My PCA result using prcomp() function is summarised and plot as followings. How to interpret the plot results? It shows in some online article that the points present the amount of variance attributed to the different principal components. However, the value seems not matching with any of the statistics, e.g., standard deviation, the proportion of variance, or cumulative proportion.
> summary(data_pca)
> plot(data_pca,type="lines")
I got the hint from #Roland and #Maurits. Here, the variance is exactly the square of standard deviation.
Related
I am using the rcorr function within the Hmisc package in R to develop Pearson correlation coefficients and corresponding p-values when analyzing the correlation of several fishery landings time series. The data isn't really important here but what I would like to know is: how are the p-values calculated for this? It states that the asymptotic P-values are approximated by using the t or F distributions but I am wondering if someone could help me find some more information on this or an equation that describes how exactly these values are calculated.
I am calculating odds ratios over several subsets of a population. Here is one subset:
"Normal" and "0" are the reference groups. "Dibirads" is the outcome and "BMIcat" are the levels of exposure. I will also attach the code I used to calculate beta coefficients and Odds Ratios
However, the results show as follows:
If you calculate them mathematically, they aren't matching with the R output. Is something wrong with my code? The Beta coefficients still match up with the Odds Ratios so something is wrong there as well.
I am assessing the accuracy of a model that predicts count data.
My actual data has quite an unusual distribution - although I have a large amount of data, the shape is unlike any standard distributions (poisson, normal, negative binomial etc.).
As part of my assessment, I want a metric for how well the distribution of the predictions match the distribution of actual data. I've tried using standard model performance metrics, such as MAE or RMSE, but they don't seem to capture how well the predictions match the expected distribution.
My initial idea was to split the predictions into deciles, and calculate what proportion fall in each decile. This would be a very rough indication of the underlying distribution. I would then calculate the same for my 'actuals' and sum the absolute differences between the proportions.
This works to some extent, but feels a bit clunky, and the split into deciles feels arbitrary. Is there a function in R to produce a single metric for how well two distributions match?
I have used the package lsmeans in R to get the average estimate for all observations for my treatment factor (across the levels of a block factor in the experimental design that has been included with systematic effect because it only had 3 levels). I have used a sqrt transformation for my response variable.
Thus I have used the following commands in R.
First defining model
model<-sqrt(response)~treatment+block
Then applying lsmeans
model_lsmeans<-lsmeans(model,~treatment)
Then plotting this
plot(model_lsmeans,ylab="treatment", xlab="response(with 95% CI)")
This gives a very nice graph with estimates and 95% confidense intervals for the different treatment.
The problems is just that this graph is for the transformed response.
How do I get this same plot with the backtransformed response (so the squared response)?
I have tried to create a new data frame and extract the lsmean, lower.CL, and upper.CL:
a<-summary(model_lsmeans)
New_dataframe<-as.data.frame(a[c("treatment","lsmean","lower.CL","upper.CL")])
And then make these squared
New_dataframe$lsmean<-New_dataframe$lsmean^2
New_dataframe$lower.CL<-New_dataframe$lower.CL^2
New_dataframe$upper.CL<-New_dataframe$upper.CL^2
New_dataframe
This gives me the estimates and CI boundaries squared that I need.
The problem is that I cannot make the same graph for thise estimates and CI as the one that I did in LS means above.
How can I do this? The reason that I ask is that I want to have graphs that are all of a similar style for my article. Since I very much like this LSmeans plot, and it is very convenient for me to use on the non-transformed response variables, I would like to have all my graphs in this style.
Thank you very much for your help! Hope everything is clear!
Kind regards
Ditlev
I would like to calculate a summary odds ratio value for two or more papers where the only information I have is the individual odds ratios with their 95% confidence intervals. Is this possible? I have been poking around in the meta package, and only figured out how to do it with crude counts.
Thanks so much!
It is quite simple.
You just need to use the natural logarithm of the odds ratio (logOR), and its standard errror (and corresponding variance). These can be easily back-calculated from the 95% confidence intervals according to the normal distribution. Finally, pool logORs with their variance.
For instance, after you have built a data frame (eg called mydata) with logOR and variance for each study, you can easily proceed with a random effect meta-analysis with the metafor package in R as follows:
res <- rma(logOR, variance, data=mydata, method="DL")
forest(res)
In the future, you may consider posting similar questions in CrossValidated.