#R studio commands regression how to#

Mean annual maximum 3-day wintertime precipitation ( L-mean), inĬoefficient of L-variation for annual maximum 3-day wintertime precipitationĬoefficient of L-skewness for annual maximum 3-day wintertime precipitationĬoefficient of L-kurtosis for annual maximum 3-day wintertime precipitation

For this workshop we will be working with two variables, which are in bold. The fifteen variables contained in this comma-separated value file are shown below. Using PRISM as the basis for the regression means that the predicted variable can be estimated almost anywhere. PRISM climate normals provide a spatially-continuous map of long-term averages of meteorological variables such as temperature, precipitation, etc. This allows for estimation of the mean annual maximum precipitation anywhere the seasonal total precipitation can be estimated. In regional precipitation-frequency analysis, it is common to try to predict the mean annual maximum precipitation at the study sites using annual or seasonal total precipitation as a predictor. 295 stations were analyzed and properties of their annual maximum precipitation as well as characteristics of the location of the station were placed in a data file.

The data you have been provided in 'stations.csv' are from a precipitation-frequency analysis for 3-day maximum wintertime precipitation in the Pacific Northwest. The data required to start this workshop can be downloaded here: stations.csv Background

#R studio commands regression how to#

If at any time you need a refresher on working with R commands or on how to use RStudio, feel free to look back at the R introduction or the RStudio introduction pages. In this part of the workshop, you will load a data file into a data frame, perform a regression analysis, and look at some of the diagnostics that you can produce in R for linear regression. Do not copy, forward, or release the information without United States Army Corps of Engineers approval. Status4 -1.094e+03 4.467e+02 -2.450 0.Disclaimer: The United States Army Corps of Engineers has granted access to these data for instructional purposes only. If i tell R that they are factor and not numeric, he correctly states the levels, for exampleīut then if i try to get the summary, i get one less intecept for each factor: Status2 2.255e+03 3.399e+02 6.635 3.34e-11 *** So many question guys, thanks in advance to any who'll help me !Īnother doubt is about the factor$ itself. Reg2=lm(Income~Consumption+Status+Qualification+Family.num+Age+Sex,data=data)

What should i do about the first column (the family id one) ? Ok but then how do i get a correlation matrix which doesn't consider 4 columns of data ? i can get R to not compute on one with the, but can i do it for multiple ones ? or should i create another object with just selected columns ?Īnother question is about the lm command itself. data$Status=factor(data$Status)ĭata$Qualification=factor(data$Qualification)Īre the factors correct ? or maybe Age should have been and Family.num shouldn't ? Then i try to get a correlation matrix.īut I get an error: ('X' must be numeric). Anyhow, i need to analyze it so i use data<-read.table('dataTest.txt',sep=' ',header=T)īut i need them to be factors, because for example sex=1 is male and viceversa. Of course the data is much bigger, this is just a sample.

So, let's assume i have this data 'Fam.ID' 'Income' 'Consumption' 'Status' 'Qualification' 'Family num' 'Age' 'Sex' Hi guys, trying to test some data witha a multiple regression model.