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Unit Cost Equation
Im moving this to a new thread so I quit hogging Scott Heberts excellent thread on Commander costs...
Heres my current formula for caulculating the cost of a unit based on (and only on) its physical stats: sqrt(((HP*(10.0+Prot)*Def*MR)/1000.0)*((Str*Att)/10.0))*((3.0+Move)/5.0)*((40.0+AP)/50.0)*(9.0/(6.0+Enc))*((Mor^log10(7))/7.0) Comments, anyone? Suggestions? Flames? Donations? http://forum.shrapnelgames.com/images/smilies/wink.gif So, back to the morale buisiness: Perhaps just a chart is the way to go, then. I hate making morale 0-5 cost exactly the same, because there is a change, however small and imperceptable http://forum.shrapnelgames.com/images/smilies/happy.gif Besides, there arent any troops with under 7 morale, are there? Theyd be useless, and Illwinter knows it http://forum.shrapnelgames.com/image...es/biggrin.gif <font class="small">Code:</font><hr /><pre> Morale Cost 0 0.38 1 0.39 2 0.40 3 0.41 4 0.43 5 0.46 6 0.50 7 0.55 8 0.65 9 0.80 10 1.00 11 1.40 12 1.70 13 1.90 14 2.00 15 2.09 16 2.17 17 2.24 18 2.30 19 2.35 20 2.39 21 2.42 22 2.44 23 2.45 24 2.46 25 2.47 26 2.48 27 2.49 28 2.50 29 2.51 30 2.52 </pre><hr /> Now, the question is, if I do get this chart accurate, should it stay as just a multiplier to what the cost would be otherwise? |
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Heres a quick graph of the data in the chart.
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That looks pretty accurate. As far as multiplier-or-not goes... On commanders, morale is (AFAIK) unimportant except for repel attempts. So it should not be applied to commanders. On normal units, it sort of depends. High morale is worth more on cheap units than expensive units, because you get lots of them. Maybe it should have an additive AND multiplicative effect? In other words, if the unit's value (aside from morale) is "X", with morale ("MV", value of morale from the chart) it might be: (X+X*MV+K*X^2*MV) The first X considers units in huge groups, or boosted by a priest casting Sermon of Courage, or with "Berserkers" cast, or patrolling, or waiting as targets forn Fires from Afar, or as bodyguards where morale is not relevant, or as arrow targets / lance fodder where morale is meaningless. The second term considers normal groups with no morale support, in normal melee battles. The third term (with an unknown constant, K) is the usefulness of high-morale units in keeping low-morale groups from fleeing, preventing complete routs (so mages can do their business), when the enemy is using units with Fear or casting Terror and Panic, and other cases where high morale is crucial. ... then again, I never stop playing with a formula until it is so complex I can no longer understand it, so maybe you should ignore everything I just said. Probably, in fact. Actually, I order you to banish it from your mind http://forum.shrapnelgames.com/images/smilies/happy.gif. Your current formula seems fine; it looks good to me, anyway http://forum.shrapnelgames.com/images/smilies/happy.gif 2.5 seems a little high as an upper asymptote, though; 2.0 might be high enough for a purely multiplicative term. I wouldn't pay 2.5 times as much for units with infinite morale as for 10 morale - the max would be, maybe, 2.0 times, at which point I'd only buy a few of them. |
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Ok, new chart http://forum.shrapnelgames.com/images/smilies/happy.gif
(can you tell I dont have a life) <font class="small">Code:</font><hr /><pre> Morale Cost 0 0.450 1 0.460 2 0.480 3 0.510 4 0.550 5 0.600 6 0.660 7 0.730 8 0.810 9 0.900 10 1.000 11 1.100 12 1.195 13 1.285 14 1.370 15 1.450 16 1.525 17 1.595 18 1.660 19 1.720 20 1.775 21 1.825 22 1.870 23 1.910 24 1.945 25 1.975 26 2.000 27 2.020 28 2.035 29 2.045 30 2.050 </pre><hr /> Attatched is another graph of the data. Its a much smoother spread, too http://forum.shrapnelgames.com/images/smilies/happy.gif However, the change close to 10 is less severe... |
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Hmmm.......
