similar to: Logistic Regression with genetic component

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

On Mon, 22 Jan 2024, Rich Shepard wrote: > 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 chemical concentrations (e.g., > in mg/L) is an appropriate representation of the expected value. If not, I > want to explain this to non-technical

I have searched the archives and instructions high and low but have not found what I need. I have a dataframe named Data with columns: AdjResND0 - the numeric result Parameter - the classification (chemical name) Sort - the order I want the chemical names to appear, with leading zeros so all are the same length Client.Name - the sample name I am trying to generate a series of 1 page barcharts,

Dear colleagues, I often use chemical formulas to label my x-axis, e.g. in barplots. I used to do this like the following example and it worked just fine: a <- seq(2,24,2) b <- seq(3,36,3) c <- rbind(a,b) barplot(c, width=c(0.9, 1.1),space=c(0.2, 0.75), col=c("indianred4", "yellow2"), beside = TRUE, xlab="Ions", yaxt="n", xaxt="n", ylab

Dear colleagues, I often use chemical formulas to label my x-axis, e.g. in barplots. I used to do this like the following example and it worked just fine: a <- seq(2,24,2) b <- seq(3,36,3) c <- rbind(a,b) barplot(c, width=c(0.9, 1.1),space=c(0.2, 0.75), col=c("indianred4", "yellow2"), beside = TRUE, xlab="Ions", yaxt="n", xaxt="n", ylab

Dear all, I am ploting two boxplot of two chemical variables using the format boxplot(Alkalinity, logAlkalinity, log="y", col=c("lightblue"), lwd=0.6, boxwex=0.3) However when I plot like this, the distance between the two boxplots is very big and as well the distance of each boxplot from the plot area, left and right. How can I minimise that distance between the boxplots and

Hi all Consider the classic data below from Darwin on the heights of 15 pairs of zea mays (corn) plants either cross-fertilized or self-fertilized, where the goal is to see if it makes a difference. > head(ZeaMays) pair pot cross self diff 1 1 1 23.500 17.375 6.125 2 2 1 12.000 20.375 -8.375 3 3 1 21.000 20.000 1.000 4 4 2 22.000 20.000 2.000 5 5 2 19.125

Hello, I used the glm function in R to fit a dose-response relationship and then have been using dose.p to calculate the LC50, however I would like to calculate the NOEL (no observed effect level), ie the lowest dose above which responses start occurring. Does anyone know how to do this? [[alternative HTML version deleted]]

[Env: R 2.14.2 / Win Xp] In the script below, I want to select some variables from rrcov::OsloTransect, delete cases with any missing data, and subset the data frame Oslo to remove cases for two levels of the factor litho that occur with low frequency. The checks I run on my new data frame Oslo look OK, but I when I try to fit a multivariate linear model with lm(), I am getting an error:

I'm trying to use either mice or norm to perform multiple imputation to fill in some missing values in my data. The data has some missing values because of a chemical detection limit (so they are left censored). I'd like to use MI because I have several variables that are highly correlated. In SAS's proc MI, there is an option with which you can limit the imputed values that are

Greetings - I am working on some data that contain chemical names with air concentrations, and I am creating a data frame with date/time and each chemical having its own column. However, these are organic chemicals (e.g. 1-butene, 2,3,4-trimethylbenzene etc). The package I am going to be using the data with is openair, and many of the great functions require you to specify a column name which

I know that simple line plots showing a point representing the mean + an error bar are difficult in R, but I am clearly missing something. I simply want to show how a the mean and 95% CI of a chemical concentration (y) vary over space (x) under four temperature regimes (T; a categorical factor). plotmeans, in the gplots library, should allow for such a figure, right? After all, it invokes lm()

Dear all, I want to plot 8 chemical variables in the same plot. I have used the interaction.plot command: > interaction.plot(speclong$Date,speclong$time2,speclong$outcome,xaxt="n",type="l",pch=20,xlab="", log="y", ylab="Concentration (ug/L)",trace.label="",col=rainbow(8)) However, what it gives me is dotted lines and the colours are

Hi - I'm just starting out with R and have come up against a problem with what should be a simple operation. I'm plotting a range of safety standards for 7 different chemicals. I don't want the standard box and whisker plot - I just a dot for each value. Whilst using "plot.default" I get the error "invalid xlim value" - which is confusing since using the same script

Folks, This is so simple is driving me crazy. It's not really an R question is more an Statistics question. I applied a chemical in 3 different events during a growing season. I took 3 samples in each event to estimate mean applied chemical. Thus, I can easily estimate means and st.err by event. I can then estimate the total chemical applied for the whole season by adding the 3 means

I am just starting to code in R and need some help as I am used to doing this in SAS. I have a dataset that looks like this: Chemical Well1 Well2 Well3 Well4 BOD 13.2 14.2 15.5 14.2 O2 7.8 2.6 3.5 2.4 TURB 10.2 14.6 18.5 17.3 and so on with more chemicals.... I would like to transpose my data so that it looks like this: Chemical WellID Value BOD Well1 13.2 BOD Well2 14.2 BOD Well3 15.5 BOD

Good Day The attached document refers. I have recently attended an introduction course to R, and now wish to apply the program into my work analysis. The spreadsheet contains water quality data sampled from different locations within a catchment. I wish to group the variables per point (Point Name), Year (all samples per year eg 1985 etc) These should be averages of each year. Lastly, I wish

How do I post text-string dates along the x-axis instead of the excel_date position values? I would like to plot a time-series of chemical values changing with time The first column is the internal date from excel. This should be the x-axis position of a graph of the plotted data. The second column is a text-string of date. This should be what is posted (instead of the excel_date). The third

Dear R community, Please, can you help me with a problem concerning bootstrap. The data table called «RMika», contained times (Tps) and corresponding concentration of a chemical in a soil (SolA). I would like to get, by bootstraping, 10 estimations of the parameters C0 and k from the function: SolA = C0*exp(-k*Tps). # First, I fit the data and all is OK >

Dear All, I hope this is not too off-topic. Essentially, I need to know if there is any R package which can help me with a deformulation project. Suppose e.g. that you know from a chemical analysis the fat, mineral, vitamin, energy [and so on] content of a certain food product. You also know the ingredients of this product (e.g. milk, lactose, vegetable oil) and you know the chemical