-Thanks! We assessed the ability of 50 age-matched insect lines/populations from 30 diverse geographic regions to survive infection. Median lethal times (LT50) were monitored using five replicates (20-30 insects each) per sex per insect line, and experiments were repeated at least twice. Some locations have more than one insect line collected, especially 10 insect lines from Tokyo, Japan. We want to know whether there are correlations/differences between LT50 and maximum temperature at 30 geographic regions/localities. There will be ten pairs of X, Y variables for the one Tokyo, Japan location as in the previous inserted figure. Should we use the average LT50 of 10 lines from Tokyo or show both?    Another question, we tried to divide 50 insect lines into 8 biome groups, each group has 3-13 insect lines. How do we compare the group LT50 by using the original 10-15 replicates of each of 50 insect lines with replicate N numbers, or the mean LT50 of insect lines with line N numbers in 8 groups?  ... View more

Thanks Koen for your help! As p>n, tried to use both Lasso and elasticnet, and got inconsistent outputs too. ... View more

Thank you so much for your help! Please find the inserted photo. The insect collector told us those ten lines were actually from two close regions of a big city. However, we want to study the environmental factors, from 0.5 degree of latitude and longitude grid database, may be related to LT50 values in 30 different worldwide sites for 50 insect lines. Therefore, it's may be reasonable to use the mean LT50 from that location to do correlation analyses? By the way, there is a good correlation between LT50 and 25-30C Tmax (p=0.0003). ... View more

Thanks for your help!   I did LT50 assay for 50 insect lines with individual stock number, which were collected from 30 global locations and colored differently in the attached figure. At 30-35C maximum temperature, there is 0 or 1 days at 1/3 localities. Also, there are 10 insect lines with slightly different LT50s from a large city circled in yellow. My questions are: 1) how to analyze the data with "0" values in x variables circled in green color as "normality" concern? and 2) should we take the average LT50 value from those 10 lines from the same locality?   ... View more

Example 2.3 Analysis Using Fisher’s z Transformation The following statements request Pearson correlation statistics by using Fisher’s z transformation for the data set  Fitness :   proc corr data=Fitness nosimple fisher; var weight oxygen runtime; run; found above proc corr in https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/procstat/procstat_corr_examples03.htm . How to add Class "female LT50 or male LT50" to proc corr to check the effect of different % relative humidity on the slopes with LT50s? ... View more

Thanks Koen and how to code the following Fisher Z test in  SAS ? Zr1-r2=(Z’1-Z’2)/((1/(N1-3)+ 1/(N2-3))1/2 Zcritical=±1.96   @sbxkoenk wrote: Hello,   Usage Note 24177: Comparing parameters (slopes) from a model fit to two or more groups https://support.sas.com/kb/24/177.html   In general ... Make use of CONTRAST or ESTIMATE statements !!   The magical ESTIMATE (and CONTRAST) statements By Chris Daman on SAS Learning Post April 23, 2012 https://blogs.sas.com/content/sastraining/2012/04/23/the-magical-estimate-and-contrast-statements/   "Easy button" for ESTIMATE statements By Chris Daman on SAS Learning Post April 25, 2012 https://blogs.sas.com/content/sastraining/2012/04/25/easy-button-for-estimate-statements/   ESTIMATE Statements - the final installment By Chris Daman on SAS Learning Post May 2, 2012 https://blogs.sas.com/content/sastraining/2012/05/02/estimate-statements-the-final-installment/   How to write CONTRAST and ESTIMATE statements in SAS regression procedures? By Rick Wicklin on The DO Loop June 6, 2016 https://blogs.sas.com/content/iml/2016/06/06/write-contrast-estimate-statements-sas-regression-procedures.html   Usage Note 24447: Examples of writing CONTRAST and ESTIMATE statements https://support.sas.com/kb/24/447.html   Usage Note 67024: Using the ESTIMATE or CONTRAST statement or Margins macro to assess continuous variable effects in interactions and splines 67024 - Using the ESTIMATE or CONTRAST statement or Margins macro to assess continuous variable effects in interactions and splines (sas.com)   When a model contains interactions, it is often of interest to assess the effect of one of the interacting variables. When the variable of interest is categorical, and therefore is specified in the CLASS statement, this is most easily done using the LSMEANS, SLICE, or LSMESTIMATE statement. But when the variable of interest is continuous, these statements cannot be used. Two procedures, LOGISTIC and PHREG, provide statements that can estimate the effect of increasing a continuous predictor by a specified number of units. However, when the modeled response is not binomial or a time to event, these procedures are not appropriate. Nevertheless, the HAZARDRATIO statement in PROC PHREG can still be used to provide contrast coefficients that can be used in CONTRAST or ESTIMATE statements to test or estimate the effect of a continuous predictor. This can be done for a continuous predictor involved in one or more interactions and even in constructed effects such as splines. In the case of generalized linear models that don’t use the identity link, it is important to note that the estimated effect using these coefficients is not a difference in response means. For these models, an alternative and generally easier solution is provided by the Margins macro, which can estimate the required difference in means. Both of these approaches are discussed and illustrated in this note.   BR, Koen ... View more

Thanks and can those code be used for proc GLM, PLOTS=ALL, not a proc sgplot? ... View more

Ksharp,   Thanks and I actually want to change "50" to subscript in the variables "LT50" in y axis in the proc GLM?   Best,   Dave ... View more

Hi Dan,   Thank you for your help and I'm using ODS style=statistical, proc GLM data=LT50 plots all; not SGPLOT _____ %Macro LT50(var1, var2); proc glm data=LT50 plots=all; model &var1=&var2; run; _________   ... View more

used Label Female_LT50 = 'Female LT^{sub 50}'; Showed in Y axis Female LT__SUB_50_SUB__ ... View more