Yeah, it has better endpoints. Is it possible to increase the slope around 10 while keeping the endpoints and the smoother line? |
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And why is the spacing all wacked out in the 2nd chart? |
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<font class="small">Code:</font><hr /><pre>
Morale Cost 0 0.500 1 0.510 2 0.523 3 0.541 4 0.564 5 0.596 6 0.638 7 0.694 8 0.768 9 0.868 10 1.000 11 1.251 12 1.439 13 1.580 14 1.686 15 1.765 16 1.824 17 1.869 18 1.903 19 1.928 20 1.947 21 1.961 22 1.971 23 1.979 24 1.985 25 1.990 26 1.993 27 1.995 28 1.997 29 1.999 30 2.000 </pre><hr /> |
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Perfect!
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Here is a quick and dirty formula based on a multiple linear regression that
included only units with morale below 30. (AFAIK, the 30 and 50 morale numbers are only symbolic and represent a qualitatively different state.) I didn't include movement because the spreadsheet I have needs work to adjust those numbers into a useable format. (Plenty of other boring qualifiers omitted.) Cost = -386.0 + (11.9 * Size) + (2.3 * HP) + (-2.0 * Prot) + (11.0 * Morale) + (21.8 * MagicRes) + (1.4 * Enc) + (-4.8 * Str) + (2.8 * Att) + (.7 * Def) + (4.0 * Prec) The adjusted multiple R-squared is .70, so about 70% of the variance in cost is explained by the predictors. How does that compare to the formula in the first post? In case you are not familiar with multiple regression, don't be fooled by the size of the coefficients. There is some collinearity -- some of the stats used as predictors are correlated with each other, so their unique predictive contribution is affected. For example, strength has a zero-order correlation of .24 with cost, but in the multiple regression, its coefficient is negative. Edit: Commander vs. non-commander makes a difference, so here are the formulae after splitting the two groups. Non-commander formula (adjusted R-squared = .81): Cost = -60.1 + (12.3 * Size) + (2.9 * HP) + (.8 * Prot) + (5.3 * Morale) + (4.1 * MagicRes) + (-1.9 * Encumb) + (-8.0 * Str) + (1.5 * Att) + (.5 * Def) + (-1.9 * Prec) Commander formula (adjusted R-squared = .71): Cost = -459.7 + (16.8 * Size) + (.3 * HP) + (-2.5 * Prot) + (11.0 * Morale) + (25.4 * MagicRes) + (5.0 * Encumb) + (.8 * Str) + (-1.1 * Att) + (6.2 * Def) + (-.1 * Prec) |
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How on Earth did you come up with these? I assume you were using data compiled in Excel spreadsheets and some automated technique, but...
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I have to wonder how well a forumla with negative coefficients for strength, encumbrance, and precision will predict the value of future units... but since you out-math me, I'll gracefully bow before you http://forum.shrapnelgames.com/images/smilies/happy.gif |
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50 morale is symbolic, but I think 30 morale is real...
