I am working on a difficult (for my abilities) dataset. The dependent variable is continuous [0-3] bounded and was measured between 2000 to 2019 in several locations (not necessarily the same location in each year). So I have a spatial component that would like to account for.
I have data with many missing observations in an excel file. Missing cells have a period ".".
When using proc import, the "." is recognized as a level in subsequent analysis.
However, when I paste the data directly in SAS, the "." is correctly identified as missing value.
I have a split-plot design and I want to test the effect of 2 factors on the disease incidence (continuous proportion). I am using Beta dist. which is appropriate for these data (bounded within 0-1).
The fit statistics look OK (Pearson Chi-square/DF close to 1)
Fit Statistics for...
I am analyzing data from a multi-location trial (5 locations) to test the effectiveness of a treatment with 2 levels.
The design is RCB with 3-4 replications in every location.
I use the model below:
proc mixed data=mydata;
class location rep trt ;
model Y=trt/ddfm=kr2 residual...
I have a 5 x 5 Latin square design which is replicated 5 times within each location (same rows and columns in each location). The same design was used in 10 different locations and I was asked to perform a combined location analysis.
I have done it before with other designs, such as RCB...
I have data from 200 similar studies, all measuring the same effect of a continuous independent variable on the same continuous response. I say similar because the designs are different (split plot vs. rcbd) and the levels of the independent variable is not the same across all studies.
I am new to R and I was reading about conditional decision trees.
In the "party" package there is an option to select number of permutations (nresample=...). However, that is not the case with "partykit".
So does it use permutations, even if it is a constant number and I can't...
I am trying to analyze data from a split-split-plot design. The sub-plot is a continuous factor and since we suspect a non-linear relationship, the quadratic form needs to be tested as well.
Factors: a-main plot-5 levels
To test the quadratic...
I have a CRD with 4 reps and 4 treatments (A, B, C, D). The study took place in 1 location for 3 years. I want to pool over years (so treat year as random effect). I am interested in main effects and up to 2-way interactions.
So I am using the following model and random statement...
I run multiple regression with 2 continuous and 1 categorical variable (3 levels).
SAS will hold the last level of the categorical variable and will not give an estimate. I know that this is the intercept.
My question is how to calculate the interaction of the continuous variable with the 3rd...
I have 4 outcomes (A B C D) that one of them is calculated as a weighted average of the first 2 (D=0.6*B + 0.4*C).
Then I ran ANOVA to examine the effect of 2 factors on the 4 outcomes.
My analysis was rejected because they said that D is not mutually exclusive from all other factors...
I am interested in Time series regression and I would like to find 1-2 good books that explain Time series with examples using SAS.
Does anybody know and can propose good books that I can purchase?
I am into agriculture/environmental science if that matters.
Does anyone has experience using AMMI (additive main effects and multiplicative interactions)?
If yes, could you please provide practical information on how it works and SAS code that performs the analysis?
This is a technique used in multi-location, multi-year plant breeding...
I am using PCA to avoid multicollinearity problems and then I want to use the first 2 PCs in linear regression.
The 1st PC contains 60% of variability and the 2nd 32%.
Is it valid if I use a data step in SAS, create a new variable which is the interaction of these 2 PCs and say that...
I have a question about comparing F statistics among effects.
I have the following anova table
Parameter Estimate Standard Error t Value Pr > |t|
Factor1 12088.62915 2249.053598 5.37 <.0001
I am using PCA to generate PCs and then using them as independent variables in regression.
I added an extra data-step after PCA which is transformation of the PCs e.g. X=prin1^2
This significantly improved the subsequent regression model, however I am not sure if transforming the...