search for: humidities

Hello, I can get nut to work with this UPS using the apcsmart driver, but only with limited functionality. It is only reporting the OL/OB status and that's about it. None of the other useful variables such as run time, load etc are reported and initiating battery tests does not work either. Has anyone managed to get nut to report more than these variables with a Matrix type UPS? I see

Hi, We have a several AP9612TH environmental cards (they have 2 probe connectors) inserted into our APC UPS devices which monitor temperature and humidity. The apcsmart nut module knows how to query the ambient.temperature and ambient.humidity using the 't' and 'h' commands of the UPS (refer to apcsmart.h). The results of the 't' and 'h' commands are from probe 1.

Hi, To my knowledge, an excellent of ggplot with a second y-axis is https://rpubs.com/MarkusLoew/226759 In this example, the author uses two colors for the two lines, but the line shapes are the same -- both are solid. Could each line have its own color as well as its own shape? For example, can I make the red line with the linetype "twodash", while the blue line with the

Hi I am using R version 2.7.2. on a windows XP OS and have a question concerning an analysis of covariance with count data I am trying to do, I will give details of a scaled down version of the analysis (as I have more covariates and need to take account of over-dispersion etc etc) but as I am sure it is only a simple problem but I just can't see how to fix it. I have a data set with count

Hi John, You can try the following: override.linetype=c("twodash","solid") p <- ggplot(obs, aes(x = Timestamp)) p <- p + geom_line(aes(y = air_temp, colour = "Temperature", linetype ="Temperature")) p <- p + geom_line(aes(y = rel_hum/5, colour = "Humidity", linetype="Humidity")) p <- p +

Sorry let me clarify. If I modify the line p <- p + geom_line(aes(y = air_temp, colour = "Temperature")) by p <- p + geom_line(aes(y = air_temp, colour = "Temperature", linetype ="Temperature")) and p <- p + geom_line(aes(y = rel_hum/5, colour = "Humidity")) by p <- p + geom_line(aes(y = rel_hum/5, colour = "Humidity",

Hi, We have several APC Smart UPSes (serial port/apcsmart) with the APC Temp/Humidity sensor cards (AP9612TH). The sensor cards have 2 plugs for 2 separate sensors. So far, we have only used one of the sensors in conjunction with the apcsmart module from nut. This has been working great. Example output: [nut at nut]# upsc APC11 ambient.humidity: 037.2 ambient.humidity.alarm.maximum: NO,NO

See ?rpart.control. I get: > golf.rp = rpart(Outlook ~ ., golf, control=rpart.control(minsplit=1)) > golf.rp n= 14 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 14 9 rain (0.2857143 0.3571429 0.3571429) 2) Temperature< 71.5 6 2 rain (0.1666667 0.6666667 0.1666667) 4) Temperature< 64.5 1 0 overcast (1.0000000 0.0000000 0.0000000) * 5)

Dear all, I am using RDA to study plant communities in various land uses (variable LU with values M, U, etc.). For each land use, I sample 3 to 5 fields (M1, M2, U1, U2, etc). I make 5 measurements for both plant communities and environmental variables in each field. I repeat the process every 6 months to study the effect of time and season (D16 for dry season 2017, R16 for rainy season 2016,

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:

Dear friends, I have used R for some time now and have a tricky question about the coxph-function: To sum it up, I am not sure whether I can use coxph in conjunction with missing covariate data in a model with time-variant covariates. The point is: I know how "old" every piece that I oberserve is, but do not have fully historical information about the corresponding covariates. Maybe you

I want to estimate the effect of several independent variables on several dependent variables. In the example below I wanted to estimate the effect of three independent variables on ozone and temperature. My aim is to create a list of dependent and independent variables and automate the process rather than writing every dependent and independent variable in each model as I have done below.

On 2011-01-27 14:22, Rasti Matus wrote: > I have a few questions regarding machine learning packages in R, > e.g. earth package ... > > 1) How could I force the algorithm to generate knots only with > values < 90 for variable doy? Not possible in the current implementation. In principle it would be possible to extend earth so the user-defined "allowed" function

Sorry for the extra email. It send to quickly. procmail: Assigning "SUBJECT= Tornado Monday, 03/27/2017 at 20:27:02. The Point BB.OBSURGRH is" jerry

Ah yes I will have to use the predict function. But the predict function will not get me there really. If I can take the example that I have a model predicting whether or not I will play golf (this is the dependent value), and there are three independent variables Humidity(High, Medium, Low), Pending_Chores(Taxes, None, Laundry, Car Maintenance) and Wind (High, Low). I would like rules like

Hi guys, Sorry for the OT. For the last couple of weeks I notice that the static in my server room is worrisomely noticeable. I cannot see what may be causing it.... Care to share some of your experience what may be the cause and the remedy? Thank you.

Hi, I have a few questions regarding machine learning packages in R, e.g. earth package and its forward pass and prunning pass, and hinge functions selection in the example below. 1) How could I force the algorithm to generate knots in hinge function only with values < 90 for e.g. variable doy? 2) Is it possible to restrict the algorithm to generate maximum number of terms per a selected

I have a SmartUPS 750 (SUA750) which has the temperature and humidity SmartSlot card installed. When NUT 2.2 communicates with this UPS over /dev/ttyS0 using apcsmart, using the APC Smart cable connected to the SUA750 serial port, and this ups.conf: [su700] driver=apcsmart port = /dev/ttyS0 I can see the ambient temperature and humidity data with "upsc su700 at

Hi All, Days <- matrix(c("Monday", "Tuesday", "Wed", "Thu", "Fri", "Sat", "Sun"),7,1) Hum <-matrix(c(56,57,60,75,62,67,70), Temp<-matrix(c(76,77,81,95,82,77,83), Using the above information I want plot humidity and temperature on Y-axis and days on X-axis Any help is appreciated! [[alternative HTML version

Hi, Being a novice this is my first usage of R. I am trying to use rpart for building a decision tree in R. And I have the following dataframe Outlook Temp Humidity Windy Class Sunny 75 70 Yes Play Sunny 80 90 Yes Don't Play Sunny 85 85 No Don't Play Sunny 72 95 No Don't Play Sunny 69 70 No Play Overcast 72 90 Yes Play Overcast 83 78 No Play Overcast 64 65 Yes Play Overcast 81 75