As for your equation, while it may be perfect for all units already in the game, it doesnt do much for modding, which is what I was interested in; I ran the Size 1, 5 HP, 3 Prot, 10 Mor, 5 MR, 2 Enc, 5 Str, 11 Att, 10 Def unit Im including in my mod through your equations and he came out costing -133 from your combined euation, and 20 from the unit only one. So, it isnt really a global equation that can be applied to any unit a modder might come up with, which is what I was hoping to eventually get. Not that Im claiming any equation can fully encompass this complicated game http://forum.shrapnelgames.com/images/smilies/happy.gif I just want to make a stab at it... Now, I have a couple questions: Can the same 'diminishing returns' idea be applied to attack and defence? That is, is the difference between 10 and 13 attack greater than between 20 and 23? How much attack or defense skill would be required to double the effectiveness of a 'normal' unit (that is, 10 HP, 0 Prot, 10 Att, 10 Def, etc.)? Is defense really worth more than attack? For some reason I have gotten that into my head, but then, Im a n00b at Dominions... In my initial equation, should the magic resistance be pulled out of the square root? Should it be wieghted more? Does low magic resistance make an otherwise powerful unit worthless? new equation: (thanks Saber Cherry http://forum.shrapnelgames.com/images/smilies/happy.gif) sqrt( ( ( HP * ( 10 + Prot ) * Def * MR ) / 1000 ) * ( ( Str * Att ) / 10 ) ) * ( ( 3 + Move ) / 5 ) * ( ( 40 + AP ) / 50 ) * ( 9 / ( 6 + Enc ) ) * f( Mor ) f( x ) = 0.5 + ( ( 0.75 ) ^ ( 10 - x ) ) / 2 | x < 10 f( x ) = 2 - ( ( 0.75 ) ^ ( x - 10 ) ) | x >= 10 Heres another (crazy) idea: sqrt( HP * ( 10 + Prot ) * f( Def ) * f( Str ) * f( Att ) ) * ( ( 3 + Move ) / 5 ) * ( ( 40 + AP ) / 50 ) * ( 9 / ( 6 + Enc ) ) * f( Mor ) * f( MR ) f( x ) = -sqrt( 10 - x ) / 3 + 1 | x < 10 f( x ) = sqrt( x - 10 ) / 3 + 1 | x >= 10 This is assuming that morale, magic resistance, attack, strength, and defense all follow the same cost distribution... Attatched is a graph of this f(x). |
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How did I do it? Many statistics programs exist that can do a multiple
regression in a few seconds. The tricky part is recognizing problems and interpreting the output. What use is it to a modder? Use it as you will. It is an imperfect description of the way that the unit stats are related to unit costs in the original game. Below are some new formulae. I added some stats, and I removed stats post hoc that didn't make a significant unique contribution. Something I might do in the future is separate mages from non-mages. Non-commander (adjusted R-squared = .81) Cost = -51.7 + (11.2 * Size) + (3.1 * HP) + (.9 * Prot) + (5.9 * Morale) + (4.5 * MagicRes) + (-1.6 * Encumb) + (-8.6 * Str) + (-1.8 * Prec) + (.5 * TacMove) Commander (adjusted R-squared = .82) Cost = -269.6 + (1.6 * HP) + (-1.7 * Prot) + (8.9 * Morale) + (12.4 * MagicRes) + (3.6 * Def) + (2.2 * TacMove) + (.6 * Ldr_Norm) + (1.5 * Ldr_Undead) + (3.3 * Ldr_Magic) Edit: OK, here are separate formulae for non-mage and mage commanders. Non-mage Commander (adjusted R-squared = .77) Cost = -125.6 + (9.2 * Size) + (1.4 * Prot) + (10.9 * Morale) + (7.6 * MagicRes) + (-6.6 * Encumb) + (-3.5 * Str) + (-2.0 * Def) + (2.3 * TacMove) Mage Commander (adjusted R-squared = .79) Cost = -233.0 + (7.7 * Morale) + (16.0 * MagicRes) + (4.1 * Str) + (-10.7 * Att) + (11.5 * Def) + (-5.7 * Prec) + (.9 * Ldr_Norm) + (1.1 * Ldr_Undead) + (3.5 * Ldr_Magic) |
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Sorry, but I'm pretty sure these regressions are mostly useless (and I've some stat background..). Namely because they do work *only* if the relationships between attributes and the attributes scales themselves are in the range of the sample studied.
So if I make yet another jav totting Lt Inf it will be ok, but if I make something unusual, for example a 1-hp Ethereal elephant, a 100-hp rock-hard militia or some nasty B3W3?3 mage (called "Ice Devils Factory" :lol ), they would be totally out. Thay also don't take at all into account the equipment of the unit (the values are averaged, difference going to the error margin) : yet these are pretty important... |
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Equipment is generally accounted for in the resource cost whereas skill is accounted for by the unit cost. The value of units such as mages is extraordinarily difficult to determine because their spells and combinations of spells (even if you leave out item bonuses and communion) is subjective and certain combinations of spells have greater "value" that the sum of the "values" of the spells alone (e.g. Body Ethereal combined with Bone Grinding > Body Ethereal + Bone Grinding).
