similar to: Interpreting Multiple Linear Regression Summary

This is my first excursion into using reshape2 and I want to ensure that the melt() function call is syntactically correct. The unmodifed data frame is organized this way: head(tds.anal) site sampdate param quant 1 UDS-O 2006-12-06 TDS 10800 4 STC-FS 1996-06-14 Cond 280 7 UDS-O 2007-10-04 Mg 1620 9 UDS-O 2007-10-04 SO4 7580 19 JCM-10B 2007-06-21 Ca 79 20

Thanks to David's help I subset my large data set and produced a smaller one for a single stream and 7 factors of interest. The structure of this data frame is: str(burns.tds.anal) 'data.frame': 718 obs. of 4 variables: $ site : Factor w/ 143 levels "BC-0.5","BC-1",..: 1 1 4 6 4 4 4 5 5 5 $ sampdate: Date, format: "1996-06-02"

I have measured values for 47 chemicals in a stream. After processing the original data frame through reshape2, the recast data frame has this structure: 'data.frame': 256 obs. of 47 variables: $ site : Factor w/ 143 levels "BC-0.5","BC-1",..: 1 1 1 2 2 2 2 2 2 2 ... $ sampdate : Date, format: "1996-04-19" "1996-05-21" ... $ Acid :

All but one of the summaries of multiple linear regressions in this analysis set present the residuals by min, 1Q, median, 3Q, and max. Example: lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4, data = snow.cast) Residuals: Min 1Q Median 3Q Max -277.351 -32.551 -2.621 40.812 245.272 The one that doesn't has only a small number of rows (23) and presents the

I have a data frame with 1 factor, one date, and 37 numeric values: str(waterchem) 'data.frame': 3525 obs. of 39 variables: site : Factor w/ 64 levels "D-1","D-2","D-3",..: 1 1 1 1 1 ... $ sampdate : Date, format: "2007-12-12" "2008-03-15" ... $ CO3 : num 1 1 6.7 1 1 1 1 1 1 1 ... $ HCO3 : num 231 228 118 246

Working with 5 subset streams from my source data frame, three of them successfully call dcast(), but two fail: jerritt.cast <- dcast(jerritt.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length and winters.cast <- dcast(winters.melt, site + sampdate ~ param) Aggregation function missing: defaulting to length Yet both data frames have the values in their

A data frame has one factor, one date, and one numeric column. When I plot these using the default pch of the open circle (first attachment), xyplot(TDS ~ sampdate | she.s, data = sheep.cast, main = 'TDS in Sheep Creek', ylab = 'Concentration (mg/L)', xlab = 'Time') I see the higher concentration points toward the right on several panels. But, when I change to using a

Because of regulatory requirement changes over several decades and weather conditions preventing site access the variables in my data set have different lengths. I'd like guidance on how to perform linear regressions and other models with these variables. For example, there are 2206 rows for the parameter "TDS" but only 1191 rows for the parameter "Cond." Such

I have read ?zoo but am not sure how to relate the parameters (x, order.by, frequency, and style) to my data.frame. The structure of the data.frame is 'data.frame': 11169 obs. of 4 variables: $ stream : Factor w/ 37 levels "Burns","CIL",..: 1 1 1 1 1 1 1 1 1 1 ... $ sampdate: Date, format: "1987-07-23" "1987-09-17" ... $ param : Factor w/

I believe that I have a basic understanding of zoo and how to use read.zoo on a text file, What I have not seen in the zoo help files and vignettes is how to convert a data frame to a zoo matrix for irregular time series analyses. An example data frame is structured like this: str(burns.cast) 'data.frame': 256 obs. of 47 variables: $ site : Factor w/ 143 levels

I am not sure if this is an R-users question, but since most of you here are statisticians, I decided to give it a shot. I am using the lm() function in R to fit a dependent variable to a set of 3 to 5 independent variables. For this, I used the following commands: >model1<-lm(function=PBW~SO4+NO3+NH4) Coefficients: (Intercept) SO4 NO3 NH4 0.01323 0.01968

Hello I would like to transfer some Splus functions in R. But I have a problem first about this assignation in Splus : xnom <- deparse(substitute(x)) I am a bad programmer : I don't understand the R help How to modify these functions ? Thank you very much for your help Here are the four functions and a data test

I am wondering how to interpret the parameter estimates that lm() reports in this sort of situation: y = round(rnorm(n=24,mean=5,sd=2),2) A = gl(3,2,24,labels=c("one","two","three")) B = gl(4,6,24,labels=c("i","ii","iii","iv")) # Make both observations for A=1, B=4 missing y[19] = NA y[20] = NA data.frame(y,A,B) nonadd = lm(y ~

Hi, I am not a statistics expert, so I have this question. A linear model gives me the following summary: Call: lm(formula = N ~ N_alt) Residuals: Min 1Q Median 3Q Max -110.30 -35.80 -22.77 38.07 122.76 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.5177 229.0764 0.059 0.9535 N_alt 0.2832 0.1501 1.886 0.0739

Dear R users, I think i have a simple question which i want to explain by an example; i have several 2-digit industry codes that i want to use for conducting by-industry analysis but i think there is a problem with the degrees of freedom! for example, when i do my analysis without any 2-digit industry code, i got the following summary (i have 146574 observations in total): >

R 2.7 Windows XP I have two model that have been run using exactly the same data, both fit using glm(). One model is a linear regression (gaussian(link = "identity")) the other a quasipoisson(link = "log"). I have log likelihoods from each model. Is there any way I can determine which model is a better fit to the data? anova() does not appear to work as the models have the

I am trying to do a quasipoisson regression to know if the frequency of drinking of my subject is related to temperature. The problem is that I'm not sure how to interpret my result. 1) Since my result is signifiant, can I tell that the frequency of drinking of my subject increase linearly or exponentially? 2) When I want to quantify the increase, do I need to do an exponential

THANKS so very much for your help (previous and future!). I have a two follow-up questions. 1) You say that dispersion = 1 by definition ....dispersion changes from 1 to 13.5 when I go from binomial to quasibinomial....does this suggest that I should use the binomial? i.e., is the dispersion factor more important that the 2) Is there a cutoff for too much overdispersion - mine seems to be

Dear R-users, After running a logistic regression, I need to calculate OR by exponentiating the coefficient, and then I need the 95% CL for the OR as well. For the following example (taken from P. Dalaagard's book), what would be the most straightforward method of getting what I need? Could anyone enlight me please? Thank you! Lucho > summary(glm(menarche~age,binomial)) Call:

A statistical question, not specific to R. I'm asking for a pointer for a source of definitive descriptions of what types of data are best summarized by the arithmetic, geometric, and harmonic means. As an aquatic ecologist I see regulators apply the geometric mean to geochemical concentrations rather than using the arithmetic mean. I want to know whether the geometric mean of a set of