similar to: Permutations in RDA for repeated measures, using how()

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 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",

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 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:

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

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

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

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

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

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

Hello. Just wanted to report this sysOID to you. The UPSC output is below. Network UPS Tools - Generic SNMP UPS driver 0.70 (2.7.1) kill: No such process No matching MIB found for sysOID '.1.3.6.1.4.1.318.1.3.2.11'! Please report it to NUT developers, with an 'upsc' output for your device. Going back to the classic MIB detection method. Detected SMART-UPS 2200 RM XL on host

I have nut/trusty,now 2.7.1-1ubuntu1 and nut-snmp/trusty,now 2.7.1-1ubuntu1 up and running from packages, but during the configuration process, upsdrvctl start included this output: No matching MIB found for sysOID '.1.3.6.1.4.1.318.1.3.27'! Please report it to NUT developers, with an 'upsc' output for your device. As such, I am trying to report this to any developer who may be

Newbie question, but I've checked archives etc. Am trying to reproduce in R Quinlan's trivial example of the "golf" decision tree. The data file of 14 examples follows (read in via read.table()): Outlook Temperature Humidity Windy PlayDontPlay 1 sunny 85 85 false DontPlay 2 sunny 80 90 true DontPlay 3 overcast 83 78 false Play 4 rain 70 96 false Play 5 rain 68 80 false Play 6

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 =

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.

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

Since you only have 3 predictors, each categorical with a small number of categories, you can use expand.grid to make a data.frame containing all possible combinations and give that the predict method for your model to get all possible predictions. Something like the following untested code. newdata <- expand.grid( Humidity = levels(Humidity), #(High, Medium,Low)

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