Unit cost should be a measure of their statistical efficacy versus the "average" unit whose cost could be arbitrarily assigned. The "average" unit's stats are not all 10's and his cost is not 10gp... Once you have a sample space of units, you can determine the most effective ones. The combat simulator is a good tool for this, but it doesn't produce excel-like results. Any unit whose effectiveness against "Average" is 50% should cost the same as the "Average" unit. I'm not sure how you'd scale up the value as the percentages vary....How much more should a unit with 95% effectiveness cost? |
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A better model for these powerful units might be to do a similar study of summonable units and convert from gems to gold (using ermor, 10-15 gems us ~equal to 200 gold). Unfortunately you can't use the same equation for commanders and regulars, but seperate units would be available. An even better model would be to use best fit nonlinear equations for the summonable units, then convert to gold. Cheap regression software can only check your guesses for nonlinear equations. Probably the best method would be to use an expert system (engineering term - artificial intelligence for programmers) to compute costs. Using this method, you could convert summonable unit cost to gold first (or use the system to do it accurately), then input ALL of the units. Then, by pumping your unit through the system you would get an accurate price. The system itself would be somewhat complex, with nodes for each aspect (sacred, etheareal, etc) of the unit. You can get a good quality student expert system program for "free," with the purchase of a US$100-150 expert system text. It MAY allow enough nodes for this system. You may be able to download one for free, but I haven't looked. It could take you a while to learn how to use it. . . SUMMARY: Use the linear model or 1gold per point model for units within normal "human" unit ranges (maybe up to 14 skill) with typical aspects (not ethereal). This is how devs appear to have done it. . . For powerful units, make a model using summonable units and convert to gold, using ermor income to gold (10-15 gems per 200 gold). Use of linear lines here will cause barely powerful units to be too expensive and truly powerful units to be too cheap, but mid range guys should be right on. Models for extreme units should use a nonlinear or expert system model. OR you could "just" shake the info out of the devs. . . |
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Prediction is not what you might think. Of course, I can't predict what a
modder is going to do. A modder could change Militia to be 10,000 gold. The regression equations describe how stats are related to price for existing units. To the extent that you generally like what Illwinter has done with pricing units, the equations might give you a good starting point. If you want to make drastic changes, you may be less interested. My guess is that the Illwinter guys don't use equations. One of them probably makes a unit and assigns a price based on an intuitive understanding of its usefulness. After playtesting, the price is modified. I can put things like magic paths and special abilities (e.g., ethereal) into the mix. I already did so with flying, which turned out not to be uniquely important enough to keep in. Keep in mind, however, that the equations above are already explaining about 80% of the variance. That is the equivalent of a correlation of .90. Whatever gains come in the future will be very modest at best. As for using nonlinear equations, I have looked at polynomial regressions for some of the predictor variables. Morale, for example, is pretty much linear. The squared term adds a little, and the cubed term is negligible. In other contexts, I have played around with log, square root, and reciprocal transformations. My experience has been that they are most useful when the vanilla linear regression isn't working well. Here, vanilla works like gangbusters, so the room for improvement is small. |
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Why is size a positive value? I guess the ultimate answer would have to come
from Illwinter. If I remember correctly, size, HP, and strength are highly correlated with each other, so having more than one in the equation makes interpretation tricky. You are welcome to use the equations in any way you like. At the moment, they cover only national units, not independents. (Maybe next weekend I'll add the indies.) I have not included magic paths for mages yet. The only special ability I have looked at is flying. I would like to add other special abilities and magic paths later. The three equations below are the ones to use for now. Non-commander Cost = -51.7 + (11.2 * Size) + (3.1 * HP) + (.9 * Prot) + (5.9 * Morale) + (4.5 * MagicRes) + (-1.6 * Encumb) + (-8.6 * Str) + (-1.8 * Prec) + (.5 * TacMove) Non-mage Commander Cost = -125.6 + (9.2 * Size) + (1.4 * Prot) + (10.9 * Morale) + (7.6 * MagicRes) + (-6.6 * Encumb) + (-3.5 * Str) + (-2.0 * Def) + (2.3 * TacMove) Mage Commander Cost = -233.0 + (7.7 * Morale) + (16.0 * MagicRes) + (4.1 * Str) + (-10.7 * Att) + (11.5 * Def) + (-5.7 * Prec) + (.9 * Ldr_Norm) + (1.1 * Ldr_Undead) + (3.5 * Ldr_Magic) |
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If I could give you values for commanders without any 'specials', could you calculate a regression formula based on that? |
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Also, why is the mage commander different from the non-mage commander?
