 1.223 0.22320 gra...

I have drawed a picture with persp, it's 3d map with different color, indicate different altitude. In gnuplot, the corresponding command 'splot' will generate a picture beside to indicate the relationship between color and altitude. But in R, how to draw it? I have read the manual of legend, but they are all about how to draw a legend with colored text, not a continuous varing color wit...

...n the help I am still confused. I sampled bugs in different habitats within sites which were within rivers themselves within different regions. The habitat correspond to different substrata (not systematically present in all sites). For rivers and sites, I have environemental variables (e.g. altitude and slope of the site, drainage area and geology of the river) and I have only 2 regions. Note that sometimes I have only one site per river. I would like to know the part of each spatial scale in the species richness variance. I looked into the nlme package but I did not found how to proceed.....

...in which their BOS pages are given. This is nothing semantic, but only a convenience so we can ascertain that different Web browsers will address the same track by the same index number through JavaScript. Finally there are two rendering related fields that we propose introducing: Display-hint and Altitude (their names could of course still be changed). Each of these are specified as features of the given track, but relate to the other tracks for rendering. Altitude specified the display ordering (as z-index in HTML/CSS), and Display-hints right now has proposals for picture-in-picture display relati...

Dear list, I want to make a plot based on the following information, using the command plot. variable A for x axis : temperature (range: -20 degrees to 40 degree) variable B for y axis : altitude (range: 50 m to 2500 m ) The data below 0 degree of X variable wants to be erased tentatively. Please kindly advise the command to extract the data ranging from 0 degree to 40 degrees. Thank you. Elaine [[alternative HTML version deleted]]

Hi there, I have two data sets, one of locations at different elevations (x,y,z) and the other of points that make up topographic contours (also x,y,z). I have used: result<-apply(distppll(data2,cbind(topocon[-nrow(topocon),],topocon[-1,])),1,min) where 'data2' are my measurement coordinates (x, y) and 'topocon' are my topographic contours (x,y). While this gives me an output

Dear listers, I have written a function to facilitate the drawing of altitude profiles with x (distance), y (altitude) and a z parameter (altitude magnification). profplot<-function(x,y,z=10,...){ op <- par()$mai par(mai=c(0.95625,0.76875,0.76875,0.95625)) plot(x,y*z, type="l",asp=1,las=1,xlab="",ylab="",yaxt="n",...) axis(2,l...

...ive binomial models. 1. When fitting smoothed parameters (e.g. s(X, df=2)) changing the degrees-of-freedom has no effect on the level of smoothing (e.g. number of knots for the spline). This appears to be the case even for the example on the vgam help page. data(hunua) fit2 = vgam(agaaus ~ s(altitude, df=2), binomialff, hunua) > coef(fit2) (Intercept) s(altitude, df = 2) -1.1661259280 0.0003932463 fit10 = vgam(agaaus ~ s(altitude, df=10), binomialff, hunua) > coef(fit10) (Intercept) s(altitude, df = 10) -1.1661259280 0.0003932463 2. T...

Hi, The script below is my current coding in order to produce a contour plot of temperature across altitude and time. In my case,?time,altitude and temperature?are represented by x, y and z variables. ############################################## Brazilan.Pallete <- colorRampPalette(c("blue","green","yellow","red")) image.plot(x, y, z, col = Brazilan.Pallete(...

Dear R friends. I´m trying to fit a Logistic Regression using glm( family='binomial'). Here is the model: *model<-glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp, offset=(log(1/off)), data=mydata, family='binomial')* mydata has 76820 observations. The response variable f_ocur) is a 0-1. This data is a SAMPLE of a bigger dataset, so the idea of setting the offset is to

I tried the following ordered logistic regression in R: mod1 <- polr(altitude~sp + wind_dir + wind_speed + hr, data=altioot) But when I asked The summary of my regression I got the folloing error message: > summary (mod1) Re-fitting to get Hessian Error in optim(start, fmin, gmin, method = "BFGS", hessian = Hess, ...) : the initial value of 'vmi...

Hi i need some help with this exercise: FIles: https://mega.nz/#!JxMFGIwC!qA85SBIBRVagCzYfmLwSvGuNK_qXqCXrakPxXryCpGg #PARZIAL 3: GEO #Data: # Shapefile "INCOME" contains dummy information about revenue # Common Abbreviations in the "INCOME" variable and the centroid altitude #dell common in the variable "ALT" #Richieste # 1 #map of the variable "INCOME", choosing an appropriate color scale (save the map in pdf) #2 #map of the variable "ALT", choosing a color palette from green to white, passing for brown (save the map in pdf) # 3 #calc...

Hi everyone, Can anyone tell me if its possibility to extract the coefficients from the lm() command? For instance, imagine that we have the following data set (the number of observations for each company is actually larger than the one showed...): Company Y X1 X2 1 y_1 x1_1 x2_1 1 y_2 x1_2 x2_2 1 y_3 x1_3 x2_3 (...) 2 y_4 x1_4 x2_4 2 y_5 x1_5 x2_5 2 y_6 x1_6 x2_6 (...) n y_n x1_n x2_n n

Dear R friends I´m interested into apply a Jackknife analysis to in order to quantify the uncertainty of my coefficients estimated by the logistic regression. I´m using a glm(family=’binomial’) because my independent variable is in 0 - 1 format. My dataset has 76000 obs, and I´m using 7 independent variables plus an offset. The idea involves to split the data in let’s say 5 random subsets and