search for: altitudes

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 c...

...ay my respects to their Elders. On 31 Jan 2023 at 8:17 PM +1100, konstantinos christodoulou <konstantinos.christodoulou1 at gmail.com>, wrote: External email: Please exercise caution 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 can I find the derivatives...

Hi all. I have the problem, that the labels from a factor variable in a tree object, (more exact, 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]

...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 =

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:

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

Dear R users, I face to a nested pattern and despite the numerous examples in 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

Hi all, Discussions about a need for an extension to Skeleton to cater for multi-track media files has been going on for a while. In a recent thread here, in discussions on IRC, and at FOMS between Jan, Ralph, Viktor and I, we discussed some fields. Viktor and I continued that discussion to make more specific recommendations on what fields to add. We now have a wiki page at

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

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",...)

Hello R-Users, I have recently run into several problems using vgam() in the VGAM package. I am hoping someone might have some solutions... Briefly, I have been trying to fit GAM models for zero-altered negative 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

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 =

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 'vmin' is not

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

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