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Sushiboat's equations don't answer "why" questions, they answer only "what" questions. Such as, what is the price correlation between this and that for mage commanders. . . The fact that mixing the commander types together decreased his significance showed him that he needed to seperate them.
In this case, he's reverse engineering the pricing method. |
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Its quite nice to see regression analysis used this way. As I stated, I believed it would work quite well for "normal" units. I still have my doubts about its usefulness for powerful units, so in these cases, your use should be more reserved (higher prices).
Sushiboat, I think you'll see a significant changes to the pricing of mages when you add magic paths. I'm curious as to whether or not after testing all the magic paths really turned out "even" in terms of pricing. Don't worry, I appreciate the difficulty of your working in figuring that particular question out (are their combinations that are more expensive, do you need to put a variable in for lvl2 earth path, or just lvl2 path?). Happy hunting! |
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BigDaddy, I am toying with the idea of converting cost to percentile scores
and then giving a table for converting back from percentiles to gold cost for someone who uses the formula. I think that would address nonlinearity more efficiently than various curve fitting schemes. I bet that math professors everywhere are feeling mysteriously uneasy that someone somewhere is using linear modeling with percentile data, but I think that it should work alright from a practical perspective. Of course, it's extra work, so who know whether I will get around to it. |
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Nah, normalizing the data is a straightforward method of "tabling" it. I'm sure the practice is commonplace. It will also give people an idea of what the "upper-bound" of your study is; They would then know when they can no longer use your numbers to determine cost.
I think it might be just as easy though, to just type out a cumulative cost chart for each stat. Then you don't need to normalize, just plug and chug. Just dump the equation parts in a spread sheet and... Oh, and by the way, you may want to remove outliers from the unit lists. Any units which break the mold by being too cheap (likely for nation balance purposes by the devs) where never meant to meet your "entrance criterea." |
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Well, here's the ugly beta version for you to mess with sushi. Anyone else, use the higher numbers at your own risk!
See next post. . . |
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Presumably, a stat is a stat, right? |
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OK, so I felt guilty about dropping that crappy spreadsheet on you guys, so heres a more useful and straightforward version. . .
SushiUnitPriceCalcV02.xls Hope you have Excel. |
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Well. . .
I'd say that different stats make a mage commander better, like Defense and HP. So, the "a unit is a unit" priciple probably isn't the way it is. Anyway, without the path prices included the cost method is sketchy for mages at best. . . |
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@BigDaddy: Theoretically, since the path costs have been given by Illwinter, they really shouldn't be that hard to remove from the equation, right? Would you say that Sushiboat's split-off of Mage Commanders would mean that Mage Commanders have different priorities?
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Exactly.
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What would happen if you subtracted the path cost for each mage, and then recalculated? Do you think you would find the same occurrence?
Also, were the calculations made with equipment/path bonuses, or with just the base stats? |
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I assume he already took out the path cost, but I don't know. In any case, because the path costs may have differed from the standards of illwinter due to balancing the path cost need to be run in a model. I ASSUME he didn't count equipment (it costs resources). We need him to tell us.
On the spreadsheet... if you make an unusually strong or weak unit the cost will definetely be off. It's for "typical" units. Sushiboat, can you use Absolute value on Str and HP to force Size to be negative(with your software)? When you update I'll change the spreadsheet that bears your screen name. |
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I look forward to adding magic paths. However, it would not surprise me if they turn out to be correlated with the stats already in -- especially precision, magic resistance, and magic leadership. The number of total path levels should be highly correlated with magic leadership. Levels in death and blood paths will be highly correlated with undead leadership. Fire magic adds to attack skill, and so on. What may happen is that the path levels stay in the final equation, other stats come out, and overall variance explained is bumped up only a little. Chipping away at the last 20% of the variance will be tough, I bet.
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To repeat myself for your convenience:
-Remove outlier units that are artificially extra efficient to hire. -Add magic paths to mage units. These two will greatly increase your accuracy. -I've updated the speadsheet to be NONCRAPPY. -Look at your program options, and see if you can for HP and Str to be positive |
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Out of curiosity, what do the p-values for the individual regressors look like? |
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I would think that, since we ostensibly have what the path costs should be for a given configuration (I've worked these all out, including full randoms, if you'd like it), shouldn't you remove that from consideration before running your analysis? That done, wouldn't you gain a clearer picture of how to value the stats? What I'm getting at is perhaps by working together (using my guesses and your regression analysis), we might be able to come up with a value for any given stat configuration, and then just 'plug in' the rest of the information. Unfortunately, I don't very much at all about regression testing, so it's hard for me to grasp much of the jargon. |
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[/quote] Yes, I'm talking about the coefficients. They really would do better as positives. I thought if you could force absolute value on them, then it would force Size to be negative. You also may get this result by changing the order. To some extent I agree with Evil Dave, except that we are talking about a correlation issue, even though Size and HP are very different. I would recommend making a model for human sized units IF you where going to cut size out. Otherwise, it is important enough to leave in. Besides, I assume it makes your model more accurate. |
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The coefficients are derived from the data, so you can't just change the
positive/negative sign without hurting the equation's performance. I could take out all but one of a set of highly correlated predictors. However, all predictors in the equation are making a highly significant contribution. Perhaps a better solution would be to create a composite variable. A new variable called Bigness could be the sum of the standardized scores of Size, HP, and Strength. A little information would be lost by not keeping them separate, but not much. Edit: EvilDave, I don't have the output in front of me. (It's a Mac Classic app, and I don't want to fire up the Classic environment just now.) I do remember the predictors I kept having p-values that were very low, less than .0001. I removed the predictors with p-values of .10 and higher from the three most recent regressions. It just happened that there was a huge gap between the predictors that weren't statistically significant and the ones that were. |
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I DO realize that forcing the numbers will affect performance. But, building small strong units is trouble right now. . . SO, I would see if a Str and size composite (excluding HP) would be effective, it is a very good idea. I'd like to see HP remain alone, if possible. Then, little information would be lost.
Note to modders (who like this sort of thing), YES you can easily use this equation to min/max like never before and claim fairness. . . |
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Claim, yes. Hide behind for long, probably not. http://forum.shrapnelgames.com/images/smilies/wink.gif
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So, you'd make a model like: damage = A * strength + B * weapon damage "strength" and "weapon damage" are the regressors (or contributors). A and B are their coefficients. The regressors also each have p-values, which is the likelihood they're due to chance -- 1%, 5%, and 10% are typical cut-offs for scientific work. The whole model has an R-squared, which is the fraction of the variation of the data it explains. R-squareds range from 0 to 1. So, in this toy example, strength and weapon damage would probably have very small p-values (probably less than 0.01), but the R-squared would probably be pretty low, because the 2d6oe tends to swamp out the effects of the regressors. Hope that helps. http://forum.shrapnelgames.com/images/smilies/happy.gif |
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Scott, I would be interested in seeing your figures for magic paths and cost.
Unless you are using the Unit Database spreadsheet and have numerical entries for all mage units, I wouldn't be able to plug your numbers in directly to my approach. As I said, I do plan to add magic paths to the analysis, but no time for that today. |
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Dave: So... the p-values are like that 95% value for confidence testing, and the R-squared value is how much of the sample that the equation explains.
It sounds like math modeling and differential equations to me. http://forum.shrapnelgames.com/images/smilies/wink.gif Sushi: If I had the Unit Database, I could supply that for you. It would be easiest if it were in Excel. It uses the Illwinter cost method. |
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Scott,
Its here (and in Excel Format) http://www.dominions-2.org/files.htm#docs Under "Unit DB - by Edi" |
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Downloaded. I hope to have the magic path costs entered sometime today... if my New Era Pangaea game doesn't suck any more time away...
mmmm... Dryad Hoplites.... |
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Oh, question. Where do you want me to put the Magic path cost, Sushi?
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Hmmm. . . I think Ill work on a raw data Unit spreadsheet. . .
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